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Journal of Plant Development Sciences (An International Monthly Refereed Research Journal)

Volume 12 Number 4 April 2020

Contents

REVIEW ARTICLE

Diara cultivation of cucurbits

—Barsha Tripathy, Samapika Dalai, Meenakshi Badu, Kalyani Pradhan, M. Sai Sindhu, B. Bhagyarekha

and Sandeep Rout ----------------------------------------------------------------------------------------------------- 189-194

RESEARCH ARTICLES

Changes in the physico-chemical properties of soil in different Deodarforests of Garhwal Himalaya

—Gaurav Chand Ramola, Digvijay Rathod, Yogesh Kumar, Prajapati Dhaval, Akshit Kukreti and V.P.

Khanduri---------------------------------------------------------------------------------------------------------------- 195-205

Collection of medicinal plants in traditional and modern perspective

—Vinay M. Raole and Vaidehi V. Raole -------------------------------------------------------------------------- 207-214

Long term effect of inorganic fertilizers and organic manures on nutrient uptake, and yield of rice on Inceptisol

—Kiran Rathore, Alok Tiwari and Rahul Kumar ------------------------------------------------------------- 215-222

Assessment of medicinal plants through proximate and micronutrients analysis

—Tamanna Malik, V.K. Madan and Tanya Dhanda ---------------------------------------------------------- 223-229

Validation of mas derived lines for introgressed gene against blast and blb resistancein Southern Chhattisgarh

—Prafull Kumar ------------------------------------------------------------------------------------------------------ 231-237

Forecasting monthly precipitation model for Dantewada, Jagdalpur and Sukma region (Chhattisgarh) using

Arima model

—Anosh Graham, Avinash Yadu and Atul Galav -------------------------------------------------------------- 239-245

Disease controlling potential of Trichoderma harzianum and Trichoderma viride against collar rot of chickpea

—Shweta Mishra, Devendra Nishad and R.K.S. Tiwari ------------------------------------------------------ 247-251

Production potential and economics of intercropping in autumn planted- sugarcane under north hill zone of

Chhattisgarh

—Prakash Kumar Sahu, D.K. Gupta and V.K. Singh --------------------------------------------------------- 253-256

SHORT COMMUNICATION

Existing production patterns among the maize growers

—P.K. Netam, Basanti Netam and Virendra Kumar Painkra ----------------------------------------------- 257-259

*Corresponding Author ________________________________________________ Journal of Plant Development Sciences Vol. 12(4): 189-194. 2020

DIARA CULTIVATION OF CUCURBITS

Barsha Tripathy1, Samapika Dalai

1, Meenakshi Badu

2, Kalyani Pradhan

1, M. Sai Sindhu

1, B.

Bhagyarekha1 and Sandeep Rout*

3

1Department of Horticulture, Centurion University of Technology and Management,

Paralakhemundi, Odisha-761211 2Sri Sri University, Cuttack, Odisha

3Department of Agronomy and Agroforestry, M. S. Swaminathan School of Agriculture, Centurion

University of Technology and Management, Parlakhemundi, Odisha-761211

Email: [email protected]

Received-05.04.2020, Revised-26.04.2020

Abstracts: In recent years, the agriculture sector is adversely affected by climate change, and the rural poor are becoming more vulnerable to unsustainable livelihoods. River areas are known as “Diara”. Diara land cultivation continues to be carried out with the traditional manner. Riverbed cultivation is a type of vegetables forcing, facilitating off season production of mainly cucurbitaceous vegetables. Incomes generated by river bed vegetable growers were used primarily for meeting their household food security. On riverbed vegetable cultivation is easy with respect to land preparation, water management and other cultural practices. In north India, the cucurbits generally grown together are cucumber, bottle gourd, bitter gourd,

summer squash, round melon and long melon but ridge gourd in Rajasthan, MP and UP. The major constraints of diara farming are stray animals, strong windstorms and long spell of droughts. Diara farming is a pro-poor focused program for the rural community to increase household income and to improve the food security of landless and land poor households of India.

Keywords: Climate change, Cultivation, Cucurbits, Vegetable

INTRODUCTION

he ancient practice diara cultivation was started

during the mughal period predominantly with

various cucurbits. It was selected as an entry point to

promote inclusive economic growth for the benefit of the landless people. A piece of land created inside a

river due to deposition of sand is known as Diara

land or river bed. Cultivation in riverbeds facilitates

off season production which is a type of vegetable

forcing in many cucurbitaceous vegetables which is

purely an indigenous and innovation of Indian

vegetable grower. The term "Diara" has been

extracted from the word „Diya‟ meaning earthen

lamp. Keeping in conformity with the shape of the

„Diya‟, the bowl like systems on the surface

(depressions) situated between the natural levees on

either side of the river appear like small „Diyas‟ when rain water gets accumulated in them during the

rainy session. In survey it was observed that out of

total area under cucurbits cultivation, 60 % area is

under riverbed cultivation during the summer season

around 75-80 % of total cucurbits production is being

produced in river beds or diara land area which is

available in market from February – June. Such land

is also known as in different areas of India as

khaddar lands, char lands, dariayi, kachhar, doab,

kochar, nad, riverine area, and nadiari. Diara lands

and tal lands.

How it forms

The alluvion and diluvion action of perennial

Himalayan Rivers during South- West monsoon lead

to the formation of diara lands. During monsoon, the

vegetable crops can be grown on these lands due to

yearly deposits of fresh silt and clay. After the monsoon season, the water from the riverbeds

retreats back to its channel, leaving large areas dry.

These areas of land are generally left unused. The

subterranean moisture seeped from adjacent river,

streams, makes the upper layers of land more suitable

for growing early vegetable crops.

Where it works

The technology works well on marginal lands, in

topographically flat areas with river beds that are

dry for one crop cycle (approximately 6 months)

with arable land silted over and/or washed away

due by floods.

Distance/Adjoining to village: not more than 30

minutes on foot.

Sand must be fine and small-grained and the

groundwater table should be <1 m.

Riverbeds or riverbanks may be cultivated.

Riverbeds have a higher soil moisture content

compared to riverbanks.

Main River beds in India Main river beds which formed by different rivers are

found in 9 states (Table 1). State wise, the main river

beds in India are:

T

REVIEW ARTICLE

mailto:[email protected]

190 BARSHA TRIPATHY, SAMAPIKA DALAI, MEENAKSHI BADU, KALYANI PRADHAN, M. SAI SINDHU,

B. BHAGYAREKHA AND SANDEEP ROUT

Table 1. River beds of India

States Main river beds

Uttar Pradesh Jamuna, Hindon, Sarayu, Ganga, Ghaghra, Tank

beds, Sharada, Ramganga, Gomati

Bihar Ganga, Gandak, Sone, Kosi, Burhi Ganga

Madhya Pradesh Narmada, Tapti, Tawa, Mohana

Rajasthan MarkhedaGhat, Banas

Gujarat Sabarmati, Vatrak, Panam-Orusung, Mohi-Banas,

Tapti

Maharashtra Tapti, Burai, Purna, Vagur, Girna, MaisBhuikund,

Nirguna, Kanchan

Andhra Pradesh Tungbhadra, Krishna, Pennar, Papagni, Hundri-

Sagileru

Karnataka Channapatna, Hanganoor, Shimsha

Kerala Pamba, Manimala

Classification of Diara land on the basis of precise

location from the main stream 1. Main riverbed (low land) diara- The actual

riverbeds, which have fine sand to coarse deposits

on surface, become available during non-monsoon

seasons i.e. December/January to May/June until

early rains set in. Main crop is Bottle gourd and

Bitter gourd.

2. Main land (medium land) diara- These areas are

located on the bank of the river and its width varies considerably. They are frequently inundated during

rainy season by the swelling of flood water. The

depth of the main diara region varies considerably

at different locations. Main crop is Watermelon,

Cucumber, Luffa, Pointed gourd and Muskmelon.

3. Upland diara- Due to continuous deposition, such

areas have been elevated and are relatively less

frequently flooded than the main land diara areas.

For all operational purposes these areas are not

very different from the normal (non- diara) lands.

Main crop is pointed gourd.

Other Classification

1. Riverbed Diara- The land available for cultivation

on both sides of the flowing portion of the riverbed

during non-mansoon season.

2. Riverbank Diara- Strips of land available for

cultivation in between riverbeds and natural levees

or existing embankments.

3. Flood Affected Diara- The lands available for

cultivation adjacent to unprotected reaches.

4. Flood Prone Diara- The area on both sides

beyond the levees or embankments of the river.

Advantages of riverbed cultivation

The advantages of river bed cultivation are

High net returns per unit land area

Early and high yield Ease in irrigation

Low cost of cultivation, highly fertile lands

reduces the external mineral requirements

Limited weed growth

Pest and disease are controlled by cultural

practices, cost effective labour facilities

No land ownership required Income and food

security of landless and marginal farmers

Local adaptation to climate change.

Characteristics of Riverbed soils

The soil in river beds contains mostly sand and

moisture is seeped from the adjacent river. Well-

drained loamy soils are preferred for cucurbit

cultivation. Soil moisture is also important for rapid

growth and it should be at least 10% to 15% above

the wilting point. For early yields lighter soils and for

getting late crop heavier soils are usually used. Sub-

terranean moisture of river streams and alluvial

substrate in sandy river-beds support the growth of

cucurbits. The soils should not crack and should not

be water-logged in summer and rainy season respectively. It should be provided with adequate

organic matter. pH below 5.5 is not suitable for

cucurbits cultivation and most of the cucurbits prefer

a neutral soil pH i.e. between 6.0 and 7.0 is optimum

for cucurbits. Water melon is the only cucurbit which

is slightly salt tolerant and Musk melon is slightly

tolerant to acidic soils. (Patel et al., 2016). For proper

growth and development the optimum temperature

range should be around 18-22°C. For good soil

management firstly level the lands and secondly for

alkali soil application of gypsum and for acid soil there is application of lime in diara lands.

Cultivation

Land Preparation: Riverbed plots are chosen by

JOURNAL OF PLANT DEVELOPMENT SCIENCES VOL. 12(4) 191

farmers, with plots perpendicular to the river‟s flow.

After recession of flood during October- November

and the cessation of the south-west monsoon pits or

trenches or channels are prepared.

Systems of planting

Majority of farmers choose the following system of planting depending on personal preferences and

availability of labour. They are –

i. Pit system of planting

ii. Ditch system of planting

Pit system of Planting - For the pit system, pits of

0.5 m diameter are dug 1 m deep and 1 to 3 meters

apart depending on the crops, and planted with

numerous seeds, the feeble of which are thinned out.

Sometimes circular pits with diameter of about 35-

45cm and a depth of 90 cm are prepared.

Ditch system of planting - To manage the

availability of moisture and higher temperature the trenches are dug in North-West direction. To use the

ditch system, a trench 1 m deep is dug along the row,

with 1 to 2 m (cucumber, bitter gourd) or 3 m

(watermelon, bottle gourd, pumpkin) space between

rows. Seeds are planted/spaced 1 m (watermelon,

bottle gourd, and pumpkin) and 0.5 m (cucumber,

bitter gourd) apart in the ditch. The pits/trenches are

filled with organic decomposed waste or oil cakes or

FYM or any other which is mix in the soil.

Seed rate, seed treatment and

sowing/transplanting time Seed rate varies according crops to be grown.

Sowing is usually done for early crop in 1st fortnight

of November and 1st week of December. 1st week of

January is the best time for late sowing. The seeds

are sown in trench at a distance of 45-60 cm and at a

depth of 3 to 4 cm. Two seeds are usually sown at

one place. Pre sprouted seeds are sown for smooth

germination when the temperature is very low. For

this, pre-soak the seeds for 24 hours and place the

moist seeds on a gunny bag and cover them with a

cotton cloth and keep them in a warm place for about

a week for sprouting to start. Sometimes the moistened seeds are wrapped in

gunny bags are left near the fire for quick

germination and in this way sprouting start after 5-6

days. As soon as sprouts emerged outside the seed

coat they are planted. Generally, 3-4 pre-germinated

seeds/hill area sown in pits.

Nutrient management Earlier manures/fertilizers were not used in diara

land cultivation but nowadays farmers are slightly

using fertilizers and manures for proper growth of

the crop. Since this cultivation practice is taken only for one season, so inorganic fertilizers and organic

manures are used in a limited quantity with extra

caution. Well decomposed FYM or compost, caster

cake or groundnut is applied first. To enhance

retentively of moisture in the feeding zone River silt

is generally used. Germinating seeds or growing

transplants are provided with warmth from the

organic manure. At the time of thinning 30-60 g urea

per pit will be useful. After 25-30 days of sowing,

depending on weather conditions and growth,

chemical fertilizers are top dressed in two split doses,

especially fertilizer mixtures or nitrogenous

fertilizers like urea. This top dressing is applied in shallow trenches away from the plants.

Water management The deep root system in cucurbits, enables the plant

to survive in diara land. Pitcher irrigation is given in

the initial stages of germination and growth till the

roots of the plants touch the water regime below the

sand or left as such. Trickle or sprinkler irrigation

system is quite advantageous to avoid leaching losses

of the nutrients in sandy soils.

Weed management Major weeds in diara land areas are Euphorbia hirta,

Polygonum sp., Eclipta prostrata, Fimbristlylis dichotoma, Sida sp. etc. These weeds can be

eradicated manually by pulling, since soil is quite

loosened due to excess sands. Herbicides should be

avoided completely as it may prove to be hazardous

to human, animal and fishes when mixed with

running river water.

Thatch preparation In north-west India, when the winter goes down 1-20

C in Dec-Jan, young plants should be protected from

low temperature and frost in their early stages. The

thatch screen made of locally available material like paddy straw, Saccharam grass or sugarcane leaves

provides protection for the young seedlings.

Grass is spread in the month of February over the

sand as a bedding and mulch, to protect the tender

and young plants and fruits from scorching of heat

sand during summer and also stops the vines to drift

during strong winds. Polyethylene cover as a method

for frost protection is still to be developed. This will

be affordable and will be available easily for

ordinary growers.

Cropping pattern Mixed cropping is usually practiced in riverbeds. Water melon and Musk melon generally go together.

Other cucurbits mainly grown together are summer

squash, bottle gourd, round melon, cucumber, sponge

gourd, bitter gourd, long melon in north India, ridge

gourd in Rajasthan, MP, and UP and pointed gourd

in Bihar.

Harvesting and yield

Harvesting should be done when fruits are quite

tender and edible. Fruits which attain edible maturity

should be harvested at 2-3 days interval, or else, the,

quality deteriorates and fruits are hardened due to seed maturity. By the end of June to end of October

harvesting at regular interval can be done. Table 2

contains the Potential yield of various vegetables.

Harvesting of fruits starts in Feb-March (off-season)

and gives early yield and higher return (Selvakumar,

2014). After harvest, crops are transported to local

market centers for sale.



Table 2. Crop duration and yield of cucurbitaceous vegetables in diara lands

S.No. Vegetables Seed rate

(kg/ha)

Planting Time Harvesting Time Average Yield

(q/ha)

1 Bottle gourd 3-4 Nov-Dec March-July 200-350

2 Bitter gourd 4-5.5 Feb-March May-July 100-150

3 Pointed gourd - Nov-Dec March-July 350-400

4 Ridge gourd 3.5-5 Apr-May June-July 100-200

5 Sponge gourd 2.5-3.5 Jan-Feb April-May 100-200

6 Cucumber 2.5-3.5 Jan-Feb March-June 225-250

Table 3. Important considerations for Cucurbits harvesting/marketing

Sl.

No

Crops Harvesting Test method Stage of fruit Remark

1 Cucumber 60-70 days after

sowing

Anthesis

duration

Tender green

fruit

Optimum length 20-25

cm (depending upon

variety/consumers

demand)

2 Bitter gourd 55-100 days after

sowing (depending upon variety)

Anthesis

duration

Tender green

fruit


cm

3 Pointed gourd 80-90 days after

transplanting

Anthesis

duration

Green fruits

having tender

seeds


cm

4 Ivy gourd Tender immature

fruits

Anthesis

duration

Green fruits

having tender

seeds


cm

5 Ash gourd 75-125 days after

sowing

Anthesis

duration

Full mature

stage

White wax deposition on

skin

6 Bottle gourd 60-100 days after

sowing,

12-15 days after fruit

setting

Pressing the

skin and little

pubescence

persisting on

the skin Nail

test

Light green

colour

Seed should be soft, if

examined in transverse

section

7 Luffa species 55-60 days after

sowing, 6-7 days after

anthesis

Anthesis

duration

Fruit should

not turn fibrous and

picking should

be done earlier

Picking at 4-5 days

interval

Table 4. Important Diseases and pests in diara land

Diseases

Sl.

No

Name of the

Disease

Causal organism Symptoms Control

1 Powdery mildew Sphaerotheca

fulignea

White fluffy, circular patches

on the under surface of leaves.

At later stages brown surface

with shrivelled leaves appear and lastly defoliation occurs.

Spray diathane M-45 an

early stage and repeated

2 to 3 times.

2 Fusarium wilt Fusarium

oxysporium s.sp.

nivarum

In young seedlings, cotyledons

droop and wither.

In older plants, leaves wilt

suddenly.

Soil dressing by captan

or hexocaptan or thiride

0.2 to 0.3% solution.


3 Anthracnose Colletotrichum

spp.

Reddish brown dry leaf spots

are formed which often

coalesce and cause shriveling

and death of leaf.

Water soaked and yellowish

lesions on petiole and stems.

Repeated spraying at 5 to

7 days interval with

dithane M-45 0.2% or

diathane Z-78 0.2%

solutions.

4 Viral disease Cucumber mosaic

virus, Water melon mosaic virus,

Tobacco mosaic

virus, Kakari

mosaic virus and

Luffa mosaic virus

The leaves show a molting,

mosaic, crinkling and twisting and stunted internodes and

flowering in adversely affected.

Take precaution in case

of mechanical transmission.

Use of insecticide.

Avoid seeds from the

virus affected plants.

Insects

The pests like aphids and red pumpkin beetle are

usually noted in early stages of crops. The fruit-fly

incidence is more in pointed gourd and bitter gourd

and mite infestation increases in arid situations, as

the day temperature rises above 40°C.

Mineral deficiencies Non-pathogenic diseases mostly caused by mineral

deficiencies are also prevalent in some situations.

This is a special problem in river-beds. Absence of

rich sub-soil, silt or alluvium beneath the sandy layer

and leaching of nutrients due to sandy substrate

sometimes cause deficiencies of macro and micro

nutrients.

Constraints Non availability of quality seed

Most of these fruits are produced by cross

pollination before selection of the fruit for seed extraction. That is the reason why the

fruits coming from river-beds are of

undependable quality, especially in sweetness

and flesh color of which urban consumers

often complains.

It has enabled the perpetuation of natural

variability and there has been a continuous

process of recombination and selection

involuntarily promoted by the farmers.

Non availability of land

Sometime the river-bed remains underwater for a longer period.

The vegetable growers are not the owner of

available land.

Due to heavy leaching of the soils, fertility is

very low.

River-bed cultivation practice does not fit in any

of the crop rotation, and cucurbits are especially

adopted for such type of cultivation.

The major share of benefit is usually taken by

the business man and middle man who purchase

the cucurbits vegetables in summer and sale it in

market. Thus, the small and marginal farmers who raised a good crop on river-bed or

practically on sand, is deprived of his major

share of profits.

CONCLUSION

Riverbed farming may increase farmers‟

vulnerability environmental shocks because riverbed

cultivation is low-environmental-impact, easy-to-

learn, cost effective technology allowing landless

households to produce unused marginal lands. This type of cultivation is best suited for the small farmers

and marginal farmers who can work themselves

along with their families in the fields, producing a

large number of cucurbits and other vegetables

economically. By utilizing an under exploited

resources and enhancing small holders productive

skills on marginal soils, it increases marginal

farmers‟ options for sustainable coping with the

effects of environmental shocks like floods. For this

the Indian Institute of Vegetable Research, Varanasi

has taken the responsibility and initiated a multi-institutional project “Evaluation of high yielding

varieties/hybrids of cucurbitaceous vegetables for

river bed (diara land) cultivation and standardization

of their agro-techniques” involving “Institute of

Agriculture, SHIATS, Allahabad” and “C. S. Azad

Univ. of Agriculture & Technology, Kalyanpur,

Kanpur”.

Future Thrust

There is urgent need for screening of the existing

varieties and advanced lines of cucurbitaceous

vegetables under riverbed condition. The Multiplication and distribution of seed of

such landraces are should be done by

Horticultural Research Institute/SAUs /Local

agricultural, horticultural departments in nearby

experiment station.

Standardization of riverbed technologies for

cucurbits so that scientific information regarding

varietal suitability and other input parameters of

river bed technology can be refined, and

documented.

REFERENCES

Chaudhary, P. and Chatterjee, R. (2009). “Off

season cultivation of Cucurbits in diara land”, A

livelihood Alternative.



Kumari, Reena, Sharma, Ankita, Bhagta, Shikha

and Kumar, Ramesh (2018). River Bed Cultivation:

A Kind of Vegetable Forcing for Remunerative Returns. Int.J.Curr.Microbiol.App.Sci. 7(04): 359-

365.

Pandey, S. and Karmakar, P. (2014). New

initiative- River bed cultivation of cucurbits,

vegetable newsletter, ICAR-IIVR, Varanasi-221305.

1(1): 2-3.

Pandey, S. and Karmakar, P. (2015). River bed

cultivation of cucurbitaceous vegetable crops, E-

manual on improved production technologies in

vegetable crops. DOE, Ministry of Agriculture &

Cooperation, Government of India, New Delhi,16th -

23rd January. 49- 54.

Patel, H.B., Saravaiya, S.N., Kumar, S. and Patel, A.I. (2016). Riverbed farming. Innovative Farming -

An International Journal of Agriculture 1(3): 106-

107.

Ramjan, Md, Kumar, V. and Chhetri, A. (2018).

Production technology of cucurbits in riverbeds.

Indian farmer. 5(4): 434-438. Selvakumar, R. (2014). A text book of Glaustus

Olericulture. New Vishal Publications. New Delhi.

Singh, P.K. (2012). “Cucurbits Cultivation under

Diara-Land”, Asian Journal of Agriculture and Rural

Development. 2(2): 248-252.

*Corresponding Author

________________________________________________ Journal of Plant Development Sciences Vol. 12(4): 195-205. 2020

CHANGES IN THE PHYSICO-CHEMICAL PROPERTIES OF SOIL IN

DIFFERENT DEODARFORESTS OF GARHWAL HIMALAYA

Gaurav Chand Ramola1, Digvijay Rathod

2, Yogesh Kumar*

3, Prajapati Dhaval

4, Akshit

Kukreti5 and V.P. Khanduri

6

1,2,4,5ForestResearch Institute, Dehradun(Uttarakhand)

6College of Forestry, VCSG, UUHF, RanichauriTehriGarhwal(Uttrakhand)

3Ministry of Environment Forest and Climate Change, New Delhi

Received-09.04.2020, Revised-28.04.2020 Abstracts: The present study was undertaken in different deodar temperate forest of Uttrakhand in Garhwal Himalaya, India. The aim of the study was to evaluate the changes in the physico-chemical properties of soils in different deodar forest of Garhwal Himalaya after 15 years as previous study was carried out in 2000 in the same studied sites by Bhatt et al. The

changes in physico-chemical properties of soil were assessed by laying out five 0.1 ha sample plots by recognizing GPS location of the earlier study on each location. The composite soil samples were collected from each sample plot at three different soil depths (0-10 cm, 11-20 cm and 21-30 cm). The standard method was used to analyze the soil sample. To study the Physico-chemical properties of soil various parameter viz. Soil organic carbon %, available phosphorus, available potassium, pH and moisture content % was analyzed. The outcome of the study revealed that the values of soil organic carbon %, available phosphorus, available potassium, pH and moisture content % ranged between 0.24% to 0.68 %, 7.76 to 64.21 kg/ha, 63.5 kg/ha to 406.6 kg/ha, 5.07 to 5.87, 14.72 % to 41.99 % respectively. In the present re-visitation study, the huge changes was seen in the physico-chemical properties of soil mainly in Organic Carbon %, soil pH and moisture content

% as they all decreases due steep topographic condition, slow decomposition rate whereas there was increase in the available Phosphorus. These changes are more likely attributable to the combined effect of growth and use of soil nutrients by the trees in respective sites.

Keywords: Decomposition, Deodar, Garhwal Himalaya, Nutrient changes, Physico-chemical properties

INTRODUCTION

oil is a complex system wherein living soil

organisms belonging to different taxonomic

groups interact at different levels within the

community and plays a significant role in the

maintenance of soil properties (Garbeva et al., 2004, Van et al., 2002). Soil microorganisms constitute a

source and sink for nutrients and are involved in

decomposition of wood, litter, organic matter,

generating organic C, N and energy from these

organic substrates (Ganjegunte et al., 2004, Lindahl

et al., 2007).

Soils in the Himalayan region are very well suited for

high productivity and sustainability. But due to

increased anthropogenic activities like rapid

urbanization and infrastructure development in the

naturally delicate ecosystem with unstable geology,

steep slopes and heavy rains had hastened the degradation process of fertile soil in the Himalayan

region. Many studies confined to agricultural soils

have been performed to determine the ecological and

environmental factors regulating microbial

community structure (Baek and Kim, 2009, Grayston

et al., 2001, Hogberg et al., 2007). Besides, the soil

and vegetation have a complex interrelation because

they develop together over a long period. Soil

analysis shows the forest types and plant density of

any area because the different species of plants need

different types of soils. The selective absorption of nutrient elements by different plant species and their

capacity to return them to the soil brings about

changes in soil properties (Singh et al., 1986).The

properties of the soil are an important factor for the

growth of the plants. Some of these properties

including the percentage of nitrogen, phosphorus,

potassium, soil acidity, soil salinity, and pH affect

vegetation cover in an ecosystem (Zarinkafsh, 1987). Bhatt in 2000 studied five different forests of deodar

at five sites namely Ghimtoli, Dhanolti, Dewarkhal,

Devidhar and Jhandidhar of Garhwal Himalaya to

study the physico-chemical properties of soil. The

present study was also conducted on the same sites

mentioned above by recognizing the GPS location of

the earlier study as the re-visitation study, which was

aimed to understand the changes in the various

physico-chemical characteristics of soil of Cedrus

deodara forests of Garhwal Himalayas over 15 years.

MATERIALS AND METHODS

Five forest stands of Cedrusdeodara in different

parts of Garhwal Himalayas (Lat.290 26‟ to 310 28‟ N

and Longi.770 49‟ to 800 06‟ E) were recognized with

the help of GPS coordinates for identifying the

changes in the physico-chemical properties of soil in

a re-visitation study over 15 years (Fig.1). The earlier

study was made by Bhatt et al. (2000) at these

studied sites. The same sites in the present study

were located with the help of geographic information

as presented in Table 1.

S

RESEARCH ARTICLE

196 GAURAV CHAND RAMOLA, DIGVIJAY RATHOD, YOGESH KUMAR, PRAJAPATI DHAVAL, AKSHIT

KUKRETI AND V.P. KHANDURI

Table 1. The geographic information of different studied sites

S.No Locality /District Alt (m) Longitude Latitude

1 Ghimtoli / Rudraprayag 2300 780 15‟ 300 „23

2 Dhanolti / TehriGarhwal 2200 780 52‟ 300 23‟

3 Dewarkhal / Uttarkashi 2300 780 26‟ 390 44‟

4 Devidhar / Rudraprayag 1900 780 15 290 25‟

5 Jhandidhar / Pauri 2000 780 46‟ 300 8‟

METHODOLOGY

To investigate the changes in the physico-chemical

properties of soil in Cedrus deodara forests, five

sample plots of 0.1 ha were laid out on each location.

Thus a total of 25 sample plots were laid out in all

the five locations.

Soil Analysis

Composite soil samples (four samples from four

corners and one from the center of the sample plot)

were taken and later on were mixed-depth-wise like 0-10 cm soil of one corner of sample plot mixed with

0-10 cm soil of other four corners of the same

sample plot and like that other soil samples were

mixed depth-wise). The samples were collected from

three different depths viz., (i) upper (0–10 cm), (ii)

middle (11–20 cm) and (iii) lower (21–30 cm) for

assessing the physical and chemical properties of soil

in all the five selected forest of deodar. The total

number of composite soil samples from the single

site was 15 and total composite soil samples among

all the five sites were 75.

The samples were brought to the laboratory in tightly

closed polythene bags and fresh weight of each

composite sample was recorded. The samples were

air-dried, grinned and passed through 2 mm sieve for

pH, soil moisture content %, organic carbon content, available phosphorus, and exchangeable potassium

by technique shown in Table 2. The soil analysis was

done in the soil science laboratory of New Tehri,

Tehri Garhwal, Uttrakhand.

Table 2. Physico-chemical properties

S.

No. Component Method adopted References

1 Soil pH Standard paste technique using Ec and

pH meters

Rhoades,1982

2 Organic Carbon % Potassium dichromate reduction of

organic carbon and spectrophotometric

measurements

Walkey and Black, 1982

3 Available

phosphorus

Olsen‟s method, Colorimetry Olsen et al. (1954)

4 Available

potassium

Neutral normal NH4OAc, Flame

photometry

Stanford and English (1949)

5 Moisture Content

%

(Fresh weight-Dry Weight) ×100

Dry weight

Misra, 1968.

Calculation of Correlation coefficient Correlation coefficient between Growing stock

versus Organic carbon, Available phosphorus and Available potassium were calculated as per Freese

(1967).

RESULTS AND DISCUSSION

Changes in Physico-chemical properties of soils

under different C. deodara forests of Garhwal

Himalaya:

Changes in Soil Organic Carbonpercentage: The comparison of present soil organic percentage

reported by Bhatt, reveals that over these 15 years, organic carbon percentage decreased in all the

studied sites because of poor understory vegetation

among all the sites which leads to the slow

decomposition of organic matter. A decrease in

organic carbon percentage may also due to slower

microbial activities among all the sites. According to

Bhatt, the average organic carbon % was 1.64 %

(Ghimtoli), 1.47% (Dhanolti), 1.56% (Dewarkhal),


2.07 % (Devidhar) and 1.18 % (Jhandidhar), whereas

after 15 years in the same studied sites it was 0.57 %

(Ghimtoli),0.62% (Dhanolti), 0.24% (Dewarkhal),

0.68% (Devidhar) and 0.58% (Jhandidhar).(Table

no.3 and Fig.1).Sharma et al., (2012) while working

on the soil chemical properties in relation to forest composition in moist temperate valley slopes of

Garhwal Himalaya observed 2.29 % organic carbon

in mixed Abiespindrow forest and 4.31 %organic

carbon in Aesculus indica forest typessimilarly.

Digvijay et al., (2015) has worked on biomass and

carbon stocks in different deodar forests of Garhwal

Himalaya. In this study they ranged soil organic

carbon percent in all the sites from 1.42 to 1.70 %, 1.29 to 1.67 % and 1.41 to 1.61 % for 0-15 cm, 16-30

cm and 31- 45 cm soil depth respectively.

Fig. 1. Changes in soil organic carbon percentage over 15 years

The highest soil organic carbon % was obtained

under Devidhar forest and lowest under Dewarkhal

forest. The soil enrichment with soil organic carbon

content in Devidhar could be due to the addition of

litter, thick humus layer and minimum soil organic

carbon % were recorded in Dewarkhal due to poor

understory vegetation and heavy rainfalls recorded in

Uttarkashi region as compared to Rudraprayag

district. This might have a key factor contributing to

decreasing soil organic carbon content, which may

erode the soil surface and removed organic matter-

rich fine sediments from the soil surface.

Table 3. Changes in the soil organic carbon percentage

S.NO LOCALITY SOIL ORGANIC CARBON % IN

2000

SOIL ORGANIC CARBON % IN

2016

RANGE X* SE* RANGE X* SE*

1 GHIMTOLI

A

B

C

1.01-2.25

0.91-

1.86

1.35-

2.10

1.81 1.39

1.73

0.21 0.61

1.73

0.03-1.40 0.03-1.17

0.02-1.31

0.66 0.35

0.71

0.29 0.31

0.28

Average 1.64 0.13 0.57 0.26

2 DHANOLTI

A

B

C

0.62-

2.27

0.49-

2.21

0.41-

2.69

1.35

1.32

1.81

0.31

0.31

0.48

0.27-1.05

0.33-0.90

0.30-1.05

0.21

0.26

0.25

0.06

0.01

0.07

Average 1.47 0.18 0.62 0.12

1.641.46

1.56

2.07

1.18

0.57 0.62

0.24

0.680.58

0

0.5

1

1.5

2

2.5

OR

GA

NIC

CA

RB

ON

%

SITES

2002

2016

2000



3 DEWARKHAL

A

B

C

1.07-

2.02

0.61-2.02

1.07-

2.21

1.61

1.35

1.73

0.22

0.25

0.22

0.12-0.44

0.21-0.32

0.05-0.45

0.21

0.26

0.25

0.06

0.01

0.07

Average 1.56 0.18 0.24 0.04

4 DEVIDHAR

A

B

C

0.76-

6.70 0.33-

6.21

0.25-

1.28

3.42

0.66 0.76

1.35

0.20 0.18

0.02-1.44

0.08-1.32 0.02-1.35

0.83

0.43 0.78

0.32

0.23 0.30

Average 2.07 0.90 0.68 0.28

5. JHANDIDHAR

A

B

C

0.49-2.05

0.58-

1.61

0.61-

1.54

1.16 1.09

0.85

0.25 0.19

0.18

0.03-1.35 0.06-1.35

0.20-1.35

0.58 0.80

0.37

0.20 0.29

0.25

Average 1.18 0.22 0.58 0.24

*X=MEAN,SE= STANDARD ERROR

Changes in Soil pH

The present study results showed in Fig.2 , according

to Bhatt, the average soil pH of the same studied sites

was 6.37 (Ghimtoli), 6.43 (Dhanolti), 6.45

(Dewarkhal), 6.16 (Devidhar), and 6.12 (Jhandidhar).

However, after 15 years the soil pH decreased and in

the present study, it was 5.82 (Ghimtoli), 5.07

(Dhanolti), 5.87 (Dewarkhal), 5.64 (Devidhar) and

5.55 (Jhandidhar). The decrease in soil pH over a

period of 15 years clearly depicting that the nature of

conifer forests became acidic in due course of time as a result of podzolozation. (Table no. 4). Gairola et

al., (2012) studied the Conifer mixed broadleaf forest

and Abiespindrow forest in Mandal – chopta,

Chamoli Garhwal region and Khera et al. (2001) for

Quercusleucotrichophora and Q. floribunda forest in

Uttarkashi Garhwal region have also reported acidic

pH values i.e. 5.47 and 5.20. This may be due to

higher organic matter content and protected nature of

forest. Tiwari et al., (2013) studied the Physico-

chemical properties of soils in cool-temperate forests

of the “Nanda Devi Biosphere Reserve” in

Uttarakhand (India). Their study revealed that the

parent material of the study area represents

crystalline rocks and comprises of garnetiferous mica, schists, garnet mica and mica quartzite. The

soil was found acidic in nature, which ranged from

5.09 ± 0.06 to 6.46 ± 0.05 for 0 to 45 cm depth.

Fig. 2. Changes in soil ph over 15 years

6.37 6.43 6.45 6.16 6.12

5.825.07

5.87 5.64 5.55

0

1

2

3

4

5

6

7

SO

IL p

H

SITES

2002

2016

2000


Table 4. Changes in the soil ph

S.NO LOCALITY SOIL pH IN 2000 SOIL pH IN 2016


1 GHIMTOLI

A

B

C

6.2-6.6

6.3-6.5 6.2-6.6

6.38

6.38 6.36

0.07

0.04 0.07

5.30-6.50

5.30-6.80 5.10-5.80

5.88

6.14 5.44

0.19

0.31 0.15

Average 6.37 0.01 5.82 0.21

2 DHANOLTI

A

B

C

6.4-6.5

6.3-6.5

6.4-6.6

6.42

6.42

6.46

0.02

0.02

0.04

4.60-5.30

4.70-5.50

4.60-5.50

4.96

5.14

5.12

0.13

0.16

0.18

Average 6.43 0.01 5.07 0.15

3 DEWARKHAL

A

B

C

6.4-6.5

6.3-6.5

6.4-6.6

6.44

6.44

6.48

0.02

0.04

0.05

5.90-6.62

5.50-6.62

4.90-6.30

6.04

5.80

5.78

0.05

0.15

0.24

Average 6.45 0.01 5.87 0.14

4 DEVIDHAR

A

B

C

5.9-6.2 6.0-6.3

6.1-6.4

6.12 6.12

6.24

0.06 0.06

0.05

4.80-7.30 5.30-6.10

5.50-6.00

5.68 5.50

5.74

0.42 0.15

0.09

Average 6.16 0.04 5.64 0.22

5. JHANDIDHAR

A

B

C

6.0-6.2

5.9-6.1

6.0-6.5

6.12

6.02

6.22

0.04

0.04

0.09

5.30-6.80

4.90-5.80

5.20-5.80

5.92

5.28

5.24

0.27

0.15

0.06

Average 6.12 0.06 5.55 0.16

* X= MEAN, SE= STANDARD ERROR

Table 5. Changes in the soil available phosphorus

S.NO LOCALITY AVAILABLE PHOSPHORUS

(kg/ha) IN 2000

AVAILABLE PHOSPHORUS

(kg/ha) IN 2016


1 GHIMTOLI

A

B

C

3.95-14.78

3.25-7.88

3.45-11.23

6.59

6.34

7.17

2.07

0.90

2.24

8.96-22.4

8.96-26.88

8.93-17.92

15.23

15.23

15.23

2.28

3.35

1.79

Average 6.70 0.25 15.23 4.49

2 DHANOLTI

A

B

C

13.34-

22.65

13.74-22.65

16.75-

31.52

17.84

19.08

22.43

1.52

1.78

3.18

31.36-

62.72

31.36-67.2 40.32-

76.12

51.07

51.96

49.27

6.43

5.94

6.78

Average 19.75 1.34 50.77 6.38

3 DEWARKHAL

A

B

C

10.44-

13.99

9.46-13.99

4.21-13.99

12.33

10.95

9.86

0.63

0.79

1.59

44.8-85.12

67.2-76.12

49.28-

53.12

71.68

68.18

52.86

7.88

2.60

0.89

Average 11.05 0.71 64.21 3.79



4 DEVIDHAR

A

B

C

4.83-10.84

6.00-14.78

7.09-9.26

7.78

9.96

8.36

1.12

9.96

8.36

4.28-22.4

4.48-26.28

4.48-22.04

14.19

18.91

9.85

2.93

4.10

3.29

Average 8.70 0.65 14.28 3.44

5. JHANDIDHAR

A

B

C

6.59-9.85

7.49-11.23

4.83-8.07

8.40

9.30

6.54

0.63

0.69

0.61

4.48-17.92

4.48-13.44

4.48-8.96

9.85

8.06

5.37

2.61

1.67

0.89

Average 8.08 0.81 7.76 1.72

*X= MEAN, SE= STANDARD ERROR

Table 6. Changes in the soil available potassium

S.NO LOCALITY AVAILABLE POTASSIUM

(kg/ha) IN 2000

AVAILABLE POTASSIUM (kg/ha)

IN 2016


1 GHIMTOLI

A

B

C

116-224

128-172

120-148

167.20

164.40

133.20

22.07

10.48

4.72

54-117

5-135

45.5-153

84.8

78.2

93.7

11.11

26.99

18.72

Average 154.33 10.90 85.57 18.04

2 DHANOLTI

A

B

C

200-320

160-320 148-324

251.20

237.80 230.40

23.81

28.07 30.58

315-396

342-423 333-405

360.8

370.8 354.6

13.16

13.76 13.52

Average 239.80 6.08 362.4 13.48

3 DEWARKHAL

A

B

C

312-452

312-440

312-440

369.60

363.20

384.00

26

25.12

29.26

356-466

324-432

356-423

430.4

392.4

397

20.09

19.42

11.50

Average 372.27 6.15 406.6 17

4 DEVIDHAR

A

B

C

136-288

168-300

132-352

204

215.20

177.80

29.23

24.18

19.87

41-144

27-72

27-90

73.2

48.8

68.5

18.42

8.32

11.53

Average 199 11.08 63.5 12.85

5. JHANDIDHAR

A

B

C

112-342

192-312

124-428

220.40

201.60

276.00

43.21

31.84

64.50

99-153

41-113

41-153

118

77

92

8.96

12.40

18.60

Average 232.67 22.34 95.67 13.66

* X= MEAN, SE= STANDARD ERROR

Table 7. Changes in the moisture content percentage

S.NO LOCALITY MOISTURE % IN 2000 MOISTURE % IN 2016


1 GHIMTOLI

A

B

C

10.0-31.25

10.0-

31.25

7.96-

27.78

19.25 20.59

15.33

3.68 3.87

3.41

18.68-42.2

20.62-

57.92

23.51-

42.4

33.18 34.08

39.9

4.13 6.15

7.96

Average 18.39 1.58 35.69 6.08


2 DHANOLTI

A

B

C

42.86-

83.33

33.33-

75.00

25.00-

66.67

62..86

62.18

43.67

7.32

16.17

8.24

12.94-

84.97

24.71-

44.52

37.47-

54.08

44.59

36.14

45.15

11.52

3.64

3.34

Average 56.24 6.28 41.99 6.16

3 DEWARKHAL

A

B

C

15.00-

56.25

20.00-

71.42

7.14-

33.33

28.25

32.17

18.84

7.24

9.84

4.72

12.94-

21.5

9.91-

57.92

16.41-

42.16

16.83

29.40

27.29

1.54

7.87

4.50

Average 26.42 3.95 24.48 4.63

4 DEVIDHAR

A

B

C

7.96-

40.00

9.09-

25.00

11.11-27.27

17.86

15.68

19.18

5.91

3.03

3.24

8.95-

25.25

4.73-

23.82

5.06-14.07

16.55

16.36

11.25

3.34

7.75

2.07

Average 17.57 1.02 14.72 4.38

5. JHANDIDHAR

A

B

C

10.00-

33.33

9.09-

28.83

8.33-

16.67

24.11

17.14

12.28

4.26

3.51

1.67

7.36-23.3

2.56-

51.56

6.95-

18.39

16.71

21.68

13.85

2.74

8.33

2.25

Average 18.01 3.30 17.41 4.44

*X= MEAN, SE= STANDARD ERROR

Present study reveals that soils were slightly acidic to

buffer in nature on all the 5 sites and the pH values

of these soils ranged from 5.07 to 5.87. It has been

reported that forest soils should be slightly acidic for

nutrient supply to be balanced (Leskiw 1998).

Changes in Available Phosphorus

Bhatt revealed that the maximum average

phosphorus (19.75±1.34 kg/ha) was recorded in the

soils of site-2 (Dhanolti), followed by Dewarkhal

(11.05 kg/ha), Devidhar (8.70 kg/ha), Jhandidhar

(8.08 kg/ha) and minimum average (6.70 kg/h) was

recorded in site-1 (Ghimtoli). After 15 years in a re-

visitation study results showed in figure. 3, the

available average phosphorus was increased in four

similar sites as 15.23 kg/ha in Ghimtoli, followed by

50.77 kg/ha in Dhanolti, 64.21 kg/ha in Dewarkhal,

14.28 kg/ha in Devidhar. However, in the Jhandidhar

site, it declined (7.76 kg/ha) as compared to that of

the previous study of Bhatt in 2000. (Table no. 5).

Bhatt et al. (2014) have studied the physico-chemical

properties of the soil in Central Himalaya and

observed that the available phosphorus is varied

between 16.12 kg/ha in the Oak-mixed conifer forest

and 35.15 Kg/ ha in Pine-Oak forest.



Fig. 3. Changes in phosphorus (kg/ha.) over 15 years

Shrestha (1979) has reported the available

Phosphorus range from 1.03 to 71.15 kg/ha in the

specified part of the Godavari hill forest area, Kathmandu. However, Baral (1983) have reported

the available Phosphorus range 44.66 to 90.66 kg/ha

from the same sites.

In the present study, the minimum average

phosphorus (7.76 kg/ha) was recorded in the soils of

site-5 (Jhandidhar) whereas the maximum average

phosphorus (64.21 kg/ha) was recorded in the soils of

site-3 (Dewarkhal). The values of available

Phosphorus in the present study are much higher than

those recorded by some other investigators in similar

and other parts of the Garhwal region of Central Himalaya (Bhatt et al., 2000, Digvijay et al., 2015).

It may be because in low pH, the Phosphorus reacts

with Iron, Aluminium, Calcium and other minerals to

form Iron phosphate, Aluminium phosphate and

Calcium phosphate, which is unavailable to the

plants in higher amount. The Phosphorus was also

found higher in the lower horizons of all the forest

types, which may be due to the leaching properties of

the soils.

Changes in Available Potassium

According to Bhatt, the mean maximum (372.27

kg/ha) potassium was recorded in the soils of site-3

(Dewarkhal) followed by (239.80 kg/ha) Dhanolti,

(232.67 kg/ha) Jhandidhar, (199.00 kg/ha) Devidhar

and (154.93 kg/ha) in Ghimtoli. After 15 years in a re-visitation studyindicated by figure. 4, it is evident

that average potassium increased in Dhanolti (362.4

kg/ha) and Dewarkhal site (406.6 kg/ha), whereas it

decreases in Ghimtoli (85.57 kg/ha), Devidhar (63.5

kg/ha) and Jhandidhar site (95.67 kg/ha).These

changes are more likely attributable to the combined

effect of growth and the use of soil nutrients by the

trees in respective sites. (Table no. 6). Digvijay et

al.,(2015) has worked on biomass and carbon stocks

in different deodar forests of Garhwal Himalaya. In

this study, they ranged soil potassium in all the sites from 84.56 kg/ha to 243.4/ha.

Kaushal et al., (1997) have observed the available K

status of the dry temperate zone of Cedrus deodara

in surface soil from 188.4 - 860.0 kg ha-1 and sub-

surface soils from 67.2 - 710.0 kg ha-1 respectively.

In the present study, the mean maximum (406 kg/ha)

potassium was recorded at site-3 (Dewarkhal) and

mean minimum (63.5 kg/ha) was recorded at site-4

(Devidhar).The reduction in availability of potassium

is due to leaching and drainage, which results in the

destruction of vegetation (Basumatary and Bordoloi,

1992).

6.7

19.75

11.05

8.7

8.0815.23

50.77

64.21

14.28

7.760

10

20

30

40

50

60

70

PH

OS

PH

OR

US

(K

G/H

A)

SITES

2002

2016

2000


Fig. 4. Changes in available potassium (kg/ha) over 15 year

Changes in Moisture content Bhatt has also recorded the average moisture content

percentage on same sites i.e. Ghimtoli (18.39±1.58

%), Dhanolti (56.24±6.28), Dewarkhal (26.42±3.95

%), Devidhar (17.57±1.02 %) and Jhandidhar

(18.01±3.30 %) which was within the moderate

range of its availability.

After 15 years, the average moisture content

percentage showed in figure 5. at Ghimtoli

(35.69±6.08) increased, whereas it decreased in other sites like Dhanolti (41.99±6.16), Dewarkhal

(24.48±4.63), Devidhar (14.72±4.48) and Jhandidhar

(17.41±4.44) which may be due to less utilization of

water by plants or may be due to less transpiration

rate. The highest average moisture content (41.99 %)

was recorded on site-2 (Dhanolti) because of more

atmospheric precipitation on this site (Table-no.10).

Fig. 5. Changes in moisture content of soil (%) over 15 years

Bhatt et al., (2014) studied the Analysis of the

physico-chemical properties of the soil and climatic

attribute on vegetation in Central Himalaya in which

forest types were Pine- Oak forest, mixed oak-

conifer forest, mixed broadleaved conifer forest,

conifer forest. According to him the average

moisture content in these forests were 18.8% in pine-

oak forest, 24.66 % in mixed oak-conifer forest,

154.93239.8

372.27

199232.67

85.87

362.40

406.60

63.5095.67

0

50

100

150

200

250

300

350

400

450P

OT

AS

SIU

M (

KG

/HA

)

SITES

2002

2016

18.39

56.24

26.42

17.57 18.01

35.6941.99

24.48

14.72 17.41

0

10

20

30

40

50

60

MO

IST

UR

E C

ON

TE

NT

%

SITES

2002

2016

2000



26.33 % in mixed broadleaved conifer forest and

20.5 % in a conifer forest.

The average moisture contents on three study sites i.e., Dewarkhal (24.48 %), Devidhar (14.72 %) and

Jhandidhar (17.41 %) although found within the

moderate range of its availability which was

significantly higher (average 35.59 % and 41.99 %)

on site-1 (Ghimtoli) and site-2 (Dhanolti). An

increase in the retention of soil moisture through the

incorporation of humus has been reported by Biswas

and Ali (1969).

CONCLUSION

The present study revealed important results over a

long period in changesof physicochemical properties.

It will help all the scientific community, researches

and forester to understand the changes in soil over a

long period. The present study will be a key factor to

manage and maintain for the deteriorating plant

growing media to improving its fertility and

productivity per hectare.

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Beer, C.,Lucht, W., Schmullius, C. and

Shvidenko, A.(2006).Small Net Carbon Dioxide

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1999.Geophysical Research Letters, 33, Article ID:

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Bhatt, A., Sharma, C.M. and Khanduri, V.P.

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University.





COLLECTION OF MEDICINAL PLANTS IN TRADITIONAL AND MODERN

PERSPECTIVE

Vinay M. Raole*1 and Vaidehi V. Raole

2

1Department of Botany, Faculty of Science, The Maharaja Sayajirao University of Baroda,

Vadodara, Gujarat, India

2Department of Sharir Kriya, Parul Institute of Ayurveda, Parul University, Limda,

Waghodia Vadodara

Email: [email protected]; [email protected]


Abstract: In the recent past surveys of medicinal plants and plant products all over the globe is increased. Use of medicinal plants and its products is going on since the beginning of human civilization. Traditional knowledge is very important for sustainability of natural wealth including medicinal plants. Medicinal plants form the major natural resources base of the Indian indigenous health care tradition. Conservation of these plants can be learnt from specific local knowledge and transmission of facts, skills and strategies, concern for well-being of future generations. Due to global popularity of Ayurveda

there is enhanced demand of herbal drugs which is exerting enormous pressure on natural assets. Healing plants form the major natural resources base of the Indian indigenous health care system. In the medicinal plants, the secondary metabolites or active principle are made available through biosynthetic pathway and proper harvesting techniques. The science behind ancient Ayurvedic harvesting techniques was narrated in various earlier treaties and commentaries. To achieve good therapeutic result it is mandatory to collect the drug plants in a modern collection procedure and is also proven by modern scientific methods. In Ayurvedic literature, drug collection has been mentioned according to different parts of the plant in respective seasons and basis of therapeutic uses. According to modern botanical and pharmaceutical science, drugs possess highest prospects during its collection period. The soil condition, climatic factors, temperature, rain fall, duration of light

exposure, altitude, collection from wild area, and methods of collection, processing and storage have impact on the secondary metabolites of the plant ultimately which affect the therapeutic efficiency of the drug. General guidelines for drug plants, plant parts as per botanical field collection, safety issues and recommendations for collection practices, and future scope of procedure has been given.

Keywords: Ayurvedic, Medicinal plants, Modern, Traditional

INTRODUCTION

an since time immortal, has constantly

struggled to achieve mastery over the very

many forces of the nature, and plants has all the time

helped us to reach the goal. Nature provided man with

all the basic requirements for his existence which is

one of the reasons why nature was respected in the form of mother. The early civilizations have always

valued nature and nature worship was common in

those times. After coming in contact with plants,

people began to realize their significance and how

they could be of great benefit to them. There are also

some evidences relating to nature worship in the

Indian subcontinent around 3000 BC as well as

admiration for nature as a source of medicine.

Conventional therapeutic systems of India employ

large numbers of plant species viz., Ayurveda (2000

spp.), Siddha (1121 spp.), Unani (751 spp.) and Tibetan (337 spp.) (Anonymous, 2004 a, b).

Aforesaid long-established medical systems are

generally based on the exploitation of available

natural and local products which are commonly

related to the people's perspective on the world and

life (Toledo et al., 2009).

India as a nation has different traditional culture,

distributed throughout, and follows different custom

and systems of medicine. It also harbours a rich

variety of floral and faunal diversity. The peninsular

region is well known for rich Ethnobotanical wealth

in traditional knowledge particularly medicinal plants

since ancient time. Furthermore, it might have started

with ancient myths, lores and beliefs in addition to

other occult practices and developed into folk

medicine which advanced into herbal medicinal

practices viz. Ayurveda and siddha etc. Ancient Unani manuscripts, Egyptian papyrus and Chinese writings

have also described the use of herbs in their medicinal

texts. After prolonged time, these traditional systems

of green medicine are coming back to centre stage of

our health care and hygiene (Balasubramanian, 2000).

Moreover, demand for these medicines and systems

have started gaining the respectability among the

scientific society all over the globe despite the

development of synthetic drugs, and demand for plant

based medicines is growing. The main rationale for

this growing drift is increasing public concerns about the adverse effects of synthetic medicines. Traditional

medicine and Ethnobotany are two important subjects

that should be noted to achieve effective herbal

medicines with considerable therapeutic effects.

Conventional medicine is based on experience of

citizens over centuries and ethno-botany is based on

recognition of the indigenous plants prevailing in the

vicinity of the human habitat. Natural products have

been the backbone of traditional system of healing

M

RESEARCH ARTICLE



208 VINAY M. RAOLE AND VAIDEHI V. RAOLE

throughout the globe, and have also been an integral

part of history and culture ((Shankar et al., 2000).

Ayurveda is more than the experience with the nature

as it is nothing but the knowledge of life. Ayurveda

(drug school) a supplement of Atharvaveda (charm

school) which we can notice an admixture of drug school and charm school is used in concept of drug.

And, the drug concept in Ayurveda is totally different

from modern school of medicine. As modern

medicine heavily based on principles of physical

sciences only, while Ayurveda includes physical,

living and conscious phenomenon also. The term

drug is derived from the French word „drogue‟ i.e.

dry/dried herb. It is defined as „any substance or

product used to modify and/or explore the

physiological systems or pathological states for the

benefit of the recipient‟. In terms of Ayurveda drug is

a ‘bhesaja’ or ‘aushadha’ which will overcome the disease or fear of disease. Although, in Ayurveda 3

types of drugs Audbhida, Jangama and Parthiva

derived from plants, animals and minerals includes

salts respectively (Sinha, 1984; Majumdar, 1989).

The link between available phytodiversity and

medicine was appreciated early in India. Caraka

Samhita, mentions that the remedies for the diseases

prevalent in a given region can be found in the herbs

growing naturally in that region. Folk medicines are

widely practiced for primary healthcare, underlying

factors such as economy, education, religion, culture, and environment. The local society and herbalist

primarily use different Barks, Roots, Rhizomes,

Leaves, Flowers, Fruits, Seeds, Herbs or other

common items available in and around their

homestead, collected from their vicinity and even

from remote hills/forests, as well as grown through

cultivation (Sivarajan and Balalchandran, 1999). In

some cases, they also perform rituals based on faiths,

and recite holy verses (mantras). Three factors which

legitimize the role of the folk healers include: their

own beliefs, the beliefs of the community and the

success of their actions. Nearly half of rural community members present in this universe have

superstitions and strong beliefs on herbs and

approximately 15%- 25% treats simple ailments with

herbs. They mostly use different plant as a whole in

diverse forms and simple extracts or polyherbal

formulation for different diseases for cure. These

systems are still in place today because of their

organizational strengths, and they focus primarily on

multicomponent mixtures (Bannerman et al, 1983).

India, seventh largest country of the world covering

total area of 32,87,263 square Kilometers, ranks 6 among the 12 mega biodiversity centres of the world

and home for 4 main hotspots. The Indian homeland

is divided into 10 bio-geographical zones and 25

provinces and also has 15 agroclimatic zones and

17,000–18,000 species of flowering plants of which

6000–7000 are estimated to have medicinal usage in

folk and documented systems of medicines. Our

nation has a wide range of medical experiences due to

historical precedent and diversity of race, religion,

ethnicity, language, and climate. The herbal

medicines are considered to be of great importance

especially, for tribal populations and residents settled

in the treacherous part of the land. On the other hand,

climatic and environmental variability between regions leads to growth of different types of plant

species rather large diversity. In recent years owing to

habitat destruction and indefensible harvesting of

natural phyto-resources including medicinal plants

pose a serious threat not only to biodiversity but,

even to native medicinal resources in India because of

progressively loss of forest cover in every region

(Mukherjee et al. 2013)

METHODOLOGY

Ayurveda classical texts and their commentaries,

various available compendia and translations,

lexicons, literature on collection and cultivation

methods as well as on modern plant inventory

methods have been referred. Some of the harvesting

practices and research publications related to

documentation and inventorization of forest wealth

for research data also taken into account. Main

emphasis has been about these collection techniques

and its utility for collecting medicinal plants are

considered while compiling the information

(Anonymous 1948-1976; Anonymous 2005).

Plant Diversity and Ayurvedic Medicinal Plant

Collection

Plants are used as a medicines in the treatment of

various diseases in civilised society since times gone

by. Most of the medicinal plant species used in trade

is always carrying on to be sourced from natural

forest and most of these are harvested by destructive

means leading to rapidly decreasing in the vicinity.

Ongoing dilapidation of ecosystem through ruthless

exploitation of natural resources and inconsistent

collection practices, collection of the drug plants with

high therapeutic activity is declining. This is quite

true with respect to medicinal plants whose roots are

collected and used for very many herbal formulations.

The naturally synthesised phytocompounds are

having better patient tolerance and acceptance.

Approximately 30,000-70,000 plant species all

around the globe have been screened for their

medicinal properties and used by aboriginals. Plants

have provided us with some of our most effective

drugs, including aspirin, made from willow bark.

Moreover, at least 7,000 medical compounds in the

modern pharmacopoeia are derived from plants. And,

these plants in particular those with ethno

pharmacological uses have been the major starting

place of medicine in the early drug discovery.

Fabricant and Farnsworth, (2001) reported that, 80%


of 122 plant derived drugs were related to their

original ethno pharmacological purposes used by

indigenous people. Current drug discovery from

plants mainly relied on bioactivity guided principle as

well as fractionation and led to isolation of many

important anticancer drugs. Nearly two thirds of

traditional medicinal plants are as effective as medical

drugs but it is still difficult to get proper sound advice

due to variability of quality of these drugs as an

individual or in polyherbal formulations. Medical

pluralism has led to an intrinsic feature of its medical

system in historical and contemporary contexts.

Acharya Charaka described the technical excellence

in the field of medicinal plants pharmacognostical,

pharmaceutical and therapeutic sciences as

“Tasyapium pariksha idam evam Prakruti” etc. Here

“evam rutu” the season for collection of drug plays an

important role in the field of drug research. Shankar et

al., (2000) explained about both siddha and unani

systems were enriched by and in turn contributed to

the enrichment of Ayurveda. While the Vedic texts

written between c. 1500-1000 B.C. list only 289

medicinal plants, this number increased to around 650

in Caraka and Susruta Samhitas composed around

500 A.D., and further to about 1800 in various

„Nighantus‟ (ayurveda texts) that were compiled

between 500 and 1900 A.D. (Shankar et al., 2000). In

Ayurvedic classics, Charak samhita, sushrut samhita,

bhavaprakasha, Raj nighantu etc. drug collection has

been described in four major steps i.e. (( Table 1 & 2)

Bhumi pariksha (Selection of land), Medicinal plant

materials should be collected from the appropriate

place, and during the appropriate season or time

period to ensure the best possible quality of both

source materials and finished products. The soil

which is unctuous, smooth, blackish-white or reddish

in colour, nearer to water sources ,tight(non fragile),

devoid of big stones, ditch, excessive water, gravels,

sand particles, valmika (ant hills) is recommended

best for finding best quality medicinal herb as a

whole and for the collection of raw materials in the

part of medicinal shrubs or trees.

Therefore, one should have the basic knowledge of

the soil types from which drug plant is going to be

collected. This soil texture is also based on the

availability of water at that particular region. Three

main types of soil classes have been given; Jangal

desha, Aanoop desha and Sadharan desha repectively.

Over and above, on the basis of five fundmental

elements (Panchmahabhootas) soil is further sub

divided into parthiv, jaliya, aagneya, vayaneeya and

aakashiya. Furthermore, on the basis of other

characters it is termed as prashasta and aprahsasta

bhoomi i e. Nonpolluted and polluted soil. ( Table 3)

Sangrahaniya dravyas (Selection of drug), It is well

known that the quantitative concentration of

biologically active constituent‟s i.e. veerya of a drug,

varies with the stage of plant growth, its stage of

development and place of its occurrence this includes

sheetveerdravya and ushanveerdravya which varies

according to phytoconstituetns. The best time for

collection should be determined according to the

quality and quantity of biologically active constituents

rather than the total vegetative yield of the targeted

medicinal plant parts. For herbs we should look into

some of the following characteristics (Charak and

Sushruta samhita).

It should not be affected by smoke, rain, air or

water.

It should be collected in respective seasons, from

prasastabhumi which should be free from pests,

poisonous weapon, severe sunlight, high breeze,

fire, excessive moisture and any kind of disease.

It should not be collected from road sides, should

be well developed and deeply rooted in the soil,

must have a single predominant taste in it with its

natural odor, color and taste.

It should be new and must be used within one

year of its collection from field and must be

collected from eastern and northern side for

therapeutic utility.

Sangrahaniya Vidhi (Method of collection) to procure

best qualities of drug the proper place of collection,

part, method and time for collection are most

important. Charaka prescribes certain instructions

regarding mode of collection. They include

performance of auspicious rituals and practices,

taking of sacred bath to become mentally and

physically clean, wearing sacred dress, worshipping

of gods including Ashwinikumar‟s and the sacred

cow, performance of religious fast overnight etc. In

addition to the collector should collect the raw

material plant or plant part mainly from east or north

direction according to part-used, season of collection

and its potency, in a specific manner.

Sangrahaniya Kala (Time for collection).

Sangrahakala of various medicinal plants and plant

organs in different seasons give you an idea about a

systematic scientific reason of transportation of

synthesised secondary metabolites from one part to

another part of the plant to live fit in that particular

ecological condition. While, collecting the medicinal

plants in nature certain factors such as Guna, Desha,

Kala, Pakva-apakva avastha, Navpurana avastha,

Prayojyanga, Karma and Disha should also be given

importance.


Table 1. Season and collection period According to 3 main Ayurveda samhitas

Part used Season

Charaka Sushruta Raj nighantu

Whole plant - - Late autumn

Roots Summers and winter Summer and monsoon Late winter

Tubers and rhizomes - Early winter

Branches and Leaves

Tender Leaves

Autumn and spring Monsoon and early

Winter

Late winter

late summer

Stem bark and latex Autumn and winter Late autumn and early

Winter

-

Heart wood and sap wood Early winter Spring -

Flowers and inflorescence According to season - Spring

Fruits and seeds (mature) According to season Summer -

Modern Collection Technique

On the subject of huge diversity and distribution of

medicinal plants in India, botanical research and

collection of data obtained from other researches can

provides a good recognition of this great treasure for

us and future generation. Botanical studies of a

neighbouring region not only are of an educational

value, but they are also the source of information for

students on diversity of nature and a necessity for

protection of the province they reside in. Going

outdoors, visiting nearby and farther surroundings,

pupils together with their teachers, can observe the

morphological properties record and collect plants

including medicinal plants. It gives an input for

analyzing and describing plant species present in the

community and vegetation of the nearby region which

always forms basic source of number of medicinal

plants.

Table 2. Collection schedule of medicinal plant and plant parts according to Rutus (Seasons)

Part used Rutus (Season)

Charaka Sushruta Raj nighantu

Whole plant - - Sharad

Roots Grishama, Shishira - Shishira

Tubers and rhizomes Sharad - Hemant

Branches

Varsha, vasant - -

Leaves Varsha, vasant Varsha Shishira

Tender Leaves Varsha, vasant Grishama,

Stem bark Sharad Sharad -

Latex Sharad Hemant -

Heart wood and sap wood Hemant Vansnat -

Flowers and inflorescence As per rutu - Vasant

Fruits As per rutu

Seeds - - -

Table 3. Collection of medicinal plant and vegetation based on traditional soil types

Mahabhuta (Elemental composition)

Soil nature Colour Vegetation

Prithvi Pebbly Dark blue or black Rich vegetation

Jala Unctous, cool White Grass

Agni Stony Multicolour Small sized tree

Vayu Rough Grey Small trees

Akash Soft No colour Trees of no value


In order to gain proper knowledge of medicinal plants

we should have the information on the flora and the

vegetation of a particular or certain region. It is

necessary to carry both, field and laboratory studies.

Field studies encompass, first of all, listing/registering

and collecting of the plant material, as well as, identification and description of plant communities

that are made of that plant material. The collected

material is prepared in laboratories for preservation

and a further utilization for stereoscopic and

microscopic analyses if any for proper identification.

The field investigations can also include different

microclimatic measurements (solar radiation, solar

light, air and soil temperatures, air humidity,

evaporation, etc.) that provide data on a habitat

conditions under which living creatures live i e.

abiotic and biotic factors prevailing in the area. By

and large, considerable importance is given for the season during collection of different parts of

economical plants as it governs not only the total

quantity of active constituents produced but also the

relative proportions of the components of the active

mixture.

Presently plant collections methods always suggest

looking for the morphologically best specimen from

that particular habitat and ecological niche. Mostly we

have to select vigorous, typical specimens for

identification and after confirmation of particular

medicinal species we can go for bulk collection. By and large, non destructive system of the plant

collection should be employed. Specimens should be

representative of the population, but should include

the range of morpho-variation of the plant species.

Dioecious plant species should be represented by both

sexes in the collection. Roots, bulbs, and other

underground parts should be carefully dug up, and the

soil removed with care if it is useful for proper

identification such as Cyperaceae and Araceae

members. In collecting large herbs, shrubs and trees,

different types of foliage, flowers and fruits should be

collected from the same plant. Collect sufficient material to prepare the herbarium sheet and still leave

enough room for the labelling and field notes. This

will be useful for authentification of the plant species

and will require during the use as well as research

purpose. Moreover, if we are going to collect the

whole population then avoid insect or pathogen

damaged plants and plant parts of pharmacological

interest. These will vary widely from species to

species and the area from which we are going to

collect the raw material according to necessity.

While, collecting whole plant we have to follow above procedure. Leafy drug plants which are

discoloured or attacked by insects or strike must be

rejected as it has the main quality of the medicinally

important phytochemicals. At the same time, we have

to keep away the leaves covered with rain or dew

drops (Annonymous, 2005).

Season of collection: The season and time at which

each drug is collected is usually a matter of

consideration, since the amount and sometimes nature

of active constituents varies throughout the year.

There is increasing evidence that composition of

number of secondary plant metabolites varies

appreciably throughout day and night. e.g., Rhubarb

(Rheum emodi) has maximum Anthraquinone in summer season which is an active constituent of this

plant while same constituent is minimum or absent if

one collect the drug in winter. Likewise, the amount

of glycoside present in the leaves of Digitalis

purpurea remains at its highest level only in the day

time, it breaks down in the night. So, the leaves of

Digitalis should be collected in the day time and

rhubarb should be collected in summer for desired

action.

Collection period: The best period for collection of

herbaceous species is during September -November,

but in August-October in the area above 3000m., as, snowfall starts from November. In evergreen forest

the best period for collection of trees and shrubs is

March-May, as most of the species are in flowering

condition during this period, while in deciduous forest

the collection should be avoided in winter season

because falling of leaves is common during this

season.

Stage of plant: It is also observed that the quality and

quantity of chemical constituents varies according to

stage/growth of plant. Therefore, it is very essential to

know the optimum stage of the plant parts/plant for collection of right material for getting best therapeutic

effects. It is generally suggested to collect the leaves

just before flowering, flowers when fully opened, and

rhizome and root when aerial parts are matured.

Herbage: The aerial or top parts of the plant are

collected with flower or fruit bearing stem. In case of

herbage, seasonal studies must be conducted to

pinpoint the period when optimum active principles

are present in the plant. The care should be taken

during the collection that the mature branches of the

stem must be harvested and never remove all the

branches of the plant. Care should be taken to exclude vegetable debris as far as possible.

Roots: Roots of annuals are usually not collected but

in case where the whole plant is used, the roots of an

annual plant along with aerial part of the plant are

collected. At the same time, while collecting roots of

trees and bushes, the main roots should not be cut or

dug up, and moreover, severing the tap root of trees

and bushes should be avoided. Only some of the

lateral roots should be located and collected. The

biennials and perennials are generally collected in

autumn of the first year growth or in spring before the beginning of the second year growth. This is because

the roots are storage organ for the plant and

accumulate active principles during the summer.

However, there are some exceptions also viz. the root

of Withania somnifera are normally collected when

the plants are 6-8 months old. Likewise the roots of

Saussurea lappa, Innula racemosa, Glycirrhizia

glabra should be collected when the plants are of 3-5


years old. Moreover, seasonal variation is recorded in

overall various active biomolecules content, as well as

variation among different phytochemicals present

their in. This is an exceedingly valuable finding if

roots are being grown for individual compounds or if

manufactured products are harvested at different times and standardized to a specific compound that

varies from month to month, week to week, or even

day to day.

Stem and bark: It is collected either in spring when

the trees and shrubs begin to bud or in autumn after

they have shed their leaves. This is the time of year

when the flow of sap is at its maximum and bark

radially detach from the wood. However, the

collection time of every individual plant or part of the

plant differs depending upon the climate and altitude.

The bark should be collected from the branches

instead of main trunk and do not peel whole bark of the plant. It is also important to strip the bark

longitudinally and not all over the circumference to

the trunk/ branches. Bark and wood samples are often

desirable additions when collecting woody plants.

There are special requirements for the identification

of some plants; e.g. Eucalyptus specimen, should

must include mature leaves, juvenile leaves, buds,

fruits, and bark wherever possible.

When collecting species whose bark is the main

material to be used, the tree should not be girdled or

completely stripped of its bark, it will result into death of that plant. So longitudinal strips of stem bark along

one side of the tree should be cut and collected. Bark

is usually collected after a period of damp weather.

Collection of gums, gum resins etc. should be made in

dry weather.

Leaves: They are collected throughout the whole

growing period. The leaves show that loss on drying

is minimum when they are fully grown. When

majority of leaves dries up and new leaves are

coming. Young leaves, however, contain highest

quality of active principle, but they must be free from

diseases, insect etc. This indicates that new leaves have more tendencies to absorb more moisture

helping in proper storage of the drug. Leaves, flowers

and fruits should not be collected when covered with

dew or rain. Care should be taken to exclude

vegetable debris as far as possible.

Tubers/bulb: These should be collected during

flowering period because this aid in identification of

the species. It is notable that the deep digging is

avoided during the collection of underground parts.

Underground parts must be freed from soil to

minimise the microbial contamination. Flower: Flowers or whole inflorescences are gathered

at the start of the flowering period and leave some

floral parts on the plants to facilitate natural

regeneration.

Fruits and the seeds: Fruits and the seeds are

collected when they are fully matured. In the case of

cultivated crops which are harvested by machine, this

is done just before they are fully ripe so that fruits do

not crumble or the seeds fallout in the field.

Medicinal Property of the Plants

The beneficial medicinal effects of plant materials

typically result from the combinations of secondary

products present in the plant. That the medicinal actions of plants are unique to particular plant species

or groups is consistent with this concept as the

combinations of secondary products in a particular

plant are often taxonomically distinct (Wink, 1999).

These secondary products defined as biochemicals

which do not have any vital biochemical role in

maintaining plant cells. But, these chemicals play an

important role in ecophysiology of plants such as

defensive role against herbivory, pathogen attack, and

inter-plant competition. In addition they play

attractant role toward beneficial organisms such as

pollinators or symbionts (Kaufman et al., 1999; Wink and Schimmer, 1999). Plant secondary products also

have protective actions in relation to abiotic stresses

exerted on the plants. Secondary metabolites involved

in plant defense toward microbial pathogens proved

useful as antimicrobial medicines in humans.

Likewise, defence against herbivores through

neurotoxin activity may have beneficial effects in

humans through their action on the central nervous

system. To promote the ecological survival of plants,

secondary products can be affecting the physiological

functions in competing microorganisms, plants, and animals (Wink and Schimmer, 1999). Over and

above, some plant metabolites may have beneficial

medicinal effects on humans due to similarities in

their potential target sites (e.g. central nervous

system, endocrine system, etc.) (Kaufman et al.,

1999). Many phytomedicines exert their beneficial

effects through the additive or synergistic action of

several chemical compounds acting at single or

multiple target sites associated with a physiological

process (Briskin, 2000).

Secondary metabolites also contribute to the specific

odours, tastes and colours in plants (Bennett and Wallsgrove, 1994). Plant secondary metabolites are

only one of its kind as a source for number of food

additives, flavors, pharmaceuticals and industrially

important pharmaceuticals (Ravishankar and

Venkataraman, 1990; Ravishankar and Rao, 2002).

Chemicals include calcium, abscisic acid (ABA),

salicylic acid (SA), polyamines and Jasmonates (JA),

nitric oxide are involved in stress responses in plants

(Tuteja and Sopory, 2008). Accumulation of

metabolites often occurs in plants subjected to stresses

including various elicitors or signal molecules. Secondary metabolites have significant practical

applications in medicinal, nutritive and cosmetic

purposes, besides, importance in plant stress

physiology for adaptation.The production of these

compounds is often low (less than 1% dry weight) and

depends greatly on the physiological and

developmental stage of the plant. Some of the plant

derived natural products include drugs such as


morphine, codeine, cocaine, quinine etc.

Catharanthus alkaloids, Belladonna alkaloids,

colchicines, phytostigminine, pilocarpine, reserpine

and steroids like diosgenin, digoxin and digitoxin,

flavonoids, phenolics etc. (Rao and Ravishankar,

2002).

General guidelines for collection

In the course of collection, efforts should be made to

remove parts of the selected plants that are not

required. In addition to above, foreign material in any

form natural or artificial such as particular toxic

weeds or the anthropogenic waste also should be

taken away. Decomposed and decayed medicinal

plant parts as a raw material should be discarded.

Medicinal plants should not be collected in or around

areas of heavily pollution. Local sites having high

levels of pesticides, herbicides, insecticides, chemical

fertilizer or other possible contaminants are always neglected. Even, road sides, drainage, ditches, mines

tailing, garbage dumps and industrial surroundings

which may produce toxic emissions should not be

considered. In addition, the collection of medicinal

plants in and around active pastures, including

riverbanks downstream from pastures, should be

avoided in order to avoid microbial contamination

from any type of waste as it has the capacity to have

numerous microorganisms. In general, the collected

medicinal plant and raw materials of any plant part

should not come into direct contact with the soil. If underground parts are used, any adhering soil should

always be removed from the plants and plant part as

soon as they are collected. Collected material must be

placed in clean containers, basket, mesh bags, other

well aerated containers or drape clothes that are free

from any foreign matter, including plant remnants

from previous collecting activities. This concern is of

extreme importance to present day society that has

been over exploiting the drugs for commercial gains

without much reciprocal social gains as a result many

commonly used medicinal plants are on the verge of

extinction (Annonymus, 2005).

Safety issues and recommendations

Lack of side effects of herbals does not mean taking a

conventional medicine without any expert‟s

recommendations. With traditional medicine, there is

always risk of counterfeits or adulterations or

admixture of unwanted material. And, while their

effectiveness varies from place, method and season of

collection of plant material and also person to person.

Such traditional medicinal systems are often the last

resort for people, especially when the western medical

treatments fail them to cure. Definitely we get energized with a number of active herbal ingredients

posing on the package but a little but relevant

question rise how all these ingredients are altogether

found at a time as plant constituents varies in different

seasons. Moreover, some plants should not be found

anytime and every time. And if we are convinced with

the fact that they collected and stored for selling year-

round, where‟s the guaranty that they were stored in a

prescribed manner after following standard operating

procedure for plant collection. Rapidly increasing of

local healer‟s or quack shops clearly visible with their

unusual claim of complete recovery from any

complications. A growing number of people are

looking for guidance on the Internet while others believe dishonest ads. It is strictly recommended that

plant drugs are miraculous, better avoid plant drugs

only as food supplements, they can‟t be always safe

as they are natural and taking expert‟s advice before

use.

Conclusion and future scope

Thus, on the one hand, habitat destruction and

unsustainable harvesting of medicinal plants pose a

serious threat to biodiversity, as well as the native

medicinal resources in India, where many states and

regions are progressively losing their forest cover

(FSI, 1999). Therefore, collection of these resourceful plants in most appropriate manner is essential. Vedic,

Ayurvedic literature on medicinal plants has

furthermore described the collection practices in well

documented manner and its utility is also corroborated

by modern chemical tools. Therapeutic efficiency is

presumed to depend on the quality and quantity of the

secondary metabolites which are major active

principles. The quantity and quality are influenced by

the method of collection. For our ancient

Ayurvedacharya‟s medicinal plants were not just

resources for exploitation of natural resources for man‟s need/greed. They were the auspicious fellow

inhabitants of the sacred planet „Mother Earth‟ which

is being exploited only to protect and promote the

health of living being during the time of natural or life

style problems. In fact they believed that the drug will

be blessed with optimum medicinal value if and only

if their purpose is genuine and proper collection of the

plants or plant parts with due respect in a proper

season.

This communication highlights the importance of

collection practices to achieve desired beneficial

effect, as herbs without good effectiveness become useless to physician as well as pharmaceuticals

industries. Traditional plants have vast opportunities

to explore and conducting extensive research is

necessary for their rational use. In order to expedite

medicinal plant breeding and transform them into

living factories of medicinal compounds proper

collection of important medicinal plants is essential.

And, even use of the emerging high-end modern

technologies can be expanded to traditional medicinal

plants to understand and extract newer phyto-

molecules for very many health problems of the human being.

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LONG TERM EFFECT OF INORGANIC FERTILIZERS AND ORGANIC

MANURES ON NUTRIENT UPTAKE, AND YIELD OF RICE ON INCEPTISOL

Kiran Rathore1, Alok Tiwari

2and Rahul Kumar*

3

Department of Soil Science & Agricultural Chemistry, College of Agriculture, Raipur1,2

College of Agriculture, Bharatpur(Raj)3



Abstracts: The experiment was conducted during the kharif season at research farm, Indira Gandhi Krishi Viswavidyalaya, Raipur to investigate the long term effect of Inorganic fertilizer and organic manures on nutrient uptake and yield or rice. The soil was sandy loan and locally known as matasi, Low in nitrogen, medium in P and K. the experiment was laid in RBD and replicated three times with eleven treatment T1 -No Fertilizer, No Organic manure (Control),T2 -50% Recommended NPK (40:30:20), T3-75% Recommended NPK,T4-100% Recommended NPK (80:60:40),T5-50% Recommended NPK +50%N through Farm yard manure,T6 -75% Recommended NPK +25%N through Farm yard manure,T7-50% Recommended NPK +50%N through rice residue,T8-75% Recommended NPK +25%N through rice residue,T9-50% Recommended NPK +50%N through Green manure,T10-75% Recommended NPK +25%N through Green manure,T11-

Conventional Farmer’ Practice (50:30:20). A medium duration high yielding rice variety Mahamaya was taken as test crop. The results revealed that combination application of inorganic fertilizer and organic manure i.e. integrated of fertilizer and manure improve chemical propertied of soil. The macro nutrient uptake yield and attributing parameter and grain yield of rice were found superior in different organic and inorganic treatment combination at 25, 50 % and along with Green manuring and / FYM as compared to 50 % or 75% RDF to rice crop

Keyword: Rice, Nutrient uptake,Nutrient content, Organic, Inorganic fertilizer

INTRODUCTION

ndia is the second largest rice producing country in

the world after China. Although rice planted area

in India is 40 per cent higher than in China, Indian

rice production is 30 percent below than Chinese

production because of lower yields (2.3 tonnes per

hectare in India vs. 4.7 tonnes in China). Indian rice

yields are well below the world average (2.9 tonnes/hectare), implying there is a great potential

for increasing production.Chhattisgarh state occupies

13.51 million hectares with a gross cropped area of

about 5.68 million ha. The geographical area of the

state is situated between 17046’ to 2406’ N latitude

and 80015’ to 84051’ E longitude.Inceptisol are

shallow, well-drained, loamy soils on the gentle

sloping and undulating plateau (slightly dissected)

with moderate erosion and occurrence of stones.

They are classified as loamy, kaolinitic,

isohyperthermic, Lithic Ustropepts. Inceptisol are

locally called matasi soil. They have a light texture and a shallow to moderate depth.

Integrated nutrient management is one of the most

important components of the production technology

to sustain soil fertility and crop productivity. The

combined use of organic and inorganic sources of

plant nutrients not only pushed the production and

profitability of field crops but also helped in

maintaining the fertility status of the soil

(Chandrasoorian et al. 1994). The advantage of

combining organic and inorganic sources of nutrients

in integrated nutrient management has been proved superior to the use of each component separately

(Palaniappan and Annadurai 2007). Inorganic

fertilizers, especially nitrogen (N), phosphorus (P)

and potassium (K), not only serve to maintain or

improve crop yields, but their application also

directly or indirectly induce changes in soil chemical,

physical and biological properties. Some studies

showed that chemical fertilizers increase biomass C

and N, Sarathchandra et al. (2001) reported that

nitrogen and phosphatefertilizers had no significant effects on soil microbial populations and N

application reduced the functional microbial diversity

in pasture soils.

Applying organic amendments to soil not only

increases the total organic carbon content and its

different fractions but also has a series of effects on

microbial proliferation and activity (Tejada et al.,

2006; Ros et al., 2003). Soil microbial biomass is

undoubtedly a valuable tool for understanding and

predicting changes in soil fertility management and

associated soil conditions such as nutrient dynamics

and soil reaction (Sharma et al., 2004; Yougunet al., 2007). It has assumed greater significance and

increasing interest in its determination (Azamet al.,

2003).Long term effect of inorganic fertilizers and

organic manures on nutrient uptake and yield of rice

on inceptisol”


The field experiment was conducted at the research

farm, Indira Gandhi Krishi Vishwavidyalaya, Raipur

(Chhattisgarh) during kharif. Raipur comes under agro-climatic plain Zone of Chhattisgarh State and

I

RESEARCH ARTICLE


216 KIRAN RATHORE, ALOK TIWARI AND RAHUL KUMAR

lie at 21016’ N latitude and 81026’ East longitude

with an altitude of 289.56 m above the mean sea

level. The experiment were laid out in eleven

treatment combinations.T1 -No Fertilizer, No

Organic manure (Control),T2 -50% Recommended

NPK (40:30:20), T3-75% Recommended NPK,T4-100% Recommended NPK (80:60:40),T5-50%

Recommended NPK +50%N through Farm yard

manure,T6 -75% Recommended NPK +25%N

through Farm yard manure,T7-50% Recommended

NPK +50%N through rice residue,T8-75%

Recommended NPK +25%N through rice residue,T9-

50% Recommended NPK +50%N through Green

manure,T10-75% Recommended NPK +25%N

through Green manure,T11- Conventional Farmer’

Practice (50:30:20). A medium duration high

yielding rice variety Mahamaya was taken as test

crop with threereplications. Nitrogen content in plant Sample was determined by

using micro-Kjeldahl method as described by

Chapman and Pratt (1961). Phosphorus content was

determined by Vanadomolybdo-phosphoric acid

yellow color method using blue filter as described by

Jackson (1958). Potassium content was determined

by flame photometer method as described by

Chapman and Pratt (1961).The growth and yield of

rice crop depend upon all growth parameters.viz

Number of total and effective tillers, Panicle length,

1000 grain weight etc.


Effect of various combinations of inorganic

fertilizer and organic manure application on

nutrient content and their uptake

Content in rice grain and straw

Nitrogen, Phosphorus and potassium content in grain

as well as straw are given in table 1 The different

treatment combination significantly affected the N, P

and K concentration in grain and straw at harvest

stage.

Nitrogen content The nitrogen content in grain and straw was

increased significantly with integrated nutrient

management practices over control. The nitrogen content in grain and straw was ranged from 7.9 to

10.9 and 3.8 to 6.5 gm kg-1, respectively. The higher

nitrogen content in grain was noted under treatment

50%RDF+ 50%GM followed by 50% RDF+

50%FYM and lowest under the control. Similar kind

of result in nitrogen content was obtained in straw

also.

Phosphorus content

Phosphorus content in grain and straw of rice under

different treatment combination is given in table 1.

The P content in grain was ranged from 1.6 to 3.2 gm

kg-1 amongst different treatment. The highest (3.2 gm kg-1) P content in grain was recorded in 50%RDF+

50%GM-N and lowest in control plot. Almost similar

kind of pattern was observed in phosphorus content

in straw amongst various treatment combinations.

Potassium content

Potassium content in grain was ranged from 2.1 to

2.9gm kg-1 in different treatments. The higher (2.9

gm kg-1) potassium content in grain was recorded in

50% RDF+50%FYM-N and 50% RDF+ 50%GM-N

and lowest (2.1gm kg-1) in control plots. Potassium

content in straw was ranged from 13.2 to 19.7gm kg-1 and increased significantly with applied all treatment

combination over control. The highest (9.7gmkg-1)

potassium content in straw was measured in 50%

RDF+ 50%FYM-N treated plot, while lowest (16.9

gm kg-1) in farmer’s practices. The results are in

agreement with the finding of Sarwar (2005) who

studied that increased concentration of N, P and K in

paddy grain and straw of rice with the combined use

of FYM, Sesbania green manure and chemical

fertilizer compared with application of chemical

fertilizer and organic manures alone.

Table 1. Effect ofintegrated nutrient management fertilizer and organic manure application on N, P and K

content in grain and straw of rice (after 21 crop cycle)

Treatments

Nutrient Content (gm kg-1

)

Grain Straw Grain Straw Grain Straw

N P K

T1-Control 7.9d 3.8e 1.6e 0.4d

2.1d 1.32d

T2-50% RDF (40:30:20) 8.4d 4.3de 2.4d 0.5cd 2.3cd 1.72bc

T3-75% RDF 8.5cd 5.1bc 2.4cd 0.5c 2.5abcd 1.79bc

T4-100% RDF (80:60:40) 9.4abcd 5.0cd 2.6bcd 0.6bc 2.6abc 1.81abc

T5-50% RDF+50% FYM-N 10.7ab 6.4a 2.9ab 0.8ab 2.9a 1.97a

T6-75%RDF+25% FYM-N 10.2abc 5.5bc 2.7bcd 0.7abc 2.7ab 1.74bc

T7-50%RDF+50% RR-N 8.8cd 5.9ab 2.8abc 0.7abc 2.6abc 1.81abc

T8-75% RDF+25% RR-N 9.2bcd 5.4bc 2.6bcd 0.7abc 2.3bcd 1.76bc


T9-50% RDF+50% GM-N 10.9a 6.5a 3.2a 0.8a 2.9a 1.88ab

T10-75% RDF+25% GM-N 10.2abc 5.9ab 2.8ab 0.7ab 2.8a 1.72bc

T11-Farmer practices (50:30:20) 8.6cd 4.9cd 2.6cd 0.6c 2.5abcd 1.69c

SEm+ 0.56 0.27 0.15 0.05 0.14 0.55

CD (P= 0.05) 1.65 0.79 0.43 0.14 0.40 1.63

FYM-farm yard manure, RR- rice residues, GM- green manure

Uptake of N, P and K by grain and straw as

influenced by different fertilizer and integrated

nutrient management practices

Nitrogen uptake

Nitrogen uptake by grain, straw and total by rice is

given in table 2. The nitrogen uptake by grain was ranged from 10.34 to 76.29 kg ha-1. The 50% RDF+

50% GM-N had highest nitrogen uptake 76.29 kg ha-

1 by grain followed by 74.33 kg ha-1 in 50% RDF +

50% FYM-N and the lowest (10.34 kg ha-1 ) under

control plots. Almost similar trend was observed in

nitrogen uptake by straw among various treatment

combinations. Due to incorporation of 50% or 25%

RR-N with 50% or 75% RDF the N uptake by straw

was similar to that obtained in 100% RDF.

Among INM combinations 50% RDF + 50% GM –N

gave highest N uptake by grain as compared to other

INM treatment at 50% RDF (T5, T7 and T9). The INM treatments at 75% RDF level were almost

similar (T6, T8 and T10) in N uptake by grain. The

results are in agreement with the findings of Dar et

al. (2012) who studied that uptake of nitrogen by

paddy and straw was higher under integrated nutrient

treatments.

Table 2. Nitrogen uptake by rice as affected by nutrient management practices (after 21 crop cycle)

Treatments Nitrogen uptake (kg ha

-1)

Grain Straw Total

T1-Control 10.34 8.78 19.12 h

T2-50% RDF (40:30:20) 36.45 29.00 65.45 g

T3-75% RDF 52.72 47.10 99.82 dc

T4-100% RDF (80:60:40) 65.69 47.42 113.11 cd

T5-50% RDF+50% FYM-N 74.33 59.26 133.59 ab

T6-75%RDF+25% FYM-N 68.71 51.21 119.92 bc

T7-50%RDF+50% RR-N 53.70 47.75 101.45 dc

T8-75% RDF+25% RR-N 52.90 41.50 94.40 ef

T9-50% RDF+50% GM-N 76.29 61.87 138.20 a

T10-75% RDF+25% GM-N 69.47 56.54 126.01 abc

T11-Farmer practices (50:30:20) 44.13 37.39 81.53 f

SEm+ 3.68 2.34 4.76

CD (P= 0.05) 10.87 6.92 14.03

FYM-farm yard manure, RR- rice residues, GM- green manure

Total uptake

The total nitrogen uptake by rice crop was ranged

from 19.12 to 138.2 kg ha-1 and increased significantly with applied fertilizer treatment over

control. Significantly higher (138.2 kg ha-1) N uptake

was recorded in treatment 50%RDF +50% GM-N,

followed by 133.59 kg ha1in 50%RDF + 50% FYM-

N and lowest (65.45 kg ha-1) under 50% RDF among

fertilizer treatment plot ( Table 2) The results are in

agreement with Laxminarayana and Patiram (2006),

revealed that application of optimum doses of NPK

in combination with green manure @ 5 Mg ha−1

recorded highest grain and straw yields and uptake of

N, P and K followed by 100% NPK + poultry

manure and 100% NPK + FYM

Phosphorus uptake

Phosphorus uptake by rice grain, straw and total

under different treatment is given in table 3. The increase in application of inorganic fertilizer

significantly increased the P uptake by grain and

simultaneously the uptake by straw and total also.

The phosphorus uptake by grain ranged from 2.13 to

22.09 kg ha-1. The highest (22.09 kg ha-1) phosphorus

uptake by grain was noted under 50%RDF +50%

GM-N, followed by 19.83 kg ha-1 in

50%RDF+50%FYM-N and lowest (10.32 kg ha-1)

under 50% RDF with respect to applied treatment.

The highest (18.3 kg ha-1) uptake was recorded in

100% RDF compared to all inorganic fertilizer.

Similar trend was observed in phosphorus uptake by straw among various treatment combinations.


Table 3.Phosphorus uptake by rice as affected by nutrient management practices (after 21 crop cycle)

Treatments Phosphorus uptake (kg ha

-1)

Grain Straw Total

T1-Control 2.13 0.91 3.05 i

T2-50% RDF (40:30:20) 10.32 3.57 13.9 h

T3-75% RDF 14.74 4.97 19.7 f

T4-100% RDF (80:60:40) 18.30 5.95 24.3 cd

T5-50% RDF+50% FYM-N 19.83 7.1 26.9 b

T6-75%RDF+25% FYM-N 18.01 6.18 24.2 cd

T7-50%RDF+50% RR-N 17.01 5.46 22.5 de

T8-75% RDF+25% RR-N 15.19 5.03 20.2 ef

T9-50% RDF+50% GM-N 22.09 7.52 29.6 a

T10-75% RDF+25% GM-N 19.11 6.86 26 bc

T11-Farmer practices (50:30:20) 13.04 4.21 17.2 g

SEm+ 0.93 0.45 0.79

CD (P= 0.05) 2.73 1.32 2.35

FYM- farm yard manure, RR- rice residues, GM- green manure

Total uptake

Total phosphorus uptake by rice ranged from 3.05 to

29.6 kg ha-1 and increasing significantly with

increasing dose of applied fertilizer and manure over

control. The significantly highest (29.6 kg ha-1) P uptake was recorded in 50% RDF + 50% GM-

Nclosely followed (26.9 kg ha-1) by 50%RDF +

50%FYM-N and lowest (13.9 kg ha-1) in 50% RDF

with respect to applied inorganic fertilizer. The

nitrogen and phosphorus uptake by grain and straw

were also increased due to increased grain yield and

concentration of the nutrients in respective

treatments (Table 3). Similar finding were observed

by Makarim et al. (2005).

Potassium uptake

Potassium uptake by grain, straw and total under

inorganic fertilizer and INM treatments is given in

table 4. The K uptake by grain ranged from2.80 to

20.18 kg ha-1. The highest (20.18 kg ha-1) K uptake was recorded in 50%RDF + 50% GM-N followed by

19.88 kg ha-1 in 50%RDF + 50%FYM-N and lowest

(2.80 kg ha-1) in control plots. The K uptake by straw

ranged from 30.85 to 183.34 kg ha-1. The highest

(183.34 kg ha-1) K uptake by straw was noted in

50%RDF + 50%FYM-N followed by (179.79 kg ha-

1) in 50%RDF + 50% GM-N and the lowest 30.85 kg

ha-1 in control plots.

Table 4. Potassium uptake by rice as affected by different treatment (after 21 crop cycle)

Treatments

Potassium uptake (kg ha-1

)

Grain Straw Total

T1-Control 2.8 30.85 33.65 f

T2-50% RDF (40:30:20) 9.84 117.16 127.00 e

T3-75% RDF 15.4 165.03 180.43 b

T4-100% RDF (80:60:40) 18.18 171.72 189.90 ab

T5-50% RDF+50% FYM-N 19.88 183.34 203.22 a

T6-75%RDF+25% FYM-N 18.11 162.79 180.91 b

T7-50%RDF+50% RR-N 15.75 147.34 163.09 c

T8-75% RDF+25% RR-N 13.45 136.16 149.61 d

T9-50% RDF+50% GM-N 20.18 179.79 199.97 a

T10-75% RDF+25% GM-N 18.96 165.92 184.88 b

T11-Farmer practices (50:30:20) 12.74 129.32 142.06 d

SEm+ 0.78 4.53 4.55

CD (P= 0.05) 2.29 13.36 13.41


Total uptake The total potassium uptake by rice ranged from 33.65

to 203.22 kg ha-1 and increased significantly by

increasing dose of applied fertilizer and manure over

control. Significantly highest 203.22 kg ha-1 K

uptake was recorded in 50%RDF + 50% FYM-N

closely followed by 199.97 kg ha-1 in 50%RDF +

50% GM-N and lowest 127 kg ha-1in 50%RDF with


respect to applied treatment. The results are in

agreement with the findings of Das et al. (2013),

showed that FYM application @ 15 t ha -1 along with

100% NPK fertilizers produced maximum yields,

nutrients uptake and improve in soil properties.

Effect of combined application of inorganic

fertilizer and organic manure on nutrient balance

sheet

It is always desirable to calculate the apparent

nutrient balance to attain the desirable level of

production without depleting the native reserves and

ensuring the maintenance and improvement in soil

fertility. Nutrient’s drain has been calculated on the

basis of the removal by grain and straw of the crops

which were harvested. The balance sheets of

available N, P and K in rice as influenced by

inorganic fertilizer and organic manure are presented

in Table 5. The data showed that on inputs (nutrient applied) and outputs (nutrient uptake) of nutrients

indicated the N, P and K removal was the highest in

50%RDF+50%FYM-N followed by 50%

RDF+50%GM-N over rest of the treatments. The

maximum negative N balance was recorded in

treatment 50%RDF+50%FYM-N (-54.83).

Phosphorus is relatively immobile in soil as

compared to N and K. The maximum positive

balance of P was recorded in treatment 75%

RDF+25% RR-N (+5.8 kg ha-1) followed by 50%RDF+50% RR-N (+3.5 kg ha-1) while,

maximum negative balance of P was recorded in

treatment Farmer practices (-4.2 kg ha-1). The

negative P balance is obviously due to absence

and/or lower dose of P in fertilization schedule

whereas positive P balance is because of addition of

P in excess of its uptake by crops (Dwivedi et al.

2007). The maximum negative balance of K in soil

was recorded under 50%RDF+50%FYM-N (-170 kg

ha-1) treatment. Thakur et al. (2011) reported that

balanced use of fertilizers alone significantly

responsible forbuildup of organic carbon and available P. A declining trend of available N and K

from its initial status was noticed as a result of

continuous cropping, which indicated considerable

soil mining of available N and K.

Table 5. Effect of application of inorganic fertilizer and organic manure on nutrients (N, P and K) balance sheet

(after 21 crop cycle)

Treatments

Balance sheet of Nutrients ( kg ha-1

)

Nutrient added through

fertilizer and manure

Nutrient removed by grain

+straw

Net gain or loss

N P K N P K N P K

T1-Control 0 0 0 19.12 3.05 33.65 -19.12 -3.05 -33.65

T2-50% RDF (40:30:20) 40 13 17 65.45 13.9 127 -25.45 -0.9 -110

T3-75% RDF 60 20 25 99.82 19.7 180.43 -39.82 +0.3 -155.43

T4-100% RDF (80:60:40) 80 26 33 113.11 24.3 189.9 -33.11 +1.7 -156.9

T5-50% RDF+50% FYM-N 80 26 33 134.83 26.9 203.22 -54.83 -0.9 -170.22

T6-75%RDF+25% FYM-N 80 26 33 119.92 24.2 180.91 -39.92 +1.8 -147.91

T7-50%RDF+50% RR-N 80 26 33 101.45 22.5 163.09 -21.45 +3.5 -130.09

T8-75% RDF+25% RR-N 80 26 33 94.40 20.2 149.61 -14.4 +5.8 -116.61

T9-50% RDF+50% GM-N 80 26 33 132.98 29.6 199.97 -52.98 -3.6 -166.97

T10-75% RDF+25% GM-N 80 26 33 126.01 26 184.88 -46.01 0 -151.88

T11-Farmer practices (50:30:20) 50 13 17 81.53 17.2 142.06 -31.53 -4.2 -125.06


Effect of different nutrient management practices

on yield attributing characters and yield of rice

Yield attributing character of rice

Number of tiller per m2

Data pertaining on number of effective tillers per m2 (Table 6) showed that this parameter was

significantly influenced with combination of applied

inorganic fertilizer and organic manure over control.

The 50% RDF+ 50% GM-N showed higher response

on number of tillers as compare to other treatment

combination. The integration of recommended

fertilizer dose along with organic residues like 50%

N through GM (483 m2), FYM (456 m2), and RS

(424 m2), and 25% N through GM (453 m2), FYM

(440 m2) and RS (434 m2) along with 50 and 75%

RDF showed comparable and/or on par number of

effective tillers amongst them. At harvest, all the treatments combination proved

significantly superior over control in producing

effective tillers per square meter. The 50% RDF+

50% GM-N gave maximum (453 m2) number of

effective tillers and minimum (298 m2) in farmer’s

practice plot.

Table 6. Effect of continuous cropping and fertilization on yield attributing characters of Rice (after 21 crop

cycle)

Treatments

Effective tiller

(m-2

)

Panicle length

(cm)

Test weight

(gm)

T1-Control 113 f 16.04 c 30.86 c

T2-50% RDF (40:30:20) 353 d 21.3 b 31.89 bc

T3-75% RDF 348 d 21.83 ab 33.01 ab

T4-100% RDF (80:60:40) 460 a 22.52 ab 33.56 a

T5-50% RDF+50% FYM-N 456 b 24 ab 34.21 a

T6-75%RDF+25% FYM-N 440 b 22.38 ab 33.94 a

T7-50%RDF+50% RR-N 424 c 21.23 b 33.92 a

T8-75% RDF+25% RR-N 434 b 22.32 ab 33.46 a

T9-50% RDF+50% GM-N 483 a 24.55 a 33.31 ab

T10-75% RDF+25% GM-N 453 b 22.56 ab 32.86 ab

T11-Farmer practices (50:30:20) 298 e 21.5 ab 33.85 a

SEm+ 9 1.06 0.48

CD (P= 0.05) 26 3.11 1.43


Panicle length The data on panicle length is given in Table 6

showed that all the treatments had significant

differences over the control. The highest (24.55 cm)

panicle length was recorded in 50% RDF+ 50% GM-

N followed by 24 cm in 50% RDF+ 50% FYM-N

and lowest (16.04 cm) under control plots. The 50,

75 and 100% RDF alone and in combination with 25 and 50% RR-N and farmer practice were at par on

panicle length and significantly inferior over rest of

the INM treatments. The results are in agreement

with the finding of Chaudhary et al. (2007) who

observed that maximum panicle length was obtained

under 120 kg N ha-1 (27.80 cm) as compared to in

control (20.22 m).While the effect of N at 80 or 120

kg ha-1 with FYM on 1000-grain weight was equally

effective as reflected in their statistical methods also.

Test weight

The data on test weight (1000- seeds) is presented in Table 6. The highest test weight, 34.21 g was

recorded in 50%RDF+ 50% FYM-N followed by

33.31 g in 50%RDF+ 50% GM-N and lowest (30.86

g) in control plot. The results are conformity with the

findings of Hossaen et al. (2011) who revealed that

the maximum number of total grain per plant (97.45),

the highest weight of 1000 seeds (21.80 g), the

maximumgrain yield (7.30 t ha-1) and straw yield

(7.64 t ha-1) were recorded in treatment T5 (70%

NPKS+2.4 t poultry manure ha-1

) whereas the lowest

number of effective tillers per hill was 6.1

Effect of different nutrient management practices

on rice yield The yield of rice increased with increasing the levels

of mineral nutrients from 50 to 100% RDF.

Treatment T9 consisting of 50% RDF + 50% GM-N

as received from green manuring registered highest

grain yield (70.23 q/ha) of rice which was

significantly superior to the control, farmer’s practice

and different levels of mineral nutrients i.e. from 50

to 100 % RDF. It was because of the immobilization

of nitrogen and comparable to that of 100%

inorganic fertilizer treatment (T4), 50% (T5) and 75%

(T6) RDF +25/50% N as received from FYM and 25% N received from GM in T10 respectively (Table

7). Significant residual effect of FYM and GM

incorporation in soil was recorded on grain yield of

rice. Thus, the use of FYM and GM with fertilizer N

has helped in sustaining the yield of rice as reported


by Singh et al. (2001). Rice was found to be more

responsive than rabi crops to green manuring, which

might be due to direct effect of green manure in

supplying nutrient to rice crop and beneficial effect

on soil health as reported by Kumar and Singh

(2010). Mehdi et al. (2011) studied the comparison of Sesbania and FYM applied at 20 ton ha-1 showed

that Sesbania remained superior over the farm yard

manure for improving the paddy and straw yield. The

increased efficiency of NPK fertilizer with green

manuring may be due to chemical, enzymatic and

metabolic transformation of organic material, as the

green manuring iscontinuously subject to

degradation, thus more susceptible to change in metal uptakethan inorganic soil fractions.

Table 7. Effect of long term inorganic fertilizer and organic manure application on rice yield

Treatments Yield q/ha

Grain Straw

T1 Control 13.13 e 23.29 e

T2 50% RDF (40:30:20) 43.54 cd 68.13 cd

T3 75% RDF 61.77 b 92.08 a

T4 100% RDF (80:60:40) 70.1 a 94.79 a

T5 50% RDF+50% FYM-N 69.27 a 93.13 a

T6 75%RDF+25% FYM-N 67.5 a 93.75 a

T7 50%RDF+50% RR-N 60.73 b 81.46 b

T8 75% RDF+25% RR-N 57.6 b 77.5 b

T9 50% RDF+50% GM-N 70.23 a 95.83 a

T10 75% RDF+25% GM-N 68.29 a 96.25 a

T11 Farmer practices (50:30:20) 51.04 c 76.46 b

SEm± 2.78 3.72

CD (P= 0.05) 8.15 10.90


CONCLUSION

The above result the following conclusions can be

drawn:-Higher response was observed in integrated use of organic along with inorganic fertilizer for the

nutrient supply of rice crop and further it improves

the organic carbon and available nitrogen and

potassium content of soil. The balance sheets of N, P

and K in rice as markedly influenced by inorganic

fertilizer and organic manures, The maximum

negative N balance (-54.83)was recorded in

treatment 50%RDF+50%FYM-N. The maximum

positive balance (+5.8 kg ha-1) of P was recorded in

treatment 75% RDF+25% RR-N followed by

50%RDF+ 50% RR-N (+3.5 kg ha1) while, maximum negative balance (-4.2 kg ha-1) of P was

recorded in Farmer practices treatment. The

maximum negative balance (-170 kg ha-1) of K in

soil was recorded under 50%RDF+50%FYM-N

treatment.Green manure is second best option for

long-term sustainability and high yield in this system

followed by chemical fertilizer alone and integration

with FYM. All the yield attributing character and

yield of rice significantly increased due to residual

effect of GM/FYM/RR residues along with 75% and

100% RDF in rice over control. The yield of rice

increased with increasing the levels of mineral nutrients from 50 to 100% RDF. Treatment T9

consisting of 50% RDF + 50% GM-N as received

from green manuring registered highest grain yield

(70.23 q/ha).

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http://ovidsp.tx.ovid.com/sp-3.7.1b/ovidweb.cgi?&S=ABFJFPHGOBDDKEBLNCPKICDCFOEDAA00&Complete+Reference=S.sh.14%7c14%7c1



ASSESSMENT OF MEDICINAL PLANTS THROUGH PROXIMATE AND

MICRONUTRIENTS ANALYSIS

Tamanna Malik*, V.K. Madan2 and Tanya Dhanda

1

1Department of Chemistry, Chaudhary Charan Singh Haryana Agricultural University;

2MAP Section, Department of Genetics and Plant Breeding, Chaudhary Charan Singh Haryana

Agricultural University; [email protected]



Abstracts: The leaves, roots, bark and fruits of medicinal plants have various health-promoting effects on human and animals. These materials may be suitable singly or in combination as therapeutic agents and are important raw materials for manufacturing traditional and modern medicines. Indigenous medicinal plants have been playing a significant role in the economy of our country. Proximate compositions of seeds, aerial parts and roots of amla (Emblica officinalis), Bahera (Terminalia belerica) and Harad (Terminalia chebula) of indigenous origin were determined. The mineral contents [Iron (Fe), Copper (Cu), Zinc (Zn) and Manganese (Mn)] from the fruit pulp of these plants were determined. The moisture

content (%), crude fat (%), ash (%), crude protein (%), crude fibre (%) and total carbohydrates (%) were evaluated in the proximate composition. It was found that the overall proximate composition in seeds was highest when compared to aerial parts and roots. Therefore, fruits of Amla, Bahera and Harad have good nutritional value and hold their potential for nutraceutical development.

Keywords: Medicinal plants, Micronutrients, Modern, Traditional

INTRODUCTION

ver the past decade, herbal medicine has

become a topic of global importance, making an

impact on both world health and international trade.

Among ancient civilizations, India has been known

to be rich repository of medicinal plants. The forest

in India is the principal repository of large number of

medicinal and aromatic plants, which are largely collected as raw materials for manufacture of drugs

and perfumery products. About 8,000 herbal

remedies have been codified in AYUSH systems in

INDIA. Ayurveda, Unani, Siddha and Folk (tribal)

medicines are the major systems of indigenous

medicines. Among these systems, Ayurveda and

Unani Medicine are most developed and widely

practiced in India. Recently, WHO (World Health

Organization) estimated that 80 percent of people

worldwide rely on herbal medicines for some aspect

of their primary health care needs. According to WHO, around 21,000 plant species have the potential

for being used as medicinal plants.

Medicinal plants continue to play a vital role in the

healthcare system of large proportions of the world’s

population [1]. Different herbs are obtained from

different parts of the plant they may come from

roots, leaves, barks, seeds or flowers of a plant [2].

Triphala is a widespread polyherbal drug, which has

been used to treat a number of diseases in the

traditional systems since the ancient times. Triphala

is a composite mixture of three herbs Amalaki

(Emblica officinalis), Haritaki (Terminalia chebula) and Vibhitaki (Terminalia belerica) also known as

the ‘three myrobalans’. Emblica officinalis Gaertn.

belongs to Euphorbiaceae, and Terminalia chebula

Retz. Terminalia belerica belongs to Combretaceae

family.

Different parts of Terminalia chebula Retz,

Terminalia belerica Roxb, and Emblica officinalis

Gaertn are widely used in the Indian traditional

system of medicine [3]. The half ripe fruit of T.

belerica and the pericarp of T. chebula fruit were

reported to be purgative [3]. The fruit of T. chebula

was traditionally used to cure asthma, urinary disorders, heart disease and it has cardiotonic activity

[4,5]. In Ayurveda, the fruit of E. officinalis is used

as a cardiotonic, cerebral and intestinal tonic [6], and

it is also reported to have anticancer properties [7,6].

The fruit of E. officinalis is a rich source of vitamin

C, a well-known antioxidant [8]. The crude extract of

E. officinalis was reported to counteract the

hepatotoxic and renotoxic effects of metals [7] due to

antioxidant properties.

Emblica officinalis is commonly called the ‘Indian

gooseberry’. It belongs to the family Euphorbiaceae, and is known as Amla in Hindi, and Amalaki in

Sanskrit. It is a small to medium-sized tree with a

crooked trunk and spreading branches, and grayish-

green bark that peels off in flakes. The branchlets are

glabrous or finely pubescent, 10–20 cm long, usually

deciduous, with the leaves simple, subsessile, and

closely set along the branchlets. The leaves are light

green, resembling pinnate leaves. The flowers are

greenish-yellow, born in axillary fascicles, and give

way to globose fruit. The fruits are depressed globose

in shape, 1–2.5 cm in diameter, fleshy, and obscurely

six-lobed, containing six trigonous seeds. They are green when unripe, and turn light yellow or brick red

when mature.

O

RESEARCH ARTICLE

224 TAMANNA MALIK, V.K. MADAN AND TANYA DHANDA

Terminalia chebula is a medium to large deciduous

tree growing to 30 m (98 ft) tall, with a trunk up to 1

m (3 ft 3 in) in diameter. The leaves are alternate to

sub opposite in arrangement, oval, 7–8 cm (2.8–3.1

in) long and 4.5–10 cm (1.8–3.9 in) broad with a 1–3

cm (0.39–1.18 in) petiole. They have an acute tip, cordate at the base, margins entire, glabrous above

with a yellowish pubescence below. The fruit is

drupe-like, 2–4.5 cm (0.79–1.77 in) long and 1.2–2.5

cm (0.47–0.98 in) broad, blackish, with five

longitudinal ridges. The dull white to yellow flowers

are monoecious, and have a strong, unpleasant odor.

They are borne in terminal spikes or short panicles.

The fruits are smooth ellipsoid to ovoid drupes,

yellow to orange-brown in colour, with a single

angled stone.

Terminalia belerica (Gaertn.) Roxb. leaves are about

15 cm long and crowded toward the ends of the branches. It is considered a good fodder for cattle.

Terminalia belerica seeds have an oil content of 40%,

whose fatty-acid methyl ester meets all of the major

biodiesel requirements in the USA, Germany and

European Union. The seeds are called bedda nuts.

Triphala (in Sanskrit, tri = three and phala = fruits) is

another important Ayurvedic medicinal preparation

comprising three fruits: Phyllanthus emblica or

Emblica officinalis, Terminalia chebula, and

Terminalia belerica. Triphala is one of the most

well-studied Ayurvedic formulations, and experiments have shown it to possess antibacterial,

antifungal, free radical scavenging, antioxidant, anti-

inflammatory, laxative, antiarthritic, anticataleptic,

hypolipidemic, antihyperlipidimic, hepatoprotective,

anti-stress, antidiabetic, antimutagenic, anticancer,

chemopreventive, chemoprotective, radioprotective,

and immunomodulatory properties.

Although, the medicinal properties and presence of

antioxidants in these plant products are well

recognized, data with regard to their chemical

composition is scanty. It is necessary to evaluate the

proximate and nutraceutical composition of those plants in addition to their components that promote

health care. So, this present study is aimed to assess

the proximate composition and nutritional parameters

in different parts of Amla, Bahera and Harad viz.

seeds, aerial parts and roots.


Apparatus used: In the present studies, the

apparatus used are Spatula, Filter Paper, Water

bath, Oven, Beaker, Test tubes, Sieve, Funnel, Measuring cylinder, Soxhlet Extraction unit,

Hand grinder, Sample bottle, Detergent, Wash

Bottle, Aluminium foil etc.

Sample collection: The different parts of were

Amla, Bahera and Harad viz. seeds, aerial parts

and roots were procured from Chaudhary Charan

Singh Haryana Agricultural University, Hisar.

Sterilization of glass wares: All glass wares

used during the experimental investigation were

washed properly with detergent, rinsed with

distilled water and air dried. They were also then

sterilized in hot air oven by wrapping with

Aluminium foil. Sample preparation: The procured plant

samples were dried under shade and grinded into

fine powder followed by transferring to airtight

containers with proper labelling for future use.

Proximate analysis: The proximate analysis

(moisture, crude fibre, ash, crude fat, crude

protein and Total carbohydrates) of all the

samples were determined in triplicates as per the

standard technique of AOAC.

Estimation of moisture content

The dried powdered samples (seed, aerial parts and

roots) of Amla, Bahera and Harad (2g each) were taken in triplicates and dried initially at lower

temperature (80-90°C) and finally at higher

temperature (100-102°C) then weight of dried

samples was noted until constant weights were

obtained. The moisture content (%) was estimated

using the formula as follows:

Moisture content (%) =

Wt. of fresh plant part – Wt. of dry plant part

X 100

Wt. of fresh plant part

Estimation of crude fat content The dried powdered samples (seed, aerial parts and

roots) of Amla, Bahera and Harad (2g each) were

taken in a thimble and placed in a Soxhlet extractor.

Approximately 150-175 ml of petroleum ether was

added up to one and a half siphons in a dried and pre-

weighed round bottom flask connected to soxhlet

assembly. The extraction was carried as long as for

8h. After extraction, weight of the round bottomed

flask along with the extract was determined again

when almost all the petroleum ether gets evaporated

from RB flask upon heating. The crude fat contents

(%) were calculated as follows:

Crude Fat content % = Weight of fat

Weight of sample ×100

Estimation of ash content In a pre-weighed and ignited crucibles, the powdered

samples (seed, aerial parts and roots) of Amla,

Bahera and Harad (2g each) were transferred and

placed in a muffle furnace (pre-heated 600°C). The crucibles having samples were placed directly from

the furnace into a desiccator, and weight was taken

after cooling. The formula for ash contents (%) is as

follows:

Ash content % = Weight of ash

Initial weight of sample×100

Estimation of Crude protein content

The conventional microkjeldahl’s method was

adopted for the estimation of Crude protein content.

Multiplication % of N with the factor of 6.25 is done

for calculating crude protein.


Reagents

Digestion mixture: sulphuric acid and perchloric

acid were mixed in the ratio of 9:1.

N/100 sulphuric acid

N/100 sodium hydroxide

40% sodium hydroxide

Methyl red indicator

Method 100 mg of sample was taken in 100 mL micro-

kjeldahl’s digestion flask. (9:1) K2SO4 ∶ CuSO4

(⁓1g each) was added to it followed by 10 mL conc.

sulphuric acid. Continuous heating was done to

obtain clear solution. The flask was cooled and the

sample contents were diluted and final volume was

made upto 100 mL with double distilled water. 5 mL

aliquots of the digest were transferred to conical flask. 10 mL of N/100 sulphuric acid was taken in

the receiving flask. Then, 10 mL of the digested

sample was transferred to the steam chamber of the

apparatus followed by the addition of 10 mL of 40%

NaOH. Ammonia was distilled till 30-40 mL of

distillate was collected in the receiving flask. The

contents in the receiving flask were titrated against

N/100 NaOH and volume of NaOH used was noted.

The end point was reached when colour changed

from pink to yellow. The titration is performed with

the blank under the identical conditions.

Calculations Amount of nitrogen (%) = A-B

Where, A = Volume of NaOH used for blank

B = Volume of NaOH used for sample

Crude Protein content (%) in sample = Nitrogen

content in sample × 6.25

Crude fibre content

The modified method of Maynard (1970) was

followed for the estimation of crude fibre content

(%).

Reagents

1) 1.25% Sulphuric acid 2) 1.25% Sodium hydroxide

Method

The powdered sample (seed, aerial parts and roots) of

Amla, Bahera and Harad (2g each) was weighed and

transferred to a clean one litre beaker and added 200

mL of 1.25% (w/v) sulphuric acid. For heating, hot

plate was used to place the beaker and the contents

were allowed to reflux for 30 minutes’ time was

noted from onset of boiling and shaking is done after

every 5 min. After 30 min beaker was removed from

hot plate and filtration was performed using suction

through a muslin cloth. Washing of the residue was done with hot water till it became free from acid,

then the material was transferred to the same beaker

and after the addition of 200 mL of 1.25% NaOH

solution, the contents were again refluxed for 30 min.

It was followed by the filtration again through muslin

cloth with the help of suction pump and to free the

residue from alkali, it was washed with hot water and

then transferred to a crucible and placed in hot air

oven, allowed to dry to constant weight at 80-110°C

and recorded its weight. The residue was ignited in

muffle furnace at 550-660°C for 2-3 h, then cooled

and weighed again. The loss of weight due to

ignition is weight of crude fibre.

100sample of wt.Original

fibre crude of Wt. (%)content fibre Crude

Total carbohydrates content

The difference is used for the calculation of Total

carbohydrates content as follows:

Total carbohydrates content (%) =

100 – [Moisture (%) + Crude Fat (%) + Ash (%) +

Crude Protein (%) + Crude fibre (%)]

Minerals content

Reagents 1) Diacid mixture: HNO3: HClO4 (4:1) just before use.

2) Hydrochloric mixture (1%): Conc. HCl (1 ml) in

50 mL distilled water and Distilled water was used in

making Vfinal as 100 mL.

Method The powdered sample of fruit pulp of Amla, Bahera

and Harad (⁓1g) was digested with 15 mL of di-acid

mixture (4HNO3:1HClO4) in a conical flask by

heating on hot plate in open space till clear white

precipitates settled down at bottom of conical flask.

The precipitates were dissolved in 1% HCl prepared by dilution with double distilled water, filtered and

Vfinal of filterate was made 50 mL using double

distilled water.


Proximate composition

Moisture content

The statistics of moisture content in different parts of

Amla, Bahera and Harad is given in Table 4.1.

Moisture content in seeds, aerial parts and roots of Amla was 34.12, 7.85 and 5.36%, respectively.

Moisture content in seeds, aerial parts and roots of

Bahera was 32.35, 7.64 and 4.48%, respectively. The

corresponding values of moisture content in Harad

were 17.27, 7.58 and 3.28%, respectively.

Crude Fat content The data of crude fat content in different parts of

Amla, Bahera and Harad is given in Table 1. Fat

content in seeds, aerial parts and roots of Amla was

2.95, 1.10 and 0.19%, respectively. Fat content in

seeds, aerial parts and roots of Bahera was 3.44, 0.55

and 4.23%, respectively. The corresponding values of fat content in Harad were 2.97, 1.40 and 0.58%,

respectively.

Ash content

The data of ash content in different parts of Amla,

Bahera and Harad is given in Table 1. Ash content in

seeds, aerial parts and roots of Amla was 11.28, 3.75

and 4.78%, respectively. Ash content in seeds, aerial

parts and roots of Bahera was 11.75, 2.75 and 0.84%,

respectively. The corresponding values of ash


content in Harad were 8.56, 2.61 and 3.48%,

respectively.

Crude Protein content The data of crude protein content in different parts of

Amla, Bahera and Harad is given in Table 1. Crude

Protein content in seeds, aerial parts and roots of Amla was 10.39, 13.93 and 7.63%, respectively.

Protein content in seeds, aerial parts and roots of

Bahera was 18.29, 3.17 and 1.71%, respectively. The

corresponding values of crude protein content in

Harad were 18.45, 3.70 and 2.24%, respectively.

Crude fibre content

The data of crude fibre content in different parts of

Amla, Bahera and Harad is given in Table 1. Crude

fibre content in seeds, aerial parts and roots of Amla

was 5.23, 33.45 and 36.22%, respectively. Crude

fibre content in seeds, aerial parts and roots of

Bahera was 14.84, 42.85 and 43.57%, respectively.

The corresponding values of crude fibre content in

Harad were 16.45, 48.62 and 40.67%, respectively

Total carbohydrates content The data of total carbohydrates content in different

parts of Amla, Bahera and Harad is given in Table 1.

Total carbohydrates content in seeds, aerial parts and

roots of Amla was 35.98, 40.42 and 45.30%,

respectively. Total carbohydrates content in seeds,

aerial parts and roots of Bahera was 19.30, 43.23 and

49.22%, respectively. The corresponding values of

total carbohydrates content in Harad were 36.31,

43.49 and 49.76%, respectively.


Fig. 1. Proximate composition in different parts of Amla, Bahera and Harad

Table 1. Proximate composition in different parts of Amla, Bahera and Harad

Plant Parameter Seeds Aerial Parts Roots

AMLA

Moisture (%) 34.12 ± 0.1 7.85±0.03 5.36±0.01

Crude Fat (%) 2.95 ± 0.03 1.10±0.003 0.70±0.06

Ash (%) 11.28 ±0.01 3.25±0.01 4.78±0.01

Crude Protein (%) 10.39±0.02 13.93±0.02 7.63±0.01

Crude fibre (%) 5.23±0.02 33.45±0.01 36.22±0.02

Total Carbohydrates (%) 35.98±0.10 40.42±0.04 45.30±0.01

BAHERA

Moisture (%) 32.35 ± 0.03 7.64±0.02 4.48±0.01

Crude Fat (%) 3.44 ± 0.01 0.55±0.01 0.19±0.01

Ash (%) 11.75±0.01 2.56±0.02 0.84±0.01

Crude Protein (%) 18.29±0.02 3.17±0.02 1.71±0.01

Crude fibre (%) 14.84±0.02 42.85±0.01 43.57±0.01


HARAD

Moisture (%) 17.27± 0.01 7.58±0.02 3.28±0.01

Crude Fat (%) 2.97 ± 0.01 1.40±0.11 0.577±0.01

Ash (%) 8.56±0.003 2.61±0.01 3.48±0.01

Crude Protein (%) 18.45±0.03 3.70±0.01 2.24±0.01

Crude fibre (%) 16.45±0.02 48.62±0.01 40.67±0.01


The above mentioned plants were selected to

compare the proximate parameters and

micronutrients composition. Since many of these

herbal products are used orally, therefore, to know

proximate and nutrient analysis of these products and

raw material used therein plays a crucial role in

assessing nutritional significance and health effects [9-11]. The result of proximate analysis shows

variant concentration/proportions of bio-chemicals

and other contents. The difference found in the

proportion of proximate parameter of these medicinal

plants might be attributed to the conditions on which

the plant species are harvested along with

environmental parameters [12,13].

Mineral composition

Iron (Fe) content

The data of Fe content in fruit pulp of Amla, Bahera

and Hard is given in Table 2. Fe content in the fruit

pulp of Amla, Bahera and Harad was 62.13, 220.53

and 30.05 ppm, respectively.

Copper (Cu) content

The data of Cu content in fruit pulp of Amla, Bahera

and Harad is given in Table 2. Cu content in the fruit

pulp of Amla, Bahera and Harad was 5.94, 6.37 and

7.33 ppm, respectively.

Zinc (Zn) content

The data of Zn content in the fruit pulp of Amla,

Bahera and Harad is given in Table 2. Zn content in

the fruit pulp of Amla, Bahera and Harad was 38.04,

32.87 and 20.18 ppm, respectively.

Manganese (Mn) content

The data of Mn content in the fruit pulp of Amla,

Bahera and Harad is given in Table 2. Mn content in

the fruit pulp of Amla, Bahera and Harad was

204.74, 50.38 and 34.57 ppm, respectively.


Table 2. Minerals content (ppm) in the fruit pulp of Amla, Bahera and Harad

Plant

part

Minerals

Plants

Mineral content (ppm)

Amla Bahera Harad

Fruit

Fe 62.13 ±0.04 220.53 ± 0.238 30.05±0.019

Cu 5.94 ± 0.052 6.37 ± 0.120 7.33±0.145

Zn 38.04±0.012 32.87±0.291 20.18±0.007

Mn 204.74 ± 0.15 50.38± 0.01 34.57±0.088

SE(m) 0.09 0.225 0.085

CD at 5% 0.316 0.795 0.301

CV% 0.20 0.503 0.642

SE(d) 0.13 0.319 0.121

Fig. 2. Minerals content (ppm) in the fruit pulp of Amla, Bahera and Harad

The mineral content analysis of the medicinal plant

species showed considerable variation among

different micronutrients. However, for some species

difference or higher concentration was recorded,

which may be due to prevailing environmental and

soil conditions and the season when the plants were

collected for analysis [13-15].

CONCLUDING RESULTS

Trends in Proximate Composition

Parameter Order

Moisture (%) Amla>Bahera>Harad (Seeds>Aerial parts> Roots)

Crude Fat (%) Bahera>Harad>Amla (Seeds>Aerial parts> Roots)

Ash (%) Bahera>Amla> Harad (Seeds>Roots>Aerial parts)

Crude Protein (%) Harad>Bahera>Amla (Seeds>Aerial parts> Roots)

Crude Fibre (%) Harad>Bahera>Amla (Roots>Aerial parts> Seeds)

Total Carbohydrates (%) Harad>Amla>Bahera (Seeds>Aerial parts> Roots)


Trends in Minerals Composition

CONCLUSION

Different parts of these medicinal plants namely

Amla, Bahera and Harad were analyzed in order to

get some useful information to be used in the

preparation of therapeutic and nutraceutical foods.

As there are no chief reports in literature on detailed

proximate composition and nutritional parameters of medicinal plants’ parts, this paper should be

considered as a contribution to the course, being, a

far from the knowledge for the active constituents

formation from these medicinal parts of herbal

plants.

ACKNOWLEDGEMENT

We are thankful to CSIR, New Delhi for providing

the financial support and help during the research

work.

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[7] Rajarama, Rao, M.R. and Siddiqui, H.H. (1964). Pharmacological studies on Emblica

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[8] Halliwell, B. and Gutteridge, J.M.C. (1985). Free radicals, ageing and disease. In Free Radicals in

Biology and Medicine Clarendon: Oxford;279-315.

[9] Kochhar, A., Nagi, M. and Sachdeva, R. (2006). Proximate Composition, Available

Carbohydrates, Dietary Fibre and Anti Nutritional

Factors of Selected Traditional Medicinal Plants. J.

Hum. Ecol., 19(3): 195-199.

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R.P. (2006). Nutritional Evaluation of Leafy

Vegetable Paratha J. Hum. Ecol. 19 (2): 155-156

[11] Taiga, A., Suleiman, M.N., Aina, D.O., Sule, W.F. and Alege, G.O. (2008). Proximate analysis of

some dry season vegetables in Anyigba, Kogi State,

Nigeria. Afr. J. Biotechnol. 7(10): 1588-1590.

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E. and Oshaug, A. (1996). Nutrient composition and

nutritional importance of green leaves and wild food

resources in an agricultural district, Koutiala, in

southern Mali. Int. J. Food Sci. Nutr., 47: 455–478.

[13] Kutbay, H.G. and Ok, T. (2001). Foliar N and

P resorbtion and nutrient levels along an elevational

gradient Zelkova carpinifolia (Pall.) C. Koch subsp.

Yomraensis. Ann. Agric. Bio. Res., 6:1-8. [14] Patterson, E. (1996). Standardized extracts:

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Minerals composition Order

Fe content (ppm) Bahera>Amla>Harad

Cu Content(ppm) Harad>Bahera>Amla

Zn Content (ppm) Amla>Bahera>Harad

Mn Content (ppm) Amla>Bahera>Harad




VALIDATION OF MAS DERIVED LINES FOR INTROGRESSED GENE AGAINST

BLAST AND BLB RESISTANCEIN SOUTHERN CHHATTISGARH

Prafull Kumar*

S.G. College of Agriculture and research Station, Jagdalpur, IGKV, Raipur, Chhattisgarh



Abstract: The experiment was carried out at SGCARS, Jagdalpur, IGKV Raipur, Chhattisgarh to validate Marker Assisted Selection (MAS) derived genotypes, from ICAR-IIRR, Hyderabad, against blast and bacterial leaf blight resistance and access recurrent parent recovery. BPT 5204 (Samba Mahsuri) the recurrent parent 01 (RP 1) for the four test genotype recorded average plot yield of 4.00 kg/ha placing second in the experiment. When the recurrent parent 02 (Improved Sambha Mahsuri) was taken into account, genotype RP-Patho-1-2-15 recorded higher plot yield (4.23 kg). RP-Patho-1-2-15 and RP-Patho-3-56-11 were similar to the recurrent parent with heading spanof 78 and 79 DAS accordingly while RP-Patho-3-73-6

was six days in advance (70 days) to the recurrent parent (76 days). The entire test Near Isogenic Lines (NILs) with a plant height of 79-85 cm were similar to the recurrent parent (82 cm). Blast and bacterial leaf blight resistance gene carrying genotypes RP-Patho-2-18-5 and RP-Patho-2-16-4gave plot grain yield 3.77kg, which out yielded recurrent parent 02 but lesser than recurrent parent 01.Incidence of blast reported in Tetep (1-2%), C 101 LAC (8-10%)and average (5-8%) in all NILs.Blast resistance genes Pi 1 carrying genotypes RP-Patho-1-2-15 and RP-Patho-1-6-5, the infestation the comparatively higher (Score 6) than those with Pi 54 (RP-Patho-3-56-11 and RP-Patho-3-73-6, RP-Patho-3-56-11 and RP-Patho-3-73-6 (Score 4). However, dual genetic resistance background i.e. blast and bacterial blight resistance genes Xa 21+ Pi-54, provided excellent resistance even in hot spot centre for the disease. There was noincidence of Bacterial Leaf Blight (BLB)in

all the isogenic line including donor and recurrent parents which may because of plant defence system or incompatible environmental condition for disease prevalence. Keywords: NILs, Marker Assisted Selection, Recurrent parent, Blast, Bacterial Leaf Blight

INTRODUCTION

ice (Oryza sativa L.) is one of the vital

cultivated crop, which provides food for more

than half of the world’s population and constitutes a major source of calories for urban and rural

inhabitants (Singh et al., 2015,Kumar et al., 2015a),

regrettably, whose production is constrained by

substantial number of fungal, bacterial and viral

origin diseases. Rice blast (caused by

Magnaporthegrisea) and bacterial leaf blight (BLB,

caused by Xanthomonas oryzaepv. Oryzae, Xoo) are

two most destructive diseases leading to severe yield

losses in rice production worldwide (Zhan et al.,

2012). Bacterial leaf blight (BLB) is one of the most

devastating diseases affecting entire rice acreages

and causes severe yield losses of up to 74-81% depending upon crop stage, cultivar susceptibility

index and the environmental phenomenon

(Srinivasan et al., 2005). Rice blast, caused by the

filamentous ascomycete fungus Magnaportheoryzae

(anamorph Pyriculariaoryzae), is another major

threats for rice production and leads to significant

yield loss, as high as 70–80% during an epidemic

(Khushet al., 2009).The most effective approach to

prevent the two diseases is the genetic improvement

using resistant varieties however, narrow genetic

diversity in existing gene pool is problematic in breeding for adaption to these major biotic stresses

(Sattariet al., 2014). Therefore, exploitation of host

plant resistance is emerging as most effective,

economical and environmentally safe measure for

controlling paddy blast and bacterial leaf blight in

combination with pathological management. So far,

73 blast resistance genes and 31 BLB resistance

genes have been identified (Balliniet al, 2008,

Ruanet al., 2008, Cheema et al., 2008, Sujatha et al., 2011) and some of them have been incorporated into

modern rice varieties (Sundaram et al., 2008, 2009)

through marker assisted selection (MAS).Among

these, few genes like Pi-1, Pi-2, Pi-9, Pi-54 etc. (for

blast resistance) and Xa5, Xa 13,Xa21, etc. (for

bacterial leaf blight resistance) are being extensively

used in ricebreeding programmes globally as are

highly effective and having tightly linked molecular

markers.

Marker Assisted Backcross Selection (MABS) has

previously been used in rice breeding toincorporate

Sub1gene of mega-variety Swarna to a submergencetolerant variety and IR64SUB1 for

developing a newsubmergence tolerant rice variety

ASS996-SUB1(Neerajaet al. 2007, Septiningsihet al.

2009, Luuetal. 2012). It was also used to

incorporatebadh2 and Wxgene from Basmati into

Manawthukha for cookingquality parameters (Yi et

al. 2009), and Pup1 under Phosphorus

deficientlowland/irrigated conditions into

SituBagendit and Batur (Chin et al. 2011). Rice

salttolerance on BT7 cultivar, FL478 was used as a

donorparent of SaltolQTL (Linh et al. 2012) and threeresistance genes (Xa4 + xa5 + Xa21) to bacterial

leafblight were transferred from an indica donor

(IRBB57)to Korean rice Mangeumbyeo (Suh et al.

2013). More recently Fatimah et al., (2014)

R

RESEARCH ARTICLE


232 PRAFULL KUMAR

successfully transferred hd2genein rice for early

heading date.With these background, some of

theICAR-IIRR (Formerly DRR) Hyderabad

developed MAS breeding lines, obtained under

AICRIP programme, possessing high level of

resistance against blast (conferred by Pi1 or Pi54) and bacterialblight (conferred by Xa21) was

evaluated for trait verification and for substantial

equivalence with the recurrent parents in southern

Chhattisgarh province.


The Study Material

The breeding materials were obtained from Indian

Institute of Rice Research, (ICAR-IIRR, formerly

DRR), Hyderabad under AICRIP programme in

Advanced Varietal Trial 01- Near Isogenic Lines (AVT-1-NILs). The trial was constituted with 6 test

entries, 2 recurrentparents and 2 donor parents. Entry

RP-Patho-1-2-15 and RP-Patho-1-6-5 possess Pi-1,

resistant to leaf blast. Entry RP-Patho-3-56-11 and

RP-Patho-3-73-6 possess Pi-54, conferring resistance

to leaf blast(DRR, 2014). Entry RP-Patho-2-18-5 and

RP-Patho-2-16-4were derived from backcross

between Improved Samba Mahsuri(recurrent parent)

and Tetep (donor parent) possessing combined

resistance to leaf blastand bacterial leaf blight as they

possess the resistance genes Pi-54 and Xa21 respectively(DRR, 2014, Table 01).

Experiment Conduction and Statistical Analysis

The trireplicated field experiment was conducted at

Rice Research Block of S. G. College of Agriculture

and Research Station, Jagdalpur, Chhattisgarh, India

in Kharif 2013-14 with 5x2.6m (Net plot) plot size,

Randomized Complete Block Design. Standard

agronomic package was followed to raise the crop.

Restriction selection indice was constructed based on

previous research review and four quantitative

parameters were selected for genetic evaluation. The

observation was recorded on net plot basis (heading date and plot yield), unit plot basis (panicles/sq M)

and arithmetic mean basis among random

selections.Days to 50 percent flowering was recorded

when half of plant flowered among the plot. Panicle

count was made with one square meter square at

maturity. The disease scoring was made

visually giving the score 1-9. Statistical analysis was

done with SPARK 2.


Rice is oldest domesticated cereal crop imparting diet

to three billions globally (Shrivastava et al., 2014) of

which irrigated rice account for 55 percent of area

and 75 percent of production (Kumar et al., 2015b).

Era of input oriented agriculture and changing

climate have led this important crop susceptible to

many diseases mainly Blast and BLB and scenario

became more critical when narrowed crop genetic

base and expending pathogen biotype failed the

monogenic plant breeding approach.Recently,

pyramiding of more than one major resistance gene

has been proven to deliver durable resistance

(Rajpurohitet al., 2010).Since, conventional breeding

tools are inefficient for gene pyramiding, particularly in recessively inherited resistance, such as xa5 and

xa13, marker assisted selection (MAS) enables to

address these limitations by the evaluation of the

expression status of resistance gene(s).

Genotypic response referring to Recurrent and

Donor parent

The test entries were evaluated at Jagdalpur centre,

hot spot location for rice blast disease, under natural

field conditions. The experimental yield ranged from

2.67kg/plot(Improved sambhaMahsuri) to 4.23/plot

(RP-Patho-1-2-15). The Recurrent parent 01 (BPT

5204) was the earliest to flower with 50% flowering in 91 DASfollowed by donor parent 01 (92 DAS)

while RP-Patho-1-2-15 and RP-Patho-3-73-6

recorded to be late flowering genotypes with 113

DAS days to 50 percent flowering. The experimental

mean for blooming period was 101 DAS. RP-Patho-

3-73-6, BPT 5204 and RP-Patho-1-2-15 were short

in plant height (70, 76 and 78cms respectively) while

Tetep was recorded to be tallest (115cms) (Table 02)

whereas, the mean plant height was reported to be

84.5 cms. Panicles count wassignificantly higher for

BPT 5204, RP-Patho-3-73-11 and RP-Patho-1-6-5 (348, 324 and 312 correspondingly) while RP-Patho-

1-2-15 and Tetep exhibited comparative lower count.

The grain yield was in perfect direction with panicles

number. BPT 5204 (Samba Mahsuri) the recurrent

parent 1 (RP 1) for the four test genotype

recordedaverageplot yield of 4.00 kg/ha placing

second in the experiment. None of the entry recorded

on par with recurrent parent 01 statically however, all

the genotypes no significant difference was observed

with respect to grain yield. When the recurrent parent

02 (Improved Sambha Mahsuri) was taken into

account, genotype RP-Patho-1-2-15 recorded higher plot yield (4.23 kg) and was at par. Flowering

duration wise, RP-Patho-1-2-15 and RP-Patho-3-56-

11 were similar to the recurrent parent with

flowering of 78 and 79 DAS accordingly while RP-

Patho-3-73-6 was six daysin advance (70 days) to the

recurrentparent (76 days). All the test isogenic lines

with a plant height of 79-85 cm were similar to

therecurrent parent (82 cm).Blast and bacterial leaf

blight resistance gene carrying genotypes RP-Patho-

2-18-5 and RP-Patho-2-16-4gave plot grain yield

3.77kg, which out yielded recurrent parent 02 but lesser than recurrent parent 01. However flowering

was delayed (96 DAS and 99 DAS) and plant height

was increased in both the isogenic lines (83cms and

81 cms). The same trend was continued with panicle

count. The donor lines Tetep (DP 1) and C 101 LAC

(DP 2) showed optimal adaption to Southern

Chhattisgarh rice growing ecology. Average plant

height of Tetep was 91 cms and attained 50 percent


flowering by 98 DAS. Since vegetative span was

lengthened, panicle count (300) and plot yield also

increased accordingly (3.43). Taking into

accountanother donor parent (Tetep), it exhibited

faster growth rate and attained 115cms plant height

by 92 DAS of preflowering period. Compare to DP 2produced lesser panicle population (264) and plot

grain yield (3.15kg).

Resistance breeding by deploying target genes has

always been a topic of interest and intense research

for breeders and molecular biologist (Sundramet al.,

2008, Zhan et al., 2012). Pyramiding major genes to

enhance resistance spectrum in commercial cultivars

has been proven by many workers (Hittalmaniet al,

2000, Chen et al, 2001, Zhan and Cheng, 2001,

Narayanan et al, 2002, 2004, Joseph et al, 2004,

Neerja et al., 2007, Chen et al., 2008, 2009, Fujita et

al., 2010).Marker-assisted selection for genes and disease evaluation for genetic verification can be

conducted in laboratory conditions during seedling

stage at first followed by field evaluation to access

the environmental play. The subsequent selection for

complex quantitative traits such as combining ability,

grain qualityand grain yield can be carried out in the

fields during heading and maturity stage. Combined

laboratory and field evaluation at early and late

stages make MAS breeding more efficient and

assured than conventional breeding (Zhan et al.,

2012). Once disease resistance genes are standardised by MAS, rice breeding can be focused

on combining and restoring the agronomic traits

(Lang et al., 2008, Pinta et al., 2013). The biphasic

selection in breeding programmeis promising to

overcome the defects of conventional hybridization

breeding in which restorer genes and resistance genes

usually have low recombinant frequency (Cao et al.,

2003) that requires immense effort to identify the

individuals combining both the resistance and

restoring ability in research programme.

Scoring for Blast and Bacterial Leaf Blight

Morphologically all the isogenic lines were parallel to the respective recurrent parent, blast prevalence

was modest inboth Recurrent Parents. Incidence of

blast reported in Tetep (1-2%), C 101 LAC (8-10%),

all isogenic lines (5-8%).Blast resistance genes Pi 1

carrying genotypes RP-Patho-1-2-15 and RP-Patho-

1-6-5, the infestation the comparatively higher (Score

6) however those with Pi 54 (RP-Patho-3-56-11 and

RP-Patho-3-73-6). Similarly RP-Patho-3-56-11 and

RP-Patho-3-73-6, having the blast resistance gene,

(Pi 54) showed average disease incidence (Score 4).

However, dual genetic resistance background i.e. both blast and bacterial blight resistance genes Xa

21+ Pi-54, provided excellent resistance even in hot

spot centre for the disease. In previous studies

multiple resistance gene pyramiding has been

reported by Chen et al., 2009 and Zhan et al., 2012.

In genotype RP-Patho-2-16-4 the disease was scaled

to be 01, which was quite similar to recurrent parent

02 (Improved Sambha Mahsuri). Visual evaluation of

donor parents revealed average disease incidence

(score 4).There was noincidence of Bacterial Leaf

Blightin all the isogenic line including donor and

recurrent parents which may because of plant

defence system or incompatible environmental

condition for disease prevalence.Plant adopts several strategies to defend against pathogens, including the

production of antibacterialchemicals, which are

either preformed (i.e. already present in plant tissue

in variableamounts) or induced following infection

(e.g. de novo synthesized phytoalexins) (Sattariet al.,

2014). Many of the phytoalexins or pre-formed

chemicals belong to the phenolic group (Latif, 2007).

Phenolic compounds are secondary metabolites

synthesized in plantsand play a role in plant defence

against pathogens through antimicrobial properties,

involvement in cell wall reinforcement, modulation

and induction of plant responses (Aly, 2002). Generally, when a plant is infected, its phenolic

content increases in response todefence reaction (Ma,

2002). In addition, biochemical resistance is another

complex but equally vital mechanism in plant

pathogen defence specifically accumulation of

peroxidase, catalase and other pathogenesis related

proteins.

Recovery of Recurrent Parent

Almost all the genotypes exhibited stable recovery of

one or both recurrent parents indicating stabilization

of population allelic recombination. Perusing radar chart of recurrent parent recovery (Fig 01)

reproductive behaviour were similar to BPT 5205

and Improved SambhaMahsuri for all genotypes

except RP-Patho-1-2-15 and RP-Patho-2-16-4 but it

may attribute to G x E interactions rather than

genetic segregation. Similarly, for above ground

plant canopy length all test genotypes were relatively

more closure to both the recurrent parents which

shows comparative expression of photosynthates

source sink balance in Near Isogenic Lines (NILs).

However, donor parent i.e. Tetep, plant height was

significantly higher might be due to regional quantitative adaptability. Looking for sink strength,

BPT 5205 recorded significantly higher potential of

dry matter accumulation than remaining population

due to genetic establishment in given environment.

Parallel observation was recorded for RP-Patho-3-

56-11, RP-Patho-3-73-6 and RP-Patho-2-18-5

showing potent leaf blast and bacterial leaf blight

resistance for Southern province of Chhattisgarh.

Ultimately, when crop yield was taken into account,

the normal distribution curve shifted towards the

recurrent parent 01 and the entire test entries lied between 1750 kg/ha to 3075 kg/ha. Summarily,

sustainable yield potential was realized despite of

target disease incidence all genotypes may be

considered as resistance genotypes.

The varietal improvement for resistance to major

prevalent and destructive diseases is necessary for

sustainable grain yield. Past attempts to achieve

Bacterial Leaf Blight (BLB) resistance is not very

234 PRAFULL KUMAR

much encouraging, largely due to disease variability

level in growing areas (Sreewongchai, 2010).

Pyramiding disease resistant genes into a single

genetic background might be expected to give more

durable disease resistance, as more resistant genes

are incorporated into single genotypes (Koide, 2010).Till date, as many as 24 major genes of host

plant resistance which have been identified and used

in rice improvement programme (Rao, 2002).The

BLB resistance becomes quantitative when using

NILs with four resistance genes (R gene) (Xa4, xa5,

xa13, and Xa21) that express a higher level and more

durable resistance. Further, the resistant genes to

BLB, Xa4, and xa13, links to microsatellites markers

RM144 and RM122, respectively, and xa5 links to

STS marker (RG136) (Sattariet al., 2014).

CONCLUSION

Many disease resistant varieties have been developed

but they have not been widely adopted as tolerant

varieties lack many of desirable traits of the widely

grown mega varieties and hencereplacement of these

megavarieties with modern cultivars cannot be

possible.However, these megavarieties despite

having many agronomically desirablecharacters,

often susceptible to biotic and abiotic stress. With advances in molecular biology, breeding for

resistance genes has become wise and efficient

strategy in crop improvement. In present

investigation evaluation was made of DRR bred

materials for hot spot of paddy blast. All the

genotypes were similar to recurrent parent with

respect to flowering duration, canopy length, and plot

grain yield and disease reaction indicating the

recovery of crossed material to desired genetic

background after introgression of dual resistance

gene and equally and effectivelyeven in hot spot

centre for the disease. No incidence of bacterial leaf blight (BLB) was observed and the reason may be

excellent resistant source or environmental cause.

Table 1. Composition of Entries (Near Isogenic Lines) for Recurrent Parent Recovery Analysis (RPRA) S

No

Genotype Cross Combination Resistance Gene Grain

Type

01. RP-Patho-1-2-15 BPT 5204*2/C 101 LAC Blast Resistance Gene Pi-1 MS

02. RP-Patho-1-6-5 BPT 5204*2/C 101 LAC Blast Resistance Gene Pi-1 MS

03. RP-Patho-3-56-11 BPT 5204*2/ Tetep Blast Resistance Gene Pi-kh (Pi-54) MS

04. RP-Patho-3-73-6 BPT 5204*2/Tetep Blast Resistance Gene Pi-kh (Pi-54) MS

05. RP-Patho-2-18-5 Improved Sambha

Mahsuri/Tetep

Blast and Bacterial Blight Resistant Gene

Xa21+Pi-kh (Pi-54)

MS

06. RP-Patho-1-2-15 Improved Sambha

Mahsuri/Tetep

Blast and Bacterial Blight Resistant Gene

Xa21+Pi-kh (Pi-54)

MS

07. BPT 5204 Recurrent Parent 01(RP 01)

08. Improved Sambha

Mahsuri

Recurrent Parent 02 (RP 02)

09. Tetep Donor parent 01 (DP 01)

10. C 101 LAC Donor Parent 02 (DP 02)

Source: DRR Annual Report, 2014.

Table 2. Ancillary Traits Data following Restriction Selection Indices Plant Height (cm) Days to 50 percent Flowering Panicles/sqm Grain Yield/Plot

Mean SE Mean SE (±) Mean SE (±) Mean SE (±)

RP- Patho-1-2-15 78 4.98 113 0.88 257 12.42 4.23 0.15

RP- Patho-1-6-5 90 3.84 108 0.33 312 24.12 3.87 0.41

RP- Patho-3-56-11 79 4.33 113 0.83 324 12.2 3.7 0.3

RP- Patho-3-73-6 70 4.97 107 1.16 331 12.44 3.03 0.35

RP- Patho-2-18-5 83 4.33 99 0.88 286 7.26 3.87 0.26

RP- Patho-2-16-4 81 3.18 96 0.81 272 5.81 3.87 0.23

BPT 5204 (RP 1) 76 4.63 91 0.67 348 10.1 4 0.17

Improved Samba Mahsuri (RP 2) 82 2.72 94 0.58 276 8.95 2.67 0.09

Tetep (DP 1) 115 4.53 92 0.57 264 6.96 3.17 0.34


C 101 LAC (DP 2) 91 3.53 98 0.33 300 10.44 3.43 0.2

Critical Difference 8.74 2.39 37.54 0.68

Standard Error (difference) 4.13 1.13 17.73 0.32

Standard Error (mean) 2.92 0.79 12.54 0.23

Coefficient of Variation (%)

(Relative measure of dispersion) 5.97 1.37 7.31 11.01

Fig 1. Radar chart of Recurrent parent recovery pattern

*A- Days to 50 percent flowering, B-Plant height, C-Panicle per square meter, D-Grain Yield

ACKNOWLEDGEMENT

The authors acknowledge Indian Institute of Rice Research (ICAR-IIRR formerly DRR), Hyderabad

for providing study material and financial support.

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238 PRAFULL KUMAR



FORECASTING MONTHLY PRECIPITATION MODEL FOR DANTEWADA,

JAGDALPUR AND SUKMA REGION (CHHATTISGARH) USING ARIMA MODEL

Anosh Graham*1, Avinash Yadu

2 and Atul Galav

3

1,3

Department of Environmental Sciences and NRM, College of Forestry, Sam Higginbottom

University of Agriculture, Technology & Sciences Allahabad-211007,

Uttar Pradesh, India 2Department of Agrometeorology, Indra Gandhi Krishi Vishwavidyalaya,

Raipur-492012, Chhattisgarh (India)


Abstract: Earlier forecasting was based on observing weather patterns. Latter-days weather forecasting involves a combination of computer models, observations and knowledge of tends and patterns. This paper describes the Box-Jenkins time series seasonal ARIMA (Auto Regression Integrated Moving Average) approach for prediction of rainfall on monthly scales. ARIMA model of Dantewada (0, 0, 1) (0, 1, 1), Jagdalpur (0, 0, 0) (1, 1, 1), Sukma (0, 0, 1) (1, 1, 1) for rainfall was identified the best model to forecast rainfall for next 5 years with confidence level of 95 percent by analyzing last 30 year ’s

data (1989-20018). Previous years data is used to formulate the seasonal ARIMA model and in determination of model parameters. The performance evaluations of the adopted models are carried out on the basis of correlation coefficient (R2) and root mean square error (RMSE). The study conducted at three cities Dantewada, Jagdalpur & Sukma, Chhattisgarh (India). The results indicate that the ARIMA model provide consistent and satisfactory predictions for rainfall parameters on monthly scale.

Keywords: Rainfall, ARIMA, Correlation Coefficient (R2), Root Mean Square Error (RMSE)

INTRODUCTION

ainfall is a stochastic process which depends on

so many parameters and these properties make

forecasting of rainfall a formidable challenge.

Information about rainfall is really essential for the

planning and management of water resources.

Forecasting of rainfall is vital as it is very much

important for flood warning. Rainfall mostly occurs

during a season called Monsoon and major part of

the annual rainfall occurs in this monsoon.

A wide range of rainfall forecast methods are

employed in weather prediction at regional and

national levels. According to Somvanshi et al.

(2006), rainfall is natural climatic occurrences and its

prediction remains a difficult challenge as a result of

climatic variability. The forecast of precipitation is

particularly relevant to agriculture, growth of plants

and development, which profoundly contribute to the

economy of Africa. In the statement of the above

authors, attempts have been made to predict

behavioral pattern of rainfall using autoregressive

integrated moving average (ARIMA) technique. In

agricultural planning the understanding of rainfall

variability and its prediction has great significance in

the agricultural management and helps in decision

making process. Rainfall information is an important

input in the hydrological modeling, predicting

extreme precipitation events such as droughts and

floods, for planning and management of irrigation

projects and agricultural production is very important

[Nirmala 2015]. Etuk and Mohamed (2014) fitted a

SARIMA (0, 0, 0) x (0, 1, 1)12 model to monthly

rainfall in Gadaref, Sudan. The Box-Jenkins

Seasonal ARIMA (SARIMA) model has several

advantages over other models, particularly over

exponential smoothing and neural network, due to its

forecasting capability and richer information on

time-related changes. ARIMA model consider the

serial correlation which is the most important

characteristic of time series data. ARIMA model also

provides a systematic option to identify a better

model. Another advantage of ARIMA model is that

the model uses less parameter to describe a time

series.


Study area

Dantewada - The Dantewada lies on 370m above sea

level. The climate here is tropical. When compared

with winter, the summers have much more rainfall.

This location is classified as Aw by Köppen and

Geiger. The temperature here averages 26.2 °C | 79.1

°F. The annual rainfall is 1391 mm | 54.8 inch. The

driest month is December, with 3 mm | 0.1 inch of

rain. In August, the precipitation reaches its peak,

with an average of 405 mm | 15.9 inch.

Sukma - The Sukma lies on 216m above sea level.

Sukma has a tropical climate. The summers are much

rainier than the winters in Sukma. The Köppen-

Geiger climate classification is Aw. The temperature

R

RESEARCH ARTICLE

240 ANOSH GRAHAM, AVINASH YADU AND ATUL GALAV

here averages 27.0 °C | 80.6 °F. The rainfall here is

around 1477 mm | 58.1 inch per year. The driest

month is February, with 3 mm | 0.1 inch of rainfall.

The greatest amount of precipitation occurs in July,

with an average of 416 mm | 16.4 inch.

Jagdalpur - The Jagdalpur lies on 558m above sea

level .The climate here is tropical. When compared

with winter, the summers have much more rainfall.

The climate here is classified as Aw by the Köppen-

Geiger system. The average annual temperature is

25.0 °C | 77.0 °F in Jagdalpur. The annual rainfall is

1451 mm | 57.1 inch. The driest month is January,

with 7 mm | 0.3 inch of rain. In August, the

precipitation reaches its peak, with an average of 371

mm | 14.6 inch.

Data Collection

Daily rainfall data (mm) for the past 30 years from

1989 to 2018 was collected from Department of

Agrometeorology IGKV Raipur, for forecasting.

Software used

SPSS Auto Regressive Integrated Moving Average

(ARIMA) models were selected using SPSS software

to find the best fit of a time series to past values of

this time series in order to make forecasts.

METHODOLOGY

Box and Jenkins (1976) have effectively put

together in a comprehensive manner, the relevant

information required to understand and use time

series ARIMA models. A detailed strategy for the

construction of linear stochastic equation describing

the behavior of time series was examined. Consider

the function Zt represents forecasted rainfall and

temperature at time t month. Yt is series of observed

data of rainfall and temperature at time t. If series is

stationary, then an ARIMA process can be

represented as

∇pZt = ∇qYt …. (1)

Where ∇ is a back shift operator. If series Y is not

stationary then it can be reduced to a stationary series

by differencing a finite number of times.

∇pZt = ∇q (1-B) d Yt …. (2)

Where d is a positive integer, and B is back shift

operator on the index of time series so that

B Yt = Yt -1; B2Yt = Yt -2 and so on. Thus further

equation (2) can be simplified into following

equation.

(1-Ф1B-Ф2B2-…….-ФpBp) Zt = θ0+ (1-θ1B-θ2B2-

………- θqBq) at ….. (3)

Where at’s a sequence of identically distributed

uncorrelated deviates, referred to as “white noise”.

Combining equations (2) and (3) yields the basic

Box-Jenkins models for non stationary time series (1-

Ф1B-Ф2B2-…….-ФpBp) (1-B)d Yt =θ0+ (1-θ1B-

θ2B2- ………-θqBq) at ….(4) Equation (4)

represents an ARIMA process of order (p,d,q).

Seasonal ARIMA model represented as follows for a

stationary series i.e. differencing parameters (d &ds

= 0) equal to Zero, used for forecasting rainfall.

∇ps ∇pZt =∇ qs ∇q Yt ….(5)

Where ps and qs are the seasonal parameters

corresponding to AR and MA process. Model of type

of equation (5) was fitted to given set of data using

an approach consists of mainly three steps (a)

identification (b) estimation (c) application

(forecasting) or diagnostic checking. At the

identification stage tentative values of p, d, q and ps,

ds, qs were chosen. Coefficients of variables used in

model were estimated. Finally diagnostic checks

were made to determine, whether the model fitted

adequately describes the given time series. Any

inadequacies discovered might suggest an alternative

form of the model, and whole iterative cycle of

identification, estimation and application was

repeated until a satisfactory model was obtained.


The model that seems to represent the behavior of the

series is searched, by the means of autocorrelation

function (ACF) and partial auto correlation function

(PACF), for further investigation and parameter

estimation. The behavior of ACF and PACF is to see

whether the series is stationary or not. For modeling

by ACF and PACF methods, examination of values

relative to auto regression and moving average were

made. An appropriate model for estimation of

monthly rainfall for Dantewada, Jagdalpur, Sukma

was finally found. Many models for Dantewada,

Jagdalpur, Sukma, according to the ACF and PACF

of the data, were examined to determine the best

model. The model that gives the minimum Bayer’s

Information Criterion (BIC) is selected as best fit

model, as shown in Table 1. Obviously, model

ARIMA Dantewada (0, 0, 1) (0, 1, 1), Jagdalpur (0,

0, 0) (1, 1, 1), Sukma (0, 0, 1) (1, 1, 1) has the

smallest values of BIC. Observed and predicted

values of next five years are determined and plotted

as shown in figure: 8,9,10.


Fig. 1. Observed rainfall of Sukma, Jagdalpur & Dantewada district (Jan1989-Dec2018)

Fig. 2. Autocorrelation function of rainfall Sukma.

Fig. 3. Partial Autocorrelation function of rainfall Sukma.

0

500

1000

1500

2000

2500

Jan

-89

Mar

-90

May

-91

Jul-

92

Sep

-93

No

v-9

4

Jan

-96

Mar

-97

May

-98

Jul-

99

Sep

-00

No

v-0

1

Jan

-03

Mar

-04

May

-05

Jul-

06

Sep

-07

No

v-0

8

Jan

-10

Mar

-11

May

-12

Jul-

13

Sep

-14

No

v-1

5

Jan

-17

Mar

-18

sukma jagdalpur dantewada


Fig. 4. Autocorrelation function of rainfall Jagdalpur.

Fig. 5. Partial Autocorrelation function of rainfall Jagdalpur.


Fig. 6. Autocorrelation function of rainfall Dantewada.

Fig. 7. Partial Autocorrelation function of rainfall Dantewada.


Fig. 8. Observed and fitted values of rainfall for Sukma.

Fig. 9. Observed and fitted values of rainfall for Jagdalpur.

Fig. 10. Observed and fitted values of rainfall for Dantewada


Table 1.

Dantewada Jagdalpur Sukma

Rainfall model (0, 0, 1) (0, 1, 1), (0, 0, 0) (1, 1, 1), (0, 0, 1) (1, 1, 1)

R2 0.788 0.76 0.68

RMSE 78.678 71.713 104.698

CONCLUSION

The Box-Jenkins ARIMA methodology was used to

develop monthly rainfall of Dantewada, Jagdalpur,

Sukma. The monthly rainfall is panning over the

period of 1989-2018 at Dantewada and the results are good for this region. In Jagdalpur region the

observed and forecasted data are fitted and showed

good results, In Sukma region the results were not so

good. The performance of resulting ARIMA model

was evaluated by using the data from the year 1989-

2018 through graphical comparison between the

forecasted and observed data. In ARIMA model the

forecasted and observed data of rainfall showed good

results. The study reveals that Box-Jenkins

methodology can be used as an appropriate tool to

forecast rainfall in Dantewada, Jagdalpur, Sukma for upcoming years.

REFERENCES

Box, G.E.P. and Jenkins, G.M. (1976). Time series

analysis: forecasting and control, Prentince Hall, Inc,

575.

Nirmala, M. and Sundaram, S.M. (2010). A

Seasonal Arima Model for Forecasting monthly

rainfall in Tamil Nadu. National. Journal on

Advances in Building Sciences and Mechanics.

1(2):43-47.

Etuk, E.H. and Mohamed, T.M. (2014). Time

Series Analysis of Monthly Rainfall data for the

Gadaref rainfall station, Sudan, by SARIMA

Methods. International Journal of Scientific Research in Knowledge. 2(7):320-327.

Reddy, J.C., Ganesh, T., Venkateswaran, M. and

Reddy, P.R.S. (2017). Forecasting of Monthly Mean

Rainfall in Coastal Andhra International Journal of

Statistics and Applications, 7(4): 197-204

Kaushik, I. and Singh, S.M. (2008). Seasonal

ARIMA model for forecasting of monthly rainfall

and temperature. International Journal of Agriculture

Sciences. 5(8):112-25.

Mohamed, T.M. and Ibrahim, A. (2016). Time

Series Analysis of Nyala Rainfall Using ARIMA Method Journal of Engineering and Computer

Science (JECS). 17(1):5-11.

Somvanshi, V., Pandey, O., Agrawal, P.,

Kalanker, N., Prakash, M.R. and Chand, R.

(2006). Modeling and prediction of rainfall using

artificial neural network and ARIMA techniques.

The Journal of Indian Geophysical Union. Vol. 10.

No. 2 p. 141–151.

Nirmala, M. (2015). Computational models for

forecasting annual rainfall in Tamilnadu. Applied

Mathematical Sciences. Vol. 9. Iss. 13 p. 617–621.


*Corresponding Author ________________________________________________ Journal of Plant Development Sciences Vol. 12(4): 247-251. 2020

DISEASE CONTROLLING POTENTIAL OF TRICHODERMA HARZIANUM AND

TRICHODERMA VIRIDE AGAINST COLLAR ROT OF CHICKPEA

Shweta Mishra*, Devendra Nishad and R.K.S. Tiwari

Department of Plant Pathology, Indira Gandhi Krishi Viswavidyalaya

Raipur (492001) Chhattisgarh


Received-06.04.2020, Revised-25.04.2020 Abstracts: Disease controlling potential of Trichoderma strains are evaluated in vivo against collar rot in chickpea. Ten Trichoderma strains (T1,T2,T3,T4,T5,T6,T7,T18,T28) were taken among which nine were Trichoderma harzianum and one Trichoderma viride (T18) .All strains of Trichoderma harzianum / Trichoderma viride was superior over control for disease controlling parameters i. e mortality percentage , no. of pods per / plant ,yield (quintal/hectare), yield (g/plot),Test weight. Plant population / plot were counted from each plot after 25 days of sowing Trichoderma strains T18 and T28 were more effective showing higher degree of parasitism on Sclerotium rolfsii under field against collar rot in chickpea. Keywords: Chickpea, Sclerotium rolfsii ,Trichoderma harzianum, Trichoderma viride

INTRODUCTION

hickpea is known in this country since ancient

times. It is a widely grown major pulse crop in

India, accounts for nearly 75 per cent of the total

pulse production in the world. Chickpea crop is

prone to many diseases viz., Fusarium wilt, dry root

rot, collar rot, Ascochyta blight, Verticillium wilt,

black root rot, Phytophthora root rot, wet root rot,

foot rot, Pythium rot and seed rot etc. Among these,

collar rot caused by Sclerotium rolfsii which is

gaining importance. Sclerotium rolfsii is an

economically important pathogen on numerous crops worldwide. It has an extensive host range; at least

500 species in 100 families are susceptible, the most

common hosts are legumes, crucifers and cucurbits,

and commonly occurs in the tropics, subtropics, and

other warm temperate regions (Punja, 1985).

Sclerotium rolfsii has wide host range, abundant

growth of the pathogen and its capability of

producing excessive sclerotia that may persist in soil

for several years (Chet and Henis, 1972). Hence

management of Sclerotium rolfsii causing collar rot

of chickpea is difficult to achieve chemically, in this

context plant extracts and bioagents can be used as an alternative source for controlling soil-borne

diseases.


Experiment was conducted under direct sown

conditions chickpea cultivar Garouv in upland

chickpea field having clay loam soil. The land was

well prepared by ploughing two three times. Sick soil

was prepared using Sclerotia of Sclerotium rolfsii @

3800/ plot. Talc powder based formulations of different strains of Trichoderma spp. were developed

and used as seed treatment. Seeds of chickpea were

treated with Different strains of Trichoderma spp. @

10 g /kg seed. Hexaconazole +Zineb were used @ 3

g kg/seed. Untreated control was kept for making

comparison. Seeds @ 100 kg/ha were sown in each plot under randomized block design with three

replications. Fertilizers i.e. NPK @ 20:60:0/ha were

applied as basal dose. Plant population / plot were

counted from each plot after 25 days of sowing.

Plant growth parameters i.e. root & shoot length /

plant, fresh /dry weight of root and shoot/ plant,

number of nodules / plant and weight of nodules

/plant were recorded randomly from three plants of

each plot. Number of dead plants were counted form

each plot at an interval of 15 days till the harvest of

crop. Mortality per cent was calculated taking number of dead plant and total number of plant from

each plot. Number of pods / plant, yield g/plot, yield

q/ha were calculated from each treatment.

Glasswares and plasticwares

Whenever required, the glasswares of Borosil make

plastic plates of Tarson make, blotter paper of

standard grade and chemicals of standard grade

(Merck, Qualigens, S.D. fine etc.) were used during

the course of investigation. All the glasswares,

polythene bags, ethyl alcohol, formalin, chemicals

and other materials were procured from the Thakur

Chhedilal Barrister College of Agriculture and Research Station Bilaspur (C.G.).

Equipments used

The following equipments or materials used in

present investigation were-

1. Autoclave for media sterilization

2. BOD incubator for incubation

3. Binocular research microscope

4. Compound microscope

5. Hot air oven for glassware sterilization

6. Forceps, needles, blades, inoculation needle, cork

borer, petri dishes 7. Growth chamber

8. Laminar air flow for isolation and purification

Preparation of bio mass of strains of Trichoderma

viride and Trichoderma harzianum

C

RESEARCH ARTICLE


248 SHWETA MISHRA, DEVENDRA NISHAD AND R.K.S. TIWARI

Potato dextrose broth was prepared and sterilized in

500 ml conical flasks. Sterilization of media was

done by autoclaving at 1.41 kg cm-² pressure for 20

minutes. Broth containing flasks were further

inoculated with fungal disc of different strains and

incubated at 25 ± 20C inside the B.O.D. Incubator for 15 days. Green biomass along with extract was

homogenized using grinder mixer for developing

formulations.

Preparation of Talc based formulations of strains

of Trichoderma viride and Trichoderma harzianum Talc powder in 250 g poly bags was sterilized.

Sterilization of talc powder was done by autoclaving

at 1.41 kg cm-² pressure for 20 minutes.

Homogenized green biomass of each strain was

incorporated in talc powder in the ration of 1: 10

(one part of bio mass in 10 parts of talc powder) and

thus 10 % (W/V) talc based formulation of each strain was obtained. Talc based formulations of

different strains were used as seed treatment, soil

treatment and foliar on different crops under various

sets of experiments. CFUs were also counted from

talc based formulations and it was ranged between

109 to 1010 / g sample.

Chemicals used

Analytical grade chemicals supplied by different

manufacturers and some of the chemicals were

procured from Thakur Chhedilal College of

Agriculture and Research Station Bilaspur (C.G.). 4.Disease parameters : Mortality per cent

(Number of wilted plant / total number of plant x

100)


Experiment was conducted under in vivo to study

the plant growth promoting and disease controlling

potential of Trichoderma harzianum / Trichoderma

viride strains against collar rot of chickpea caused

by Sclerotium rolfsii. All strain used as seed

treatment @10g/kg seed along with recommended

dose of fertilizer and agronomical practices. Data

indicates that all strains of Trichoderma harzianum

and Trichoderma viride were found significantly

effective in reducing mortality % and enhancing

yield and yield components over control.However, Trichoderma strains i.e Trichoderma viride (T 18),

Trichoderma harzianum (T 28) were more effective

in controlling collar rot of chickpea. All strains of

Trichoderma harzianum / Trichoderma viride was

superior over control for disease controlling

parameters i. e mortality percentage , no. of pods

per / plant ,yield (quintal/hectare), yield (g/plot).

Minimum mortality percentage was recorded in

Trichoderma viride strain number T18 followed by

T8, T28 and maximum mortality percentage was

recorded in Trichoderma strain T1 and T7. Highest

number of pod /plant was recorded in Trichoderma strain number T6 (55.20) followed by T5 whereas

least number of pod / plant recorded in T4 (26.80)

but superior over control (7.40). Higher yield in

quintal / hectare was recorded in Trichoderma strain

T18 (18.97) followed by T 28(17.05) ,T 6(16.99) ,T

7 (16.20) whereas least yield in quintal/hectare was

recorded in Trichoderma strain T3 (10.84) .

similarly higher yield g/ plot was recorded in T18

(0.22),T8 (0.21), T6 (0.19),T5 (0.17),T7 (0.17)

whereas least in strain T2 (0.12 ).test weight (weight

of 100 grain ) was maximum recorded in strain T6 (16.02), T18 (13.54) which is statistically at par

among themselves followed by strain T8 (13.04),T3

(12.08), T6 (12.06) and least test weight was

recorded in strain T2 (10.10). Similar study were

proposed by Jabber et al. (2014) in vitro shows the

bio efficacy of eight antagonists tested through dual

culture technique respectively against S. rolfsii

causing collar rot of chickpea. Among the eight

bioagents tested against S. rolfsii, Trichoderma

harzianum-55 IIHR recorded maximum inhibition of

70% followed by T. harzianum (62%).

Table 1. Disease controlling potential of indigenous strains of Trichoderma harzianum / Trichoderma viride as

seed treatment against collar rot of chickpea.

Trichoderma strains Designation

Mortality

%

No. of

pods /

plant

Yield

qt./ ha

Yield

g/plot Test

weight

Trichoderma

harzianum

T1 9.04 31.60 11.41 0.13 11.90

Trichoderma

harzianum

T2 6.94 19.60 11.93 0.12 10.10

Trichoderma

harzianum

T3 7.71 16.00 10.84 0.13 12.08

Trichoderma

harzianum

T4 4.83 26.80 14.71 0.15 11.70

Trichoderma

harzianum

T5 6.15 51.40 15.98 0.17 12.06

Trichoderma

harzianum

T6 5.81 55.20 16.99 0.19 16.02

Trichoderma

harzianum

T7 9.89 27.60 16.20 0.17 11.30


Trichoderma

harzianum

T8 3.23 28.80 11.66 0.21 13.04

Trichoderma

viride

T18 1.14 37.80 18.97 0.22 13.54

Trichoderma

harzianum

T28 3.76 37.40 17.05 0.19 15.02

Control

12.29 7.40 7.64 0.13 6.65

S E m( ±)

CD 5%

0.39

1.16

0.51

1.50

0.61

1.84

0.01

0.05

0.83

2.48

Fig. 1. Disease controlling potential of indigenous strains of Trichoderma harzianum / Trichoderma viride as

seed treatment against collar rot of chickpea.

Plate 1: Effect of different indigenous strains of Trichoderma viride and Trichoderma harzianum against disease

controlling potential against Collar rot of chickpea.

0

10

20

30

40

50

60

70

80

90

100

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Test weight

Yield g/plot

Yield

No. of pods / plant

Designation



REFERENCES

Chet, I. and Henis, Y. (1972). The response of two

type of Sclerotium rolfsii to factors affecting

Sclerotium formation. J. Gen. Microbol., 73: 483-

486.

Kulkarni, V. R. (2007). Epidemiology and

integrated management of potato wilt caused by

Sclerotium rolfsii Sacc. Ph. D. Thesis, Univ. Agric. Sci. Dharwad. p. 191. Manu, T. G. 2012. Studies on

Manu, T. G. (2012). Studies on Sclerotium rolfsii

(Sacc.) causing foot rot disease on finger millet

M.Sc. (Agri) Thesis, Univ. Agric. Sci., Bangalore.

pp. 1-76. Morton, D. T. and Stoub

Punja, Z. K. (1985). The biology, ecology, and

control of Sclerotium rolfsii. Annu. Review of

Phytopathology. 23: 97-127.

Sivan, A., Elad, Y. and Chet, I. (1984). Biological

control effects of a new isolate of Trichoderma

harzianum on Pythium aphanidermatum. J.

Phytopathol. 74: 498-501.

Seshakiran, K. (2002). Use of phytochemicals in the

management of stem rot of groundnut caused by

Sclerotium rolfsii Sacc. M.Sc. (Agri.) Thesis, Uni.

Agric. Sci Dharwad.

Singh, S. R., Prajapati, R. K., Srivastava, S. S. L., Pandey, R. K. and Gupta, P. K. (2007). Evaluation

of different botanicals and non-target pesticides

against Sclerotium rolfsii causing collar rot of lentil.

Indian Phytopathol. 60(4): 499-501.

Sab, J., Nagraja, A. and Nagamma, G. (2014).

Efficasy of biopesticide against Sclerotium rolfsii

SACC. Causing collar rot of chickpea(Cicer

arientum L.).The bioscan. 9(1): 335-339.




PRODUCTION POTENTIAL AND ECONOMICS OF INTERCROPPING IN

AUTUMN PLANTED- SUGARCANE UNDER NORTH HILL ZONE OF

CHHATTISGARH

Prakash Kumar Sahu, D.K. Gupta* and V.K. Singh

Department of Agronomy, RMD College of Agriculture and Research Station, Ambikapur (C.G.)- 497001


Abstract: A field experiment was conducted during autumn season of 2017-18 at Instructional-cum-research farm RMD CARS, Ambikapur to evaluate the most profitable crops grown as intercrops with winter planted sugarcane under thirteen

treatments formulated with intercropping i.e. sugarcane sole, sugarcane + onion (1:3), sugarcane + onion (1:4), sugarcane + potato (1:1), sugarcane + potato (1:2) , sugarcane + sweetcorn (1:1), sugarcane + sweetcorn (1:2), sugarcane + wheat (1:2), sugarcane + wheat (1:3), sugarcane + frenchbean (1:2), sugarcane + frenchbean (1:3), sugarcane + mustard (1:1) and sugarcane + mustard (1:2) in randomized block design. Based on the one year study, onion (1:3) intercropping was selected as most remunerative in autumn/winter cane with the highest no. of millable cane (93.69 x 103 ha-1), millable cane length (309.26 cm), cane weight (2.72 kg cane-1), cane yield (255.41 t ha-1), cane equivalent yield (295.95 t ha-1) and net return and B:C ratio (Rs. 799244 ha-1 and 9.08) among all the intercropping systems. Sugarcane + onion (1:4) and sugarcane + potato (1:1) intercropping were also found comparable with sugarcane + onion (1:3). Whereas, lowest no. of millable cane (44.55 x

103 ha-1), millable cane length (258.33 cm), cane weight (1.61 kg cane-1), cane yield (71.79 t ha-1), cane equivalent yield (89.58 t ha-1) and net return and B:C ratio (Rs. 189227 ha-1and 2.38 ) recorded under sugarcane + wheat (1:3) intercropping system among the intercrops.

Keywords: Production potential, Economics, Sugarcane, Intercropping, Cane equivalent yield

INTRODUCTION

utumn planted sugarcane is most suitable for

growing intercrops due to its delayed

germination and slow growth because of low

temperature during December to February and

condition are favorable for short duration crops. Sugarcane planted under autumn season gives about

20-25 % higher cane yield and also 0.5 unit higher

sugar recovery as compared to spring cane. Inspite of

these benefits, farmers are least interested to grow

autumn sugarcane and are growing cereal, oilseed

and pulse rabi crops, as per demand in the area of

priority in autumn and sugarcane in spring which

leads to loss in production potential per unit area and

time. Sugarcane requires 4 to 6 weeks for

germination and initial growth is also very slow for

first two months. This time required for germination and subsequent initial slow growing period can be

made better use of growing short duration intercrop

as a bonus crop. As sugarcane is planted at wider row

spacing and this inter row space practically remains

vacant in early growth stages which extends nearly 3

to 4 months where suitable short duration winter

crops may be grown as intercrop that increase total

crop equivalent yield, higher net return and greater

resource utilization and fulfils the diversified needs

of the farmers and also introduce mechanization in

sugarcane to reduce cost of production in constrast to

conventional method of planting. Intercropping in sugarcane with various short duration crops like

onion, potato, mungbean and cabbage etc. have been

proven beneficial in comparison to growing

sugarcane as sole crop (Alam et al., 2000, Panghal,

2010 and Chaudhary et al., 2010). Hence, the

experiment had been conducted to know the

production potential of vegetables as intercrops in

autumn planted sugarcane under North hill zone of

Chhattisgarh.


The present field experiment was conducted during

rabi seasons of 2017-18 at Instructional-cum-

research farm RMD CARS, Ambikapur, Surguja

(Chhattisgarh). The soil of experimental field was

‘Inceptisols’ which is locally known as ‘Chawar’.

The soil was acidic (pH 5.7) in nature with low

fertility having 0.35% soil organic carbon, low N

(235 kg ha-1) and P2O5 (12.5 kg ha-1) and medium

K2O (290 kg ha-1). The experiment comprised of thirteen (13) treatments i.e. sugarcane sole, sugarcane

+ onion (1:3), sugarcane + onion (1:4), sugarcane +

potato (1:1), sugarcane + potato (1:2) , sugarcane +

sweetcorn (1:1), sugarcane + sweetcorn (1:2),

sugarcane + wheat (1:2), sugarcane + wheat (1:3),

sugarcane + french bean (1:2), sugarcane + french

bean (1:3), sugarcane + mustard (1:1) and sugarcane

+ mustard (1:2) in randomized block design with

three replications. Autumn cane (CO-8036) was

planted in first week of December. Recommended

dose of fertilizer on sugarcane viz., 250:100:150 N:

P: K kg ha-1 with 10 ton FYM ha-1 was used for field experiment. At the time of sowing of crop give 34 kg

N, 10 kg P2O5 and 8 kg K2O as a basal and the

remaining dose of fertilizer of N, P and K was

A

RESEARCH ARTICLE

254 PRAKASH KUMAR SAHU, D.K. GUPTA AND V.K. SINGH

applied through drip fertigation at different stages of

crop growth. In case of intercrops gives there

recommended dose of fertilizer as a basal and side

placement at the time as per recommended of

practices viz., onion, potato, wheat, mustard, sweet

corn and french bean.


Yield Attributes and Yield

Millable cane length (cm):- A critical analysis of

data clearly reveals that in general, gradual increase

in cane length with intercropping was observed

significant by during harvesting stage which has been

presented in Table 1. The maximum millable cane

length (309.26 cm) was observed when sugarcane

was intercropped with onion (1:3) followed by

sugarcane+ onion (1:4) , sugarcane + potato (1:1), sugarcane + potato (1:2) , sole sugarcane and the

lowest millable cane length (258.33 cm) recorded

when sugarcane intercropped with sugarcane + wheat

(1:3), which were statistically equal to that sugarcane

intercropped with wheat (1:2), mustard (1:1),

mustard (1:3), frenchbean (1:2), frenchbean (1:3),

sweetcorn (1:1) and sweetcorn (1:2).

Onion and potato as intercropping with sugarcane

does not show any adverse effect on sugarcane yield

and growth. Potato intercropped plots produced

second highest tiller, highest millable cane, maximum height, diameter, unit stalk weight and

yield of sugarcane. These findings were strongly

corroborated with Miah et al. (1994). This might be

possible due to beneficial effects of crop

management practices for onion and potato that

ultimately helped to produce better yield attributes

and yield.

Cane weight (kg cane-1

):- The maximum cane

weight (2.72 kg plant-1) was recorded when

sugarcane was intercropped with onion (1:3)

followed by sugarcane + onion (1:4), sole sugarcane,

sugarcane + potato (1:1) and sugarcane + potato (1:2). The lowest cane weight (1.61 kg plant-1) was

recorded when sugarcane was intercropped with

wheat (1:3) which was found at par with sugarcane +

wheat (1:2), sugarcane + mustard (1:2), sugarcane +

mustard (1:1), sugarcane + sweetcorn (1:2)

sugarcane + sweetcorn (1:1) sugarcane + frenchbean

(1:3) and sugarcane + frenchbean (1:2) Singh et al.

(2010) who also gave similar reports that the onion

as vegetable produced canes of similar weight and

were significantly heavier than all the other

intercropping systems. The production of taller and heavier canes under the onion intercropping systems

indicated that these intercrops did not compete with

main crop.

Number of millable cane (x103

ha-1

):- It is clear

from data that number of millable canes was

significantly influenced due to sugarcane

intercropped with onion (1:3) and it recorded

maximum number of millable canes (93.69 x103 ha

-1)

which was comparable to sugarcane + onion (1:4), sugarcane + potato (1:1), sole sugarcane and

sugarcane + potato (1:2) but significantly superior

over sugarcane + wheat (1:3), sugarcane + wheat

(1:2), sugarcane + mustard (1:2), sugarcane +

mustard (1:1), sugarcane + sweetcorn (1:2),

sugarcane + sweetcorn (1:1), sugarcane + frenchbean

(1:2) and sugarcane + frenchbean (1:3). The lowest

number of millable cane (44.55 x103 ha-1) was

recorded when sugarcane was intercropped in wheat

(1:3).The reduction in number of millable canes was

attributed to poor short proliferation under

intercropping situation as a result of higher inter-specific competition.

Cane yield and cane equivalent yield (t ha-1

):- The

maximum cane yield and cane equivalent yield

(255.41 and 295.75 t ha-1) was recorded when

sugarcane was intercropped with onion (1:3) and it

was statistically at par with sugarcane + onion (1:4),

sole sugarcane, sugarcane + potato (1:1) and

sugarcane + potato (1:2) but significantly superior

over sugarcane + wheat (1:3), sugarcane + wheat

(1:2), sugarcane + mustard (1:2), sugarcane +

mustard (1:1), sugarcane + sweetcorn (1:2), sugarcane+ sweetcorn (1:1), sugarcane + frenchbean

(1:2) and sugarcane + frenchbean (1:3). The lowest

cane yield and cane equivalent yield (71.79 and

89.58 t ha-1) was recorded when sugarcane was

intercropped in wheat (1:3).Similar findings was

reported by Kumar et al. (2003) they reported that

the reduction in cane yield as a result of sarson and

wheat was attributed to exhaustive competition

between the component crops for essential nutrients,

water and other growth factors. Lower yield was

observed when sugarcane was intercropped with

wheat may be due to late vacation of field and competition between wheat and cane plant during

that period resulted in production of lower number of

tillers and millable canes and cane yield as compared

to sole sugarcane.

Economics

Gross return, Net returns (Rs ha-1

) and B: C

ratio: - Higher gross return, net return and B:C ratio

(Rs. 887236, 799244 ha-1 and 9.08 ) was obtained

with sugarcane intercropped with onion (1:3)

followed by sugarcane + onion (1:4), sugarcane +

potato (1:1) . The lowest gross return, net return and B:C ratio (Rs. 268727, 189227 ha-1and 2.38) was

recorded when sugarcane was intercropped with

wheat (1:3) followed by sugarcane + mustard (1:1)

and sugarcane + wheat (1:2).


Table 1. Effect of different intercropping system on no. of millable cane, cane length and cane weight of

sugarcane

Treatments No. of Millable

cane

(x103

ha-1

)

Millable cane length

(cm)

Cane weight

(kg cane-1

)

T1- S.cane + Onion (1:3) 93.69 309.26 2.72

T2- S.cane + Onion (1:4) 92.99 303.00 2.52

T3- S.cane + Potato (1:1) 86.75 300.06 2.45

T4–S.cane + Potato (1:2) 83.33 298.46 2.44

T5-S.cane + Sweetcorn (1:1) 60.79 270.00 1.90

T6-S.cane + Sweetcorn (1:2) 58.99 266.00 1.87

T7-S.cane + Wheat (1:2) 55.38 260.40 1.67

T8- S.cane + Wheat (1:3) 44.55 258.33 1.61

T9-S.cane + Frenchbean (1:2) 77.36 277.86 2.01

T10-S.cane + Frenchbean (1:3) 77.36 275.40 2.00

T11- S.cane + Mustard (1:1) 56.39 264.33 1.69

T12- S.cane + Mustard (1:2) 56.12 262.40 1.69

T13- Sole Sugarcane 86.29 299.53 2.45

SEm± 5.39 6.28 0.24

CD (P=0.05) 16.29 19.50 0.70

Table 2. Effect of different intercropping system on sugarcane yield, intercrop yield and sugarcane equivalent

yield

Treatments S. cane yield

(t ha-1

)

Intercrop yield

(q ha-1

)

S. cane equivalent

yield (CEY , t ha-

1)

T1-Sugarcane + Onion (1:3) 255.41 110 (43.3) 295.75

T2- Sugarcane + Onion (1:4) 236.50 144 (52.80) 289.30

T3 -Sugarcane + Potato (1:1) 212.76 80 (27.46) 240.22

T4 - Sugarcane + Potato (1:2) 204.53 103 (21.33) 225.87

T5-Sugarcane + Sweetcorn (1:1) 115.43 83 (27.66) 143.09

T6-Sugarcane + Sweetcorn (1:2) 110.11 90 (30.0) 140.12

T7- Sugarcane + Wheat (1:2) 92.55 21 (12.88) 105.44

T8- Sugarcane + Wheat (1:3) 71.79 29 (17.78) 89.58

T9-Sugarcane + Frenchbean (1:2) 162.67 12 (4.00) 166.67

256 PRAKASH KUMAR SAHU, D.K. GUPTA AND V.K. SINGH

T10-Sugarcane + Frenchbean (1:3) 160.14 13 (4.33) 164.47

T11- Sugarcane + Mustard (1:1) 95.51 05 (7.00) 102.50

T12- Sugarcane + Mustard (1:2) 94.66 07 (9.80) 104.47

T13- Sole Sugarcane 213.74 - 213.74

SEm± 22.90 - 22.88

CD (P=0.05) 67.24 - 67.17

Table 3. Effect of different intercropping system on gross return, net return and B:C ratio on sugarcane

Treatments Cost of

cultivation (Rs

ha-1

)

Gross return (Rs

ha-1

)

Net return (Rs

ha-1

)

B:C

ratio

T1- S. cane + Onion (1:3) 87992 887236 799244 9.08

T2- S. cane + Onion (1:4) 88900 867910 779010 8.76

T3- S. cane + Potato (1:1) 90900 720662 629762 6.93

T4 -S. cane + Potato(1:2) 92917 677597 584680 6.29

T5-S.cane + Sweetcorn (1:1) 78667 429272 350605 4.46

T6-S.cane + Sweetcorn (1:2) 79700 420352 340652 4.27

T7- S.cane + Wheat (1:2) 78900 316320 237420 3.01

T8- S.cane + Wheat (1:3) 79500 268727 189227 2.38

T9-S.cane + Frenchbean (1:2) 76600 500016 423416 5.53

T10-S.cane + Frenchbean (1:3) 77000 493421 416421 5.41

T11- S.cane + Mustard (1:1) 78950 307515 228565 2.90

T12- S.cane + Mustard (1:2) 79300 313402 234102 2.95

T13- Sole Sugarcane 73166 641240 568074 7.76

SEm± - 68632 68633 0.84

CD (P=0.05) - 201518 201518 2.47

REFERENCES

Alam, M.J., Rahman, M.M. and Zaman, A.K.M.M. (2000). Impact of paired row Sugarcane

with double intercrops. Bangladesh Journal of

Sugarcane, 22: 1- 9.

Chaudhary, S., Dorge, J.T. and Tilekar, S.N. (2010). Impact of agricultural technologies and

development of cane productivity of sugarcane in

Western Maharashtra. Cooperative Sugar, 41(11):

69-74.

Kumar, S., Rana, N.S. and Saini, S.K. (2003).

Effect of NPK fertilization on production potential of

autumn cane based intercropping system. Indian

journal sugarcane technology, 18 (1/2):55-58.

Miah, M.A.M., Sabur, S.A. and Islam, M.S. (1994). Comparative economics of sugarcane with

intercrops in Jaipurhat. Sugar mill area, Bangladesh

journal of sugarcane, 16: 5-9.

Panghal, S.S. (2010). Cane production

mechanization – A solution for labour problems.

Indian Sugar, 45: 27-32.

Singh, K., Singh, A., Gill, M. S., Singh, D., Uppal,

S. K. and Bhullar, M. S. (2010). Intercropping in

single bud vertical planted sugarcane. Journal

Research Punjab Agriculture University, 47(3 & 4):

138-42.



EXISTING PRODUCTION PATTERNS AMONG THE MAIZE GROWERS

P.K. Netam*, Basanti Netam and Virendra Kumar Painkra

1Department of Agricultural Extension CARS,Kanker, IGKV, Raipur, Chhattisgarh 2Department of Senior Agriculture Development Officer, Dhamtari,Chhattisgarh

3Department of Agricultural Extension CoA, IGKV, Raipur, Chhattisgarh


Received-01.04.2020, Revised-22.04.2020 Abstracts: This investigation was carried out in three district of Bastar plateau of Chhattisgarh State to assess the level of existing production pattern among the respondents. 270 farmers were considering as respondents for this study. Respondents were interviewed through personal interview. Collected data were analyzed with the help of suitable statistical methods. The analysis of the results showed that major crop prevailed in Kharif season among different respondents was rice followed by maize, while predominant crop in Rabi season was maize covering 64.44 percent area.

Keywords: Production pattern, Area, Productivity, Economic assessment

INTRODUCTION

aize (Zea mays L.) is one of the most

important cereal crops in the world and has the

highest production among all the cereals. It is a

miracle crop, it has very high yield potential, there is

no cereal on the earth which has so immense

potentiality and that is why it is called ‘queen of

cereal’. Besides, maize has many types like normal

yellow, white grain, sweet corn, baby corn, pop corn,

waxy corn, high amylase corn, high oil corn, quality

protein maize, etc. Maize is the most important crop in the world after wheat and rice (Verheys,Undated).

It is an important staple food in many countries and

is also used as animal feed and many industrial

applications. Maize is 3rd major crop in India after

rice and wheat (Cox, R., 1956 & Reddy et. al. 2013).

Maize is important cereal crop which provides food,

feed, fodder and serves as a source of basic raw

material for a number of industrial products viz,

starch, protein, oil, food sweeteners, alcoholic

beverages, cosmetics, bio-fuel etc, it is cultivated

over 8.12 million hectare area with an annual production of 19.77 million tones and an average

productivity of 2,435 kg ha-1 (Langade et. al. 2013).

Maize is the third most important food grain in India

after wheat and rice. In India, about 28% of maize

produced is used for food purpose, 11% as livestock

feed, 48% as poultry feed, 12% in wet milling

industry (for example starch and oil production) and

1% as seed (AICRP on Maize, 2007). Maize crop in

the state has an area of 123430 ha with the

production 254134 MT (C.G. Agriculture Statistic

Report 2014).The area and production of Maize crop

in Kanker district was 11511 ha and 25705 MT respectively, area of maize crop in Kondagaon

district is 13586 ha with production of 31831 MT

while the coverage of maize in Bastar district is 9560

ha with the production of 22398 (C.G. Ag. statistic

Report 2014). The existing production pattern

indicate the used to pattern which is now present in

operation is made according to the exact dimension of particular style with allowance. The present study

was undertaken with specific objectives to assess the

existing production pattern of maize of the maize

growers of Bastar plateau of Chhattisgarh.


The present study was carried out in Bastar plateau

of Chhattisgarh State. Three districts in the zone i.e.

Kanker, Kondagaon and Bastar were undertaken for

the study. Two blocks from each of the selected

district Block Antagarh and Koylibeda in Kanker

District, Keshkal and Baderajpur in Kondagaon,

Bastar and Bakawand in Bastar District. Each

selected block 3 villages viz. Irrabodi, Amagaon,

Godri, in Antagarh Block, Chotekapsi,

Kodosalhebhat, Manegaon, in Koylibeda Block,

Cherbeda, Toraibeda, Amoda in Keshkal Block,

Baderajpur, Toraipara, Khargaon(Manduki) in

Baderajpur Block, Ikchapur, Bagmohlai,

Dubeumargaon in Bastar Block, Belputi, Khotlapal

and Mangnar in Bakawand Block were selected and

from each selected village, 15 farmers were selected

randomly. In this way total two hundred seventy

respondents were selected to response as per the

interview schedule designed for the study. Collected

data were analyzed by the help of various statistical

tools i.e. frequency, percentage, mean, standard

deviation, correlation and regression, etc. In this

study, the existing production pattern indicate the

used to pattern which is now present in operation is

made according to the exact dimension of particular

style with allowance.


The result and discussion of the present study have

been summarized on the basis of response of

M

SHORT COMMUNICATION


258 P.K. NETAM, BASANTI NETAM AND VIRENDRA KUMAR PAINKRA

respondents regarding to area, productivity and

economics of the major Kharif and Rabi crops

prevailing among the respondents are represented in

the Table No.1. Major crop prevailed in Kharif

season among different respondents was rice

followed by maize, while predominant crop in Rabi season was maize covering 64.44 percent area while,

it covered 87.77 percent area covered in Kharif

season. The major variety of Kharif rice taken up by

the respondents was hybrid, MTU-1010, MTU-1001

and IR-64. Irrigated area under Kharif maize was 48

ha. while 129ha. area was unirrigated. The total

irrigated area under Rabi maize among different

respondents was 187.2 ha. Average productivity of

Kharif maize under irrigated condition and was

47qha-1 whereas, it is lower in unirrigated condition

and found 38 qha-1 comparatively higher productivity

of Rabi maize was found as 58 qha-1 . Average productivity of rice hybrid under irrigated condition

was 46qha-1 whereas, it is 44 qha-1 under unirrigated

condition. Average productivity of rice variety viz

MTU-1010, MTU-1001and IR-64 under irrigated

condition was 46, 32, 25 and 29 qha-1 respectively,

whereas under unirrigated condition it gave

productivity of 44, 31, 24 and 27 qha-1.

Average cost of cultivation of rice varieties viz.

Hybrid, MTU-1010, MTU-1001 and IR-64 was

Rs.33000, Rs.27000, Rs.26000 and Rs.27000 per

hectare under irrigated condition which was almost same or slightly lower under unirrigated condition.

Gross return among the rice variety was maximum in

rice hybrids followed by MTU-1010,IR-64 and

MTU-1001. Net return (Rs.ha-1) also followed the

same trends. Among the different rice variety

maximum benefit: cost ratio was obtained with rice

hybrid (1.81) followed by MTU-1010(1.54), IR-

64(1.39) and MTU-1001(1.25).The highest net return

of Rs. 51,170 ha-1 in Rabi maize whereas, the net

return in Kharif maize under irrigated condition was

Rs.36155ha-1 and under unirrigated condition it was

Rs.28870ha-1 . The maximum benefit cost ratio of 2.82 was obtained in Rabi maize compared to 2.19 in

Kharif season under irrigated condition. Under

unirrigated condition the B:C rato in Kharif maize

was 1.57.

Table 1. Area, Productivity and economics assessment of major crops among the respondents during Kharif and

Rabi season

Season /

Crop

No percent Area (ha) AC (Rs.000/ha.)

AP (Q./ha)

GR (Rs./ha.)

Net return (Rs./ha.)

B:CRatio

I UI I UI I UI I UI I UI I UI

Kharif

Rice (vr.)

Hybrid 191 70.74 100 38 33 32 46 44 59800 57200 26800 25200 1.81 1.78

MTU-1010 207 76.67 51 85 27 27 32 31 41600 40300 14600 13300 1.54 1.49

MTU- 1001 34 12.59 4.4 24 26 26 25 24 32500 31200 6500 5200 1.25 1.20

HMT 13 4.81 2.8 7.6 29 28 31 27 40300 35100 11300 9100 1.38 1.25

IR-64 20 7.40 2.8 14.8 27 26 29 27 37700 35100 10700 9100 1.39 1.35

Safri 36 13.33 3.6 24 26 25 25 24 32500 31200 6500 6200 1.25 1.24

Gurmutiya 12 4.44 2 6.4 25 25 21 21 27300 27300 2300 2300 1.09 1.09

Maize 237 87.77 48 129 28 23 47 38 64155 51870 36155 28870 2.19 1.57

Rabi Maize 164 64.44 187.2 - 28 - 58 - 79170 51170 - 2.82 -

*Data are based on multiple responses

Note: Data are based on multiple responses, I- irrigated, UI-un irrigated, AC-average cost, AP- average

production,

GR- gross return, NR-net return, B: C- benefit: cost, vr- variety


Fig. 1. Distribution of the respondents on the basis of their Major crops

CONCLUSION

From the above research findings it can be concluded

that Major crop prevailed in Kharif season among

different respondents was rice followed by maize,

while predominant crop in Rabi season was maize

covering 64.44 percent area.

REFERENCES

Chhattisgarh (2014). Annual statistics report.

CIMMYT (2005). Maize in India: production

systems, constraints, and research priorities.

FAOSTAT (2012). Statistical databases and data

sets of the food and agriculture organization of the

United Nations.

FARA (2009). Pattern of change in maize production

in Africa: implication for policy development.

Ministerial policy brief services, No.3 December

2009 Accra, Ghana: forum for Agricultural Research

in Africa (FARA). Gecho, Yishak and Punjabi, N.K. (2011).

Determination of adoption of improved Maize

technology in Damot Gale, Wolaita, Ethiopia. Raj. J.

Ext. Edu., 19: 1-9.

Gupta, Km. Saroj and Gyanpur, S.R.N. (2012).

Sustainability of scientific maize cultivation practice

in Uttar Pradesh, India. Journal of Agricultural

Technology. 8 (3): 1089-1098.

Kumari, Sunita, Sharma, F.L. and Nidhi (2017).

Study of profile characteristics of wheat and maize

growers in Udaipur District of Rajasthan. IMPACT:

International Journal of Research in Applied, Natural

and Social Sciences, 5(2): 1-12.

Langade, D. M., Shahi, J.P., Agrawal, V. K. and

Sharma, A. (2013). Maize as emerging source of oil

in india: an overview. Maydica, 58(3/4): 224-230.

Nyangena, Wilfred and Juma, Ogada Maurice (2014). Impact of Improved Farm Technologies on

Yields: The case of improved Maize Varieties and

Inorganic fertilizer in Kenya. Environment for

Development.

R. Cox (1956). Control of helminthosporium

turcicum blight disease of sweet corn in South

Florida. Phytopathology, 5: 68-70.

Singha, K. and Chakravorty, A. (2013). Crop

diversification in India: a study of maize cultivation

in Karnataka. Scientific Journal of Review, 2(1): 1-10.

Wokabi, S. M. (1998). Sustainability of maize

production in Kenya. Kenya Agricultural Research

Institute, Nairobi, Kenya. p. 2.

191 207 34 13 20 36 12 237 164

70.74% 76.67% 12.59% 4.81% 7.40% 13.33% 4.44% 87.77% 64.44%

99%

100%

100%

100%

260 P.K. NETAM, BASANTI NETAM AND VIRENDRA KUMAR PAINKRA

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