DISTRIBUTION of ASCOCHYTA BLIGHT (Didymella Rabiei) and EVALUATION of CHICKPEA (Cicer Arietinum L.) VARIETIES to the DISEASE in EAST SHEWA, CENTRAL ETHIOPIA

DISTRIBUTION OF ASCOCHYTA BLIGHT (Didymella rabiei) AND EVALUATION OF CHICKPEA (Cicer arietinum L.) VARIETIES TO THE DISEASE IN EAST SHEWA, CENTRAL ETHIOPIA

MSc THESIS

EDEO NEGASH TUFA

FABURARY 2021

HARAMAYA UNIVERSITY, HARAMAYA

Distribution of Ascochyta Blight [Didymella rabiei) and Evaluation of Chickpea (Cicer arietinum L.) Varieties to the Disease in East Shewa, Central Ethiopia

A Thesis Submitted to the School of Plant Sciences,

Postgraduate Programs Directorate

HARAMAYA UNIVERSITY

In Partial Fulfillment of the Requirements for the Degree of

MASTER OF SCIENCE IN PLANT PATHOLOGY

Edeo NegashTufa

APRIL 2021

Haramaya University, Haramaya, Ethiopia

HARAMAYA UNIVERSITY

POSTGRADUATE PROGRAMS DIRECTORATE

We hereby certify that we have read and evaluated this Thesis entitled ‘Distribution of Ascochyta Blight [Didymella Rabiei) and Evaluation of Chickpea (Cicer Arietinum L.) Varieties to the Disease in East Shewa, Central Ethiopia’ prepared under our guidance by Edeo Negash Tufa. We recommend that it be submitted as fulfilling the Thesis requirements.

Zelalem Bekeko (PhD) ______Major Advisor Signature Date

Abdi Mohammed (PhD) ______Co-advisor Signature Date

As members of the Board of Examiners of the M.Sc. Thesis Open Defense Examination, we certify that we have read and evaluated the Thesis prepared by Edeo Negash Tufa and examined the candidate. We recommend that the Thesis be accepted as fulfilling the Thesis requirements for the Degree of Master of Science in Plant Pathology.

______Chairman Signature Date

______Internal Examiner Signature Date

______

External Examiner Signature Date

Final approval and acceptance of the Thesis is contingent upon the submission of the final Copy of the thesis to the Council of Graduate Studies (CGS) through the School Graduate Committee (SGC) of the candidate’s School.

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DEDICATION

I dedicate this thesis to my mother Danse Midakso, my father Negash Tufa and my younger brother RegasaNegash for nursing me with affection and love and for their dedicated partnership in the success of my life.

STATEMENT OF AUTHOR

First, I declare that this thesis is my genuine work and all sources of materials used for this thesis have been duly acknowledged. I have followed all ethical and technical principles of scholarship in the preparation, data collection, data analysis, and compilation of this thesis. Any scholarly matter that is included in the thesis has been given recognition through citation. This thesis is submitted in partial fulfillment of the requirements for the award of M.Sc. degree in Plant Pathology at Haramaya University. The thesis is deposited in the Haramaya University’s Library and is made available to borrowers under the rules of the Library. I solemnly declare that this thesis has not been submitted to any other institution anywhere for the award of any academic degree, diploma, or certificate

Brief quotations from this thesis are allowable without special permission provided that accurate acknowledgement of source is made. Requests for permission extended quotation from or reproduction of this manuscript in whole or in part may be granted by Head of the School of Plant Sciences or Director of Postgraduate Programs Directorate when in his or her judgment the proposed use of the material is in the interests of scholarship. In all other instances, however, permission must be obtained from the author.

Name: EdeoNegash Tufa Signature: ––––––––––––––––––––

Date: ––––––––––––––––––––

School: Plant Sciences, Haramaya University

BIOGRAPHICAL SKETCH

The author was born in East Shewa Zone, Adama Woreda from a rural family in 1992. He attended his Primary School from 1999-2002 at Balale and joined Wonji Shewa Elementary School, at Adama, from 2003 to 2007. He completed his high school education at Wonji Shewa Secondary School from 2008 to 2009 at Adama and then joined Hawas Preparatory School, located in Adama City from 2010 to 2011. After the completion of his high school education, he joined Haramaya University in 2012 and graduated with a Bachelor of Science degree in Plant Sciences in 2015.

After his graduation, the author was employed at the Ethiopian Institute of Agricultural Research as a researcher and assigned at Holetta Agricultural Research Center on Temperate and Indigenous Fruits Research Program as a Junior Researcher in September 2016. Following, he joined the Postgraduate Programs Directorate of Haramaya University in October 2018 to pursue his study leading to M.Sc. Degree in Plant Pathology.

ACKNOWLEDGEMENTS

First, I would like to thank God for helping and guiding me throughout my entire life.My deepest and heartfelt gratitude goes to my major advisor, Dr. Zelalem Bekeko and my co-advisor Dr. Abdi Mohammed for their guidance, assistance, support and encouragement throughout the entire period of this thesis work. Their keen eye for details and their pointed questions have steered me in the right direction at every turn. I deem it my duty to acknowledge my debt of gratitude to Dr. Mashilla Dejene, for his substantial advice, guidance throughout the progress of this study and for your words of wisdom and encouragement during tough time. I feel a great pleasure to place on record my deep sense of appreciation and heartfelt thanks to Mr. Shugute Addisu (Debre Zeit Agricultural Research Center, Researcher and Candidate of PhD in plant pathology) for extending his keen interest, sincere guidance, continuous encouragement and sympathetic attitude that enabled me to complete these studies.

My heartfelt gratitude also goes to Mr. Kecha Geda, Rebira Kecha and Dechu Mendafrawu for their help in conducting the survey as well as field assistance and technical support in execution of the research. I would like to forward my appreciation to Mr. Erasi Megersa and Debre Zeit Agricultural Research Center Plant Pathology Laboratories staff members for their assistance in the laboratory works. I gratefully acknowledges to the management of Debre Zeit Agricultural Research Center for allowing and facilitating to use their experimental material (Chickpea variety) that make true the thesis research. I also recognized Debre Zeit Agricultural Research Center Plant Pathology laboratory for fulfilling the laboratory Equipment has to grow and identify the plant disease.

Dr. Girma Demissie of Holetta Research Center Plant Protection Director and Dr. Mohammed Yusuf of EIAR Crop Protection Research Directorate are highly acknowledged for their kind and sincere support morally, technically and financially. A word of thanks will not suffice to all the people who contributed to this work physically and emotionally, who stood beside me throughout the journey may almighty God bless you all. I am blessed to have my parents’ sacrifice and constant love from my family and my friends Hawi Geda, without which I would not be here today.

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ACRONYMS AND ABBREVIATIONS

AB Ascochyta Blight ANOVA Analysis of Variance AUDPC Area Under Disease Progressive Curve CSA Central Statistical Agency CSMA Chickpea Seed Meal Agar DAP Days After Planting DI Diseases Incidence DZARC Debre Zeit Agricultural Research Center FA Farmers’ Associations FAO Food and Agriculture Organization FOSTAT Food and Agriculture Organization Statistics Database G*E Variety by Environment GPS Global Positioning System GRDC Grain Research and Development Corporation ICARDA International Center for Agricultural Research in the Dryland Areas ICRISAT International Crop research Institute for the Smi-Arid Tropics m.a.s.l. metres above sea level NaOCl Sodium Hypochlorite (Chlorox) (detergent) PDA Potato Dextrose Agar PDI Percent Disease Incidence PDS Percent Disease Severity PSI Percentage Severity Index SA Salsalic Acid SAS Statistical Analysis System WP Wettable Powder (Formulation)

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TABLE OF CONTENTS

DEDICATION IV

STATEMENT OF AUTHOR V

BIOGRAPHICAL SKETCH VI

ACKNOWLEDGEMENTS VII

ACRONYMS AND ABBREVIATIONS VIII

LIST OF TABLES XIII

LIST OF FIGURES XV

LIST OF TABLES IN THE APPENDIX XVI

ABSTRACT XVII

1. INTRODUCTION 1

2. LITERATURE REVIEW 5

2.1. Origin and Distribution of Chickpea 5

2.2. Trends of Chickpea Production Status and Types 5

2.3. Status of Chickpea Production and Productivity in Ethiopia 6

2.4. Importance of Chickpea in Ethiopia 7

2.5. Ecological Requirements for Chickpea Production 8

2.6. Major Constraints of Chickpea Production 9

2.7. Chickpeas Ascochyta Blight (Didymella rabiei) 10

2.7.1. Pathogen biology and host range 10

2.7.2. Ecology and epidemiology of ascochyta blight 12

2.7.3. Symptoms of ascochyta blight on chickpea 13

2.7.4. The spread of ascochyta blight on chickpea 13

2.8. Managements of Ascochyta Blight of Chickpea 14

2.8.1. Cultural practices 15

2.8.2. Host plant resistance 15

2.8.3. Biological control 17

2.8.4. Chemical control 18

2.8.5. Integrated disease management (IDM) against Ascochyta blight 19

3. MATERIALS AND METHODS 21

3.1. Description of the Study Areas 21

3.2. Distribution and Association of Chickpea Ascochya Blight with

Biophysical Factors in East Shewa, Central Ethiopia 22

3.2.1. Ascochyta blight disease survey 22

3.2.2. Assessment of chickpea Ascochyta blight 22

3.2.3. Collection of diseased plant samples 23 x

3.2.4. Data analyses 24

3.3. Culural and Morphological variability of Didymella rabiei Isolates

collected from East Shewa, Central Ethiopia. 24

3.3.1. Isolation of the pathogen 24

3.3.2. Sub-culturing and characterization of Didymella rebiei 25

3.4. Evaluation of Chickpea Varieties Resistance Reaction Againist

Ascochyta Blight under Field Condition 26

3.4.1. Treatments, procedures and Experimental design 26

3.4.2. Diseases assessment 28

3.4.3. Yield and yield related parameters 29

3.5. Data Analysis 29

4. RESULT AND DISCUSSIONS 32

4.1. Field Survey 32

4.1.1. Prevalence and distribution of Ascochyta blight 32

4.1.2. Ascochyta blight incidence and severity 33

4.2. Association of Ascochyta Blight Disease with Biophysical Factors 36

4.3. Morphological and cultural characteristics of Didymella rabiei (Target

pathogen) 44

4.4. Evaluation of Chickpea Varieties Resistance Reaction against Chickpeas

Ascochyta Blight 48

4.4.1. Disease incidence 48

4.4.2. Percentage severity index 48

4.4.3. The Area Under the Disease Progress Curve (AUDPC) 50

4.4.4. Disease Progress Rate (r) 52

4.4.5. Reaction of chickpea varieties against chickpea AB under field condition 54

4.5. Mean Values of Growth Parameters and Grain Yields of Chickpea

Varieties at Both Locations 56

4.6. Pod Infection 60

4.7. Correlations of Disease Incidence and Severity with Yield Components

and Yield at both sites 62

5. SUMMARY AND CONCLUSION 67

6. REFERENCES 70

7. APPENDICES 93

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LIST of TABLES

Table Page

1. Lists of chickpea varieties used for field experiment at Bora and Dhera for their reaction against Ascochyta blight. 27

2. Categorization of variables used in analysis for the distribution of AB of chickpea epidemics in four districts (n = 68) of Central Ethiopia, during the 2019 main growing season. 31

3. Prevalence of chickpea Ascochyta blight across four districts in the Central Ethiopia during 2019/20 main cropping season. 32

4. Incidence and severity (mean ± SE) of AB of chickpea for different independent variables during the 2019/20 main growing season, East Shewa, Central Ethiopia. 35

5. Logistic regression model for chickpea Ascochyta blight (Dedimela rabei) incidence and severity, and likelihood ratio test on independent variables in East Shewa, Central Ethiopia, during 2019 main cropping season. 40

6. Analysis of deviance, natural logarithms of odds ratio and standard error of chickpea AB incidence (%) and likelihood ratio test on independent variables in reduced regression model in East Shewa, centeral Ethiopia, during 2019 main cropping season. 41

7. Analysis of deviance, natural logarithms of odds ratio and standard error of chickpea Ascochyta blight severity (%) and likelihood ratio test on independent variables in reduced regression model in East Shewa, Central Ethiopia, during 2019 main cropping season. 42

8. List of isolated fungal pathogens and frequency of occurrence on blighted chickpea samples collected from Adaa, Bora, Lume and Gumbichu districts during 2019. 43

9. Didymella rabiei isolates collected from four districts of East Shewa, Central Ethiopia in 2019 showing variation in pigmentation on CSMA and PDA 46

10. Frequency of occurrence of Didymella rabiei (Ascochyta rabiei) cuse of chickpea Ascochyta blight on PDA and CSMA medium. 47

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11. Response of chickpea varieties for chickpea AB incidence and severity (%) at Bora and Dhera, Central Ethiopia, during 2019 main cropping season 49

12. Area under disease progress curve (AUDPC) for chickpea varieties against AB in the fields at Bora and Dhera, Eats Shewa, Central Ethiopia, during 2019/2020 main cropping season. 51

Table 13. Description of disease progress rate by variety and location during 2019/2020 main cropping season 53

14. Average disease reaction of 25 chickpea varieties against AB at two locations (Bora and Dhera) of East Shewa, Central Ethiopia, during 2019/2020 main cropping season. 55

15. Mean values of growth parameters and grain yields of the test varieties at Dhera of East Shewa, Central Ethiopia in 2019/2020 cropping season. 58

16. Mean values of growth parameters and grain yield of the test varieties at Bora of East Shewa, Central Ethiopia in 2019/2020 cropping season. 59

17. Ascochyta blight pod infection at two locations (Dhera and Bora) East Shewa, Central Ethiopia in 2019/2020 cropping season. 61

18. The correlation coefficient (r) between Ascochyta blight incidence, severities index, AUDPC and infection rate with yield and yield components of chickpea at Bora, Central Ethiopia, in 2019/2020 cropping season. 66

19. The correlation coefficient (r) between Ascochyta blight incidence, severities index, AUDPC and infection rate with yield and yield components of chickpea at Dhera, Central Ethiopia, in 2019/2020 cropping season. 66

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LIST of FIGURES

Figure Page

1. Map of chickpea Ascochyta blight surveyed areas in 2019/20 cropping season at East Shewa, Central Ethiopia. 21

2. (A, B) Didymella rabiei (Ascochyta rabiei) cuase of chickpea Ascochyta blight grown on both PDA and CSMA media in the laboratory and Conidia of Didymella rabiei f. sp. ciceris (C, D) under microscope observation 47

3. Ascochyta blight Pod infection 60

LIST OF TABLES IN THE APPENDIX

Appendix Table Page

1. Questionnaire for assessing the distribution and significance AB of chickpea in the Central of Ethiopia. 93

2. Summarized ANOVA table for chickpea varieties evaluated for their reaction to AB under field conditions at Dhera, central, Ethiopia during 2019/20 in the cropping season 95

3. Summarized ANOVA table for chickpea varieties evaluated for their reaction to AB under field conditions at Bora, Central Ethiopia during 2019/20 in the cropping season 95

4. Models fitted for disease progress rate of Chickpea Ascochyta blight severity (%) at Dhera and Bora, Central Ethiopia, during 2019 main cropping season. 96

5. Combined analysis mean square value for chickpea varieties evaluated for AB resistance reaction under field conditions at Dhera and Bora, Central Ethiopia, during 2019/20 main cropping seasons 97

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DISTRIBUTION OF ASCOCHYTA BLIGHT [Didymella rabiei) AND EVALUATION OF CHICKPEA (Cicer arietinum L.) VARIETIES TO THE DISEASE IN EAST SHEWA, CENTRAL ETHIOPIA

ABSTRACT

Ascochyta blight (AB) has emerged as a threat of chickpea production in Central Rift Valley of Ethiopia. The present study was conducted with the objectives of assessing the distribution of Ascochyta blight and associated factors and evaluation of chickpea varieties against the pathogen in East Shewa, Ethiopia. Field survey was conducted across four major chickpea growing districts, in Central Rift Valley of Ethiopia during 2019 main cropping season. Ascochyta blight Isolates collected from the study areas were identified based on cultural and morphological features under laboratory conditions. Twenty-five chickpea variety were evaluated for blight resistance at two sites. The treatments were laid out in a randomized complete block design with three replications. During the survey, 68 chickpea grown fields were assessed, of which the disease was prevalent on 23(33.8%) of the fields with mean incidence and severity of 15.2 and 26.2%, respectively. Thus, among districts (Adea, Lume, Gumbichu and Bora), the highest prevalence and incidence of the disease were recorded in Bora district with 100 and 49.4%, respectively, whereas Adea district showed the lowest disease prevalence and incidence 10.0 and 3.9%, respectively. The association of disease incidence and severity with biophysical factors was assessed using a logistic regression model. The current study confirmed that districts, planting date, varieties, previous crop history, crop density, and seed source were significantly associated with both AB incidence and severity. The highest (>25.0%) AB incidence and severity were highly associated with Bora and Lume districts, with field previously sown field pea and wheat, early august sown field and planting material sourced from other farmers. Morphological and cultural characterization of 68 isolate showed highest frequencies of Fusarium spp (53.53%) followed by Penicillium spp. (19.71%) and D. rabei (10.88%). Fungal isolates representatives of four districts of Central Rift Valley of Ethiopia showed variation in colony color as mouse gray, the light gray and gray with dark brown center when grown on CSMA. However, gray-black and black colony color was observed on PDA. fungus grew best on CSMA medium. Regarding the field experiment, analysis of variance revealed that there were highly significant differences (P≤0.001) among the tested varieties against AB disease. A high degree of disease severity was observed at Dhera site than Bora. Out of that 25, the resistance of the tested varieties to AB, when mapped against location, showed, 11 were found resistant, 10 were moderately resistant, 3 were susceptible, while 2 varieties were found highly susceptible. None of the varieties was found highly resistant. Thus, it is better to use both Arerti and Dhera variety with proper management practices in production to reduce AB epidemics in Central Rift Valey and related agro-ecologies. Future research work should focus on screening several chickpea varieties for the source of resistance at different locations over seasons.

Keywords: Association: Incidence; reaction; Severity; grain yield. xvii

1. INTRODUCTION

Chickpea (Cicer arietinum L.) is the second most important legume crop after common bean (Phaseolus vulgaris L.) and ranks third in production globally, next to harricot bean (Phaseolus vulgaris L.) and field pea (Pisum sativum L.) (Diapari et al., 2014). According to CSA (2018) report, Ethiopia is the largest producer, consumer, and exporter of chickpea in Africa and accounted for 4.5 and 60% of the global and Africa’s chickpea market, respectively. In Ethiopia, chickpea is one among the main cultivated annual crops in both its total coverage area compared to pulses and its role as in indirect human consumption (Sisay, 2018). In the country, over 921,552 farmers are engaged in chickpea production (CSA, 2019). It is widely grown in marginal lands and usually as crop rotation in highland and semi-highland regions of the country. Apperently, it is one of the economically important grain legume crops in Africa, particularly in Ethiopia as a cash crop for small-scale farmers and beyond for the country through earning the foreign currency as an export commodity (Daniel et al., 2019). For instance, according to FAO (2019), the country exported 473,570 metric tons of chickpea, and earned US$ 301 million, whereas the dried haricot bean was the top-exported pulse, accounting for about half of all export volumes.

Despite the importance of chickpea in Ethiopia, its production and productivity is relatively low (2000 kg ha-1) (CSA, 2020) compared to its potential yield (5000 kg ha-1) under well- managed production (Gemechu et al., 2012). Several factors are contributing to the low productivity of chickpea. The primary causes of low yields are its susceptibility to various biotic and abiotic stresses. Among the abunduantly reported biotic diseases, Ascochyta blight (AB), caused by Didymella rabiei (syn. Ascochyta rabiei, anamorph), fusarium wilt (Fusarium oxysporum f.sp. ciceri), phytophthora root rot (Phytophthora medicaginis) and botrytis grey mold (Botrytis cinerea) (Li et al., 2015).

Ascochyta blight caused by Didymella rabiei (Pass.), is an important foliar disease of chickpea worldwide and its epidemics can cause grain yield and quality losses up to 100% in most regions of the world (Wazir, 2019). It is one of the most important foliar diseases of chickpea (Tilaye et al., 1994; Asrat, 2015) and leads to yield losses up to 41.3% in Ethiopia (Amin and Melkamu, 2014). When the crop is planted earlier, the maximum mean relative grain yield losses of 44.97% (1514 kg ha-1) was also recorded from Teje variety (Yigrem et al, 2019).

Ascochyta blight has been reported in Ethiopia (Asfaw et al., 1994) as well as in 35 countries across six continents (Labdi et al., 2013). The disease was not well known in Ethiopia until it was first observed around 1969 at Kulumsa and there was no known evidence whether the disease was exogenic or indigenous to the country (Geletu, 1984). The teleomorph Didymella rabiei (syn. Ascochyta rabiei, anamorph) produces characteristic necrotic lesions on all foliage parts causing the collapse of tissue, stem breakage, and plant death (Pande et al., 2005). Frequent rainfall at flowering and pod formation stages can create conducive environment for the infection and symptoms expression (Malik et al., 2005).

Ascochyta blight has been reduced using some methods, such as planting pattern (Gan et al., 2009), row spacing and seeding rate (Chang et al., 2007), identification of optimum plant populations (Gan et al., 2007), application of salicylic acid (SA) (Bayraktar and Dolar, 2002) and fungicide (Rauf et al., 1996; Shtienberg et al., 2006; Armstrong-Cho et al., 2008; Gan et al., 2009). However, these approaches are not feasible at farmer’s fields because of D.rabiei is a seed-borne pathogen, and infected seed can be the most important source of inoculums for long-distance dispersal (Tivoli and Banniza, 2007). Although several fungicides have proved effective as seed treatment, the need for their repeated application often makes them uneconomical where crop yields are low (Pande et al, 2005) and the only practical management of AB is thought to produce continuously resistant genotypes against the pathogen.

In Ethiopia, various researches have conducted mainly by Debre Zeit Agricultural Research Center (DZARC) over the past three decades to improve the productivity of this crop (Shiferaw et al., 2007). Through this processes DZARC and collaborative research programs through the involvements of ICRISAT and ICARDA most of the improved chickpea varieties with their appropriate agronomic practices have been demonstrated across some districts, such as; Adea-Liben, Akaki and Gumbichu for further technologies dissemination (Legesse et al., 2005). However, those cultivars available in ICARDA lack complete resistance to Didymella rabiei (Singh et al., 1992) and Ethiopian strains are break after certain production period. Meanwhile, the virulence of the pathogen is changed in a high degree of genetic diversity constantly changing in nature and previously released resistant cultivars have become susceptible due to the appearance of new physiological races of the pathogen (Tekeoglu et al., 2000).

As an evidence, some of the released varieties have been affected and some of them, like Shasho, are now out of production and have lost their resistance in the Central Rift Valley and other parts of the country. However, researchers have identified a number of resistant chickpea genotypes against AB at National and International levels (Hawtin and Singh, 1984; Nene and Reddy, 1987). Either that, sources of resistance have been reported during the last 50 years and generally based on field observation during natural epidemics or artificial inoculation tests in the field or greenhouse (Alam et al., 2003; Iqbal et al., 2004; Chaudhary et al., 2005; Basher et al., 2006). In Pakistan Iqbal et al. (2010) screened about 145 varieties against AB and wilt diseases, in which most of the genotypes ranged from susceptible to highly susceptible. The authors also evaluated 356 chickpea germplasm accessions from different origins and none of them was highly resistant (Iqbal, 2002), while 7 genotypess were found resistant and 75 were moderately resistant. Similarly, Bokhari et al. (2011) evaluated the resistance levels of 10 cultivars of chickpea and revealed that, the maximum number of varieties was susceptible under field conditions. In addition, similar activities have been conducted in Ethiopia; Megersa (2016) screened a set of chickpea genotypess against AB and demonstrated that most genotypess were resistant against chickpea blight, indicating a good source of resistance genes. Likewise, Yigrem et al. (2019) reported that using resistant varieties with optimum sowing date gave reasonable grain yields and reduced disease incidence and severity in the country.

Crop disease survey represents a basic essential step facilitating loss determination, as the pivot to articulate and implement management schemes aimed at economic control. A few surveys on the intensity of AB in the Central Rift Valley of Ethiopia have been reported previously, but no attempts were made to relate biophysical factors to disease epidemics and the causal strains of AB were not well characterized yet. In addition, there is a need to get knowledge on the current status of the diseases to formulate effective control measures of the pathogens associated with the diseases. Knowing which areeas are worst affected, and which are currently threatened is vital for the effective formulation of control interventions (James, 1968). Nevertheless, severity of the disease varies with crop varieties, pathogen species, geographic area, environmental conditions, and cultural practices (Yusuf and Sangchote, 2005).

For instance, the result of survey report condected by (Asrat, 2015) on 2013/2014 cropping season were indicated that disease seems increasing in intensity and distribution in chickpea growing areas of Ethiopia, mainly at the Central Rift Valley due to the dissemination of infected seed. However, Megarsa (2016) reported the mean low incidence and severity of the disease across the country in the next year that might be associated with the drought occasioned by El-Nino conditions. Associations of this disease with El Nino have been made and indicate the potential for future epidemics if weather patterns become more variable (Checkley et al., 2000; Rodó et al., 2002).

On the other hand, however some studies on screening of chickpea genotypes and lines already been conducted by (Megarsa, 2016 and Asrat, 2015) but there is not much work done up until now in Ethiopia on screening of already released varieties. Since host plant resistance is not stable due to emergence of new pathotypes of Didymella rabiei, identification of resistant sources against the prevalent pathotypes/isolates should be considered. Knowledge on Ascochyta blight intensity and the effect of the disease on grain yields of chickpea in the country will enable farmers and other stakeholders to take appropriate control measures thus paving way for commercial production of the crop. Farmers will also use identified resistant varieties from this study to enhance food security in the region and to have an additional source of income.

Therefore, the current study was undertaken with the following specific objectives to:

1) Investigate the distribution of Ascochyta blight of chickpea and its association with biophysical factors in East Shewa Zone; 2) Determine cultural and morphological characteristics of Didymella rabiei isolates collected from diseased chickpea samples and; 3) Evaluate chickpea Varieties for resistance to Acochyta blight under natural infections in East Shewa, Central Ethiopia.

2. LITERATURE REVIEW

2.1. Origin and Distribution of Chickpea

The centre of origin for chickpea (Cicer arietinum L.) are Turkey and Syria (Singh, 1997), but it is developed as a post-rainy season, spring-sown crop, early in its evolution and spread into sub-tropical regions, in contrast to its wild relatives that have remained as winter annuals in the West and Central Asia (Berger et al., 2005). It is also likely to have been domesticated for the first time in southeast Turkey (Redden and Berger, 2007). Among the pulses, chickpea is one of the most important cool season food grain legumes in the world after common bean (Phaseolus vulgaris L.) and field pea (Pisum sativum L.) (Muehlbauer and Sarker, 2017). It is the second most cultivated grain legume crop by smallholder farmers of the semi-arid regions around the world (Thudi et al., 2014). Ethiopia is a secondary center of genetic diversity for chickpea, where the wild relative of cultivated chickpea, Cicer cuneatum L., is found in the Tigray region of Ethiopia (Yadeta and Geletu, 2002; Gemechu et al., 2012).

2.2. Trends of Chickpea Production Status and Types

Globally, chickpea is cultivated over 13.2 million hectares with an annual production of 13.1 million tons and productivity less than 1000 kg ha-1 much less than estimated potential of 6000 kg ha-1 under optimum growing conditions (Amare et al., 2020). It is grown in more than 57 countries (Bulti and Jema, 2019), in which about 83% is produced in Asian countries, followed by Oceania (6%), Africa (5%), Americas (4%) and Europe (2%) (FAOSTAT, 2019). India had the lion’s share of the world’s total chickpea production, which accounts for 64.6%, followed by Australia (7.2%), Myanmar (4.6%), Pakistan (4.3%), Turkey (3.8%), and Ethiopia (3.7%) (FAOSTAT, 2018). Globally two types of chickpea are commonly cultivated these are Desi and Kabuli types. Chickpea with colored and thick seed coats is called Desi type, which is identified by its small size, angular shape, rugose surface, and colored seeds (black, brown, green and yellow), thick seed coat and mainly grown in India. They are normally dehulled and split to obtain dhal (split grain) and are favored in the Asian sub-continent. Desi type is generally earlier maturing and higher yielding than the Kabuli type, particularly the larger seeded Kabuli (GRDC, 2017). Seeds are mostly decorticated and processed into flour but some consumed as a whole grain (Wood et al., 2012).

The flowers are generally pink and the plants show various degrees of anthocyanin pigmentation even though some Desi types have white flowers without anthocyanin pigmentation on the stem. This type of chickpea accounts for 80-85% of chickpea production. Kabuli type chickpea has larger, rounder seeds than Desi type, and weighs about 400 mg. It is sold whole, so seed size and appearance are critically important. Yields are generally lower and more variable than in Desi varieties (GRDC, 2017). The Kabuli type chickpea is identified by it is large to medium seed size, ram’s head-shaped, and beige to white seeds, smooth to scarcely rugose surface texture, thin seed coat, and lack anthocyanin pigmentation on the stem and mainly cultivated in the Mediterranean Basin. The Kabuli type is rich in sucrose, low in fiber as compared to Desi type. Since the Kabuli type of chickpea has a larger seed size than Desi type, it receives a higher market price than the Desi type (Singh, 1997; Corp et al., 2004; Gaur et al., 2010). Kabuli type covers 15% of the world chickpea and grown mostly in Mediterranean regions, North Africa, North America and West Asia (Gaur et al., 2008). Ethiopian chickpea has enjoyed by the Kabuli types joined since the mid-1980s and accelerates over the Desi types due to some peculiar traits (AB-resistance, taste, seed texture, etc.) liked by the market and consumers (Asnake and Dagnachew, 2020). Virtually, consumption and production of Desi type chickpea is restricted primarily to the Middle East and Southeast Asia, whereas Kabuli is a type popular and valuable global commodity.

2.3. Status of Chickpea Production and Productivity in Ethiopia

In Ethiopia, chickpea is mainly grown in the central, eastern, northern highlands and southern regions of Ethiopia with an altitude ranging from 1400 to 2300 m.a.s.l, where annual rainfall ranges between 700 to 2000 mm (Asrat, 2015). Chickpea is categorized as a highland pulse crop grown in the cooler highlands of the country (Asfaw, 2010). Ethiopia is the first and largest producer of chickpea in Africa, which accounts for about 88.5% of the continent's production and the sixth largest producer worldwide and contributes to about 3.7% to the total world chickpea production (FAOSTAT, 2018). Although this crop is one of the major pulse crops, including faba bean, field pea, grass pea, haricot bean and lentil in the country, the average productivity of chickpea in 2018/19 cropping season was 2.0 ton ha-1(CSA, 2019), which is still uncertain compared to 5 ton ha-1 that to be harvested under optimum conditions (Gemachu, 2012).

In the country, the major chickpea producing areas are concentrated in two regional states, Amhara and Oromia regions, which covers more than 76.6% of the entire chickpea area and constitute about 94.2% of the total chickpea production (CSA, 2020). From Amhara region, the major chickpea producing zones are: East Gojam; North, South and Central Gondar; North Shewa; North Wollo; South Gondar; South Wollo and West Gojam (CSA, 2020). In the Oromia region, the major producing zones are East Shewa, South-West Shewa, North Shewa, and West Harargie, which account for a lion’s share in the total area and production in this regional state (CSA, 2020). The Desi type of chickpea is dominant in Ethiopia (Singh, 1997; Corp et al., 2004; Gaur et al., 2010). During 1995-2005, Ethiopian chickpea planted area and chickpea production in Africa showed an annual growth rate of 2.1 and 7.6%, respectively (Kassie et al., 2009). During 2013-2017, Ethiopian chickpea harvested area in hectares, chickpea productivity (kg/ha), and Production (ton) was 237,182, 1,943.82 and 2,307,096000, respectively (FAOSTAT, 2019).

2.4. Importance of Chickpea in Ethiopia

In Ethiopia, chickpea is an important grain legume next to faba bean and common bean both in terms of area coverage and production. It is mainly grown as a source of food protein, income generation, and soil fertility restoration and used for animal feed (Nigussu et al., 2019). The consumption of chickpea is also increasing among the urban population mainly because of the growing recognition of its health benefits and affordable source of proteins (Shiv, 2007). Currently, it has become an important high-value crop that promotes commercialization (Setotaw et al., 2018). In addition to being a source of cash for smallholder producers, chickpea increases livestock productivity as the crop residue is rich in digestible crude protein compared to cereals (Menale et al., 2009). It is used to prepare various food products in different parts of the world. The chickpea seed is a good source of carbohydrates and proteins, which collectively constitute 80% of the total dry seed weight (Upadhyaya et al., 2008). Chickpea also plays an important role in maintaining soil fertility by fixing nitrogen at rates of up to 140 kg ha-1year-1 (Flowers et al., 2010). Therefore, this crop can grow under the condition of low fertility and varying conditions of soil and climate in the country (Asnake, 2014).

In Ethiopia, the periodic profitability of chickpea production increased from ETB 20,000 ha-1 at the beginning of the decade to the current level of ETB 90,000 ha-1, indicating positive and significant production gain (Asnake et al., 2018). Its seed contains 59% carbohydrate, 29% protein, 5% oil, 4% ash and 3% fiber (Melak, 2014). In the 2014/2015 crop season, the country produced 458,682 tons of chickpea, of which 80% was consumed domestically and the remaining 20% was exported to markets in different countries (CSA, 2015).

2.5. Ecological Requirements for Chickpea Production

This crop is traditionally grown in the northern hemisphere, mostly at relatively high elevations in India and Ethiopia (Berhanu, 2017). It is grown between 20–40°N in the northern hemisphere and 27–38°S in the southern hemisphere (Imtiaz et al., 2011). Chickpea is a drought-tolerant legume and is grown as a summer crop in temperate environments. Genetic variability exists in chickpea germplasm for the response to variation in day length (photoperiod sensitivity) and response to variation in temperature (thermal sensitivity) and has been exploited in the development of short-duration cultivars (Ketema et al., 2016). It is sensitive to high (maximum daily temperature >35 °C) as well as low (mean of maximum and minimum daily temperatures < 15 °C) temperatures at the reproductive stage. Both extremes of temperatures lead to flower drop and reduced pod set (Kiprop, 2016).

Globally, chickpea is adapted to black soils in the cool semi-arid areas of the tropics, sub- tropics as well as temperate areas (Joshi et al., 2001). It does best on fertile sandy, loam soils with good internal drainage. Good drainage is necessary because even short periods of flooded or waterlogged fields reduce growth and increase susceptibility to root and stem rots (Oplinger et al., 1990). Chickpea, like other pulse crops, requires phosphorus (P), potassium (K), and micronutrients for their growth and development. A research result showed that chickpea crop significantly responses to a combined application of P and bio-fertilizers (Rhizobium and phosphobacterin) (Dhananjoy and Bandyopadhyay, 2009). Nitrogen is known to be an essential nutrient for plant growth and development (Werner and Newton, 2005) due to its role in biochemical, physiological, and morphological processes of plant production (Novoa and Loomis, 1981). Next to nitrogen, phosphorus is the most important element for adequate grain production (Ryan et al., 2012).

Application of P fertilizer enhanced mid-season dry matter accumulation and tissue P accumulation of both Desi and Kabuli chickpea, but grain yield was increased only modestly for Desi chickpea, while the yield of Kabuli chickpea was not affected by the application of P (Walley et al., 2005). In Ethiopia, chickpea is mainly grown in the central, northern, and eastern highland areas of the country at an altitude of 1400-2300 meters above sea level (m.a.s.l.), where annual rainfall ranges between 500 and 2000 mm (Geletu and Maesen, 2006). However, when the rainfall is heavier in the seasons it shows reduced yields due to disease outbreaks and stem lodging problems from the excessive vegetative growth. Areas with lighter, well-distributed rainfall patterns have produced the highest yield and quality chickpea seed (Oplinger et al., 1990). Similarly, early chickpea sowing using a drainage system on Vertisol has an increased risk of outbreaks of Ascochyta blight leading to heavy losses of yield (Nigussie et al., 2008).

2.6. Major Constraints of Chickpea Production

In Ethiopia, several abiotic and biotic factors are contributing to the low production and productivity of chickpea. Unpredictable rainfall, soil types, and agro-ecological adaptations are among abiotic factors. However, of biotic factors, which lead to an overall reduction in chickpea annual production, fungal diseases are of prime importance, followed by viral and bacterial diseases, which affect all parts of the plant at all stages of growth (Megarsa, 2016). In Ethiopia, 16 economically important diseases were reported on chickpea, of which about 50% are caused by fungal species and 38% are caused by viral pathogens (Nigussie et al., 2008). The remaining diseases are caused by nematodes and mycoplasma like organisms. However, the major chickpea diseases reported in Ethiopia are; Ascochyta blight (Didymella rabiei), Fusarium wilt (Fusarium oxysporum f.sp. ciceris), Dry root rot (Rhizoctonia bataticola), Black root rot (Fusarium solani), Wet root rot (Rhizoctonia solani), Collar rot (Sclerotium rolfsii), Root knot (Meloidogyne spp), Powdery mildew (Leveillula tauricacv. Arn), Rust (Uromyces ciceri-arietinum), Phyllody (Mycoplasma-like organisms), Stunt (Beet western yellows virus (BWYV), Alfalfa mosaic virus (AMV), Broad bean mosaic (BBSV), Pea seed-borne mosaic (Pea seed-borne mosaic virus (PSbMV), Faba bean necrotic yellows virus (FBNYV), Broad bean wilt virus (BBWV) (Abraham, 2008).

2.7. Chickpeas Ascochyta Blight (Didymella rabiei)

2.7.1. Pathogen biology and host range

The Dothideomycetes is the largest class of fungi within the phylum Ascomycota and represents diverse forms of life, such as saprobes, endophytes, mycorrhizae, marine fungi, lichenized fungi and pathogens (Robin et al., 2012). The Ascochyta blight diseases on different legume plants are caused by Ascochyta and related taxa, such as Phoma (Kim and Chen, 2019). Ascochyta blight, caused by Didymella rabiei (Pass.), belongs to phylum Ascomycota; Class Ascomycetes; and Genus Didymella Labr. [Teleomorph: Didymella rabiei (Kovacheski) von Arx] is haploid, heterothallic ascomyceteous fungus, which causes Ascochyta blight of chickpea (Chilvers et al., 2007). Didymella rabiei, the causal agent of Ascochyta blight of chickpea, exists as both an anamorph (A. rabiei) and a teleomorph (D. rabiei).

The anamorph is characterized by the formation of spherical or pear-shaped black fruiting bodies, called pycnidia. A pycnidium contains numerous hyaline unicellular and occasionally bicellular spores, pycnidiospores, or conidia, developed on short conidiophores (stalks) embedded in a mucilaginous mass. Pycnidiospores are oval to oblong, straight, or slightly bent at one or both ends and measure 6–12 μm by 4–6 μm spore size when grown on nutrient media (Punithalingam and Holliday, 1972; Nene, 1982). The teleomorph, D. rabiei (Kovacheski) von Arx (Syn. Mycosphaerella rabiei Kovacheski), is a bipolar heterothallic Ascomycete and is characterized by pseudothecia developing on chickpea crop residues that overwinter in the field (Pande et al., 2005). For successful sexual reproduction, the teleomorph requires pairing of 2 compatible mating types (MAT1-1 and MAT1-2), which are widely distributed in several major chickpea-growing areas of the world (Kaiser, 1997; Armstrong et al., 2001). Kovachevski first recorded the teleomorph (D. rabiei Pass) on overwintering chickpea debris in Bulgaria (Kovics et al., 1986; Trapero-Casas et al., 1996). Kaiser (1997) also reported that fertile pseudothecia of a fungus developed on naturally infected debris from several chickpea-growing countries in North Africa, North America, West Asia, and East and West Europe, indicating the widespread distribution of the two mating types (MAT1-1 and MAT1-2) in nature.

However, Khan et al. (2002) suggested that D. rabiei is heterothallic and the two mating types are not present in all chickpea growing areas. Sexual recombination occurs regularly in the life cycle of D. rabieiwherever both mating types occur; it contributes to increased diversity in the population (Armstrong et al., 2001; Peever et al., 2004), which leads to the erosion of resistant host genotypess (Peever et al., 2012; Mahiout et al., 2015; Tekin et al., 2017). Didymella rabiei has both sexual and asexual fruiting bodies. Therefore, a high degree of genetic diversity and pathogenicity has been reported in most epidemic regions for D. rabiei(Rhaiem et al., 2008; Atik et al., 2011; Sharma and Ghosh, 2016). This variability in reported pathotypes/races for chickpea might be due to the unavailability of standardized differentials and existence of high genetic variation of the D. rabieipopulation worldwide (Baite and Dubey, 2018). As a result, characterization and development of new resistance sources to Ascochyta blight has been challenging and affected by several factors, such as complex genetic basis of resistance and high pressure on the pathogen populations due to widespread cultivation of unique improved chickpea cultivars that confer to high variability in pathogen population (Collard et al., 2003; Mehrabi et al., 2015). Therefore, knowledge about pathogen aggressiveness and pathotypes differentiation could likely lead to better management of disease and proper decisions in breeding programs (Aghamiri et al., 2015; Farahani et al., 2019).

In Ethiopia, reports on Ascochyta blight mating types are scanty. Various crop and weed species were infected naturally by Didymella rabiei (anamorph: Ascochyta rabiei) in blight- affected chickpea fields. Didymella rabiei naturally infected 31 accessions of 12 Cicer spp. and the teleomorph developed on the overwintered debris of all accessions, including those of three highly resistant perennial species (Trapero and Kaiser, 2009). Well, known hosts include chickpea (Cicer arietinum), faba bean (Vicia faba), lentil (Lens culinaris), pea (Pisum sativum), vetches (Vicia spp.) and their wild relatives. The pathogens are often host-specific, each species causing the disease with economic significance only on specific crops, e.g. Ascochyta rabiei on chickpea, A. fabae on faba bean, A. lentiS on lentil, and A. pisi (teleomorph Mycosphaerella pinodes), and Phoma medicaginis var. pinodella on field pea (Peever, 2007). However, D. rabiei is pathogenic on lentil (Lens culinaris Medik), field pea (Pisum sativum L.), vetch (Vicia spp.), common bean (Phaseolus vulgaris L.), and cowpea (Vigna unguiculata L.) after artificial inoculation (Kaiser, 1991).

The pathogen additionally infects prickly lettuce (Lactuca serriola L.) and field pennycress (Thlapsi arvense L.), while reproducing (producing pycnidia) on necrotic tissues of alfalfa (Medicago sativa L.) and white sweet clover [Melilotus alba (L.) Lam.] (Kaiser, 1991). Ascochyta rabiei has also been isolated from Brassica nigra, Descurainiasophia, Galium apanine, Lamium amplexicaule, and Triticum aestivum, grown in fields where infected chickpea debris of the previous season remained on the soil surface during the off-season (Kaiser, 1991). Isolates of the pathogen from crop and weed species were pathogenic to chickpea and indistinguishable in cultural and morphological characteristics from isolates of D. rabiei from chickpea (Trapero and Kaiser, 2009).

2.7.2. Ecology and epidemiology of ascochyta blight

The fungus D. rabiei (syn. Phoma rabiei) can infect all parts of the plant aboveground, and at any growth stage (Sharma and Ghosh, 2016), and it is a seedborne diseaese. Ascochyta blight is associated with debris leftover in the ﬁelds also serves as a source of primary inoculum. Ascospores were also found to play a role in the initiation of disease epidemics. The secondary spread is through pycnidiospores (Nene et al., 2012). After coming into contact with host tissue, conidia of D. rabieibegin to germinate 12 hr after inoculation and penetration through the leaf cuticle, stem cuticle, and through stomatal openings into the host tissue normally occurs 24 hr after inoculation (Pande et al., 1987). Didymella rabiei forms typical appressoria associated with stomatal penetration (Illarslan and Dolar, 2002). Five or six days after inoculation, the pycnidia, arranged in a circular pattern on the infected host tissue. By the seventh day, most of the non-lignified host cells are destroyed (Pandey et al., 1987; Illarslan and Dollar, 2002). The disease builds up and spreads fast when night temperatures are around 10 °C, day temperatures are around 20 °C, and rains are accompanied by cloudy days (Nene et al., 2012). Ascospores produced from the sexual stage, developing on stubble or seed, are airborne, and can be spread over long distances. Under favorable conditions, i.e. at least two hours of wetness on the leaves, the ascospores germinate but the likelihood of establishment increases if the wetness continues for more than six hours (Markell et al., 2008). The germinated spore forms an appressorium from which the germ tube penetrates the plant tissue within 12-24 hr. Once the fungus has successfully invaded the plant, it starts to kill plant tissues and the disease lesions will be visible within 4 to 6 days after infection (Faye et al., 2010).

Ascochyta blight infection and disease progression would occur at 5 to 25 °C with an optimum temperature range of 16 to 20 °C and a minimum of 6 hrs leaf wetness. Disease severity increases with the increase in relative humidity (Trapero Casas and Kaiser, 1992).

2.7.3. Symptoms of ascochyta blight on chickpea

Symptoms of AB can develop on all the aboveground parts of the plant (Chongo and Gossen, 2001). It first appears as gray areas on the leaves, stems, or pods that quickly turn into brown lesions with dark borders. As the disease progresses, small, circular, brown-black dots (pycnidia) develop in the center of the lesions, are frequently arranged in concentric circles (zones), and resemble a bull's-eye (Markell et al., 2008). The apical twigs, branches, and stem often show girdling, and the plant parts above the girdled portion are killed or break off even before drying (Pande et al. 2010). The pale green/yellow discoloration on the leaves is often referred to as ―ghosting‖ (Moore et al., 2011). These symptoms become visible in 4 to 6 days after infection (Faye et al., 2010).

Concentric rings of pycnidia are the most diagnostic characteristics of chickpea Ascochyta blight. Infected seeds may be discolored, shrunken, or shriveled and when at high severity, lesions with dark pycnidia may be present on the seeds (Markell et al., 2008; Moore et al., 2011). In the field, Ascochyta blight appears as small circular patches of dead plants. However, if the source of inoculum is seed-borne, disease symptoms are often scattered uniformly across the field. On resistant cultivars, although the lesions appear as small dark- brown spots, they may not progress further (Chongo and Gossen, 2003).

2.7.4. The spread of ascochyta blight on chickpea

Ascochyta rabiei may spread with the aid of various agents, which include planting infected seeds from diseased crops, through wind and water, transporting infected crop residues, and stock, people and machinery moving through infected crops. Chickpea stubbles remain infected in the field would be a source of inoculum for the subsequent cropping seasons.

Crops grown close to infected residues or volunteer plants may also become infected (Singh et al., 1994). Once infections are established, numerous asexual spores (conidia) are produced in the pycnidia on blighted plants and then cause the secondary spread of the disease within the field (Conventry, 2011). Although produced even when minimal moisture is available, these asexual spores are spread by rain splash and somewhat by wind.

They may also be dispersed with infected living plant parts, within crop residues, on contaminated machinery, on seed surface, and within the seed (Kevin et al., 2013), particularly when the foliage is wet and the spores have been released from the pycnidia (Pritchard, 2000). Because trace quantities of D. rabieiin and on seed are difficult to detect, the Ascochyta blight fungus is readily dispersed in and on chickpea seeds. In addition to seeds, wind-blown ascopsores are other major sources of primary inoculum that can initiate blight infection. Ascospores are produced abundantly on infested crop residues that persist and overwinter on the soil surface (Kaiser, 1989). It has been recognized that spore dispersal has a major impact on the onset and development of epidemics. However, long-distance spread, in general, occurs when airborne sexual ascospores are produced and moved by air currents and wind (Tivoli et al., 2007).

2.8. Managements of Ascochyta Blight of Chickpea

Management of AB is essential to provide increased and stable chickpea yields throughout the world. Where possible, host resistance should be emphasized over chemical protection as the most environment friendly and economic disease management strategy (Pande et al., 2005). Since no single method of management against this disease exists, integrated disease management is the ideal approach to deal with the disease. Several different strategies that include cultural practices (e.g. rotating chickpea with non-host crops every three to four years, use of the clean seed, and burial of infested residue, using host plant resistance if available), biological control and chemical protection (judicious use of fungicides when warranted) are important practices (Kiprop, 2016). Indeed, AB is most effectively managed with the integration of different strategies/tactics. Some of the effectively management approaches are reviewed below.

2.8.1. Cultural practices

Cultural practices that reduce the main sources of inoculum are most important in effective disease management under field conditions. Since chickpea is susceptible to AB, several cultural practices, such as rotation with non-host crops, not growing chickpea more frequently than every 3-4 years, and not planting new chickpea crops near previous blighted fields, the use of disease-free seeds, adjusting planting date and destruction of diseased plant debris, will all help to reduce inoculum level and inhibit severe epidemics (Robert, 2013). Since D. rabiei is specific for legumes, rotating with non-host cereal crop will help reduce inoculum levels in the soil medium as a seedbed. Chickpea should not be grown more frequently than every three to four years, and new crops should not be planted near previously blighted fields since there could be imminent risk of infection of the new crops.

Tilling and burial of residue will speed up the decomposition of infected residue (debris), which also will reduce inoculum by removing the source for the pathogen to survive in soils (Robert, 2013). Under low disease pressure, agronomic practices, such as delayed sowing, lower seed rate, and wider row and plant spacing can reduce the incidence and severity of Ascochyta blight. Application of potassium fertilizers, especially in soils with high nitrogen content, can enhance chickpea yields and retard AB (Kader et al., 1990). Tillage can be used to reduce ascospore production since stubble burial inhibits the teleomorph formation and maturation on infected residues (Navas-Cortes et al., 1995). Moderately resistant cultivars sown early produced 15–300% greater grain yields than those sown late (Siddique and Sedgley, 1986). When cultivars that are more susceptible are used, a decision to sow early must be taken into account the potential for an Ascochyta blight epidemic as the emerging plants may be exposed to the peak period for spore dispersal from residues (Asrat, 2015).

2.8.2. Host plant resistance

The most recommended method of managing the disease is to use resistant varieties (Raju et al., 2013; Rubiales et al., 2018), economically feasible in reducing the costs of control and environmental friendly, reducing health impact residues (Rubiales et al., 2018). Ascochyta blight resistance of chickpea is determined by a diverse set of anatomical, biochemical, physiological, and genetic characters.

Host metabolic activities that inhibit the pathogen invasion include induction of hypersensitive response/reaction (Hr), cell wall reinforcement by deposition of callose, lignin, ester bound cinnamic acids/polyphenols, and hydroxyproline-rich glycoproteins, induction of phytoalexins, and proteins that inhibit the pathogen growth or reduce its virulence (Pande et al., 2005). Additionally, partial resistance in the chickpea cultivars declines at flowering when the weather and crop canopy conditions often favor blight development (Chongo et al., 2003). Resistance begins to break down shortly after flowering and pod formation, thus implying the need for seeking alternative measures after this period (Shahid et al., 2008). This can help recognize disease symptoms at an initial stage that is essential in blight management.The number of improved chickpea disease-resistant varieties have been multiplied and disseminated to farmers in many districts of Ethiopia. However, their current performance in farmers' fields and the severity of the Ascochyta blight has not been documented well. Therefore, timely measures at all growth stages should be taken to prevent chickpea from blight foliar fungal disease and to maximize the yield in Ethiopia.

A comprehensive study on the number of genes conferring resistance against chickpea blight, their nature, and diversity is essential for exploiting resistance sources in resistance breeding programs (Ilyas et al., 2007). The available information on the resistance to D. rabiei detected in recent investigation provided a clear clue that there is sufficient genetic variation in chickpea for this trait that can be exploited for disease management through pyramiding disease resistance genes (Megersa, 2016). When compared to other management options, breeding host resistance against any disease is the most efficient and eco-friendly to manage the disease (Li et al., 2015).

Breeding host resistance against AB includes conventional field screening experiments (Pande et al., 2005) as well as recent molecular breeding techniques (Labdi et al., 2013). However, the production of resistant genotypess has become a supreme challenge due to continuously evolving new pathotypes of D. rabiei (Singh and Reddy, 1991; Sharma and Ghosh, 2016). This is due to the contribution of the sexual stage (teleomorph, D. rabiei ) of the pathogen to the emergence of new physiological races or pathotypes (Barve et al., 2003; Bretag et al., 2006; Gan et al., 2006) and Current cultivars only possess low resistance, which can breakdown easily, to the pathogen (Gan et al., 2006).

That is why we need to continuously explore and identify new sources of resistant chickpea germplasm and its incorporation into high yielding quality commercial chickpea varieties is important, i.e. evaluating chickpea varieties for resistance to Ascochyta blight disease (Asrat and Neguisse, 2018). Formerly, Ethiopia produced mainly small-seeded Desi type chickpeas. Large seeded Kabuli chickpeas, which offer higher yields and fetch better price, were usually ruled out because of their vulnerability to disease. Kabuli varieties, which are resistant to multiple diseases, are now available.

Joint evaluation of ICARDA breeding lines led to the release of four improved Ascochyta blight and wilt- resistant Kabuli varieties targeted primarily at mid-altitude area in the Shewa region. Three Kabuli type chickpea varieties, namely Teji, Habru and Ejere, selected directly from ICARDA lines were released in 2004 and 2005. They yield up to 4-ton ha-1, compared to 1-ton ha-1 from landraces; and are resistant to Ascochyta blight. They are large-seeded, fetching almost double the market price of traditional varieties. Habru is now in its third year of dissemination, involving demonstration plots at DebrZeit and more than 200 farmers’ fields in East Shewa Zone (Oromia Region) (ICARDA, 2010). 2.8.3. Biological control

Using biological control method is also important in managing AB. This has emerged as an alternative to the use of chemicals, mostly fungicides, and offers economically viable and ecologically sustainable management of crop diseases. Biological control of soil and seed- borne plant pathogenic fungi can be addressed using bacterial and fungal antagonists. Strains of Pseudomonas, Bacillus and Trichoderma spp. and non-pathogenic isolates of Fusarium oxysporum, isolated from the rhizosphere of crop plants and composts, are effective not only to manage plant pathogens but also in helping the plants to mobilize and acquire nutrients (Khan et al., 2004). As an evidence, Azospirillum is a free‐living nitrogen‐fixing bacterium and is intimately associated with plants of broad range, especially grasses, could promote plant growth using several mechanisms like plant hormone production, N fixation, favoring root growth, etc (Dobbelaere et al., 2001; Bashan and de‐Bashan, 2010; Parmasi, 2019). Although Azospirillum induced disease resistance in chickpea genotype against Ascochyta blight, but it had no significant improving resistance effect against the disease in susceptible chickpea cultivars (Parmasi et al., 2019).

In 2019 (Roopa and Maya, 2020) evaluated the effect of some microbial antagonists on the growth of three foliar fungal pathogens (F.oxysporum, A. rabie and Alternaria alternata) under laboratory condition with dual and triple inoculation culture technique using Trichoderma harzianum+Bacillus subtilis, T. harzianum+Pseudomonas fluorescens, T. viridae + B .subtilis and T. viride+ P.fluorescens. The maximum inhibition of D. rabieiwas observed in the dual antagonists inoculated treatment with T. viride + P. fluorescens (85.52%) followed by treatment inoculated with T. viride + B. subtilis (84.00%) which were significantly different from all the other treatments. The biocontrol potential of the fungal antagonist, Chaetomium globosum was the most effective antagonist against AB causing 73.12% reduction in disease index when used as post inoculation spray.

2.8.4. Chemical control

Fungicide seed treatment, especially when used on seeds of low vigor or infected seed lot, remains the most effective means of increasing seedling emergence and delay early foliar infections, reducing disease severity significantly (Mancini and Romanazzi, 2014). Ndungu (2016) demonestrated that the most effective fungicidal seed treatment against AB was azoxystrobin+difenoconazole combination and azoxystrobin (Quadris) alone under greenhouse and field conditions and there was up to 18.9 and 16.1% increase in seedling emergence when seeds were dressed with azoxystrobin alone, and azoxystrobin+difenoconazole in combination, respectively. Systemic fungicides have generally not been effective against D. rabiei (Shtienberg et al., 2000). Broad-spectrum fungicides can be used in seed treatment to limit fungal pathogens that may be present on the seed or in the soil.

Small-sized seeds usually have a higher level of Ascochyta infection than large-sized seeds. This is because diseased pods typically produce seed with a smaller size. The use of large seed screened from a seed lot may reduce the risk of Ascochyta infection (Gan et al., 2006). However, research has shown that seed dressings will only protect emerging seedling from seed-borne Ascochyta and seed-borne Botrytis. Seed dressing will not protect the emerging seedling from raindrop splashed Ascochyta or windborne Botrytis (Markell et al., 2008). Several fungicides are labeled against this disease, including Bravo (Chlorothalanil), Quadris (Azoxystrobin), and Headline (Pyraclostrobin) as spray fungicides (Megarsa, 2016).

All fungicides have been demonstrated to increase yields and reduce losses from the pathogen, but their use will not be economically feasible unless disease pressure is high. Watching weather reports and monitoring environmental conditions for disease development are useful methods for estimating (forecasting) need and timing for fungicide applications. Scouting fields early for the presence of isolated, infected plants is also an important part of this process (Ali et al., 2010; Amin and Melkamu, 2014). Recently researchers evaluated the efficiency of five fungicides, including Carbendazim, Hexaconozole, Propiconozole, Mancozeb and Thiram at five different concentrations (250, 500, 1000, 2000 and 3000) ppm (Roopa and Maya, 2020).

The authors found that highest percent inhibition of radial growth of the foliar fungal pathogens was recorded at 250-ppm concentration of carbendazim 50% WP for all the fungal foliar pathogens of chickpea. Relative yield losses due to Ascochyta blight reached 41.3% on control (untreated) and 10.9% on mancozeb treated plot, respectively (Amin and Melkamu, 2014). Similar resaearch done by Amelework et al. (2017) revealed that up to 59.27% relative yield a loss was recorded on the control (untreated) plots while 11.97 % on Othello Top Spray treated plot. Fungicide treated plots consumed; the higher total variable costs than untreated. Likewise, minimum variable costs were observed in untreated plots. Nevertheless, the highest (EB 50818.5 ha-1) gross return was obtained from fungicide treated plots.

2.8.5. Integrated disease management (IDM) against Ascochyta blight

Integration of host resistance with other cultural practices and minimal chemical protection has been suggested. A combination of disease management options utilizing cultural practices, such as deep plowing of infested crop debris and crop rotation of at least 4 years moderate host plant resistance, foliar and seed treatment fungicides, are economical production practices (Reddy and Singh, 1990). These strategies include crop rotation with non-host crops, not planting chickpea more frequently than 3-4 years, the use of disease-free seeds, destruction of plant debris, and selection of fields without a previous history of blight (Shahid et al., 2008). However, the use of resistant cultivars is the most effective and economical management strategy for Ascochyta blight, the level of resistance currently available is not sufficient to withstand high disease pressure under favorable disease conditions (Nene and Reddy, 1987; Akem, 1999; Chongo et al., 2000).

Due to narrow genetic base in chickpea to biotic and abiotic stresses (Ghaffari et al., 2014). Most of the known AB-resistance sources used in breeding programs have no complete resistance (Chen and Muehlbaur, 2003). The best option available for an integrated management strategy to manage these diseases is to exploit the host plant resistance mechanism to identify the sources of resistance in existing chickpea germplasm (Duzdemir et al., 2014). But, managing the genetic resistance in chickpea against Ascochyta blight is a challenge because of a high level of diversity in the primary gene pool of host, complexity in molecular bases in QTLs (Islam et al., 2017) and the resistance mechanism is not stable due to the introduction of new pathotypes/isolates (Mahiout et al., 2015; Islam et al., 2017).

3. MATERIALS AND METHODS

3.1. Description of the Study Areas

A field Survey was conducted in four administrative districts of East Shewa Zone of Oromia Region. Field experiments were carried out in two sites (Bora and Dhera) of East Shewa. Bora is located at latitude 8o18’24.4‖ N, longitude 38o57’05.3‖ E altitude of 1700 m.a.s.l. and receives an average annual rainfall of about 728 mm and with an average annual temperature ranges 13-270c, whereas the soil type is light light sandy (Megarsa, 2016). Dhera is situated at latitude 08o19’10‖ N, longitude 38o19’13‖ E, altitude of 1650 m.a.s.l. and the average annual rainfall is about 620 mm and with the minimum and maximum average annual temperatures of 14.0 and 27.8 oC, while the soil type is light silty-loam (DZARC, 2013).

Figure 1. Map of chickpea Ascochyta blight surveyed areas in 2019/20 cropping season at East Shewa, Central Ethiopia.

3.2. Distribution and Association of Chickpea Ascochya Blight with Biophysical Factors in East Shewa, Central Ethiopia 3.2.1. Ascochyta blight disease survey

Field surveys were conducted between September to November 2019 during the chickpea- growing season across 4 representative chickpea growing districts (Adea, Bora, Lume and Gumbichu) of East Shewa Zone of Oromia Region, Ethiopia, to assess the distribution, relative importance and intensity of ascochyta blight of chickpea, and determine the associations of Ascochyta blight epidemics with biophysical factors. Those districts were selected purposively based on the chickpea production potential and diseases problems. Selection of fields per Farmers Association (FA) was done randomly at intervals of 2–5 km travelling along the main and feeder roads. The plant population and weed density were assessed using 1 m2 quadrate set by ―X‖ fashion throwing four quadrates within 10 m apart per field.

The mean plant and weed population densities were estimated by averaging the number of populations in the 4 quadrates for analysis. During the survey, additional information, such as previous field history, variety, cropping systems, planting date, weeding practices and fertilizer usage, seed source, crop and weed density were recorded to observe their relationship with both disease incidence and severity.

3.2.2. Assessment of chickpea Ascochyta blight

In each field, where disease assessment was conducted, 4 spots were examined by moving in a diagonal path (using a 1 m2 quadrate) to avoid biasness in recording of the blight. As aforementioned, 68 chickpea fields were assessed, the plants within the quadrant were counted and recorded as diseased/infected, and healthy/non infected based on visual symptoms they showed for Ascochyta blight and other diseases. Disease prevalence and the average Ascochyta blight incidence in each sampled field were calculated as follows:

Number of fields affected by AB Disease prevalence (%) = 푥100 Total number of fields assessed per quadrate

Number of plant diseased per quadrat Diseases incidence = 푥100 Total no of plant observed

Disease severity was also recorded by using severity scale (1–9 rating scale; where 1 = no visible symptom; 2 = minute lesions prominent on the apical stems; 3 = lesions up to 5‒10 mm in size and slight drooping of apical stems; 4 = lesions obvious on all plant parts and clear drooping of apical stems; 5 = lesions on all plants parts, defoliation initiated, breaking and drying of branches slight to moderate; 6 = lesions as in 5, defoliation, broken, dry branches common, some plants killed; 7 = lesions as in 5, defoliation, broken, dry branches very common, up to 25% of plants killed; 8 = symptoms as in 7 but up to 50% of the plants killed and 9 = symptoms as in 7 but up to 100% of the plants killed will be recorded for each field (Jan and Wiese, 1991; Chen and Muehlbauer, 2003; Chen et al., 2004; Sharma et al., 2005; Pande et al., 2011). The severity grades obtained were converted into percent severity index (PSI) based on Wheeler (1969) for analysis as follows:

Sum of numerical ratings PSI = 푋100 푇표푡푎푙 푛푢푚푏푒푟 표푓 푝푙푎푛푡푠 푠푐표푟푒푑 푥 푀푎푥푖푚푚푢푚 푠푐표푟푒 표푛 푠푐푎푙푒

3.2.3. Collection of diseased plant samples

About 5-10 infected chickpea plants were uprooted following hierarchical sampling strategy comprising 10 plants from 4 randomly identified spots based on a field size of the assessed field crossing a diagonal in each of the selected field, according to McDonald and Martinez (1990); specimens were kept in paper bags for further isolation of the pathogen in the laboratory to determine associated symptomatic plants with the blight causing pathogen. With each sample, the required information [name of the region, district, and GPS readings (altitude, latitude, and longitude)] and date of collection were recorded accordingly. All collected 68 samples were brought to Debre Zeit Plant Pathology Laboratory based at DZARC for further isolation, identification, and characterization of the pathogen.

3.2.4. Data analyses

Disease incidence and severity were classified into distinct groups of binomial qualitative data (Chemeda and Yuen, 2001 and Misganaw et al, 2019). Disease incidence and severity data were classified into distinct classes of bivariate qualitative data.

Class boundaries were chosen so that classes contained approximately equal numbers. Contingency table of independent variables by disease intensity was built to represent the bivariate distribution of fields according to two classifications (Table 3). The importance of the independent variables (risk factors) was evaluated in two ways; first, the association of AB disease incidence and severity with independent variables was tested in a single-variable model. Second, those independent variables with high association to AB incidence or severity were added to reduce multiple variable models.

Deviance reduction was calculated for each variable as it was added to the reduced model and likelihood ratio test was used to examine the importance of the variable and was tested against χ2 - value (McCullagh and Nelder, 1989), as described by McCullagh and Nelder (1989), Chemeda and Yuen (2001), Samuel et al. (2008) with the SAS procedure GENMOND. Exponentiation the parameter estimates of each variable class results the odds ratio, which are interpreted as relative risk that is the higher the odds ratio of a variable the higher diseases infection (Yuen et al., 1996). The logistic regression model assesses the importance of multiple independent variables that affect the response variable. It calculates the probability of a given binary outcome as a function of the independent variables. The binary outcome was the probability that Ascochyta blight severity exceeds 25% and incidence exceeds 15% in a given chickpea field.

3.3. Culural and Morphological variability of Didymella rabiei Isolates collected from East Shewa, Central Ethiopia.

3.3.1. Isolation of the pathogen

During the field survey period, 68 chickpea samples were collected from the selected surveyed districts. The pathogen was isolated from leaflets, stems, and pod samples collected during the surveys. Both potato dextrose agar (PDA) general-purpose medium and chickpea

seed meal agar (CSMA) were used for isolation of the pathogens associated with diseased chickpea plant samples. Under aseptic conditions, specimens were cut into 2 to 5mm long pieces by leaving a small healthy portion on either side, and surface sterilized through dipping into a bleach solution (1% NaOCl) for 2–3 min, and then rinsed with sterile distilled water in 3 consecutive beackers, then dried with sterile filter paper.

Following it, five selected samples were placed (four pieces at the corner with equal distance and one at the center of the plate) into plates containing PDA and CSMA separately using 2 replications for each sample. Plates were incubated at 24±2 °C for 7 to 14 days and observed every 72 hrs. The isolated pathogens frequency was calculated using formula proposed by Shivendra Pathak, (2016) to determine the percentage of occurrence of the different pathogens in the culture.

Percentage of occurrence= X/N x 100%

X= total number of each organisms in all the samples

N= total number of the entire organisms in all the samples

Then the pure cultures of tagdeted pathogen (Didymella rabiei) were examined both under compound microscope to identify which cultures were held responsible for the target pathogen (D. rabiei ) and which were not; and then, cultures with the target pathogen were transferred aseptically to both CSMA and PDA for further cultural and morphological characterization.

3.3.2. Sub-culturing and characterization of Didymella rebiei

About 7-14 days after incubations, a disc of fungal cultures from the edge of plate was transferred to newly prepared medium of CSMA and PDA, separately again incubated for the same days at the same temperature until the cultures produced pycnidia (Alam and Strange, 1987), for further species identifications. Then identification was carried out based on cultural and morphological characteristics. Characters, like colony texture and colony color of all isolates, were observed macroscopically. Isolates were also sub-cultured on CSMA and PDA to ascertain their macroscopic and microscopic morphologies. Phenotypic identification was

done using standard mycological procedures and comparing with the literature (Aveskamp et al., 2010).

3.4. Evaluation of Chickpea Varieties Resistance Reaction Againist Ascochyta Blight under Field Condition

3.4.1. Treatments, procedures and Experimental design

The field trial was conducted at two hot spot locations i.e. Bora and Dhera in 2019/20 cropping season. It was carried out based on the protocol developed by Nene et al. (1981), which gave a detailed account of developing screening techniques for chickpea variety against Ascochyta blight. Preparation of raised seedbed with 15 cm height was performed during late August 2019 to drain excess moisture. Twenty-five chickpea varieties were obtained from DAZRC (Table 1). Then the test entries were planted in 2 m x 2 m plots by using 40 cm × 10 cm inter and intra–row spacing, respectively, with three replications. Each plot had 5 rows of 20 plants each and all normal agronomic practices were undertaken. Phosphorus fertilizer was applied at the rate of 50 kg ha-1 to all experimental plots equally at the time of planting. The highly susceptible variety, Shasho, which was used as a check and indicator-cum-spreader rows were planted as a single row after every 4 test lines of the variety for disease spread in the field and its distribution to test variety on each side. The effect of the disease on each plant was monitored throughout the growth period and disease scoring was carried out starting from the onset of the first symptom (chickpea blight) by using 1-9 rating scale and continue every week until the crop was harvested.

Table 1. Lists of chickpea varieties used for field experiment at Bora and Dhera for their reaction against Ascochyta blight. Variety Type Year of Origin Pedigree Release 1. Acos-Dubie Kabuli 2009 Mexico Monino 2. Akeki Desi 1995 ICRISAT ICCL 82106 3. Akuri Kabuli 2011 ICRISAT ICCV03402 4. Arerti Kabuli 1999 ICARDA FLIP 8984c 5. Chefe Kabuli 2004 ICARDA ICCV 92318 6. Dalota Desi 2013 ICRISAT ICCX-940002-F5-242PO-1-1-01 7. Dhera Kabuli 2016 ICARDA X98TH30FLIP-93-55C XS-96231 8. Dimtu Desi 2016 ICRISAT ICCV-93954 X ICC –5003 9. Dubie Kabuli 1978 Ethiopia PGRC 10. Dz-10-11 Desi 1974 Ethiopia Dz-10-11 11. Dz-10-4 Kabuli 1974 Ethiopia Dz-10-4 12. Ejere Kabuli 2005 ICARDA FLIP 97263c 13. Fetenech Desi 2006 ICRISAT ICCV-92069 14. Habru Kabuli 2004 ICARDA FLIP 8842c 15. Hora Kabuli 2016 ICARDA X2000TH50/FLIP98-52C X FLIP 98-12C 16. Kasech Kabuli 2011 ICRISAT FLIP 95-31C 17. Kobo Kabuli 2012 ICRISAT ICCV-01308

18. Kutaye Desi 2005 ICRISAT ICCV 92003 19. Mastewel Desi 2006 ICRISAT ICCV-92006 20. Minjar Desi 2010 ICRISAT CC97103 21. Natoli Desi 2007 ICRISAT ICCX-910112-6 22. Shasho Kabuli 1999 ICARDA ICCV 93512 23. Teji Kabuli 2005 ICARDA FLIP-97-266c 24. Worku Desi 1994 ICRISAT ICCL 82104 25. Yelibe Kabuli 2006 ICRISAT ICCV-14808

3.4.2. Diseases assessment

Disease incidence: Disease incidence was recorded starting from the appearance of the disease in the experimental plots. Numbers of plants infected in the three middle rows was recorded and their means were converted into percentage as the total plant observation. Disease incidence on each plot was calculated as the formula mentioned above (section 3.2.2).

Disease severity: The disease severity scoring was started immediately after disease onset from 6 randomly taken and tagged plants per plot and assessment continued up to five times every seven days' interval using a 1-9 rating scale (Jan and Wiese, 1991; Chen and Muehlbauer, 2003; Chen et al., 2004; Sharma et al., 2005; Pande et al., 2011). The level of resistance and susceptibility of each test line was determined by using 1-9, rating scale as mentioned above. Then test varieties were further categorized for their reaction to AB infection based on Pande et al. (2006) diseases scale; according to this scale; 1= asymptomatic (HR); 1.1–3.0= resistant (R); 3.1–5.0= moderately resistant (MR); 5.1–7.0= susceptible (S); and 7.1–9.0= highly susceptible (HS). Then the severity grades were converted into percentage severity index (PSI) for analysis (Wheeler, 1969). Then PSI was calculated as the formula mentioned above (section 3.2.1). AUDPC was calculated with the formula suggested by Shaner and Finney (1977):

i + i + 1 AUDPC = ∑ ( ) (푡푖 + 1 − 푡푖) 2

th Where, xi is the cumulative disease severity expressed as a proportion at the i observation, th, where ti is the time (days after sowing) at the i observation and n are a total number of observations.

Pod infection: was also recorded since it started at podding stage on whole plot based on 1-9 scale, as above mentioned.

3.4.3. Yield and yield related parameters

Plant height (cm): The height of the plant was taken from the ground to the highest point of plants at physiological maturity stage that recorded in centimeters. For this experiment, the average of five randomly selected chickpea plants was used to calculate plant height.

Number of pods per plant (No): Total number of pods found per a given chickpea plant at maturity was recorded. For this experiment, the average of five randomly selected chickpea plants was used to calculate the pod number per plant.

Number of seeds per pod (No): Total number of seeds counted per pod. For this experiment, the average of five randomly selected pods was used to calculate the seed number per pod.

Thousand seed weight (g): The weight of 1000 chickpea seeds.

Grain yield per plot (Kg): Total yield of chickpea, which was harvested from the three central rows of the plot.

3.5. Data Analysis

Data on AB disease parameters and other agronomic data were subjected to analysis of variance (ANOVA) using SAS (9.1 version) GLM procedure. Mean separation was based on the LSD at the 5% probability level. The disease severity scores were used to calculate infection rate and AUDPC for each treatment. Since Ascochyta blight severity was expressed in percent and time (t) in days, AUDPC values were expressed in %-days (Wilcoxson et al., 1975). Correlation analysis was also carried out to determine the effect of disease severity on yield and yield components. Logistic [ln(y/1-y)] (Van der Plank, 1963) and Gompertz [-ln(- ln(y))] (Berger, 1981) models were used based on values of coefficient of determination (R2) and standard error to determine the disease progress rate from the linear regression for each separate treatment (Van der Plank, 1963). The fitness of the models was tested based on magnitude of R2 and residuals SE obtained using each model (Campbell and Madden, 1990). Logistic model showed higher R2 and lower SE than Gompertz model at both locations. Thus, rate of increase in chickpea Ascochyta blight was estimated and compared using Logistic model at both locations (Appendix Table 4). The two locations were environmentally different

because of heterogeneity of variance as tested by Bartlett’s test and results of the F-test for most of the parameters were found significant at p≤ 0.001 (Gomez and Gomez, 1984). Thus, the data were not combined for analysis (Appendix table 5). The rate of foliar disease development was quantified by repeated assessments of the percentage of leaf, stem and pod area affected by Ascochyta blight in each plot started from the first day of disease onset after some days from DAP during the disease epidemic. The data were transformed using their linear equation and were regressed over time (DAP) to determine the disease progress rate or the slope of the regression line. Regression equations were computed using Minitab version 17.3 to determine rate parameters.

Table 2. Categorization of variables used in analysis for the distribution of AB of chickpea epidemics in four districts (n = 68) of Central Ethiopia, during the 2019 main growing season. Variables Variable Class No Intensity of chickpea AB Variables Variable Class No Intensity of chickpea AB of Incidence (%) Severity (%) of Incidence (%) Severity (%) field ≤15 >15 ≤25 >25 field ≤15 >15 ≤25 >25 Districts Bora 8 8 0 8 0 Previous Chickpea 7 4 3 4 3 Lume 20 11 9 12 8 Crop Lentil 3 2 1 3 0 history Adea 20 18 2 11 9 Field pea 4 3 1 3 1 Gumbichu 20 16 4 16 4 Haricot bean 2 1 1 1 1 Growth Seedling 4 4 0 4 0 Fababen 4 3 1 2 2 stage Vegetative 2 1 1 1 1 Wheat 15 6 9 4 11 Flowering 10 9 1 3 7 Maize 5 2 3 2 3 Flower/podding 44 30 14 29 15 Barley 17 14 3 12 5 Full podding 8 8 0 7 1 Teff 11 10 1 8 3 Altitudea 1500-2000 42 26 16 20 22 Seed Farmer to farmer 21 7 14 6 15 <2000 26 19 7 19 7 source Market 17 11 6 9 8 Planting August 35 20 15 19 16 Own saved 17 15 2 14 3 b date September 33 25 8 20 13 Research center 13 12 1 10 3 Weeding No 36 16 20 10 26 Cropd >33 plants m-2 26 22 4 20 6 practice Yes 32 29 3 29 3 density <33 plants m-2 42 23 19 19 23 Fertilizer No 60 41 19 35 25 Weed <30 weeds m-2 48 36 12 32 16 Practice Yes 8 4 4 4 4 density ≥30 weeds m-2 20 9 11 7 13 Chickpea Local 22 14 8 12 10 cultivar Improved 46 31 15 27 19 aAltitude were classified based on chickpea production agro ecology requirement (>2300 and <2300 m.a.s.l.). bAugust planting starts from early July up to august and fields sown in the beginning of puwagume and forward were considered as early September planting. cFields without weeds were considered as weeded fields due to frequent weeding practices and fields infested by weeds were considered as un weeded. dPlant population based on number of population expected from a hectare.

4. RESULT AND DISCUSSIONS

4.1. Field Survey

4.1.1. Prevalence and distribution of Ascochyta blight

Prevalence of AB was assessed and 33.4% (n=23) fields were diseased across the surveyed districts, with 100.0% prevalence at Bora, followed by Lume district (45%). However, Gumbichu (20.0%) and Adea (10.0%) had relatively less recorded prevalence of Ascochyta blight. The prevalence of the disease in the surveyed areas varied from 0 to 100%. The results of this study demonstrate low distribution of the disease in Adea and Gumbichu districts. Chickpea AB was found prevalent in some assessed districts, of course with varying magnitudes as distribution of AB is dependent upon the primary infections from diseased debris, or infected seeds (Islam et al., 2017). In a similar study, Megarsa et al. (2016) recorded a range of 0 - 25% mean disease prevalence with an overall mean prevalence of 12.0% from all 83 surveyed districts of 4 major chickpea growing regions of Ethiopia. This is in agreement with the observations in the present finding that showed variation in distribution of this disease in the Central Rift Valley area, which is one of the major chickpea growing areas of Ethiopia. The observed difference in disease prevalence might be depending on the agro-ecological and environmental conditions prevailing in each locality, diversified weather conditions, and variation in sowing dates in different fields (Daniel and Tilahun, 2020).

Table 3. Prevalence of chickpea Ascochyta blight across four districts in the Central Ethiopia during 2019/20 main cropping season. District Number of fields inspected Number of fields exhibit the disease Prevalence (%) Adea 20 2 10 Bora 8 8 100 Gumbichu 20 4 20 Lume 20 9 45 Total 68 23 92.3

4.1.2. Ascochyta blight incidence and severity

Ascochyta blight incidence and severity of chickpea were recorded in four districts. The average incidence values ranged from 0 to 73.3%. The mean incidence of ascochyta blight was higher in Bora (49.4%) followed by Lume (19.3%) districts. Conversely, the lowest (3.9%) mean disease incidence of was recorded at Adea district in the 2019 cropping season. In addition, highest (53.3%) mean AB severity was observed in Bora district followed by Lume (27.6%) districts. On the contrary, low mean AB severities were recorded in Adea (21.8%) and Gumbichu (18.4%) districts (Table 4). The lower Ascochyta blight incidence in Adaa district as compared to all other surveyed districts might be due to that the farmers in the districts were using a high adoption rate for improved chickpea varieties in the study areas with exception of Lume district. Chickpea fields covered with planting materials obtained from other farmers or farmer to farmer seed exchange showed a higher DI (28.6%), followed by seed bought from market, with mean DI of (16.8%).

Similarly, higher (38.7%) mean PSI was recorded from fields sown with planting materials collected from other farmers, followed by seed bought from market, with mean DI of (27.2%) (Table 4). This might be due to seed-borne nature of the diseases. The lowest (4.1%) incidence was recorded from seed source obtained from research center; however, the lowest (16.1%) severity was recorded from own saved seed. Similarly, Asrat et al. (2018) reported that seed lots collected from local market had more source of inoculums for disease because of seed collection and exchange from several locations and mixture compositions of Desi with kabuli type of chickpea particular in union and other key actors in informal seed system. Of chickpea fields assessed, higher (17.24%) mean disease incidence of was obtained in altitude of 1500- 2000 m.a.s.l than higher altitude class, while altitudes >2000 m.a.s.l. had the lowest mean incidence (11.91%) and severity (16.3%). These results indicated that the chickpea AB intensity was increased as the altitude range decreased and the disease intensity decreased as the altitudinal range increased.

In contrast to the present finding, many scholars say that the intensity of this disease increases as altitude increases. Nevertheless, this needs further investigation to find out whether this factor has significant contribution on the intensity of chickpea Ascochyta blight in Ethiopia.

Similarly Pande et a.l. (2005) were suggested that although several studies have been conducted to determine epidemiological factors that favor AB development, many gaps still exist in our understanding of disease development and prediction of epidemics. A relatively higher (26.7%) mean disease incidence and severity (36.7%) of chickpea AB were obtained from poorly weeded farms than weed free ones, which recorded 2.1% of incidence and 14.7% severity. Chickpea fields sown during August showed high (20.1%) chickpea Ascochyta blight incidence, while the lowest (10.1%) incidence was recorded from chickpea fields planted starting from early September. The highest mean AB severity was recorded from chickpea fields planted in August compared with early planted in September. The highest (30%) and lowest (4.4%) mean blight incidence was recorded in fields previously sown with wheat and tef respectively.

Similarly, the highest (41.2%) mean AB severity was recorded from chickpea fields preceded by wheat; however, the lowest (11.1%) diseases severity was recorded from chickpea fields sown with lentil in the previous cropping years. Regarding cropping pattern, these conditions are often conducive to disease development. In the present study, it was observed that there was no variation in chickpea fields surveyed in the cropping system, as all fields are sown broadcast. Similarly, during the survey in all the fields, it was observed that none of the chickpea fields was irrigated; the crop was cultivated under residual soil moisture or rainfall during the crop growth. Of course, this result coincides with the finding of Megarsa et al. (2016) who reported Chickpea production system in Ethiopia is generally dominated by the rain fed production system. The mean disease incidence and severity was higher (26.29 and 36.78%) in highly weed infested farms >30 /m2 than farms with low to free weed infestations. Higher disease incidence and severity were recorded from poorly managed and densely populated chickpea fields (Table 4).

Table 4. Incidence and severity (mean ± SE) of AB of chickpea for different independent variables during the 2019/20 main growing season, East Shewa, Central Ethiopia. Variable Variable Class Incidence (%) Severity (%) Variable Variable Class Incidence (%) Severity (%) District Bora 49.4 ±4.45 53.33±3.09 Chickpea Local 16.9±4.07 28.2±4.43 Lume 19.7±4.92 27.6±4.58 cultivar Improved 14.4±3.5 25.3±2.86 Adea 3.9±2.73 21.8±3.04 Previous crop Chickpea 16.6±2.93 25.1±3.93 Gumbichu 8.4±3.94 18.4±3.54 history Lentil 7.9±2.93 11.1±0 Growth stage Seedling 0±0.00 11.1±0 Field pea 8±2.7 19.4±3.44 Vegetative 0±0.00 11.1±0 Haricot bean 24.4±4.4 30±3.89 Flowering 4.6±4.61 26.9±4.34 Fababean 7.6±2.96 22.2±2.92 Flower/podding 14.2±3.26 24.6±2.95 Wheat 30±4.87 41.2±4.14 Full podding 45.2±4.74 45.8±4.68 Maize 24.1±2.43 31.6±3.32 Altitudea 1500-2000 17.24±3.64 29.8±3.07 Barley 9.7±4.64 21.8±3.14 <2000 11.91±4.07 16.3±3.76 Teff 4.4±2.72 18.2±3.54 Planting dateb August 20.1±4.17 29.1±3.67 Seed source Farmers 28.6±3.75 38.7±4.14 September 10.1±3.32 23.2±3 Market 16.8±4.48 27.2±3.86 Weeding No 26.83±3.96 36.66±1.67 Own saved 5.5±3.31 16.1±2.97 practicec Yes 2.12±3.77 14.66±3.45 Research center 4.1±4.11 18±2.64 Fertilizer No 14.9±2.76 25.6±2.64 Crop densitye <33 /m2 8±3.67 19.74±3.33 practice Yes 17.4±4.43 26.3±4.88 >33 /m2 21.6±3.64 30.21±3.03 Weed <30 /m2 10.59±2.78 21.8±2.49 d density ≥30 /m2 26.29±4.40 36.78±4.87 aIn Ethiopia, areas with 1500 - 2000 and >2000 m.a.s.l are classified as midland and highland, respectively. bEarly August planting starts from August 2 up to 31 and sowing made in the beginning of September considered as early September planting. cWeeding practice was recorded as weeded or not weeded; dWeed density was determined as number of weeds less than or equal to 30 or greater than 30 per 1m2 quadrat. eCrop density = based on number of population expected from a hectare.

4.2. Association of Ascochyta Blight Disease with Biophysical Factors

The association of independent variables with Ascochyta blight incidence and severity was presented in Table 5. Among the biophysical factors, district, crop growth stage, previous crop history, weeding practice, crop density, and weed density had shown very highly significant (p<0.0001) associations with disease incidence in the logistic model as a single variable and they also maintained their association with incidence when entered last into the model. Similarly, weeding practice and variety were very highly significantly associated with disease incidence when entered first into the model but they lost their significant association when entered last into the model. Altitude, showed a highly significant (p<0.001) association with disease incidence when it entered first into the model but it lost its association when entered last into the model. All the variables were significantly associated with disease severity when entered first into a logistic regression model. However, growth stage, fertilizer practice, variety, and weed density lost their significance when entered last into the model with the addition of other variables. Chickpea variety was lost its significant association with both AB incidence and severity when entered last into the model (Table 5). District, previous crop history, crop density, and seed source resumed their very highly significance (P<0.0001) association with both AB incidence and severity when entered last into the model along with other variables.

All significantly associated independent variables were tested in reduced multiple variable models with both AB severity and incidence as the dependent variable. For added variables analysis of deviance, parameter estimates, standard errors resulting from the reduced regression model are given in (Table 6 and 7). Of all the independent variables, district (χ 2 = 894.7 and 67.96, 3df), planting date (χ 2 = 42.61 and 5.18, 1df), growth stage (χ 2 = 374.42 and 29.48, 4df), fertilizer practice (χ 2 = 10.65 and 6.27, 1df), previous crop history (χ 2 = 259.17 and 78.2, 8df), crop density (χ 2 = 71.18 and 47.59, 1df), weed density (χ 2 = 43.98 and 36.92, 1df) and seed source (χ 2 = 57.63 and 57.63, 3df) were the most important variables in their association with mean disease incidence when entered first and last into the model, respectively (Table 5). The deviation analysis of these variables in reduced multiple variable models showed different levels of importance of their association with chickpea Ascochyta blight incidence (Table 6).

Similarly, a group of seven variables: district, altitude, planting date, weeding practice, previous crop history, seed source, and crop density significance were tested in reduced multiple variable models with chickpea Ascochyta blight severity. Analysis of deviance for these variables added one by one to the reduced model showed the importance of each variable and variable class. The parameter estimates resulting from the reduced regression model and their standard error are presented (Table 7). The probability of mean disease severity of >25% was highly associated with Bora, Lume, and Adea district, when crop sown early august, unwedded chickpeas field, previously wheat and field pea sown field and planting material sourced from other farmers. In contrast, lower Ascochyta blight severity (≤25%) had shown that a high probability of association with Gumbichu district, altitude found (1500-2000 m.a.s.l), late sown fields, on the weeded farm, with previously Lentil, Haricot bean, Fababean, Chickpea, Maize and Barley planted fields, seed source from the market, own saved seed and research center and weed density <30/m2 while their compared variable classes had a higher probability of association to AB severity (>25%) (Table 7).

The mean higher Ascochyta blight severity and incidence were recorded on the Bora and Lume district. Chickpea production at Lume and Bora districts showed a 3 and 5 times high probability of association with high disease incidence when compared with chickpea farms at Gumbichu district, respectively. Concerning disease severity, Bora district (5 times), Lume and Adea districts each recorded twice the high probability of relationship with high disease severity compared with Gumbichu district. This might be due to the seed born nature of the diseases and farmer to farmer seed exchange is common in the Bora district that seed from different source harboring high level of inoculum sources for the next cropping season and mechanism for long-distance dispersal across the region (Asrat, 2015). Likewise, Megarsa et al., (2017) reported the importance of these diseases in the Lume district.

High incidence in some locations, such as East Shewa (Lume) district was probably due to the presence of effective vectors and environmental conditions favoring their population buildup and movement (Megarsa et al., 2017). The findings of this study revealed that mean disease severity was more pronounced in areas with altitude 1500-2000 m.a.s.l than altitude ranged >2000 m.a.s.l. Altitudinal ranges of >2000 m.a.s.l. reduced disease severity by 44.05% compared with lower altitudes.

The population of weeds influenced the intensity of the disease; the chickpea field with more weed population had relatively high severity (36.78%) and incidence (26.29%) of the disease whereas with less population of weed had less severity (21.8%) and incidence (10.59%). This could be due to an increase of relative humidity on the crop microclimate, which favors the pathogen and their completion for nutrients. Consequently, a less vigorous chickpea plant is likely to be more susceptible to disease (Sultan et al., 2018), which may account for the higher Ascochyta blight incidence and PSI in un-weeded fields. On other hand, weeds might harbor causal pathogens to host plants (Wandimu, 2019). Similarly, in this study unwedded farms recorded the highest (36.66%) disease severity and incidence (26.83%) since the presences of weeds do have an avoidable effect on disease development (Habtamu and Getachew, 2019).

Higher plant populations might increase the disease intensity, increase competition among plants, and reduce yield. Besides, high relative humidity resulting from a higher plant population and a favorable temperature can increase AB incidence and severity on chickpea (Jettner et al., 1999, Siddique et al., 1998). Low Ascochyta blight epidemics were observed in lower chickpea densities. This could be attributed to fewer plants and leaves infected with the same inoculums level (Chemeda and Yuen, 2001). Higher populations might increase the disease epidemics, increase competition among plants, and reduce yield. Besides, high relative humidity resulting from a higher plant population and a favorable temperature can increase AB incidence and severity on chickpea (Jettner et al., 1999, Siddique et al., 1998).

Preceding crops were found to significantly associate with Ascochyta blight epidemics in the study areas. Fields previously planted with wheat, followed by haricot bean showed relatively higher AB incidence than other preceding crops. However, those fields previously planted teff showed relatively lower disease severity than others. The farmers of the surveyed area produce chickpea crop with different cereal and legume crop and the preceding crop residual appears to be a source of inoculums for the subsequent cropping seasons. Previous studies also confirmed that around sixteen crops including wheat, cloves, and pea and weed species have been reported to be infected by D. rabiei naturally in North Idaho and East Washington (Sally, 2005). As evidence Kaiser (1991) isolated D. rabiei from Triticum aestivum leftover debris, grown in fields where infected chickpea debris of the previous season remained on the soil surface during the off-season.

Ascochyta blight is associated with debris leftover in the field that serves as a source of primary inoculums. The secondary spread is through pycnidiospores (Nene et al., 2012). Ascospores are produced abundantly on infested crop residues that persist overwinter on the soil surface (Kaiser, 1989). The release of ascospores from debris remaining between cropping seasons has been reported in many areas of the world, including Australia (Galloway and MacLeod, 2003). The high AB incidence and severity in the chickpea plants sown in August might be due to the extended infection period of the pathogen in the soil. A high weed infestation resulting from early-planted chickpea fields also increases AB infection. In addition, this might be due to late or delayed planting reduced time to disease onset, which means that favorable conditions did not coincide and interact at the proper time or for sufficient duration (Girmay et al., 2019). To avoid the disease, most chickpea farmers planted late which also helped to limit disease occurrence and development. This result is in opposition with other scholars who reported that a high incidence of Ascochyta blight was observed in late-planted chickpea crops (Asrat, 2015).

Regarding the growth stage, the disease incidence was more severe starting from flowering to full pod formation than at other growth stages of the plants. This might be due to the percentage of infected plants was increased with the progress of time and the creation of new physiological stages. Increasing the percentage of infected plants, at the reproductive in comparison with the seedlings stage was due to increasing temperature (Darvishnia et al., 2014). The effects of growth stage on disease severity might explain the fact that as far as the environmental condition is conducive and once the disease infects the plant, it might damage the whole growth stages during the growing season (Misganaw et al., 2019). Similarly, Sharma and Ghosh. (2016), reported that the destructive potential of D. rabiei is most severe under favorable weather conditions at the flowering stage, which can result in near-complete yield loss. Logistic regression analysis identified variables that are associated with chickpea epidemics either singly or in combination. The model also quantified the relative importance of the multiple explanatory variables indicating how much they increased or decreased disease. The present study identified districts, altitude, growth stage, previous crop history, fertilizer practice, and crop and weeds density, weeding practice, sowing date, and seed source as important variables that influenced chickpea Ascochyta blight epidemics.

Table 5. Logistic regression model for chickpea Ascochyta blight (Dedimela rabei) incidence and severity, and likelihood ratio test on independent variables in East Shewa, Central Ethiopia, during 2019 main cropping season.

Independent variable DF AB Incidence LRTa AB Severity LRTa Type 1 analysis Type 3 analysis Type 1 analysis Type 3 analysis DR Pr>χ2 DR Pr>χ2 DR Pr>χ2 DR Pr>χ2 District 3 894.7 <.0001 67.96 <.0001 354.32 <.0001 76.06 <.0001 Altitude 1 8.69 0.0032 2.2 0.1381 15.87 <.0001 22.51 <.0001 Planting date 1 42.61 <.0001 5.18 0.0228 15.63 <.0001 6.53 0.0106 Growth stage 4 374.42 <.0001 29.48 <.0001 176.42 <.0001 7.35 0.1185 Fertilizer practice 1 10.65 0.0011 6.27 0.0123 7.24 0.0071 0.42 0.5152 Weeding practice 1 477.77 <.0001 0 . 205.11 <.0001 172.2 <.0001 Variety 1 49.96 <.0001 3.26 0.0712 10.63 0.0011 0 0.9719 Previous crop history 8 259.17 <.0001 78.2 <.0001 130.51 <.0001 51.48 <.0001 Crop density 1 71.18 <.0001 47.59 <.0001 31.68 <.0001 38.43 <.0001 Weed density 1 43.98 <.0001 36.92 <.0001 12.25 0.0005 1.49 0.2224 Seed source 3 57.63 <.0001 57.63 <.0001 68.62 <.0001 68.62 <.0001 aLRT likelihood ratio test; Type 1 analysis, variable entered first; Type 3 analysis, variable entered last; df = Degrees of freedom; DR Deviance reduction; Pr = Probability of a value χ2 exceeding the deviance reduction.

Table 6. Analysis of deviance, natural logarithms of odds ratio and standard error of chickpea AB incidence (%) and likelihood ratio test on independent variables in reduced regression model in East Shewa, centeral Ethiopia, during 2019 main cropping season. Variable Residual df AB incidence Variable Estimate SEd Odds a b deviance LRT Class Loge (odds ratio DR Pr>χ2 ratio)c District 1871.63 3 24.7 <.0001 Bora 1.69 0.34 5.4 18.3 <.0001 Lume 1.07 0.25 2.91 4.86 0.0275 Adea -1.26 0.57 0.28 Gumbichu 0* 0 1 Planting 1820.33 1 5.17 0.023 August 0.37 0.16 1.45 date September 0* 0 1 Growth 1445.91 4 0 0.9993 Seedling -21.41 23018.87 0 stage 0 0.9993 Vegetative -23.17 28122 0 0.83 0.3609 Flowering -1.6 0.5 1.67 0.83 0.3624 Flower+pod -0.2 0.2 1.24 Full podding 0* 0 1 Fertilizer 1435.26 6.26 0.0123 No -0.49 0.19 0.62 practice Yes 0* 0 1 Previous 648.36 8 2.39 0.1218 Chickpea 0.53 0.35 1.71 crop 0.24 0.6233 Lentil 0.19 0.38 1.21 history 11.5 0.0007 Field pea 1.79 0.53 5.98 3.38 0.0658 Haricot bean 0.7 0.38 2.01 2.98 0.0844 Fababean 0.72 0.42 2.05 22.3 <.0001 Wheat 1.59 0.34 4.93 1.62 0.2027 Maize 0.45 0.35 1.57 11.2 0.0008 Barley 0.96 0.29 2.62 Teff 0* 0 1 Seed 475.57 3 27.8 <.0001 Farmer 1.27 0.24 3.57 source 4.74 0.0294 Market 0.55 0.25 1.74 2.02 0.1556 Own saved 0.42 0.3 1.53 Research 0* 0 1 center Crop 577.19 1 44.7 <.0001 <33/m2 -1.11 0.17 0.33 density >33 /m2 0* 0 1 Weed 533.2 2 36.6 <.0001 <30/ m2 -0.82 0.14 0.44 density >30/ m2 0* 0 1 a Residual deviance = unexplained variation after fitting model; b Likelihood ratio test; DR = deviance reduction; df = degrees of freedom; Pr = probability of χ2 value exceeding the deviance reduction; c* = reference group; d SE = standard Error.

Table 7. Analysis of deviance, natural logarithms of odds ratio and standard error of chickpea Ascochyta blight severity (%) and likelihood ratio test on independent variables in reduced regression model in East Shewa, Central Ethiopia, during 2019 main cropping season. Variable Residual df AB Severity Variable Estimate SE Odds a b Deviance LRT Class Loge (odds ratio c DR Pr>χ2 ratio) District 961.05 3 67.64 <.0001 Bora 1.57 0.19 4.82 22.87 <.0001 Lume 0.71 0.15 2.03 16.56 <.0001 Adea 0.77 0.19 2.17 Gumbichu 0* 0 1 Altitude 945.18 1 22.34 <.0001 1500-2000 -0.72 0.15 0.49 >2000 0* 0 1 Planting 929.55 1 6.52 0.0107 August 0.21 0.08 1.23 date September 0* 0 1 Weeding 540.77 1 163.96 <.0001 No 1.08 0.08 2.96 practice . . Yes 0* 0 1 Previous 399.63 8 2.33 0.1265 Chickpea -0.24 0.15 0.79 crop history 19.21 <.0001 Lentil -0.99 0.23 0.37 0.12 0.7308 Field pea 0.06 0.18 1.06 2.97 0.085 Haricot bean -0.37 0.22 0.69 2.07 0.1503 Fababean -0.27 0.19 0.76 4.06 0.0439 Wheat 0.25 0.13 1.29 1.46 0.2268 Maize -0.21 0.17 0.81 0.86 0.3524 Barley -0.11 0.12 0.9 Teff 0* 0 1 Seed source 287.08 3 12.32 0.0004 Farmer 0.39 0.11 1.48 0.99 0.3197 Market -0.13 0.13 0.87 11.39 0.0007 Own saved -0.42 0.12 0.66 Research center 0* 0 1 Crop 367.95 1 37.59 <.0001 <33/m2 -0.5 0.08 0.6 density >33 /m2 0* 0 1 a Residual deviance = unexplained variation after fitting model; b Likelihood ratio test; DR = deviance reduction; df = degrees of freedom; Pr = probability of χ2 value exceeding the deviance reduction; c* = reference group; d SE = standard Error.

4.3. Isolation of Didymella rabiei

Isolation of the AB causing pathogen (D. rabiei) was carried out from infected chickpea samples (leaves, stems, and pods) collected from the four surveyed areas of the Central Rift Valley, Ethiopia. The sterilized cuttings of infected portion of leave, pod, and stems were placed on both PDA and CSMA medium for isolation. Several fungal species became obvious from both growth media 7-14 days after incubation. In the majority of the field, there was contamination with other fungal pathogens such as Ascochyta blight, Aspergillusspp, Fusarium spp, and Penicillium spp from microscopic observation in the surveyed sample. The frequency of occurrence of Ascochyta blight, Fusarium spp, Alternaria, Penicillium, and Aspergillus regardless of the districts was 10.88%, 53.53%, 4.41%, 19.71% and 7.35% respectively (Table 8 ). Similarly, Asrat et al. (2015) reported that the presence of Ascochyta blight was associated with other Aspergillus spp, Fusarium spp, and Penicillium spp in culture Petri plates.

Based on isolation frequency from infected tissue pieces, Fusarium spp was the most common fungus and comprised a greater proportion than the other four pathogens combined. This indicated that Fusarium spp is the most prevalent fungal pathogen responsible for wilting in chickpea cultivated fields in central Ethiopia and as a major problem in most chickpea growing areas of the central parts of the country, particularly in Gumbichu, Lume and Adaa districts of the studied area. The laboratory results revealed that from 68 blighted plant samples grown on PDA and CSMA medium only 23 samples were developed as a pure culture for further cultural and morphological characteristics.

Table 8. List of isolated fungal pathogens and frequency of occurrence on blighted chickpea samples collected from Adaa, Bora, Lume and Gumbichu districts during 2019. Fungal Pathogen Number of cultures* Frequency of occurrence (%) Ascochyta blight 37 10.88 Fusarium oxysporum 182 53.53 Alternaria 15 4.41 Penicillium 67 19.71 Aspergillus 25 7.35 Un identified 14 4.12 Total 340 100%

4.3. Morphological and cultural characteristics of Didymella rabiei (Target pathogen)

The fungus from the pure culture was thoroughly examined under microscope colony color, colony texture, and growth rate of isolates. The pathogenic cultures isolated from the collected samples were identified based on the cultural and morphological characters as Didymella rabiei f.sp. ciceri compared with the laboratory fungal identifications manual (Aveskamp et al., 2010). The present study revealed that out of the collected 68 samples about 40 isolates of D. rabie f. sp. ciceri grew were grown on PDA and CSMA medium (Table 10). Seventeen (25.0%) of D.rabie isolates were recovered on PDA medium, while the abundant proportions (33.8%) of 23 isoltes were grown on CSMA medium. Identification of colony cultures revealed that the highest (100%) and (75%) percentage of isolates was found in Bora district followed by Lume (45%) and (30%), Gumbichu (20%) and (15%), and Adea districts (10%) and (10%) on CSMA and PDA medium, respectively.

According to the obtained results, CSMA media should be used for the isolation and identification of D. rabiei infections in chickpea samples. The variations in cultural and morphological features are one of the main criteria used widely for their identification and taxonomic placement. Then fungus the color goes to dark with septate and branched mycilium under microscope (Figure 2). The pycnidia were circular or ovular in shape, yellow to brown. Significant variations of isolates were observed in cultural and morphological characteristics. Cultures of D. rabiei on PDA and CSMA showed different growth rates, colony morphologies and colors. When maintained on CSMA, the color of the colony, in the beginning was white then shortly turned to mouse gray/light gray because of the formation of pycnidia. Only one isolate from Lume district showed fully gray colony color. These results are similar to those of Mahiout (2015) who observed the same phenomenon in D.rabiei grown on CSMA. When strains were cultivated on PDA, the isolates grew as dark colonies, being grey-black on the surface and brown-black on the reverse on PDA plates (Figure 2: A and B).

Variation among isolates in mycelium color was reported by Baite et al. (2016) that the mycelium was pale cream at first but later turned greyish white or green to greenish dark and creamy white. From the 7th to the 14th day, the cultures on PDA and CSMA had different growth rates. Conidia formation was better observed in CSMA medium and the fungal colony growth was slow and limited conidia were formed on PDA. Differences in growth of the strains indicated that richer media promote better development of colonies, as expected. These observations are consistent with the findings of Bahr et al. (2016) who reported D. rebei conidia formation occurred on CSMA, 10 to 12 days after culturing was more abundant than that of PDA medium that might be due to the presence of chickpea as one of the ingredients in the medium, which provided additional nutrients for the growth of the pathogen. Several other workers also stated that CSMA is the best medium for mycelial growth of D. rebei (Can et al., 2007; Bahr et al., 2016).

Table 9. Didymella rabiei isolates collected from four districts of East Shewa, Central Ethiopia in 2019 showing variation in pigmentation on CSMA and PDA Disticts Isolates/Districs Cultures colours on Cultures colours on PDA CSMA Adea AD1 Gray-black Light gray AD2 Gray-black Mouse gray Bora B1 Gray-black Mouse gray B2 Gray-black Mouse gray B3 Gray-black Mouse gray B4 Gray-black Gray B5 Gray-black Mouse gray B6 Gray-black Mouse gray B7 Gray-black Gray B8 Gray-black Mouse gray Gumbichu G1 Gray-black Light gray G2 Gray-black Mouse gray G3 Gray-black Light gray G4 Gray-black Mouse gray Lume L1 Gray-black Mouse gray L2 ray-black Dark Brown L3 Gray-black Mouse gray L4 Gray-black Mouse gray L5 Gray-black Light gray L6 Gray-black Mouse gray L7 Gray Light gray L8 Gray-black Mouse gray L9 Gray-black Mouse gray

Table 10. Frequency of occurrence of Didymella rabiei (Ascochyta rabiei) cuse of chickpea Ascochyta blight on PDA and CSMA medium. S/N Districs Number of Frequencies of Frequencies of Total samples Didymella rabiei Didymella rabiei isolate on PDA isolate on CSMA 1 Adea 20 2(10%) 2(10%) 4 2 Lume 20 6(30%) 9(45%) 15 3 Gumbichu 20 3(15%) 4(20%) 7 4 Bora 8 6(75%) 8(100%) 14 Total 68 17(25%) 23(33.82%) 40

Figure 2. (A, B) Didymella rabiei (Ascochyta rabiei) cuase of chickpea Ascochyta blight grown on both PDA and CSMA media in the laboratory and Conidia of Didymella rabiei f. sp. ciceris (C, D) under microscope observation

4.4. Evaluation of Chickpea Varieties Resistance Reaction against Chickpeas Ascochyta Blight

4.4.1. Disease incidence

There was highly significant (P ≤ 0.0001) difference in AB incidence levels among the test Varieties at both sites. Disease incidence and interaction between variety and site among the test varieties was also significantly (P ≤ 0.0001) different across the two sites. In general, combined analysis of disease parameters, yield, and yield related components data showed significant variation between Dhera and Bora experimental sites (Appendix Table 5) and therefore, data for each location were analyzed separately. At Dhera, the disease incidence result of field experiment showed that there was a highly significant (p<0.001) difference at among the screened varieties to AB reaction response. The highest (90.1%) percentage of incidence was recorded from the variety Dimtu while the lowest (18.6%) incidence was recorded from the Arerti variety at this site (Table 10). Arerti were reduced disease incidence by 79.4 when compared with Dimtu variety. The disease incidence result of the field experiment reveals that there was a highly significant difference at (p<0.001) among the screened chickpea variety. At Bora, the highest percentage of disease incidence (84.5 %) was recorded from the variety Shasho (susceptible check) while the lowest incidence (9.8%) was recorded from the variety Arerti (resistant check). Similarly, Kiprop et al. (2016) screened 25 chickpea varieties against AB across three locations and found that most of the chickpea varieties exhibited different reactions against D. rebie f. spp. ciceri through each location.

4.4.2. Percentage severity index

The percentage severity index by main effects of varieties revealed that there was a highly significant (p<0.001) difference among the tested verities at both locations (Table 11). At Dhera, the highest disease severity (90.6%) was recorded from the Shasho variety. However, it was not significantly (p>0.01) different from Dimtu (89.6%). The lowest severity mean disease severity was recorded from Arerti and Dhera varieties at both locations (Table 11). At Bora, the highest mean disease severity (70.2%) was recorded on the Shasho and the lowest mean severity (17.3%) was recorded from Arerti variety.

Table 11. Response of chickpea varieties for chickpea AB incidence and severity (%) at Bora and Dhera, Central Ethiopia, during 2019 main cropping season Chickpea Bora Dhera Varieties DI (%) PSI (%) DI (%) PSI (%) Acos-Dubie 46.1d 35.4h 59.4g 47.3i Akeki 36.3e 34.8h 64.1f 62.7e Akuri 20.3j 23.1k 68.7e 48.4h Arerti 9.8n 17.2o 18.6m 13.5r Chefe 15.0kl 20.5lm 29.7k 23.3pq Dalota 53.8c 48.7c 87.6ab 68.6bc Dhera 11.3mn 18.2o 21.2lm 22.7q Dimtu 59.8b 68.1b 90.1a 89.8a Dubie 33.2f 25.6j 52.6h 45.9j Dz-10-11 31.0g 24.6j 70.2e 57.0g Dz-10-4 35.3e 23.2k 45.1i 42.1l Ejere 13.3lm 20.3klm 28.6k 24p Fetenech 46.1d 36.8g 83.1c 65.7d Habru 11.4mn 20.4lm 23.1l 23.2pq Hora 20.21j 19.1n 21.7lm 23pq Kasech 23.4i 20.7l 32.8jk 25.2o Kobo 16.2k 20.7l 31.1k 23.7pq Kutaye 35.9e 31.6i 51.1h 44.5k Mastewel 46.1d 44.2d 87.3abc 69.6b Minjar 34.6ef 39.0f 84.4bc 68.4c Natoli 44.8d 38.5 74.9d 60.7f Shasho 84.5a 70.2a 89.1a 90.6a Teji 28.6h 34.8h 51.1h 34.9m Worku 46.6d 40.7e 69.9e 56.8g Yelibe 10.6n 19.5mn 36.8j 31n Mean 32.6 31.8 55 46.5 CV (%) 3.72 2.31 4.86 1.35

4.4.3. The Area Under the Disease Progress Curve (AUDPC)

The area under disease progress curve (AUDPC) is a very convenient summary of plant disease epidemics that incorporates initial intensity, the rate parameter, and the duration (Madden et al., 2007) of the epidemic, which determines the final disease intensity. In short, the disease progress curve is a cumulative expression of all the components of disease development activities at different stages of the progress of the epidemic. The analysis of variance (ANOVA) of AUDPC revealed significant (P<0.001) differences among varieties in terms of resistance to AB at both locations. The area under the disease progress curve was highest in Dhera. At this site, among tested variety, the minimum AUDPC value of 212.4%-days was recorded from Arerti variety, followed by Dhera, which recorded 360.5%-day, and the maximum AUDPC value of 1472.8% day was calculated on variety Shasho (Table 12). At Bora, among tested varieties, the minimum 267.9%-days AUDPC value of was recorded from Arerti variety, followed by Dhera, which recorded 287.9%-day, and the highest (1127.7% days) AUDPC was calculated for the highly susceptible variety Shasho (susceptible check). Similarly, kiprop, (2016) reported that varietys that had a higher area under the disease progress curve also had higher disease severity and were found to be more susceptible to Ascochyta blight disease than the resistant variety.

It was observed that variety Arerti, Dhera, Hora, Habru, Chefe, Ejere, Kobo, Kesech, Yelibe and Dz-10-4 had smaller AUDPC and was the most resistant varieties at both locations (Table 12). In contrast, the two varieties Shasho and Dimtu showed the highest levels of susceptibility to infection in the two field environments. In general, AUDPC values varied among the chickpea variety depending on the resistance levels of the varieties and it is known that AUDPC is directly related to the yield loss (Singh, 1989). Therefore, the selection of Chickpea cultivars having low AUDPC values is acceptable for practical purposes.

Table 12. Area under disease progress curve (AUDPC) for chickpea varieties against AB in the fields at Bora and Dhera, Eats Shewa, Central Ethiopia, during 2019/2020 main cropping season. Varieties Bora Dhera Mean Acos-Dubie 566.1gh 748.2i 657.1 Akeki 559.5h 1019.8d 789.6 Akuri 361.7l 773.7h 567.7 Arerti 267.9p 212.4q 240.1 Chefe 328.3m 359.3p 343.8 Dalota 773.3c 1103b 938.2 Dhera 287.9o 360.5p 324.2 Dimtu 1093.6b 1461.1a 1277.4 Dubie 404.9j 804.3g 604.6 Dz-10-11 387.7jk 911.1f 649.4 Dz-10-4 372.8kl 674.1k 523.5 Ejere 323.5mn 380.3no 351.9 Fetenech 585.1g 1055.6c 820.3 Habru 325.9m 362op 344.0 Hora 306.2no 366.7op 336.4 Kasech 332.1m 392.4n 362.3 Kobo 328.4m 379.8no 354.1 Kutaye 492.6i 712.4j 602.5 Mastewel 692.6d 1120.9b 906.7 Minjar 631.0e 1118.5b 874.8 Natoli 608.6e 967.9e 788.3 Shasho 1127.7a 1472.8a 1300.3 l Teji 556.8h 549.4 553.1 Worku 646.9e 906.2f 776.5 Yelibe 313.6mn 487.6m 400.6 Mean 507.0 748.0 627.5 CV (%) 1.54 2.31 1.93

4.4.4. Disease Progress Rate (r)

Chickpea AB development showed different rates in chickpea varieties evaluated at both experimental locations. Comparisons of the rates of development of the disease among the treatments were subsequently made based on the Logistic Model by fitting the PSI data with dates of assessment (Table 13). At Bora, the highest (0.135 units day-1) disease progress rate was computed from Shasho, followed by Dimtu (0.129 units day-1) and Worku (0.08 units day-1) and the lowest (0.014 units day-1) disease progress rate recorded from Yelibe followed by Habru (0.015units day-1) and Ejere (0.016units day-1). However, at Dhera, the highest (0.176 units day- 1) disease progress rate was computed from Shasho followed by Dimtu variety (0.156 units day-1) and the lowest (0.02 units day-1) disease progress rate calculated from Arerti followed by Hora (0.026 units day-1) and Kobo (0.027 units day-1).

The lowest progress rate was calculated for the variety Yelibe that reduced disease progress rate by 89.6% as compared to Shasho (susceptible check) at Bora, but 92.04% at Dhera. Again, at Dhera site, the lowest disease progress rate was calculated from the Arerti variety (resistant check) that could reduce disease progress rate by 98.86 as compared to Shasho (susceptible check). Generally, variation in chickpea Ascochyta blighst progress rate due to variety was clearly observed and rate of disease progress was higher at Dhera site than that at Bora. The higher blight disease infection rate (0.176) progressed rapidly on the susceptible variety Shasho and the lowest (0.021) was calculated for the resistant variety Areri, which was slow infection rate, noticed at Dhera, which is in line with some previous findings (Reddy and Kabbabeh, 1985; Reddy, 1992).

Table 13. Description of disease progress rate by variety and location during 2019/2020 main cropping season Diseases Progress rate (r) at Bora Diseases Progress rate (r) at Dhera Chickpea Disease Disease Variety R2 R2 A progress rate SEb A progress rate SEb (%)c (%)c (unit/day)a (unit/day)a Acosdube -2.497 0.037 0.001 99.1 -4.226 0.081 0.002 99.0 Akeki -2.886 0.044 0.002 97.0 -6.242 0.137 0.005 98.1 Akuri -2.666 0.028 0.002 91.1 -4.109 0.079 0.002 99.2 Arerti -2.498 0.018 0.003 73.1 -2.938 0.021 0.003 81.9 Chefe -2.53 0.023 0.002 92.2 -3.348 0.041 0.004 89.5 Dalota -3.175 0.061 0.002 99.1 -3.431 0.085 0.003 98.2 Dhera -2.932 0.027 0.003 85.0 -3.758 0.048 0.002 98.0 Dimtu -5.479 0.129 0.008 95.3 -6.76 0.156 0.002 91.53 Dubie -4.188 0.059 0.002 98.5 -4.326 0.081 0.003 97.9 Dz-10-11 -3.473 0.045 0.002 97.7 -5.886 0.124 0.004 98.7 Dz-10-4 -3.743 0.049 0.004 93.5 -5.449 0.099 0.003 99.2 Ejere -2.217 0.016 0.001 90.4 -2.695 0.030 0.001 97.9 Fetenech -2.259 0.033 0.001 98.4 -4.5 0.104 0.004 98.2 Habru -2.152 0.015 0.001 92.1 -3.732 0.049 0.002 97.5 Hora -2.302 0.017 0.001 91.7 -2.572 0.026 0.001 97.8 Kasech -2.405 0.021 0.001 95.4 -3.743 0.051 0.003 96.0 Kobo -2.469 0.022 0.002 91.3 -2.563 0.027 0.001 97.2 Kutaye -3.855 0.059 0.003 95.9 -6.104 0.114 0.003 99.3 Mastewel -3.12 0.056 0.003 95.6 -6.41 0.151 0.009 95.9 Minjar -2.977 0.049 0.003 95.5 -3.808 0.093 0.005 95.9 Natoli -4.16 0.071 0.004 96.8 -2.93 0.067 0.003 98.2 Shasho -5.658 0.135 0.01 93.7 -6.02 0.176 0.011 87.82 Teji -2.891 0.044 0.001 98.9 -3.28 0.051 0.003 95.7 Worku -4.516 0.080 0.003 98.7 -4.563 0.096 0.003 98.6 Yelibe -2.117 0.014 0.002 76.6 -4.104 0.063 0.001 99.6 A = Y-intercept; a Disease progress rate obtained from PSI against time of disease assessment (days). b Standard error of rate. d Coefficient of determination for the Logistic model.

4.4.5. Reaction of chickpea varieties against chickpea AB under field condition

There were significant differences in AB responses between the varieties at this site (Table 14). Out of the total 25 varieties tested, nine varieties, viz. Arerti, Dhera, Hora, Habru, Chefe, Kobo, Ejere, Kesech, and Yelibe exhibited resistance reaction against chickpea Ascochyta blight. Even though the resistant varieties could not be regarded as symptom-free as they exhibited small dark spots as well but due to the genetic resistance the spots could not progress further (Chongo and Gossen, 2003). Six varieties viz. Teji, Dz-10-4, Kutaye, Dubie, Acos-Dubie and Akuri exhibited moderately resistant reaction against the disease and 8 varieties, that is, Worku, Dz-10-11, Natoli, Akeki, Fetenech, Minjar, Dalota and Mastawel revealed susceptible reaction to the disease. Two varieties, namely Shasho (Susceptible check) and Dimtu showed highly susceptible reaction to the disease because they showed severe symptoms of disease in all parts of the plant with a mean disease severity-rating mean of 8.2 (highly susceptible) (Table 14). None of the tested varieties possessed immunity against this pathogen (Didymella rabiei) as the pathogen continued to evolve and to overcome resistant chickpea varieties (Chen et al., 2004) that shows Ascochyta blight epidemics were a recurrent phenomenon in Ethiopia (Asrat et al., 2015) and presently available varieties are mostly susceptible to chickpea AB (Megarsa, 2016).

At this site, all tested varieties differed significantly in their response to AB disease. Out of the total 25 varieties tested two varieties Dimtu and Shasho (Susceptible check) revealed susceptible reaction to the disease (Table 14). Fourteen varieties viz. Arerti, Dhera, Hora, Habru, Chefe, Kobo, Ejere, Kesech, Dubie, Dz-10-4, Akuri, Kutaye, Dz-10-11 and Yelibe exhibited resistance against these diseases. Nine varieties viz. Teji, Acos-Dubie, worku, Mastawel, Natoli, Akeki, Fetenech, Minjar and Dalota exhibited moderately resistant reaction against the disease (Table 14). None of the tested varieties possessed immunity against this pathogen as the pathogen continues to evolve the ability to overcome resistant varieties (Chen et al., 2004) that shows AB epidemics are a recurrent phenomenon in Ethiopia (Asrat et al., 2015). In contrast to this, none of the tested varieties possessed high susceptibility against this pathogen that indicating a good source of resistance genes in Ethiopia chickpea germplasm at this site (Megarsa, 2016).

Table 14. Average disease reaction of 25 chickpea varieties against AB at two locations (Bora and Dhera) of East Shewa, Central Ethiopia, during 2019/2020 main cropping season. Variety Bora Resistance level Dhera Resistance level Acos-Dubie 3.2h MR 4.3k MR Akeki 3.1h MR 5.6f S Akuri 2.1k R 4.4j MR Arerti 1.5p R 1.2s R Chefe 1.9lm R 2.1qr R Dalota 4.4c MR 6.2cd S Dhera 1.6op R 2.0r MR Dimtu 6.1b S 8.1b HS Dubie 2.3j R 4.5i MR Dz-10-11 2.2j R 5.1h S Dz-10-4 2.1k R 3.8m MR Ejere 1.8lmn R 2.2q R Fetenech 3.3g MR 5.9e S Habru 1.8lm R 2.1qr R Hora 1.7no R 2.1qr R Kasech 1.9l R 2.3p R Kobo 1.9l R 2.1qr R Kutaye 2.9i R 4.0l MR Mastewel 4d MR 6.3c S Minjar 3.5f MR 6.2d S Natoli 3.5f MR 5.5g S Shasho 6.3a S 8.2a HS Teji 3.1h MR 3.1n MR Worku 3.7e MR 5.1h S Yelibe 1.8mn R 2.8o R Mean 2.9 4.2 CV (%) 2.34 1.39 *** =indicate significance level at 0.001.

4.5. Mean Values of Growth Parameters and Grain Yields of Chickpea Varieties at Both Locations

Significant differences were observed among chickpea varieties for all measurements as the effect of Ascochyta blight on yield components appeared to be highly variable (Table 15 and 16.) (Tivoli et al., 1996). The separate analysis of variance for all yield-related traits at each location exhibited highly significant (P≤0.01) differences among the test varieties for Agronomic characters such as number of seeds per pod, number of pods per plant, plant height and hundred seed weight at both locations. This indicates that relative performance of varieties was quite inconsistent across the environments. Significant differences were observed for all the characters studied among the tested varieties. At Dhera, all interaction effects showed significantly (P<0.05) different on grain yield (Table 15). The highest (23.6) grain yield was obtained from Dhera variety. However, the lowest (0) mean grain yield was recorded from varieties Shasho, Fetenech, Dimtu, Dalota, Minjar, Akaki and Dz-10-11. This result is consistent with previously reported findings by researchers (Kimurto et al., 2013) who indicated that interaction between variety and environment (sites) also significantly (P<0.01) affected the grain yield production amongst test varieties. This is an indication that environmental influence is a major factor in the yield performance of chickpea in Ethiopia (Megarsa et al., 2016).

At Bora significant differences were observed among chickpea varieties for all measurements (Table 16). Analysis of variance revealed significant (P<0.05) differences at among the varieties in grain yield at this site. The highest grain yield was produced by variety Arerti (31.7) while, Shasho produced minimum grain yield (8.8). The high yield loss of this variety was due to higher disease incidence and severity, which damaged vegetative plant parts and killed some plants, reducing yields considerately (Kiprop et al., 2016). The interaction effects of variety and location concerning the number of seeds per pod showed a significant (P < 0.01) difference.

At Dhera, the number of seeds per pod significantly varied with Ascochyta blight infection that ranged from 0 to 1.3 with the highest number of seeds per pod (1.3) and was recorded from variety Habru. The lowest (0) number of seeds per pod was recorded from variety Fetenech, Dimtu, Dalota, Minjar, Akaki, and Dz-10-11. At Bora, the highest number of seeds per pod (1.4) was recorded from the varieties Arerti and Ejere and the lowest (0.8) number of seeds per pod

was recorded from variety Dimtu that showed Ascochyta blight had a deleterious effect on a number of seeds per pod (Amelework et al., 2017). According to an analysis of variance, significant effects were recorded on plant height not only for varieties but also for locations and variety by environment interaction. This is due to the fact that Plant height was sensitive to environmental fluctuations and it indicated that the relative performance of varieties was markedly inconsistent over the locations (Getachew et al., 2015).

At Dhera, the tallest (57.5cm) plant height obtained from Dhera variety and the shortest (0cm) was recorded from variety Fetenech, Dimtu, Dalota, Minjar, Akaki, and Dz-10-11. At Bora, the tallest plant height (60.9cm) was recorded from Dhera variety, while the shortest (23.8cm) was recorded from Dimtu variety (Table 16). The interaction effects of variety and location concerning the number of pods per plant showed a highly significant (P < 0.01) difference. At Dhera, the highest mean number of pods per plant was recorded for Dhera variety (54.3) followed by Arerti (52.1) and the lowest(0) number of pod per plant was recorded from variety Fetenech, Dimtu, Dalota, Minjar, Akaki, and Dz-10-11. These results are consistent with the findings of Keyvan et al. (2011) and Singh et al. (1998) that varietal differences are more associated with the number of pods per plant and used as criteria for selection of best materials. At Bora, the highest (69.4) mean number of pods per plant was recorded for varieties Dhera followed by Arerti (67.7); however, the lowest (26.4) number of pod per plant was recorded from the Dalota variety .

The data from the experiment showed that 1000 seed weight had varied significantly from 0 to 481.3 g at Dhera (Table 15). This indicated the presence of high variability among the varieties for 1000 seed weight. The maximum (481.3 g) 1000 seed weight was recorded for Acos-Dubie and minimum (0 g) for variety Fetenech, Dimtu, Dalota, Minjar, Akaki, and Dz-10-11 (Table15). At Bora, the maximum plant height (524.2 g) was recorded from the Acos-Dubie variety while the minimum (100.7 g) was recorded from Dz-10-11 variety (Table 16).

Table 15. Mean values of growth parameters and grain yields of the test varieties at Dhera of East Shewa, Central Ethiopia in 2019/2020 cropping season. Variety NSPP NPPP PH (cm) GY SSW Acos-Dubie 0.5 29.5 46.6 5.8 481.3 Akuri 0.9 41.2 36.5 5.8 261.0 Arerti 1.2 52.1 45.3 22.3 256.3 Chefe 1.2 47.9 49.9 17.3 297.7 Dhera 1.2 54.3 57.5 23.6 324.0 Dubie 1.0 27.1 41.1 8.4 179.3 Dz-10-4 1.1 33.5 41.5 10.2 322.3 Ejere 1.2 49.1 46.8 14.1 262.7 Habru 1.2 48.7 41.1 21.1 250.3 Hora 1.3 50.2 46.7 21.3 307.7 Kasech 1.1 31.3 35.2 14.1 315.7 Kobo 1.1 45.5 47.5 17.0 318.7 Kutaye 0.9 43.0 41.2 8.5 168.0 Mastewel 0.9 23.1 32.1 2.5 241.3 Natoli 0.6 25.1 35.4 4.6 236.3 Teji 1.1 35.3 40.5 10.4 243.3 Worku 0.6 24.7 35.8 4.6 331.7 Yelibe 1.1 41.3 43.6 13.3 297.5 Mean 0.77 28.53 31.37 9.09 213.33 CV (%) 21.14 2.98 3.35 13.06 8.59 Means followed by a different letter within a column are significantly different from each other at P ≤ 0.05, according to DMRT.PH - Plant height, NPP - Number of Pods per Plant, HSW - Hundred seed weight, GY - Grain yield, NSP-Number of Seed per Pod.

Table 16. Mean values of growth parameters and grain yield of the test varieties at Bora of East Shewa, Central Ethiopia in 2019/2020 cropping season. Variety NSPP NPPP PH GY SSW Acos-Dubie 1.0 34.3 37.4 15.1 524.2 Akeki 1.1 39.1 46.9 15.0 191.4 Akuri 1.1 48.1 45.8 17.2 288.3 Arerti 1.4 67.7 57.9 31.6 242.3 Chefe 1.3 54.3 49.6 23.3 298.7 Dalota 1.0 26.4 36.4 14.5 270.3 Dhera 1.2 69.4 60.9 29.7 308.0 Dimtu 0.8 30.5 23.8 12.6 283.5 Dubie 1.1 43.7 39.8 16.3 195.1 Dz-10-11 1.1 45.5 42.4 14.7 100.7 Dz-10-4 1.2 41.5 42.1 14.3 104.0 Ejere 1.4 51.4 40.7 21.8 291.5 Fetenech 1.2 34.5 35.5 14.7 148.0 Habru 1.1 67.2 52.9 27.3 266.2 Hora 1.2 62.4 58.5 27.7 302.4 Kasech 1.0 37.4 47.3 15.1 289.2 Kobo 1.2 54.5 44.9 19.3 290.0 Kutaye 1.0 44.7 33.2 16.3 169.3 Mastewel 0.9 33.6 38.1 15.6 183.3 Minjar 1.0 56.8 56.9 20.4 167.4 Natoli 1.2 59.5 35.1 22.3 243.0 Shasho 1.0 34.2 34.1 8.8 274.2 Teji 1.1 45.0 41.8 16.4 309.7 Worku 0.9 43.7 37.8 16.0 187.5 Yelibe 1.0 48.9 53.3 15.3 276.6 Mean 1.1 47 43.7 18.5 248.2 CV (%) 9.19 6.63 5.08 5.50 5.80 Means followed by different letters within a column are significantly different from each other at P ≤ 0.05, according to DMRT. PH - Plant height, NPP - Number of Pods per Plant, HSW - Hundred seed weight, GY - Grain yield, NSP-Number of seeds per Pod.

4.6. Pod Infection

The analysis of variance (ANOVA) revealed a highly significant (P< 0.01) difference among varieties in pod infection level as well as interaction effects of chickpea variety and location with respect to pod infect was recorded at both locations. At Bora, the highest (4.3) pod infection level was recorded on the variety Shasho which was at highly susceptible reaction, and the lowest (0.9) pod infection was recorded on the variety Dhera (Table 17). On the other hand, the maximum (4.6) pod infection level was recorded from Dimtu chickpea, while the pod infection level (0.9) was recorded on Arerti variety at Dhera (Table 17). This result is in line with the investigation of Asrat (2015), who reported that the minimum pod infection level was recorded in the resistance check variety (Arerti). Diseased pods with visible blight symptoms often failed to develop any seed. Pod infection often leads to seed infection through the testa and cotyledons. Infected seed can be discolored and possess deep, round, or irregular cankers, sometimes bearing pycnidia visible to the naked eye. Infection during the pod formation stage results in shriveled and infected seed (Pande et al., 2010).

Figure 3. Ascochyta blight Pod infection

Table 17. Ascochyta blight pod infection at two locations (Dhera and Bora) East Shewa, Central Ethiopia in 2019/2020 cropping season. Chickpea varieties Pod Infection at: Dhera Bora Acos-Dubie 3.3defg 2.6def Akeki 3.9bcd 2.0ghi Akuri 3.3defg 1.85hij Arerti 0.9l 1.1lm Chefe 1.9jk 1.3klm Dalota 4.4ab 3.2c Dhera 1.3kl 0.9m Dimtu 4.6a 3.7b Dubie 3.2efgh 1.9hij Dz-10-11 3.5def 1.9hij Dz-10-4 3.0fhi 2.0ghi Ejere 2.6hi 1.3klm Fetenech 3.9bcd 2.4efg Habru 1.48kl 1.5jkl Hora 1.3kl 1.1lm Kasech 2.8ghi 2.0ghi Kobo 2.4ij 1.67ijk Kutaye 3fghi 2.2fgh Mastewel 4.3abc 2.6def Minjar 3.7cde 2.8cde Natoli 3.7cde 2.4efg Shasho 4.4ab 4.3a Teji 3fghi 2.2fgh Worku 3.5def 3.0cd Yelibe 2.8ghi 1.5jkl Mean 3 2.1 CV (%) 12.07 15.06

4.7. Correlations of Disease Incidence and Severity with Yield Components and Yield at both sites

The correlation coefficient between yield and yield parameters was strong, positive, and significant (P ≤ 0.01) at both locations (Tables 18 and 19). The positive association between grain yield and yield attributes is also in accord with an earlier study on character association in chickpea (Arshad et al., 2004; Atta et al., 2008; Padmavathi et al., 2013). Furthermore, the greater magnitudes of variability among varieties for all traits associated with grain yield provide a large scope of selection based on these traits (Tariq et al., 2007). At Bora, grain yield was positively and significantly associated with number of seeds per pod, number of pods per plant, plant height and 1000 seed weight with values of r= 0.55, 0.90, 0.730 and 0.15 respectively, and negative association with pod infection (r = -0.71), diseases incidence (r = -0.73), disease severity (r = -0.64) and area under diseases progress curve (r = -0.65).

At Dhera site, the grain yield was positively and significantly related to the number of seeds per pod, the number of pods per plant, plant height, and 1000 seed weight with r= 0.85, 0.90, 0.81 and 0.66, respectively. Nevertheless, association of grain yield was negatively correlated with pod infection r = -0.93, disease incidence, r = -0.95, disease severity, r = -0.91 and area under diseases progress curve (r = -0.91). These results are in agreement with findings of Asrat et al (2017) and Haque et al. (2014) who reported that the increase in grain yield would have a positive and significant association with plant height, number of seeds per pod, number of pods per plant and 1000 seed mass. Similarly, Megarsa (2016) reported that plant height, plant biomass, branches/plant, and days to maturity resulted in a significant positive correlation with grain yield. The lowest yielding varieties had the highest AB susceptibility. The low yields indicate that AB could have destroyed most plant parts resulting in damage to the photosynthetic area, direct damage on seeds, and death of many plants in the field (Kimurto et al., 2013).

At Bora, a highly significant variation and positive correlation was observed in plant height, with number of seeds per pod (r = 0.46), number of pods per plant (r = 0.76), grain yield (r = 0.73), and non-significant variation with 1000 seed mass (r = 0.06ns) but it showed a highly negative correlation with pod infection (r = - 0.73), disease incidence (r = - 0.78), diseases severity (r = -

0.70) and area under diseases progress curve (r = -0.73). This findings indicate that the trend is almost similar with that of Dhera site.

Plant height was positively and significantly correlated with number of seeds per pod (r = 0.91), number of pods per plant (r = 0.95), grain yield (r = 0.81), and 1000 seed mass (r = 0.91) and it was negatively and significantly correlated with pod infection (r = - 0.72), disease incidence (r = - 0.78), disease severity (r = - 0.81) and area under disease progress curve (r = -0.82) at Dhera. This indicates healthy (blight free) chickpea crop would have the tallest plant height because of absence of the inhibiting phytotoxins released by the pathogen (Khan et al., 2004). Similarly, Megarsa et al. (2016b) reported that plant height showed a positive correlation with seed yield, number of pods per plant, and number of seeds per pod. Kumar et al. (2004) have also reported a positive correlation of plant height with seed yield.

At Bora, there was highly significant (P < 0.0001) and positively correlated between number of seeds per pod with number of pod per plant (r = 0.53), plant height (r=46), grain yield (r=55) but non-significant correlation with 1000 seed mass (r = 0.0.03 ns) and correlated negatively and highly significant with pod infection (r = - 0.62), diseases incidence (r = - 0.51) disease severity (r = - 0.58) and area under disease progress curve (r = -0.57). Similarly, at Dhera site, number of seeds per pod was correlated positively and highly significantly (P ≤ 0.01) related to number of pods per plant (r = 0.93), plant height (r=91), grain yield (r=85) and 1000 seed mass (r = 0.078) and correlated negatively and highly significant with pod infection (r = - 0.76), disease incidence (r = - 0.82) disease severity (r = - 0.84) and area under disease progress curve (r = -0.84).

These results are in harmony with findings of Megarsa et al. (2016) who reported a significant and positive correlation was found between the number of seeds per pod with a number of pods per plant. The same was true for number of pods per plant that exhibited highly significant at (P ≤ 0.0001) and positively correlated with number of seeds per pod (r = 0.93), plant height (r=0.95), grain yield (r=0.90) and 1000 seed mass (r = 0.81) and correlated negatively and highly significant with pod infection (r = - 0.82), disease incidence (r = - 0.87) disease severity (r = - 0.89) and area under disease progress curve (r = -0.89) at Dhera site. At Bora there was a significant and positive correlation of Number of pod per plant to seeds per pod (r = 0.53), plant height (r=0.76), grain yield (r=0.90) but, correlated non-sinificantly to 1000 seed mass (r = 0.03ns) and significantly and negatively associated with pod infection (r = - 0.73), disease

incidence (r = - 0.78) diseases severity (r = - 0.70) and area under disease progress curve (r = - 0.70) at p < 0.01 and improving these traits increases the development of number of pods per plant that support to increase in grain yield (Fasil, 2019).

The present study showed that 1000 seed weight had insignificant either positively to number of pods per plant (r = 0.03 ns), seeds per pod (r = 0.03 ns), plant height (r= 0.06), grain yield (r=0.15) or negatively with pod infection (r = - 0.06 ns), disease incidence (r = - 0.10) and disease severity (r = - 0.02 ns) relationship with all parameters at Bora site. On the contrary, thausand seed weight had significant and negative trend of relationship with number of pod per plant (r = 0.81), seeds per pod (r = 0.78), plant height (r= 0.91), grain yield (r= 0.66) this may indicate the fact that when thausand seed weight (seed size) increases, the number of seeds per pod and number of pods per plant reduced in most cases, which, in turn, leads to reduction in yield (Fasil, 2019) and showed negative significant relationship with pod infection (r = - 0.56), diseases incidence (r = - 0.64 ), disease severity (r = - 0.69) and area under disease progress curve (r = - 0.70) at Dhera location.

The current significant and negative correlation of grain yield with thousand seed weight is in accordance with the findings of Samad et al. (2014) and Banic et al. (2017) that they were reported genotypic correlation coefficients of grain yield with thousand seed weight expressed significance with negative values. The association of disease incidence was negatively correlated with plant height (r = - 0.78), number of seeds per pod (r = - 0.82), number of pods per plant (r = - 0.87), and 1000 seed weight (r = - 0.64). However, it showed significant positive association with pod infection (r = 0.91), disease severity (r = 0.96), area under disease progress curve (r = 0.96) at Dhera site. Similarly, disease incidence correlation indicated a highly significant difference and negatively correlation with plant height (r = - 0.78), number of seeds per pod (r = - 0.51), number of pods per plant (r = - 0.78), and non - significant correlation with 1000 seed weight (r = - 0.10 ns) and highly significant and positively correlated with pod infection (r = 0.90), disease severity (r = 0.93), area under disease progress curve (r = 0.93) at Bora.

Ascochyta blight incidence increased and affected the physiological process of crops since it is a foliar disease that decreased yield and yield components (Bashir et al., 1985; Irena and Ruta, 2013).

Again the same was true for disease severity that exhibited highly significant (P < 0.001) and negatively correlated with plant height (r = - 0.74), number of seeds per pod (r = - 0.58), number of pods per plant (r = - 0.70), grain yield (r=-64), however it showed non - significant correlation with 1000 seed mass (r = - 0.02 ns) and strong positive correlation with pod infection (r = 0.91), diseases incidence (r = 0.91), and area under disease progress curve (r =1) at Bora.

At Dhera, a highly significant variation and positive correlation were observed in disease severity with pod infection (r = 0.89), disease incidence (r = 0.96), area under disease progress curve (r =1) but it showed a highly negative correlation with plant height (r = - 0.81), number of seeds per pod (r = - 0.84), number of pods per plant (r = - 0.89), grain yield (r=- 91) and 1000 seed mass (r = - 69). Pod infection and AUDPC had highly significant negative correlation coefficients (r = - 0.93) and (r = -0.91) with yield, (r = -0.56) and (r = -0.70) with 1000 seed mass (r = - 69), ( r = - 0.72) and (r = -0.82) with plant height (r = -0.76) and(r = -0.84) with number of seed per pod (r = -0.82) and (r = -0.89) with number of pods per plant respectively, while AUDPC and Pod infection themselves were even highly and positively (r = 0.89) correlated with each other and ( r = 0.91) and (r = 0.96) with disease incidence (r = 0.89 ) and (r = 0.96 with ) disease severity. Similarly, pod infection and AUDPC exhibited highly significant (P ≤ 0.01) and negatively correlated with grain yield (r = - 0.71 and- 0.65), plant height (r = - 0.73 and- 0.73), with number of seeds per pod (r = - 0.62 and- 0.57), with number of pods per plant (r = - 0.73 and- 0.70), and with 1000 seed mass (r = - 0.06 ns and- 0.01 ns). On the other hand, there was strong positive and significant correlation with disease incidence (r = 0.90 and 0.93), with disease severity (r = 0.91 and 1), and highly and positively (r = 0.91) correlated with each other, respectively, at Bora site.

Table 18. The correlation coefficient (r) between Ascochyta blight incidence, severities index, AUDPC and infection rate with yield and yield components of chickpea at Bora, Central Ethiopia, in 2019/2020 cropping season.

CORR. NSPP NPPP PH PI ABDI ABDS ABPSI GY SSW AUDPC NSPP 1 NPPP 0.53*** 1 PH 0.46*** 0.76*** 1 PI -0.62*** -0.73*** -0.73*** 1 ABDI -0.51*** -0.78*** -0.78*** 0.90*** 1 ABDS -0.58*** -0.70*** -0.74*** 0.91*** 0.93*** 1 ABPSI -0.58*** -0.70*** -0.74*** 0.91*** 0.93*** 1.00*** 1 GY 0.55*** 0.90*** 0.73*** -0.71*** -0.73*** -0.64*** -0.64*** 1 SSW 0.03ns 0.03ns 0.06ns -0.06ns -0.10ns -0.02ns -0.02ns 0.15ns 1 AUDPC -0.57*** -0.70*** -0.73*** 0.91*** 0.93*** 1.00*** 1.00*** -0.65*** -0.01ns 1 Mean Values in the same letter within a column are not significantly different at 5% probability level; ns = non- significant, where *** = significance at p<0.0001, **= significant at p< 0.01, and *= significant at p<0.05. LSD = Least significant different, AUDPC - Area under disease progress curve, PH - Plant height, NSPP Number of seed per pod, NP Number of pod per plant, TSW - Thousand seed weight, GY - Grain yield, DS - Disease severity, DI - Disease incidence.

Table 19. The correlation coefficient (r) between Ascochyta blight incidence, severities index, AUDPC and infection rate with yield and yield components of chickpea at Dhera, Central Ethiopia, in 2019/2020 cropping season.

CORRE NSPP NPPP PH PI ABDI ABDS ABPSI GY SSW AUDPC NSPP 1.00

NPPP 0.93*** 1

PH 0.91*** 0.95*** 1

PI -0.76*** -0.82*** -0.72*** 1

ABDI -0.82*** -0.87*** -0.78*** 0.91*** 1

ABDS -0.84*** -0.89*** -0.81*** 0.89*** 0.96*** 1

ABPSI -0.84*** -0.89*** -0.81*** 0.89** 0.96*** 1.00 1

GY 0.85*** 0.90*** 0.81*** -0.93*** -0.95*** -0.91*** -0.91*** 1

SSW 0.78*** 0.81*** 0.91*** -0.56*** -0.64*** -0.69*** -0.69*** 0.66*** 1

AUDPC -0.84*** -0.89*** -0.82*** 0.89*** 0.96*** 1.00*** 1.00*** -0.91*** -0.70*** 1 Mean Values in the same letter within a column are not significantly different at 5% probability level; * = significant, ** = highly significant and ns = non-significant; LSD = Least significant different, AUDPC - Area under disease progress curve, PH - Plant height, NSPP - Number of seeds per pod, NP - Number of pod per plant, TSW - Thousand seed weight, GY - Grain yield, DS - Disease severity, DI - Disease incidence.

5. SUMMARY AND CONCLUSION

Chickpea is one of the main annual leguminous crops in Ethiopia both in its share of the total cultivated area of pulse crops and its role in direct human consumption. The low productivity of the crop in the country is attributed to susceptibility to biotic and abiotic stresses. Among various diseases of chickpea caused by fungi, bacteria and viruses, Ascochyta blight (AB), caused by Didymella rabiei and fusarium wilt, caused by Fusarium oxysporum f. spp. ciceris (FOC) are the most common and cause qualitative and yield damages. The present study was undertaken to assess the distribution and importance of Ascochyta blight of chickpea and factors affecting disease intensity in the central Rift Valley of Ethiopia. Furthermore, isolation and identification Didymella rabiei isolates, based on morphological characteristics have been conducted. Besides, the potentials of host resistance to this disease are investigated.

A field survey was carried out in purposively selected FAs of four districts, namely Adea, Lume, Bora, and Gumbichu of East Shewa, Ethiopia. Among 68 chickpea fields surveyed in all districts, the overall mean prevalence and incidence of the disease were 43.8% and 15.2% respectively. The higher prevalence and incidence of AB were recorded at Bora district with 100 and 73.3%, respectively, while in Lume district it was 45 and 64.2%, respectively. The observed difference in disease intensity might be due to the agro-ecological and environmental conditions prevailing their population buildup and movement and diversified weather conditions. Depending on the environmental conditions and the availability of alternate hosts, the deleterious effect of blight can vary from season to season in the same area. The logistic regression model analysis identified that district (except Gumbichu), when crop sown early in august, unwedded chickpeas field, field previously sown any crop, flowering to full podding crop growth stages, and planting material sourced from any source had a high association with disease epidemics and significantly contributed to the dynamics of the epidemic of the disease.

In contrast, Ascochyta blight epidemics were low in the Gumbichu district, on late sown fields, on the weeded farm and low crop density. The findings of the present study recognized that AB incidence and severity vary among districts, planting date, previous crop history, crop density, and seed source. Our results from this study suggest proper weeding practices, late planting; optimum spacing and following of other related farm practices should be carried out to reduce AB impact on chickpea production.

Widespread distribution of AB in all Kebeles of Bora district irrespective of all cultivars grown by the district farmers was local and it is a matter of concern. In the study area, several factors were positively or negatively influenced the intensity of the disease. In some surveyed areas, however, the farmers used improved chickpea varieties the disease intensity was high. This can be due to the absence of strong seed certification and seed health testing in Ethiopia that can lead to the infested/infected seed being dispatched to farmers and that cultivar can often be used without treatment by farmers from season to another season, which contributes to increasing the inoculums level because diseases is seed-borne. In the present survey, the farmers of the study area responded that Ascochyta blight (Didymella rabiei) and association of Ascochyta blight with fusarium wilt (Fusarium oxysporum f.spp ciceri) were the prioritized constraints of chickpea production. To prove the pathogen being D. rebie f.sp. cicer isolation and identification was performed in the laboratories. In the present investigation, high mycelial growth of D.rabiei was obtained in CSMA media. Cultures of D. rabiei developed on PDA and CSMA showed different growth rates, colony colors, and morphology. Conidia formation was better observed in CSMA than PDA.

Isolation and characterization test under microscopic observation in most of the survey sample revealed that contamination with another fungal pathogen such as Ascochyta rebie, Fusarium oxysporum, Penicillium, Aspergillus and Alternaria. The frequency of occurrence of Ascochyta rebie, Fusarium oxysporum, Penicillium, Aspergillus and Alternaria regardless of the districts were 10.88, 53.53%, 19.71%, 7.35 and 4.41%, respectively. This study indicated that among the fungal pathogens Fusarium oxysporum is the most prevalent fungal pathogen responsible for wilting in chickpea cultivated fields in central Ethiopia.

Evaluation of genetic variation in chickpea varieties is essential for effective selection in genetic improvement for disease resistance, agronomic and yield traits due to the possibility that the varieties deployed in the promotional activities lack resistance to AB disease. The present field experiments were carried out in two farmers' fields (Bora and Dhera) sites to evaluate chickpea varieties against AB of chickpea. The field experiments were done using twenty-five varieties released in Ethiopia, screened, and promoted under various projects for resistance to AB at Dhera and Bora farmers’ fields. Of these, most varieties showed different levels of reactions to disease. Ascochyta blight incidence and severity level were higher in Dhera than in Bora location. The

varieties Arerti, Dhera, Hora, Habru, Chefe, Ejere, Kobo, Kesech, and Yelibe had lower disease severities with high grain yield and proved the idea that varieties that had higher resistance to AB also have higher grain yield although this varied between the two sites (Dhera and Bora). The varieties Dalota, Dimtu and Shasho, had high disease severity with low grain yields production at both locations.

At Dhera, the lowest (0) mean grain yield was recorded from the varieties Fetenech, Dimtu, Dalota, Minjar, Akaki and Dz-10-11 as the disease severity increased and destroyed all aboveground parts of the plant. The interactions between variety, variety, and site (G×E) greatly affected grain yields in chickpea production. This study showed significant variation among tested chickpea varieties, which may facilitate the selection of varieties possessing the greatest potential to produce a better yield in diverse environments. Relying on these facts, here are issues that need work in the future: (1) Regular monitoring strategy has to be designed countrywide for early detection. (2) There is a need to conduct surveys to understand the presence of different pathotypes of D. rabiei in varied agro-ecozones since knowledge on the variability of the pathogen is essential in breeding for durable resistance and would overcome the challenge of the breakdown of resistance due to development of new pathotypes. (3) A detailed analysis of the factors responsible for the widespread incidence and prevalence of these diseases in the area with the lowest altitude needs further investigations. (4). Further research should be conducted on expanded multi location and multi season field trials are essential before varieties are released to farmers to widen the scope of available AB resistant varieties. (5) The field experiment should be repeated including the newly developed chickpea varieties at different location to get the reliable result since these research results dealing with one-year data and only over two testing locations. (6) Farmers to increase yield and production of this crop in Ethiopia can cultivate the Ascochyta blight-resistant chickpea varieties.

Lastly, (7) many researchers prove that Desi type germplasm has a better source of resistance than Kabuli material in most of the country. However, in Ethiopia, some previously conducted research reports including the present findings indicate the presence of low resistance of Desi chickpea variety to Ascochyta blight. Thus, any research towards this cause could be highly appreciated/recommended by supporting with modern molecular tools and scientific techniques.

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7. APPENDICES

Appendix Table 1. Questionnaire for assessing the distribution and significance AB of chickpea in the Central of Ethiopia.

1. Site Information 1.1. Region: ______1.2. Zone: ______1.3. Woreda (districts): ______1.4. Kebele (Farmer association): ______1.5. Altitude:______latitude (N/S): ______longitude (E/W):______1.6. Stop No. ______2. Field Information 2.1. Estimated field size (ha):______2.2. Planting date: ______2.3. Cultivar type: 0. Local 1. Improved 2.4. From where are you got this seed? a. farmer-to-farmer b. Market c. own saved d.research center 2.5. Previous Field history (what was planted last year and the production capacity of the field) ______2.6. Cropping system: a. broadcast b. row planting 2.7. Crop growth stage:______2.8. Do you apply fertilizer on this field? 1.Yes 0.No 2.9. Weeding practice: 1.Yes 0.No 2.10. Plant density (no. of plants/m2): Q1___Q2____ Q3___ Q4___ Q (Quadrant) Mean ___ 2.11. weed density (no. of weeds/m2): Q1___Q2____ Q3___ Q4___ Q (Quadrant) Mean ___ 3. Farmers perception: 3.1. Do you experience AB symptom disease on chickpea? Yes___ No__ 3.2. Did you notice any new (relatively) chickpea disease occurring in the area? Yes or No. if yes list A.______B. ______C. ______

3.3. List climatic/other factors that aggravate the severity/incidence of AB of chickpea ______4. Assessment of chickpea Ascochyta blight severity and incidence 4.1. Mean AB severity at Field level: ______Quadrant AB severity (1-9) Plant number Others 1 2 3 4 5 6 Mean diseases insects Remark I II III IV Total For disease severity Score was recorded as scale mentioned above 4.2. Mean AB Incidence at Field level: ______

Quadrant No of Plant per NO. of plants with AB Other Remark quadrant blight symptom incidence diseases and (%) insects I II III IV Total

Appendix Table 2. Summarized ANOVA table for chickpea varieties evaluated for their reaction to AB under field conditions at Dhera, central, Ethiopia during 2019/20 in the cropping season Parameters Df MS ME CV (%) PSI 24 11.68** 0.06 1.39 Incidence 24 1844.19** 2.67 4.86 NSPP 24 0.70** 0.16 20.22 NPPP 24 1133.15** 0.86 3.00 PH 24 1115.16** 1.07 3.41 PI 24 10.56** 0.66 12.07 GY 24 190.86** 1.18 12.94 SSW 24 55204.32** 17.80 8.35 AUDPC 24 387799.14** 11.55 1.54 MS = means of squares; ME = mean square of error; CV = coefficient of variation (%); NPPP = number of pods per plant; NSPP = number seeds per pod; HSW = hundred seed weight; PH = plant height, and GY = grain yield. AUDPC = area under disease progress curve. * And ** refer to level of significance at p<0.01 and p<0.001 according DMRT df = degree of freedom.

Appendix Table 3. Summarized ANOVA table for chickpea varieties evaluated for their reaction to AB under field conditions at Bora, Central Ethiopia during 2019/20 in the cropping season Parameters Df MS ME CV (%) PSI 24 631.69** 0.74 2.33 Incidence 24 1009.25** 1.21 3.72 NSPP 24 0.06** 0.10 9.19 NPPP 24 442.59** 2.87 6.11 PH 24 258.13** 2.51 5.74 PI 24 6.48** 0.58 15.09 GY 24 97.28** 1.01 5.46 SSW 24 22351.05** 14.41 5.81 AUDPC 24 162821.00** 11.70 2.31 MS = means of squares; ME = mean square of error; CV = coefficient of variation (%); NPPP = number of pods per plant; NSPP = number seeds per pod; HSW = hundred seed weight; PH = plant height, and GY = grain yield. AUDPC = area under disease progress curve. * And ** refer to level of significance at p<0.01 and p<0.001 according DMRT df = degree of freedom.

Appendix Table 4. Models fitted for disease progress rate of Chickpea Ascochyta blight severity (%) at Dhera and Bora, Central Ethiopia, during 2019 main cropping season. Logistic Model Gompertz Model Varieties Dhera Bora Dhera Bora SE R2 SE R2 SE R2 SE R2 Dalota 0.003 98.21 0.002 99.05 0.003 97.32 0.002 97.85 Dimtu 0.002 91.53 0.008 95.33 0.004 78.89 0.008 92.92 Kasech 0.003 96.04 0.001 95.42 0.002 95.07 0.001 95.54 Mastewel 0.009 95.88 0.003 95.56 0.009 94.78 0.003 93.34 Fetenech 0.004 98.21 0.001 98.41 0.004 97.51 0.001 97.82 Dz-10-11 0.004 98.7 0.002 97.72 0.005 96.37 0.001 96.97 Akeki 0.005 98.09 0.002 96.96 0.005 97.02 0.001 97.53 Kutaye 0.003 99.29 0.003 95.94 0.001 99.82 0.002 94.19 Acos-Dubie 0.002 99.01 0.001 99.08 0.003 97.38 0.001 99.36 Yelibe 0.001 99.64 0.002 76.57 0.001 98.53 0.001 76.06 Shasho 0.011 87.82 0.01 93.67 0.023 82.14 0.010 91 Ejere 0.001 97.87 0.001 90.39 0.001 97.55 0.001 90.11 Arerti 0.003 81.92 0.003 73.09 0.001 81.97 0.001 72.45 Natoli 0.003 98.19 0.004 96.82 0.003 96.92 0.002 96.81 Chefe 0.004 89.47 0.002 92.22 0.002 87.46 0.001 92.96 Hora 0.001 97.81 0.001 91.71 0.001 98.03 0.001 91.6 Dubie 0.003 97.9 0.002 98.5 0.002 98.32 0.001 98.58 Worku 0.003 98.63 0.003 98.65 0.004 97.17 0.002 98.08 Teji 0.003 95.66 0.001 98.86 0.002 94.54 0.001 99.47 Minjar 0.005 95.89 0.003 95.53 0.004 96.89 0.001 97.25 Dhera 0.002 98 0.003 85.02 0.001 98.21 0.001 86.94 Akuri 0.002 99.22 0.002 91.13 0.002 98.54 0.001 89.33 Dz-10-4 0.003 99.15 0.004 93.46 0.001 99.6 0.002 94.52 Kobo 0.001 97.22 0.002 91.29 0.001 97.92 0.001 90.18 Habru 0.002 97.52 0.001 92.05 0.001 96 0.001 92.03 R2 = Coefficient of determination. SE= Standard error of parameter estimates.

Appendix Table 5. Combined analysis mean square value for chickpea varieties evaluated for AB resistance reaction under field conditions at Dhera and Bora, Central Ethiopia, during 2019/20 main cropping seasons Parameters Variety Replication Location effect Experimental Location CV effect Vs Error Vs Variety (%) Location PSI 1895.75** 0.31** 8240.66** 0.69 177.61** 1.77** Incidence 2273.94** 5.32NS 21591.84** 2.08 341.27** 4.89** NSPP 0.47** 0.02NS 4.27** 0.13 0.30** 13.45** NPPP 1218.93** 5.40NS 13510.34** 2.22 335.55** 5.85** PH 859.18** 0.37NS 6347.51** 1.97 507.32** 5.20** PI 13.47** 0.29NS 119.71** 0.61 1.67** 13.39** GY 234.43** 0.79NS 3619.36** 1.13 41.76** 8.08** SSW 58573.90** 577.91NS 45579.80** 16.20 18981.46** 7.02** AUDPC 501183.8** 74.02NS 2178037.50** 11.62 49436.30** 1.85** Rate 2.2** 0.0NS 8.7** 0.04 0.24** 31.4** NPPP = number of pods per plant; NSPP = number seeds per pod; HSW = hundred seed weight; PH = plant height, and GY = grain yield. AUDPC = area under disease progress curve. * And ** refer to level of significance at p<0.01 and p<0.001 and NS= non-significant according DMRT.