A DISEASE PREDICTIVE MODEL FOR THE MANAGEMENT OF
BEMISIA TABACI (GENN.) POPULATION AND TOMATO
LEAF CURL VIRUS DISEASE INCIDENCE
Muhammad Ahmad Zeshan (Regd. No. 2005-ag-1566)
M.Sc. (Hons.) Plant Pathology
A dissertation submitted in partial fulfillment
of the requirements for the degree of
Doctor of Philosophy
in
Plant Pathology
DEPARTMENT OF PLANT PATHOLOGY
FACULTY OF AGRICULTURE
UNIVERSITY OF AGRICULTURE
FAISALABAD
PAKISTAN
2015
DECLARATION
I hereby declare that the contents of the dissertation entitled “A disease predictive model for
the management of Bemisia tabaci (Genn.) population and tomato leaf curl virus disease
incidence” are product of my own research and no part has been copied from any published
source (except the references, standard mathematical or biochemical models/equations/
formulae/protocol etc.) I further declare that this work has not been submitted for the award
of any other diploma/degree. The university may take action if the information provided is
found inaccurate at any stage, as per Higher Education Commission, plagiarism policy.
Muhammad Ahmad Zeshan
(Regd. No. 2005-ag-1566)
The Controller of Examinations,
University of Agriculture,
Faisalabad.
We the Supervisory Committee, certify that the contents and form of dissertation
submitted by Mr. Muhammad Ahmad Zeshan (Regd. No. 2005- ag-1566), have been found
satisfactory and recommend that it be processed for evaluation by the external examiner(s) for
the award of degree.
SUPERVISORY COMMITTEE
Prof. Dr. Muhammad Aslam Khan : (Chairman)
Dr. Safdar Ali : (Member)
Dr. Muhammad Arshad : (Member)
DEDICATED
To
My beloved Parents and family members
Who wished to see me Doctor of Philosophy
All praises and thanks are for ALMIGHTY ALLAH (Jalla-Jalalaho), The
Compassionate, The Merciful, The only Creator of The Universe and the source of all
Knowledge and Wisdom Who blessed me with health, thoughts, talented teachers, co-
operative friends and opportunity to make some contribution to the already existing body of
knowledge. I offer my humblest thanks to the greatest social reformer The Holy Prophet
Hazrat Muhammad (Sallallah-O-Allah-e-Wasallum) for His services for the humanity.
The work presented in this manuscript was accomplished under the sympathetic
attitude, animate direction, scholarly criticism and enlightened supervision of Prof. Dr.
Muhammad Aslam Khan, Ex. Chairman, Department of Plant Pathology and Principal
Officer (Library), University of Agriculture, Faisalabad. I owe my deepest gratitude to his
ever-inspiring guidance, constructive suggestions and support from the initial to final level
enabling me to develop an understanding of the subject. I am greatly indebted to him for
finding me worthy of being promoted to a doctor.
It is my pleasure to extend heartiest gratitude to Dr. Safdar Ali (Lecturer, Department
of Plant Pathology, UAF) whose presence was always a source of confidence for me. I am
highly obliged and grateful to Dr. Muhammad Atiq (Assistant Professor, Department of
Plant Pathology UAF), for his valuable guidance and positive criticism. I would like to
acknowledge Dr. Muhammad Arshad (Assistant Professor, Department of Entomology,
UAF), for his dynamic and inspiring guidance. Special thanks for Dr. Ummad-ud-din Umar
for helping in serological assays.
I would like to pay special thanks to Mr. Nadeem Ahmad Ph.D. Scholar, Department
of Plant Pathology, for his guidance regarding statistical analysis. I am also thankful to my
friends Hafiz Muneeb Ahmad, Abdul Khaliq, Hafiz Attique, Asif Nadeem, Hafiz Rizwan,
Hafiz Sajid and Hafiz Arslan for their cooperation and moral support during the completion of
this project.
I don’t have words at command in acknowledging that all the credit goes to my
loving parents and brothers (Muhammad Naveed Asim, Muhammad Munir Sarwar and
Muhammad Usman Ghani) for their amicable attitude, mellifluous affections and inspiration
which hearten me to achieve success in every sphere of life.
Muhammad Ahmad Zeshan
LIST OF CONTENTS
Sr. No. CONTENTS Page
No.
DEDICATION i
ACKNOWLEDGEMENTS ii
LIST OF CONTENTS iii
LIST OF TABLES vi
LIST OF FIGURES viii
ABSTRACT ix
1 INTRODUCTION 1
2 REVIEW OF LITERATURE 5
2.1 History and taxonomy of tomato leaf curl virus disease (TLCVD) 5
2.2 Symptomology of TLCVD 7
2.3 Screening of tomato germplasm against TLCVD 7
2.4 Screening of tomato germplasm against whitefly 10
2.5 Biological assays for TLCV 12
2.5.1 Through B. tabaci 12
2.5.2 Through grafting 14
2.6 Serological assay for confirmation of TLCV 15
2.7 Host range of TLCVD 16
2.7.1 Host range of B. tabaci 18
2.8 Epidemiology of TLCVD and B. tabaci 18
2.9 TLCVD incidence and B. tabaci population predictive model 21
2.10 Management of TLCVD and B. tabaci 23
2.10.1 Management through insecticides 23
2.10.2 Management through nutrients and systemic acquired resistance 26
2.10.3 Management through plant extracts 28
3 MATERIALS AND METHODS 31
3.1 Screening of tomato germplasm against tomato leaf curl virus disease
(TLCVD) and whitefly
31
3.2 Biological assays 32
3.2.1 Through whitefly 32
3.2.2 Through grafting 32
3.3 Serological assay 33
3.3.1 Buffer formulations 33
3.3.2 DAS-ELISA procedure 34
3.3.3 Color development 35
3.4 Area under disease progress curve 35
3.5 Recording of whitefly population data from disease screening nursery 36
3.6 Collection of environmental conditions data 36
3.7 Development of predictive model for B. tabaci population and TLCVD
incidence
36
3.7.1 Establishment of experiment and data recording 36
3.7.2 Analysis of data 37
3.7.3 Evaluation of model 37
3.8 Management of TLCVD and B. tabaci 38
3.8.1 Evaluation of insecticides, nutrients and plant extracts against TLCVD and
B. tabaci
38
3.8.2 Preparation of plant extracts 38
3.8.3 Data analysis 39
4 RESULTS 37
4.1 Symptomology and disease development during two years (2012 and 2013) 40
4.1.1 Screening of tomato germplasm against tomato leaf curl virus disease
(TLCVD) during 2012 under natural environmental conditions
41
4.1.2 Screening of tomato germplasm against TLCVD during 2013 under
natural environmental conditions
43
4.2 Screening of tomato germplasm against Bemisia tabaci population during
two years (2012 and 2013) under natural conditions
45
4.3 Confirmation of TLCV through ELISA and grafting 49
4.4 Correlation of environmental factors with TLCVD incidence on tomato
varieties/lines during 2012 and 2013
49
4.5 Correlation of environmental factors with B. tabaci population on different
tomato varieties/lines during 2012 and 2013
53
4.6
Characterization of environmental conditions conducive for the
development of TLCVD on five susceptible and highly susceptible
varieties/lines during two years (2012 and 2013)
55
4.7
Characterization of environmental conditions conducive for the
development of B. tabaci population on five varieties/lines during two
years (2012 and 2013)
59
4.8 Analysis of variance for B. tabaci population during two years (2012 and
2013)
62
4.8.1 Analysis of variance of environmental conditions during two years (2012
and 2013)
62
4.8.2 Comparison of environmental conditions during the years 2012 and 2013 63
4.8.3 Overall correlation of weekly environmental conditions with TLCVD
incidence during the years 2012 and 2013
63
4.8.4 Year wise correlation of weekly environmental conditions with TLCVD
incidence during 2012 and 2013 on five varieties/lines
63
4.9 Development of TLCVD predictive model based on two years data (2012
and 2013)
66
4.9.1 TLCVD predictive model assessment during two years (2012 and 2013) 67
4.9.2 Comparison of the dependent variable (TLCVD) and regression co-
efficient with physical theory
67
4.9.3 Variety wise predictive model for TLCVD incidence 69
4.9.4 Evaluation of model by comparing the observed and predicted data 70
4.9.5 Graphical representation of TLCVD predictive model based on two years
data
70
4.10 Analysis of variance of B. tabaci population during two years (2012 and
2013)
72
4.10.1 ANOVA of environmental conditions during two years (2012 and 2013) 72
4.10.2 Comparison of environmental conditions during the years 2012 and 2013 72
4.10.3 Correlation of weekly environmental conditions with B. tabaci population
during 2012 and 2013
73
4.10.4 Year wise correlation of weekly environmental conditions with B. tabaci
population during 2012 and 2013
73
4.11 Development of B. tabaci population predictive model based on two years
data (2012 and 2013)
76
4.11.1 Comparison of the dependent variable (B. tabaci) and regression
coefficients with physical theory
77
4.11.2 Variety wise predictive model for B. tabaci population 78
4.11.3 Evaluation of model by comparing the observed and predicted data 78
4.11.4 Graphical representation of B. tabaci population predictive model based on
two years data (2012 and 2013)
78
4.12 Management 80
4.12 Evaluation of different treatments against TLCVD during two years (2012
and 2013)
81
4.12.1 Analysis of variance for TLCVD management during the years 2012 and
2013
81
4.12.2 Comparison of different treatments against TLCVD incidence 81
4.12.3 Comparisons of TLCVD incidence with spray and year 83
4.12.4 Comparisons of treatments and years against TLCVD incidence 83
4.12.5 Comparisons of TLCVD incidence with variety and spray 83
4.12.6 Comparisons of TLCVD incidence with variety, spray and year 84
4.13 Analysis of variance for B. tabaci management during 2012 and 2013 85
4.13.1 Comparisons of different treatments against B. tabaci population 85
4.13.2 Comparisons of B. tabaci population with spray and year 87
4.13.3 Comparison of treatments and years against B. tabaci population 87
4.13.4 Comparisons of B. tabaci population with variety and spray 87
4.13.5 Comparisons of B. tabaci population with variety, spray and year 88
5 DISCUSSIONS 90
6 SUMMARY 99
CONCLUSION 101
RECOMMENDATIONS 102
LITERATURE CITED 103
LIST OF TABLES
Table
No.
Title Page
No.
3.1 Disease rating scale 32
3.2 Treatments used against TLCVD and B. tabaci 39
3.3 Plant extracts used against TLCVD and B. tabaci 39
4.1 Resistance level of tomato germplasm against TLCVD under natural
conditions during the year 2012 42
4.2 Resistance level of varieties/lines to TLCVD under natural conditions
during the year 2013 44
4.3 Resistance level of tomato germplasm against B. tabaci population
during 2012 46
4.4 Resistanec level of tomato germplasm against B. tabaci population
during 2013 48
4.5 Confirmation of resistance level against TLCV through graft
inoculation and ELISA 50
4.6
Pearson’s correlation coefficients of environmental factors with
TLCVD incidence on different tomato varieties/lines during 2012
and 2013
51
4.7 Pearson’s correlation coefficients of environmental factors with B.
tabaci population on tomato varieties during 2012 and 2013 53
4.8 ANOVA for TLCVD incidence during 2012 and 2013 63
4.9 Comparison of environmental conditions for TLCVD incidence
during two years (2012 and 2013)
64
4.10a Analysis of variance of environmental factors (maximum and
minimum temperature) during 2012 and 2013 65
4.10b ANOVA of environmental factors (relative humidity, rainfall and
wind speed) during 2012 and 2013 65
4.11 Overall correlation of weekly environmental conditions with TLCVD
incidence during 2012 and 2013 66
4.12 Year wise correlation of environmental conditions with TLCVD
incidence during two 2012 and 2013 66
4.13 Summary of stepwise regression model to predict TLCVD incidence
during two years 2012 and 2013 67
4.14 Regression statistics of the predictive model for TLCVD based on
two years (2012 and 2013) data 68
4.15 ANOVA of the TLCVD predictive model for based on two years
environmental conditions data 68
4.16 Co-efficient of variables, their standard error, t Stat, P-value and
Significance 68
4.17
Summary of stepwise regression model developed to predict TLCVD
incidence with respect to environmental factors on five tomato
varieties/lines during two years
69
4.18 Multiple regression equations based on environmental conditions and
predicted TLCVD incidence values during two years 71
4.19 ANOVA for B. tabaci population during two years (2012 and 2013) 72
4.20 Comparison of environmental conditions for B. tabaci population
during two years (2012 and 2013) 73
4.21 Correlation of weekly environmental conditions with B. tabaci
population during 2012 and 2013 74
4.22 Year wise correlation of weekly environmental conditions with B.
tabaci population during 2012 and 2013 on five varieties/lines 74
4.23a Analysis of variance of environmental factors (maximum and
minimum temperature) during two years 75
4.23b Analysis of variance of environmental factors (relative humidity,
rainfall and wind speed) during two years (2012 and 2013) 75
4.24 Summary of stepwise regression model to predict B. tabaci
population during 2012 and 2013 76
4.25 Regression statistics of the predictive model for B. tabaci based on
two years (2012 and 2013) 77
4.26 Analysis of variance of the predictive model for B. tabaci based on
two years (2012-2013) 77
4.27 Coefficients of variables, their standard error, t Stat, P-value and
significance 77
4.28
Summary of stepwise regression model developed to predict B.
tabaci population with respect to environmental factors on five
tomato varieties/lines during two years
78
4.29 Multiple regression equations based on environmental conditions and
predicted B. tabaci population values during two years 80
4.30 ANOVA for TLCVD management during 2012 and 2013 82
4.31 Comparisons of different treatments against TLCVD incidence 82
4.32 Comparisons of TLCVD incidence with spray and year 83
4.33 Comparison of treatments and years against TLCVD incidence 84
4.34 Comparisons of TLCVD incidence with variety and spray 84
4.35 Comparisons of TLCVD incidence with variety, spray and year 85
4.36 ANOVA for B. tabaci population during two years (2012 and 2013) 86
4.37 Comparisons of different treatments against B. tabaci population
during two years 86
4.38 Comparisons of B. tabaci population with spray and year 87
4.39 Comparison of treatments and years against B. tabaci population 88
4.40 Comparisons of B. tabaci population with variety and spray 88
4.41 Comparison of B. tabaci population with variety, spray and year 89
LIST OF FIGURES
Figure
No.
Title Page
No.
3.1 ELISA results 35
4.1 Upward curling and yellowing of leaves due to TLCVD 40
4.2 Tomato plant with stunting and cupping symptoms caused by TLCVD 41
4.3 Relationship of maximum temperature with TLCVD incidence on five
tomato varieties during 2012 and 2013 56
4.4 Relationship of minimum temperature with TLCVD incidence on five
tomato varieties during 2012 and 2013 57
4.5 Relationship of relative humidity with TLCVD incidence on five tomato
varieties during 2012 and 2013 57
4.6 Relationship of rainfall with TLCVD incidence on five tomato varieties
during 2012 and 2013 58
4.7 Relationship of wind speed with TLCVD incidence on five tomato
varieties during 2012 and 2013 58
4.8 Relationship of maximum temperature with B. tabaci population on five
tomato varieties during 2012 and 2013 60
4.9 Relationship of minimum temperature with B. tabaci population on five
tomato varieties during 2012 and 2013 60
4.10 Relationship of relative humidity with B. tabaci population on five tomato
varieties during 2012 and 2013 61
4.11 Relationship of rainfall with B. tabaci population on five tomato varieties
during 2012 and 2013 61
4.12 Relationship of wind speed with B. tabaci population on five tomato
varieties during 2012 and 2013 62
4.13 Normal probability plot and residuals versus fit for TLCVD predictive
model
70
4.14 Normal probability plot and residuals versus fit for the B. tabaci
population predictive model
81
ABSTRACT
Tomato is an important vegetable crop of global importance. Tomato leaf curl virus disease
(TLCVD) transmitted by whitefly Bemisia tabaci (Genn.) is a serious threat for the
successful tomato production under field conditions. Twenty seven varieties/lines were
screened against TLCVD and B. tabaci under natural conditions. None of the screened
varieties/advanced lines was found to be highly resistant against TLCVD and varied greatly
in disease incidence during both years (2012 and 2013). Eight varieties/lines (Naqeeb, Pakit,
Nagina, Riogrande, 09080, Roma, 09091 and Nutyt-04-11) were found to be resistant against
TLCVD. Ten varieties/lines (Carmen, Roker, Lyp#1, 09079, Nutyt-25-11, 09088, Uovo
Roseo, Nutyt-9-11, Po-02 and 10113 were categorized as moderately resistant and
moderately susceptible respectively. Nine varieties/lines (Salma, 014276, Sitara-TS-101,
10125, 10127, Libnan Arif, BL-1-176-Riostone-1-1, Big Beef and Caldera) were found to be
highly susceptible and susceptible against TLCVD incidence during two years 2012 and
2013. A significant (P<0.05) correlation was observed between maximum and minimum
temperature and TLCVD. The correlation of minimum temperature and B. tabaci population
was significantly positive while the correlation of relative humidity with B. tabaci population
and TLCVD incidence was negative i.e. lower humidity has more B. tabaci and TLCVD. The
relationship of B. tabaci population and TLCVD incidence with rainfall and wind velocity
was found non-significant during two years (2012 and 2013). Precise prediction of whitefly
and TLCVD could be helpful in deciding the timely application of treatments. A disease and
vector predictive model based on 2 years of epidemiological data was developed for the
prediction of TLCVD and B. tabaci population infestation. Y= 0.532+ 0.053x1+0.97x2-
0.081x3+0.15x4 R2= 0.85 where Y= TLCVD, x1= Maximum temperature, x2= Minimum
temperature, x3= Relative humidity, x4= Rainfall, Y= -7.76+0.231x1+0.21x2-
0.092x3+0.11x4+0.086x5 R2= 0.92 where Y= B. tabaci, x1= Maximum temperature, x2=
Minimum temperature, x3= Relative humidity, x4= Rainfall, x5= Wind speed. Different
pesticides/biopesticides were evaluated for management of Bemisia tabaci and the disease.
All the six treatments reduced B. tabaci population and TLCVD incidence significantly
compared to untreated control. Imidacloprid was the most effective to manage the B. tabaci
population. Acetamiprid was at number second and Azadirachta indica (Neem) was at
number third whereas Salicylic acid, Classic (Zn and Boron) solution and Eucalyptus
globules (Eucalyptus) were at number four, fifth and sixth respectively in managing the B.
tabaci population and TLCVD incidence.
CHAPTER 1 INTRODUCTION
Tomato (Lycopersicon esculentum Mill.) belongs to the Solanaceae family which
also contains other important species such as potato, tobacco, peppers and eggplant. It
originated in Latin America and has become one of the most widely grown vegetables with
ability to survive in diverse environmental conditions (Rice et al., 1987). Tomatoes are
generally used as a model crop for various cellular, biochemical, molecular, genetic and
physiological studies because they are easily grown, have a short life cycle and are easy to
manipulate (Dan et al., 2006). Tomatoes contribute to a healthy diet by providing rich
amounts of minerals, essential amino acids, sugars and dietary fibers etc. It contains abundant
vitamin B, C, iron and phosphorus. Canned and dried tomatoes are economically important
processed products (Glick et al., 2009). Tomato production has been emphasized not only as
source of vitamins but also as a source of income and food security because it has become a
high value cash crop and subsistence vegetable for farmers (Nagaraju et al., 2002). Tomato
provide high profits to farmers and employment opportunities to rural laborers because this
crop requires more labor inputs as compared to other crops (Mari et al., 2007). Current
production of tomato is approximately 150 million tons in the world which is cultivated on
4.6 million hectares (FAO, 2011). The area under tomato production in Pakistan is 52.3
thousand hectares and annual yield is 529.6 thousand tons (GOP, 2011).
The yield and quality of the tomato is severely affected by different biotic and abiotic
stresses. Tomato crop is susceptible to a large number of diseases caused by different fungi,
bacteria, nematode and viruses (Rivard and Louws, 2008). Several viruses like tobacco
mosaic virus (TMV), potato virus X (PVX), tomato yellow leaf curl virus (TYLCV), potato
virus Y (PVY) beet curly top virus (BCTV) and cucumber mosaic virus (CMV) etc. attack
tomato crop (Navas-Castillo, 1999; Moriones and Navas-Castillo, 2000). Tomato leaf curl
disease (TLCD) is a remarkable biotic stress for production of tomato in the tropics and
subtropics, commonly in South and Southeast Asia (Chakraborty, 2008). This disease is of
economic importance (Valizadeh et al., 2011) as the yield of TYLCV-infected plants is
reduced qualitatively and quantitatively (Makkouk et al., 1979; Al-Musa, 1982; Fang et al.,
2013). In case of severe attacks, infection of the plants range from 5 to 100% (Varma and
Malathi, 2003). TLCVD is the most widespread among viral diseases and found in several
Middle Eastern, African, Asian and Mediterranean countries. TLCVD is caused by a
complex group of viruses including TYLCV and TLCV (Fauquet and Stanely, 2005).
TYLCV can be divided into three major clusters worldwide based on geographical origin
(Mediterranean/Middle East/African region, India/Far East/Australia and Americas)
(Czosnek and Laterrot, 1997). In Asia (India, Pakistan) and Australia this virus is recognized
as tomato leaf curl virus (TLCV) which is transmitted by whitefly Bemisia tabaci
(Muniyappa et al., 2000; Stonor et al., 2003). TLCV causes tomato leaf curl virus disease
(TLCVD) in Pakistan (Mansoor et al., 1997). TLCV belongs to the Geminiviridae family
which contains plant viruses with a circular, single-stranded DNA genome and two
incomplete icosahedral geminate particles (Pandey et al., 2009). Geminiviridae is classified
into four genera on the basis of vector type, host range and genome sequences (Fauquet and
Stanley, 2003). Begomoviruses such as TLCV are the most devastating genera for tomato
plants worldwide especially in tropical and subtropical regions (Czosnek and Laterrot, 1997).
TLCVD is differentiated by stunting, chlorosis, upward curling of leaves, crinkling,
puckering and yellowing with reduced flower and fruit setting. Infected plants have a bushy
appearance due to shortening of internodal length with more lateral branches (Kumar et al.,
2012). TLCV is transmitted by whitefly B. tabaci (Gennadious) which belongs to order
Hemiptera and family Aleyrodidae in a circulative and persistent manner (Boykin et al.,
2007). B. tabaci can acquire the virus from an infected source in five minutes of acquisition
access period (Atzmon et al., 1998). A single whitefly can transmit TLCV successfully after
4-8 hours of inoculation access period (Hidayat and Rahmayani, 2007). B. tabaci can
transmit TLCV horizontally as well as vertically by sexual and transovarial passage
respectively (Ghanim et al., 2007). The latent period of TYLCV in B. tabaci is between 8-24
hours (Ghanim et al., 2001). The virus can also be transmitted through grafting because it
involves the union of cambial layers of stock and scion, either of which might be infected
with a virus (Mathews, 1970).
Environmental conditions play a vital role in the spread of the disease epidemics and
vector population buildup (Khan and Khan, 2000). TLCVD incidence and whitefly
population tend to increase during high temperature, low rainfall and relative humidity
(Sastry et al., 1978). The developmental time of whitefly increases with a decrease in
temperature (Bonato et al., 2007). The temperature range from 25°C to 30°C is favorable for
whitefly build up and rapid generation time (Tiwari et al., 2013). Disease predictive model is
used to explore the possibility of disease outbreaks by studying the inoculum in a particular
area and the suitable environmental conditions for the pathogen which lead to forecast
disease, provide significant information to decide risk, cost-benefit ratio, site selection,
selection of propagative material and implementation of a timely disease management plan to
protect crop precisely (Morales et al., 2004; Naerstad et al., 2007). In southern India, a
disease predictive model was developed by using biological and epidemiological data for the
management of TLCVD where alternate host plants of virus and whitefly are frequent (Holt
et al., 1999). Vector activity and behavior, particularly with respect to virus transmission are
key factors for the frequency and amount of epidemic development (Jeger et al., 2004).
Several pesticides applied against the insects failed to control the B. tabaci which attributed
to the development of insecticidal resistance (Costa and Brown, 1991). The non-judicious use
of pesticides causes environmental pollution and increases the cost of crop production (Xiliu,
2000).
Keeping in view the heavy losses caused by the TLCVD and B. tabaci, varietal
resistance would be the best option for disease management. Resistant plant hosts would be a
cheap, efficient and viable option as no chemical options available for the suitable
management of TLCVD problem. Therefore, it was necessary to record the disease incidence
and vector population with respect to environmental conditions in Pakistan. The relationship
of environmental conditions with TLCVD and B. tabaci provided a base for the development
of the disease and vector predictive models which would eventually help the farmer to
recognize, evaluate and choose proper management approaches against these pests. The
present research includes the development of epidemiological models to predict the TLCVD
incidence and B. tabaci population buildup. Adaptation of different management options
comprising pesticides/biopesticides and nutrients would help to compare their efficacy
against TLCVD and B. tabaci. Use of environment friendly bio-products to manage the
insect vector population and the viral pathogen in an economical way is a pre-requisite to
successful tomato production. The hypothesis of the current study was that the incidence of
TLCVD could be predicted by determining the role of environmental factors for its timely
management. The objectives of the present study were following:
To evaluate tomato germplasm for the identification of sources of resistance against
TLCVD and B. tabaci
To develop predictive models for the management of B. tabaci population and TLCVD
incidence
To evaluate different pesticides/biopesticides and nutrients against the TLCVD and B.
tabaci
In order to fulfill the above mentioned objectives following line of work was adopted.
a) Evaluation of the tomato germplasm against TLCVD incidence and B. tabaci population,
so that resistant, tolerant and susceptible varieties/lines could be identified. The resistant
varieties could be given to farmers directly or incorporated in the breeding program. The
tolerant varieties could be exploited by chemicals/nutrients application. Susceptible to
highly susceptible varieties could be used for TLCVD and whitefly predictive model.
b) Confirmation of virus through grafting and whitefly mediated inoculation and (double
antibody sandwich-enzyme linked immune sorbent assay) DAS-ELISA
c) Characterization of environmental conditions conducive for TLCVD and B. tabaci
d) Development of TLCVD and B. tabaci population predictive models for their timely
management through pesticides/biopesticides and nutrients
e) Evaluation of pesticides and biopesticides and nutrients for the management of TLCVD
and B. tabaci.
CHAPTER 2 REVIEW OF LITERATURE
2.1. History and taxonomy of tomato leaf curl virus disease (TLCVD)
TYLCD-like symptoms were firstly described in the late 1920s in the Jordan valley of
Israel and severe disease epidemics occurred in the early 1960s. From the late 1980s, a rapid
geographical spread of TYLCD started on large scale and its dissemination stretched from
Japan in the east to Spain in the west, Reunion Island and Australia in the south (Cohen and
Lapidot, 2007). Lefeuvre et al., (2010) proposed that TYLCV was first examined in Middle
East between 1930s and 1950s but the worldwide spread of this virus started in 1980s after
the evolution of the mild (Mld) and Israel (IL) strains of TYLCV. The Mediterranean basin is
the central launching point for the global movements of TYLCV. According to Cohen and
Harpaz (1964) the name TYLCV was devised in the early sixties for the description of a
whitefly transmitted virus that attacked the tomato crop in Middle East. TYLCV disease
incidence were sporadic in the sixties but became a severe economic problem in the early
seventies, when yield losses mostly reached upto 100%. All the tomato growing areas in
Middle East were affected by the virus untill the end of 1970s (Ioannou, 1985). In the 1990s,
TYLCV attacked tomato crops in several countries of the New World and abruptly spread in
North America and in the Caribbean (Polston and Anderson, 1997). Currently, TYLCV is
present in most Mediterranean countries and regions of sub-Saharan Africa, Asia, Australia,
Caribbean Islands, Central America, Japan and Mexico (Glick et al., 2009).
TYLCD is associated to a complex of viral species, including TYLCV, tomato yellow
leaf curl Axarquia virus (TYLCAxV), tomato yellow leaf curl Malaga virus (TYLCMalV),
tomato yellow leaf curl Mali virus (TYLCMLV) and tomato yellow leaf curl Sardinia virus
(TYLCSV) all inducing similar symptoms on tomato plants (Anfoka et al., 2005). TYLCV
consists of single stranded DNA and single genomic components with the exception of
TYLCTHV from Thailand which consists of two genomic components (Rochester et al.,
1994). Several related whitefly-transmitted viruses infecting tomato are known as TLCV and
have been identified in Australia and India. TLCV isolates from Australia, southern India
(Bangalore) and Taiwan have a single genomic component (DNA-A) whereas in northern
India isolates with two genomic components were found (Muniyappa et al., 2000). Based on
the worldwide surveys, DNA and protein sequence comparison, TYLCV can be
approximately grouped into three main groups specifying viruses from the
Mediterranean/Middle Eastern/African region, Indian/Far Eastern/Australian region and the
American region (Czosnek and Laterrot, 1997). In Southeast and East Asia, as well as in
various countries of the Old World, the viruses associated with leaf curl disease have been
termed as TYLCV or TLCV (Zeidan et al., 1998). TYLCV in India, sub-Saharan Africa
(Nigeria and Senegal) and Southeast Asia exists as different strains from the Mediterranean
virus, which are known as western Mediterranean, Sardinian, Israeli and eastern
Mediterranean strains (Abou-Jawdah et al., 1995; Padidam et al., 1995; Deng et al., 1994;
Nakhla et al., 1993). Many viruses are described as TYLCV generically but they are different
from each other. Recently recommends nomenclature recommends the addition of the
location from where the virus was isolated (Fauquet et al., 2008).
A review of the discussions on the taxonomy of the begomoviruses that cause leaf
curl symptoms in the tomato plants suggest that it is a complex of viruses (Glick et al., 2009)
with different names across the world. The symptoms caused by these begomoviruses are the
same (curling, cupping upward, stunting and yellowing), all are transmitted by the B. tabaci
in a persistently circulative mode and belong to family Geminiviridae (Mazyad et al., 2007).
Despite all these facts, these viruses are divided into three different clusters in the world
(Czosnek and Laterrot, 1997). The viral complex in India and Australia is termed TLCV
instead of TYLCV because the species complex in these regions is bipartite while TYLCV is
monopartite (Muniyappa et al., 2000). However of TYLCV strains, TYLCV-Th is bipartite
(Rochester et al., 1994) and TLCV-Banglore is monopartite like most TYLCV complex. The
yellowing symptoms are produced by both type of complexes. Some workers say that the
viral complex should followed by the name of region/country in which the particular virus is
discovered (Fauquet et al., 2008). To resolve the taxonomical issues of TLCV and TYLCV,
virologists should consider symptomology, biology and serology as well as location the virus
of a particular sequence is discovered.
2.2. Symptomology of TLCVD
The symptoms of TLCV include vein clearing, reduction in leaf size, stunted growth,
deformation of leaflets, puckering of leaflets, thickening, epinasty, crumpling, blade
reduction, abnormal shoot proliferation, internode reduction, leaves curling inward and
outward result in bushy appearance. The infected plants produce few fruits which are small
and no fruits, if infected at very early stage (Diaz-Pendon et al., 2010). The leaflets in
TYLCV infected plants cup downward and inward in a hook-like shape, become yellow and
show interveinal and marginal chlorosis (Zhang et al., 2008).
2.3. Screening of tomato germplasm against TLCVD
Tomato leaf curl virus (TLCV) causes severe damage to tomato crop worldwide
every year (Kumar et al., 2012). TLCV disease is mainly the cause of intensive cultivation of
susceptible tomato germplasm (Seal et al., 2006). A study based upon the symptom severity
and yield loss was conducted to screen tomato germplasm against TLCVD in different agro-
ecological zones of Indian Gujrat. Almost complete destruction of tomato crop was observed
in Kheda district followed by other districts with 50-80% disease severity and heavy yield
losses depending upon the environmental conditions and cultural practices (Shelat et al.,
2014). The most effective and eco-friendly approach for the management of TLCVD is the
cultivation of resistant varieties/lines (Kasrawi et al., 1988). In order to obtain stable and
durable resistances, forty one tomato varieties collected from diverse locations were screened
to evaluate their response against TYLCVD. Results revealed 12 resistant, 16 tolerant and 8
susceptible varieties (Camara et al., 2013). The resistance and tolerance of different tomato
varieties were estimated by ratio of infected plants, virus titre and symptom intensity (Rubio
et al., 2003). Infected plants showed that leaf relative water contents (RWC), total soluble
sugars (TSS), fresh and dry biomass, photosynthetic pigments level were less as compared to
healthy plants (Mushtaq et al., 2014). Out of six tomato cultivars only three (Hatouf, Douna
and Saria) were resistant against TLCVD with low incidence while Super Marmande, Speedy
and Ginan showed susceptible reaction with varying levels of disease incidence in nursery
and field conditions (Al-Refai et al., 2007). The accessions with less number of infected
plants could be the result of late TYLCV infection related to whitefly population variation.
This difference in response might be due to the virus strain, vector genotype or different
feeding conditions of the vector (Delatte et al., 2006; Navas-Castillo et al., 1999). During
autumn season sixty accessions of tomato were evaluated against TLCVD under natural
conditions followed by artificial screening under glasshouse through whitefly and grafting.
The resistant reaction was confirmed by only three lines viz. 58-11-1-1, LCT-8-5 and 115-1-
8-1 because no viral symptom appeared on all grafted plants of these genotypes even after 50
days of grafting (Gaikwad et al., 2009). There is a lack of natural resistance in domesticated
varieties of tomato as compared to wild species (Pico et al., 1998). Singh (2014) found three
resistant and eleven moderately resistant genotypes out of thirty two screened under
glasshouse conditions. Only wild genotype H-88-78-1 showed immunity against TLCVD.
Wild tomato species were screened for the identification of resistant source in tomato
as no resistance was found in domesticated tomato (Lapidot and Friedmann, 2002). Under
field conditions, one hundred and sixty cultivars of tomato were evaluated for resistance
against TLCVD. Only two wild genotypes Lycopersicon hirsutum (LA 1223) and L. hirsutum
(LA 1353) were immune to TLCVD incidence (Ragupathi and Narayanaswamy, 2000). In
India, thirty four wild and domesticated tomato cultivars were screened against TLCV in
glasshouse and field based upon symptomology. Wild relatives of tomato, L. hirsutum LA
1777 and PI 390659 were found as the durable resistance source against TLCV (Maruthi et
al., 2003). In L. hirsutum, two epistatic genes are associated with TYLCV resistance (Hanson
et al., 2000). Only wild relatives from the L. chilense and L. peruvianum showed highly
resistant response against TYLCV when twelve tomato genotypes were screened using
whitefly mediated inoculation techniques (Pico et al., 1998). TYLCV resistance is controlled
by Ty-1 gene in L. chilense, which reduces virus titres and movement of the virus for long
distance in the plant (Michelson et al., 1994). Likewise, L. glabratum (B6013) and L.
typicum (A1904) proved to be highly resistant against TLCV after screening under three
different environmental conditions (Banerjee and Kallo, 1987). More than one thousand
domesticated and wild tomato accessions were evaluated for TYLCV resistance under field
conditions in United Arab Emirates based on phenotypic response. Domestic varieties were
found more susceptible to TYLCV infection as compared to wild accessions (Hassan et al.,
1991).
Assessment of virus titer along with phenotypic evaluation of disease severity is
necessary for germplasm screening. Therefore, one hundred and thirty four domesticated
accessions and six wild tomato lines were screened against TYLCV based on symptom
development and DNA amplification. None of the varieties was resistant to TYLCV in
domesticated tomato while all six lines of wild species were resistant (Azizi et al., 2008). The
resistance in the wild relatives of tomato L. peruvianum could be due to the high acyl sugar
contents which are considered to be whitefly repellent (Liedli et al., 1995). Virus
accumulation was very low in four tomato lines developed by introgression from L. chilense
as compared to commercial F1 hybrids ARO 8479 and HA 3108 in which high virus titre was
detected (Gomez et al., 2004). Different TYLCV tolerant and susceptible tomato lines were
checked for viral DNA accumulation. DNA was analyzed by alkaline transfer and dot spot
hybridization using cloned viral DNA as a probe. Results showed that tolerant lines
contained 10-50% less DNA as compared to susceptible ones (Rom et al., 1993). Virus titre
and symptom severity showed positive correlation (Pico et al., 2001). Phenotypic and
molecular screening of thirty accessions from Solanum lycopersicum L. was done for
resistance against TYLCV. All the tomato accessions exhibited different grades of disease
symptoms. Phenotypic evaluation was confirmed by amplification of viral DNA fragment in
all tested accessions. None of the accessions showed complete resistance to TYLCV in
Ghana based on the phenotypic and molecular evaluations. Accessions having milder
symptoms of TYLCVD under field conditions were considered as tolerant (Osei et al., 2012).
Stress responses of tomato plants revealed that susceptible plants were higher in reactive
oxygen species (ROS) compounds, the anti-oxidative compounds, pathogenesis-related (PR)
and wound-induced proteins than resistant ones. Sources of carbon and nitrogen were more
in resistant than susceptible plants, which could make resistant plants more balanced and fit
to sustain viral infection (Moshe et al., 2012). Furthermore, chemical components of tomato
leaves, mainly chlorophyll (a and b), lipids, fatty acids, proteins and reducing sugars were
decreased in infected leaves as compared to healthy leaves of test plants. Whereas, infected
leaves exhibited more phenol accumulations than healthy ones. Electron microscopy of
TYLCV infected leaves showed ultrastructural changes in various organelles such as empty
vacuoles, irregular oily inclusions, severe damage in chloroplasts and uneven thickenings of
phloem tissues (Montasser et al., 2012).
Breeding programs have been successful by transferring resistance genes from wild
accessions into cultivated tomato (Vidavsky and Czosnek, 1998). Extensive experiments
were conducted for the development of resistant cultivars against TYLCV. Highly resistant
breeding lines were developed by evaluating the wild Lycopersicon spp. (Pilowsky and
Cohen, 1990) which included Solanum cheesmaniae, S. chilense, S. habrochaites, S.
peruvianum and S. pimpinellifolium (Pico et al., 1996; Vidavsky et al., 1998) and degree of
resistance was checked based on symptomology (El-Dougdoud et al., 2013). The genes
controlling TYLCV resistance were characterized from the wild species by using classical
genetic methodologies (Ilana et al., 2009). After the wide germplasm screening of Solanum
habrochaites, only two TYLCD resistant genotypes (EELM-388 and EELM-889) were
obtained and characterized further. It was found that two independent dominant and recessive
loci were linked with resistance in EELM-889 which were different from Ty-1 resistance
gene usually introgressed in domestic tomato genotypes (Tomas et al., 2011). Two TYLCV
resistant lines (BC1F1 and BC1F4) were obtained after crossing wild relatives. Analysis of
segregation showed that resistance is controlled by two to three recessive genes while
tolerance by a single dominant gene (Vidavsky and Czosnek, 1998).
2.4. Screening of tomato germplasm against whitefly
B. tabaci is the most serious pest which harms tomato plants by feeding causing leaf
and fruit spotting, irregular fruit ripening and honeydew secretion followed by sooty mold
growth (Byrne and Miller, 1990). The major economic threat is from the whitefly transmitted
begomoviruses viruses, especially TYLCV (Lapidot and Polston 2006). Due to the very low
level of resistance in domesticated tomato (Lycopersicon esculentum) against whitefly
(Freitas et al., 2002) as well as expensive pesticides that are hazardous to humans and
environment (Morales, 2007), natural plant defenses present in wild relatives of tomato were
manipulated against whitefly. The best resistance source was an accession of Solanum
galapagense (Firdaus et al., 2012) which has abundant type IV trichomes (Simmons and
Gurr, 2005). Non-preference of whitefly to the wild tomato species is due to the high
trichome density (Sanchez-Pena et al., 2006). Exudates secreted by the trichomes are of
prime importance in resistance against whitefly (Fancelli et al., 2005). These exudates have
insecticidal methylketones such as 2-tridecanone and 2-undecanone (McDowell et al., 2011).
Quantitative trait loci (QTL) for reduced whitefly egg deposition were found in Solanum
habrochaites LA1777 (Momotaz et al., 2010). Six tomato varieties (Gress, Idola, Ovation,
BTM-855, Martha and Cosmonot) were evaluated for eggs, nymphs and adult of B. tabaci on
the upper, middle and lower leaflets, percentage of geminivirus infected plant and marketable
yield. The results showed significant infestation of B. tabaci in Gress, Idola and BTM-855 as
compared to Martha, Cosmonot and Ovation. None of the varieties was found to be resistant
against geminivirus, however Martha was relatively resistant to B. tabaci and geminivirus
with the highest yield of 42.09 t/ha. This variety had high density of glandular trichome,
which was effective in reducing oviposition and nymphal feeding. The number of B. tabaci
was found higher at the upper leaf than the middle and lower leaves (Setiawati et al., 2009).
Socio-economic studies showed that farmers could gain up to 10 times more profit by
growing the resistant varieties against TLCV as compared to the susceptible varieties.
Cultivation of resistant varieties was also resulted in reduced pesticide use. Therefore, three
high yielding resistant tomato varieties were developed against TLCV using conventional
breeding and screening techniques comprising inoculation by viruliferous whitefly (Colvin et
al., 2012). The tomato genotypes were screened against TYLCV, viruliferous B. tabaci were
used for inoculation in insect proof cages following 48 and 72 hours of acquisition and
inoculation feeding periods, respectively. Disease severity data was recorded weekly
following a 0-4 disease rating scale. The susceptible cultivar Moneymaker was severely
affected by TYLCV while resistant tomato line TY172 showed no symptoms (Kashina et al.,
2004). Resistant cultivars TY 172 and TY 197 inhibited TYLCV effects and exhibited less
yield loss regarding average fruit weight and fruit size as compared to susceptible varieties
(Lapidot et al., 1997). Ten determinate tomato cultivars were screened in order to find the
correlation between against TLCVD incidence and whitefly population at different days after
planting (DAP). Results showed that 45 DAP was the critical time for viral infection in the
plants in relation to disease incidence, vector population and yield losses. The cultivar Punjab
Chhuhara was the most resistant, followed by Sel-7 (Ali et al., 2002). Wild and cultivated
tomato varieties were evaluated against whitefly. Wild tomato leaves showed resistant
response against whitefly oviposition while significant whitefly oviposition was found on
young leaves as compared to older leaves in cultivated tomatoes (Guo et al., 2013).
Similarly, young seedlings were preferred by the whiteflies for oviposition during in-vivo
screening (Campos et al., 2005).
2.5. Biological assays for TLCV
2.5.1. Through B. tabaci
B. tabaci has become a global threat for many greenhouse crops (Martin et al., 2000).
B. tabaci cause huge losses to crops by phloem feeding, induction of phytotoxic disorders,
excretion of honeydew and transmission of plant viruses. The whitefly is described as
‘superbug’ because of its effect on agricultural production (Dalton, 2006; De Barro, 2008;
Liu et al., 2007). B. tabaci can transmit more than 15 viruses that cause 40 plant diseases
(Brown and Bird 1992). A single B. tabaci can transmit TYLCV after feeding on infected
plants for 48 hours. The transmission rate of the virus was 70% when groups of five and ten
whiteflies were used (Green and Sulyo 1987). B. tabaci transmit TYLCV persistently. The
latent period of TYLCV in its vector is 20-24 hours (Ghanim et al., 2001). TYLCV is
transmitted to healthy plants after a 6-8 hours period of inoculation feeding (Berlinger et al.,
2002). The virus develops within the phloem and induces cytological changes
(Channarayappa et al., 1992).
Biotype B of B. tabaci (Mehta et al,. 1994) transmits TYLCV more efficiently
(McGrath and Harrison, 1995). The endosymbiotic bacteria of B. tabaci produced a 63-kDa
GroEL protein which help in TYLCV transmission. Biological assays by B. tabaci showed
that the biotype B was more capable to transmit TYLCV as compared to biotype Q. In
biotype B, GroEL protein produced by Hamiltonella interacts with coat protein (CP) of
TYLCV while GroEL produced by Rickettsia and Portiera does not interact with CP of the
virus (Gottlieb et al., 2010). Survey indicated that the rapid spread of TYLCV may be
associated with Hamiltonella infection of B. tabaci. Five endosymbiotic bacteria from
various B. tabaci populations were analyzed by comparing rDNA sequences. Hamiltonella
was detected in all the populations tested (Park et al., 2012).
During a study of virus vector relationship it was found that a single whitefly can
transmit TYLCV. The minimum requirement of the whitefly was 30 minutes for each of the
acquisition feeding, inoculation feeding, pre-acquisition starvation and post-acquisition
starvation periods, to transmit the TYLCV. The young seedlings of 20 days were highly
susceptible against TYLCV (Rashid et al., 2008b). B. tabaci inoculates young leaves more
efficiently as compared to older leaves. The symptoms appeared 15 days after inoculation
(Ber et al., 1990). The efficiency of TYLCV transmission was increased by increasing
acquisition access period (AAP), inoculation access period (IAP) and insect number when
the relationship of TYLCV and B. tabaci were studied in Saudi Arabia. TYLCV disease
incidence ranged from 85-96% in different regions of Saudi Arabia both in tunnel and field
conditions (Ajlan et al., 2007). The acquisition of TYLCV from a tolerant or resistant plant,
and its transmission by whiteflies is less efficient than those for a susceptible plant (Lapidot
et al., 2001). The TYLCV infection rates varied from 40 to 87% in susceptible genotypes and
the rate of virus acquisition from resistant genotypes was less than from susceptible
genotypes during the evaluation of tomato germplasm against whiteflies and TYLCV. The
results showed that the resistant genotypes can also influence disease epidemics by serving as
reservoirs of TYLCV and whitefly (Srinivasan et al., 2012). Whiteflies can acquire TYLCV
from infected tomato fruits and consequently transmit it to the healthy tomato plants (Delatte
et al., 2003).
The efficiency of TYLCV acquisition and transmission varies with the gender and
age of whitefly. Female whiteflies transmit TYLCV and tomato leaf curl Banglore virus
(TLCBV) with higher efficiency than male whiteflies (Cohen and Nitzany, 1966; Muniyappa
et al., 2000). Adult female B. tabaci of 1-2 week age could infect tomato plants after a 48
hours inoculation access period (IAP). In contrast, almost 20% of the male whiteflies of the
similar age were capable of infecting the plants. Inoculation capability decreased with the age
of the insects; 60% of the 3 week old females were able to cause infection, whereas male
whiteflies of similar age did not infect any plant. Only 20% of the 6 week old female B.
tabaci were capable of infecting the tomato plants (Czosnek et al., 2001). Insects that
emerged during a period of 24 hours, were caged with TYLCV infected plants for a
acquisition access period of 48 hours. The capability of the viruliferous B. tabaci for the
transmittion of TYLCV in tomato plants gradually decreased with age but did not vanish
entirely. Transmission by viruliferous whiteflies decreased from 100% to 10-20% during
their adult lifetime (Rubinstein and Czosnek, 1997). TYLCV can be transmitted between
male and female B. tabaci during sexual intercourse in the absence of any virus source
(Ghanim and Czosnek, 2000). Effects of TYLCV acquisition on the physiology of B. tabaci
were studied by comparing lifespan of viruliferous (V) and non-viruliferous (NV) B. tabaci.
The lifecycle of V whiteflies was 10.64 days shorter than NV whiteflies which was up to
62.5 days. The susceptibility of whiteflies to temperature was investigated by comparing
mortality rate and level of mRNA in heat shock proteins (hsp) of both the V and NV
whiteflies. Both NV and V whiteflies were subjected to 3°C and 35°C for 4 and 25 hours,
respectively. The mortality rate in V whiteflies was higher than NV ones. Results showed
that TYLCV acquisition increased the susceptibility of whitefly against thermal stress which
reduced its longevity due to enhanced metabolic energy consumption (Pusag et al., 2012).
By immuno-electron microscopy it was shown that begomovirus TYLCV can enter
midgut epithelial cells of the vector whitefly but not those of a non-vector whitefly,
Trialeurodes vaporariorum, belonging to the same family. In midgut epithelial cells of
viruliferous whitefly, the virus was localized in vesicle like structures, suggesting
endocytosis as an entry mechanism (Uchibori et al., 2013). The insect feeds on phloem sap of
TYLCV infected plants and ingests the virus. TYLCV particles then pass through the food
canal, esophagus and filter chamber, which filters out sugar and water. Virus particles are
transported from the gastric caeca into the descending midgut, which contains a single layer
of epithelial cells and is the main virus entry site. TYLCV then enters primary salivary gland
cells and finally is excreted into the saliva as another source of inoculum (Morin et al.,
2000). TYLCV enter as virions or ssDNA to the nucleus and form a chromatin for further
replication, using a polymerase machinery in host cells. This chromatin is the dsDNA
wrapped around 13 nucleosomes at maximum. For interactions with factors that drive
transcriptions and translations, this chromatin is opened at certain genomic points. The mode
of replication is similar to phages and uses a rolling circle mechanism (Jeske et al., 2001).
After injection into the phloem by B. tabaci, TYLCV replicates in infected cell nuclei and
spreads systemically through the plant. After the whitefly injects its stylets intercellularly
between epidermal cells, virions are usually deposited into the sieve elements (SE), although
in some cases they are deposited into companion cells or vascular parenchyma cells (Wege,
2007). For replication, the genomic DNA must enter a nucleus via coat protein (CP)
mediation of the TYLCV genome (Kunik et al., 1998; Rojas et al., 2001). After entering the
nucleus, viral DNA moves systemically through the plant via sieve tubes assisted by the
capsid and movement proteins (Gronenborn, 2007).
2.5.2. Through grafting
TYLCV is not transmitted through seeds or mechanically. The other successful mean
of TYLCV transmission is through grafting (Kashina et al., 2007). Graft inoculation was
done under glasshouse conditions in 36 F1 hybrids and 13 parents of tomato for their
resistance to TLCV. A wedge shaped virus infected scion was inserted into a similar cut of
the stock. The grafted plants were kept in screen house (at 25±°C and 72.4% relative
humidity) to check graft success, virus prevalence and symptoms severity. The symptoms of
TLCV developed within 2-4 weeks. The hybrids FLCR5 x MLCR4 and FLCR5 x MLCR1 and
the parents FLCR1, FLCR3, FLCR5, MLCR4, MLCR5 and MLCR6 recorded the lowest
disease incidence (Sankari et al., 2002). The differences in disease incidence and symptom
severity could be attributed to different virus concentrations in scions, physiological
conditions and initial recognition activities between scion and stock (Ioannou, 1985).
2.6. Serological assay for confirmation of TLCV
Serological assays are widely used in identification of TYLCV despite the limitations
of obtaining abundant purified coat protein for the production of antisera (Chiemsombat et
al., 1991). Enzyme linked immunosorbent assay (ELISA) is the most common technique for
the detection of viruses in insect vectors, plant material, seeds and vegetative materials
(Clark and Adams, 1977). ELISA is used to test a large number of samples in a quite short
time period due to its flexibility, sensitivity and economy in use of reagents (Almasi et al.,
2013). The procedure of ELISA is based upon binding reaction of antigen with antibody on
epitopes surface of viral particles along with specific binding sites for antiviral antibodies
(Cohen et al., 1989). Two types of antibodies are produced by injecting antigen protein into a
suitable animal. Polyclonal antibodies bind on different epitopes of the antigenic protein
(Guo et al., 2006) and monoclonal antibodies binds to one specific antigenic determinant on
the antigen (Wu et al., 2012). Coat protein of bean golden mosaic virus Brazil isolate
(BGMV), cabbage leaf curl virus (CabLCV), TYLCV and tomato mottle virus (TMoV) were
used for the production of polyclonal rabbit antisera. The polyclonal antisera were found
suitable for the detecting the begomoviruses in different assays (Abouzid et al., 2002) while
Muniyappa et al., (1991) detected and characterized TLCV through monoclonal antibodies.
The purpose of using monoclonal antibodies was to study the relationship between
geminiviruses (Macintosh et al., 1992) from different geographic areas that share specific
epitopes (Harrison et al., 1991). In triple antibody sandwich (TAS-ELISA) monoclonal
antibodies were used to detect tomato geminiviruses (Credi et al., 1989). TAS-ELISA was
used for accurate differentiation between highly susceptible and highly resistant genotypes
(Abou-Jawdah et al., 1996).
2.7. Host range of TLCVD
The domesticated tomato L. esculentum is the main host of TYLCV. Many wild
relatives of tomato such as S. chilense, L. hirsutum, S. peruvianum and S. pimpinellifolium
contain symptomless carriers that are used as progenitors in breeding programs for resistance
against TYLCV (Zakay et al., 1991). Laboratory inoculation by viruliferous whiteflies and
field sampling surveys have indicated a potentially wide host range of TLCV, covering 13
plant species in 9 botanical families. Host plant families include Asclepiadaceae, Asteraceae,
Fabaceae, Malvaceae, Solanaceae, Gentianaceae, Cleomaceae, Cucurbitaceae and Apiaceae
(Kegler, 1994). Pepper Capsicum species were screened against TYLCV and C. baccatum,
C. chinense and C. frutescens found susceptible. Moreover, B. tabaci were found capable to
acquire TYLCV from infected pepper plants and transmit it to the healthy tomato plants
(Polston et al., 2006). TYLCV-Eg isolate was transmitted by whiteflies and syringe injection
in different plant species belonging to families Chenopodiaceae, Cucurbitaceae, Fabaceae
and Solanaceae with 80% and 100% transmission efficiency, respectively (El-Monem et al.,
2011). A severe attack of leaf curl virus affected 80-90% of sunhemp plants in research fields
of NBRI, Lucknow. Whitefly transmitted the virus from infected to healthy sunhemp plants.
The PCR amplification of the viral DNA with begomovirus specific primers and its
hybridization with a DNA-A probe of Indian tomato leaf curl virus indicated that it was a
begomovirus (Khan et al., 2002). TLCV caused yellow leaf disease in cantaloupe and wax
gourd (Samretwanich et al., 2000) in Thailand.
Some cultivated plants including bean (Phaseolus vulgaris), petunia (Petunia
hybrida) and lisianthus (Eustoma grandiflorum) are hosts of TYLCV and developed severe
symptoms with whitefly mediated inoculation. Further plant species such as the weeds
species Cleome viscose (Caparidaceae) and Croton lobatus (Euphorbiaceae) were found
susceptible against TYLCV but did not produce disease symptoms (Salati et al., 2002). An
extensive study was conducted in Cyprus to screen naturally infected weed species against
TYLCV disease incidence and prevalance. About 4,000 dicotyledonous plants from 122
species and 25 families were tested against TYLCV through serological and molecular
techniques. Different plant families such as Amaranthaceae, Chenopodiaceae, Solanaceae
and Urticaceae were found infected with TYLCV when checked using real-time PCR. It was
concluded that destruction of alternate hosts may be the easiest management strategy for
TYLCV (Papayiannis et al., 2011).
After testing 210 samples of 95 weed species, Conyza sumatrensis, Chenopodium
murale, Datura stramonium, Dittrichia viscosa, Malva parviflora, Solanum nigrum,
Convolvulus sp. and Cuscuta sp. were found infected with TYLCV (Jorda et al., 2000).
Weeds, such as D. stramonium and Cynanchum acutum showed distinct symptoms, while M.
parviflora was symptomless carrier (Czosnek et al., 1993). In order to find out the weed
hosts of TYLCV, different weeds were inoculated with viruliferous whiteflies. Amaranthus
dubius was found the only infected weed when detected based on PCR amplification. In A.
dubius, viral symptoms were observed 11 days after inoculation and transmission rate was
83%. The successful back transmission of TYLCV from A. dubius to tomato plant was also
checked by using whitefly adults (Guerere et al., 2012). Solanum nigrum, collected from a
field in southeast Spain and exhibiting leaf curl symptoms, was squash blotted onto nylon
membrane and gave a positive signal when hybridized to a TYLCV-Is DNA probe.
Laboratory tests showed that whitefly transmitted the TYLCV-AL from infected tomato
plants to healthy S. nigrum seedlings. The virus could be acquired by whitefly and
transmitted back from infected S. nigrum plants to tomato plants, inducing typical TYLCV
disease symptoms. These results indicate the importance of S. nigrum as a weed
host/reservoir for a TYLCV and its possible role in the spread of this virus within Europe
(Bedford et al., 1998).
A survey of natural weed hosts that could be reservoirs of TYLCV was performed in
major tomato production areas of Korea. About 530 samples were collected and identified as
belonging to 25 species from 11 families. PCR and Southern hybridization were used to
detect TYLCV in samples and replicating forms of TYLCV DNA were detected in three
species (Achyranthes bidentata, Lamium amplexicaule and Veronica persica) by Southern
hybridization. TYLCV transmission mediated by B. tabaci from TYLCVinfected tomato
plants to L. amplexicaule was confirmed and TYLCV-infected L. amplexicaule showed
symptoms such as yellowing, stunting and leaf curling. TYLCV from infected L.
amplexicaule was also transmitted to healthy tomato and L. amplexicaule plants by B. tabaci.
The rate of infection of L. amplexicaule by TYLCV was similar to that of tomato. These
results were the proof that L. amplexicaule is a reservoir weed host for TYLCV (Kil et al.,
2014). TLCV was also detected in 13 weed species generally found in Karnataka, based on
symptomology and TAS-ELISA. TLCV was transmitted successfully from infected weeds to
healthy tomato plants by whitefly (Ramappa et al., 1998).
2.7.1. Host range of B. tabaci
B. tabaci cause huge losses in tomato crop by direct feeding and transmitting
geminiviruses worldwide (Inbar and Gerling, 2008). B. tabaci attack more than 600 plant
species including a number of weed hosts such as Borreria verticilliata (Rubiaceae), Cleome
espinosa (Cleomaceae), Herisanthia hemoralis (Malvaceae), Richardia grandiflora, Senna
obtusifolia (Fabaceae), Stachytarpheta sanguinea (Verbenaceae), Waltheria indica, W.
rotundifolia (Sterculicaceae) (Oliveira et al., 2001). Whitefly infestation on tomato was
higher than weeds (Bezerra et al., 2004). Legaspi et al., (2006) found whitefly eggs and
nymphs on cotton, collards, cowpea, tomato and hibiscus. B. tabaci selectively colonizes
cassava and sweet potato (Legg, 1996) while Butler et al., (1986) observed that whitefly
prefers cotton for oviposition.
2.8. Epidemiology of TLCVD and B. tabaci
In India after a number of experiments it was found that the most effective time for
planting of tomato is October to Mid-December followed by January to first March. Further,
it was added that TLCV disease appeared very early (25 to 45 days) when the crop was
planted between 16th March to 16th September and there was delayed appearance (132 to 162
days) of the disease between October to Mid-December (Saklani and Mathai, 1977). Tomato
crops planted during the months of December to May are subjected to low rainfall, low
humidity and high temperature which helped for high population of whitefly and high TLCV
incidence resulting in low yield. Whereas the tomato planted during July to November are
subjected to high rainfall, high humidity and low temperature, resulting in low whitefly
population, low incidence of TLCV with better yield of tomatoes (Sastry et al., 1978). In
Saudi Arabia TYLCV caused severe epidemics in summer and early autumn due to favorable
conditions for whitefly population build up whereas winter planting exhibited low infection
with minor symptoms. Tomato genotypes showed varying response of susceptibility against
the viral infection (Mazyad et al., 1979). B. tabaci attacks tomato from April to November
with highest infestation in August to October. Tomato sown in February was rarely infested
with B. tabaci but plants sown in April became severely infested during the flowering and
fruiting stage resulting in 40% crop loss (Shaheen, 1983). TLCVD in Sudan was most severe
during the hottest months of the year, even though vector populations during this period were
relatively low. This could be due to increased vector activity and host vulnerability under
very high temperature conditions (Yassin, 1983).
The seasonal pattern of disease incidence and severity determined in Mediterranean
and Middle Eastern countries indicated that disease incidence was highest and symptoms
were most severe during the hot and dry summer months but negligible during the cold and
rainy winter months (Makkouk and Laterrot, 1983). Epidemiological studies of TYLCV
revealed that sowing time significantly reduces the disease. The disease incidence, severity
and rate of spread were maximum in summer and early autumn transplanted crops because of
abundant whitefly populations while crops transplanted in winter and early spring escaped
TYLCV infection completely (Ioannou and Lordanou, 1985). All growth stages of tomato
plants were found susceptible to TLCV infection. TLCVD incidence in Karnataka was 17-
53% in July-November sowing as compared to 100% in February-May crop. In late sowings,
50-70% less yield losses were observed as compared to early sowings (Saikia and
Muniyappa, 1989). TYLCVD incidence was more severe in August transplanted crops as
compared to October transplanted crops. A good correlation between whitefly populations
and TYLCVD incidence was found during hot months. Yield losses ranged from 24.6 to
80.7% in relation to the infection period. The symptomatology depends on the temperature
and the time of infection (Polizzi and Asero, 1993). The severity of tomato mosaic virus
disease (TMVD) was increased when the pepper plants were inoculated during warmer
months of the year as compared to inoculation during cooler months. This result suggested
that increase in temperature is directly proportional to disease severity (Schuerger and
Hammer, 1995).
TYLCV outbreaks always followed in months with a mean relative humidity less than
60% and mean maximum temperature of 30°C in Israel (Nitzany, 1975). Effect of
environmental factors was studied on the TLCV disease incidence in different tomato
cultivars in India. It was found that high temperature and humidity increased TLCV disease
incidence in the plants with the maximum infection was obtained at 25°C and 79.73%
relative humidity (Rai et al., 2001). Correlation of environmental conditions (maximum
temperature, minimum temperature, relative humidity, rainfall, clouds and wind velocity)
with okra yellow vein mosaic virus (OYVMV) disease severity and whitefly population was
determined on commercially grown okra varieties. Minimum temperature and relative
humidity had significant correlation with OYVMV disease severity and whitefly population.
The disease incidence was positively correlated with minimum temperature while the
whitefly population was negatively correlated with relative humidity (Ali et al., 2005a). The
influence of air temperatures, rainfall and relative humidity on whitefly and MYMV severity
was found significant through stepwise regression analysis two years (2003-2004) data
(Khan et al., 2006). Similarly, hot weather with little or no rainfall was found conducive for
OYVMV disease development and also for B. tabaci multiplication (Singh, 1990). Beniwal
et al., (2006) also found the negative correlation between CLCuVD and maximum
temperature and relative humidity. A negative correlation was found between TLCV disease
incidence and wind direction when observations were made in Sudan for five growing
seasons. The highest rate of TLCV spread was found in the early stages of growth, mostly
within 7-10 weeks after planting (Yassin, 1975).
The effect of temperature (17, 21, 25, 30 and 35°C) on life history parameters of
whitefly population was studied. Temperature dependent interactions were described for
immature developmental rate, immature survival, fecundity and longevity. Development time
was 20 days at 30°C and 56 days at 17°C with the lowest thermal threshold was observed at
10.2°C. The optimum temperature for immature development was 32.5°C. Total fecundity
(eggs per female) ranged from 105.3 (at 21°C) to 41 (at 35°C). The longevity decreased with
the increase in temperature. The relationships between temperature and life history traits
provided a basis for development of population models (Bonato et al., 2007). The optimum
temperature and relative humidity ranged for the buildup of whitefly population was 20-24°C
and 46-60%, respectively (Bishnoi et al., 1996). The mean development time in days from
egg to adult was 37 at 20°C and 20 at 25-30°C. Temperatures of 25°C and 30°C were found
to be the most favourable for the development of egg and nymph stages of B. tabaci
(Darwish et al., 2000). Maximum temperature was significantly correlated with whitefly
density in the semi-arid region of Rajisthan, India (Kumhawat et al., 2000).
2.9. TLCVD incidence and B. tabaci population predictive model
Plant diseases carry major health, economical, environmental and social problems
around the world. Therefore, it is necessary to describe the dynamics of plant disease for
sustainable disease management strategies and reduce the effect of diseases in crops.
Dynamics of epidemic is described by using different mathematical tools including models,
area under disease progress curve (AUDPC), linked differential equation (LDE) and
computer simulation. The mathematical tools are selected according to the nature of the
problem and requirements of the epidemiologist (Medina et al., 2009). Several disease
progress measurements are combined through AUDPC into a single value when assessments
in the first or last observations have a relatively large variance (Simko and Piepho, 2012).
The temporal dynamics and spatial patterns of epidemics are jointly determined by the
pathosystem characteristics and environmental conditions using mathematical and statistical
modeling (Maanen and Xu, 2003). Models predict the likelihood of disease outbreak on the
basis of past and future (Shtienberg, 2000). Vector transmitted viral diseases were predicted
and analyzed by developing disease predictive models (Pethybridge and Madden, 2003). A
model was developed and analyzed to determine the effect of vector transmission on plant
virus disease epidemic development (Jeger et al., 2009). Analysis of an epidemiological
model revealed that varietal resistance is the most appropriate way of TLCVD management
and the infected tomato plants has little impact on disease incidence. Application of
insecticides to reduce the whitefly population is also necessary (Holt et al., 1999).
The relation between feeding behavior of whitefly and transmission of TYLCV was
studied. There was a positively significant relationship between phloem contacts and
transmission efficiency. The minimum duration of contact between B. tabaci and phloem of
the tomato plant for transmission of TYLCV was 1.8 minutes (Jiang et al., 2000). The
relationship between TYLCV transmission and whitefly population on tomato varieties was
determined under the field conditions. There was non-significant quadratic polynomial
relationship (y = -0.005x2 + 0.28x – 1.54 and R2 = 0.96) between temperature and whitefly
population build up. A negatively significant relationship was found between relative
humidity and whitefly population (y = - 0.032x2 +4.55x – 159.44 and R2 = 0.67). There was a
positively significant correlation between number of whiteflies and TYLCV transmission in
the tomato field (y = - 0.001x2 + 0.03x + 1.06 and R2 = 0.66). In all the varieties, virus
prevalence was found higher at mid stage as compared to late and early stages of infection
(Rahman et al., 2006). Epidemiological studies showed that a significant and positive linear
relationship (Y = 23.24+0.74x and R2
= 0.61) was found between the whitefly population and
TYLCV infection under field conditions. Likewise, the whitefly population was positively
correlated with temperature and negatively correlated with relative humidity (Aktar et al.,
2008).
An experiment was conducted to evaluate the effect of different planting dates on
TYLCV incidence and whitefly population in tomato fields. The highest TYLCV incidence
(%) was observed at 75 DAP during the period of March and April planting followed by May
planting, but the lowest TYLCV incidence (%) was found in November planting followed by
December planting. A strong correlation was obtained between TYLCVD incidence and
number of whitefly in tomato plants. A regression line was fitted between whitefly
population and TYLCV incidence. The correlation coefficient (r) was 0.81** and the
contribution of the regression (R2 = 0.65) indicated that 65% TYLCV infection increased by
whitefly (Rashid et al., 2008a). A disease predictive model was developed for the
management of tomato spotted wilt virus (TSWV) and its vector based upon weather factors
in tobacco. It was observed that the weather affected thrips activity and disease incidence
during summer, particularly during the acqusition of virus from natural reservoirs and
transmission to healthy host plants. There was a positive correlation between thrips activity
and spring rainfall regarding disease incidence (Chappel et al., 2013).
A climate probability model was developed through Flora Map in Latin America
where whitefly and geminiviruses cause heavy losses in vegetables. The data were collected
and grouped from 304 geo-referenced points indicated low rainfall of 80 mm and
temperature above 21°C. A modified Koeppen climate classification revealed that the
geminiviruses attacked 55% areas are in the tropical wet or dry climates, 22% areas in
tropical and subtropical dry or humid climates and 23% areas in wet equatorial and trade
wind litoral climates. These results contributed towards the understanding of whitefly and
geminivirus epidemics as well as adoption of integrated pest and disease management
strategies (Morales and Jones, 2004). The population of B. tabaci was initiated at about 48
standard meteorological weeks (sMw), increased at first slowly up to 1 sMw then steadily up
to 5 sMw attaining the maximum at about 6 sMw which was maintained up to about 9 sMw.
The population then declined at first slowly then abruptly. Incidence of TYLCV was
correlated with B. tabaci population. Maximum and minimum value of TYLCV was noted at
about 15 and 50 sMw respectively. Abiotic conditions had significant negative influence on
B. tabaci population. In case of relative humidity gradient a positive influence was observed
(Kaushik, 2012).
2.10. Management of TLCVD and B. tabaci
2.10.1. Management through insecticides
The chloronicotinyls or neonicotinoids (imidacloprid, acetamiprid, nitenpyram and
thiamethoxam) have shown good efficacy in controlling aphids, whiteflies and other insects
(Bacci et al., 2007; Ishaaya et al., 2007). These compounds bind with acetylcholine receptor
(nAChR) in the CNS of insects. Neonicotinoids mimic acetylcholine and induce unusual
excitement in the insect by disturbing the normal synaptic transmission. Eventually, the
insect suffers from excitation and paralysis, followed by death. Neonicotinoids are effective
against the insects on contact and through stomach action (Tomizawa et al., 1995; Lind et al.,
1999). Translaminar movement permits the insecticide to control pests on both sides of the
leaf. The insecticides with translaminar movement capability are of significance importance
against sucking pests such as aphids and whiteflies that live and feed primarily on lowerside
of the leaves (Natwick, 2001; Parrish et al., 2001). Confidor (Imidacloprid) and Megamos
(Acetamaprid) along with other insecticides were evaluated at field recommended dose
against whitefly population. All the insecticides were applied at economic threshold level
(ETL) of whitefly. Confidor and megamos caused significant mortality of whitefly as
compared with other insecticides (Amjad et al., 2009). Four insecticides were evaluated
against B. tabaci on tomato plants. The results showed that imidacloprid gave highly
reduction in the mean number of B. tabaci (0.97 nymph/leaf) followed by etofenprox (1.22
nymph/leaf), thiocloprid (1.33 nymph/leaf) and thiamethoxam(1.82 nymph/ leaf) (El-Sayed,
2013). The effects of different insecticides were checked against nymphs and adult whitefly.
Buprofezin was found effective against nymphs while acetamiprid, diafenthiuron and
imidacloprid were effective against the whitefly adults (Ali et al., 2005b).
Neonicotinoids have low hydrophobicity due to their excellent systemic and
translaminar movement. The systemic activity of neonicotinoids were studied in cotton,
wheat and sugar beet. These studies revealed that neonicotinoids transport in the xylem
(Westwood et al., 1998). The crop species also affects the systemic efficacy of the active
ingredient. The penetration and translocation of Imidacloprid was less obvious in cotton
leaves as compared to cabbage (Bucholz and Nauen, 2001). An experiment was conducted to
determine efficacy of four neonicotinoids viz; nitenpyram 10SL, thiacloprid 480SC,
imidacloprid 200SL, acetamaprid 20SL and four traditional insecticides at their
recommended field doses against sucking insect pests of cotton and their natural enemies at a
farmers’ field. The results showed that nitenpyram, thiacloprid and imidacloprid found safer
against natural enemies and toxic for the sucking pests as compared to conventional
insecticides when the number of insects per leaf were counted to find difference among
treatments (Ahmed et al., 2014). The efficacy of four insecticides was assessed for
controlling jassid, whitefly and thrips. Novastar 56 EC (bifenthrin + abamectin), Deltaphos
(deltamethrin + triazophos), Confidor 20 SL + Tracer and Confidor 20 SL were sprayed
twice every two weeks to ascertain the mortality of the pests on NIAB-111 variety of cotton.
The lowest populations of jassids (2.54), whiteflies (1.79) and thrips (4.16) per leaf after
application of insecticides were shown by Novastar followed by Confidor (Tayyib et al.,
2005). Seven insecticides were used against sucking insect pests of cotton. Fenpropathrin
proved as the most effective against all the insect pests followed by the imidacloprid and
acetamaprid while dimethioate significantly reduced the whitefly population followed by the
imidacloprid and acetamaprid (Shivana et al., 2011). Acetamaprid and imidacloprid gave the
significant reduction in whitefly population on all cotton cultivars during an experiment
when different pesticides and bio-control agents were used (Abbas et al., 2012).
An experiment was conducted to evaluate the efficacy of different insecticides and
biopesticides against TYLCV disease. Disease incidence was reduced by 1.7 to 3 times
depending on chemicals. Efficiency of chemical insecticides was better than botanical
pesticides against TYLCV disease (Muqit et al., 2006). In vitro efficacy of insecticide
molecules on whitefly mortality and TLCV transmission revealed that adult mortality varies
with the increase in the concentration of insecticides. Among the different concentrations of
cyantraniliprole (45, 60 and 75 g.a.i/ha) tested, highest concentration 75 g.a.i/ha were found
more effective in reducing both whitefly population and TLCVD incidence. Whiteflies
remained active and caused 100% transmission of TLCV in the untreated check (Govindappa
et al., 2013). Comparative efficacy of Acetamiprid 20 SP, Imidacloprid 25% WP, Bifenthrin
10 EC, Cypermethrin 10 EC, Triazophos 40 EC, Lambda Cyhalothrin 2.5EC and Rani 20SL
against sucking insect pests (whitefly, Jassid and Thrips) of cotton was checked. Among
insecticides, Rani 20 SL and Acetamiprid 20 SP were more effective against the sucking
insect pests and in increasing seed cotton yield as compared to the other tested insecticides
(Khan, 2011).
The systemic efficacy of neonicotinoids was correlated with the method of
application. Soil application was found suitable for systemic activities of Imidacloprid while
acetamiprid performed better after foliar application (Horowitz et al., 1998). In Florida and
Israel neonicotinoids (thiomethoxam, imidacloprid, and dinotefuron) are applied as drenches
and sprays for the management of TYLCV. Neonicotinoids were used at a reduced rate in
nursery and then at recommended doses in the standing water at the time of transplanting.
The application in standing water controlled the whitefly for about 8 weeks. Insecticide
resistance can be avoided by the application of non-neonicotinoids such as soaps, oils, insect
growth regulators, and many contact insecticides until the harvesting of the tomato crop
(Elbert and Nauen, 2000). Effectiveness of imidacloprid and thiamethoxam, was evaluated
using each active ingredient separately as seed treatments and foliar applications against
thrips, jassid, whitefly and cotton aphid. Seed treatment with Imidacloprid and
Thiamethoxam found were effective against thrips up to 6 weeks from the start of seed
sowing. Imidacloprid had a better efficiency against whitefly than thiamethoxam. Foliar
treatments with imidacloprid and thiamethoxam were highly effective against aphids and
jassids as compared to whiteflies (El-Naggar and Zidan, 2013).
Imidacloprid reduces plant damage by virus infection through the interruption in
feeding of insect instead of causing rapid knockdown of sucking insects. Consequently,
neonicotinoids have substantially reduced virus infcidence in several field crops (Bethke et
al., 2001). Moreover, drenching of thiamethoxam protected the tomato plants from TYLCV
infection up to twenty two days, while foliar spray was effective for eight days only. High
residual activities of neonicotenoids make them effective against virus transmission (Mason
et al., 2000). Application of imidacloprid in the early growth stages of tomato, follow the
systemic pathway in the plant and protect the crop from seedling to flowering stage by
delaying the infection in early stages (Ahmed et al., 2001; Karim et al., 2008). Imidacloprid
was used for indirectly controlling TYLCV in tomato. In three seasons, the mean incidence
of TYLCV was 42.7% in untreated plots as compared with 15.7% in treated plots. Disease
incidence in imidacloprid treated plots was reduced from 17% to 2.2%. Higher yields were
recorded from treated plots and the yields decreased with decrease in the rate of insecticide
application (Ahmed et al., 2001).
Seed treatment with imidacloprid reduces the insect feeding and provides indirect
protection against disease transmission in different crops (Gourmet et al., 1994). In
Bangladesh, foliar spray as well as seed treatment of BARI hybrid tomatoes with
imidacloprid significantly reduced the TYLCVD incidence and increase the yield (Karim and
Rehman, 2012). The effect of Admire (Imidacloprid 0.1%) and Cymbush (Cypermethrin
0.1%) was checked on the growth and yield of tomato plants due to TYLCV infection under
natural field conditions. The Admire exhibited better results as compared to Cymbush. The
TYLCV disease incidence and percent reduction in fruit yield was significantly and
positively correlated with one another (Aktar et al., 2008).
2.10.2. Management through nutrients and systemic acquired resistance
Plant health plays an important role in the pest management (Altieri and Nicholls,
2003). Nutrient management improves plant health, which enables the plant to tolerate the
incidence and herbivory of sucking as well as of chewing insect-pests. Therefore, the effect
of various nutrients (N, P, K, Zn, B), on infestation of whitefly was investigated. The
nutrients significantly reduced the population of whitefly in treated plots as compared to
control (Gogi et al., 2012). The application of micro and macro-nutrients to crop plants may
affect the relationship between plants and insects (Abro et al., 2004) as nutrient deficient
plants are weak and susceptible to disease incidence and insect pest attack (Marschner, 1995;
Thompson and Huber, 2007). Micronutrients take part in all the metabolic and cellular
functions of the cell. Plants have different requirements for micronutrients e.g. boron (B),
chlorine (Cl), copper (Cu), iron (Fe), manganese (Mn), molybdenum (Mo), nickel (Ni) and
zinc (Zn). Some of these elements are redox-active act as cofactors in enzymes, others
activate the enzymes and accomplish a structural role in stabilizing proteins (Hansch and
Mendel, 2009). Viruses alter the physiology of plants by affecting the growth and
development and interacting with defense mechanism. The concentration of reactive oxygen
species (ROS) and free radicals increases upto two fold due to the viral attack in Zn deficient
cells causing significant damage to the plants. Zinc improves the defense system of plant
cells against ROS by interfering with membrane-bound NADPH oxidase that produces ROS
and protects membrane lipids, proteins, chlorophyll, enzymes and DNA of the cell from
oxidation (Cakmak, 2000).
The role of different nutrients, such as nitrogen (N), phosphorus (P), potassium (K),
Mn, Zn, B, Cl and silicon (Si) in disease management was described (Dordas, 2008). Plants
with high N supplies reduced the infection severity caused by facultative parasites. Potassium
decreased the susceptibility of host plants. Mn was found effective as it has vital role in
photosynthesis, lignin and phenol biosynthesis. Boron reduced the severity of many diseases
as well as susceptibility of plants because it affects structure of cell wall, plant membranes
and metabolism of phenolics or lignin (Brown et al., 2002). Boron binds the apoplastic
proteins to cis-hydroxyl groups of cell wall and membranes for the interruption of
manganese-dependent enzymatic responses and affecting the metabolic pathways of plants
(Blevins and Leukaszewski, 1998). In B deficient cells oxidative burst, cell death, H2O2
production and phenolic leakage increased indicating loss of membrane integrity (Dordas and
Brown, 2005).
Plant defense responses are regulated by a complex network of signal molecules and
growth regulators. Resistance genes identifies the pathogen specifically and start defense
responses. Salicylic acid (SA), jasmonic acid (JA), naphthalene acetic acid (NAA) and
ethylene (ET) mediated appearance of both specific as well as basal defense responses (Jalali
et al., 2006). Dipotassium hydrogen phosphate (K2HPO4), potassium dihydrogen phosphate
(KH2PO4) and salicylic acid at 2 and 3% concentrations were drenched in pots kept under
screen house and as foliar applications under field conditions on different cotton varieties. B.
tabaci collected from virus infected plants were released on the plants placed in wooden
cages. Salicylic acid at 3% concentration indicated best results in reducing egg hatching
ability, adult emergence, adult B. tabaci population and CLCuVD severity followed by
KH2PO4 and K2HPO4 (Khan et al., 2003).
Salicylic acid (SA) was an efficient inducer of resistance against tobamoviruses in
tomato and bell pepper. The seedlings were sprayed with salicylic acid (50 mM) and the
severity of viral diseases was assessed by number of local lesions. The results showed that
the seedling treatment with SA minimized the number of local lesions when compared with
untreated ones (Madhusudhan et al., 2005). Tobacco mosaic virus (TMV) RNA and coat
protein levels were reduced in susceptible tobacco tissues by treating with SA because it
inhibits the replication of TMV. Salicylhydroxamic acid (SHAM) which inhibits the
alternative oxidase of mitochondria, antagonized the SA induced resistance against TMV
both in susceptible and resistant tobacco plants (Chivasa et al., 1997). SA induced the
resistance against Cucumber mosaic virus (CMV) in tobacco (Nicotiana tabacum) by
inhibiting the systemic movement of the virus from cell to cell and induced by a signal
transduction pathway (Mayers et al., 2005).
2.10.3. Management through plant extracts
Chemical control methods remained the major approach for the management of insect
infestations, but this approach has become less effective because the insect populations
develop resistance against insecticides (Siebert et al., 2012). Apart from this, B. tabaci
adults, eggs and nymphs are found on the lower side of leaves where these remain safe from
insecticide application on upper leaf surfaces. Hence, chemical control of whiteflies is costly
and not effective always because of whiteflies treated with chemical pesticides develop
resistance against these pesticides (Palumbo et al., 2001). Therefore, the pesticides produced
from natural products are helpful in reducing the problems such as insecticide resistance and
environmental hazards caused by synthetic compounds (Abou-Yousef et al., 2010). The
efficacy of neem based pesticides azadirachtin, neema (liquid type) and neema-plus (pellet
type) were checked against the mortality rate and developmental inhibition of the B. tabaci.
Azadirachtin reduced the rates of female oviposition, egg hatching and adult exclusion to
23.1, 53.2 and 26.6% respectively. Foliar spray of neema reduced the rates of adult
colonization, oviposition and egg hatch up to 78.2, 47.0 and 71.2% respectively while soil
treatment with neema-plus reduced up to 31.3, 34.1 and 66.8%, respectively (Lynn et al.,
2010). The insecticidal activity of neem extracts is due to the components that are capable of
influencing the physiology and behaviour of a wide range of insects (Schaaf et al., 2000).
Major biologically active components of A. indica extract are azadirachtin, triterpenoids and
essential oils etc. These components suppress the insects’ desire for food as well as destroy
eggs and immature insects (Siddiqui et al., 2000). The azadirachtin being the main ingredient
of the neem extract disturbs the functioning of corpus cardiacum and molting hormone. This
compound is also used as an insect growth regulator which suppresses egg laying, molting,
pupation and adult formation of the whitefly (Ascher, 1993).
The eggs and nymphs of B. tabaci were managed by aqueous and ethanolic extracts
of Acalypha gaumeri, Annona squamosa, Carlowrightia myriantha, Petiveria alliaceae,
Trichilia arborea and Azadirachta indica. Results suggest that ethanolic extracts of P.
alliaceae and T. arborea leaves showed the highest insecticidal effects on eggs and nymphs
of B. tabaci followed by the extracts of A. indica (Cruz-Estrada et al., 2013). By using plant
derived oil, a reduction of 62-75% was observed in B. tabaci population (Butler et al., 1991)
while Butler and Henneberry (1992) suggested that the immature and adults of whitefly
could be killed or repelled by one or two applications of plant oils (cotton seed and soybean
oils) at the concentration of 1-2% without any phytotoxicity. Neem oil and neem seed water
extract at different levels of concentration were applied against some sucking insect pests.
Neem oil at 2% and neem seed water extract at 3% significantly reduced the population of
whitefly, jassids and thrips on cotton up to 168 hours after spray but lost their efficacy up to
336 hours after spray. Reduction in the test insects population 24 hours after spray at 1.5 %
and 2% neem oil and 3% neem seed water extract increased 168 hours after spray which may
be the cause of anti-feedant and deterrent effect of neem that had forced the test insects to
leave the locality or chronic effect of the neem compounds (Khattak et al., 2006).
Melia dubia and neem products were evaluated against pests of tomato. Melia seed
kernel extract (MSKE) and neem seed kernel extract (NSKE) at 5% concentration reduced
60.19 and 69.37% B. tabaci, respectively (Senguttuvan et al., 2005). Bioassays with aqueous
extracts of Melia azedarach L. (chinaberry) leaves and fruits were conducted against 3rd and
4th instar nymphs of B. tabaci on tomato crop. Results indicated that all Melia extracts caused
significant mortality of whitefly than the controls. Extracts along with the surfactant were
significantly more toxic than those sprayed alone (Jazzar and Hammad, 2003). The toxic
effects of Nicotiana tobacum and Eucalyptus globulus extracts were examined against
second instar larvae of Lycoriella auripila, by agar dilution technique. Plant extracts were
applied at seven concentrations against second instar larvae and their mortality were assessed
after 24, 48 and 72 hours. N. tabacum and E. globulus extracts resulted in 77.55 and 72.5%
mortality of larvae at 4000 ppm concentration after 72 hours, respectively (Farsani et al.,
2011). The effect of neem oil, garlic, eucalyptus and datura extracts on the population of
jassid, whitefly and thrips were tested in Bt cotton under field conditions. All the plant
products showed varying toxicity against sucking complex of Bt cotton 24, 72, 168 and 240
hours after application. Datura proved to be the most effective bringing about significant
reduction in the pest population followed by neem oil. Garlic and eucalyptus also produced
significant results as compared to untreated check (Khan et al., 2013).
As the TYLCV is transmitted by B. tabaci, extracts of mehogoni (Swietenia
macrophylla) seeds, garlic (Allium sativum) bulbs, karamja (Pongamia pinnata) leaves and
neem (A. indica) fruits, were used against TYLCVD incidence. Disease incidence was high
in control as compared to treated plants (Bhyan et al., 2007). Efficacy of six plant products
was evaluated in the field for the management of TLCVD and B. tabaci. Spraying with neem
seed kernel extracts and leaf extract of Pinus, Thuja, Araucaria, Cupressus and Cycas proved
effective in reducing the disease incidence, whitefly population and also in increasing the
yield (Ansari et al., 2007). Plant extracts of Mirabilis jalapa, Charthamus roseus, Dathura
melta, Bougainvillea spectabilis, Boerhaavia diffusa and A. indica reduced maximum
incidence of urdbean leaf crinkle virus (Reddy et al., 2006). The extracts of A. indica,
Calotropics procera, Eucalyptus globules L., Allium sativum L., Datura stramonium L., and
Aloe barbadensis Mill. were evaluated against B. tabaci and cotton leaf curl virus disease
(CLCuVD) under field conditions. A. indica and E. globules extracts controlled the B. tabaci
as well as CLCuVD most effectively (Ali et al., 2010).
CHAPTER 3 MATERIALS AND METHODS
3.1. Screening of tomato germplasm against tomato leaf curl virus disease (TLCVD)
and whitefly Bemisia tabaci
To evaluate tomato germplasm against TLCVD and B. tabaci, an experiment was
established during two years (2012 and 2013) in the Research Area of Department of Plant
Pathology, University of Agriculture Faisalabad. Twenty seven varieties/lines (Roker, Big
Beef, 09079, Uovo Roseo, Naqeeb, Caldera, Sitara-TS-101, Pakit, Riogrande, Nuyt-9-11,
Nagina, Lyp#1, Nuyt-25-11, Carmen, BL-1176-Riostone-1-1, Libnan Arif, Nuyt -04-11,
Salma, Po-02, 09088, 09080, 10127, 10113, 09091, 10125, 014276 and Roma) were obtained
from Ayub Agricultural Research Institute (AARI) Faisalabad. Row to row and plant to plant
distance of 70cm and 30cm was maintained, respectively. The experiment was conducted in
augmented design. To ensure the presence of virus source in the field, a row of spreader
(Fanto) was sown after every three rows of varieties/lines to be tested for resistance. All the
recommended agronomic practices were followed to keep the tomato crop in good condition.
However, no insecticide was used in order to develop maximum whitefly population and
disease pressure. Disease incidence of TLCV infected plants on each variety/line was
recorded on weekly basis according to the following formula:
No. of infected plants
Disease incidence = -------------------------------------- x 100
Total No. of plants
The resistance or susceptibility status of the screened varieties/lines against disease was
determined by using modified Ssekyewa, 2006 scale. This helped to determine susceptible
and tolerant varieties/lines for model development and plant disease management,
respectively.
Table 3.1. Disease rating scale
Grades Disease incidence (%) Level of
resistance/susceptibility
0 All plant free of virus symptoms HR
1 1-20% R
2 21-40% MR
3 41-60% MS
4 61-80% S
5 81-100% HS
HR= Highly Resistant, R= Resistant, MR= Moderately Resistant, MS= Moderately
Susceptible, S= Susceptible and HS= Highly Susceptible
3.2. Biological assays
Two types of pathogenicity tests (whitefly transmission and graft inoculation) were
performed for the confirmation of viral infection in tomato plants.
3.2.1. Through whitefly
Ten plants of highly susceptible variety were grown in pots and kept in insect free
cage. These plants were inoculated through whitefly transmission technique (Lapidot et al.,
2001). Twenty whiteflies were introduced into the cage containing TLCVD infected tomato
plants and given an acquisition access period of two days. Impregnated whitefly was
collected from the muslin cage and transferred to the healthy plants at second leaf stage for a
period of two days. Later on, these plants were sprayed with insecticide (imidacloprid) to kill
the whitefly. The symptoms were recorded after four weeks by visual observations.
3.2.2. Through grafting
TLCV infected plants were collected from the field for grafting on to healthy plants in
the pots. Plants were selected as soon as top leaves showed the TLCVD symptoms. A
slanting cut of 2cm long and 0.2cm deep was made on the stem of infected plant. Wedge
grafting was performed as suggested by Kashina et al., (2007). The grafted portion was
wrapped tightly with parafilm and covered with polyethylene bags. Non-grafted plants were
kept as control.
3.3. Serological assay
The infected samples were collected from the field for the confirmation of TLCV by
double antibody sandwich (DAS-ELISA) as described by (Clark and Adams, 1977). Bioreba
(www.bioreba.com) polyclonal antibodies were used for ELISA test.
3.3.1. Buffer formulations
1. Carbonate coating buffer/Liter:
Sodium carbonate (anhydrous) 1.59 g
Sodium biocarbonate 2.93 g
Sodium azide 0.20 g
pH 9.6 and stored at 4°C
2. PBST buffer (Wash buffer)/Liter:
Sodium chloride 8.00 g
Sodium phosphate (dibasic) 1.15 g
Potassium phosphate (monobasic) 0.20 g
Potassium chloride 0.20 g
Tween-20 20.0 ml
pH 7.4 and stored at 4°C
3. Extraction buffer/Liter:
Extraction buffer was prepared by dissolving following chemicals to PBST.
Sodium sulfite (anhydrous) 1.30 g
Polyvinylpyrrolidone (PVP) 20.0 g
Sodium azide 2.00 g
Egg (chicken) albumin 2.00 g
Tween-20 20.0 g
pH 7.4 and stored at 4°C
4. Conjugate buffer:
Alkaline phosphatase labeled antibodies were added to extraction buffer at a dilution of
1:1000.
Bovine serum albumin (BSA) 2.00 g
Polyvinylpyrrolidone (PVP) 20.0 g
Sodium azide 0.20 g
pH 7.4 and stored at 4°C
5. PNP or Substrate buffer:
Magnesium chloride hexahydrate 0.10 g
Sodium azide 0.20 g
Dithanolamine 97.0 ml
Distilled water 800 ml
Volume was adjusted to one liter and pH 9.8 with HCl. It was prepared just 5 minutes before
use and p-nitrophenyl phosphate was dissolved @1mg/1ml.
3.3.2. DAS-ELISA procedure
Microtiter plates were coated with TLCV specific antibody diluted 1000 fold in
coating buffer.
ELISA plates were incubated at 30°C for 4 hours.
ELISA plates were washed thrice with washing buffer.
Freshly prepared antigen (1:10 w/v) in extraction buffer was loaded (100μl/well) and
the plates were incubated overnight at 4ºC followed by washing.
Reference blank, negative and positive controls were also included.
TLCV conjugated antibody diluted 1000 fold in conjugate, added 100μl/well and
incubated at 30°C for 5 hours followed by washing.
Substrate (p-nitrophenyl-phosphate) was added 100 μl/well @ 1mg/ml and incubated
at room temperature for an hour at least.
The reaction strength was rated visually as
= no reaction
+ = weak reaction
++ = definite reaction,
+++ = strong reaction
++++ = very strong reaction
3.3.3. Color development
Development of yellow color in the wells indicated the presence of TLCV and the
intensity was proportional to the concentration of virus in the plant. Therefore, the positive
and negative samples were sorted out by visual observation of yellow color. Reaction was
stopped by the addition of 50μl 1N NaOH solution and the plate was photographed.
Fig. 3.1. ELISA results darker color indicating highly susceptible varieties/lines. Clear wells
indicating negative control
3.4. Area under disease progress curve
Area under disease progressive curve (AUDPC) was calculated by the trapezoidal
integration of the disease incidence over time for each variety/advance line, considering the
whole period evaluated according to the following formula as described by Shaner and
Finney (1977):
n-1
AUDPC = Σ [(xi+xi+1)/2] (ti+1-ti)
i=1
Where n is the number of assessment; x, disease incidence (%); and (ti+1-ti), duration
between two consecutive assessments. The TLCVD incidence over weekly basis was
recorded for each variety/advance line during the experiment (2012 and 2013) and the
resistance/susceptibility level of each variety/line was determined according to the AUDPC
units as for resistant (200-725), moderately resistant (725-1300), moderately susceptible
(1300-1920), susceptible (1920-2675) and highly susceptible varieties/lines (2675-3350),
respectively.
3.5. Recording of whitefly population data from disease screening nursery
Whitefly population data was recorded from disease screening nursery by randomly
selecting three diseased plants from each variety/line. The insect population from upper,
middle and lower leaves of the plants was estimated and average was calculated on weekly
basis. For the identification of B. tabaci, pseudo pupae were examined under microscope and
pairs of setae and transverse molting suture was examined (Bellows et al., 1994).
3.6. Collection of environmental conditions data
Data of environmental conditions comprising maximum and minimum temperatures,
relative humidity, average rainfall and wind speed was collected from (www.uaf.edu.pk)
recorded by Meteorological Station, University of Agriculture, Faisalabad, situated adjacent
(50 meters) to research area of Plant Pathology Department on daily basis from March to
June during the year 2012 and 2013 and weekly averages were calculated.
3.7. Development of predictive model for TLCVD incidence and B. tabaci population
3.7.1. Establishment of experiment and data recording
In order to develop TLCVD incidence and B. tabaci population predictive models,
five susceptible and highly susceptible varieties/lines (Big Beef, Caldera, Sitara-TS-101,
014276 and Salma) were sown in randomized complete block design (RCBD) with three
replications in research area of Plant Pathology Department University of Agriculture,
Faisalabad during two years (2012 and 2013). Each variety/line was planted in a block of
15m length and row to row and plant to plant distance was maintained 70 cm and 30 cm,
respectively. The data of disease incidence and vector population were recorded on weekly
basis in five varieties/lines during 2012 and 2013.
3.7.2. Analysis of data
The data were analyzed using statistical analysis software SAS 9.3 (SAS institute,
1990). Analysis of variance (ANOVA) and comparison between disease incidence and
environmental conditions were determined by least significance difference test (LSD at
P<0.05). Effects of environmental variables (maximum and minimum temperatures, relative
humidity, rainfall and wind speed) on disease incidence were determined by correlation
analysis (Steel et al., 1997). Environmental factors having significant correlation with
disease incidence and whitefly population was subjected to regression analysis. Predictive
model for TLCVD incidence and B. tabaci population based on two years (2012 and 2013)
environmental variables was developed using stepwise regression analysis (Myers, 1990).
Environmental conditions exhibiting significant correlation with disease incidence and
vector population were graphically plotted and their critical ranges conducive for TLCVD
incidence and B. tabaci population were determined. The accuracy of developed models was
studied by the influence of environmental conditions on TLCVD incidence and B. tabaci
population on five varieties/lines (Big Beef, Caldera, Sitara-TS-101, 014276 and Salma) by
comparing the observed disease incidence and vector population with those values predicted
by multiple regression models.
3.7.3. Evaluation of model
After the development of the model through stepwise regression, the model was
evaluated according to the procedures described by Snee (1977); Chattefuee and Hadi
(2006).
1) Comparison of dependent variable and regression coefficients with physical theory
2) Comparison of observed vs. predicted data
3) Collection of new data to check predictions
Assessment of predictions was done by computing statistic indices like; root mean
square error (RMSE) and % error (Wallach and Goffinet, 1989). The formulas used for
RMSE and % error were:
RMSE = ∑ in= 1 = [(Oi - Pi)2÷n]0.5
Observed value – Predicted value
% Error = x 100
Observed value
Where Pi and Oi are the predicted and observed data points for studied parameters,
respectively, and n is the number of observations. Model performance is considered good if
the values of RMSE and % error are below or equal to ± 20 (Willmott, 1982).
3.8. Management of TLCVD and B. tabaci
3.8.1. Evaluation of insecticides, nutrients and plant extracts against TLCVD and B.
tabaci
Five varieties Carmen, Roker, Uovo Roseo, Po-02 and Lyp#1 were sown in the
management experiment. The trial was conducted in randomized complete block design
(RCBD) with three replications. Seven treatments including one untreated control was used
for each entry in every replication.
For management of TLCVD and B. tabaci, insecticides (Imidacloprid and
Acetamaprid), plant extracts (Neem and Eucalyptus) and nutrients consisting of (Zn & B
solution) and salicylic acid (0.02%) were applied randomly to each row of experimental plot.
The detailed description of the above mentioned treatments is follows (Table. 3.2 and 3.3). In
order to make the required concentrations 3ml of insecticides and 5 ml of nutrients were
measured and dissolved in 1000 ml water.
3.8.2. Preparation of plant extracts
For the preparation of aqueous extracts, fresh leaves and bulbs from above mentioned
healthy plants were collected and macerated with distilled water at Kg/L and then thoroughly
homogenized. The macerated extracts were passed through two folds of muslin cloth and
diluted up to ten times and stored at 4°C until use. To prepare the required concentration, 5ml
of each plant extract were measured and dissolved in 100ml of water. A knapsack sprayer
was used to apply these solutions. The spray was applied until leaf run-off and control plants
were not sprayed with any insecticide/chemical (Ashfaq et al., 2006).
Table. 3.2. Treatments used against TLCVD and B. tabaci
Common Name Active Ingredient Recommended
dose
Manufacturer
Acelan Acetamaprid 125ml/acre FMC
Amedaclopard Imidacloprid 250ml/acre FMC
Classic Zn and Boron
solution
500ml/acre Ali Akbar
Table. 3.3. Plant extracts used against TLCVD and B. tabaci
Common
name
Botanical name Family Parts used Recommended dose
Neem Azadirachta indica
A. Juss.
Meliaceae Leaves 5ml/liter
Sufaida Eucalyptus globulus
Labill.
Myrtaceae Leaves 5ml/liter
3.8.3. Data analysis
Data for the evaluation of above mentioned treatments on TLCVD incidence and B.
tabaci population was recorded before and after the application of treatments and analyzed
through statistix 8.1 software, all possible interactions and comparisons of treatments were
determined through ANOVA. All the treatments were compared with one another and with
control by least significant difference (LSD) test at P= 0.05 (Steel et al., 1997).
CHAPTER 4 RESULTS
4.1. Symptomology and disease development during two years (2012 and 2013)
Tomato leaf curl virus disease symptoms appeared on all the varieties/lines. The
earliest symptoms were observed on a highly susceptible variety Salma followed by the line
014276. The symptoms started by upward and downward curling of leaves in infected plants
(Fig. 4.1). Infected plants remained stunted (Fig. 4.2) and became yellowish in color with
less fruit formation. The disease was present throughout the tomato growing season with
maximum in the months of high temperature, low rainfall and low relative humidity.
Minimum disease symptoms were observed on variety Naqeeb during both years (2012 and
2013).
Fig.4.1. Upward curling and yellowing of leaves due to natural infection of TLCVD on Pakit variety
four weeks after sowing
Fig. 4.2. Tomato plant with stunting and cupping symptoms caused by natural infection of
TLCVD on Nagina variety three weeks after sowing
4.1.1. Screening of tomato germplasm against tomato leaf curl virus disease (TLCVD)
during 2012 under natural environmental conditions
Twenty seven varieties/lines were sown for the screening purpose under natural
infestation of whitefly. Maximum disease incidence (95.29%) was recorded on variety
Salma, followed by 86.15% on advance line 014276 and 82.71% on Sitara-TS-101. These
varieties/lines were highly susceptible with disease rating 5 and AUDPC in the range of
2912.35-3352.65 (Table. 4.1). The advance line 10125 exhibited susceptible response with
74.09% disease incidence, followed by line 10127 (71.64%), Libnan Arif (69.16%), BL-1-
176-Riostone-1-1 (67.39%), Big Beef (64.87%) and Caldera (63.38%) during the year 2012
(Table. 4.23). These susceptible varieties/lines were graded as 4 in the disease rating scale
with AUDPC in the range of 2287.95-2610.65. The disease incidence was minimum on
Naqeeb (6.26%), followed by Pakit (8.34%), Nagina (10.81%), Riogrande (13.76%), 09080
(15.31%), Roma (17.85%), 09091 (19.67%) and Nuyt-04-11 (18.83%) during the year 2012.
These varieties/advanced lines were graded as resistant with disease rating 1 and AUDPC in
the range 236.6-705.95. Moderately resistant varieties/lines (Carmen, Roker, Lyp#1, 09079,
Nuyt-25-11 and 09088) showed disease incidence 22.45%, 24.67%, 27.53%, 29.42%,
32.12% and 35.27% respectively with disease rating 2 and AUDPC in the range 803.25-
1251.95. Uovo Roseo, Nuyt-9-11, Po-02 and 10113 were categorized as moderately
susceptible by showing 43.24%, 47.15%, 50.73% and 53.48% TLCVD incidence,
respectively with disease rating 3 and AUDPC in the range 1530.9-1889.3.
Table 4.1. Resistance level of tomato germplasm against TLCVD under natural
conditions during the year 2012
Serial No. Varieties/lines
Disease
incidence
(%)
Ratings AUDPC Response
1 Roker 24.67 2 880.95 MR*
2 Big Beef 64.87 4 2287.95 S
3 09079 29.42 2 1062.95 MR
4 Uovo Roseo 43.24 3 1530.93 MS
5 Naqeeb 6.26 1 236.61 R
6 Roma 17.85 1 642.25 R
7 Caldera 63.38 4 2235.84 S
8 Sitara-TS-101 82.71 5 2912.35 HS
9 Pakit 8.34 1 309.43 R
10 Riogrande 13.76 1 499.12 R
11 Nuyt-9-11 47.15 3 1667.75 MS
12 Nagina 10.81 1 395.85 R
13 Lyp#1 27.53 2 981.05 MR
14 Nuyt-25-11 32.12 2 1141.73 MR
15 Carmen 22.45 2 803.25 MR
16 BL-1176-Riostone-1-1 67.39 4 2376.15 S
17 Libnan Arif 69.16 4 2438.12 S
18 Nuyt -04-11 18.83 1 676.55 R
19 Salma 95.29 5 3352.65 HS
20 Po-02 50.73 3 1793.05 MS
21 09088 35.27 2 1251.95 MR
22 09080 15.31 1 553.35 R
23 10127 71.64 4 2524.92 S
24 10113 53.48 3 1889.34 MS
25 09091 19.67 1 705.95 R
26 10125 74.09 4 2610.65 S
27 014276 86.15 5 3032.75 HS
*R= Resistant, MR= Moderately Resistant, MS= Moderately Susceptible, S= Susceptible and
HS= Highly Susceptible
Fig. 4.3. Comparison of disease incidence (%) on 27 varieties/lines during 2012
4.1.2. Screening of tomato germplasm against TLCVD during 2013 under natural
environmental conditions
Tomato varieties/lines exhibited similar response against TLCVD during the year
2013. None of the screened varieties/lines was found to be highly resistant against TLCVD.
All the varieties/lines were categorized in the same disease ratings as in the year 2012 with
more or less TLCVD incidence percentage (Table. 4.2). During the year 2013, all the
varieties/lines showed different response regarding the area under disease progress curve
(AUDPC) which ranged from 207.2-649.6, 844.9-1290.1, 1575.7-1912.4, 2202.9-2653.35
and 2935.45-3332.7 for the resistant, moderately resistant, moderately susceptible,
susceptible and highly susceptible varieties/lines, respectively.
Uovo Roseo
Sitara-TS-101
Salma
Rom
a
Roker
Riogran
de
Po-02
Pakit
Nuy
t -04-11
Nuyt-9-11
Nuyt-25-11
Naqee
b
Nagina
Lyp#
1
Libnan Arif
Carmen
Caldera
BL-1176-Riostone-1-1
Big Bee
f
9091
9088
9080
9079
1427
6
1012
7
10125
10113
100
80
60
40
20
0
Varieties
Dis
ea
se
in
cid
en
ce
(%
)
44.52
83.37
94.72
16.58
25.16
12.57
51.58
7.43
18.35
46.23
31.25
5.4210.98
28.31
68.53
23.64
62.4466.48
63.28
18.06
36.36
14.79
28.24
88.06
72.0575.31
54.14
Table 4.2. Resistance level of tomato varieties/lines to TLCVD under natural conditions
during the year 2013
Serial No. Varieties/lines
Disease
incidence (%)
Ratings AUDPC Response
1 Roker 25.16 2 898.14 MR*
2 Big Beef 63.28 4 2232.36 S
3 09079 28.24 2 1005.92 MR
4 Uovo Roseo 44.52 3 1575.73 MS
5 Naqeeb 5.42 1 207.22 R
6 Roma 16.58 1 597.81 R
7 Caldera 62.44 4 2202.93 S
8 Sitara-TS-101 83.37 5 2935.45 HS
9 Pakit 7.43 1 277.55 R
10 Riogrande 12.57 1 457.45 R
11 Nuyt-9-11 46.23 3 1635.55 MS
12 Nagina 10.98 1 401.87 R
13 Lyp#1 28.31 2 1008.35 MR
14 Nuyt-25-11 31.25 2 1111.25 MR
15 Carmen 23.64 2 844.94 MR
16 BL-1176-Riostone-1-1 66.48 4 2344.32 S
17 Libnan Arif 68.53 4 2416.05 S
18 Nuyt -04-11 18.35 1 659.75 R
19 Salma 94.72 5 3332.74 HS
20 Po-02 51.58 3 1822.83 MS
21 09088 36.36 2 1290.12 MR
22 09080 14.79 1 535.15 R
23 10127 72.05 4 2539.25 S
24 10113 54.14 3 1912.42 MS
25 09091 18.06 1 649.64 R
26 10125 75.31 4 2653.35 S
27 014276 88.06 5 3099.63 HS
*R= Resistant, MR= Moderately Resistant, MS= Moderately Susceptible, S= Susceptible and
HS= Highly Susceptible
Fig. 4.4. Comparison of disease incidence (%) on 27 varieties/lines during 2013
4.2. Screening of tomato germplasm against Bemisia tabaci population during two years
(2012 and 2013) under natural conditions
B. tabaci infested tomato crop during the whole growing seasons of 2012 and 2013.
The peak activity of B. tabaci was observed on warm and sunny days with high temperature
and low relative humidity. The duration of B. tabaci developmental stages depend upon the
prevailing temperature. Whitefly population was minimum during the months of July and
August because of high rainfall and relative humidity. Whitefly attacked the plants and
transmitted TLCV and secreted honey dew that resulted in the development of sooty mold
and stunted growth. Maximum whitefly population (8.23 and 8.18) was found on variety
Salma during two years 2012 and 2013, respectively (Table. 4.3 and 4.4)). Minimum
whitefly population (2.17 and 2.14) was found on resistant variety Naqeeb during 2012 and
2013, respectively.
Uovo
Ros
eo
Sita
ra-T
S-10
1
Salm
a
Roma
Roke
r
Riog
rand
e
Po-0
2Pa
kit
Nuy t
-04-
11
Nuyt
-9-1
1
Nuyt
-25-
11
Naqe
eb
Nagin
a
Lyp#
1
Libna
n Ar
if
Carm
en
Calder
a
BL-1
176-
Rios
tone
-1-1
Big Be
ef
9091
9088
9080
9079
1427
6
1012
7
1012
5
1011
3
100
80
60
40
20
0
Varieties
Dis
ea
se
in
cid
en
ce
(%
)
43.24
82.71
95.29
17.85
24.67
13.76
50.73
8.34
18.83
47.15
32.12
6.2610.81
27.53
69.16
22.45
63.3867.39
64.87
19.67
35.27
15.31
29.42
86.15
71.6474.09
53.48
Table 4.3. Resistance level of tomato germplasm against B. tabaci population during
2012
Serial
No. Varieties/lines Average whitefly population Resistance level
1 Roker 3.51 MR
2 Big Beef 5.36 S
3 09079 3.82 MR
4 Uovo Roseo 4.09 MS
5 Naqeeb 2.17 R
6 Roma 3.05 R
7 Caldera 5.24 S
8 Sitara-TS-101 7.56 HS
9 Pakit 2.42 R
10 Riogrande 2.75 R
11 Nuyt-9-11 4.63 MS
12 Nagina 2.67 R
13 Lyp#1 3.62 MR
14 Nuyt-25-11 3.95 MR
15 Carmen 3.37 MR
16 BL-1176-Riostone-1-1 5.58 S
17 Libnan Arif 5.79 S
18 Nuyt -04-11 3.21 R
19 Salma 8.23 HS
20 Po-02 4.81 MS
21 09088 4.02 MR
22 09080 2.98 R
23 10127 6.34 S
24 10113 5.17 MS
25 09091 3.35 R
26 10125 6.67 S
27 014276 7.22 HS
R= Resistant, MR= Moderately Resistant, MS= Moderately Susceptible, S= Susceptible and
HS= Highly Susceptible
Fig. 4.5. Comparison of whitefly infestation on 27 tomato varieties/lines during 2012
Fig. 4.6. Comparison of whitefly infestation on 27 tomato varieties/lines during 2013
Uovo
Rose
o
Sita
ra-T
S-10
1
Salm
a
Roma
Roke
r
Riog
rand
e
Po-02
Pakit
Nuyt -
04-1
1
Nuyt
-9-11
Nuyt -2
5-11
Naqee
b
Nagina
Lyp#
1
Libna
n Arif
Carm
en
Calder
a
BL-1
176-Rio
ston
e-1-
1
Big B
eef
9091
9088
9080
9079
1427
6
1012
7
1012
5
1011
3
9
8
7
6
5
4
3
2
1
0
Varieties/lines
Av
era
ge
wh
ite
fly
po
pu
lati
on
4.09
7.56
8.23
3.053.51
2.75
4.81
2.42
3.21
4.63
3.95
2.172.67
3.62
5.79
3.37
5.245.58
5.36
3.35
4.02
2.98
3.82
7.22
6.346.67
5.17
Uovo
Rose
o
Sita
ra-T
S-10
1
Salm
a
Roma
Roke
r
Riog
rand
e
Po-02
Pakit
Nuyt -
04-1
1
Nuyt
-9-11
Nuyt -2
5-11
Naqee
b
Nagina
Lyp#
1
Libna
n Arif
Carm
en
Calder
a
BL-1
176-Rio
ston
e-1-
1
Big B
eef
9091
9088
9080
9079
1427
6
1012
7
1012
5
1011
3
9
8
7
6
5
4
3
2
1
0
Varieties/lines
Av
era
ge
wh
ite
fly
po
pu
lati
on
4.16
7.62
8.18
3.02
3.64
2.69
4.89
2.34
3.19
4.56
3.91
2.142.63
3.65
5.71
3.46
5.185.52
5.24
3.45
4.07
2.87
3.68
7.47
6.386.75
5.23
Table 4.4. Resistance level of tomato germplasm against B. tabaci population during
2013
Serial No. Varieties/lines Average whitefly population Resistance level
1 Roker 3.64 MR
2 Big Beef 5.24 S
3 09079 3.68 MR
4 Uovo Roseo 4.16 MS
5 Naqeeb 2.14 R
6 Roma 3.02 R
7 Caldera 5.18 S
8 Sitara-TS-101 7.62 HS
9 Pakit 2.34 R
10 Riogrande 2.69 R
11 Nuyt-9-11 4.56 MS
12 Nagina 2.63 R
13 Lyp#1 3.65 MR
14 Nuyt-25-11 3.91 MR
15 Carmen 3.46 MR
16 BL-1176-Riostone-1-1 5.52 S
17 Libnan Arif 5.71 S
18 Nuyt -04-11 3.19 R
19 Salma 8.18 HS
20 Po-02 4.89 MS
21 09088 4.07 MR
22 09080 2.87 R
23 10127 6.38 S
24 10113 5.23 MS
25 09091 3.45 R
26 10125 6.75 S
27 014276 7.47 HS
R= Resistant, MR= Moderately Resistant, MS= Moderately Susceptible, S= Susceptible and
HS= Highly Susceptible
4.3. Confirmation of TLCV through ELISA and grafting
Double antibody sandwich (DAS) ELISA was performed to confirm the presence of
TLCV in infected plants. ELISA results were analyzed visually on the basis of colorimetric
change. ELISA results had a strong positive relationship with disease incidence of different
varieties/lines. Antigen from resistant and moderately resistant varieties/lines showed very
week reaction with antibodies (+) (Table 4.5). The ELISA results of moderately susceptible,
susceptible and highly susceptible varieties/lines were moderate (++), strong (+++) and very
strong reaction (++++), respectively.
TLCV was also confirmed through graft inoculation in all the varieties/lines. The
results of graft transmission were in confirmation with disease incidence response of
different varieties/lines. Resistant varieties/lines showed 0-20% transmission through
grafting (Table 4.5). The transmission success in case of moderately resistant, moderately
susceptible, susceptible and highly susceptible varieties/lines was (20-40%), (40-60%), (60-
80%) and (80-100%), respectively.
4.4. Correlation of environmental factors with TLCVD incidence on tomato
varieties/lines during 2012 and 2013
In general, the contribution of three environmental variables i.e. temperature
(maximum and minimum) and relative humidity was significant as compared to rainfall and
wind speed in TLCVD development (Table. 4.6). Maximum and minimum temperature had
significantly positive correlation with TLCVD incidence on all varieties/lines (Roker, Big
Beef, 09079, Uovo Roseo, Naqeeb, Roma, Caldera, Sitara-TS-101, Pakit, Riogrande, Nuyt-9-
11, Nagina, Lyp#1, Nuyt-25-11, Carmen, BL-1176-Riostone-1-1, Libnan Arif, Nuyt-04-11,
Salma, Po-02, 09088, 09080, 10127, 10113, 09091, 10125 and 014276). There was a
significant negative correlation between TLCVD incidence and relative humidity was on all
the varieties/lines. Only two varieties (Big Beef and Salma) exhibited significant correlation
with rainfall and TLCVD incidence and a remaining twenty five varieties/lines showed non-
significant correlation with rainfall and TLCVD incidence. All the varieties/lines showed
non-significant correlation with wind speed.
Table 4.5. Confirmation of resistance level against TLCV through graft inoculation and
ELISA
Serial
No.
Varieties/lines
Response
ELISA
results
Graft
inoculation Transmission
(%) Infected/total
1 Roker MR* +** 2/5 40
2 Big Beef S +++ 3/5 60
3 09079 MR + 2/5 40
4 Uovo Roseo MS ++ 3/5 60
5 Naqeeb R + 1/5 20
6 Roma R + 1/5 20
7 Caldera S +++ 3/5 60
8 Sitara-TS-101 HS ++++ 4/5 80
9 Pakit R + 0 0
10 Riogrande R + 0 0
11 Nuyt-9-11 MS ++ 2/5 40
12 Nagina R + 0 0
13 Lyp#1 MR + 1/5 20
14 Nuyt-25-11 MR + 2/5 40
15 Carmen MR + 2/5 40
16 BL-1176-Riostone-1-1 S +++ 4/5 80
17 Libnan Arif S +++ 3/5 60
18 Nuyt-04-11 R + 1/5 20
19 Salma HS ++++ 5/5 100
20 Po-02 MS ++ 2/5 40
21 09088 MR + 2/5 40
22 09080 R + 1/5 20
23 10127 S +++ 3/5 60
24 10113 MS ++ 3/5 60
25 09091 R + 1/5 20
26 10125 S ++++ 4/5 80
27 014276 HS ++++ 5/5 100
*R= Resistant, MR= Moderately Resistant, MS= Moderately Susceptible, S= Susceptible and
HS= Highly Susceptible
**+ = week reaction, ++ = moderate reaction, +++ = strong reaction and ++++ = very strong
reaction
Table 4.6. Pearson’s correlation co-efficients of environmental factors with TLCVD
incidence on tomato varieties/lines during 2012 and 2013
Varieties/lines Maximum
temperature
(°C)
Minimum
temperature
(°C)
Relative
humidity
(%)
Rainfall
(mm)
Wind
speed
(Km/h)
Roker 0.798*
0.002
0.743*
0.006
-0.769*
0.003
0.455
0.137
0.29
0.36
Big Beef 0.751*
0.005
0.647*
0.023
-0.846*
0.001
0.582*
0.047
0.232
0.467
09079 0.806*
0.002
0.703*
0.011
-0.791*
0.002
0.475
0.118
0.333
0.29
Uovo Roseo 0.845*
0.001
0.807*
0.002
-0.764*
0.004
0.533
0.074
0.243
0.447
Naqeeb 0.835*
0.001
0.793*
0.002
-0.801*
0.002
0.558
0.06
0.248
0.438
Roma 0.849*
0.001
0.777*
0.003
-0.806*
0.002
0.539
0.071
0.236
0.459
Caldera 0.763*
0.004
0.656*
0.021
-0.828*
0.001
0.558
0.059
0.255
0.423
Sitara-TS-101 0.769*
0.003
0.667*
0.018
-0.824*
0.001
0.529
0.077
0.266
0.403
Pakit 0.832*
0.001
0.787*
0.002
-0.801*
0.002
0.565
0.055
0.241
0.451
Riogrande 0.825*
0.001
0.780*
0.003
-0.797*
0.002
0.551
0.064
0.253
0.427
Nutyt-9-11 0.777*
0.003
0.743*
0.006
-0.796*
0.002
0.481
0.113
0.327
0.299
Nagina 0.828*
0.001
0.785*
0.002
-0.793*
0.002
0.57
0.053
0.251
0.432
Lyp#1 0.791*
0.002
0.722*
0.008
-0.808*
0.001
0.499
0.098
0.265
0.405
Nuyt-25-11 0.776* 0.692* -0.794* 0.493 0.388
0.003 0.013 0.002 0.104 0.212
Carmen 0.797*
0.002
0.740*
0.006
-0.763*
0.004
0.473
0.12
0.288
0.365
BL-1176-
Riostone-1-1
0.793*
0.002
0.746*
0.005
-0.824*
0.001
0.551
0.063
0.278
0.381
Libnan Arif 0.792*
0.002
0.745*
0.005
-0.826*
0.001
0.553
0.062
0.275
0.388
Nuyt-04-11 0.830*
0.001
0.752*
0.005
-0.830*
0.001
0.526
0.079
0.278
0.382
Salma 0.740*
0.006
.612*
0.034
-0.830*
0.001
.607*
0.036
0.218
0.497
Po-02 0.778*
0.003
0.743*
0.006
-0.794*
0.002
0.478
0.116
0.331
0.294
09088 0.791*
0.002
0.731*
0.007
-0.725*
0.008
0.442
0.15
0.289
0.363
09080 0.839*
0.001
0.785*
0.003
-0.792*
0.002
0.555
0.061
0.229
0.474
10127 0.790*
0.002
0.741*
0.006
-0.833*
0.001
0.547
0.066
0.276
0.385
10113 0.780*
0.003
0.750*
0.005
-0.789*
0.002
0.484
0.111
0.334
0.288
09091 0.818*
0.001
0.762*
0.004
-0.811*
0.001
0.544
0.067
0.297
0.348
10125 0.782*
0.003
0.731*
0.007
-0.836*
0.001
0.549
0.064
0.279
0.38
014276 0.794*
0.002
0.734*
0.007
-0.809*
0.001
0.563
0.056
0.228
0.476
Upper values indicate Pearson’s correlation coefficient
Lower values indicate level of probability at P = 0.05
4.5. Correlation of environmental factors with B. tabaci population on different tomato
varieties/lines during 2012 and 2013
In overall correlation analysis, the contribution of three environmental variables
maximum and minimum temperatures and relative humidity was significant as compared to
rainfall and wind speed for B. tabaci population (Table 4.7). Maximum and minimum
temperature had significantly positive correlation with B. tabaci population on all
varieties/lines (Roker, Big Beef, 09079, Uovo Roseo, Naqeeb, Roma, Caldera, Sitara-TS-
101, Pakit, Riogrande, Nuyt-9-11, Nagina, Lyp#1, Nuyt-25-11, Carmen, BL-1176-Riostone-
1-1, Libnan Arif, Nuyt -04-11, Salma, Po-02, 09088, 09080, 10127, 10113, 09091, 10125
and 014276). The correlation of relative humidity with B. tabaci population was significantly
negative on all the five varieties/lines. The correlation of B. tabaci population was non-
significant with rainfall and wind speed on all the varieties/lines.
Table 4.7. Pearson’s correlation coefficients of environmental factors with B. tabaci
population on tomato varieties/lines during 2012 and 2013
Varieties/lines Maximum
temperature
(°C)
Minimum
temperature
(°C)
Relative
humidity
(%)
Rainfall
(mm)
Wind
speed
(Km/h)
Roker 0.843*
0.001
0.671*
0.017
-0.741*
0.006
0.416
0.178
0.371
0.235
Big Beef 0.878*
0.001
0.802*
0.002
-0.750*
0.005
0.51
0.091
0.279
0.379
09079 0.862*
0.002
0.728*
0.007
-0.701*
0.011
0.453
0.139
0.351
0.263
Uovo Roseo 0.856*
0.001
0.710*
0.01
-0.751*
0.005
0.449
0.144
0.352
0.261
Naqeeb 0.902*
0.003
0.730*
0.007
-0.694*
0.012
0.475
0.119
0.357
0.254
Roma 0.833*
0.001
0.694*
0.012
-0.709*
0.01
0.427
0.166
0.304
0.338
Caldera 0.883*
0.001
0.803*
0.002
-0.757*
0.004
0.511
0.089
0.28
0.377
Sitara-TS-101 0.855* 0.787* -0.801* 0.531 0.303
0.001 0.002 0.002 0.075 0.339
Pakit 0.791*
0.002
0.645*
0.024
-0.632*
0.027
0.381
0.222
0.31
0.326
Riogrande 0.814*
0.001
0.674*
0.016
-0.673*
0.017
0.413
0.182
0.284
0.372
Nuyt-9-11 0.880*
0.003
0.774*
0.003
-0.749*
0.005
0.498
0.1
0.313
0.322
Nagina 0.798*
0.002
0.641*
0.025
-0.706*
0.01
0.395
0.204
0.302
0.341
Lyp#1
0.839*
0.001
0.685*
0.014
-0.725*
0.008
0.424
0.17
0.365
0.243
Nuyt-25-11 0.868*
0.001
0.729*
0.007
-0.723*
0.008
0.457
0.135
0.344
0.274
Carmen 0.828*
0.001
0.662*
0.019
-0.727*
0.007
0.403
0.194
0.375
0.229
BL-1176-
Riostone-1-1
0.878*
0.002
0.804*
0.002
-0.760*
0.004
0.515
0.087
0.259
0.417
Libnan Arif 0.878*
0.001
0.809*
0.001
-0.761*
0.004
0.522
0.082
0.263
0.408
Nuyt -04-11 0.815*
0.001
0.678*
0.015
-0.708*
0.01
0.412
0.184
0.322
0.308
Salma 0.857*
0.003
0.799*
0.002
-0.787*
0.002
0.531
0.076
0.292
0.356
Po-02 0.869*
0.001
0.800*
0.002
-0.704*
0.011
0.463
0.13
0.328
0.299
09088 0.850*
0.003
0.704*
0.011
-0.756*
0.004
0.454
0.138
0.355
0.258
09080 0.873*
0.002
0.709*
0.001
-0.708*
0.001
0.409
0.187
0.284
0.371
10127 0.870*
0.003
0.797*
0.002
-0.783*
0.003
0.552
0.063
0.234
0.464
10113 0.890*
0.002
0.801*
0.002
-0.760*
0.003
0.501
0.097
0.268
0.399
09091 0.825*
0.001
0.669*
0.017
-0.746*
0.005
0.413
0.183
0.355
0.258
10125 0.871*
0.003
0.799*
0.002
-0.781*
0.003
0.559
0.059
0.22
0.491
014276 0.862*
0.004
0.807*
0.002
-0.790*
0.002
0.561
0.058
0.255
0.424
Upper values indicate Pearson’s correlation coefficient
Lower values indicate level of probability at P = 0.05
4.6. Characterization of environmental conditions conducive for the development of
TLCVD on five susceptible to highly susceptible varieties/lines during two years
(2012 and 2013)
The environmental conditions conducive for TLCV disease development were
characterized on five tomato varieties/lines i.e. Big Beef, Caldera, Sitara-TS-101, 014276 and
Salma. There was significant relationship between temperature (maximum and minimum)
and TLCVD incidence (Fig. 4.3 and Fig. 4.4). The relationship between relative humidity
and TLCVD incidence was significantly negative (Fig. 4.5). The relationship of rainfall and
wind speed with TLCVD incidence was very poor (Fig. 4.6 and Fig. 4.7). Maximum
temperature ranged from 32 to 38°C during two years (Fig. 4.3). The TLCVD incidence
increased with increase in maximum temperature and explained 79 to 85% of the variability
in the disease development. Highly significant relationship of maximum temperature with
TLCVD incidence was found in case of variety Salma where it contributed 85% towards
disease development. The minimum temperature ranged from 22 to 29°C and was
significantly correlated with TLCVD incidence during two years (Fig. 4.4). The correlation
of minimum temperature with disease development was best explained by linear relationship
as indicated by higher r values. The minimum temperature explained 84 to 95% of the
variability in TLCVD development. The minimum temperature explained 95% of the
variability in disease development in advance line 014276.
Relative humidity had significant influence on TLCVD incidence and linear
relationship explained 78 to 87% variability in disease development (Fig. 4.5). There was
negative correlation between relative humidity and disease incidence. The maximum
influence of relative humidity was observed on Big Beef where it contributed 87% towards
disease development. Rainfall had non-significant influence on TLCVD incidence and
polynomial regression explained 47 to 54% of the variability in disease development (Fig.
4.6). The rainfall explained maximum 54% variability in disease development in case of
Sitara-TS-101. The wind speed had non-significant effect in the TLCVD development and its
contribution was very poor (Fig.4.7). The linear model indicated very low r values. The
wind speed exerted maximum influence of about 34% in disease development in case of
Sitara-TS-101.
Fig. 4.3: Relationship of maximum temperature with TLCVD incidence on five tomato
varieties/lines i.e. Y1=Big Beef, Y2=Caldera, Y3=Sitara-TS-101, Y4=014276 and
Y5=Salma during 2012 and 2013.
Y1 = 72.4+11.83x
r = 0.81Y2 = 85.9+12.25x
r = 0.83
Y3 = 91.9+15.57x
r = 0.79
Y4 = 82.9+15.37x
r = 0.82
Y5 = 92.3+16.56x
r = 0.85
0
10
20
30
40
50
60
70
80
90
100
32 33 34 35 36 37 38
Maximum temperature (°C)
Dis
ease
inci
den
ce (
%)
Fig. 4.4: Relationship of minimum temperature with TLCVD incidence on five tomato
varieties/lines i.e. Y1=Big Beef, Y2=Caldera, Y3=Sitara-TS-101, Y4=014276 and
Y5=Salma during 2012 and 2013.
Fig. 4.5: Relationship of relative humidity with TLCVD incidence on five tomato
varieties/lines i.e. Y1=Big Beef, Y2=Caldera, Y3=Sitara-TS-101, Y4=014276 and
Y5=Salma during 2012 and 2013.
Y1 = 25.8+8.27x
r = 0.89
Y2 = 15.6+8.26x
r = 0.84
Y3 = 21.8+9.73x
r = 0.87
Y4 = 37.9+9.69x
r = 0.95
Y5 = 35.5+9.45x
r = 0.92
0
10
20
30
40
50
60
70
80
90
100
20 22 24 26 28 30
Minimum temperature (°C)
Dis
ease
inci
den
ce (
%)
Y1 = 96.1+1.35x
r = 0.87
Y2 = 98.6+1.39x
r = 0.82
Y3 = 94.4+1.77x
r = 0.78
Y4 = 95.4+1.74x
r = 0.85
Y5 = 92.4+1.86x
r = 0.86
0
10
20
30
40
50
60
70
80
90
100
15 25 35 45 55
Relative humidity (%)
Dis
ease
inci
den
ce (
%)
Fig. 4.6: Relationship of rainfall with TLCVD incidence on five tomato varieties/lines i.e.
Y1=Big Beef, Y2=Caldera, Y3=Sitara-TS-101, Y4=014276 and Y5=Salma during
2012 and 2013
Fig. 4.7: Relationship of wind speed with TLCVD incidence on five tomato varieties/lines i.e.
Y1=Big Beef, Y2=Caldera, Y3=Sitara-TS-101, Y4=014276 and Y5=Salma during
2012 and 2013
Y1 = 35.05+13.17x-1.31x2
r = 0.52
Y2 = 36.23+13.27x-1.33x2
r = 0.53
Y3 = 44.79+14.43x-1.46x2
r = 0.54
Y4 = 47.45+13.48x-1.15x2
r = 0.47
Y5 = 48.98+12.56x-0.83x2
r = 0.48
0
10
20
30
40
50
60
70
80
90
100
0 1 2 3 4 5 6 7 8
Rainfall (mm)
Dis
ease
inci
den
ce (
%)
Y1 = 1.01+3.24x-0.41x2
r = 0.27
Y2 = 2.73+4.78x-0.37x2
r = 0.32
Y3 = 1.14+4.13x-0.51x2
r = 0.34
Y4 = 1.24+2.87x-0.53x2
r = 0.29
Y5 = 1.58+3.63x-0.58x2
r = 0.34
0
10
20
30
40
50
60
70
80
90
100
2 3 4 5 6 7 8 9 10
Wind speed (Km/h)
Dis
ease
inci
den
ce (
%)
4.7. Characterization of environmental conditions conducive for the development of B.
tabaci population on five varieties/lines during two years (2012 and 2013)
The environmental conditions conducive for the development of B. tabaci population
were characterized on five tomato varieties/lines i.e., Big Beef, Caldera, Sitara-TS-101,
014276 and Salma. There was significantly positive relationship between temperature
(maximum and minimum) and B. tabaci population (Fig. 4.8 and Fig. 4.9). The relationship
between relative humidity and B. tabaci population was significantly negative (Fig. 4.10).
The relationship of B. tabaci population was very poor with rainfall and wind speed (Fig.
4.11 and Fig. 4.12). The maximum temperature ranged from 32 to 38°C during two years
(Fig. 4.8). The B. tabaci population increased with increase in maximum temperature and
linear regression model explained 83 to 91% variability in the B. tabaci population
development. Highly significant relationship was found between maximum temperature and
B. tabaci population in case of advance line 014276 where it contributed 91% towards B.
tabaci population development. The minimum temperature ranged from 22 to 29°C
significantly correlated with B. tabaci population during two years (Fig. 4.9). The
relationship of minimum temperature was best. The minimum temperature explained 75 to
85% of the variability in B. tabaci population build up. The minimum temperature
contributed 85% towards B. tabaci population in variety Caldera.
Relative humidity had significant influence on B. tabaci population and a linear
regression model with Rh as a single variable explained 78 to 85% variability in B. tabaci
population development (Fig. 4.10). There was negative relationship i.e. as the relative
humidity increased the B. tabaci population decreased. The maximum influence of relative
humidity was observed in case of Big Beef where it contributed 85% for B. tabaci
population. Rainfall had not significant influence on B. tabaci population and polynomial
regression explained 35 to 42% of the variability in B. tabaci population development (Fig.
4.11). The rainfall explained 42% variability in B. tabaci population infestation in case of
014276. The wind speed had non-significant effect in the B. tabaci population build up and
its contribution was very poor (Fig.4.12). The polynomial regression indicated very low r
values. The wind speed exerted maximum influence of about 36% on B. tabaci population in
case of advance line 014276.
Fig. 4.8: Relationship of maximum temperature with B. tabaci population on five tomato
varieties/lines i.e. Y1=Big Beef, Y2=Caldera, Y3=Sitara-TS-101, Y4=014276 and
Y5=Salma during 2012 and 2013.
Fig. 4.9: Relationship of minimum temperature with B. tabaci population on five tomato
varieties/lines i.e. Y1=Big Beef, Y2=Caldera, Y3=Sitara-TS-101, Y4=014276 and
Y5=Salma during 2012 and 2013.
Y1 = 27.98+0.91x
r = 0.98
Y2 = 23.11+0.77x
r = 0.94
Y3 = 44.17+1.39x
r = 0.98
Y4 = 42.25+1.34x
r = 0.95
Y5 = 53.051.65x
r = 0.96
0
1
2
3
4
5
6
7
8
9
32 33 34 35 36 37 38
B.
tab
aci
popu
lati
on
Maximum temperature (°C)
Y1 = 8.11+0.55x
r = 0.83
Y2 = 8.57+0.47x
r = 0.85
Y3 = 7.74+0.85x
r = 0.80
Y4 = 7.12+0.83x
r = 0.78
Y5 = 6.29+1.03x
r = 0.75
0
1
2
3
4
5
6
7
8
9
20 22 24 26 28 30
Minimum temperature (°C)
B.
tab
aci
popu
lati
on
Fig. 4.10: Relationship of relative humidity with B. tabaci population on five tomato
varieties/lines i.e. Y1=Big Beef, Y2=Caldera, Y3=Sitara-TS-101, Y4=014276
and Y5=Salma during 2012 and 2013.
Fig. 4.11: Relationship of rainfall with B. tabaci population on five tomato varieties/lines
i.e. Y1=Big Beef, Y2=Caldera, Y3=Sitara-TS-101, Y4=014276 and Y5=Salma
during 2012 and 2013
Y1 = 7.72+0.13x
r = 0.85
Y2 = 7.22+0.83x
r = 0.83
Y3 = 9.72+0.23x
r = 0.82
Y4 = 8.14+0.45x
r = 0.79
Y5 = 7.82+0.69x
r = 0.78
0
1
2
3
4
5
6
7
8
9
10
15 20 25 30 35 40 45 50 55
Relative humidity (%)
B.
tab
aci
popu
lati
on
Y1 = 3.65+1.28x-0.16x2
r = 0.37
Y2 = 3.57+0.64x-0.05x2
r = 0.39
Y3 = 3.89+1.36x-0.11x2
r = 0.35
Y4 = 3.51+1.55x-0.16x2
r = 0.42
Y5 = 3.65+1.43x-0.17x2
r = 0.36
0
1
2
3
4
5
6
7
8
9
0 2 4 6 8 10
Rainfall (mm)
B.
tab
aci
popu
lati
on
Fig. 4.12: Relationship of wind speed with B. tabaci population on five tomato varieties/lines
i.e. Y1=Big Beef, Y2=Caldera, Y3=Sitara-TS-101, Y4=014276 and Y5=Salma
during 2012 and 2013
4.8. Analysis of variance of TLCVD incidence during two years (2012 and 2013)
During two years the individual effects of variety, week and year were highly
significant for TLCVD incidence. The two ways interaction of variety with week and week
with year was also highly significant. The two ways interaction of variety with year was non-
significant while three ways interaction of variety, week and year was significant (Table.
4.8).
4.8.1. Analysis of variance of environmental conditions during two years (2012 and
2013)
During two years (2012 and 2013) analysis of variance, the individual effect of year and
variety was significant in case of environmental variables i.e. maximum and minimum
temperature, relative humidity, rainfall and wind speed. The two way interactive effect of
year and variety was not significant (Table 4.10a and Table 4.10b).
Y1 = 2.53+0.88x-0.11x2
r = 0.18
Y2 = 3.11+1.63x-0.23x2
r = 0.23
Y3 = 4.12+3.12x-0.39x2
r = 0.29
Y4 = 6.13+2.53x-0.43x2
r = 0.36
Y5 = 5.34+1.62x-0.52x2
r = 0.32
0
1
2
3
4
5
6
7
8
9
0 2 4 6 8
B.
tab
aci
popu
lati
on
Wind speed (Km/h)
4.8.2. Comparison of environmental conditions during the years 2012 and 2013
During two years 2012 and 2013 all the environmental variables (maximum and
minimum temperature, relative humidity and wind speed) and TLCVD incidence showed
significant difference. The maximum temperature was 38.04°C and 37.57°C, whereas
TLCVD incidence was 95.18 and 94.26, respectively during two years (Table 4.9).
4.8.3. Overall correlation of weekly environmental conditions with TLCVD incidence
during two years (2012 and 2013)
Overall correlation of environmental conditions with TLCVD incidence was
significant during two years except rainfall and wind speed (Table. 4.11). The relationship of
TLCVD incidence was significantly positive with maximum and minimum temperature in all
five varieties. There was significantly negative relationship between TLCVD incidence and
relative humidity. Rainfall and wind speed were found non-significant in overall correlation
with TLCVD incidence during 2012 and 2013.
4.8.4. Year wise correlation of weekly environmental conditions with TLCVD incidence
during 2012 and 2013
A highly significant correlation was observed between environmental parameters
(maximum and minimum temperature, relative humidity, rainfall) and TLCVD incidence
during both years only the correlation of wind speed (r < 0.20 and r < 0.22) with TLCVD
incidence was found non-significant during both years (Table 4.12).
Table 4.8: ANOVA for TLCVD incidence during two years (2012 and 2013)
Source DF SS MS F-value P-value
Replication 2 0.002 0.001
Variety 4 14.91 3.73 138.42 0.001*
Week 5 290.87 58.17 2160.21 0.001*
Year 1 7.94 7.94 294.77 0.001*
Variety*Week 20 1.96 0.09 3.65 0.001*
Variety*Year 4 0.16 0.04 1.53 0.205NS
Week*Year 5 21.38 4.28 158.82 0.001*
Variety*Week*Year 20 1.03 0.05 1.92 0.018*
Error 118 3.18 0.03
Total 179 341.43
*Significant at P< 0.05 NS=Non-significant
Table 4.9: Comparison of environmental conditions for TLCVD incidence during two
years (2012 and 2013)
Environmental factors 2012 2013 LSD
Maximum temperature (°C) 38.04a
37.57b
1.53
Minimum temperature (°C) 29.46a
29.05b
1.91
Relative humidity (%) 51.51a 50.74b
6.28
Rainfall (mm) 1.2a 0.64b 0.52
Wind speed (Km/h) 5.91b 6.73a
0.77
Disease incidence 95.18b
94.26a
0.39
*Means with similar letters in a row are not significantly different at P = 0.05
Table 4.23a: Analysis of variance of environmental factors (maximum and minimum temperature) during 2012 and 2013
Table 4.23b: ANOVA of environmental factors (relative humidity, rainfall and wind speed) during 2012and 2013)
Relative humidity (%) Rainfall (mm) Wind speed (Km/h)
Source DF SS MS F P SS MS F P SS MS F P
Week 5 125.8 25.16 157.2 0.001* 244.16 48.83 168.37 0.001* 30.41 6.08 67.55 0.004*
Year 1 460.6 460.6 2878.75 0.002* 141.12 141.12 486.62 0.001* 26.62 26.6 295.5 0.001*
Variety 4 114.2 28.55 178.44 0.001* 75.34 18.83 64.93 0.002* 24.61 6.15 68.33 0.001*
Y*V 4 271.6 67.9 424.38 0.423NS 382.25 95.56 329.51 0.756NS 34.32 8.58 95.33 0.994NS
Error 165 26.4 0.16 47.87 0.29 15.24 0.09
Total 179 998.6 890.74 131.1
Maximum temperature (°C) Minimum temperature (°C)
Source DF SS MS F-value P-value SS MS F-value P-value
Week 5 802.79 160.55 1114.91 0.001* 1763.65 352.73 708.31 0.001*
Year 1 97.24 97.24 675.28 0.001* 408.15 408.15 819.58 0.001*
Variety 4 25.09 6.27 43.56 0.001* 165.12 41.28 82.89 0.004*
Y*V 4 38.26 9.57 66.42 0.991NS 112.42 28.11 56.44 0.508NS
Error 165 23.79 0.14 82.17 0.49
Total 179 987.17 2531.36
Table. 4.11: Overall correlation of weekly environmental conditions with TLCVD
incidence during 2012 and 2013
Varieties/lines
Maximum
temperature
(°C)
Minimum
temperature
(°C)
Relative
humidity
(%)
Rainfall
(mm)
Wind
speed
(Km/h)
Big Beef 0.786*
0.002
0.743*
0.006
-0.814*
0.001
0.511NS
0.090
0.275NS
0.388
Caldera 0.785*
0.002
0.739*
0.006
-0.788*
0.002
0.510NS
0.090
0.292NS
0.357
Sitara-TS101 0.772*
0.003
0.735*
0.006
-0.763*
0.004
0.510NS
0.090
0.298NS
0.348
014276 0.795*
0.002
0.743*
0.006
-0.809*
0.001
0.511NS
0.090
0.266NS
0.404
Salma 0.799*
0.002
0.745*
0.005
-0.816*
0.001
0.511NS
0.089
0.245NS
0.442
*Significant at P = 0.05 NS=Non-significant
Table. 4.12: Year wise correlation of weekly environmental conditions with TLCVD
incidence during 2012 and 2013
Environmental Conditions 2012 2013
Maximum temperature (°C) 0.927*
0.001
0.849*
0.002
Minimum temperature (°C) 0.847*
0.001
0.842*
0.002
Relative humidity (%) -0.895*
0.001
-0.945*
0.002
Rainfall (mm) 0.649*
0.004
0.624*
0.003
Wind speed (Km/h) 0.197NS
0.063
0.215NS
0.41
*Significant at P = 0.05 NS=Non-significant
4.9. Development of TLCVD disease predictive model based on two years data (2012
and 2013)
A TLCV disease predictive model was developed through stepwise regression
analysis of two years (2012 and 2013) environmental and disease incidence data. The
multiple regression model; Y= 0.532+ 0.053x1 + 0.97x2-0.081x3+0.15x4 was used to predict
the probable onset of TLCVD under given set of environmental variables. In this model, Y =
TLCVD incidence, x1= maximum temperature, x2 = minimum temperature, x3 = relative
humidity and x4 = rainfall. It is evident from the model equation that major factors
responsible for the attack of TLCV were temperature (maximum and minimum), relative
humidity and rainfall. It indicated from the above regression equation that with one unit
change in maximum temperature there would be probable change of 0.053 units in TLCVD.
The change would be 0.97 units in case of minimum temperature and with one unit increase
in relative humidity TLCVD incidence would be decreased 0.081 units. Disease would be
affected 0.15 units with one unit change in rainfall. Two years predictive model explained
85% variability in TLCVD incidence (Table 4.13). Among environmental variables relative
humidity, minimum temperature, rainfall and relative humidity appeared as the main
contributing factors in the stepwise regression analysis. The model containing these variables
explained 65 to 85% variability in disease development.
Table. 4.13: Summary of stepwise regression model to predict TLCVD during 2012 and
2013
Variable
Entered
No. in
model
Model
R2
C(p) F-value P-value
Relative humidity (%) 1 0.65 230.42 325.14 0.001*
Minimum temperature (°C) 2 0.82 29.95 175.72 0.001*
Rainfall (mm) 3 0.85 4.71 27.12 0.001*
Maximum temperature (°C) 4 0.85 4.12 4.61 0.004*
*Significant at P = 0.05
4.9.1. TLCVD disease predictive model assessment during two years (2012 and 2013)
After selecting independent variables, a regression model needs to be validated before
being used because the goal of model development is to identify the best possible variables
for a particular system. The statistical procedure as described by Chattefuee and Hadi (2006);
Snee (1977) was followed in the model assessment processes.
4.9.2. Comparison of the dependent variable (TLCVD) and regression coefficients with
physical theory
One of the most important parameter to check the model reliability is the value of
coefficient of determination, i.e., R2. In TLCVD predictive model, it was 0.85 which is
considered fairly good particularly under field conditions when one has no control on any of
the studied variables (Table. 4.14). Standard error of estimate was reasonably low (0.295).
The F-distribution of regression model was significant at P<0.05 (Table. 4.15). The relative
contribution of maximum temperature, minimum temperature, relative humidity and rainfall
towards the development of model was significant at P<0.05 and all these environmental
variables showed low standard error <1 (Table 4.16). It may be concluded on the basis of
high R2 value, low standard error and significant regression statistics that the model is good
for prediction TLCVD incidence.
Table. 4.14: Regression statistics of the predictive model for TLCVD based on two
years (2012 and 2013)
Regression Statistics
R2 0.85
Adjusted R2 0.84
MSE 0.29
Standard Error 0.29
Observations 180
Table. 4.15: ANOVA of the TLCVD predictive model for based on two years
environmental conditions data
Source DF SS MS F-value P-value
Model 4 289.68 72.42 244.91 0.001*
Error 175 51.75 0.29
Total 179 341.42
*Significant at P = 0.05
Table. 4.16: Coefficients of variables, their standard error, t Stat, P-value and
significance
Parameters Coefficients Standard
Error
Type II SS t stat P-value
Intercept 0.53 1.17 0.06 0.21 0.001*
Maximum temperature (°C) 0.053 0.032 1.72 4.61 0.004*
Minimum temperature (°C) 0.097 0.019 7.14 24.13 0.001*
Relative humidity (%) -0.081 0.005 74.07 250.49 0.001*
Rainfall (mm) 0.15 0.027 8.77 29.66 0.001*
*Significant at P=0.05
4.9.3. Variety wise predictive model for TLCVD incidence
The minimum temperature and relative humidity were epidemiologically important in
the development of TLCVD on five tomato varieties/lines. These were subjected to stepwise
regression analysis and single variety models were developed. The TLCVD values predicted
by these single variety models were in close conformity with observed values recorded on
five tomato varieties/lines viz. Big Beef, Caldera, Sitara-TS-101, 014276 and Salma.
The models with significantly important variables were developed by stepwise regression
on five tomato varieties/lines separately to predict TLCVD incidence during two years (Table
4.17). Out of five variables entered, two of them i .e. minimum temperature and relative
humidity appeared as the main contributing environmental variables and exerted significant
influence in the development of TLCVD. In stepwise regression analysis maximum
temperature, rainfall and wind speed were assessed as very poor on all five varieties/lines (Big
Beef, Caldera, Sitara-TS-101, 014276 and Salma). The model containing these variables
explained above 85 percent variability in TLCVD incidence in all varieties/lines. When these
two environmental variable models were used to predict TLCVD incidence, there was a fairly
good R2 value, low C (p) value and low RMSE value come as result.
Table 4.17: Summary of stepwise regression model developed to predict TLCVD
incidence with respect to environmental factors on five tomato
varieties/lines during two years
Environmental parameters R2 Adj. R2 C (p) RMSE Pr > F
Big Beef 0.93 0.91 0.139 0.51
Minimum temperature (°C) 0.002*
Relative humidity (%) 0.001*
Caldera 0.89 0.87 0.204 0.61
Minimum temperature (°C) 0.009*
Relative humidity (%) 0.004*
Sitara-TS-101 0.86 0.83 0.240 0.67
Minimum temperature (°C) 0.002*
Relative humidity (%) 0.001*
014276 0.92 0.91 0.083 0.55
Minimum temperature (°C) 0.003*
Relative humidity (%) 0.001*
Salma 0.93 0.92 0.096 0.53
Minimum temperature (°C) 0.001*
Relative humidity (%) 0.001*
* = Significant at P=0.05
4.9.4. Evaluation of model by comparing the observed and predicted data
Second step of model evaluation was completed by comparing observed and predicted
data. Two criteria i.e. percent error and root mean square error (RMSE) value were used to
evaluate the predictions of the model. Model efficiency is considered good if predictions of
the model having percent error and RMSE of about ± 20. In present studies, most of
predictions obtained using two years model on five genotypes, showed percent error of about ±
20 (Table 4.18). Average RMSE of total predictions (180) during two years on five genotypes
was less than ± 20 (Table 4.17).
4.9.5. Graphical representation of TLCVD predictive model based on two years data
(2012 and 2013)
The graphs of normal probability plot and disease versus fit, best explained the two
years full model (Fig. 4.13). The probability plots are frequently recommended for assessing
the goodness of fit of a hypothesized distribution and are often used as an informal means of
assessing the non normality of a set of data (Johnson and Wichern, 1982).
Fig. 4.13: Normal probability plot and residual versus fit for the model of 2012-2013
The normal probability plot for the two years full model showed that most of the data
points were placed on the reference line whereas only few data points both at the lower side
and at the higher side deviate from the reference line affecting the normal distribution of data
points; it could be the cause of an error in the regression model. Residuals are estimates of
experimental error obtained by subtracting the observed responses from the predicted
responses. Residuals can be considered as the elements of variation unexplained by the fitted
model. The purpose of this dot plot is to provide an indication of the distribution of the
residuals. The two years model showed that most of the data points were more or less
distributed uniformly around the reference line indicating a better fit of regression model.
Only few data points were not very closely distributed on the reference line leading to the
addition of an error in the regression model.
Table 4.18: Multiple regression equations based on environmental conditions and
predicted TLCVD incidence values during two years
Regression equations of TLCVD incidence
Y = bo + b1X1 + b2X2 + b3X3……..
Observed Predicted % Error
Big Beef = -0.26 + 0.27X1 – 0.085X2
(X1=Minimum temperature, X2= Relative humidity) 1.20 1.18 1.67
1.30 1.28 1.54
2.10 1.83 12.86
3.50 3.35 4.29
Caldera = -0.22 + 0.19X1 – 0.079X2
(X1=Minimum temperature, X2= Relative humidity) 1.50 1.45 3.33
1.60 1.47 9.38
3.60 3.45 4.17
2.30 1.63 29.13
Sitara-TS-101 = -0.162 + 0.193X1 – 0.74X2
(X1=Minimum temperature, X2= Relative humidity) 1.70 1.69 0.59
1.80 1.67 7.22
3.80 3.61 5.00
2.50 1.83 26.81
014276 = 0.18 + 0.21X1– 0.089X2
(X1= Minimum temperature, X2= Relative humidity) 4.10 4.02 1.95
4.20 4.08 2.86
1.90 1.73 8.95
2.60 2.06 20.77
Salma = 0.26+ 0.22X1- 0.094X2 (X1= Minimum temperature, X2= relative humidity) 4.30 4.21 2.09
1.90 1.76 7.37
2.20 1.95 11.36
2.80 2.18 22.14
4.10. Analysis of variance for B. tabaci population during two years (2012 and 2013)
During two years (2012 and 2013) the individual effects of variety and week was
significant while the effect of year was non-significant for the development of B. tabaci
population (Table. 4.19). The two way interactions of variety with week, variety with year
and week with year were significant at P<0.05. The three way interaction of variety, week
and year was also significant. This showed that B. tabaci population varied greatly with
respect to varieties, weeks and years.
4.10.1. ANOVA of environmental conditions during two years (2012 and 2013)
During two years (2012 and 2013) analysis of variance, the individual effect of year and
variety was significant in case of environmental variables i.e. maximum and minimum
temperature, relative humidity, rainfall and wind speed. The two way interactive effect of
year and variety was not significant (Table 4.23a and Table 4.23b).
4.10.2. Comparison of environmental conditions during the years 2012 and 2013
During two years 2012 and 2013 all the environmental variables (maximum and
minimum temperature, relative humidity and wind speed) and B. tabaci population showed
significant difference. The maximum temperature was 38.04°C and 37.57°C, whereas B.
tabaci population was 8.21 and 7.78, respectively during two years (Table 4.20).
Table 4.19: ANOVA for B. tabaci population during 2012 and 2013
Source DF SS MS F-value P-value
Replication 2 0.07 0.035
Variety 4 17.94 4.49 474.05 0.001*
Week 5 776.82 155.36 16419.21 0.001*
Year 1 0.027 0.027 2.84 0.094NS
Variety*Week 20 3.39 0.17 17.92 0.001*
Variety*Year 4 0.15 0.04 4.03 0.004*
Week*Year 5 0.92 0.19 19.54 0.001*
Variety*Week*Year 20 0.51 0.03 2.71 0.004*
Error 118 1.12 0.009
Total 179 800.96
*Significant at P<0.05 NS=Non-significant
Table 4.20: Comparison of environmental conditions for B. tabaci population during
two years (2012 and 2013)
Environmental factors 2012 2013 LSD
Maximum temperature (°C) 38.04a
37.57b
1.53
Minimum temperature (°C) 29.46a
29.05b
1.91
Relative humidity (%) 45.51a 35.4b
6.28
Rainfall (mm) 1.2a 0.64b 0.52
Wind speed (Km/h) 5.91b 6.73a
0.77
B. tabaci population 8.21b
7.78a
0.39
*Means with similar letters in a row are not significantly different at P = 0.05
4.10.3. Correlation of weekly environmental conditions with B. tabaci population
during 2012 and 2013
The correlation of environmental conditions with B. tabaci population was determined
on five susceptible and highly susceptible varieties/lines. A highly significant role was played
by temperature (maximum and minimum) in the development of B. tabaci population in all
five varieties/lines i.e., Big Beef, Caldera, Sitara-TS-101, 014276 and Salma during two
years (Table 4.21). The overall correlation of relative humidity was significantly negative
with B. tabaci population on all five varieties/lines during both years. Rainfall and wind
speed showed non-significant relationship with B. tabaci population on all the five
varieties/lines.
4.10.4. Year wise correlation of weekly environmental conditions with B. tabaci
population during 2012 and 2013 on five varieties/lines
A highly significant correlation was observed between environmental parameters
(maximum and minimum temperature, relative humidity and rainfall) and B. tabaci
population during both years while only the correlation of wind speed (r < 0.15) with B.
tabaci population was found non-significant on all the five varieties/lines i.e., Big Beef,
Caldera, Sitara-TS-101, 014276 and Salma (Table 4.22).
Table 4.21: Correlation of environmental conditions with B. tabaci population during
two years (2012 and 2013)
Varieties/lines Maximum
temperature
(° C)
Minimum
temperature
(° C)
Relative
humidity
(%)
Rainfall
(mm)
Wind
speed
(Km/h)
Big Beef 0.857*
0.000
0.780*
0.003
-0.788*
0.002
0.535NS
0.073
0.284NS
0.371
Caldera 0.864*
0.000
0.783*
0.003
-0.781*
0.003
0.538NS
0.071
0.289NS
0.362
Sitara-TS-101 0.873*
0.000
0.770*
0.003
-0.775*
0.003
0.532NS
0.075
0.307NS
0.332
014276 0.887*
0.000
0.774*
0.003
-0.770*
0.003
0.537NS
0.072
0.311NS
0.325
Salma 0.873*
0.000
0.771*
0.003
-0.767*
0.004
0.533NS
0.074
0.291NS
0.359
* Significant at P<0.05 NS = Non-significant
Table 4.22: Year wise correlation of environmental conditions with B. tabaci
population during two years (2012 and 2013) on five varieties/lines
Environmental Conditions 2012 2013
Maximum temperature (°C) 0.981*
0.002
0.920*
0.001
Minimum temperature (°C) 0.906*
0.003
0.900*
0.002
Relative humidity (%) -0.971*
0.001
-0.970*
0.001
Rainfall (mm) 0.619*
0.001
0.486*
0.002
Wind speed (Km/h) 0.431NS
0.063
0.147NS
0.167
* Significant at P<0.05 NS=Non-significant
Table 4.23a: Analysis of variance of environmental factors (maximum and minimum temperature) during 2012 and 2013
Table 4.23b: ANOVA of environmental factors (relative humidity, rainfall and wind speed) during 2012and 2013)
Relative humidity (%) Rainfall (mm) Wind speed (Km/h)
Source DF SS MS F P SS MS F P SS MS F P
Week 5 125.8 25.16 157.2 0.001* 244.16 48.83 168.37 0.001* 30.41 6.08 67.55 0.004*
Year 1 460.6 460.6 2878.75 0.002* 141.12 141.12 486.62 0.001* 26.62 26.6 295.5 0.001*
Variety 4 114.2 28.55 178.44 0.001* 75.34 18.83 64.93 0.002* 24.61 6.15 68.33 0.001*
Y*V 4 271.6 67.9 424.38 0.423NS 382.25 95.56 329.51 0.756NS 34.32 8.58 95.33 0.994NS
Error 165 26.4 0.16 47.87 0.29 15.24 0.09
Total 179 998.6 890.74 131.1
Maximum temperature (°C) Minimum temperature (°C)
Source DF SS MS F-value P-value SS MS F-value P-value
Week 5 802.79 160.55 1114.91 0.001* 1763.65 352.73 708.31 0.001*
Year 1 97.24 97.24 675.28 0.001* 408.15 408.15 819.58 0.001*
Variety 4 25.09 6.27 43.56 0.001* 165.12 41.28 82.89 0.004*
Y*V 4 38.26 9.57 66.42 0.991NS 112.42 28.11 56.44 0.508NS
Error 165 23.79 0.14 82.17 0.49
Total 179 987.17 2531.36
4.11. Development of B. tabaci population predictive model based on two years data
(2012 and 2013)
Two years environmental conditions and B. tabaci population data were subjected to
stepwise regression for the development of predictive model. A multiple regression model;
Y= -7.76+0.231x1+0.21x2-0.092x3+0.11x4+0.086x5 (where Y = B. tabaci population, x1=
maximum temperature, x2 = minimum temperature, x3 = relative humidity, x4 = rainfall, x5 =
wind speed) was developed to predict the probable attack of B. tabaci on tomato crop. It is
evident from the model equation that major factors responsible for the attack of whitefly
were maximum temperature, minimum temperature, relative humidity, rainfall and wind
speed prevalent at that time. It indicated that with one unit change in maximum temperature
there would be probable change of 0.231 units in B. tabaci population. The change would be
0.21 units in case of minimum temperature. Relative humidity has also significant
contributing role in built up of whitefly i.e., linear expansion in single part of relative
humidity would cause equivalent increase in 0.092 units of B. tabaci population. One unit
change in rainfall and wind speed will affect the B. tabaci population by 0.11 and 0.086 units
respectively. Model explained maximum 92% variability in B. tabaci population (Table
4.24). Maximum temperature, relative humidity and minimum temperature appeared as the
main contributing environmental variables in the stepwise regression analysis. The influence
of rainfall and wind speed was very poor. The model containing these variables explained 73
to 92 % variability in B. tabaci population development.
Table 4.24: Summary of stepwise regression model to predict B. tabaci population
during two years 2012 and 2013
Variable entered No. in model Model R2 C(p) F-value P-value
Maximum temperature (°C) 1 0.73 414.75 504.04 0.001*
Relative humidity (%) 2 0.83 202.06 101.05 0.001*
Minimum temperature (°C) 3 0.92 17.98 172.39 0.001*
Rainfall (mm) 4 0.92 10.81 8.87 0.003*
Wind speed (Km/h) 5 0.92 6.00 6.81 0.009*
* = Significant at P<0.05
4.11.1. Comparison of the dependent variable (B. tabaci) and regression co-efficient
with physical theory
One of the most important parameter to check the model reliability is the value of
coefficient of determination, i.e., R2, which was 0.92 that is considered fairly good
particularly under field conditions when one has no control on any of the studied variables.
Standard error of estimate was quite low (1.34) (Table 4.25). The F-distribution of
regression model was significant at P<0.05 (Table 4.26). The contribution of environmental
variables (maximum temperature, minimum temperature, relative humidity, rainfall and
wind speed) was significant towards B. tabaci population at P<0.05 and each of them
showed quite low standard error <1 (Table 4.27). It may be concluded that the model is
good for prediction purpose from set of the unknown variables based on physical theory.
Table 4.25: Regression statistics of the predictive model for B. tabaci based on two
years (2012 and 2013) data
Regression Statistics
R2 0.92
Adjusted R2 0.91
MSE 0.35
Standard Error 1.34
Observations 180
Table 4.26: ANOVA of the predictive model for B. tabaci based on two years data
Source DF Sum of Square Mean
Square
F-value P-value
Regression 5 739.39 147.88 417.91 0.001*
Error 174 61.57 0.35
Total 179 800.96
* = Significant at P <0.05
Table 4.27: Co-efficient of variables, their standard error, t Stat, P-value and
Significance
Parameters Coefficients Standard
Error
Type II
SS
t Stat P-value
Intercept -7.76 1.29 12.73 35.97 0.001*
Maximum temperature (°C) 0.231 0.04 14.83 41.92 0.001*
Minimum temperature (°C) 0.21 0.02 32.48 91.78 0.001*
Relative humidity (%) -0.092 0.005 88.93 251.32 0.001*
Rainfall (mm) 0.11 0.03 4.31 12.18 0.006*
Wind speed (Km/h) 0.086 0.03 2.41 6.81 0.009*
* = Significant at P <0.05
4.11.2. Variety wise predictive model for B. tabaci population
The model with significantly important variables was developed by stepwise
regression on five tomato varieties/lines separately to predict B. tabaci population during two
years (Table 4.28). Out of five variables entered, only minimum temperature and relative
humidity contributed significantly towards the development of B. tabaci population in all the
varieties/lines except in case of 014276 where maximum temperature, minimum temperature
and relative humidity appeared as the main contributing environmental variables in the
stepwise regression analysis. In stepwise regression analysis maximum temperature, rainfall
and wind speed were assessed as very poor in all five varieties/lines (Big Beef, Caldera, Sitara-
TS-101, 014276 and Salma). The model containing these variables explained above 90%
variability in B. tabaci population in all varieties/lines. When these three environmental
variable models were used to predict B. tabaci population, a fairly good R2 value; low C (p)
value and low RMSE value obtained indicating the fitness of the model.
Table 4.28: Summary of stepwise regression model developed to predict B. tabaci
population with respect to environmental factors on five tomato
varieties/lines during two years
Environmental parameters R2 Adj. R2 C (p) RMSE Pr > F
Big Beef 0.94 0.93 1.537 0.644
Minimum temperature (°C) 0.019*
Relative humidity (%) 0.045*
Caldera 0.93 0.92 2.099 0.645
Minimum temperature (°C) 0.015*
Relative humidity (%) 0.003*
Sitara-TS-101 0.91 0.89 3.049 0.711
Minimum temperature (°C) 0.015*
Relative humidity (%) 0.036*
014276 0.94 0.91 3.429 0.638
Maximum temperature (°C) 0.234
Minimum temperature (°C) 0.004*
Relative humidity (%) 0.033*
Salma 0.90 0.88 2.661 0.702
Minimum temperature (°C) 0.003*
Relative humidity (%) 0.029*
* = Significant at P < 0.05
4.11.3. Evaluation of model by comparing the observed and predicted data
Second step of model evaluation was completed by comparing observed and
predicted data. The criteria of percent error and root mean square error (RMSE) values were
used to evaluate the predictions of the model. Model efficiency is considered good if
predictions of the model having percent error and RMSE ~ ± 20. In present studies, most of
predictions obtained using two years model on five genotypes, showed percent error ~ ± 20
(Table 4.29). Average RMSE of total predictions (180) during two years on five genotypes
was low i.e. less than ± 20 (Table 4.28).
The environmental variables minimum temperature and relative humidity were
epidemiologically important in the development of B. tabaci population on five tomato
varieties/lines. These were subjected to regression analysis and for each variety model
equations were developed. The B. tabaci population values predicted by these single variety
models were in close conformity with observed values recorded on five tomato varieties/lines
viz. Big Beef, Caldera, Sitara-TS-101, 014276 and Salma.
4.11.4. Graphical representation of B. tabaci population predictive model based on two
years data
The graphs of normal probability plot and disease versus fit, best explained the two
years full model (Fig. 4.14). The probability plots are frequently recommended for assessing
the goodness of fit of a hypothesized distribution and are often used as an informal means of
assessing the non-normality of a set of data (Johnson and Wichern, 1982).
The normal probability plot for the two years full model showed that most of the data
points were placed on the reference line whereas only few data points both at the lower side
and at the higher side deviate from the reference line affecting the normal distribution of data
points; it could be the cause of an error in the regression model. Residuals are estimates of
experimental error obtained by subtracting the observed responses from the predicted
responses. Residuals can be considered as the elements of variation unexplained by the fitted
model. The purpose of this dot plot is to provide an indication of the distribution of the
residuals. The two years model showed that most of the data points were more or less
distributed uniformly around the reference line indicating a better fit of the regression model.
Only few data points were not very closely distributed on the reference line leading to the
addition of an error in the regression model.
Table 4.29: Multiple regression equations based on environmental conditions and
predicted B. tabaci population values during two years
Regression equations of B. tabaci population
Y = bo + b1X1 + b2X2 + b3X3……..
Observed Predicted %Error
Big Beef = -0.48613+ 0.33974X1 – 0.12343X2
(X1=Minimum temperature, X2= Relative Humidity) 3.60 3.59 0.28
1.30 1.27 2.31
5.40 5.06 6.30
3.60 3.05 15.21
Caldera = -0.41908+ 0.34116X1 – 0.12116X2
(X1=Minimum temperature, X2= Relative Humidity) 1.40 1.39 0.71
3.90 3.83 1.79
5.60 5.21 6.96
3.80 3.26 14.21
Sitara-TS-101 = 0.05899+ 0.33260X1 – 0.12026X2
(X1=Minimum temperature, X2= Relative Humidity) 1.50 1.46 2.67
4.50 4.16 8.17
5.90 5.43 7.97
4.20 3.58 14.76
014276 = -9.05576+ 0.27130X1+0.22468X2-0.09080X3
(X1= Maximum temperature, X2= Minimum temperature,
X3= Relative humidity) 6.80 6.75 0.74
5.90 5.51 7.07
1.90 1.73 8.95
4.30 3.77 12.33
Salma = 0.72258+ 0.30907X1- 0.10931X2
(X1= Minimum temperature, X2= relative humidity) 2.20 2.15 2.73
7.00 6.92 1.14
6.00 5.77 3.83
5.20 4.68 11.11
Fig. 4.14: Normal probability plot and residual versus fit for the model of 2012-2013
4.12. Management
4.12. Evaluation of different treatments against TLCVD incidence during two years
(2012 and 2013)
4.12.1. Analysis of variance for TLCVD management during the years 2012 and 2013
The individual effect of year, spray, variety and treatment was significant for disease
incidence (Table 4.30). The two way interactions of spray with year, variety with year,
treatment with year and variety with spray were significant; whereas the two way
interactions of variety with treatment and spray with treatment were not significant. The
three way interaction between variety, spray and year was significant. Three way
interactions between variety, spray and treatment; variety, treatment and year; spray,
treatment and year were not significant. The four way interaction of variety with spray,
treatment and year was also non-significant.
4.12.2. Comparisons of different treatments against TLCVD incidence
All the treatments were significantly effective in reducing TLCVD incidence
compared to untreated control. Comparative efficacy of all treatments was significantly
different from each other. Imidacloprid was the most effective in reducing TLCVD incidence
as compared to control followed by acetamiprid, neem extract, salicylic acid, classic (Zn and
Boron solution) and eucalyptus extract (Table 4.31).
Table 4.30: ANOVA for TLCVD management during 2012 and 2013
Source DF SS MS F-value P-value
Replication 2 78.13 39.11
Year 1 230.91 230.91 624.08 0.002*
Spray 2 340.92 170.46 460.71 0.001*
Variety 4 65958.92 16489.73 44566.84 0.001*
Treatment 6 8221.93 1370.32 3703.57 0.001*
Spray*Year 2 2.74 1.37 3.71 0.003*
Variety*Year 4 431.32 107.83 291.43 0.001*
Treatment*Year 6 57.22 9.54 25.78 0.034*
Variety*Spray 8 26.23 3.28 8.86 0.001*
Variety*Treatment 24 286.42 11.93 32.25 0.214NS
Spray*Treatment 12 13.45 1.12 3.03 0.061NS
Variety*Spray*Year 8 4.22 0.53 0.37 0.014*
Variety*Spray*Treatment 48 25.83 0.54 1.45 0.063NS
Variety*Treatment*Year 24 115.56 4.82 13.01 0.085NS
Spray*Treatment*Year 12 7.54 0.63 1.69 0.076NS
Variety*Spray*Treatment*Year 48 11.43 0.24 0.64 0.073NS
Error 418 155.92 0.37
Total 629
*Significant at P<0.05 NS= Non-significant
Table 4.31: Comparisons of different treatments against TLCVD incidence
Sr. No. Treatments Disease incidence (%)
T1 Imidacloprid 11.34 g
T2 Acetamiprid 16.47 f
T3 Classic (Zn and Boron) 26.71 c
T4 Salicylic acid 23.52 d
T5 Neem extract 20.16 e
T6 Eucalyptus Extract 28.18 b
T7 Control 44.15 a*
*Means with similar letters in a column are not significantly different at P = 0.05
LSD=0.16
4.12.3. Comparison of TLCVD incidence with spray and year
Three sprays were applied for the management of TLCVD during two years (2012 and
2013). There was significant difference in TLCVD incidence after each spray during 2012 and
2013 (Table. 4.32). After first spray, 38.65% disease incidence was recorded which reduced to
17.25% after third spray during 2012, while disease incidence reduced from 36.03% to
17.41% after first and third spray, respectively during 2013.
Table 4.32: Comparisons of TLCVD incidence with spray and year
Sprays TLCVD incidence (%)
2012 2013
Ist Spray 38.65 a 36.03 a
2nd Spray 24.73 b 27.21 b
3rd Spray 17.25 c 17.41 c
*Means with similar letters in a column are not significantly different at P = 0.05 LSD=0.17
4.12.4. Comparisons of treatments and years against TLCVD incidence
All the treatments were effective in reducing TLCVD incidence compared to untreated
control during the years 2012 and 2013 (Table 4.33). In 2012 all the treatments showed
significantly different results in reducing TLCVD incidence while in 2013 salicylic acid and
neem extract were not significantly different from each other in reducing the TLCVD
incidence. In 2012, the efficacy of imidacloprid and salicylic acid against TLCVD incidence
was significantly different from their respective treatments in 2013. In 2012 three treatments
i.e. acetamiprid, classic (Zn and Boron solution) and neem extract were not significantly
different from their respective treatments in the year 2013. During both years (2012 and 2013)
imidacloprid was the most effective in reducing TLCVD incidence as compared to other
treatments and control.
4.12.5. Comparisons of TLCVD incidence with variety and spray
The mean TLCVD incidence significantly reduced in all genotypes i.e., Carmen, Po-
02, Roker, Uovo Roseo and Lyp#1 in first, second and third sprays (Table 4.34). In first
spray, three genotypes i.e., Po-02, Uovo Roseo and Lyp#1 had significant difference with
respect to disease incidence while Carmen and Roker showed non-significant difference. All
the genotypes showed significant difference of TLCVD incidence in second spray. In third
spray, only Carmen showed significant difference as compared to all other varieties/lines,
while the disease incidence was non-significant in Po-02 and Uovo Roseo; Roker and Lyp#1.
Table 4.33: Comparison of treatments and years against TLCVD incidence
Serial No. Treatments
2012 2013
Disease incidence
(%)
Disease incidence
(%)
T1 Imidacloprid 13.83 h 11.85 i
T2 Acetamiprid 16.34 g 16.02 g
T3 Classic (Zn and
Boron)
21.24 d 20.97 d
T4 Salicylic acid 19.42 e 18.26 f
T5 Neem extract 18.12 f 17.94 f
T6 Eucalyptus Extract 24.23 c 23.71 c
T7 Control 49.09 b 54.21 a
LSD 1.03 1.32
* Means with similar letters in a column are not significantly different at P =0.05
Table 4.34: Comparisons of TLCVD incidence with variety and spray during two years
TLCVD incidence (%)
Varieties/lines 1st Spray 2nd Spray 3rd Spray
Carmen 22.13 e 13.72 hij 5.46 lm
Po-02 53.67 a 28.15 de 14.79 hi
Roker 22.19 e 15.36 h 9.33 kl
Uovo Roseo 47.55 b 31.83 c 14.99 gh
Lyp#1 27.94 de 18.19 g 9.46 k
*Means with similar letters in a row and column are not significantly different at P = 0.05
LSD=0.26
4.12.6. Comparisons of TLCVD incidence with variety, spray and year
The TLCVD incidence significantly reduced in all genotypes i.e., Carmen, Po-02,
Roker, Uovo Roseo and Lyp#1 in first, second and third sprays during two years 2012 and
2013 (Table 4.35). All genotypes had significant difference in disease incidence in third
spray with respect to first and second sprays during 2012 and 2013. In first spray all
genotypes showed significant difference in disease incidence during 2012 and 2013. In
second and third sprays, three genotypes i.e. Po-02, Uovo Roseo and Lyp#1 showed
significant difference with respect to disease incidence while two genotypes Carmen and
Roker showed non-significant difference with each other during the year 2012. All the
genotypes showed significant difference in disease incidence in second and third sprays during
the year 2013.
Table 4.35: Comparisons of TLCVD incidence with variety, spray and year
Varieties/lines
2012 2013
1st Spray 2nd Spray 3rd Spray 1st Spray 2nd Spray 3rd Spray
Carmen 28.71 mn 27.71 o 26.96 p 27.66 o 26.89 p 26.10 q
Po-02 47.73 f 47.02 g 46.26 h 47.37 f 46.65 g 45.73 i
Roker 29.95 l 27.86 o 27.26 p 29.93 l 28.52 mn 27.66 o
Uovo Roseo 50.95 b 50.45 c 49.63 d 50.36 c 49.85 d 48.82 e
Lyp#1 30.58 k 28.82 m 28.38 n 25.69 r 24.63 s 24.22 t
*Similar letters in a row and column showing significantly different values at P =0.05
LSD=0.37
4.13. Analysis of variance for B. tabaci management during 2012 and 2013
The individual effect of year, spray, variety and treatment was significant against B.
tabaci population (Table 4.36). The two way interactions of spray with year, variety with
year, treatment with year and variety with spray were significant; whereas the interaction of
variety with treatment and spray with treatment were non-significant. The three way
interaction between variety, spray and year was significant whereas the interaction of
variety with spray and treatment, variety with treatment and year, spray with treatment and
year were non-significant. The four way interaction of variety with spray, treatment and year
was also not significant.
4.13.1. Comparisons of different treatments against B. tabaci population
All the treatments were significantly effective in reducing B. tabaci population
compared to untreated control during 2012 and 2013 (Table 4.37). Comparative efficacy of
all treatments was significantly different from each other. Imidacloprid was the most
effective in reducing B. tabaci population as compared to control followed by acetamiprid,
neem extract, salicylic acid, classic (Zn and Boron solution) and eucalyptus extract.
Table 4.36: ANOVA for B. tabaci population during two years (2012 and 2013)
Source DF SS MS F-value P-value
Replication 2 3.52 1.76
Year 1 0.14 0.14 73.13 0.002*
Spray 2 248.94 124.47 62235.11 0.001*
Variety 4 84.48 21.12 10560.21 0.001*
Treatment 6 108.19 18.03 9015.83 0.001*
Spray*Year 2 1.24 0.62 310.63 0.003*
Variety*Year 4 2.76 0.69 345.11 0.001*
Treatment*Year 6 2.61 0.44 220.54 0.001*
Variety*Spray 8 0.38 0.05 25.62 0.001*
Variety*Treatment 24 2.35 0.09 45.62 0.331NS
Spray*Treatment 12 32.43 2.71 1355.27 0.087NS
Variety*Spray*Year 8 6.86 0.86 430.75 0.014*
Variety*Spray*Treatment 48 0.05 0.001 0.54 0.053NS
Variety*Treatment*Year 24 8.35 0.35 175.95 0.074NS
Spray*Treatment*Year 12 3.87 0.32 160.25 0.076NS
Variety*Spray*Treatment*Year 48 1.80 0.04 20.24 0.073NS
Error 418 0.80 0.002
Total 629
*Significant at P<0.05 NS=Non-significant
Table. 4.37: Comparisons of different treatments against B. tabaci population during
two years
Serial No. Treatments B. tabaci population
T1 Imidacloprid 1.04 g
T2 Acetamiprid 1.17 f
T3 Classic (Zn and Boron) 3.72 c
T4 Salicylic acid 2.95 d
T5 Neem extract 2.01 e
T6 Eucalyptus Extract 4.86 b
T7 Control 9.69 a*
*Means with similar letters in a column are not significantly different at P = 0.05
LSD = 0.018
4.13.2. Comparison of B. tabaci population with spray and year
Three sprays were applied for the management of B. tabaci during two years (2012 and
2013). There was significant difference in B. tabaci population after each spray during 2012
and 2013 (Table. 4.38). After first spray, B. tabaci population was recorded 3.78 which
reduced to 1.25 after third spray during 2012, while B. tabaci population reduced from 3.75 to
1.41 after first and third spray, respectively during 2013.
Table 4.38: Comparisons of B. tabaci with spray and year
Sprays B. tabaci population
2012 2013
Ist Spray 3.78 a 3.75 a
2nd Spray 2.73 b 2.84 b
3rd Spray 1.25 c 1.41 c
*Means with similar letters in a column are not significantly different at P =0.05 LSD=0.013
4.13.3. Comparison of treatments and years against B. tabaci population
All the treatments were effective in reducing B. tabaci population compared to
untreated control during the years 2012 and 2013 (Table 4.39). In 2012, all the treatments
showed significantly different results in reducing B. tabaci population while in 2013 salicylic
acid and neem extract were not significantly different from each other in reducing the B.
tabaci population as compared to control. In 2012, three treatments imidacloprid, classic (Zn
and Boron solution) and eucalyptus extract showed significantly different results as compared
to their respective treatments in 2013. In 2012, three treatments i.e. acetamiprid, salicylic acid
and neem extract were not significantly different from their respective treatments in the year
2013. During both years (2012 and 2013) imidacloprid was the most effective in reducing B.
tabaci population as compared to other treatments and control.
4.13.4. Comparisons of B. tabaci population with variety and spray
The mean B. tabaci population significantly reduced in all the genotypes i.e.,
Carmen, Po-02, Roker, Uovo Roseo and Lyp#1 in first, second and third sprays (Table 4.40).
In first spray, three genotypes i.e., Po-02, Uovo Roseo and Lyp#1 had significant difference
in B. tabaci population while Carmen and Roker showed non-significant difference. All the
genotypes showed significant difference of B. tabaci population in second spray except
Roker and Lyp#1 which showed non-significant difference with each other. In third spray,
all genotypes (Carmen, Po-02, Roker, Uovo Roseo and Lyp#1) genotype showed significant
difference in reducing B. tabaci population.
Table 4.39: Comparison of treatments and years against B. tabaci population
Serial No. Treatments
2012 2013
Mean B. tabaci
population
Mean B. tabaci
population
T1 Imidacloprid 1.28 i 1.07 j
T2 Acetamiprid 1.78 h 1.79 h
T3 Classic (Zn and
Boron)
4.87 d 4.14 e
T4 Salicylic acid 3.18 f 3.16 f
T5 Neem extract 3.06 g 3.13 g
T6 Eucalyptus Extract 6.91 b 5.89 c
T7 Control 10.71 a 10.69 a
LSD 0.031 0.032
*Means with similar letters in a column are not significantly different at P = 0.05
Table 4.40: Comparisons of B. tabaci population with variety and spray
Mean B. tabaci population
Varieties/lines 1st Spray 2nd Spray 3rd Spray
Carmen 2.94 e 2.32 m 1.62 q
Po-02 3.95 a 3.39 c 2.72 k
Roker 2.97 e 2.52 l 1.77 p
Uovo Roseo 3.48 b 2.86 f 2.18 n
Lyp#1 3.22 d 2.51 l 1.87 o
*Means with similar letters in a row and column are not significantly different at P = 0.05
LSD=0.03
4.13.5. Comparisons of B. tabaci population with variety, spray and year
The mean B. tabaci population significantly reduced in all genotypes i.e., Carmen,
Po-02, Roker, Uovo Roseo and Lyp#1 in first, second and third sprays during two years
2012 and 2013 (Table 4.41). All genotypes had significant difference in mean B. tabaci
population in third spray with respect to first and second sprays during 2012 and 2013. In
first spray all genotypes showed significant difference in mean B. tabaci population during
2012 and 2013. In second spray all the genotypes i.e. Carmen, Po-02, Roker, Uovo Roseo
and Lyp#1 showed significant difference in B. tabaci population during the year 2012 and
2013. All the genotypes showed significant difference in mean B. tabaci population in third
spray during the year 2013 but Carmen and Lyp#1 showed non-significant difference in
mean B. tabaci population during 2012.
Table 4.41: Comparison of B. tabaci population with variety, spray and year
Varieties/lines 2012 2013
1st Spray 2nd Spray 3rd Spray 1st Spray 2nd Spray 3rd Spray
Carmen 2.99 g 2.33 m 1.63 q 2.91 h 2.30 m 1.61 q
Po-02 3.97 a 3.41 c 2.61 j 3.94 a 3.34 d 2.62 j
Roker 3.14 f 2.54 k 1.78 p 3.12 f 2.41 l 1.76 p
Uovo Roseo 3.49 b 2.88 h 2.18 n 3.42 c 2.75 i 2.17 n
Lyp#1 3.23 e 2.33 m 1.63 q 3.21 e 2.52 k 1.86 o
* Means with similar letters in a row and column are not significantly different at P = 0.05
LSD=0.043
CHAPTER 5 DISCUSSION
Tomato leaf curl virus disease (TLCVD) is the most serious problem for tomato
production in the tropics and subtropics, mainly in South and Southeast Asia (Chakraborty,
2008). The susceptible germplasm, favorable environmental conditions and presence of
viruliferous whitefly contribute towards the wide spread outbreak of this disease (Habib et
al., 2007). Genetic resistance is probably the only durable and long lasting solution against
TLCVD and Bemisia tabaci which require long time period for its implication. The short
term solution to the problem would be the screening of tomato germplasm against TLCVD
for relative resistance/susceptibility (Ellis et al., 2014).
Twenty seven varieties/lines were evaluated to find out resistant source against
TLCVD. Eight varieties/lines (Naqeeb, Pakit, Nagina, Riogrande, 09080, Roma, 09091 and
Nuyt-04-11) were found to be resistant against TLCVD. Six varieties/lines were categorized
as moderately resistant and four as moderately susceptible. Nine varieties/lines were found to
be susceptible and highly susceptible against TLCVD incidence. The resistance and tolerance
of different tomato varieties were estimated by ratio of infected plants, virus titer and
symptom intensity. There was a positive relationship between virus titer and symptom
severity (Rubio et al., 2003). These results are in line with (Camara et al., 2013) who
screened forty one tomato varieties against TYLCV in order to obtain stable and durable
resistances. Results showed that there were 12 resistant, 16 tolerant and 8 susceptible
varieties. Gaikwad et al., (2009) evaluated sixty tomato accessions against TLCVD under
natural conditions followed by artificial screening under glasshouse through whitefly and
grafting. The resistant reaction was confirmed by only three lines viz. 58-11-1-1, LCT-8-5
and 115-1-8-1. After studying the stress responses Moshe et al., (2012) reported that
susceptible plants were rich in reactive oxygen species (ROS) than resistant ones and
chemical components of tomato leaves (Montasser et al., 2012) particularly chlorophyll,
lipids, fatty acids, reducing sugars and proteins decreased in diseased leaves due to which
they could not sustain viral infection.
None of the screened varieties/lines was found to be highly resistant against TLCVD.
This is because of the low natural resistance in domesticated varieties of tomato as compared
to wild species. This result can be strengthened by the findings of various workers, after
screening of one hundred and sixty tomato cultivars, only two wild species Lycopersicon
hirsutum (LA 1223) and Lycopersicon hirsutum (LA 1353) were immune to TLCVD
(Ragupathi and Narayanaswamy, 2000). Domestic varieties were found more susceptible to
TYLCV infection as compared to wild accessions after a large scale screening in the United
Arab Emirates (Hassan et al., 1991). Based on the phenotypic and molecular screening of
thirty tomato cultivars, no accession showed complete resistance to TYLCV (Osei et al.,
2012). Different tomato lines were checked for viral DNA accumulation by alkaline transfer
and dot spot hybridization. Results showed that tolerant lines contained 10-50% less DNA as
compared to susceptible ones (Rom et al., 1993). One hundred and thirty four domesticated
accessions and six wild tomato lines were screened against TYLCV based on symptom
development and DNA amplification. None of the varieties was resistant to TYLCV in
domesticated tomato while all six lines of wild species were resistant (Azizi et al., 2008).
Advanced resistant breeding lines were developed after extensive screening of wild tomato
species because all the domestic varieties were found susceptible to TYLCV (Pilowsky and
Cohen, 1990). Virus accumulation was very low in tomato lines developed by introgression
from L. chilense as compared to hybrids ARO-8479 and HA-3108 (Gomez et al., 2004).
AUDPC was used for the measurement of the disease because it reflects the disease
progress throughout the whole growing season rather than the current status of the disease as
in case of disease incidence measurements. Single incidence data do not capture changes
caused by the environmental conditions. Low AUDPC values appeared as the result of host
resistance and unavailability of the favorable conditions for the pathogen. The age of the host
plant and planting dates also played a critical role in the overall response of the host.
Environmental conditions conducive for TLCVD development and B. tabaci
population density were determined. The overall correlation of temperatures (maximum and
minimum) with B. tabaci population and TLCVD was positive while the relationship of
relative humidity was negative with B. tabaci and TLCVD. These results were according to
Rahman et al., (2006) findings that the disease incidence and vector population increased
with increase in temperature and decreased with increase in relative humidity because there
was a significantly positive correlation between number of whiteflies and TYLCV
transmission in the tomato field. Epidemiological studies showed significant linear
relationship (Y = 0.74x + 23.24, R2
= 0.61) between the B. tabaci population and TYLCVD
incidence in the field. The whitefly population was positively correlated with temperature
and negatively with relative humidity (Aktar et al., 2008). At higher temperatures, the
increase in disease incidence was due to the reduced expression of resistance genes and
inhibition of defense responses (Wang et al., 2009). Defense responses were activated by
SNC1 gene at 22°C but not at 28°C (Yang and Hua, 2004).
TLCVD incidence and whitefly population was high during the months of high
temperature and low rainfall and low relative humidity. These results are strongly supported
by the findings of Polizzi and Asero (1993) who observed more TYLCVD incidence in
August as compared to October because of decrease in temperature. Nitzany (1975) reported
that TYLCVD appeared in epidemic form during the months with relative humidity less than
60% and mean maximum temperature of 30°C because maximum temperature was
significantly correlated with whitefly density. Kumhawat et al., (2000) found a good
correlation between whitefly populations and TYLCVD incidence at higher temperatures. B.
tabaci population increased at 25-30°C due to high oviposition rates which decreased below
20°C (Gerling et al., 1986). The temperatures of 25°C and 30°C were found to be the most
favorable for the development of egg and nymph stages of B. tabaci (Darwish et al., 2000).
Mean development time from egg to adult whitefly was 20 days at 25-30°C, 37 days at 20°C
(Gonzalez and Gallardo, 1999) and 56 days at 17°C. Likewise, the optimum temperature for
juvenile development was 32.5°C (Bonato et al., 2007).
The co-efficient of correlation (r) between TLCVD and environmental conditions
(maximum and minimum temperature, relative humidity) were observed as 0.85, 0.92 and
0.87, respectively. The values of correlation coefficient between B. tabaci population and
environmental conditions (maximum temperature, minimum temperature and relative
humidity) were observed as 0.91, 0.85 and 0.85, respectively. The buildup of whitefly
population and okra yellow vein mosaic virus disease (OYVMVD) incidence were
significantly correlated with temperatures (maximum and minimum) and relative humidity
(Ali et al., 2005a). Hot weather with little or no rainfall was conducive for OYVMV disease
development and also for multiplication of B. tabaci (Singh, 1990).
Conducive environmental conditions for B. tabaci and TLCVD development were
characterized on five tomato genotypes. The maximum temperature (32-37°C), minimum
temperature (22-29°C) and relative humidity (27-51%) were determined as critical ranges for
whitefly population and disease incidence. These results corroborated with those of Bishnoi
et al., (1996) who found the optimum temperature (20-24°C) and relative humidity (46-60%)
ranged for the build-up of whitefly population, respectively. The effect of environmental
factors was also studied on the TLCV disease incidence in different tomato cultivars in India.
It was found that high temperature and humidity increased TLCV disease incidence in the
plants with the maximum infection was obtained at 25°C and 79.73% relative humidity (Rai
et al., 2001). A linear relationship was obtained between weekly air temperature, i.e.,
maximum and minimum air temperature of 33-45°C and 25-30°C, respectively, relative
humidity (70-80%), wind speed (6-12km/hour) for CLCuVD development (Khan et al.,
1998). Rainfall and wind speed showed non-significant relationship with B. tabaci
population and TLCVD incidence on all five varieties/lines. Yassin (1975) reported the
negative correlation between TLCV incidence and wind direction.
As plant diseases cause huge economic, ecological, health and social problems
around the world, it is desirable to describe the disease dynamics by using mathematical
models for its sustainable management (Medina et al., 2009). The temporal and spatial
patterns of plant disease epidemics are jointly determined by the pathosystem characteristics
and environmental conditions. Such spatio-temporal dynamics is understood via
mathematical and statistical modeling (Maanen and Xu, 2003). TLCVD predictive model
statistically justified (R2=0.85) at P<0.05, was developed to predict the probable attack of
TLCV under a set of environmental conditions on five susceptible and highly susceptible
varieties/lines. The model with good co-efficient of determination value explained maximum
(85%) disease development. The observed and predicted mean disease incidence values were
not so different in five varieties/lines. A vector (B. tabaci) predictive model was developed to
predict the buildup of B. tabaci population during two years. The environmental variables
explained 92% of the variability in whitefly population. Both models were compared and
found non-significant, indicating close association with one another for the prediction of
TLCVD incidence and B. tabaci population. Pethybridge and Madden (2003) developed
disease predictive models for the management of vector transmitted viral diseases. Holt et al.,
(1999) found the varietal resistance and insecticides application as suitable management
strategies for TLCVD after the analysis of epidemiological model. A disease predictive
model for leaf rust severity based upon three environmental variables (minimum soil
temperature, minimum air temperature and rainfall) explained 72% variability in disease
development with lowest Cp (1.89), and minimum MSE (35) (Khan, 1994).
It was found that after stepwise regression analysis that the temperatures (maximum
and minimum), relative humidity, rainfall and wind speed significantly affected the B. tabaci
population. The results of above mentioned study were similar to that of (Khan et al., 2006a)
who described significant influence of environmental variables on whitefly population and
MYMV disease severity after the stepwise regression analysis. A similar relationship of
environmental variables (minimum temperature and evening relative humidity) was found for
the prediction of leaf rust disease (Khan et al., 2006b). A disease predictive model was
developed for the management of tomato spotted wilt virus (TSWV) and its vector thrips,
based upon weather factors in tobacco. The disease incidence was affected by environmental
conditions and thrips activity in summer and spring seasons (Chappel et al., 2013).
Cultivation of resistant varieties is the most economical method to manage the disease
(Bosch et al., 2006). But when the disease appears suddenly and at a very rapid rate in the
field, farmers are left with no option except to spray the crop with some effective chemicals
(Pal and Gardener, 2006). Different insecticides, plant extracts and nutrients were applied for
the management of TLCV disease and insect vector B. tabaci. All the six treatments reduced
TLCVD incidence and B. tabaci population significantly compared to untreated control.
Among insecticides, imidacloprid was the most effective to manage the B. tabaci population
and indirectly TLCVD incidence followed by acetamiprid in that order. The imidacloprid and
acetamiprid being the member of neonicotinoids, bind to the acetylcholine receptors
(AChRs) in the CNS of insects (Zhang et al., 2000). Neonicotinoids mimic acetylcholine and
induce abnormal excitement in the insect by disturbing the systematic synaptic transmission.
Subsequently, the insect undergoes excitation and paralysis, followed by death.
Neonicotinoids are effective on contact and through stomach action (Lind et al., 1999). The
efficacy of neonicotinoids may be the result of translaminar movement that allows the
insecticide to control pests on both sides of the leaves because aphids and whiteflies feed
from loweside of leaves (Natwick, 2001; Parrish et al., 2001). Bacci et al., (2007) obtained
significant control of whiteflies and other sucking insects by the use of chloronicotinyls or
neonicotinoids (imidacloprid, acetamiprid, nitenpyram, and thiamethoxam). Ali et al.,
(2005b) checked the effects of different insecticides against nymphs and adult whitefly.
Buprofezin was found effective against nymphs while acetamiprid, diafenthiuron and
imidacloprid were effective against the whitefly adults. Confidor (imidacloprid) and
Megamos (acetamaprid) caused significant mortality of whitefly at field recommended dose
as compared with other insecticides (Amjad et al., 2009). Acetamaprid and imidacloprid
gave the significant insecticidal performance against whitefly population than bio-control
agents (Abbas et al., 2012).
Neonicotinoids have low hydrophobicity and transport acropetally in the xylem due
to their excellent systemic and translaminar activities (Westwood et al., 1998). Imidacloprid
was used for indirectly controlling TYLCV in tomato. In three seasons, the mean incidence
of TYLCV was 42.7% in untreated plots as compared with 15.7% in treated plots. Higher
yields were recorded from treated plots and the yields decreased with decrease in the rate of
insecticide application (Ahmed et al., 2001). The effect of admire (imidacloprid 0.1%) on the
growth and yield of TYLCV infected tomato plants was significant as compared to cymbush
(cypermethrin 0.1%) (Aktar et al., 2008).
Although chemical control is easy, direct and rapid action to solve pest and disease
problems but continuous dependence on pesticides has contributed towards environmental
pollution and degradation (Singh and Bhat, 2003). Furthermore, chemical control is
expensive (Palumbo et al., 2001) and has become less effective due to the development of
resistance against insecticide in insects (Siebert et al., 2012). Bio-pesticides can solve the
problems of insecticidal resistance and environmental hazards (Abou-Yousef et al., 2010). In
current experiment, the extract of A. indica (neem) was very effective against the B. tabaci
population and TLCVD incidence after the synthetic insecticides (imidacloprid and
acetamiprid) followed by the extract of E. globules (Eucalyptus). The insecticidal activity of
neem extracts is due to the components that are capable of influencing the physiology and
behaviour of a wide range of insects (Schaaf et al., 2000). Azadirachtin interacts with the
corpus cardiacum, thus blocking the activity of the molting hormone and acts as an insect
growth regulator, suppresses fecundity, molting, pupation and adult emergence (Ascher,
1993). Plant derived oil reduced the whitefly population up to 75% (Butler et al., 1991).
Neem oil at 2% and neem seed water extract at 3% significantly reduced the population of
whitefly, jassids and thrips on cotton that may be the cause of the anti-feedant and deterrent
effect of neem which had forced the insects to leave the locality or chronic effect of the neem
compounds (Khattak et al., 2006). The eggs and nymphs of B. tabaci were managed by
aqueous and ethanolic extracts of Acalypha gaumeri, Annona squamosa, Carlowrightia
myriantha, Petiveria alliaceae, Trichilia arborea and A. indica (Cruz-Estrada et al., 2013).
Neem based pesticides azadirachtin, neema (liquid type) and neema-plus (pellet type) caused
significant reduction in the rates of female oviposition, subsequent egg hatch and adult
formation (Lynn et al., 2010). Melia seed kernel extract (MSKE) and neem seed kernel
extract (NSKE) at 5% concentration reduced 60.19 and 69.37% B. tabaci, respectively in
tomato crop (Senguttuvan et al., 2005). The aqueous extracts N. tobacum and E. globulus
caused 77.55 and 72.5% mortality of Lycoriella auripila larvae, respectively (Farsani et al.,
2011). Datura reduced the whitefly population significantly followed by neem oil, garlic and
eucalyptus in Bt cotton under field conditions (Khan et al., 2013).
As the TYLCV is transmitted by whitefly, the extracts of A. indica, A. sativum, P.
pinnata and S. macrophylla were used for their efficacy against TYLCVD incidence. Phyto-
pesticides significantly reduced the TYLCVD incidence and severity (Bhyan et al., 2007).
Neem seed kernel extracts and leaf extract of Pinus, Thuja, Araucaria, Cupressus and Cycas
proved effective in reducing the TLCV disease incidence, whitefly population and also in
increasing the yield (Ansari et al., 2007). Neem and eucalyptus extracts controlled the B.
tabaci as well as CLCuVD most effectively as compared to other plant extracts (Ali et al.,
2010). Eucalyptus extract manage the disturbed balance of production and scavenging of
active oxygen species under stress situations (Wan et al., 2012) by producing catalase (CAT),
peroxidase (POD) and superoxide dismutase (SOD) (Apel and Hirt, 2004).
Pathogenic attack destroys the physiology of the plants such as nutrient uptake,
assimilation, translocation from the root to shoot and utilization (Marschner, 1995). Nutrients
improve the plant health by regulating metabolic and cellular functions, which enable the
plant to tolerate the attack of sucking and chewing insects. The nutrients such as N, P, K, Zn
and B significantly reduced whitefly population in cotton (Gogi et al., 2012). Several nutrient
elements act as catalytically active cofactors in enzymes while others stabilize the proteins
structurally (Hansch and Mendel, 2009). Viral attack results in the production of reactive
oxygen species (ROS) and free radicals which leads to the inhibited plant growth and
development. Zinc protects the oxidation of cell components by reducing the production of
ROS and free radicals through interfering with membrane-bound NADPH oxidase (Cakmak,
2000). Virus replication protein was inhibited to bind with replication origin by using
artificial zinc finger protein (AZP). Arabidopsis plants treated with AZP found highly
resistant against virus infection (Sera, 2005).
Boron may affect the physiology and biochemistry of the plants by strengthening the
cell wall and membrane through binding of apoplastic proteins to cis-hydroxyl groups and by
interfering with enzymatic reactions (Blevins and Leukaszewski, 1998). Dordas (2008)
conducted experiments to describe the role of different nutrients, such as nitrogen (N),
phosphorus (P), potassium (K), manganese (Mn), zinc (Zn), boron (B), chlorine (Cl) and
silicon (Si) in disease management. Plants with high N supplies reduced the infection
severity caused by facultative parasites. K decreased the susceptibility of host plants. Mn was
found effective as it has vital role in photosynthesis, lignin and phenol biosynthesis. B
reduced the severity of many diseases because it affects the cell wall structure, cell
membrane permeability and metabolism of phenolics or lignin against the biotic stresses
(Brown et al., 2002). The soil application of boron reduced the mungbean yellow mosaic
virus disease severity (Bimal and Ali, 2001). Pramanik and Ali (2001) reported that the
application of boron significantly reduced the severities of yellow mosaic and leaf crinkle
viruses in mungbean.
Plant defense responses are regulated by a complex network of signal molecules and
growth regulators. Resistance genes identifies the pathogen and start defense responses.
Salicylic acid (SA), jasmonic acid (JA), naphthalene acetic acid (NAA) and ethylene (ET)
mediates both specific as well as basal defense responses (Jalali et al., 2006). SA at 3%
concentration found best in reducing egg hatchability, adult emergence, adult whitefly
population and CLCuVD severity both in soil drenching and foliar sprays (Khan et al.,
2003). SA induced resistance against cucumber mosaic virus (CMV) in tobacco by inhibiting
the virus accumulation in inoculated tissues and its systemic movement virus from cell to cell
via a signal transduction pathway (Mayers et al., 2005).
The resistant varieties/lines of tomato identified in the present screening can further
be exploited as resistant sources against TLCVD, in breeding programmes for development
of resistant commercial cultivars after determining their genetics or these lines can be
released directly as commercial cultivars if these were found to possess other desirable
horticultural characters. Development of predictive models for TLCVD incidence and B.
tabaci infestation would be helpful for the farmers regarding appropriate and timely
management. Disease predictive models help not only to decide about the curative and
preventive treatments but also the time and place of sowing. By evaluating the predictive
model it could be concluded that disease has set or just ready to set in and intervene the
management options accordingly. Nutrients enable the plants to withstand adverse conditions
by improving health and also help to increase yield and quality of the produce. Plant extracts
and salicylic acid could be used as eco-friendly approaches for the management of TLCVD
and B. tabaci.
CHAPTER 6 SUMMARY
The essence of the research endeavors was to evaluate the tomato germplasm for the
source of resistance against tomato leaf curl virus disease (TLCVD) a serious threat to
successful tomato production. This disease is transmitted by whitefly Bemisia tabaci
Gennadius. The varieties/lines grouped on a 0-5 scale in terms of resistance/susceptibility
constituted a valuable source of germplasm which may be employed in breeding for genetic
resistance against TLCVD. Twenty seven tomato varieties/lines were screened against
TLCVD and B. tabaci during the year 2012 and 2013. None of the screened
varieties/advanced lines was found to be highly resistant against TLCVD and varied greatly
in response to disease incidence. Eight varieties/lines (Naqeeb, Pakit, Nagina, Riogrande,
09080, Roma, 09091 and Nuyt-04-11) were found to be resistant against TLCVD. Ten
cultivars (Carmen, Roker, Lyp#1, 09079, Nuyt-25-11, 09088, Uovo Roseo, Nuyt-9-11, Po-02
and 10113 were categorized as moderately resistant and moderately susceptible, respectively.
Nine cultivars (Salma, 014276, Sitara-TS-101, 10125, 10127, Libnan Arif, BL-1-176-
Riostone-1-1, Big Beef and Caldera) were found to be highly susceptible and susceptible
against TLCVD incidence during two years 2012 and 2013.
Environmental conditions conducive for TLCVD development and B. tabaci
population density were determined. A positively significant (P<0.05) correlation was found
among maximum and minimum temperature but negatively significant correlation was
observed among relative humidity and B. tabaci population and TLCVD incidence in case of
all five genotypes i.e., Big Beef, Caldera, Sitara-TS-101, 014276 and Salma. Rainfall and
wind speed showed non-significant relationship with B. tabaci population and TLCVD
incidence on all five varieties/lines. In year wise correlation, a significant (P<0.05) and
positive correlation was observed between temperature (maximum and minimum) and B.
tabaci population and TLCVD incidence during two years (2012 and 2013) while in case of
relative humidity significantly negative correlation was found during two years 2012 and
2013. The maximum value of correlation coefficient between TLCVD and significant
environmental variables (maximum temperature, minimum temperature and relative
humidity) were observed as (r=0.85*) (r=0.92*) and (r=0.87*) respectively. The maximum
value of correlation coefficient between B. tabaci population and significant environmental
variables (maximum temperature, minimum temperature and relative humidity) were
observed as (r=0.91*) (r=0.85*) and (r=0.85*) respectively.
A disease and vector predictive model based on 2 years epidemiological factors was
developed. TLCVD predictive model based on two years (2012 and 2013) data was
developed. Y= 0.532+ 0.053X1 + 0.97X2-0.081X3+0.15X4 R2= 0.85 where y = TLCVD, x1=
Maximum temperature, x2 = Minimum temperature, x3 = Relative humidity and x4 = rainfall.
Similarly, whitefly predictive model based on two years (2012 and 2013) data were
developed on same lines as was done for TLCVD. Y= -7.76+0.231X1+0.21X2-
0.092X3+0.11X4+0.086X5 R2= 0.92 where y = Whitefly, x1= Maximum temperature, x2 =
Minimum temperature, x3 = Relative humidity, x4 = Rainfall and x5 = Wind speed.
Disease predictive model explained 85% variability in TLCVD during two years
(2012 and 2013) while vector predictive models explained 92% variability in B. tabaci
population. The two models were found non-significant indicating close association with one
another. The major factors responsible for the attack of TLCVD were temperature and the
extent/intensity of whitefly prevalent at that time. The major factors identified for the attack
of whitefly were temperature and the relative humidity prevalent at that time.
Different pesticides/biopesticides were evaluated for management of insect vector
Bemisia tabaci and the disease. All the six treatments reduced B. tabaci population and
TLCVD incidence significantly compared to untreated control. Imidacloprid was the most
effective to manage the B. tabaci population. Acetamiprid was at number second and
Azadirachta indica (Neem) was at number third whereas Salicylic acid, Classic (Zn and
Boron) solution and Eucalyptus globules (Eucalyptus) were at number four, fifth and sixth
respectively in managing the B. tabaci population and TLCVD incidence.
CONCLUSIONS
I. Disease predictive model for TLCVD incidence based on five environmental
variables i.e. maximum and minimum temperatures, relative humidity, rainfall and
wind speed explained 85 percent variability in disease development.
II. Predictive model for (whitefly) Bemisia tabaci population based on environmental
variables i.e. maximum and minimum temperatures, relative humidity, rainfall and
wind speed explained 92 percent variability in vector population development.
III. Maximum (35-44°C) and minimum temperatures (25-37°C), relative humidity (17-
51%), were found critical environmental ranges for TLCVD and B. tabaci
population during 2012 and 2010.
IV. Among five tomato varieties, Pakit and Naqeeb showed the most resistant
reaction against the disease.
V. Maximum and minimum temperature and relative humidity played most
significant role in the development of TLCVD and B. tabaci population during
two years.
VI. Correlation of environmental conditions with TLCVD and B. tabaci population
was found significant during two years.
VII. Models on five tomato varieties Salma, 014276, Sitara-TS-101, Caldera and Big
Beef respectively, were in close conformity with observed values of TLCVD
incidence during two years models.
VIII. All the treatments were significantly effective in reducing TLCVD incidence and
B. tabaci population compared to untreated control but Imidacloprid and
Acetamiprid were the most effective treatments in controlling TLCVD and B.
tabaci population.
RECOMMENDATIONS
I. Continuous monitoring of egg, pseudo pupae and adult of vector (B. tabaci) would
be necessary for precise TLCVD prediction.
II. Environmental factors especially temperature and relative humidity would be used
in the development of a TLCV disease predictive model in future.
III. There must be installation of weather stations at major tomato growing areas of
Pakistan particularly in province Punjab, so that, environmental data may be made
available for establishing a future forecasting system.
IV. Local area environmental factors should be used for the development of a disease
predictive model to a specific area.
V. Many local area models should be integrated for the development of a Decision
Support System (DSS) at country level for the appropriate management of TLCVD.
VI. Spatio-temporal patterns of disease progress should be studied so that the
management options could be intervened accordingly.
VII. For the management of TLCVD, the use of nutrients (Zn and Boron solution),
salicylic acid and plant extracts would be helpful and environment friendly.
VIII. Treatments (preventive or curative) should be applied by taking into account the
current status of the disease.
IX. Prepare a management plan by integrating the environmental conditions.
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