ACHARYA N.G. RANGA AGRICULTURAL UNIVERSITY3)2012.pdf · P. GANESH KUMAR, R. RATNAKAR and P. GIDDA...

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ACHARYA N.G. RANGA AGRICULTURAL UNIVERSITYRajendranagar, Hyderabad - 500 030.

J. Res. ANGRAU Vol. XXXIX No.4 pp 1-134, October -December, 2011

ISSN 0970-0226

J. Res. ANGRAU Vol. XL No. 3 pp 1-146, July- September 2012

The Journal of Research ANGRAU(Published quarterly in March, June, September and December)

Dr. T. PradeepPrincipal Scientist(Breeding),Maize Research Station,ARI Campus, Rajendranagar,Hyderabad.

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Dr. M. V. RamanaSenior Scientist (Agronomy),AICRP on Integrated Farming Systems,Water Technology Centre,College of Agriculture, Rajendranagar,Hyderabad.

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Dr.(Smt.) A. ManiAssociate ProfessorDept. of Agril. Engineering & TechnologyCollege of Agriculture, Rajendranagar,Hyderabad.

Dr. T. RameshAssociate ProfessorDept. of Plant PhysiologyCollege of Agriculture, Rajendranagar,Hyderabad.

Dr. I. Sreenivas RaoProfessor and Head, Dept. of Extension Education,ANGRAU, Rajendranagar, Hyderabad.

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CONTENTS

PART I : PLANT SCIENCE

Effect of invigoration treatments on seed germination and seedling vigour in 1carry-over onion seed (Allium cepa L.)B. SOMRAJ, K. RAVINDER REDDY, K.V. RADHA KRISHNA and D. SRIHARI

Evaluation of different organic nutrient sources and varieties for organic rice 6(Oryza sativa L.) productionR. JAGADEESHWAR, N. RAMA GOPALA VARMA, B. GOPAL REDDY,P. NARSIMHA REDDY, CH. SURENDER RAJU and S. VANISREE

Study on the incidence of insect pests and natural enemies as influenced by 9organic treatments in Rice ecosystem

A. VENKAT REDDY, R. SUNITHA DEVI, G. ANITHA and D. VISHNU VARDHAN REDDY

Fertility status of rice growing soils of Nalgonda district in Andhra Pradesh - a GIS approach 14Y. SUDHA RANI and G. JAYASREE

Population of meloidogyne sp and rhizoctonia sp in tomato in different agroclimatic 18zones of Andhra PradeshB. VIDYA SAGAR, V. KRISHNA RAO and K. S. VARA PRASAD

PART II : SOCIAL SCIENCE

An empirical study of Groundnut in Ananthapur district of Andhra Pradesh 21K. SUPRIYA, A.K.GOEL, M. H. V. BHAVE and V. V. NARENDRANATH

Study of sales and services provided by the farm input retailers of West Bengal 25 A. DAS and D. BASUCritical analysis of e-Sagu- an information and communication technology project in Andhra Pradesh 29P. GANESH KUMAR, R. RATNAKAR and P. GIDDA REDDY

PART III : HOME SCIENCE

Non-woven drapery lining with Ultra Violet resistance 33D. HARINI, A. SHARADA DEVI and D. ANITHA

PART IV : RESEARCH NOTES

Mapping of QTLs for grain Iron and Zinc in samba mahsuri and wild rice 37(Oryza rufipogon) using RM and gene specific markersV. ROJA, N. SARLA, K. MANORAMA and K. RADHIKA

Identification of nutrient deficiencies by critical nutrient concentration (CNC) and 41dris methods and determination of sufficiency ranges of nutrients by dris technique in maizeCH. RAMULU and G. BHUPAL RAJ

Development of a functional food for diabetes 45S. SPANDANA and M. PENCHALARAJU

Evaluation of wheat (Triticum aestivum L.) genotypes for different sowing dates 48under irrigated conditionsJ. VIJAY, K. MADHAVI, A. RAMACHANDRA RAO and A. SAIRAM

Heavy metal removal efficiency of zeolite from sewage 51RESHMA BHADANGE, AKULA BABY, P.PRABHU PRASADINI, M.UMA DEVI andD. JAGDISHWAR REDDY

Production potential of multicut fodder bajra genotypes under varied dates of sowing 54V. CHANDRIKA, T. SHASHIKALA, M. SHANTI and K. LOKA REDDY

Per se performance and correlation studies in F1 generation of tomato (Solanum lycopersicum Mill.) 58

K.VINAY RAJU, B. NEERAJA PRABHAKAR, S. SUDHEER KUMAR and R.V.S.K. REDDY

Pesticide residues in selected fruits/vegetables of Nalgonda district of Andhra Pradesh 64SHAIK FARHATH and S. SHOBHA

Development of lycopene enriched mixed fruit bar 67M. PENCHALA RAJU , B. SHIREESHA , B. SRAVANTHI and KUNA APARNA

Study on the effect of irradiation on sensory qualities of preserved tomato products 69M. KIRTHY REDDY and V. VIJAYALAKSHMI

Combining ability studies in tomato (Solanum lycopersicum Mill.) 74K.VINAY RAJU, B. NEERAJA PRABHAKAR, S. SUDHEER KUMAR and R. V. S. K. REDDY

Attitude of the rural women towards telugu television programme -Mee Arogyam Mee Chetullo 77GRANDHI MANASA , R. GEETHA REDDY and M. PREETHI

Response of tomato (Solanumly copersicum L.) to potassium fertilization along with foliar 79application of potassium humate and triacontanolPALAKSHI BORAH, V. SAILAJA, P.CHANDRASEKHAR RAO and A. PRATAP KUMAR REDDY

Recommended specific production practices followed by farmers in supply chain management of 82horticultural productsM. BHAVYAMANJARI, M.SURYAMANI and C.PADMAVENI

Effect of heating of the gel at different temperatures on physico-chemical characteristics 86and microbial content in different accessions of aloe (Aloe barbadensis Miller).B. AMARESWARI, M.PADMA, M.RAJKUMAR and A. SIVA SHANKAR

Effect of different levels of nitrogen and sulphur on growth and yield of 90sunflower (Helianthus annuus L.)S. PAVANI, K. BHANU REKHA, S. N. SUDHAKARA BABU and G. PADMAJA

Food habits, preferences and meal pattern of the professionals in information technology sector 94PRIYA SHARMA, M. USHA RANI, K. UMA MAHESWARI and K. SUPRIYA

Nutrient content of selected herbal crops 97ELMUONZO, K. UMA MAHESWARI, ANURAG CHATURVEDI and T. SUSILA

Pesticide residues in spices of Guntur district of Andhra Pradesh 99SHAIK. SANHERA and S. SHOBHA

Response of wheat (Triticum aestivum L.) cultivars to varying levels of nitrogen 101MATHURA YADAV, V. PRAVEEN RAO, M. YAKADRI and G. JAYASREE

Association of grain iron content with grain yield and other traits in 105sorghum (Sorghum bicolor L. Moench)S. V. P. L. GAYATHRI, K. RADHIKA, A. ASHOK KUMAR and P. JANILA

Determinants of goal directed behaviour of adolescents 108NEHA JOSHI and M. SARADA DEVI

Identification of important qtls for grain number in popular rice varieties of Andhra Pradesh 110SONALI DATTA MUHURI, M. SHESHU MADHAV, CH. SURENDER RAJU and CH. V. DURGA RANI

Morphological characterization of cotton hybrids and their parental lines 113P. ARUNA, P. S. RAO, G. ANURADHA and K. KESHAVULU

Effect of age of seedlings on high yielding rice varieties 116D. NARESH, M. MALLA REDDY, D. VISHNU VARDHAN REDDY and T.V. SRIDHAR

Weed management studies in kharif Maize 121M. A. ALEEM AHMED and R. SUSHEELA

Effect of inoculum levels of rhizoctonia solani and meloidogyne incognita on root 124rot and root knot incidence on tomato CV. pusa rubyB. VIDYA SAGAR, V. KRISHNA RAO, K. S. VARA PRASAD and D. R. R. REDDY

Soil fertility mapping of pillaipally anacut command area, musi river in andhra pradesh 127Y. KRISHNAVENI, K. AVIL KUMAR, M. UMA DEVI and M. D. REDDY

Response of Cauliflower (Brassica Oleracea var. Botrytis) to potassium fertilization 132on light textured soils of Rangareddy districtK. KALYANI, V. SAILAJA, A. PRATAP KUMAR REDDY and P. CHANDRASEKHAR RAO

J.Res. ANGRAU 40(3) 1-5, 2012

Date of Receipt : 10.05.2012 Date of Acceptance : 19.06.2012

email: [email protected]

EFFECT OF INVIGORATION TREATMENTS ON SEED GERMINATION ANDSEEDLING VIGOUR IN CARRY-OVER ONION SEED (Allium cepa L.)

B. SOMRAJ, K. RAVINDER REDDY, K.V. RADHA KRISHNA and D. SRIHARIDepartment of Horticulture, College of Horticulture,

Dr.Y.S.R. Horticultural University, Rajendranagar, Hyderabad-500030.

ABSTRACT

An investigation was carried out to find out the appropriate invigoration treatment on seed germination andseedling vigour in carry-over onion seed during 2010. Six different aged seed lots with one month interval fromexpiry date along with fresh seed lot were subjected to invigoration with growth regulators and chemical nutrients.The seed treatments comprised growth regulators GA3 (100, 200, 400 ppm), NAA (50, 100, 150 ppm) and chemicalnutrients KNO3 (0.25%, 0.50%, 1.0%), Na2HPO4 (10-2M, 10-4M, 10-8M) and FeSO4 (0.25%, 0.50%, 1.0%) each withthree concentrations. Among the treatments GA3 @ 100ppm followed by NAA @ 100ppm and KNO3 @1% followedby Na2HPO4 @10-4 and FeSO4 @1% were found effective in registering higher values for seed germination andseedling vigour in all the germination and vigour tests. Irrespective of the invigoration treatments, the germinationpercentage and seedling vigour gradually declined as age of seed lot advanced.

Onion (Allium cepa L.) is one of the most

important commercial vegetable crop grown in India

and world. Of the 15 vegetable crops listed by the

FAO, onion falls 2nd only after tomato. It is a bulbous,

herbaceous, monocot cultivated as annual. But for

seed production it is biennial. Onion seeds have poor

longevity and storability which lose its viability very

rapidly (Mumtaz Khan et al., 2004). Generally, the

demand for seed fluctuates very often and sometimes

there may be a surplus of seeds which need to be

stored upto 2-3 sowing seasons. These carry-over

seeds exhibit poor germinability and less vigour.

Since onion seeds found to be poor storers,

maintenance of seed viability of carry-over seed lot

is of great importance in the sowing seasons following

the periods of low production. Hence, proper seed

treatments are needed during storage to maintain the

seed quality. Plant growth regulators and chemical

nutrients have been reported to play a major role in

controlling the biochemical changes accompanying

deterioration of seeds. Seed treatments with growth

regulators and nutrients can invigorate seeds for

better germination and seedling emergence. Keeping

this in view, present investigation was taken up in

order to find out appropriate invigoration treatments

for seed germination and seedling vigour in carry-

over onion seed.

MATERIALS AND METHODS

The present investigation was conductedduring 2010 at Department of Horticulture, College ofHorticulture, Rajendranagar, Hyderabad. Six differentaged seed lots with one month interval from the dateof expiry along with one fresh seed lot of onion having70 per cent germination were procured from privateseed dealers during 2009-10. The seed lots wereinvigorated by soaking them in double the quantityof distilled water and aqueous solutions of growthregulators and chemical nutrients over-night.Treatments consisted of growth regulators Gibberillicacid (GA

3) and Naphthalene Acetic Acid (NAA) each

with three concentrations i.e., GA3@100 ppm, 200

ppm, 400 ppm and NAA @ 50 ppm, 100 ppm, 150ppm and chemical nutrients viz., KNO

3 @0.25%,

0.5%, 1.0%; FeSO4

@0.25%, 0.5%, 1.0% andNa

2HPO

4 @10-2M, 10-4M, 10-8M as well as control with

distilled water. The experiment was laid out in afactorial completely randomized design adoptingFisher’s analysis of variance technique (Sundararaj,et. al., 1972) with three replications.

Germination tests were conducted byadopting top paper, between paper, soil media andsand media as described by ISTA (1985). Ten normalseedlings grown in moist towel paper kept at optimumtemperature were selected for measuring length incentimeters on the day of final count (12th day). Ten

normal seedlings selected at random from germinationtests on 10th day were dried in hot air oven at 80±1oCfor two days for measuring mean dry weight ofseedlings and expressed in milligrams. The seedlingvigour index was calculated by adopting the methodsuggested by Abdul-Baki and Anderson (1973). Thefield emergence was recorded on alternate days from4 to 8 DAS (Days after sowing) in all treatments foreach replication adopting the method suggested byBaskin (1969). Speed of germination was computedby using the formula given by Heydecker (1972)germination index= no. of seeds germinated/daystaken for germination (GI= n/d). Paper exertion testwas calculated by sowing the seeds on 1.25 cm ofmoist sand. It was covered with specially selecteddry filter paper (with 0.4 mm thickness). This filterpaper was again covered with 3 cm of moist sand.This was kept at 20-25oc for the days required forfinal count. The seedlings which were able topenetrate the paper were considered vigorous. Brickgravel test was calculated by selecting hundred seedsat random for each replication which were sown on2 cm thick layer of 2-3 mm size porous brick gravel.Again a 2 cm layer of moist brick gravel was laidabove the seed and wetted upto 60% water holdingcapacity. The count of normal emerged seedlingswas taken 12days after planting and expressed asgermination percentage. Electrical conductivity (E.C.)was measured by digital E.C. meter and expressedin μ mhos /cm (Dadlani and Agarwal, 1983). Coldtest was calculated by sowing seeds on 2 cm thickleveled moist soil. The same quantity of soil wasthen placed on the top of the seed. Enough cold water(10oC) was added to the soil to bring the medium to70 percent of its water holding capacity and thenincubated at 10oC for 7 days. After 7 days, it wastransferred to required temperature for germination.The lot showing minimum variation in germinationpercentage in comparison to check was consideredas vigorous.

RESULTS AND DISCUSSION

Germination percentage:

The interaction effect of invigorationtreatment in relation to the age of the onion seed lotson top of the paper (92.75% to 75.81%), between thepaper (95.20% to 81.06%), soil media method(84.62% to 64.54%) and sand media method (75.33%to 57.81%) decreased progressively from fresh seedlot to the six months old aged seed lot. Between the

paper method recorded highest germinationpercentage than other methods but the germinationpercentage gradually decreased with the age of theseed lots in all the methods. The decrease ingermination was very slow in case of invigorated seed,especially with GA

3 @100 ppm (83.98%), followed

by NAA @100 ppm (82.73%), KNO3 @ 1% (81.39%),

Na2HPO

4 @ 10-4M (80.69%) and FeSO

4 @ 1%

(78.96%) compared to the rapid decrease in untreatedor control (74.87%) seed lots. It may be due to repairor re-organization of the cell organelles, enzymaticreactions, activation of metabolic process of the foodreserves, repair and replacement of cell membraneand rejuvenation of embryo viability (Sudershan,2004).

Seedling Vigour Index

The Seedling Vigour index declinedprogressively and significantly with the age of theonion seed lots from 1503 to 974 based on theseedling length (SV I) and 1374 to 905 based on theseedling dry weight (SV II). It was high in fresh seedlot and decreased with the age of the seed lots fromone month old seed lot till six months old seed lot.Similar findings were reported by Sudershan (2004).Treatment with GA

3 @100 ppm recorded highest

SVI and SVII while FeSO4 @ 0.25% registered lower

seedling vigour indices but these indices were higherthan those in control. Irrespective of invigorationtreatments fresh seed lot recorded maximumseedlings vigour indices based on length and massof the seedling. The increased seedling length andseedling dry weight may be due to the retention ofmembrane activity and high dehydrogenase activityin the seeds (Doijode, 1989).

Seed Germination Percentage in Stress Tests

The advancing age of the seed resulted insignificantly reduced seed germination under stresstests namely, Brick gravel test (82.14% to 62.43%),Paper piercing test (73.43 to 54.43%), cold test(74.1% to 53.87%) and field emergence test (16.89to 12.14). Irrespective of the invigoration treatments,fresh seed lot recorded higher germinationpercentage, and six months old aged seed lotrecorded lower germination percentage in all thestress tests. Invigoration treatment activates themetabolic activity in the first phase of germinationbefore sowing and hence provides added advantageof better emergence, growth and establishment ofseedlings in the field (Vanangamudi and

SOMRAJ et al

EFFECT OF INVIGORATION TREATMENTS ON SEED GERMINATION

Tab

le 1

. E

ffe

ct

of

Invig

ora

tio

n t

reatm

en

ts w

ith

gro

wth

reg

ula

tors

an

d c

hem

ical

nu

trie

nts

usin

g d

iffe

ren

t m

eth

od

s i

n c

arr

y-o

ver

on

ion

s

eed

in

rela

tio

n t

o t

he a

ge o

f th

e s

eed

lot

F

resh

seed

lo

t O

ne m

on

th

old

seed

lo

t T

wo

mo

nth

s

old

seed

lo

t

Th

ree

mo

nth

s o

ld

seed

lo

t

Fo

ur

mo

nth

s

old

seed

lo

t F

ive m

on

ths

old

seed

lo

t

Six

mo

nth

s

old

seed

lo

t

Germ

inati

on

(%

)

Top

of t

he p

aper

92

.70

91.2

9 89

.14

86.2

5 83

.39

79.9

1 76

.87

Bet

wee

n th

e pa

per

95.2

0 93

.79

92.2

7 88

.95

87.5

4 84

.43

81.8

9 S

oil m

etho

d 84

.62

82.2

9 79

.27

75.2

2 72

.20

68.1

6 64

.91

San

d m

etho

d 75

.33

73.1

6 70

.83

69.1

2 66

.47

61.6

0 58

.37

Vig

ou

r in

dex

See

dlin

g vi

gour

I 13

44

1290

12

15

1125

10

72

998

905

See

dlin

g vi

gour

II

1503

14

24

1328

12

31

1167

10

60

974

Ele

ctr

ical

co

nd

ucti

vit

y (

μs/p

pt)

Ele

ctric

al c

ondu

ctiv

ity

211

231

246

266

274

292

310

Seed

germ

inati

on

in

str

ess t

ests

(%

)

Bric

k gr

avel

test

82

.14

79.4

7 76

.16

72.3

1 69

.52

66.5

2 62

.43

Fie

ld e

mer

genc

e in

dex

16.8

9 16

.22

15.2

6 14

.42

13.8

6 13

.12

12.1

4 P

aper

pie

rcin

g te

st

73.4

3 71

.47

68.4

1 65

.62

62.1

0 57

.97

53.0

4 C

old

test

74

.10

71.2

7 66

.95

63.5

6 60

.91

57.1

0 52

.83

Sp

eed

of

germ

inati

on

Spe

ed o

f ger

min

atio

n 13

.46

13.1

9 12

.75

12.1

0 11

.54

10.9

4 10

.28

SOMRAJ et al

Tab

le 2

. E

ffect

of

Invig

ora

tio

n w

ith

gro

wth

reg

ula

tors

an

d c

hem

ical

nu

trie

nts

usin

g d

iffe

ren

t m

eth

od

s i

n c

arr

y-o

ver

on

ion

seed

in

rela

tio

n t

o t

he

invig

ora

tio

n t

reatm

en

ts

G

A3

@

10

0

pp

m

GA

3

@ 2

00

pp

m

GA

3

@ 4

00

pp

m

NA

A @

50

pp

m

NA

A @

10

0

pp

m

NA

A

@ 1

50

pp

m

KN

O3

@

0.2

5%

KN

O3

@

0.5

0%

KN

O3

@

1.0

0%

FeS

O4

@

0.2

5%

FeS

O4

@

0.5

0%

FeS

O4 @

1.0

0%

N

a2H

PO

4

@ 1

0-2

M

Na

2H

PO

4

@ 1

0-4

M

Na

2H

PO

4

@ 1

0-8

M

Co

ntr

ol

Germ

inati

on

(%

)

Top

of t

he p

aper

89

.00

87.7

6 88

.38

86.6

1 87

.40

87.2

8 84

.95

85.2

3 84

.76

84.8

1 84

.00

83.6

1 84

.52

84.7

1 84

.61

83.2

3

Bet

wee

n th

e pa

per

92.6

6 89

.47

90.4

7 90

.23

92.9

5 90

.38

86.3

3 87

.85

91.5

7 85

.76

87.2

3 90

.52

85.4

7 90

.95

87.3

3 84

.66

Soi

l met

hod

82.2

3 78

.23

79.9

0 75

.19

79.1

4 76

.52

71.0

4 73

.61

77.4

2 70

.33

72.2

8 75

.61

70.9

0 76

.90

73.6

6 68

.57

San

d m

etho

d 72

.04

66.9

0 68

.19

66.4

7 71

.42

67.5

7 65

.42

67.3

8 71

.81

64.4

2 66

.09

66.0

9 65

.42

70.1

9 65

.61

63.0

0

Mea

n 83

.98

80.5

9 81

.74

79.6

3 82

.73

80.4

4 76

.94

78.5

2 81

.39

76.3

3 77

.40

78.9

6 76

.58

80.6

9 77

.80

74.8

7

Viig

our i

ndex

See

dlin

g vi

gour

I 13

.24

1204

12

47

1197

13

77

1215

99

3 10

57

1168

94

0 98

6 10

82

1051

12

48

1152

91

3

See

dlin

g vi

gour

II 14

60

1331

13

58

1195

13

86

1287

11

23

1233

13

78

1043

11

10

1219

11

61

1319

11

97

1007

Ele

ctr

ical

co

nd

ucti

vit

y (

μs/p

pt)

Ele

ctric

al

cond

uctiv

ity

183

196

191

265

239

250

296

276

267

298

286

268

296

266

284

316

Seed

germ

inati

on

in

str

ess t

ests

(%

)

Bric

k gr

avel

test

78

.00

72.6

1 75

.66

70.6

1 78

.38

73.1

9 69

.85

72.1

4 75

.95

67.6

1 70

.47

74.2

3 68

.33

75.2

3 71

.00

65.8

1

Fie

ld e

mer

genc

e in

dex

16.2

3 15

.24

15.5

6 14

.79

15.8

4 15

.11

14.0

5 14

.44

15.3

1 13

.38

13.7

5 14

.59

13.6

3 14

.69

13.9

4 12

.40

Pap

er p

ierc

ing

test

67

.66

62.6

1 64

.47

63.1

4 68

.04

64.9

5 63

.38

65.9

5 71

.00

59.9

0 62

.90

66.8

1 66

.81

68.8

1 64

.42

57.4

2

Col

d te

st

68.3

3 62

.19

64.0

0 63

.04

67.4

7 63

.09

61.5

2 63

.85

68.6

1 59

.95

62.1

9 66

.61

61.4

7 66

.85

63.1

4 58

.76

Sp

eed

of

germ

inati

on

Spe

ed o

f ge

rmin

atio

n 13

.30

12.6

9 12

.84

12.3

5 12

.84

12.3

0 11

.28

11.9

8 12

.43

10.7

0 11

.47

12.1

2 11

.29

12.3

4 12

.18

9.78

Kulandaivelu, 1989). Invigoration treatment helpedto record higher seed germination percentage in allthe stress tests with GA

3 @ 100ppm, NAA @

100ppm, KNO3 @ 1%, Na

2 HPO

4 10-4M and FeSO

4

@ 1%. Improved performance of seeds hydratedbefore sowing might be due to increased DNA andRNA and protein synthesis, higher mitochondrialactivity and allied sequential changes in the elasticityand viscosity of protoplasm (Henckel, 1964).

Speed of Germination

All the Invigoration treatments helped toregister higher speed of germination over control oruntreated seed lots, especially with GA

3@100ppm

which recorded highest speed of germination (13.46to 10.28) followed by NAA@100ppm, KNO

3 @ 1%,

Na2 HPO

4 @ 10-4M and FeSO

4 @1%.

Electrical Conductivity

There was a significant increase in theelectrical conductivity of seed leachate in relation tothe age of the Onion seed lots which increased fromfresh lot (211μs/ppt) to the six months old aged seedlot (310μs/ppt). Irrespective of the invigorationtreatment, fresh seed lot recorded lower electricalconductivity of seed leachate while the six monthsold aged seed lot recorded the higher values. It mightbe due to destructive changes in cellular membranesystem resulting in more leakage of organic solutes(free sugars, Fatty acids and Amino acids). Damageto the membrane system could be repaired andprotected against such changes by invigorationtreatment, indicated by low Electrical Conductivityof seed leachate. Protective action of invigorationchemicals could presumably have extended theviability of seeds. Seed soaking in water effectivelycontrolled the leakage of electrolytes, sugars andamino acids from the seeds (Dias et al., 2004). TheE.C of seed leachate was lowest in seed lots treatedwith GA

3 @100ppm followed by NAA@100ppm,

KNO3@1%, Na

2HPO

4 @ 10-4M and FeSO

4 @ 1.0%.

All the Invigoration treatments helped to record lowerE.C. of the seed leachate compared to the control.

In the present investigation, irrespective ofthe invigoration treatments, the seed qualityparameters declined progressively with the increasein the age of the seed lot. However, invigorationtreatments helped to register higher values for seedgermination and seedling vigour compared to control.

REFERENCES:

Abdul- Baki, A. A and Anderson, J. D 1973. Vigourdetermination in soybean seed by multiplecriteria. Crop Science 13: 630-633.

Baskin, C. C 1969. GADA and seedlingmeasurements as tests for seed quality.Proceedings of Seedsman Short Course,Mississippi State University, pp.59-64.

Dadlani, M and Agarwal, P. K. 1983. Factorsinfluencing leaching of sugars and electrolytesfrom carrot and okra seeds. ScientiaHorticulture 19: 39-44.

Dias, D. C. F. S., Freitas, R. A., Dias, L. A. S andOlivera, M. G. A 2004. Storage potential ofcotton seeds predicted by vigour tests and bio-chemical assays. Abstracts, 27th ISTACongress Seed Symposium, Budapest,Hungary, May 17-19, p.70.

Doijode, S. D 1989. Some biochemical changes inrelation to lose of seed viability in cauliflowergermplasm. Indian Journal of Plant GeneticResources 2:136-139.

Henckel, P. A 1964. Physiology of plants underdrought. A Review on Plant Physiology15: 363 -386.

Heydecker, 1972. Vigour In Roberts E H (ed.) “Viabilityof seeds” Syracuse University press SyracuseNew York. pp.209-252.

ISTA International Seed Testing Association 1985.International rules for seed testing. SeedScience and Technology 13: 307-513.

Mumtaz khan M., Javed Iqbal M., Abbas M., RazaH., Waseem, R and Arshad Ali 2004. Loss ofvigour and viability in aged onion (Allium cepaL.) seeds. International Journal of Agricultureand Biology 6 (4) : 708-71.

Sudershan, G 2004. Effect of seed vigour andinvigoration treatments on storability, seedlingvigour, growth, development and yield incotton hybrids. M.Sc. (Ag.) Thesis, AndhraPradesh Agricultural University, Hyderabad.

Sundararaj, N., Nagaraj, S., Venkataram, M. N andJagannath, M. K 1972. Design and Analysisof Field Experiments, University of AgriculturalSciencess. Bangalore.

Vanangamudi, K and Kulandaivelu, R 1989. Pre-sowing seed treatment for dryland farming.Seeds and Farms 15(9): 33-34.

EFFECT OF INVIGORATION TREATMENTS ON SEED GERMINATION

J.Res. ANGRAU 40(3) 6-8, 2012


e-mail: [email protected]

EVALUATION OF DIFFERENT ORGANIC NUTRIENT SOURCESAND VARIETIES FOR ORGANIC RICE (Oryza sativa L) PRODUCTION

R. JAGADEESHWAR, N. RAMA GOPALA VARMA, B. GOPAL REDDY,P. NARSIMHA REDDY, CH. SURENDER RAJU and S. VANISREE

Rice Section, Agricultural Research Institute,Acharya N. G. Ranga Agricultural University, Rajendranagar, Hyderabad - 500 030

ABSTRACT

Field experiment was conducted at Rice Section, ARI, Rajendranagar, Ranga Reddy district during kharif2008 and rabi 2008-09 seasons to evaluate the influence of organic nutrient sources and varieties on productivityand on major insect pests and diseases. The results revealed that among the varieties Sugandha Samba respondedwell and recorded higher grain yield (4.4t/ha) during kharif and Tellahamsa (5.1t/ha) during rabi. Higher productivitywas realized from karanj cake (4.4 and 5.2t/ha in both seasons) and neem cake treated plots (5.1t/ha during rabi).Reduced incidence of thrips with application of vermicompost and reduced incidence of blast and sheath rot withapplication of karanj cake and neem cake was observed.

Organic farming is gaining much importancein the world with more than 100 countries alreadypracticing it. The trade in world organic market hasnow touched 26 billion US$ and is expected toincrease to 102 billion US$ by 2020. According toAPEDA (2011), about 9,76,646 MT of different organicproducts worth 498 crore rupees are being exportedfrom India. One of the constraints identified forpromoting organic rice cultivation in India is lack ofsufficient FYM and also efficient production andprotection technologies that can be easily adaptedby the farmers for cultivating rice organically. Further,varieties with inherent potential to perform well evenunder low input management and can best suit toorganic farming systems have to be identified toevolve profitable and productive organic farmingpackage.


A field experiment was conducted duringkharif 2008 and rabi 2008-09 seasons at Rice Section,ARI, Rajendranagar with 5 rice varieties (SugandhaSamba, Sumati, Early Samba, Rajendra,Tellahamsa) and 4 organic nutrient sources (FYM,Green manure, Neem cake and Karanj cake @ 5.0 t/ha) and an untreated control, in randomized blockdesign replicating thrice, to compare the influence oforganic nutrient sources and varieties on productivity

and document the incidence of insect pests /diseases of major concern.

The trial was conducted in an exclusivelymaintained organic plot for last three years. The cropwas transplanted at a spacing of 15x15cm. All theorganic nutrient sources were applied only basally.The mean data of insect pests, diseases and grainyield were subjected to statistical analysis usingINDOSTAT package.


Among the varieties tested, Sugandha Samba(RNR 2465) responded very well with grain yield of4.4 t/ha (Table 1) in comparison to Rajendra (2.3t/ha). Whereas, during rabi 2008-09 Tellahamsa, acold tolerant variety produced highest grain yield of5.1 t/ha as against the same check Rajendra(3.8 t/ha). With respect to organic nutrient sources,application of karanj cake @5.0 t/ha resulted inhigher grain yield (Table 2) in both the seasons (4.4and 5.2t/ha, respectively) and next best source forrabi was neem cake (5.1 t/ha).

Surekha et al., (2010) observed that grain yieldsin the inorganic fertilizer plots were superior toorganics during first two years by 15-20%, while grainyields gradually improved with organics in the lateryears to parity with inorganics. However, in this

Table 1. Grain yield (t/ha) as influenced by varieties under organic farming at Rajendranagar during kharif 2008 and rabi 2008-09

Grain yield (t/ha) Variety

kharif 2008 rabi 2008-09 Sugandha Samba 4.4c 4.8bc

Sumati 4.0bc 4.5b

Early Samba 4.2bc 4.9c

Rajendra 2.3a 3.8a

Tellahamsa 3.9a 5.1c

Figures in a row within a season with different letters differ significantly (p=0.05)

Table 2. Grain yield (t/ha) as influenced by different organic sources at Rajendranagar,kharif 2008 and rabi 2008-09

Grain yield (t/ha) Treatments

kharif 2008 rabi 2008-09 Untreated control 3.3a 3.9a

FYM @5t/ha 3.5ab 4.3a

Vermicompost @ 5t/ha 3.9b 4.7b

Neem cake @ 5t/ha 3.9b 5.1c Karanj cake @ 5t/ha 4.4c 5.2c


Table 3. Incidence of insect pests as influenced by organic sources at Rajendranagar during kharif2008 and rabi 2008-09

% THDL %WMDL %DH %WE Treatments

Kh 08 Ra 08-09 Kh 08 Ra 08-09 Kh 08 Untreated control 16.69d 14.27b 5.26a 3.98a 8.08a 2.64a

FYM @5t/ha 13.45b 14.08b 5.28a 4.47a 9.52a 2.14a

Vermicompost @ 5t/ha 11.09a 11.31a 5.30a 4.03a 9.26a 2.49a

Neem cake @ 5t/ha 15.07c 12.17b 5.57a 3.87a 8.50a 1.23a

Karanj cake @ 5t/ha 15.17c 14.29b 5.36a 3.98a 8.26a 1.27a

Figures in a row within a season with different letters differ significantly (p=0.05)THDL: thrips damaged leaves, WMDL: Whorl maggot damaged leaves, DH: dead hearts, WE: white ears

Table 4. Incidence of blast and sheath rot as influenced by organic sources at Rajendranagarduring kharif 2008

Treatments % leaf blast % neck blast % sheath rot Untreated control 30.19d 35.06d 47.8c

FYM @5t/ha 16.80c 19.90c 26.2b

Vermicompost @ 5t/ha 16.67c 19.80c 26.0b

Neem cake @ 5t/ha 14.26b 17.01b 22.1a

Pongamia cake @ 5t/ha 13.09a 15.45a 20.1a


EVALUATION OF DIFFERENT ORGANIC NUTRIENT SOURCES

continuous study, the yields during rabi reached parityby 5th year after build up of sufficient soil fertilitythrough improved soil biological activity.

During these two seasons there was reducedincidence of thrips (11.09 and 11.31%) withvermicompost application (Table 3) compared tountreated control (16.67 and 14.27%). However, therewas no significant difference in incidence of whorlmaggot or stem borer vis a vis organic nutrientsources.

Kajimura et al., (1995) and Chandramani etal., (2009) reported reduced incidence of plant andleafhoppers when neem cake was added as organicnutrient source. Studies indicated that Karanjin (analkaloid present in Karanj cake) has a dramaticantifeedant/repellent effect, with many insectsavoiding treated crops. It suppresses ecdysonehormone and, thereby, it acts as an insect growthregulator (IGR) and antifeedant. There were claimsthat it inhibits cytochrome P450 in susceptibleinsects and mites (Copping, 2009).

Incidence of leaf blast, neck blast andsheath rot was low (Table 4) in the treatment whereKaranj cake was applied (kharif 2008). Similarobservations were made by Barnwal et al., (2007).The organic sources like karanj cake and neem cakecontain certain alakaloids which inhibit the fungalgrowth and sporulation. As there is meagerinformation on these aspects, further study wasenvisaged.

Based on the studies it can be inferred that,varieties like Sungandha Samba and Tellahamsa aresuitable for organic rice production for kharif and rabi,respectively. Addition of nutrient sources likevermicompost and karanj cake could not only helprealize higher grain yield but also to minimize problemdue to biotic stresses under organic rice cultivation.

Integration of these components in organic farmingwould pave way for managing insect pests anddiseases in rice, apart from their role as nutrientsources.

REFERENCES:

APEDA, 2011. Agricultural and Processed FoodProducts Export Development (APEDA):http://www.apeda.gov.in/apedawebsite/organic/Organic_Products.htm

Barnwal, M. K., Agarwal, B. K., Prasad, S. M. 2007.Effect of different levels of nitrogen and karanjcake in relation to occurrence of diseases andyield of rice. Journal of Plant Protection andEnvironment 2007 Vol. 4 (2): 122-125.

Chandramani P., Rajendran, R.P., Sivasubramanianand Muthiah, C. 2009. Management of hoppersin rice through host nutrition – A novelApproach. Journal of Biopesticides 2(1): 99-106.

Copping, L.G [ed.] 2009. The Manual of BiocontrolAgents. a World Compendium - 4th ed. / ed.L.G. Copping Alton. Published by BCPCcop.XLIV, pp.851

Kajimura, T., Fujisaki, K and Nakasuji, F. 1995. Effectof organic rice farming on leafhoppers andplanthoppers 2. Amino acid content in the ricephloem sap and survival rate of planthoppers.Applied Entomology and Zoology 30(1): 17-22.

Surekha K., Jhansilakshmi, V., Somasekhar, N.,Latha, P.C., Kumar, R. M., Shobha Rani, N.,Rao, K.V and Viraktamath, B.C. 2010. Statusof Organic Farming and Research Experiencesin Rice. Journal of Rice Research Vol.3 (1).23-35.

JAGADEESHWAR et al

STUDY ON THE INCIDENCE OF INSECT PESTS AND NATURAL ENEMIES ASINFLUENCED BY ORGANIC TREATMENTS IN RICE ECOSYSTEM

A. VENKAT REDDY, R. SUNITHA DEVI, G. ANITHA and D. VISHNU VARDHAN REDDYRegional Agricultural Research Station,

Acharya N.G. Ranga Agricultural University, Warangal - 506 007

J.Res. ANGRAU 40(3) 9-13, 2012


ABSTRACT

A field experiment was conducted in rice (Oryza sativa) at Regional Agricultural Research Station, AcharyaN.G. Ranga Agricultural University, Warangal, AndhraPradesh for four years i.e., during Kharif 2007-08, 2008-09,2009-10 and 2010-11 to study the effect of organic manures with and with out plant protection on the incidence ofinsect pests, natural enemies and yield. The treatments include: 100% Organic manures + Need based plantprotection, 100% Organic Manures + No plant protection, 50% Organic Manures + 50% Recommended Doses ofFertilisers + Need based plant protection, 50% Organic Manures + 50% Recommended Doses of Fertilisers + Noplant protection, 100% Recommended Doses of Fertilisers + Need based plant protection and 100% RecommendedDoses of Fertilisers + No plant protection . With regards to insect pests, the lowest percent dead hearts, silver shootsand hoppers were recorded in 100% Organic manures + Need based plant protection, while highest pest incidencewas recorded in 100% Recommended Doses of Fertilisers + No plant protection. The population of spiders, miridbugs and coccinellids was recorded to be highest in 100% Organic Manures + No plant protection and lowest in100% Organic manures + Need based plant protection. The grain yield was found to be significantly highest in 50%Organic Manures + 50% Recommended Doses of Fertilisers + Need based plant protection (5460.8 kg/ha) followedby 100% Recommended Doses of Fertilisers + Need based plant protection( 5312.5 kg/ha) and lowest in 100%Recommended Doses of Fertilisers + No plant protection (4589.5 kg/ha). Ihe benefit cost ratio was highest in 100%Recommended Doses of Fertilisers + Need based plant protection (1.85) and lowest in 100% Organic Manures + Noplant protection (1.25)

Increased and indiscriminate use of chemicalfertilizers and pesticides during green revolutionperiod resulted in several harmful effects on soil,water and air. This has reduced the productivity ofthe soil by deteriorating soil health in terms of soilfertility and biological activity. The excess /indiscriminate use of pesticides has led to the entryof harmful compounds into food chain, death ofnatural enemies and development of resurgence /resistance to pesticides. Out breaks of insect pestshave occurred after insecticides were over used andoutbreak of brown plant hopper (BPH), Nilaparvathalugens in rice is an example of this over use Wang etal (1994). Rice is the major crop that receivesmaximum quantity of fertilizers (40%) and pesticides(17-18%). Hence, enhancement and maintenanceof system productivity and resource quality isessential for sustainable agriculture. Increasinginsect pest and disease pressure in agro eco systemis due to changes that have occurred in agriculturalpractices since World War II. Usage of fertilizersand pesticides has increased rapidly during this period


and evidence suggests that such excessive use ofagro chemicals in conjunction with expandingmonocultures has exacerbated pest problems(Conway and Presty; 1991). On the other hand,proponents of alternative agricultural methods contendthat crop losses to insect and diseases are reducedwith organic farming (Merrill; 1983 ; Oelhaf; 1978).Hence, studies comparing insect pest and naturalenemies populations on plants treated with syntheticfertilizers versus organic sources of fertilizers areneeded.


A field experiment was conducted duringkharif season for four years (2007-08 to 2010-11) ona deep black soil at Regional Agricultural ResearchStation, Warangal, Andhra Pradesh, to compare theinfluence of manures and conventional fertilizertreatments on the incidence of insect pests, naturalenemies and yield.

A randomized complete block design wasset up with four replications. Each treatment has a

plot size of 200 m2. Variety BPT-5204 was selectedfor the experiment. There were six treatments (table).

The recommended dose of fertilizers were given at100-60-40 kg N, P

2O

5, K

2O/ha through Urea, Single

Super Phosphate, Muriate of potash, respectively,in addition to Recommended quantity of Farm YardManure @ 25 t/ha. Nitrogen was given in three equalsplits at basal, maximum tillering and panicle initiationstages, P as basal, while K was given at basal andpanicle initiation. Through organics, N dose wasadjusted to recommended level based on their ‘N’content on dry weight basis. Organic manures wereincorporated one day before transplanting rice. Theorganic manures used were: Farm Yard Manure,Neem Cake and Vermi compost. In organictreatments need based plant protection sprays weregiven with 5 percent Neem Seed Kernel Extract(NSKE) and in fertiliser treatments, need basedsprayings were given with recommended pesticideswhen the pest reached above Economic ThresholdLevels (ETL). The number of sprays ranged from2-4 depending on the incidence in different seasons.

For recording the incidence of insect pestsand natural enemies, field investigations were carriedout at tillering stage, elongation stage, panicleinitiation stage and milky grain stage and grainhardening stage. The data were recorded for theincidence of insect pests - stem borer, gall midge,plant hoppers and predator populations of spiders,mirid bugs and coccinellids.


I. INSECT PESTS

A. STEM BORER

The incidence of stem borer in terms of deadhearts ranged from 5.7% to 10.0%, while that of whiteear heads ranged from 8.1% to 9.0 %. Lowest meanpercent dead hearts was recorded when need basedplant protection was taken up either with 100%Organics (5.7%) or by adopting RDF with chemicalprotection (5.9%) . The highest percent of dead heartswas noticed when no plant protection measures weretaken with 100% RDF (10.0%) or with 50% OrganicsManures + 50% RDF (9.6%). With regards to percentwhite ears heads, the differences among thetreatments were found to be non significant. Lowerincidence of stem borer in organics compared to

inorganics was reported by several workers.

Surekha et al (2010) reported that, the white ears

and dead hearts in rice were slightly more in inorganic

treatments compared to organics. European corn

borer (Ostria nubilalis) females laid more eggs withchemical fertilization compared to organics (Phelan

et al, 1995.). Outbreak of stem borer in rice crop

wasreported to be low with organics compared to

synthetic inorganic fertil izers (Luong and

Heong.,2005).

B. GALLMIDGE

The percent silver shoots ranged from 15.9%to 26.5%. Significantly lowest percent of silver shoots

was reported when nutrients were met through

organics irrespective of plant protection measures

(15.9 and 18.0%). The highest incidence of gallmidge

was found with the use of fertilizers without any plant

protection (26.5%) with 100% RDF and 22.4% with50% RDF.

C. HOPPERS

The hopper population included brown plant

hoppers and white backed plant hoppers. Lowest

population of hoppers was recorded when rice was

grown solely with organics, either with organics for

plant protection (17.3%) or without plant protection(22.7%) . Their incidence was also lower (22.5%) ,

when 50% fertilizer N was substituted through

organics coupled with need based chemical plant

protection. Chemical plant protection was less

effective on hoppers when rice was grown with

fertilizers (28.3% with protection and 31.1% withoutprotection). Low incidence of insect pests such as

plant hoppers and leaf hoppers was observed in

organically farmed rice fields compared to traditional

fields (Hidaka; 1997 ; Kajimura et al., 1993, and

Kajimura .,1995).

The density of immigrants of the rice planthopper species Sogotella furcifera was significantly

lower and the settling rate of female adults and

survival rate of immature stages of ensuing

generations were generally lower in organic compared

to conventional rice field. Luong, and Heong, (2005)

shared that the organics or manures affected riceplant growth and minimized the outbreak of brownplant hopper.

VENKAT et al

In case of cotton crop, Adkinson (1985)reported nearly three times as many boll weevil larvaeon crop receiving heavy application of fertilizerscompared to unfertilized checks. Lower abundanceof herbivores in low-input system can be partlyattributed to the lower N content in organically farmedcrops (Lampkin, 1990). Increased soluble N levelsin plant tissue following N fertilization were found togenerally decrease pest resistance as reported byPhelan et al (1995).

Chemical fertil izers can dramaticallyinfluence the balance of nutritional elements in plantsand it is likely that their excessive use will createnutrient imbalances, which in turn, reduce resistanceto insect pests. In contrast, organic practicespromote an increase of soil organic matter andmicrobial activity and gradual release of plantnutrients which does not lead to enhanced N levelsin plant tissues. Organic practices can also providesupplies of secondary and trace elements thusproviding a proper balance of elements, which canstimulate resistance to insect attack (Luna; 1988).Ramesh et al (2005) stated that organic crops haveshown more tolerance to insect attacks and organicrice is reported to have thicker cell wall and lowerlevels of free amino acids than conventional rice.

II. NATURAL ENEMIES

The population of natural enemies viz.,spiders, mirid bugs, coccinellids ranged from 14.5 to25.4, 19.6 to 34.2, 11.0 to 20.3 per hill, respectively.The treatmental effects were not significant on spiderpopulation. The population of mired bugs andcoccinellids was highest when rice was grown with100% Organics with out any plant protection ( 34.2,20.3) or with need based organic protection (21.8,15.6) or when 50% fertilizer N was substituted throughorganics without any plant protection (25.8, 15.5).Use of chemicals for plant protection has broughtdown the population of mired bugs and coccinellidswhen fertilizers were used either solely or inconjunction with organics.

Lawanprasert et al (2006) reported higherpopulation of natural enemies particularly spiders,lady bird beetles and dragon flies in the naturallygrown rice than conventional rice. More number ofnatural enemies such as spiders, mirid bugs and

insect pathogenic nematodes were reported in

organically framed rice by Hidaka (1997) and Kajimura

et al (1993). Surekha et al (2010) also reported more

populations of natural enemies such as Platygasteroryzae, mirid bugs and spiders in organic treatmentscompared to inorganics. Hesler et al (1993) and Louis

et al (1993) reported significantly abundant

populations of predators like giant water bugs, back

swimmers and diving beetles in organic fields.

III. YIELD AND BENEFIT COST RATIO

Grain yield of rice was higher when need

based plant protection (5460 kg/ha) was adoptedeither with INM or with 100% RDF (5312 kg/ha).

Chemical fertilization without chemical plant

protection seemed to be less effective to realised

good yields of rice crop. Organic rice gave lower yields

with or without organic plant protection.

The highest benefit cost ratio of 1.85 was

achieved in 100% RDF + Need Based PlantProtection and the next best was INM + Need Based

Plant Protection (1.47) . The returns from organic

rice were comparatively less.

Similar reports of lower yields in organics

compared to inorganics were also reported by other

researchers. A 20-30% less yield of crops in organicfarming was reported by Rajendra Prasad (2006).

Yield loss of organically grown rice was reported to

the tune of 24% (Mader et al: 2002) and Atkison and

Betsy Woods (1999) reported organic rice yield at

50-60% of conventional yields.

The lower benefit cost ration of 1.09 wasrecorded in organic rice compared to 1.37 in in-

organic rice (Surekha et al, 2010). Lowanprasert etal; (2006) reported lower benefit cost ratio in organic

method and attributed this to highest production cost

The experimental results indicated that the

incidence of insect pests was low in organically

grown rice and the population of natural enemies /predators was highest in this system, more so when

plant protection measures were not taken. But ,

chemical farming resulted in higher grain yield and

higher benefit cost ratio. Organic rice production is

more advantageous than conventional farming when

environmental concerns become important.

STUDY ON THE INCIDENCE OF INSECT PESTS AND NATURAL ENEMIES

VENKAT et al

Tab

le. E

ffect

of

org

an

ic a

nd

in

org

an

ic t

reatm

en

ts o

n t

he i

ncid

en

ce o

f in

sect

pests

, n

atu

ral

en

em

ies a

nd

yie

ld o

f ri

ce (

mean

of

4 y

ears

)

Insect

Pests

N

atu

ral

en

em

ies

Tre

atm

en

ts

% D

ead

h

eart

s

% S

ilver

sh

oo

ts

No

. o

f h

op

pers

/hill

% W

hit

e

ears

N

o.

of

sp

iders

/

hil

l

No

. o

f m

irid

b

ug

s /

hil

l N

o.

of

co

ccin

ell

ids /

h

ill

Gra

in

yie

ld

(kg

/ha)

Ben

efi

t-

co

st

rati

o

T 1-1

00%

Org

anic

s +

N

eed

base

d pl

ant

prot

ectio

n 5.

7 15

.9

17.3

8.

9 22

.2

21.8

15

.6

4678

1.

35

T 2-1

00%

Org

anic

s +

No

plan

t pro

tect

ion

8.5

18.0

22

.7

8.7

25.4

34

.2

20.3

47

09

1.25

T 3-5

0% O

rgan

ics

+ 50

%

RD

F +

Nee

d B

ased

pl

ant p

rote

ctio

n 6.

8 19

.6

22.5

9.

0 17

.1

23.7

13

.1

5460

1.

40

T 4-5

0% O

rgan

ics

+ 50

%

RD

F +

No

plan

t pr

otec

tion

9.6

22.4

25

.9

8.3

20.6

25

.8

15.5

50

01

1.47

T 5-1

00%

RD

F +

Nee

d B

ased

Pla

nt P

rote

ctio

n 5.

9 18

.2

28.3

8.

1 14

.5

19.6

11

.0

5312

1.

85

T 6-1

00%

RD

F +

No

plan

t pro

tect

ion

10.0

26

.5

31.1

8.

8 19

.9

25.0

14

.9

4589

1.

35

SE

m ±

3.

03

2.2

5.6

NS

N

S

2.8

2.4

310

CD

at 5

%

0.99

0.

96

2.5

1.2

1.

2 1.

1 13

9

RD

F =

Rec

omm

ende

d D

ose

of F

ertil

izer

s ( 1

00:6

0:40

kg/

ha o

f NP

K)

REFERENCES

Adkisson, P.L., 1985. The influence of fertilizerapplication on population of Heliothis zea andcertain insect predators. Journal of EconomicEntomology. 51:144-149

Atkinson and Betsy Woods. 1999. Acres USA. April.P. 1-9

Conway, G.R and Presty, J. 1991. Unwelcomeharvest. Agricultural pollution. Earthscan,London.

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STUDY ON THE INCIDENCE OF INSECT PESTS AND NATURAL ENEMIES

J.Res. ANGRAU 40(3) 14-17, 2012

FERTILITY STATUS OF RICE GROWING SOILS OF NALGONDA DISTRICT INANDHRA PRADESH - A GIS APPROACH

Y. SUDHA RANI and G. JAYASREEDepartment of Soil Science and Agricultural Chemistry

College of Agriculture, Acharya N. G. Ranga Agricultural University,Rajendranagar, Hyderabad-500 030.


ABSTRACT

A study was undertaken to map the nutrient status of Nalgonda district of Andhra Pradesh. The soil analysisshowed the deficiency of N, P, K and organic carbon in the study area. From the soil fertility maps it was observed thatall rice growing soils in the district are low in organic carbon content and available nitrogen. An area of 9462 ha (6.4per cent) was in the low category and 138390 ha (93.6%) was in the medium category in available phosphorus. Anarea of 14046 ha (9.5%) was in low category and 133806 ha (90.5 %) was in medium category in availablepotassium.


Rice requires application of heavy doses of

nutrients, particularly nitrogen (N) to achieve full yield

potential. Application of fertilizers by the farmers in

the fields without prior knowledge of soil fertility status

might result in adverse effects on soils as well as

crops both in terms of nutrient deficiency and toxicity

either by adequate or overuse of fertilizers (Sharma,

2004). Scan attention was paid to collect the geo-

referenced samples taking whole district as a single

mapping unit. With the invent of modern technologies

of remote sensing, GIS and GPS, it is now possible

to monitor the changes in fertility status of the studied

area with site-specific nutrient requirement of the

crop. The present study was conducted in Nalgonda

district of Andhra Pradesh to know the spatial

distribution of soil nutrients during 2009.


Rice occupied an area of 147 thousand ha,

with production of 420 thousand tonnes and

productivity of 2.84 t ha-1 in Nalgonda district.

Surface soil samples from 0-15 cm and sub surface

soil samples from 15-30 cm depth were drawn from

the ground truth sites from intensively rice growing

areas. Sixty-one geo-referenced soil samples were

collected and location of soil sampling sites (X, Y

coordinates) of the district was worked out with the

help of global positioning system (GPS) used during

collection of soil samples. The soil samples were

ground and passed through 2 mm sieve. Important

soil physical and chemical properties were estimated

by standard procedures. The points having same

category of soil property were grouped into class as

a polygon and the maps for individual nutrients were

generated in Arc GIS.


The soils were slightly acidic to alkaline with

pH ranging from 5.5 to 8.5. Nearly 41 % of soils were

slightly acidic in nature, while 25 % samples were

neutral and 34 % of soils were in alkaline range. The

soil reaction of subsurface soils varied from 6.1 to

8.8 with a mean value of 7.6. The pH of subsurface

soils varied from 6.1 to 8.8 with a mean value of 7.6.

The reaction of applied fertilizer material with soils

colloids, results in the retention of basic cations on

the exchangeable complex of the soil (Nayak et al.,2002 and Madhuvani et al., 2000), thus influencing

the soil pH.

The electrical conductivity (EC) varied from

0.13 to 1.37 dS m-1. The normal EC may be ascribed

to leaching of salts to lower horizons. The electrical

conductivity was more in sub surface soil and ranged

from 0.56 to 1.90 with a mean value of 0.97.

The organic carbon content of the surface

soils varied from 1.1 to 4.8 g kg-1 with a mean value

of 3.7 g kg-1 and all the samples were under low

category. The organic carbon content in subsurface

soils was low and varied from 0.8 to 3.5 g kg-1 with a

mean value of 2.0 g kg-1. All sub surface soils were

also under low category. The low level was further

categorized into 2 levels for map presentation

(Fig 1).

The texture of the soils varied from sandy

loam to sandy clay loamy in nature. The clay content

in these soils, varied from 8 to 26 %, with a mean

value of 15.1 %, while the sand fraction varied from

65 to 82 % with a mean value of 73.2 %. The silt

content of these soils varied from 5 to 21 % with a

mean value of 11.7 %. The texture of the subsurface

soils varied from sandy loam to sandy clay loam.

With increase in the depth of soil, the percent of clay

and silt increased, while the sand content decreased.

The clay content of subsurface soils varied from 13

to 36 % with a mean value of 22 and standard

deviation of 4.96. The sand fraction varied from 52

to 69 % with a mean value of 61 and standard

deviation of 3.82. The silt content of these soils varied

from 10 to 25 with a mean value of 17 and standard

deviation of 3.38.

The cation exchange capacity of surface

soils under the study was in a range of 6.4 to 19.5 c

mol (p+) kg-1soil, with a mean value of 9.8 c mol (p+)

kg-1soil. Of the total samples 80 % of the soils were

low and 20 % of the soils were moderate in CEC.

The CEC content of the sub surface soils varied from

7.6 to 21.1 c mol (p+) kg-1soil, with a mean value of

11.7 c mol (p+) kg-1soil. The CEC of the soils was

low to high and was related to clay and organic carbon

content of the soils (Gangopadhyay et al.1998).

The available nitrogen of the soils varied from

125 to 321 kg ha-1, with a mean value of 212 kg ha-1

and standard deviation of 60.5. Seventy seven per

cent of soils were low in available nitrogen, while 31

% of soils were medium in available nitrogen. The

available nitrogen content of subsurface soils was

low and varied from 67 to 240 kg ha-1, with a mean

value of 125 kg ha-1 with a standard deviation of 38.5.

Organic carbon content in soils was low and thus the

soils also were low in available nitrogen status

(Suribabu et al.,2002). Low level of nitrogen was

further categorized into 3 levels for map presentation

(Fig 2). It is observed that all soils were under low

category (147853 ha).

Phosphorus status of soils under study

ranged from 14.5 to 48.5 kg ha-1, with a mean value

of 25.6 kg ha-1 with a standard deviation of 7.6. Of

the total samples, 46 % were low in available

phosphorus content and 54 % soils were medium in

available phosphorus. In sub-surface soil samples,

it ranged from 7.8 to 31.5 kg ha-1, with a mean value

of 16.1 kg ha-1. Majority of subsurface soils were low

in available P. The variations in phosphorus status

were mapped under GIS environment. (Fig 3). An

area of 9462 ha (6.4 per cent) was in the low category

and 138390 ha (93.6%) was in the medium category.

The available potassium content of soils

varied from 78 to 361 kg ha-1, with a mean value of

174 kg ha-1. Out of the total 61 soils, 41 % of the

soils were low in available potassium. Majority of

soils (56%) were medium in available potassium. The

available potassium content in subsurface soils

ranged from 47 to 280 kg ha-1, with a mean value of

102 kg ha-1. The variations in potassium status were

mapped under GIS environment (Fig 4). Potassium

content in all sub surface soils sample was low. It

could be due to high leaching losses of native and

applied potassium (Ramesh et al., 2003). It was

observed that 14046 ha (9.5%) were low in available

potassium and 133806 ha (90.5 %) were medium in

available potassium.

Based on soil analysis and nutrient maps it

was observed that the fertility status of rice growing

soils of Nalgonda district was low. The organic carbon

and available nitrogen in rice growing soils was low

and available phosphorus and potassium was low to

medium.

FERTILITY STATUS OF RICE GROWING SOILS OF NALGONDA DISTRICT

Fig 1. Variations in organic carbon (g kg-1) content inNalgonda district Fig 2. Spatial distribution of available nitrogen (kg ha-1)

in Nalgonda district

Fig 3. Spatial distribution of available phosphorus(P2O5) in Nalgonda district (kg ha-1)

Fig 4. Spatial distribution of available potassium (K2O)in Nalgonda district (kg ha-1)

SUDHA and JAYA

REFERENCES

Gangopadhyay, S. K., Walia, C. S., Chamuah, G. Sand Baruah, U. 1998. Rice growing soils ofupper Brahmaputra valley of Assam-theircharacteristics and suitability. Journal of theIndian Society of Soil Science. 46(1):103-109.

Madhuvani, P., Bhanu Prasad, V., Seshagiri Rao, Mand Prasuna Rani, P. 2000. Physical, physic-chemical and chemical properties of soilsdeveloped on granite-gnesis and sand stone.The Andhra Agricultural Journal. 47: 301-305.

Nayak, R., Sahu, G. C and Nanda, S. S. K. 2002.Characterization and classification of the soilsof Central Research Station, Bhubaneswar.Agropedology, 12 : 1-8.

Ramesh, K., Bhanu Prasad, V and Seshagiri Rao,

M. 2003. Physical nature and fertility status

of soils of Singarayakonda mandal in prakasam

district of Andhra Pradesh. The AndhraAgricultural Journal. 50:54-57.

Sharma, P. K. 2004. Emerging technologies of remote

sensing and GIS for the development of spatial

infrastructure. Journal of the Indian Society ofSoil Science 52: 384-406.

Suribabu, K., Bhanu Prasad, V., Seshagiri Rao, M

and Prasuna Rani, P. 2002. Nutrient status of

soils of A.Konduru mandal in Krishna district

of Andhra Pradesh. The Andhra AgriculturalJournal. 49: 155-158.

FERTILITY STATUS OF RICE GROWING SOILS OF NALGONDA DISTRICT

J.Res. ANGRAU 40(3) 18-20, 2012


ABSTRACT

POPULATION OF MELOIDOGYNE SP AND RHIZOCTONIA SP IN TOMATO INDIFFERENT AGRO CLIMATIC ZONES OF ANDHRA PRADESH

B. VIDYA SAGAR, V. KRISHNA RAO and K. S. VARA PRASADDepartment of Plant Pathology, College of Agriculture, Acharya NG Ranga Agricultural University,

Rajendranagar, Hyderabad-500030

A survey was conducted during 2007- 2008 to study the on the population of root knot and other plantparasitic nematodes and Rhizoctonia sp, in deferent agro climatic zones and results revealed that a high density of171.11/250 cc soil and 94.88/250 cc soil of Meloidogyne and other nematodes, respectively were recorded inSouthern zone, while a low density of 28.08/250 cc soil and 7.49/250 cc soil were recorded in North TelanganaZone. The occurrence of Rhizoctonia was also high in Southern zone with 5.19X 103 cfu/ g soil. The variation in thepopulation of both pathogens in different zones could be due to variation in cropping sequence, interaction andenvironmental factors prevailed in the respective zones.

Tomato (Lycopersicon esculentum Mill.) isan important vegetable crop grown widely throughoutthe world under various agro-climatic conditions andacquired the status of one of the world’s most popularvegetables. Plant parasitic nematodes are estimatedto cause monetary loss to the extent approximately21.0 million rupees in different crops in the country(Jain, et.al, 2007). Among these, root knot nematode,Meloidogyne incognita (Kofoid and White) Chitwood,is worldwide in occurrence in most of the vegetablecrops resulting in crop losses ranging from 15 to 60per cent and monetary loss of about 2204 millionrupees in tomato and has become a major constraintto successful cultivation of tomato in India. Similarly,Rhizoctonia solani Kuhn is also an important soil bornepathogen and is widely distributed in nature. Thepathogen causes several complex symptoms viz.,damping off, root rot and wilt symptoms etc, resultingin about 69.25 per cent yield losses in tomato crop(Mehta and Gupta, 1992). Several workers havereported that the association of root knot nematodewith Rhizoctonia solani aggravates the diseaseincidence resulting in huge yield loss (Chahal andChhabra, 1984, Khan and Reddy, 1993, Kumhar,2001). Further, the populations of the nematode andpathogen complex may vary with agroclimaticsituation. Hence, keeping the above in view, thepresent investigation was carried out to know theeffect of agroclimatic zones on population ofMeloidogyne incognita and Rhizoctonia solani ontomato.



A survey of major tomato growing areas ofAndhra Pradesh was carried out to determine theextent of root knot and root rot disease complex duringkharif, 2009. The soil and root samples werecollected from sufficiently wet field from 10-15random locations, from the rhizhosphere of tomatocrop. Each sample was filled in a polythene bag andtied with a rubber band and labelled immediately.Information pertaining to the locality, crop history etc.,was also obtained along with samples as given inthe data sheet. A composite sample of 250g of soiland 5g of root was used for further studies. The soiland root samples were analysed on the day ofcollection or after keeping for a few days underrefrigerated conditions.

The propagules of fungi were estimated byplating the rhizosphere soil following serial dilutionplate technique. Nematode Population in soil wasestimated by combined gravity sieving and Baermannfunnel technique (Christie and Perry, 1951).

Root samples of known quantity were directlyobserved under stereo binocular microscope forcounting adult females of sedentary nematodes andthe same was processed using blending andBaermann funnel method (Ayoub, 1977) for theextraction of active forms of sedentary as well asmigratory nematodes. The nematodes were identifiedas per the keys provided by Mai and Lyon (1975),Taylor and Sasser (1978) and Jepson, (1987).

POPULATION OF MELOIDOGYNE SP AND RHIZOCTONIA SP IN TOMATO

Tab

le 1

. P

opul

atio

n of

roo

t kn

ot a

nd o

ther

pla

nt p

aras

itic

nem

atod

es a

nd R

hizo

cton

ia s

p. a

ssoc

iate

d w

ith t

omat

o in

diff

eren

t ag

ro c

limat

ic z

ones

A

ndhr

a P

rade

sh

* H

elic

otyl

ench

us s

p., H

oplo

laim

us s

p., P

raty

lenc

hus

sp. R

otyl

ench

ulus

sp.

, Tyl

ench

oryn

chus

sp

Ave

rage

den

sity

(D

) fr

eque

ncy

(F)

and

Per

cent

age

(P)

of n

emat

odes

in 2

50

cc g

soi

l/ 5

g of

roo

t M

eloi

dogy

ne s

p.

Oth

ers*

T

otal

Fre

quen

cy o

f oc

curr

ence

(%

) A

gro

clim

atic

zo

ne

Tot

al

sam

ples

S

oil

Roo

t T

otal

S

oil

Roo

t T

otal

Soi

l R

oot

Gra

nd

Tot

al

Rhi

zoct

onia

pr

opag

ules

cf

u/g

soil

x 10

3 S

oil

Roo

tN

orth

coa

stal

zo

ne

10

D

F

P

67.2

80

.0

45.4

0

34.1

80

.0

71.3

3

101.

3 80

.8

80.0

13.7

40

.0

94.5

14

8.0

47.8

19

5.8

0.0

0 0

God

avar

i zo

ne

4

D

F

P

56.7

5 10

0.0

33.0

4

45.5

10

0.0

85.4

4

102.

2511

5.0

75.0

7.

75

50.0

12

2.75

17

1.75

53

.25

225.

0 0.

0 0

0

Kris

hna

zone

7

D

F

P

20.5

7 57

.14

29.5

0

14.8

5 57

.14

60.4

6

35.4

2 49

.14

71.4

9.

71

42.8

5 58

.85

69.7

1 24

.56

94.2

7 0.

0 0

0

Sou

ther

n zo

ne

34

D

F

P

103.

67

94.1

1 50

.47

67.4

4 94

.11

71.0

7

171.

1110

1.70

10

0.0

27.4

4 61

.76

129.

14

205.

37

94.8

8 30

0.25

5.

19

11.7

6 14

.70

Nor

th

Tel

anga

na

zone

12

D

F

P

22.0

41

.66

64.5

5

6.08

41

.66

81.1

7

28.0

8 12

.08

41.6

6 1.

41

24.9

13

.49

34.0

8 7.

49

41.5

7 1.

76

8.3

8.3

Sou

th

Tel

anga

na

zone

12

D

F

P

69.3

3 74

.99

47.3

5

42.9

1 74

.99

54.0

3

112.

2477

.08

91.6

6 36

.5

74.9

9 11

3.58

14

6.41

79

.41

225.

82

0.0

0 0

Cen

tral

T

elan

gana

zo

ne

10

D

F

P

36.2

60

.0

62.4

1

17.3

60

.0

81.6

0

53.5

21

.8

70

3.9

20

25.7

58

.0

21.2

79

.2

0.0

0 0

Sca

rce

Rai

nfal

l zon

e 9

D

F

P

52.8

8 66

.66

67.1

4

23.3

3 55

.55

80.1

7

76.2

1 25

.88

66.6

6 5.

7 33

.33

31.6

5 78

.76

29.1

10

7.86

0.

0 0

0

To

tal

98

4

28

.6

25

1.5

16

80

.11

48

3.4

81

06

.11

58

9.6

69

12

.08

35

7.6

9

12

69

.77

6

.95


In case of soil samples, highest density ofMeloidogyne (103.67/250cc) was observed inSouthern Zone, and lowest (22.0) in North TelanganaZone (Table 1). The occurrence was noticed in allthe samples in Godavari zone while it was noticed in41.66 % in North Telangana Zone. The percentagepopulation was more (67.14) in Scarce rainfall zoneand less (29.50) in Krishna zone.

In case of root samples, the maximumdensity of Meloidogyne sp. (67.44/5g) was observedin Southern Zone while the frequency and percentagewas high in Godavari zone with 100.0 and 85.44respectively. The density of Meloidogyne sp. wasminimum (6.08) in North Telangana Zone with a lowfrequency (41.66) and percentage (81.17). The totalMeloidogyne sp. population (soil and root) werehighest (171.11) in Southern Zone while lowest inKrishna zone. The other nematodes were also high(129.14) in Southern zone and low (13.49) in NorthTelangana Zone.

The population of Rhizoctonia sp. in soil washighest (5.19x 103) in Southern Zone (Table 1),followed by North Telangana Zone (1.76 x103). TheSouthern Zone recorded highest frequency ofoccurrence of fungus both in soil (11.76) and root(14.70).

In the present study, a high frequency ofRhizoctonia sp. and Meloidogyne sp. was observedin Southern zone. Naidu et al. (2007) and Sudheer etal. (2008) reported a high prevalence of M. incognitain Chittoor, Ananthapur and Kurnool districts. In thepresent investigation the samples from these districtspositively yielded Meloidogyne population.

The variation in the population of bothpathogens in different zones might be due to variationin cropping sequence, interaction and environmentalfactors prevailed in the respective zones.

REFERENCES

Ayoub, S. M. 1977. Plant Nematology, An AgriculturalTraining Aid. Division of Plant IndustryLaboratory Services, Nematology Sacremento.156 pp.

Chahal, P. P. K and Chhabra, K. 1984. Effect ofMeloidogyne incognita and Rhizoctonia solanion the emergence and damping off of tomatoseedlings. Journal of Research of the PunjabAgricultural University. 21 (4): 642-644

Christie, J. R and Perry V. G. 1951. Removingnematodes from the soil. Proceedings ofHelminthological Society of Washington 18;106-108

Jain, R.K., Mathrr, K.N and Singh. R.V. 2007Estimation of losses due to plant parasiticnematodes on different crops in India. IndianJournal of Nematology 37(2): 219-221.

Jain, R.K., Mathur, K.N and Singh R.V. 2007.Estimation of losses due to plant parasiticnematodes on different crops in India. IndianJournal of Nematology. 37(2): 219-221.

Jepson, S. B. 1987. Identification of Root-knotNematodes (Meloidogyne species). CABInternational, Wallingford, UK. 265 pp.

Khan,R.M Reddy and Parvatha, P. 1993.Management of disease complex. In :Nematode interactions. (ed. M.W. Khan)Chapman and Hall, London, pp. 345-365.

Kumhar, K.C. 2001. Studies on integratedmanagement of root rot of tomato caused byRhizoctonia solani Kuhn. PhD thesis. CCSHaryana Agricultural University Hisar- India.

Mai, W. F and Lyon, H. H. 1975. Pictorial key toGenera of Plant Parasitic Nematodes.Comstock Pub. Association, Cornell UniversityPress. 219 pp.

Mehta, N and Gupta, D.C. 1992. Influence of culturemedia on the pathogenic behaviour ofRhizoctonia solani and R. bataticola on varioushost plants. Plant Disease Research. 7:245-247.

Naidu, P., Harinath., Haritha, V and John sudheer,M. 2007.Community analysis of plant parasiticNematodes in Chittoor district of AndhraPradesh. Indian Journal of Nematology. 37:(2) 2007-211.

Sudheer John, M., Kalairasan, P., Senthamarai, M.,Prabhu, S. S., Prasad Rao, G.M.V., Priya, P.,Harinatha Naidu, P., Haritha,V., Reddy Kumar,M and Shailaja Rani. 2008. Diversity andcommunity structure of major plant parasiticnematodes in selected districts of AndhraPradesh, India. Indian Journal of Nematology.38 (1) 68-74.

Taylor, A.L and Sasser, J.N. 1978. Biology,identification and control of root-knotnematodes. NCSU Raleigh. 111pp.

VIDYA SAGAR et al

J.Res. ANGRAU 40(3) 21-24, 2012


AN EMPIRICAL STUDY OF GROUNDNUT IN ANANTHAPUR DISTRICTOF ANDHRA PRADESH

K. SUPRIYA, A.K.GOEL, M. H. V. BHAVE and V. V. NARENDRANATHDepartment of Statistics and Mathematics,

College of Agriculture, Acharya N.G. Ranga Agricultural University, Rajendranagar, Hyderabad- 500 030

ABSTRACT

Groundnut crop is grown extensively in Ananthapur district of Andhra Pradesh. An attempt was made tostudy the growth of area under cultivation, production and productivity of groundnut for 47 years i.e., 1961 to 2007in the district. The compound growth rates recorded for area, production and productivity were 3.84%, 3.75% and -0.19% respectively. It was also attempted to identify the rainfall patterns and their impact on groundnut crop yield inthe district. A period of 23 years was the most probable cluster for the rainfall pattern in south west monsoon duringkharif season. The average rainfall was estimated as 280 mm for the southwest monsoon. The association betweenrainfall and the crop yields for the most probable cluster was 78% with an average yield of 737 kg ha-1.


In India, during the year 2009, groundnutoccupied an area of 6700 thousand hectares withproduction of 7338 thousand tonnes and productivityof 1095 kg ha-1. Andhra Pradesh ranked second nextto Gujarat with a share of 26.31% of area (1763thousand hectares) and 13.04% of production (957thousand tonnes) with a productivity of 543 kg ha-1.

In Andhra Pradesh, Ananthapur districtranked first with 50% of total area and 30% of totalgroundnut production of the state. The present studywas taken up to examine the growth of area undercultivation, production and productivity of groundnutcrop as well as analysis of rainfall patterns and theirimpact on crop yields in the Ananthapur district onthe basis of time series data covering 47 years (1961-2007).


The crop yield and rainfall data were collectedfrom www.indiastat.com, CMIE and StatisticalAbstracts of Andhra Pradesh State. The shifts in thedata disturb the normal trend of the data and thusthe continuity. These points (years) of discontinuityreferred as ‘knots’ were identified by applying theprocedure of graphical analysis (Kulkarni and Pandit,1988). The growth in the data were measured for theperiods formed with points (years) of discontinuity orknots i.e., 1992 (1961-1991; 1992-2007), instead ofthe overall data.

In the context of agricultural data recorded

over a period of years, the Compound Growth Rate

(CGR) was derived as an exponential equation,

Y = abt

Where, Y = Crop area/production/productivity ,

t = Time (years) and (a, b) = Parameters

The Average Linkage approach proposed by

Kulkarni and Narendranath (2007) was applied to

identify the rainfall patterns. The monthly rainfall data

of 47 years was subjected to cluster analysis to obtain

clusters consisting of years with similar rainfall. The

cluster averages indicated the pattern of rainfall. The

cluster analysis also provided the patterns of rainfall

at different levels. Assured availability of rainfall,

was identified from the cluster, which is relatively

more frequent. The average rainfall corresponding to

this relatively more frequent cluster was regarded as

the most probable “Multivariate Estimate of Rainfall”.

Further, Cluster analysis approach was also

applied to the time series data on crop yield to classify

the years into different clusters representing low yield

years, high yield years and unusual/abnormal yield

years. In the present study the relation of rainfall

pattern with the groundnut yields was quantified by

applying the approach of Kulkarni et al., (2004).


The study indicated that there was aconsiderable increase in the average area ofgroundnut during 1992 to 2007 over the earlier years(Table 1). These groups (1961-91; 1992-2007) werebased on the year of discontinuity or knot. Theincrease recorded was about 90 per cent.

The production data indicated a discontinuityfrom the normal trend during the year 1995. Due tothis point of discontinuity, the data were grouped intotwo sub-periods. The growth rate was thereforemeasured separately within the periods formed bythe year of discontinuity.

Table 1. Growth rates for area, production and productivity of groundnut (1961-2007)

Area (ha)

Period Year Average CV (%) CGR (%)

1 1961-1991 414384 49.62 4.61

2 1992-2007 786513 10.54 1.51

Overall 1961-2007 538427 48.45 3.84

Production (tonnes)


1 1961-1994 310219 68.73 6.17

2 1995-2007 473715 52.66 -3.71

Overall 1961-2007 351962 65.82 3.75

Productivity (kg ha-1)


1 1961-1994 709 28.24 1.19

2 1995-2007 683 56.17 -7.74

Overall 1961-2007 702 36.96 -0.19

As evident by the data, the groundnut croprecorded a considerable increase in the averageproduction during the second period, 1995 to 2007(4,73,715 tonnes) over the first period (3,10,219tonnes). The production data of groundnut recordeda considerable growth of 6.17% during the first periodas compared to a negative growth (-3.71%) in thesecond period. Though, the production level during

the second period was at higher level, the rate ofgrowth, however had exhibited a declining trend ofgrowth. The increase in average production per year(52.7%) was due to increase in area under groundnutcultivation (89.8%). Ninety percent increase in areawith an average increase of 53% in production wasan indicator of loss in productivity per unit area. Singhand Sirohi (1974), Reddy (1978), Dandekar (1980),Mathur (2005), Upadhaya and Saxena (2009) alsoused similar approach to study the compound growthrates for various crops.

The year 1995 was identified as the year ofdiscontinuity in the productivity data. Hence, thegrowth rate was measured separately within the two

periods 1961 to 1994 and 1995 to 2007. The averageproductivity during the first period was 709 kg ha-1

and during the second period, it was 683 kg ha-1. Areduction of 3.61% in the average level of productivitywas recorded during the second period. Betweenthese two periods, the average level of productivityin the first period was relatively more consistent with28.24% coefficient of variation (cv) as compared to

SUPRIYA et al

cv of 56.17% during the second period. It wasobserved that the growth in productivity reduced from1.19% during the first period to -7.74% during thesecond period, indicating the loss in level ofproductivity over the years.Analysis of Rainfall and its Association with CropYields

The cluster analysis resulted in identifyingclusters with years of similar rainfall. The year wiserainfall of Ananthapur was classified into 7 clusters(Table 2). Among these, Clusters 3, 4, 5, 6 and 7consisting of only one to two years. The characteristicof these clusters was unusual rainfall in all the fourmonths of Kharif season(jun-sep). Cluster 2 ofrainfall patterns was formed with highest number ofyears i.e., twenty-three years, which accounts for 0.49probability of occurrence. Hence, it was regarded asmost probable ‘representative’ cluster for studyingthe pattern of rainfall. The rainfall pattern of

Table 2. Association between rainfall pattern and groundnut yield (1961-2007)

Levels of Crop Yield (Kg ha-1)

Rainfall Pattern

Rainfall

(June-

Sept)

mm

Cluster 1 Cluster 2 Cluster 3

Cluster 1 (18) 356

337 (4) 737 (9) 1147 (5)

Cluster 2 (23) 280

359 (3) 737 (18) 1136 (2)

Cluster 3 (1)

231 - 967 (1) -

Cluster 4 (1)

397 444 (1) - -

Cluster 5 (1) 251 - 689 (1) -

Cluster 6 (2) 670 324 (1) 560 (1) -

Cluster 7 (1) 559 - 680 (1) -

Average (kg/ ha-1) 355 (9) 735 (31) 1143 (7)

(Figures in parenthesis are the number of common years corresponding to thecombination of crop yield and rainfall pattern cluster)

cluster-1, consisting of eighteen years, had 0.38probability of occurrence. It was regarded as the nextbest ‘representative’ pattern of rainfall. During the fourmonths (June-Sep) of kharif season, the

characteristic of rainfall pattern was highest rainfallin September (about135 mm) and lowest in June(about 55 mm).

The 47 years of groundnut yield data (Fig.1)represented 3 different levels of average yields incluster 1, 2 and 3. Cluster 1 represented ‘low yield’years with average yield of 355 kg ha-1 based on nineyears. Cluster 2, which was the largest cluster withthirty-one years, represented average yield of 735kg ha-1; where as cluster 3 was formed with onlyseven years with a very high average yield of 1143kg ha-1. Thus, the average yield of 735 kg ha-1 of thelargest cluster i.e., cluster-2 represented the potentialyield in Ananthapur district.

The Two-way association table (Table 2)indicated the twenty three years in the cluster ofrepresentative rainfall pattern (cluster 2) wasrepresented by three years in cluster 1, eighteen

years in cluster 2 and two years in cluster 3 of thecrop yields. This clearly indicated that the averageproductivity level of cluster 2 (i.e.737 kg ha-1, basedon eighteen years) could be regarded as most

AN EMPIRICAL STUDY OF GROUNDNUT IN ANANTHAPUR DISTRICT

‘assured’ yield resulting out of the representativerainfall pattern.

Similarly, the association between the nextrainfall pattern (cluster 1) and the crop yields indicatedthat this rainfall pattern also contributed to a majorextent, in influencing the crop yields of cluster 2having nine years common with the rainfall pattern.The remaining rainfall patterns have however no major

SUPRIYA et al

influence on the crop yields, as seen from the numberof common years in the respective clustercombinations. The results of the association betweenrainfall patterns and the groundnut crop yields under‘normal’ i.e., representative rainfall pattern of cluster2 revealed the potential of 737 kg ha-1 with theassociation of 78 per cent crop yields in theAnanthapur district.

REFERENCES

Dandekar, V. M. 1980. Introduction to seminar ondatabase and methodology for the study ofgrowth rates in agriculture. Indian Journal ofAgricultural Economics. 35: 1-12.

Kulkarni, B.S and Pandit, S.N.N. 1988. A discretestep in the technology trend for sorghum yieldin Parbhani, India. Agriculture and ForestMeteorology. 42: 157-165.

Kulkarni, B.S., Sreenivasa Rao, T and Krishna Kanth

G. 2004. A study on association of combined

effect of rainfalls on crop yields. Journal of

the Indian Society of Agricultural Statistics.

58: 344-351.

Kulkarni, B.S and Narendranath, V.V. 2007. A

multivariate approach for studying the rainfall

pattern. Journal of Research ANGRAU. 35:

39-45.

Mathur, K. N. 2005. Trend analysis of area, production

and productivity of rice in India. Journal of the

Indian Society of Agriculture Statistics. 59: 39.

Reddy, V. M. 1978. Growth Rates. Economic and

Political Weekly. 132: 806-812.

Singh, C. B and Sirohi, A. S. 1974. Disparities in

agricultural growth and equality in India. Indian

Journal of Agricultural Economics. 29: 234-

247.

Upadhaya, S and Sexana, R. C. 2009. Trends in area,

production and productivity of rainfed cereal

crops in central Rajasthan. Current Agriculture.

33(1): 99-106.

J.Res. ANGRAU 40(3) 25-28, 2012



Farmers buy agricultural inputs in theirlocality more often from private traders like inputretailers than government agencies (Mitra, 1999). Inspite of the business attitude, the input retailersprovide various services to the farmers (Jana, 2005).Agro input retailers are the most visible person ofagricultural extension at the grass root with their easyaccess to the farmers. Their round the clock servicesand contributions in agriculture development has beenrelatively less recognized and cultured till date. Astudy was undertaken to understand the sales aswell as services provided by different categories ofthe farm input retailers towards development of thefarming community.


The present study was conducted in sixblocks of South 24 Parganas district of West Bengal,selected through multistage random sampling fromeighteen districts of the State during 2009. The farminput retailers who were dealing with pesticide alongwith fertilizers, seed and farm implements wereconsidered in this study and the sample was drawnfrom pesticide retailers specifically so as to getinformation regarding different categories of inputretailers in best possible way. Probability proportional

STUDY OF SALES AND SERVICES PROVIDED BY THE FARM INPUTRETAILERS OF WEST BENGAL

A. DAS and D. BASUState Agricultural Management and Extension Training Institute (SAMETI),

R. K. Mission Ashrama, Narendrapur, West Bengal-700 103

ABSTRACT

A survey of 118 farm input retailers selected from six blocks of South 24 Parganas district of West Bengalwas conducted in 2009 to study type the sales and services of agri-input retailers in promotion of agriculturalextension and development. It was observed that all the respondents sold pesticides like insecticides, fungicides,nematicides and bactericides, and fertilizers. Eighty five per cent of the amount of sale value emerged from chemicalfertilizer, chemical pesticide and seed, and the rest were contributed by other inputs. In respect of total sale value theretailers were categorized into three groups; retailers with high sale (RHS), retailers with medium sale (RMS) andretailers with low sale (RLS). Twenty six customer services offered by the retailers were ranked according to theirperformance and priority. Some of the important services which were prioritized by all the categories of retailerswere, ‘taking care all types of farmers in technology dissemination irrespective of caste and class’, displayingmagazines, booklets, leaflets, posters etc. for farmers’ awareness’, ‘selling various agricultural products in rightprice’, ‘promoting judicious use of agricultural inputs’, ‘supplying right inputs and information at right time’ etc.

to size sampling technique was followed to selectsample from the pesticide retailers from each block.One hundred eighteen retailers from the total pesticideretailers of 233 from the selected blocks wereconsidered as final sample. The sample retailers werecategorized as retailers with high sale (RHS), retailerswith medium sale (RMS) and retailers with low sale(RLS) on the basis of total sale value (mean splitwith standard deviation). The respondents were askedto respond to services provided by them in a 4 pointcontinuum of ‘always’, ‘sometimes’, ‘seldom’ and‘never’ with weights 4, 3, 2, 1 respectively. The datawere collected through personal interview.Spearman’s rank correlation coefficient was used tofind out the association among the three types ofretailers in respect of their services.


Agricultural inputs sold by the farm input retailer:Pattern of sales of agricultural inputs followed by allcategories of input retailers was similar. So, thepooled data were taken to study the sales providedby all categories of farm input retailers. There arefive kinds of agricultural inputs viz. pesticide (chemicalinsecticide, chemical fungicide, bio-insecticide andbio-fungicide, herbicide, rodenticide, nematicide and

bactericide), plant growth hormone, seed, fertilizer(chemical fertilizer, micronutrient and bio-fertilizer),and agricultural implement and machinery (paddythresher, knap-sack sprayer etc.) which are usuallysold by the farm input retailers. It has been observedthat, all 118 respondents (100 per cent) sold pesticidelike insecticide, fungicide, nematicide, bactericideand fertilizer. Ninety nine respondents (83.9 per cent)sold rodenticide and 72 respondents (61.0 per cent)sold herbicide. Fifty eight respondents (49.2 per cent)sold seed especially paddy seed of improved andhigh yielding varieties and vegetable seed in smallquantities. Ninety four respondents (79.7 per cent)sold plant growth hormone. Study also reveals that85 respondents (72.0 per cent) sold micronutrient,70 respondents (59.2 per cent) sold bio-fertilizer and60 respondents (50.8 per cent) sold bio-pesticide.Farm implement and machinery especially paddythresher and sprayer were sold by 61 respondents(51.7 per cent) in the study area. The present findingsregarding distribution of agricultural inputs sold bythe farm input retailer are in agreement with theobservations of Mitra (1999) and Jana (2005).

Share of various agricultural inputs in sale valueof the farm input retailer: The share of fertilizer wasthe highest (50.91 per cent) in terms of sale valuefollowed by pesticide (19.78 per cent) and seed (14.04per cent) in the study area. The contributions fromother inputs (bio-fertilizer, bio-pesticide, micronutrientand growth hormone, farm implement, farm machineryand plant protection equipment) were only 15 per cent.

Services provided by the farm input retailer: Thethree categories of retailers, RHS, RMS and RLS,identified twenty six services provided by them.Those services were ranked according to the priorityas perceived by the retailers. RHS ranked ‘Displayingmagazines, leaflets, booklets, posters etc. forfarmers’ awareness’ and ‘taking care all types offarmers in technology dissemination irrespective ofcaste and class’; ‘providing idea about plant protectionmeasures’ and ‘promoting judicious use of agriculturalinputs’; and, supplying right inputs at right time’ and‘selling various agricultural products in right price’ asfirst, second and third position respectively. BothRMS and RLS ranked ‘taking care all types of farmersin technology dissemination irrespective of caste and

class’, and ‘displaying magazines, leaflets, booklets,posters etc. for farmers’ awareness’ as first andsecond position respectively. ‘Promoting judicioususe of agricultural inputs’ was ranked as third positionby RMS whereas, RLS prioritized ‘selling variousagricultural products in right price’ as third position.In general, the services which were more or lessprioritized by all the categories of the retailers were‘taking care all types of farmers in technologydissemination irrespective of caste and class’,‘displaying magazines, booklets, posters etc. forfarmers’ awareness’, ‘selling various agriculturalproducts in right price’, ‘promoting judicious use ofagricultural inputs’, ‘providing idea about plantprotection measures’, ‘supplying right inputs at righttime’, ‘organizing farmers’ training programme onbehalf of agricultural department/company’ etc.(Table 1). Services like ‘paying active roles to carryout various activities of farmers’ organization’ and‘highlighting the advantages of organic farming’ wereprioritized by RHS possibly due to the fact that suchtype of retailers had enough fund and manpower tohelp the farmers’ organization effectively, and thetechnical persons who worked under them couldpromote and made the farmers aware about the

advantage of farming using organic manures and bio-

pesticides. Though there are some differences in

placing the priority on various services, a strong

significant association was observed among RHS and

RMS and, RMS and RLS. Significant association was

not observed between RHS and RLS indicating nature

and extent of services provided by RHS and RLS

were independent . Though not categorized, various

services of the input retailers were reported by many

researchers. (Jana, 2005; Parikh et al., 2007)

Therefore, it may be concluded that the farm

input retailers along with sales of various agricultural

inputs provides various services to the farmers

facilitating the farm production process both directly

and indirectly through their multidimensional services.

In order to undertake extension intervention and

developmental strategies retailers, particularly

retailers with low investment should be addressed

separately. However, this finding has far reaching

implications beyond the study area.

DAS and BASU

Services Provided

RHS RMS RLS

Mean score

Rank Mean score

Rank Mean score

Rank

Displaying magazines, leaflets, booklets,

posters etc. for farmers’ awareness

3.90 1.5 3.91 2 3.9 2

Taking care all types of farmers in

technology dissemination irrespective of

caste and class

3.90 1.5 4.00 1 4.00 1

Providing valuable suggestions to solve

different problems of cultivation

3.41 4.5 3.23 9.5 2.90 12

Selling various agril. products in right price 3.65 3.5 3.71 4 3.81 3

Providing idea about plant protection

measures

3.82 2.5 3.34 8.5 3.44 7

Organizing farmers’ training programme on

behalf of agril. department/company

2.73 9 3.34 8.5 3.50 6

Helping in linking the farmers with the

extension agencies (public and private) for

demonstration and other extension work

2.44 12 3.00 11 3.32 8

Conveying likes and dislikes of the farmers

to the wholesalers and producers and

bridges the gap between them

2.20 14 2.64 15 2.98 11

Promoting judicious use of agril. inputs 3.82 2.5 3.84 3 3.66 4

Selling products in credit specially when

farmers are affected by natural calamities

2.31 13.5 2.44 17 2.40 17

Providing credit at the time of selling inputs 2.31 13.5 3.14 10 1.88 22

Providing right information at right time 2.53 11 3.53 6 3.64 5

Supplying right inputs at right time 3.65 3.5 3.63 5 3.18 9

Giving referral services to the farmers 2.64 10 2.92 12 3.10 10

Convincing farmers about the importance of

soil testing

2.80 8 2.68 14 2.00 21

Providing guarantee for the inputs sold by

him to the farmers and assures the risk for

bad quality

1.13 15 1.93 22 2.33 18

STUDY OF SALES AND SERVICES PROVIDED BY THE FARM INPUT

Table 1. Services provided by the farm input retailer

Provi

disse

produ

Provi

which

Creat

about

Verify

Payin

activi

Provi

repai

Highl

Provi

farme

Rank

RHS = Retailers with high sale; RMS = Retailers with medium sale;

RLS = Retailers with low sale

** Significant at 0.01 level of significance; N.S.= Non-significant

REFERENCES

Jana, H. 2005. Agricultural input retailers and theirrole in extension. Ph. D. Thesis (Unpublished)submitted to Dept. of Agricultural Extension,Bidhan Chandra Krishi Viswavidyalaya, Nadia,West Bengal.

Mitra, S. 1999. Study on pesticide market asperceived by retailers. M.Sc. (Agri.) Thesis

(Unpublished) submitted to Dept. of AgriculturalExtension, Bidhan Chandra KrishiViswavidyalaya, Nadia, W. Bengal.

Parikh, T. S., Berkeley, U. C., Patel, N andSchwartzman, Y. 2007. A survey of informationsystems reaching small producers in Globalagricultural value chains. Retrieved on 15.2.09from http://www.stanford.edu /~neilp/pubs/ictd2007.pdf.

DAS and BASU

ABSTRACT

e-Sagu is an IT based personalized agro-advisory system commissioned in Warangal district of AndhraPradesh in kharif 2004. The study was under taken to find out benefits of this project. Exploratory research designwas adopted for conducting the study. The most important services that was accessible to majority of farmers werepest/disease warning and control and fertilizer management. Marketing information was not accessible to all thefarmers. Pest/Disease warning and control service was most utilized by 64 per cent farmers and fertilizer managementservice was utilized by 28 per cent farmers. Majority of farmers had low(40%) and very low (38%) level of satisfaction.

J.Res. ANGRAU 40(3) 29-32, 2012

CRITICAL ANALYSIS OF e-SAGU- AN INFORMATION AND COMMUNICATIONTECHNOLOGY PROJECT IN ANDHRA PRADESH

P. GANESH KUMAR, R. RATNAKAR and P. GIDDA REDDYExtension Education Institute, College of Agriculture

Rajendranagar, Hyderabad- 500030

The Information and CommunicationTechnology (ICT) provides the flexibility in providinginformation on various modes of farming practicesfor all the crops, specific commodities and real timeprice information and all other information related totechnological advances. Thus the ICT play anincreasingly important role in linking the research-extension-market continuum towards developingprofessional competencies and entrepreneurialcapabilit ies among specialists and farmingcommunities. There is huge potential to harnessICT for agricultural development. Only a few isolatedprojects have been initiated in different parts of India(Meera, 2002). One of the prerequisites of harnessingICTs in the field of agriculture development is to buildstrong interfaces between the farming communityand ICT. e-Sagu (Sagu means cultivation in Telugulanguage) is a tool for IT based personalized agro-advisory system commissioned in Warangal districtof Andhra Pradesh state in India by IIIT in kharif 2004.It aims to improve farm productivity by delivering highquality personalized (farm specific) agro expert advicein a timely manner to each farm at the farmers’ doorsteps. How far the e-Sagu project is successful inproviding the information services to the farmersbecomes an important issue to be explored. Thestudy was therefore undertaken to analyze thebenefits accrued to the farmers of this project area.


Exploratory research design was adopted forconducting the study. The study was conducted in

Warangal district of Andhra Pradesh. The district wasselected because e-Sagu project is operating in thedistrict for the last 3 years. Two villages were selectedrandomly for the study. The respondents who hadenrolled their names to avail the services of e-Saguwere called “users” for the purpose of study. Twentyfive farmers from each village were selected. A totalof 50 users constituted the sample for the study.

The perceived benefit was measured in termsof their access to services and their utility and overallsatisfaction of the farmers towards this initiative. Theaccess was measured based on the scheduledeveloped on six parameters for e-Sagu. Therespondents were asked to mention their access toservices based on the five-point scale developed asalways, most often, often, less often, and not at alland the scores assigned were 4, 3, 2, 1 and 0respectively.

The utility was measured based on theschedule developed for measuring access on threepoint continuum viz., mostly utilized, utilized and notat all utilized and the scores assigned were 2, 1, and0 respectively.

The farmers’ satisfaction towards servicesof ICT project was measured following five pointcontinuum with the scores of 0, 1, 2, 3 and 4 forstrongly disagree, disagree, undecided, agree andstrongly agree respectively. An index was developedfor all the above three parameters as the ratio ofscore obtained to the maximum score expressed inpercentage.



GANESH et al

In case of other dimensions, some open endedquestions were posed to the farmers, to get deeperinsight into the situations prevailed and responseswere sought. Besides this, the results of this initiativeare examined by non- participant observation and bydirect questioning of the farmers in the project area.


In case of e-sagu, the advice is provided on a regularbasis (typically once in a week) from sowing toharvesting with an aim to reduce the cost ofcultivation and increase the farm productivity as wellas quality of agri-commodities. In e-Sagu, rather thanvisiting the crop in person, the agricultural scientistdelivers the expert advice by getting the crop statusin the form of digital photographs and otherinformation. The farmers are the end users of thesystem.

The operation of e-Sagu is as follows, a teamof agricultural experts work at the e-Sagu (main lab)supported by agricultural information system. Onee-Sagu local centre (few computers and computeroperator) is established for a group of about tenvillages. Educated and experienced farmers (who arefrom the villages) work as coordinators. Dependingon the crop, the coordinator is assigned with fixednumber of farms. The coordinator collects theregistration details of the farms under line, includingsoil data, water resources and capital availability andsends the information to main e-Sagu system.Everyday, the coordinator visits a fixed number offarms (10-15) and takes 4 to 5 photographs for eachfarm. A CD is prepared with the photographs andthen information is transported to the main systemby a regular courier service. The agricultural expertswith diverse back ground (Entomology, Pathology,Agronomy, Soil Science and other subjects) at thee-Sagu lab analyse the crop situation with respect tosoil, weather and other agronomic practices andprepare a farm specific advice. At the local e-Sagucentre, the advice is down loaded electronicallythrough internet. The coordinator collects the adviceprint outs and delivers them to the concerned farmer.In this way each farm gets the proactive advice atregular intervals starting from pre sowing operationsto post-harvest precautions.

The project focuses on the followingservices. 1) pest/disease warning and control 2)

fertilizer use in terms of amount and timing 3) choiceof crops based on soil and other information 4)scheduling of crop activities 5) weather informationand 6) marketing information.

Pest/disease warning and control was themost important service that was accessible tomajority (90%) of the farmers and ranked first(Table1).Fertilizer use and management was ranked second(88%) in terms of accessibility. scheduling of cropactivities (44%) was ranked third in terms ofaccessibility. Choice of crops based on soil and otherinformation service was accessible to 38 per cent,but it was less often. Weather information was alsoless often accessible to 20 per cent farmers.Marketing information was not at all accessible tocent per cent farmers, as it was not covered by projectstaff till now.

A glance at the results, indicate that majorityof the farmers having accessibility to pest/diseasemanagement and fertilize management. This mightbe because of project functionaries may be focusingon these two activities, because they were importantto reduce cost of cultivation, minimize crop loss andincrease profitability. For the remaining services, dueconsideration was not given by project.

Pest/disease warning and control servicewas utilized by 64 per cent farmers(mostly +occasionally). Remaining 36 per cent did not utilizeany such service. Fertilizer management service wasutilized by 28 per cent farmers, and this service wasnot utilized by 72 per cent. Weather information wasutilized by 16 per cent, scheduling of crop activitiesby 10 per cent and remaining services were not at allutilised by the farmers. This is due to the fact thatthe frequency of providing remaining services wasoccasional by the functionaries.

Only 24 per cent of the farmers opined that e-Saguwas contributing in decision making of farm activitieswith regard to pest/disease management and fertilizermanagement.

With the timely information on pest/diseaseoccurrence they were able to identify the problem inearly stages and could manage the pest/disease withsuitable plant protection measures. Farmers were ableto apply recommended dose of fertilizers, whichhelped in reduced cost of cultivation.

Farmers felt that services offered at kiosk, takes 24-36 hrs for advices and they were spending Rs. 200-300/- per season for getting e-Sagu services.70 percent of the farmers opined that they were not in aposition to afford the services.

Farmers felt that the project should provideservices related to input supply, credit facilities,marketing services, weather forecasting, training onnew technologies and package of practices on cropsgrown in that area.

Overall satisfaction of farmers towards services ofe-Sagu was rated as low by 40 per cent and as verylow by another 38 per cent.No farmer stated the overall satisfaction. Twelve per cent stated thesatisfaction to be medium and ten per cent as high.Majority farmers had low level of satisfaction withrespect to the technical information, coordinatorscompetence and timeliness of services.

For more effective selection of coordinatorsopinion leadership can be identified from personsthrough socio metric technique. This techniqueensures wide acceptability of coordinators byvillagers.

In the study area some farmers noticedreduction in cost of cultivation, with reduction in costof pesticides and fertilizers in cotton crop. This shows

early identification of pest and diseases wouldminimises input cost in crops like cotton and chillies.Hence conducting diagnostic visits by the experts inthe project area would improve the knowledge offarmers and gross root extension workers(coordinators) which results in reduced cost ofcultivation.

e-Sagu extension system should be gearedthrough regular assessment of needs of farmers,improved participation of farmers in e-Sagu extensionmechanism. Project officials should conduct massivecampaigns in the service villages through extensionworkers about the utility of ICT led extensionservices.

Creating awareness is another importantfactor in the successful operation. The servicesoperators should reach out to every village andexplain the benefits of availing services through theinformation kiosk.

Successful implementation of any program / projectwould depend on human resources. They act asinterface between ICT tools and farming community.By giving incentives to the grass root level extensionworkers and extension personnel by fixing targets,access to information services could be improved.

Level of access (in %) S. No.

Service

Farmers having

access (in %)

Always Most often Often Less

often Not at

all Mean score

Rank

1. Pest/disease warning and control

90 64 10 16 - 10 3.58 I

2. Fertiliser use -interms of amount and timing

88 4 8 36 40 12 1.7 II

3. Choice of crops based on soil and other information

38 - - - 38 62 0.41 IV

4. Scheduling of crop activities

44 - - - 44 56 0.44 III

5. Weather information

20 - - - 20 80 0.4 V

6. Marketing information

0 - - - - 100 0 VI

Table 1. Access to various services of e-sagu

CRITICAL ANALYSIS OF e-SAGU- AN INFORMATION AND COMMUNICATION PROJECT

N=50

Table 2. Utility of various services of e-sagu by farmers N=50

Level of utility (%) S.NO Service Mostly

utilized Occasionally

utilized Not at all utilized

1 Pest/disease warning and control 40 24 36 2 Fertilizer use in terms of amount

and timing 4 24 72

3 Choice of crops based on soil and other information

- - 100

4 Scheduling of crop activities - 10 90

5 Weather information - 16 84

6 Marketing information - - 100

REFERENCES

Meera, N. Shaik. 2002. A Critical Analysis ofInformation Technology in AgriculturalDevelopment: Impact and Implications. Ph.D.Thesis submitted to IARI, New Delhi.

Meera, S N., Jhamtani A and Rao, D.U.M. 2004.Information and Communication Technologyon Agricultural Development: A ComparativeAnalysis of Three Projects from India: AgRENNetwork Paper no.135

GANESH et al

J.Res. ANGRAU 40(3) 33- 36, 2012


NON-WOVEN DRAPERY LINING WITH ULTRA VIOLET RESISTANCED. HARINI, A. SHARADA DEVI and D. ANITHA

Department of Apparel and Textiles, College of Home Science,Acharya N.G. Ranga Agricultural University, Saifabad, Hyderabad - 500 004


ABSTRACT

To overcome the negative effects of UV radiation in households, a study had been undertaken to formulatea suitable non-woven drapery lining fabric with UV protective finish. Non-woven fabrics were manufactured throughneedle punching technique with different combinations of cotton, virgin polyester and recycled polyester fibres.Inorganic UV blocking agent ZnO nano particles were selected for UV resistance finish. Finishing process wascarried out through pad-dry-cure technique. This study revealed that finished fabrics composed of 50 % cotton-50%recycled polyester, 30 % cotton- 70 % recycled polyester and 30 % cotton- 70 % virgin polyester scored maximumUPF of 50+ and were found more suitable for drapery lining fabrics.

Approximately 10 per cent of Sun’s radiationis the Ultra violet radiation of which only 5 per centreaches the surface of the earth. The gradualdepletion of ozone layer around the earth has alarmedmany environmentalists through out the globe.

Exposure to ultraviolet radiation is a risk factorin for people spending in outdoor activities. Long-term exposure to UV radiation can result in tanning,ageing of skin, photodermatosis, skin reddening, andsunburn, increased risk of skin cancer, eye damageand DNA damage. To combat the problems encountedwith the increased UV radiation, scientists have beensearching for different ways. In textiles UV protectivefinish is a latest technological advancement.

Nature of the fibre influences the UVtransmittance. Natural fibres like cotton, silk and woolhave lower degree UVR absorption than syntheticfibres such as polyester, because they have hasaromatic group in the molecular structure (Hussainand Jahan, 2010). Cotton fabric in a grey stateprovides a higher Ultraviolet Protection Factorbecause of natural pigment pectin and waxes. The

polyester fibres absorb more in the UV-A and UV-Bregions than aliphatic polyamides. Among the naturalfibres, protein fibres such as silk and wool have betterresistance against UV radiation. The other factorsthat influence the UV resistance include weavedensity, cover factor, porosity, weight and thickness.

UV-A rays are the least powerful of UV rays.They penetrate more deeply into the skin andcontribute to premature ageing of the skin and skincancers, when not significantly filtered by theatmosphere. UV-B rays are the most powerful andpotentially harmful form of radiation. It is the mostcommon cause of sunburn, ageing, wrinkling and skincancer. UVB is particularly strong at the equator, athigh elevations or during the summer months.(Hussain and Jahan, 2010).

The protection extended by the textilematerials is denoted by Ultraviolet Protection Factor(UPF). UPF measures both UV-A & UV-B radiationblocked. A fabric with a UPF 15 allows only 1/15th(6.66 per cent) of the UV-radiation to penetrate theskin as compared to uncovered skin. This wasmeasured as follows:

Table 1. UVR rating scheme

UVR Protection Category UV Protection Factor Mean % UVR Transmission

Moderate UPF 10 to 19 10 to 5.1

High UPF 20 to 29 5.0 to 3.4

Very high UPF 30 to 49 3.3 to 2.0

Maximum UPF ? 50 ? 2.0

(Source: Menezes, 2009).

> >

The fabric construction factors are the mostimportant determinants of UV- radiation. The closerthe weave, the higher the UV radiation protectionbecause the fibres of tightly woven fabrics are closertogether, less UV radiation is able to pass through tothe skin. But later, after the introduction of nonwovens,it was found that these non-woven fabrics have goodUV resistance and flame retardent property than thewoven fabrics. Therefore, nonwovens are replacingthe woven fabrics in the present scenario of hometextiles. (Srinivasan, 2001) Generally curtains are

provided lining material to protect from sunlight and

UV rays. This requires another backing fabric whichalso adds to the weight and cost of the drapery. If a

non-woven lining is applied, it provides light weight

curtain with reduced cost besides providing UV

resistance.

Nano technology is a recent innovation that

covers a wide range of technologies concerned with

structures and procedures on the nanometer scale.Nano technology has a real commercial potential for

the textile industry and considered as latest finishing

technology. Inorganic UV blockers like ZnO, TiO2,

large surface area-to-volume ratio and high surfaceenergy. (Shanmugasundaram,2010)

MATERIAL AND METHODS

An attempt has been made to formulatesuitable non-woven drapery lining fabrics with acombination of cotton, virgin and recycled polyesterand finished with ZnO nano particles. These particlesare advantageous over other inorganic UV blockers,as being chemically stable, environmental friendly,good transparency, better UV-blocking properties,ability to absorb electromagnetic waves, easysynthesis and also commercial availability. Thefinishing process was carried out at differentconcentration levels of 0.2 %, 0.6 % and 1 % usingpad-dry-cure technique. The ZnO nano particles weretaken based on weight of fabric. Material to liquorratio was considered as 1:40. Nano finishing solutionwas prepared by dispersing the soluble starch intowater to make sure of complete exhaustion of nanoparticles in water and maintained in acidic conditionat 60°C. These fabrics were exposed to sunlight for24 hours to assess the efficiency of the finish. Thefabrics are coded as furnished in table 2.

Table 2. Code number for different fabrics

Non-woven Curtain Lining Farbric Control 0.2% UV 0.6% UV 1% UVNano Nano Nano

Finish Finish Finish

50% Cotton + 50% Virgin Polyester A A1

A2

A3

Samples exposed for 24 hrs A4

A5

A6

30% Cotton + 70% Virgin Polyester B B1

B2

B3

Samples exposed for 24 hrs B4

B5

B6

50% Cotton + 50% Recycled Polyester C C1

C2

C3

Samples exposed for 24 hrs C4

C5

C6

30% Cotton + 70% Recycled Polyster D D1

D2

D3

Samples exposed for 24 hrs D4

D5

D6

SiO2 are more stable and prefered than the organicUV blockers as they are non-toxic and chemicallystable under high temperature through nano coatingof the fabrics with UV blocking agents. Nanofinishingis a cost effective technology that provides moreeffective and durable textiles as nano particles have


The fabrics were assessed for theirfunctionality through objective evaluation. The meanweight and tear strength of control and finished fabricsare presented in table 3.

HARINI et al

Table 3. Fabric weight and tear strength of nonwoven fabric

S.No Non-woven Fabrics Exposed samples (24 hrs)

Fabriccode

Fabricweightg/m2

Tearstrength(kgf) kilo-

gram force

Increase%

Fabriccode

Fabricweightg/m2

Tearstrength

kgf)kilogram

force

Increase%

1 A 1.20 64.3 A 1.20 64.3

2 A 1

1.18 66.6 3.5 A4

1.17 67.2 4.5

3 A2

1.15 72.6 12.9 A5

1.14 73.1 13.6

4 A3

1.13 78 21.3 A6

1.13 78.9 22.7

5 B 1.42 66.3 B 1.42 66.3

6 B 1

1.39 70.3 6.0 B 4

1.38 71 7.0

7 B2

1.34 73.3 10.5 B5

1.33 74.1 11.7

8 B3 1.27 78.6 18.5 B6 1.26 79.4 19.7

9 C 1.44 66.6 C 1.44 66.6

10 C 1

1.43 70 5.1 C 4

1.41 71 6.6

11 C 2

1.41 73.6 10.5 C 5

1.40 75 12.6

12 C 3

1.39 76.6 15.0 C 6

1.39 78 17.1

13 D 1.27 72 D 1.27 72

14 D 1

1.18 76.6 6.3 D 4

1.17 76 5.5

15 D 2

1.14 80.3 11.5 D 5

1.14 81.5 13.1

16 D 3

1.11 85.3 18.4 D 6

1.10 86.07 19.5

The weight of non-woven control fabrics orthe GSM ranged from 1.20 in case of sample A to1.44 in case of sample C. Among the finished fabricssample C

1 was heavy and sample A

6 was light weight.

It was observed that all finished fabrics decreased inweight after finishing. It was also obvious that weightloss increased with the increase in the concentrationof the nano ZnO. However, the change in the weightwas not significant statistically.

The tear strength of the fabric is an averageforce required to tear a fabric which was determinedby measuring the work done in tearing a fabric at afixed distance. It is an indication of the efficiency offabrics against UV protection (Booth, 1983).

The tear strength of the control samplesranged from 64.3 kgf in case of sample A and 72 kgfin case of sample D. All treated samples increasedin tear strength. The increase in tear strength was

progressive with increase in concentration of the

finish. At 0.2 per cent level concentration, sample D1

(6.3 per cent) showed maximum increase; at 0.6 per

cent, sample A2 showed maximum of 12.9 per cent

and at 1 per cent level, A3 sample showed maximum

increase of 21.3 per cent. It is interesting to note

that all exposed samples increased in tear strength

when compared to unexposed samples.

The increase in tear strength in all treated

samples might be attributed to criss cross layering

of fibre arrays during the formation of web of non-

wovens, the padding and UV finish. This clearly

indicated the efficiency of the finish as exposure to

sunlight would always lower the strength of textile as

indicated through many studies. (Katangur, 2009).

The finished fabrics were tested for UV

transmittance through UPF rating.

NON-WOVEN DRAPERY LINING WITH UV RESISTANCE

Table 4. UPF values of finished non-woven fabrics

S.No. Fabric UV (A) % UV (B) % UPF rating

1 A 9.79 0.82 30

2 A 1

13.14 1.42 35

3 A2

14.05 1.44 40

4 A3

13.18 1.54 45

5 B 11.12 0.53 40

6 B 1 11.75 0.53 45

7 B2

9.59 0.42 50+

8 B3

10.78 0.46 50+

9 C 1.93 0.18 40

10 C 1

2.51 0.20 50+

11 C 2

3.00 0.24 50+

12 C 3

4.55 0.43 50+

13 D 2.87 0.26 40

14 D 1

3.50 0.31 50+

15 D 2

3.64 0.32 50+

16 D 3

2.68 0.24 50+

Among the control samples, sample B hadhigher UV –A rating than other samples. Sample B

2

had lower UV-A than other treated samples. SampleC and D had lesser UV-A values than other controlsamples. The UV-A values were increased withincrease in finishing concentrations. There was notmuch difference between the control and finishedsamples of fabrics C and D. But the UV-B value hadprogressively increased after each treatment for allthe control samples. The highest UV-B value wasobserved in sample A

3 and least was found in sample

C. The UV-B was high in sample A than other controlfabrics.

The finished fabrics scored maximum UPFrating 50+ than control fabrics with 30 and 40. Thisindicated that the finished non-woven fabrics weremore suitable for drapery lining to protect from UVrays.

By taking UV-A, UV-B and UPF values alongwith tear strength results into consideration, thecontrol and finished samples of B-30% cotton+ 70%virgin polyester, C-50% cotton+50%recycledpolyester and D-30% cotton+70% recycled polyesterfabrics showed high resistance. However, all finishedfabrics were found to be suitable for durable drapery

lining which could provide necessary UV protectionfor the households.

REFERENCES

Booth, J. E, 1983. Principles of Textile Testing – Anintroduction to physical methods of testingtextile fibers, yarns and fabrics. Butter worth’spublications, London:209

Hussain, A and Jahan, S. 2010. Protective Textiles:Protection against UV Radiation. The IndianTextile Journal, 129 (9):20-32.http://www.arpansa.gov.au/uvindex/daily/ausuvindex.htm

Katangur, 2009. http://www.scribd.com/doc/19791954/Nonwoven-Fabrics

Menezes, E. 2009. UV protective finish. Man-madeTextiles Journal. February; 22(2): 59-62.

Srinivasan, R, 2001. Nonwoven fabric having bothUV stability and flame retardancy, Patentno:US 6,309,97 B1. October 30:1-16

Shanmugasundaram, O. L. 2010. Application ofnanotech in textile finishing: A review, TheIndian Textile Journal, November 121(2):16-22

HARINI et al

Research NoteJ.Res. ANGRAU 40(3) 37-40, 2012

MAPPING OF QTLS FOR GRAIN IRON AND ZINC IN SAMBA MAHSURI AND WILDRICE (Oryza rufipogon) USING RM AND GENE SPECIFIC MARKERS

V. ROJA, N. SARLA, K. MANORAMA and K. RADHIKADepartment of Agricultural Biotechnology, College of Agriculture,

Acharya N.G. Ranga Agricultural University, Rajendranagar, Hyderabad -500 030



Rice is one of the cheapest sources of foodenergy and protein. However, it is deficient inessential micronutrients such as iron (Fe) and zinc(Zn). It has limited contents of Fe (2.0-5.0 mg kg-1)and Zn (1.3 -5.0 mg kg-1) after milling (IRRI,1991).The loss of minerals, particularly of Fe, during ricemill ing is high because most of the iron isaccumulated in the aleurone layer. Therefore,micronutrient enrichment i.e. biofortification of foodcrops is the best approach for alleviatingmicronutrient deficiencies. Wild rice (Oryza rufipogon)contributed favorable alleles for Fe, Zn, Mn, Cu, Ca,Mg, P and K. Three QTLs for Zn content wereidentified on chromosomes 5, 8 and 12. The favorableallele for iron content on chromosome 2 wascontributed by Oryza rufipogon. (Garcia-Oliveira etal., 2009).

In this study the parents selected for parentalpolymorphism were Samba Mahsuri (BPT 5204) andO.rufipogon screened with RM primers and genespecific primers. 188 RM markers located on all the12 rice chromosomes and 36 gene specific primersdesigned from genes

YS (Yellow Stripe), FRO (Fe3+-chelatereductase oxidase), ZIP (Zinc regulated transporter /Iron regulated transporter Protein), NRAMP (NaturalResistance - Associated Macrophage Protein),reported to be associated with Fe and Zn homeostasisin plants were used for screening the parents. Totalgenomic DNA was isolated from the leaf samples ofparents using the method described by Zheng et al.,(1991) with some modifications. A mini-preparationprocedure adopted for extraction of total genomicDNA from the leaf samples of 15-30 days old riceseedlings grown in the nurseries of Directorate of RiceResearch, Hyderabad.The purity and concentration

of the isolated genomic DNA samples were estimatedby Nano-drop (Nanodrop Technologies (U.S.A).

PCR was carried out using a programmablethermocycler (Eppendorf, Germany). The PCR plateswere taken and 50ng of template DNA was pipettedout into each of the PCR tubes after proper labelingand kept the PCR plate at 40C. Master mix wasprepared by taking 10 picomolar of each primer (bothforward and reverse primer), 2.5 mM deoxyribonucleotides (dNTPS), Genei 10 X assay buffer(10 mM Tris-Cl (pH 8.3,50 mM KCl,1.5mMMgCl

2,0.01% gelatin) and 1U/ml Taq DNA polymerase

(Bangalore Genei private Limited, Bangalore) andsterile distilled water was added to make up thereaction volume to 10 ml. The quantity of mastermix to be prepared depends on the number of samplesthat are set for PCR. The master mix was centrifugedfor short duration of about 10 sec for thorough mixingof the components and added to each of PCR tubehaving template DNA. The PCR plate was coveredand kept in a thermo cycler for the reaction to takeplace, which was set to thermal profile for Initialdenaturation at 94oC for 5 minutes, Primer annealingat 55oC for 30 seconds and final elongation at 72oCfor 7 minutes. After completion of the PCR, the stripwas stored at 4oC and the amplified products werelater resolved on 3% agarose gel.

Parental polymorphism survey betweenindica cultivar, Samba Mahsuri and wild rice,O.rufipogon was studied using 188 rice micro satellitemarkers chosen based on their distribution throughthe genome and 36 gene specific markers designedfrom the genes to be associated with iron and zinchomeostasis in plants. Among 188 RM primers, 51(27%) were polymorphic and among 36 gene specificmarkers only 3 (8%) were polymorphic. (Table1).

Polymorphic markers identified in the present

investigation were spread over all the chromosomes

except on 5, 10 and 11 used in the present study for

parental polymorphism survey. Out of 54 polymorphic

loci most of the loci showed lighter band in SambaMahsuri and heavier band in O.rufipogon, indicating

that the repeat length of these SSR loci is more in

the wild accession (Fig-1). Polymorphism is the

measure of genetic diversity and varies with the

parents. Earlier reports of mapping yield QTLs using

single accession of O. rufipogon (IRGC105491) asdonor parent and different O. sativa accessions

revealed 60 to 90% polymorphism (Xiao et al., 1998;

Septiningsih et al., 2003; Thomson et al., 2003; Marriet al., 2005). The polymorphism reported in thepresent study is lower compared to the earlier reports.

The lower level of polymorphism may be because ofthe agarose gels used to resolve the bands as minorbase changes may not be detected, or the randomSSR markers used were probably from conservedregions of chromosomes. The polymorphism betweencultivar and O. rufipogon accessions also dependson their genetic similarity (Londo et al., 2006).

Screening of markers for parentalpolymorphism among the parents forms the basisfor tagging and subsequent Marker Assisted Selection(MAS) programmes. The polymorphic RM markerscan be used in studying the segregation pattern inmapping population derived from the cross betweenthese two parents and also identifying the QTLs/genes for high iron and zinc concentration in brownrice.

Table1. List of polymorphic markers between O. rufipogon and Samba Mahsuri (BPT 5204)

S. SSR Chromosome S. SSR ChromosomeNo. markers No. No. markers No.

1 2 3 4 5 6

1 RM 237 1 28 RM 13656 2

2 RM 490 1 29 RM 3688 2

3 RM 1 1 30 RM 3762 2

4 RM 272 1 31 RM 110 2

5 RM 283 1 32 RM 6881 3

6 RM 579 1 33 RM 85 3

ROJA et al

Contd...

S. SSR Chromosome S. SSR ChromosomeNo. markers No. No. markers No.

1 2 3 4 5 6

7 RM 423 1 34 RM 517 3

8 RM 11968 1 35 RM 16 3

9 RM 11952 1 36 NAS 1 3

10 RM 11969 1 37 RM 156 3

11 RM 11962 1 38 RM 448 3

12 RM 11997 1 39 RM 15448 3

13 RM 226 1 40 OSZIP 11 3

14 RM 3515 2 41 ZIPRM5511 4

15 RM 6379 2 42 RM 518 4

16 RM 5651 2 43 RM 470 4

17 RM 5699 2 44 RM 19263 6

18 RM 535 2 45 RM 18 7

19 RM 12486 2 46 RM 21589 7

20 RM 13530 2 47 RM 21596 721 RM 12487 2 48 RM 223 8

22 RM 279 2 49 RM 5485 8

23 RM 13599 2 50 RM 264 8

24 RM 263 2 51 RM 28361 9

25 RM 13514 2 52 RM 205 9

26 RM 12496 2 53 RM 24829 9

27 RM 13630 2 54 RM 235 12

REFERENCES

Garcia-Oliveira, A.L., Lubin, T., Yongcai, F andChuanqing, S. 2009. Genetic Identification ofQuantitative Trait Loci for Contents of MineralNutrients in Rice Grain. Journal of IntegrativePlant Biology. 51 (1): 84–92.

IRRI. 1991. Rice grain marketing and quality issues.Manila: International Rice Research Institute.

Londo, J.P., Chiang, Y., Hung, K., Chiang, T andSchaal, B.A. 2006. Phylogeography of Asianwild rice, Oryza rufipogon, reveals multipleindependent domestications of cultivated rice,Oryza sativa. Proceedings of Nationalof Academy of Sciences. 103: 9578-9583.

Marri, P.R., Sarla, N., Reddy, V.L.N and Siddiq, E.A.2005. Identification and mapping of yield andyield related QTLs from an Indian accessionof Oryza rufipogon. BMC Genetics. 6: 33.

Septiningsih, E.M., Prasetiyono, J., Lubis, E., Tai,

T.H., Tjubaryat, T., Moeljopawiro, S and Mc

Couch, S.R. 2003. Identification of quantitative

trait loci for yield and yield components in an

advanced backcross population derived from

the Oryza sativa variety IR64 and the wild

relative O. rufipogon. Theoretical and Applied

Genetics. 107: 1419-1432.

Thomson, M.J., Tai, T.H., Mc Clung, A.M., Lai,

X.H., Hinga, M.E., Lobos, K.B., Xu, Y.,

Martinez, C.P and Mc Couch, S.R. 2003.

Mapping quantitative trait loci for yield, yield

components and morphological traits in an

advanced backcross population between Oryzarufipogon and the Oryza sativa cultivar

Jefferson .Theoretical and Applied Genetics.

107:479-493.

MAPPING OF QTLS FOR GRAIN IRON AND ZINC IN SAMBA MAHSURI

Xiao, J., Li, J., Grandillo, S., Ahn, S.N., Yuan, L.,Steven, D and Mc Couch, S.R. 1998.Identification of trait-improving quantitative traitloci alleles from a wild rice relative. Oryzarufipogon. Genetics. 150:899-909.

Zheng, K.L., Shen, B and Qian, H. R. 1991. DNApolymorphism generated by arbitrary primedPCR in rice. Rice Genetics Newsletter. 8: 134-136.

ACKNOWLEGEMENTS

We thank our Professor and Head, Dept ofBiotechnology, ANGRAU and the ProjectDirector, DRR for encouragement and facilities.Financial support from ICAR-NTPC Functionalgenomics project on Iron and Zinc in rice grainsto NS is gratefully acknowledged.

ROJA et al

IDENTIFICATION OF NUTRIENT DEFICIENCIES BY CRITICAL NUTRIENTCONCENTRATION (CNC) AND DRIS METHODS AND DETERMINATION OFSUFFICIENCY RANGES OF NUTRIENTS BY DRIS TECHNIQUE IN MAIZE

CH. RAMULU and G. BHUPAL RAJDepartment of Soil Science and Agricultural Chemistry,College of Agriculture,Acharya N.G.Ranga Agricultural University, Rajendranagar, Hyderabad-500 030


Maize is an important crop grown in AndhraPradesh in an area of 7.83 lakh hectares with theproduction of 27.61 lakh tonnes (DES, 2010). Thirtyone percent of the total maize area and twenty ninepercent of the total production in this state is locatedin three districts viz. Karimnagar, Nizamabad andWarangal.

Foliar analysis is usually considered to bean important method for monitoring the nutrient statusof the plants and to identify the nutrient deficiencies.Sufficiency range approach and the DRIS techniqueare most common methods for interpretation of leaftissue nutrient status of field crops. The interpretationis done based on comparing the level of nutrient (s)in the index tissue with critical or optimum range ofthe concerned nutrient. In case of maize, the indicatedtissue to be sampled has been identified as ear leaf(leaf opposite to cob at tasseling or silking stage) ofthe plant (Escano et al., 1981 and Walworth et al.,1988).

In the conventional approach, the diagnosisof nutrient deficiencies was done on the basis ofactual tissue concentrations comparing with CriticalNutrient Concentrations (CNC) or Sufficiency Range(SR) of the nutrients. The interpretation of the yieldlimiting nutrients based on this CNC of one nutrientdoes not take into consideration the level of othernutrients. Moreover this conventional approach hasseveral weaknesses. These CNC vary with localclimatic and soil conditions, as well as with cultivartype, age and portion of the plant sampled (Escanoet al., 1981).

Both the methods CNC and DRIS in maizewere compared in the present study.


Index leaves (ear leaves opposite to cob atsilking or tasseling stage) were collected at randomfrom the 150 selected fields covering 50 locations ineach district in 10 mandals covering 3-5 locations ineach mandal for the analysis of nutrients. 15-20 indexleaves at random were collected in each field andcomposite sample was prepared. Leaf samplescollected were immediately washed first with tap waterfollowed by 0.1 N HCl and then followed by repeatedwashings with running tap water. The samples werethen rinsed in distilled water and finally with doubledistilled water. They were first dried under shade andthen in hot air oven at 700C. Oven dried plant sampleswere powdered in a stainless steel grinder to afineness of 40 meshes and stored in butter papercovers. Powdered plant samples were analyzed forN, P, K, Zn, Cu, Fe and Mn following standardprocedures.

Nitrogen content of samples was calculated(AOAC, 1980) by microkjeldhal method.

Phosphorus was estimated in the diacidextract by vanadomolybdate phosphoric yellow colourmethod as described by Jackson (1973) andpotassium was estimated with flame photometermethod (Muhr et al., 1965) and expressed in per cent.

The Zinc content in diacid extract was measuredwith Atomic Absorption Spectrophotometer of modelVarian 240 FS (AOAC, 1980) and expressed in mgkg-1.

There was a total divergence of interpretationof nutrient requirements by conventional CNC andDRIS methods. Based on CNC, the deficiencies ofN were in 110 fields, P in 18 fields, K in no fields andZn in 26 fields, but based on DRIS, the deficiency of


N, P, K and Zn were in 74, 52, 45 and 71 fieldsrespectively (Table 1).

While based on CNC, the deficiency of Nwas identified in 26 mandals, P and Zn were deficientin two mandals each and K was adequate in all 30mandals, but based on DRIS, N, P, K and Zn wereyield limiting in 15, 10, 8 and 13 mandalsrespectively.

Based on both CNC and DRIS, the deficiencyof N was identified in all the three districts understudy. P, K and Zn were identified as adequate basedon conventional CNC whereas only P and K wereidentified as yield limiting based on DRIS inNizamabad district. In case of Zn, it was adequatebased on both CNC and DRIS in all the three districtsunder study.

Based on CNC, no deficiency of K wasobserved in all the selected farmers fields under study,but DRIS diagnosed this as yield limiting to an extentof 30 percent. Nitrogen was yield limiting based onDRIS indices, to an extent of 49 per cent, but itsdeficiency was more to an extent of 73 per cent basedon CNC method. Phosphorus was deficient to anextent of 12 percent samples through CNC, but itwas yield limiting to an extent of 35 per cent basedon DRIS. Zinc was identified as a yield limiting in 48per cent samples through DRIS, but its deficiencywas only to an extent of only 17 per cent based onCNC method. These deficiencies of nutrients basedon CNC and DRIS indices are quite divergent.

Majority of the nutrients indicated asdeficient by CNC method in this study, also find a

place among the nutrients diagnosed as limiting by

DRIS indices, but many of the nutrients diagnosed

as limiting by DRIS method do not find place among

the nutrients indicated as deficient by CNC method.

Bhupal Raj (2006), also reported similar findings inmango. Singh and Agarwal (2007), also found that

some extra nutrients were identified as yield limiting

by DRIS other than those found to be deficient by

CNC in rice crop. Similar observations were alsomade by Dharmender (2002) and Ravi (2010), insweet orange and rice crops, respectively in AndhraPradesh. The poor correlation between leaf

composition of individual nutrients and yield was alsoone of the reasons for not getting the correct diagnosisof nutrients by CNC method. The variance ratiobetween the low and high yielding populations fordifferent forms of expressing the nutrients (say fornutrients N and P, five possible forms are N/P, P/N,N*P, N and P) clearly shows that for none of thenutrients tested, the variance ratio for differentnutrients content (from N and P for the nutrients Nand P, in the above examples) is the highest. It isonly for the ratio of these two nutrients and theirproducts, the variance ratio is high. Based on thisalone, such forms of expressions find a place in DRISindices developed. In other words it implies that, theadequacy of any nutrient is to be judged in terms ofits level in relation to that of all other nutrients, whichis the basic philosophy of DRIS method, Walworthand Sumner (1987), in their extensive review on DRISindices, concluded the superiority of the DRISdiagnosis over the conventional Critical NutrientConcentration (CNC) or sufficiency range (SR)method.

Therefore, the diagnosis of nutritionaldisorders by DRIS indices was found superior ascompared to that of CNC method. DRIS not onlydiagnoses the limiting nutrients may be of evenhidden hunger but also predict the nutrients likely tobe deficient in priority wise.

The sufficient ranges of nutrients derived byDRIS technique are given in Table 2. It was foundthat the optimum ranges for N, P, K and Zn were2.27 - 2.72, 0.23 -0.39, 2.43 - 2.87 per cent and 26.6- 66.2 mg kg–1 respectively. It was also determinedthat the nutrients N, P, K and Zn can be said deficient,when the concentration of these nutrients were lessthan 2.27%, 0.23 %, 2.43 %, and 26.62 mg kg–1

respectively. These values derived in this study forN and P were lower / near to the values suggestedby Melsted et al.(1969) and Neubert et al. (1969),respectively, but the derived value of K was higherthan suggested by Melsted et al.(1969), Neubert etal. (1969) and Jones et al. (1990). In case of Zn, thederived value in this study was higher than thatsuggested by Melsted et al. (1969) and Jones et al.(1990), but lower than that by Neubert et al. (1969).

RAMULU and BHUPAL

Table 1. Extent of deficiencies indicated by CNC and DRIS(Total fieds 150, mandals 30 and districts 3)

CNC DRIS Nutrient

No. of fields deficient

No. of mandals deficient

No. of districts

deficient

No. of fields deficient

No. of mandals deficient

No. of districts

deficient N 110 26 3 74 15 3

P 18 2 0 52 10 1

K 0 0 0 45 8 1

Zn 26 2 0 71 13 0

Table 2. Sufficiency ranges of nutrients derived by DRIS technique from nutrientindexing survey of maize in three districts under study

Nutrient Mean S.D Low Optimum High Excessive

N (%) 2.47 0.19 <2.27 2.27-2.72 2.72-2.97 >2.97

P (%) 0.31 0.06 <0.23 0.23-0.39 0.39-0.47 >0.47

K (%) 2.65 0.16 <2.43 2.43-2.87 2.87 -3.01 >3.01

Zn (mg kg–1) 39.56 19.96 <26.62 26.62-66.17 66.17-92.79 >92.79

REFERENCES

AOAC,1980. Official and Tentative Methods ofAnalysis. Association of Official AgriculturalChemistry. Washington DC.

Bhupal Raj, G. 2006. Identification of yield-limitingnutrients in mango through DRIS indices.Communications in Soil Science and PlantAnalysis. 37:1761-1774.

DES, 2010. Statistical Abstracts. Directorate ofEconomics and Statistics. Government ofAndhra Pradesh.

Dharmendar, R. G. 2002. Identification of yield limitingnutrients in sweet orange (Citrus sinensis L.)through DRIS. M. Sc. Thesis. Acharya N GRanga Agricultural University, Hyderabad,India.

Escano, C. R., Jones, C. A and Uehara, 1981.Nutrient diagnosis in corn grown on hydricdystrandepts: Optimum tissue nutrientconcentrations. Soil Science Society ofAmerica Journal. 45: 1135-1139.

Jackson, M. L. 1973. Soil Chemical Analysis,Prentice Hall of India Pvt. Ltd., New Delhi.

Jones, J. B., Eck, H. V and Voss, R. 1990. Plant

analysis as an aid in fertilizing corn and grain

sorghum. Soil testing and plant analysis. 3rd

ed. SSSA Book Ser. 3. SSSA, Madison, WI.

Melsted, S. W., Motto, H. L and Peck, T. R. 1969.Critical plant nutrition composition values

useful in interpreting plant analysis data.

Agronomy Journal.61:17-20.

Muhr, R., Gilbert., Dutta, N and Sankara Subramoney,

H., Dever, R. F., Laley, V. K and Donahue, R.

L. 1965. Soil testing in India.United StatesAgency for International Development Mission

to India, New Delhi.

Neubert, P. W., Wrazidlo, N. P., Vielmeyer I Hundt,

F., Gullmick and Bergman, W. 1969. Tabellen

zur Pflanzenanelze- ersteorientierrende

Ubersicht.Institut fur Pflanzenerah rung Jena,

Berlin.

Ravi, P. 2010. Diagnosis of yield limiting nutrients in

rice (Oryza sativa L.) through DRIS in

Karimnagar district. M. Sc. Thesis. Acharya

N G Ranga Agricultural University, Hyderabad,

India.

IDENTIFICATION OF NUTRIENT DEFICIENCIES BY CRITICAL NUTRIENT

Singh, V. K and Agarwal, H. P. 2007. Developmentof DRIS norms for evaluating N, P, K and Srequirements of rice crop. Journal of the IndianSociety of Soil Science. 55(3): 294-303.

Walworth, J. L.,Woodard, H. J and Sumner, M. E.1988. Generation of corn tissue norms from asmall, high-yield data base. Communicationsin soil science and plant analysis.19(5):563-577.

Walworth, J. L and Sumner, M. E. 1987. Diagnosisand Recommendation Integrated S y s t e m .Advances in Soil Science. 6: 149-188.

RAMULU and BHUPAL


DEVELOPMENT OF A FUNCTIONAL FOOD FOR DIABETES S. SPANDANA and M. PENCHALARAJU Department of Food Technology, Post Graduate and Research Centre, Acharya N.G.Ranga Agricultural University, Rajendranagar, Hyderabad-500030.


Over 220 million people worldwide havediabetes. In 2005, an estimated 1.1 million peopledied from diabetes (the actual number is much higherbecause people may live for years with diabetes andtheir cause of death is often recorded as heart diseaseor kidney failure – brought on by their diabetes.)Nearly 80% of diabetes deaths occur in low- andmiddle income countries. About half of diabetesdeaths occur in people under the age of 70 years;55% of diabetes deaths are in women. The WHOprojects that diabetes deaths will double between 2005and 2030 (Taylor, 2010).

Diabetes is being treated with drugs andinsulin with emphasis on dietary modifications.Utilization of the functional properties like thehypoglycemic effect of different foods will be of usein the diets of diabetic patients. Sorghum, soybeanand Ocimum sanctum have been shown to havefunctional and medicinal properties.

Certain varieties of sorghum bran mayaffect critical biological processes that are importantin diabetes and insulin resistance (Farrar et al., 2008).Grain sorghum contains beneficial components thatcould be used as food ingredients or dietarysupplements to manage cholesterol levels in humans(Carr et al., 2005).

Legumes such as soybeans contain complexcarbohydrates, minerals, phytoestrogens, vegetableprotein, soluble fiber, oligosaccharides, particularlythe isoflavones genistein and daidzein that may bebeneficial in the management of diabetes (NSRL,2012).

Diets containing soy protein rich inisoflavones have been shown to improve insulinresistance in ovariectomized cynomolgus monkeysand to reduce insulin levels in healthy postmenopausalwomen (Goodman and Kritz, 2001).

Ocimum sanctum (holy basil) called Tulsi inIndia, is ubiquitous in Hindu tradition. Ocimum is


explored as medicinal plant and it occupies anenviable position in the holistic system of Indianmedicine ‘Ayurveda’ which has its root in antiquityand has been practiced for centuries. Ocimum is anerect, herbaceous, much branched, soft hairy, annualwith purple or crimson flowers. Leaves of Ocimumsanctum were found to be rich in Vitamin C, VitaminE and phytochemicals, possessing antioxidantproperties beneficial to health (Prakash and Gupta,2005).The juice of the leaves is given to the childrenfor cold and bronchitis. The leaves are also used forsauces, soups and salads (Sethi, et al., 2004).

Keeping the properties of the abovefunctional foods in controlling diabetes, it was feltthat a food product which could incorporate all threeof them, if developed and accepted, would bebeneficial to diabetic patients. Therefore this studywas designed and conducted in the department ofFood Technology, Post Graduate and ResearchCentre, Acharya N.G. Ranga Agricultural University,Rajendra Nagar, Hyderabad.

For the present investigation, soybean flourand sorghum flour were procured from a localsupermarket in Hyderabad as a single lot. Tulasi(Ocimum sanctum) leaves were procured from theHerbal Garden, Acharya N.G. Ranga AgriculturalUniversity, Rajendra Nagar, Hyderabad. The Ocimumsanctum leaves were washed under running water toremove any adhering particles of dirt. The leaves werethen dried in a hot air oven at low temperatures (50-60oC), powdered, sieved and stored in airtightcontainers in a refrigerator till standardization ofproducts and analysis for various parameters wascarried out at a later date.

Two products namely a baked and anextruded product were standardized incorporatingOcimum sanctum leaf powder at various levels.

Table 1. Different combinations carried out for product development

Numcombi

The two products developed were subjected

to Organoleptic evaluation (appearance, colour,

flavour, texture and overall acceptability) by fifteen

(15) trained taste panel members. A score card with

five point hedonic scale was used for the purpose of

evaluation. After product development trials and

acceptability studies, it was observed that the

extruded product was inferior in all aspects as

compared to the baked product. Whichever

combination of sorghum and soy were used (75:20,

80:12.5) the product became hard with the strands

mingling into a single mass which tend to break easily

into small pieces. Incorporation of maida at 40%

level also did not produce a good product. Hence the

extruded product was excluded and the baked product

was selected for further study.

Dry Ocimum sanctum leaf powder and the

baked product were analyzed for their proximate viz.

moisture, protein, fat, vitamin C, total carotenes, iron,

calcium and zinc, adopting standard procedures.

Since both sorghum and soy have been

analyzed in a number of studies, the table values

have been taken. Soy contained the highest amount

of protein i.e. 43.2 g, followed by sorghum and

ocimum leaf powder (table 2). Ocimum leaf powder

contained 2.98 g of fat and it was high, whereas in

sorghum it was found to be 1.9 g. There was little

amount of fat in defatted soy flour. Vitamin C was

absent in both sorghum and soy whereas it was 81.96

mg in Ocimum leaf powder so also the highest

amount of total carotenes was present in Ocimum

leaf powder (653.6 mg). Higher amount of iron was

present in defatted soy flour (10.4 mg). The calcium

content of defatted soy flour was found to be 240 mg

where as in sorghum it was 25 mg and in Ocimum

leaf powder it was found to be only 1.25 mg. The

foods were also analysed for zinc and it was found

that least quantity was observed in Ocimum leaf

powder (1.34 mg).

The product prepared from the above three

ingredients and selected for experimental procedure

was a baked product – Biscuit. The product was

analysed in the lab for different nutrients.

The moisture content of the biscuit was

5.53g. The protein percentage was 4.5 g. The fat

content was found to be 12.5 g. The vitamin C content

was found to be 0.34/100g. The iron and calcium

content was 0.03 mg and 0.05 mg whereas zinc

content was found to be 0.18 mg. The carbohydrate

content was 69.4g and those of total carotenes

was153.1 mcg. Thus each biscuit weighing 9 grams

contained about 0.4g of protein, 1.1 g of fat 0.03 mg

of vitamin C, 13.7 mg of total carotenes, 0.0027 mg

of iron, 0.0045 mg of calcium and zinc was 0.0162

mg.

Organoleptic evaluation of the biscuit was

carried out by a trained panel and the mean scores

obtained for standard (baked product) and selected

experimental product for various parameters were

presented in Table 3.

SPANDANA and RAJU

Table 2. The nutrient content of dried Ocimum leaf powder, sorghum and defatted soy flour

S. No Nutrient per 100g Sorghum * Defatted soy flour *

Ocimum leaf powder

1.

2.

3.

4.

5.

6.

7.

8.

Moisture (g)

Protein (g)

Fat (g)

Vitamin – C (mg)

Total carotenes (mcg)

Iron (mg)

Calcium (mg)

Zinc(mg)

11.9

10.4

1.9

-

47

4.1

25

1.6

8.1

43.2

1.2

-

426

10.4

240

3.4

11.58

3.5

2.98

81.96

654

0.086

1.25

1.31

* Calculated as per data - Nutritive value of Indian foods ICMR (2002)

Table 3. Mean scores obtained by standard and experimental product to different variables

S.No. Variable Extruded Bakedproduct (Experimental

product)

1. Colour 3.6 + 0.63 3.5 + 0.61

2. Flavour 3.9 + 0.35 4.0 + 0.53

3. Texture 4.0 + 0.53 4.0 + 0.53

4. Taste 4.1 + 0.63 4.0 + 0.59

5. Overall acceptability 3.7 + 0.49 3.9 + 0.35

The differences between standard productand experimental product for all the organolepticscores were meagre (Table 3) indicating that theexperimental product was comparable with thestandard product.

REFERENCES

Carr, T.P., Weller, C.L., Schlegel, V.L., Cuppett,S.L., Guderian, D.M. Jr and Johnson,K.R.2005. Grain sorghum lipid extract reducescholesterol absorption and plasma non-HDLcholesterol concentration in hamsters. Journalof nutrition. (9): 2236-40.

Farrar,J.L., Hartle,D.K., Hargrove, J.L and Greenspan, P. 2008. A novel nutraceuticalproperty of select sorghum (Sorghum bicolor)brans: inhibition of protein glycation.NRL.22(8):1052-6.

Goodman-Gruen, D and Kritz-Silverstein, D. 2001.Usual dietary isoflavour intake in association

with cardio vascular disease risk factors inpostmenopausal women. Journal of Nutrition.131:1202-1206.

National Soybean Research Laboratory(NSRL). SoyBenefits. Retrieved February 16, 2012.

Prakash, P and Gupta, N. (April 2005). Therapeuticuses of Ocimum sanctum Linn (Tulsi) with anote on eugenol and its pharmacologicalactions: A short review. Indian Journal ofPhysiology and Pharmacology 49 (2): 125–131.

Sethi., Jyoti, Sood., Sushma, Seth., Shashi, Talwarand Anjana. 2004. ”Evaluation ofhypoglycemic and antioxidant effectof Ocimum sanctum”. Indian Journal ofClinical Biochemistry. 19(2): 152–155.

Taylor, D.W. 2010.The Burden of Non-CommunicableDiseases in India, Hamilton ON: The CameronInstitute.

DEVELOPMENT OF A FUNCTIONAL FOOD FOR DIABETES



EVALUATION OF WHEAT (Triticum aestivum L.) GENOTYPES FOR DIFFERENTSOWING DATES UNDER IRRIGATED CONDITIONS

J. VIJAY, K. MADHAVI, A. RAMACHANDRA RAO and A. SAIRAMDepartment of Agronomy, College of Agriculture,

Acharya. N.G Ranga Agricultural University, Rajendranagar, Hyderabad-5000 30


Wheat (Triticum aestivum L.) is the secondmost important food crop after rice in India whichcontributes about 35 percent to total food grain basketof our country from about 28.37 million hectares witha production of 80.80 million tonnes and productivityof 2.87 tonnes ha-1 (CMIE, 2010). In order to meetthe projected wheat demand of 109 million tonnesfor the growing population of about 1.3 billion by 2020AD, it is essential to sustain the productivity at muchhigher level. At global level, India ranks as secondlargest wheat producing nation and contributesapproximately 11.9 percent to the world’s wheatproduction from about 12 percent of global areameeting 20% of the total food requirement of the Worldpopulation (USDA, 2010).

The late sown wheat crop gets exposed toboth extremes of temperature; i.e. low temperatureduring early growth period that restricts vegetativephase and high temperature during grain filling andripening phases and this is one of the major causesof poor wheat growth and productivity. Temperaturecannot be manipulated easily under field conditionsbut seeding time can be so adjusted that the variousphysiological stages can meet their specifictemperature requirements during cycles (Tiwari andSingh 1993). The adverse effect of delayed sowingcan be minimized by selecting suitable cultivars asthe magnitude of yield reduction varies with varieties.

A field experiment was conducted during rabi2010 – 2011 on sandy loam soil at AgriculturalResearch Institute (ARI), Rajendranagar, Hyderabad.The experiment was laid out in split plot design andreplicated thrice. Four sowing dates were maintreatments viz., D

1 (15th October), D

2 (1st November),

D3 (15th November) and D4 (1st December) and three

genotypes were the sub treatments viz., V1

(RAJ

4037), V2 (HP 4080) and V

3 (PDW 315). The soil was

low in available nitrogen (195.0 kg ha -1), high inphosphorus (62.5 kg ha -1) and potassium (352.5 kgha -1).

Plant height at harvest was found to beunaffected by different sowing dates. Among thegenotypes PDW 315 was significantly taller than therest. Dry matter production was significantly higherin 15th November sowing compared to 1st Decemberand 15th October sowings but on par with 1st Novembersowing. All the genotypes were on par with each otherin dry matter production. Number of tillers m -2 weresignificantly higher with 15th November sowing over1st December sowing but were comparable with 15th

October and 1st November sowings. All the genotypeswere comparable with each other for tillers at harvest.These results are in conformity with the findings ofTahir et al (2009) and Pandey et al. (2010).

More ears m -2 and longer ear length werewith 15th November sowing compared to 1st Decembersowing but were on par with 1st November and 15th

October sowings. The wheat crop sown on 15th

November had more grains per ear. Test weight wasnumerically high in 15th October sown crop but wascomparable with that of late sowings till 15th

November. The test weight decreased sharply in caseof December 1st sown crop. Regarding the genotypestested, all of them were comparable to each otherwith respect to number of ears m-2 and test weight.The ears of HP 4080 were longer compared to others,especially PDW 315, and had significantly moregrains ear-1.

Among the sowing dates 1st November and15th November sowings recorded comparable grainand straw yields but significantly higher than 15th

October and December 1st sowings. Among the

EVALUATION OF WHEAT (Triticum aestivum L.) GENOTYPES

Tab

le 1

. E

ffect

of

dif

fere

nt

so

win

g d

ate

s a

nd

wh

eat

gen

oty

pes o

n g

row

th p

ara

mete

rs,

yie

ld a

ttri

bu

tes,

harv

est

ind

ex,

gra

in a

nd

str

aw

yie

ld

So

win

g d

ate

s

Pla

nt

heig

ht

(cm

)

Dry

matt

er

(g

pla

nt-1

) T

ille

rs

m2

Ears

m

-2

Ear

len

gth

(c

m)

Gra

ins

ear-

1

Tes

t w

eig

ht

(g)

Gra

in

yie

ld

(k

g h

a1)

Str

aw

yie

ld

(kg

ha

-1)

Harv

est

ind

ex (

%)

D1

(15th

Oct

ober

) 68

26

.3

347

336

7.0

35

47.2

27

30

2940

48

.1

D2

(1st N

ovem

ber)

69

31

.5

355

335

7.0

41

45.5

42

68

4851

46

.8

D3 (1

5th N

ovem

ber)

69

33

.8

362

341

7.5

45

44.4

43

99

5071

46

.5

D4

(1st D

ecem

ber)

66

21

.8

316

283

6.4

39

28.3

21

01

2354

47

.2

SE

m ±

2

.6

2.7

11

9.9

0.2

2.4

3.8

122

203

2.8

CD

at 5

%

NS

5.

3 22

19

.4

0.5

4.8

9.4

299

497

NS

Gen

oty

pe

s

V1

(Raj

403

7)

65

25.5

34

8 32

0 7.

2 35

42

.1

3162

35

94

46.8

V2

(HP

408

0)

66

30.4

34

1 32

1 7.

8 47

39

.6

3404

38

44

47.0

V3 (P

DW

315

) 74

29

.1

345

330

6.0

36

42.5

35

58

3979

47

.2

SE

m ±

2.

4 2.

5 7

12.0

0.

3 2.

2 3.

5 11

1 91

1.

7

CD

at 5

%

4.6

NS

N

S

NS

0.

7 4.

3 N

S

236

229

NS

Inte

racti

on

(D

×V

)

Su

b a

t s

am

e l

ev

el

main

SE

m ±

4.

7 5.

0 14

24

.0

0.7

4.4

7.0

223

182

3.4

CD

at 5

%

NS

N

S

NS

N

S

1.4

8.7

NS

47

2 38

6 N

S

Main

at

sam

e o

r d

iffe

ren

t le

vels

of

su

b

SE

m ±

4.

6 4.

8 18

.2

19.7

0.

5 4.

3 6.

8 21

5 30

6 4.

4

CD

at 5

%

NS

N

S

NS

N

S

1.0

8.4

NS

47

8 71

4 N

S

genotypes PDW 315 and HP 4080 recordedcomparable yields but significantly higher than Raj4037. Singh and Ahmad (1997) expressed that higherpost anthesis temperature contributed to decline yieldattributing parameters and yield of grain and straw.Kulhari et al. (2003), Shah et al. (2006), Pardeshi etal. (2009), Muduli et al. (2010) and Pandey et al. (2010)viewed that grain and straw yields were significantlyhigher with the crop sown on normal (15th November)compared to delayed sowings.

REFERENCES

CMIE, 2010. Centre for monitoring Indian Economy(CMIE). Apple heritage, Mumbai. URL: http://www.CMIE.com/. Mumbai. 96-97.

Kulhari, S.C., Sharma, S.L and Kantwa, S.R. 2003.Effect of Varieties, sowing dates and nitrogenlevels on yield nutrient uptake and quality ofdurum wheat (Triticum deurum DESF). Annalsof Agriculture Research. 24 (2): 332-336.

Muduli, K.C., Kar, A.K., Sahu, K.C., Mishra, B.K.,Swain, S. K and Nayak, R. N. 2010. Influenceof sowing dates of seed crops on seed yieldand quality parameters in wheat cv Sonalika.Environment and Ecology. 28 (1A): 284-287.

Pandey, I.B., Pandey, R.K., Dwivedi, D.K and Singh,R.S. 2010. Phenology, heat unit requirementand yield of wheat (Triticum aestivum) varietiesunder different crop- growing environment.Indian Journal of Agricultural Sciences.80 (2): 136-140.

Pardeshi, H.P., Jadav, K.V and Bagul, R.S. 2009.

Effect of sowing dates and fertilizer levels on

yield attributes and grain yield of wheat

(Triticum aestivum L.). International Journal

of Agricultural Sciences. 5 (1): 148-150.

Shah, W.A., Bakht, J., Tehreen Hullah, Khan, A.W.,

Zubair, M.D and Khakwani, A.A. 2006. Effect

of sowing on the yield and yield components

of different wheat varieties. Journal of

Agronomy. 5 (1): 106-110.

Singh, N.B and Ahmed, Z. 1997. Response of wheat

(Triticum aestivum) varieties to different dates

of sowing. Indian Journal of Agricultural

Sciences. 67 (5): 208-211.

Tahir, M.D., ALI, A., Nadeem, M.A., Hussain, A and

Farhan Khalid. 2009. Effect of different sowing

dates on growth and yield of wheat (Triticumaestivum L.) varieties in district Jhang.

Pakistan Journal of Life and Social Sciences.

7 (1): 68-69.

Tiwari, S.K and Singh, M. 1993. Yielding ability of

wheat (Triticum aestivum) at different dates

of sowing-a temperature – dependent

performance. Indian Journal of Agronomy.

38 (2): 204-209.

USDA, 2010. Grain: World Market and Trade Jult,

www.fas.usda.gov/psdonline, 45 & 51.

VIJAY et al


HEAVY METAL REMOVAL EFFICIENCY OF ZEOLITE FROM SEWAGERESHMA BHADANGE, AKULA BABY , P.PRABHU PRASADINI, M.UMA DEVI and

D. JAGDISHWAR REDDYDepartment of Environmental Science and Technology

College of Agriculture, Acharya N.G. Ranga Agricultural University,Rajendranagar, Hyderabad-500 030.



Noor Mohammed Kunta (NMK) situated nearANGRAU campus and providing irrigation water tothe university forms has been contaminated withvarious pollutants which come from two sources viz.,industries situated in Katedan, and pollutants fromthe domestic untreated waste water of the surroundingperi urban areas. Mir Alam Tank water was also usedfor the study as second source. Waste water of NMKcould be put to use for irrigation provided the pollutantsdischarge could be checked and the lake waterdecontaminated using appropriate technologies.

Zeolite (an aluminosilicate clay mineral) hasshown a great capacity for metal adsorption of Cu,Cd, Pb and Zn (Kim et al., 2009). Hence, zeolite (Naform) has been used in the present study to checkits efficiency as a nanofilter by virtue of its veryregular pore structure of molecular dimensions.

A column experiment was carried out, usingpolyvinyl chloride (PVC) tubes of 50 cm height and10.5 cm of diameter. The filter paper was placed atthe bottom of the column; over it nylon cloth wassecured tightly. The column was divided into threelayers viz., 0-15 cm, 15-30 cm and 30-45 cm. Thesieved red sandy loam soil (< 2mm) was filled in thelower 30 cm of the column maintaining 1.54 g cm-3 ofbulk density. The soil has 7.2 pH and EC of 0.34dsm-1. The organic carbon content was 0.6%,available N, P and K contents were 50.17, 12.67 and360 kg ha-1. The upper 15 cm layer of soil columnwas left as such and treated as control beside fillingmixing of Zeolite in soil on weight basis @ 0.25 %,0.5 %, 0.75 %, 1.0 %, 1.25 % and 1.50 %. The upper5 cm of space in the column was kept for applicationof sewage. The column was saturated for 24 hoursby adding 1440 ml of sewage (pore volume basis),collected from inlet of two sources viz., NoorMohammed Kunta sewage treatment plant (NMKSTP) and Mir Alam sewage treatment plant (Mir Alam

STP). Leachates were collected by adding 480 ml ofwater, at an interval of 24 hours, up to nine leachingevents from L

1 to L

9. The collected leachates were

analysed for heavy metal content, using atomicabsorption spectrometer and the metal contentpresented.

The removal efficiency of zeolite for heavymetals was worked out by using following formula,

C Ion initial

- C Ion final

—————————— X100

C Ion initial

Where, R Ion,

% - Ion removal efficiency, C

Ion

initial - Initial concentration of ion

and C

Ion final - Final

concentration of ion. The experimental results wereanalyzed in Completely Randomized Design (CRD),with factorial concept

The sewage brought from NMK STPcontained heavy metals like cobalt (0.487 mg l-1) andcadmium (0.21 mg l-1) which were found to be abovethe permissible limits, while lead (1.70 mg l-1) wasfound within the permissible limits. Similarly, theheavy metals from the Mir Alam STP also containedcobalt (0.507 mg l-1) and cadmium (0.25 mg l-1), abovethe permissible limits and lead (1.73 mg l-1) withinthe permissible limits, as per the guidelines of ISI.

Cobalt content obtained after nine leachingevents in NMK recorded a decrease with theincreased addition of Zeolite in the column studies(0.459 to 0.427). Leachate from Zeolite @1.50%column recorded maximum cobalt removal (0.427mgL-1) as compared to untreated control (0.478). Theefficiency of removal was 11.73%. In Miralam tank,the Cobalt content obtained after nine leaching eventsrecorded a decrease with the increased addition ofZeolite in the column studies (0.472 to 0.431).Leachate from Zeolite @1.50% column recordedmaximum cobalt removal (0.431 mgL-1) as compared

Ion removal efficiency (R Ion,

%) =

RESHMA et al

Tab

le .

1. E

ffic

ien

cy o

f Z

eo

lite

on

rem

oval o

f C

ob

alt

, L

ead

an

d C

ad

miu

m

co

nte

nt

(mg

L-1

) fr

om

sew

ag

e e

fflu

en

t o

f N

MK

, M

irala

m S

TP

aft

er

nin

e

leach

ing

eve

nts

.

Co

balt

co

nte

nt

Lead

co

nte

nt

Cad

miu

m

Zeo

lite

% in

co

lum

n

NM

K

Mir

Ala

m

NM

K

Mir

Ala

m

MK

M

ir A

lam

T1-

0.2

5

0.4

59

0.4

72

1.4

6

1.4

7

0.1

71

0.2

21

T2 -

0.5

0

0.4

56

0.4

65

1.3

4

1.3

8

0.1

66

0.2

14

T3 -

0.7

5

0.4

46

0.4

59

1.2

0

1.2

9

0.1

60

0.2

04

T4 -

1.0

0.4

33

0.4

52

1.1

8

1.2

1

0.1

44

0.1

96

T5 -

1.2

5

0.4

26

0.4

41

1.1

0

1.1

2

0.1

32

0.1

90

T6 -

1.5

0

0.4

27

0.4

31

0.9

7

1.0

7

0.1

32

0.1

89

T7-

co

ntr

ol

0.4

78

0.4

92

1.5

6

1.5

9

0.1

99

0.2

37

Eff

icie

ncy*

11.7

3

12.3

6

34.7

7

32.7

6

34.2

9

20.6

7

Sem

+

0.0

05

0.0

05

0.0

48

0.0

43

0.0

04

0.0

03

CD

at

5%

N

S

NS

N

S

NS

N

S

NS

*Eff

icie

ncy o

f T

6 o

ver

co

ntr

ol

to untreated control (0.492). The efficiency of removalwas 12.36 % (Table 1).

Lead content obtained after nine leachingevents in NMK recorded a decrease with theincreased addition of Zeolite in the column studies(1.46 to 0.97). Leachate from Zeolite @1.50% columnrecorded maximum cobalt removal (0.97 mgL-1 ) ascompared to untreated control (1.56). The efficiencyof removal was 34.77%. Where as in Miralam tankthe Lead content obtained after nine leaching eventsrecorded a decrease with the increased addition ofZeolite in the column studies (1.47 to 1.07). Leachatefrom Zeolite @1.50% column recorded maximumLead removal (1.07 mgL-1 ) as compared to untreatedcontrol (1.59). The efficiency of removal was 32.76%(Table 1)

Cadmium content obtained after nineleaching events in NMK recorded a decrease withthe increased addition of Zeolite in the column studies(0.171 to 0.132). Leachate from Zeolite @1.50%column recorded maximum cadmium removal (0.132mgL-1 ) as compared to untreated control (0.199). Theefficiency of removal was 34.29%. Where as inMiralam tank the Cadmium content obtained afternine leaching events recorded a decrease with theincreased addition of Zeolite in the column studies(0.221 to 0.189). Leachate from Zeolite @1.50%column recorded maximum cadmium removal(0.189mgL-1 ) as compared to untreated control(0.237). The efficiency of removal was 20.67%. (Table1)

It was observed that increase in amount ofzeolite resulted in increase in efficiency of cadmiumremoval from leachates of both the sources (Table)This decrease may be due to binding of Zeolite withCd2+ ions and thus, forming ion-exchanged zeolitecomplex James and Sampath, 1999, Lin and Hsi.(1995) reported decrease in cadmium concentrationin leachates with increase in zeolite concentration.These may be due to exchangeable cationsdistributed on surface as well as in the pores of zeoliteand adsorption of the ions on its charged surface.Baker et al. (2008) studied the adsorption behaviorof zeolite for heavy metals such as cadmium bycolumn experiment and opined similarly.

At lower concentration the exchange processis limited to the exchange on sites of the zeolite,and hence, huge concentration of cadmium wasnoticed in the leachates. In contrast, at higher

concentration diffusion also occurs within the poresof zeolite which results in less heavy metal contentin leachates. Thus, the results revealed that zeolitewas an efficient ion exchanger for removing heavymetals from sewage.

Several studies emphasized the differentremoval efficiency and selectivity of Zeolite towardsheavy metals (Querol et al., 2002).

Conclusively, if sewage is to be used for theirrigation by the farmers in periurban areas ofHyderabad, the application of zeolite can be used asan nanofilter to reduce the load of pollutants viz.,heavy metals.

Zeolite at the rate of 1.50 % w/w of soil,though showed the better removal efficiency of heavymetals but not up to permissible limits, emphasizesthe necessity to carry further research at elevatedlevels of concentrations to ascertain its role in wastewater management.

REFFERENCES

Baker, H.M., Massadeh, A.M and Younes, H.A. 2008.Natural Jordanian zeolite: removal of heavymetal ions from water samples using columnand batch methods. Environmental Monitoringand Assessment. 157(1-4):319-30.

James, R and Sampath, K. 1999. Effect of Zeoliteon the Reduction of Cadmium Toxicity inWater and a Freshwater Fish, Oreochromismossambicus. Environmental Contaminationand Toxicology. 62:222-229.

Kim, T.H., Nam, Y.K and Lee, M. 2009. Nitrogenand Phosphorus removal from livestock wastewater by zeolite ion exchange and ionizingradiation. World Academy of Science,Engineering and Technology. (54) pp 9-13.

Lin, C.F and Hsi, H.C. 1995. Resource recovery ofwaste fly-ash synthesis of zeolite-likematerials, Environmental Science andTechnology. 29: 1109–1117.

Querol, X., Moreno, N., Umana, J.C., Juan, R.,Hernandez S., Fernandez-Pereira C., AyoraC., Janssen M., Garcia-Martinez J., Linares-Solano, A and Cazorla-Amoros D. 2002.Application of zeolitic material synthesisedfrom fly ash to the decontamination of wastewater and flue gas. Journal of ChemicalTechnology and Biotechnology. 77 292–298.

HEAVY METAL REMOVAL EFFICIENCY OF ZEOLITE FROM SEWAGE


PRODUCTION POTENTIAL OF MULTICUT FODDER BAJRA GENOTYPES UNDERVARIED DATES OF SOWING

V. CHANDRIKA, T. SHASHIKALA, M. SHANTI and K. LOKA REDDYAICRP on Forage Crops, Acharya N.G. Ranga Agricultural University,Agricultural Research Institurte, Rajendranagar, Hyderabad - 500 030.



Bajra is a fast growing short duration cerealcrop. It has high biomass production potential andserves as an ideal crop under low rainfall, with hightillering ability, high dry matter production, high proteincontent (10-12% CP) and ratoonability, thus makingit as an outstanding summer growing fodder crop forthe dry farming areas or rainfed situations (Patel etal., 2008). The green fodder of bajra is leafy, palatableand very nutritious feed stock for cattle ensuring goodmilk yield. It has no HCN content as compared tosorghum and can be fed to cattle at any stage of thecrop. Multicut bajra types have a potential ofproducing about 70-80 t of green fodder / ha in 3-4cuts. The varieties ICMV 08111 and Rijko bajrarecorded 812 and 791 q/ha of green fodder yield,respectively in three cuts during summer season atRajendranagar, Hyderabad (Shashikala et al., 2011).Green fodder scarcity is the most common problemfrom November to June due to various reasons.Therefore a study was conducted at AICRP on ForageCrops, Hyderabad centre to identify a suitablegenotype of multicut bajra and to find out an optimumdate of sowing to achieve higher green fodder yieldduring summer season /lean period to over come thescarcity.

A field experiment was conducted during laterabi / early summer, 2011 at AICRP on Forage Crops,ARI, Rajendranagar, Hyderabad, Acarya N.G. RangaAgricultural University. The soil was sandy loam intexture with pH of 8.13, low in available nitrogen andmedium in available phosphorus and available K2O.The experiment was laid out in randomized blockdesign with factorial concept replicated thrice. Twovarieties of multicut bajra viz., V1-Gaint Bajra andV2- BAIF Bajra were sown at four different datesstarting from January I fortnight at fortnightly intervals.The crop was supplied with recommended dose of

fertilizer 100- 50- 40 kg N, P2O5 and K2O /ha. Nitrogenwas given in two splits, half as basal and theremaining half at 30 days after sowing. After everycut, 30 kg N/ha was given as top dressing. Crop wassown at a row distance of 30 cm. First cut was takenat 50% flowering stage (65 days after sowing). From2nd cut onwards, harvesting was done at 40 daysinterval after previous cut. Irrigations were givenimmediately after each cut and whenever necessary.

The crop sown during I fortnight of Februaryrecorded taller plants (Table-1), higher green fodderyield, dry matter yield and crude protein yield(Table-2). In first cut, higher green fodder yield wasrecorded with D2 (30.1 t/ha) and it was on par withD3 (27.6 t/ha) and both were significantly superiorover D1 (23.0 t/ha) and D4 (19.6 t/ha). Similar trendwas observed with dry matter yield (5.73 and 5.34 t/ha, respectively) and crude protein yield (406 and379 kg/ha, respectively). In second cut, irrespectiveof genotypes green fodder yield was higher over firstcut (Table-2). Higher green fodder yield was noticedwith D2 (31.1 t/ha) in second cut and it was on parwith D1 (29.6 t/ha) and D3 (29.1 t/ha). Similar trendwas noticed with 3rd and 4th cuts also (Table-2).Significant differences were not observed with leaf:stem ratio with respect to genotypes and dates ofsowing (Table-1). In all the cuts D4 recordedsignificantly lower green fodder yield, dry fodder yieldand crude protein yield. Delayed sowing might havereduced the yields due to increase in temperatures.When the data were pooled over four cuts, significantlyhigher green fodder yield (118 t/ha) was recorded withD2 over other dates of sowing (Table-2). Where as,D3 (107 t/ha) was on par with D1 (103 t/ha) with regardto the green fodder yield. Similar trend was noticedwith dry fodder yield and crude protein yield also.Similar results were obtained by Patel et al., (2008)with the summer sown bajra crop.

PRODUCTION POTENTIAL OF MULTICUT FODDER BAJRA GENOTYPES

Tab

le 1

. Y

ield

pa

ram

ete

rs o

f m

ult

icu

t fo

dd

er

bajr

a g

en

oty

pe

s

Pla

nt

heig

ht

(cm

) L

eaf

: ste

m r

ati

o

Tre

atm

en

ts

1s

t cu

t 2

nd c

ut

3rd

cu

t 4

th c

ut

1s

t cu

t 2

nd c

ut

3rd

cu

t 4

th c

ut

Da

tes o

f so

win

g

D1

– Ja

n II

FN

14

1.06

13

2.50

13

2.50

13

2.80

0.

22

0.20

0.

20

0.17

D2

– F

eb I

FN

18

8.55

16

7.27

16

5.16

16

4.43

0.

22

0.23

0.

21

0.18

D3

– F

eb II

FN

17

2.06

13

5.66

15

0.26

15

0.70

0.

20

0.23

0.

21

0.18

D4–

Mar

ch I

FN

13

2.05

11

2.36

14

1.73

13

2.53

0.

20

0.24

0.

20

0.16

S E

m+

5.

4 6.

4 6.

6 6.

2 0.

009

0.12

0.

009

0.00

9

CD

at 5

%

16.5

19

.3

20.0

18

.8

NS

N

S

NS

N

S

En

trie

s

V1-

Gai

nt B

ajra

14

8.05

12

7.17

15

1.93

14

4.55

0.

20

0.24

0.

21

0.18

V2-

BA

IF B

ajra

16

1.80

12

6.74

14

2.90

14

5.67

0.

20

0.23

0.

21

0.18

SE

m +

3.

9 4.

5 4.

7 4.

4 0.

006

0.00

8 0.

007

0.00

6

CD

at 5

%

11.7

N

S

NS

N

S

NS

N

S

NS

N

S

D

x V

SE

m+

7.

7 9.

0 9.

3 8.

8 0.

012

0.01

7 0.

013

0.01

3

CD

at 5

%

34.6

N

S

NS

N

S

NS

N

S

NS

N

S

CHANDRIKA et al

Tab

le 2

. G

reen

fo

dd

er

yie

ld,

dry

matt

er

yie

ld a

nd

cru

de p

rote

in y

ield

of

mu

ltic

ut

fod

der

bajr

a g

en

oty

pe

s

G

FY

(t/h

a)

DM

Y(t

/ha)

CP

Y(k

g/h

a)

Tre

atm

en

ts

1s

t cu

t 2

nd

cu

t 3

rdcu

t 4

th

cu

t p

oo

led

1s

t cu

t 2

nd

cu

t 3

rd

cu

t 4

th

cu

t p

oo

led

1s

t

cu

t 2

nd

cu

t 3

rd

cu

t 4

th

cu

t p

oo

led

Da

tes o

f so

win

g

D1

– Ja

n II

FN

23

.0

29.6

22

.2

27.9

103

4.42

5.

884.

074.

86

19.3

314

406

281

319

1318

D2

– F

eb

I F

N

30.1

31

.1

24.0

32

.511

8 5.

73

6.19

4.46

5.70

22

.140

6 42

7 30

837

1 15

12

D3

– F

eb I

I F

N

27.6

29

.1

22.5

28

.110

7 5.

34

5.77

4.17

4.93

20

.237

9 39

8 28

832

1 13

86

D4–

Mar

ch

I F

N

19.6

19

.5

20.1

19

.078

3.

72

3.88

3.73

3.34

14

.726

5 26

8 25

821

7 10

06

S E

m+

0.

0009

1.

01

1.29

1.

482.

49

0.27

0.

200.

230.

26

0.45

19

14

1617

31

CD

at 5

%

NS

3.

06

3.91

4.

487.

4 0.

8 0.

60.

710.

71

1.4

57

42

4951

95

En

trie

s

V1-

Gai

nt

Baj

ra

23.7

27

.8

21.3

26

.299

4.

43

5.52

3.93

4.56

18

.531

5 38

0 27

129

8 12

66

V2-

BA

IF

Baj

ra

26.5

26

.8

23.1

27

.610

4 5.

17

5.34

4.29

4.82

19

.636

7 36

8 29

631

5 13

46

S E

m+

1.

00

0.71

0.

91

1.05

1.69

0.

19

0.14

0.17

0.19

0.

3214

10

11

12

22

CD

at 5

%

3.0

NS

N

S

NS

5.3

0.58

N

SN

SN

S

0.97

41

30

NS

NS

67

D x

V

S E

m+

2.

0 1.

43

1.83

2.

093.

5 0.

38

0.28

0.33

0.37

0.

6427

19

23

24

44

CD

at 5

%

NS

N

S

NS

N

S10

.5

NS

N

SN

SN

S

2.88

NS

N

S

NS

NS

19

9

Both the varieties performed similarly withrespect to plant height, L: S ratio, green fodder yield,dry fodder yield and crude protein yield. In first cut,V2 performance was superior over V1 (Table 1 and2). Where as in second cut V1 performed better withrespect to green fodder yield, dry matter yield andcrude protein yield (Table2). No significant differenceswere observed during third and fourth cut. Thoughgreen fodder yield, dry matter yield and crude proteinyield were higher with BAIF Bajra, it was on par with

Giant Bajra when pooled over four cuts. Genotypic

variations in forage bajra were reported by Pathan

and Bhilare, (2009). Desale et al., (2000) reported

that Giant Bajra recorded significantly higher green

fodder yield (918.2 q/ha) and dry matter yield (205.7q/ha) over other varieties.

It can be inferred from the above study that

sowing of either giant bajra or BAIF multicut fodder

bajra during I fortnight of February will be beneficialto the dairy farmers to over come the green fodderscarcity during the lean period by providing sufficientgreen fodder.

REFERENCES

Desale, J.S., Bhilare, R.L., Pathan, S.H and Babar,R.M. 2000. Response of multicut fodder bajravarieties to nitrogen fertilizer levels., Journalof Maharastra Agricultural Universities. 25(1):74-75.

Patel, M.R., Sadhu, A.C., Patel, R.M., Parmar, H.Pand Kher, H.R., 2008. Cutting management indifferent genotypes of forage bajra duringsummer season. Research on Crops. 9(2) :325-327.

Pathan, S.H and Bhilare, R.L. 2009. Growthparameters and seed yield of forage pearl milletvarieties as influenced by nitrogen levels.Journal of Maharastra Agricultural Universities.34(1): 101-102.

Shashikala, T., Rai, K.N., Chandrika, V., Shanti, Mand Loka Reddy. K. 2011. Fodder yieldpotential of multicut pearl millet genotypesduring summer under irrigation. Proceedingsof National Symposium on “Forage resourceand livestock for livelihood, environment andnutritional security”. Jhansi, 10-11 September,2011. pp76

PRODUCTION POTENTIAL OF MULTICUT FODDER BAJRA GENOTYPES



PER SE PERFORMANCE AND CORRELATION STUDIES IN F1 GENERATION OFTOMATO (Solanum lycopersicum Mill.)

K.VINAY RAJU, B. NEERAJA PRABHAKAR, S. SUDHEER KUMAR and R.V.S.K. REDDYCollege of Horticulture, Andhra Pradesh Horticultural University,

Rajendranagar, Hyderabad - 500 030

The fruit yield in tomato as in most other

field crops is a very complex character and is

dependent on a number of yield components. A

knowledge of the association of quantitative

characters especially the yield and its attributes will

be of immense practical interest in the field of plant

breeding.

An experiment was conducted at NBPGR,

Regional Station, Rajendranagar, Hyderabad during

rabi and summer 2008-09. In randomized block design

with three replications. The treatments included eight

diverse genotypes of tomato used as lines (female

parents) and four popular varieties used as Testers

(male parents). The selfed seeds of twelve parents

were raised in the green house of NBPGR, Regional

Station, Rajendranagar during rabi 2008 and were

crossed in a Line x Tester mating design. The riped

fruits were harvested, seeds from each crossed fruit

were collected by using fermentation method for seed

extraction. Twenty six days old tomato seedlings were

transplanted on 13th April 2009 with a spacing of 60 x

45 cm The experimental unit consisted of parents

followed by F1s in all the three blocks. All the

treatments were assigned randomly to the

experimental units. The data were recorded on 13

characters in F1s and parents and correlations were

worked out using the method given by Johnson etal., (1955).


The mean performance of the crosses EC

163663 x Pusa Ruby, EC 257489 x Pusa Ruby, EC

257489 x Arka Saurabh for fruit yield per plant was

better , but none of the hybrids exhibited better yield

over the standard check (Arka Vikas). The crosses

EC 145057 x Pusa Ruby, EC 338717 x PED, EC

338717 x Marutham exhibited high rind thickness

over standard check. Higher ascorbic acid content

over standard check was recorded in crosses EC

257489 X Arka Saurabh, EC 238308 x Marutham,

EC145057 x Marutham, EC145057 x PED

respectively. TSS was high in crosses EC145057 x

Arka Saurabh, EC145057 x Pusa Ruby and EC

163663 x Pusa Ruby over the standard check. In

general, the genotypic correlations were found to be

higher than phenotypic correlations. The study of

correlations revealed that fruit yield per plant was

positive and significantly correlated with fruit weight,

fruit length, fruit diameter, fruit volume, rind thickness,

TSS and fruit juice content. Similar results have been

reported earlier in tomato for the association of fruit

yield with fruit weight (Harer et al., 2002; Pradeep etal., 2002, Brar and Hari Singh, 1998), Fruit length

(Tiwari, 2002 and Arun Joshi et al., 2004)., Plant

height, number of branches per plant, days to 50 %

flowering (Shifaraw Nesgea et al., 2002), rind

thickness (Kumar et al., 2003). The crosses that

proved to be better than the elite parent are useful

for further exploitation.

PER SE PERFORMANCE AND CORRELATION STUDIES IN F1 GENERATION

Tab

le 1

. M

ean

Perf

orm

an

ce o

f p

are

nts

, cro

sses a

nd

sta

nd

ard

ch

eck f

or

yie

ld a

nd

yie

ld c

on

trib

uti

ng

ch

ara

cte

rs i

n T

om

ato

Tre

atm

en

ts

Pla

nt

h

eig

ht

(cm

)

Nu

mb

er

of

bra

nc

he

s

Da

ys

to

5

0%

flo

we

rin

g

Fru

it

we

igh

t (g

)

Fru

it

len

gth

(cm

)

Fru

it

dia

mete

r

(cm

)

Fru

it

vo

lum

e

(cc

)

Yie

ld p

er

pla

nt

(g)

Rin

d

thic

kn

es

s

(mm

)

Asc

orb

ic

ac

id

(mg

/100

g

pu

lp)

Fru

it ju

ice

co

nte

nt

(%)

See

d

co

nte

nt

(%)

Lin

es

EC

145

057

118.

23

9.00

61

.00

46.3

2.

67

2.67

40

.33

1153

3.

27

23.7

5 48

.30

0.37

EC

163

663

65.7

0 14

.66

52.6

7 42

.3

2.50

2.

57

41.5

3 11

26

3.33

25

.03

50.1

4 0.

32

EC

238

308

78.0

3 12

.33

56.0

0 38

.0

2.53

2.

37

26.5

3 12

23

3.40

23

.20

50.0

3 1.

24

EC

257

489

89.0

0 11

.33

54.0

0 46

.0

2.50

2.

73

41.8

7 11

66

3.47

25

.23

50.6

9 0.

66

EC

320

574-

1 11

3.83

9.

66

61.6

7 51

.6

2.77

3.

00

45.8

3 12

76

3.53

25

.60

42.7

3 1.

20

EC

338

714

81.6

0 10

.66

60.0

0 59

.0

3.13

3.

53

55.9

7 14

50

3.37

26

.63

44.1

0 0.

64

EC

338

717

98.0

3 13

.33

56.3

3 46

.6

2.47

2.

73

41.6

7 12

36

3.23

25

.35

41.9

7 0.

93

EC

338

735

102.

87

11.3

3 61

.33

45.0

2.

70

2.90

39

.60

1226

3.

00

24.8

0 46

.30

0.86

Lin

es

Me

an

9

3.4

1

11.5

3

57.8

8

46.8

2.6

6

2.8

1

41.6

7

123

2

3.3

3

24

.95

4

6.7

8

0.7

8

Tes

ters

Pus

a R

uby

90.2

0 11

.66

63.6

7 26

.6

2.27

2.

27

23.5

7 97

6 3.

13

26.2

5 38

.47

1.63

PE

D

93.1

0 12

.00

67.3

3 33

.0

2.70

3.

37

52.5

3 10

53

3.47

20

.27

42.1

7 1.

27

Ark

a S

oura

bh

111.

43

13.0

0 64

.00

27.6

2.

13

2.43

25

.17

1188

2.

27

23.5

5 16

.63

1.78

Mar

utha

m

102.

83

12.6

6 63

.67

32.6

2.

30

2.70

38

.33

1085

2.

33

23.8

0 26

.34

1.07

Te

ste

rs M

ea

n

99

.39

1

2.3

3

64.6

7

30.0

2.3

5

2.6

9

34.9

0

107

5

2.8

0

23

.47

3

0.9

0

1.4

4

Cro

sse

s

EC

145

057

× P

usa

Rub

y 10

8.10

14

.66

62.0

0 65

.3

3.60

3.

87

66.1

0 12

86

5.73

24

.15

35.0

7 0.

54

EC

145

057

× P

ED

98

.30

13.0

0 65

.67

56.3

3.

27

3.70

54

.00

1303

4.

27

26.6

3 42

.00

0.81

EC

145

057

× A

rka

Sou

rabh

98

.93

13.3

3 64

.67

47.6

2.

97

2.63

45

.30

1293

3.

23

24.9

5 53

.23

0.69

EC

145

057

× M

arut

ham

10

3.00

12

.66

65.3

3 48

.0

2.80

2.

73

39.0

7 12

60

3.23

26

.70

37.6

0 0.

66

VINAY et al

Tre

atm

en

ts

Pla

nt

h

eig

ht

(cm

)

Nu

mb

er

of

bra

nc

he

s

Da

ys

to

5

0%

flo

we

rin

g

Fru

it

we

igh

t (g

)

Fru

it

len

gth

(cm

)

Fru

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r

(cm

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(cc

)

Yie

ld p

er

pla

nt

(g)

Rin

d

thic

kn

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(mm

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Asc

orb

ic

ac

id

(mg

/100

g

pu

lp)

Fru

it ju

ice

co

nte

nt

(%)

See

d

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nte

nt

(%)

EC

163

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× P

usa

Rub

y 12

1.97

11

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49.3

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36

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20

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7 0.

77

EC

163

663

× P

ED

86

.10

14.0

0 62

.00

52.3

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10

3.40

56

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3.

27

26.1

1 52

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0.71

EC

163

663

× A

rka

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ra

96.6

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61.3

3 44

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2.73

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90

42.5

0 12

36

3.77

21

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42.2

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93

EC

163

663

× M

arut

ham

10

2.73

11

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63.6

7 48

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83

47.0

0 12

83

4.03

24

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48.2

0 1.

13

EC

238

308

× P

usa

Rub

y 11

9.67

10

.66

55.6

7 37

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2.37

2.

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29.3

3 11

93

3.37

23

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36.9

0 1.

63

EC

238

308

× P

ED

99

.50

13.3

3 62

.33

26.0

1.

83

2.20

23

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1043

3.

13

25.5

1 42

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1.90

EC

238

308

× A

rka

Sou

ra

105.

93

11.0

0 62

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32.6

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33

2.33

28

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1033

3.

43

23.8

5 40

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1.65

EC

238

308

× M

arut

ham

85

.77

12.6

6 62

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27.0

1.

90

2.23

26

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1060

3.

33

27.2

3 36

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1.82

EC

257

489

× P

usa

Rub

y 98

.43

13.3

3 64

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56.6

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23

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53

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1393

4.

03

20.1

1 40

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0.70

EC

257

489

× P

ED

82

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13.3

3 65

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51.0

2.

60

2.90

49

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1226

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13

22.7

3 41

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0.73

EC

257

489

× A

rka

Sou

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82.9

7 12

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62.6

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3 13

45

3.03

27

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38.0

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EC

257

489

× M

arut

ham

79

.17

13.0

0 64

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38.6

2.

23

2.50

44

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1176

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37

23.4

0 40

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0.81

EC

320

574-

1 ×

Pus

a R

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123.

10

13.3

3 53

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35.0

2.

30

2.20

27

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1216

2.

23

24.2

3 15

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1.79

EC

320

574

-1×

PE

D

82.7

3 8.

33

62.3

3 28

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2.13

2.

30

30.1

0 11

70

2.23

21

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17.7

3 1.

84

EC

320

574-

1 ×

Ark

a S

oura

10

2.20

12

.00

54.0

0 25

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2.00

2.

13

25.1

3 10

66

2.20

22

.71

16.4

7 1.

76

EC

320

574-

1 ×

Mar

utha

m

96.5

7 10

.00

58.0

0 26

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2.00

2.

13

24.2

0 10

80

2.20

24

.32

16.7

7 1.

73

EC

338

714

× P

usa

Rub

y 12

5.97

13

.00

58.6

7 45

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2.40

2.

37

37.0

7 11

73

2.77

26

.28

28.6

7 0.

65

EC

338

714

× P

ED

73

.00

12.3

3 55

.00

47.6

3.

10

3.27

52

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1200

2.

90

19.6

3 42

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0.67

EC

338

714

× A

rka

Sou

ra

80.0

7 13

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57.0

0 49

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2.73

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87

41.2

7 11

76

3.00

22

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40.1

3 0.

62


Tre

atm

en

ts

Pla

nt

h

eig

ht

(cm

)

Nu

mb

er

of

bra

nc

he

s

Da

ys

to

5

0%

flo

we

rin

g

Fru

it

we

igh

t (g

)

Fru

it

len

gth

(cm

)

Fru

it

dia

mete

r

(cm

)

Fru

it

vo

lum

e

(cc

)

Yie

ld p

er

pla

nt

(g)

Rin

d

thic

kn

es

s

(mm

)

Asc

orb

ic

ac

id

(mg

/100

g

pu

lp)

Fru

it ju

ice

co

nte

nt

(%)

See

d

co

nte

nt

(%)

EC

338

714

× M

arut

ham

82

.97

13.3

3 58

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40.0

2.

23

2.60

39

.97

1113

2.

77

20.5

7 39

.47

0.68

EC

338

717

× P

usa

Rub

y 12

8.03

12

.66

54.6

7 55

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2.97

3.

20

50.6

3 12

76

5.17

23

.20

35.2

7 0.

46

EC

338

717

× P

ED

77

.53

10.3

3 55

.33

50.3

2.

97

3.23

43

.97

1303

5.

47

24.2

7 42

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0.84

EC

338

717

× A

rka

Sou

ra

104.

00

11.0

0 52

.00

48.0

2.

50

2.70

44

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1233

5.

17

23.8

5 38

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0.74

EC

338

717

× M

arut

ham

10

6.80

8.

33

55.0

0 47

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2.77

2.

47

39.5

0 12

13

5.47

26

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39.5

3 0.

82

EC

338

735

× P

usa

Rub

y 11

5.20

13

.66

62.0

0 46

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2.87

2.

97

39.9

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53

3.70

25

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38.7

3 0.

37

EC

338

735

× P

ED

69

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12.3

3 62

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48.0

2.

90

2.97

44

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1158

3.

63

24.5

0 42

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0.41

EC

338

735

× A

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Sou

ra

98.8

0 11

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60.0

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3.30

23

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41.8

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EC

338

735

× M

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10

3.63

12

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53.0

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54.3

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10

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41.5

6 0.

34

Cro

ss

Me

an

98

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12

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59

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2.6

7

2.8

4

42

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1

211

3

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23

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37

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0.9

5

Ark

a vi

kas

(che

ck)

72.5

7 12

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58.6

7 64

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3.73

3.

40

60.2

0 17

90

3.16

26

.24

51.7

3 0.

79

Gen

era

l m

ea

n

96

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12

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59

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44

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2.6

7

2.8

3

41

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1

215

3

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24

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38

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0.9

6

S.E

. 1.

60

0.58

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94

2.0

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0.

07

2.26

32

0.

10

0.06

0.

50

0.05

C.D

at 5

%

4.51

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65

2.64

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7 0.

22

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34

90

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17

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13

VINAY et al

Tab

le 2

. P

hen

oty

pic

(P)

an

d G

en

oty

pic

(G)

co

rrela

tio

n c

oeff

icie

nts

betw

een

yie

ld c

on

trib

uti

ng

ch

ara

cte

rs i

n T

om

ato

Tre

atm

en

ts

Pla

nt

he

igh

t (c

m)

Nu

mb

er

of

bra

nc

hes

/ p

lan

t

Day

s t

o

50%

fl

ow

eri

ng

Fru

it

weig

ht

(g)

Fru

it

len

gth

(c

m)

Fru

it

dia

me

ter

(cm

)

Fru

it

vo

lum

e

(cc

)

Rin

d

thic

kn

es

s (

mm

)

TS

S

(0b

rix

)

As

co

rbic

a

cid

(m

g/1

00

g p

ulp

)

% o

f fr

uit

ju

ice

% o

f se

ed

c

on

ten

t

Yie

ld/

pla

nt

(gm

P

1.0

00

0

0.19

25*

-0.0

155

-0.0

246

-0.0

805

-0.0

91

-0.1

513

0.12

04

0.03

93

0.00

61

-0.3

497**

0

.121

4 -0

.077

2 P

lant

hei

ght

(cm

) G

1

.00

00

0.72

83**

-0.0

202

-0.0

301

-0.0

834

-0.0

969

-0.1

660

0.12

49

0.05

70

0.00

45

-0.3

570**

0.

1198

-0

.080

3

P

1.0

000

0.

1239

-0

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6 -0

.018

8 0.

0615

0.

0224

-0

.166

3 -0

.073

3 -0

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1* -0

.074

0 0.

0999

0.

0110

N

umbe

r of

br

anch

es/ p

lant

G

1.0

000

1.

0964

**

-0.2

422**

-0

.035

6 0.

1898

* 0.

1067

-1

.147

6**

-0.5

606**

-1

.214

2**

-0.4

683**

0.

4526

**

-0.0

493

P

1.0

000

-0

.058

3 0.

0244

0.

1514

0.

1080

-0

.118

4 -0

.053

3 0.

0838

0.

0675

0.

1092

-0

.006

2 D

ays

to 5

0%

flow

erin

g G

1.0

000

-0

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2 0.

0259

0.

1543

0.

1203

-0

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5 -0

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4 0.

0902

0.

0706

0.

1186

-0

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3

P

1

.00

00

0.

8653

**

0.80

77**

0.87

87**

0.52

46**

0.30

99**

0.09

25

0.50

89**

-0.7

601**

0.

7306

**

Fru

it w

eigh

t (gm

)

G

1

.00

00

0.

9213

**

0.84

94**

0.89

42**

0.56

82**

0.37

72**

0.09

63

0.53

92**

-0.8

089**

0.

7616

**

P

1.0

00

0

0.87

26**

0.82

38**

0.46

07**

0.27

73**

0.00

27

0.45

21**

-0.6

496**

0.

6925

**

Fru

it le

ngth

(cm

)

G

1.0

00

0

0.90

48**

0.88

92**

0.48

86**

0.35

85**

0.00

43

0.47

56**

-0.6

973**

0.

7558

**

P

1

.00

00

0.

8793

**

0.46

22**

0.20

81*

-0.0

823

0.41

28**

-0.5

940**

0.

5947

**

Fru

it di

amet

er

(cm

) G

1.0

000

0.

9419

**

0.48

94**

0.25

08**

-0.0

853

0.42

80**

-0.6

193**

0.

6227

**

P

1.0

00

0

0.44

28**

0.22

18*

-0.0

477

0.46

85**

-0.6

964**

0.

6227

**

Fru

it vo

lum

e (c

c)

G

1.0

00

0

0.48

00*

0.26

82**

-0.0

557

0.50

07**

-0.7

438**

0.

6612

**

P

1

.00

00

0.37

75**

0.06

61

0.37

37**

-0.4

057**

0.

2499

**

Rin

d th

ickn

ess

(mm

) G

1.0

00

0

0.46

19**

0.06

74

0.38

19**

-0.4

238**

0.

2838

**

P

1.0

000

0.

1102

0.

5949

**

-0.2

925**

0.

1711

* T

SS

(0 br

ix)

G

1.0

000

0.

1359

0.

7078

**

-0.3

582**

0.

2358

**

P

1.0

00

0

0.14

70

0.02

16

0.11

06

Asc

orbi

c ac

id

(mg/

100g

pul

p)

G

1.0

00

0

0.14

79

0.02

10

0.12

27

P

1.0

000

-0

.555

2**

0.30

38**

% o

f fru

it ju

ice

G

1.0

000

-0

.565

7**

0.32

87**

P

1.0

00

0

-0.3

521**

%

of s

eed

cont

ent

G

1.0

00

0

-0.3

782**

P

1.0

000

Y

ield

/ pl

ant (

g)

G

1.0

000

* , *

* S

ignific

ant

at

5%

and 1

% levels

resp

ect

ivel

REFERENCES

Arun, Joshi, Amit, Vikram and Thakur, M. C. 2004Studies on genetic variability, correlation andpath analysis for yield and physico-chemicaltraits in tomato (Lycopersicon esculentumMill.). Progressive Horticulture 36 (1) : 51-58.

Brar, P. S and Hari Singh. 1998. Variability andcorrelation studies in different varieties oftomato (Lycopersicon esculentum Mill.).Punjab Vegetable Grower 33: 23-26.

Harer, P. N., Lad D. B and Bhor, T .J 2002.Correlation and path analysis studies intomato. Journal of Maharashtra AgriculturalUniversities 27 (3) : 302-303.

Johnson, H. W., Robinson, H. F and Comstock, R.E. 1955. Estimates of genetic andenvironmental variability of Soybeans.Agronomy Journal 47: 314-318.

Kumar, V. R. A., Thakur M.C and Hedau, N. K. 2003.Correlation and path coefficient analysis intomato (Lycopersicon esculentum Mill.).Annalsof Agricultural Research 24(1) :175-177.

Pradheep, K.,Veeraragavathatham, D and Auxcilia,J. 2002. Correlation analysis in tomato(Lycopersicon esculentum Mill.) with anemphasis to virus resistance. South IndianHorticulture 55 (1-6) : 12-19.

Shiferaw Nesgea, Krishnappa, K. S and Raju, T. B.P. 2002. Correlation coefficient analysis intomato. Current Research - University ofAgricultural Sciences (Bangalore) 31(7/8)127-130.

Tiwari, J. K. 2002. Correlation studies in tomato.Haryana Journal of Horticultural Sciences.31 (1/2) 146-147.




PESTICIDE RESIDUES IN SELECTED FRUITS/VEGETABLES OF NALGONDADISTRICT OF ANDHRA PRADESH

SHAIK FARHATH and S. SHOBHA Department of Foods and Nutrition, Post Graduate and Research Centre,

Acharya N.G. Ranga Agricultural University, Rajendranagar, Hyderabad-500 030.

Regarding the consumption of pesticide for

different crops, cotton consumes 55 per cent of total

pesticides in India while the total area under cotton

is only 5 per cent. Similarly in the case of paddy, 17

per cent of the pesticide was consumed, while the

total area under paddy is 24 per cent. In fruits and

vegetables, the usage is 13 per cent and the area is

only 3 per cent of the total cultivated area (Dikshit,

2008).

This study was planned to monitor pesticide

residues in sweet lime, tomato and bitter gourd

collected from different locations of Madhariguda,

Ranga reddy guda, Ganapavaram and Pedda

Madhapur adisherlapally villages of Nalgoda districtof Andhra Pradesh.

Samples of sweet lime, tomato and bitter

gourd were procured from farmers in the selected

villages of Nalgonda district of Southern Telangana

Zone of Andhra Pradesh. The selected samples were

procured after interviewing the farmers growing them.

The samples were obtained after proper sampling was

done using standard sampling tecthniques.


Selected whole and processed fruits and

vegetables were analyzed for the pesticides such as

monocrotophos, quinalphos, endosulfan,

chloripyriphos, acephate and carbofuran. Most non-

ionic residues were extracted with acetone and the

residues were portioned from aqueous acetone to

dichloromethane/ hexane phase. After removing the

traces of dichloromethane, final extract was made

with acetone. Organophosphate residues were

determined directly by gas liquid chromatography with

FPD-P/NPD. For determination of organochlorine and

synthetic pyrethroid residues, the extract was

subjected to cleanup using florisil column.(Sharma,

2007).

The data revealed that pesticide residues in

processed as well as unprocessed samples were

below detectable limits (BDL) in all the samples.

Therefore, statistical analysis was not carriedout. The

chromatograms of the standards and samples are

given Table no 1, 2 and 3 and figure 1,2 and 3.

Table 1. Standard chromatographs-I

S.No

1.

2.

3.

4.

Table 2. Standard chromatograph-II

S.No Name of the

Pesticide standard Retention time

(in minutes) 1 Monocrotophos 16.034

2 Quinalphos 23.332

Table 3. Standard chromatographs-III

S.No Name of the

Pesticide standard Retention time

(in minutes) 1. Carbofuran 19.297

2. Quinalphos 23.312

Chromatograms of pesticides obtained from theselected crops

As the samples selected had enough safetylimiting period, the pesticide metabolites weredegraded. Pesticide residues of samples were innegligible quantities that were below detectable limits(BDL). That might be because of pre-processingtechniques contributed to the declinement of pesticideresidues.

Rao et al.(2009) studied pesticide residues

in vegetables (brinjal, cucumber, okra, ridge gourd

and tomato) and water samples collected from

Kothapally Adarsha watershed in Rangareddy district,

Andhra Pradesh, India during 2007.The resultsrevealed the presence of monocrotophos (range

0.001-0.044 mg kg-1), chlorpyrifos (0.001 to 5.154 mg

kg-1), cypermethrin (0.001 to 0.352 mg kg-1) and

endosulfan (0.001 to 0.784 mg kg-1). The residues of

monocrotophos and endosulfan were below maximum

residue limit (MRL) in all the 59 vegetable samples.The water samples also revealed the presence of

pesticide residues, but below MRLs.

Similar study was conducted by Srivastava

et al. (2011). on 20 vegetables including leafy, root,

modified stem, and fruity vegetables like bitter gourd,

jack fruit, french-bean, onion, colocassia, pointedgourd, capsicum, spinach, potato, fenugreek seeds,

carrot, radish, cucumber, beetroot, brinjal, cauliflower,

cabbage, tomato, okra, and bottle gourd. Forty-eight

pesticides including 13 organochlorines (OCs), 17

organophosphates (OPs), 10 synthetic pyrethriods

(SPs), and eight herbicides (H) pesticides were

analyzed. A total number of 60 samples, each in

triplicates, However the limit of detection ranged from

0.001-0.009 mg kg-1 for OCs, SPs, OPs, and H,

respectively. Twenty-three pesticides were detected

from total 48 analyzed pesticides in the samples with

the range of 0.005-12.35 mg kg-1. The detected

pesticides were: Sigma -HCH, Dicofol, Sigma -

Endosulfan, Fenpropathrin, Permethrin-II, beta -

cyfluthrin-II, Fenvalerate-I, Dichlorvos, Dimethoate,

Diazinon, Malathion, Chlorofenvinfos, Anilophos, and

Dimethachlor. In some vegetables like radish,

cucumber, cauliflower, cabbage, and okra, the

detected pesticides ( Sigma -HCH, Permethrin-II,

Dichlorvos, and Chlorofenvinfos) were above

maximum residues limit (MRL) (PFA 1954). However,

in other vegetables the level of pesticide residues

was either below detection limit or MRL.

Unit operations normally employed in

processing food crops reduce or remove residues of

insecticides and other pesticides that are present in

them. These operations such as washing, peeling,

blanching and cooking play a role in the reduction of

residues

Studies with tomatoes fortified with (14 C)

ETU (0.006 ppm) showed that 70% of the radioactivity

was lost during washing of the tomatoes in water

(Knio et al., 2000).

The initial Diazinon residue level (0.822 ppm)

on cucumbers was decreased by 22.3% by washing

for 15sec by rubbing under running water (Cengiz etal., 2006).

PESTICIDE RESIDUES IN SELECTED FRUITS/VEGETABLES

The residues of Azoxystrobin on grapes were0.49–1.84 ppm and washing removed 75% of theresidues (Lentza et al., 2006).

The initial procymidone residue level (0.86ppm) on tomatoes was decreased 68% by washingfor 15sec by rubbing under running water (Cengiz etal., 2007).

Captan residues in apples washed for 10–15sec with continuous hand rubbing were 50% lowerthan in those apples that received no post-harvestwashing (25.5–5100 ng/g) (Rawn et al., 2008).

Washing is generally the first step in varioustypes of treatments which are given to foodcommodities in combination (like washing followedby cooking, washing and drying, washing and peelingand washing, peeling and juicing to allow for effectivedecontamination from pesticides.

REFERENCES

Cengiz, M. F., Certel, M., Karakas, B and Gocmen,H. 2006. Residue contents of DDVP(Dichlorvos) and diazinon applied oncucumbers grown in greenhouses and theirreduction by duration of a pre-harvest intervaland post-harvest culinary applications. FoodChemistry, 98, 127–135.

Cengiz, M. F., Certel, M., Karakas, B and Gocmen,H. 2007. Residue contents of captan andprocymidone applied on tomatoes grown ingreenhouses and their reduction by durationof a pre-harvest interval and post-harvestculinaryapplications. Food Chemistry, 100,1611–1619.

Dikshit, A. K. 2008. Pesticide protecting crops andenvironment: The Journey has Just Began.Corp Care. 34 (3): 37-41.

Knio, K. M., Saad, A and Dagher, S. 2000. The fateand persistence of zineb, maneb andethylenethiourea on fresh and processedtomatoes. Food Additives and Contaminants,17(5), 393–398.

Lentza-Rizos, C., Avramides, E. J and Kokkinaki,K. 2006. Residues of azoxystrobin from grapesto raisins. Journal of Agricultural and FoodChemistry, 54(1),138–141.

Rao, G. V. R., Sahrawat, K. L., Rao, C. S., BinithaDas Reddy, K. K., Bharath, B. S., Rao, V. R.,Murthy, K. V. S and Wani, S. P. 2009.Insecticide residues in vegetable crops grownin Kothapalli watershed, Andhra Pradesh,India: a case study. Indian Journal of DrylandAgricultural Research and Development.24(2):21-27.

Rawn, D. F. K., Quade, S. C., Sun, W., Fouguet, A.,Belanger, A and Smith, M. 2008.

Captan residue reduction in apples as a result ofrinsing and peeling. Food Chemistry, 109, 790–796.

Srivastava, A. K Purushottam Trivedi Srivastava, M.K. Lohani, M and Srivastava, L. P. 2011.Monitoring of pesticide residues in marketbasket samples of vegetable from LucknowCity, India: QuEChERS method. Environ-mental Monitoring and Assessment. 176(1/4):465-472.

Sharma, K. K. 2007. Cereals, pulses and grains.Pesticide Residue Analysis Manual. All IndiaNetwork Project on Pesticide Residues.Directorate of Information and Publication ofAgriculture. New Delhi.

FARHATH and SHOBHA

DEVELOPMENT OF LYCOPENE ENRICHED MIXED FRUIT BARM. PENCHALA RAJU , B. SHIREESHA , B. SRAVANTHI and KUNA APARNA

Department of Food Technology, Post Graduate and Research Centre,Acharya N.G. Ranga Agricultural University, Rajendranagar, Hyderabad-500 030.


Lycopene is a phytochemical nutrient thatis found in many fruits and vegetables but abundantlyin tomatoes and tomato products, contributing about80 to 90 percent of total pigments. The lycopeneamount in tomato juice varies from 4.0 to 11.6mgper 100g depending on tomato cultivars. Other sourcesof lycopene are watermelon (2.3 – 7.4 mg), papaya(2.0 – 5.3 mg), grape fruit (0.2 – 3.4 mg.) and guava(4.5 – 5.5 mg) per 100 gm on gross weight basis(Pohar et al., 2003)

Lycopene provides the mechanism thatinhibits the oxidation of low density lipoproteins (LDL)and in reducing the circulating LDL cholesterol.Reducing the LDL oxidation in a long term could inhibitatherosclerosis that may lower coronary risk (Balch,2001).

The present study was undertaken with theobjective to standardize the method for preparationof lycopene enriched mixed fruit bar and to evaluatethe best acceptable product.

Firm ripe red coloured mature tomatoes(Lycopersican esculentum), green coloured uniformlyripened banana (Musa paradasica), fully matured ripepapaya (Carica papya), sugar and liquid glucose wereused for the development of bars. All the ingredientsrequired were obtained from local markets ofHyderabad. Fruits were washed thoroughly to removedust from surface. Tomatoes were blanched in hotwater at 75 to 80 0C for 10 min to loosen the skin andsoften the pulp.

Blanched tomatoes were cut into halves andpassed through the pulper and filtered through muslincloth. Residue was returned to the pulper twice toextract maximum amount of pulp.

This extracted pulp was boiled at 85 0C for25 min to concentrate the pulp and to remove theraw tomato flavour.



Banana and papaya were peeled and cut into½ inch and 5x3 inch slices respectively. The pulpwas made by grinding and it was passed through a30 mm stainless steel sieve to obtain a homogenouspulp free from skin and seeds. Extracted pulp wassubjected to boiling separately. Three products weredeveloped with variations in fruit pulp as given inTable 1.

All the three (Tomato, banana & papaya)pulps were weighed according to formulations; 15%sugar, 15% liquid glucose were added to the pulpand mixed properly to get a homogenized pulp. Thepulp was subjected to boiling for 25min at 80 0C. Thehomogenized and boiled pulp was then spread ongreased trays at a rate of 250g/sq.ft. The trays wereallowed to dry at 60 to 65 0C in a tray drier for 6hours. After drying, the dried pulp layer was removedfrom the tray and six layers were piled and cut into 3x 9 cm and packed in flexible packaging covers andstored in air tight container. The developed mixedfruit bars were subjected to organoleptic evaluation.For this purpose ten semi-trained panel membersalready involved in other studies were selected and5-point hedonic scale score card was designed forevaluation. The results of the study were subjectedto statistical analysis for Means and Standarddeviation.

The colour of T1 fruit bar was more liked bythe panelists .This could be due to the incorporationof 30% banana pulp and 30% papaya pulp in T1 fruitbar.

The flavour of T2 was best as per the sensorypanel. This could be due to a balance of fruit pulpadded in the formulation ie., 20% tomato , 25%banana & 25% papaya where as the otherformulations had either 30% tomato or 30% papayadue to which either of the flavour dominated the otherflavour.

The taste of T2 was ranked higher than T1and T3 which could be due to the flavour componentas discussed earlier. The texture of T2 was rankedhigher which could be due to balance of ingredients.There was an after taste reported in T1 as comparedto T2 and T3 which could be due to 30% papayaincorporation in T1 formulation which gave a strongpapaya after taste as per the sensory panelobservations.

The overall acceptability of T2 was bestfollowed by T3 and T1. The individual attributes suchas colour, flavour, taste, texture added to the overallacceptability of the T2 formulation. Further studiescan be carried out on the T2 formulation to study theshelf life and packaging parameters which can provideinputs for commercialition of the product.Lycopenecontent in developed products was estimated. It was1.4mg in the product T1, 2.8mg in T2 and 4.0mg inT3.

Formulations of mixed fruit bars developed.

Mean score of sensory evaluation of lycopene enriched mixed fruit bar

Ingredients

(%) Treatments Characteristic Treatments

T1 T2 T3 T1 T2 T3 Tomato 10 20 30 Colour 4.4 ± 0.8

4.4 ± 0.5

4.3 ± 0.6

Banana 30 25 20 Flavour 3.7 ± 1.0

4.0 ± 0.6

3.9 ± 0.9

Papaya 30 25 20 Taste 3.7 ± 1.0

4.5 ± 0.5

3.9 ± 0.8

Sugar 15 15 15 Texture 3.7 ± 1.0

4.0 ± 0.6

3.4 ± 0.9

Liquid Glucose

15 15 15 After taste 4.2 ± 0.7

4.0 ± 0.4

3.9 ± 0.7

Overall Acceptability 3.9 ± 0.9 4.2 ± 0.4 3.9 ± 0.8

Note: Values are expressed as mean ± SD

Table 1. Mean score of sensory evaluation and formulation of mixed fruit bars developed

References

Balch, J. 2001. Tomato Phytonutrients: Contributorsin the Battle Against DegenerativeDiseases.Total health. 21(4): 54-55.

Pohar, K.S., Gong, M.C., Bahnson, R., Miller, E.Cand Clinton, S.K.2003.Tomatoes, lycopeneand prostate cancer: a clinicians guide forcounselling those at risk for prostate cancer.World Journal of Urology. 21: 9-14.

RAJU et al

STUDY ON THE EFFECT OF IRRADIATION ON SENSORY QUALITIES OFPRESERVED TOMATO PRODUCTSM. KIRTHY REDDY and V. VIJAYALAKSHMI

College of Home Science, Acharya N.G. Ranga Agricultural UniversitySaifabad, Hyderabad-500004



Increasing levels of dietary lycopene through

the consumption of fresh tomatoes and tomato

products has been recommended by many health

experts (Tonucci et al. 1995; Giovannucci, 1999).

Fruits and vegetables are living entities and undergo

several physiological changes (Haard and Salunkhe,

1980). Moreover most of them contain more than 80

percent water and some even upto 90-95 per cent.

Even 5 per cent loss of water causes many

vegetables to appear wilted or shriveled and

eventually unmarketable. These characters

significantly limit the storage life of fruits and

vegetables (Madan et al, 1993). Tomato has limited

shelf life under ambient conditions and is highly

perishable. It creates glut during peak season of

production and becomes scanty during off-season.

Short shelf life coupled with inadequate processing

facilities results in heavy revenue loss to the country.

Food irradiation has been identified as a safe

and alternative technology of food preservation to

reduce the risk of food borne illness as a part of high

quality food production, processing, handling and

preparation. The process has been approved by more

than 40 countries around the world. The technology

utilizes a source of ionizing energy that passes

through food to destroy harmful bacteria and other

organisms. Often it is referred to as “cold

pasteurization”.

An attempt was made to process and

preserve tomato products viz. puree and crush study

the effect of irradiation on sensory qualities on storage

for 60 days.

Tomato puree: Tomatoes were thoroughly washed

and blanched at 85oC for 2-3 min. Blanching helps in


loosening the skin, then after washing hands

thoroughly skin was removed Tomatoes were passed

through pulper to get the pulp and strained to remove

seeds. The strained pulp was concentrated to 12

percent total soluble solids (heavy tomato puree).In

the control product 0.5g of sodium benzoate per kg

was added as a preservative in the final stage of

preparation, cooled packed in polyethylene

bags.(Srivastava, 2004)

Tomato crush: Cut pieces of tomatoes were

concentrated to a brix of 15 by boiling to which glacial

acetic acid (5ml/kg) was added in the final stage of

preparation. In the control product, the preservatives

(0.4g of potassium metabisulphite+0.2g of sodium

benzoate per kg) were added in the final stage of

preparation and after cooling packed in polythene

bags.(Anand and Vijay, 1977).

Irradiation: Both the products were subjected to

gamma irradiations at dosages of 0.5kGy, 1.00kGy

and 2.00kGy. Control product in which chemical

preservatives were added. In puree (0.5g of sodium

benzoate per kg) and crush (0.4g of potassium

metabisulphite+0.2g of sodium benzoate per kg,

glacial acetic acid 5ml/kg) and no preservatives were

added in remaining treatments. Other three products

were subjected to 0.50 kilo Gray for duration of

11mins and 12seconds, 1.00 kilo Gray for duration

0f 22mins and 35 seconds, 2.00 kilo Gray for duration

0f 45mins and 7 seconds.

Sensory evaluation and storage: The evaluation

was done by a 10 trained panelists at Post Graduate

and Research Centre and College of Home Science,

Hyderabad. Judgments for the products were made

through rating products on a 9 point Hedonic scale

with corresponding descriptive terms ranging from

9 ‘like extremely’ to 1 ‘dislike extremely’. Parameters

like color, flavor, taste, texture and overall

acceptability were evaluated on 0 day, 30th day and

60th day. (Marek Sikaro et al. 2007).

The data obtained were subjected to statistical

analysis as per procedure outlined by Snedecor and

Cochran, (1983).

There were no significant changes observed

during storage period in the color of puree. Lu et al.(1986) reported that doses of gamma irradiation up

to 2000 Gy showed no effect on color of sweet potato.

Non significant interactions were observed between

treatments and storage periods in case of the colour

of tomato crush. Anuradha et al. (2002) also found

similar results in diced tomatoes by irradiation and

on storage. (Table 1).

Flavor development in tomato is

accompanied by the maturation process, which

involves many changes such as metabolic reactions

of synthesis and degradation of many compounds,

besides the complex energy transfer associated with

various phases of tomato development. (Hayasa etal. 1984)

Significant changes (P <0.05) were noticed

during the storage period, decrease in the flavor was

observed from 0 day (8.00%) to 60th day (7.60%) in

flavor of puree. The interaction between treatments

and storage periods were not significant. The flavor

of the tomato crush was not influenced by any of the

irradiation treatments, storage period or their

interaction. Fetter et al. (1969) found that doses up

to 5 kGy had no effect on flavor of commercial

tomato, orange, guava, red currant, black currant,

apricot, peach, pear and grape juices. (Table 2).

Significant changes (P<0.05) were observed

among treatments in tomato puree with irradiation

treatment of 1.00kGy (7.93%) scoring highest for taste

followed by irradiation treatment of 2.00kGy (7.73%)

and lowest score was recorded in irradiation treatment

of 0.50kGy (7.50%). There were no significant

changes noticed during storage period and

interactions were not significant. The decrease could

be due to the loss of volatile aromatic substances

responsible for taste during storage (Reddy, 2004).

Effect of gamma irradiation on taste of crush was

significant during the storage period. There was

decrease in the taste of crush from 0 day (8.02%) to

60th day (7.68%). The effects of treatments and their

interaction period of storage were found to be non

significant in case of the taste of crush. (Table 3).

The texture of both the products – puree and

crush was not influenced by treatments, periods of

storage on their interactions (Table 4). Higher

dosages of gamma irradiation had an impact on

aroma, flavor and textural properties of diced

tomatoes Anuradha et al. (2002). With increasing

levels of irradiation, panelists noted a decrease in

fresh tomato aroma and flavor and an increase in

ripe tomato aroma. Treatment with 1.24 and 3.70 kGy

significantly decreased firmness.

The consistency of tomato puree decreased

significantly by storing 60 days. There was no effect

of irradiation on the consistency of puree and crush,

the effects of storage were also not seen in case of

crush. (Table 5).

The overall acceptability of tomato puree was

not affected till 30 days, but decreased significantly

by storing for 60 days. The treatments of irradiation

did not show any influence on the overall acceptability

of puree as well as crush (Table 6).The effects of

storage were not seen on crush. The interaction

effects were also not significant. Bregvadze, (1963)

also found that no significant changes were observed

in overall acceptability of sterilized apple juice at 0.5

kGy.

The results of sensory evaluation indicated

that due to irradiation at 1.00kGy level the tomato

crush can be stored safely upto 60 days as there

was no change observed in various attributes like

taste, texture, color, flavor and overall acceptability.

The tomato puree can be stored safely for 30 days.

REDDY and LAKSHMI

Table 1. Color of preserved tomato products

Treatments 0 day 30th day 60th day Mean Tomato

puree Tomato crush

Tomato puree

Tomato crush

Tomato puree

Tomato crush

Tomato puree

Tomato crush

T1 (Control) 8.5 8 8.5 7.7 8.2 7.6 8.40 7.77 T2 (0.50kGy) 8.5 8 8.4 8 8.2 7.8 8.36 7.93 T3 (1.00kGy) 8.5 8 8.4 8 8.3 7.9 8.40 7.97 T4 (2.00kGy) 8.5 8.1 8.4 8 8.2 7.8 8.37 7.96 Mean 8.50 8.02 8.45 7.98 8.30 7.82

Tomato puree Tomato crush S.Ed CD at 5% S.Ed CD at 5%

ForTreatments 0.134 NS 0.134 NS For periods 0.116 NS 0.116 NS For T*P 0.233 NS 0.233 NS

NS –Non Significant

Table 2. Flavor of preserved tomato products


puree Tomato crush

Tomato puree

Tomato crush

Tomato puree

Tomato crush

Tomato puree

Tomato crush

T1 (Control) 8 7.8 7.7 7.8 7.3 7.7 7.67 7.77 T2 (0.50kGy) 8 7.9 7.8 7.7 7.6 7.6 7.80 7.73 T3 (1.00kGy) 8 7.9 8 7.8 7.6 7.7 7.87 7.80 T4 (2.00kGy) 8 7.8 7.9 7.7 7.8 7.7 7.90 7.73 Mean 8.00 7.85 7.85 7.75 7.60 7.68


ForTreatments 0.151 NS 0.134 NS For periods 0.131 0.256* 0.116 NS For T*P 0.262 NS 0.232 NS


Table 3. Taste of preserved tomato products


puree Tomato crush

Tomato puree

Tomato crush

Tomato puree

Tomato crush

Tomato puree

Tomato crush

T1 (Control) 7.6 8 7.6 7.8 7.5 7.7 7.66 7.83 T2 (0.50kGy) 7.6 8.2 7.5 7.9 7.4 7.8 7.50 7.97 T3 (1.00kGy) 8 7.9 7.9 7.7 7.9 7.6 7.93 7.73 T4 (2.00kGy) 7.9 8 7.7 7.8 7.6 7.6 7.73 7.8 Mean 7.77 8.02 7.67 7.80 7.60 7.68


ForTreatments 0.131 0.256* 0.134 NS For periods 0.113 NS 0.116 0.227* For T*P 0.226 NS 0.232 NS


STUDY ON THE EFFECT OF IRRADIATION

Table 4. Textural properties of preserved tomato products

Trea

T1 (Co

T2 (0.5

T3 (1.0

T4 (2.0

Mean

Fortrea

For pe

For T*

Table 5. Consistency of preserved tomato products

Treatments 0th day 30th day 60th day Mean Tomato

puree Tomato crush

Tomato puree

Tomato crush

Tomato puree

Tomato crush

Tomato puree

Tomato crush

T1 (Control) 8.2 7.8 7.8 7.9 7.7 7.8 7.90 7.83

T2 (0.50kGy) 8 7.8 7.9 7.8 7.8 7.7 7.90 7.67 T3 (1.00kGy) 8.3 8 8.3 7.8 7.9 7.8 8.20 7.83 T4 (2.00kGy) 8.1 7.9 7.9 7.8 7.7 7.7 7.90 7.00 Mean 8.15 7.87 7.98 7.82 7.78 7.72


ForTreatments 0.123 0.245 0.130 0.256

For periods 0.107 0.210* 0.113 0.222 For T*P 0.214 0.420 0.226 0.443



Table 6. Overall acceptability of preserved tomato products


puree Tomato crush

Tomato puree

Tomato crush

Tomato puree

Tomato crush

Tomato puree

Tomato crush

T1 (Control) 8.1 7.9 8 7.6 7.7 7.5 7.93 7.67 T2 (0.50kGy) 8 7.9 8 7.8 7.8 7.7 7.93 7.83 T3 (1.00kGy) 8.2 8 8 7.9 7.8 7.8 8.06 7.86 T4 (2.00kGy) 8 7.9 7.9 7.9 7.8 7.7 7.90 7.83 Mean 8.08 7.92 7.98 7.80 7.78 7.67


ForTreatments 0.100 NS 0.142 NS For periods 0.086 0.170* 0.123 NS For T*P 0.173 NS 0.246 NS


REDDY and LAKSHMI

REFERENCES

Anand, J. C and vijay sethi 1977. Whole tomatoconcentrate. Indian Horticulture. 22(2) :14.

Anuradha Prakasha, Jacqueline Manleya, SureshDeCostaa, Fred Caporasoa and Denise Foleyb.2002. The effects of gamma irradiation on themicrobiological, physical and sensory qualitiesof diced tomatoes. Radiation Physics andChemistry. 63: 387–390.

Brevadge, U. D. 1963. Preserving fruit juices by thecombined effects of gamma irradiation andascorbic acid. Navy Fiz. Metody ObrabotkiPisch. Produktov Kiev . sb. 2: 199-205.

Fetter, F., Stehlik, G K., ovascs, J and Weiss, S.1969. The Flavour halten einigergammabetrahlter Fruchtsaefte. Mitteilung-enirebe, Wein , Obstbau and Fruechtt-everwertung 19 :140-151.

Giovannucci, E. 1999. Tomatoes, tomato-basedproducts, lycopene, and cancer: review of theepidemiologic literature. Journal NationalisedCancer Institute. 91(4):317–331.

Haard, N. F and Salunkhe D.K. 1980. Perspectivesof post harvest physiology. In symposium:post harvest biology and handling of fruits andvegetables. AVI publishing Company,USA.pp:1-4.

Hayase, F., Chung, T,Y and Kato, H. 1984. Changesof volatile components of tomato fruits duringripening. Food Chemistry. 14: 113–124.

Lu, J.Y., S .White, P., Yakubu and Loretan, P.A.1986. Effects of gamma irradiation on nutritiveand sensory qualities of sweet potato storageroots. Journal Food Quality. 9: 425-435.

Madan, M S, Ullasa, B. A and Gopalakrishna, RaoK., P. 1993. Post harvest loses in vegetables.In advances in Horticulture vol 6-vegetablecrops part 2 (ed Chanda KL and Kalloo G)Malhotra Publishing House, New Delhi pp:1059.

Marek Sikaro., Stainslaw Kowalski., Piotr Tomasikand Marek Sedy. 2007. Rheological andsensory properties of dessert saucesthickened by starch-xanthan gum. Journal ofFood Engineering. 79: 1144-1151.

Reddy, G.N.V.V. 2004. Studies on methods onextraction of sapota juice for optimum yieldand quality. MSc. Thesis submitted toAcharya N.G. Ranga Agricultural University,Hyderabad, India.

Snedecor, G.W and Cochran, W.G.1983.StatisticalMethods Oxford and IBH publishingcompany New Delhi.

Srivastava, R.P. 2004. Fruits and Vegetablepreservation. Principles and practices. Tomatoprocessing. 265-266.

Tonucci, L.H., Holden, J.M., Beecher, G.R., Khachik,F., Davis, C.S and Mulokozi. G. 1995.Carotenoid content of thermally processedtomato-based food products. Journal ofAgricultural Food Chemistry. 43:579–586.

STUDY ON THE EFFECT OF IRRADIATION



COMBINING ABILITY STUDIES IN TOMATO (Solanum lycopersicum Mill.)

K.VINAY RAJU, B. NEERAJA PRABHAKAR, S. SUDHEER KUMAR and R. V. S. K. REDDY College of Horticulture, Andhra Pradesh Horticultural University,

Rajendranagar, Hyderabad-500 030.

This experiment was conducted at NBPGR,Regional Station, Rajendranagar, Hyderabad duringrabi and summer 2008-09. The treatments includedeight diverse genotypes of tomato used as lines(female parents) and four popular varieties used astesters (male parents). The selfed seeds of twelveparents were raised in the green house of NBPGR,Regional Station, Rajendranagar during rabi 2008 andwere crossed in a Line x Tester mating design. Theriped fruits were harvested, seeds from each crossedfruit were collected by using fermentation method forseed extraction. Twenty six days old tomatoseedlings were transplanted on 13th April 2009 with aspacing of 60 x 45 cm in a randomized block designwith three replications. The experimental unitconsisted of parents followed by F

1s in all the three

blocks. All the treatments were assigned randomlyto the experimental units. The data were recorded on13 characters in F1s and parents and analyzed foranalysis of variance and combining ability studies(Kempthorne, 1957).

Significant differences were noticed amongthe genotypes for all the traits studied. The Line xTester was found significant for all the charactersunder study except for ascorbic acid content . Thegca effects of the parents (Table 1) revealed that theline EC 145057 recorded significant positive gcaeffects for fruit weight, fruit length, fruit diameter andascorbic acid content. EC 163663 recorded significantpositive gca effects for TSS, fruit juice and yield perplant. Another line EC 338717 showed positivelysignificant gca effects for plant height, rind thicknessand negative gca effects for days to 50% flowering.

Among the testers, Pusa Ruby and PED werefound to be good general combiners for plant height,days to 50% flowering, fruit weight ,fruit length, fruitdiameter, fruit volume, rind thickness, TSS, fruit juice,yield and seed content. None of the parents was found


to be good general combiner for all the characters.These results are inconformity with findings ofPremalakshmi et al (2006) and Rao et al (2007).

The sca effects of the crosses (Table 2)revealed that three crosses had significant andpositive sca effects for fruit yield per plant.EC 163663 x Pusa Ruby, EC 257489 x Arka Saurabh,EC 338735 x Marutham were found to be bestcombiners for fruit yield per plant. Kamal Veer et al.(2006), Pradeep Kumar Singh et al. (2006),Premalakshmi et al. (2006) and Rao et al. (2007) alsoreported positive significant affects of sca for fruityield per plant.

Based on the magnitude of sca effects indesirable direction for various characters, the crossEC 538735 x Marutham was found to be the bestperforming cross for five characters namely days to50 % flowering, fruit weight, fruit length, fruit diameterand fruit yield per plant. The next best hybrids wereEC 14507 x Pusa Ruby for fruit volume and rindthickness, EC 145057 x Arka Sourabh for TSS, fruitjuice (%), EC 163663 x PED for less seed content(%), EC 338714 x Pusa Ruby for more plant heightand ascorbic acid content. Hence, these crosseswere identified as good specific combiners for theabove characters. Similar views have also beenreported by Arun Joshi and Kohli (2006) and PujaRattan et al. (2008).

In general, the hybrids with significant scaeffects in the desirable direction involved parents withhigh x high, low x high and low x low gca effects,which indicated that both additive and non additivegene actions were involved in the inheritance of thesetraits.

Fruit yield is the ultimate result, which isdependent on its components. Griffing (1956)suggested that there would be no separate genesystem for yield per se and it is an end product of

COMBINING ABILITY STUDIES IN TOMATO (Solanum lycopersicum Mill.)

Tab

le 1

. Esti

mate

s o

f g

en

era

l co

mb

inin

g a

bilit

y f

or

lin

es a

nd

teste

rs f

or

thir

teen

ch

ara

cte

rs i

n T

om

ato

Lin

es

Pla

nt

h

eig

ht

(cm

)

Nu

mb

er

of

bra

nc

he

s

Da

ys t

o

50%

flo

we

rin

g

Fru

it w

eig

ht

(g)

Fru

it

len

gth

(c

m)

Fru

it

dia

mete

r (c

m)

Fru

it

vo

lum

e

(cc)

Yie

ld p

er

pla

nt

(g)

Rin

d

thic

kn

es

s

(mm

)

TS

S

(0b

rix)

Asc

orb

ic

ac

id(m

g/

100

g

pu

lp)

Fru

it

juic

e(%

)

see

d

co

nte

nt

(%)

EC

145

057

3.96

**

1.20

**

4.47

**9.

41**

0.

49**

0.40

**8.

83**

74

.69*

*0.

56**

0.

29**

1.78

**

4.44

**

-0.2

8**

EC

163

663

3.72

**

0.28

2.

22**

4.24

**

0.15

**0.

28**

6.47

**

103.

02**

0.19

**

0.33

**-0

.78*

* 7.

66**

-0

.07*

*

EC

238

308

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l;

multiplicative interaction between yield and its

components. In the present investigation the cross

EC 338735 x Marutham with high per se performance

and sca effects for fruit yield per plant also exhibited

high sca effects for other important yield components

like fruit weight, fruit length and diameter.

REFERENCES

Arun Joshi and Kohli, U. K. 2006. Combining ability

and gene action studies for processing quality

attributes in tomato ( Lycopersicon esculentumMill.). Indian Journal of Horticulture

63(3): 289-293.

Griffing, B. 1956. Concept of general and specific

combining ability in relation to diallel crossing

system. Australian Journal of Biological

Sciences 9: 463-493.

Kamal Veer., Sharma, V. K and Uniyal S. P. 2006.

Combining ability studies in tomato [Solanumlycopersicon (Mill.) Wettsd.]. VegetableScience 33(1): 76-78.

Kempthorne, O. 1957. An introduction to GeneticStatistics.

Pradeep Kumar Singh., Singh B Singh, J. P andSantosh Singh., 2006. Combining ability intomato (Solanum lycopersicon Mill.).Vegetable science 33(1) : 85-87.

Premalakshmi V. ,Thangaraj, T Veeraragavathatham,D and Arumugam, T 2006. Heterosis andcombining ability analysis in tomato (Solanumlycopersicon Mill.) for yield and yieldcontributing traits. Vegetable Science33( 1): 5-9.

Puja Rattan,, Vidyasagar and Sanjeev Kumar., 2008.Line x tester analysis for combining abilitystudies involving bacterial wilt resistantgenotypes across environments in tomato.Indian Journal of Horticulture 65(2) 239-242.

Rao, E. S., Munshi, A. D Balraj Singh and Raj Kumar2007 Studies on heterosis and combiningability for yield and resistance to early blightin tomato. Indian Journal of Horticulture64 (3): 331-334.

VINAY et al

ATTITUDE OF THE RURAL WOMEN TOWARDS TELUGU TELEVISIONPROGRAMME -MEE AROGYAM MEE CHETULLOGRANDHI MANASA , R. GEETHA REDDY and M. PREETHI

Department of Home Science Extension and Communication Management, College of Home Science,Acharya N.G. Ranga Agricultural University, Saifabad, Hyderabad- 500 030



Television has been acclaimed to be themost effective media for diffusing information in torural areas and particularly to rural women than anyother mass media sources. (Meena and RekhaBhagat, 2011). Many health and nutritionalprogrammes are being produced in different channelsto improve the knowledge levels of the women infamily. “Mee Arogyam Mee Chetullo” is one of theprogrammes which gained lot of popularity andcredibility among the audience of Andhra Pradeshwith high Television rating points (TRP). Taking abovefacts into consideration the present study wasattempted to find out the attitude of rural womentowards ‘Mee Arogyam Mee Chetullo’ Programme.

Ex post facto research design was adoptedand Ranga Reddy District was purposively selectedfor the study. Out of the 37 Mandals of the RangaReddy district, one Mandal- Shamshabad wasselected by using random sampling Method. The listof villages under the selected mandal was collectedand four villages viz., Mutchintal, Kothwalguda,


Peddatopra, and Kavvaguda were selected randomly.120 rural women televiewers were selected asrespondents with the help of Sarpanch, anganwadiworkers and village level workers by usingproportionate random sampling technique from theselected villages. Personal interview method wasapplied to collect data by adopting the attitudinal scaledeveloped by Ganesh Kumar (2005) with suitablemodifications.

Attitude in the present study wasoperationalised as the degree of positive or negativeeffect associated with the programme Mee ArogyamChetullo with specific reference to the content,presentation and utility of the programme among thewomen respondents of the selected sample.

Based on the attitude of the respondentstowards the aspects like content, presentation andutility of the ‘Mee Arogyam Mee Chetullo’ Programmethey were classified into three categories by takingrange into the consideration – less favourable,favourable and more favourable attitude.

Table 1. Distribution of respondents according to their attitude towards the TV Programme

(n=120)

Mutchintal (n=30)

Kothwalguda (n=30)

Peddathopra (n=30)

Kavvaguda (n=30)

Total (n=120)

SI. No

Attitude

% % % % (%)

1. Less favourable ( below 51)

30.00

20.00 6.66 20.00 19.16

2. Favourable (52-57)

53.30 73.33 63.33 40.00 57.50

3. More favourable ( above 57)

16.66 6.66 30.00 40.00 23.33

Total 100 100 100 100 100

Majority of the respondents in Mutchintalvillage had favourable attitude towards theprogramme. A little less than three fourths of therespondents in Kothwalguda village had favourableattitude. In Peddathopra village two thirds of therespondents had favourable attitude. Forty per centof the women in Kavvaguda village had favourableattitude and another 40.00 per cent had morefavourable attitude towards the programme. On thewhole 57.50\ per cent of the respondents had afavourable attitude

Right from the inception of the programme ithad been covering wide range of topics on nutritionand health irrespective of gender, age, along with

remedies that can be practiced at home level withavailable resources.

This may be the reason for the womenteleviewers to develop favourable attitude towardsthe programme.

Reddy (2002) in his study on AnnadataVelugubata a farm telecast programme reported that60.8 per cent of the viewers had medium useful levelof opinion about the programme.

Profile characteristics of the rural womenwere studied and correlation analysis was carried outin order to study the nature of relationship betweenthe independent variables and attitude of therespondents towards the programme.

Table 2. Correlation analysis of independent variables with dependent variable - Attitude

(n= 120)

S.No Independent Correlation coefficient (r)Variables values

1. Age -0.0026 NS

2. Education 0.2059 *

3. Family type 0.0507 NS

4. SES 0.2054 *

5. Extension contact 0.1650 NS

6. Mass media exposure 0.1238 NS

7. Social participation 0.2798 **

8. Urban contact 0.0863 NS

9. Nutrition and health orientation 0.2590 **

10. Frequency of viewing 0.4579 **

11. Length of viewing 0.2693 **

*: Significant at 0.05 level of probability, ** : Significant at 0.01 level of probability

NS= non significant

Social participation, nutrition and healthorientation, frequency of viewing and length ofviewing had positive and significant relationship withthe attitude at 1% level of significance where asEducation and SES, had positive and significantrelationship at 5% level of significance with theattitude of the respondents towards the programme.

REFERENCES

Ganesh Kumar, P. 2005. A study on Rythumitratelevision programme for farm televiewers inChitttoor District of Andhra Pradesh.

M.Sc(Ag).Thesis. Acharya N. G RangaAgricultural University. Hyderabad.

Meena, K.C and Rekha Bhagat. 2011. Socio profilecharacteristics and psychological factorsinfluencing the farm telecast viewing. Journalof Communication Studies. 29(2): 47-54.

Reddy, K. 2002. A critical analysis of AnnadataVelugubata - Farm telecast programme inAndhra Pradesh. M.sc. (Ag) Thesis. AcharyaN.G. Ranga Agricultural University,Hyderabad.

MANASA et al.


Fertilizers play an important role in increasingthe production and improving the quality of vegetablesand among them Potassium is considered to be primenutrient because of its role in various physiologicalprocesses. Besides potassium, organic products likeTriacontanol (a neem based product with the formulaCH3(CH2)28CH2OH) and K- humate also play animportant role in manipulating the yield potential andaugmenting the uptake of nutrients in many vegetablespecies including tomato. Hence, keeping all this inview an experiment was conducted.

A field experiment was conducted on a sandyclay soil at College Farm, College of Agriculture,Rajendranagar, Hyderabad during kharif season of2011. It was laid out in Randomized Block Designwith 3 replications and 16 treatments. The soil understudy was sandy clay in texture, slightly alkaline (7.64pH) and normal in salt content (0.19 dS m-1). Thecation exchange capacity of the soil was 35.8 c mol(p+) kg-1. The soil was medium in organic carbon(0.67%), low in available nitrogen (156.7 kg N ha-1),high in available phosphorus (57.0 kg P ha-1) andpotassium (449.0 kg K ha-1).Tomato fruits wereharvested at five days interval and a total of 14pickings were taken and yield was recorded, the drymatter yield at flowering and harvest were alsorecorded and expressed in q ha-1.

The dry matter put forth by tomato atflowering and harvest was significantly influenced bythe combined application of inorganic K, potassiumhumate and triacontanol.

At flowering, the dry matter produced withthe individual application of recommended doseinorganic K @ 60 kg K

2O ha-1, foliar applications of

triacontanol and potassium humate were 23.79, 21.57and 20.30 q ha-1 respectively.

RESPONSE OF TOMATO (Solanum lycopersicum L.) TO POTASSIUMFERTILIZATION ALONG WITH FOLIAR APPLICATION OF POTASSIUM

HUMATE AND TRIACONTANOLPALAKSHI BORAH, V. SAILAJA, P.CHANDRASEKHAR RAO and A. PRATAP KUMAR REDDY

Department of Soil Science and Agricultural ChemistryCollege of Agriculture, Acharya N.G. Ranga Agricultural University,

Rajendranagar, Hyderabad -500 030


A higher dose of 90 kg K2O ha-1 also was

found to be inferior with a dry matter production of25.17 q ha-1. Individual application of eithertriacontanol or potassium humate or their combinedapplication without inorganic K produced significantlylower dry matter yields of tomato.

Among the treatments, T4 receiving 60 kgK

2O ha-1 along with foliar application of potassium

humate and triacontanol showed significantly higherdry matter production of 30.79 q ha-1 which was 29% more when compared to control receivingrecommended dose of 60 kg K

2O ha-1. However, this

was on par with the dry matter produced by integratedapplication of potassium humate and triacontanolalong with a lower dose of 45 kg K

2Oha-1.

The mean dry matter yield at harvest showeda significant increase from 21.17 q ha-1 with 30 kgK

2O ha-1 alone to 34.19 q ha-1 obtained from the

treatment receiving 60 kg K2O ha-1along with the foliar

applications of triacontanol and potassium humatewhich surpassed the dry matter yield obtained withinorganic K alone at the same level of application by35 %.

These findings are in agreement with thereports of Besford and Maw (1975). Potassium playsa vital role in growth and plant productivity,metabolism, ionic balance, activation of severalenzymes and plant defense systems (Marschner,2002). Plant growth regulator triacontanol plays animportant role in manipulating the yield potential andaugmenting the uptake of nutrients in many vegetablespecies including tomato (Muthuvelet al., 2001). TRIAtreated plants had a significantly higher rate ofphotosynthesis and hence increased the dry weightby 30 per cent as was documented by Eriksen et al.(1981). The positive influence of TRIA on tomato isphotosynthesis stimulation and enhanced water


uptake, which eventually results in increase in thedry weight.

The yield of tomato varied from 102.7 q ha-1

in the treatment receiving 30 kg K2O ha-1 alone to a

significantly higher yield of 135.0 q ha-1 when 60 kgK

2O ha-1 was integrated with triacontanol and

potassium humate, which was 18 per cent more whencompared to control (T1) receiving recommended doseof K.

However, the fruit yields obtained from thetreatments integrating the triacontanol and potassiumhumate with either 45 or 60 kg K

2O ha-1were all at a

par. Individual applications of either inorganic K atany level or triacontanol or potassium humate werefound to be significantly inferior to the best treatment.

Conjunctive use of chemical fertilizer withfoliar application of potassium humate and triacontanol

produced higher yields as compared to their individualapplications due to increase in nutrient uptake fromsoil and effective utilization of foliar applied nutrients.This might be due to soil application of inorganicpotassium resulting in increased uptake of nutrientsand improved translocation of photosynthates andother metabolites to the reproductive parts and foliarapplication of potassium humate and triacontanolresulted in accumulation of increased assimilates inthe sink and increase in chlorophyll content andphotosynthetic area of the plant, which is responsiblefor the increased photosynthetic efficiency (Muthuvelet al., 2001 and Marschner, 2002). The results are inagreement with the findings of Duraisamy and Mani(2002) in tomato. Schnitzer (1978) reported that thefavorable effect of humic substances in stimulatinggrowth, yield and yield attributes could be attributedto the presence of auxin like properties in humic acid.

T1 : 60 kg K2O ha-1 (Recommended K) as basal application 23.79 25.24 114.2

T2 : T1 + Foliar application of Triacontanol 28.61 32.47 125.8

T3 : T1 + Foliar application of K- humate 26.84 30.79 128.8

T4 : T1 + Foliar application of Triacontanol and K- humate 30.79 34.19 135.0

T5 : 45 kg K2O ha-1 as basal application 21.80 23.44 106.7

T6 : T5 + Foliar application of Triacontanol 25.45 27.89 127.2


T8 : T5 + Foliar application of Triacontanol and K- humate 29.91 29.47 124.7

T9 : 30 kg K2O ha-1 as basal application 21.15 21.17 102.7

T10 : T9+ Foliar application of Triacontanol 23.13 25.68 124.7


T12 : T9+ Foliar application of Triacontanol and K- humate 25.91 28.41 125.1

T13 : Foliar application of Triacontanol 21.57 24.23 115.2

T14 : Foliar application of K- humate 20.30 23.22 114.0

T15 : Foliar application of Triacontanol and K- humate 22.19 26.66 119.0

T16 : Inorganic K @ 90 kg K2O ha-1 as basal application 25.17 27.13 117.4

CD at 5% 4.93 5.37 11.26

SE(d) ± 2.40 2.62 5.48

Table1. Effect of individual and integrated application of inorganic potassium, potassium humateand triacontanol on dry matter production and fruit yield of tomato

Treatments Dry matter Fruitproduction (q ha-1) yield

Flowering Harvest

BORAH et al

REFERENCES

Besford, R. T and Maw, G. A. 1975.Effect ofpotassium nutrition on tomato plant growth andfruit development. Plant and soil. 42: 395-412.

Duraisami, V. P and Mani, A. K. 2002. Effect of majornutrients on yield and fruit quality of tomatounder rainfed condition in Entisol. South IndianHorticulture. 50(1-3): 56- 64.

Eriksen, A. B., Sellden, G., Skogen, D and Nilsen,S. 1981. Comparative analyses of the effectof triacontanol on photosynthesis,photorespirstion and growth of tomato(C3-plant) and maize (C4-plant). Planta.152: 44-49.

Marschner, H. 2002. Mineral nutrition of higher plants.

Academic Press, London.

Muthuvel, P., Muralidharan, R and Saravanan,

A. 2001. Effect of bio-regulators on soil

available nutrients, yield and uptake of nutrients

by Tomato (Lycopersicon esculentum, Mill.).

Madras Agricultural Journal. 115-117.

Schnitzer, M. 1978. Humic substances- Chemistry

and reactions. In: Schnitzer, M and Khan,

S. V (Eds.) Soil Organic Matter (pp. 1-64).

Elsevier Scientific Publishing Company,

Amsterdam.

RESPONSE OF TOMATO TO POTASSIUM FERTILIZATION

RECOMMENDED SPECIFIC PRODUCTION PRACTICES FOLLOWED BY FARMERS INSUPPLY CHAIN MANAGEMENT OF HORTICULTURAL PRODUCTS

M.BHAVYAMANJARI, M.SURYAMANI and C.PADMAVENIExtension Education Institute, Acharya N.G.Ranga Agricultural University

Rajendranagar, Hyderabad -500030


Supply Chain Management refers to the

management of the entire set of production,

distribution, and marketing processes by which a

consumer is supplied with a desired product. (Gadre

and Bomandes, 2004). The present study was taken

up with the objective to find out recommended

specific production practices followed by farmers in

SCM of horticultural products.

An exploratory research design was followed

to conduct the present study. Three districts namely,

East Godavari, Ranga Reddy and Cuddapah of

Andhra Pradesh were selected based on the highest

area and production of horticultural crops. 40 banana

growing farmers, 30 onion farmers, 40 tomato farmers

and 40 mango farmers were selected for the study.

Thus a total of 150 farmers were selected from

existing models of SCM at Mandal level by following

proportionate random sampling. The interview

schedule consisted of six major components viz.,

land preparation, seed and sowing, manure and

fertilizer application, weeding, irrigation and plant

protection measures. Schedule was administered to

farmers. Responses were obtained and frequency

and percentage were calculated for each practice.

The details of important production practices

as per the recommendation and the frequency of

adoption by different categories of horticultural

farmers are furnished in Table1.

Over all distribution of the farmers into three

categories (low, medium and high) as per the adoption

of specific production practices is given in Table 2.

Majority of the banana farmers adopted most

of the specific practices. This might be due to the

fact that, the percentage of rejection of produce was

high during the initial stages of establishment because

farmers were not accustomed to producing good

quality produce in a scientific manner. Hence the

farmers changed their practices to provide required

good quality products to the retail outlets.

In case of tomato farmers, majority of the

SCM farmers adopted specific production practices.

Here again, farmers changed their cultivation

practices due to larger scale rejection of produce

during initial period for want of quality specifications.

Due to change in cultivation practices the rate of

rejection was reduced.

Majority of Onion farmers preferred to supply

their produce to the Consolidation Center, as it

provided them stable prices and assured market,

compared to the highly volatile prices at the wholesale

market. To supply the required quality, farmers

adopted specific practices required by the

Consolidation Center.

Regarding mango farmers, majority of them

fell under high category. Large land holding, medium

level of risk taking behavior, medium income level

and medium economic orientation were the attributes

for changing the farmers attitude to adopt the

recommended practices. It was aimed to provide the

quality produce to retail outlets.

email : [email protected]


RECOMMENDED SPECIFIC PRODUCTION PRACTICES FOLLOWED BY FARMERS

Table1. Distribution of SCM farmers according to their recommended specific production practices

Banana (N=40)

Tomato (N=40)

Onion (N=30)

Mango (N=40)

S.No Specific Production practices Practices/

F & (%) Practices/

F & (%) Practices/

F & (%) Practices F & (%)

1. Soil and field preparation

a. Soil testing

Conducted 24

(60.00)

Conducted 24

(60.00)

Conducted 17

(56.67)

Conducted 24

(60.00) b.

Ploughings for field preparation

3 ploughings

25 (62.50)

3 ploughings

25 (62.50)

3 ploughings

20 (66.67)

3 ploughings

31 (77.50)

2. Seed and Sowing

a. Recommended varieties

Chakrakeli, Amruthapani,

Dwarf Cavendish and

Robusta 25

(62.50)

Hybrids 25

(62.50)

Agri found dark red/ light

red, Arka pragati,

Agrifound rose and

Early Grano 21

(70.00)

Totapuri, Neelam and

Badami 25

(62.50)

b. Method of propagation

Tissue culture 25

(62.50) - - -

c. Time of planting February – April

26 (65.00)

June – July 31

(77.50)

August – September

22 (73.33)

-

d Size of pits 60x60x60

27 (67.50)

- - 1mx1mx1m

25 (62.50)

e Recommended spacing

Dwarf- 1.8mx1.8m Robusta – 2.1mx2.1m

27 (67.50)

60cmx 45cm 28

(70.00)

Bulbs dibbled at 15 cm on

the side of 45 cm wide ridges

23 (76.67)

10mx 10m 28

(70.00)

f Method of seed treatment -

Seeds are treated with

Carbendazim Seedlings are treated with

0.2% Dithane M-45 and 0.1%

Nuvacron 26

(65.00)

Seeds are treated with fungicides

20 (66.67)

Seeds are treated with fungicides

29 (72.50)

g Recommender seed rate -

75-100g seeds per hectare

32 (80.00)

10-12 kg seeds per

acre 22

(73.33)

-

BHAVYA et al

h Recommended method of sowing

-

Raising seedlings and Transplanting

29 (72.50)

Direct sowing 21

(70.00) -

i Recommended

dose of FYM before sowing

-

5-8 kg of manure

before sowing 28

(70.00)

20-25 tonnes/ ha 24

(80.00)

-

3. Manure and fertilizer application

a. Use of manures/ Vermi-compost

5-8 tonnes vermi compost

10-12 tonnes FYM/acre

26 (65.00)

5-10 tonnes of FYM 28

(70.00)

14-16 tonnes of FYM

20 (66.67)

10-15 kg FYM with top soil

used as filling material

27 (67.50)

b. Use of fertilizers

1-2 kg complex fertilizers

27 (67.50)

100kg/ha complex fertilizers

32 (80.00)

50 kg/ ha DAP and

potassium 22

(73.33)

1-3 kg/ tree complex fertilizer

24 (60.00)

4. Inter cropping

a. Inter crops

Leguminous crops

25 (62.50)

- -

Maize and red gram

29 (72.50)

5. Weeding and inter cultivation

a. Time of weeding

Once in a month after planting

27 (67.50)

40-45 days after transplanting

28 (70.00)

After 45 days 18

(60.00) -

b. Use of weedicides

0.4% Glycel 25

(62.50)

Pendimethalin @ 1kg ai/ha

28 (70.00)

Basalin @ 1 lit/ha 20

(66.67)

-

c. After care De suckering

25 (62.50)

- - -

6. Irrigation

a. Recommended no. of Irrigations

3-5 days after planting

25 (62.50)

8-12 days after sowing

29 (72.50)

8-20 days after sowing

21 (70.00)

15 days after planting

26 (65.00)

7. Plant protection measures

a. Disease control measures

25 (62.50)

30 (75.00)

- 25

(62.50)

b. Pest control measures

25 (62.50)

25 (62.50)

22 (73.33)

26 (65.00)

Figures in parentheses indicate percentage

REFERENCES

Gadre, H and Bomandes, J. 2004. An Introductoryof supply chain management.The International Journal of LogisticsManagement. 8 (1): 15–24.

RECOMMENDED SPECIFIC PRODUCTION PRACTICES FOLLOWED BY FARMERS

Table2. Overall distribution of SCM farmers towards adoption of specific practices

.

Figures in parentheses indicate percentage

Banana (N=40)

Tomato (N=40)

Onion (N=30)

Mango (N=40)

Category

F & (%) F & (%) F & (%) F & (%)

Low 13 (32.50)

8 (20.00)

4 (13.33)

10 (25.00)

Medium 1 (2.50)

6 (15.00)

7 (23.33)

4 (10.00)

High 26 (65.00)

26 (65.00)

19 (63.33)

26 (65.00)

The herb Aloe is as old as human civilization.It belongs to the family “Liliaceae”. Aloe was knownto Indians for its medicinal value since timeimmemorial in the name of Ghrit kumaree orKanyasara. The Aloe is known as “Mussambar” inIndian market (Saroj et al., 2004). The inner mostpart of the leaf is a clean, soft, moist and slipperytissue where water is held in the form of viscousmucilage called gel (Newton, 2004). The gel is therich source of polysaccharides, antioxidants,enzymes, minerals and vitamins (Chauhan et al.,2007). For centuries, this plant has been used for itsmedicinal and therapeutic properties. It has a historyof use in folk medicine for treating skin and otherdisorders. Today, the Aloe industry is flourishing andthe gel is being used in many products, such asfresh gel juice and other formulations for health,medical and cosmetic purposes. The leaves are tobe harvested at the right age and cut exactly at rightplace on the plant to ensure the best gel (Chauhanet al., 2007). Heating of gel is an effective method ofpasteurization and add better flavour (He et al.,2005). Gel heating may change the compositionwhich also has effect on storage. Aloe gel can bestored for more number of days (up to 30 days) at5°C without any deterioration in quality (Hemalathaet al., 2008). Keeping in view the composition,importance and usage of Aloe gel, the presentinvestigation was carried out.

The experiment was conducted during 2010at Herbal garden, College of Horticulture,Rajendranagar, Hyderabad to study the effect oftemperatures on physico- chemical characteristicslike pH, acidity and gel microbial content in differentaccessions of Aloe during storage. Three accessionsof Aloe viz., yellow flowering accession-1, yellow

EFFECT OF HEATING OF THE GEL AT DIFFERENT TEMPERATURES ONPHYSICO-CHEMICAL CHARACTERISTICS AND MICROBIAL CONTENT IN

DIFFERENT ACCESSIONS OF ALOE (Aloe barbadensis Miller)

B. AMARESWARI, M.PADMA, M.RAJKUMAR and A. SIVA SHANKARHerbal garden, College of Horticulture, Andhra Pradesh Horticultural University,



flowering accession-2 and orange floweringaccession-3 were studied at three temperatures viz.,50°C, 75°C and 100°C. Nine treatments wereevaluated in Completely Randomized Design withfactorial concept in three replications.

Healthy and matured leaves of differentaccessions were harvested manually. Afterharvesting, Aloe leaves were washed thoroughly. Thealoetic juice was separated from the leaves by cuttingthem transversely at the base and keeping the cutportion touching the ground. The leaf was allowed tostand in slanting position for half an hour to removeyellow latex from leaf. The leaves were again washedthoroughly and cut into pieces by stainless steel knifeunder hygienic conditions. Then the outer peels wereseparated. Then the extracted gel from the leaveswas thoroughly homogenized in a mixer and gel ofeach accession was heated as per treatments for 15minutes and stored at room temperature. Standardpreservative (Sodium benzoate 1000 ppm + Citricacid 1%) was added to aloe gel. Then the juice wasanalyzed for pH, acidity and microbial content at every10 days interval up to 30th day of storage.

The characteristics of three accessions wereas given under.

Accession-1 leaves were dark green in colorwith purple tinge. On an average leaf size, weightand thickness were more than the accession-2 andaccession-3. Spines on leaf margin were set closerwhen compared to accession-2 and accession-3. Gelcolor was yellow.

Accession-2 leaves were green in color.Distance between the spines on leaf margin wasmedium when compared to accession-1 andaccession-3. Gel color was yellow.



Accession-3 leaves were green in color asaccession-2. On an average leaf size, weight andthickness were less than accession-1 and accession-2. Small white spots appeared on lower surface ofthe leaf. Spines on leaf margin were set widely whencompared to accession-1 and accession-2. Gel colorwas pale yellow and very sticky when compared toyellow flowering accessions.

Among the three accessions, the highest pH(5.17, 5.30, 5.63 and 5.89) was recorded by orangeflowering accession-3 followed by yellow floweringaccession-2 (4.76, 4.83, 5.11 and 5.41) and yellow

flowering accession-1 (4.41, 4.48, 4.64 and 4.77) atday1, 10th, 20th and 30th day of storage respectively.Among the three temperatures, the highest pH wasrecorded by heating at 50°C (4.79, 4.90, 5.20 and5.46) and 100°C temperature (4.79, 4.89, 5.19 and5.46) followed by 75°C (4.76, 4.82, 5.0 and 5.16).

There was a significant increase in pH withincrease in storage period in all the accessions. Itmight be due to the fact that, acidity of Aloe geldecreases with the extension of storage periodirrespective of the temperature. Similar observationswere reported by Hemalatha et al. (2005) in Aloe and

( pH) (ACIDITY) Days after storage Days after storage

Fresh 10 20 30 Fresh 10 20 30

A1 4.41 4.48 4.64 4.77 0.24 0.24 0.22 0.22 A2 4.76 4.83 5.11 5.41 0.16 0.16 0.15 0.14 A3 5.17 5.30 5.63 5.89 0.07 0.07 0.07 0.06

SEm ± 0.0471 0.0497 0.0537 0.0595 0.0057 0.0057 0.0057 0.0056

CD at 5% 0.0991 0.1044 0.1128 0.1250 0.0120 0.0120 0.0120 0.0117

t1 4.79 4.90 5.20 5.46 0.15 0.15 0.14 0.14

t2 4.76 4.82 5.00 5.16 0.16 0.16 0.15 0.15

t3 4.79 4.89 5.19 5.46 0.15 0.15 0.14 0.14

SEm ± 0.0471 0.0497 0.0537 0.0595 0.0057 0.0057 0.0057 0.0056

CD at 5% 0.0991 0.1044 0.1128 0.1250 0.0120 0.0120 0.0120 0.0117

Accessions × Temperatures

A1 t1 4.43 4.50 4.77 4.83 0.24 0.23 0.22 0.22

A1 t2 4.40 4.43 4.53 4.67 0.24 0.24 0.23 0.23

A1 t3 4.40 4.50 4.63 4.80 0.24 0.23 0.22 0.22

A2 t1 4.77 4.87 5.17 5.53 0.16 0.16 0.15 0.14

A2 t2 4.73 4.77 5.03 5.20 0.16 0.16 0.15 0.14

A2 t3 4.77 4.87 5.13 5.50 0.15 0.15 0.15 0.14

A3 t1 5.17 5.33 5.67 6.00 0.07 0.07 0.06 0.06

A3 t2 5.13 5.27 5.43 5.60 0.08 0.08 0.07 0.07

A3 t3 5.20 5.30 5.80 6.07 0.07 0.07 0.06 0.06

SEm ± 0.0817 0.0861 0.0930 0.1030 0.0099 0.0099 0.0099 0.0097

CD t 5% 0 1716 0 1808 0 1953 0 2165 0 0208 0 0208 0 0208 0 0203

TREAT-MENTS

Table 1. Effect of heating at different temperatures on gel pH and acidity indifferentaccessions of Aloe.

EFFECT OF HEATING OF THE GEL AT DIFFERENT TEMPERATURES

Gajanana (2002) in amla juice. A combination of

orange flowering accession-3 heated at 100°C has

recorded the maximum pH (5.20, 5.30, 5.80 and 6.07)

at all storage intervals. But the optimum pH of Aloe

was maintained by yellow flowering accession-1heated at 75°C (4.40, 4.43, 4.53 and 4.67) at day1,

10th, 20th and 30th day of storage respectively.

The highest gel acidity (0.24, 0.24, 0.22 and

0.22%) was recorded by yellow flowering accession-

1 followed by yellow flowering accession-2 (0.16,

0.16, 0.15 and 0.14%) and orange flowering

accession-3 (0.07, 0.07, 0.07 and 0.06%) at day1,10th, 20th and 30th day of storage respectively. Among

the temperatures, 75°C has recorded the maximum

gel acidity (0.16, 0.16, 0.15 and 0.15%) at all storage

intervals.

The results indicated that there was acorresponding decrease in acidity during the storageperiod. Similar observations were reported in Aloeby Hemalatha et al. (2005). The result showed thatthere was an inverse relationship between the pHand acidity in Aloe gel. The maximum gel acidity(0.24, 0.24, 0.23 and 0.23%) was recorded by yellowflowering accession-1 heated at 75°C at all storageintervals.

Similarly, Miranda et al. (2009) reported thata drying temperature of 80°C and 90°C resulted insignificant variation and/ loss of the physico-chemicaland nutritional properties of the gel. But, there was aminor alteration in the physico-chemical andnutritional properties of Aloe gel produced at dryingtemperature of 60-70°C, which resulted in theproduction of highquality gel.

Table 2 . Effect of heating at different temperatures on microbial count indifferent accessions of Aloe.

Microbial count

1st day 10th day 20th day 30th day

Treatments

B Y/M B Y/M B Y/M B Y/M A1 t1 - - 28 15 63 38 >100 >100

A1 t2 - - 10 5 35 27 87 75

A1 t3 - - 22 12 50 36 >100 93

A2 t1 - - 37 21 75 54 >100 >100

A2 t2 - - 15 9 44 32 92 79

A2 t3 - - 29 15 50 41 >100 >100

A3 t1 - - 40 30 92 65 >100 >100

A3 t2 - - 17 10 60 35 >100 85

A3 t3 - - 35 17 73 47 >100 >100

The gel microbial count increased graduallywith increase in storage period up to 30th day in allthe treatments (Table 2). On day 1, no microbial countwas observed in all the treatments. On 10th day ofstorage highest microbial count was observed inorange flowering accession-3 heated at 50°C (bacterialcount-40, mould count-30) which increased further(bacterial count- >100, mould count- >100) at 30th

day of storage. The lowest microbial count wasrecorded with yellow flowering accession-1 heated

B - Bacterial count, Y/M - Yeast / Mould count

at 75°C temperature on 10th day of storage (bacterialcount-10, mould count- 5) which increasedconsiderably (bacterial count- 87, mould count- 75)at 30th day of storage.

Aloe gel and leaf itself has an antimicrobialactivity. The results obtained showed that there wasslight increase in microbial load in all the treatmentsat all heating temperatures during the storage period.The Aloe gel can be stored for 20 days at roomtemperature without deterioration in quality. Similar

AMARESWARI et al

observations were reported by Hemalatha et al.(2005) in Aloe.

At 30th day of storage of Aloe gel, significantmicrobial load was observed in all the treatments.But, Aloe gel heated at 75°C had more storagestability. He and Changhang (2006) reported that thepolysaccharide from Aloe exhibited a maximal

stability at temperature of 70°C where as the stabilitydecreased either at higher or lower temperatures. Theincreased microbial count was attributed to decreasedacidity and increased sugars during the storageperiod. The experimental results indicated that,Yellow flowering accession-1 heated at 75°C wasgood in keeping quality.

REFERENCES

Chauhan, O. P., Raju, P. S., Farhat Khanum andBawa, A. S. 2007 Aloe vera - Therapeuticand Food applications Indian Food Industry.26(3):43-51.

Hemalatha, P., Vadivel, E., Saraswathi, T andRajamani, K. 2005 Role of preservatives andstorage temperature on the post harvestquality of Aloe vera gel. Academy of PlantSciences. 21:2, 471-473. 5 ref.

He, Q., Changhong, L., Koje, E and Tian, Z. 2005Quality and safety assurance in the processingof Aloe vera gel juice. Food Control. 16:95-104.

Miranda, M., Maureira, H., Rodriguez, K and Vega-Galvez, A. 2009 Influence of temperature onthe drying kinetics, physicochemicalproperties, and antioxidant capacity of Aloevera gel.(Aloe barbadensis Miller). Journal ofFood Engineering. 91:2, 297-304.

Newton, L. E. 2004 Aloes in habitat at In aloes thegenus Aloe; Reynolds T Ed CRS press BocaRaton pp:3- 36.

Saroj, P. L., Dhandar, D.G and Singh, R.S. 2004Indian Aloe. Central Institute for AridHorticulture, Bikaner, Rajastha, pp-3-6.

EFFECT OF HEATING OF THE GEL AT DIFFERENT TEMPERATURES

Sunflower (Helianthus annuus L.) holds greatpromise as an edible oilseed crop because of its shortduration, photo-insensitivity, drought tolerance andwide adaptability to different agro-climatic regions andsoil types. Sunflower can play a key role in meetingthe shortage of edible oils in the country. Lowproductivity of sunflower (625 kg ha-1) is mainly dueto its cultivation with low and imbalanced nutrition.

Nitrogen is crucial for growth anddevelopment while, sulphur fertilization is most criticalfor oil and protein synthesis besides seed yieldenhancement. After N, P and K, S is the fourthnutrient, whose deficiency is widespread in India(Sakal et al., 2001). N and S deficiencies are commonas the crop is grown mostly on energy starvedconditions with poor organic matter content. A suitablecombination of major and secondary nutrients is byand large the most important factor that affects theyield and quality of sunflower oil. This warants thatnutrient schedule should be worked out for differentgrowing conditions and to sustain soil health and cropproduction. Keeping this in view the present studywas undertaken to find out the response of sunflowerto different levels of nitrogen and sulphur during kharif2011 in Alfisols.

The experiment was conducted at Collegefarm, College of Agriculture, Rajendranagar. The soilwas sandy loam in texture, slightly alkaline in reaction(pH 7.5), low in organic carbon (0.38 %), low inavailable nitrogen (226.4 kg ha-1), medium in availablephosphorous (38.7 kg ha-1) and potassium (258.0 kgha-1) and low in available sulphur (17.9 kg ha-1).

The experiment was laid out in a randomizedblock design with factorial concept, replicated thrice,having nine treatment combinations comprising threenitrogen levels (60, 90 and 120 kg ha-1) and threesulphur levels (0, 15 and 30 kg ha-1). A uniform doseof 60 kg P

2O

5 ha-1 and 30 kg K

2O ha-1 was applied


EFFECT OF DIFFERENT LEVELS OF NITROGEN AND SULPHUR ON GROWTHAND YIELD OF SUNFLOWER (Helianthus annuus L.)

S. PAVANI, K. BHANU REKHA, S. N. SUDHAKARA BABU and G. PADMAJADepartment of Agronomy, College of Agriculture

Acharya N.G. Ranga Agricultural University, Rajendranagar, Hyderabad -500 030

email : [email protected]

through di-ammonium phosphate and muriate ofpotash respectively as basal dose to all the plots.Nitrogen was applied through urea as per thetreatments in three splits i.e. half at sowing, one fourthat bud initiation stage and the remaining one fourthat flowering stage. Sulphur through elemental sulphur(Gromor S - 90 % S) was applied as per treatments15 days before sowing, in presence of moisture andincorporated into the soil. The crop (DRSH-1 hybrid)was sown on 16th July adopting a spacing of 60 cm x30 cm.

Irrigation was given as and when requiredconsidering the rainfall. During the crop period a totalof 466.1 mm rainfall was received in 29 rainy days.No specific pest and disease incidence was noticed.Pre-emergence herbicide pendimethalin 30% EC @240 ml ha-1 was sprayed one day after sowing inoptimum soil moisture followed by two hand weedingsat 20 and 40 days after sowing. The crop washarvested on 19th October, threshed, dried and seedyield was recorded.

Application of nitrogen and sulphursignificantly influenced the growth parameters, yieldattributes and yield of sunflower. The increase in theplant height, dry matter accumulation, stem girth, headdiameter, number of filled seeds head-1, seed yieldand stalk yield were significant with each incrementlevel of nitrogen up to 120 kg N ha-1 and sulphur upto 30 kg S ha-1.

Significant N x S interaction was observed onplant height, dry matter accumulation, number of filledseeds head-1, seed yield and stalk yield.

Application of 120 kg N ha-1 registeredsignificantly higher seed yield (2108 kg ha-1) overcorresponding lower levels of 60 and 90 kg N ha-1.Among the sulphur treatments application of 30 kg Sha-1 recorded significantly higher seed yield (2048 kgha-1) over 0 and 15 kg S ha-1. The results of Maity


EFFECT OF DIFFERENT LEVELS OF NITROGEN AND SULPHUR ON GROWTH

Ta

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36

PAVANI et al

Ta

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kg

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1 63

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6 15

36

1629

18

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3451

35

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3867

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68

0 75

2 78

4 17

10

1977

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39

40

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36

and Gajendra Giri (2003) and Satish Kumar and Singh(2005) corroborate with the above findings. Applicationof 120 kg N ha-1 recorded significantly higher stalkyield (4072 kg ha-1) in comparison to 60 and 90 kg Nha-1. Application of 30 kg S ha-1 recorded more stalkyield (4028 kg ha-1) when compared to 15 kg S ha-1

and the plots with no sulphur application. Theseresults are in agreement with the observations madeby Sumathi and Rao (2007).

N x S interaction effect on seed yield and stalkyield was significant. Nitrogen @ 120 kg ha-1 alongwith sulphur application @ 30 kg S ha-1 recordedsignificantly higher seed and stalk yield and this wasfollowed by 120 kg N ha-1 along with 15 kg S ha-1.Similar results of increased seed yield due to sulphurand nitrogen application were reported by Reddi Ramuand Maheshwara Reddy (2003) and Sarkar andMallick (2009).

Among three different levels of nitrogen and

sulphur studied, crop applied with 120 kg N ha-1 and

30 kg S ha-1 performed better in terms of growth

characters, yield attributes and yield.

REFERENCES

Maity, S.K and Gajendra Giri, 2003. Influence of

phosphorus and sulphur fertilization onproductivity and oil yield of ground nut and

sunflower (Helianthus annuus) in intercroppingwith simultaneous and staggered planting.Indian Journal of Agronomy. 48 (4): 267-270.

Reddi Ramu, Y and Maheswara Reddy, P. 2003.Growth and yield of sunflower as influencedby nitrogen and sulphur nutrition. Indian Journalof Dry Land Agriculture Research &Development. 18 (2): 192-195.

Sakal, R., Singh, A.P., Choudhary, B.C and Shahi,B. 2001. Sulphur status of Usifluvents andresponse of crops to sulphur application.Fertilizer News. 46 (10): 61-65.

Sarkar, R.K and Mallick, R.B. 2009. Effect ofnitrogen, sulphur and foliar spray of nitratesalts on performance of spring sunflower(Helianthus annuus). Indian Journal ofAgricultural Sciences. 79 (12): 986-990.

Satish Kumar and Singh, S.S. 2005. Effect of differentlevels of phosphorous and sulphur on thegrowth, yield and oil content of sunflower(Helianthus annuus L.). Journal of OilseedsResearch. 22 (2): 404-409.

Sumathi, V and Rao, D.S.K. 2007. Effect of organicsources of nitrogen with different irrigationschedules on growth and yield of sunflower.Indian Journal of Agronomy. 52 (1): 77-79.

EFFECT OF DIFFERENT LEVELS OF NITROGEN AND SULPHUR ON GROWTH


Globalization, mall culture and urbanizationhave brought about radical changes in the mealpattern of the people across the nation, resulting intothe escalating intake of unhealthy food. Meal patternrepresent a picture of the type of food consumptionand may thus be more predictive of the food choicesand eating habits of the population. Correct foodchoices and healthy eating practices related tovarious meals are believed to be a cause of presentconcern. Thus, the aim of this study was to examinethe food habits, preferences and meal pattern of theprofessionals in information technology (IT) sector.

The selected sample consisted of 40 ITprofessionals (29 males and 11 females). Entiresample was drawn from four IT companies ofHyderabad city using stratified random samplingtechnique. A structured questionnaire was used forcollection of the data. The schedule assessed thebasic information, meal pattern, food habits and foodpreferences of the subjects.

Age wise distribution of IT professionalsshowed that 70% of the respondents were between20-29 years and 30% of them were of 30-39 years.87.5% of the respondents were from Andhra Pradeshand the rest were from other Southern states (12.5%).



FOOD HABITS, PREFERENCES AND MEAL PATTERN OF THE PROFESSIONALSIN INFORMATION TECHNOLOGY SECTOR

PRIYA SHARMA, M. USHA RANI, K. UMA MAHESWARI and K. SUPRIYADepartment of Food and Nutrition, College of Home Science, Saifabad,

Acharya N.G. Ranga Agricultural University, Rajendranagar, Hyderabad-500030

All the respondents were literate with 72.5%of postgraduates and 27.5% graduates. Among allthe subjects, 67.5% were unmarried and the rest32.5% were married. It was observed that 65% ofthe subjects (72.4% males and 45.5% females) hadmonthly income ranging from Rs. 21,000 to Rs.30,000 while 22.5 percent earned Rs. 31,000 to Rs.40,000 and the rest had income of Rs. 10,000 to20,000 per month.

Data reveals that the major meals i.e., lunchand dinner were regularly consumed by all thesubjects (100%). The general meal pattern of the ITprofessionals is presented in the Table 1.

It was reported that 82.5% subjects (82.8%males and 81.8% females) consumed breakfastregularly; 70% (69% males and 72.7% females) ofthe subjects consuming evening snacks whereas;57.5 percent and 42.5 percent of the subjects werehaving mid-morning and bedtime meal, respectivelyin the general meal pattern.

Majority of the subjects were following asimilar four meal pattern i.e., breakfast, lunch,snacks and dinner. This could be due to culturaldietary habits.

Table 1. General meal pattern of IT professionals

N=40

Males (n=29) Females (n=11) Total (N=40) S. No. Meal % % %

1 Breakfast 82.8 81.8 82.5 2 Mid-morning 55.2 63.6 57.5 3 Lunch 100 100 100 4 Evening snacks 69 72.7 70 5 Dinner 100 100 100 6 Bedtime 41.4 45.5 42.5

Food habits and preferences of ITprofessionals include their liking towards fried foods,frequency of eating junk foods, eating outside thehome and habit of snacking at different occassions.It was found that majority (70%) of the subjects werenon-vegetarian (79.4% males and 45% females) while20% were ova-vegetarian (17.2% males and 27%females) and 10% were vegetarians.

The preference for fried foods like frenchfries, potato chips, samosas, chips, puffs, pattiesetc. was reported high by the males as compared tothe females. Among the females, 18.2% preferredfried foods and 27.3% preferred and liked moderatelyand 54.5% responded that they do not like, whereas20.7% of the males preferred fried foods and 62.1%preferred and liked moderately and 17.2%respondents did not like. Polednak (1997) reportedthat the mean estimated fat intake from the 13 fattyitems was significantly greater for men than thewomen especially for french fries.

Junk foods like pizzas, burgers, noodles,chocolate, icecream, soft drinks etc. which areconsidered as favorite snacks of the professionalswere consumed by 35% of the subjects daily and

Table 2. Food preferences of IT professionals

N=40

Males (n=29) Females (n=11) Total (N=40) Food preferences towards % % %

Fried/fatty foods

a) Like very much 20.7 18.2 20

b) Like moderately 62.1 27.3 52.5

c) Don’t like 17.2 54.5 27.5

Junk foods

a) Daily 37.9 27.3 35

b) 2-3 times/week 51.7 27.3 45

c) Occasionally 10.3 45.5 20 Consumption of food outside home

a) Daily 17.2 - 12.5

b) 2-3 times/week 75.9 36.4 65

c) Occasionally 6.9 63.6 22.5

Consumption of snacks during Working on computers

a) Yes 82.8 72.7 80

b) No 17.2 27.3 20

45% of them consume atleast 2-3 times a week andthe remaining 20% consuming occasionally. Similarresults have also been reported by Frary et al. (2001),who indicated that 77.5% of participants ate junk fooddaily and the majority consumed junk food severaltimes a day. The daily consumption of junk food waspreferred by 37.9% of the males and 27.3% of thefemale subjects. 51.7% of the male subjects and27.3% of the females reported to consume junk food2-3 times a week. Junk food pervades virtually allsegment of the society including local communities,privates institutions, industries, companies. Thesetrends seem to have been driven by massiveadvertising and marketing compaigns aimed at thesedentary lifestyle.

According to Demory et al. (2004), changesin eating behaviors, diet quality and physical activitymay result in an increased risk of obesity. It wasfound that young adulthood consumption was greaterfor sweetened beverages, salty snacks compared tochildhood.

The consumption of food outside home incase of males was high and 75.9% of them consumethese atleast 2-3 times a week, 17.2% of them ondaily basis and rest consume occasionally. Whereas,

FOOD HABITS, PREFERENCES AND MEAL PATTERN OF THE PROFESSIONALS

36.4% of females consume the food outside home2-3 times a week and rest of 63.6% eat outsideoccasionally.

It was observed that about 80% of the totalsubjects (82.8% males and 72.7% females) used toconsume snacks during working on computers/laptops. It was seen that the habit of eating andsnacking during TV viewing and working oncomputers/laptops was more in case of males as

compared to females. Kourlaba et al. (2008) reported

that the time spent watching television was positively

associated with the ‘junk food’ pattern and inversely

associated with the ‘healthy’ pattern.

The results of the study revealed that the

male subjects were to prefer more of fried food, junk

food, food outside home and snacks while working

on computers as compared to the female subjects.

REFERENCES

Demory-Luce, D., Morales, M., Nicklas, T.,Baranowski, T., Zakeri, I and Berenson, G.2004. Changes in food group consumptionpatterns from childhood to young adulthood:The Bogalusa Heart Study. Journal of theAmerican Dietetic Association. 104 (11): 1684-1691.

Frary, C.D., John Son, R.K and Wang, M.Q. 2001.Children and adolescents choices of food andbeverages high in added sugars areassociated with intakes of key nutrients andfood groups. Journal of Adolescent Health. 28:16-25.

Kourlaba, G., Panagiotakos, D.B., Mihas, K.,Alevizos, A., Marayiannis, K., Mariolis, A andTountas, Y. 2008. Dietary patterns in relationto socio-economic and lifestyle characteristicsamong Greek adolescents: a multivariateanalysis. Public Health Nutrition. 12 (9): 1366-1372.

Polednak, A.P. 1997. Use of selected high fat foodsby Hispanic adults in the North eastern US.Ethnicity & Health. 2 (1 & 2): 71-76.

PRIYA et al

It is very important to establish a system of

evaluation for every plant medicine in the market,

since the scope for variation in different batches of

medicine is enormous. Nutrition is the provision, to

cells and organisms, of the materials necessary (in

the form of food) to support life. A poor diet can have

an injurious impact on health, causing deficiency

diseases. Herbal nutritional supplements provide

essential nutrients that are not present or present in

less amount in diet (Soni et al., 2010).

The samples {Basella rubra, Centellaasiatica, Withania somnifera (Ashwagandha) and

Leucas aspera} were procured from Herbal garden,

Dr. Y. S. R. Horticulture University, Hyderabad. The

plant material used were in dried form except for

moisture analysis. The fresh leaves of Basella rubra,Centella asiatica, Leucas aspera and root of Withaniasomnifera were cleaned and dried in air drier, ground

to powder and used for analysis in triplicate. The

recommended methods of the Association of Official

Analytical Chemists (AOAC, 1990 and 2005) were

used for the determination of moisture, fat, ash, crude

fiber and protein.

Moisture content of the analyzed samples

ranged between 65.63 % and 90.99 %, highest being

in Basella rubra (90.99 %). Withania somnifera had

the lowest moisture content of 65.63 %. The variations

in moisture content were found to be statistically

significant.

The protein content ranged between 1.38g –

2.12 g/100g. Of the four herbs analyzed, Basellarubra had the highest protein content (2.12g) followed

NUTRIENT CONTENT OF SELECTED HERBAL CROPSELMUONZO, K. UMA MAHESWARI, ANURAG CHATURVEDI and T. SUSILA

Department of Foods and Nutrition, Post Graduate and Research CentreANGRAU, Rajendranagar, Hyderabad -500 030.




by Cantella asiatica (1.86 g/100g) which is similar to

the finding reported by Gupta et al. (2005). Withaniasomnifera had the lowest protein content (1.38 g/

100g). Similar value was reported by Krishnamurthy

and Sarala (2011).

All the samples were found to be low in fat

content, which ranged between 1.38 and 2.12 g/100g.

The maximum amount of fat was observed in Basellarubra and Withania somnifera was found to have a

low fat content of 0.82 g/100g.

The ash content ranged from 0.88 g/100g to

2.21 g/100g. The ash content of the samples was

high except for Leucas aspera (0.88 g/100g). Variation

in ash content were statistically significant. Crude

fibre content in the sample varied from 1.15 g/100 to

18.21g/100g. Withania somnifera showed the highest

value of 18.21g/100g while Basella rubra showed the

lowest crude fibre value 1.15 g/100 among the four

samples but Mensah et al. (2008) reported only 0.6g

/100 g crude fibre in Basella rubra. Significant

difference was observed in crude fibre content among

the samples.

The analyzed herbal samples were high in

moisture. They also had appreciable amount of fibre

content except Basella rubra and protein and fat

content was found to be low.

The analyzed herbal samples were high in

moisture. They also had appreciable amount of fibre

content except Basella rubra and protein and fat

content was found to be low.

Sample Moisture

(%)

Protein

(g/100g)

Fat

(g/100g)

Crude fibre

(g/100g)

Ash

(g/100g)

Withania somnifera 65.63 ± 1.39 1.38 ± 0.38 1.38 ± 0.38 18.21 ±0.08 2.21 ± 0.02

Centella asiatica 73.90 ± 0.12 1.86 ± 0.21 1.86 ± 0.21 15.04 ±0.02 1.93 ± 0.03

Leucas aspera 79.39 ± 1.47 1.51 ± 0.31 1.51 ± 0.31 4.56 ± 0.03 0.88 ± 0.12

Basella rubra 90.99 ± 0.91 2.12 ± 0.26 2.12 ± 0.26 1.15 ± 0.03 1.03 ± 0.04

Sem + 0.64 0.17 0.18 2.59 3.88

CD at 5% 2.29* 0.47 0.50 0.07* 0.11*

Table 1. Nutritive value of herbal crops

REFERENCES

AOAC, 1990. Official methods of Analysis for protein,fat, and ash 15th edition. Association ofAnalytical Chemist, Washington, D.C.

AOAC, 2005. Official methods of Analysis formoisture and crude fibre 18th edition.Association of Analytical Chemist,Washington, D.C.

Gupta, S., Lakshmi, A. J., Manjunath, M. N andPrakash J. 2005. Analysis of nutrient andantinutrient content of underutilized green leafyvegetables. LWT Food Science andTechnology. 38( 4): 339-345.

Krishnamurthy, S. R. and Sarala, P. 2010. ProximateNutritive Values and Mineral Components of

Withania Somnifera (Linn.) Dunal. E-Journal

of Chemistry. 7(3): 985-996.

* Significant at 5 % level, Values expressed are mean of three replicates ± SD

Mensah, J. K., Okoli, R. I., Ohaju-Obodo, J. O and

Eifediyi, K. 2008. Phytochemical, nutritional

and medical properties of some leafy

vegetables consumed by Edo people of

Nigeria. African Journal of Biotechnology. 7

(14): 2304-2309.

Soni, H. K., Ribadiya, N. C., Bhatt, S. B and Sheth,

N. R. 2010. Revaluation of herbal formulation

(capsule) containing Ashwagandha as a single

herb with their nutritional value determination.

International Journal of Applied Biology and

Pharmaceutical Technology. 1(3): 960-967.

ELMUONZO et al



Of late, there has been a 30% decline in

exports of spices due to the presence of agriculturalcontaminants and pesticides. The particularinterest

is residues of the chlorinated hydrocarbon.

Pesticides, including organophosphate (OP),

Organochlorine (OC), and Carbamate (CB)

compounds, are widely used in the production of

spices.

Thoughspices are used in less quantity in

the diet which imparts flavor and make the food

palatable. Thus they are important components of

our food. These factors have influenced to take up

the study and investigate the pesticide residues and

nutrients in commonly grown spices in Guntur district.

Spice samples viz.chilli, turmeric and

coriander seeds were procured from farmers

cultivating spices in Guntur district. The pesticide

standards with 98% purity were procured from

Dr.Ehrenstorfer GmbH. (Germany), were used to

analyze the residues in samples. All the solventsused in the study were glass distilled at their boiling

point. All the glassware were soaked in chromic acid

solution and washed thoroughly with water. These

were rinsed with acetone and air-dried before use.

spice samples (500 g) from each replicate were

collected from farmers, from each replicate spiceswere cut into small pieces (whole) and powdered

(processed), representative 50 g sample (in duplicate)

was obtained by quartering method from entire

sample. All the dried samples were processed andsealed in packing material (polyethylene) and thenstored at ambient temperature for analysis.

PESTICIDE RESIDUES IN SPICES OF GUNTUR DISTRICT OFANDHRA PRADESH

SHAIK. SANHERA and S. SHOBHADepartment of Foods and Nutrition, College of Home Science,

Post Graduate & Research Centre, Acharya N. G. Ranga Agricultural University, Hyderabad-500030.


Residues were analyzed adopting methodsuggested bySharma, 2007 on gas chromatograph(GLC) equipped with electron capture detector (ECD)and Col –Elite -1 HT-30M, 0.250mm × 0.1 um filmcolumn. The operating conditions for determinationof Chlorophyriphos, Endosulfan, Acephate,Carbofuran, Monocrotophos and Quinalphoswere:temperatures, injector 225°C and detector275°C,oven temperature at initial of 150°C with holdtime of 1.00min and Ramp 1 programmed as 2°C for1 min, Ramp 2, at 10°Cfor 5 min. Total run time was46.00min. The retention time of standardsareChlorophyriphos (21.294),Quinolphos (23.317),Endosulphan (24.212) and Acephate (25 .996),Monocrotophos (16.034),Quinolphos (23.312) andCarbofuran (19.297) minutes.

The pesticide residue values were belowdetectable levels (BDL) in the whole and processedspice samples analyzed (Table 1). The finding of thisstudy is in concordance with findings of Papiaet al.(2010) According to the study no residues were foundfor two treatments studied in15 days after applicationof pesticides. It was dissipatedup to 83.45% and78.65% in 10 days. Similar inference could be drawnfrom the present investigation that post-harvesttechniques and storage of spices contributed towardsdegradation of residues thus the spices are safe forconsumption.

The MRL values were not determined forspices; however adequate dietary intake (ADI mg/kgbody weight per day) values are available forChlorophyriphos, Endosulfan, Acephate, Carbofuran,Monocrotophos and Quinalphos residues for spices.

Residues were analyzed adopting methodsuggested bySharma, (2007) on gas chromatograph(GLC) equipped with electron capture detector (ECD)and Col –Elite -1 HT-30M, 0.250mm × 0.1 um filmcolumn. The operating conditions for determinationof Chlorophyriphos, Endosulfan, Acephate,Carbofuran, Monocrotophos and Quinalphoswere:temperatures, injector 225°C and detector 275°C,oven temperature at initial of 150 °C with holdtime of 1.00min and Ramp 1 programmed as 2°C for1 min, Ramp 2, at 10°Cfor 5 min. Total run time was46.00min. The retention time of standards areChlorophyriphos (21.294),Quinolphos (23.317),Endosulphan (24.212) and Acephate (25 .996),Monocrotophos (16.034),Quinolphos (23.312) andCarbofuran (19.297) minutes.

The MRL values were not determined for

spices; however adequate dietary intake (ADI mg/kg

body weight per day) values are available for

Chlorophyriphos, Endosulfan, Acephate, Carbofuran,

Monocrotophos and Quinalphos residues for spices.

Processing of spices includes drying, roasting and

grinding and storage, consumed after sufficient

storage. These processing and storage of spices are

factors for degrading pesticide residual content since,

there was a time gap between harvesting and

analyzing the pesticide residues and a well-known

fact that pesticide residues get degraded often.

Thus, these three spices are considered tobe safe for consumption and without any harmful

Source*: Sharma, 2007.BDL-Below Detectable Level

Table 1. Results of Pesticide residues in selected spices

Pesticide Residues ADI mg/kg bodyweight per day*

Residuedetected

Chlorophyriphos 0.01 BDL

Quinalphos 0.01 BDL

Endosulfan 0.006 BDL

Acephate 0.01 BDL

Monocrotophos 0.01 BDL

Carbofuran 0.01 BDL

The pesticide residue values were belowdetectable levels (BDL) in the whole and processedspice samples analyzed (Table 1). The finding of thisstudy is in concordance with findings of Papia et al.(2010) According to the study no residues were foundfor two treatments studied in15 days after applicationof pesticides. It was dissipatedup to 83.45% and78.65% in 10 days. Similar inference could be drawnfrom the present investigation that post-harvesttechniques and storage of spices contributed towards

degradation of residues thus the spices are safe for

consumption.

effects, as they contained no pesticide residues asrevealed from present investigation.

REFERENCES

Papia, Ditya., Das, S. P., Sarkar, P. K and AnjanBhattacharyya. 2010. Degradation Dynamicsof Chlorfenapyr Residue in Chili, Cabbage andSoil. Bull Environ ContamToxicol. 84:602–605.

Sharma, K.K.2007.PesticideResidue AnalysisManual.ICAR.Directorate of information andpublications of Agriculture, New Delhi.

SANHERA and SHOBHA

Wheat (Triticum aestivum L.) is an importantpost-rainy season crop in central and northernTelangana zone of Andhra Pradesh. It occupies anarea of 13000 ha with a total production of 10,000tonnes (CMIE, 2010). The role of improved variety inenhancing the wheat crop yield is well documented(Mukherjee, 2008 and Singh et al., 2010).Replacement of low potential and pest susceptibleold varieties by new high yielding varieties withpromising yield potential is a continuous process andmay be significant to increase the productivity toovercome the present low yield level of 769 kg ha-1inthe state. However, new high yielding varieties differin their optimum nitrogen requirement. Knowing theoptimum yield as affected by nitrogen level is alsovaluable when farmers choose the varieties to plantin the context of commodity prices, production costand environmental conditions.

Keeping the above in view a field experimentwas conducted during rabi season of 2010–11 atAgricultural Research Station, Basantpur, Medakdistrict in Central Telangana Zone of Andhra Pradesh.The soil was loamy sand in texture with a soil pH of6.4 and had S1 salinity class (EC 0.96 dS m-1). Theexperimental soil was low in available nitrogen (265kg N ha-1), phosphorus (19.59 kg P

2O

5 ha-1) and

medium in available potassium (219.4 kg K2O ha-1).

The treatments consisted of five wheat cultivars viz.,Sonalika (V

1 ), NIDW 295 (V

2), UAS 415 (V

3), NIAW

917 (V4) and DWR 162 (V

5) as main plot treatments

and five levels of nitrogen 0, 80, 120, 160 and 200 kgN ha-1 as sub-plot treatments summing up to 25treatment combinations laid out in split-plot designwith three replications. The crop was sown on 1st

November, 2010.

The growth characters except tiller productionwere significantly influenced by different varieties

RESPONSE OF WHEAT (Triticum aestivum L.) CULTIVARSTO VARYING LEVELS OF NITROGEN

MATHURA YADAV, V. PRAVEEN RAO, M. YAKADRI AND G. JAYASREEDepartment of Agronomy, College of Agriculture,

Acharya N.G. Ranga Agricultural University, Rajendranagar, Hyderabad-500 030



(Table 1). Sonalika was significantly taller. Leaf areaindex was significantly higher in Sonalika and NIDW295; and dry matter production was markedly highestunder Sonalika, NIDW 295 and UAS 415 incomparison to other tested varieties. Several workershave documented variation in plant height, tillerproduction and leaf area index among varietiesdiffering in their genetic makeup (Parihar and Tiwari,2003 and Pandey et al.,1999) under diverseenvironments.Further the yield attributing charactersviz., ears m-2, grains ear-1 and test weight weresignificantly higher in NIDW 295 and UAS 415 wheatvarieties which were on par with each other andsuperior over other tested varieties (Table 1). Furthergrain yield was significantly higher in NIDW 295 andUAS 415 owing to improved growth and yieldattributes. Similarly, both these varieties recordedhigher harvest index than remaining varieties. Theseresults are in conformity with the findings of Singh etal. (2009) and Singh et al. (2010).

Each higher level of nitrogen significantlyimproved growth traits viz., plant height, tillerproduction, leaf area index and dry matter productionand yield attributes viz., ears m-2, grains m-1 and testweight over its lower level up to 160 kg N ha-1. Theseimproved growth and yield attributes in turncontributed to significantly higher yield under 160 kgN ha-1 fertilized crop over its lower levels (Singh etal., 2011 and Mattas et al., 2011). Application of 200kg N ha-1 did not prove to be advantageous over 160kg N ha-1. Harvest index was inversely related tonitrogen levels (Behera and Ghosh, 2009).

Table 2 presents the empirical results of all the linearfits established between grain yield (dependentvariable) of wheat and growth traits, yield componentsand nutrient uptake values (independent variables).All the independent variables showed a significant


YADAV et al

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RESPONSE OF WHEAT CULTIVARS

Table 2. Linear estimates for the relationship between grain yield and other parameters

**= Significant at 1% (P = 0.01)

Relationship a b R2

Yield – Plant height (cm) −4.451168 0.0813098∗∗ 0.986∗∗

Yield – Tillers m-2 −1.634572 0.0139447∗∗ 0.991∗∗

Yield – Leaf area index −2.971010 1.4028101∗∗ 0.984∗∗

Yield – Dry matter (g m-2) 0.515290 0.0026607∗∗ 0.895∗∗

Yield – Days to flowering 38.91386 −0.5124024∗∗ 0.820∗∗

Yield – Ears m-2 −1.449538 0.0148671∗∗ 0.987∗∗

Yield – Grains ear-1 −4.116270 0.1791787∗∗ 0.889∗∗

Yield – Test weight (g) −7.494776 0.2697872∗∗ 0.993∗∗

Yield – Harvest index −42.500520 0.8642943∗∗ 0.693∗∗

Yield – N uptake (kg ha-1) 0.9644030 0.0302001∗∗ 0.986∗∗

Yield – P uptake (kg ha-1) 0.1822273 0.1146001∗∗ 0.997∗∗

Yield – K uptake (kg ha-1) 0.1574281 0.0673001∗∗ 0.999∗∗

positive and linear relationship with grain yield exceptfor days to 50% flowering (Table 2) suggesting thereinforcement of the effects of a given pair of yieldand component relation (Tayyar, 2008). However, themagnitude of this reinforcement varied with the growthtrait, yield component, nutrient uptake values andtheir units. The explained total variation as indicatedby coefficient of determination (R2) in grain yield byvarious growth traits (plant height, tiller production,leaf area index, dry matter production and days to

flowering), yield components (ears m-2, grains ear-1,test weight and harvest index) and nutrient uptake(NPK uptake) chosen as independent variablesindividually ranged from 69.0 to 99.9%. The varianceratio for testing R2 was highly significant (P = 0.01)in all the relations. This suggests that the grain yieldof wheat can be adequately predicted using the testedindependent variables viz., growth traits, yieldcomponents and nutrient uptake.

REFERENCES

Behera, U.K and Ghosh, P.K. 2009. Response ofvery late sown bread wheat cultivars todifferent levels of nitrogen in vertisols. IndianAgriculturist. 53: 21 – 26.

CMIE, 2010. Economic intelligence service,Agriculture June, 2010. Centre for monitoringIndian Economy Pvt. Ltd., Mumbai. pp. 96 –99.

Mattas, K.K., Uppal, R.S and Singh, R.P. 2011. Effectof Varieties and Nitrogen Management on theGrowth, Yield and Nitrogen Uptake of DurumWheat. Research Journal of AgriculturalSciences. 2: 376 – 380.

Mukherjee, D. 2008. Effect of levels of nitrogen ondifferent wheat cultivar under Mid Hill situation.RAU Journal of Research. 18: 37 – 40.

YADAV et al

Pandey, I.B., Thakur, S.S and Singh, S.K. 1999.Response of timely sown wheat (Triticumaestivum) varieties to seed rate and fertilitylevel. Indian Journal of Agronomy. 44: 745 –749.

Parihar S.S and Tiwari, R.B. 2003. Effect of irrigationand nitrogen level on yield, nutrient uptake and

water use of late sown wheat (Triticumaestivum). Indian Journal of Agronomy. 48:103

– 107.

Singh, P., Singh, P., Singh, K.N. Singh, R. Faaga,.

Bahar, F and Waseem Raja. 2010. Evaluation

wheat (Triticum aestivum) genotypes for

productivity and economics under graded

levels of nitrogen in Kashmir. Indian Journalof Agricultural sciences. 80: 380 – 384.

Singh, B. R., Singh, R. V and Rajput,O. P. 2009.

Effect of nitrogen, phosphorous and zinc on

growth, yield and nutrient uptake of wheat.

Current Advances in agricultural Sciences.1(2): 133-134.

Singh, C.M., Sharma, P.K., Kishore, P., Mishra,

P.K., Singh, A.P., Verma, R and Raha, P.

2011. Impact of integrated nutrient

management on growth, yield and nutrient

uptake by wheat. Asian Journal of AgriculturalResearch. 5: 76 – 82.

Tayyar, S. 2008. Grain yield and agronomic

characteristics of Romanian bread wheat

varieties under the conditions of North western

Turkey. African Journal of Biotechnology. 7:

1479 – 1486.



Sorghum (Sorghum bicolor L. Moench), thefifth most important cereal crop in the world afterwheat, maize, rice and barley, is a major cereal staplefood and forage crop of the semi-arid tropics of Indiansub-continent and several African regions. In AndhraPradesh, it covers an area of 0.28 m ha with an annualproduction of 0.44 m t and productivity of 1420 kgha-1 (CMIE, 2009). Sorghum is the second cheapestsource of energy in the form of starch (63.4 - 72.5 %)and micronutrients such as iron (Fe) and zinc (Zn)after pearl millet.

In order to realize the potential impact of themicronutrient-dense cultivars, the high-yieldingvarieties / hybrids with farmer’s preferred traits suchas early maturity and large seed size coupled withincrease in micronutrient concentration in grain mustbe made available for commercial cultivation (Kumaret al., 2010). Information about the association ofmicronutrients such as grain iron with grain yield andother important traits can help the breeders to devisea suitable breeding strategy for enhancingmicronutrient density in sorghum cultivars. However,limited information is available on the characterassociation of grain iron content with grain yield andother important traits and also the direct and indirecteffects of grain yield and other important traits ongrain iron content. Hence, an attempt was made inthe present investigation to analyse and determinethe traits having greater interrelationship with grainiron utilizing correlation and path analysis.

The present investigation was carried outusing four parental lines (IS 2263, IS 13211, IS 10305and SPV 1359) and 12 hybrids generated by crossingthe parents in a full-diallel fashion during postrainyseason, 2011-12 in Randomized Block Design (RBD)with three replications under high fertility conditions

(80 N : 40 P) on vertisols at ICRISAT farm inPatancheru, located at an altitude of 545 m abovemean sea level, latitude of 17.53° N and longitude of78.27° E. Each genotype was grown in four rows of 2m length with 75 cm spacing between the rows and10 cm between the plants. All the recommendedagronomic practices were followed for raising a goodcrop. The data were recorded on four important traitsi. e., plant height, days to 50 % flowering, 100-grainweight and grain yield. The cleaned seeds of eachgenotype were used to measure the iron content withOxford X-supreme 8000 model X-ray flourescenceanalyzer (XRF). Correlation coefficients werecalculated at phenotypic level using the formulaesuggested by Falconer (1981). The direct and indirecteffects at phenotypic level were estimated by takinggrain iron content as dependent variable, using pathcoefficient analysis as suggested by Dewey and Lu(1959).

In the present investigation, plant height, 100-grain weight and grain (Table 1) exhibited non-significant and lower magnitude of negativeassociation with grain iron, while days to 50 %flowering showed non-significant positive correlation

with grain iron content. Plant aspect score recorded

significant positive correlation with grain iron content.

Reddy et al. (2005) earlier obtained statistically

significant negative but a rather weaker correlation

of grain iron content with grain yield and poorassociation of agronomic traits such as days to 50

% flowering and plant height with grain iron content

and indicated the possibility of developingmicronutrient-dense cultivars in desired maturityduration and plant height background with littlecompromise in grain yield. Reddy et al. (2010) alsoreported weak association of grain iron with agronomic


ASSOCIATION OF GRAIN IRON CONTENT WITH GRAIN YIELD AND OTHERTRAITS IN SORGHUM (Sorghum bicolor L. Moench)

S. V. P. L. GAYATHRI, K. RADHIKA, A. ASHOK KUMAR and P. JANILADepartment of Genetics and Plant Breeding, College of Agriculture,

Acharya N.G. Ranga Agricultural University, Rajendranagar, Hyderabad-500030.

Table 1. Phenotypic correlation co-efficient matrix of grain iron content withgrain yield and other traits

Character Plant height

Days to 50 % flowering

Plant aspect score

100-grain weight

Grain yield

Correlation with grain

iron Plant height 1.000

0.377**

-0.696**

0.877**

0.811**

-0.133


1.000 -0.191

0.370**

0.472**

0.224

Plant aspect score

1.000 -0.757**

-0.767**

0.372**

100-grain weight 1.000

0.901**

-0.097

Grain yield 1.000 -0.177

Grain iron

1.000

* indicates significance at 5 % probability i.e., r=>0.2845

** indicates significance at 1 % probability i.e., r=>0.3683

Table 2. Phenotypic path matrix showing direct and indirect effects of various agronomic traits on grain iron

Character Plant height

Days to 50 %

flowering

Plant aspect score

100-grain weight Grain yield

Correlation with grain

iron Plant height -0.167 0.125 -0.366 0.651 -0.376 -0.133


-0.063 0.331 -0.101 0.275 -0.219 0.224

Plant aspect score

0.116 -0.063 0.526 -0.562 0.356 0.372**

100-grain weight

-0.146 0.123 -0.398 0.743 -0.418 -0.097

Grain yield -0.135 0.157 -0.403 0.670 -0.464 -0.177

Residual effect = 0.836

and grain traits indicating possible positive outcomes

from breeding for high grain iron content in varied

agronomic backgrounds. The non-significant

correlation of 100-grain weight with grain iron content

in pearl millet populations was earlier reported by

Gupta et al. (2009), who further indicated that breeding

for higher levels of micronutrients could be achieved

without compromising the improvement for larger

grain size. They also obtained negative correlation

between seed yield and iron content in pearl millet

populations indicating that selection for iron can be

accomplished without compromising on grain yield.

Banziger and Long (2000) found negative correlationof grain iron content with grain yield in maize. The

non-significant association of iron content with grain

yield in rice was earlier reported by Nagesh et al.(2012).

GAYATHRI et al

Out of the five traits taken for path analysis,100-grain weight had maximum positive direct effect(0.743) on the grain iron content followed by plantaspect score (0.526) and days to 50 % flowering(0.331), while, grain yield and plant height showednegative direct effects on the grain iron content (Table2). All the characters studied in the presentinvestigation showed positive indirect effect through100-grain weight and days to 50 % flowering indicatingthat direct selection for grain size might be helpful inimproving the grain iron content. Negative indirecteffects of 100-grain weight through other characterswere more prominent than its positive direct effecton the grain iron content thus resulting in its negativecorrelation with grain iron content and in such asituation direct selection for grain weight should bepracticed to reduce the undesirable indirect effect.Days to 50 % flowering exhibited a positive directeffect and a positive non-significant association withgrain iron content. Hence selection for this trait maybe rewarding during the development of iron richcultivars in sorghum. A very high residual effect(0.836) revealed that some more characters closelyrelated with grain iron content need to be includedapart from the characters studied in this investigation.

REFERENCES

Banziger, M and Long, J. 2000. The potential forincreasing the iron and zinc density of maizethrough plant breeding. Food and NutritionBulletin. 21: 397-400

CMIE. 2009. Area, production and productivity ofsorghum in India and Andhra Pradesh. Centrefor Monitoring Indian Economy (CMIE) Pvt.Ltd. Mumbai

Dewey, D.R and Lu, K.H. 1959. A correlation and

path co-efficient analysis of components of

crested wheat grass seed production.

Agronomy Journal. 51: 515-518

Falconer, D.C. 1981. An introduction to quantitative

genetics. Longman, New York. pp. 67-68

Gupta, S.K., Velu, G., Rai, K.N and Sumalini, K.

2009. Association of grain iron and zinc

content with grain yield and other traits in pearl

millet (Pennisetum glaucum (L.) R. Br.). Crop

Improvement. 36 (2): 4-7

Kumar, A.A., Reddy, B.V.S., Sahrawat, K.L and

Ramaiah, B. 2010. Combating micronutrient

malformation: Identification of commercial

sorghum cultivars with high grain iron and zinc.

SAT e Journal. 8: 1-5

Nagesh, Ravindrababu, V., Usharani, G and Dayakar

Reddy, T. 2012.Grain iron and zinc association

studies in rice (Oryza sativa L.) F1 progenies.

Archives of Applied Science Research. 4 (1):

696-702

Reddy, B.V.S., Ramesh, S and Longvah, T. 2005.

Prospects of breeding for micronutrients and

â-carotene-dense sorghums. Journal of SAT

Agricultural Research. 1 (1): 1-4

Reddy, P.S., Reddy, B.V.S., Kumar, A.A., Ramesh,

S., Sahrawat, K.L and Rao P. V. 2010.

Association of grain Fe and Zn contents with

agronomic traits in sorghum. Indian Journal of

Plant Genetic Resources. 23 (3): 280-284

ASSOCIATION OF GRAIN IRON CONTENT

When a goal is set at a difficult level a personis required to put forth more effort to meet it. Thiseffort is motivation dependent. Goals, therefore, aremotivation-based outcomes leading to personalsatisfaction. Goal content is typically age-graded andreflects the important issues of a particular period ofthe life span (Ogilvie et al. 2001). During the transitionfrom childhood to adulthood, adolescents set andpursue goals unique to this period. Within this life-phase, goal priorities change. Hence to promote theunderstanding of goal directed behaviour ofadolescents, it is very essential to study thedeterminants of goal directed behaviour ofadolescents.

Expost facto research design was used toinvestigate the present study. The total sample sizeof the study was 200, out of which 50 respondentswere from professional and non-professionalinstitution each. The respondents were distributedinto four groups and these were professional girls,professional boys, non-professional girls and non-professional boys. The data were collected throughquestionnaire and modified scales. The data obtainedwere coded, consolidated, tabulated and analyzedby using step down regression analysis.

Four strong predictors of the goal directedbehaviour of the professional adolescents based onthe analysis were self-efficacy, achievementmotivation, independence and organization. Higherthe self beliefs, higher would be the goal directedbehaviour.

Self- efficacy is a central predictor ofintentions and behaviour (Ajzen, 1991 and Kramper,2000). The development of self-efficacy reflects thedevelopment of actual efficacy that is thedevelpoment of the capacity to act autonomouslyand efficiently.

Likewise goals required adolescents’motivation to meet the desired ambitions and for this

DETERMINANTS OF GOAL DIRECTED BEHAVIOUR OF ADOLESCENTSNEHA JOSHI and M. SARADA DEVI

Department of Human Development and Family Studies, College of Home Science, Acharya N.G. Ranga Agricultural University, Hyderabad - 500 004.



they need to be focused about the course of actions,thus this relates to their goal directed behaviour. Sothis achievement motivation also has a positiverelationship with the goal directed behaviour. Similarlythe rest two variables were equally important inpredicting the goal directed behaviour of professionaladolescents.

The strong determinants of goal directedbehaviour of non-professional adolescents were self-efficacy, achievement motivation, expressiveness,acceptance & caring, control and active-recreationalorientation. Non-professionals have self-efficacy andachievement motivation as strong predictors, whichwas similar to that of professionals. Other than thatexpressiveness was having direct relationshipbecause, adolescents who were expressive feel freeto express their feelings, intentions and goals infrontof parents, peers and teachers which made themmore goal directed in life. Acceptance & caring wouldhelp them to think realistically and because of thatthey easily accept the situations in life. Thus in thisway this aspect of family environment made non-professional adolescents more goal directed towardstheir lives. Similarly active-recreational orientationmight have helped them to live happily with theirfamily. So higher the active-recreational orientation,higher would be the goal directed behaviour.

The strong determinants of goal directedbehaviour of all the adolescents including bothprofessionals and non-professionals were self-efficacy, achievement motivation, expressiveness,acceptance & caring, independence and organization.Out of eleven only six independent variables wereleft out as strong determinants.

Hence self-efficacy and achievementmotivation turned out to be very strong determinantsof goal directed behaviour of adolescents. The betterunderstanding of self-efficacy and achievementmotivation will help the professionals working foradolescents to improve their job performance.


Table 1. Determinants of goal directed behaviour of professional adolescents

S.No. Variables Beta value t value

1 Self-efficacy .211 2.527

2 Achievement motivation .586 6.905

3 Independence .206 2.539

4 Organization -.283 -3.543

Coefficient of determination (R²) = .578

Table 2. Determinants of goal directed behaviour of non-professional adolescents


1 Self-Efficacy .394 4.243

2 Achievement Motivation .491 5.354

3 Expressiveness .239 2.493

4 Acceptance & Caring -.372 -3.536

5 Control .126 1.677

6 Active-Recreational Orientation .158 2.253

Coefficient of determination (R²) = .683

Table 3. Determinants of goal directed behaviour of professional and non-professional adolescents


1 Self-Efficacy .273 4.429

2 Achievement Motivation .554 8.923

3 Expressiveness .120 1.678

4 Acceptance & Caring -.139 -2.078

5 Independence .130 2.367

6 Organization -.141 -2.193

Coefficient of determination (R²) =.626

REFERENCES

Ajzen, I. 1991. The theory of planned behavior.Organizational Behavior and Human DecisionProcesses. 50: 179-211.

Kramper ,G. 2000 . HandlungstheoretischePerssnlichkeitspsychologie [Action-

theoretical psychology of personality] .2nd ed.Gsttingen, FRG: Hogrefe.

Ogilvie, D. M., Rose, K. M and Heppen, J. B. 2001.A comparison of personal project motives inthree age groups. Basic and Applied SocialPsychology. 23: 207–215.

DETERMINANTS OF GOAL DIRECTED BEHAVIOUR

Most agronomically useful traits existing innature have continuous phenotypic distributions,implying that many genes with relatively minor effects,termed quantitative trait loci (QTLs), control thosetraits. The identification of QTLs provides the startingpoint to dissect the molecular basis of natural allelicdiversity, which in turn will be very useful for futurecrop improvement. Rice as the main staple food ofworld’s two-third’s population, it provide 23% ofcalories of daily requirement. Among theagronomically important yield related traits, grainnumber controlled by different QTLs in rice isimportant.

IDENTIFICATION OF IMPORTANT QTLs FOR GRAIN NUMBER IN POPULAR RICEVARIETIES OF ANDHRA PRADESH

SONALI DATTA MUHURI, M. SHESHU MADHAV, CH. SURENDER RAJU and CH. V. DURGA RANI Department of Plant Molecular Biology and Biotechnology

College of Agriculture, Acharya N.G. Ranga Agricultural University, Rajendranagar, Hyderabad -500 030.



A total of fifty six popular varieties of Andhra

Pradesh were collected and raised in the College

Farm, ANGRAU, Rajendranagar, Hyderabad during

kharif, 2011-2012 and the experiment was carried out

at the Directorate of Rice Research (DRR). Based

on literature survey, three QTL’s for grain number

trait viz., QPGN1a, ng1, gpp2.1 on chromosome 1

and 2 were selected. SSR markers with their physical

location were identified for their QTLs using online

web tool ‘gramene’ (www.gramene.org.) (Table 1).

SL. NO QTL NAME CHROMOSOME NO

SELECTED MARKERS

POSITION OF SELECTED MARKERS

1 QPGN1a 1 HRM 11570 HRM 10167

29.27 Mb 33.93 Mb

4 ng1 1 HRM 11865 36.2 Mb

5 gpp2.1 2 RM 208 37.29 Mb

Table 1. List of selected markers for QTL analysis

The good quality DNA of fifty six varietieswere used for PCR analysis using the abovementioned primers. IR 64 was used as positive control.

The scoring of gels was done based on the presenceand absence of expected bands/alleles as comparedto positive control.

SL.No Steps Temperature Time

1. Initial denaturation 94° C 5 min

2. Denaturation 94° C 30 sec

3. Annealing 59° C 1 min

4. Extension 72° C 1 min 5. Final extension 72° C 10 min

6. Cooling 4° C Long time


Table 2. PCR reaction protocol

The reported linked markers of QPGN1a are

RM237- M473A. Physical position of RM237 was

determined on chromosome 1 and in that region two

hypervariable SSR markers were selected namely

HRM11570 and HRM 10167. Using IR 64 as positive

control for both the markers since it was one parent

used in the detection of QPGN1a (Ahamadi et al.,2008), genotyping was done. Upon genotyping with

HRM 10167, out of fifty six varieties, only twenty

eight varieties showed the allele similar to that of

IR64 allele (figure 1) and rest showed the other allele.

In case of HRM 11570, out of fifty six varieties, thirty

five varieties showed the allele similar to IR64 and

rest showed other allele. It indicated that, this QTLoperated in some of the tested varieties, and otherswere affected by other QTLs.

The linked markers of ng1 are RG374- RG394 (Lin etal., 1996). The physical position for both these linkedmarkers on chromosome no 1 was determined andfrom that region, hyper variable SSR marker wasselected i.e. HRM11865. Taking IR64 as the positivecontrol, genotyping was done (figure 2). Out of fiftysix varieties, 46 varieties showed the IR64 allele andrest of the varieties showed other allele. This clearlyshowed that this QTL operated in majority of thetested varieties.

IR64 (Positive control)

Figure 1. QTL detection with primer HRM10167 (QTL QPGN1a)


Figure 2. QTL detection with primer HRM11865 (QTL ng1).

Figure 3. QTL detection with primer RM208 (QTL gpp2.1).


Similarly, the linked marker i.e. RM208 of

another QTL (present in chromosome 2) gpp2.1

(Septiningsih et al., 2003) was selected. Here also,

genotyping was done by taking IR64 as positive

control (figure 3). Out of fifty six varieties, 39 varieties

showed same IR64 allele. Results indicated that, gpp

2.1 QTL was present in some of the tested varieties.

REFERENCES

Ahamadi, J., Fotokian, M.H and Fabriki-Orang, S.

2008. Detection of QTLs Influencing Panicle

IDENTIFICATION OF IMPORTANT QTLs FOR GRAIN NUMBER IN POPULAR RICE VARIETIES

Length, Panicle Grain Number and Panicle

Grain Sterility in Rice (Oryza sativa L.). Journal

of Crop Science and Biotechnology. 11 (3):

163-170.

Lin, H.X., Qian, H.R., Zhuang, J.Y., Lu, J., Min, S.K.,

Xiong, Z.M., Huang, N and Zheng, K.L. 1996.

RFLP mapping of QTLs for yield and related

characters in rice (Oryza sativa L.). Theoretical

and Applied Genetics. 92: 920-927.

Septiningsih, E.M., Prasetiyono, J., Lubis, E., Tai,

T.H., Tjubaryat, T., Moeljopawiro, S and

McCouch, S.R. 2003. Identification of

quantitative trait loci for yield and yield

components in an advanced backcross

population derived from the Oryza sativa

variety IR64 and the wild relative O. rufipogon.

2003. Theoretical and Applied Genetics.107:

1419–1432.

SONALI et al

Cotton (Gossypium hirsutum L.) is the most

important commercial fibre crop contributing to 75%of total raw material needs of textile industry andalso provides employment to about 60 million people.To sustain high production and productivity of cotton,a large number of hybrids and high yielding varietieshave been developed. So far 50 cotton hybrids havebeen released for commercial cultivation in thecountry through the All India Co-ordinated CottonImprovement Project (Rana et al., 2006). Thevarieties and hybrids attain acceptance when thefarmer gets genetically pure seeds of high standards.For this purpose, each cultivar should be properlydefined with suitable descriptors, so as to maintainits identity during seed production through fieldinspection and certification. In India, Protection ofPlant Varieties and Farmer’s Rights Act (PPV & FRA2001) envisages the registration and protection ofnew and notified/extant plant varieties based

on the criteria of Distinctness, Uniformity andStability (DUS) of morphological characteristics andincreasing attention is being paid towardscomprehensive characterization, identification anddocumentation of plant genetic resources. TheNational Test Guidelines have been developed forcharacterization based on the criteria of DUSmorphological characteristics. Present study wasundertaken to characterize six hybrids and theirparental lines that are in active seed multiplicationchain on the basis of qualitative and quantitativemorphological characters.

The field studies were carried during kharif2011 season. The seed material comprised of sixhybrids (NDLHH 240, WGHH 2, NSPHH 5, CSH 198,CICR 2 and LAHH 4) and their 12 parents(NARASIMHA, MCU 5, NA 1678, NA 4084, NA 1325,

MORPHOLOGICAL CHARACTERIZATION OF COTTON HYBRIDS AND THEIR PARENTAL LINES

P. ARUNA, P. S. RAO, G. ANURADHA and K. KESHAVULUDepartment of Seed Science and Technology, College of Agriculture,

Acharya N.G. Ranga Agricultural University, Rajendranagar, Hyderabad-500 030

Research NoteJ.Res. ANGRAU 40(3) 113-115 , 2012


L604, CSH 19, CSH8, DS-5, LD 32, AB 6 and M 2

respectively) of cotton. The experiment was laid outin a randomized block design with three replications.

All the recommended agronomic practices were

followed to raise the crop. Morphological characters

based on National DUS test guidelines descriptors

for cotton were used for characterization.

The qualitative and quantitative morphologicalcharacters of genotypes in each treatment and

replication were used to differentiate the cotton

hybrids and their parental lines. Based on leaf size

cotton genotypes were grouped as small (4), medium

(12) and large (2). As per leaf colour, they were

grouped as light green (4), medium green (9) and darkgreen (5). Variation in the leaf size pattern and colour

of the leaf was observed among the genotypes and

it may be due to genetic characters of parents and

may vary according to soil, environmental, cultural

and nutritional factors during crop growth. The shape

of leaves varied as palmate (15), semi digitate (2)and digitate (1) and variation in the shape of the leaves

attributed mainly to the genetic characters. Based

on leaf pubescence genotypes were grouped as

glabrous (4), medium hairy (11) and hairy (3) and leaf

pubescence was largely be controlled by different

genes (Wright et al., 1999). The absence (4) orpresence (14) of stem pigmentation was also helpful

in differentiating the genotypes. The character of

stem was the hairiness which was categorized as

sparsely hairy (10), medium hairy (8). The petal colour

also one of important characters, for characterization

and was classified as cream (12) and yellow colour(6). The presence (4) or absence (14) of petal spot

was also used as the marker for varietal identification

(Ahuja and Dhayal 2006) The variation in the anther

colour is due to herbal characters of genotypes and


Gen

otyp

es:

1: A

B 6

, 2:

M 2

, 3:

LA

HH

4,

4: D

S 5

, 5:

LD

327

, 6:

CIC

R 2

, 7:

Nar

asim

ha 8

: M

CU

5,

9: N

DLH

H 2

40,

10:

CS

H 1

9, 1

1: C

SH

8,

12:

CS

H 1

98,

13:

NA

167

8,

14:

NA

408

4, 1

5: W

GH

H 2

, 16

: N

A 1

325,

17:

L 6

04,

18:

NS

PH

H 5

Tabl

e 1.

Des

crip

tion

of m

orph

olog

ical

DU

S d

escr

ipto

rs fo

r eig

htee

n co

tton

gen

otyp

esARUNA et al

Cul

tivar

S

. N

o.

Des

crip

tor

1 2

3 4

5 6

7 8

9 10

11

12

13

14

15

16

17

18

R

emar

ks

1 S

tem

pig

men

tatio

n 9

1 1

9 9

9 9

9 9

9 9

9 1

9 9

9 9

1 1:

abs

ent,

9: p

rese

nt

2 S

tem

pub

esce

nce

5 3

5 3

3 3

5 5

5 3

3 3

3 3

5 5

3 5

1: a

bsen

t, 3:

spa

rse,

5: m

ediu

m, 7

: st

rong

hai

ry

3 Le

af s

hape

1

1 1

2 3*

2

1 1

1 1

1 1

1 1

1 1

1 1

1: p

alm

ate,

2: s

emi-d

igita

te, 3

: di

gita

te, 4

: lan

ceol

ate

4 Le

af s

ize

5 5

5 5

7 5

5 5

5 7

3 5

3 3

3 5

5 5

3: s

mal

l, 5:

med

ium

, 7: l

arge

5 Le

af c

olou

r 1

1 2

3 3

3 2

3 2

1 2

2 2

1 2

2 3

1 1:

ligh

t gre

en, 2

: med

ium

gre

en,

3:da

rk g

reen

6

Leaf

pub

esce

nce

1 1

5 1

1 5

5 5

5 9

9 9

5 5

5 5

5 5

1:ab

sent

/spa

rse,

5: m

ediu

m, 9

: st

rong

7 P

etal

col

or

2 2

2 3

3 3

3 2

2 2

2 2

2 2

2 3

3 2

1:w

hite

, 2:c

ream

, 3: y

ello

w,4

: pin

k, 5

: red

,

8 P

etal

spo

t 1

1 1

9 1

1 1

9 9

1 1

1 1

1 1

1 1

9 1:

abse

nt, 9

: pre

sent

9 P

ositi

on s

tigm

a 1

1 2

2 1

1 2

2 2

1 1

1 2

2 2

2 1

1 1:

em

bded

, 2: e

xert

ed

10

Ant

her

colo

ur

3 1

3 3

3 3

2 3

3 2

2 2

1 1

1 2

1 2

1:w

hite

, 2: c

ream

, 3: y

ello

w, 4

: pur

ple

11

Bol

l sha

pe

2 1

1 2

2 3

2 1

2 2

2 2

3 3

3 2

2 3

1: r

ound

, 2: o

vate

, 3: e

llipt

ic

12

Pla

nt h

eigh

t 9

9 9

7 7

9 7

9 9

7 9

7 7

7 7

7 9

9 1:

ver

y sh

ort,

3: s

hort

, 5: m

ediu

m,

7: ta

ll, 9

: ver

y ta

ll 13

P

lant

type

3

2 2

1 1

1 1

3 3

1 1

1 3

3 2

1 2

2 1:

cyl

indr

ical

, 2; c

onic

al, 3

: glo

bose

14

D

ays

to 5

0%

flow

erin

g 5

5 5

5 7

7 5

5 5

7 3*

5

5 5

5 5

5 5

3:ea

rly, 5

: med

ium

, 7: l

ate

classified as white (5), cream (6) and yellow (7) and

its usefulness to identify genotypes was already

reported by (Ankaiah et al., 2005). Based on shape

of bolls, these were classified as round (3), ovate

(10) and elliptic (5). Embded stigma (9) or exertedstigma (9) position was used in characterization which

was genetically controlled. The plant type varied as

globose (5), cylindrical (8) and remaining 5 genotypes

showed conical shape and presence or absence of

filament colour among the genotypes was also useful

for characterization. The wide variation in plant heightwas noticed and grouped as tall (9) and very tall (9),

thus plant height could be extremely useful for varietal

identification and genetic purity testing. Based on

days to 50% flowering genotypes grouped as early

(1), medium (14) and late duration (3). Manjunath

Reddy et al. (2007) and Ramanadham (2000) using

key morphological characters for cotton hybrids and

their parents, stated that they were least influencedby growing conditions and found distinct, uniform and

stable throughout growing season.

On the basis of morphological characters

unique morphological profiles were obtained for two

cotton genotypes viz., CSH 8, LD 327. LD 327 have

digitate leaf shape unique from other genotypes and

CSH 8 was identified from other genotypes bypossessing early days to 50% flowering.

REFERENCES

Ramanadham, S. 2000. Assessment of genetic

purity, identity and seed vigour in F1 hybrids

and F2 population in cotton. M. Sc Thesis

submitted to Acharya N.G. Ranga Agricultural

University, Rajendranagar, Hyderabad.

Rana, M. K., Singh, S and Bhat, K.V. 2006. RAPD,

STMS and ISSR markers for genetic diversity

and hybrid seed purity testing in cotton. Seed

Science and Technology. 35: 709-721.

Wright, R.J., Thaxton, P.M., El-Zik, K.M and

Paterson, A.H. 1999. Molecular mapping of

genes affecting pubescence of cotton. Journal

of Heredity. 90: 215–219.

Ahuja, S.L and Dhayal, L.S. 2006. Identification ofpetal spotted mutant in upland cotton(Gossypium hirsutum L.) with some desirablemorphological and technological haracters.Indian Journal of Genetics and Plant Breeding.66 (3): 255-256.

Ankaiah, R., Subba Rao, L.V, Kumar, K.V.S.M.,Chaudhary, C.P and Vidya Sagar, C. 2005.Significance of grow out test in assessinggenetic purity of cotton hybrids and theirparental lines. Structural Genomics. 1: 53–54.

Manjunath Reddy, Ravi Hunje, Biradar, D.P andVyakarnahal, B.S. 2007. Characterization ofcotton hybrids and parental lines usingmorphological characters. Karnataka Journalof Agriculture Science. 20 (3): 511-513.

MORPHOLOGICAL CHARACTERIZATION OF COTTON HYBRIDS

Timely planting and use of appropriate aged

seedlings for transplanting are important non cash

inputs for realizing the higher productivity in rice

(Pattar et al., 2001). Tillering is an important

agronomic trait which finally determines the number

of panicles, grains and grain yield per unit land area.

Tillering dynamics of the rice plant greatly depends

on the age of seedling at transplanting (Pasuquin etal., 2008). All the varieties will not perform equally,

when transplanted with aged seedlings. The present

investigation was taken up to know the influence of

age of seedlings on growth and yield of different high

yielding rice varieties during kharif 2011 at RARS,

Warangal. The site is situated at 180 00' 42.1'’ N

latitude and 790 36' 04.5'’ E longitude and 262 m

altitude. The soil was sandy loam in texture, medium

in organic carbon (0.58 %), neutral in reaction (pH

7.4), low in available nitrogen (225.8 kg ha-1), medium

in available phosphorus (21.0 kg ha-1) and low in

available potassium (95.5 kg ha-1). The weekly mean

temperature ranged between 22.6 to 28.6 0C. A total

rainfall of 349.4 mm was received during the crop

growth period spread over 26 rainy days. The

experiment was laid out in a randomized block design

(factorial concept) with 15 treatments replicated

thrice. 20, 30, 40, 50 and 60 days old seedlings and

three varieties i.e., BPT 5204, MTU 1001 and JGL

384 were the treatments. The plot size was 5.4 m X

4.2 m. A uniform dose of 60, 40, 50 kg ha-1 P

2O

5: K

2O:

ZnSO4 was applied basally to all the treatments.

Nitrogen @ 120 kg ha-1 was applied in three equal

split doses each at basal, active tillering and panicle

initiation stages. Need based plant protection

measures were taken up, whenever the incidence

was more than economic threshold level. The other

EFFECT OF AGE OF SEEDLINGS ON HIGH YIELDING RICE VARIETIESD. NARESH, M. MALLA REDDY, D. VISHNU VARDHAN REDDY AND T.V. SRIDHAR

Department of Agronomy, Agricultural College,Aswaraopet, Khammam – 507 301, A.P.




recommended cultural practices were followed for

raising the crop.

The growth parameters of rice such as plant

height, LAI and dry matter production were

significantly influenced by the age of seedlings and

varieties (Table 1). Taller plants, higher LAI and dry

matter production were obtained by transplanting 20

days aged seedlings or 30 days old seedlings.

Reduction in these growth parameters was noticed

when 40, 50 and 60 days old seedlings were used.

Bommayasamy et al. (2010) also found the similar

performance of younger seedlings of rice over the

aged seedlings. Plant height, LAI and dry matter

production were significantly higher with MTU 1001

compared to BPT 5204. The number of tillers hill-1

was high when crop was planted with 20 days

seedlings (Fig. 1.a). There was 43 % reduction in

maximum tillering capacity when crop was planted

with 60 days old seedlings. The tillering period was

also extended upto 60 days when young seedlings

(20 or 30 days old seedlings) were transplanted, while

crop reached maximum tillering by 45 DAT when

overaged seedlings were transplanted (40, 50 or 60

days). When a seedling is transplanted carefully at

the initial growth stage, the trauma of root damage

caused during uprooting is minimized following a rapid

growth with short phyllochrons. Mobasser et al.(2007) observed that when seedlings stay for a longer

period of time in the nursery beds, primary tiller buds

on the lower nodes of the main culm become

degenerated leading to reduced tiller production.

Among the three tested varieties tillers hill-1 were

more in BPT 5204 followed by MTU 1001 and JGL

384 at all the stages of observation (Fig. 1.b). The

number of days to 50% flowering and maturity were

increased gradually with successive advancement

in the age of seedlings (Table 1). The crop

transplanted with 20 and 30 days seedlings took less

number of days to flowering and maturity. The

cultivar, MTU 1001 flowered and matured earlier than

JGL 384 and BPT 5204 even though the differences

were not statistically significant.

Yield attributes were also significantly

influenced by the age of seedling and varieties (Table

2). The number of panicles m-2, number of filled grains

panicle-1 were significantly higher in crop planted with

20 days seedlings but on a par with 30 days old

seedlings. Number of chaffy grains panicle-1 was

lower with the usage of 20 days seedlings. The test

weight was not influenced by age of seedlings.

Among the three varieties, the yield attributes like

number of panicles m-2 and test weight were

significantly higher in cultivar MTU 1001 followed by

JGL 384 and BPT 5204. Number of chaffy grains

were also minimum in MTU 1001 followed by JGL

384 and BPT 5204. Whereas, number of filled grains

panicle-1 were more in JGL 384 followed by BPT 5204

and MTU 1001.

The age of seedlings significantly influenced

the grain yield, straw yield and harvest index

(Table 2). Among the different aged seedlings, 20

followed by 30 days seedlings recorded significantly

higher grain yield, straw yield and harvest index.

Delay in transplanting beyond 30 days after sowing

caused 24, 36 and 42 per cent reduction in grain yield

with 40, 50 and 60 days old seedlings, respectively.

The better initial growth coupled with superior yield

attributes (panicles m-2, panicle length and filled

grains panicle-1) besides less chaffy grains panicle-1

might be ascribed the reasons for higher grain and

straw yield under 20 day old seedlings. Pasuquin etal. (2008) and Manjunatha et al. (2010) also found

the superior performance of younger seedlings of rice

over the aged seedlings. The cultivar MTU 1001

recorded significantly higher yield and harvest index

compared with JGL 384 and BPT 5204.

The interaction between the age of seedlings

and varieties for all the growth parameters, yield

attributes and yield was found to be non-significant.

The gross returns, net returns and return per rupee

invested were higher with the use of younger seedlings

(20 or 30 days seedlings). Similarly, higher returns

and return per rupee invested were recorded with MTU

1001. The experimental results revealed that

transplanting of 20 or 30 days old seedlings of MTU

1001 was found to be optimum to realize higher yield

and returns.

0

5

10

15

20

25

15 30 45 60

Days after transplantin g

No.

of t

iller

s h

ill-1 20 days

30 days 40 days

50 days

60 days

Fig. 1.a. Number of tillers hill-1 at different growth stages as influenced by age of seedlings

EFFECT OF AGE OF SEEDLINGS ON HIGH YIELDING RICE VARIETIES

Table 1. Growth parameters of rice as influenced by age of seedlings and varieties

Treatment Plant height

(cm) at PI

Dry matter (g hill-1) at

PI

LAI at PI

Days to 50% flowering

Days to maturity

Age of seedlings (days)

20 103.7 46 6.9 99 133

30 98.0 41 6.4 104 138

40 95.0 35 5.3 110 143

50 89.3 29 4.4 118 150

60 86.0 24 3.6 123 154

SEm± 2.9 2.3 0.3 2 2

CD at 5% 8.6 6.8 0.9 7 7

Variety

BPT 5204 87.8 29 4.4 114 146

MTU 1001 100.6 39 6.4 109 141

JGL 384 94.8 37 5.1 110 143

SEm± 2.3 1.8 0.2 2 2

CD at 5% 6.6 5.2 0.7 NS NS

Interaction

SEm± 5.1 4.0 0.5 4 4

CD at 5% NS NS NS NS NS

0

2

4

6

8

10

12

14

16

18

15 30 45 60

Days after tran splanting

No

. of t

iller

s h

ill-1

BPT 5204

MTU 1001

JGL 384

Fig. 1.b. Number of tillers hill-1 at different growth stages as influenced by varieties

NARESH et al

EFFECT OF AGE OF SEEDLINGS ON HIGH YIELDING RICE VARIETIES T

ab

le 2

. Y

ield

att

rib

ute

s,

yie

ld a

nd

eco

no

mic

s o

f ri

ce a

s i

nfl

uen

ced

by a

ge o

f seed

lin

gs a

nd

vari

eti

es

Tre

atm

en

t

Pan

icle

s

m-2

Fille

d

gra

ins

pan

icle

-1

Ch

aff

y

gra

ins

pan

icle

-1

10

00-g

rain

weig

ht

(g)

Gra

in

yie

ld

(kg

ha

-1)

Str

aw

yie

ld

(kg

ha

-1)

Harv

est

ind

ex (

%)

Gro

ss

retu

rns

(Rs h

a-1

)

Net

retu

rns

(Rs h

a-1

)

Re

turn

per

rup

ee

investe

d

Ag

e o

f se

ed

lin

gs (

days)

20

347

240

19

17.8

61

40

7464

42

.1

(45.

0)

79,2

57

53,5

80

2.1

30

316

218

24

17.3

56

20

7006

41

.7

(44.

3)

72,6

27

46,9

50

1.8

40

286

205

31

17.7

42

60

5913

40

.2

(41.

7)

62,0

54

36,3

77

1.4

50

238

190

40

17.5

35

70

5093

39

.7

(40.

8)

53,6

92

28,0

15

1.1

60

196

145

52

17.0

32

50

4837

39

.1

(39.

8)

48,1

15

26,4

38

0.9

SE

m±

13

13

3

0.3

233

234

0.3

- -

-

CD

at

5%

40

39

9

NS

67

6 67

8 0.

9 -

- -

Vari

ety

BP

T 5

204

253

185

42

13.3

36

64

5137

39

.8

(41.

0)

55,9

50

30,2

73

1.2

MT

U 1

001

300

157

25

24.4

55

76

7107

41

.3

(43.

6)

71,6

90

46,0

13

1.8

JG

L 3

84

276

257

33

14.7

44

64

5945

40

.6

(42.

5)

61,8

07

36,1

30

1.4

SE

m±

10

10

2

0.2

181

181

0.2

- -

-

CD

at

5%

29

30

6

0.7

523

525

0.6

- -

-

Inte

racti

on

SE

m±

22

23

5

0.5

404

405

0.5

- -

-

CD

at

5%

N

S

NS

N

S

NS

N

S

NS

N

S

- -

-

The

val

ues

in th

e pa

rent

hesi

s ar

e or

igin

al v

alue

s [a

ngul

ar tr

ansf

orm

atio

n]

REFERENCES

Bommayasamy, N., Ravisankar, N and Subramani,T. 2010. Influence of non-monetary inputs ongrowth and yield of rice (Oryza sativa L.) undersystem of rice intensification (SRI). IndianJournal of Agronomy. 55(2): 95-99.

Manjunatha, B.N., Basavarajappa, R and Pujari, B.2010. Effect of age of seedlings on growth,yield and water requirement by differentsystem of rice intensification. KarnatakaJournal of Agricultural Sciences. 23(2): 231-234.

Mobasser, H. R., Tari, D.B., Vojdani, M., Abadi, R.Sand Eftekhari, A. 2007. Effect of seedling ageand planting space on yield and yieldcomponents of rice (Neda Variety). AsianJournal of Plant Sciences. 6(2): 438-440.

Pasuquin, E., Lafarge, T and Tubana, B. 2008.Transplanting young seedlings in irrigated ricefields: Early and high tiller productionenhanced grain yield. Field Crops Research.105(1-2): 141-155.

Pattar, P.S., Masthana Reddy and Kuchanur, P.H.2001. Yield and yield parameters of rice (Oryzasativa L.) as influenced by date of plantingand age of seedlings. Indian Journal ofAgricultural Sciences. 71(8): 521-522.

NARESH et al


In India, maize occupies third position nextto rice and wheat cultivation and plays a pivotal rolein Indian agricultural economy. Yield loss due toweeds in maize (Zea mays L.) varies from 28 to 93%,depending on the type of weed flora and the intensityand duration o crop-weed competition (Sharma andThakur, 1998).

A field experiment entitled “Weedmanagement studies in kharif maize” was conductedon sandy loam soil during kharif 2010.The fieldexperiment was conducted at college farm, Collegeof Agriculture, Rajendranagar, Hyderabad. The farmis located at an altitude of 542.6 m above mean sealevel (MSL) and 780.291 E longitudes and 170 191 Nlatitude. The soil of experimental site was sandy loamin texture, low in available N (193.4 kg ha-1) andmedium in phosphorus (49 kg P

2O

5 ha-1) and high in

potassium (168 kg K2O ha-1) with pH 7.4. The

experiment was laid out in randomized block designwith 10 treatments and three replications.

Medium duration maize hybrid DHM 117was sown on 24 July at a spacing of 60 x 25 cm.The common fertilizer schedule adopted for all thetreatments was 200-60-40 kg N, P

2O

5 and K

2O ha-1 in

the form of urea, single super phosphate and muriateof potash respectively. Entire dose of P

2O

5 and K

2O

and 1/3 rd N were applied as basal, rest of the nitrogenwas applied in two equal splits at knee high andtasseling stage, respectively. The required quantitiesof herbicides were calculated as per the treatmentsand applied as aqueous spray by using the sprayfluid (water @ 500 litres ha -1) using Knapsack sprayerwith flat fan nozzle. Rainfall received during the cropgrowth period was 610.6 mm with 38 rainy daysindicating well distributed rainfall during crop growingseason.

The major weed flora associated with maizecrop in the experimental site consisted of four

WEED MANAGEMENT STUDIES IN KHARIF MAIZEM. A. ALEEM AHMED and R. SUSHEELA

Department of Agronomy, College of Agriculture,Acharya N.G. Ranga Agricultural University, Rajendranagar, Hyderabad -500 030


monocots viz., Cyperus rotundus, Cynodon dactylon,Digitaria sanguinalis, Dactyloctenium aegyptium andeight dicots viz., Parthtenium hysterophorus,Commelina benghalensis, Amaranthus viridis,Euphorbia geniculata, Celosia argentia, Digeriaarvensis, Trichodesma indicum, Legasca mollies.Cyperus rotundus and Cynodon dactylon weredominant and problematic weeds observed in theexperimental field. The weeds were better controlledby Intercultivation followed by hand weeding (T-

9) and

atrazine 1.0 kg a.i. ha-1 at 1-2 DAS followed by post-emergence spray topramezone 40ml ha-1 at 25 DAS(T-

5).Thakur and Singh (1989) reported less number

of Cyprus spices with the inclusion of post emergenceherbicide glyphosate while Reddy (2003) observedthat the intensity of this weed spices was greatlysuppressed by the inclusion of post emergenceherbicide 2,4-D Na salt.

The results showed that the farmer’s practiceof eliminating weeds through intercultivation at 20DAS fb HW at 30 DAS (T-

9) reduced the weed density

and weed dry matter production significantly andthereby increased the weed control efficiency (%)Next best was pre emergence spray of atrazine @1.0kg a.i. ha-1at 1-2 DAS followed by intercultivation at20 DAS (T2),The herbicide treatment involving pre-emergence spray of atrazine @1.0 kg a.i.ha-1 fb post-emergence spray of topramezone @ 40 ml ha-1 was

on par with (T-9) and the post-emergence spray of

metribuzin @ 0.75 kg a.i. ha-1 exhibited phyto-toxicity

on maize . Mundra et al. (2003) found that pre

emergence application of atrazine @ 0.50 kg a.i.

ha-1 followed by intercultivation at 35 days after sowinggave the highest weed control efficiency (81.0%).

Plant height, plant dry matter production,number of seed rows cob-1, number of grains cob-1,

100 grain weight (g) were highest in T9 (Intercultivation

at 20 DAS followed by hand weeding at 30 DAS)


AHMED and SUSHEEL

Tab

le 1

. A

gro

no

mic

tra

its o

f m

aiz

e in

flu

en

ced

un

der

dif

fere

nt

weed

co

ntr

ol

man

ag

em

en

t p

racti

ces

T

reat

-m

ents

Wee

d de

nsity

at

60

DA

S

(No.

m-

2 )

Wee

d dr

y m

atte

r at

60

DA

S (

g m

-2)

Wee

d co

ntro

l ef

ficie

ncy

at 6

0 D

AS

(%

)

Pla

nt

heig

ht

at 6

0 D

AS

(c

ms)

Pla

nt

dry

mat

ter

at 6

0 D

AS

(g

m-2

)

Leaf

ar

ea

inde

x at

60

DA

S

No

of

seed

ro

ws

cob-1

No

of

seed

s co

b-1

100

seed

w

eigh

t (g

)

See

d yi

eld

kg

ha-

1

Sta

lk

yiel

d

kg h

a-

1

Har

ves

t in

dex

(%

)

Gro

ss

retu

rns

(Rs

ha-1

)

Net

re

turn

s (R

s ha

-

1 )

B:C

R

atio

T1

76

.3

(8.8

) 68

.9

71.3

18

9.9

64.8

2.

92

14.3

35

3.6

19.7

41

32

6876

31

.2

4737

0 30

055

1.73

T2

37

.6

(6.2

) 32

.6

86.4

19

6.6

76.6

3.

51

14.6

40

1.0

20.8

48

52

7383

33

.1

5493

3 35

493

1.82

T3

63

.7

(8.0

) 54

.2

77.5

19

4.2

71.6

3.

15

14.3

36

1.1

20.1

43

15

6949

31

.6

4923

6 31

421

1.76

T4

39

.6

(6.4

) 37

.7

84.3

19

5.8

72.3

3.

28

14.4

37

3.2

20.4

44

88

7261

32

.4

5173

3 33

418

1.82

T5

33

.3

(5.8

) 28

.6

85.3

20

3.6

73.5

3.

43

14.5

38

4.5

20.6

46

46

7304

32

.6

5283

5 34

470

1.88

T6

63

(7

.9)

41.7

82

.6

192.

6 68

.8

3.40

14

.3

347.

4 20

.3

4317

70

45

30.6

49

355

3149

0 1.

76

T7

79

(8

.9)

63.1

73

.8

186.

3 61

.3

2.80

14

.1

311.

8 20

.1

3807

63

70

30.1

43

697

2633

2 1.

51

T8

68

.3

(8.3

) 46

.4

80.7

16

7.3

57.6

2.

48

14.1

15

8.8

20.0

19

17

3893

29

.8

2268

0 39

15

0.20

T9

29

(5

.4)

26.0

89

.2

208.

6 78

.3

3.73

14

.9

424.

1 21

.0

5122

79

34

34.2

58

179

3754

9 1.

77

T10

288

(16.

9)

240.

7 0.

0 15

8.3

52.0

2.

57

13.6

21

3.5

17.9

25

85

5383

24

.8

3071

6 14

401

0.88

SE

m (

+ )

0.

4 3.

2 1.

6 1.

9 2.

2 0.

06

0.11

7.

2 0.

22

99

280

1.1

0 0

0

CD

at

5%

1.

3 9.

5 4.

9 5.

6 6.

6 0.

18

0.32

21

.8

0.65

27

7 83

9 3.

2 0

0 0

T1

A

traz

ine

@ 1

.0 k

g a.

i. ha

-1 P

re-e

m

T6

Atr

azin

e @

0.5

kg

a.i.

ha-1

+ to

pram

ezon

e 40

ml.h

a-1 (

both

at 1

2DA

S)

T2

A

traz

ine

@ 1

.0 k

g a.

i. ha

-1 fb

IC

(20

DA

S)

T

7

Top

ram

ezon

e @

40 m

l ha-1

(

25 D

AS

)

T

3

Atr

azin

e @

1.5

kg

a.i.

ha-1

Pre

-em

T8

Met

ribuz

in @

0.7

5kg

a.i.h

a-1 (

16

DA

S)

T4

A

traz

ine

@ 1

.0 k

g a.

i. ha

-1 f

b at

razi

ne 1

.0 k

g a.

i. ha

-1 (

16 D

AS

)

T

9

IC (

20D

AS

) fb

HW

(30

DA

S)

T5

Atr

azin

e @

1.0

kg

a.i.

ha-1

fb

topr

amez

one4

0 m

l ha-1

(2

5 D

AS

)

T

10

Con

trol

(W

eedy

che

ck)

This was on par with T2 (atrazine @1.0 kg a.i.ha-1 fb

IC and T5 (Pre emergence application of atrazine @

1.0 kg a.i. ha-1 fb topramezone 40 ml ha-1 at 25 DAS).No. of cob plant-1 was not influenced by weed controltreatments. Similar findings were also reported byPandey et al (2001). Thomas et al. (2010) conductedtwo field experiment in northern greece in 2008 and2009 to determine the response of grain maize totopramezone applied as post emergence at 2-4, 4-6and 6-8 maize leaf stages .In both the years grainyields were not differently affected with the use ofherbicide at these growth stages and yields weresimilar with that of hand weeded control.

Higher grain yield was achieved with T9, T2

and T5 were on par with each other. Stover yield

followed the same trend as that of grain yield.

Maximum gross returns and net returns wererealized from (T

9) followed by T

2 and T

5. Benefit cost

WEED MANAGEMENT STUDIES IN KHARIF MAIZE

ratio was higher in T5 (Pre- emergence application of

atrazine @1.0 kg a.i. ha-1 fb topramezone@ 40 ml

ha-1at 25 DAS)

It is clear that weed menace in maize during

kharif season where continuous rains pose a serious

problem, can be managed efficiently through

integration of pre-emergence application of atrazine

@ 1.0 kg a.i. ha-1 fb topramezone @ 40 ml ha-1 as

post-emergence spray to sustain high productivity

and profitability under situations of scarcity and non

availability labour and slushy soil conditions.

Topramezone would be complement to current weed

management program in grain maize. Alternatively

it can be used in sequential application to pre

emergence soil applied treatments or in a total post

emergence program in mixtures with other herbicides

(Porter et al., 2005)

References

Mundra, S. L., Vyas, A. K and Maliwal, P. L. 2003.Effect of weed and nutrient management onweed growth and productivity of maize (Zeamays L.) . Indian Journal of Weed Science.

35(1&2): 57-61

Pandey, A. K ., Singh. P. , Prakash, V., Singh, R. D

and Mani, V. P. 2001. Integrated weed

management in maize(Zea mays) . Indianjournal of Agronomy 46 (2) : 260-265.

Porter, R.M., Vaculin, P. D., Orrr, J. E., Immaraju,

J.A and O Neal, W.B. 2005. Topramezone a

new active for post-emergence weed control

in corn. In: North Central Weed S c i e n c e

Society Proceedings. 60:93

Reddy, U, S, 2003. Integrated weed management in

rainfed maize M Sc (Ag) Thesis submitted

to Acharya N G Ranga Agricultural University,

Hyderabad.

Sharma, V and Thakur, D. R. 1998. Integrated weed

management in maize (Zea mays) under mid-

hill condition of north-western himalayas.

Indian journal of Weed Science 30(3&4): 158-

162.

Thakur, D. R and Singh, K. K. 1989. Effects of weed

management systems on weeds under varying

fertility levels in rainfed maize. Indian Journalof Weed Science 21 (3&4):10-14

Thakur, D. R and Sharma, V.1996. Integrated weed

management in rainfed maize. Indianjournal of Weed Science 28(3&4): 207-208.

Thomas, K., Gitsopoulos., Melidis, V and Evgenidis,

G. 2010. Response of maize (Zea mays L.)

to post-emergence applications of

topramezone. Crop protection 29 :1091- 1093

Tomato (Lycopersicon esculentum Mill.) isan important vegetable crop grown widely throughoutthe world under various agro-climatic conditions. InIndia, the estimated annual production of tomato isabout 11.98 million tonnes with an area of about 3.65lakh ha with an average productivity of 19.32 tonnesper ha. The crop is grown in an area of about 7.41lakh ha with an annual production of 140.8 lakhtonnes in Andhra Pradesh (Indiastat, 2011). The rootrot caused by Rhizoctonia solani is a major problemin monocropping areas and it is reported fromHaryana, Tamilnadu and Andhra Pradesh. Thepathogen generally attacks root region and causespre and post emergence root rot. The root knotnematode is a widespread problem in tomato.Inoculum levels of these pathogens determine theyield losses associated with tomato crop. Thepresent study was carried out to investigate effect ofinoculum levels of Rhizoctonia solani and Meloidogyneincognita on root rot and root knot incidence ontomato cv. Pusa ruby.

The fungus Rhizoctonia solani was isolatedfrom roots of infected disease tomato plants collectedfrom farmers field during survey from Chittoor districtwith a history of maximum disease incidence in thefield during kharif season of 2008. The nematodeM. incognita was extracted from roots of tomatoplants. Based on the morphological characters andperineal pattern the nematode was identified asMeloidogyne incognita following the key provided byHartman and Sasser (1985).

The test pathogen Rhizoctonia solani wasgrown on potato dextrose broth for ten days and usedas inoculum for pathogenicity studies. The pathogenwas tested at different inoculum levels i.e., 0 g., 1.0g., 2.0 g. and 4.0 g / kg soil and the inoculum wasapplied to top 6-7 cm layer of sterilized soil in one kg

EFFECT OF INOCULUM LEVELS OF RHIZOCTONIA SOLANI AND MELOIDOGYNEINCOGNITA ON ROOT ROT AND ROOT KNOT INCIDENCE ON

TOMATO CV. PUSA RUBYB. VIDYA SAGAR, V. KRISHNA RAO, K. S. VARA PRASAD and D. R. R. REDDY

Department of Plant Pathology, College of Agriculture,Acharya NG Ranga Agricultural University, Rajendranagar, Hyderabad-500030.



plastic pots and was mixed thoroughly and moistenedregularly for maintenance of the inoculum. The potswithout pathogen inoculum served as control. Fivereplications were maintained per each treatment andfifteen seeds of tomato cv. Pusa ruby disinfectedwith sodium hypochlorite were sown in the pots.Plants were kept under constant observation forappearance of symptoms. After getting symptoms,the test plants were uprooted and washed thoroughlyand the pathogen was re-isolated from the artificiallyinoculated root rot infested plants. The fungusobtained was compared with the original isolate in allaspects to prove pathogenicity. The data on preemergence rotting was recorded at 12 DAS and postemergence rotting at 50 days after sowing.

The different levels of second stage juveniles(J

2) of M. incognita viz., 0, 1000, 2000 and 4000 J

2

per 1000g of soil were inoculated at root zone depth,of 7 days old tomato seedlings. Uninoculated potsserved as control. The data on shoot and root length,fresh and dry shoot and root weight and root knotindex were recorded at 50 days after inculcation andstatistically analyzed with necessary trans-formations.

An increase of pre and post emergencerotting (Table 1) was observed with increase ininoculum levels of Rhizoctonia solani. Soil inoculationwith 1.0 g mycelium recorded 10.00 and 11.86 percent of pre and post emergence rootrot and the preand post emergence root rot increased to 30.67 and42.54 at a higher inoculum level i.e. 4.0 g ofmycelium. The total rotting was 67.25 and 73.26 percent with 2.0g and 4.0 g mycelium respectively. Theinoculum level of 2.0 g of mycelium per kg soil wasidentified as optimum inoculum threshold level andwas used for further studies.


Table 2. Effect of inoculum levels Meloidogyne incognita on root knot incidence of tomato cv.Pusa ruby (mean of 5 replications)

Table 1. Effect of inoculum levels of Rhizoctonia solani on root rot incidence of tomato cv. Pusaruby (mean of 5 replications)

Per cent disease incidence* Treatment (g mycelium / kg

soil) Pre-emergence

rotting Post-emergence

rotting Total rotting

0 0.00

(4.05)

0.00

(4.05)

0.00

(4.05)

1.0 10.00

(18.24)

11.86

(19.83)

21.86

(27.66)

2.0 30.00

(33.07)

37.25

(37.58)

67.25

(55.30)

4.0 30.67

(33.49)

42.54

(40.70)

73.26

(59.13)

Mean 17.67 22.93 40.59

CD at 5% 4.91 3.73 6.82

* Figures in parenthesis are angular transformed values

Shoot weight/plant

Root weight/Plant Treatment

(J2 /1000 g soil)

**Root knot Index

Shoot length (cm/

plant) Fresh

(g) Dry (mg)

Root length

(cm/ plant)

Fresh (g)

Dry (mg)

0 1.00 (1.23)

28.60 11.24 338 9.06 3.37 145

1000 3.60 (2.00)

22.40 9.97 298 7.72 2.97 127

2000 4.40 (2.21)

20.06 8.65 275 6.84 2.51 117

4000 4.60 (2.25)

19.24 8.54 269 6.57 2.50 115

3.40 22.58 9.60 295 7.55 2.84 126

CD at 5% 0.22 0.87 15.57 9.3 2 0.34 6.65 2.59

Figures in parenthesis are square root transformed values

EFFECT OF INOCULUM LEVELS OF RHIZOCTONIA SOLANI AND MELOIDOGYNE

Similar result was observed by Hadwan andKhara (1992) where in they recorded 19 per centdisease at 1: 24 level of inoculum and 90 per centdisease at 1:3 ratio. Similar results were alsoreported by Jiskani et al. (2007).

The root knot index increased with increasein inoculum levels. The maximum root knot index(4.60) was recorded when plants were inoculated with4000 J

2 /kg soil (Table 2).

The overall plant growth was also decreasedwith the increased inoculum levels of nematode.Shoot length (19.24), fresh (8.54g) and dry shoot(269mg) weight were reduced greatly at high inoculumlevel (4000 J

2/kg soil) when compared to 28.60, 11.24,

338 in control. Similarly, there was maximumreduction in the root length, fresh and dry weight ofroot where plants were inoculated with 4000 J

2 /kg

soil compared to control.

REFERENCES

Hafeezullah Khan., Riaz Ahmad, Akhtar, A.S., Arshad

Mahmood., Tahir Basit and Tariq Niaz. 2000.

Effect of Inoculum Density of Meloidogyneincognita and plant age on the severity of Root

Knot disease in Tomato. International Journal

of Agriculture & Biology. 2(4):360-63

Hadwan, A.H and Khara, H.S. 1992. Studies on the

effect of inoculum level and temperature on

the incidence of damping off and root rot of

tomato by Rhizoctonia solani. Plant Disease

Research. 7 (2) :242-244

Hartman, K. M and Sasser, J.N. 1985. Identification

of Meloidogyne species on the basis of

differential host test and perineal pattern

The root knot index was maximum (4.6) athigh inoculum level (4000 J

2/kg soil) and there was a

progressive decrease in growth parameters of tomatowith increase of inoculum levels. Similar results werealso reported by Hafeezulla Khan (2000) where asignificant reduction of growth parameters of tomato

with increased inoculum levels of nematode wasobserved.

As per the results, the damaging thresholdlevel of R. solani on tomato cv. Pusa ruby was 2.0 gof mycelium and that of M. incognita was2000 J

2/ kg soil.

morphology. In Advanced Treatise on

Meloidogyne. Vol.II Methodology(Eds.

K.R.Barker, C.C. Carter and J. N.Sasser)

NCSU. Raleigh,pp. 69-77

Indiastat. 2011. Crop statistics in India

www.indiastat.com

Jiskani, M.M., Pathan, M.A., Wagan, K.H.L, Imran,

M and Abro, H.2007. Studies on the control of

tomato Damping off disease caused by

Rhizoctonia solani Kuhn. Pakistan Journal of

Botany. 39 (7) 2749-2754.

SAGAR et al



The present investigation was carried out

during post kharif, 2011 to characterize the soils of

command area of Pillaipally anicut, Musi river,

Nalgonda district of Andhra Pradesh and to map them

by using the GIS tools which can be used for soil

test based, crop specific fertilizer recommendations.

The water from Musi river is source of

irrigation water to different villages of 20 mandals in

Rangareddy and Nalgonda districts, irrigating an area

of 12750 ha through different (23) Anicuts and

Pillaipally anicut is one such anicut among them

selected for the study. Surface (0-15 cm) soil

samples(73) were collected from the farmers’ fields

in Pillaipally anicut command area along with GPS

readings and analyzed for texture, pH, Electrical

conductivity, organic carbon and available

Nitrogren(N), Phosporous (P) and Potassium (K) in

laboratory by using standard procedures suggested

by Piper (1966) and Tandon (1993). The Survey of

India (SOI) topographical maps of 57K7, 57K

11 in

1:50,000 scale covering the command area of

Pillaipally anicut of Musi river, Nalgonda district,

Andhra Pradesh were used as reference maps for

demarcating the study area. The Musi command area

map was vectorised by using Raster to Vector

software (ERDAS 9.3 software), and then it was

exported into Arc GIS 9.3 Software. Database on

soil fertility status of the study area was developed

using Microsoft Excel package.

Then the database was exported to Arc GIS

software, thematic maps (base maps) on spatial

variability of soil fertility were generated by ordinary

krigging method available in the sub mode of

interpolation in the spatial analyst tools of Arc map

9.3 GIS software.

SOIL FERTILITY MAPPING OF PILLAIPALLY ANICUT COMMAND AREA, MUSI RIVER IN ANDHRA PRADESH

Y. KRISHNAVENI, K.AVIL KUMAR, M. UMA DEVI, and M. D. REDDYWater Technology Centre, Achary N.G.Ranaga Agricultural University,



Soil texture of surface (0-15 cm) samplescollected from command area of Pillaipally anicutranged from sandy clay loam to loam. Among these,70 per cent of soils were sandy clay loam, 25 percent of soils were sandy loam in texture and 5 percent of them were loam in texture. The soilscontained 48 to 72 per cent of sand, 4 to 34 per centof silt and 13 to 32 per cent of clay contents, withoverall means of 62 per cent, 15 per cent and 24 percent of sand, silt and clay contents, respectively.The clay soil particles deposited while irrigating thecrops with Musi water might be responsible for thehigher percentage of soils having relatively finetexture. Rattan et al. (2005) indicated that fieldsirrigated with sewage irrigation water around the peri-urban areas of Keshopur Sewage Treatment Plantwere sandy loam in texture.

Among all the samples in study area ofPillaipally anicut, the pH of the surface soil samplesirrigated with canal water varied from 7.8 to 8.7 withoverall mean value of 8.2 while the pH of the soilsirrigated with ground water varied from 6.9 to 8.8 withoverall mean value of 8.1. Among the soils irrigatedwith canal water, 15.8 per cent of the samples werelow alkaline, 71 per cent were medium alkaline and13 per cent were strong alkaline whereas in case ofsoils irrigated with ground water 6 per cent wereneutral, 35 per cent were low alkaline, 56 per centwere medium alkaline and 3 per cent were strongalkaline (Fig.1). Alkalinity of irrigation water in thecommand area (Musi canal / bore wells / open wells)might be responsible for the higher percentage of soilshaving alkalinity. Tiwari et al. (1996) observed thatsoils irrigated with treated sewage water had relativelyhigher pH (8.0) and alkaline in reaction as comparedwith soils irrigated with tube well water (7.5) atVaranasi, and U.P.

Electrical conductivity of the surface soilsamples irrigated with canal water ranged from 0.1to 1.0 dS m-1 with a mean value of 0.52 dS m-1

whereas surface soil samples irrigated with groundwater ranged from 0.2 to 1.3 dS m-1 with a meanvalue of 0.61 dS m-1(Fig.2). Tiwari et al. (1996) andRamesh (2003) also reported relatively higher totalsoluble contents in sewage water irrigated fields thansoils irrigated with normal water.

Organic carbon content of surface soilsamples irrigated with canal water ranged from 0.23to 2.01 per cent with mean 0.88 per cent while thesoils irrigated with ground water ranged from 0.2 to2.1 per cent with mean 0.8 per cent(Fig.3). Musi canalwater carrying sufficient loads of suspended organicmatter might be responsible for higher organic carboncontents in the study area. The results are inconformity with the findings of Ramesh (2003), whoreported higher organic carbon contents in soilsirrigated with sewage water than with normal water.

Available nitrogen content of surface soilsamples irrigated with canal water ranged from 50.2to 589.6 kg N ha-1 with overall mean value of 189.4kg N ha-1 while available nitrogen content of thesurface soil samples irrigated with ground waterranged from 71.3 to 313.6 kg N ha-1 with overall meanvalue of 149.04 kg N ha-1(Fig.4). These resultscorroborates with findings of Azad et al. (1987) whofound that the normal soils contained very lowamounts of available nitrogen in surface andsubsurface layers, while in case of soils fed withsewage waste water had low to medium availablenitrogen in surface, subsurface layers of the soils ofvillages around ‘Budha Nallah’, Ludhiana.

Available phosphorus content of surface soilsamples irrigated with canal water ranged from 62.8to 549.2 kg P ha-1 with overall mean value of 238.4kg P ha-1 while phosphorus content of the surfacesoil samples irrigated with ground water ranged from73.4 to 514.7 kg P ha-1 with overall mean value of249.4 kg P ha-1(Fig.5). High available phosphoruscontent could be ascribed to enrichment of soils withsewage canal water, heavy application of phosphaticfertilizers by farmers through complex fertilizersapplied as basal as well as top dressing and organicmatter, which favours the solubilisation of fixedphosphorus releasing more quantity to the available

pool. Kharche et al. (2011) also concluded similarresults of higher available P in sewage-irrigated soilsindicating significant addition of P through sewagewater.

Available potassium content of surface soilsamples irrigated with canal water ranged from 90.7to 813.1 kg K ha-1 with overall mean value of 264.5kg K ha-1 while available potassium content of thesurface soil samples irrigated with ground waterranged from 63.8 to 432.3 kg K ha-1 with overall meanvalue of 174.3 kg K ha-1. All the soils are found to behigh to very high in available K status (Fig.3).Additions of potassium by Musi river water could beresponsible for such higher status of K in these soils.The results of high available potassium are inconformity with Priyanieamerasinghe et al. (2008) andKharche et al. (2011).

REFERENCES

Azad, A.S., Arora, B.R., Bijay Singh and Sekhan,G.S. 1987. Effect of sewage waste water onsome soil properties. Indian Journal ofEconomy. 14(1) : 7-13.

Kharche, V.K., Desai, V.N and Pharande, A.L. 2011.Effect of sewage irrigation on soil properties,essential nutrient and pollutant element statusof soils and plants in a vegetable growing areaaround Ahmednagar city in Maharashtra.Journal of the Indian Society of SoilScience.59 (2): 177-184.

Priyanieamerasinghe Philipp Weckenbrock RobertSimmons SreedharAcharya and AxelDrescher. 2008. An atlas of water quality,health and agronomic risks and benefitsassociated with wastewater irrigatedagriculture-A study from the banks of theMusiriver, India.

Piper, C.S. 1966.Soil and Plant Analysis.HansPublishers, Bombay.137-153.

Ramesh, M. 2003. Soil and water resourcecharacteristics in relation to land disposal ofsewage effluents and suitability of sewagewater for irrigation. M.Sc. (Ag.) ThesisAcharyaN.G. Ranga Agricultural University, Hyderabad.

Rattan. R.K., Datta.S.P.,Chhonkar,P.K., Suribabu,Kand Singh, A.K. 2005. Long-term impact of

KRISHNAVENI et al

Fig.1. Spatial variability of soil pH in command area of Pillaipally anicut, Nalognda district, A.P.

Fig.2. Spatial variability of soil EC in command area of Pillaipally anicut, Nalognda district, A.P.

SOIL FERTILITY MAPPING OF PILLAIPALLY ANICUT COMMAND AREA

Fig.3. Spatial variability of soil organic carbon and Potassium in command area of Pillaipally anicut,Nalognda district, A.P.

Fig.4. Spatial variability of available soil nitrogen in Pillaipally anicut command area, Nalognda district, A.P.

KRISHNAVENI et al

irrigation with sewage effluents on heavy metalcontent in soils, crops and groundwater—acase study. Agriculture, Ecosystems andEnvironment 109 (2005): 310–322.

Tandon, H. L .S. 1993.Methods of analysis of soilsplants, water and fertilizers. FertilizersDevelopment and Consultation Organisation,New Delhi, India.

Tiwari, R. C., Arvind Kumar and Mishra, A. K .1996.Influence of treated sewage and tube wellwater irrigation with different fertilizer levelson rice and soil properties. Journal of the IndianSociety of Soil Science. 44(3): 547-549.

Fig.5: Spatial variability of available soil phosphorus in command area of Pillaipally anicut, Nalognda district, A.P.

SOIL FERTILITY MAPPING OF PILLAIPALLY ANICUT COMMAND AREA



Fertilizers play an important role in increasing

the production and improving the quality of vegetables

specially cauliflower, and among them potassium is

prime because of its role in various physiological

processes. Not much work has been done in light

textured soils of Rangareddy district on the effect of

levels of potassium on cauliflower. Hence, a study

was made in green house of Department of Soil

Science, College of Agriculture, Rajendranagar during

Rabi, 2010-11. Surface soils in bulk were collected

from thirty locations of Rangareddy district.

Greenhouse experiment was conducted with four

different levels of potassium viz., 0, 50, 100 and 150

kg K2O ha-1, each replicated thrice. The soils were

analysed for pH, EC, OC and CEC by standard

procedures (Jackson, 1973). The texture of the soils

ranged from loamy sand to sandy loam except the

soils of Shamshabad, Emamguda and Tukkuguda

which was sandy soil and the soil of Mominpet which

was sandy clay loam. They were slightly acidic to

slightly alkaline with mean pH value 7.9, non-saline

with EC ranging from 0.06 to 0.26 dsm-1, cation

exchange capacity ranging from 14.39 to 20.02 c mol

(p+) kg-1 , organic carbon content ranging from 0.11

to 0.65 per cent. The soils were low to medium (231.1

kg ha-1 to 306.5 kg ha-1) in available N, low to medium

(10.2 kg ha-1 to 48.43 P2O

5 kg ha-1) in available P and

medium to high (121.5 kg ha-1 to 542.9 K2O kg ha-1)

in available K. At harvest, curd yield of cauliflower

was recorded and plant samples were analysed for

K content in diacid (HNO3: HClO

4) extract by adopting

standard procedure (Jackson, 1973).

Curd yield of cauliflower was influenced

significantly by levels of K, soil type and the


RESPONSE OF CAULIFLOWER (Brassica Oleracea var. Botrytis) TO POTASSIUMFERTILIZATION ON LIGHT TEXTURED SOILS OF RANGAREDDY DISTRICT

K. KALYANI, V. SAILAJA, A. PRATAP KUMAR REDDY and P. CHANDRASEKHAR RAODepartment of Soil Science and Agricultural chemistry

College of Agriculture, ANGRAU, Rajendranagar, Hyderabad -500 030

interaction of both. Significantly higher values were

associated with high native K status. The mean curd

yield was 352.4 g plant-1 in the control which showed

a significant increase to 454.8 g plant-1 with the

application of 150 kg K2O ha-1. The mean curd yield

of 582.6 g plant-1 was obtained from Mominpet soil

(sandy clay loam) which was significantly higher than

the curd yield obtained from all other soils. Curd yield

from Emamguda (sand) was significantly lower, the

mean value being 279.4 g plant-1 which was on par

with the mean yield of 285.3 g plant-1 obtained on

Shamshabad (sand) soils. The interaction effect was

significant. The response to the applied K in terms

of curd yield was 12.2 per cent with Mominpet soils,

where as it was 52.5 per cent with Emamguda soil.

The mean response to the application of K was 25.28

kg cauliflower curd per kg of potassium applied. This

was mainly due to positive response of the yield

contributing characters like equatorial and polar

diameter and average head weight (Table 1).

Sarkar et al., (1994) also obtained 41.46 per

cent increase in the curd yield due to 100 kg K2O per

ha-1. Hariprakasa Rao (1994) proved that the higher

levels of K application significantly reduced the

number of barren plants and number of days taken

for bolting (curd initiation).

The mean K content in the curd of cauliflowerwas significantly influenced by both the levels of K

application and the soils. There was a significant

increase in the mean K content from 1.53 per cent in

the control to 2.13 per cent in the treatment receiving

150 kg K2O ha-1. The mean K content was significantly

higher in the curd of Aglanooru; the value being 2.48

per cent and lower from the soil of Azithnagar with

Table 1. Curd yield (g plant-1) as influenced by K-levels and soil types

Curd Yield (g plant-1)

S.No Village Name Avail. K(kg K2O

ha-1)

Levels of K (kg K2O ha-1) Mean

0 50 100 150

1 Azithnagar 190.8 260.1 301.9 346.1 380.9 322.3

2 Himayathnagar 348.9 374.8 395.8 442.2 497.8 427.7

3 Kammeta 215.5 287.7 321.9 368.2 401.0 344.7

4 Chevella 355.2 362.4 396.0 442.7 471.1 418.1

5 Velverthi 345.3 391.5 428.4 469.8 511.4 450.3

6 Manneguda 237.8 304.8 352.7 412.4 453.7 380.9

7 Yennepalli 412.6 488.4 492.4 535.5 547.0 515.8

8 Parigi 346.7 389.5 391.2 435.8 469.4 421.5

9 Baspally 221.7 271.8 315.5 359.2 390.7 334.3

10 Turkaimzal 204.8 262.4 306.3 375.3 410.1 338.5

11 Navabpet 342.1 383.0 419.3 447.4 482.5 433.1

12 Mominpet 542.9 546.2 576.4 594.7 612.9 582.6

13 Morangapally 448.6 508.9 525.9 556.6 575.5 541.7

14 Siripuram 373.1 409.9 421.3 453.3 487.8 443.1

15 Station marpally 352.2 437.4 441.3 482.3 520.8 470.5

16 Yacharam 154.3 251.8 294.0 329.0 360.8 308.9

17 Nagarambandar 238.2 301.2 349.2 393.5 432.0 369.0

18 Peddamal 323.1 369.8 383.3 425.3 458.8 409.3

19 Thandur 402.1 464.2 482.2 513.3 541.1 500.2

20 Aglanooru 382.6 451.8 472.4 502.1 542.5 492.2

21 Basheerabad 346.2 428.1 445.6 494.8 525.9 473.6

22 College farm (RNGR) 290.1 338.6 362.7 393.0 421.1 378.9

23 Student farm(RNGR) 223.2 268.6 293.7 344.8 374.7 320.5

24 Shamshabad 139.2 232.2 262.5 301.4 344.9 285.3

25 Haithnagar 218.8 291.4 339.9 377.5 407.9 354.2

26 Emamguda 121.5 224.8 251.5 298.1 343.0 279.4

27 Thukkuguda 147.8 242.9 293.8 321.4 369.3 306.9

28 Singaram 252.5 322.9 349.2 392.5 427.3 373.0

29 Aagapalli 311.4 359.6 374.3 421.4 462.0 404.3

30 Seriguda 270.6 345.4 362.6 393.3 420.9 380.6

Mean 292.0 352.4 380.1 420.8 454.8

SEm ± CD at 5%

Factor (A) K-levels 1.18 3.29 Factor (B) Soil types 3.24 9.03Factor (AxB) 6.48 18.0

RESPONSE OF CAULIFLOWER (Brassica Oleracea var. Botrytis) TO POTASSIUM

Table. 2. K Concentration (%) in Curd as influenced by K-levels and soil types

Levels of K (kg K2O ha-1)

S.No Village Name Levels of K (kg K2O ha-1) Mean

0 50 100 150

SEm ± CD at 5%

1. Azithnagar 1.16 1.26 1.33 1.42 1.29

2. Himayathnagar 1.34 1.65 1.75 1.78 1.63

3. Kammeta 1.43 1.54 1.74 1.69 1.60

4. Chevella 1.65 1.75 1.80 1.82 1.76

5. Velverthi 1.26 1.28 1.32 2.01 1.47

6. Manneguda 1.33 1.52 1.74 1.90 1.62

7. Yennepalli 1.25 1.33 1.56 1.72 1.47

8. Parigi 1.62 2.29 2.56 2.72 2.30

9. Baspally 1.54 2.19 2.52 2.68 2.23

10. Turkaimzal 1.25 1.33 1.33 1.49 1.35

11. Navabpet 1.34 2.03 1.80 1.96 1.78

12. Mominpet 1.91 2.44 2.53 2.69 2.39

13. Morangapally 1.71 2.47 2.72 2.88 2.45

14. Siripuram 1.39 1.88 1.99 2.15 1.85

15. Station marpally 1.50 2.10 2.42 2.56 2.15

16. Yacharam 1.67 1.86 2.02 2.18 1.93

17. Nagasambandar 1.72 1.87 1.96 2.12 1.92

18. Peddamal 2.15 2.23 2.44 2.60 2.36

19. Thandur 1.79 2.42 2.59 2.75 2.39

20. Aglanooru 2.12 2.34 2.65 2.81 2.48

21. Basheerabad 1.42 1.56 1.69 1.85 1.63

22. College farm (RNGR) 1.59 2.31 2.64 2.81 2.34

23. Student farm(RNGR) 1.59 1.63 1.74 1.90 1.72

24. Shamshabad 1.54 1.76 1.93 2.09 1.83

25. Haithnagar 1.31 1.43 1.57 1.62 1.48

26. Emamguda 1.28 1.54 1.75 1.91 1.62

27. Thukkuguda 1.79 2.11 2.39 2.55 2.21

28. Singaram 1.37 1.48 1.52 1.68 1.51

29. Aagapalli 1.22 1.41 1.76 1.92 1.58

30. Seriguda 1.56 1.61 1.67 1.72 1.64

Mean 1.53 1.82 1.98 2.13

Factor (A) K-levels 0.008 0.02Factor (B) Soil types 0.023 0.06Factor (AxB) 0.046 0.12

KALYANI et al

1.29 per cent. Higher K content of 2.15 per cent in

the control was observed in the Peddamal soil which

increased significantly to 2.60 per cent due to 150

kg K2O ha-1. In Azithnagar soil, the curd had a

significantly lower K content of 1.16 per cent which

increased significantly to 1.42 per cent with the

application of 150 kg K2O ha-1. This increase in K

content of with K application could be due to the

efficient partitioning of K into the curd (Table 2).

From the study it was evident that the light

textured soils of Rangareddy district need 150 kg

K2O ha-1 for getting optimum yields of cauliflower.

REFERENCES

Jackson, M.L. 1973. Soil Chemical Analysis. PrenticeHall of India Pvt. Ltd: New Delhi.

Hariprakasa Rao, M. 1994. Growth, Yield and Qualityof Tomato, Carrot and Cauliflower asInfluenced by Levels and Sources ofPotassium. Journal of Potassium Research.10 (4): 402-406.

Sarkar, S. K., Singh, S.P and Jain, B.P. 1994.Response of Cabbage to Potassium and Limein Bihar Plateau. Journal of PotassiumResearch. 10(4): 398-401.

RESPONSE OF CAULIFLOWER (Brassica Oleracea var. Botrytis) TO POTASSIUM

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Journals and Bulletins

Abdul Salam, M and Mazrooe, S.A. 2007. Water requirement of maize (Zea mays L.) as influenced byplanting dates in Kuwait. Journal of Agrometeorology. 9 (1) : 34-41

Hu, J., Yue, B and Vick, B.A. 2007. Integration of trap makers onto a sunflower SSR marker linkage mapconstructed from 92 recombinant inbred lines. Helia. 30 (46) :25-36.

Books

AOAC. 1990. Official methods of analysis. Association of official analytical chemists. 15th Ed. WashingtonDC. USA. pp. 256

Federer, W.T. 1993. Statistical design and analysis for intercropping experiments. Volume I: two crops.Springer – Verlag, Cornell University, Ithaca, New York, USA. pp. 298-305

Thesis

Ibrahim, F. 2007. Genetic variability for resistance to sorghum aphid (Melanaphis sacchari, Zentner) insorghum. Ph.D. Thesis submitted to Acharya N.G. Ranga Agricultural University, Hyderabad.

Seminars / Symposia / Workshops

Naveen Kumar, P.G and Shaik Mohammad 2007. Farming Systems approach – A way towards organicfarming. Paper presented at the National symposium on integrated farming systems and its roletowards livelihood improvement. Jaipur, 26 – 28 October 2007. pp.43-46

Proceedings of Seminars / Symposia

Bind, M and Howden, M. 2004. Challenges and opportunities for cropping systems in a changing climate.Proceedings of International crop science congress. Brisbane –Australia. 26 September – 1 October2004. pp. 52-54

(www.cropscience 2004.com 03-11-2004)

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ESSENTIAL REQUIREMENTS FOR CONSIDERATION OF PUBLICATIONOF ARTICLES

1. Research of not less than 2 years and of high standard will be considered as full length paper. Ifnecessary, it will be considered for short communication.

2. Research of one year should be submitted in the style and format of short communication.

3. The total number of pages should not exceed 10 for full paper and 5 pages for short communicationincluding tables and figures. The figures should be legible.

4. Old research which terminated 5 years before the date of submission will not be considered.

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least in short forms if they are lengthy, but not abbreviated as T1, T

2 and T

3 etc. The weights and

measures should be given in the metric system following the latest units eg. kg ha-1, kg ha-1 cm, mgg-1, ds m-1, g m-3, C mol kg-1 etc.

Typing : The article should be typed in 12pt font on A4 size paper leaving a margin of 10 cm on all

sides. There should be a single line space between the rows in abstract and double line in rest. Checkthe manuscript thoroughly for errors before submitting it for publication.

Note : Latest issue of the Journal may be consulted. Further details can be obtained from the book“Editors style Manual, edn 4. American Institute of Biological Sciences, Washington DC”.

Website : www. angrau.net

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