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A DISEASE PREDICTIVE MODEL FOR THE MANAGEMENT OF
CHICKPEA BLIGHT (Ascochyta rabiei) (Pass.) Lab
By
Salman Ahmad
M.Sc. (Hons) Agri.
Regd. No. 2000-ag-1176
A thesis 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 thesis entitled “A disease predictive model for the
management of chickpea blight (Ascochyta rabiei) (Pass.) Lab” are product of my own
research and no part has been copied from any published source (except the references,
standard mathematical or biochemical models/rating scales/equations/ formulae/protocol
etc.). I further declare that this work has not been submitted for award of any other
diploma/ degree. The university may take action if the information provided is found
inaccurate at any stage. In case of any default the scholar will be proceeded against as per
HEC plagiarism policy.
Salman Ahmad
2000-ag-1176
To
The Controller of Examinations,
University of Agriculture,
Faisalabad.
We, the Supervisory Committee, certify that the contents and form of thesis
submitted by Mr. Salman Ahmad, Regd. No. 2000-ag-1176, have been found
satisfactory and recommend that it be processed for evaluation by the External
Examiner(s) for the award of the degree.
SUPERVISORY COMMITTEE
1. Chairman: ________________________________
Prof. Dr. Muhammad Aslam Khan
2. Member: ________________________________
Prof. Dr. Shahbaz Talib Sahi
3. Member: ________________________________
Prof. Dr. Riaz Ahmad
DEDICATED
To
My mother who could not see the reward of her prayers!
i
ACKNOWLEDGEMENTS
All supplications and praises are for The Almighty ALLAH, the only creator of
this universe. ALLAH, who teaches us how to read and write. I am obliged with adoration
for the mercy shown by The Almighty ALLAH by giving me health, vigour and
intelligence to accomplish this manuscript. With deep reverence and solemnity, I offer
indefinite salaams (Darood-e-paak) upon the Holy Prophet Hazrat Muhammad Mustafa
(Sallallah-o-Allah-e-Wasallum), the merciful Prophet to all mankind. I accept and
acknowledge with extreme submissiveness that Almighty ALLAH rendered me this
opportunity of writing this manuscript being the follower of The Holy Prophet
Muhammad (PBUH).
I am very thankful to Prof. Dr. Mohammad Aslam Khan, dissertation supervisor,
for his kind attention and guidance during course of research and write up. I also
appreciate his valuable suggestions and comments which made this manuscript improved
and quality one. He always encouraged and supported me where I felt any problem
(academically and logistically) during my stay at the Department of Plant Pathology,
University of Agriculture, Faisalabad (U.A.F).
Dr. Shahbaz Talib Sahi, Professor and Chairman, Department of Plant Pathology
has been kind during my stay at U.A.F. Being the member of supervisory committee, he
always assisted me in every difficulty which I faced during my research. I will never
forget his numerous kindheartedness over me during ten years liaison at U.A.F. I am
appreciative to Dr. Nazir Javed, Professor and ex-chairman, Department of Plant
Pathology for his abundant kindnesses over me in academics and career building. I am
also thankful to Dr. Riaz Ahmad, Professor, Department of Agronomy, U.A.F, for his
valued assistance in research planning and dissertation.
This research would never complete if the continuous logistic assistance never
remained with me from Pulses section, AARI, Faisalabad. They have not only provided
me the data of chickpea blight disease severity of five years (2006-10) on which the five
year model was developed but also made available all the chickpea germplasm used in
this study. I am obliged, and really appreciate their this kindness and co-operation.
Special thanks to Dr. Irfan Ahmad and Asif Ali Chohan, Assistant Professor and Lecturer,
respectively, Department of Forestry, Range and Wildlife management, U.A.F, for their
continuous encouragement. I also appreciate the collaboration of Dr. Safdar Ali, Lecturer,
Plant Pathology, Department of Plant Pathology, U.A.F in data analysis.
Best wishes for my dear friends, Dr. H.M Aatif and Mohammad Raheel Shafique
for their motivation and inspiration during write up. At the end, I acknowledge and pay
special thanks to Higher Education Commission of Pakistan (HEC) for financial
assistance throughout my Ph.D. studies.
Salman Ahmad
April 25, 2015
ii
TABLE OF CONTENTS
No. Contents Page
No.
DEDICATION
ACKNOWLEDGEMENTS I
TABLE OF CONTENTS II
LIST OF TABLES IV
LIST OF FIGURES VI LIST OF SYMBOLS AND ABBREVIATIONS VII
ABSTRACT VIII
1 INTRODUCTION 1
2 REVIEW OF LITERATURE 5
2.1 History and economic importance of chickpea blight 5
2.2 Symptoms of the disease 6
2.3 Chickpea blight taxonomy, biology and host range 7
2.3.1 Taxonomy 7
2.3.2 Biology 7
2.3.3 Host range 8
2.4 Pathogenic variability 9
2.5 Epidemiology of A. rabiei 10
2.5.1 Disease cycle 10
2.5.2 Disease spread 11
2.5.3 Anamorph 11
2.5.4 Teleomorph 12
2.5.5 Environmental conditions 13
2.5.6 Disease forecasting models 14
2.6 Chickpea breeding for blight resistance 17
2.7 Management of chickpea blight 18
2.7.1 Foliar applications of fungicides 18
2.7.2 Evaluation of fungicides 19
2.7.3 Management through plant extracts 20
2.7.4 Management through antagonists 22
3 MATERIALS AND METHODS 24
3.1 Collection of diseased samples 24
3.1.1 Preparation of culture medium 24
3.1.2 Isolation and purification of A. rabiei 25
3.1.3 Preparation of mass culture 25
3.2 Plant materials 25
3.3 Environmental data 26
3.4 Development of disease predictive model based on five (2006-10) years data 26
3.4.1 Data collection of chickpea blight disease severity 26
3.4.2 Analysis of data 26
3.4.3 Evaluation of Model 27
3.4.4 Validation of model 27
3.5 Identification of resistant, tolerant and susceptible sources under field
conditions
28
3.6 Area under disease progress curve (AUDPC) 29
iii
3.7 Management of chickpea blight disease 30
3.7.1 In vitro evaluation of fungicides and plant extracts against A. rabiei 30
3.7.2 In vitro evaluation of fungal antagonists against A. rabiei 31
3.7.3 In vivo evaluation of treatments against A. rabiei 34
4 RESULTS AND DISCUSSION 36
4.1 Analysis of variance of five years (2006-10) environmental parameters and
chickpea blight disease severity
36
4.2 Analysis of variance of two years (2011-12) environmental parameters and
chickpea blight disease severity
37
4.3 Comparison of environmental parameters and chickpea blight disease severity
during 2006-10
38
4.4 Comparison of environmental parameters and chickpea blight disease severity
during 2011-12
39
4.5 Relationship of environmental parameters with chickpea blight disease
severity during five years (2006-10)
39
4.6 Relationship of environmental parameters with chickpea blight disease
severity on different genotypes/advanced lines during two years (2011-12)
40
4.7 Chickpea blight disease predictive model based on five years data (2006-10) 45
4.8 Model assessment 45
4.8.1 Comparison of the dependent variable and regression coefficients with
physical theory
45
4.8.2 Model evaluation by comparing observed and predicted data 47
4.9 Chickpea blight disease predictive model based on two years data (2011-12) 50
4.10 Model assessment 50
4.10.1 Comparison of the dependent variable and regression coefficients with
physical theory
50
4.10.2 Model evaluation by comparing observed and predicted data 51
4.11 Chickpea blight disease predictive model validation 53
4.12 Characterization of environmental parameters conducive for chickpea blight
disease severity during 2006-10
53
4.13 Characterization of environmental parameters conducive for chickpea blight
disease severity during 2011-12
60
4.14 Identification of resistant sources in chickpea against chickpea blight disease 63
4.15 Management 72
4.15.1 In vitro evaluation of fungicides against A. rabiei 72
4.15.2 In vitro evaluation of plant extracts against A. rabiei 75
4.15.3 In vitro evaluation of antagonists against A. rabiei 78
4.16 Evaluation of fungicides, plant extracts and antagonist against chickpea blight
under field conditions during two years
82
DISCUSSION 87
5 SUMMARY 98
CONCLUSIONS 101
RECOMMENDATIONS 102
LITERATURE CITED 103
APPENDIX-I 127
iv
LIST OF TABLES
Table
No.
Title Page
No.
3.1 Fungicides used against A. rabiei management 31
3.2 Plant materials used against A. rabiei management 31
4.1 Analysis of variance of chickpea blight disease severity during 2006-10 36
4.2 Mean chickpea blight disease severity on four genotypes during five years (2006-10) 37
4.3 Analysis of variance of chickpea blight disease severity during 2011-12 38
4.4 Mean chickpea blight disease severity on four genotypes during two years (2011-12) 38
4.5 Comparison of environmental parameters and chickpea blight disease severity during
2006-10
39
4.6 Comparison of environmental parameters and chickpea blight disease severity during
2011-12
39
4.7 Correlation of environmental parameters with chickpea blight disease severity during
(2006-10)
40
4.8 Overall correlation of environmental parameters with chickpea blight disease severity
during 2011-12
42
4.9 Correlation of environmental parameters with chickpea blight disease severity on
different genotypes/advanced lines during 2011
43
4.10 Correlation of environmental parameters with chickpea blight disease severity on
different genotypes/advanced lines during 2012
44
4.11 Summary of stepwise regression models to predict chickpea blight disease during period
of five years (2006-10)
45
4.12 Regression statistics of chickpea blight disease predictive model during (2006-10) 46
4.13 Analysis of variance of chickpea blight disease predictive model based on five
years (2006-10) data
46
4.14 Coefficients of variables, their standard error, t Stat, P-value and their significance 46
4.15 Observed and predicted chickpea blight disease severity (%) on genotypes (K-97006)
and (K-97007) during five years (2006-10)
48
4.16 Observed and predicted chickpea blight disease severity (%) on genotypes (K-95058)
and (D-91224) during five years (2006-10)
49
4.17 Summary of stepwise regression models to predict chickpea blight disease during period
of two years (2011-12)
50
4.18 Regression statistics of chickpea blight disease predictive model during (2011-12) 51
4.19 Analysis of variance of chickpea blight disease predictive model based on two years
(2011-12) data
51
4.20 Coefficients of variables, their standard error, t Stat, P-value and their significance 51
4.21 Observed and predicted chickpea blight disease severity (%) on genotypes (K-97006),
(K-97007), (K-95058) and (D-91224) during two years (2011-12) 52
4.22 Comparison of the two models for validation of chickpea blight disease severity 53
4.23 Response of genotypes to chickpea blight disease during 2011 65
4.24 Response of genotypes to chickpea blight disease during 2012 67
4.25 Analysis of variance for the effect of different concentrations of fungicides on colony
growth inhibition and colony diameter of A. rabiei
73
4.26 Effect of fungicides and their concentrations on colony growth inhibition and colony
diameter of A. rabiei
74
4.27 Analysis of variance for the effect of different concentrations of plant extracts on colony
growth inhibition and colony diameter of A. rabiei
76
v
4.28 Effect of plant extracts and their concentrations on colony growth inhibition and colony
diameter of A. rabiei
77
4.29 Analysis of variance for the effect of different concentrations of antagonists on colony
growth inhibition and colony diameter of A. rabiei
79
4.30 Effect of plant antagonists and their concentrations on colony growth inhibition and
colony diameter of A. rabiei
80
4.31 ANOVA for the evaluation of fungicides, plant extracts and antagonist to control
chickpea blight disease under field conditions
84
4.32 Evaluation of fungicides, plant extracts and antagonist to control chickpea blight disease
under field conditions
84
4.33 Interaction of treatments, years and sprays for the evaluation of fungicides, plant extracts
and antagonist to control chickpea blight disease under field conditions 85
4.34 Interaction of treatments, varieties and sprays for the evaluation of fungicides, plant
extracts and antagonist to control chickpea blight disease under field conditions
86
vi
LIST OF FIGURES
No. List of Figurs
Page
No.
3.1 In vitro evaluation of treatments against A. rabiei 33
3.2 Preparation of mass culture of A. rabiei and screening of chickpea
germplasm under field conditions
35
4.1 Relationship of maximum temperature with chickpea blight disease severity
during 2006-10
55
4.2 Relationship of minimum temperature with chickpea blight disease severity
during 2006-10
56
4.3 Relationship of relative humidity with chickpea blight disease severity
during 2006-10
57
4.4 Relationship of rainfall with chickpea blight disease severity during 2006-10 58
4.5 Relationship of wind speed with chickpea blight disease severity during
2006-10
59
4.6 Relationship of maximum temperature with chickpea blight disease severity
recorded on K-97006(V1), K-97007(V2), K-95058(V3) and D-91224(V4)
during years 2011-12
61
4.7 Relationship of minimum temperature with chickpea blight disease severity
recorded on K-97006(V1), K-97007(V2), K-95058(V3) and D-91224(V4)
during years 2011-12
61
4.8 Relationship of relative humidity with chickpea blight disease severity
recorded on K-97006(V1), K-97007(V2), K-95058(V3) and D-91224(V4)
during years 2011-12
62
4.9 Relationship of rainfall with chickpea blight disease severity recorded on
K-97006(V1), K-97007(V2), K-95058(V3) and D-91224(V4) during years
2011-12
62
4.10 Relationship of wind speed with chickpea blight disease severity recorded
on K-97006(V1), K-97007(V2), K-95058(V3) and D-91224(V4) during
years 2011-12
63
4.11 Symptoms of chickpea blight disease exhibiting necrotic lesions on leaves
(a) and pods (b)
69
4.12 Symptoms of chickpea blight disease exhibiting necrotic lesions on leaves
(a) and girdling effect of stem lesions (b)
70
4.13 A view of disease screening nursery before and after inoculation on
genotype K-94002
71
4.14 Inhibition of colony growth of A. rabiei by two systemic fungicides
using poisoned food technique
81
vii
LIST OF SYMBOLS AND ABBREVIATIONS
Sr. No Description Symbols or
Abbreviations
1 International Crops Research Institute for the Semi-Arid
Tropics
ICRISAT
2 Food and Agriculture Organization FAO
3 Government of Pakistan GOP
4 Millimetre mm
5 Ascochyta blight AB
6 Centigrade °C
7 Australian Bureau of Agriculture and Resource Economics ABARE
8 Centimetre cm
9 Random Amplified Polymorphic DNA RAPD
10 International Centre for Agricultural Research in the Dry
Areas
ICARDA
11 Ascochyta rabiei A. rabiei
12 Didymella rabiei D. rabiei
13 Micrometre µm
14 Relative humidity RH
15 Ayub Agricultural Research Institute ARRI
16 Chickpea seed meal agar medium CSMA
17 Gram g
18 Pound lbs
19 Per Square Inch psi
20 Randomized Complete Block Design RCBD
21 Completely Randomized Design CRD
22 Area under disease progress curve AUDPC
23 Least Significant Difference LSD
24 Root Mean Square Error RMSE
25 Mean Square Error MSE
26 Milligram mg
27 Potato Dextrose Medium PDA
28 Disease Severity Index DSI
29 Coefficient of Determination R2
30 Correlation Coefficient r
31 Observed Obs
32 Predicted Pre
33 Analysis of Variance ANOVA
viii
ABSTRACT
Chickpea blight caused by Ascochyta rabiei (Pass.) Lab. (teleomorph: Didymella rabiei)
(Kovachevski) v. Arx, is the most devastating disease of gram crop in the world. Disease
can induce 100% yield losses under epidemic condition. In Pakistan, chickpea blight is
causing heavy yield losses annually. Due to lack of resistance in indigenous chickpea
germplasm, disease is controlled through fungicides by the growers of Pakistan. Excessive
use of fungicides causes resistance in the pathogen and creates fatalistic effect on the
environment. Chickpea blight disease predictive model under such situation may be
effective tool to predict early onset of disease. In this way excessive use of fungicides may
be avoided. Plant extracts and antagonists may also provide a replacement of chemical
control of chickpea blight. The objective of this study was to develop a disease predictive
model based upon environmental variables i.e. maximum and minimum temperatures,
relative humidity, rainfall and wind speed to predict chickpea blight and devise an eco-
friendly management strategy for this disease. Correlation and regression analysis was
performed to determine the relationship of environmental variables with disease severity.
Significant correlation was found between all environmental variables and disease
severity. Maximum temperature showed negative correlation, while minimum
temperature, relative humidity, rainfall and wind speed exhibited positive correlation with
disease severity. Environmental factors and disease severity data of five years (2006-10)
was used to develop a disease predictive model using stepwise regression analysis.
Maximum and minimum temperatures, relative humidity, rainfall and wind speed
significantly contributed in disease development and explained 72% variability in disease
severity. This model, based on five years data, was then validated with two years (2011-
12) environmental and disease severity data. In two years model all environmental
parameters explained 82% variability in blight severity. Both models i.e. based upon five
and two years data validated each other on the basis of homogeneity of regression lines.
Blight severity was high at maximum (20-24°C) and minimum (12-14°C) temperatures,
relative humidity (65-70%), rainfall (5-6 mm) and wind speed (5-6.5 km/h), respectively.
Chickpea germplasm comprised of 48 genotypes was screened against chickpea blight
during years 2011-12. Advanced lines exhibited resistant and moderately resistant
response were viz; K-60013, K-98008, K-96001, K-96022, D-97092, D-91055, D-90272,
D-96050, D-Pb2008 and D-Pu502-362, and K-96033, K-89169, K-90395, D-91017, D-
89044, D-05006, D-96018, D-86030, D-96032, D-03009 and D-1CC-5127, respectively.
For the management of disease, five fungicides, five plant extracts and two bio-control
agents were evaluated under in vitro conditions. The concentrations of fungicides were
0.05%, 0.10% and 0.15%, respectively while concentrations of plant extracts were 3%,
5% and 8%, respectively. Spore suspension of bio-controls was kept as; 105, 10
6, and 10
7
conidia/mL, respectively. Means of treatments were compared using least significant
difference (LSD) test. Two fungicides i.e. Alliete and ThiovetJet @ 0.15%, two plants
extracts i.e. Melia azedarach and Azadirachta indica @ 8% and bio-control agent
(Trichoderma harzianum at 107 conidia/mL) proved significantly effective under in vitro
conditions. These most effective treatments were then applied under in vivo to check their
efficacy for two years. Significant disease severity was reduced by fungicides i.e. Alliete
(17%) and Thiovetjet (23%) followed by plant extracts, in which M. azedarach and A.
indica reduced disease severity to 50% and 56%, respectively compared to control (75%).
T. harzianum proved third good against chickpea blight disease in field after fungicides
and plant extracts.
1
CHAPTER 1 INTRODUCTION
Chickpea (Cicer arietinum L.) is an annual self-pollinated, diploid pulse crop
having 16 chromosomes with genome of 740 Mb (Arumuganathan and Earle, 1991).
Chickpea was first cultivated in South-Eastern Anatolia (Turkey) (Ladizinsky, 1975), then
brought for cultivation to the Mediterranean, Asian, Northern African and Middle East
countries. During that time, chickpea cultivation also spread to entire temperate regions of
the globe and to some new countries, those countries included; Mexico, Canada and
Australia (Duke, 1981). The height of chickpea plant is generally 0.2-1m with excessive
nodules on roots. The life cycle of chickpea crop is three to six months and usually gets
mature after one month of flowering. Chickpea is highly nutritious having protein (20-
30%), carbohydrates (40%) and less amount of oil which is only 6% (Gil et al., 1996).
Chickpeas are vital source of protein for developing nations and thus mostly used for
human consumption. Seeds of chickpeas are used in different ways either eaten as whole,
as flour, or the young plants consumed as vegetables. In some parts of the world,
chickpea is also cultivated for livestock feed. On the basis of diversity, chickpeas are
divided into two groups i.e. Kabuli and Desi. Kabuli are large in size, creamy in colour
and ram-head in shape, while, Desi are angular, smaller sized and dark in colour (Van der
Maeson, 1972; Cubrero, 1987). Desi chickpeas cover approximately 85% of the total
world cultivation and are mostly grown in the South Asia, Iran, Mexico and Ethiopia,
while Kabuli are grown in the Latin American and Mediterranean regions (ICRISAT,
2002). The chickpea crop needs minimum amount of fertilizers, less number of irrigations
and pesticides (Malik, 1993). In subcontinent, especially in cool season, chickpeas are
sown after the harvest of cereal crops but under temperate regions like the Mediterranean
countries, grams are grown as a winter annual (Langer and Hill, 1982). The best soil for
chickpea cultivation is light loam and well-drained clays. Chickpeas sustain the soil
fertility by fixing atmospheric nitrogen thus are routinely cultivated in rotation with
different agronomically important crops. In Pakistan, chickpea is often cultivated as a
post moon-soon rotation crop with wheat. This assists in maintaining the soil fertility by
enhancing nitrogen availability, and increases water use efficiency (Iqtidar and
Amanullah, 2002).
Chickpea is the third most vital crop (legume) in the world after peas (Pisum
sativum L.) and dry beans (Phaseolus vulgaris L.) (Pande et al., 2005). Chickpea is
2
cultivated worldwide on 13.5 million hectares with production of 13.1 million tonnes.
Pakistan ranks third in the world in chickpea production (FAO, 2013). Chickpea is the
largest Rabi crop in Pakistan, mostly cultivated in Barani areas on an area of 975 thousand
hectares with yield of 475 thousand tonnes (GOP, 2014).
Chickpeas are grown in semi-arid and sub-tropical zones of Pakistan (Iqtidar and
Amanullah, 2002). Major chickpea culativation i.e. 90% is under rainfed areas of Punjab
Province, and the districts where this crop is cultivated include; Mianwali, Khushab,
Bhakkar, Chakwal, Faisalabad, Jhang and Layyah (Khan et al., 1991c). Environment of
these districts is highly favourable for chickpea crop, but optimum yield is not being
obtained in these areas due to a fungal disease i.e. chickpea blight (Ali et al., 2011).
Chickpea blight or ascochyta blight (AB) caused by Ascochyta rabiei (Pass.) Lab.
(teleomorph: Didymella rabiei) (Kovachevski) v. Arx, is the most important limiting
factor in gram production around the globe (Haqqani et al., 2000). Ascochyta blight
disease in severe form may result 100% yield losses (Reddy and Singh, 1990a; Pande et
al., 2005). In Pakistan, this disease is a major threat to chickpea production (Syed et al.,
2009). Fungus overwinters in the form of pycnidia in crop disease debris left in field, on
seeds and in soil. Survival of A. rabiei depends highly on rainfall and low temperatures
(Ram and Mahender, 1993). In Pakistan, hot weather and heavy rainfall in summer
decreases the survival duration of chickpea blight fungus (Kausar, 1965).
Pakistan had faced severe chickpea blight epidemics during the period of 1979-80
that resulted in heavy yield losses (Nene, 1982; Bashir and Ilyas, 1986). Factors including;
susceptible cultivars and high inoculum pressure at early stages of the plant coupled with
suitable climatic parameters, favour chickpea blight epiphytotics. Ascochyta blight can
ruin whole crop in those areas where temperature of 15-25°C and rainfall of more than
150 mm occur, and cool and cloudy weather mostly prevails during the growing season
(Nene, 1982).
Cultivation of disease resistant varieties of chickpea is an effective way to avoid
ascochyta blight (Ilyas et al., 2007). But because of the emergence of new
pathotypes/races, available cultivars are becoming susceptible rapidly in Pakistan. Eight to
twelve isolates (I-68, I-70, I-71, I-72, I-74, KN-1, KN-30, C-50, C-51, A-32, P-16, and M-12)
having different races have been reported in Pakistan (Jamil et al., 1995, 2010). Resistance
3
has been developed in advanced lines against these pathotypes, but cultivars become
susceptible after few years because of appearance of new pathotype. At present in
Pakistan, very few cultivars are showing resistance to chickpea blight, while most of the
germplasm is susceptible (Shah et al., 2005; Ilyas et al., 2007; Ghazanfar et al., 2010).
Under these circumstances, farmers use excessive fungicides (3-5 foliar sprays) to control
chickpea blight. Foliar chemicals are helping farmers to encounter blight disease, but are
also producing resistance in the pathogen and causing deleterious effects on the
environment (Rial-Otero et al., 2005). As public awareness is increasing about the lethal
effects of these chemicals, therefore, there is need to replace these fungicides with
environment friendly compounds. Plant extracts and antagonists may help in this regard
(Hashim and Devi, 2003; Benzohra et al., 2011). Though plant extracts and antagonists
are not available in bulk quantity, yet their use may play considerable role in cutting down
the use of fungicides. This will help in developing a sustainable management for chickpea
blight to avert future epiphytotics.
To rationalize the use of any protective measure (fungicides, extracts or
antagonists) against chickpea blight complete knowledge of epidemiology of this disease
is required. Study of relationship of environmental conditions with chickpea blight gives
a better understanding of factors that influence the development of an epiphytotic (Kimber
et al., 2007). A disease predictive model may reduce uncertainty about the management
decisions by providing a quantitative description of disease pressure. Forecasting model
gives an early prediction that may help to control ascochyta blight without or with
minimum number of fungicidal sprays (Shtienberg, 2010). Jhorar et al. (1997) developed
a disease predictive model based on 15 years data and found that environmental factors
(temperature and relative humidity) played significant role in disease severity. Similarly,
Trapero-Casas and Kaiser (1992) found that wetness and temperature were the key
climatic factors in augmenting the disease severity. In Pakistan, criteria were set for
chickpea blight epidemics in 1965 based upon annual rainfall occurrence (Kausar, 1965).
According to those criteria disease appears in epidemic form when rainfall is more than 6
inches during crop season. Later on, Khan et al. (1999) characterized the meteorological
conditions favourable for chickpea blight disease. They found that maximum (25-31°C)
and minimum (16-22°C) temperatures and relative humidity (62-70%) were conducive.
4
Still studies regarding multiple regression models based on environmental conditions to
predict chickpea blight have not been conducted in Pakistan.
Cultivation of resistant varieties is the cheapest source to manage chickpea blight, but
lack of resistant source, and for rationale use of control measures, study of environmental
conditions with ascochyta blight is vital. Thus, the hypothesis of this study was developed
that “on the basis of knowledge about the environmental conditions expected severity of
blight on chickpea crop could be predicted for timely management of chickpea blight
through fungicides, plant extracts and antagonists”. Screening of chickpea advanced lines
were also performed to look for resistant source as it is economical and hazardous free.
Further, as resistant cultivars have less disease therefore would be helpful to avoid or
lessen fungicidal usage. Hence, a three dimensional study was designed with the
following objectives;
I. To develop a chickpea blight disease predictive model based on environmental
conditions of five crop seasons and to validate it on two crop seasons.
II. To identify resistant source by screening of chickpea germplasm; to explore
tolerant source for its exploitation through chemotherapy.
III. To evaluate fungicides, botanical extracts and antagonists against chickpea blight
under in vitro and in vivo conditions.
The model developed during this study would give an advance prediction of chickpea
blight which would help in timing spray schedule. Current research would also facilitate
devising an eco-friendly management strategy with less use of chemicals. The line of
work adopted during this study was;
I. Development of a chickpea blight disease predictive model based on 5 years
environmental variables and its validation based on 2 years environmental
variables for the prediction of chickpea blight.
II. Characterization of epidemiological conditions favourable for chickpea blight
disease severity.
III. Screening of chickpea germplasm collected from Ayub Agricultural Research
Institute (ARRI), Faisalabad, through artificial inoculation under field conditions.
IV. In vitro and in vivo evaluation of fungicides, plant extracts and antagonists for the
management of A. rabiei.
5
CHAPTER 2 REVIEW OF LITERATURE
Reviews written by the researchers, Vail (2005), Coram (2006) and Ghazanfar (2010)
remained very good source of information during the write up of this review of literature.
2.1 History and economic importance of chickpea blight
Chickpea blight has been known for its wreck since ancient times. The seriousness of
the disease was initially observed in Indian Punjab in 1911 (Butler, 1918). The disease
had been appearing frequently in Barani areas of Pakistan (Kausar, 1965). Investigations
on gram blight were taken up in 1926 and continued up to 1952 in which different aspects
of fungus like life history, transmission, epidemiology, seed treatment and toxins
produced by ascochyta blight were studied by different researchers (Luthra and Bedi,
1932; Sattar, 1933; Luthra et al., 1935; Sattar and Hafiz; 1951; Nene, 1982). Currently,
the disease is present in almost all chickpea growing countries of the globe and is reaching
in new regions of the world where it was not previously reported (CAB, 2000; Kaiser et
al., 2000; Pande et al., 2005). Blight is the most important limiting factor to chickpea
production in areas lying between 31° and 45° north, and is appearing sporadically
between 26° and 30° N (Singh and Sharma, 1998; Akem, 1999).
Australia was among the top producers of chickpea before the introduction of AB, but
later on, owing to the outbreaks of chickpea blight epidemics, decline was observed in
chickpea yield (Ackland et al., 1998; Knights and Siddique, 2002). It is potentially
damaging 95% area of chickpea in different parts of Australia (Knights and Sidddique,
2002). Incidence of AB in Western Canada declined chickpea yield more than 70%
(Pande et al., 2005). Nene (1980, 82) and Kaiser et al., (1998) reported ascochyta blight
in France, Greece, India, Iran, Iraq, Israel, Italy, Jordan, Lebanon, Mexico, Morocco,
Pakistan, Romania, Spain, Syria, Tanzania, Tunisia, Algeria, America, Australia,
Bangladesh, Bulgaria, Turkey Russia, Canada, Cyprus and Ethiopia. However, disease is
much commonly experienced in Iraq, Israel, Jordan, Lebanon, Morocco, Algeria,
Bulgaria, Cyprus, Greece, Russia, Spain, Romania, Tunisia, Turkey, Syria and Pakistan.
The disease causes heavy losses i.e. up to 100% under cool and moistened
environment (Pande et al., 2005). Kovachevski (1936) investigated and reported 20 to
50% losses due to chickpea blight in Bulgaria annually. However, whenever this disease
prevailed epiphytotically in Bulgaria caused complete loss of the crop. In 1929, the
6
disease appeared in destructive form in Morocco (Labrousse, 1930). During 1956, in
Dnepropetrovsk USSR, now Russia, blight was in severe form and caused 100% loss
(Nemlienko and Lukashevich, 1957), while 10–20% damage occurred during late fifties in
Greece (Demetriades et al., 1959). Wherever chickpea was grown in Spain, AB caused
heavy yield losses (Puerta, 1964). In Australia, ascochyta blight appeared during 1973
when contaminated seed were used at research stations. During 1997, reduced level of
infection was reported and the crop faced 40% less losses, while in 1998, blight epidemic
erupted in South Australia and some parts of Victoria and resulted heavy losses in those
states (Kaiser, 1997; Khan et al., 1999a; ABARE, 2006). In Western Canada, chickpea is
the main crop and always threatened by ascochyta blight. When conditions get favourable
then even the most resistant varieties become susceptible and experience damage >70%
(Saskatchewan Agriculture and Food Agriculture Knowledge Centre, 2007). In Indo-Pak
subcontinent, research on chickpea blight was started in 1922 and 25 to 50% annual losses
were reported by this disease (Sattar, 1933). During 1979-1980, ascochyta blight evolved
in Pakistan as an epidemic and brought severe threats to chickpea production in Pakistan.
Different researchers who worked on this particular problem in Pakistan during those
years reported up to 70% losses in chickpea yield (Bashir and Ilyas, 1986; Nene, 1982).
2.2 Symptoms of the disease
A number of pathologists have explained remarkably similar symptoms of ascochyta
blight since it is present in different parts of the world (Shahid et al., 2008). All above
ground parts of the plant including leaflets, petioles and young branches are attacked by
chickpea blight. Round or elongated lesions appear on leaflets. The lesions comprise
unevenly sunken brown dots with brownish red margins which surround them. Circular
lesions with dark margins emerge on the green pods; the lesions have typical pycnidia
with concentric rings. Infected seeds usually carry lesions. Lesions remain brown,
elongated (3–4 cm) on the stem and petiole, having black dots which often result in
girdling of infected portion. The portion above the point of attack rapidly dies when
lesions girdle the stem. If the pathogen attacks on collar region of main stem, the entire
plant girdles down and dies. As the severity of the disease increases, diseased plants
patches become quite prominent in the field which slowly advance and the whole field
shows blight symptoms (Singh and Shrama, 1998; Akem, 1999).
7
2.3 Chickpea blight taxonomy, biology and host range
2.3.1 Taxonomy
Chickpea blight is caused by Ascochyta rabiei (Pass) Labrousse (teleomorph:
Didymella rabiei (Kovachevski) v. Arx. Syn. Mycosphaerella rabiei (Kovachevski).
Firstly in 1918, Butler illustrated the fungus as Ascochyta rabiei in Pakistan. The causal
organism was attributed as Phyllosticta rabiei by a number of researchers (Ciferri, 1927;
Sprague, 1930; Luthra et al., 1935). Later on, similar fungus was isolated from gram
plants, and it was suggested that the fungus was in fact Ascochyta rabiei (Pass.) Lab.
(Labrousse, 1931). Different scientists have also proposed fungus name as Phoma rabiei
on the basis of morphological characters (Labrousse, 1931; Sprague, 1932; Sattar, 1934;
Luthra and Bedi, 1932; Kovachevski, 1936; Khune and Kapoor, 1980; Bruns and Barz,
2001). The recognized name of the fungus is Ascochyta rabiei, though it lacks basal frills
on disectioned conidia, outer bounding wall of conidia and internal conidiogenous cell
wall of conidiogenous locus. Isolates of A. rabiei demonstrate much variability in cells
number in single spore and have 1-4 nuclei (Bruns and Barz, 2001). The sexual stage of
Didymella rabiei resembles with respect to its larger ascomata, nonfasciculate asci
arrangement, pseudoparaphyses presence and ascospores structure with Ascochyta spp.
(Wilson and Kaiser, 1995).
2.3.2 Biology
Conidia start germination on host surfaces after 12 to 48 hours of landing, germ tubes
grow longer and give ramifications on leaves. Secretion of mucilaginous substance takes
place which assist attachment to the plant surface, and cell wall secreted lytic enzymes
facilitate entry of germ tube in the host tissue (Hohl et al., 1990). Appressoria produced
from hyphal branches use mechanical force and penetrate through two epidermal layers of
cuticle to reach subcuticle cells. Hypha pushes subcuticle cells alongside the connection of
epidermal cells before entering into subsidiary and guard cells, even when the stoma is not
closed (Pande et al., 1987; Hohl et al., 1990). There are also reports that hypha breaks in
hydathodes (Kohler et al., 1995). During early stages of disease penetration, hypha grows
separately between palisade parenchyma and epidermal cells by stretching inside the
intercellular spaces to form gloomy aggregates, this usually happens after four hours of
inoculation. Epidermal cells remain intact after three days post infection (dpi), however
8
prominent symptoms emerge after four days as yellow flecks on the surface of the stem
showing deadening of epidermal cells. In later stages of disease (5 dpi), the whole cortex
and pith are collapsed. Disintegration of tissues away from attacking hyphae has also been
noticed showing the prevalence of cell wall disintegrating enzymes in non-infected
tissues. Accumulation of hyphae in cortical tissue produces fake mass of parenchymatous
mycelium which is then converted into pycnidia. After eight days of inoculation, pycnidia
become visible as black dots, come out from epidermal cells of stem, and conidia are
liberated through an ostiole (Pande et al., 1987; Hohl et al., 1990). After seven days of
infection, majority of non-lignified tissues are collapsed and resultantly girdling of plants
occur (Pande et al., 1987). Gram blight is usually identified in the phloem and not
commonly in the xylem of petioles. This suggests that AB mainly receives its food from
phloem or in other words, these tissues are much easy to destroy. At the extreme level of
disease, roughly all cells are damaged and become packed with fungal biomass, showing
distinctive nectrotrophic approach of infection (Pande et al., 1987; Hohl et al., 1990).
2.3.3 Host range
The results of artificial inoculation of ascochyta blight fungus on different hosts like;
common beans, vetch, filed peas, cow peas and lentil reveal that the disease is as
dangerous on these crops as on chickpea (Zachos et al., 1963; Nene and Reddy, 1987;
Khan et al., 1999a). AB can also cause the disease on Vigna unguiculata (cowpea), P.
vulgaris (Common bean) (Kaiser, 1973; Pande et al., 2005). Lactuca serriola (prickly
lettuce), Lamium amplexicaule (common dead nettle), Medicago sativa (alfalfa), Melilotus
alba (sweet clover) and Thlapis arvense (field penny-cress) have also been found hosts of
ascochyta blight, as these plants grow alongside the gram crop in different countries.
There are strong reports of pycnidial development in Medicago sativa and Melilotus alba.
There are also reports of Ascochyta rabiei isolation from Descurainia sophia (flix weed),
Galium apanine (goose grass), Lamium amplexicaule (common dead nettle) and Triticum
aestivum (wheat) from those fields where chickpea debris of earlier season was present
(Kaiser, 1991).
9
2.4 Pathogenic variability
Ascochyta rabiei is continually creating variability in nature and making the
resistant cultivars susceptible (Jamil et al., 2000). Therefore, consistent studies have been
made to characterize the blight pathogen pathotypes/races (Taleei et al., 2010).
Variability in isolates of A. rabiei pathogen has been studied and documented in gram
cultivating regions all around the globe. Firstly, the work started in India on classification
of AB isolates and 6-12 pathotypes/races on different host differentials were observed in
different parts of India (Vir and Grewal, 1974; Singh, 1990). Later on, in India, 11
pathotypes and 5 isolates were collected from 3-15 differentials (Navas-Cortés et al.,
1998). Very recently in Pakistan, Jamil et al., (2010) reported 12 isolates of A. rabiei on
the basis of genetic dissimilarity using DNA finger printing and random amplified
polymorphic DNA (RAPD) analysis. Previously, Jamil et al. (2000) categorized 130
isolates with 3 differential lines into 3 pathotypes on the basis of virulence. Similar studies
discovered 6 pathotypes in Syria (Reddy and Kabbabeh, 1985). Further, Udupa and
Weigand (1997) grouped 53 isolates into 3 distinctive classes on the basis of virulences on
nine differentials. In USA, an experiment was conducted on 15 differentials in the Palouse
region in which 11 races were filed from 39 isolates (Jan and Wiese, 1991). To compare
the races of isolates from different chickpea cultivating regions in the world, a study was
designed by Navas-Cortés et al. (1998) who reported races 11, 14, 7, 11, 2 and 1 from
India, Spain, USA, Pakistan, Morocco and Greece, respectively. Using cluster analysis, 17
representative pathotypes of A. rabiei from the Beja region of Tunisia were identified on 8
differential lines. Among those, 5 were found extremely virulent (Hamza et al., 2000). In
spite of these numerous studies, since different procedures, like inoculation with respect to
plant age, dissimilarity among rating scales and different gram differential genotypes were
adopted, therefore it is tough to compare results of variability in these studies (Navas-
Cortés et al., 1998). Chongo et al. (2004) employed a set of 8 differentials, 5 white and 3
black accessions to organize 40 isolates of chickpea blight received largely in 1998 from
commercial gram fields in Saskatchewan. The isolates were classified into 14 pathotypes
on the basis of resistant/susceptible response of differentials by keeping 1-8 isolates in
each virulence group. As small as 3 differentials from highly resistant, moderately
resistant and moderately susceptible lines are enough for categorizing the pathotypes of A.
rabiei into 3 groups on the basis of virulences (Udupa et al., 1998). At International
10
Center for Agricultural Research in the Dry Areas (ICARDA), the standard differentials
being used comprised of ‘ILC 482’ as a tolerant, ‘ILC 1929’ as a susceptible and ILC
3279’ as a resistant line (Jamil et al., 2000). Likewise, the United States Department of
Agriculture (USDA) makes use of varieties ‘Dwelley’ and ‘French White’ for virulence
studies of isolates categorizing them into genotype- I and genotype- II, correspondingly
(Chen et al., 2004).
2.5 Epidemiology of A. rabiei
2.5.1 Disease cycle
Fungus overwinters in the form of asexual fruiting body pycnidia (Nene and Reddy,
1987). Firstly, the disease appears on limited area in the field but under most favourable
environment, extends rapidly throughout the field (Zachos et al., 1963; Kaiser, 1973).
Infested gram stubbles are the most particular medium of inoculum for subsequent
growing seasons (Luthra et al., 1935; Dickinson and Sheridan, 1968; Kaiser, 1973). The
development of D. rabiei, the sexual stage (teleomorph) of the fungus, can appear in
infested stubbles of chickpea (Galloway and MacLeod, 2003). In the winter, pseudothecia
build up on residues and continue to expand until the coming spring. In USA,
pseudothecia grow at the commencement of March and ascospores remain releasing
continuously for the period of two months while major release of ascospores occurs after
the second week of April (Trapero-Casas and Kaiser, 1992a). However, in Canada, sexual
spores (ascospores) remain present in the spring and the summer (Armstrong et al., 2001),
but there are no exact reports regarding the time of release and termination of ascospores.
It is strongly believed that wind-borne ascospores can cause ascochyta blight epidemics
(Pedersen et al., 1994). Blight infection due to wind-borne ascospores and conidia have
been observed in chickpea fields which were free of disease debris or in which disease-
free seeds of chickpea were sown (Trapero-Casas and Kaiser, 1992a; Kaiser, 1997; Bretag
et al., 2006). Additionally, survival of the pathogen from one growing season to the other
is associated with ascospores present on disease debris (Gossen and Morrall, 1986; Kaiser
et al., 1987; Navas-Cortés et al., 1995).
11
2.5.2 Disease spread
Movement of infected seed is the primary source of spreading A. rabiei over large
distances. A. rabiei had the ability to survive in chickpea infected seed under storage
conditions for over 5 years. Also, infected seeds act as inoculum from year to year and
infect the healthy fields (Maden, 1983; Kaiser, 1987, 1997; Kaiser and Hannan, 1988; Dey
and Singh, 1994). In Southern Spain, ascospores of D. rabiei are the main starting
medium of epidemics. Ascospores present in asci are liberated forcibly from pseudothecia
into air, then these are carried by wind, land on healthy chickpea plants and cause
infection (Kaiser, 1987, 1992; Trapero-Casas and Kaiser, 1992a; Trapero-Casas et al.,
1996; Davidson et al., 2006).
Rain splashes are the major source of spread of conidia of A. rabiei and are the cause
of recurrence of secondary infections with in the field during the entire growing season
(Maden et al., 1975; Wilson and Kaiser, 1995; Chongo and Gossen, 2001). Other than rain
splashes, agricultural implements used in infected fields, people, animals and vehicles
passing through chickpea areas having infestations of blight, may also be a factor involved
in spreading the disease (Kaiser, 1973; Pearse et al., 2000). Agronomic practice in which
tillage is minimized to conserve moisture, in this case infected leaves and debris present in
the soil contribute in the expansion of pseudothecia of D. rabiei during the winter
(Trapero-Casas and Kaiser, 1992a; Gamliel et al., 2000). Pycnidia and pseudothecia when
present on soil surface can survive for longer period and stay viable for almost 2 years, but
when disease debris is buried fruiting bodies remain viable only for 2 to 5 months (Wallen
and Jeun, 1968; Navas-Cortés et al., 1995; Gossen and Miller, 2004).
2.5.3 Anamorph
In Ascochyta blight, the asexual fruiting body in which asexual spores (conidia) are
produced, is pycnidia. Pycnidia emerge as minute concentric dots in lesions developed on
chickpea plants. There is a prominent ostiole in the pycnidium while conidia are
colourless, oblong-oval, in a straight line or somewhat curved in shape and are 6-12 x 4-
12 µm in sizes (Punithalingam and Holliday, 1972; Nene, 1982). The most favourable
temperature for the growth and germination of spore is 20ºC, however temperature <10ºC
and >30ºC halt the development of conidia. Moreover, continuous light span has been
accounted for both increase and decrease in sporulation (Chauhan and Sinha, 1973;
12
Kaiser, 1973). Relative humidity (RH) plays a vital role in the development of pycnidia
on chickpea debris (Navas-Cortés et al., 1998b). Decrease in RH i.e. from 100 to 86%
limits spread of disease regardless what range of temperature prevails during that period
(Gaur and Singh, 1993; Navas-Cortés et al., 1998b). RH also influences the sporulation of
other fungi (Mondal et al., 2003). Disease severity increases when after inoculation
wetness phase is terminated by dry period; however dry period more than twevlve hours
after intial wetting period usually decreases the development of disease (Trapero-Casas
and Kaiser, 1992). Jhorar et al. (1998) found that disease occurrence was negatively
correlated with dry period. Very minute infection was developed when leaf wetness was
deficient although RH was more than 90%. Similarly, without leaf wetness there was
complete arrest of blight at 95% RH. Pycnidia and conidia production increases when leaf
wetness is maintained at 8 hours a day. Pycnidia are disseminated by wind (Singh et al.,
1995; Schoeny et al., 2008).
2.5.4 Teleomorph
For the development of pseudothecia, nutrients are supplied by the chickpea debris
(Trapero-Casas and Kaiser, 1992). Dark brown to black in colour, erumpet and
subglobose in shape with 120 to 270 μm in diameter, pseudothecia are positioned in lines
on gram straw with an ostiole at maturity. Bitunicate walled asci of D. rabiei are
subclavate to cylindrical and 50-80 x 10-12 μm in diameter. Ascospores are
morphologically bicelled, biconic to ellipsoidal in shape and hyaline, narrow at the septum
and 9.5-10 x 4.5-7 μm in size. In one square millimeter of infected tissue, 15000
ascospores are produced (Trapero-Casas and Kaiser, 1992a; Pande et al., 2005).
Temperature has partial effect on the stimulation of pseudothecia but pronounced
influence on their maturation. Maturation of ascospores becomes delayed when dry
periods are long (Trapero-Casas and Kaiser, 1992a; Galloway et al., 2004). Frequency of
rainfall positively influences the release of ascospores than the intensity of rainfall (Fitt et
al., 1989; Navas-Cortés et al., 1998). Infection process begins with the formation of
pseudothecia during rainy days. Rainfall makes plant disease debris wet which is
necessary for the development and maturity of pseudothecia. After that, rainfall makes the
temperature mild which helps in triggering the early events of maturation of
pseudothecium (Trapero-Casas and Kaiser, 1992a; MacLeod and Galloway, 2003; Pande
et al., 2005). Later on, pseudothecia finalize their development, ascospores are liberated
13
from developed pseudothecia by rain and are disseminated by wind currents to nearby
fields (Trapero-Casas and Kaiser, 1987, 1992a). Similarly, teleomorphic stage of
ascochyta blight is also affected by relative humidity (Navas-Cortés et al., 1995).
Production of ascospores from psuedothecia in Didymella rabiei increases with the
increase of RH. Jhorar et al. (1998) found more production of asci and ascospores in
artificially inoculated fields where relative humidity level was kept at 100% than naturally
infested fields.
2.5.5 Environmental conditions
Rainfall, wind and temperature are crucial meteorological parameters having
significant impact on AB disease development (Nene, 1982; Weltzien and Kaack, 1984).
Environmental factors with high inoculum pressure also affect the resistance of
germplasm (Pande et al., 2011). Rainfall with conducive temperature has strong
correlation with the disease intensity (Atinsky et al., 2005; Schoeny et al., 2007).
Ascochyta blight increases with the amount of rainfall (Kaiser, 1973, 1992; Thind et al.,
2007). Chickpea blight can erupt in epidemic proportions when there is more than 60%
relative humidity, 10-20°C leaf temperatures, and annual excessive rainfall (more than
150 mm) coupled with 7 days of leaf wetness period for at least 7 hours (Reddy and
Singh, 1990c). Trapero-Casas and Kaiser (1992) found that by increasing wetness
duration to minimum of 6 hours along with increasing maximum temperature to 20oC,
incidence and severity of AB was enhanced on different cultivars. Upper and lower
temperature limits for disease development and infection are about 30oC and 5
oC,
respectively (Pande et al., 2005). In Pakistan, Khan et al. (1999) conducted experiments to
determine the relationship of different environmental conditions with gram blight on four
varieties. It was found that maximum and minimum temperatures showed fairly good
correlation with blight severity. Further, it was also observed that relative humidity (60-
88%) and rainfall (0-20 mm) had relationship with ascochyta blight severities. Fairly good
correlation between environmental variables and chickpea blight has also been reported by
Ahmad et al., (1985). In their studies, they found that disease increased and decreased
with fluctuation in different environmental variables. Monthly average temperature with
minimum of 8oC along with minimum 40 mm rainfall is empirical for the causation of an
epidemic (Ketelaer et al., 1988). Similarly, Pande et al. (2005) concluded that
temperatures between 5-30°C with 20°C optimum temperature is essential for disease
14
development and infection while 17 hours moisture is critical for epidemic. Symptom
expressions are also affected by temperature after infection (Jayakumar et al., 2005).
Studies have shown significant effect of temperature on incubation and latent periods
(Chauhan and Sinha, 1973; Ram and Mahender, 1993). Thind et al. (2007) validated the
relationship of meteorological variables with ascochyta blight of gram under field
conditions for two growing seasons. It was observed that the incubation phase prolonged
for 2-3 weeks during January and declined down to 3–5 days in the months of February
and March, and there was a significant correlation between these variables and blight. It
was also concluded that temperature was more appropriate due to high frequency of
rainfall from February to end-March for disease development and spread. Their study also
revealed that leaf wetness minimum for 4 hours was essential for infection, initiation of
disease and disease symptoms. On the basis of empirical data, number of researchers have
found significant correlation of environmental factors i.e. maximum and minimum
temperatures, rainfall, relative humidity and wind speed with disease severity, and
concluded that these four variables might be used to develop forecasting models to give
prediction of ascochyta blight (Akem, 1999; Bretag et al., 2006; Pande et al., 2005; Thind
et al., 2007).
2.5.6 Disease forecasting models
Correlation analysis is a simple relationship and does not provide exact variability
in response variable (disease severity). Thus, to quantify chickpea blight disease severity
in relation to environmental variables, regression analysis is used (Shtienberg, 2010;
Salam et al., 2003). Therefore, maximum and minimum temperatures, rainfall, relative
humidity and wind, and function of these five variables produce a strong foundation for
the development of a predictive model to forecast the ascochyta disease (Tivoli and
Bennizia, 2007). Forecasting model (regression models) may be helpful for studying the
dynamics of AB and knowing probable development of blight incursions (Conventry et
al., 2008). Predictive models have been employed to analyze ascochyta blight peril in
different ecological zones at various growth stages (Frenkel et al., 2008). Jhorar et al.,
(1997) studied the relationship between climatic variables and gram blight occurrence
over duration of 15 years at different locations with different disease intensities. They
illustrated that afternoon relative humidity and maximum temperature were two crucial
reasons in the development and spread of chickpea blight, and they named this
15
relationship as humid thermal ratio (HTR) for forecasting the eruption of chickpea blight.
Mean daily precipitation, temperature and rainfall during the first two months of growing
season in different ecological zones may be used in the prediction of AB (Diekmann,
1992). In the Mediterranean basin during late winter environment, repeated spells of
rainfall, moderate temperature and winds may cause full destruction of the cultivated
crops by ascochyta blight (Frenkel et al., 2008). In Israel, chickpea was autumn sowing
crop but because of its less germination in autumn, it was started to cultivate in spring
(Ben-David et al., 2006). This shift in cultivation led the chickpea crop to extreme
vulnerability of AB in rainy season (Abbo et al., 2007). Because of this change in
cropping pattern, diversity was observed in the pathogen of gram blight in terms of
ecology and virulence. To cover these two features of the pathogen, ecological studies
were designed to predict the disease in rainy season with mild temperature (Shtienberg et
al., 2005). This aided highly in understanding the temporal and spatial movement of the
disease. Shtienberg et al. (2005) tested the implication of avoiding primary infections
ensuing from the sexual stage of Didymella rabiei under field conditions during 1998-
2000. To protect the plants from aerial ascospores infection, fungicides were sprayed.
Forty empirical models showing the impact of interrupted wetness and temperature on
early maturation of pseudothecia of D. rabiei were developed and confirmed by means of
data collected from chickpea fields in 1998. Out of ten models, 7 were validated with data
recorded from 1999 to 2000. Model gave the most excellent predictions of maturity of
pseudothecia and release of ascospores regarding the disease initiation in next year at the
start of rainy season (October to December). Disease predictive models help growers to
target management approach during the critical stages of AB. Release of timing of
primary inoculum from diseased debris is important in the causation of epiphytotic of
gram blight. This was studied in the field using susceptible grams in pots which were
badly infested with D. rabiei at two different ecological sites (Chilvers et al., 2007).
Studies showed that primary inoculum discharge started from 15th
March while maximum
lesions linked with rainfall, were recorded during May in each year. Release remained
persistent up to the commencement of June when the number of lesions declined on
spreader plants in spite of frequent incidence of rainfall. Correlation analysis was
performed to know the relationship of primary inoculum discharge with already published
data on maturity of pseudothecia and ascospore liberation in the laboratory. On the basis
of these studies, a simple model was established for forecasting the release of primary
16
inoculum from heavily disease infested debris and the ejection of primary inoculum was
best explained by rainfall variable (Chilvers et al., 2007). Infrequent rainfall and low
temperatures are involved in uncommon occurrence of teleomorphic stage of gram blight
(Atinsky et al., 2005). Attempts have been made to elucidate either moisture and
temperatures are essential for the development and maturation of D. rabiei or not. It has
been found that average temperature (maximum and minimum) with precipitation events
determine the maturity of pseudothecia and release of ascospores (Navas-Cortés et al.,
1998a). Salam et al. (2011) developed the model to predict the disease severity of AB
with respect to ascospore release. The model showed coefficient of determination value
i.e. R2
88% with 13.18 slopes and -10.67 intercept. It was observed that temperature and
precipitation played vital role in the discharge of ascospore, and thus augmented disease
severity. They also developed another model on same parameters (temperature and
moisture) which successfully forecasted ascochyta blight severity in relation to dispersal
of ascospores from infected field; as model was statistically fit (R2 = 0.88) (Salam et al.,
2011a).
Strong winds and splashing rains may disperse pycnidiospores of AB or diseased
plant debris over large distances (Bretag, 1991). Wind can transfer primary inoculum from
infected fields to healthy fields on a distance of 1.6 km (Schoeny et al., 2007). Wind speed
and wind direction affect ascospores distribution (Salam et al., 2011c). Initial inoculum is
always air-borne in gram blight (Atinsky et al., 2005; Zhang et al., 2005). Schoeny et al.
(2007) developed a forecasting model to give prediction of AB disease on field peas
depending on daily environmental conditions. It was found that changes in environmental
variables brought considerable fluctuation in disease development. In addition, there was a
strong role of wind in disease transmission. Simulation models provide an increased
understanding of epidemiological variables that create certain impacts on the development
of epidemics of AB from diseased seed. An ample amount of rain in combination with
wind speed favours the disease on susceptible genotypes from infected seeds. It highly
supports the theory that epidemics occur because of accumulation of seed-borne inoculum
in most parts of the world under repeated spells of rain (Bretag et al., 1995).
Although, numerous researches have characterized the environmental variables
that are conducive for blight epidemics, yet many loopholes exist in understanding
epidemiological aspects for AB epidemics forecasting. For this reason, additional
17
systematic assessments of inoculum sources, dissemination, host-pathogen interactions,
disease perpetuation, favourable weather factors and host range are necessary to cover
information gaps in disease prediction (Pande et al., 2005).
2.6 Chickpea breeding for blight resistance
Use of resistant varieties is the most efficient method to control chickpea blight
(McMurray et al., 2006; Iqbal et al., 2010; Bokhari et al., 2011). Host resistance is
thought to have greatest effect on ascochyta blight epidemics (Gan et al., 2006; Davidson
and Kimber, 2007). Numerous breeding programmes are in progress for genetic
improvement against A. rabiei of chickpea worldwide (Reddy and Singh, 1984; Singh and
Reddy, 1990; Taleei et al., 2010; Peever et al., 2012). Process of development of
resistance in varieties against AB remained slow for the last sixty years by virtue of low-
level and unstable basis of resistance (Reddy and Singh, 1990a; Reddy et al., 1992).
Since, genetics of chickpea blight resistance is controlled by single dominant gene or
recessive gene (Singh and Reddy, 1983, 1989, 1991); therefore is not long lasting.
Emergence of new races is also cause of unstable resistance in grams (Reddy and
Kabbabeh, 1985; Ilyas et al., 2007). Up till now, studies on screening of genotypes against
A. rabiei demonstrated that the resistant germplasm stock was extremely small and that
grams had a narrow genetic base (Reddy and Singh, 1984; Udupa et al., 1993; Ghazanfar
et al., 2010). Horizontal resistance was tried to establish against AB at ICARDA, but left
when advanced lines did not show remarkable performance (Vanrheenen and Harware,
1994; Singh, 1997). Successful efforts have been made to introduce different genes
through gene pyramiding into single lines by crossing resistant genotypes of diverse
origins (Vanrheenen and Harware, 1994; Singh et al., 1994; Qurban et al., 2011).
Development of cultivars by gene pyramiding through marker-assisted selection (MAS)
was tried at the International Crops Research Institute for the Semi-Arid Tropics
(ICRISAT) which remained effective (Singh and Reddy, 1996). Breeders are also trying
to look for wild relatives of gram for resistant genes. The main attempt of several chickpea
breeders around the globe is to screen out wild Cicer species as they may be a novel
source of resistance (Singh et al., 1994). Resistance to chickpea blight has been reported
in accessions of C. bijugum K.H. Rech., C. reticulatum Ladiz., C. pinnatifidum Jaub., C.
judaicum Boiss. and C. echinospermum (Harware et al., 1992; Singh and Reddy, 1993;
Collard et al., 2001, 2003).
18
Species of C. reticulatum and C. echinospermum are very important and
immediate sources of resistance because these may be crossed with C. arietinum (Singh
and Ocampo, 1993; Singh et al., 1999). Many genotypes of chickpea which display
resistance to AB in the vegetative phases do not maintain resistance at reproductive stage
(Reddy and Singh 1984; Chongo and Gossen, 2001). Fresh vegetative development on
resistant plants was less susceptible to chickpea blight than older ones, but contrary to
this, in susceptible plants all parts and stages of plants were extremely susceptible
(Chongo and Gossen, 2001). Furthermore, gene expression was different in leaves and
stem because leaves were less infected than stem against AB (Chongo et al., 2000). Reddy
et al. (1992) revealed that black gram germplasm had much higher resistance level than
the white germplasm and, black gram varieties showed resistance during the early stage of
growth at which the chances of attack of blight were maximum (Singh and Reddy, 1993a).
There was a positive association of AB resistance with later stage of growth, erect growth
habit and increased plant height in both white and black accessions whereas seed size had
negative correlation with resistance in white-type accessions (Reddy and Singh 1984;
Singh and Reddy, 1993a). Morphological, physiological and biochemical traits play key
role in introducing resistance in chickpea plants (Pande et al., 2005). Angelini et al.
(1993) reported that varieties having thicker stem hypodermis and epidermis were more
resistant against A. rabiei than those lacking this character. This suggests that stem
hypodermis and epidermis thickness may give resistance to chickpea plants by defending
the cortical and vascular tissues from pathogen attack and by providing a guarding effect.
Bio-chemicals like; phenylalanine ammonia lyase (PAL), peroxidase, copper amine
oxidase, polyphenoloxidase and catalase are found more in resistant genotypes of
chickpea than susceptible (Angelini et al., 1993; Nehra et al., 1994; Sindhu et al., 1995;
Rea et al., 2002; Sarwar et al., 2001, 2003). These enzymes produce resistance in
chickpea varieties by triggering hypersensitive response. Working on aforementioned
studies, resistance may be enhanced in chickpea cultivars.
2.7 Management of chickpea blight
2.7.1 Foliar applications of fungicides
Recent genetic development in chickpea cultivars has helped a lot to produce
moderate resistance against ascochyta blight. But ascochyta blight continues to be a chief
19
constraint worldwide. Yield losses in gram are always in exponential fashion because of
chickpea blight disease severities even when moderately resistant varieties are grown.
Timely use of efficient fungicides play a crucial role in curtailing the damage caused by
the AB and thus enhancing production (MacLeod and Galloway, 2002; Chongo et al.,
2003a,b; Bretag et al., 2008). In Saskatchewan Canada, the incidence of chickpea blight
on partially resistant gram was 45% without using fungicide, but two applications of
chlorothalonil decreased the incidence to 8% and production was almost double (Chongo
et al., 2003a). Partial resistance does not provide sufficient management against blight
under high inoculum pressure and favourable environmental conditions (Reddy and Singh,
1990b; Rahat et al., 1993; Chongo et al., 2003a). Therefore, it is vital to combine aerial
sprays of fungicides for an integrated plan.
Efficacy of foliar applications of fungicides mainly depends upon host resistance
level, fungicide efficacy, foliar coverage of the fungicide treatment, disease pressure and
environmental conditions under which the aerial sprays are applied (Gan et al., 2006;
Davidson and Kimber, 2007). So, these 5 features jointly measure the effectiveness of
foliar fungicide applications. Protective sprays are not recommended for winter cultivation
and under conducive environments (Bretag et al., 2003). Curative applications with
systemic fungicides are best option under conducive environment and for winter
cultivation (Shtienberg et al., 2000; Bretag et al., 2003). Systemic nature of fungicides
makes them durable for blight check for longer period of time (Shtienberg et al., 2000). In
Australia, chickpea blight is being controlled effectively using systemic fungicides
(Bretag et al., 2003). As, not all systemic fungicides show translocality in newly
developed tissues, therefore their efficacy must be checked time to time (Gan et al., 2006;
Davidson and Kimber, 2007).
2.7.2 Evaluation of fungicides
Numerous fungicides have been analyzed and employed for foliar application in
gram but their effectiveness remained variable from one region to the other. Protective
fungicides such as; wettable sulphur, bordeaux mixture, maneb and captan have controlled
AB under some circumstances (Solel and Kostrinski, 1964), but these fungicides were
found in general to be ineffective when sprayed on susceptible genotypes during
epidemics (Nene, 1982; Shtienberg et al., 2000; Demirci et al., 2003). In recent years,
20
high efficacy fungicides have been developed for foliar applications in gram. Propineb,
captan, dithianon, captafol, zineb, chlorothalonil, maneb, ferbam, penconazole,
thiabendzole and sulfur fungicides have been extensively used against gram blight and
have effectively reduced its further development and spread (Bashir and Ilyas, 1983;
Bashir et al., 1987; Pande et al., 2005). Similary, tebuconazole, boscalid, azoxystrobin,
mancozeb, difenoconazole and pyraclostrobin are also being used widely and providing
substantial results (Gan et al., 2006). On the other hand, tebuconazole, difenoconazole and
carbendazim are being used on a fairly small scale in sub-continent (Singh et al., 1992;
Gaur and singh, 1996b). The active ingredients of systemic fungicides control fungal
growth by a number of ways. For example in fungi, sulfur deactivates sulfhydryl groups
of amino acids and enzymes, interferes electron transport, and reduces sulfur to hydrogen
sulfide which kills the mycelium of fungus. In the same way, aluminium ethyle phosphate
inhibits cell division and enzyme synthesis, and causes denaturation of proteins. Under
field conditions, systemic fungicides, particularly having Fosetyl-Al, activate the defense
mechanism of the plants by producing phytoalexins (Agrios, 2005).
Since regulatory checks, some of these fungicides have been registered for
utilization in some countries while in other countries they are still under experimental
phases. Partially resistant varieties need one or two applications of an effective fungicide
for the control of AB under dry conditions or where ascochyta blight appears in mild form
(Reddy and Singh, 1990b; Shtienberg et al., 2000; Chongo et al., 2003a). Under high
disease pressure and favourble weather conditions, more than three applications of
fungicides, which may exceed up to ten, may be required on susceptible varieties to
control the disease (Kimber and Ramsey, 2001). In these situations, those fungicides
should be employed as aerial spray which could remain effective in plant for an extended
duration after the application and consequently curtail number of sprays (Demirci et al.,
2003). Thus, under these circumstances, systemic fungicides remain the only choice to be
used as foliar sprays. Systemic fungicides, because of their systemic nature have the
ability to eliminate established infection (Shtienberg et al., 2000; Gan et al., 2006).
2.7.3 Management through plant extracts
Plants are rich source of allelopathic chemicals and can be used as fungicides
(Jabeen and Javaid, 2008, 2010). In certain plant extracts, secondary metabolites are
21
present which are antifungal (Nunez et al., 2006; Lee, 2007). Sisti et al. (2008) reported
that methanolic extract of Rubus species of vegetables had antifungal compounds
(phenolics) against 37 pathogenic fungi. Methyl jasmonates and jasmonic acid are growth
regulators which occur naturally in different plants. Methyl jasmonates have been found to
be effective against grey mold of strawberry (Creelman and Mullet, 1997; Droby et al.,
1999). Citrus oils, and extracts of Apiaceae, Acanthaceae, Amranthaceae and
Magnoliaceae families have antifungal and cytotoxic properties (Caccioni et al., 1998;
Mansilla and Palenzuela, 1999; Neerman, 2003).
Research references were found, documenting the effects of different extracts in
controlling ascochyta blight. For example, efficacy of aqueous extracts derived from
different plant parts of Tagetes erectus L. under laboratory conditions were checked
against chickpea blight and found to be effective in limiting the mycelial growth in the
range of 4-73% (Shazia et al., 2011). Similarly, Jabeen et al. (2007) found that M.
azedarach leaves extracts had 24-54% fungitoxicity against A. rabiei. Allelopathic trees
extracts have also antifungal activity against chickpea blight. Jabeen and Javaid (2008)
evaluated the extracts collected from different parts of allelopathic trees and reported 25-
42% colony growth inhibition of blight fungus. Extracts of different plant parts have
different antifungal activity. Jabeen and Javaid, (2010) found that aqueous extracts taken
from different parts i.e. leaves, root and stem bark of Syzygium cumini (L.) were different
in their effectiveness against A. rabiei. They evaluated different concentrations (1-5%) of
extracts of leaves, root and stem bark which inhibited mycelial growth of A. rabiei 7–
30%, 21-64%, and 23-39%, respectively. Jabeen et al. (2011) probed out the antifungal
action of M. azedarach L. leaves for the management of chickpea blight. Six compounds
were isolated from test leaves and were found, having main role in suppression of
chickpea blight pathogen. Furthermore, all the concentrations showed highly significant
results in suppressing the mycelial growth of A. rabiei under in vitro conditions. Similarly,
Datura metel extracts from root and shoot with different concentrations (1-4% w/v) can
suppress colony diameter of A. rabiei (Shafique and Shafique, 2008). Biochemical
compounds viz; obacunone, limonin, nomilin and limonoids have also been isolated from
M. azedarach and have been found to have pesticidal properties (Koul et al., 2004).
Plant extracts have also been found in different studies as a resistance inducer (Guleria
and Kumar, 2006). Reports are there for the control of chickpea blight through induced
22
resistance by the application of cuprous chloride, dipotassium hydrogen sulphate and
salicylic acid because these compounds increase phenolic contents (Chaudhry et al.,
2001). Similarly, foliar applications of Bion, salicyclic acid and A. indica induce systemic
resistance against chickpea blight (Sarwar et al., 2011).
2.7.4 Management through antagonists
Antagonistic microbes are being used in the management of economically
important crop diseases (Harman et al., 2004). Antagonists can limit the growth of
different fungal pathogens up to 64% under in vitro conditions (Rajendiran et al., 2010).
Studies were conducted in the past to evaluate the efficacy of different antagonists. For
example, Rajakumar et al. (2005) assessed the potential of fugal antagonists like; T.
viride, Acremonium implicatum and Chaetomium globosum under field and laboratory
conditions against A. rabiei. In those studies, A. viride caused lysis of the A. rabiei
mycelium, whilst A. implicatum created inhibition zone and C. globosum abundantly grew
on the mycelium of host fungus. Further, culture filtrate bioassays of all antagonists
significantly inhibited pycnidiospore germination and colony development. Similarly,
under green house conditions, there was a decrease in disease progress during both pre
and post-inoculations. However, C. globosum gave the most efficient results in declining
disease development during post-inoculation applications. Benzohra et al. (2011)
evaluated the effect of T. harzianum on the growth of A. rabiei mycelium and found that
antagonistic fungi showed substantial limiting effects on all 16 isolates of chickpea blight.
Likewise, Küçük et al. (2007) conducted experiments to study the interaction of T.
harzianum and A. rabiei and found considerable inhibition of A. rabiei mycelium. In fact,
enzymes; chitinase and β-1, 3-glucanase produced by T. harzianum play role in the
inhibition of ascochyta fungus (Küçük et al., 2007). Proteases and amylases have also
been reported from T. harzianum (Azevedo et al., 2000). These enzymes cause lysis of
cell wall of pathogenic fungi. T. harzianum may also activate the defense mechanism of
chickpea plants (Jayalakshmi et al., 2009). Navas-Cortés (1992) observed that under
sterilized soil conditions, production of pycnidia and pseudothecia was higher than under
natural conditions which was indication that ascochyta blight was influenced by
antagonistic saprophytes inhabited in that natural soil. Khokhar et al. (2001) tested
Rhizobium strain (Thal-8) against chickpea blight and found it highly effective in
hindering the growth of fungus. Under laboratory conditions, Conostachya rosea and
23
Aureobasidium pullulans both significantly restrain the growth of ascochyta blight (Dugan
et al., 2005). Bacterial antagonists also contribute towards the management of gram blight
by triggering defense mechanism of chickpea plants. Pseudomonas fluorescens two strains
i.e. Serratia spp. (5 strains) and Bacillus spp. (2 strains) have produced very good results
against chickpea blight (Wang et al., 2003). However, regarding effectiveness of bacterial
antagonists against chickpea blight a lot of research work is still required to be
undertaken.
24
CHAPTER 3 MATERIALS AND METHODS
The current research work presented in this dissertation was conducted at the
research area of Department of Plant pathology, University of Agriculture, Faisalabad
during the two chickpea growing seasons from November 2010 to April 2011 and
November 2011 to April 2012. The allocated research area is an open vast field where
crops of different seasons are grown for research objectives.
3.1 Collection of diseased samples
Chickpea blight infected pods were collected from Ayub Agricultural Research
Institute (ARRI) located near to University of Agriculture, Faisalabad. The pods were
placed in refrigerator at 5-8°C. These samples were then used for isolation and
purification of A. rabiei.
3.1.1 Preparation of culture medium
Chickpea seed meal agar medium (CSMA) was used to isolate the fungus A. rabiei
from diseased samples. For purification of A. rabiei same medium (CSMA) was used. The
recipe used for one liter of CSMA was;
Chickpea seed meal = 20 g
Agar = 20 g
Glucose = 20 g
Sterilized water = 1 Liter
20 g chickpea seed meal and 20 g glucose was dissolved in 500 mL sterilized
water in a conical flask. 20 g agar was dissolved in a separate conical flask in 500 mL
sterilized water. Solutions of both flasks were mixed together in a 1000 mL conical flask
and final volume of one liter was made. The pH of the medium was adjusted at 7. Cotton
plug was tightly fixed in the opening of conical flask. Then, the medium was kept at
121oC and 15 lbs psi for 20 minutes in an autoclave for sterilization. Glassware (Petri-
dishes) were sterilized in hot air oven at the temperature of 180 oC for two hours.
25
3.1.2 Isolation and purification of A. rabiei
Fungus (A. rabiei) was isolated by the procedure followed by Ilyas and Iqbal,
(1986). Pods were placed in forceps grip and heated on sprit lamp flame in a way that
outer surface of pods could be sterilized, while inner pod layer remained undamaged.
Surface sterilized pods were then opened and infected seed were brought out from the
pods with sterilized forceps. Infected seeds were placed on the autoclaved CSMA medium
and placed in incubator at 20 ± 2°C for fortnight. When colonies of A. rabiei formed
around the plated infected material on CSMA medium, they were isolated, and purified by
single spore culture method (Choi et al., 1999). Purified culture of A. rabiei was prepared
and maintained at 5°C.
3.1.3 Preparation of mass culture
The chickpea seeds were soaked in tap water for overnight (Ilyas and Khan, 1986).
The soaked seeds were spread on paper towels so that excessive moisture could be
absorbed. The soaked chickpea seeds were then placed into conical flasks at the rate of
250 g/1liter flask. A cotton plug was tightly fixed into the opening of conical flask. The
flasks containing chickpea seeds were placed in an autoclave at 121oC and 15 lbs psi for
half an hour and same process was repeated after an interval of 24 hours to eliminate the
traces of bacterial endospores, if any. On the autoclaved seeds, 6 mm agar plugs (3-4 in
numbers) taken from a fortnightly old culture of A. rabiei with sterile cork borer were
inserted into seeds. Streptomycin (25 mg) was also mixed in autoclaved seeds i.e. 250 g in
conical flasks to avoid bacterial contamination. After closing the mouth of flasks with
cotton plug, flasks were incubated at 20 ± 2°C for ten days for further growth of pycnidial
culture of A. rabiei.
3.2 Plant materials
The germplasm used in this research was collected from Pulses section, AARI,
Faisalabad (Punjab) Pakistan. Recommended agronomic practices were carried out during
growing seasons to keep the crop in good condition and no pesticide/fungicide was
sprayed to induce maximum stress condition for the development of high disease pressure.
26
3.3 Environmental data
Consecutive five years (2006-10) environmental data comprising of maximum and
minimum temperatures, relative humidity, rainfall and wind speed of two months i.e.
February to March were collected from Agromet Department, ARRI, Faisalabad during
five years (2006-10) to develop chickpea blight predictive model. Environmental data of
two months i.e. February to March for two years (2011-12) were collected from
Meteorological station, Department of Crop Physiology, University of Agriculture,
Faisalabad for validation of five years disease predictive model.
3.4 Development of disease predictive model based on five (2006-10) years
data
3.4.1 Data collection of chickpea blight disease severity
For the development of chickpea blight disease predictive model, five years
disease severity data were taken from AARI. Four highly susceptible to susceptible
genotypes (K-97006, K-97007, K-95058 and D-91224) were consecutively sown for five
years (2006-10) at ARRI, Faisalabad. Genotypes were sown in randomized complete
block design (RCBD), each genotype had three replications. Each entry was planted in a
5m length row. The seeds were planted through dibbler at a distance of 10 cm. The row to
row distance between two entries was 50 cm. Plots were inoculated with spore suspension
of A. rabiei (105 spores/mL). Disease severity data were recorded on weekly basis from
the month of February to March on four genotypes during all five years (2006-10) using
1-10 modified rating scale (Khan et al., 1999a).
3.4.2 Analysis of data
The data were analyzed using two statistical softwares packages i.e. Meet Minitab
15 by Minitab Inc. U.S.A and SAS/STAT (SAS institute, 1990). Analysis of variance
(ANOVA), and comparison between disease severity and environmental conditions were
made through least significant difference test (LSD at P<0.05).
Effects of environmental parameters (maximum and minimum temperatures,
relative humidity, rainfall and wind speed) on disease severity were determined by
correlation analysis (Steel et al., 1997). Disease predictive model for chickpea blight
disease based on five years (2006-10) environmental variables was developed using
27
stepwise regression analysis (Myers, 1990). Environmental factors exhibited significant
relationship with disease severity were graphically plotted and critical ranges of
environmental variables conducive for chickpea blight disease development were
determined.
3.4.3 Evaluation of Model
Model was evaluated according to the procedures described by Chattefuee and Hadi
(2006) and Snee (1977). They suggested the following options to assess the model;
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 = [∑(𝑝𝑖 − 𝑜𝑖)2
𝑛⁄
𝑛
𝑖=1
]
0.5
𝐸𝑟𝑟𝑜𝑟(%) = ((𝑝 − 𝑜)
𝑜) 100
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).
3.4.4 Validation of model
For the validation of five years model, a separate experiment was conducted at the
field area of Department of Plant Pathology, University of Agriculture, Faisalabad in the
same manner as it was conducted in AARI during last five years. Four genotypes viz; K-
97006, K-97007, K-95058 and D-91224 were sown in RCBD with three replications
(maintaining R x R and P x P distance as mentioned in section (3.4.1). For inoculation and
disease rating same procedures were adopted, used for screening of genotypes against
chickpea blight mentioned in section (3.5). Disease severity data were recorded with the
28
help of 1-10 disease rating scale modified by (Khan et al., 1999a) on weekly basis from
February to March, 2011 and February to March, 2012 on four genotypes. Disease
severity on four advanced lines and environmental data of two years were employed for
model validation. Correlation analysis was used to determine the relationship between
environmental variables and disease severity. Stepwise regression analysis was used to
develop model based on two years environmental data and disease severity. Both
regression models based on five years (2006-10) and two years (2011-12) were validated
by comparing homogeneity of regression coefficients of F-test (Harrell, 2001).
3.5 Identification of resistant, tolerant and susceptible sources under field conditions
To evaluate various levels of resistance or susceptibility against ascochyta blight
disease, forty eight genotypes (white and black forms) of chickpea were sown in
augmented design under field conditions, at the Department of Plant Pathology,
University of Agriculture, Faisalabad for two years (2010-11 and 2011-12). Each line was
sown in two rows of 3 meter length, and row to row and plant to plant distance of 30 cm
and 15 cm, respectively was maintained. Punjab-1 (Pb-1) being the most susceptible
variety was sown after each two test genotypes as a spreader row. When the crop reached
at flowering or early pod formation stage test lines were sprayed on daily basis with spore
suspension of 105 spores/mL of A. rabiei. Number of spores were counted by using
haemocytometer. The process of spraying of inoculum continued until susceptible
spreader (Pb-1) showed the symptom expression to severe conditions i.e. death of
spreader line. To expedite the process of disease development plants were sprayed five
times daily with tap water to create conditions of artificial humidity. Disease ratings of
ascochyta blight were made by using 1-10 rating scale (Gowen et al., 1989), modified by
(Khan et al., 1999a).
29
Infection
%age
1-10 Point Scale Symptoms Reaction
1-10 1-<2 No infection or small lesions Highly
Resistant
11-20 2-<3 Some stem lesions - minor stem breakage
in upper foliage
Resistant
21-30 3-<4 1-2 branches broken. Several girdling
stem lesions low down on some branches
Resistant
31-40 4-<5 Large basal stem lesions or several
branches broken near to main stem
Moderately
Resistant
41-50 5-<6 Half foliage dead Moderately
Resistant
51-60 6-<7 Half foliage dead or dying, but young
shoots still actively growing from base
Moderately
Susceptible
61-70 7-<8 Most foliage dead - some healthy stem
tissue with lateral buds
Susceptible
71-80 8-<9 Most foliage dead, no healthy lateral buds
in leaf axils
Susceptible
81-90 9-<10 Most foliage dead, decreasing areas of
living stem tissue
Highly
Susceptible
91-100 10 Plants completely dead Highly
Susceptible
(Khan et al., 1999a)
3.6 Area under disease progress curve (AUDPC)
The AUDPC was computed using the trapezoidal integration of the blight severity
over time, assuming the whole period evaluated (Madden et al., 2007). The formula is
given as follows:
In this equation, (n) is number of assessments; (X) is disease severity and (ti+1-ti) is the
time interval between two consecutive data recording. AUDPC was divided by its
n-1
AUDPC = ∑ [(xi+xi+1)/2] (ti+1-ti)
i=1
30
respective observation period (ti+1-ti) to allow comparison between different treatments
that were assessed during different periods of time. Thus, AUDPC was the standard area
under chickpea blight severity progress curve and interpreted as; higher the value of
AUDPC, more susceptible the genotype was, and vice versa.
3.7 Management of chickpea blight disease
3.7.1 In vitro evaluation of fungicides and plant extracts against A. rabiei
Five fungicides and five plant extracts (Table.3.1 and Table.3.2) were tested at
0.05, 0.10, 0.15, and 3, 5 and 8 percent concentrations, respectively through poisoned food
technique (Nene and Thapliyal, 2002). To make the required percent concentrations of
fungicides, amount of each fungicide (50, 100 and 150 mg) was weighed and dissolved in
100 mL distilled water. For the preparation of aqueous plant extracts, actively growing
leaves, cloves, roots and shoots were taken and surface sterilized with 1% sodium
hypochlorite solution, then thoroughly washed with distilled sterilized water. After that,
the material was dried at 40°C in an oven and then grinded in electric grinder. This
grinded material was then soaked in sufficient amount of sterilized water to get 20% W/V
concentration of aqueous extract. After that, concentrated solution of plant extracts was
filtered through muslin cloth and filter papers. Plant extracts were stored at 4°C and used
within four days to ensure the antifungal efficacy. The required concentration of plant
extracts were made in sterilized water.
CSMA medium was prepared and sterilized in autoclave at 121oC and 15 lbs psi
for 20 minutes. Petri-dishes were sterilized in oven at 180 oC for two hours. CSMA
medium was then poured in sterilized petri-dishes in laminar flow chamber. After pouring,
petri-dishes (poisoned plates) were prepared by saturating fungicides/extracts in the
medium in laminar flow chamber to ensure aseptic conditions. Disks of 7 mm A. rabiei
culture with sterile cork borer were taken and punched in the center of each plate. The
plates were then kept in an incubator at 20 ± 2°C until full growth of A. rabiei appeared in
the control plate (having no fungicide/plant extract). Percentage inhibition of colony
growth was recorded by measuring colony diameter of treatments (poisoned plates) and
control plates by using following formula:
Colony Growth Inhibition (%) = Growth in control − Growth in treatment
Growth in control × 100
31
The present studies on in vitro bioassays of fungicides/plant extracts were accomplished
using completely randomized design (CRD) with three replications within each treatment.
The data were subjected to ANOVA for the determination of main and interactive effects
of treatments. For comparison of means Least Significant Difference (LSD) test at P <
0.05 was used.
Table 3.1 Fungicides used against A. rabiei management
Sr.
No
Common
Name
Chemical Name/percent
composition
Formulation Manufacturer
1 Alliete Aluminium ethyle phosphate 80 W.P Rhoune-poulenc-
Agrochimie
2 CabrioTop Dithiocarbamate (55%) +
Methoxycarbamate (5%)
5/55 WDG F.M.C
3 ThiovetJet Sulfur 80WG Syngenta
4 Nativo Tuboconazole 50% W/W Bayer Crop Science
5 Antracol Propinels 80% W/W Bayer Crop Science
Table 3.2 Plant materials used against A. rabiei management
Sr.
No
Common
Name
Botanical Name Family Parts
used
1 Neem Azadirachta indica A. Juss. Meliaceae Leaves
2 Garlic Allium sativum L. Alliaceae Cloves
3 Datura Datura stramonium L. Solanaceae Roots
and
shoots
4 Bakain Melia azedarach Meliaceae Leaves
5 Ak,
Akund
Calotropics procera Wild Drayand
ex.W. Ait.
Asclepiadiaceae Leaves
3.7.2 In vitro evaluation of fungal antagonists against A. rabiei
The spore suspensions of antagonists i.e. Aspergillus flavus and Trichoderma
harziaum at 105, 10
6, and 10
7 conidia/mL were made in sterilized water with the help of
haemocytometer, and effectiveness was checked by dual culture assay (Morton and
Strouble, 1955). Antagonistic fungi were grown on potato dextrose medium (PDA). PDA
was prepared by mixing 20 g potato dextrose, 20 g agar and 20 g glucose in 1000 mL
distilled water in conical flask. The whole material was then placed in autoclave for
sterilization. Sterilized medium was poured in petri-plates in laminar flow chamber. Later
on, the culture of above mentioned fungi was placed in each plate and incubated at 25°C.
32
When the colonies of antagonists were established, required spore concentration was
prepared. For dual culture, 7 mm disk of fungal culture of A. rabiei was taken and put on
one side of the petri-plate, then with sterilized syringe the required concentration of
antagonists was taken and placed on the other side of the plate. The plates were then
placed in incubator at 20 ± 2°C until the full growth appeared in the control. In this study,
CRD design was used and each treatment was replicated thrice. Percentage inhibition of
colony growth of A. rabiei was determined by following formula (Küçük et al., 2007).
Percentage Inhibition = R2 - R1 × 100
R2
R2 = Radial growth in control
R1 = Radial growth in treatment
33
Measuring colony diameter of A. rabiei
Comparing control with Thiovetjet fungicidal treatment @ 0.15%
Fig. 3.1. In vitro evaluation of treatments against A. rabiei
34
3.7.3 In vivo Evaluation of treatments against A. rabiei
Treatments performed best under in vitro conditions were tested under field
conditions. The treatments were;
T0 = Control (Water)
T1 = Alliette @ 0.15%
T2 = ThiovetJet @ 0.15%
T3 = M. azedarach @ 8%
T4 = A. indica @ 8%
T5 = T. harzianum @107
For the field evaluation of treatments, three varieties viz; (CM-2000, CM-98, Pb-1)
susceptible to chickpea blight were sown in RCBD with three replications. In each block
there were six rows of each variety, five for treatments and one served as control. All the
blocks were sprayed with inoculum of A. rabiei (105 spores/mL) until the appearance of
disease. When disease symptoms initiated treatments were applied with knapsack sprayer
by making required formulations. While, only distilled water was sprayed on the control.
Data regarding disease severity index (DSI) was recorded selecting 10 plants by the
formula suggested by (Bashir and Ilyas, 1986).
DSI(%) =Total of all ratings
No. of Plants examined×
100
Max. Disease rating
To evaluate the interactive effects of different treatments, data were subjected to ANOVA
and LSD statistical analysis tests (Steel et al., 1997).
35
Mass culture of A. rabiei
Field Experiment
Fig. 3.2. Preparation of mass culture of A. rabiei and screening of chickpea
germplasm under field conditions
36
CHAPTER 4 RESULTS AND DISCUSSION
4.1 Analysis of variance of five years (2006-10) environmental parameters and
chickpea blight disease severity
Individual effects of weeks, years and genotypes for chickpea blight disease severity
were significant during five years (Table 4.1). Two way interactive effects of genotypes
and years were also significant. Two way interactive effects of weeks and genotypes and
weeks and years were not significant (Table 4.1). Similarly, three way interactive effects
of weeks, years and genotypes were also not significant. It showed that with respect to
genotypes, weeks and years there was less variation in disease severity of chickpea blight
during the five years period.
Chickpea blight disease severity was observed higher in years 2006 and 2007 on all
four genotypes, while it was significantly less in year 2008 (Table 4.2). The difference
among disease severities of all four genotypes was significant in year 2006. The difference
between disease severities of K-97006 and K-97007 was also significant in year 2007.
There was no significant difference between disease severities of genotypes K-97006 and
K-97007 during years 2008, 2009 and 2010. Similarly, genotypes K-95058 and D-91224
also did not show significant difference between their disease severities during years 2007
and 2010. Mean disease severity was significantly higher on genotypes K-97006 and K-
97007 than K-95058 and D-91224 during five years (Table 4.2).
Table 4.1 Analysis of variance of chickpea blight disease severity during 2006-10
Sources DF SS MSE F-value P-value
Replication 2 109617 54808.6
Weeks 6 45145 7524.2 50.30 0.0001*
Years 4 10087 2521.7 16.86 0.0001*
Genotypes 3 12353 4117.5 27.53 0.0001*
Weeks x Years 24 1341 55.87 0.37 0.9974NS
Weeks x Genotypes 18 3170 176.1 1.18 0.2792NS
Years x Genotypes 12 114585 9548.75 63.82 0.0001*
Weeks x Years x
Genotypes
72 13607 189.0 1.26 0.0945 NS
Error 278 41583 149.6
Total 419 351488
*= Significant NS = Non-significant CV= 25
37
Table 4.2 Mean chickpea blight disease severity on four genotypes during five years
(2006-10)
Years Genotypes
K-97006 K-97007 K-95058 D-91224
2006 55.00 a* 59.28 b 48.57 c 44.28 d
2007 67.57 e 71.42 f 62.14 ab 60.00 ab
2008 38.57 g 39.28 g 30.71 h 33.57 ij
2009 44.28 d 42.85 d 33.57 ij 37.14 g
2010 47.14 c 48.57 c 37.85 g 39.28 g
LSD 14.75
*Means with similar letters in a row are not significantly different at P = 0.05
4.2 Analysis of variance of two years (2011-12) environmental parameters and
chickpea blight disease severity
During years 2011-12, the individual effects of genotypes, weeks and years were
significant (Table 4.3). Two way interaction between genotypes and years was also
significant. Two way interactive effects of weeks and genotypes and weeks and years
were not significant (Table 4.3). Three way interaction among weeks, genotypes and years
were also not significant.
Chickpea blight disease severity was significantly higher on genotypes K-97006
and K-97007 compared to genotypes K-95058 and D-91224 during two years (2011-12)
(Table 4.4). Difference in disease severity of genotype K-97007 was significant during
two years. Genotypes i.e. K-97006, K-95058 and D-91224 did not show significant
difference in their disease severities during both years (2011-12). In 2011, genotypes K-
95058 and D-91224 showed significant difference between their disease severities while
there was no significant difference between disease severities of K-97006 and K-97007.
However, there was significant difference among disease severities of all genotypes
during year 2012 (Table 4.4).
38
Table 4.3 Analysis of variance of chickpea blight disease severity during 2011-12
Sources DF SS MSE F-value P-value
Replication 2 2357.4 1178.7
Weeks 6 1596.9 266.15 3.60 0.0026*
Genotypes 3 12472.3 4157.43 56.25 0.0001*
Years 1 14393.0 14393.0 194.84 0.0001*
Weeks x Genotypes 18 972.9 54.1 0.73 0.7720NS
Weeks x Years 6 514.8 85.8 1.16 0.3334NS
Genotypes x Years 3 650.2 216.7 2.93 0.0368*
Weeks x Genotypes
x Years
18 819.3 45.5 0.62 0.8803 NS
Error 110 8125.9 73.9
Total 167 41902.7
*= Significant NS = Non-significant CV = 17.43
Table 4.4 Mean chickpea blight disease severity on four genotypes during two years
(2011-12)
Years Genotypes
K-97006 K-97007 K-95058 D-91224
2011 43.57 a* 45.00 a 34.28 b 39.28 c
2012 46.42 a 50.00 d 35.71 b 41.14 c
LSD 9.08
*Means with similar letters in a row are not significantly different at P = 0.05
4.3 Comparison of environmental parameters and chickpea blight disease severity
during 2006-10
Difference in maximum temperature was significant during years 2006 and 2007
while it was not significant during 2008-10 (Table 4.5). Difference in minimum
temperature was significant during years 2006 and 2008, and was not significant during
2007-10. Relative humidity differed significantly among years. Amount of rainfall
showed significant difference during three years i.e. 2006-08 and exhibited non-
significant difference in two years i.e. 2009-10. Rainfall was significantly higher in 2007
as compared to other years. There was no significant difference in wind speed during
years 2006, 2007 and 2009. Furthermore, wind speed also did not show significant
difference during 2008 and 2010. Maximum disease severity of chickpea blight i.e. 65.3%
and 51.8% was recorded during 2007 and 2006, respectively compared to other years.
Minimum level of disease severity i.e. 27.1% was observed during year 2008. In years
2009 and 2010, disease severity was 39.5% and 43.2%, respectively (Table 4.5).
39
Table 4.5 Comparison of environmental parameters and chickpea blight disease
severity during 2006-10
Environmental factors 2006 2007 2008 2009 2010 LSD
Maximum temperature 27.6 a 21.7 b 24.6 c 24.7 c 24.7 c 2.6
Minimum temperature 13.6 b 11.0 ab 9.22 ac 11.4 ab 11.9 ab 1.9
Relative humidity 48.3 a 60.3 b 38.4 e 56.2 d 62.1 b 12.1
Rainfall 4.08 e 14.0 bc 0.97 d 2.77 ef 2.95 ef 6.4
Wind speed 3.43 a 2.52 ab 5.92 c 2.63 ab 4.37 c 1.7
Disease severity 51.8 a 65.3 b 27.1 c 39.5 d 43.2 a 17.6
*Mean values within rows not sharing the same letter differ significantly at 0.05 level of
Probability
4.4 Comparison of environmental parameters and chickpea blight disease severity
during 2011-12
Except wind speed, all other environmental factors i.e. maximum and minimum
temperatures, relative humidity and rainfall were significantly different during 2011-12
(Table 4.6). Chickpea blight disease severity was also statistically significantly different
during two years. Blight severity was high i.e. 43.3% during year 2012 while it was low
i.e. 40.5% during year 2011(Table 4.6).
Table 4.6 Comparison of environmental parameters and chickpea blight disease
severity during 2011-12
*Mean values within rows not sharing the same letter differ significantly at 0.05 level of
Probability
4.5 Relationship of environmental parameters with chickpea blight disease severity
during five years (2006-10)
Environmental parameters significantly influenced chickpea blight disease severity
during 2006-10 (Table 4.7). There was negative correlation between maximum
temperature and disease severity during five years. Minimum temperature was positively
correlated with disease severity during four years i.e. 2006-7 and 2009-10 and was not
significantly correlated with disease severity during 2008 (Table 4.7). Except year 2008,
relative humidity showed positive correlation with disease severity (Table 4.7).
Environmental factors 2011 2012 LSD
Maximum temperature 24.2 a 21.1 b 1.09
Minimum temperature 9.50 a 6.96 b 0.88
Relative humidity 50.0 a 61.2 b 2.20
Rainfall 0.64 a 1.35 b 0.38
Wind speed 4.15 a 5.25 ab 0.25
Disease severity 40.5 a 43.3 b 4.01
40
Relationship of rainfall and wind speed with disease severity remained positive during
five years.
Table 4.7 Correlation of environmental parameters with chickpea blight disease
severity during (2006-10)
Environmental
Parameters
Disease severity
2006 2007 2008 2009 2010
Maximum
temperature
-0.912*
0.0001
-0.584*
0.0001
-0.555*
0.002
-0.678*
0.0001
-0.723*
0.0001
Minimum
temperature
0.448*
0.041
0.495*
0.007
0.359ns
0.061
0.648*
0.002
0.517*
0.005
Relative humidity 0.778*
0.0001
0.618*
0.0001
-0.601*
0.0001
0.791*
0.0001
0.777*
0.0001
Rainfall 0.846*
0.0001
0.806*
0.0001
0.719*
0.0001
0.767*
0.0001
0.874*
0.0001
Wind speed 0.870*
0.0001
0.554*
0.0001
0.920*
0.0001
0.902*
0.0001
0.850*
0.0001
Upper values indicate Pearson’s correlation coefficient
Lower values indicate level of probability at P = 0.05
4.6 Relationship of environmental parameters with chickpea blight disease severity
on different genotypes/advanced lines during two years (2011-12)
On the basis of overall correlation, all environmental parameters were significantly
correlated with chickpea blight disease severity during years 2011 and 2012 (Table 4.8).
Maximum temperature was negatively correlated while minimum temperature, relative
humidity, rainfall and wind speed was positively correlated with chickpea blight disease
severity during two years (2011-12).
To see the relationship of environmental parameters with chickpea blight disease
severity on different genotypes, the advanced lines having highly susceptible, susceptible
and moderately susceptible reactions were selected. No advanced line was selected
showing resistant to moderately resistant reactions because such lines had less disease
severity due to their resistant reactions. When the data were split by genotypes the level of
correlation was different during two years. During 2011, environmental variables i.e.
maximum and minimum temperatures, relative humidity, rainfall and wind speed
expressed significant correlation with blight severity on fifteen lines, those were; K-
97006, K-97007, K-98009, K-94002, K-98014, K-52721, K-95058, K-60028, K-93001,
K-92030, D-91005, D-91224, D-97074, D-96022 and Punjab-1(check). On the basis of
single environmental variable, maximum temperature had statistically significant
41
correlation with blight severity on twenty five advanced lines while had non-significant
correlation with three lines i.e. K-50076, K-95041 and D-03006. Minimum temperature
showed significant correlation with blight severity on twenty two lines whereas revealed
non-significant correlation with six lines (K-98012, K-95041, K-60034, D-CAM68, D-
03006 and D-03019). Relative humidity had significant correlation with blight severity on
twenty two genotypes and demonstrated non-significant correlation on six genotypes i.e.
K-98007, K-50076, K-95041, K-60016, K-60048 and D-03006. Except two genotypes i.e.
D-03006 and D-03019, rainfall had significant correlation with blight severity over rest of
twenty six advanced lines. Except four lines i.e. D-91005, D-97074, D-03006 and D-
03019, wind speed had significant correlation with blight severity on twenty four
advanced lines (Table 4.9).
In year 2012, all environmental parameters exhibited significant correlation with
chickpea blight disease severity on eighteen genotypes i.e. K-97006, K-97007, K-98009,
K-98007, K-52721, K-50076, K-95041, K-95041, K-95058, K-60016, K-60028, K-93001,
K-60034, K-60048, D-91224, D-CM98, D-91013 and Punjab-1(check). While, on the
basis of single environmental variable, maximum temperature was significantly correlated
with blight severity on twenty six advanced lines excluding two i.e. K-94002 and D-
96022. Minimum temperature was significantly correlated with blight severity on twenty
four genotypes and was not significantly correlated on four genotypes (K-94002, K-
98014, K-98012 and D-CAM68). Except three genotypes i.e. K-92030, D-91005 and D-
05028, relative humidity had significant correlation with twenty eight genotypes. Rainfall
was significantly correlated with blight severity on all genotypes. There was significant
correlation between wind speed and blight severity on twenty four genotypes and had non-
significant correlation on four genotypes i.e. D-91005, D-97074, D-03006 and D-03019
(Table 4.10).
42
Table 4.8 Overall correlation of environmental parameters with chickpea blight
disease severity during 2011-12
Environmental Parameters 2011 2012
Maximum temperature -0.838*
0.0001
-0.769*
0.0001
Minimum temperature 0.622*
0.0001
0.542*
0.003
Relative humidity 0.764*
0.0001
0.689*
0.0001
Rainfall 0.740*
0.0001
0.614*
0.0001
Wind speed 0.570*
0.002
0.628*
0.0001
Upper values indicate Pearson’s correlation coefficient
Lower values indicate level of probability at P = 0.05
43
Table 4.9 Correlation of environmental parameters with chickpea blight disease
severity on different genotypes/advanced lines during 2011
Sr.
No
Genotypes Maximum
Temperature
(°C)
Minimum
Temperature
(°C)
Relative
humidity
(%)
Rain fall
(mm)
Wind
Speed
(Km/h)
1 K-97006 -0.884** 0.613* 0.779* 0.700* 0.581*
2 K-97007 -0.809** 0.651* 0.716* 0.778* 0.617*
3 K-98009 -0.810** 0.556* 0.648* 0. 801** 0.694*
4 K-94002 -0.634* 0.580* 0.599* 0. 807** 0.527*
5 K-98007 -0.784* 0.554* 0.324 Ns 0.592* 0.612*
6 K-98014 -0.676* 0.691* 0.729* 0.806** 0.726*
7 K-98012 -0.752* 0.436 Ns 0.733* 0.742* 0.597*
8 K-52721 -0.625* 0.675* 0.650* 0.810** 0.778*
9 K-50076 -0.426 Ns 0.612* 0.397 Ns 0.568* 0.581*
10 K-95041 -0.414 Ns 0.255 Ns 0.417 Ns 0.614* 0.592*
11 K-95058 -0.764* 0.558* 0.721* 0.649* 0.536*
12 K-60016 -0.741* 0.681* 0.376 Ns 0.876 ** 0.757*
13 K-60028 -0.712* 0.501* 0.510* 0.788* 0.716*
14 K-93001 -0.748* 0.671* 0.735* 0.736* 0.662*
15 K-60034 -0.689* 0.433 Ns 0.695* 0.724* 0.702*
16 K-60048 -0.743* 0.619* 0.400 Ns 0.679* 0.681*
17 K-92030 -0.811** 0.614* 0.640* 0.804** 0.739*
18 D-91005 -0.837** 0.674* 0.797* 0.736* 0.746*
19 D-91224 -0.740* 0.571* 0.774* 0.667* 0.519*
20 D-CM98 -0.659* 0.682* 0.784* 0.626* 0.429Ns
21 D-CAM68 -0.818** 0.405 Ns 0.669* 0.808** 0.653*
22 D-91013 -0.710* 0.697* 0.656* 0.748* 0.406 Ns
23 D-97074 -0.685* 0.677* 0.782* 0.797* 0.676**
24 D-96022 -0.870** 0.527* 0.760* 0.736* 0.805**
25 D-03006 -0.312 Ns 0.290 Ns 0.438 Ns 0.440Ns 0.604*
26 D-03019 -0.581* 0.417 Ns 0.536* 0.425Ns 0.612*
27 D-1CC-5127 -0.747* 0.689* 0.649* 0.661* 0.370 Ns
28 Punjab-
1(check)
-0.874* * 0.539* 0.789* 0.878** 0.649*
Ns = Non-significant
* = Significant
** = Highly significant
44
Table 4.10 Correlation of environmental parameters with chickpea blight disease
severity on different genotypes/advanced lines during 2012
Sr.
No
Genotypes Maximum
Temperature
(°C)
Minimum
Temperature
(°C)
Relative
humidity
(%)
Rain fall
(mm)
Wind
Speed
(Km/h)
1 K-97006 -0.780* 0.573* 0.659* 0.736* 0.656*
2 K-97007 -0.761* 0.542* 0.572* 0.604* 0.612*
3 K-98009 -0.767* 0.521* 0.511* 0.596* 0.587*
4 K-94002 -0.441 Ns 0.365 Ns 0.629* 0.660* 0.509*
5 K-98007 -0.642* 0.579* 0.546* 0.619* 0.631*
6 K-98014 -0.776* 0.338 Ns 0.626* 0.786* 0.661*
7 K-98012 -0.791* 0.285 Ns 0.648* 0.617* 0.566*
8 K-52721 -0.777* 0.596* 0.758* 0.592* 0.503*
9 K-50076 -0.578* 0.599* 0.575* 0.687* 0.609*
10 K-95041 -0.527* 0.561* 0.574* 0.691* 0.617*
11 K-95058 -0.712* 0.531* 0.689* 0.646* 0.602*
12 K-60016 -0.743* 0.643* 0.726* 0.744* 0.789*
13 K-60028 -0.652* 0.549* 0.673* 0. 755* 0.711*
14 K-93001 -0.678* 0.628* 0.684* 0.503* 0.615*
15 K-60034 -0.801** 0.536* 0.583* 0.615* 0.622*
16 K-60048 -0.774* 0.568* 0.511* 0.598* 0.518*
17 K-92030 -0.685* 0.534* 0.370 Ns 0.760* 0.625*
18 D-91005 -0.836** 0.536* 0.410 Ns 0.729* 0.435 Ns
19 D-91224 -0.703* 0.516* 0.639* 0.535* 0.571*
20 D-CM98 -0.716* 0.509* 0.666* 0.596* 0.649*
21 D-CAM68 -0.862** 0.297 Ns 0.741* 0.795* 0.677*
22 D-91013 -0.694* 0.558* 0.705* 0.611* 0.508
23 D-97074 -0.818** 0.564* 0.767* 0.824** 0.324 Ns
24 D-96022 -0.429 Ns 0.610* 0.612* 0.870** 0.630*
25 D-03006 -0.523* 0.590* 0.666* 0.619* 0.381 Ns
26 D-03019 -0.716* 0.586* 0. 544* 0.551* 0.354 Ns
27 D-05028 -0.717* 0.592* 0. 206Ns 0.624* 0.523*
28 Punjab-1(check) -0.780* 0.648* 0.658* 0.788* 0.555*
Ns = Non-significant
* = Significant
** = Highly significant
45
4.7 Chickpea blight disease predictive model based on five years data (2006-10)
Five years environmental conditions and chickpea blight disease severity data of
four genotypes was subjected to stepwise regression analysis to develop disease predictive
model. A multiple regression model Y= 81.8 - 3.17x1 + 1.99x2 + 0.257x3 + 0.885x4 +
1.85x5 (where y= chickpea blight severity, x1= maximum temperature, x2 = minimum
temperature, x3 = relative humidity, x4 = rainfall, x5 = wind speed) was developed to
predict the outbreak of chickpea blight. Model showed variability in disease severity up to
72% (Table 4.11). All the environmental conditions significantly influenced severity of
blight during five years.
Table 4.11 Summary of stepwise regression models to predict chickpea blight disease
during period of five years (2006-10)
Parameters No. in
Model
Model
R2
C(p) MSE F value Prb. > F
Rainfall 1 0.50 106.5 202 138.20 0.0001*
Maximum
temperature
2 0.67 24.7 133 141.01 0.0001*
Minimum
temperature
3 0.69 17.2 126 102.15 0.0041*
Wind speed 4 0.71 10.5 119 82.86 0.0042*
Relative
humidity
5 0.72 6.0 115 70.26 0.0012*
4.8 Model assessment
The above model was assessed by Chattefuee and Hadi (2006) procedure. The
suggested procedure include following steps to evaluate regression model.
(1) Comparison of the dependent variable and regression coefficients with physical theory
(2) Comparison of observed vs. predicted data
(3) Collection of new data to check predictions
4.8.1 Comparison of the dependent variable and regression coefficients with physical
theory
Coefficient of determination (R2), F-statistics and standard error are effective
criteria to check the reliability of regression model. Chickpea disease predictive model
based upon five years environmental parameters exhibited fairly good R2 value i.e. 72
percent (Table 4.12). Though it was not quite high yet under field conditions it is
considered good because of no control over the environmental conditions. Secondly,
standard error was low i.e. ≤20 (Table 4.12). F-distribution of regression statistics was
46
significant (Table 4.13). Environmental parameters (maximum and minimum
temperatures, relative humidity, rainfall and wind speed) contributed in the chickpea
blight disease predictive model were significant at P <0.05 (Table 4.14). Each
independent variable in the model showed quite low standard error ≤5 (Table 4.14). These
criteria indicated that the model was statistically good, and may effectively predict
chickpea blight disease severity.
Table 4.12 Regression statistics of chickpea blight disease predictive model during
(2006-10)
Regression statistics
R-Square 72.4%
Adj.R-Square 71.4%
Standard Error 10
Observations 140
Table 4.13 Analysis of variance of chickpea blight disease predictive model based on
five years (2006-10) data
ANOVA
Source Df SS MS F P-value Significance
F
Regression 5 40504.1 8100.8 70.27 0.001 **
Residual
error
134 15448.1 115.3
Total 135 55952.3
Table 4.14 Coefficients of variables, their standard error, t Stat, P-value and their
significance
Parameters Coefficients Standard
Error
t-Stat P-value
Intercept 81.81 14.15 5.78 0.001 *
Maximum
Temperature
-3.1707 0.6854 -4.63 0.001*
Minimum
Temperature
1.9935 0.7931 2.51 0.031*
Relative Humidity 0.2570 0.1011 2.54 0.031*
Rainfall 0.8847 0.1620 5.46 0.021*
Wind speed 1.8456 0.5258 3.51 0.001*
*Significance at P < 0.05
47
4.8.2 Model evaluation by comparing 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) 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 five years model on four genotypes, showed percent error ≤
±20 (Table 4.15-16). Only 25 predicted values out of 140 showed percent error more than
±20. Average percent error and RMSE of total predictions (140) during five years on four
genotypes was low i.e. less than ±20 (Table 4.15-16). Most of the predicted values gave
difference of less than ten as manifested by predicted values on four advanced lines (Table
4.15-16). Conformity was observed between most of observed and predicted values (Table
4.15-16).
48
Table 4.15 Observed and predicted chickpea blight disease severity (%) on genotypes (K-97006) and (K-97007) during five years
(2006-10)
Genotypes 2006 2007 2008 2009 2010
K-97006 Obs* Pre** Error Obs Pre Error Obs Pre Error Obs Pre Error Obs Pre Error
(%) (%) (%) (%) (%
30 28 -7 30 32 6.6 10 28 110 20 18 -10 25 28 12
35 34 -3 50 57 14 25 23 -8 25 26 4 30 31 3
45 39 -13 60 56 -7 20 24 -7 25 28 12 35 33 -6
55 51 -7 70 73 4 35 31 -11 30 32 7 40 44 10
60 60 0 85 70 -18 50 40 -7 50 39 -22 50 48 -4
75 69 -8 90 78 -13 45 54 -2 70 60 -14 65 67 3
85 80 -6 90 88 -2 70 68 -1 90 82 -9 85 78 -8
RMSE (Error %) 9.07(-6.23) 11(-6.39) 4.19 (5.09) 9.44(-8.06) 0.37(-0.30)
K-97007 25 28 12 40 32 -20 10 28 110 15 18 20 15 28 87
40 34 -15 65 57 -12 20 23 15 25 26 4 25 31 24
45 39 -13 65 56 -14 30 24 -7 30 28 -7 40 33 -18
60 51 -15 70 73 4 30 31 3 35 32 -9 45 44 -2
70 60 -14 80 70 -13 50 40 -16 45 39 -13 55 48 -13
85 69 -19 90 78 -13 60 54 -10 65 60 -8 70 67 -4
90 80 -11 90 88 -2 75 68 -8 85 82 -4 90 78 -13
RMSE (Error %) 18 (-14.02) 17.38 (-9.2) 2.54 (-2.64) 5.66(-4.83) 4.15(-3.33)
*Observed
**Predicted
49
Table 4.16 Observed and predicted chickpea blight disease severity (%) on genotypes (K-95058) and (D-91224) during five years
(2006-10)
Genotypes 2006 2007 2008 2009 2010
K-95058 Obs* Pre** Error Obs Pre Error Obs Pre Error Obs Pre Error Obs Pre Error
(%) (%) (%) (%) (%
20 28 40 20 32 60 10 28 110 10 18 80 10 28 180
30 34 13 40 57 43 15 23 53 20 26 30 20 31 55
40 39 -3 60 56 -7 25 24 12 20 28 40 30 33 10
50 51 2 65 73 12 25 31 24 25 32 28 40 44 10
55 60 9 70 70 0 35 40 20 35 39 11 40 48 20
70 69 -1 70 78 11 45 54 20 55 60 9 60 67 12
75 80 7 85 88 4 60 68 15 70 82 17 65 78 20
RMSE (Error %) 7.93(5.45) 16.63 (10.73) 18.65 (10.03)
18.89(16.12) 14.18(19.39)
D-91224 20 28 40 30 32 7 10 28 110 15 18 20 10 28 180
25 34 36 50 57 14 20 23 15 20 26 30 10 31 210
35 39 11 65 56 -14 20 24 40 25 28 12 30 33 10
45 51 13 65 73 12 30 31 3 25 32 28 45 44 -2
50 60 20 70 70 0 40 40 5 40 39 -3 50 48 -4
65 69 6 75 78 4 50 54 8 60 60 0 60 67 12
70 80 14 85 88 4 65 68 6 75 82 9 70 78 11
RMSE (Error %) 19.27(13.24) 5.29 (3.18) 12.17(-14.04) 9.44(8.06) 10.41(16.36)
*Observed
**Predicted
50
4.9 Chickpea blight disease predictive model based on two years data (2011-12)
A multiple regression model; Y = 73.8 - 3.11x1 + 1.60x2 + 0.40X3 + 1.44X4 +
2.34X5 based upon two years environmental conditions data was developed to give the
prediction of chickpea blight diesease. Environmental conditions i.e. maximum and
minimum temperatures, relative humidity, rainfall and wind speed contributed
significantly in disease development (Table 4.17). Two years model explained 82%
variability in disease development. In this model, Y= chickpea bligth severity, X1 =
maximum temperature, X2 = minimum temperature, X3 = relative humdity, X4 = rainfall and
X5 = wind speed.
Table 4.17 Summary of stepwise regression models to predict chickpea blight disease
during period of two years (2011-12)
Parameters No. in
Model
Model
R2
C(p) MSE F value Prb. > F
Maximum
temperature
1 0.71 26.6 66 132.57 0.001*
Minimum
temperature
2 0.76 15.7 56 83.07 0.001*
Wind speed 3 0.78 10.9 51 62.55 0.001*
Relative
humidity
4 0.80 8.6 48 50.72 0.001*
Rainfall 5 0.82 6.0 45 44.33 0.001*
4.10 Model assessment
4.10.1 Comparison of the dependent variable and regression coefficients with
physical theory
Predictive model based upon two years environmental parameters (maximum and
minimum temperatures, relative humidity, rainfall and wind speed) showed high R2 value
i.e. 81.6 percent with low standard error i.e. ≤ 20 (Table 4.18). F-distribution analysis
exhibited significant regression statistics (Table 4.19). Maximum and minimum
temperatures, relative humidity, rainfall and wind speed were found significant in the
chickpea blight disease severity at P < 0.05 (Table 4.20). High R2 value, low standard
error and significance of regression statistics showed that model was good to predict
chickpea blight disease severity (Table 4.18-20).
51
Table 4.18 Regression statistics of chickpea blight disease predictive model during
(2011-12)
Regression statistics
R-Square 81.6%
Adj.R-Square 83.6%
Standard Error 6.73
Observations 55
Table 4.19 Analysis of variance of chickpea blight disease predictive model based on
two years (2011-12) data
ANOVA
Source Df SS MS F P-value Significance
F
Regression 5 10063.7 2012.7 44.33 0.001 **
Residual
error
50 2270.2 45.4
Total 55 12333.9
Table 4.20 Coefficients of variables, their standard error, t Stat, P-value and their
significance
Parameters Coefficients Standard
Error
t-Stat P-value
Intercept 73.80 20.11 3.67 0.001*
Maximum
Temperature
-3.1092 0.6349 -4.90 0.001*
Minimum
Temperature
1.6022 0.6156 2.60 0.021*
Relative Humidity 0.4077 0.1866 2.19 0.034*
Rainfall 1.4364 0.6725 2.14 0.038*
Wind speed 2.344 1.147 2.04 0.046*
*Significance at P<0.05
4.10.2 Model evaluation by comparing observed and predicted data
For the evaluation of two years (2011-12) model, predictions were obtained using
two years model and evaluated on two criteria i.e. percent error and RMSE. Percent error
of most of the predictions was less than ±20 (Table 4.21). Only seven predictions out of
fifty six showed high percent error i.e. >±20. Average percent error and RMSE of all
predictions given by the two years model was low (≤±20) (Table 4.21). Observed and
predicted data points of four advanced lines showed close conformation. Out of fifty six
data points only nine showed difference of more than ten (Table 4.21). This shows that
model may give good predictions of blight severities on chickpea crop.
52
Table 4.21 Observed and predicted chickpea blight disease severity (%) on
genotypes (K-97006), (K-97007), (K-95058) and (D-91224) during
two years (2011-12)
Genotypes 2011 2012
K-97006 Obs* Pre** Error Obs Pre Error
(%) (%)
15 22 47 20 18 -10
25 21 -16 25 22 -12
35 28 -20 40 41 2
40 36 -10 40 46 15
50 45 -10 55 52 -5
65 60 -8 60 58 -3
85 76 -11 85 78 -8
RMSE (Error %) 3.02 (2.85) -3.07 (-7.54)
K-97007 10 22 120 15 18 20
20 21 5 20 22 10
30 28 -7 45 41 -9
35 36 3 50 46 -8
50 45 -10 60 52 -13
75 60 -20 70 58 -17
85 76 -11 90 78 -13
RMSE (Error %) 6.42 (-5.57) 13.22 (10)
K- K-95058 10 22 120 15 18 20
20 21 5 15 22 -25
20 28 40 20 38 90
30 36 20 35 41 17
40 45 12 45 46 -2
55 60 9 55 58 5
65 76 17 65 78 20
RMSE (Error %) 18.14 (12.08) 19.20 (15.11)
D-91224 10 22 120 15 18 20
20 21 5 20 22 10
30 28 -7 35 38 9
40 36 -10 40 41 2
45 45 0 50 46 -8
65 60 -8 60 58 -3
65 76 17 70 78 11
RMSE (Error %) 4.91(4.72) 4.15(3.79)
*Observed
**Predicted
53
4.11 Chickpea blight disease predictive model validation
Chickpea blight disease predictive model based upon five years (2006-10)
environmental data, collected from ARRI, Faisalabad was validated on two years (2011-
12) data collected from Department of Plant Pathology, University of Agriculture,
Faisalabad. Regression equations of two models were fairly matchable to each other,
showing good fit into the data (Table 4.22). Slopes of both models were close to each
other. Coefficients of determination of five years model (R2=0.72) and two years model
(R2=0.82) were also comparable to each other. Both regression equations (Model-I and
Model-II) showed quite good proximity. P-value showed that two models were not
significantly different from each other indicating the homogeneity of regression (Table
4.22).
Table 4.22 Comparison of the two models for validation of chickpea blight disease
severity
Model Regression equation R2 F-
value
P-
value
Model(I)
Model(II)
Y = 81.8 - 3.17x1 + 1.99x2 + 0.25x3 + 0.88x4 + 1.85x5
Vs
Y = 73.8 - 3.11x1 + 1.60x2 + 0.40X3 + 1.44X4 + 2.34X5
0.72
0.82
0.542
0.999
Model (I) = Model based on five years data Significant at P< at 0.05
Model (II) = Model based on two years data
Where, x1= Maximum Temperature, x2 = Minimum Temperature, x3 = Relative humidity,
x4= Rainfall, x5= Wind speed, respectively
4.12 Characterization of environmental parameters conducive for chickpea blight
disease severity during 2006-10
Scatter plots were used to determine the relationship of environmental parameters
i.e. maximum and minimum temperatures, relative humidity, rainfall and wind speed with
chickpea blight disease severity during five years (2006-10). Linear and negative
relationship was found between maximum temperature and blight severity except year
2008 in which it was polynomial (Fig. 4.1). Negative relationship of maximum
temperature means that with the increase of maximum temperature disease severity
decreased. Linear regression models explained up to 91 percent variability in disease
severity due to maximum temperature (Fig. 4.1). Minimum temperature showed
polynomial relationship with disease severity during five years (Fig. 4.2). Polynomial
relationship explains that disease severity gradually increased up to 14°C minimum
temperature and decreased after >14°C. Polynomial regression models explained up to 64
54
percent variability in disease severity due to minimum temperature (Fig. 4.2). Average
maximum and minimum temperatures during five years remained in the range of 15-35°C
and 4-18°C, respectively (Fig. 4.1-4.2). Except year 2008, relative humidity in the range
of 35-75% showed positive relationship with disease severity (Fig. 4.3), which means
chickpea blight disease severity increased with the increase of relative humidity. There
was linear relationship between relative humidity and chickpea blight severity during five
years period (Fig. 4.3). Maximum 79% variability was found in disease severity because
of relative humidity (Fig. 4.3). Total rainfall during five years was not so high; maximum
total rainfall was recorded 36.5 mm only in year 2007 (Fig. 4.4). Relationship between
rainfall and disease severity was linear and positive that explains that disease severity
increased with the increase of rainfall. Rainfall showed up to 87 percent variability in
disease severity during year 2010. Positive relationship was also observed in case of wind
speed with chickpea blight severity during five years (Fig. 4.5). With the increase of wind
speed disease severity increased. Regression models displayed up to 92 percent variability
in disease severity due to wind speed (Fig. 4.5). The range of wind speed during five years
(2006-10) was 2-10 km/h (Fig. 4.5).
55
Scatter plot of chickpea blight disease severity vs maximum temperature
Dis
ease
sev
erit
y (
%)
Maximum temperature (°C)
Y = Disease severity, x = Maximum temperature
Fig. 4.1 Relationship of maximum temperature with chickpea blight disease severity during 2006-10
y = 96.21-1.55x r = 0.91 Year 2006
0
20
40
60
80
100
24 26 28 30 32
y = 57.26 - 0.90x r = 0.58 Year 2007
0
20
40
60
80
100
18 20 22 24 26
y = - 71.51+ 9.19x - 0.42x2 r= 0.55 Year 2008
0
10
20
30
40
50
60
70
16 21 26 31
y = 54.22-1.63x r = 0.67 Year 2009
0
20
40
60
80
100
20 25 30
y = 25.52 - 3.39x r = 0.72 Year 2010
0
20
40
60
80
100
18 21 24 27 30 33
y = 113.28-2.73x r= 0.61 Average
0
20
40
60
80
100
15 20 25 30 35
56
Scatter plot of chickpea blight disease severity vs minimum temperature
Dis
ease
sev
erit
y (
%)
Minimum temperature (°C)
Y = Disease severity, x = Minimum temperature
Fig. 4.2 Relationship of minimum temperature with chickpea blight disease severity during 2006-10
y = - 73.86+121.06x -4.546x2 r = 0.44 Year 2006
0
20
40
60
80
100
10 12 14 16
y = 42.2 - 77.15x + 3.97x2 r =0.49 Year 2007
0
20
40
60
80
100
8 10 12 14
y = - 47.97 + 20.61x-1.03x2 r = 0.35 Year 2008
0
20
40
60
80
3 8 13 18
y = - 41.98 + 71.27x -2.66x2 r = 0.64 Year 2009
0
20
40
60
80
100
9 10 11 12 13 14 15 16
y = - 30.72+ 46.40x-1.78x2 r = 0.51 Year 2010
0
20
40
60
80
100
7 9 11 13 15 17
y = - 42.83+14.86x-0.56x2 r = 0.40 Average
20
40
60
80
100
4 6 8 10 12 14 16 18
57
Scatter plot of chickpea blight disease severity vs relative humidity
Dis
ease
sev
erit
y (
%)
Relative humidity (%)
Y = Disease severity, x = Relative humidity
Fig. 4.3 Relationship of relative humidity with chickpea blight disease severity during 2006-10
y = - 56.03+ 2.20x r = 0.77 Year 2006
0
20
40
60
80
100
35 45 55
y = - 41.21+1.77x r = 0.61 Year 2007
0
20
40
60
80
100
50 60 70
y = 160.93-3.37x r = 0.60 Year 2008
0
20
40
60
80
32 34 36 38 40 42 44
y = - 96.15 + 2.35x r = 0.79 Year 2009
0
20
40
60
80
100
40 50 60 70
y = - 94.82 + 2.23x r = 0.77 Year 2010
0
20
40
60
80
100
50 55 60 65 70 75 80
y = - 9.96+1.07x r = 0.56 Average
0
20
40
60
80
100
35 55 75
58
Scatter plot of chickpea blight disease severity vs rainfall
Dis
ease
sev
erit
y (
%)
Rainfall (mm)
Y = Disease severity, x = Rainfall
Fig. 4.4 Relationship of rainfall with chickpea blight disease severity during 2006-10
y = 38.73+3.19x r = 0.84 Year 2006
0
20
40
60
80
100
0 5 10 15 20
y = 48.31 + 1.22x r = 0.80 Year 2007
0
20
40
60
80
100
0 10 20 30 40
y = 29.58 + 6.12x r = 0.71 Year 2008
0
20
40
60
80
0 2 4 6 8
y = 31.99 + 3.01x r = 0.76 Year 2009
0
20
40
60
80
100
0 5 10 15 20
y = 28.73 + 4.35x r = 0.87 Year 2010
0
20
40
60
80
100
0 5 10 15
y = + 36.53+2.09x r = 0.75 Average
0
20
40
60
80
100
120
0 10 20 30 40
59
Scatter plot of chickpea blight disease severity vs wind speed
Dis
ease
sev
erit
y (
%)
Wind speed (Km/h)
Y = Disease severity, x = Wind speed
Fig. 4.5 Relationship of wind speed with chickpea blight disease severity during 2006-10
y = - 12.47+17.92x r = 0.87 Year 2006
0
20
40
60
80
100
2 3 4 5 6
y = 39.06 + 10.47x r = 0.55 Year 2007
0
20
40
60
80
100
0 2 4 6
y = - 41.14+ 12.94x r = 0.92 Year 2008
0
20
40
60
80
4 6 8
y = 10.41 + 11.12x r = 0.90 Year 2009
0
20
40
60
80
100
0 2 4 6 8
y = 1.76 + 8.67x r = 0.85 Year 2010
0
20
40
60
80
100
3 5 7 9
y = 31.08 +4.13x r =0.35 Average
0
20
40
60
80
100
2 4 6 8 10
60
4.13 Characterization of environmental parameters conducive for chickpea blight
disease severity during 2011-12
On four lines i.e. K-97006, K-97007, K-95058 and D-91224 (highly susceptible to
susceptible), significant relationship was found between environmental parameters and
disease severity. Maximum temperature contributed significantly in the development of
ascochyta blight of chickpea on four genotypes as indicated by correlation coefficients (r)
values 0.70, 0.71, 0.76 and 0.74, respectively (Fig. 4.6). The relationship between
maximum temperature and blight severity was polynomial. It was observed that with the
increase of maximum temperature from 24°C to 32°C disease severity decreased.
Maximum disease severity was recorded at maximum temperature limit of 20-24°C (Fig.
4.6). Minimum temperature in the range of 12-14°C was found favourable for chickpea
blight. Minimum temperature demonstrated positive and linear relationship with blight
severity (Fig. 4.7). Regression models developed on four genotypes on the basis of
minimum temperature showed “r” values as; 0.61, 0.65, 0.66 and 0.68, respectively (Fig.
4.7). Influence of relative humidity on ascochyta blight severity on four lines was positive
(Fig. 4.8). Maximum severity was observed in the range of 60 to 70% relative humidity
(Fig. 4.8). Linear regression models explained significant relationship between relative
humidity and disease severity on four lines as indicated by higher “r” values; 0.88, 0.82,
0.82, and 0.77, respectively. Rainfall was positively correlated with blight severity i.e.
disease severity increased with the increase of rainfall. Maximum disease severity was
recorded above 5 mm rainfall (Fig. 4.9). There was significant contribution of rainfall in
disease severity as indicated by “r” values, i.e. 0.80, 0.78, 0.76 and 0.72, respectively.
Wind speed also displayed positive correlation with blight severity. With the increase of
wind speed disease severity increased (Fig. 4.10). However, highest level of disease
severity was seen on all four advanced lines at 6.5 Km/h range of wind speed (Fig. 4.10).
Regression models developed on four lines to explain the relationship of wind speed with
chickpea blight severity showed significant higher “r” values (0.88, 0.86, 0.84 and 0.85)
(Fig. 4.10).
61
Fig. 4.6 Relationship of maximum temperature with chickpea blight disease severity
recorded on K-97006(V1), K-97007(V2), K-95058(V3) and D-91224(V4)
during years 2011-12
Fig. 4.7 Relationship of minimum temperature with chickpea blight disease severity
recorded on K-97006(V1), K-97007(V2), K-95058(V3) and D-91224(V4)
during years 2011-12
= - 111.91+17.8x -0.46x2 r = 0.70 = - 78.59 + 14.15x-0.37x2 r = 0.71 = - 67.79 + 12.83x-0.34x2 r = 0.76 = - 31.08 - 0.25x2 + 8.76x r = 0.74
-20
0
20
40
60
80
100
18 20 22 24 26 28 30 32
Dis
ease
sev
erit
y (
%)
Maximum temperature (°C)
V1V2V3V4
= - 5.82x + 5.47 r = 0.61 = -16.53 + 7.21 r = 0.65 = -11.27 + 5.22 r = 0.66 = -8.72 + 5.58 r = 0.68
0
20
40
60
80
100
4 6 8 10 12 14 16
Dis
ease
sev
erit
y (
%)
Minimum temperature (°C)
V1V2V3V4
62
Fig. 4.8 Relationship of relative humidity with chickpea blight disease severity
recorded on K-97006(V1), K-97007(V2), K-95058(V3) and D-91224(V4)
during years 2011-12
Fig. 4.9 Relationship of rainfall with chickpea blight disease severity recorded on
K-97006(V1), K-97007(V2), K-95058(V3) and D-91224(V4) during years
2011-12
= -89 + 2.43x r = 0.88 = -76 + 2.93x r = 0.82 = -83 + 2.12x r = 0.82 = -88 + 2.31x r = 0.77
0
10
20
30
40
50
60
70
80
90
100
35 40 45 50 55 60 65 70
Dis
ease
sev
erit
y (
%)
Relative humidity (%)
V1V2V3V4
= 33 + 12.96x r = 0.80 = 32+ 13.96 x r = 0.78 = 24+ 13.96 x r = 0.76 = 30+ 10.17x r = 0.72
0
10
20
30
40
50
60
70
80
90
100
0 1 2 3 4 5 6
Dis
ease
sev
erit
y (
%)
Rainfall (mm)
V1V2V3V4
63
Fig. 4.10 Relationship of wind speed with chickpea blight disease severity recorded
on K- 97006(V1), K-97007(V2), K-95058(V3) and D-91224(V4) during years
2011-12
4.14 Identification of resistant sources in chickpea against chickpea blight disease
Screening trails were conducted for two years i.e. 2011-12 to evaluate chickpea
germplasm against ascochyta blight. No genotype/advanced line was found highly
resistant. Among 48 lines, 10 and 11 lines showed resistant and moderately resistant
response, respectively. Most of the lines were susceptible to highly susceptible while
others exhibited moderately susceptible response. Sixteen lines were highly susceptible
and eight were susceptible. Three lines displayed moderately susceptible response. Area
under disease progress curve (AUDPC) was also calculated for all types of responses.
Maximum percent disease severity (up to 81.25%) and AUDPC (1715-1750) was
recorded on Punjab-1(check) during both years. Genotypes did not show variation in their
responses i.e. resistant, moderately resistant, highly susceptible, susceptible and
moderately susceptible during two years (2011-12), however, the level of disease severity
and AUDPC over different genotypes differed in two years. Responses of all genotypes
are given in Table 4.23-24.
Genotypes K-60013, K-98008, K-96001, K-96022, D-97092, D-91055, D-90272,
D-96050, D-Pb2008 and D-Pu502-362 displayed resistant reaction (Table 4.23-24).
Chickpea lines exhibited moderately resistant response against blight were; K-96033, K-
89169, K-90395, D-91017, D-89044, D-05006, D-96018, D-86030, D-96032, D-03009
= - 53+ 20.67x r = 0.88 = - 51+19.17x r = 0.86 = - 67+ 24.02x r = 0.84 = - 51+18.08x r = 0.85
0
10
20
30
40
50
60
70
80
90
100
3 4 5 6 7
Dis
ease
sev
erit
y (
%)
Wind speed (Km/h)
V1V2V3V4
64
and D-1CC-5127 (Table 4.23-24). During year 2011, minimum and maximum range of
percent disease severity on resistant and moderately resistant genotypes was 3.75-8.75 and
11.25-17.50, respectively. While, range of percent disease severity on resistant and
moderately resistant advanced lines during year 2012 was 3.75-10 and 12.5-20,
respectively. Range of AUDPC for resistant and moderately resistant advanced lines
during year 2011 was 52.5-192.5 and 210-367.5, respectively. In year 2012, resistant and
moderately resistant advanced lines had AUDPC range as; 70-227.5 and 245-420,
respectively (Table 4.23-24).
Advanced lines showed highly susceptible response were viz; K-97006, K-97007,
K-98009, K-94002, K-98014, K-98012, K-52721, K-60028, K-93001, K-92030, D-91005,
D-CM98, D-CAM68, D-91013, D-97074, D-96022 and Punjab-1(check). Whereas, with
susceptible level included; K-95058, K-60016, K-60034, K-60048, D-91224, D-03006, D-
03019 and D-05028. Only three lines, K-98007, K-50076 and K-95041 showed
moderately susceptible response (Table 4.23-24). In year 2011, percent disease severity
range for highly susceptible, susceptible and moderately susceptible advanced lines was
found as 52.5-80, 38.75-43.75 and 31.25-35, respectively, while in year 2012, same
responses exhibited their percent disease severity range as 56.25-81.25, 40-45 and 32.5-
36.25, respectively. During year 2011, highly susceptible, susceptible and moderately
susceptible genotypes showed AUDPC range as; 1085-1715, 787.5-910 and 647.5-735,
respectively. Whereas, AUDPC range for highly susceptible, susceptible and moderately
susceptible genotypes during year 2012 remained as; 1172.5-1750, 805-962.5 and 665-
752.5, respectively (Table 4.23-24).
65
Table 4.23 Response of genotypes to chickpea blight disease during 2011
Sr. No Genotypes Disease
rating
Percent disease
Severity
AUDPC Reaction
1 K-97006 9.0 52.5 1085 HS*
2 K-97007 9.0 52.5 1137.5 HS
3 K-60013 3.0 3.75 70 R
4 K-98009 9.0 58.75 1242.5 HS
5 K-94002 9.0 55 1155 HS
6 K-98007 6.0 33.75 717.5 MS
7 K-98008 3.0 6.25 122.5 R
8 K-98014 9.0 53.75 1120 HS
9 K-98012 9.0 55 1155 HS
10 K-52721 9.0 58.75 1242.5 HS
11 K-50076 6.0 35 735 MS
12 K-95041 6.0 31.25 647.5 MS
13 K-95058 7.0 43.75 910 S
14 K-96033 4.0 15 315 MR
15 K-96001 3.0 7.5 140 R
16 K-60016 7.0 40 822.5 S
17 K-60028 9.0 62.5 1330 HS
18 K-93001 9.0 57.5 1207.5 HS
19 K-60034 7.0 38.75 787.5 S
20 K-60048 7.0 40 857.5 S
21 K-89169 4.0 11.25 210 MR
22 K-90395 4.0 12.5 245 MR
23 K-92030 9.0 60 1260 HS
24 K-96022 3.0 5 87.5 R
25 D-91005 9.0 53.75 1137.5 HS
26 D-97092 3.0 10 210 R
27 D-91224 7.0 41.25 875 S
28 D-CM98 9.0 55 1155 HS
29 D-91055 3.0 8.75 175 R
30 D-91017 4.0 15 315 MR
31 D-CAM68 9.0 53.75 1120 HS
32 D-90272 3.0 7.5 157.5 R
33 D-89044 4.0 15 297.5 MR
34 D-91013 9.0 58.75 1260 HS
35 D-05006 4.0 16.25 332.5 MR
36 D-97074 9.0 53.75 1137.5 HS
37 D-96050 3.0 6.25 140 R
38 D-96022 9.0 56.25 1172.5 HS
39 D-96018 4.0 17.5 367.5 MR
40 D-86030 4.0 16.25 332.5 MR
41 D-03006 7.0 38.75 787.5 S
42 D-03019 7.0 41.25 857.5 S
43 D-05028 7.0 40 840 S
44 D-96032 4.0 12.5 245 MR
45 D-03009 4.0 16.25 332.5 MR
66
46 D-1CC-5127 4.0 17.5 367.5 MR
47 D-Pb2008 3.0 5 52.5 R
48 D-Pu502-362 3.0 3.75 87.5 R
49 Punjab-1(check) 10 80 1715 HS
*HS= Highly susceptible, S= Susceptible, MS= Moderately susceptible, MR= Moderately
resistant, R= Resistant
67
Table 4.24 Response of genotypes to chickpea blight disease during 2012
Sr. No Genotypes Disease
rating
Percent
disease
Severity
AUDPC Reaction
1 K-97006 9.0 60 1242.5 HS*
2 K-97007 9.0 58.75 1225 HS
3 K-60013 3.0 3.75 70 R
4 K-98009 9.0 56.25 1172.5 HS
5 K-94002 9.0 63.75 1330 HS
6 K-98007 6.0 36.25 752.5 MS
7 K-98008 3.0 7.5 157.5 R
8 K-98014 9.0 63.75 1347.5 HS
9 K-98012 9.0 56.25 1190 HS
10 K-52721 9.0 61.25 1312.5 HS
11 K-50076 6.0 36.25 752.5 MS
12 K-95041 6.0 32.5 665 MS
13 K-95058 7.0 45 927.5 S
14 K-96033 4.0 16.25 350 MR
15 K-96001 3.0 7.5 140 R
16 K-60016 7.0 41.25 840 S
17 K-60028 9.0 65 1382.5 HS
18 K-93001 9.0 58.75 1242.5 HS
19 K-60034 7.0 40 805 S
20 K-60048 7.0 43.75 910 S
21 K-89169 4.0 12.5 245 MR
22 K-90395 4.0 13.75 280 MR
23 K-92030 9.0 67.5 1435 HS
24 K-96022 3.0 5 87.5 R
25 D-91005 9.0 58.75 1242.5 HS
26 D-97092 3.0 10 210 R
27 D-91224 7.0 45 962.5 S
28 D-CM98 9.0 58.75 1242.5 HS
29 D-91055 3.0 10 210 R
30 D-91017 4.0 15 315 MR
31 D-CAM68 9.0 56.25 1172.5 HS
32 D-90272 3.0 10 210 R
33 D-89044 4.0 15 297.5 MR
34 D-91013 9.0 63.75 1347.5 HS
35 D-05006 4.0 17.5 367.5 MR
36 D-97074 9.0 56.25 1190 HS
37 D-96050 3.0 10 227.5 R
38 D-96022 9.0 60 1260 HS
39 D-96018 4.0 17.5 350 MR
40 D-86030 4.0 16.25 332.5 MR
41 D-03006 7.0 40 822.5 S
42 D-03019 7.0 43.75 927.5 S
43 D-05028 7.0 42.5 875 S
44 D-96032 4.0 15 297.5 MR
68
45 D-03009 4.0 17.5 367.5 MR
46 D-1CC-5127 4.0 20 420 MR
47 D-Pb2008 3.0 5 70 R
48 D-Pu502-362 3.0 3.75 105 R
49 Punjab-1(check) 10 81.25 1750 HS
*HS= Highly susceptible, S= Susceptible, MS= Moderately susceptible, MR= Moderately
resistant, R= Resistant
69
a.
b.
Fig. 4.11. Symptoms of chickpea blight disease exhibiting necrotic lesions
on leaves (a) and pods (b)
70
a.
b.
Fig. 4.12. Symptoms of chickpea blight disease exhibiting necrotic lesions on leaves
(a) and girdling effect of stem lesions (b)
71
Before inoculation After inoculation
Fig. 4.13 A view of disease screening nursery before and after inoculation on
genotype K-94002
72
4.15 Management
4.15.1 In vitro evaluation of fungicides against A. rabiei
Analysis of variance of fungicidal treatments indicated that the individual effects
of treatments and concentrations were significant. Two way interactive effects of
treatments and concentrations were also significant (Table 4.25). Fungicides at
concentrations i.e. 0.05%, 0.01% and 0.15% significantly decreased colony growth of A.
rabiei compared to control. Colony inhibition was significantly higher at 0.15% compared
to other concentrations (Table 4.26). CabrioTop, ThiovetJet, Alliete and Antracol were
statistically different (P≤0.05) at three concentrations than Nativo which was statistically
same at 0.05 and 0.10%. Out of five fungicides, two fungicides Alliete and ThiovetJet were
significantly superior to other fungicides. Alliete at 0.15% concentration inhibited 87.20%
colony growth than 0.10(83.86%) and 0.05(80.53%), respectively. Similarly, ThiovetJet
exhibited 82.93% inhibition at 0.15% than 0.10(78.26%) and 0.05(74%), respectively.
CabrioTop and Nativo at their concentrations of 0.05%, 0.10% and 0.15% decreased colony
growth of A. rabiei as; 53.46%, 61.86% and 75.60%, and 63.33%, 65.33% and 69.86%,
respectively. Fungicide Antracol was found significantly less effective compared to other
treatments in decreasing colony growth of A. rabiei at all its three concentrations i.e.
0.05(43.20%), 0.10(47.60%) and 0.15(63.20%), respectively (Table 4.26).
Effects of fungicidal treatments and concentrations on colony diameter are given in Table
4.26.
73
Table 4.25 Analysis of variance for the effect of different concentrations of fungicides
on colony growth inhibition and colony diameter of A. rabiei
*Significance at 0.05 difference
S.O.V DF SS MS F Pr > F
Colony Growth Inhibition
Replication 2 11.0 5.5
Treatments 4 4291.2 1072.8 626.21 0.0001*
Concentrations 3 52843.0 17614.3 10281.6 0.0001*
Treatments ×
Concentrations
12 1795.5 149.6 87.34 0.0001*
Error 38 65.1 1.7
Total 59 59005.9
C.V = 5.27 (%)
Colony diameter
Replication 2 7.06 3.53
Treatments 4 29.78 7.44 26.26 0.0001*
Concentrations 3 278.14 92.71 326.92 0.0001*
Treatments ×
Concentrations
12 9.16 0.76 2.69 0.0001*
Error 38 10.77 0.28
Total 59 334.93
C.V = 14.88 (%)
74
Table 4.26 Effect of fungicides and their concentrations on colony growth inhibition and colony diameter of A. rabiei
Treatments Concentrations
0.05% 0.10% 0.15%
Colony Growth
Inhibition (%)
Colony
Diameter
(cm)
Colony Growth
Inhibition (%)
Colony Diameter
(cm)
Colony Growth
Inhibition (%)
Colony Diameter
(cm)
T1=CabrioTop 53.46* i 3.49 cd 61.86 h 2.86 de 75.60 f 1.83 ghij
T2=ThiovetJet 74.00 e 1.95 fghi 78.26 d 1.63 hij 82.93 b 1.28 ij
T3=Alliete 80.53 c 1.46 hij 83.86 b 1.21 ij 87.20 a 0.96 j
T4=Antracol 43.20 k 4.26 c 47.60 j 3.93 c 63.20 g 2.76 def
T5=Nativo 63.33 g 2.75 def 65.33 g 2.60 efg 69.86 f 2.26 efgh
Untreated 0.00 l 7.50 a 0.00 l 7.50 a 0.00 l 7.50 a
LSD 3.03 2.04 2.96 2.06 3.76 2.08
*Mean values sharing similar letters in a row do not differ significantly as determined by the LSD test at 5% level of probability
75
4.15.2 In vitro evaluation of plant extracts against A. rabiei
Analysis of variance showed that the individual effects of treatments and
concentrations were significant. Two way interaction between treatments and
concentrations was also significant (Table 4.27). Plant extracts at different concentrations
significantly affected colony growth (Table 4. 28). Mean percentage inhibition of colony
growth by plant extracts i.e. M. azedarach, A. indica, A. sativum and D. stramonium was
statistically different (P≤0.05) at different concentrations. While, mean percentage
inhibition by C. procera was statistically at par at different concentrations. Plant extracts
i.e. M. azedarach and A. indica performed best compared to other plant extracts. M.
azedarach and A. indica at 2%, 3% and 8% concentrations inhibited colony growth of A.
rabiei as; 37.28 %, 42.37% and 47.45%, and 20.33%, 25.42% and 35.59%, respectively
(Table 4.28). Plant extracts i.e. A. sativum and D. stramonium remained second good and
inhibited colony growth as; 2%(13.55%), 3%(18.64%) and 8%(23.72%), and 2%(10.16%),
3%(13.55%) and 8%(15.25%), respectively (Table 4.28). Significantly less percentage
inhibition of colony was recorded in terms of C. procera. Three concentrations i.e. 2%, 3%
and 8% of C. procera resulted in 5.08%, 6.77% and 8.47% reduction in colony growth,
respectively. All plant extracts were significantly more active at their higher concentration
i.e. 8% than low (2%).
Data relating the effects of plant extracts and concentrations on colony diameter of A.
rabiei are shown in Table 4.28.
76
Table 4.27 Analysis of variance for the effect of different concentrations of plant
extracts on colony growth inhibition and colony diameter of A. rabiei
*Significance at 0.05 difference
S.O.V DF SS MS F Pr > F
Colony Growth Inhibition
Replication 2 7.7 3.87
Treatments 4 5407.7 1351.92 522.89 0.0001*
Concentrations 3 5566.3 1855.44 717.65 0.0001*
Treatments ×
Concentrations
12 1922.6 160.22 61.97 0.0001*
Error 38 98.2 2.59
Total 59 13002.6
C.V: 10.20 (%)
Colony diameter
Replication 2 4.41 2.20
Treatments 4 18.92 4.73 402.88 0.0001*
Concentrations 3 19.47 6.49 552.70 0.0001*
Treatments ×
Concentrations
12 6.72 0.56 47.69 0.0001*
Error 38 0.44 0.01
Total 59 49.97
C.V = 2.17 (%)
77
Table 4.28 Effect of plant extracts and their concentrations on colony growth inhibition and colony diameter of A. rabiei
Treatments Concentrations
3% 5% 8%
Colony Growth
Inhibition (%)
Colony
Diameter
(cm)
Colony
Growth
Inhibition (%)
Colony
Diameter
(cm)
Colony
Growth
Inhibition
(%)
Colony
Diameter
(cm)
T1= M. azedarach 37.28* c 3.7 jk 42.37 b 3.4 kl 47.45 a 3.1 l
T2= A. indica A. Juss. 20.33 e 4.7 ghi 25.42 d 4.4 i 35.59 c 3.8 j
T3= A. sativum L. 13.55 g 5.1 def 18.64 f 4.8 fgh 23.72 e 4.5 hi
T4= D. stramonium L. 10.16 h 5.3 cde 13.55 g 5.1 def 15.25 f 5.0 efg
T5= C. procera Wild Drayand ex.W.
Ait.
5.08 i 5.6 ab 6.77 ij 5.5 abc 8.47 hi 5.4 bcd
Untreated 0.00 k 5.9 a 0.00 k 5.9 a 0.00 k 5.9 a
LSD 5.02 0.19 1.90 0.13 1.99 0.14
*Mean values sharing similar letters in a row do not differ significantly as determined by the LSD test at 5% level of probability
78
4.15.3 In vitro evaluation of antagonists against A. rabiei
Individual effects of treatments and concentrations of antagonists were significant
(Table 4.29). Interactive effects of treatments and concentrations were also significant
(Table 4.29). Both antagonists i.e. T. harzianum and A. flavus showed significantly more
inhibition of colony growth at higher spore concentrations than low (Table 4.30). A. flavus
at spore concentration of 107
conidia/mL and T. harzianum at 10
6 conidia/mL showed
statistically same (P≤0.05) percent inhibition. Inhibition by A. flavus at spore
concentrations of (105,10
6 conidia/mL) and T. harzianum at spore concentrations of (10
5,
107 conidia/mL) was statistically different (P≤0.05). T. harzianum at spore concentrations
i.e. 105, 10
6 and 10
7 conidia/mL inhibited colony growth as; 16.98%, 28.30% and 41.50%,
respectively. While, A. flavus at spore concentrations of 105, 10
6 and 10
7 conidia/mL
showed 9.43%, 20.28% and 30.18% decrease in colony growth, respectively (Table 4.30).
Bio-control T. harzianum at 107conidia/mL was found best.
Effects of antagonists and concentrations on colony diameter are shown in Table 4.30.
79
Table 4.29 Analysis of variance for the effect of different concentrations of
antagonists on colony growth inhibition and colony diameter of
A. rabiei
*Significance at 0.05 difference
S.O.V DF SS MS F Pr > F
Colony Growth Inhibition
Replication 2 43.28 21.64
Treatments 1 296.18 296.18 85.58 0.0001*
Concentrations 3 4026.87 1342.29 387.86 0.0001*
Treatments ×
Concentrations
3 108.93 36.31 10.49 0.0007*
Error 14 48.45 3.46
Total 23 4523.71
C.V = 10.21(%)
Colony diameter
Replication 2 1.54 0.77
Treatments 1 0.82 0.82 64.70 0.0001*
Concentrations 3 11.48 3.82 300.13 0.0001*
Treatments ×
Concentrations
3 0.30 0.10 7.89 0.0025*
Error 14 0.17 0.01
Total 23 14.33
C.V = 2.59 (%)
80
Table 4.30 Effect of plant antagonists and their concentrations on colony growth inhibition and colony diameter of A. rabiei
Treatments Concentrations
1x105
conidia/mL 1x 106conidia/mL 1x 10
7conidia/mL
Colony Growth
Inhibition (%)
Colony
Diameter
(cm)
Colony
Growth
Inhibition (%)
Colony
Diameter
(cm)
Colony
Growth
Inhibition
(%)
Colony
Diameter
(cm)
T1= A. flavus 9.43* d 4.8 b 20.28 c 4.2 c 30.18 b 3.7 d
T2= T. harzianum 16.98 c 4.4 c 28.30 b 3.8 d 41.50 a 3.1 e
Untreated 0.000 e 5.3 a 0.000 e 5.3 a 0.000 e 5.3 a
LSD 3.70 0.20 4.35 0.27 6.79 0.42
*Mean values sharing similar letters in a row do not differ significantly as determined by the LSD test at 5% level of probability
81
Alliete@ 0.15% Control
ThiovetJet@ 0.15% Control
Fig. 4.14 Inhibition of colony growth of A. rabiei by two systemic fungicides
using poisoned food technique
82
4.16 Evaluation of fungicides, plant extracts and antagonist against chickpea blight
under field conditions during two years
Fungicides, plant extracts and antagonist found most effective during in vitro
studies were evaluated under field conditions. Analysis of variance (ANOVA) showed
that individual effects of years, sprays, varieties and treatments were significant (Table
4.31). Two way interactive effects of years and treatments, sprays and varieties, sprays
and treatments, and varieties and treatments were also significant. While, two way
interactive effects of years and sprays and years and varieties were not significant. Three
way interactive effects of years, varieties and treatments, and sprays, varieties and
treatments were significant (Table 4.31). Three way interactive effects of years, sprays
and varieties, and years, sprays and treatments were not significant. Four way interactive
effects of years, sprays, varieties and treatments were also not significant (Table 4.31).
Fungicides i.e. Alliete and Thiovetjet at 0.15%, plant extracts i.e. M. azedarach
and A. indica at 8% and antagonist T. harzianum at 107 conidia/mL were evaluated against
chickpea blight. All the treatments were significantly effective compared to control.
Comparative efficacy of different treatments was statistically different (Table 4.32).
Significant disease severity was reduced by fungicides i.e. Alliete (17.38%) and Thiovetjet
(22.66%) followed by plant extracts in which M. azedarach and A. indica reduced 49.63%
and 56.46%, respectively compared to control (75.25%). Antagonist i.e. T. harzianum was
significantly less effective (63.48%) in controlling blight disease severity compared to
fungicides and plant extracts (Table 4.32).
Three way interaction among treatments, years and sprays showed that treatments
i.e. fungicides, plant extracts and antagonist inhibited significant disease severity
compared to control during two years (2011-12). There was statistically significant
difference in disease severity control by different treatments (Table 4.33). Significant
more disease control was observed in year 2012 by all treatments than year 2011.
Fungicides proved best during two years followed by plant extracts and antagonist (Table
4.33). Three way interaction revealed that disease severity decreased with increasing
number of sprays (Table 4.33). Effect of Alliete, Thiovetjet, M. azedarach and A. indica on
disease severity control was not statistically similar at different sprays. However, effect of
T. harzianum at spray second and third was statistically equally effective. Third spray of
83
all treatments significantly controlled more disease severity compared to spray first and
second. (Table 4.33).
Three factor interaction among treatments, varieties and sprays indicated that
control of chickpea blight disease severity at spray first, second and third of fungicides i.e.
Alliete and Thiovetjet on varieties CM-2000 and Pb-1 was statistically equal except CM-
98 (Table 4.34). While, effects of plant extracts and antagonist on chickpea blight disease
control were statistically not at par on three varieties i.e. CM-2000, CM-98 and Pb-1.
Fungicides proved best in reducing disease on all three varieties followed by plant extracts
compared to control. Antagonist i.e. T. harzianum proved least effective on three varieties
in controlling disease severity (Table 4.34).
Two way interactions of treatments with years, sprays and varieties are given in appendix-
I.
84
Table 4.31 ANOVA for the evaluation of fungicides, plant extracts and antagonist to
control chickpea blight disease under field conditions
Source of variation DF SS MS F P
Replication 2 103 51.4
Years 1 1985 1985.2 452.65 0.0001*
Sprays 2 5280 2639.8 601.92 0.0001*
Varieties 2 12418 6209.0 1415.75 0.0001*
Treatments 5 142248 28449.6 6486.93 0.0001*
Years*sprays 2 14 7.05 1.60 0.2111ns
Years* Varieties 2 1 0.5 0.10 0.9011ns
Years*Treatments 5 517 103.4 23.58 0.0001*
Sprays* Varieties 4 665 166.2 37.90 0.0026*
Sprays*Treatments 10 1379 137.9 31.44 0.0001*
Varieties*Treatments 10 4451 445.1 101.49 0.0001*
Years*Sprays* Varieties 4 11 2.7 0.62 0.6492ns
Years*Sprays*Treatments 10 73 7.3 1.67 0.0886ns
Years* Varieties*Treatments 10 114 11.4 2.60 0.0054*
Sprays*Varieties*Treatments 20 270 13.5 3.08 0.0001*
Years*Sprays* Varieties
*Treatments
20 46 2.3 0.52 0.9555ns
Error 214 939 4.4
Total 323 170513
*Significant at 0.05 difference CV= 14.41
Table 4.32 Evaluation of fungicides, plant extracts and antagonist to control
chickpea blight disease under field conditions
Sr. No. Treatments Mean values of chickpea blight percent
disease severity
T1 Alliete @ 0.15% 17.38* f
T2 ThiovetJet @ 0.15% 22.66 e
T4 M. azedarach @ 8% 49.63 d
T3 A. indica A. Juss.@ 8% 56.46 c
T5 T. harzianum @ 1x 107 63.48 b
T0 Control 75.25 a
L.S.D 1.14
*Means with similar letters in a row are not significantly different at P = 0.05
85
Table 4.33 Interaction of treatments, years and sprays for the evaluation of fungicides, plant extracts and antagonist to control
chickpea blight disease under field conditions
Treatment 1st Year
2011
2nd year
2012
Spray1 Spray2 Spray3 Spray1 Spray2 Spray3
Alliete @ 0.15% 29.11* k 20.56 l 14.22 m 19.33 l 12.67 m 8.44 n
ThiovetJet @ 0.15% 34.00 j 25.33 k 19.11 l 25.89 k 18.22 l 13.44 m
M. azedarach @ 8% 60.22 ef 52.22 g 45.78 h 53.00 g 45.44 h 41.11 i
A. indica A. Juss.@ 8% 65.89 bc 58.67 f 52.67 g 60.22 ef 53.00 g 48.33 h
T. harzianum @ 1x 107 69.22 b 63.33 cde 62.56 cde 65.00 cd 61.22 def 59.56 ef
Control 75.67 a 73.67 a 77.00 a 76.56 a 73.67 a 75.00 a
L.S.D 11.42 10.83 10.64 11.26 10.89 10.64
L.S.D 5.98 5.97
* Means with similar letters in a row are not significantly different at P = 0.05
86
Table 4.34 Interaction of treatments, varieties and sprays for the evaluation of fungicides, plant extracts and antagonist to control
chickpea blight disease under field conditions Treatments CM-2000 CM-98 Pb-1
Spray1 Spray2 Spray3 Spray1 Spray2 Spray3 Spray1 Spray2 Spray3
Alliete @ 0.15% 22.0* tuv 12.66 xy 7.50 z 30.33 r 20.50 uvw 14.16 xy 20.33 vw 16.66 wxy 12.33 yz
ThiovetJet @ 0.15% 27.83 rst 17.50 vwx 12.83 xy 37.00 q 26.50 rst 19.33 vw 25.00 stu 21.33 uvw 16.66 wxy
M. azedarach @ 8% 56.16 jkl 45.83 no 42.50 op 67.00 fgh 57.00 jkl 49.16 mn 46.66 no 43.66 o 38.66 pq
A. indica @ 8% 67.00 fgh 55.50 kl 52.50 lm 72.83 de 65.33 hi 56.33 jkl 49.33 mn 46.66 no 42.66 op
T. harzianum @ 1x 10
7
71.00 efg 65.83 h 66.33 gh 77.00 cd 71.33 ef 69.50 efgh 53.33 lm 49.66 mn 47.33 no
Control 81.50 abc 78.33 bc 79.16 bc 86.16 a 85.00 a 82.83 ab 60.66 ij 57.66 jk 66.00 h
L.S.D 4.77 4.28 3.37 5.02 5.53 3.76 5.04 4.04 3.49
L.S.D 4.12 4.78 3.08
* Means with similar letters in a row are not significantly different at P = 0.05
87
DISCUSSION
Environmental parameters i.e. maximum and minimum temperatures, relative
humidity, rainfall and wind speed influence chickpea blight disease (Nene, 1982; Weltzien
and Kaack, 1984), so these can be used to predict chickpea blight disease severity (Pande
et al., 2005). During this study, fluctuation in disease severity was observed during five
(2006-10) and two years (2011-12). During years 2006-10, maximum mean disease
severity i.e. 65.3% and 51.8% was observed in year 2007 and year 2006, respectively.
Similarly, during 2011-12, mean disease severity was high (43.3%) in year 2012 than
2011. High level of disease severity in these years may be attributed to conducive
environmental conditions, and most importantly to more rainfall in these years. Though
the amount of rainfall was not so high during years 2007, 2006 and 2012, yet it created
pronounced impact on blight severity. Many researchers have reported that chickpea
blight disease increases with frequent showers of rainfall. In fact, rainfall after inoculation
promotes the rapid spread of chickpea blight from spreader to experimental plots which
facilitates sporulation of A. rabiei and dispersal of conidia, thus enhancing inoculum level
in the field which results in more disease severity (Kaiser, 1973, 1992; Akem, 1999;
Thind et al., 2007).
Maximum temperature showed negative correlation with disease severity during
the period of five (2006-10) and two years (2011-12). Minimum temperature presented
positive correlation with blight severity during 2006-07 and 2009-12, however in year
2008, it showed non-significant correlation with disease severity. This non-significant
correlation may be either due to low level of disease severity in year 2008 or variation in
disease severity data. The other reason may be unfavourable/unusual ranges i.e. 3°C and
>14°C of minimum temperature which prevailed during growing season. Correlation of
relative humidity (except year 2008 in which it was negative), rainfall and wind speed
with blight severity was also positive and significant during seven years of period i.e.
2006-10 and 2011-12. Negative correlation of relative humidity (RH) with disease
severity in year 2008 is due to low level of RH in this year. This is in agreement with
88
(Chauhan and Sinha, 1973) who reported that RH showed negative correlation with
ascochyta blight when its threshold was quite low.
Significant relationship of environmental variables with ascochyta blight severity
found during this study is in line with the findings of Ahmad et al., (1985) who found
fairly good correlation between chickpea blight disease and environmental factors.
Significant correlation of temperature with chickpea blight disease can be explained with
the fact that it is critical in different aspects of disease development. For example,
temperature along with sufficient amount of moisture (precipitation) has the significant
role in pseudothecial maturation, liberation of ascospores and disease establishment (Fitt
et al., 1989; Navas-Cortés et al., 1998; Galloway et al., 2004; Atinsky et al., 2005).
Symptom expressions are also affected by temperature after infection (Jayakumar et al.,
2005). There are also findings showing significant effect of temperature on incubation and
latent periods (Chauhan and Sinha, 1973; Reddy and Singh, 1990c; Ram and Mahender,
1993). Further, mycelial growth, spore germination and pycnidial formation of A. rabiei
also increase by increasing temperature, but to certain limits of temperature (optimum
20°C) (Chauhan and Sinha, 1973; Kaiser, 1973). Significant correlation of RH with
ascochyta disease is ascribed to its contribution in the development of chickpea blight
disease (Navas-Cortés et al., 1998b). As there was positive correlation of RH with disease
i.e. chickpea blight enhanced with the increase of RH. This agrees with the findings of
(Gaur and Singh, 1993; Navas-Cortés et al., 1998b) who found decline in blight incidence
when RH decreased from 100 to 86%. Relative humidity is also an influential factor in
sporulation (Mondal et al., 2003). Formation of conidia in pycnidia particularly relies on
relative humidity (Muller, 1979; Platt and Morrall, 1980). Similarly, teleomorphic stage of
ascochyta blight is also affected by relative humidity (Navas-Cortés et al., 1995, 1998b).
Production of ascospores from psuedothecia in D. rabiei increases with the increase of
RH. Jhorar et al. (1998) found more production of asci and ascospores in artificially
inoculated fields where RH level was kept at 100% than naturally infested fields.
89
Significant correlation between rainfall and disease severity is due to its key role in
infection process. Infection process begins with the formation of pseudothecia during
rainy days. Rainfall makes plant disease debris wet and keeps the temperature mild which
is necessary for the development and maturity of pseudothecia (Trapero-Casas and Kaiser,
1992a; MacLeod and Galloway, 2003; Pande et al., 2005). After that ascospores are
liberated from developed pseudothecia by rainfall and are disseminated by wind currents
to nearby fields (Trapero-Casas and Kaiser, 1987, 1992a). Rainfall also contributes in
secondary disease cycles caused by rain-splashing conidia during the whole growing
season (Maden et al., 1975; Wilson and Kaiser, 1995). Frequent showers of rainfall
received during growing season, are therefore, key factors in chickpea blight epidemics
(Bretag et al., 1995). Elucidation behind significant correlation of wind speed with
chickpea blight disease is that, it transfers conidia and ascospores from diseased fields to
healthy fields (Trapero-Casas and Kaiser, 1992a; Kaiser, 1997; Bretag et al., 2006).
Ascochyta blight is a polycyclic disease and spreads by primary and secondary inocula.
Wind transfers primary inoculum from infected fields to healthy fields, sometimes on a
distance of 1.6 km (Schoeny et al., 2007), thus, in this way, also initiates ascochyta
disease and increases its severity in the chickpea fields.
It is concluded from significant correlation of environmental conditions with
chickpea blight that these factors influence disease development, so can be used to predict
its onset. Thus to quantify chickpea blight disease severity in relation to environmental
variables, regression analysis was used.
Multiple regression model based upon five years (2006-10) data i.e. Y = 81.8 -
3.17x1 + 1.99x2 + 0.257x3 + 0.885x4 + 1.85x5 showing R2 = 0.72, when validated with two
years (2011-12) data i.e. Y = 73.8 - 3.11x1 + 1.60x2 + 0.40X3 + 1.44X4 + 2.34X5 explained
82% variability in disease development, and these models validated each other as there
was homogeneity of regression lines. Environmental variables present in these models are
important as reported by other researchers. Shtienberg et al. (2005) developed a disease
predictive model to forecast teleomorphic stage of ascochyta blight on the basis of
90
temperature and wetness period. Model was then validated with independent set of data of
two years. It was found that average temperature (maximum and minimum) below 15°C
and precipitation events above 10 mm played a vital role in maturity of ascospores of
ascochyta blight. Salam et al. (2011) developed a model to predict the disease severity of
AB with respect to ascospore release. The model showed 0.88 R2 with 13.18 slopes and -
10.67 intercept. It was observed that temperature and precipitation played vital role in the
discharge of ascospores, and thus, augmented disease severity. They also developed
another model based upon two weather inputs; daily mean temperature and rainfall. When
that model was validated at different locations proved effective in giving the simulations
of ascospore dispersal and AB severity. It was also observed that different regimes of
temperature and rainfall during the growing season at different locations of Australia
helped in predicting accurate blight severity (Salam et al., 2011a.b).
Role of RH in model was also significant. For forecasting ascochyta blight disease,
> 60% RH threshold provides best results (Reddy and Singh, 1990c). Jhorar et al. (1997)
used 15 years environmental data (temperature and RH) to predict chickpea blight disease.
Model developed on the basis of humid thermal ratio (HTR) showed high coefficient of
determination i.e. R2= 0.89 to predict chickpea blight.
Wind speed was also an important predictor in present model. One reason behind
this is that, primary infection is caused by primary air born inoculum (Zhang et al., 2005).
Secondly, wind having significant role in the dispersal of pycnidiospores and ascospores
(Pedersen et al., 1994; Singh et al., 1995; Davidson et al., 2006). Because the disease is
spread in most of the countries by the infected stubbles present in the previously grown
chickpea fields. Thus, when pycnidium outbreaks, pycnidiospores liberate and spread to
nearby fields by wind (Bretag et al., 2006; Schoeny et al., 2008). Schoeny et al. (2007)
established a model and explained that disease appeared only when particular weather
based variables were conducive, and wind speed remained the most prominent parameter
of the model in their studies.
91
Current multiple regression model i.e. five years model is the first time study in
Pakistan according to best information to predict chickpea blight disease. Previously,
Ahmad et al., (1985) and Khan et al., (1999) conducted researches on epidemiology of
chickpea blight and characterized suitable environmental conditions on the basis of one
growing season data, but have not been reliable because of smaller data set used in their
studies. Similarly, Kausar, (1965) set criteria for chickpea blight epidemics which were
based on annual rainfall. Though these criteria proved to be effective and are still being
used in Pakistan, but do not cover the role of other environmental parameters including
maximum and minimum temperatures, relative humidity and wind speed. Further,
previous studies do not explain the variability in chickpea blight disease which may be
produced due to different environmental conditions. The multiple regression model
developed during this research answers these lacunas. Model contains fairly large data set,
have been validated with two years data set and satisfactory good results have been
achieved regarding chickpea blight predictions. Secondly, model has good forecasting
power i.e. R2. Thus, model would surely help in taking accurate predictions of blight
disease.
For successful prediction at different locations necessitates the characterization of
suitable environmental variables for chickpea blight disease development, therefore
environmental conditions favourable for this disease were characterized during two years
(2011-12). Maximum and minimum temperatures in the range of 20-24°C and 12-14°C
were found favourable for ascochyta blight disease. Similarly, relative humidity, rainfall
and wind speed in the range of 60-70%, >5 mm and 6.5 Km/h, respectively were
conducive for chickpea blight disease. This is in accordance with the results of many
research workers who concluded that variation in environmental variables affected the
chickpea blight disease (Pande et al., 2005; Thind et al., 2007; Tivoli and Banniza, 2007).
In this study, higher level of disease severity was recorded at low temperature and high
rainfall ranges, explaining that, rainfall by keeping temperature low, favours ascochyta
disease. There is also possibility that low temperature and high rainfall influence disease
92
severity by maintaining the leaf wetness period, which is critical for the development and
intensity of blight disease. Reason behind this is that low temperature declines down
evaporation rate which helps in maintaining the leaf wetness. Reddy and Singh (1990c)
found that chickpea blight can erupt in epidemic proportions when there is more than 60%
relative humidity, 10-20°C leaf temperatures, and annual excessive rainfall is more than
150 mm coupled with 7 days of leaf wetness period for at least 7 hours. Similar
investigations are reported by (Trapero-Casas and Kaiser, 1992). Pande et al. (2005)
concluded that temperature ranges between 5-30°C with 20°C optimum temperature, are
essential for the establishment of infection and spread, while 17 hours moisture is critical
for an epidemic. It is concluded from current studies, that low temperature and high
rainfall have been conducive, for disease development and infection. This is supported by
previous findings of Ketelaer et al., (1988), who reported that monthly average
temperature minimum of 8oC along with minimum 40 mm rainfall might cause an
epidemic. This is also possible that conducive temperature ranges and high rainfall found,
may favour the disease by bringing change in incubation period. Thind et al. (2007)
validated the relationship of meteorological variables with ascochyta blight of gram under
field conditions for two growing seasons. They observed that incubation phase was more
(2-3 weeks) during January and declined to 3–5 days in the months of February-March,
because during that period (February-March) high frequency of rainfall made the
temperature more appropriate for disease development, therefore incubation period
decreased from weeks to days.
As, leaf wetness minimum for 4 hours is essential for infection, initiation and
symptoms of ascochyta blight disease (Thind et al., 2007); which is only likely when
there is high rainfall. Therefore, blight disease intensifies with the increasing amount of
rainfall. Excessive moisture also causes pycnidiospore germination and growth of fungus;
hence speeds up the infection process, and enhancing blight incidence and severity in
chickpea fields (Bedi and Ajula, 1970; Akem, 1999). Similar interpretations about rainfall
were referred by other researchers (Weltzien and Kaack, 1984; Chilvers et al., 2007).
93
Currently, disease severity also increased with the increase of wind speed. This is
due to the role of wind in spreading inoculum from one plant to other plants in the field
(Tivoli and Benniza, 2007). Wind also affects ascochyta blight disease by its speed and
direction (Pedersen and Morrall, 1995; Zhang et al., 2005; Salam et al., 2011c).
Environmental factors with high inoculum pressure may also affect the resistance
or susceptibility of germplasm (Pande et al., 2011). Host resistance is thought to have
greatest effect on ascochyta blight epidemics (Gan et al., 2006; Davidson and Kimber,
2007). Chickpea blight damage can be minimized by using resistant cultivars (Gan et al.,
2006; McMurray et al., 2006). Effectiveness of chemical control measure also depends
upon resistance level of genotype (Shtienberg et al., 2000). Hence, introducing genes of
resistance to chickpea varieties is indispensible, but as it is laborious and lengthy process
therefore screening of advanced lines provides an easy and short term way out. Thus
during this study, resistance and susceptibility levels of 48 genotypes of chickpea were
checked, only few of these advanced lines were found resistant showing scarcity of
resistance in chickpea germplasm against blight (Reddy and Singh, 1984; Udupa et al.,
1993; Ghazanfar et al., 2010). Lack of resistance in chickpea germplasm is referred to;
continuous competition between host and pathogen, and failure of horizontal resistance in
chickpea (Reddy and Kabbabeh, 1985; VanRheenen and Harware, 1994; Singh, 1997;
Ilyas et al., 2007). The other reason behind this scarcity, is the genetics of chickpea blight
resistance, which is controlled by either single dominant gene or recessive gene (Singh
and Reddy, 1983, 1989, 1991), and genotypes easily become susceptible by the
appearance of new race (Reddy and Kabbabeh, 1985; Ilyas et al., 2007).
Pyramiding of resistant genes into commercially grown varieties may be effective
to stabilize chickpea resistance against ascochyta blight (VanRheenen and Harware, 1994;
Singh et al., 1994; Qurban et al., 2011). Resistance can also be increased in chickpea
plants by working on those genes controlling different morphological, biochemical and
physiological characters (Pande et al., 2005). Morphological characters i.e. erect growth
habit, thicker stem hypodermis and epidermis, and increased plant height in both white
94
and black chickpeas, increase resistance (Reddy and Singh, 1984; Angelini et al., 1993;
Singh and Reddy, 1993a). Phenylalanine ammonia lyase (PAL), peroxidase, copper amine
oxidase, polyphenyloxidase and catalase having key role in hypersensitive response are
reported higher in resistant plants than susceptible (Angelini et al., 1993; Nehra et al.,
1994; Sindhu et al., 1995; Rea et al., 2002; Sarwar et al., 2001, 2003). These informations
are useful to develop durable resistance in indigenous chickpea cultivars. Black gram
showed more resistance against blight than white gram during current research. Similar,
reports about black gram have been given by (Singh and Reddy, 1993a). So, sources of
resistance may also be seen in black accessions. Resistant and moderately resistant
genotypes found during this study under high inoculum pressure may be cultivated under
field conditions, because under natural conditions inoculum pressure is always less
(Ahmad et al., 2013). Secondly, excessive use of fungicides cab be avoided by the
cultivation of these genotypes, because would have less disease incidence compared to
susceptible cultivars.
Sometimes chickpea blight disease appears suddenly and rapidly, then farmers
have no option other than effective fungicides. However, frequent use of fungicides is
neither cost-effective nor eco-friendly. To curb this issue, management strategy including
both chemicals and natural ways i.e. plant extracts and bio-controls may be useful
(Hashim and Devi, 2003; Benzohra et al., 2011), as this will reduce complete reliance on
fungicides.
Fungicides, plant extracts and bio-control agents (antagonists) were evaluated
against chickpea blight under in vitro and in vivo conditions. Among fungicides, Alliete
and Thiovetjet inhibited significantly colony growth of A. rabiei. Significant inhibition of
colony growth by these two fungicides may be attributed to their active ingredients i.e.
aluminium ethyle phosphate (Alliete) and sulfur (Thiovetjet). For example, sulfur containg
fungicides inhibit fungus growth by inactivating sulfhydryl groups in amino acids and in
enzymes, interfering electron transport, and reducing sulfur to hydrogen sulfide which is
highly toxic to fungus. Similarly, aluminium ethyle phosphate may have reduced the
95
mycelial growth by inhibiting cell division and enzyme synthesis, and by denaturation of
proteins (Agrios, 2005). When these two fungicides (Alliete and Thiovetjet) at 0.15%
concentration were applied under field conditions also significantly controlled disease
severity. Significant control of chickpea blight by these fungicides under in vivo
conditions is due to their systemic ability which allowed these fungicides to kill the
fungus in established infection. This is in line with the finding of (Shtienberg et al., 2000).
Organophosphate systemic fungicides, containg Fosetyl-Al, protect the plants by
activating defense mechanism and synthesizing phytoalexins (Agrios, 2005). Maximum
control of chickpea blight was obtained using three foliar applications of Alliete and
Thiovetjet. This shows that curative applications significantly control chickpea blight. This
is in agreement with Gan et al. (2006) who reported that with fewer applications of
curative systemic fungicides, ascochyta blight can be controlled effectively. Successful
control of ascochyta blight through these two systemic fungicides, might also have
resulted because of their good translocation into tissues of the host. Previous researches
have shown that only those systemic fungicides perform well which show movement into
newly developed tissues (Gan et al., 2006; Davidson and Kimber, 2007). Thus, this study
recommends that continuous effort should be made to look for those systemic fungicides
which show more translocation in the system of the plant. This will help to fix most
appropriate fungicides i.e. systemic, for chickpea blight management.
Plant extracts M. azedarach and A. indica remained effective against ascochyta
blight, but less than fungicides during current research. Under in vitro conditions, these
extracts may have inhibited the ascochyta fungus by different antifungal compounds
which they contain (Nunez et al., 2006; Lee, 2007). Jabeen et al. (2011) found that M.
azedarach contained; benzoic acid, ursolic acid, maesol, 3,5 dimethoxybenzoic acid, ß-
sitosterol and ß-amyrin which were highly toxic to chickpea blight fungus. Similarly, from
M. azedarach, obacunone, nomilin, limonoids and limonin have also been isolated and
proved to be effective against different insects (Koul et al., 2004). Botanical extracts also
contain secondary metabolites which are antifungal and restrict the mycelial growth of
96
fungi (Sisti et al., 2008). Disease control by these plant extracts under field conditions
may be ascribed to their ability of inducing systemic acquired resistance (SAR) (Guleria
and Kumar, 2006). Foliar applications of A. indica produce SAR effectively in chickpea
cultivars against ascochyta blight (Sarwar et al., 2011).
Two bio-control agents i.e. antagonists were evaluated against blight. Both bio-
controls showed good effectiveness against A. rabiei by inhibiting colony growth.
However, T. harzianum was significantly more effective at spore concentration of 107
conidia/mL under laboratory conditions. When this antagonist i.e. T. harzianum at 107
conidia/mL was applied in field to control chickpea blight, though its effectiveness
remained significantly less than fungicides and plant extracts, yet, an inhibition of
chickpea blight disease was observed. Studies are available on T. harzianum, confirming
their efficacy against A. rabiei under controlled conditions (Küçük et al., 2007; Benzohra
et al., 2011). Inhibition of mycelial growth of A. rabiei under in vitro conditions is caused
by the lysis of its mycelium by T. harzianum. This is as per findings of (Rajakumar et al.,
2005; Benzohra et al., 2011). Antagonists also produce enzymes which inhibit the growth
of target fungi and kill them. Chitinases, β-1, 3-glucanases, proteases and amylases have
been reported to be produced by antagonists (Azevedo et al., 2000; Küçük et al., 2007).
Under in vivo (field) conditions, biological organisms confer resistance in plants by
stimulating their defense mechanisms, and thus, play role in disease control (Alabouvette
et al., 2006; Jayalakshmi et al., 2009).
The present research revealed that chickpea blight disease was found to be very
responsive to low temperatures and high rainfall, relative humidity and wind speed.
Multiple regression model developed during this study is completely based on
environmental variables; this means no field data i.e. measurement or record is pre-
requisite to predict chickpea blight disease under field conditions. This avoids direct
observation or survey of this disease which is expensive, time consuming and requires
expertise. The model provides good early advance predictions which could be used to give
early warnings to farmers to take timely control measures against chickpea blight in
97
Faisalabad. This study reveals scope for plant extracts and biological organisms
(antagonists) that they are replacement of fungicides, but at moderate levels of ascochyta
blight severities. They can effectively control blight disease, if level of disease severity is
early known which can easily be determined by currently developed model. And if, the
infection is predicted at low level, plant extracts and bio-control particulary T. harzianum
can be used instead of fungicides.
Future research could be designed to work on (1) establishing a decision support
system for chickpea blight disease (2) predicting onset of pycnidia maturity and release of
conidia in relation to environment (3) exploring most appropriate antifungal compounds
from M. azedarach and A. indica against chickpea blight (4) preparing synthetic products
of antimicrobial compounds of M. azedarach and A. indica (5) selecting appropriate
formulation and method of application of plant products to improve their effectiveness.
98
CHAPTER 5 SUMMARY
Present study was designed to develop a disease predictive model for chickpea
blight disease and its management through fungicides, plant extracts and bio-control
agents. Model would help the farmers to decide management options (fungicides, plant
extracts or bio-controls) in advance before the occurrence of disease to avoid an epidemic.
Current research would also provide a base to establish a forecasting system for ascochyta
blight in Pakistan. Hypothesis of this study was developed as “On the basis of knowledge
about the environmental conditions expected severity of blight on chickpea crop could be
predicted for timely management of chickpea blight through fungicides, plant extracts and
antagonists. Objectives of the study were; (a) To develop a chickpea blight disease
predictive model based on environmental conditions of five crop seasons and to validate it
on two crop seasons. (b) To identify resistant source by screening of chickpea germplasm;
to explore tolerant source for its exploitation through chemotherapy. (c) To evaluate
fungicides, botanical extracts and antagonists against chickpea blight under in vitro and in
vivo conditions.
Correlation analysis was used to determine the relationship of environmental
conditions with ascochyta blight. Multiple regression analysis was employed to develop a
disease predictive model based upon environmental conditions. For the evaluation of
treatments (fungicides, plant extracts and bio-controls), analysis of variance was
performed to determine the individual and interactive effects, both for in vitro and in vivo
experiments. Least significant difference (LSD) test was done for the comparison of
means of different treatments.
Environmental factors i.e. maximum temperature, relative humidity, rainfall and
wind speed were significantly correlated (as indicated by significant correlation
coefficients (r)) with blight severity during five years (2006-10). While, minimum
temperature was significantly correlated during four years i.e. 2006-2007 and 2009-10. In
year 2008, minimum temperature was not significantly correlated with disease severity.
Maximum temperature was negatively, while rainfall and wind speed was positively
correlated with disease severity during five years. Minimum temperature showed positive
correlation with disease severity. Except year 2008, relative humidity was positively
correlated with disease severity in rest of years i.e. 2006-07 and 2009-10. Similarly, in
years 2011-12, maximum temperature exhibited significant and negative correlation with
99
blight severity, whereas minimum temperature, relative humidity, rainfall and wind speed
displayed positive and significant relationship with disease severity.
A multiple regression model was developed on five years (2006-10) data set and
validated with two years (2011-12) independent data set. Five years model i.e. Y= 81.8 -
3.17x1 + 1.99x2 + 0.257x3 + 0.885x4 + 1.85x5, based on maximum (X1) and minimum (X2)
temperatures, relative humidity (X3), rainfall (X4) and wind speed (X5) exhibited variability
in disease severity up to 72%. Two years model i.e. Y = 73.8 - 3.11x1 + 1.60x2 + 0.40X3 +
1.44X4 + 2.34X5 with R2= 0.82, based on maximum (X1) and minimum (X2) temperatures,
relative humidity (X3), rainfall (X4) and wind speed (X5) showed 82% variability in blight
severity. Both models were assessed by; comparing dependent variable and regression
coefficients with physical theory, comparing observed and predicted values, and collection
of new data. Comparison of observed and predicted data points was done using statistical
indices i.e. root mean square error (RMSE) and % error. Model assessment revealed that
both models were statistically significant and reliable to predict chickpea blight disease, as
both predictive models had significant coefficient of determination i.e. R2 which was 0.72
in terms of five years, and 0.82 in terms of two years predictive models. Further, both
models showed significant F-statistics and low standard error. For example, five years
model indicated (10) and two years model displayed (7) standard error. When comparison
of observed and predicted values was done on the basis of percent error and root mean
square error (RMSE), all predictions were correct on the basis of average RMSE and %
error i.e. below ± 20. Validation of five years model with two years model showed that
two equations of regression models exhibited good fit into the data. Coefficient of
determination (R2) of five years model i.e. 72% and two years model i.e. 82% was also in
close proximity. Five years model when compared with two years model showed non-
significant P-value. This indicated that both models were closely associated. Closeness of
regression equations and nearness of coefficients of determination of two models (five and
two years) indicated that two models validated each other. In both models, influence of
maximum and minimum temperatures, relative humidity, rainfall and wind speed was
significant. Therefore, in stepwise regression analysis no single variable was eliminated.
Hence, predictive model developed in present study on five years data was fit to predict
chickpea blight disease.
100
Critical ranges of environmental variables i.e. maximum and minimum
temperatures, relative humidity, rainfall and wind speed for chickpea blight were
determined. Blight severity was high at maximum (20-24°C) and minimum (12-14°C)
temperatures, relative humidity (65-70%), rainfall (5-6 mm) and wind speed (5-6.5 km/h),
respectively.
Screening trails were conducted for two years to identify resistant sources against
chickpea blight. Among forty eight genotypes/advanced lines, 10 were found resistant
while 11 displayed moderately resistant reactions. The resistant advanced lines were K-
60013, K-98008, K-96001, K-96022, D-97092, D-91055, D-90272, D-96050, D-Pb2008
and D-Pu502-362. The advanced lines showed moderately resistant reactions were K-
96033, K-89169, K-90395, D-91017, D-89044, D-05006, D-96018, D-86030, D-96032,
D-03009 and D-1CC-5127. The rest of genotypes showed highly susceptible, susceptible
and moderately susceptible reactions.
Fungicides, botanical extracts and antagonists (bio-controls) were tested at
different concentrations under laboratory conditions. Fungicides gave significantly more
control over rest of the treatments during in vitro studies. Two fungicides i.e. Alliete and
ThiovetJet, two plant extracts i.e. M. azedarach and A. indica, and a bio-control agent i.e.
T. harzianum remained significant in their efficacy under laboratory conditions. Alliete
and ThiovetJet @ 0.15% reduced 87% and 83% colony growth of A. rabiei, respectively
while botanical extracts; M. azedarach and A. indica @ 8% inhibited 47% and 36%
colony growth, respectively. T. harzianum (antagonist) at 1 x 107 conidia/mL, checked
colony growth up to 41% of A. rabiei. These effective treatments were then tested under
in vivo conditions and proved effective. Under field conditions, Alliete controlled
significantly more disease severity than ThiovetJet, while among plant extracts, M.
azedarach remained significantly more effective than A. indica. T. harzianum showed
significant less effect over ascochyta blight. Alliete, ThiovetJet, M. azedarach, A. indica
and T. harzianum restricted disease severity to; 17%, 23%, 50%, 56% and 63%,
respectively compared to control (75%) under field conditions.
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CONCLUSIONS
I. Environmental parameters i.e. maximum and minimum temperatures, relative
humidity, rainfall and wind speed were significantly correlated with chickpea
blight disease.
II. Five years chickpea blight disease predictive model based on five environmental
variables i.e. maximum and minimum temperatures, relative humidity, rainfall and
wind speed explained 72 percent variability in disease development.
III. Two years model based on five environmental variables explained 82 percent
variability in disease development.
IV. Maximum (20-24°C) and minimum temperatures (12-14°C), relative humidity (65-
70%), rainfall (5-6 mm) and wind speed (5-6.5 km/h) were found critical
environmental ranges for chickpea blight disease.
V. Evaluation of chickpea germplasm showed that there was scarcity of resistance in
chickpea germplasm against ascochyta blight.
VI. Alliete proved best among all fungicides.
VII. Plant extract M. azedarach gave better results to control ascochyta blight.
102
RECOMMENDATIONS
I. Disease predictive model should be used for deciding application of fungicide.
II. Continuous monitoring of quantity, quality, movement and distribution of conidia
would be necessary for precise chickpea blight disease prediction.
III. There must be installation of weather stations at major chickpea growing areas of
Pakistan particularly in province Punjab, so that, environmental data may be made
available for establishing future forecasting system.
IV. Genotypes; K-60013, K-98008, K-96001, K-96022, D-97092, D-91055, D-90272,
D-96050, D-Pb2008 and D-Pu502-362 displayed resistant reaction should be
cultivated on farmer ´s field to check their resistance stability. Their cultivation
will also help in mitigating the use of fungicides because these genotypes would
have less disease compared to susceptible cultivars.
V. Systemic fungicides should be used as post-infection spray.
VI. Antimicrobial action of T. harzianum against chickpea blight disease on chickpea
plant is required to be studied under controlled environment to explore full
potential of this antagonist against ascochyta blight.
103
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APPENDIX-I
Table a. Interaction of treatments and years for the evaluation of fungicides, plant
extracts and antagonist to control chickpea blight disease under field
conditions
Treatments 1st Year
2011
2nd year
2012
Alliete @ 0.15% 21.29 h 13.48 j
ThiovetJet @ 0.15% 26.14 g 19.18 i
M. azedarach @ 8% 52.74 e 46.51 f
A. indica @ 8% 59.07 d 53.85 e
T. harzianum @
1x 107
65.03 b 61.92 c
Control 75.44 a 75.07 a
L.S.D 1.86 1.14
*Means with similar letters in a row are not significantly different at P = 0.05
Table b. Interaction of treatments and sprays for the evaluation of fungicides, plant
extracts and antagonist to control chickpea blight disease under field
conditions
Treatments Sprays
Spray1 Spray2 Spray3
Alliete @ 0.15% 24.22 i 16.61 k 11.33 l
ThiovetJet @ 0.15% 29.94 h 21.77 j 16.27 k
M. azedarach @ 8% 56.61 e 48.83 f 43.44 g
A. indica @ 8% 63.05 d 55.83 e 50.50 f
T. harzianum @
1x 107
67.11 c 62.27 d
61.05 d
Control 76.11 a 73.66 b 76.00 ab
L.S.D 2.43 1.94 1.71
*Means with similar letters in a row are not significantly different at P = 0.05
128
Table c. Interaction of treatments and varieties for the evaluation of fungicides, plant
extracts and antagonist to control chickpea blight disease under field
conditions
Treatment Varieties
CM-2000 CM-98 Pb-1
Alliete @ 0.15% 14.05 m 21.66 l 16.44 m
ThiovetJet @ 0.15% 19.38 l 27.61 k 21.00 l
M. azedarach @ 8% 48.16 hi 57.72 g 43.00 j
A. indica @ 8% 58.33 g 64.83 e 46.22 i
T. harzianum @
1x 107
67.72 d 72.61 c 50.11 h
Control 79.66 b 84.66 a 61.44 f
L.S.D 2.84 2.04 1.97
*Means with similar letters in a row are not significantly different at P = 0.05
