Angular Leaf Spot

Management of common bean (Phaseolus vulgaris) angular leaf spot (Pseudocercospora griseola) using cultural practices and development of disease-weather models for prediction of the disease and host characteristics

David Icishahayo

A thesis submitted in partial fulfillment of the requirements of the degree of Doctor of Philosophy

Department of Crop Science Faculty of Agriculture University of Zimbabwe

September 2014

ABSTRACT

Bean seed collected from 82 households growing beans in Chinyika Resettlement Area indicated that sugar (sweet) bean type was the most preferred and cultivated by 84.1 % of farmers. The most common seed source was home-saved seed as indicated by 73.2 % of the farmers. Common seedborne fungi isolated from the different seed lots using the blotter method included; Fusarium oxysporum (73.2 %), Alternaria alternata (70.7 %) and Colletotrichum lindemuthianum (51.2 %). The major sources of inoculum identified for Pseudocercospora griseola were; infected seed, concomitant infected plants in the field, the soil at the end of the growing period, the air, rainfall and irrigation water. The north and south facing slides on the local trap were the most efficient in conidia trapping and the north conformed to the main wind direction which was north-north-east.

A field study conducted on the control of common bean angular leaf spot (Pseudocercospora griseola) during two years (2002/3 and 2003/4) indicated that, as a consequence of unfavourable weather conditions for disease development, winter and early planting in summer associated with any irrigation method can be adopted. To target high yield, early planting can be complemented with any irrigation method, whereas the winter crop worked best with sprinkler irrigation. However, the high amounts of water received and high humidity conditions that prevail during maturity when beans are planted early might interfere negatively with harvesting operations and seed quality. This situation will require harvesting at physiological maturity, and putting in place facilities for drying plants/pods and seed after processing.

The variables estimated at the start of the disease and leaf disease variables from 4 to 8 weeks after planting (WAP) [incidence, severity and defoliation], pod disease variables [incidence and severity], and weather variables from 4 to 10 WAP [duration of humidity and temperature, and daily mean humidity) were positively correlated with the main disease variables and negatively correlated with yield. Days to first disease infection and daily mean temperature were correlated negatively with the main disease variables and positively with yield. Duration of water and daily mean water were the most variable across evaluation periods, and correlations were specific to evaluation periods.

For each one of the dependent variables, one most appropriate equation was developed and the number of predictors was reduced to 7 - 10. The most important predictors included in the equations developed; disease incidence and severity, mean daily humidity and water, duration of humidity and water, were associated with specific evaluation periods. Consequently, decisions can be made early enough to permit disease control measures and warn farmers. As soon as the conditions of water received and humidity favourable to the disease are recorded, farmers should be warned and control measures should be applied when necessary. The critical periods for disease infection were 4, 6 and 10 WAP.

ACKNOWLEDGMENTS

I am grateful to my supervisor, Dr S. Dimbi for her advice and guidance. Her patience and understanding were a source of strength during difficult moments. I would like to thank Dr J. Sibiya, my former supervisor from the start of the research work, who offered a tremendous support, and constantly shared her knowledge. I also gratefully acknowledge invaluable guidance and advice from Mr B. Chipindu in agrometeorological data management, analysis and modeling. Dr S. Kageler, enthusiastically and freely gave me specialist biometrics advice especially in modeling. I would like to thank Prof. B.V. Maasdorp for having finalized the final proposal.

Doctor E. Ngadze and Mrs E. Masenda should be commended for their guidance in research methodology and assistance in the laboratory work. Many useful comments were received from Drs W. Manyangarirwa, I. Makanda, A. B. Mashingaidze, R. and C. Madakadze. I am grateful to Dr A. Murwira for his help in the estimation of the distances between stations, Dr S. Dzikiti in weather data calculations, Dr E. Mashonjowa for his advice in the management of wind direction data, and Dr A. Senzanje for his contribution in sprinkler and furrow irrigation weather data adjustment.

I am grateful to Dr C. Mguni who allowed me to use Mazowe Quarantine laboratory facilities and to the personnel of the institution especially Mrs M. Mabika, D. Mukwena and A. Semani. I thank also A. Matikiti, G. Ashley, M. Cavill and A. Mare for their assistance in laboratory work. I am also grateful to Agricultural and Rural Development Authority Muzarabani (ARDA Mz) staff, to P. Kambidzi, L.Tumbare and R. Tumbare for their contribution in field experimentation. Givemore Parirenyatwa was the key person in the Chinyika seed collection. I would like to thank Agricultural Technical and Extension Services (AGRITEX) personnel, S. Marimo, N. Gachange and all the people who assisted in seed collection. I would like to thank Dr S. Dimbi and Mrs T. Sigobodhla for having provided the Burkard trap used in this research. I thank A. Chirwa, M. Mlambo and R. Cikotosa of the Engineering Department for their contribution in making the local trap, installing and maintaining the traps used in the study.

I am grateful to Belvedere weather station staff especially Mr T. Gwaze, and to Mr T. Soko of Seed-Co Rattray Arnold Research Station for sourcing meteorological data used in this study.

Financial assistance from The Rockefeller Foundation, the Southern Africa Bean Research Network and the Crop Breeding Institute is gratefully acknowledged.

I thank Fr F. Chanterie, Mr M. Triest, Miss T. Masekesa and Mrs S. Svova for their contribution in this study.

There are many others who directly and indirectly contributed to the success of this study, and to you all, I say: thank you. Lastly I thank the Virgin Mary and Christ.

TABLE OF CONTENTS

ABSTRACT ...... i ACKNOWLEDGMENTS ...... ii TABLE OF CONTENTS ...... iii LIST OF TABLES ...... viii LIST OF FIGURES ...... x LIST OF APPENDICES ...... xiii LIST OF ABBREVIATIONS ...... xiv

CHAPTER 1: INTRODUCTION AND JUSTIFICATION ...... 1 1.1 General introduction ...... 1 1.2 Objectives of the study...... 6 1.3 Study hypotheses ...... 7

CHAPTER 2: GENERAL LITERATURE REVIEW ...... 9 2.1 Introduction…...... 9 2.2 Angular leaf spot ...... 9 2.2.1 Sources of inoculum, survival and symptom development ...... 10 2.2.2 Control of angular leaf spot disease in beans...... 11 2.3 Effect of weather factors on plant disease development ...... 15 2.3.1 Temperature ...... 15 2.3.2 Moisture ...... 16 2.3.3 Combination of weather variables ...... 17 2.4 Conditions suitable for disease-free bean production ...... 17 2.5 Irrigation……...... 19 2.5.1 Sprinkler irrigation ...... 20 2.5.2 Effect of irrigation systems on diseases infection and yield ...... 20 2.6 Date of planting...... 22 2.7 Overview of modeling possibilities ...... 23 2.7.1 Simulation models ...... 24 2.7.2 Statistical models ...... 24 2.7.3 Modeling for yield ...... 26 2.7.4 Decisions in disease management ...... 27 2.7.5 Forecasting ...... 28 2.8 Implication of literature review key issues in the conception of the study………… ...... 29

CHAPTER 3: ASSESSMENT OF QUALITY OF FIELD BEAN SEEDS HOME- SAVED BY SMALLHOLDER FARMERS ...... 31 3.1 Introduction…...... 31 3.2 Materials and methods ...... 34 3.2.1 Study area...... 34 3.2.2 Seed collection ...... 35

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3.2.3 Identification of fungal pathogens ...... 37 3.2.4 Data analysis ...... 38 3.3 Results………...... 38 3.3.1 Seed types, sources and quality ...... 38 3.3.2 Seed health ...... 39 3.4 Discussion…… ...... 39 3.5 Conclusion…...... 43

CHAPTER 4: STUDIES ON SOURCES OF INOCULUM OF PSEUDOCERCOSPORA GRISEOLA ...... 44 4.1 Introduction…...... 44 4.2 Materials and methods ...... 46 4.2.1 Field evaluation ...... 46 4.2.2 Environmental data ...... 47 4.2.3 Seed health evaluation ...... 48 4.2.4 Detection of soilborne inoculum ...... 48 4.2.5 Detection of airborne inoculum ...... 49 4.2.6 Detection of rainfall and irrigation waterborne inoculum ...... 51 4.3 Data analysis… ...... 52 4.3.1 Environmental data ...... 52 4.3.2 Field evaluation ...... 52 4.3.3 Seed health ...... 53 4.3.4 Data management, summaries and analysis ...... 53 4.4 Results………...... 53 4.4.1 Weather data ...... 53 4.4.2 Field evaluation ...... 54 4.4.3 Seed health ...... 56 4.4.4 Soilborne inoculum ...... 57 4.4.5 Airborne inoculum ...... 57 4.4.6 Waterborne inoculum...... 57 4.5 Discussion…… ...... 57 4.6 Conclusion…...... 62

CHAPTER 5: THE EFFECTS OF TYPE OF IRRIGATION AND PLANTING DATE ON ANGULAR LEAF SPOT DISEASE, SEED YIELD AND SEED INFECTION ...... 63 5.1 Introduction…...... 63 5.2 Materials and methods ...... 68 5.2.1 Background to sites selected ...... 68 5.2.1.1 University of Zimbabwe Farm ...... 68 5.2.1.2 Agricultural and Rural Development Authority Muzarabani ...... 68 5.2.1.3 Comparison of weather conditions between sites ...... 69 5.2.2 Land preparation and crop husbandry...... 69 5.2.3 Treatments and experimental design ...... 70

5.2.4 Characteristics of the furrrow ...... 70 5.2.5 Data collection ...... 72 5.2.5.1 Weather data ...... 72 5.2.5.2 Disease incidence ...... 72 5.2.5.3 Disease severity ...... 72 5.2.5.4 Defoliation ...... 73 5.2.5.5 Seed yield ...... 73 5.2.5.6 Testing for Pseudocercospora griseola ...... 73 5.2.6 Data management...... 74 5.2.6.1 Irrigation ...... 74 5.2.6.2 Weather missing data ...... 75 5.2.6.3 Changes in rainfall, temperature and relative humidity associated with evaporation ...... 76 5.2.6.4 Adjustment of weather data with irrigation methods ...... 76 5.2.6.5 Selection of weather variables ...... 77 5.2.6.6 Calculation of the integral variables ...... 77 5.3 Data analysis… ...... 78 5.3.1 Weather data ...... 78 5.3.2 Angular leaf spot variables, seed yield and seed infection ...... 79 5.4 Results………...... 80 5.4.1 Weather ...... 80 5.4.2 Disease incidence at UZ Farm ...... 82 5.4.2.1 Number of days to the start of the disease ...... 82 5.4.2.2 Leaf disease incidence at the start of the disease ...... 82 5.4.2.3 Relationship between number of days and leaf disease incidence at the start of the disease ...... 84 5.4.2.4 Leaf disease incidence during the growing period ...... 84 5.4.2.5 Area under disease progress curve leaf incidence ...... 87 5.4.2.6 Progression of disease incidence ...... 87 5.4.2.7 Pod disease incidence ...... 89 5.4.3 Disease incidence at ARDA Muzarabani ...... 89 5.4.4 Disease severity at UZ Farm ...... 91 5.4.4.1 Leaf disease severity at the initial development of the disease ...... 91 5.4.4.2 Relationship between number of days and leaf disease severity at the start of the disease ...... 91 5.4.4.3 Leaf disease severity during the growing period ...... 91 5.4.4.4 Area under leaf disease severity progress curve ...... 94 5.4.4.5 Progression of disease severity ...... 94 5.4.4.6 Pod disease severity ...... 97 5.4.5 Disease severity at ARDA Muzarabani ...... 97 5.4.6 Defoliation at UZ Farm ...... 97 5.4.6.1 Defoliation at the start of the disease ...... 97 5.4.6.2 Relationship between number of days and disease defoliation at the start of the disease ...... 102 5.4.6.3 Defoliation during the growing period ...... 102

5.4.6.4 Area under disease progress curve defoliation ...... 103 5.4.6.5 Progression of leaf defoliation due to disease...... 103 5.4.7 Defoliation at ARDA Muzarabani ...... 104 5.4.8 Seed yield at UZ Farm ...... 104 5.4.9 Seed yield at ARDA Muzarabani ...... 106 5.4.10 Seed health at UZ Farm ...... 106 5.4.10.1 Seed planted ...... 106 5.4.10.2 Seed harvested ...... 106 5.4.11 Seed health at ARDA Muzarabani ...... 109 5.5 Discussion…… ...... 109 5.6 Conclusion…...... 116

CHAPTER 6: CORRELATION STUDIES BETWEEN DISEASE, HOST AND WEATHER VARIABLES...... 118 6.1 Introduction…...... 118 6.2 Materials and methods ...... 119 6.2.1 Data analysis ...... 120 6.3 Results………...... 120 6.3.1 Relation between main characteristics ...... 120 6.3.2 Relation between disease characteristics with the main variables ...... 120 6.3.3 Relation between characteristics recorded at the start of disease infection, pod incidence and severity with the main variables ...... 121 6.3.4 Relation between weather and the main variables ...... 122 6.4 Discussion…… ...... 124 6.5 Conclusion…...... 127

CHAPTER 7: PREDICTION OF ANGULAR LEAF SPOT EFFECT IN COMMON BEAN USING WEATHER AND DISEASE PREDICTORS ...... 128 7.1 Introduction…...... 128 7.2 Materials and methods ...... 131 7.2.1 Conceptual method ...... 131 7.2.2 Calibration...... 131 7.2.3 Models development and selection ...... 132 7.2.4 Models validation...... 133 7.3 Results………...... 134 7.3.1 Disease and weather predictors associated with final leaf disease characteristics at 10 WAP ...... 134 7.3.2 Disease and weather predictors associated with area under disease progress curves ...... 135 7.3.3 Disease and weather predictors associated with seed yield and seed infection ...... 136 7.3.4 Validation of the models ...... 136 7.3.4.1 Comparison of different models obtained by data

splitting and removal...... 136 7.3.4.2 Regression between responses and fitted values ...... 138 7.3.4.3 “Leave out more than one observation” approach ...... 139 7.3.5 Assumptions and considerations ...... 139 7.4 Discussion…… ...... 145 7.5 Conclusion…...... 147

CHAPTER 8: GENERAL DISCUSSION AND RECOMMENDATIONS ...... 150 8.1 An assessment of quality and health of field bean seeds home- saved by smallholder farmers ...... 150 8.2 Studies on sources of inoculum of Pseudocercospora griseola ...... 151 8.3 Effect of cultural practices on common bean angular leaf spot disease characteristics ...... 152 8.3.1 Weather variables...... 152 8.3.2 Leaf characteristics ...... 153 8.3.3 Seed yield ...... 154 8.3.4 Seed health ...... 155 8.4 Correlation…...... 156 8.5 Prediction of angular leaf spot effect in common bean using weather and disease predictors ...... 156 8.6 Recommendations ...... 158

REFERENCES ...... 162 APPENDICES ...... 189

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LIST OF TABLES

Table 3.1: Characteristics of sweet bean types collected from Chinyika Resettlement Area in November 2002/3 season (n = 82)...... 41 Table 3. 2: Levels of seedborne fungal pathogens in sweet bean seed samples collected from 82 farmers in Chinyika Resettlelement Area in November 2002/3 season...... 41 Table 4.1: Prevailing wind direction recorded during dry and wet days, and water characteristics recorded during rainy and irrigated days during the growing period in 2009/10 and 2010/11 respectively at the Crop Science Department site...... 54 Table 4.2: Prevailing wind direction and means of other weather characteristics recorded at the Crop Science Department site during two years...... 54 Table 4.3: Seed infection of planted and harvested seed during summer season obtained from two years data sets...... 56 Table 5.1: Effect of planting date on the percentage of common bean plants with leaves infected with ALS (transformed by sqrt) at 6 weeks after planting in 2002/3 and 2003/4 seasons...... 85 Table 5.2: Effect of irrigation and planting date on the percentage of common bean plants with leaves infected with ALS (transformed by sqrt) at 8 weeks in 2003/4 and 10 weeks after planting in 2002/3 and 2003/4...... 85 Table 5.3: Effect of planting date on the AUDPC-N (transformed by sqrt) calculated from the percentage of common bean plants attacked by ALS from 4 to 10 WAP in 2002/3 and 2003/4...... 87 Table 5.4: Effect of irrigation and planting date on the percentage of leaf area (transformed by sqrt) attacked by ALS at the start of disease infection at UZ Farm in 2002/3 and 2003/4...... 92 Table 5.5: Effect of planting date on the percentage of common bean leaf area (transformed by sqrt) affected by ALS obtained at UZ Farm in 2002/3. . 93 Table 5.6: Effect of irrigation and planting date on the percentage of common bean leaf area (transformed by sqrt) attacked by ALS observed at UZ Farm, 4 WAP with LPD and 8 WAP in 2003/4 with all planting dates...... 93 Table 5.7: Effect of irrigation and planting date of common bean on the AUDPC-V (transformed by sqrt) calculated from the percentage of leaf area attacked by ALS observed at UZ Farm in 2002/3 and 2003/4 from 4 to 10 weeks after planting...... 96 Table 5.8: Effect of irrigation and planting date of common bean on the relative rate of defoliation (transformed by sqrt) obtained at UZ Farm at 10 WAP in years 2002/3 and 2003/4...... 101 Table 5.9: Effect of irrigation and planting date of common bean on the AUDPC-L obtained at UZ Farm in 2002/3 and 2003/4...... 106 Table 5.10: Effect of planting date and irrigation system on common bean seed yield (kg/ha) obtained at UZ Farm in 2002/3 and 2003/4...... 108 Table 5.11: Effect of year and irrigation system on common bean seed yield (kg/ha) obtained at ARDA Muzarabani in 2002/3 and 2003/4...... 108

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Table 6.1: Correlation between main variables of common bean ALS obtained at UZ Farm and ARDA Mz in 2002/3 and 2003/4...... 121 Table 6.2: Correlation between main variables and disease characteristics of common bean ALS recorded at UZ Farm and ARDA Mz from 4 to 8 WAP...... 121 Table 6.3: Correlation between common bean ALS characteristics recorded at UZ Farm and ARDA Muzarabani from 2002/3 to 2003/4 at the start of the disease and pod filling with the main variables...... 122 Table 6.4: Correlation between common bean ALS leaf incidence, severity and defoliation recorded at 10 WAP; area under disease progress curve for incidence, severity and defoliation with weather variables recorded from 4 to 10 WAP at UZ Farm and ARDA Mz in 2002/3 and 2003/4...... 123 Table 6.5: Correlation between seed infection and yield of common bean, and weather variables recorded from 4 WAP to maturity at UZ Farm and ARDA Mz in 2002/3 and 2003/4...... 124 Table 7.1: Relationship between dependent and independent variables from the common bean trial conducted at UZ Farm and ARDA Muzarabani in 2002/3 and 2003/4 with the percentage calculated from the number of dependent variables in which the independent variable is significant. ... 137 Table 7.2: Coefficients of determination (R2s) (significant at P < 0.001) and standard deviations of the residuals (Ss) (between brackets) of the models developed with combined data for both years, individual data for each year with data splitting and removal...... 138 Table 7.3: Comparison made in each year (2002/3 and 2003/4) of responses and estimated values of final disease incidence [sr(li10)], severity [sr(ls10)] and defoliation [sr(rrdef10)] by multiple regression equations by paired t-test after removal of one observation in each planting date repeated three times...... 144 Table 7.4: Comparison made in each year (2002/3 and 2003/4) of responses and estimated values of final area under disease progress curve incidence [sr(dcn)], severity [sr(dcv)] and defoliation (dcl) by multiple regression equations by paired t-test after removal of one observation in each planting date repeated three times...... 144 Table 7.5: Comparison made in each year (2002/3 and 2003/4) of responses and estimated values of final seed yield (yd) and seed infection [sr(iv)] by multiple regression equations by paired t-test after removal of one observation in each planting date repeated three times...... 144

LIST OF FIGURES

Figure 2.1: Symptoms of angular leaf spot on seeds – arrows indicate the lesions. ... 12 Figure 2.2: Angular leaf spot lesions on the upperside of infected bean leaflets – arrows indicate the lesions...... 12 Figure 2.3: Angular leaf spot lesions on the underside of infected bean leaflets – arrows indicate the lesions...... 13 Figure 2.4: Angular leaf spot lesions on bean pods – arrows indicate the lesions...... 13 Figure 2.5: Map of Zimbabwe showing Natural Regions...... 18 Figure 3.1: Location of Chinyika Resettlement Area, Zimbabwe...... 36 Figure 3.2: Sweet beans percentage of bad seed and its preference by farmers in Chinyika Resettlement Area: (a) Farmers growing different varieties expressed as a percentage of the total surveyed (98), (b) Spread of the percentage of bad seed identified from 82 farmers seed samples collected in CRA in November 2002/3...... 40 Figure 4. 1: Pseudocercospora griseola habit characters on a bean seed – arrows indicate colonies...... 50 Figure 4. 2: Conidia of Pseudocercospora griseola...... 50 Figure 4. 3: Evolution of disease severity (square root transformed and centred in 2009/10) across weeks after emergence (centred) obtained from data collected on 25 varieties grown at the Crop Science Department site during 2009/10 and 2010/11 summer seasons. Analysis of variance between weeks: 2009/10 (P < 0.001), 2010/11 (P < 0.001). sqrt: square root, **: significant at P < 0.01, ***: significant at P < 0.001...... 55 Figure 4. 4: Evolution of Natal Sugar disease severity across weeks after emergence obtained from data collected at the Crop Science Department site during 2009/10 and 2010/11 summer seasons. Analysis of variance between weeks: 2009/10 (P < 0.001), 2010/11 (P < 0.001). sqrt: square root, **: significant at P < 0.01, ***: significant at P < 0.001...... 56 Figure 5. 1: Characteristics of furrows at the beginning of the experiment ...... 71 Figure 5. 2: Characteristics of furrows at the end of the experiment ...... 71 Figure 5. 3: Effect of irrigation (S: Sprinkler, F: Furrow) and planting date (E: Early, M: Medium, L: Late) of common bean on the number of days to first ALS disease symptoms after planting (dot: grand median line, data label: rank). Friedman test adjusted for ties: 2002/3 (P < 0.001), 2003/4 (P < 0.001). 83 Figure 5. 4: Effect of irrigation (S: Sprinkler, F: Furrow) and planting date (E: Early, M: Medium, L: Late) of common bean on leaf incidence at first ALS disease symptoms (dot: grand median line, data label: rank). Friedman test adjusted for ties: 2002/3 (P = 0.003), 2003/4 (P = 0.009)...... 83 Figure 5. 5: Effect of irrigation (S: Sprinkler, F: Furrow) and planting date (E: Early, M: Medium, L: Late) of common bean on the percentage of plants with leaves infected with ALS (dot: grand median line, data label: rank). Friedman test adjusted for ties: 4 WAP (2002/3) (P = 0.003), 8 WAP (2002/3) (P = 0.002), 4 WAP (2003/4) (P < 0.001)...... 86

Figure 5. 6: Development of angular leaf spot disease leaf incidence of common bean within planting dates across evaluation periods (weeks) obtained from data observed at UZ Farm in 2002/3 and 2003/4...... 88 Figure 5. 7: Effect of irrigation (S: Sprinkler, F: Furrow) and planting date (E: Early, M: Medium, L: Late) of common bean on pod disease incidence: (a) 2002/3 (dot: grand median line, data label: rank), (b) Comparison of planting dates at each irrigation system in 2003/4, (c) Comparison of irrigation systems at each planting date in 2003/4. Friedman test adjusted for ties: 2002/3 (P = 0.023), F-test 2003/4: PD (P < 0.001), IR x PD (P < 0.001)...... 90 Figure 5. 8: Comparison of factor levels at the interaction between other factors using the percentage of common bean leaf area attacked by ALS observed at 10 WAP in 2002/3 and 2003/4 at UZ Farm: (a) Planting dates (E,M,L) at each combination irrigation x year, (b) Irrigation (IR) levels (S,F) at each combination planting date x year, (c) Years (YR 1,YR 2) at each combination planting date x irrigation. F-test YR (P = 0.048), IR (P < 0.001), PD (P < 0.001), YR x PD (P < 0.001), IR x PD (P < 0.001), YR x IR x PD (P < 0.001)...... 95 Figure 5. 9: Effect of irrigation (S, F) and planting date (E, M, L) of common bean on the percentage of leaf area attacked by ALS at 6 WAP (dot: grand median line, data label: rank) obtained at UZ Farm: (a) in 2002/3, (b) in 2003/4. Friedman test adjusted for ties: 2002/3 (P = 0.002), 2003/4 (P = 0.001). 96 Figure 5. 10: Evolution of common bean ALS disease leaf severity within planting dates across evaluation periods (weeks) obtained from data observed at UZ Farm in 2002/3 and 2003/4...... 98 Figure 5. 11: Effect of irrigation (S, F) and planting date (E,M,L) of common bean on pod ALS severity obtained at UZ Farm: (a) 2002/3 (dot: grand median line, data label: rank), (b) Comparison of planting dates at each irrigation system in 2003/4, (c) Comparison of irrigation systems at each planting date in 2003/4. Friedman test adjusted for ties: 2002/3 (P = 0.041). F-test 2003/4: PD (P < 0.001), IR x PD (P < 0.001)...... 99 Figure 5. 12: Effect of studied factors on common bean leaf defoliation observed at the start of the ALS disease at UZ Farm in 2002/3 (YR 1) and 2003/4 (YR 2), at the interaction of other factors: (a) Comparison of YRs at the interaction IR x PD, (b) Comparison of irrigation levels at the interaction YR x PD, (c) Comparison of planting dates at the interaction YR x IR. F-test: YR (P = 0.002), IR (P < 0.001), YR x IR (P = 0.02), PD (P < 0.001), YR x PD (P < 0.001), IR x PD (P < 0.001), YR x IR x PD (P < 0.001)...... 100 Figure 5. 13: Effect of factors studied on common bean defoliation observed at UZ Farm from 4 to 8 WAP in 2002/3 (YR 1) and 2003/4 (YR 2), at the interaction of other factors: Comparison of years at the interaction irrigation (S, F) x planting date (PD) (E,M,L) using rrdef4 (a), rrdef6 (d), rrdef8 (g); Comparison of irrigation levels at the interaction year x planting date using rrdef4 (b), rrdef6 (e), rrdef8 (h); Comparison of planting dates at the interaction year x irrigation using rrdef4 (c), rrdef6 (f), rrdef8 (i). F-test (rrdef4, rrdef6, (rrdef8): YR x IR x PD (< 0.001)...... 105

Figure 5. 14: Progression of common bean leaf defoliation due to ALS within planting dates across evaluation periods obtained from data observed at UZ Farm in 2002/3 and 2003/4...... 107 Figure 5. 15: Effect of irrigation system (Sprinkler: S, Furrow: F) and planting date (Early: E, Medium: M, Late: L) on the percentage of common bean seed harvested at UZ Farm infected by P. griseola (dot: grand median line, data label: rank): a) in 2002/3, b) in 2003/4. Friedman test adjusted for ties: 2002/3 (P = 0.003), 2003/4 (P = 0.009)...... 109 Figure 7. 1: Regression analysis between leaf disease incidence and severity responses and fitted values by multiple regression with disease and weather variables in 2002/3 and 2003/4 (solid squares are model-data pairs, the broken line is 1:1 and the solid line is the regression line). ns: gradient and intercept not significantly different from 1 and 0 respectively using t-test, *: intercept significantly different from 0 at 0.05 level using t-test, **: intercept significantly different from 0 at 0.01 level using t-test...... 140 Figure 7. 2: Regression analysis between leaf disease defoliation and area under disease progress curve incidence responses and fitted values by multiple regression with disease and weather variables in 2002/3 and 2003/4 (solid squares are model-data pairs, the broken line is 1:1 and the solid line is the regression line). ns: gradient and intercept not significantly different from 1 and 0 respectively using t-test. *: intercept significantly different from 0 at 0.05 level using t-test, **: intercept significantly different from 0 at 0.01 level using t-test...... 141 Figure 7. 3: Regression analysis between areas under disease progress curves (AUDPCs) severity and defoliation responses and fitted values by multiple regression with disease and weather variables in 2002/3 and 2003/4 (solid squares are model-data pairs, the broken line is 1:1 and the solid line is the regression line). ns: gradient and intercept not significantly different from 1 and 0 respectively using t-test...... 142 Figure 7. 4: Regression analysis between seed yield and seed infection responses and fitted values by multiple regression with disease and weather variables in 2002/3 and 2003/4 (solid squares are model-data pairs, the broken line is 1:1 and the solid line is the regression line). ns: gradient and intercept not significantly different from 1 and 0 respectively using t-test, *: intercept not significantly different from 0 at 0.05 level using t-test, **: intercept not significantly different from 0 at 0.01 level using t-test, ***: intercept not significantly different from 0 at 0.001 level using t-test...... 143

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LIST OF APPENDICES

Appendix 1: Studies on sources of inoculum of Pseudocercospora griseola ...... 189 Appendix 2: General analyses of weather data presenting the degrees of freedom (DF) and the probabilities related to F-test...... 192 Appendix 3: Analysis of ALS disease incidence characteristics observed at UZ Farm in 2002/3 and 2003/4...... 192 Appendix 4: Analysis of ALS disease severity characteristics observed at UZ Farm in 2002/3 and 2003/4...... 193 Appendix 5: Analysis of ALS disease defoliation characteristics observed at UZ Farm in 2002/3 and 2003/4 ...... 194 Appendix 6: Analysis of seed yield (kg/ha) and seed infection (%) recorded at UZ Farm and ARDA Mz in 2002/3 and 2003/4 ...... 195 Appendix 7: Multiple regression of leaf severity at 10 weeks after planting with disease and weather predictors ...... 196 Appendix 8: Regression of fitted values and responses for leaf disease severity in 2002/3...... 205 Appendix 9: Regression of fitted values and responses for disease leaf severity in 2003/4...... 206 Appendix 10: Removal of observations from 2002/3 for leaf disease severity ...... 207

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LIST OF ABBREVIATIONS

AGRITEX Agricultural Technical and Extension Services ALS Angular leaf spot ARDA Mz Agricultural and Rural Development Authority Muzarabani AREX Department of Agricultural Research and Extension AUDP Area under disease progress AUDPC-L or dcl Area under disease progress curve defoliation AUDPC-N or dcn Area under disease progress curve incidence AUDPC-V or dcv Area under disease progress curve severity CMI Common Wealth Mycological Institute CRA Chinyika Resettlement Area d Duration DACE Days after crop emergence ddis Number of days to first disease infection dfdis Disease defoliation at the start of the disease dh Duration of relative humidity DRC Democratic Republic of Congo dt Duration of temperature dw duration of water E Early-season EOL Encyclopedia of Life EP Evaluation period F Furrow FAO Food and Agricultural Organisation of the United Nations h Hour hi Daily mean relative humidity IR Irrigation IPM Integrated Pest Management ISTA International Seed Testing Association iv Harvested seed infection L Late-season LAALS Leaf area infected by ALS li Disease leaf incidence lidis Disease incidence at first disease infection ls Disease leaf severity lsdis Leaf severity at the start of the disease M Mid-season mat Maturity MRA Multiple regression analysis N Sugar Natal Sugar NECTAR Natura-European Community Training Programme of Agricultural Universities in southern Regions NNE North-north-east NR Natural Region

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NUV Near-ultra-violet oi Disease pod incidence os Disease pod severity PAALS Pod area infected by ALS PD Planting date RCW Red Canadian Wonder RH Relative humidity rrdef Relative rate of defoliation S Sprinkler SH Smallholder sqrt or sr Square root ti Daily mean temperature TLALS Plants with leaves infected by ALS TPALS Plants with pods infected by ALS USDA-APHIS The United Stated Department of Agriculture-Animal and Health Inspectorate Services UZ Farm University of Zimbabwe Farm VIF Variance inflation factor W Winter WACE Weeks after crop emergence WAP Weeks after planting wi Daily mean of water applied WMO World Meteorogical Organization of the United Nations yd Seed yield YR Year

CHAPTER 1: INTRODUCTION AND JUSTIFICATION

1.1 General introduction Field bean (Phaseolus vulgaris L.) is the most important food grain legume, and dry beans form the most important source of protein in the diet of many tropical people. Beans contain an average protein content of 24 % (Neergaard, 1988), which are rich in lysine and tryptophan and are therefore used to supplement the carbohydrate-based staple diets of people in the tropics and subtropics (Onweme and Sinha, 1991). In eastern and southern Africa, dry beans are now recognized as the second most important source of proteins for humans, and the third most important source of calories of all agricultural commodities produced in the region (Pachico, 1993). In terms of bean production, Latin America is the leader, producing approximately 30 % or 4.0 million tonnes per year of the world’s total dry beans (Shellie-Dessert and Bliss, 1991). Africa produces approximately 10 % (1.4 million t/year) on 3.7 million hectares every year, with nearly 60 % being produced in five countries of the Central and Eastern region; namely Uganda (13 %), Kenya (9 %), Burundi (12 %), Rwanda (13 %) and Tanzania (11 %). Bean production provides both food and income to at least 100 million people in eastern, central and southern Africa (Schoonoven, 1980; Kimani et al., 2005).

In Zimbabwe beans are grown on 72 500 ha in the high- mid- and low-veld areas (Wortman et al., 1998, FAO, 2012). The bulk of field bean production is from smallholder (SH) farmers and only a small portion of field beans are grown by large-scale farmers. The SH farmers either grow beans as a monocrop or in intercrop systems, notably with maize (Magabala and Saettler, 1993). Others grow beans in rotation with field or vegetable crops because of the legume’s ability to maintain or improve soil fertility (Mandari and Minjas, 1984). Smallholder farmers produce beans mainly for home consumption (Giga, 1989). The estimated bean production in Zimbabwe is 28 000 tonnes per annum resulting in a mean bean yield of 386 kg/ha, which is lower than the African and world averages of 648 and 806 kg/ha, respectively (FAO, 2012). Unfortunately, yields as low as 300 kg/ha can be obtained in the smallholder sector

(Chiduza, 1994). But, a potential yield of 3.0 t/ha can be achieved on some commercial farms using suitable input, appropriate management and better cultivars (Giga, 1989).

Diseases and pests are amongst the important limiting constraints to bean production (Rheenen et al., 1981). According to Schwartz and Gálvez (1980), more than 50 % of the major bean diseases are seedborne. Seedborne diseases affect seed germination, initial stand establishment, and yield, by causing abortion, shrinking, rotting, necrosis, discolouration, and weaker vigour of seeds, and carrying over of infection across seasons (Shetty, 1992). Infected seed forms the major source of primary inoculum facilitating both local and long distance disease spread (Saettler et al., 1995). In the SH farming sector, due to financial constraints, few farmers are able to buy certified or treated seed, fungicides or bactericides to spray for diseases. The use of retained, untreated seed in the SH farming sector is therefore a very common practice (Chiduza, 1994). Fungal and bacterial pathogens were recovered from seeds collected from farmers in Zimbabwe and had been stored for 1 - 2 years (Kutywayo, 2000). As a result of using home-saved seed, the pathogens are carried over from season to season, resulting in a build-up of inoculum which could eventually lead to outbreak of disease epidemics.

Amongst the important and common fungal diseases affecting beans in the tropical and sub-tropical regions is angular leaf spot (ALS) caused by Pseudocercospora griseola (Sacc.) Crous & U. Braun (Crous et al., 2006; Lima et al., 2010). It is the second important limiting factor after nitrogen deficiency in Africa causing yield losses of 40 - 80 % (Wortman et al., 1998; Muthomi et al., 2011). Yield losses of 10 - 50 % in northern US and up to 80 % in tropical and subtropical countries were reported by Celetti et al. (2005, 2006) on snap bean. In the absence of adequate control measures, yield reductions of 70 % were observed in Brazil (Jesus et al., 2001) and 80 % in Colombia (Schwartz et al., 1981). Seed infection is believed to be one of the important transmission routes for ALS disease (Saettler, 1994). Variations exist in P. griseola seed infection. The pathogen was not recovered in seed samples from Kenya (Buruchara, 1990) and from Chinyika Resettlement Area (CRA) in Zimbabwe (Kutywayo, 2000). Manyangarirwa (2001), on the other hand, recovered the fungus from 26 % of bean seed samples collected from

2 various places in Zimbabwe with 7.2 % seed infection level. This variability of the pathogen in farmers’ seed lots implies a need for wider evaluation of the pathogen in all agro-ecological zones in Zimbabwe in relation to growing seasons and planting periods. Most SH farmers use farm-saved seed, which is likely to be of poor quality and consequently seedborne pathogens accumulate with time. They cannot afford purchasing certified seed because it is expensive. Sometimes the economic gains from using higher seed quality do not justify the purchase of seed (Almekinders and Louwaars, 1999).

Seed certification schemes do not exist in some tropical countries (Mathur, 1983). In Zimbabwe, seed certification is regulated by the Seed Services Institute under the Department of Agricultural Research and Extension (AREX). Seeds are tested for seed quality, which covers purity analysis, germination tests, moisture determination and trueness to variety. They are only tested for seedborne diseases as a result of poor germination (Jere, 2004). Weaknesses by seed companies to follow proper certification procedures result in certified seed lots infected by seedborne diseases (Mathur, 1983). These weaknesses affect crop production, and there is need for laboratory testing to follow proper techniques. This justifies the adoption of this study on locally produced farm-saved common bean seed and seed from seedhouses. This might become a tool for formal seed suppliers to promote seed quality testing and correct weaknesses in their seed production chain.

The main sources of inoculum for ALS disease are freshly infected soil, infected straws, infected seed, off season crops and volunteer plants (Sengooba and Mukiibi, 1986; Common Wealth Mycological Institute (CMI), 1986; Correa and Saettler, 1987; Kutywayo, 2000; Manyangarirwa, 2001). The agents of dissemination of the pathogen are wind-blown particles from infected soil, wind-blown spores and rain-droplet-borne spores (Cardona-Alvarez and Walker, 1956; Allen et al., 1996). This information is more documented in other environments and there is a need to increase the available knowledge locally.

Date of planting is another factor that determines whether a plant will be infected or not. It influences the ability of the crop to escape or avoid infection by pathogens (Cook and Baker, 1996). Generally, ALS infection and subsequent yield loss has been reported to vary with planting dates or years (Sindham and Bose, 1980a; Rodriguez et al., 1999; Jesus et al., 2001) with diverging trends as a result of different climatic factors such as relative humidity and temperature prevailing during growing seasons. Production of disease-free bean seed can be achieved at low cost by growing beans under conditions that limit leaf wetness, thereby discouraging development of foliar diseases (Maude, 1996). In such environments, supplementary sprinkler or furrow irrigation systems can be used when needed during the growing period. However, as reported by Zadocks and Schein (1979), sprinkler irrigation has effects similar to rain, which are influenced by macro and microclimatic conditions. It can lower leaf and canopy temperature, increase relative humidity, and thereby prolong dew periods. It also enhances splash dispersal and may remove spores from the atmosphere and deposit them on susceptible plant surfaces. Fungi and bacteria, which are transmitted under cool moist conditions and spread by rain splash, can therefore be suppressed by growing crops for seed production in arid or semi- arid climates (Maude, 1996). Schwartz and Mohan (2008) reported an almost complete eradication of the sour disease skin of onions caused by Pseudomonas cepacia (Burkholder) Palleroni and Holmes, when furrow irrigation was used, while sprinkler irrigation resulted in disease increase. Baker (1980) reported that the spread of splash- dispersed pathogens in low rainfall areas was restricted by use of furrow irrigation.

The above discussion, however, underscores the intricate interaction amongst the pathogen, effects of the environment and the effectiveness of various disease management strategies with regards to bean diseases. Agrios (2005) suggested the use of plant disease models in studying these factors together with aspects of host resistance and the length of time for host and pathogen interaction. A plant disease model is a mathematical description of the interaction between environmental, host, and pathogen variables that can result in disease (Broome et al., 2002). According to the same authors a model can be presented as a simple rule, an equation, a graph, or a table. The output of a

4 model can be a numerical index of disease risk, predicted disease incidence or severity, and/or predicted inoculum development.

Plant disease models can be used for two main purposes, which are simulation and forecasting (Carter, 1986; Madden and Ellis, 1988; Rabbinge and de Wilt, 1989). Disease simulators aim at increasing and organizing the understanding of the epidemiology processes (France and Thornley, 1984). They require many parameters and elaborate data and cannot, therefore, be used in operational disease forecasting. Forecasting models express the interrelationships between the occurrence of the disease and the environmental conditions, and are normally constructed by making a lot of assumptions regarding the nature of the system (Manners, 1993). Models that accurately simulate disease incidence and crop loss allow for preliminary evaluations of different disease control strategies and cost-benefit analysis of the various control options, thus avoiding the time-consuming and expensive use of field surveys and experiments (Thresh, 1986).

Farmers need disease forecasts that will help them to determine whether plant infection is likely to occur so that they can adopt economically efficient management strategies without increasing the risk of losing their crops (Agrios, 2005). Development of models for predicting potential disease epidemic outbreaks would assist in evaluating ALS incidence and severity, and bean yield losses due to the disease in Zimbabwe once the economic yield thresholds have been specified. Models developed can be of use to crop yield forecasters, researchers, extension workers and farmers.

Past research activities on beans in Zimbabwe have focused on pathogen identification and survival, effects of intercropping with maize and chemical control measures such as seed dressing and foliar sprays (Kutywayo, 2000; Mwashaireni, 2001). However, the extent to which cultural practices such as planting dates and type of irrigation interact with pathogens under local conditions is not known. ALS disease effects and subsequent seed yield have been reported to vary with planting dates across different environments and the use of supplementary irrigation systems. The effect of irrigation on the disease

5 and seed yield is not well documented, especially in the local context. Also little is known on how these factors interact in the control of ALS. Establishing the relationship between environmental conditions and disease development in Zimbabwe will enable to forecast disease outbreaks and alert farmers. The variation in disease infection and subsequent yield produced by planting dates and supplementary irrigation systems is associated with weather factors. Therefore, the variation of weather factors with planting dates and irrigation systems need to be evaluated in relation to disease infection and seed yield. Weather variables need to be screened according to their consistency with disease infection and seed yield. Several models may be used to simulate or forecast disease infection and subsequent yield, yield loss or increase. The input of disease and weather variables can be done as constants to build disease-weather models (Eversmeyer and Burleigh, 1970; Talboys and Wilson, 1970; Burleigh et al., 1972). This will provide more documentation on the usefulness of weather values in disease and seed yield modeling. The use of multiple linear regression analysis coupled with multiple-points models to build predictive statistical models integrating disease and weather predictors into a single equation to forecast disease infection and seed yield is not well documented in local conditions. There is a need to build predictive models of that kind, which can be used and adapted by other researchers. They might provide a list of factors influencing local spread of the disease and short to long-term solutions.

1.2 Objectives of the study The overall goal of the study was to develop strategies for improving seed quality by lowering angular leaf spot infection on plants originating from good quality and healthy seed by use of cultural practices namely sites, date of planting and irrigation systems, as well as to develop models to forecast ALS disease and consequent yield using specific disease and weather predictors. The objectives of the study were: 1) To gather information on bean types grown by SH farmers, their acceptability and farmers bean seed sources by surveying and seed quality by laboratory testing. 2) To quantify Pseudocercospora griseola soilborne inoculum before planting and after harvest, airborne inoculum trapped using a Burkard and a locally

made trap using slides and waterborne inoculum collected from rainfall and irrigation water. 3) To determine the effect of cultural practices, namely site, planting date and type of irrigation on angular leaf spot incidence, severity, defoliation, and subsequent seed yield and infection. 4) To identify the main factors under study associated with variation in weather factors using statistical tests. 5) To determine the relationship between specific disease and weather factors using correlation analysis. 6) To develop statistical models using multiple-points and multiple regression analysis to forecast specific disease incidence, severity, defoliation characteristics, seed infection and yield, based on selected disease and weather variables.

1.3 Study hypotheses In order to control angular leaf spot infection through the choice of production sites, irrigation systems and planting dates, the following hypotheses were formulated: a) Seed collection campaigns can provide information on acceptability of sweet beans by SH farmers and their bean seed sources for planting, and laboratory testing can assess seed quality. b) It is possible to quantify Pseudocercospora griseola soilborne inoculum before planting and after harvest, airborne inoculum trapped using a Burkard and a locally made trap using slides, and waterborne inoculum collected from rainfall and irrigation water. c) The effect of cultural practices, namely site, planting date and type of irrigation on angular leaf spot incidence, severity, defoliation, subsequent seed yield and infection, can be determined through field experiments and laboratory tests. d) The main factors associated with variation in weather factors can be identified using statistical tests.

7 e) The relationship between specific disease and weather factors can be determined using correlation analysis. f) Selected disease and weather variables can be used to develop statistical models using multiple-points and multiple regression analysis to forecast specific disease incidence, severity and defoliation characteristics, seed infection and yield.

CHAPTER 2: GENERAL LITERATURE REVIEW

2.1 Introduction

Variable weather conditions, poor soil fertility, bean diseases and insect pests appear to be amongst the important factors contributing to declining bean production and erratic yields (Schoonoven, 1974; Graham, 1978). Bean diseases are especially important in bean production as 50 % of them are seedborne (Schwartz and Gálvez, 1980). However, weather is an important factor influencing diseases in crops, including transmission of the pathogens from seed to seedlings. Weather variables tend to control the development and spread of plant diseases and therefore knowledge of weather influence will guide in choosing appropriate agrometeorological measurements and designing adequate schemes for disease forecasting and management. Plant disease severity is dependent on the interaction of host, pathogen and environment. The disease may occur only if a susceptible host, a virulent pathogen and a favourable environment coexist. Several strategies can be used to manage ALS including the use of fungicides, but this is beyond the means of most smallholder farmers. The most practical means of control that are inexpensive include the use of resistant cultivars and cultural practices such as changing of planting dates to avoid infection by pathogens (Cook and Baker, 1996). Since infected seed constitutes the primary source of inoculum, the use of disease-free bean seed is very important. Pathogen-free seed can be achieved at low cost by growing beans early or late in the season with supplementary irrigation or growing the crop in sites unfavourable to diseases development. The choice of an appropriate irrigation method limiting leaf wetness would discourage the development of foliar diseases. The spread of diseases can be restricted by using furrow irrigation that limits splash dispersal (Baker, 1980).

2.2 Angular leaf spot

The causal agent of ALS is the fungus Pseudocercospora griseola that belongs to the phylum (Ascomycota), class (Dothideomycetes), order (Capnodiales), family (Mycosphaerellaceae), genus (Pseudocercospora), species (Pseudocercospora griseola) (Crous et al., 2006; Encyclopedia of Life (EOL), 2013). The fungus has undergone parallel coevolution with its host and two formae, P. griseola f. griseola and P. griseola

9 f. mesoamericana, have been identified (Crous et al., 2006). They attack genotypes belonging to Andean and Mesoamerican gene pools respectively (Sapparat et al., 2009). Infection by angular leaf spot is characterized as multiple, as more than one pathotype can be identified in one lesion (Mahuku et al., 2002; Garcia et al., 2006).

2.2.1 Sources of inoculum, survival and symptom development The main sources of ALS inoculum serve as the main survival sites for the pathogen (Sohi and Sharma, 1967; CMI, 1986). The fungus (Pseudocercospora griseola) can survive under soil for only two months, under indoor and outside conditions for four to six months, and in infected crop debris for a maximum of nine months (Sengooba and Mukiibi, 1986). Dormant mycelium in the seeds remains viable for more than one year (CMI, 1986). Richardson (1990) reported a decrease of infection to 10 % after 9 months of seed conservation and to 0 % after 1 year.

Angular leaf spot is seedborne and seed contamination may be external or internal (Saettler, 1994). According to Richardson (1990), bean seeds become infected primarily through the hilum, when the hilum is directly under a lesion. Most internally seedborne bean pathogens are located inside the seed coat and some infection may occur in the cotyledon or embryo (Bolkan et al., 1976). External contamination by P. griseola is associated with fungal development in the hilum area only, or in the hilum and other areas of the seed coat depending on the cultivar (Saettler, 1994) and this is responsible for the brown or red-brown discolouration observed on beans (Neergaard, 1988) (Figure 2.1). Seed infection has been reported to vary from less than 2 % (CMI, 1986) to 7.2 % (Manyangarirwa, 2001). However, Richardson (1990) reported the absence of correlation between disease observed on pods and subsequent level of seed infection.

Symptoms occur on all aerial plant parts. Lesions are generally circular on primary leaves. They become more characteristic on leaves, 8 - 12 days after inoculation (Saettler, 1994). They appear initially as irregular spots gray or brown that can be surrounded by a chlorotic halo, and then they become necrotic and assume the angular shape characteristic of the disease by nine days (Figure 2.2) (Saettler, 1994). After 9 - 12 days, sporulation

10 occurs during periods of high humidity and the spores produced cause secondary spread of the disease. The secondary spores appear on primary leaves, but usually become prevalent on subsequent foliage until late flowering or early pod set (Ferraz, 1980). Severe symptoms include coalescence of necrotic lesions, chlorosis and premature abscission of affected leaves leading to heavy plant defoliation (Saettler, 1994). In the lesions located on the abaxial surface of the trifoliate leaf are produced black synnemata and conidia (Figure 2.3). Besides infecting leaves, the pathogen also attacks plant stems, branches, petioles, pods and seeds. Lesions on stems and petioles are dark brown and elongate, whereas on pods, spots are oval to circular with reddish-brown centers surrounded by darker coloured borders (Figure 2.4).

2.2.2 Control of angular leaf spot disease in beans Cultural control measures that are recommended for ALS include use of resistant varieties, crop rotation for at least two years, planting pathogen-free seed, planting in well-drained soils, and removal of previously infected crop debris (Caldona-Alvarez, 1956; Barros et al., 1958). Chemical control measures include the use of ferbam-sulfur- adherent, zineb, benomyl, thiophanate, maneb, ziram, copper oxychloride, bordeaux mixture, mancozeb, captafol and metiram (Barros et al., 1958; Singh and Sharma, 1975). Seed treatment with benomyl can reduce subsequent leaf infection (Ferraz, 1980). Other cultural control strategies warrant investigation, for example, the choice of supplementary irrigation, or adjustment of planting dates to escape or avoid disease.

The use of resistant varieties is the cheapest, easiest, safest, and most effective means of controlling plant diseases (Agrios, 2005). It eliminates losses from diseases and avoids the contamination of the environment with toxic chemicals that would otherwise be used to control plant diseases. Varieties resistant to ALS are available and three countries represent 57.2 % of the resistant germplasm: Peru (14.3 %), Mexico (28.6 %) and

Figure 2.1: Symptoms of angular leaf spot on seeds – arrows indicate the lesions.

Figure 2.2: Angular leaf spot lesions on the upperside of infected bean leaflets – arrows indicate the lesions.

Figure 2.3: Angular leaf spot lesions on the underside of infected bean leaflets – arrows indicate the lesions.

Figure 2.4: Angular leaf spot lesions on bean pods – arrows indicate the lesions.

Democratic Republic of Congo (DRC) (14.3 %) (Garcia et al., 2006). Regionally and locally, resistant/tolerant varieties to ALS are being developped. Varieties combining vertical and horizontal resistance are the most suitable, and actions should be taken to prolong genotypes resistance such as sanitation, seed treatment, or fungicide application (Agrios, 2005). Unfortuntely, the appearance of new races might lead to breakdown of resistance of the old varieties.

One aim of using pathogen free seed is the exclusion of a pathogen from a host. Since pathogen infection decreases seed vigour, germination rates and longevity in storage, healthy and pathogen-free seeds should be able to germinate and give rise to vigorous plants with high yielding capacity. Production of disease free-seed requires the development of seed industry which needs skilled technical staff and specialized well- equipped laboratories; the selection of area free of/or isolated from the pathogen or not suitable for the pathogen; and the application of cultural practices such as efficient weed and disease control, and optimum irrigation and fertilization (Maude, 1996; Agrios, 2005; Singh, 2006). Because the pathogen will be present in the field at the beginning of the growing season, even a small portion of infected seeds is sufficient to provide enough inoculum to spread and infect many plants early, thus causing severe losses (Agrios, 2005). This emphasizes the need of following a good testing procedure for seed certification by seed certification agencies.

As reported by Agrios (2005), crop rotation is used for the reduction of inoculum with time. It is efficient with pathogens that are soil invaders. Planting in well drained soils contribute to create conditions not favourable to the pathogen, and good soil drainage reduces the number and activity of certain fungal pathogens. There is a cost related to soil drainage, making of ridges or mounds. Removal of previously infected crop debris prevents infection build up. This activity requires more equipment and labour.

Chemical compounds that are toxic to the pathogen inhibit its germination, growth, and multiplication or are lethal to the pathogen (Agrios, 2005). Chemicals can be used as

14 seed treatment or as foliar application. The use of chemicals faces the problems of environment contamination and the appearance of new resistant races or strains.

2.3 Effect of weather factors on plant disease development According to Vanderplank (1975), the presence of a pathogen on a host does not necessarily lead to development of disease. The environment has a controlling effect and has to be favourable for the disease to develop (Jones, 1987; McGee, 1995). The environment affects levels of disease epidemics, influencing processes from preinfection, inoculum survival to spore germination, penetration, colonization, sporulation and dispersal (Jones, 1987). Amongst the most important environmental factors that favour disease development are temperature and moisture in addition to a susceptible host and virulent pathogen (Gabrielson, 1988; Agrios, 2005). The main factors affecting the time and extent of disease development in irrigated crops include rain, air humidity and temperature (Arnon, 1972).

2.3.1 Temperature Temperature controls all biological processes (e.g. infection, colonization, reproduction and dispersal) and correlations exist between disease levels and prevailing temperature levels (Manners, 1993). As presented by Natura-European Community Training Programme of Agricultural Universities in southern Regions (NECTAR) (1998), temperature determines the rate at which growth, reproduction rate and dispersal processes occur in plant pathogens. It also affects hyphal growth and spore formation in fungi and spore germination period, spore mortality and the rate of infection. One example of the effect of temperature is spore production; germination and the latent period in Cercospora Personata (Berk & M.A.) Ellis & Everh, the causal agent of groundnut late leaf spot. When temperature is too high or too low, germination is slower, fewer spores are produced, and infections take place longer to produce secondary spores for secondary disease spread (Rabbinge and Ward, 1989). Most plant pathogens have an optimum temperature at which they grow and multiply best. Temperatures deviating from the optimum will either delay germination of the pathogen spores and germ tube growth, reduce the rate of vegetative growth or multiplication within the host. This subsequently

15 increases the latent period in the disease cycle and decreases the rate of spore production (Jones, 1987; Agrios, 2005). For P. griseola, optimum temperature conditions for development of synnemata and conidia in culture and under natural conditions range from 20 to 25 ºC (Cardona-Alvarez, 1956). Sporulation is favoured by temperature between 16 - 26 ºC (Saettler, 1994). Infection and disease occur generally at moderate temperature (16 - 28 ºC) and develop maximally at 24 ºC (Saettler, 1994).

2.3.2 Moisture Relative humidity affects the rate of development of plant disease epidemics because micro-organisms such as fungi are often sensitive to dry conditions and will grow only when there is sufficient moisture (NECTAR, 1998). It may also affect the infection and reproduction phases. Some pathogens such as Colletotrichum gloeosporioides (Penz.) Penz. & Sacc. develop faster epidemics with increased rainfall duration and intensity (NECTAR, 1998). The duration of plant surface wetness must be sufficient to enable the pathogen to penetrate the plant and be established in the cells. Minimum and maximum wetness duration values for almost 90 % of the pathogens studied by Magarey et al. (2005) were respectively < 20 h and < 75 h with means of 9 and 30 h durations, respectively. Wetness duration varies not only with the prevailing weather conditions but also with the type and developmental stage of the crop; the position, angle, and geometry of the leaves; and the specific location on the individual leaf (Sutton et al., 1984). Droplet size during rainfall or irrigation is one of the factors determining wetness duration. This depends on the nature of precipitation, the plant surface, ambient temperature and wind speed, which affect droplet-drying rate. Free water is not always necessary for the germination of spores as some fungal spores can germinate at relative humidity (RH) above 90 % (Manners, 1993). Agrios (2005) noted that the number of infection cycles per season is related to the number of rainfall events per season. Running and/or splashing rain aid in the distribution and spread of pathogens on the same plant and from one plant to another. Spatial distribution of rain is of particular importance on farm-scale or regional epidemiological work (Sutton et al., 1984). Rain, mainly time, frequency, and duration were reported to be critical for disease progress. Pseudocercospora griseola may be disseminated from the debris by splashing water and wind-blown soil particles

(Cardona-Alvarez, 1956). Spores produced in host tissue are disseminated to leaves by wind, rain splash, or both (Saettler, 1994). In pods, stems, and petiole lesions, sporulation occurs only after 24 - 48 h of continuous humid or moist conditions (Saettler, 1994).

2.3.3 Combination of weather variables Moisture and temperature interact on the development of diseases. According to Agrios (2005), a pathogen fails to cause disease during its establishment on the host, if the length of the wetting period is less than the minimum required for a particular temperature. Angular leaf spot spreads and develops rapidly if conditions of moderate temperature, prolonged periods of wet weather or high humidity already mentioned earlier are alternating with dry windy conditions resulting in yield loss (Celetti et al., 2005; 2006). Sindham and Bose (1980a) observed that the first symptoms of ALS disease were associated with air temperature between 13.3 º - 23.4 ºC, and relative humidity around 86 - 96 %. They also noted that relative humidity and precipitation were more important than temperature as there was no disease appearance during the period when temperature was favourable but humidity low.

2.4 Conditions suitable for disease-free bean production In Zimbabwe, annual rainfall varies from as low as 300 mm in the lowveld to as high as 3000 mm in the highveld area of the Eastern highlands, with 675 mm being the average mean for the country (Hussein, 1987). In addition annual rainfalls are markedly variable, particularly over the drier regions of the country in the West and South, with coefficients of variation more than 40 % being recorded. Zimbabwe has six agro-ecological zones, demarcated primarily on basis of rainfall (Vincent and Thomas, 1960) (Figure 2.5). Zimbabwe lies within the tropics, with mean annual temperatures of about 18º - 19 ºC and altitude ranges between 197 m to 2592 m (Gambizi and Nyama, 2000). However, temperature is not usually a limiting factor to agricultural production although areas of high altitude do experience cold temperatures (Hussein, 1987). Average monthly temperatures are usually higher than 20ºC from October to March/April, which constitute the main growing season. Generally temperatures are low in June/July and highest in October prior to rains.

Figure 2.5: Map of Zimbabwe showing Natural Regions. Source: Department of the Surveyor-General (2002).

Areas that are environmentally unsuitable for the development of diseases because of climatic, edaphic or other factors can be used to produce disease-free seeds (Copeland et al., 1975; Schwartz and Gálvez, 1980; Sheppard, 1983; Maude, 1996). An ideal site for the production of disease-free bean seed should have an annual rainfall of less than 300 mm, a daily relative humidity less than 60 %, a daily temperature regime between 25º and 35ºC, and gravity irrigation facilities (Schwartz and Gálvez, 1980). Fungi and bacteria, which are transmitted under cool moist conditions and spread by rain splash, can be suppressed by growing crops for seed production in arid or semi-arid climates but with supplemental irrigation (Maude, 1996).

The optimum temperature for the growth of the common bean ranges from 15.6 to 21.1 ºC; the maximum is near 27 ºC and the minimum near 10oC (Michaeis, 1994). Most (80 %) bean production areas in eastern and southern Africa have a mean temperature (15 - 23 ºC). This temperature is favourable for bean growth for at least part of the year (Wortman et al., 1998). The requirement of bean is met with 300 - 600 mm rainfall depending on the soil and climatic factors (Onweme and Sinha, 1991; Alberta, 2000). Moisture deficits can frequently result in complete crop loss. For a potential yield of 3000 kg/ha, the estimated losses of a pure bean crop attributable to moisture deficits increased from 0 to 1000 kg/ha with decreased rainfall from > 450 to < 300 mm per season (Wortman et al., 1998). Water supply should be adequate during early growth to assure rapid and complete development of the root system. This can often be accomplished by a preplanting irrigation when rainfall is not expected soon after planting. High soil water availability is desirable during flowering and early grain development stages since these periods are so critical to successful production. Short periods of stress are less serious for field bean because its capacity for progressive pod setting and pod load is high except for bean lines with determinate growth habit.

2.5 Irrigation In Zimbabwe, Natural Regions differ in occurrence and severity of mid-season droughts. As a result, irrigation needs during summer season increase from sub-humid to semi-arid regions to supplement water to the crop to prevent total crop failure. Irrigation is also

19 useful during winter, which is not a rainy season, for production of special crops in areas where temperatures remain favourable for crop growth. However, as explained by Arnon (1972), irrigation may favour the incidence and spread of some crop diseases, for example, sprinkler irrigation influences the macroclimate as well as the microclimate of the plant and also affects soil moisture. The long periods that the stomata are widely open, the increased succulence of the tissues as a result of favourable moisture supply, can all contribute to susceptibility of the crop to disease and create more favourable conditions for infection. The more luxuriant vegetative growth resulting from a more favourable moisture regime ensures more shade and longer periods of high air-humidity under the leaf canopy and accordingly slower drying of the soil surface. These conditions are favourable for the infection of the lower leaves of field-crops. Irrigation resulted in increased incidence of downey mildew of peas and lucerne (Rotem and Palti, 1968) and of Sclerospora graminicola (Sacc.) J. Schröt. on pearl millet (Kenneth, 1966).

2.5.1 Sprinkler irrigation Sprinkler irrigation is easy to manage and can be used on steeper slopes than would be suitable for most other irrigation systems (Nyakanda, 1998). However sprinklers require high capital investment and the operating costs may be high. The most commonly used types are the small to medium sprinklers with capacities ranging from 7.5 to 75 litres/min, which cover an area of 10 to 40 m (Christiansen and Davis, 1976). Square spacings of sprinklers tend to be less affected by wind than rectangular spacings [(AGRITEX), 1986].

2.5.2 Effect of irrigation systems on diseases infection and yield Overhead sprinkler irrigation is assumed to be more conducive to diseases pressure than furrow irrigation, but yield may not follow the same trend. There has been variable reports on the effect of sprinkler and furrow irrigation on bean yields. For example, White and Singh (1991), reported more yield with furrow (1350 kg/ha) than sprinkler (730 kg/ha) when they tested 25 lines under drought conditions. Robertson and Frazier (1982), on the other hand, obtained 6.8 % dry seed increase from furrow (2694 kg/ha) to sprinkler (2878 kg/ha) used as supplementary irrigation.

Natural precipitation generally favours the incidence of fungal foliar diseases thus it is reasonable to assume that overhead irrigation may have similar effects (Arnon, 1972). According to Zadoks and Schein (1979), sprinkling can lower leaf and canopy temperature, increase relative humidity, may prolong dew periods, enhance splash dispersal and may remove spores from the atmosphere and deposit them on susceptible plant surfaces. However, there are differences between natural precipitation and overhead irrigation that may be relevant to the incidence and spread of plant diseases, for example, natural precipitation usually occurs after a period of cloud weather, lowered temperatures and increased air humidity than overhead irrigation (Arnon, 1972). This might affect the optimum temperatures for spore production, germination and infection. Whereas overhead irrigation applies water suddenly under conditions of high temperatures, light intensity and low air humidity; the irrigated area will therefore be surrounded by hot and dry air, which in many cases, may effectively preclude the survival and spread of inoculum.

Most studies with foliar pathogens compared overhead (sprinkler) and surface (furrow) irrigated fields for microclimate and disease development (Rotem et al., 1962; Rotem and Cohen, 1966; Rotem and Palti, 1969; Lomas, 1991). Usually, more disease was observed under sprinkler irrigation, due to longer leaf wetness durations, increased canopy air humidity, and/or enhanced dispersal of inoculum by splashing (Scherm and van Bruggen, 1995). Although overhead irrigation periods may be of short duration than rain, they may extend by a few hours the period of wetness resulting from dew, which also favours the spread of certain diseases. It therefore creates conditions under which the host is more prone to disease. These effects are also dependent on the time at which irrigation is applied. Rotem et al. (1970) found that the impacts of sprinkler irrigation on microclimate and potato late blight caused by Phytophthora infestans (Mont.) de Bary development under semi-arid conditions are reduced when sprinklers are operated in the afternoon or evening rather than in the morning, probably because sporangia of P. infestans are released in the morning and die under adverse conditions later during the day (Rotem and Cohen, 1974).

Sprinkler irrigation affects leaf temperature, and in a study done in Israel reported by Arnon (1972), it was found that when air temperature was 24 and 29 ºC in the morning and at noon respectively, the leaf temperature of unirrigated potatoes was 30 and 36 ºC, whilst that of the irrigated leaves was only 22 ºC. When the irrigation was stopped, leaf temperatures began to rise, but remained below that of unirrigated plants for the whole day. Sprinkler irrigation precipitation rate of 3 mm/h increased relative humidity and reduced temperature by 3 to 4 ºC, while the watering was taking place from 2 to 3 h (Recasens et al., 1998). Phytopthora infestans was found by Rotem et al., (1968) to develop profusely under overhead irrigation, whilst it did not occur with furrow irrigation. Cercospora leaf spot (C. personata) on peanuts reacted in the same way (Palti and Stettiner, 1959). Schwartz and Mohan (2008) reported that furrow irrigation resulted in almost complete eradication of the sour disease skin of onions caused by Pseudomonas cepacia, while sprinkler irrigation increased the disease development.

2.6 Date of planting Date of planting is another factor that determines the ability of a crop to either escape or avoid infection by pathogens (Cook and Baker, 1996). Planting either early or late can allow the host to pass through a vulnerable stage either before or after the pathogen produces inoculum (Altieri, 1998). As indicated by Delahaut et al. (2000) adjusting planting dates can contribute in reducing disease infection, and this can be achieved by planting early in the season. Early planting date reduced the incidence of Fusarium head blight (Subedi et al., 2007) and lowered ALS intensity in French bean (Sindham and Bose, 1980a). It reduced the severity of wheat leaf spots (Sudebi et al., 2007). Orawu et al. (2001) observed increased incidences and severities of cowpea diseases with delayed planting, which coincided with increased rainfall. Early planting delayed also Fusarium epidemic onset, slowered its rate of development, and reduced the final amount of disease (Navas-Cortés et al., 1998). On the other hand, several researchers obtained reduced disease infection with delayed planting: University of Nebraska (2000) with wheat crown and root-infecting fungi, Schlachter et al. (2000) with kidney and butter bean diseases, Bhatti and Kraft (1992) and Sugha et al. (1994) with wheat Fusarium wilt. As reported by Monted-Belmont et al. (2003), the influence of planting dates on the development of

22 sorghum grain mold varied with the highest incidence observed from 1 June to 13 July during three years of evaluation. Angular leaf spot infection and subsequent yield vary with planting dates or years (Sindham and Bose, 1980a; Rodriguez et al., 1999; Jesus et al., 2001) due to different climatic factors such as relative humidity and temperature. The periods of planting in summer season are January for High-Veld, December for Mid-Veld and November for Mid-Veld fringes (Wortman et al., 1998). Schlachter et al. (2000) proposed late planting of kidney and butter bean (late January through late February) in Zimbabwe to avoid excessive wet weather, which favours disease outbreaks.

2.7 Overview of modeling possibilities A model is a simplified representation of a system, for example, a pathosystem, which may include host and parasite populations, and vectors and their mutual interactions (Wilt and Goudrian, 1978; Rabbinge and de Wilt, 1989). The pathosystem may be affected by climate and man and it is part of a cropping system. A cropping system on the other hand not only includes crop protection aspects but also crop agronomic activities. Norton et al. (1993) defines a pest model as a mathematical, or at least computer-based, representation of a pest population, its development and mortality processes. It may also include a pest’s relationship with the crop or livestock host, and/or the processes involved in its control. A plant disease model can be a mathematical description of the interaction between environmental, host, and pathogen variables that can result in disease (Broome et al., 2002). A model can be presented as a simple rule, an equation, a graph, or a table. The final product of a modeling process is a prescriptive decision, which may be a mathematical solution, a numerical index of disease risk, predicted disease incidence or severity, and/or predicted inoculum development.

Model-based decisions are needed by agricultural decision-makers in order to reduce the time and human resources for analyzing complex decisions. Plant disease models can be used basically for simulation and forecasting. Although the two modeling approaches require the knowledge of the pathogen cycle, simulation requires more elaborate data than forecasting.

2.7.1 Simulation models As described by Rabbinge and de Wilt (1989), dynamic models describe the way in which a system changes over time. Descriptive dynamic models show the existence of relations between elements, but do not explain the relations. The development of dynamic models and the study of their behaviour is frequently called simulation. Kranz (1974) defined mathematical simulation as the logical assembly of knowledge of the interaction among pathogen, host and weather for calculating the course of epidemics.

Simulation is worthwhile if it increases knowledge of a system or if it leads to original experiments (Carter, 1986). Simulation models help in understanding pest population dynamics and damage, in improving research and development strategies and in providing a basis for pest management advice (Holt and Norton, 1993). Ways in which simulation models can provide a valuable contribution to pest management fall into the following categories: understanding pest population dynamics and damage (for example crop-pest simulation), improving research and development strategies (biological control: identification of the best agent, pesticide: investigation of the benefit of more persistant formulation of a synthetic pyrethroid for the control of Heliothis on cotton) and providing a basis for pest management advice (for example derivation of general rules). Simulation models are easy to construct, but as they frequently have many parameters they can be cumbersome to work with (Carter, 1986).

Examples of simulation models that have been developed all over the world on various crops and diseases include: SUCROS (Spitters et al., 1989), EPIDEM (Waggoner and Horsfall, 1969), EPIMAY (Waggoner et al., 1972), EPIMUL (Kampmeijer and Zadocks, 1977), EPIPRE (Zadocks et al., 1984; Zadocks, 1988) and SATSUMA (NECTAR, 1998).

2.7.2 Statistical models The basic aim of modeling is to provide a mathematical representation of the relationship between an observed response variable and a number of explanatory variables, together with a measure of the inherent uncertainty of any such relationship (Collet, 1991).

Statistical models including various forms of regression analysis determine the value of a response variable using a small number of explanatory variables (Norton et al., 1993), and consequently can be used in other models. They are essentially descriptive and as they are based on experimental observational data, may be described as empirical models (Collet, 1991). Therefore, they make no attempt to describe processes, although the terms included in the regression equation may say something about the nature of the relationship.

Statistical mathematical models can use linear multiple regression equations in systems analysis (Carter, 1986). Regression analysis assumes that the dependent variables data are independent of each other. With disease progression, the level of disease at time i is correlated with level at time i-1 owing to the cumulative process of the epidemics (Madden and Campbell, 1986). As stated by Butt and Royle (1974), multiple regression analysis (MRA) can be used to describe the quantitative relationships between time and successional observations of the dependent variable. In this case, independent variables, which are themselves measures of time and inevitably correlated with the dependant variable are used.

In multiple regression analysis, the investigator may want to explore the level of disease severity reached at one time during a season. The total disease present at the end of a season is analyzed in terms of biological and physical factors occurring before or during the development of the disease. The independent variables can be expressed for various periods in the same growing season. In this case, observations do not form a time series and there is no interest in forecasting disease progress per se. Models based on disease severity succeed only when leaf area is the same in the treatments that are being compared (Jesus et al., 2001).

The ability to measure the net effect of each independent variable makes MRA a valuable analytical method in epidemiology (Butt and Royle, 1974). However, the use of regression equations has some limitations as explained by Kranz (1974), and Butt and Royle (1974). Regression equations are empirical by nature, being the product of experiment and observation, and are based upon inductive reasoning from particular observed responses

(to controlled or uncontrolled variables). This is an inherent weakness of the MRA because the models make no provision for the effects of unexpected factors such as changes in the weather pattern, pathogen population or system crop management. The empirical equation does not explain all the variations of the disease.

2.7.3 Modeling for yield

Plant disease depends on the interaction of host, pathogen and environment, and yield is affected by disease incidence and severity. Common bean grain yield also depends on host characteristics and was defined as the product of number of plants (or fruitful axes) per unit area, number of pods per plant, number of seeds per pod and thousand/hundred seed weight (White and Izquierdo, 1991, Amede et al., 2004). Waggoner and Berger (1987) defined common bean yield (or dry matter) in a pathological way and highlighted the importance of disease severity by relating yield to the healthy leaf area index and to the radiation intercepted by the healthy area on a given day, both integrated over the growing season. This shows very well the relation between host components performance and the extent of disease incidence and severity in a given environment.

Disease-based models are based on yield triangle. Yield being a function of host, pathogen and environment, is therefore a function of disease, as supported by negative correlation between yield and amount of disease (Pegoraro et al., 2001; Wiatrack et al., 2004). Simulation models provide insight into the mechanism of growth and yield reduction and can be used into the quantification of yield reduction.

The common approach used is to model yield loss (Butt and Royle, 1974). However Jesus et al. (2001), while modeling angular leaf spot and rust, observed a lack of transfer of disease-crop-loss models to other seasons and locations. This was due primarily to the great variation in the base yield of a disease free-crop and the weak or indirect relationship between yield and disease. In this situation the use of crop yield instead of yield loss may present an advantage. Another problem in modeling yield loss is the appearance of partial regression coefficients with a negative sign as observed by James et al. (1972) and Burleigh et al. (1972). Those coefficients indicated in the case of Burleigh

26 et al. (1972), that either the net effect of the disease was to increase grain weight, or that of other factors positively correlated with this independent variable, and this can be detrimental to grain yield. In the study of James et al. (1972), those coefficients were omitted arguing that such variables have no logical place in a model which estimates crop loss.

Multiple-point disease-based models can be used for studying a single disease (NECTAR, 1998). With multiple-point models, the injury is measured at several dates or stages. Those models use multiple linear regression equations, and in general three assessments of the disease are required. Compared to single variable models (R2 range of 0.04 to 0.20), multiple-point regression models (R2 range of 0.51 to 0.80), using several assessments dates as independent variables, were more suitable for the explanation of variation in yield attributable to late blight severity (Olanya et al., 2001).

2.7.4 Decisions in disease management

Measuring or predicting the effects of diseases leads to important decisions for disease management. Four types of decisions are considered by Savary et al. (2006): . Tactical short-term decisions Short-term strategic decisions are made during the crop growing season and include: the choice of crop (which can be established as a mono-species or as interspecific association), the choice of tillage, and the choice of genotype (single or several genotypes) (Zadocks and Schein, 1980). Other choices are: timing of crop establishment, the type of seed treatment used, the sowing density, and the spatial pattern of plants. . Strategic short-term decisions are made between-seasons (for example preplanting for annual crops). . Strategic long-term decisions include for example the design of a breeding program and the developing of Integrated Pest Management (IPM) strategies within domains. . Very long-term decisions pertaining to research prioritization.

2.7.5 Forecasting

Disease forecasting is about defining the conditions under which a pathogen, when in contact with a susceptible host, can infect and become established (Hardwick, 1998). Forecasting provides an indication of when the disease is likely to go ‘critical’ and has an economic impact. In the development of forecasting models, data must be available on the crop and its development through the season, the pathogen and the interactive effects of the environment on disease development. In forecasting models, the process of model construction is deductive and data dependent (Hau, 1990). The model is constructed using certain assumptions concerning the nature of the system and expresses the interrelationships between the occurrence of the disease and the environmental conditions (Manners, 1993). These predictive models are mainly used in operational diseases forecasting, as they do not require many parameters. The aim of devising a forecasting scheme is to define a set of conditions that, when satisfied, would lead to an outbreak of the disease. Short range disease forecasting is usually aimed at predicting when a pathogen, which is already present, will develop to epidemic proportions. The development and implementation of disease management systems often utilize models for the environment and the pathogen, and a few schemes consider the growth and development of the host. The local climate and the microclimate conditions within the plant canopy are usually used in developing pathogen management systems (Zhang et al., 2005; Ghini et al., 2012).

In disease forecasting systems based on the amount of initial inoculum, the predictions of disease levels for a season are made based on the number of pathogens or vectors that survived from the previous season. In systems based on weather factors, predictions are made on the occurrence of favourable weather conditions for the development of the disease. Positive/negative forecasters are based on the assumption that damaging out breaks are/are not likely to occur (Tainter and Baker, 1996). Accurate positive pre-planting forecasters are obviously of great value but they are not always feasible (Mclean et al., 1986).

There are numerous specific forecasting systems published worldwide on a wide range of

28 crops, pests and pathogens. The success of a forecasting system depends on its adoption by farmers in view of specific and tangible benefits from using it. Attributes that will ensure the success of a forecasting system include reliability, simplicity, importance, usefulness, availability, applicability to several diseases and cost effectiveness (Campbell and Madden, 1990). All the models are trying to interpret the biology of the pathogen in the context of conditions that affects its development, survival and ability to colonize the host (Hardwick, 1998). Biological processes being in a constant rate of flux, it is unlikely that all eventualities will be covered by even the most complex model. This is an important constraint, because if a model is too complex it may be impractical, particularly if it depends on information supplied by farmers.

Several forecasters were developed all over the world on various crops and diseases. The model TOM-CAST was developed by Pitblado (1988) to forecast early blight, Septoria leaf spot and anthracnose development on tomato crop. Potato late blight forecasting models were developed by different researchers (Beaumont and Stanilund, 1933; 1934; 1937; Bourke, 1953; Smith; 1956; Lutman, 1991) and the most known models actually include BLITECAST (Hyre, 1954; 1955; Wallin, 1962; Krause et al., 1975), NegFry (Fry et al., 1983; Hansen et al., 1995; Schrödter and Ulrich, 1965; Grünwald et al., 2000), PhytoPRE (Forrer et al., 1993) and Prophy (Schepers, 1995).

2.8 Implication of literature review key issues in the conception of the study It is known that P. griseola is seedborne (externally and internally) and that dormant mycelium in the seeds can remain viable for more than one year, with consequent decreasing infection with time of conservation (CMI, 1986; Richardson, 1990; Seattler, 1994). Little is known about the seedborne status of the pathogen and its variability in seed lots under local SH conservation conditions.

Angular leaf spot attacks all the aerial parts of the plant (leaves, stems, petioles, pods) and seeds. There might be absence of correlation between the disease observed on pods and the subsequent level of seed infection, and little is known about the relation between the infection of aerial parts and seed.

Control methods proposed for ALS include the use of early and late planting dates supplemented partially or totally by irrigation systems such as sprinkler and furrow irrigation. Diverging results on the effect of planting dates on disease infection, development and progress were reported by different researchers depending on the nature of the disease and local environmental conditions. Consequently planting early might increase or decrease the disease. Less disease might lead to yield advantages, but the trend is not always followed. Planting early might require irrigation in the beginning of the season and planting late at the end of the season, in addition to irrigation due to dry spells in the growing season. The effect of supplementary irrigation is related to prior effect of rain, which is more conducive to disease. Sprinkler irrigation is assumed to be less conducive to diseases than rain, but more than furrow irrigation, which wets a small portion of the field. Sprinkler irrigation is affected by microclimatic conditions, which can preclude the survival and spread of the inoculum or create conditions under which the host is more prone to diseases. But, yield may not follow the same trend. There seems to be a lack of consistency of the main effects of planting date and irrigation systems on disease and subsequent yield. There is also little information about their combined effect on the same variables. Specifically ALS disease effects and subsequent seed yield have been reported to vary with planting dates across different environments and the use of supplementary irrigation systems on the disease and seed yield is not well documented, especially in the local context. Also little is known on how these factors interact in the control of ALS.

CHAPTER 3: ASSESSMENT OF QUALITY OF FIELD BEAN SEEDS HOME-SAVED BY SMALLHOLDER FARMERS

3.1 Introduction The use of home-saved, untreated seed in the smallholder-farming sector is a common practice (Chiduza, 1994; Chinwada, 1994; Hansen, 1995). As a result of this practice there are concerns about seed quality and health. Seed quality is the total sum of many seed attributes like genetic purity, moisture content, mechanical damage, viability and vigour, size and appearance. Health of seed refers to the presence or absence of disease- causing organisms, such as fungi, bacteria and viruses, and animal pests, such as eelworms and insects (International Seed Testing Association (ISTA), 1999; 2014).

Micro-organisms may be associated internally or externally with the seed or as concomitant contamination as sclerotial bodies, fungal bodies, nematode galls, infected plant parts and soil particles mixed with the seed. As reported by Bhale et al. (2001), seed health testing is important because: (i) seed may harbour a virulent strain of the disease pathogen, (ii) the presence of the pathogen in the seed implies the earliest possible establishment of the infection in seedlings, (iii) pathogen-free soil may be infected as a result of planting infected seeds thereby increasing the soil-borne inoculum which may eventually affect subsequent crops raised from healthy seed, (iv) new physiological races/strains may be introduced with the seed so that varieties resistant to endemic races of the organism become affected, and (v) it is important to know the health status of seed lots and to keep their quality under storage and consequently their planting value. Seedborne pathogens assume a great importance to the seed industry from the point of seed, meant for sowing and seed in trade. Given the practice of using retained seed, there is always a possibility that seedborne diseases get transmitted to the next crop every year. Physiological conditions such as trace elements deficiencies may also contribute to poor seed quality.

The common bean is planted in Zimbabwe three times during summer. It is sown early during October-November, mid-season in December, and late from January onwards. (Kutywayo, 2000). Some farmers plant the bean crop in small gardens under irrigation 31 from September to November. The cream-coloured types, which include Sugar (sweet) beans, account for 10 % of bean production in Africa (Wortman et al., 1998). In Zimbabwe, these seed types are of high and moderate importance in the mid- and highveld growing areas, respectively. Natal sugar is the commonest cream-coloured variety preferred by farmers in Chinyika Resettlement Area (CRA) of Zimbabwe (Kutywayo 2000). The variety was described as Type II indeterminate upright cranberry bean (Mariga and Munetsi, 1988), with oblong, medium to large seeds (45 - 55 g/100 seeds) and having a yield of 1544 kg/ha. The seed has a red variegated pattern against a cream background (Voysest and Dessert, 1991).

Annual production losses in world bean production as a result of disease averages about 10 % (Hall, 1994). Fungi cause most bean diseases (Hall, 1994), and more than 50 % of the major bean diseases are seedborne (Schwartz and Gálvez, 1980). Infection of seedborne pathogens results in seed rots, seedling decay, pre and post emergence mortality and abnormalities (Shetty, 1992; Barathi et al., 2013). Therefore seedborne diseases affect seed germination, seedling emergence and vigour, and initial stand establishment in a negative way. Diseases at various stages of crop growth result in leaf spots, leaf blights, stem rots, discolourations and fruit infections (Small and Whiting, 2013).

Infected seed is considered as a main origin of primary inoculums, and an essential determinant of short and long disease spread (Saettler et al., 1995). Sengooba (1976) recovered P. griseola infection in 15 - 22 % of seedlings raised from infected seeds. Seedborne diseases carry over the infection across seasons, cause poor crop establishment and low yields. Damages due to seedborne pathogens affect seed formation, growth, final size and viability (Shetty, 1992; Maude, 1996). Seed quality adversely affects crop establishment and the capacity to realize yield potential. Therefore, healthy and pathogen-free seeds should be able to germinate and give rise to vigorous plants with high yielding capacity. Alternaria alternata (Fr.) Keissel, Colletotrichum lindemuthianum (Sacc. & Magnus), Fusarium oxysporum Schelchtend.: Fr. f. sp. phaseoli J.B. Kendrick & W.C. Snyder, Macrophomina phaseolina (Tassi) Goidanick,

Pseudocercospora griseola and Phoma exigua var. exigua Sutton & Waterston, were reported as common bean seedborne diseases by Mathur and Kongsdal (2001). According to Alberta (2008), Fusarium solani [Mart.] Sacc. f. sp. Phaseoli [Burk.] W.C. Snyder & H.M. Hans. does not infect the common bean seed but the spores may be carried in dust, on seed, or in seed bags. But Gupta and Saharan (1973) and Tseng et al. (1995) considered the pathogen as seedborne. Rhizoctonia solani Kühn is considered as seed contaminant (Neergaard, 1979), was identified on bean seed by Mathur and Kongsdal (2001) and reported as bean seedborne fungus by Godoy-Lutz et al. (1996). Cercospora canescens Ellis & G. Martin was identified on common bean seed by Dhingra and Asmus (1983), Buruchara (1990) and Kutywayo (2000), and reported as bean seedborne fungus by Dhingra and Asmus (1983) as well as Zhang and Pernezny (2003).

Fusarium solani (Fusarium foot rot or dry rot), and Rhizoctonia solani (Rhizoctonia root rot) attack the subterranean parts of the common bean plant. With severe attacks, yield reductions due to Fusarium solani can reach 95 % (Anonymous, 1941). Yield losses of 5 to 10 % are common with Rhizoctonia solani, but can go up to 60 % (Hagedorn and Inglis, 1986; Hagedorn, 1994). Losses of 12 % due to Alternaria alternata (Alternaria leaf and pod spot) were reported by Tu (1994) in snap beans whereas, yield losses of 10 - 50 % due to Pseudocercospora griseola (angular leaf spot) in northern US and up to 80 % in tropical and subtropical countries have been reported by Celetti et al. (2005, 2006) on snap bean. Colletotrichum lindemuthianum (anthracnose), on the other hand, can cause losses of up to 100 %, if contaminated seed is planted and prolonged conditions favourable to disease development occur during the crop cycle (Schwartz, 1994a). Yield losses of 41 - 75 % have been reported with Phoma exigua (Ascochyta leaf spot) by Schwartz et al. (1981) and Schwartz (1994b). A minimum of 10 % yield loss was reported by Salgado et al. (1995) for Fusarium oxysporum (Fusarium yellows). The fungus attacks the plant vascular tissues, which become reddish brown, and pods. Yield losses due to Macrophomina phaseolina (charcoal rot, ashy stem blight) can reach 65 % (Zaumeyer and Thomas, 1957). Cercospora canescens (Cercospora leaf spot and blotch) is a minor disease (Dhinhgra and Asmus, 1983; Schwartz, 1994c).

In Chinyika Resettlement Area (CRA), Zimbabwe, the common bean is infected in the fields by different seedborne fungi such as Pseudocercospora griseola, Macrophomina phaseolina, Colletotrichum lindemuthianum, Rhizoctonia solani, Phoma spp., Fusarium spp. and Alternaria alternata (Manyangarirwa, 2001). Based on the percentage of growers with fields infected by specific pathogens the most frequent under smallholder conditions are suggested to be Ascochyta phaseolorum (85.4 %), Cercospora canescens (81.9%) Macrophomina phaseolina (48.2 %), Colletotrichum lindemuthianum (44.7 %), and Fusarium solani (31.6 %) (Kutywayo, 2000). However, the impact of using retained seed at each planting remains unknown.

The objectives of the study were: 1) To gather information on the different sweet bean types grown by smallholder farmers in north-eastern Zimbabwe. 2) To assess seed quality of the sweet bean types collected from these smallholder farmers. 3) To identify the major bean seedborne fungi present on retained seed and to determine infection levels present in the seed.

The study sought to test the following hypotheses: a) There is a variation in the degree of acceptability of bean types grown by farmers. b) Sweet bean seed samples differ in quality and farmers producing good quality seed can be identified. c) Sweet bean seed is infected by different fungi with varying levels of association, seed infection and spread.

3.2 Materials and methods 3.2.1 Study area The field study was conducted in CRA (18º10’S and 32º17’E)] in the north-eastern district of Makoni, Manicaland Province (Figure 3.1). The area has 112 villages and covers 121 280 ha (Nyakanda, 2003), and landholdings vary in size, but on average two hectares per

34 family. The CRA also spans three Natural Agro-ecological Regions; II, III and IV with mean annual rainfall of 760 - 1000 mm, 650 - 760 mm, and 450 - 650 mm, respectively. The area has a subtropical climate with three distinct seasons. The dry winter is from May to August and temperatures range from 7 to 21 ºC. Winter is followed by a dry hot season covering the months September to November, when mean temperatures reach 28 ºC. The rainy season starts in mid November and ends in late March to early April and temperatures range between 19 and 21 oC. The length of the rainy season varies from year to year, but in general, the area is subjected to mid-season dry spells. The soils of the area are medium grained sandy loams. For the study, three cluster areas of Bingaguru (1670 masl, Region IIa), Chinyudze (1300 masl, Region IIb), and Gowakowa (1250 masl, Regions III and IV) were used.

3.2.2 Seed collection Households were selected using simple random sampling without replacement, from a list of farmers of the three cluster areas using random numbers technique. A household was identified by its cluster area, village and stand. Seed was then collected by five trained surveyors. During the collection exercise, they asked the farmer the names of the varieties grown and his/her source of bean seed (merchant, home-saved and others). The collection was first done with 10 farmers, and then extended to 88 others. The collection exercise was done in November 2002, during the planting period. Each representative of the household (farmer) exchanged 500 g of his own home-saved sweet bean with seed of Natal Sugar which was purchased from seed-houses. From the 98 households surveyed, 82 (83.6 %) had bean seed. The number of villages and households in the cluster areas where bean seed was collected are shown in paranthesis and these were, respectively: Chinyudze (6, 38), Gowakowa (4, 34) and Bingaguru (4, 10). The collected home-saved bean seed was then used for seed quality and health assessments. After removal of impurities and off-types the collected seed was visually separated into two categories: bad and good. The bad seed category represented poor seed with wrinkles and/or discolouration on the seed coat. On the other hand good seed represented pure seed (all seeds are same variety and size), with no discolouration, wrinkles or cracking on the seed coat.

: Area surveyed

Figure 3.1: Location of Chinyika Resettlement Area, Zimbabwe. Source: Nyakanda (2003)

3.2.3 Identification of fungal pathogens Compared to agar method, the blotter method is deemed cheaper (use of filter papers), easier (does no require asceptic techniques), and reduces the probability of contamination of plates by profuse growth of saprophytes and external microbes, which can interfere with identification of pathogens (Seattler et al., 1995; Kutywayo, 2000). The two methods are identical in major fungi species identification (Kutywayo, 2000). The blotter method was then adopted for testing the presence or absence of a specific organism. The method involves incubating on well sterilized water-soaked blotters (filter papers), and incubated usually for 7 days at 20 - 25 ºC under alternating cycles of 12 h light [near- ultra-violet (NUV)] and 12 h darkness (Mathur and Kongsdal, 2001; Bhale et al., 2001). After incubation, fungi that developed on each seed were examined under different magnification of a stereomicroscope Wild Heerbrugg M3B and identified. The identification of the fungi was based on the way they grow on seeds (habit characters), and on the morphological characters of fruiting bodies, spores/conidia observed under a compound microscope. All the testing operations in this study were carried out under a sterilized cabinet. No seed surface sterilization was applied so as to identify also the seed contaminants and externally seedborne fungi.

Ten seeds were picked up with a sterilized pair of forceps and plated on four circular blotter papers pre-soaked in water and arranged in a Petri dish. Seeds were plated equidistant to each other with nine seeds in the outer ring and one in the centre. The testing unit was the Petri dish and there were five replicate Petri dishes per sample. This test was run four different times, giving a total of 200 seeds tested per sample. Petri dishes were arranged in trays and on shelves in the incubation room at the Mazowe Plant Quarantine Seed Laboratory. Seeds were incubated for 7 days at 22 ºC under 12 h alternating cycles of NUV light and darkness. At the end of the incubation period, each seed was examined under a stereomicroscope Wild Heerbrugg M38 at X6.4 to X40 magnification for the growth of fungi. Fungi found associated with the seeds were carefully examined and identified based on habit characters or by preparing a slide of fruiting structures, which were observed under a compound microscope at magnifications of X10 to X40 objective lens, following Mathur and Kongsdal (2001), descriptions by the

CMI (1986), Seung-Hunyu et al. (1982), Kulshrestha et al. (1976) and Nath et al. (1970).

Rhizoctonia solani produces thick brown mycelium which spreads from the seed to the filter paper and may cover the entire Petri dish, therefore a slide preparation of the mycelium under higher magnification of the compound microscope would show the characteristic perpendicular branching pattern of the hyphae. Observation of conidia and spores were done for other fungi under the same magnifications of the compound microscope. Whenever identifiable growth of a fungus was seen on a seed, the respective seed was considered infected even if only one fructification was observed. Infected seed was marked by writing on the blotter paper a code for the infecting fungi. Seeds infected with different fungi in different Petri dishes were counted and their numbers and type of infecting fungi recorded. Disease incidence was quantified by calculating the percentage of seeds infected by a pathogen.

3.2.4 Data analysis Data were explored and summarized using the Statistical Package for Social Sciences (SPSS 17) and Minitab 16.

3.3 Results 3.3.1 Seed types, sources and quality The different bean types cultivated in CRA are shown in Figure 3.2a. Sweet bean type was cultivated by 84.1 % of the farmers surveyed. The representation of the other bean types ranged from 1.2 % (Brown types) to 20.7 % (White types). This confirms the preference for sweet bean type in this region. The percentage of farmers who used home- saved, merchant and seed from other farmers was 73.2, 22.0 and 3.7 %, respectively. This indicated that home-saved seed was the most important seed source for the farmers. The observed seed quality parameters are summarized in Table 3.1. The mean proportion of impurities was estimated at 0.6 %, and reached a maximum of 4.5 %. The percentage of farmers who had clean seed and less than 1 % impurities in their seed was estimated at 4.9 and 19.0 %, respectively. On average 3.7 % of the farmers had pure sweet bean seed, without any adulteration from the other types. Most farmers (96.3 %) kept their

38 seed as a mixture of the main cream component with varying levels of the other seed types (0.4 - 69.5 % and a mean of 5.5 %). For an estimated 85.4 % of the farmers the percentage of bad seed varied from 1.0 to 48.0 %, with a mean of 12.0 %. Four bad seed classes were identified using the characteristics of the boxplot as their limits: minimum (0.0 %), first quartile (3.0 %), median (8.0 %), third quartile (15.0 %) and the upper limit (33.0 %) (Figure 3.2b). The proportion of farmers who had clean (0 %) and less than 10 % of bad seed was 14.6 and 61.0 %, respectively. Sweet bean seed collected covered a range of seed sizes with 100 seed weight ranging from 18.4 to 52.8 g, with a mean of 31.1 g.

3.3.2 Seed health The levels of seedborne fungal pathogens observed in sweet bean seed samples collected in CRA are presented in Table 3.2. Although the pathogen Pseudocercospora griseola was not identified; eight other seedborne fungi were identified in the seed samples collected. Considering the percentage of sweet bean samples infected by the different seedborne fungi; F. oxysporum 73.2 %, A. alternata 70.7 % and C. lindemuthianum 51.2 % were the most frequent. The other fungi were less frequent with percentages of seed samples infected varying from 34.1 % for M. phaseolina to 6.1 % for R. solani. Despite the mean seed infection levels varying from 0.8 % for C. canescens and M. phaseolina to 2.2 % for F. oxysporum and A. alternata, some high values per farmer were observed with F. oxysporum (20.5 %), C. lindemuthianum (14.5 %) and A. alternata (13.5 %). An estimated 3.7 % samples had seed without any seedborne fungus infection, and 96.3 % had 1 to 6 fungi associated per seed sample with a mean of 3.0.

3.4 Discussion Sweet bean type was the most preferred cultivated by 84.1 % of farmers surveyed. The observation that sweet bean type is the most cultivated in eastern Zimbabwe is supported by Kutywayo (2000) who reported that the cultivar Natal Sugar was the most preferred by smallholder farmers in the area. Home saved-seed was the most important source of seed used by 73.2 % of farmers. The indication that home saved-seed is the major source

90 Sweet White Red 80 Black Purple Yellow 70 Carioca Grey Brown 60 50 40

Growers (%) Growers 30 20 10 0 a

Figure 3.2: Sweet beans percentage of bad seed and its preference by farmers in Chinyika Resettlement Area: (a) Farmers growing different varieties expressed as a percentage of the total surveyed (98), (b) Spread of the percentage of bad seed identified from 82 farmers seed samples collected in CRA in November 2002/3.

Table 3.1: Characteristics of sweet bean types collected from Chinyika Resettlement Area in November 2002/3 season (n = 82).

Characteristic Frequency ( %) in Mean per Range of samples sample sample Impurities (%w/w) 95.1 0.6 0.0 - 4.5* Off types (%w/w) 96.3 5.5 0.4 - 69.5 Bad seed (%w/w) 85.4 12.0 1.0 - 48.0 100 seed weight (g) 100.0 31.1 18.4 - 52.8 Pathogens number/seed 96.3 3.0 1 - 6 *: Rounded to nearest decimal, w: weight

Table 3. 2: Levels of seedborne fungal pathogens in sweet bean seed samples collected from 82 farmers in Chinyika Resettlelement Area in November 2002/3 season.

Pathogen Frequency of Infection range in Mean infection per detection in samples infected samples infected sample (%) tested (%) (%) F. oxysporum 73.2 0.5 - 20.5 2.2 F. solani 23.2 0.5 - 5.0 1.2 A. alternata 70.7 0.5 - 13.5 2.2 C. canescens 18.3 0.5 - 1.0 0.8 C. lindemuthianum 51.2 0.5 - 14.5 1.6 P. exigua 13.4 0.5 - 3.5 1.0 M. phaseolina 34.1 0.5 - 2.5 0.8 R. solani 6.1 0.5 - 1.5 0.9 of seed used by farmers in CRA is corroborated by Chiduza (1994) and Chinwada (1994). Hansen (1995) also reported that in Silobela communal area of Zimbabwe, 90 % of the seed planted is derived from home-saved seed by farmers, and 10 % of the farmers got their seed through barter trading with neighbours. In this study, 22 % of farmers used merchant seed, while 3.7 % obtained their seed from other farmers. However, the bulk of the sweet bean seeds collected were mixed with up to six varieties of the other bean types. Farmers may actually be exposed to management of both pure varieties and mixtures. They can also maintain the main component of the mixture by careful selection. Off-types include seeds from natural outcrossing which is less than 5 % for common bean (Graham and Ranalli, 1997). These off-types could be used to develop new varieties by careful selection. Martin (1984) observed a maximum outcrossing rate of 2.23 % and postulated that a low level of outcrossing exerts a profound impact on the generation and

41 maintenance of variability in the Malawian bean landraces. Since P. griseola was not identified in the seed samples from CRA farmers, angular leaf spot source of inoculum was probably largely environmental. The inoculum would come from freshly infected soil, infected straws, off season crops, volunteer plants (Sengooba and Mukiibi, 1986), air, rainfall and irrigation water. The association with unfavourable weather conditions might have resulted in lower incidence and severity of the disease in the area and consequently lower seed infection. The pathogens identification tests revealed that F. oxysporum, A. alternata, and C. lindemuthianum were more frequent than the other five fungi. These results are in general agreement with those from different studies done elsewhere on seedborne fungi in the genera studied. These fungi were reported to infect collected seed lots as follows: Alternaria spp. 55.8 - 85.0 % (Neergaard, 1988; Sivapalan and Browning, 1992; Tseng et al., 1995), Fusarium spp. 18.0 - 81.0 % in general [with F. oxysporum affecting 2.6 - 69.2 % and F. solani 3.2 - 26.9 % (Buruchara, 1990; Neergaard, 1988; Richardson, 1990; Tseng et al., 1995; Manyangarirwa, 2001], Colletotrichum lindemuthianum 3.9 - 50.0 % (Neergaard, 1988; Buruchara, 1990; Richardson, 1990; Manyangarirwa, 2001), Phoma spp. 31.6 - 70.0 % (Hewett, 1971; Buruchara, 1990; Manyangarirwa, 2001), Cercospora spp. 3.9 - 32.0 % in general with C. canescens infecting 13.5 - 32.0 % (Dhindra and Asmus, 1983; Buruchara, 1990), Macrophomina phaseolina 2.0 - 43.8 % (Buruchara, 1990; Manyangarirwa, 2001), Pseudocercospora griseola 26.3 % (Manyangarirwa, 2001); Rhizoctonia solani 3.9 - 10.5 % (Buruchara, 1990; Manyangarirwa, 2001).

The mean infection per infected sample was generally low for all the pathogens evaluated. Higher infection levels were obtained in other environments and/or on large- scale collections. Mean infection by C. lindemuthianum was reported to be in the range 1.8 - 50.0 % (Richardson, 1990; Manyangarirwa, 2001;), Fusarium spp. in general 3.8 - 18.0 % (Tseng et al., 1995; Manyangarirwa, 2001;), Fusarium solani 76.0 % (Gupta and Saharan, 1973); Alternaria spp. 24.0 - 61.1 % (Sivapalan and Browning, 1992; Tseng et al., 1995), Rhizoctonia spp. 1.0 - 6.1 % (Tseng et al., 1995; Manyangarirwa, 2001), P. griseola 7.2, M. phaseolina 3.1, and Phoma spp. 1.7 % (Manyangarirwa, 2001) and C. canescens 13.5 % (Dhingra and Asmus, 1983).

The results also indicated that some farmers were able to achieve good seed purity and low levels of seed infection. Seed companies might train those farmers who can complement their effort in good quality seed production. The quality standard for bean seed was fixed at 10 % (David, 1998) for faded (discoloured) seed and 1 % for foreign matter (cleanliness). A maximum tolerance level of 1 % (Schwartz and Gálvez, 1980) was deemed acceptable for any pathogen for seed produced in tropical environments conditions, marginal for successful clean seed production. The respective tolerance levels for some specific fungi are: Alternaria spp. 5:100 seeds (Anonymous, 1985), Cercospora sojina 4:100 [The United States Department of Agriculture (USDA)-Animal and Health Inspectorate Services (APHIS), 1986], Colletotrichum lindemuthianum 2:1000 seeds (Tunwar and Singh, 1988), Fusarium spp. 4:100 (USDA-APHIS, 1986) and Phoma exigua 1:100 (Anonymous, 1985). In this study some of the seed batches studied had infection levels below the accepted tolerance levels. It is therefore possible to produce disease-free seed in the smallholder-farming sector.

3.5 Conclusion

Sweet bean was the most preferred type by the smallholder farmers. Most farmers used home-saved seed, which in general had different levels of seedborne fungi infection. Since some of them were able to produce seed with good purity and low levels of infection, the production of improved seed by farmers could be promoted. Farmers may be exposed to the advantages of using clean and graded seed and be trained in producing improved seed. The preferred sweet bean type is susceptible to seedborne fungi, mainly F. oxysporum, A. alternata and C. lindemuthianum. There is a need to promote farmer friendly protocols that-test bean seed for seedborne fungi, to minimize their frequency. This study gives clear indications on bean seed quality and health under smallholder farmer conditions. There is, however, a need for wider evaluation of bean seedborne fungi in all agro-ecological zones in relation to growing seasons and planting periods using other methods along with the blotter method.

CHAPTER 4: STUDIES ON SOURCES OF INOCULUM OF PSEUDOCERCOSPORA GRISEOLA

4.1 Introduction The main sources of inoculum for angular leaf spot (ALS) disease are freshly infected soil, infected straws, infected seed, off season crops and volunteer plants. The fungus (Pseudocercospora griseola) can survive in the soil for only two months, under indoor and outside conditions for four to six months, and in infected crop debris for a maximum of nine months (Sengooba and Mukiibi, 1986). Dormant mycelium of the fungus in the seeds remains viable for more than one year (Commonwealth Mycological Institute (CMI), 1986). The main host for the fungus is Phaseolus vulgaris, but a number of other Leguminosae plants have been recorded including Desmodium cephalotes Wall., D. gangeticum (L.) DC., D. pulchellum Benth., Lablab purpureus (L.) Sweet, L. niger Medik., Lathyrus odoratus L., Macroptilium atropurpureum (DC.) Urb., Phaseolus acutifolius A. Gray, P. coccineus L., P. lunatus L., Vigna angularis (Willd.) Ohwi & H. Ohashi, V. mungo (L.) Hepper, V. umbellata (Thumb.) Ohwi & Ohashi, V. radiata (L.) R. Wilczek, V. sinensis (L.) Savi ex Hausskn., V. unguiculata L. (Walp.), Pisum sativum L. and also okra (Abelmoschus esculentus (L.) Moench) (Malvaceae) (CMI, 1986; Crous et al., 2006). These sources serve as the main survival sites (Sohi and Sharma, 1967; CMI, 1986).

Generally, spores are produced in high numbers by fungi under natural environments, and fungal spores are amongst the most commonly encountered airborne particles (Wong, 2003; Celenk et al., 2007). After their formation, some spores are released and transported by air before landing at a susceptible substrate to start infection, if viable. During the transport phase, airborne pathogen inoculum can be trapped and detected. In nature, spores are always a component of bioaerosol which is why their occurrence in the air has been frequently studied (Adams, 1964; Larsen, 1981; Nikkels et al., 1996; Diaz et al., 1998). Studies on the occurrence and spread of fungal conidia and spores in the air were concentrated on taxa that caused plant diseases, and these results have practical application in agriculture (Hirst, 1991; Diaz et al., 1998; Frenguelli, 1998). In addition,

44 aerobiology has put more emphasis on fungal spores contributing to respiratory diseases and allergic reactions in humans (Isaac, 1996). Airspora fungi such as Penicillium, Aspergillus, Cladosporium and Alternaria have been studied (Ingold, 1971; Tang, 2009), but little is known about Pseudocercospora griseola. Both rain and wind appear to liberate and disperse spores of P. griseola (Cardona-Alvarez and Walker, 1956). Generally, transportation of spores released to the air is blocked by the laminar flow and the crop boundary layers (Natura-European Community Training Programme of Agricultural Universities in southern Regions (NECTAR), 1998). Consequently, an escape fraction of only 10 % of the total spores released from a crop plant is able to reach the upper parts of the atmosphere and will be subjected to transportation (Gregory, 1973). It appears that, contaminated water received from rainfall and applied through irrigation can be a potential source of inoculum. It can bring to the crop the inoculum from the crop canopy and soil surface through water splashes. It is able to wash out spores present in the air and deposit them on the crop. But little is known about the levels of infection of rainfall and irrigation water by P. griseola. It was reported by Sartorato et al. (2005) that ALS epidemics start from spores that come from outside the bean field. The most important environmental factors that are associated with the composition of the airborne fungal spora include; relative humidity, precipitation (rainfall, dew and fog deposition), temperature and wind (shear, gustiness, speed and direction) (Li and Kendrick, 1994; World Meteorological Organization (WMO), 2010). Therefore, aspects of inoculum source and quantification might be important in the study of events appearing in brief periods of time in the disease cycle (infection, incubation, sporulation and dispersal of the pathogen) in terms of the environmental factors occurring within the same time periods (Butt and Royle, 1974).

The objectives of the study were to quantify Pseudocercospora griseola 1) Soilborne inoculum by analysis of soil samples collected before planting and after harvesting a susceptible variety (Natal Sugar) using the direct method. 2) Airborne inoculum by counting spores caught daily by a locally made trap using slides, and weekly by a Burkard seven day recording volumetric spore sampler using a compound microscope.

3) Waterborne (rainfall and irrigation) inoculum collected weekly by two rain gauges installed in the field for both sources and from irrigation water source using the direct method.

The hypotheses tested for this study were: a) Pseudocercospora griseola inoculum can be quantified from the soil since infected soil and straws are known to be sources of inoculum. b) Pseudocercospora griseola inoculum can be quantified from the air since a fraction of spores from crop plants can be transported by air before landing on susceptible plant surfaces and starting infection. c) Pseudocercospora griseola inoculum can be quantified from contaminated rainfall and irrigation water.

4.2 Materials and methods 4.2.1 Field evaluation The study was carried out in Harare, Zimbabwe (latitude: 17o49’S, longitude: 31o04’E) at the University of Zimbabwe campus in the Crop Science Department field. The site has fersialitic red clay soil. It is situated in the highveld area, Natural Region II. The region lies between 1000 and 1830 masl, and is characterized by moderate rainfall averaging 750 - 1000 mm per annum with an average of 16 to 18 rainy pentads, mean temperatures of 21 - 27 ºC and relative humidity of 42 - 75 %. These conditions are conducive for high disease pressure during summer.

Glyphosate (10 ml/l water) and Paraquat (20 ml/l water) were applied for weed control two and one week before planting, respectively, in 2009/10 and 2010/11 summer seasons. The land was prepared to a fine tilth with hoes in the first year, but no tillage was done in the second year. Two different bean trials with 25 varieties each, received from the Crop Breeding Institute, were established in a 69 m x 12 m field in 2009/10 and 2010/11 summer seasons. In both seasons, the trials were laid out in a 5 x 5 triple lattice with three replicates. The variety Natal Sugar (N Sugar), susceptible to angular leaf spot, was planted in the border area. The experimental varieties were planted on 8 January 2010

46 and 7 January 2011 in moist soils. Each plot consisted of three rows, 2 m long with inter-row spacing of 0.5 m. Seeding rate was 33 seeds/m2, and thinning was done two weeks after crop emergence (WACE) to achieve a population of 222 222 plants/ha.

Compound D (7 % N, 14 % P2O5, and 7 % K2O) was applied as basal fertiliser to all plots at 300 kg/ha. The crop was top dressed with ammonium nitrate (34.5 % N) at a rate of 200 kg/ha at 4 WACE. Weeds were controlled through hoeing at the second and fifth WACE.

Disease severity was evaluated in the trials weekly from crop emergence to the start of maturity using a 1 - 9 visual scale (Schoonoven and Pastor-Corrales, 1987). The assessment was done by randomly choosing 12 plants from each plot and visually scoring the area infected of three randomly chosen leaves (lower, middle and upper leaves).

The variety, Natal Sugar was planted and managed in the border area the same way as the experimental varieties. Twenty seven plots (1.5 m x 2 m) were randomly selected from the border area each year and a total of 15 randomly selected plants per plot were scored weekly for ALS spot severity from crop emergence to the start of maturity.

4.2.2 Environmental data The quantity of 9.6 mm/h of water was applied during 2 h in 2009/10 and 3.5 h in 2010/11. Rainfall and overhead irrigation water were captured using two rain gauges mounted in the middle width line of the field, respectively at 1/3 and 2/3 of its length, and mounted at the height of 1 m above the ground. Rainfall and irrigation water were collected on rainy and irrigation days and measured using a graduated cylinder. Data on temperature (ºC), relative humidity (%) and wind speed (m/s) were collected from Rattray Arnold station (1452 masl, latitude: 17o40’S, longitude: 31º10’E) during the same period. Hourly wind direction (º) was collected every year from Belvedere station (altitude: 1472 masl, latitude: 17º50’S, longitude: 31º01’E) in March and April, corresponding to the time of conidia catch by the traps, and observation in rainfall and irrigation water.

4.2.3 Seed health evaluation Each year, 14 varieties were selected from each trial for seed evaluation. In 2009/10, the susceptible controls Natal Sugar and Red Canadian Wonder (RCW) were added to the 12 experimental varieties selected randomly from the trial. In 2010/11, thirteen varieties were selected plus the susceptible check AFR 663. The seed of selected varieties was evaluated for P. griseola infection at the University of Zimbabwe Crop Science Department and Biological Science Department laboratories both before planting and after harvesting. Seeds sample were surface sterilized in 1 % sodium hypochlorite, rinsed twice in sterile distilled water to remove traces of sodium hypochlorite, and then dried on sterile blotter papers before plating. Ten seeds of each variety per replicate were analyzed by plating the seed on three blotter papers soaked in sterilized water. The blotter papers were placed in a Petri dish and incubated at 25 ºC for 7 days under alternating 12 h near- ultra-violet (NUV) light and 12 h darkness in a completely randomized design (CRD) with three replicates of three Petri dishes each. At the end of the incubation period, each seed was examined under a stereomicroscope Wild Heerbrugg M3B with X6.4 to X40 magnification for fungal growth. The pathogen, Pseudocercospora griseola, associated with the seeds was carefully examined and identified based on habit characters or by preparing a slide of fruiting structures, which were observed under a compound microscope at magnifications of X10 to X40 objective lens, following Mathur and Kongsdal (2001) and the descriptions by the (CMI, 1986). Disease infection (percentage of seeds infected per replicate) was then determined for each variety.

4.2.4 Detection of soilborne inoculum Nine double top wet soil cores (300 g x 2) were collected in 2010/11 summer season, randomly from the border area of the uppermost 5 cm horizon, before planting using a Dutch auger. Individual composite soil cores sampled were pooled and mixed to obtain a representative sample. A composite soil sample weighing 600 g was dried at room temperature for two weeks, and sieved over 2 mm sieves. Five samples of one gram each of sieved soil were suspended in 10 ml of sterile water and 6-fold dilutions made using sterile pipettes. At each dilution the solution was agitated manually for 5 minutes (McKenny and Lindsey, 1987). Drops of the solution were placed on the grid of the

Neubauer chamber using dropper pipettes. The counting chamber was placed under a compound microscope after waiting for 20 minutes to allow the fungal cells to settle. The number of conidia was counted on 5 randomly selected squares. Conidia concentrations observed in serial dilutions were adjusted to one ml volume and expressed per g of soil. All the testing operations were carried out in the Crop Science Department laboratory. The experimental design was a CRD with six dilutions as treatments and 15 replications. The same procedure was used at harvest using a bucket auger since the soil was dry. The following formula was used for calculating the conidia counts per litre:

Conidia count = (4.1)

Where:

N = the number of conidia

DF = the dilution factor

A = the area of the chamber counted

106 = the conversion factor to conidia per litre

D = the depth of the chamber

4.2.5 Detection of airborne inoculum A Burkard seven day recording volumetric spore sampler trap was installed towards the end of the field to accommodate the construction of a shade far from the variety trial in 2010/11. The Burkard trap was mounted at 2 m height and air sucked at 0.5 m above the ground. It was operated from planting to harvest at an air sampling rate of 10 l per minute corresponding to 14.4 m3 in 24 hours. The flow rate (l per minute) was measured by the flow meter Platon A6HS816PC during one week in the middle of the growing period. Six

Figure 4. 1: Pseudocercospora griseola habit characters on a bean seed – arrows indicate colonies. Source: Mathur and Kongsdal (2001)

Figure 4. 2: Conidia of Pseudocercospora griseola. Source: Mathur and Kongsdal (2001)

50 readings were made daily at two hours intervals from 8 to 18 h. A double sided tape (345 mm long) coated with vaseline was attached to the drum at 10:00 am every week. The tape was collected weekly at 9:00 am and cut by fine scissors into seven small sections (18 mm x 49.3 mm) each corresponding to a day. These sections were then mounted on microscope slides under cover glasses and observed under a compound microscope using the continuous sweeps method with four horizontal sweeps as described by Mandrioli (2000). Conidia were counted in the middle section of 14 mm x 48 mm area at the magnification of X20 (0.9 unit diameter of the field) objective lens. The number of conidia counted was recorded per day.

A locally made trap was placed in the shade together with the Burkard during the same season. Slides coated with vaseline on one side were mounted at 0.5, 1.0, 1.5 and 2.0 mm above the ground every day. For each height, four exposure positions were compared; horizontal facing up and down, and vertical facing north and south. These sticky slides were collected daily at 8:00 am and replaced by new ones. Spores were counted using a compound microscope from the middle (14 mm x 48 mm) area according to Mandrioli (2000). All testing operations were carried out in the Crop Science Department laboratory. The number of conidia counted was recorded per day.

4.2.6 Detection of rainfall and irrigation waterborne inoculum Daily rainfall and irrigation water (mm) were collected in the field as described in the previous sections from January to April in both years, mixed separately into two containers and analyzed weekly. Three samples of 2 l of water were collected directly from the reservoir receiving borehole water but exposed to rainfall, or at the exit of water from irrigation pipes. The three samples were mixed to form a composite of 6 l, from which a sample of 300 ml was drawn and kept in a 2 l container. This sampling was repeated daily for a week before analysis. Twenty seven water samples were then centrifuged every week at 5 000 rpm for 5 minutes and 1 ml of the bottom contents retained for analysis. Conidia were counted from the bottom 1 ml following the same procedure described earlier for testing soilborne inoculum. Conidia concentrations observed were expressed per ml of water per day.

4.3 Data analysis 4.3.1 Environmental data Mean daily rainfall and irrigation water (mm), temperature (ºC), relative humidity (%) and wind speed (m/s) were calculated across the growing period. The most prevailing wind direction (º) was obtained by identifying angles with high frequencies on histograms of hourly data in March and April of each year. Histograms were plotted to find the general wind trend for the site and the main prevailing directions during rainy and irrigated days.

4.3.2 Field evaluation All disease severity data sets obtained from the 25 varieties in both years were explored for normality by Anderson-Darling test (P < 0.05) and for homogeneity by Bartlett’s and Levene’s tests (P < 0.05) and the plot of residuals was done using Minitab 16 to check assumptions for the analysis of variance (independence, normality, homogeneity). Additivity was checked by plotting replications against varieties and confirmed by Tukey’s test for non-additivity (P < 0.05). Weekly severity scores obtained in both years were transformed using square root (sqrt) to meet the assumptions for the analysis of variance. This analysis was done to compare different WACE. Polynomial regression was done between disease severity as dependent variable and WACE as predictor. Centring was used during regression analysis to reduce collinearity (Kleinbaum et al., 1988). In 2009/10, disease severity square root transformed data were centred by dividing each value by the standard deviation (0.5681). In 2010/11 no centring was done. Disease severity scores were cleaned by removing data collected at 3 WACE. In both years, WACE was centred by subtracting from each value the respective means of 7.5 and 8.0 in 2009/10 and 2010/11. The analysis of variance of the original disease severity data collected on Natal Sugar planted in the border area in 2009/10 and 2010/11 was done to compare different WACE. Polynomial regression was also applied to the original data on severity as dependent variable and WACE as predictor.

4.3.3 Seed health The infection (%) of planted and harvested seed obtained in both years was transformed by root square to meet the assumptions of the analysis of variance. Combined analysis of data before planting and after harvest in each year was done using repeated measurements. In 2009/10, all the 14 varieties were used in individual and combined data analysis since they were all infected. In 2010/11, three varieties with seed infected were used in the analyses.

4.3.4 Data management, summaries and analysis Collected data were managed and summarized using Excel, and analysed with Minitab 16 and GenStat 14.

4.4 Results 4.4.1 Weather data The characteristics of water received through rainfall and irrigation are presented in Table 4.1. In 2009/10, the crop received 505.6 mm of water during 22 rainy days and 6 days of irrigation representing respectively 60.3 and 39.7 % of the total water received. In 2010/11, 462.6 mm were received during 42 rainy days and 7 days of irrigation which represented respectively 71.1 and 28.9 % of the total water. Water was the second most variable characteristic and rainfall (2009/10: 0.0 to 85.0 mm, 2010/11: 0.0 to 41.1 mm) was more variable than irrigation (2009/10: 0.0 to 42.9 mm, 2010/11: 0.0 to 25.4 mm). Due to few rainy and irrigation days, daily mean water received through rainfall (2009/10: 3.14 mm, 2010/11: 3.29 mm) and irrigation (2009/10: 2.07 mm, 2010/11: 1.34 mm) was low. Data of other weather characteristics are presented in Table 4.2. The ranges of temperatures observed were 16.0 - 23.0 ºC in 2009/10 and 17.0 - 22.5 ºC in 2010/11, with respective means of 19.13 and 19.90 ºC. Daily relative humidity varied from 30.8 to 91.5 % in 2009/10 and from 25.5 to 91.5 % in 2010/11 with respective means of 62.57 and 66.76 %. Wind speed varied from 2.1 to 6.1 m/s in 2009/10 and from 1.7 to 5.8 m/s in 2010/11, with respective means of 4.12 and 3.86 m/s. Wind direction was the most variable characteristic in both years from 0 to 360º, but the general angle

53 was between 0 and 15º indicating a direction north-north-east (NNE) (Tables 4.1 and 4.2).

4.4.2 Field evaluation The analysis of variance of disease severity recorded in 2009/10 and 20010/11 on two different sets of 25 varieties each showed that there was a significant difference in that characteristic (P < 0.001) between weeks (Figure 4.3). In both years, there was an increase in disease severity as shown by the significant quadratic relationships between

Table 4.1: Prevailing wind direction recorded during dry and wet days, and water characteristics recorded during rainy and irrigated days during the growing period in 2009/10 and 2010/11 respectively at the Crop Science Department site.

Characteristic 2009/10 2010/11 Rainfall Irrigation Total Rainfall Irrigation General Minimum (mm) 0.0 0.0 0.0 0.0 0.0 0.0 Mean (mm) 3.14 2.07 5.21 3.29 1.34 4.63 Maximum (mm) 85.0 42.9 85.0 41.1 25.4 41.1 Watered days 22 6 25 42 7 46 Total (mm) 304.7 200.9 505.6 328.7 133.9 462.6 Percent 60.3 39.7 100.0 71.1 28.9 100.0

Wind directionnwd (º) 0 - 5 0 - 5 0 - 5 0 - 15 0 - 15 0 - 15

Wind directionnwd NNE NNE NNE NNE NNE NNE N: north, E: east wd: watered days, nwd: non-watered days

Table 4.2: Prevailing wind direction and means of other weather characteristics recorded at the Crop Science Department site during two years.

Characteristic 2009/10 2010/11 Min General Max Min General Max Temperature (ºC) 16.0 19.13 23.0 17.0 19.90 22.5 Relative humidity (%) 30.8 62.57 91.5 25.5 66.76 91.5 Wind speed (m/s) 2.1 4.12 6.1 1.7 3.86 5.8 Wind direction (º) 0 0 - 10 360 0 0 - 15 360 Wind direction N NNE N N NNE N N: north, E: east

54 angular leaf spot severity and time in weeks with respective high and significant R2 of 98.3**and 99.4***in the first and second year (Figure 4.3). The analysis of variance of disease severity recorded on Natal Sugar planted in the border area in 2009/10 and 2010/11 showed that there was a significant difference in weeks (P < 0.001) in each year (Figure 4.4). Regarding disease severity progression across time with the variety Natal Sugar, in both years, there was an increase in disease severity. That increase was shown by the significant cubic (R2 = 99.3**) and the fourth order polynomial (R2 = 99.7***) regression relationships between angular leaf spot severity and time in weeks, respectively, in 2009/10 and 2010/11 (Figure 4.4).

2009/10 2010/11

Figure 4. 3: Evolution of disease severity (square root transformed and centred in 2009/10) across weeks after emergence (centred) obtained from data collected on 25 varieties grown at the Crop Science Department site during 2009/10 and 2010/11 summer seasons. Analysis of variance between weeks: 2009/10 (P < 0.001), 2010/11 (P < 0.001). sqrt: square root, **: significant at P < 0.01, ***: significant at P < 0.001.

2009/10 2010/11

Figure 4. 4: Evolution of Natal Sugar disease severity across weeks after emergence obtained from data collected at the Crop Science Department site during 2009/10 and 2010/11 summer seasons. Analysis of variance between weeks: 2009/10 (P < 0.001), 2010/11 (P < 0.001). sqrt: square root, **: significant at P < 0.01, ***: significant at P < 0.001.

4.4.3 Seed health Planted and harvested seed exhibited equivalent levels of seed infection each year (Table 4.3). The percentage of seed infected was 20.2 for planted seed and 14.7 for harvested seed in 2009/10. The respective corresponding percentage was 2.6 and 7.4 in 2010/11.

Table 4.3: Seed infection of planted and harvested seed during summer season obtained from two years data sets.

Period 2009/10 2010/11 Before planting 4.01(20.2) 1.56(2.6) After harvest 3.61(14.7) 2.42(7.4) P 0.318 0.156 LSD (0.05) NS NS CV (%) 31.7 53.3 NS: non significant at P = 0.05 Normal data: square root transformed Between brackets: non transformed data (%)

4.4.4 Soilborne inoculum The pathogen Pseudocercospora griseola was not observed in the soil at planting. The mean number of conidia observed per g of soil at harvest was 8.67 ± 1.70 x 105.

4.4.5 Airborne inoculum The number of conidia (3.9/day) was trapped late in the 2010/11 summer season, from 13 March to 5 April during 2 days (15 and 16 March) for the Burkard and 3 days (13 March, 19 March and 5 April) for the local trap. The combination of height and slides orientation in capturing conidia for the local trap was 13 March (2.0 m, vertical north), 19 March (1.0 m, vertical north) and 5 April (0.5 m, vertical south). The pathogen was found at most heights (2.0, 1.0 and 0.5 m) at the rate of 3.9 conidia/height/day. The north and south vertical orientations were the most efficient, since no conidium was captured at up and down horizontal orientations across different heights evaluated. The prevailing wind angle was between 0 and 15º indicating a NNE wind direction.

4.4.6 Waterborne inoculum No P. griseola conidia were observed in irrigation water from the source. The number of conidia was recorded late in the summer season for both years, during weeks which ended from 12 March to 14 April. The mean number of conidia 5.57 ± 0.57 x 105 /ml/day was observed from rainfall during three weeks which ended 12 March, 2 April and 14 April in 2009/10. A corresponding mean number of 6.10 ± 0.68 x 105conidia/ml/day was observed during five weeks which ended 12 March, 19 March, 2 April, 8 April and 14 April in 2010/11. For sprinkler irrigation, 5.0 x 105 conidia/ml/day were observed during the week which ended 19 March. The prevailing angle was again between 0 and 15º indicating a NNE wind direction (Tables 4.1 and 4.2).

4.5 Discussion Late planting was adopted in order to take advantage of air inoculum from the field and other sources. As reported by Dube (2009), fungi in the atmosphere are mostly present in the troposphere, up to a height of 10 km. The effect of remote airborne inoculum might be important since the ascomycetes represent between 1.21 to 34 % of the fungi found in

57 the continental air (Sateesh and Rao, 1994; Fröhlich-Nowoisky et al., 2012). In addition, the Mycosphaerellaceae and specifically Pseudocercospora were identified in the atmospheric air (Gregory and Hirst, 1957; Hawksworth, 1981). Since no spores were trapped in the air or observed in rain or irrigation water early in the season, this implied that there was not enough inoculum coming from these sources at that period.

In the present study the pathogen P. griseola was not identified in the soil before planting. This might be due to cropping systems used for plot management. The field was occupied with sorghum and maize in 2008/9 summer season. The trial was planted in 2009/10 after a period of natural fallow from the end of April 2009 to the beginning of January 2010. Before planting the trial in 2010/11 summer season, the field had been planted in 2010 to cabbages and onions after winter season. This management strategy was able to suppress the amount of inoculum present in the soil during nine months between cropping seasons. As reported by Sengooba and Mukiibi (1986), the fungus is able to survive in infected crop debris for a maximum of nine months, six months under outside conditions and in the soil for only two months. But as reported by Correa and Saettler (1987), the pathogen is able to survive two successive winters in the debris of previously infected crops. The pathogen was observed in the soil at harvest with 8.67 ± 1.70 x 105 conidia per g of soil. In Zimbabwe, most farmers plant common bean early, from October to December, and a few plant late in January upwards (Kutywayo, 2000). The soil as a source of inoculum might be of much concern in our local conditions where staggered planting is practiced, and if there is no crop rotation or winter crop between summer seasons.

Planted and harvested seed exhibited the same levels of seed infection each year. Infected seed as a source of infection is important, since farmers plant home-saved seed allowing diseases to be transmitted from season to season, depending on the level of seed to seedling transmission. Infected planted seed and the resulting diseased plants will represent a source of inoculum for the soil, healthy susceptible plants and neighboring fields. Sengooba (1976) confirmed that diseased seed is a source of inoculum for a

58 subsequent crop by recovering P. griseola infection in 15 - 22 % of seedlings raised from visually infected seeds.

There was a significant difference (P < 0.001) in disease severity between weeks in 2009/10 and 2010/11 trials evaluating 25 varieties. In both years, there was an increase in disease severity as shown by the significant quadratic relationships between angular leaf spot severity and time in weeks with respective high and significant R2 of 98.3** and 99.4***in the first and second year. There was a significant difference (P < 0.001) in disease severity between weeks in 2009/10 and 2010/11 trials evaluating the variety Natal Sugar. There was an increase in Natal Sugar disease severity across time in both years. That increase was shown by the significant cubic (R2 = 99.3**) and fourth order polynomial (R2 = 99.7***) regression relationships between angular leaf spot severity and time in weeks, respectively in 2009/10 and 2010/11. When 25 varieties were used, there was a continuous increase in disease severity during the growing period. That increase in disease severity was due to the mixture in the same trial of varieties with different growth habits, times to maturity and levels of disease resistance. But when one variety was used, there was a decrease in disease severity from 9 WACE, which was caused by defoliation. Angular leaf spot is polycyclic and spores produced 9 - 12 days after inoculation cause the secondary spread of the disease up to late flowering or early pod set (Feraz, 1980; Saettler, 1994). This was supported by Rezende et al. (2014) who observed that the best time to evaluate the disease was 33 days after flowering. Plant defoliation occurring 70 - 80 days after planting results from severe symptoms (Saettler, 1994; Allorent and Savary, 2005).The results from field evaluation of 25 varieties and Natal Sugar showed that the release of spores in the atmosphere from the field would have gradually increased during the growing period.

The fungus was trapped towards the end of the season, from 13 March to 5 April, by the Burkard during two days and by the local trap during three days, with low concentration of conidia (3.9 conidia/day). The conidia were trapped late in the season due to the increased inoculum from the sources. The low concentration of conidia in the air might be due to different factors including pathogen characteristics, barriers and environmental

59 conditions. The fungus is able to produce enough conidia, but its natural spread remains low (Smith et al., 1992). As reported by Wagara et al. (1999) and Arx (1983), 30 - 52 (mean 41) synnemata per lesion and 48 - 106 (mean 72) conidiophores bearing solitary conidia were produced by the fungus 15 days after inoculation. The fungus has a lesion density estimated at 13.3 ± 2.6 lesions/per cm2 of leaf (Wagara et al., 2007) and a population density of 5 - 800 spores/ mm2 of lesion (Correa and Saettler, 1987). Inglis et al. (1988) obtained conidial suspensions of P. griseola per gram of dried leaves ranged from 1.1 x 104 to 5.8 x 104 conidia/ml. Spores of the fungus are produced in infectious sites present in the canopy and on defoliated leaves, and stocked in the same canopy and defoliated leaves as well (Allorent and Savary, 2005). Spore production by the pathogen is influenced by temperature (10 - 30ºC) (Campos-Avila and Fucikovsky Zak, 1980; Saettler, 1994) and relative humidity not less than 71 % (Sindham and Bose, 1980b). In 2010/11 field experiments, temperatures range from 17.0 to 22.5 ºC which was between 10 and 30 ºC hence favourable to spore production, but relative humidity was a limiting factor since only 32 % of days had relative humidity ≥ 71 %.

After the spores are formed, they are subjected to three episodes: spore liberation, transportation and deposition. Both rain and wind appear to liberate and disperse spores of P. griseola. According to Cardona-Alvarez and Walker (1956), the effective agents of dissemination of the pathogen are wind-blown particles from infected soil, windblown spores and rain droplet-born spores. Wind-blown spores can carry the infections of the disease up to 7 m high and spores in the soil particles within 2 m area (Cardona-Alvarez and Walker, 1956). However, McDonald and McCartney (1988) observed that 90 % of splash droplets produced on bean leaves did not reach a height of 5 cm above the leaves. Rain and wind specifically influence P. griseola rates of liberation and deposition of spores from the canopy and defoliated leaves (Allorent and Savary, 2005). The percentage of rainy days observed was 42.0 % in 2010/11 summer season. Wind speed varied from 1.7 to 5.8 m/s in the same season. Wind speed usually greatly exceeds terminal velocity at 10 m above ground level, decreases by 20 % between 10 and 2 m, and to 8 - 16 % of the 2 m value in the lower half of the vegetation canopies (Scott, 1978). This gives in our case an approximate range between 1.14 to 4.64 m/s between the

60 vegetation canopy and 2 to 10 m above ground level. The lower fraction of this speed (1.14 - 2.0 m/s) is appropriate for the removal of biological material from the plants, which varies from 0.5 to 2.0 m/s (Jones and Harrison, 2004). Since the removal of the material from the ground requires 3.0 to 5.4 m/s (Jones and Harrison, 2004), only a fraction of the wind speed between 3.0 and 4.64 m/s is adequate. Allorent and Savary (2005) stated that the pathogen spore loss is assumed to occur through escape from the system in the atmosphere and deposition on the ground.

The pathogen was found at most heights 2.0, 1.0 and 0.5 m at a low rate of 3.9 conidia/height/day. The same rate was obtained by the Burkard sucking the air at 0.5 m. These results are in agreement with Cox and Wathes (1995) who reported that conflicting results can be obtained sometimes in comparing particles catches made at different heights. The north and south orientations were the most efficient, since no conidia were captured at both sides of the horizontal slides across different heights evaluated. Guo and Fernando (2005) reported that more ascospores and pycnidiospores were carried in the direction of prevailing winds than in other directions. In our field experiments, there was a high variation of wind speed, but the prevailing wind direction was NNE and conformed to conidia direction. Ingold (1971) stated again that the use of a vertical slide, facing the wind, is preferable to a horizontal position.

The absence of conidia in irrigation water source confirmed that the inoculum present in rainfall and irrigation water was mostly coming from water splashes or the atmosphere surrounding the field. It was observed that water was more efficient than air in trapping P. griseola conidia. Generally, spores can be transported long distances by splashing rains as bio-aerosols. But, Smith et al. (1992) reported that airborne water was less efficient in carrying P. griseola spores than dry air currents which are able to transport contaminated soil particles. It is difficult to classify P. griseola spores as splash or dry dispersed. Splash-dispersed spores are characterized by the production of structures such as pycnidia, acervuli and stromata (Gregory, 1973; Fitt et al., 1989). They are wettable, have smooth surfaces, thin, hyaline walls and elongate shapes (Fitt et al., 1989). On the other hand, dry-dispersed spores are borne dry, are non-wettable, have rough surfaces,

61 thick coloured walls and are round. P. griseola produces dark stromata on lesions (Cardona-Alvarez, 1956). The conidia of the fungus are obclavate-cylindrical, broadly subfusiform, subhyaline to pale olivaceous or olivaceous-brown, thin-walled, smooth, sometimes rough-walled, 20 - 85 um long and 3 - 9 um thick (Saettler, 1994; Mathur and Kongsdal, 2001; Crous et al., 2006). The difference between rainfall and irrigation might be due to different environmental conditions around rainy and irrigated days. Natural precipitation usually occurs after a period of cloud weather, lowered temperatures and increased air humidity than overhead irrigation (Arnon, 1972). This might affect the optimum temperatures for spore production, germination and infection. Whereas overhead irrigation applies water suddenly under conditions of high temperatures, light intensity and low air humidity; the irrigated area will therefore be surrounded by hot and dry air, which in many cases, may effectively preclude the survival and spread of inoculum. Also, as reported by (Fitt et al., 1989) rain is the principal agent in dispersal of pathogens by splash, but overhead irrigation may also spread diseases.

4.6 Conclusion These investigations have shown that infected seed, diseased plants in the field, the soil at the end of the growing period and the air are possible sources of inoculum. Since farmers plant home-saved seed, infected seed can transmit the disease across seasons. Infected soil is important if associated with staggered planting, the absence of crop rotation or winter crop between summer seasons. The inoculum from the air, rainfall and irrigation water was important towards the end of the growing season when disease severity was high. This suggests that staggered planting might result in spread of the inoculum from early to mid and late planted crop.

CHAPTER 5: THE EFFECTS OF TYPE OF IRRIGATION AND PLANTING DATE ON ANGULAR LEAF SPOT DISEASE, SEED YIELD AND SEED INFECTION

5.1 Introduction Control measures that are recommended for ALS include the use of resistant varieties, crop rotation for at least two years, planting pathogen-free seed, planting in well-drained soils, and removal of previously infected crop debris (Cardona-Alvarez, 1956; Barros et al., 1958). The use of disease-free seed and other cultural practices such as adjusting planting and harvesting dates, and irrigation scheduling can also contribute in reducing the likelihood of an outbreak (Delahaut et al., 2000). Date of planting is one of the factors that determine the ability of the crop to either escape or avoid infection by pathogens (Cook and Baker, 1996; Amede et al., 2004). In Zimbabwe, different planting dates are used in bean production; however, all the planting dates need supplementary irrigation during the growing season due to the erratic rainfall pattern. Early planting date requires irrigation mostly at the beginning of the growing season for the crop establishment and late planting date towards the end when rains tend to disappear. Different types of irrigation can be used and these include overhead and furrow irrigation. Overhead irrigation has been considered to be more conducive to disease pressure by creating favourable microclimates, which may increase disease development compared to furrow irrigation (Schwartz and Mohan, 2008). Different planting dates are also associated with changes in weather variables especially temperature and relative humidity and also determine the amount of supplementary irrigation water that can be applied and this in turn influences disease development (Orawu et al., 2001; Monted-Belmont et al., 2003).

Disease assessment for angular leaf spot can be done by measuring disease incidence or severity on the leaves and pods. Disease incidence refers to both the number of plants or plant units diseased out of the total number assessed, expressed as a proportion (NECTAR, 1998; Madden and Hughes, 1999). Disease development progresses when the amount of diseased tissue increases as new spores are produced on that tissue, thus increasing the probability of new infections in healthy tissue (NECTAR, 1998). The advantages of using disease incidence as an assessment tool include less time for disease

63 estimation and more samples can be assessed. It increases early in the growing season which permits easy detection of the disease.

Disease severity, on the other hand, refers to the quantity of disease affecting entities within a sampling unit (Seem, 1984) and indicates the amount of tissue diseased relative to the total amount of tissue (NECTAR, 1998). Estimates of severity are frequently based on lesion area but may also be based on lesion numbers (Obanor et al., 2005). Disease severity is a continuous variable and may be low early in the growing season. For many plant diseases, only disease severity estimates are considered to give an accurate indication of their effects on the plants or of the efficacy of control treatments (Obanor et al., 2005). Visual estimates of severity have been used almost exclusively, but they can vary substantially among assessors (Nutter and Schultz, 1995).

Severe symptoms caused by ALS on common bean have been reported to lead to heavy plant defoliation (Cardona-Alvarez and Walker, 1956; Ferraz, 1980; Saettler, 1994). Defoliation can be expressed as score, percentage of leaves defoliated to the total number of leaves per plant, percent defoliation from visual ratings, number of leaflets defoliated per day (rate of defoliation) and daily multiplication factor (Foolad et al., 2002; Willocquet et al., 2004; Jones et al., 2005; Allorent et al., 2005). Compared to other diseases, ALS causes higher defoliation, and relatively low levels of disease severity can generate significant leaf loss. For example, a severity of 10 %, results in an estimated relative rate of leaflet defoliation (rrdef) of 0.17/day in common bean ALS (Willocquet et al., 2004); 0.035 in rice blast (Bastiaans, 1993), and 0.024 in groundnut leaf spot (Savary et al., 1990). As reported by Bergamin Filho et al. (1997), for pathosystems in which defoliation is a major part of the disease syndrome, and for hosts with an indeterminate growth habit; the same proportion of disease severity on different leaf areas would not have the same impact on yield. Willocquet et al. (2004) stated that defoliation is related to disease severity. Defoliation caused by ALS has strong implications on bean growth and yield as it causes a decrease in green leaf area index (LAI) (Bergamin Filho et al., 1997; Jesus et al., 2001) causing reductions in photosynthesis.

Yield is affected by weather, pests and diseases, of which many are also weather dependent (FAO, 2002). Climate or weather patterns play an important part in determining yields of crop species (Dennett, 1984). Yield is a quantitative character and consequently affected by the environment. The phenotypic performance of a plant/line/population is determined by its genotype, its environment and the genotype x environment interaction. Diseases as well as other factors both biotic and abiotic will influence the complete expression of genotypic potential. Diseases reduce total biomass (dry matter) production by the crop in the following ways: killing of plants and branches, general stunting, damage to leaf tissues and to the reproductive organs including fruits and seeds (Singh, 2006). The prevailing environment affects the functions of both the host and pathogen. Contact and infection stages are generally affected by the environment and provide the means for disease escape. Disease escape occurs primarily by avoiding contact, whereas unfavourable weather conditions may prevent infection. Escape may be achieved by change of planting date and sites. Survival, infection and spread of pathogenic organisms can be limited by using sites with unfavourable conditions created by rainfall/water, humidity and temperature conditions (Schwartz and Gálvez, 1980; Sheppard, 1983; Maude, 1996), with probable consequent yield increase if associated with good crop management. The effect of planting dates on bean yield was studied by different researchers with contrasting results (Sindham and Bose, 1980a; Rodriguez et al., 1999; Hang and Priest, 2002) due to differential environmental conditions and diseases infections.

Sprinkler irrigation may cause more foliar disease than furrow irrigation due to its effect on environmental conditions, for example longer leaf wetness duration, higher canopy humidity, increased dispersal of inoculum by splashing, and decreased leaf temperature (Scherm and van Bruggen, 1995). Despite that sprinkler irrigation favours disease infection and spread, yield losses do not follow always the same trend (Robertson and Frazier, 1982; White and Singh, 1991). Weather conditions are the main factors causing variations in crop yields (FAO, 2002). Those conditions interact with host and diseases like ALS, and are sources of yield variation across planting dates and years.

As reported by ISTA (1999), the objective of seed health testing is to determine the health status of a seed sample, and by inference that of the seed lot, thus gaining the information that can be used to compare the value of different seed lots. Health testing of seed is important because seedborne inoculum may give rise to progressive disease development in the field and reduce the commercial value of the crop. Also, health testing may elucidate seedling evaluation and causes of poor germination or field establishment and thus supplement germination testing. Infection with fungal pathogens may damage the seed by interrupting seed maturation as the pathogen feeds on the developing embryo and damages seed surface, endosperm or embryo. Effects of pathogen infection may include decreased seed vigour, germination rates and longevity in storage as the fungal pathogens continue to grow in the seed post-harvest. Therefore, healthy and pathogen-free seeds should be able to germinate and give rise to vigorous plants with high yielding capacity. Cleaning seed may reach the ideal tolerance of 0 %, which may be raised to 0.5 - 1 % in tropical conditions marginal for clean seed production (Schwartz and Gálvez, 1980). Seed health testing is part of seed certification and plant quarantine practices aimed at reducing the distribution of seedborne pathogens by national and international trade of seeds (Mathur and Manandhar, 1993). Seed health is of great concern to farmers and seed producing agencies in tropical and subtropical countries (Mathur and Kongsdal, 2001). In Zimbabwe, seed certification is practiced by Seed Services Institutes under the Department of Agricultural Research and Extension (AREX), but-testing for seed quality mainly covers purity analysis, germination tests, moisture determination and trueness to variety. Seeds are seldom tested routinely for seedborne diseases, but on request usually as a result of low germination (Jere, 2004). Also little attention has been paid to the transmission of pathogens from seed to plant and eventually to seed. The probable reason behind below standard seed health is perhaps the shortage of testing equipment and trained seed pathologists (Mathur, 1983) critical even now in some developing countries. Seed health testing might not be properly done leading to infested seeds being dispatched to farmers (McDonald and Copeland, 1998). There might be also a preference by seed companies to follow proper certification procedures for larger grains cash crops such as maize and soybean (Jere, 2004).

Climatic conditions leading to severe field infections of seedborne pathogens usually result in increased seedborne inoculum. According to Tarr (1955), few seedborne pathogens are present in arid or semi-arid regions. They are abundantly present in rainy regions. Under semi-arid conditions, infection or contamination of seed is largely prevented resulting in low percentage of infected or contaminated seed lots, and low average infection percentages in seed lots (Neergaard, 1979). Semi-arid or less humid conditions are therefore largely selected for seed production. Occurrence of pathogens on seed produced in a definite region may fluctuate considerably from year to year, reflecting yearly variation of weather conditions (Neergaard, 1979). Weather conditions in the period of flowering and seed development are decisive for infection. The conditions most conducive to seed infection include cool temperatures, high humidity, and wet conditions present during rainy periods (Neergaard, 1979; Maude, 1996). The exact time of infection within this period again determines the extent of infection in individual seeds and number of seeds infected. Cool weather conditions favour the disease by prolonging the duration of the formation of fruiting bodies and the period of pollination and maturation of the seed, and also by increasing the extent of secondary conidial infection (Wilson et al., 1945).

The objectives of the study were: 1) To determine the effects of planting date and irrigation system on the incidence of angular leaf spot on both bean leaves and pods. 2) To determine the effects of planting date and irrigation system on the severity of angular leaf spot on both bean leaves and pods. 3) To determine the effects of planting date and irrigation system on defoliation variables due to angular leaf spot. 4) To determine the effects of planting date and irrigation system on seed yield. 5) To determine the effects of planting date and irrigation system on seed infection by Pseudocercospora griseola.

The hypotheses tested were: a) The incidence of angular leaf spot on both bean leaves and pods varies with planting date and irrigation system. b) The severity of angular leaf spot on both bean leaves and pods varies with planting date and irrigation system. c) Defoliation due to angular leaf spot varies with planting date and irrigation system. d) Seed yield varies with planting date and irrigation system. e) Seed infection by Pseudocercospora griseola varies with planting date and irrigation system used during the season.

5.2 Materials and methods 5.2.1 Background to sites selected 5.2.1.1 University of Zimbabwe Farm The first site was UZ Farm (17º48’S and 31º00’E, 1460 masl) in Harare. The farm is situated in the High-Veld area, NR II. The region is characterized by moderate rainfall averaging 750 - 1000 mm per annum with an average of 16 to 18 rainy pentads, mean temperatures of 21 - 27 ºC and relative humidity of 42 - 75 %. These conditions are conducive for high disease pressure during summer. The site has red soils belonging to fersiallitic group and a slope less than 2 %.

5.2.1.2 Agricultural and Rural Development Authority Muzarabani The second site was ARDA Mz (altitude: 427 masl, latitude: 16º25’S, longitude: 31º01’E) in Mashonaland Central Province. The site is situated in the lowveld area in NR IV. This region is characterized by low rainfall averaging 450 - 650 mm per annum, mean temperatures of 24 - 28ºC and relative humidity of 27 - 55 %. The rainy season at ARDA Mz is in the order of 105 days (21 pentads). During this short season, frequent 5 day dry spells can be expected (44 % of the time during the main season), 10 day dry spells are likely to occur four times and 15 day dry spells twice during the main rainy season. These conditions are not that favourable for disease development in both summer

68 and the cool winter when the crops are grown under irrigation. The site has fersiallitic soils of sandy clay loam texture (medium grained) and a slope less than 2 %.

5.2.1.3 Comparison of weather conditions between sites The comparison of the sites was done by the analysis of original daily mean temperature (oC) and relative humidity (%) collected during two years (2002/3 and 2003/4) using the two sample t-test and Pearson’s correlation using Minitab 16. The periods considered covered 85 days in each year, from the first January to 26 March at UZ Farm and from the first June to 24 August for ARDA Mz. The mean temperatures (UZ Farm: 21.02 oC and ARDA Mz: 21.26 oC) were not significantly different, whereas UZ Farm had the highest relative humidity estimated at 80.10 % and ARDA Mz the lowest evaluated at 59.09 % (P < 0.001). There was no relationship between temperatures recorded in the two sites (r = 0.472, P = 0.201), but a negative relationship was obtained for relative humidity (r = -0.210, P = 0.006). The two sites were different in relative humidity mean values and trend, UZ Farm conditions being more conducive to ALS than ARDA Mz.

5.2.2 Land preparation and crop husbandry The trial was planted in summer (November - April) of 2002/3 and 2003/4 seasons and in winter (May/June - August/September) of 2003 and 2004 seasons. To reduce spore movement between plots, each bean plot was surrounded by one row of maize. Plots measured 5 m x 2.25 m, and intra and inter row spacing was 10 x 45 cm. Compound D (7

% N, 14 % P2O5, 7 % K2O) was applied as basal dressing to all plots at 300 kg/ha. A bait composed of a mixture of 100 g carbaryl 85 WP with 20 kg maize meal watered, was applied per row to control seedling insects. Dimethoate 40 EC (1 ml/l water) was applied at 10 - 20, 35 - 45 and 55 - 85 days after crop emergence (DACE) to control leaf and pod insects. Thinning was done two weeks after crop emergence to achieve a population of 222 222 plants/ha. The crop was top dressed with ammonium nitrate (34.5 % N) at a rate of 200 kg/ha at 4 weeks after crop emergence (WACE). Weeds were controlled through hoeing at the second and fifth WACE.

5.2.3 Treatments and experimental design The seed used in the trials was purchased from seed-houses and had low infection levels of Pseudocercospora griseola evaluated at 0 % in year one (YR 1) and 0.2 % in YR 2. The two Irrigation (IR) systems used were Sprinkler (S) and Furrow (F). The three dates of planting (PDs) used at UZ Farm were: October-November for Early-season (E), December for Mid-season (M) and later than January for Late-season (L) crop (Kutywayo, 2000). A Winter (WPD) crop was established at ARDA Mz from the end of May/beginning of June to the end of August/beginning of September of 2003 and 2004.

At UZ Farm, two irrigation levels and three planting dates were evaluated in a split-plot arranged in a randomized complete block design (RCBD) with 4 blocks and 4 replications per subplot treatment. The main plot factor was irrigation and the subplot factor, planting date. At ARDA Mz the design was reduced to a RCBD by the absence of the planting date factor. Field trials were conducted during summer at UZ Farm and winter at ARDA Mz from November 2002 to September 2004.

5.2.4 Characteristics of the furrrow The characteristics of the furrow at the beginning of the trial were: length (5 m), width front and back (top: 25.0 cm, bottom: 17.0 cm), depth (front: 10.0 cm, back: 7.5 cm) (Figure 5.1). Three furrows were made; the first one between one maize and one bean row, and the two other furrows were built each one after two bean rows (Figure 5.1). Furrows were maintained regularly avoiding working in wet conditions.

The final size of the furrow was estimated by measuring the length, the front and back width (top and bottom), and the front and back depth at pod maturity. The characteristics of the furrow at UZ Farm and ARDA Mz were: length (5 m), width front (top: 28.3 cm, bottom: 19.0 cm), width back (top: 29.4 cm, bottom: 18.1 cm), depth (front: 13.2 cm, back: 11.2 cm) (Figure 5.2). Water characteristics measured in motion were the time used to fill a 5 litres container (respectively 9.0 and 6.11’ for UZ Farm and ARDA Mz) and a furrow (respectively 10.0 and 6.12’ for UZ Farm and ARDA Mz). During sampling, the two outer rows and 0.5 m at each plot end were discarded as borders.

FRONT

45.0 cm 25.0 cm 45.0 cm 45.0 cm

10.0 cm

17.0 cm

5.0 m

7.5 cm

45.0 cm 25.0 cm 45.0 cm BACK

: Maize row : bean row

Figure 5. 1: Characteristics of furrows at the beginning of the experiment

FRONT

45.0 cm 28.3 cm 45.0 cm 45.0 cm

13.2 cm

19.0 cm

5.0 m

18.1 cm

11.2 cm

45.0 cm 29.4 cm 45.0 cm BACK

: Maize row : bean row

Figure 5. 2: Characteristics of furrows at the end of the experiment

5.2.5 Data collection 5.2.5.1 Weather data Rainfall (mm), temperature (ºC) and relative humidity (%) were collected hourly, and evaporation (mm) daily. For UZ Farm [(altitude: 1460.0 masl, latitude: 17º48’S, longitude: 31º00’E] data were collected from the Belvedere weather station (altitude: 1472.0 masl, latitude: 17º50’S, longitude: 31º01’E, distance from UZ Farm: 17 km). For ARDA Mz (altitude: 450.0 masl, latitude: 16º25’S, longitude: 31º01’E), data were collected from Kanyemba weather station (altitude: 329.0 masl, latitude: 15º38’S, longitude: 30º25’E, distance from Kanyemba: 100 km). At UZ Farm water was received through rainfall and irrigation. In contrast, at ARDA Mz water was applied only through irrigation.

5.2.5.2 Disease incidence During sampling, evaluation of diseased plants was done on an array of plants selected using a quadrant (1m x 1m) thrown randomly twice in the net plot measuring 1.35 m (3 rows) x 4 m. Field disease observations were done every 14 days starting from crop emergence. Disease leaf incidence data (li) were collected from 4 (li4) to 10 (li10) weeks after planting (WAP) and expressed as the percentage of infected plants (%TLALS). Leaf disease incidence at the beginning of the symptoms was used to estimate the number of days to first disease lesions (ddis) and leaf incidence at the same period (lidis). Disease pod incidence (oi) was assessed during pod filling and expressed as the percentage of infected plants (%TPALS).

5.2.5.3 Disease severity Evaluation of leaves was done on 18 leaves selected from 6 plants selected randomly from the array in the net plot by taking one upper, middle and lower leaf. Field disease observations were done every 14 days starting from crop emergence. Disease leaf severity (ls) was collected from 4 (ls4) to 10 (ls10) WAP and expressed as the percentage of leaf area (%LAALS). Leaf severity (lsdis) was evaluated at the start of disease symptoms. Disease pod severity (os) was collected during pod filling and expressed as the percentage of pod area (%PAALS).

5.2.5.4 Defoliation Evaluation of non-defoliated leaflets was done on an array of plants selected using a quadrant (1 m x 1 m) thrown randomly twice in the net plot measuring 1.35 m (3 rows) x 4 m. Field disease observations were done fortnightly starting from crop emergence. Leaf disease incidence at the start of the infection was used to estimate the number of days to first disease lesions (ddis).

The relative rate of defoliation (rrdef) due to ALS was estimated using the methodology described by Willocquet et al. (2004). When plant growth is halted and no new leaves are emitted, the dynamics of the number of live leaflets on a plant can be written as follows: dNFV/dt = -RRDEF x NFV, (5.1) which can be integrated as: ln(NFVt) = ln(NFVto) - RRDEF x t, (5.2) where; RRDEF is the relative rate of defoliation per day, NFVt is the number of live leaflets at time t (in days), and

to is the time of appearance of symptoms

5.2.5.5 Seed yield Plants were harvested in a net plot measuring 1.35 m (3 rows) x 4 m. Dry seed weight was collected after processing on the net plot basis, adjusted at 12 % moisture, and expressed in seed yield (kg/ha).

5.2.5.6 Testing for Pseudocercospora griseola Seed health evaluations were carried out from November 2002 to June 2005 at the Mazowe Plant Quarantine Seed Laboratory. The blotter method was adopted for testing the presence or absence of Pseudocercospora griseola. All testing operations were carried out under a sterilized cabinet. Ten seeds were picked up with a sterilized pair of forceps and plated on four circular blotter papers pre-soaked in water and arranged in a Petri dish. Seeds were plated equidistantly with nine seeds in the outer ring and one in the

73 centre. The testing unit was the Petri dish and there were five replicate Petri dishes per sample. This test was run four different times, giving a total of 200 seeds tested per sample. Petri dishes were arranged in trays and on shelves in the incubation room. Seeds were incubated for 7 days at 22 ºC under 12 h alternating cycles of near-ultra-violet (NUV) light and darkness. Pseudocercospora griseola was identified using the blotter technique as described by Mathur and Kongsdal (2001) and descriptions by CMI (1986). After incubation, colonies which developed on each seed were examined under different magnifications of a stereomicroscope Wild Heerbrugg M3B with X6.4 to X40 magnification. Conidia and their arrangement were observed under a compound microscope at magnifications of X10 to X40 objective lens. The seed was considered infected even if only one fructification was observed. Infected seed was marked by writing a code on the blotter paper. Seeds infected by the fungus in different Petri dishes were counted and their numbers recorded. Disease incidence was quantified by calculating the percentage of seeds infected by the pathogen. The completely randomized design was used to test the infection level by the pathogen of sixteen random samples of seed bought from seedhouses. For the seed harvested from different trials, the same design used in the field, split-plot arranged in a randomized complete block design for UZ farm; and randomized complete block design for ARDA Muzarabani, were followed.

5.2.6 Data management 5.2.6.1 Irrigation The main factors, which can be used in irrigation systems, are evapotranspiration and rainfall. The reference evapotranspiration (ETo) from a class A evaporation pan and crop evapotranspiration (ETc) were calculated as outlined by Allen et al. (1998). The calculation of effective rainfall is based on the principle that rainfall less than 5 mm does not add any moisture to the soil (British Columbia, 2004), and if rain is over 5 mm, only 75 % of it will be considered as effective. Irrigation water needs were calculated according to Brouwer and Heibloem (1986). The discharge rate from one individual sprinkler (m3/h) was calculated from the flow rate (l/sec) (Eisenhauer and Fischback, 1996; Nyakanda, 1998). The system capacity (c) in m3/h was obtained by multiplying the discharge rate by the number of operating sprinklers (Nyakanda, 1998). The depth of

74 water application per irrigation (mm) per hour was deducted from the system capacity (Eisenhauer and Fischback, 1996; Nyakanda, 1998). The net precipitations (mm/h) for sprinkler irrigation were respectively 11.8 at UZ Farm and 11.3 at ARDA MZ. Respective net depths of water application for furrow irrigation were equivalent to 5.0 and 6.2 mm/h. Gross water precipitation applied per hour for sprinkler irrigation was respectively 15.7 and 15.1 mm at UZ Farm and ARDA Mz. For Furrow irrigation, the amount of water applied per hour was respectively 6.3 and 7.7 mm for the same sites.

5.2.6.2 Weather missing data Missing data for one hour or day were estimated using the mean of two values before and after. For more missing data at UZ Farm from January to April 2004 for relative humidity (RH), and at ARDA Mz from 1st June to 5th July of the same year for air temperature and RH, the procedure was as follows:

Hourly temperature was estimated at ARDA Mz from daily maximum (Tmax) and minimum temperature (Tmin), using the Parton and Logan (1981) model. The equations for estimating hourly temperatures with the lag coefficient a, for Tmax modified to suit Zimbabwean conditions (Murwisi, 2003) were used. The equation to calculate day length was run according to the methodology defined by Giesen (2003).

Hourly values of RH were derived from the actual vapour pressure (ea) and saturated vapour pressure (es) values according to the procedure outlined by Allen et al. (1998). At sunrise (6.00), when the air temperature is close to Tmin, the air is nearly saturated with water vapour and the relative humidity is nearly 100 %. Then Tmin can be used to represent Tdew. Tmin in the equation was approximated by substracting 1 at UZ Farm and 2 at ARDA Mz according to the adjustment method suggested by Allen et al. (1998). Since vapour pressure is one of the most conservative quantities in an air mass (WMO, 1988), hourly crop RH was estimated from hourly [es(T)] and once daily vapour pressure (ea) computed from Tm adjusted.

5.2.6.3 Changes in rainfall, temperature and relative humidity associated with evaporation The changes in evaporation, temperature, and relative humidity were calculated by subtracting from the actual daily means the reference day values of 29 January 2004 with evaporation (0.0 mm), temperature (19.2ºC), rainfall (15.3 mm) and relative humidity (91.5 %).

A regression analysis was conducted between changes in evaporation (mm) (x) and temperature (ºC) (y). There was a significant (P < 0.001) temperature increase associated with evaporation (y = 0.443x - 0.054, R2 = 0.48***). Another regression analysis was done between changes in evaporation (mm) (x) and in relative humidity (%) (y). It showed a significant (P < 0.001) decrease in humidity associated with increase in evaporation (y = - 3.558x + 1.981, R2 = 0.60***). There was a significant (P < 0.01) quadratic relationship between days after rainfall (x) and changes in temperature (ºC) (y) (y = - 0.0972x2 + 0.8725x, R2 = 0.74***). Up to 9 days after rain a significant linear relationship (P < 0.01) existed between changes in temperature (ºC) (x) and in relative humidity (%) (y) (y = - 6.6904x, R2 = 0.74**).

5.2.6.4 Adjustment of weather data with irrigation methods On the irrigation day, the net irrigation water amount, which was the same for sprinkler and furrow irrigation, was based on water deficit computed for each irrigation period. Gross water application was calculated by adjusting the net amount for the loss caused by each irrigation system (25 % for sprinkler irrigation and 20 % for furrow). Gross application depths used for sprinkler irrigation were 15.7 mm/h for UZ Farm and 15.1 mm/h for ARDA Mz respectively. For furrow irrigation the gross depths used were 6.3 mm/h for UZ Farm and 7.7 mm/h for ARDA Mz respectively. Irrigation durations adopted for each system aimed at applying the same amount of net water for corresponding sprinkler and furrow irrigation periods. Evaporation losses for each irrigation system were calculated by applying the evaporation rates of 11.00 % for sprinkler and 3.22 % for furrow irrigation to gross water applied, as estimated by Rogers et al. (1997) and Younts et al. (2000). The regression equations with evaporation losses

76 as independent variable were used to calculate daily losses in temperature and gain in relative humidity due to irrigation systems. Those values were used to compute daily mean values and to adjust hourly temperatures and relative humidity figures for irrigation daily hours and after for the same day from the start of irrigation at 9.00 am for UZ Farm and 14.00 pm for ARDA Muzarabani.

With days after irrigation, changes in temperature were computed for each of the nine dry days after irrigation using the quadratic relation. Those changes were used to calculate daily mean temperatures for each irrigation system starting the first day after irrigation. Daily changes in relative humidity (%) were calculated using the equation relating changes in temperature to relative humidity developed for days after irrigation. Those changes were used to calculate daily mean relative humidity (%) for both irrigation systems starting the first day after irrigation. The values obtained were used to adjust hourly temperatures and relative humidity figures.

5.2.6.5 Selection of weather variables Daily means of water applied (mm) (wi), temperature (oC) (ti) and humidity (%) (hi) were calculated during 2 weeks of evaluation period. As already mentioned, the levels applied to hourly data for duration (d) calculations were water applied ( 0.1 mm), temperature ( 16 oC and  28 oC), and humidity ( 90 %). The duration represented the number of hours per day the level was applied. The periods of evaluation associated with those weather variables were 2 to 10 WAP and maturity (mat).

5.2.6.6 Calculation of the integral variables

Areas under disease progress curves for different disease characteristics, disease incidence (AUDPC-N or dcn), severity (AUDPC-V or dcv) and defoliation (AUDPC-L or dcl), were calculated with leaf data collected from 4 to 10 WAP, using the following equation (Shanner and Finney, 1977; Bergamin Filho et al., 1997):

n1

AUDPC =  [(Xi + Xi+1)/2][(t i+1 - ti)] (5.3) i1

Where;

Xi = X(ti) n = number of assessments X = disease characteristic

(ti+1 - ti) = interval between two consecutive assessments

5.3 Data analysis All data sets obtained in both years were managed using excel spreadsheets. They were explored for normality by the Anderson-Darling test (P < 0.05) and for homogeneity by Bartlett’s and Levene’s tests (P < 0.05) and the plot of residuals was done using Minitab 16 to check assumptions for the analysis of variance (independence, normality, homogeneity).

5.3.1 Weather data

Daily mean water and duration variables except temperature duration were transformed by square root (sqrt or sr). With the aim of exploring all the options of data analysis, the factors [year (YR), irrigation (IR), planting date (PD) and evaluation period (EP)] were used in the statistical analysis to select the most efficient models and to identify single factors, double and triple factors interaction effects using the software Genstat 14. When F-test was significant (P < 0.05), mean separation was performed. Means of water duration for sprinkler and furrow irrigation were separated using LSD at 5 % level. Means of the interaction EP x PD were separated for both years using Tukey’s Honestly Significant Difference Test (HSD) at 5 % level. Upper and low limits were determined according to the position of the values in the top and last groups defined by HSD.

The biplot analysis (Yan and Tinker, 2006) was done to show the dissimilarities among planting dates in discriminating evaluation periods. The relationship among PDs used the PD-vector view which is based on a PD-centred (centring = 2) EP by PD table without any scaling (scaling = 0) and it is PD-metric preserving (SVP = 2) with axes drawn to scale. The concentric circles of the biplot helped to visualize the length of the PD vectors, which was a measure of the discriminating ability of the PDs. A long vector meaning that

78 the PD was more discriminating, more informative, providing more information on the EPs. At the other end, a short vector meaning that the PD was less discriminating, providing less information on the EPs. Evaluation periods were compared on the basis of their mean performance and stability across PDs. The average-PD coordination view, EP- metric preserving (SVP = 1) appropriate for EP evaluation was used. In this plot, the single-arrowed line, the average-PD coordination abscissa, points to higher mean value across PDs, and the double-arrowed line, the average-PD coordination ordinate points to greater variability (poor stability) in either direction.

5.3.2 Angular leaf spot variables, seed yield and seed infection Additivity was checked by plotting blocks against treatments and confirmed by Tukey’s test for non-additivity (P < 0.05). Where possible, disease variables were transformed using square root (sqrt or sr) to meet the assumptions for the analysis of variance. Combined analysis over years, was performed for each site after testing homogeneity of variances (McIntosh, 1983; Gomez and Gomez, 1984). Individual and combined analysis over years of different data collected was carried out using GenStat 14. When the F-test was significant (P < 0.05) with adequate degrees of freedom of the residuals (six and more) (Gomez and Gomez, 1984), means were separated using LSD (0.05).

In the cases where no normalization was obtained following transformation, the Friedman test was performed on original data. The evolution of disease incidence, severity and defoliation for each planting date, from 2 to 10 WAP in 2002/3 and 2003/4 was described using curves linking each set of data during the evaluation period in each irrigation level, due to different exceptions and interactions encountered in data analysis.

In the case of disease incidence at UZ Farm, individual data analysis of variance per year of sqrt transformed data was done for (li6, li10 and AUDPC-N) in both years, and (li8 and the %TPALS) in the second year. Friedman test was applied on characteristics collected at the start of disease symptoms (ddis and lidis) for both years. It was also applied at 4 WAP for both years, and at 8 WAP in the first year for the %TLALS. It was again applied on the %TPALS in the first year.

For disease severity at UZ Farm, in order to meet the assumptions for the analysis of variance, in the first year, at 4 and 8 WAP, EPD with no lesions was removed from individual analysis of variance per year of the %LAALS. The same process was applied in the second year to E and MPD at 4 WAP for the same characteristic. Individual analysis per year of sqrt transformed data was done for (lsdis, ls4, ls8 and AUDPC-V) in both years and the %PAALS in the second year. Combined analysis of sqrt transformed over years was done for ls10. The Friedman test was performed on the %LAALS at 6 WAP in both years and on the %PAALS in the first year.

For defoliation, individual (rrdef10 and AUDPC-L) and combined analysis over years (start of disease symptoms to 8 WAP) were carried out on different data estimated.

Individual and combined analysis over years of seed yield (kg/ha) was carried out respectively for UZ Farm and ARDA Muzarabani.

In the case of harvested seed, no normalization was obtained through different transformations, and the Friedman test was performed on different percentages of seed infected by P. griseola at UZ Farm.

5.4 Results 5.4.1 Weather The two sites differed in mean relative humidity, lower for ARDA Mz (59.09 %) and higher for UZ Farm (80.10 %) (P < 0.001), and in its trend showing a negative relationship (r = -0.210, P = 0.006). The most important feature of the study was the triple interaction YR*PD*EP which was significant (P < 0.001) for all the variables studied. Planting date was identified as the principal source of weather variation and year the second factor. High and low limits obtained for duration variables (h) were temperature (22.00, 18.65), relative humidity (10.90, 4.15) and water (4.40, 1.50). The corresponding limits for mean daily variables were temperature (oC) (21.95, 19.20), relative humidity (%) (82.00, 66.30) and water (mm/h) (5.55, 2.70).

Compared to year 2, year 1 had the conditions of longer duration of temperature (≥ 16 oC and ≤ 28 oC) and water (≥ 0.1 mm), lower temperature and relative humidity daily means. Compared to medium and late planting dates, early and winter were less favourable to the disease. Weather conditions favourable to the disease increased from winter to E, L and MPD. Winter planting date had more intermediate and low values, and EPD more intermediate values. Late and medium planting dates had varying high values across EPs in the variables studied. Winter and medium planting dates were the least discriminating, providing little information on EPs in duration and daily mean variables in both years. There was a variation between EPs in the variables studied with regards to their mean values and data variability. High values were observed at 4, 2 and 6 WAP (data not included). Values were more variable at 2 and 6 WAP than at 4 WAP (data not included). Low values were observed at maturity, 10 WAP and 8 WAP. Weather values were more variable at maturity than at 10 and 8 WAP.

Weather conditions at maturity were favourable for clean seed harvest for winter and late planting dates which had low to intermediate values for all duration variables, daily mean relative humidity and water; and high daily mean temperature conditions in both years. Medium and early planting dates on the other hand, had at least a high or intermediate value at maturity for any variable in one of the years. Therefore, conditions of high humidity and water at maturity for early and medium planting dates might interfere negatively with harvest operations and seed quality.

Weather conditions prevailing in early and winter planting dates were less favourable to angular leaf spot. Therefore, those two planting dates can be adopted to control the disease. Since there were no difference between sprinkler and furrow irrigation, those two planting dates can be associated with any irrigation system. Due to the humid conditions prevailing at maturity with early planting date, the crop might be harvested early at physiological maturity and artificial means put in place to dry pods and seeds. Conditions prevailing from 2 to 6 WAP were favourable to the disease (data not included). This implies that for medium and late planting dates, control measures have to start early in the cropping season.

5.4.2 Disease incidence at UZ Farm 5.4.2.1 Number of days to the start of the disease The Friedman test was significant (P < 0.001) in both years for ddis when the test statistic was adjusted for ties (Figure 5.3). The number of days to the start of the disease for the various irrigation and planting date combinations is summarized in Figure 5.3. Sprinkler- irrigated crop had a higher ddis median and rank than furrow-irrigated crop for early planting date in 2003/4 season (Figure 5.3b). For the other planting dates, the crop after sprinkler water application was not significantly different from the crop after furrow irrigation in both years (Figures 5.3a, b).

With regards to planting date; early planted crop showed the highest ddis medians and associated ranks in both years under both irrigation systems (Figure 5.3a, b). In 2002/3 season, days to disease development for mid-season and late planted crops were not significantly different. However, in the 2003/4 season, the disease started much earlier for late than for mid-season planted crop under both irrigation systems.

5.4.2.2 Leaf disease incidence at the start of the disease The effects of irrigation and planting date on leaf disease incidence are summarized in Figure 5.4. The Friedman test was significant (P < 0.01) in both years for lidis with the test statistic adjusted for ties. Sprinkler-irrigated crop had high lidis and associated ranks in 2003/4 season for early and late plantings (Figure 5.4b). In all the other treatment combinations the effects of irrigation system were not significant. With regard to planting date, leaf disease incidence in the early stages of disease development and associated ranks were highest for early, lowest and equivalent for mid-season and late planted crops for both irrigation systems in year 1 (Figure 5.4a). In year 2, lidis and associated ranks were highest for late, lowest and equivalent for early and mid-season planted crops (Figure 5.4b).

70 70

20.0

60 21.5 22.5 60 24.0 2002/3 15.0 50 50

40 40 13.0

6.5 10.0 10.0 Days to start of disease 10.0 30 10.0 30 5.5 Days to start of disease

FE FL FM SE SL SM FE FL FM SE SL SM Treatment Treatment a b 2002/3 2003/4

Figure 5. 3: Effect of irrigation (S: Sprinkler, F: Furrow) and planting date (E: Early, M: Medium, L: Late) of common bean on the number of days to first ALS disease symptoms after planting (dot: grand median line, data label: rank). Friedman test adjusted for ties: 2002/3 (P < 0.001), 2003/4 (P < 0.001).

100 100

90 90 23.5 80 80 21.5 22.5 70 17.5 70 60 60 50 50 40 16.5 10.0 30 40 9.0 11.0 9.0 11.0 20 5.0 30 11.5 Leaf incidence at start of disease 10 Leaf incidence at start of disease FE FL FM SE SL SM FE FL FM SE SL SM

Treatement Treatment a b 2002/3 2003/4

Figure 5. 4: Effect of irrigation (S: Sprinkler, F: Furrow) and planting date (E: Early, M: Medium, L: Late) of common bean on leaf incidence at first ALS disease symptoms (dot: grand median line, data label: rank). Friedman test adjusted for ties: 2002/3 (P = 0.003), 2003/4 (P = 0.009).

5.4.2.3 Relationship between number of days and leaf disease incidence at the start of the disease There was a significant positive correlation between days to the initial disease development (Figure 5.3) and leaf disease incidence (Figure 5.4) with early planting date (r: 0.853***) and medium planting date (r: 0.858**). The correlation was not significant ns with late planting date (r: 0.148 ).

5.4.2.4 Leaf disease incidence during the growing period The analysis of disease incidence data collected during the growing season is presented in Tables 5.1, 5.2 and in Figure 5.5. The analysis of variance showed that at 6 and 10 weeks after planting (WAP) for both years, and at 8 WAP in 2003/4 season, planting date effects were significant (P < 0.001). The irrigation x planting date interaction was significant (P < 0.05) at 8 WAP in 2003/4 season and 10 WAP in both years. The Friedman test was significant with the test statistic adjusted for ties (P < 0.01) at 4 WAP in both years and at 8 WAP in 2002/3 season.

In the case of irrigation, sprinkler had more plants with leaves affected by ALS (%TLALS) than furrow irrigation at 4 WAP for the late planting date in year 2 (Figure 5.5), and at 10 WAP with early planting date in both years (Table 5.2). Sprinkler irrigation had less %TLALS in year 1 at 10 WAP with medium planting date and in year 2 with early planting date at 8 WAP (Table 5.2). In other treatments, sprinkler-irrigated crop was not significantly different from furrow-irrigated crop (Figures 5.5a, b, c; Table 5.2).

In the case of planting date, early planting date had no %TLALS at 4 WAP in year 1 (Figure 5.5a). In year 2, early and medium planting date had no %TLALS at 4 WAP (Figure 5.5c). In year 1, with sprinkler irrigation, at 10 WAP, early planted crop was not significantly different from late planted crop, and both were attacked more by the pathogen than mid-season planted crop (Table 5.2). With furrow irrigation, in the same year, mid-season planted crop was the least attacked, and was followed by early and late planted crops. At 10 WAP, in year 2, with sprinkler irrigation, planting dates were not

Table 5.1: Effect of planting date on the percentage of common bean plants with leaves infected with ALS (transformed by sqrt) at 6 weeks after planting in 2002/3 and 2003/4 seasons.

6 weeks Planting date 2002/3 2003/4 Early 1.03 a (1.6) 1.56 a (4.7) Medium 10.03 b (100.0) 6.66 b (51.6) Late 9.97 b (99.0) 9.92 c (97.9) P < 0.001 < 0.001 LSD (0.05) 0.31 0.89 CV (%) 8.7 29.3 Means followed by a common letter in a column are not significantly different at LSD (0.05) Figures in bracket are the non-transformed data

Table 5.2: Effect of irrigation and planting date on the percentage of common bean plants with leaves infected with ALS (transformed by sqrt) at 8 weeks in 2003/4 and 10 weeks after planting in 2002/3 and 2003/4.

Planting Sampling date/irrigation system date 10 weeks 2002/3 8 weeks 2003/4 10 weeks 2003/4 Sprinkler Furrow Sprinkler Furrow Sprinkler Furrow Early 10.03 b (100.0) 9.58 b (92.0) 1.99 a (7.3) 4.62 a (26.0) 9.92 a (97.9) 7.75 a (64.6) Medium 8.63 a (74.4) 8.92 a (79.1) 8.21 b (69.6) 7.30 b (58.6) 10.02 a (100.0) 10.02 b (100.0) Late 10.03 b (100.0) 10.03 c (100.0) 10.02 c (100.0) 9.97 c (99.0) 10.02 a (100.0) 10.02 b (100.0) P 0.013 < 0.001 < 0.001 LSD 0.34 1.18 0.68 (0.05) CV (%) 5.1 23.9 10.1 Means followed by a common letter in a column are not significantly different at LSD (0.05). LSD (0.05) to compare means in a row: 0.28 at 10 WAP in 2002/3, 1.10 at 8 WAP in 2003/4, 0.65 at 10 WAP in 2003/4 Figures in bracket are the non-transformed data

F planting date: 10 WAP (2002/3) (P < 0.001), 8 WAP (2003/4) (P < 0.001), 10 WAP (2003/4) (P < 0.001)

100 100 90 24 80 70 60 50 50 20 17 40 19 17 19 30 % plants% ALS % plants% ALS 20 10 10 10 10 10 6 6 0 0 FE FL FM SE SL SM FE FL FM SE SL SM Treatment Treatment a a c 4 WAP (2002/3) 4 WAP (2003/4)

100 19.5 19.5 19.5

13.5

%plants ALS

6.0 6.0 0

FE FL FM SE SL SM Treatment

b 8 WAP (2002/3)

Figure 5. 5: Effect of irrigation (S: Sprinkler, F: Furrow) and planting date (E: Early, M: Medium, L: Late) of common bean on the percentage of plants with leaves infected with ALS (dot: grand median line, data label: rank). Friedman test adjusted for ties: 4 WAP (2002/3) (P = 0.003), 8 WAP (2002/3) (P = 0.002), 4 WAP (2003/4) (P < 0.001).

significantly different (Table 5.2). In other cases early planted crop had lower %TLALS (Tables 5.1 and 5.2) than mid-season and late planted crops. In most cases, mid-season and/or late planted crops followed early planted crop and had high disease incidence (Figures 5.5a, b, c; Tables 5.1, 5.2).

5.4.2.5 Area under disease progress curve leaf incidence For both years, planting date effects were significant (P < 0.001) while the irrigation x planting date interaction was non-significant (Table 5.3). Early planted crop had the lowest area under disease progress curve in both years, and was followed by mid-season and/or late planted crop (Table 5.3). In 2002/3 season, medium and late planting dates were not significantly different. In 2003/4 season, late planted crop had a higher area than mid-season planted crop.

Table 5.3: Effect of planting date on the AUDPC-N (transformed by sqrt) calculated from the percentage of common bean plants attacked by ALS from 4 to 10 WAP in 2002/3 and 2003/4.

Area under disease progress curve leaf incidence Planting date 2002/3 2003/4 Early 10.05 a (102.3) 10.96 a (124.0) Medium 22.58 b (510.1) 17.84 b (331.4) Late 22.82 b (520.8) 24.03 c (577.6) P < 0.001 < 0.001 LSD (0.05) 0.37 1.16 CV (%) 4.0 13.3 Means followed by a common letter in a column are not significantly different at LSD (0.05) Figures in bracket are the non-transformed data

5.4.2.6 Progression of disease incidence The progression of disease incidence is summarized in Figure 5.6 for the two years. The disease started late between 4 and 6 WAP under both irrigation systems, with early planting date for both years and medium planting date in the second year. It started early between 2 and 4 WAP for late planting date in both years and medium planting date in the first year, under both irrigation systems. In 2002/3 mid-season and late-planted crops, for both irrigation systems, had similar disease development patterns. These showed a sharp increase in diseased plants from week 2 and reached a high level of 10.0 (AUDP) by week 6. The early-planted crop showed visible infection only at week 8. This was followed by a steep increase in the number of plants diseased to reach a maximum at week 10. In 2003/4 the disease development patterns were distinct for the three planting

12.0 12.0

10.0 10.0

8.0 8.0 EPD 6.0 MPD 6.0 EPD plants ALS) LPD MPD

4.0 sqrt(% 4.0 LPD sqrt (% plants ALS)

2.0 2.0

0.0 0.0 2W 4W 6W 8W 10W 2W 4W 6W 8W 10W Weeks after planting Weeks after planting 2002/3 sprinkler irrigation 2002/3 furrow irrigation

12.0 12.0

10.0 10.0

8.0 8.0

6.0 6.0

EPD EPD

sqrt(% plants4.0 ALS) sqrt(% plants4.0 ALS) MPD MPD LPD LPD 2.0 2.0

0.0 0.0 2W 4W 6W 8W 10W 2W 4W 6W 8W 10W Weeks after planting Weeks after planting 2003/4 furrow irrigation 2003/4 sprinkler irrigation

88 dates. For both irrigation systems disease development was fastest for late-planted crop and slowest for the early planted. Mid-season planted crop showed an intermediate disease development pattern. The final amount of disease and rate of increase in the numbers of infected plants were lower for the furrow- than sprinkler-irrigated crops only in 2003/4 with early planting date. These differential rates and absolute totals of infected plants corroborate the irrigation x planting date interactions noted for several measurements.

5.4.2.7 Pod disease incidence The analysis of pod incidence data is presented in Figure 5.7. Friedman test was significant with the test statistic adjusted for ties (P < 0.05) in 2002/3. The analysis of variance showed that planting date and the interaction irrigation x planting date were significant (P < 0.001) in 2003/4.

In the case of irrigation, sprinkler-irrigated crop had more percentage of plants with pods infected by ALS (%TPALS) than furrow-irrigated crop in year 2 with early and medium planting dates (Figure 5.7c). Sprinkler irrigation had less %TPALS than furrow irrigation in year 1 with medium planting date (Figure 5.7a). In the other treatments, the two irrigation levels were not significantly different in pod incidence (Figures 5.7a, c).

In the case of planting date, in 2002/3 season, under furrow irrigation, medium planting date had more diseased pods after early and late planting dates which were not significantly different (Figure 5.7a). In 2003/4 season, with furrow irrigation; medium planting date had less diseased pods, followed by early and late planting dates (Figure 5.7b). In the other treatments, planting dates were not significantly different in pod incidence (Figures 5.7a, b).

5.4.3 Disease incidence at ARDA Muzarabani There was no ALS disease infection in the field on both the leaves and pods in both years at ARDA Muzarabani site.

100 13.5 13.5 24.0

15.5 50

11.0 6.5 % plant% pods ALS 0 FE FL FM SE SL SM Treatment a

2002/3

12.0

12.0 10.0

10.0 8.0

8.0

6.0 6.0

4.0

4.0 sqrt(%) plant pods ALS) E M L S F sqrt (%) plant pods ALS) 2.0 2.0

0.0 0.0 E M L S F c b 2003/4 2003/4

Figure 5. 7: Effect of irrigation (S: Sprinkler, F: Furrow) and planting date (E: Early, M: Medium, L: Late) of common bean on pod disease incidence: (a) 2002/3 (dot: grand median line, data label: rank), (b) Comparison of planting dates at each irrigation system in 2003/4, (c) Comparison of irrigation systems at each planting date in 2003/4. Friedman test adjusted for ties: 2002/3 (P = 0.023), F-test 2003/4: PD (P < 0.001), IR x PD (P < 0.001).

5.4.4 Disease severity at UZ Farm 5.4.4.1 Leaf disease severity at the initial development of the disease Planting date and the interaction irrigation x planting date were significant (P < 0.001) for both years (Table 5.4). Irrigation level means computed at each planting date, and planting date means computed at each irrigation level, are presented also in Table 5.4. For irrigation: sprinkler irrigated crop had high lsdis in 2002/3 season with early planting date, and in 2003/4 season with early and late planting date. For the other treatments, irrigation levels at each planting date were not significantly different in lsdis. For planting date, in 2002/3 season, early planted crop had more severe disease at both irrigation levels, although it was not significantly different from the late planted crop with furrow irrigation. Mid-season planted crop had the least disease severity at both irrigation levels in 2002/3 season. In 2003/4 season, under sprinkler irrigation, early planting date was intermediate between medium and late planting date. Under furrow irrigation, there were no significant differences in disease severity amongst the three planting dates.

5.4.4.2 Relationship between number of days and leaf disease severity at the start of the disease There was a significant positive correlation between days to the start of the disease (Figure 5.3) and leaf disease severity (Table 5.4) with early planting date (r: 0.66***). The correlation coefficients were not significant with medium (r: -0.30ns) and late planting ns date (r: -0.08 ).

5.4.4.3 Leaf disease severity during the growing period The analysis of disease severity characteristics from 4 to 10 WAP is presented in Tables 5.5 and 5.6, and in Figures 5.8 and 5.9. Planting date was significant (P < 0.001) at 4 WAP in 2002/3, 8 WAP in 2003/4, and at 10 WAP with combined data analysis over years. Irrigation was significant (P < 0.001) at 4 WAP in 2003/4 and 10 WAP with combined analysis over years. Year was significant (P < 0.05) at 10 WAP. The interaction irrigation x planting date was significant (P < 0.001) at 8 WAP in 2003/4 and 10 WAP. The interactions: year x planting date and year x irrigation x planting date were

91 significant (P < 0.001) at 10 WAP. The Friedman test was significant at 6 WAP (P < 0.01) with the test statistic adjusted for ties in both years.

Table 5.4: Effect of irrigation and planting date on the percentage of leaf area (transformed by sqrt) attacked by ALS at the start of disease infection at UZ Farm in 2002/3 and 2003/4.

Planting date 2002/3 2003/4 Sprinkler Furrow Sprinkler Furrow Early 4.49 c (22.1) 2.53 b (7.7) 2.18 b (6.2) 0.89 a (0.3) Medium 1.56 a (2.0) 1.49 a (1.8) 1.26 a (1.2) 1.19 a (1.0) Late 2.25b (4.7) 2.15 b (4.2) 3.34 c (11.8) 1.20 a (1.0) P < 0.001 < 0.001 LSD (0.05) 0.63 0.50 CV (%) 37.4 42.5 Means followed by a common letter in a column are not significantly at LSD (0.05) LSD (0.05) to compare means in a row for each year: 0.58 in 2002/3, 0.80 in 2003/4 Figures in bracket are the non-transformed data

F-test PD: 2002/3 (P < 0.001), 2003/4 (P < 0.001)

At 10 WAP in 2002/3, early and mid-season planted crops had more %LAALS than in 2003/4 season under both irrigation systems (Figure 5.8c), whereas late planted crop in 2002/3 season had less %LAALS than in 2003/4 again under both irrigation levels. Sprinkler-irrigated crop had more %LAALS than furrow irrigated crop in both years at 10 WAP with early planting date, in 2002/3 with late planting date, and in 2003/4 with medium planting date (Figure 5.8b). Plants under sprinkler irrigation were more affected by the pathogen than those watered using furrow irrigation in 2003/4 for the late planting date at 6 WAP (Figure 5.9b). Similar observations were noted in the second year for the late planted crop at 4 and 8 WAP, and for mid-season planted crop at 8 WAP (Table 5.6). For the other treatments, sprinkler irrigated crop was not significantly different in disease severity from furrow irrigated crop (Table 5.6; Figures 5.8 and 5.9).

Data related to planting dates are summarized in Figures 5.8a and 5.9a, b; and Tables 5.5 and 5.6. Zero %LAALS was observed for early planting date in 2002/3 from 4 to 8 WAP

92 under both irrigation systems, and in 2003/4 under sprinkler irrigation. In 2003/4, no disease was observed for early planting date and medium planting date at 4 WAP under Table 5.5: Effect of planting date on the percentage of common bean leaf area (transformed by sqrt) affected by ALS obtained at UZ Farm in 2002/3.

4 WAP 8 WAP Planting date 2002/3 2002/3 Medium 1.52 a (1.9) 4.06 a (16.5) Late 2.20 b (4.4) 3.74 a (14.1) P < 0.001 0.05 LSD(0.05) 0.15 NS CV (%) 16.3 16.8 Means followed by a common letter in a column are not significantly different at LSD (0.05) Figures in brackets are the non-transformed data

Table 5.6: Effect of irrigation and planting date on the percentage of common bean leaf area (transformed by sqrt) attacked by ALS observed at UZ Farm, 4 WAP with LPD and 8 WAP in 2003/4 with all planting dates.

Planting date Sprinkler Furrow 4WAP Late 3.34 (11.8) 1.14 (0.9) P < 0.001 CV (%) 29.5 8WAP Early 0.73 a (0.0) 0.88 a (0.3) Medium 2.75 b (7.3) 1.87 b (3.2) Late 5.85 c (33.8) 4.68 c (21.7) P < 0.001 LSD(0.05) 0.29 CV (%) 14.6 Means followed by a common letter in a column at 8 WAP are not significantly different at LSD (0.05) LSD (0.05) to compare means in a row: 0.48 at 4 WAP, 0.30 at 8 WAP Figures in bracket are the non-transformed data

F-test PD 8 WAP: 2003/4 (P < 0.001).

93 both irrigation systems (Table 5.6). Generally, early planting date had plants with less %LAALS than other planting dates at each evaluation period, although the level was not significantly different from medium planting date at 4 WAP in 2003/4 under both irrigation levels. Mid-season planted crop was the most affected in 2002/3 season, under both irrigation systems at 10 WAP. For the other treatments, late planted crop was the most affected by the disease.

5.4.4.4 Area under leaf disease severity progress curve Data related to AUDPC-V are summarized in Table 5.7. In both years, planting date was significant (P < 0.001). The double interaction irrigation x planting date was significant (P < 0.001) in both years. Sprinkler-irrigated crop had higher AUDPC-V than furrow- irrigated crop with early and late planting date in both years, and with medium planting date in 2003/4. Sprinkler was not significantly different from furrow irrigation with medium planting date in 2002/3. In both years and irrigation levels, early planted crop had the lowest AUDPC-V. In 2002/3, mid-season planted crop had the highest area under furrow irrigation, and was not significantly different from late planted crop under sprinkler irrigation. In 2003/4, late planted crop had the highest AUDPC-V.

5.4.4.5 Progression of disease severity Curves describing the progression of common bean ALS disease leaf severity across evaluation periods for each planting date under different irrigation methods are presented in Figure 5.10. Sprinkler-irrigated crop had a higher rate of disease increase and final leaf severity than furrow-irrigated crop, with early and late planting date in 2002/3; and in 2003/4 with all planting dates. In 2002/3, for medium planting date; furrow-irrigated crop had higher disease rate than sprinkler-irrigated crop. In the same season, from 2 to 8 WAP, under both irrigation methods, early planted crop had the lowest rate of disease increase, and consequently low final disease severity. After that 8 WAP, mid-season planted crop showed the highest rate of increase over early and late planted crops under both irrigation methods. In year 2003/4, from 2 to 10 WAP, early planted crop had the lowest

8 8 EPD MPD LPD YR1 YR2 7 7

6 6

5 5

4 4 sqrt(% leaf area ALS) leaf sqrt(% 3 3 sqrt(% leaf area ALS) leaf area sqrt(% 2 2

1 1

0 0 SYR1 FYR1 SYR2 FYR2 ESIR MSIR LSIR EFIR MFIR LFIR

a c Irrigation x year Planting date x irrigation

8 SIR FIR 7

3 sqrt(% leaf area ALS) leaf area sqrt(% 2

0 EYR1 MYR1 LYR1 EYR2 MYR2 LYR2

b Planting date x year

Figure 5. 8: Comparison of factor levels at the interaction between other factors using the percentage of common bean leaf area attacked by ALS observed at 10 WAP in 2002/3 and 2003/4 at UZ Farm: (a) Planting dates (E,M,L) at each combination irrigation x year, (b) Irrigation (IR) levels (S,F) at each combination planting date x year, (c) Years (YR 1,YR 2) at each combination planting date x irrigation. F-test YR (P = 0.048), IR (P < 0.001), PD (P < 0.001), YR x PD (P < 0.001), IR x PD (P < 0.001), YR x IR x PD (P < 0.001).

30 30

20 20 21.0 23.0

10 24 10 14.0 14.0 20 % leaf% area ALS leaf% area ALS 12 16 6.5 5.5 7 5 0 0 FE FL FM SE SL SM FE FL FM SE SL SM

Treatment Treatment a a b b

2002/3 2003/4

Figure 5. 9: Effect of irrigation (S, F) and planting date (E, M, L) of common bean on the percentage of leaf area attacked by ALS at 6 WAP (dot: grand median line, data label: rank) obtained at UZ Farm: (a) in 2002/3, (b) in 2003/4. Friedman test adjusted for ties: 2002/3 (P = 0.002), 2003/4 (P = 0.001).

Table 5.7: Effect of irrigation and planting date of common bean on the AUDPC-V (transformed by sqrt) calculated from the percentage of leaf area attacked by ALS observed at UZ Farm in 2002/3 and 2003/4 from 4 to 10 weeks after planting.

Planting date 2002/3 2003/4 Sprinkler Furrow Sprinkler Furrow Early 4.74 a (24.0) 2.61 a (8.6) 3.02 a (10.0) 1.52 a (2.7) Medium 9.58 b (92.9) 9.60 c (93.3) 6.93 b (48.8) 4.77 b (23.2) Late 9.31 b (88.0) 7.84 b (62.4) 12.35 c (153.6) 9.32 c (87.7) P < 0.001 < 0.001 LSD (0.05) 0.82 0.55 CV (%) 16.1 12.3 Figures followed by the same letter in a column are not significantly different at LSD (0.05) LSD (0.05) to compare means in a row: 0.82 for 2002/3, 1.03 for 2003/4 Figures in bracket are the non-transformed data

F-test PD: 2002/3 (P < 0.001), 2003/4 (P < 0.001)

96 disease rate and final disease severity, followed by mid-season and late planted crops. Percentage LAALS increase for early planted crop occurred at 8 - 10 WAP in year 1 under both irrigation levels, and in year 2 under sprinkler irrigation. It started increasing 6 - 8 WAP in 2003/4 season under furrow irrigation. It started again at 4 - 6 WAP in 2003/4 for mid-season planted crop under both irrigation systems. Disease started increasing at 2 - 4 WAP for the late planted crop in both years and mid-season planted crop in 2002/3 under both irrigation systems.

5.4.4.6 Pod disease severity Data describing pod disease severity are presented in Figure 5.11. Planting date and the interaction irrigation x planting date were significant (P < 0.001) in 2003/4 season. The Friedman test was significant (P < 0.05) in 2002/3 with the test statistic adjusted for ties. In the case of irrigation, sprinkler and furrow systems, there were no significant pod disease severity differences in 2002/3, and in 2003/4, plants after sprinkler irrigation were more affected than after furrow irrigation for all planting dates. In 2003/4, under furrow irrigation, early planted crop was intermediate in pod severity between mid-season and late planted crops. For the other treatments, early planted crop had low pod disease severity, and was not, in some cases, significantly different from mid-season and/or late planted crop under both irrigation systems. Mid-season planted crop was the most affected in 2002/3 under furrow irrigation, and late planted crop in 2003/4 under both irrigation systems.

5.4.5 Disease severity at ARDA Muzarabani No disease severity scores were made at Muzarabani as there was no disease infection in the field on both the leaves and pods.

5.4.6 Defoliation at UZ Farm 5.4.6.1 Defoliation at the start of the disease All the factors studied (year, irrigation, planting date) and their interactions were significantly different (P < 0.05) (Figure 5.12).

Figure 5. 10: Evolution of common bean ALS disease leaf severity within planting dates across evaluation periods (weeks) obtained from data observed at UZ Farm in 2002/3 and 2003/4.

23.0 15.0 8 7

6 5

3 2 15.5 pod% area ALS 1 12.0 8.0 10.5 0

FE FL FM SE SL SM a 2002/3

8 8 7 7 E ) S 6 6 M 5 5 F 4 L 4

sqrt (% pod areaALS) 3

3 sqrt (% pod areaALS

2 2

1 1

0 0 S F E M L

b c 2003/4 2003/4

Figure 5. 11: Effect of irrigation (S, F) and planting date (E,M,L) of common bean on pod ALS severity obtained at UZ Farm: (a) 2002/3 (dot: grand median line, data label: rank), (b) Comparison of planting dates at each irrigation system in 2003/4, (c) Comparison of irrigation systems at each planting date in 2003/4. Friedman test adjusted for ties: 2002/3 (P = 0.041). F-test 2003/4: PD (P < 0.001), IR x PD (P < 0.001).

0.9 0.9

0.88 EPD YR1 YR2 0.85 0.86 MPD

0.84 LPD 0.8 0.82

0.75 0.8 sqrt(rrdef)

sqrt(rrdef) 0.78 0.7 0.76

0.74 0.65 0.72

0.6 0.7 SE SM SL FE FM FL YR1S YR1F YR2S YR2F

a c Irrigation x planting date Year x irrigation 0.9

0.85 S F

0.8

0.75 sqrt(rrdef)

0.7

0.65

0.6 YR1E YR1M YR1L YR2E YR2M YR2L

b Year x planting date

Figure 5. 12: Effect of studied factors on common bean leaf defoliation observed at the start of the ALS disease at UZ Farm in 2002/3 (YR 1) and 2003/4 (YR 2), at the interaction of other factors: (a) Comparison of YRs at the interaction IR x PD, (b) Comparison of irrigation levels at the interaction YR x PD, (c) Comparison of planting dates at the interaction YR x IR. F-test: YR (P = 0.002), IR (P < 0.001), YR x IR (P = 0.02), PD (P < 0.001), YR x PD (P < 0.001), IR x PD (P < 0.001), YR x IR x PD (P < 0.001).

100

Table 5.8: Effect of irrigation and planting date of common bean on the relative rate of defoliation (transformed by sqrt) obtained at UZ Farm at 10 WAP in years 2002/3 and 2003/4.

Planting date 2002/3 2003/4 Sprinkler Furrow Sprinkler Furrow Early 0.8551 ab (0.23) 0.7973 a (0.14) 0.8154 a (0.17) 0.7532 a (0.07) Medium 0.8876b (0.29) 0.8912 c (0.29) 0.8715 b (0.26) 0.8389 b (0.20) Late 0.8547a (0.23) 0.8229 b (0.18) 0.8779 b (0.27) 0.8742 c (0.26) P < 0.001 < 0.001 LSD(0.05) 0.0178 0.0127 CV(%) 3.0 2.2 Means followed by a common letter in a column are not significantly at LSD (0.05) LSD (0.05) to compare means in a row for each year: 0.0185 in 2002/3, 0.0161 in 2003/4 Figures in bracket are the non-transformed data

F-test planting date: 2002/3 (P < 0.001), 2003/4 (P < 0.001)

The means of the relative rate of defoliation of the common bean for the two years are presented in Figure 5.12a. The 2002/3 crop was more defoliated than the 2003/4 crop under both irrigation systems with early planting date. More defoliation for year 1 crop was also observed in the late planting date under furrow irrigation. On the other hand, the 2002/3 crop had less defoliation than the 2003/4 crop under sprinkler irrigation with late planting date. There were no significant differences in plant defoliation between years at both irrigation systems for medium planting.

The means of the relative rate of defoliation for irrigation systems are presented in Figure 5.12b. Plants watered with sprinkler irrigation were more defoliated than those under furrow irrigation for early planting date in both years, and with late planting date in year 2. In other cases, irrigation levels were not significantly different in plant defoliation.

The means of the relative rate of defoliation for planting dates are presented in Figure 5.12c. Generally, medium planting date resulted in less plant defoliation than the other planting dates. This scenario was observed in 2002/3 season under both levels of irrigation, and in 2003/4 season under sprinkler irrigation. In this situation, late and early planted crops were the highly defoliated. In year 2, with furrow irrigation, early planted 101 crop had low defoliation. Late planted crop was the most defoliated and not significantly different from mid-season planted crop in year 2 under furrow irrigation.

5.4.6.2 Relationship between number of days and disease defoliation at the start of the disease There was a significant positive correlation between days to the start of the disease (Figure 5.3) and disease defoliation (Figure 5.12) with early planting date (r: 0.75***). The correlation coefficients were not significant with medium (r: -0.23ns) and late ns planting date (r: -0.08 ).

5.4.6.3 Defoliation during the growing period Defoliation characteristics from 4 to 10 WAP are presented in Table 5.8 and Figure 5.13. The factor year and the interaction year x irrigation were significant (P < 0.01) at 4 and 6 WAP. The factor irrigation, the interactions year x planting date and year x irrigation x planting date, were significant (P < 0.001) from 4 to 8 WAP. The factor planting date and the interaction irrigation x planting date were significant (P < 0.001) across all evaluation periods.

Defoliation data summary for the two years are presented in Figures 5.13a, d and g, and in the Table 5.8. Generally more defoliation occurred in 2002/3 than 2003/4 crop, although there were treatments where defoliation in 2003/4 crop was high or not significantly different to the one observed in 2002/3 crop. There was more defoliation in 2002/3 than in 2003/4 crop for the medium planting date across all evaluation periods, and also for the late planting date under furrow irrigation at 4 and 6 WAP. Leaf disease defoliation was higher in 2003/4 than 2002/3 crop for the late planting date under sprinkler irrigation at 4 WAP, and under both irrigation levels at 8 WAP. For the other treatments, the two years were not significantly different in plant defoliation.

Defoliation data summary for irrigation methods are presented in Figures 5.13 b, e and h, and in the Table 5.8. Generally defoliation was not significantly different for plants after sprinkler and furrow irrigation. However, plants after sprinkler had more defoliation than

102 after furrow irrigation in both years, at 10 WAP for the early planting date, and 8 WAP for the late planting date. A similar observation was made at 10 WAP for the late planted crop in 2002/3 and the mid-season planted crop in 2003/4, at 4 WAP for the late planted crop, and at 6 WAP for the mid-season and late planted crops. The rest of the treatments under sprinkler irrigation were not significantly different in plant defoliation from treatments under furrow irrigation.

Defoliation data summary for planting dates is presented in Figures 5.13c, f and i, and in the Table 5.8. No defoliation was observed for early planted crop in year 1 from 4 to 8 WAP under both irrigation systems, and in year 2 under sprinkler irrigation. In year 2, defoliation was not observed for early planted crop from 4 to 6 WAP under furrow irrigation, and for mid-season planted crop at 4 WAP under both irrigation levels. Generally, early planted crop had lower defoliation, although there were cases where it was not significantly different from mid-season and/or late planted crop. In most cases, the late planted crop was the most defoliated.

5.4.6.4 Area under disease progress curve defoliation The analysis of variance showed that planting date for both years and the interaction irrigation x planting date in 2003/4 were significant (P < 0.001) (Table 5.9). Mean separation of different significant factors’ levels is presented in the Table 5.9. In the case of irrigation, the area under disease progress curve defoliation (AUDPC-L) was higher for plants after sprinkler than after furrow irrigation in 2003/4 for medium and late planting date. The two irrigation levels of the early planted crop in 2003/4 were not significantly different. In all planting date cases studied, there was an increase of the AUDPC-L from early to late planted crop.

5.4.6.5 Progression of leaf defoliation due to disease Curves describing the progression of leaf defoliation due to the disease across evaluation periods for each planting date under different irrigation methods are presented in Figure 5.14. Plants after sprinkler irrigation had a higher rate of defoliation increase and a final high value than plants after furrow irrigation, with early and late planting date in 2002/3;

103 and in 2003/4 with all planting dates. In 2002/3, plants after sprinkler and after furrow irrigation had the same rate of defoliation increase for the medium planting date. In 2002/3 from 2 to 8 WAP, under both irrigation methods, early planted crop had the lowest rate of defoliation increase, and consequently low final leaf defoliation due to the disease. After that period, mid-season and late planted crops cross, and mid-season crop showed the highest rate of increase over early and late planted crops under both irrigation methods. In 2003/4, from 2 to 10 WAP, early planted crop had the lowest defoliation rate and final disease defoliation. It was followed by mid-season and late planted crops. The earlier observation of disease defoliation for early planted crop was situated at 4 - 6 WAP in 2002/3 under both irrigation levels, and in 2003/4 under furrow irrigation. In 2003/4, with early planted crop under sprinkler irrigation, it was situated at 6 - 8 WAP. For mid- season planted crop, it was situated at 2 - 4 WAP in 2002/3 and 4 - 6 WAP in 2003/4 under both irrigation systems. It was situated again at 2 - 4 WAP with late planted crop in both years under both irrigation systems.

5.4.7 Defoliation at ARDA Muzarabani There was no disease symptom on leaves and pods, indicating that there was no defoliation due to ALS.

5.4.8 Seed yield at UZ Farm The analysis of variance done for each year showed that planting date was significant (P < 0.001) in both years, and the interaction irrigation x planting date in year 2 (Table 5.10). Irrigation systems and planting date means are presented in Table 5.10. In 2003/4, with late planting date, furrow-irrigated crop resulted in plants with higher yields than sprinkler-irrigated crop. In the same year, for early and medium planting date, the yield of plants after sprinkler irrigation was not significantly different from the yield of plants after furrow irrigation. Early planting date resulted in greater yield than the other planting dates. Mid-season planted crop had greater yield than late planted crop in 2003/4 under sprinkler irrigation. For the other treatments, mid-season and late planted crops were not significantly different for seed yield.

104

0.84 0.90 YR1 YR2 0.90 YR1 YR2 0.82 YR1

0.80 YR2 0.85 0.85 0.78 sqrt(rrdef) sqrt(rrdef) sqrt(rrdef) 0.76 0.80 0.80 0.74

0.72 0.75 0.75

0.70

0.68 0.70 0.70

0.66

0.64 0.65 0.65 SE SM SL FE FM FL SE SM SL FE FM FL SE SM SL FE FM FL a g d

0.84 0.90 0.90 0.82 S F S F S F 0.80 0.85 0.85

0.78 sqrt(rrdef) sqrt(rrdef) sqrt(rrdef) 0.76 0.80 0.80 0.74 0.72 0.70 0.75 0.75 0.68 0.66 0.70 0.70 0.64 0.62 0.65 0.65 YR1E YR1M YR1L YR2E YR2M YR2L YR1E YR1M YR1L YR2E YR2M YR2L YR1E YR1M YR1L YR2E YR2M YR2L b e h

0.84 0.90 EPD 0.90 EPD MPD LPD EPD MPD LPD 0.82 MPD

0.80 LPD 0.85 0.85 sqrt(rrdef) sqrt(rrdef)

0.78 sqrt(rrdef)

0.76 0.80 0.80 0.74

0.72 0.75 0.75 0.70

0.68 0.70 0.70 0.66

0.64 0.65 0.65 YR1S YR1F YR2S YR2F YR1S YR1F YR2S YR2F YR1S YR1F YR2S YR2F c f i

Figure 5. 13: Effect of factors studied on common bean defoliation observed at UZ Farm from 4 to 8 WAP in 2002/3 (YR 1) and 2003/4 (YR 2), at the interaction of other factors: Comparison of years at the interaction irrigation (S, F) x planting date (PD) (E,M,L) using rrdef4 (a), rrdef6 (d), rrdef8 (g); Comparison of irrigation levels at the interaction year x planting date using rrdef4 (b), rrdef6 (e), rrdef8 (h); Comparison of planting dates at the interaction year x irrigation using rrdef4 (c), rrdef6 (f), rrdef8 (i). F-test (rrdef4, rrdef6, (rrdef8): YR x IR x PD (< 0.001).

105

Table 5.9: Effect of irrigation and planting date of common bean on the AUDPC-L obtained at UZ Farm in 2002/3 and 2003/4.

Planting date 2002/3 2003/4 Mean Sprinkler Furrow Early 0.09 a 0.08 a 0.05 a Medium 0.52 b 0.33 b 0.23 b Late 0.55 c 0.71 c 0.51 c P < 0.001 < 0.001 LSD (0.05) 0.03 0.03 CV ( %) 13.2 12.6 Means followed by a common letter in a column are not significantly different at LSD (0.05) LSD (0.05) to compare means in a row: 0.04 in 2003/4

F-test 2003/4: PD (P < 0.001)

5.4.9 Seed yield at ARDA Muzarabani The combined analysis over years showed that year, irrigation and the interaction year x irrigation were significant (P < 0.01) (Table 5.11). Means for years and irrigation systems are presented in Table 5.11. Plants grown in 2003/4 achieved greater yields than plants grown in 2002/3 under furrow irrigation, and plants grown in the two years were equally yielding under sprinkler irrigation. In both years, plants after sprinkler irrigation achieved greater seed yields than after furrow irrigation.

5.4.10 Seed health at UZ Farm 5.4.10.1 Seed planted In year 1 there was no Pseudocercospora griseola infection on seed planted. In year 2, seed infection was low evaluated at 0.2 %.

5.4.10.2 Seed harvested Data comparing irrigation systems and planting dates are presented in Figures 5.15a and b. The stastistic test adjusted for ties was significant (P < 0.01) in both years (Figure 5.15).

106

0.9000 0.9000 EPD EPD MPD MPD 0.8500 0.8500 LPD LPD

0.8000 0.8000

0.7500 0.7500 sqrt(rrdef) sqrt(rrdef)

0.7000 0.7000

0.6500 0.6500 2W 4W 6W 8W 10W 2W 4W 6W 8W 10W

Time of evaluation Time of evaluation 2002/3 Sprinkler irrigation 2002/3 Furrow irrigation 0.9000 EPD 0.9000 MPD EPD 0.8500 LPD 0.8500 MPD LPD

0.8000 0.8000 sqrt(rrdef) sqrt(rrdef) 0.7500 0.7500

0.7000 0.7000

0.6500 0.6500 2W 4W 6W 8W 10W 2W 4W 6W 8W 10W Time of evaluation Time of evaluation

2003/4 Sprinkler irrigation 2003/4 Furrow irrigation

Figure 5. 14: Progression of common bean leaf defoliation due to ALS within planting dates across evaluation periods obtained from data observed at UZ Farm in 2002/3 and 2003/4.

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Table 5.10: Effect of planting date and irrigation system on common bean seed yield (kg/ha) obtained at UZ Farm in 2002/3 and 2003/4.

Planting date 2002/3 2003/4 Mean Sprinkler Furrow Early 1661 b 1451c 1234b Medium 716 a 745b 809a Late 652 a 510a 785a P < 0.001 < 0.001 LSD (0.05) 178 148 CV (%) 35.4 22.8 Means followed by a common letter in a column are not significantly different at LSD (0.05) LSD (0.05) to compare means in a row: 228 in 2003/4

F-test 2003/4: PD (P < 0.001)

Table 5.11: Effect of year and irrigation system on common bean seed yield (kg/ha) obtained at ARDA Muzarabani in 2002/3 and 2003/4.

Year Sprinkler Furrow 2002/3 2827 a 1276 a 2003/4 3099 a 2267 b P 0.002 LSD (0.05) 285 CV (%) 18.4 Figures followed by the same letter in a column are not significantly different at LSD (0.05) LSD (0.05) to compare means in a row: 309

Ftest: YR (P < 0.001), IR (P < 0.001)

Considering irrigation systems, with medium planting date, seed obtained from plants after sprinkler was more infected than that from plants after furrow irrigation in year 1; and less in year 2. In other combinations, seed infection of plants after sprinkler irrigation and plants after furrow irrigation was equivalent. Early planting resulted in low seed infection which sometimes was not different from medium and/or late plantings. Mid-season and late planted crops had the highest seed infection levels. Generally seed harvested in year 1 was less infected than that of year 2 with late planting date at both irrigation systems; and

108 with medium planting date under furrow irrigation. In other cases, both years were equivalent in seed infection.

5.4.11 Seed health at ARDA Muzarabani As for UZ Farm, in year 1 there was no P. griseola infection on seed planted, and in year 2, seed infection was low evaluated at 0.2 %. Seed harvested was not infected by P. griseola.

30 30

20 20 21

18 10 10 13 24.0 % seed% P. griseola % seed% griseola P. 11.5 11.5 11.5 11.5 14.0 7 6 0 0

FE FL FM SE SL SM FE FL FM SE SL SM

a Treatment b

2002/3 2003/4

Figure 5. 15: Effect of irrigation system (Sprinkler: S, Furrow: F) and planting date (Early: E, Medium: M, Late: L) on the percentage of common bean seed harvested at UZ Farm infected by P. griseola (dot: grand median line, data label: rank): a) in 2002/3, b) in 2003/4. Friedman test adjusted for ties: 2002/3 (P = 0.003), 2003/4 (P = 0.009).

5.5 Discussion At UZ Farm, summer early planted crop was generally the least affected as shown by the disease incidence, severity and defoliation results. In other studies, early planting date reduced disease incidence, intensity, severity (Sindham and Bose, 1980a; Orawu et al., 2001; Subedi et al., 2007) and consequently defoliation (Sindham and Bose, 1980a; Saettler, 1994; Willocquet et al., 2004). Planting early delayed disease epidemic onset by decreasing its rate of development, and further reducing the final amount of disease

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(Navas-Cortés et al., 1998). On the other hand, several researchers obtained reduced disease infection with delayed planting (Bhatti and Kraft, 1992; Sugha et al., 1994; University of Nebraska, 2000; Schlachter et al., 2000). At ARDA Muzarabani, no ALS infection was observed during the two years of this study, making winter planting the best in the disease control.

The effect of planting date on disease infection can be explained by the analysis of weather variables. Compared to summer medium and late planting dates; weather conditions prevailing in winter and summer early planting date in both years were less favourable for disease infection and development. Most disease epidemics are associated with increased rainfall (Orawu et al., 2001), excessive wet weather (Schlachter et al., 2000), increased temperature (Bhatti and Kraft, 1992; Sugha et al., 1994) and increased relative humidity (Sindham and Bose, 1980a). Differences in relative humidity and temperature (Sindham and Bose, 1980a; Rodriguez et al., 1999; Jesus et al., 2001), rainfall and temperature (Navas-Cortés et al., 2000), and relative humidity and precipitation (Sindham and Bose, 1980a) are important factors in determining the extent of diseases development. The common bean plant and the fungus P. griseola share a range of temperatures. The optimum temperature for plant growth ranges from 15.6 to 21.1 oC; the maximum near 27 oC and the minimum near 10 oC (Michaeis, 1994), whereas infection by P. griseola and disease occur generally at moderate temperature (16 - 28 ºC) and develop maximally at 24 ºC (Saettler, 1994). Therefore, there could be a better crop growth and favourable climatic conditions which could be a congenial base for the development of the fungus (Sindham and Bose, 1980a).

There was a significant positive correlation between days to the start of the disease and leaf disease incidence at the same period with early and medium planting dates. The correlation was not significant with late planting date (r: 0.148ns). The long time taken for plant infection was also associated with a high disease severity and defoliation for early planting date. This is in accordance with the findings of Plaut and Berger (1981) in their studies of diseases infection rates, who concluded that low initial disease was compensated for by faster rates of disease increase. This behavior may be pathological,

110 resulting from ALS disease infection and physiological, linked with the developmental stage.

For disease incidence, the earliest infection observed in this study was between 2 and 4 WAP for late planted crop date in both years and mid-season planted crop in year 1. This may find its explanation with weather data. Weather conditions prevailing in medium and late planting dates were more favourable to disease infection. For these two planting dates, most high weather values started early (2 to 4 WAP) and thus contributing to early disease development. This is in accordance with the findings of Sindham and Bose (1980a), who observed that three week old plants were less susceptible than 4, 5 and 6 weeks old plants. This is supported by Agrios (2005), who reported that the hosts or their parts are resistant during the early adult period for potato late blight and tomato early blight. Disease severity and defoliation were not good indicators of the start of disease infection, as shown by the lack of disease expression in the case of early planting date in year 2 from 4 to 6 WAP under sprinkler irrigation.

The lack of difference between the two irrigation systems can be explained by the analysis of weather variables. Except the duration of water longer for sprinkler than furrow irrigation, all the other weather characteristics were not significantly different in duration and daily mean variables between irrigation systems. This is not in agreement with Palti and Stettiner (1959), Rotem et al. (1968), Scherm and van Bruggen (1995), and Schwartz and Mohan (2008) who found more disease development in crops watered with sprinkler than furrow irrigation. Despite some trends observed with planting dates and irrigation systems at UZ Farm, the effect of those factors on disease incidence, severity and defoliation changed from the start of the disease to 10 WAP. As indicated by Navas- Cortés et al. (2000), the difference in disease progression as shown by progress curves may be due to weather modification during the season. In addition, a number of factors such as conduciveness of weather, virulence of race prevalent, and their interactions may be responsible for the observed deviations in the disease progress curves (Navas-Cortés et al., 2000). The fungus exhibits multiple infection, pathogenic variability and pathotypes

111 have been divided into Andean, Mesoamerican and Afro-Andean (Mahuku et al., 2002; Garcia et al., 2006).

The decrease in disease incidence in medium planting date from 8 to 10 WAP in year 1 after sprinkler and furrow irrigation was associated with increased disease severity and leaf defoliation. Disease severity and leaf defoliation increased with sprinkler and furrow irrigation from 8 WAP to 10 WAP. This could have been both due to disease and non- disease induced defoliation associated with sampling methods. The result of disease- induced defoliation includes increased disease infection in medium or lower layers of the canopy than at its upper level (Allorent et al., 2005). For common bean indeterminate varieties, leaves at the lower nodes die first, followed by leaves at progressively higher nodes on the main stem, and later on branches (Laing et al., 1984). This resulted in plants with few upper leaves or completely defoliated. In addition the basis of sampling was the area (the quadrant) for disease incidence, and the plant for disease severity. Defoliation was computed from the number of leaflets per plant counted on plants sampled using a quadrant.

Winter and early summer resulted in best yields, followed by medium and/or late planting dates. This trend was supported by the results obtained with weather factors which affected disease incidence, severity and defoliation. These conditions could have prevented infection, as they were unfavourable as stated by Singh (2006). Survival, infection and spread of pathogenic organisms can be controlled using unfavourable climatic conditions such as rainfall, relative humidity and temperature (Schwartz and Gálvez, 1980; Sheppard, 1983; Maude, 1996). Daily weather variables which affect yield include rainfall, temperature and air moisture. Temperature is an important weather factor influencing yield, and is controlled mainly by radiation (Fisher, 1984; Squire 1990). At UZ Farm, seed yield decreased from early to late planting date due to increased disease infection. The results are in agreement with the findings of Rodriguez et al. (1999) who observed a decrease in yield from the 1st to the 3rd planting date due to subsequent increase of ALS and rust severity. Sindham and Bose (1980a) obtained bean yield increase from 1st April to 1st May, and yield decrease from 15th May to 1st August

112 planting dates, but this was not related to disease intensity. Hang and Priest (2002) found that late planting date treatment yields tended to be higher than the early planted date treatments regardless of the bean line’s length of growing season requirement. Shao (1984) identified mid-March as the best season for yield for four bean cultivars tested in Mbeya (Tanzania) which was characterized by lowest disease pressure. Kelly (2001) obtained a reduction of wheat yield by barley yellow dwarf virus infection with early crop planted late September in southern Kansas from 1990 to 1995. Grain yield of late planted wheat in November was more variable and depended on environmental conditions. The best grain yield was observed with intermediate planting dates in mid to late October in most years.

Dry matter production can be limited by the rate of transpiration as affected by restrictions of the canopy (e.g. senescence) (Fisher, 1984). The effect of defoliation on bean yield was demonstrated by Edje and Seyani (1979), who obtained different yield levels (kg/ha) for different artificially defoliated zones on Canadian Wonder bean type, cv 1199: top third (A) (800.06), middle third (B) (962.7), bottom third (C) (706.3) and control non defoliated (1083.9). In this regard all the zones functioned below their photosynthetic efficiency, zone B being the most efficient and zone C the least.

Generally, with summer crop, where the two irrigation systems were used to supplement rainfall, the yield obtained with plants after sprinkler was not different from that of plants after furrow irrigation. On the other hand, in winter, where all the water was applied by irrigation, the yield of the crop after sprinkler irrigation was greater than the corresponding yield after furrow water supply. These results were supported by the findings of Boldt et al. (1996) who reported that sprinkler system corn yields were reduced by 13 %, while furrow irrigation showed a reduction varying from 7 to 19 %. This indicated that plants subjected to sprinkler irrigation could be low, equally or high yielding than those subjected to furrow irrigation. After sprinkler irrigation, Russet Burbank potato was less yielding (Trout et al., 2008) than after furrow irrigation, common bean more (Robertson and Frazier, 1982) and less yielding (White and Singh, 1991), corn (Turkington et al., 2004), potato (Firouzabadi, 2012) and cotton (Cetin and

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Bilgel, 2002) less yielding. With the summer crop, results from disease incidence, severity and defoliation analysis supported those obtained with irrigation systems. The use of sprinkler irrigation created an environment conducive to better seed yield than furrow irrigation in the winter crop. As explained by Trout et al. (2008), compared to furrow, sprinkler irrigation creates less water stress by more uniform application of water, causes less leaching of nitrogen from the root zone, and lower soil temperatures due to the sprinkler evaporative cooling system.

The infection by Pseudocercospora griseola of seed planted was very low with the maximum of 0.2 % observed in year 2. This is in accordance with laboratory findings showing the absence of the pathogen in home-saved seed from Chinyika Resettlement Area, and with the same results obtained by Kutywayo (2000) using the same seed type. The pathogen was not identified in seed varieties in Kenya by Buruchara (1990). Nevertheless, Manyangarirwa (2001) reported a mean seed infection of 7.2 % obtained in a countrywide large-scale collection. This low infection of planted seed was an indication that the main source of field primary inoculum was environmental. The inoculum might come from freshly infected soil, infected straws, off season crops and volunteer plants (Sengooba 1976), the air, rainfall and irrigation water. Tolerance levels for seedborne fungal pathogens vary from 0 to 7 % of seed infected (Gabrielson, 1988). Triza (2009) using the common bean cultivar Rosecco-GLP-2 found that a seed sample having 15 % incidence level of P. griseola, when planted in the field could influence disease progress and the resulting yield.

Considering irrigation systems, generally, the seed harvested after sprinkler irrigation was equally infected than the seed harvested after furrow irrigation. In the case of planting dates, the winter crop seed was not infected by the pathogen, early planting resulted in low seed infection, mid-season and late planted crops had the highest seed infection as the result of the combined action of weather and infection of the host by the disease. Seed infection levels were affected by weather conditions prevailing in EPs within planting dates. Variations were observed also in weather variables during the growing season and the periods of flowering and seed development (4 - 10 WAP), which are decisive for

114 infection as indicated by Neergaard (1979). According to the same author wet weather, warm conditions, and high temperatures during that period are conducive to high seed infection.

The levels of seed infection were supported by the absence of disease infection in the winter crop and less disease observed on leaves and pods with early planted crop in summer. In fact, weather conditions in those two planting dates were limiting disease infection, development and progression. On the other hand weather conditions prevailing in medium and late planting dates were more favourable to disease development and progression. Weather conditions prevailing at maturity were favourable to harvest with winter and LPD. Avila et al. (2003) observed that sowing soybean mid-season in November resulted in seeds with superior physiological and health quality. The same authors reported that advancing or delaying sowing dates had adverse effects on soybean seed production with regard to their sanitary quality. Conditions of high humidity and water at maturity for E and MPDs, which varied with years, might interfere negatively with harvest operations and seed quality. Those special conditions presume harvesting the crop just at physiological maturity to avoid prolonged humid conditions, and improving facilities for drying plants/pods after harvest and seed after processing.

Total seed cleaning was obtained in winter crop in both years under both irrigation systems. A low level of P. griseola infection was obtained for early planting date, with a maximum median of 0.06 % in year 2 under furrow irrigation. Good seed was still obtained since the maximum level of the pathogen in that case was less than 1 % as defined by Schwartz and Gálvez (1980). Conditions conducive to high seed infection occurred with late planting date in year 2 under sprinkler irrigation (median: 15.2 %) and furrow irrigation (median: 14.8 %).

Differences between years were observed at UZ Farm with disease infection (high infection obtained in year 1), seed infection (high infection obtained in year 2), and at ARDA Mz with seed yield of plants watered with furrow irrigation (greater yield obtained in year 2). Variations in disease infection were observed with years with

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Fusarium wilt of chickpea (Navas-Cortés et al., 2000), maize eye spot (Pron’czuk et al., 2004) and wheat Fusarium head blight (Subedi et al., 2007). These differences between years were due to variations in weather conditions such as temperature, relative humidity, and increase in inocula and pathogens populations. Despite that weather conditions prevailing in year 1 were favourable to the disease at UZ Farm, the trend observed in seed infection with years was the reverse of the one observed with disease infection. In the present study, the interaction year x planting date x evaluation period was significant (P < 0.001) for all the variables studied. Seed infection was also a result of the variation of disease incidence, severity and defoliation between years. The findings were supported by Neergaard (1979), who reported that the occurrence of pathogens on seed fluctuates considerably from year to year, reflecting yearly variation of weather conditions. The improvement of seed yield under furrow irrigation observed at ARDA Mz in year 2 was due to the change of trial emplacement.

Variations were observed in disease incidence, severity and defoliation with irrigation methods, planting dates and evaluation periods. Exceptions were also observed with seed yield and seed infection with years, irrigation methods and planting dates. Those special cases identified might be due to the effect on the characteristics studied of the interactions between weather and disease factors, irrigation, planting dates and host characteristics during plant development, flowering and seed development stages. They might have been influenced also by the starting points for the disease since the source of inoculum was mainly environmental, disease pressure, its progression, and the indeterminate nature of the growth habit of the host (type IIb and IIIa).

5.6 Conclusion At the UZ Farm, planting date was the most important factor in influencing the disease. In general, early planted drop had less disease incidence, severity, defoliation and seed infection. The disease developed late between 4 and 6 WAP, in addition to a slow disease progression. The winter crop in Muzarabani did not have ALS infection on either the leaves or pods, and there was no defoliation due to the disease. Consequently, winter and early planting can be adopted if ALS has to be avoided or reduced.

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In general, irrigation levels at UZ Farm did not result in significant differences in disease incidence, severity, defoliation and seed infection. In Muzarabani there was no disease and seed infection under both irrigation systems. Therefore, any irrigation method can be used with winter and early planting date for disease control.

In general, at UZ Farm, 2002/3 crop had high disease incidence, severity and defoliation than 2003/4 crop, but lower seed infection over the study duration due to variable weather. At ARDA Muzarabani, there was no disease infection during the two years. Planting date was also the most important factor in influencing seed yield. Generally with the summer crop where the two irrigation systems were used as supplement to rainfall, seed yield obtained under sprinkler irrigation was not different from seed yield obtained under furrow irrigation. In the winter crop, where water was totally supplied by irrigation, the crop which received sprinkler irrigation produced greater yields than the crop watered with furrow irrigation. Therefore, early planting date might be supplemented by any irrigation method, and winter crop watered with sprinkler irrigation to achieve greater yields. The use of other planting dates supplemented by irrigation methods in summer could be associated with appropriate control measures for the disease. The analysis of weather data showed that there were humid conditions prevailing at the end of the growing season for early planting date. This situation requires harvesting just at physiological maturity to avoid prolonged humid conditions, and improved facilities for drying plants/pods after harvest and seed after processing.

Based on the results obtained, the management of ALS may depend on delaying the infection and decreasing disease incidence, severity and defoliation variables by acting on weather conditions that favour disease infection and spread. This will consequently lead to higher seed yields. Seed cleaning for P. griseola can be achieved by the association of good quality seed, cultural practices and environmental conditions minimizing disease inoculum levels and spread.

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CHAPTER 6: CORRELATION STUDIES BETWEEN DISEASE, HOST AND WEATHER VARIABLES

6.1 Introduction The correlation measures the degree of association between variables of equal status; there need be no concept of cause and effect (Butt and Royle, 1974). Association means the lack of statistical independence between two variables (Kleinbaum et al., 1988). Positive values of the coefficient of correlation indicate that the two variables are positively correlated, meaning that large values of one variable occur with large values of the other (Mead and Curnow, 1987). Negative values show negative association, that is, an individual with a high value for one variable is likely to have a low value for the other. A value of zero indicates that there is no linear relationship between the two variables.

In addition to the measure of the degree of association between variables, the correlation can be used to test for periodicity of events, and to evaluate stickiness between observations in time series (Butt and Royle, 1974). Analytis (1973) used the correlation matrix to reduce the number of variables from 16 to 12. As reported by Agrios (2005), the environment may affect the availability, growth stage, succulence, and genetic susceptibility of the host. It may also affect the survival, vigor, rate of multiplication, rate of sporulation, ease, direction, and distance of dispersal of the pathogen, and the rate of spore germination and penetration. The most important environmental factors that affect the development of plant disease epidemics are moisture, temperature, and the activities of humans in terms of cultural practices (planting dates and irrigation systems) and control measures. Several researchers studied the correlation of diseases characteristics with plants and weather factors. Disease characteristics were negatively associated with yield (Pegoraro et al., 2001; Wiatrack et al., 2004), and positively with weather characteristics such as relative humidity (Sharma, 1986; Sen, 1987) and rainfall (Prashar, 1986; Sharma, 1986; Sen, 1987; Saharan and Saharan, 2004; Bhardwaj et al., 2005). Temperature characteristics might be associated positively (Sen, 1987; Stone et al., 2007) and negatively (Saharan and Saharan, 2004) with diseases characteristics. Royle and Thomas (1972) found several significant correlation coefficients between the release period of hop (Humulus lupulus L.) downy mildew (Pseudoperonospora humuli (Miyabe 118

&Takah.) G.W. Wilson) and weather factors such as relative humidity (h ≥ 90 %), temperature (mean) and rainfall (mm and h). Royle (1973) studied the relationship between one biological variable (air-borne spore concentration) and eight meteorological variables while monitoring natural infections of downy mildew in potted hop plants over three growing seasons and identified five intercorrelated variables describing wet conditions correlated significantly with infection severity.

Studying the relationships between disease, and weather variables will highlight the association between them, which can help in understanding the evolution of the disease during the growing period, the effect on seed yield and infection.

The objective of the study was: To identify associations existing between weather factors, disease characteristics, seed yield and infection.

The hypothesis tested was: Relationships exist between weather factors, disease characteristics, seed yield and infection.

6.2 Materials and methods The data used for the correlation studies in common bean-angular leaf spot pathosystem were collected during two years in an experiment laid out in summer at UZ Farm (site conducive to high disease infection) during three planting dates (early, medium and late), and ARDA Mz (site non conducive to disease infection) in winter crop, using the variety Natal Sugar susceptible to the disease, and two irrigation systems (sprinkler: known to be conducive to disease infection, furrow: known to be non-conducive to disease infection) as explained in chapter 5. The trend observed with all weather factors and some disease variables was a variation of planting dates within years and evaluation periods within planting dates as indicated in chapter 5. Disease variables [leaf, pod incidence (li, oi) and severity (ls, os); leaf defoliation (rrdef); area under disease progress curve for incidence (dcn), severity (dcv) and defoliation (dcl); harvested seed infection level (iv)], host [yield

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(yd)], and weather variables {[duration: water (dw), temperature (dt), humidity (dh)]; [daily mean values: water (wi), temperature (ti), humidity (hi)]} were collected from UZ Farm and ARDA Muzarabani and used to compute correlation coefficients. Correlation coefficients were computed between li 10 WAP (li10), ls 10WAP (ls10), rrdef 10 WAP

(rrdef10), dcn, dcv, dcl, iv and yd. The rest of the variables were correlated to those eight to study their associations.

6.2.1 Data analysis Data were managed using excel spreadsheets and explored for normality using the Anderson-Darling test (P < 0.05). Data analysis was performed in Minitab 16 and plots between two variables made to ascertain the linear relationships. Humidity, temperature, dcl, yd, ddis, lidis and dt variables were not transformed; while the remaining variables were transformed using square root (sr). Correlation coefficients and associated probabilities were then computed using Minitab 16.

6.3 Results 6.3.1 Relation between main characteristics Correlation coefficients calculated between the eight main characteristics in common bean are presented in Table 6.1. The main disease variables [sr(li10), sr(ls10), sr(rrdef10), sr(dcn), sr(dcv), dcl and sr(iv)] were significantly positively intercorrelated (P < 0.001), but were significantly negatively correlated (P < 0.001) with seed yield (yd).

6.3.2 Relation between disease characteristics with the main variables Disease leaf incidence, severity and defoliation from 4 to 8 WAP were significantly positively correlated (P < 0.001) with the main disease variables (Table 6.2). They were significantly negatively correlated (P < 0.001) with seed yield.

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Table 6.1: Correlation between main variables of common bean ALS obtained at UZ Farm and ARDA Mz in 2002/3 and 2003/4.

Variable sr(ls10) sr(rrdef10) sr(dcn) sr(dcv) dcl sr(iv) yd *** sr(li10) 0.50 0.85*** 0.83*** 0.69*** 0.62*** 0.30*** -0.70*** sr(ls10) 0.97*** 0.84*** 0.91*** 0.81*** 0.40*** -0.66*** sr(rrdef10) 0.88*** 0.89*** 0.80*** 0.35*** -0.70*** sr(dcn) 0.92*** 0.92*** 0.45*** -0.80*** sr(dcv) 0.98*** 0.46*** -0.72*** dcl 0.44*** -0.72*** sr(iv) -0.36*** ***: significant at P < 0.001 sr: square root, li10: leaf incidence 10 WAP, ls10: leaf severity 10 WAP, rrdef10: leaf disease defoliation 10 WAP, dcn: area under disease progress curve incidence, dcv: area under disease progress curve severity, dcl: area under disease progress curve defoliation, yd: seed yield, iv: seed infection

Table 6.2: Correlation between main variables and disease characteristics of common bean ALS recorded at UZ Farm and ARDA Mz from 4 to 8 WAP.

sr(li10) sr(ls10) sr(rrdef10) sr(dcn) sr(dcv) dcl sr(iv) yd Variable Leaf disease incidence *** sr(li4) 0.42 0.63*** 0.60*** 0.77*** 0.85*** 0.89*** 0.49*** -0.56*** sr(li6) 0.57*** 0.75*** 0.75*** 0.93*** 0.89*** 0.93*** 0.44*** -0.72*** sr(li8) 0.62*** 0.74*** 0.75*** 0.94*** 0.87*** 0.91*** 0.45*** -0.74*** Leaf disease severity sr(ls4) 0.36*** 0.45*** 0.45*** 0.62*** 0.75*** 0.81*** 0.27*** -0.48*** sr(ls6) 0.45*** 0.56*** 0.56*** 0.76*** 0.85*** 0.92*** 0.31*** -0.59*** sr(ls8) 0.52*** 0.76*** 0.72*** 0.85*** 0.94*** 0.96*** 0.50*** -0.67*** Leaf defoliation sr(rrdef4) 0.39*** 0.50*** 0.50*** 0.68*** 0.80*** 0.87*** 0.26*** -0.53*** sr(rrdef6) 0.49*** 0.63*** 0.63*** 0.83*** 0.89*** 0.96*** 0.35*** -0.64*** sr(rrdef8) 0.56*** 0.78*** 0.75*** 0.89*** 0.94*** 0.97*** 0.50*** -0.71*** ***: significant at P < 0.001 sr: square root, li4 to li10: leaf incidence from 4 to 10 WAP, ls4 to ls10: leaf severity from 4 to 10 WAP, rrdef4 to rrdef10: leaf disease defoliation from 4 to 10 WAP, dcn: area under disease progress curve incidence, dcv: area under disease progress curve severity, dcl: area under disease progress curve defoliation, yd: seed yield, iv: seed infection

6.3.3 Relation between characteristics recorded at the start of disease infection, pod incidence and severity with the main variables Correlation coefficients calculated between characteristics recorded at the start of disease infection, pod incidence and severity with the main variables are presented in Table 6.3. The characteristic number of days to the start of the disease (ddis) was significantly negatively correlated with the main disease variables and significantly positively with

121 seed yield (P < 0.001). The reverse situation was generally observed with other characteristics, except disease severity at the start of the disease (lsdis) and disease defoliation at the start of the disease [sr(dfdis)] which were not correlated with seed infection.

Table 6.3: Correlation between common bean ALS characteristics recorded at UZ Farm and ARDA Muzarabani from 2002/3 to 2003/4 at the start of the disease and pod filling with the main variables.

Variables sr(li10) sr(ls10) sr(rrdef10) sr(dcn) sr(dcv) dcl sr(iv) yd Characteristics at the start of the disease ddis -0.79*** -0.80*** -0.84*** -0.98*** -0.89*** -0.90*** -0.40*** 0.80*** lidis 0.70*** 0.50*** 0.59*** 0.48*** 0.43*** 0.32*** 0.41*** -0.38*** sr(lsdis) 0.50*** 0.43*** 0.49*** 0.29*** 0.41*** 0.31*** 0.05ns -0.22*** sr(dfdis) 0.61*** 0.51*** 0.59*** 0.43*** 0.53*** 0.45*** 0.10ns -0.34*** Pod incidence and severity sr(oi) 0.42*** 0.58*** 0.60*** 0.52*** 0.46*** 0.37*** 0.41*** -0.42*** sr(os) 0.47*** 0.50*** 0.49*** 0.51*** 0.55*** 0.50*** 0.64*** -0.40*** ***: significant at P < 0.001, ns: non significant sr: square root, li10: leaf incidence 10 WAP, ls10: leaf severity 10 WAP, rrdef10: leaf disease defoliation 10 WAP, dcn: area under disease progress curve incidence, dcv: area under disease progress curve severity, dcl: area under disease progress curve defoliation, yd: seed yield, iv: seed infection, ddis: number of days to the start of the disease, lidis: disease incidence at the start of the disease, lsdis: disease severity at the start of the disease, dfdis: disease defoliation at the start of the disease, oi: pod incidence, os: pod severity

6.3.4 Relation between weather and the main variables A relation was qualified as consistent positive/negative, when uniformly significant (P < 0.05) from 4 to 10 WAP for leaf variables, pod variables, seed yield and infection. It was qualified as non-consistent when there were different levels of association between positive, negative and non-significant relations across evaluation periods due to subsequent variation of the variable. The variables duration of humidity (dh) and mean daily humidity (hi) were consistently positively correlated with the main disease variables and negatively with yield (P < 0.001) (Tables 6.4 and 6.5). The variable (dt) was consistently positively correlated with seed infection and most leaf disease variables (P < 0.001) (Tables 6.4 and 6.5). Non-consistent relationships were observed with (dt) for (dcl) (Table 6.4) and seed yield (Table 6.5). The variable (ti) was consistently negatively correlated with AUDPCs (dcn, dcv, dcl) (P < 0.01) (Table 6.4) and positively correlated with seed yield (P < 0.05) (Table 6.5). It had non-consistent relation with leaf disease

122 variables from 4 to 10 WAP (Table 6.4) and seed infection (Table 6.5). Other weather variables (dw, wi) showed different levels of non-consistent relations with the main variables and yield across evaluation periods (Tables 6.4 and 6.5).

Table 6.4: Correlation between common bean ALS leaf incidence, severity and defoliation recorded at 10 WAP; area under disease progress curve for incidence, severity and defoliation with weather variables recorded from 4 to 10 WAP at UZ Farm and ARDA Mz in 2002/3 and 2003/4.

Variable sr(li10) sr(ls10) 4 6 8 10 4 6 8 10 Duration dt 0.66*** 0.70*** 0.77*** 0.50*** 0.71*** 0.56*** 0.66*** 0.35*** sr(dw) -0.02ns 0.04ns -0.29*** -0.38*** 0.32*** 0.24*** -0.17*** -0.01ns sr(dh) 0.74*** 0.70*** 0.77*** 0.68*** 0.74*** 0.71*** 0.67*** 0.44*** Daily mean values hi 0.76*** 0.74*** 0.73*** 0.69*** 0.80*** 0.72*** 0.64*** 0.51*** ti 0.10ns 0.17** 0.09ns -0.35*** -0.10ns -0.14* -0.12ns -0.28*** sr(wi) 0.67*** 0.11ns 0.52*** -0.19** 0.80*** 0.33*** 0.68*** -0.21*** Variable sr(dcn) sr(dcv) 4 6 8 10 4 6 8 10 Duration dt 0.59*** 0.61*** 0.58*** 0.24*** 0.58*** 0.53*** 0.49*** 0.16* sr(dw) 0.08ns 0.34*** -0.14* -0.17** 0.16** 0.28*** -0.06ns -0.02ns sr(dh) 0.83*** 0.72*** 0.68*** 0.48*** 0.75*** 0.58*** 0.56*** 0.35*** Daily mean values hi 0.86*** 0.78*** 0.67*** 0.51*** 0.80*** 0.65*** 0.55*** 0.38*** ti -0.21** -0.18** -0.32*** -0.57*** -0.34*** -0.19** -0.40*** -0.49*** sr(wi) 0.76*** 0.13* 0.43*** 0.01ns 0.77*** 0.13* 0.45*** 0.00ns

Variable sr(rrdef10) dcl 4 6 8 10 4 6 8 10 Duration dt 0.71*** 0.61*** 0.71*** 0.39*** 0.46*** 0.46*** 0.36*** 0.02ns sr(dw) 0.21** 0.20*** -0.21** -0.11ns 0.10ns 0.35*** -0.03ns -0.01ns sr(dh) 0.80*** 0.74*** 0.72*** 0.51*** 0.74*** 0.53*** 0.47*** 0.27*** Daily mean values hi 0.84*** 0.76*** 0.68*** 0.57*** 0.78*** 0.61*** 0.47*** 0.28*** ti -0.06ns -0.08ns -0.09ns -0.34*** -0.44*** -0.28*** -0.54*** -0.60*** sr(wi) 0.79*** 0.25*** 0.65*** -0.21** 0.72*** 0.06ns 0.31*** 0.09ns ***: significant at P < 0.001, **: significant at P < 0.01, *: significant at P < 0.05, ns: non significant sr: square root, li10: leaf incidence 10 WAP, ls10: leaf severity 10 WAP, rrdef10: leaf disease defoliation 10 WAP, dcn: area under disease progress curve incidence, dcv: area under disease progress curve severity, dcl: area under disease progress curve defoliation, dt: duration of temperature, dw: duration of water, dh: duration of relative humidity, hi: daily mean relative humidity, ti: daily mean temperature, wi: daily mean water

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Table 6.5: Correlation between seed infection and yield of common bean, and weather variables recorded from 4 WAP to maturity at UZ Farm and ARDA Mz in 2002/3 and 2003/4.

Variable sr(iv) yd 4 6 8 10 mat 4 6 8 10 Mat Duration dt 0.48*** 0.49*** 0.47*** 0.46*** 0.26*** -0.42*** -0.55*** -0.54*** -0.24*** 0.01ns sr(dw) 0.09ns -0.09ns 0.28*** 0.06ns -0.18** -0.06ns -0.47*** -0.04ns 0.08ns -0.07ns sr(dh) 0.44*** 0.30*** 0.48*** 0.36*** 0.23*** -0.75*** -0.67*** -0.65*** -0.43*** -0.54*** Daily mean values hi 0.53*** 0.44*** 0.51*** 0.43*** 0.34*** -0.73*** -0.71*** -0.60*** -0.51*** -0.51*** ti -0.01ns -0.02ns -0.06ns 0.10ns -0.14* 0.14* 0.20** 0.21** 0.52*** 0.74*** sr(wi) 0.25*** -0.03ns 0.39*** 0.33*** -0.08ns -0.51*** -0.20** -0.35*** 0.01ns -0.04ns ***: significant at P < 0.001, **: significant at P < 0.01, *: significant at P < 0.05, ns: non significant sr: square root, iv: seed infection,yd: seed yield, dt: duration of temperature, dw: duration of water, dh: duration of relative humidity, hi: daily mean relative humidity, ti: daily mean temperature, wi: daily mean water

6.4 Discussion The relationships obtained are consistent with reports by Rashed et al. (2002) who found disease incidence and severity to be important factors affecting other diseases and host variables like seed yield. This study also found bean plant defoliation due to the disease as an important factor to be added to disease incidence and severity. Pod incidence and severity were influenced by the main leaf disease variables and were functions of disease variables observed earlier. They increased as seed infection increased and seed yield increased when they decreased as shown by the correlation coefficients obtained. These results imply that the control of the disease has to start as early as 20 to 45 days after planting to avoid seed infection and yield loss and achierve high net returns (González et al., 1977; Jesus et al., 2004; Fontem et al., 2007). The main weather variables consistently involved were the duration of humidity and temperature, and mean daily humidity observed during the growing period. The variable estimated at the start of the disease (ddis) was strongly negatively correlated with the main disease variables and positively correlated with seed yield. The variable (ti) was consistently negatively correlated with area under disease progress curves (dcn, dcv, dcl) and positively correlated with seed yield, but had a non-consistent relation with leaf disease variables

124 and seed infection. This indicates that delaying disease infection and increasing daily mean temperature during crop growth might limit plant disease infection, increase seed yield and seed health.

The correlation studies have identified the variables (dt, ti, dw, wi) with non-consistent relation showing a variation of environmental conditions across evaluation periods. The frequency and length of those periods might favour or limit the pathogen events. The presence during the crop growing cycle of periods with unfavourable environmental conditions such as temperature and moisture for disease development is very important in epidemiology. According to Sharma (2004), temperature affects spore germination, host penetration, pathogen growth or reproduction, invasion of the host, and sporulation. When temperature stays within favourable range for each of these stages, a polycyclic pathogen can complete its disease cycle within the shortest possible length of time, resulting in many disease cycles within a growing season, leading to severe epidemic. In the presence of unfavourable temperature conditions during a certain period, which does not affect host growth, these events may be disturbed and the amount of inoculum reduced, slowing down epidemics (Agrios, 2005). In addition, moisture facilitates spore release by many fungi to the host surface, and enables to spores to germinate (Sambamurty, 2006). The presence of high moisture levels allows these events to take place constantly and repeatedly leading to epidemics (Sambamurty, 2006; Agrios, 2005). In contrast, the absence of moisture for even a few days prevents all these events to take place, so the epidemics are interrupted or completely stopped (Agrios, 2005). The significant correlation coefficients of disease characteristics with weather factors observed in this study are consistent with reports by other researchers. Saharan and Saharan (2004) obtained significant correlation of rainfall characteristics (cumulative rainy days and cumulative rainfall) with Alternaria blight severity of cluster bean (Cyamopsis tetragonolaba (L.) Taub.). Prados-Ligero et al. (2003) obtained significant correlations between airborne spore concentrations of Stemphylium vesicarium (Wallr.) E.G. Simmons, the severity of consequent leaf blight of garlic and weather factors (rainfall, relative humidity and temperature). Goulson et al. (2005) found a strong correlation between calyptrate fly populations and weather factors (rainfall, humidity and

125 temperature) with R2 values ranged from 0.52 to 0.84. Sharma (1986) and Sen (1987) reported a positive correlation between relative humidity, and the development and spread of web blight on strawberry. In other specific studies, relative humidity was correlated positively with disease infection (Chakraborty and Ballard, 1995), incidence (Duggal, 2001), severity (Chandel, 2003; Bhardwaj et al., 2005), intensity (Gautam, 1987; Duggal, 2001) and area under disease progress curve around the inflection point (Godoy et al., 2003). The duration of humidity expressed as the number of hours of relative humidity > 95 % was positively correlated with Colletotrichum gloeosporioides infection of Stylosanthes scabra Vogel (Chakraborty and Ballard, 1995). Godoy et al. (2003) reported an association of southern rust epidemic rate on maize and relative humidity. With regards to temperature, Sen (1987) observed a positive correlation of mean temperature and Phytophthora infestans blight on Solanum laciniatum Aiton. Stone et al. (2007) found a positive correlation of June average temperature and winter with the abundance of Phaeocryptopus gaeumannii (T. Rhodes) Petr. in Douglas-fir (Pseudotsuga menziessii (Mirb.) Franco). Temperature was specifically correlated with disease incidence (Duggal, 2001; Kaushal, 2003), severity (Duggal, 2001) and epidemic rate (Godoy et al., 2003). Where supplementary irrigation is applied, the distribution and quantity of water received through rainfall determines disease infection, development and spread. There is a consistent significant positive association between rainfall and disease (Prashar, 1986; Sharma, 1986; Sen, 1987; Bhardwaj et al., 2005). Specifically, rainfall was positively correlated with disease infection (Chakraborty and Ballard, 1995), incidence (Duggal, 2001; Kaushal, 2003) and severity (Duggal, 2001; Chandel, 2003). Disease infection is detrimental to yield, as reported by Wiatrack et al. (2004) who noticed a negative correlation between rust ratings and corn yield. Pegoraro et al. (2001) also observed a negative correlation between Phaeosphaeria leaf spot severity and maize yield.

This study and previous studies suggest that the control of angular leaf spot might be centred on the management of the duration of humidity and temperature, as well as their daily means. The control of water is possible when the crop is grown under irrigation. Therefore, manipulating cultural practices with a view of controlling diseases is feasible

126 for small-scale farmers, since the use of early planting date in summer and winter crop in the tropical lowlands where winters are mild resulted in low disease infection, enhanced seed health and increased seed yield.

6.5 Conclusion Leaf disease variables from 4 to 8 WAP [sr(li), sr(ls) and sr(rrdef)], pod disease variables [sr(oi) and sr(os)], variables estimated at the start of the disease [lidis, sr(lsdis) and sr(dfdis)], weather variables from 4 to 10 WAP [sr(dh), (hi) and (dt)] were positively correlated with the main disease variables and negatively with yield. The variable estimated at the start of disease (ddis) and the weather variable (ti) were negatively correlated with the main disease variables and positively with yield. The variables [sr(dw) and sr(wi)] were the most variable across evaluation periods, and correlations were specific to evaluation periods.

This study demonstrated that it is feasible to manage diseases through careful selection of environment and disease control strategies based on early detection. The most important weather factors in disease infection, development and spread included mean humidity, duration of humidity and temperature positively correlated with the main disease variables and negatively with yield. They included also mean temperature negatively correlated with the main disease variables and positively with yield.

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CHAPTER 7: PREDICTION OF ANGULAR LEAF SPOT EFFECT IN COMMON BEAN USING WEATHER AND DISEASE PREDICTORS

7.1 Introduction Modeling provides a great deal of information regarding the amount and efficacy of the initial disease inoculum, the effects of environment, host resistance, the length of time for host and pathogen interaction, and the effectiveness of various disease management strategies (Agrios, 2005). The epidemiologist may focus on the progress of the epidemic in time, concentrate on the rate of disease increase, be concerned with the factors determining the level of disease severity reached on one occasion, and be centred on the events in the disease cycle (infection, incubation and sporulation) (Butt and Royle, 1974). In plant disease epidemics, new spores are very sensitive to death by dessication, and the rate at which they can penetrate the leaf depends on warmness. Therefore, many disease predictors utilize temperature in combination with some measure of moisture such as humidity, rainfall, or duration of leaf wetness (Gillepsie, 1994).

A multiple regression model explains responses in one variable (the dependent) as the sum of linear functions of other variables (independent), with little regard to the manner in which the independent variables exercise their control. Regression models perform best in predicting the mean performance of a population of fields (Rabbinge and Rijsdijk, 1981), and the ability of regression analysis to measure the net effect of each independent variable makes it a valuable analytical method in epidemiology (Butt and Royle, 1974). In modeling, the first step involves the construction of a conceptual framework followed by the formulation of hypotheses and function equations (Brockington, 1979). The models developed in this case will require more testing to different diseases and environments prior to their final use. The choice of the specific model to use and its calibration are some of the important considerations to be taken into account in modeling. The choice of the model comprises the decision on the processes that control the variables of concern, the knowledge of the main assumptions and simplifications of the model and the understanding by the user of the range of its applicability. Calibration is the selection of the parameters to be used in the model. Data for calibration can be 128 obtained from literature and experiments. The parameters can be adjusted using a set of data from the past or from a designed experiment. The data requirement of a model must be reasonable, if the model is to be of practical use. When observations do not form a time series and there is no interest in forecasting disease progress per se (Butt and Royle, 1974), variables values can be input as constants in modeling. The use of constants does not present any problem with sufficiently large populations (Norman and Bailey, 1975), and the mean values computed using unprocessed values of the components might be equal to the values calculated using their expected values. Burleigh et al. (1972) and Eversemeyer and Burleigh (1970) used the following weather means (minimum and maximum temperature; hours of wetness, dew or rain; precipitation and number of days with rainfall ≥ 0.25 mm) in cereal rust multiple regression analysis. Talboys and Wilson (1970) regressed mean soil temperature and total rainfall expressed for various periods in the growing season on the percentage of hops wilted plants due to Verticillium albo- atrum Reinke and Berthold. Validation means testing of the model in the field over several cropping seasons and/or locations to evaluate its ability to assess or predict disease (Broome et al., 2002) and yield. The validation of a model is an important step in data modeling and refers to its reproducibility, by testing its performance on an underlying population (Steyerberg and Harrell, 2002).

Modeling can include biological (host and disease/pathogen) and meteorological variables to develop equations describing disease variables, yield and seed infection. The use of biological and meteorological variables alone as predictors to assemble multiple regression equations related to disease/pathogen characters may yield the best equations if the factors cause wide fluctuations in disease multiplication (Cole, 1966; Butt, 1968; Snow et al., 1968; Dirks and Romig, 1970; Eversmeyer and Burleigh, 1970; Analytis, 1973). But, problems may be encountered in using insufficient/sole biological and meteorological variables in separate multiple regression models. Schrödter and Ullrich (1965) observed a large error while modeling potato blight (Phytophthora infestans) biological variables using some meteorological factors as predictors. It was thought that the addition of other meteorological factors such as wind or radiation would improve the results. Royle and Thomas (1972) in their attempt to produce a predictive equation to

129 enable forecasting of downy mildew spore concentration in hope gardens using meteorological measurements, obtained inconsistent partial regression coefficients suggesting that biological variables which play a key role in spore production were missing from the equation. Successive stages of crop development were included in multiple regression models by Burleigh et al. (1972), while assembling regression equations for the prediction of grain wheat loss. They used rust severity (% rust infection) on the flag leaf or per tiller recorded at boot, heading, early berry and dough stage as predictors.

The modeling process will take advantage of the combination in one single equation of biological and meteorological factors, and of multiple-point models using data recorded at different evaluation periods. Developing a model of potential epidemics will help to predict in advance the increase in ALS incidence, severity and defoliation; and to assess crop yield loss caused by the disease in the country. Models developed can be of use to crop yield forecasters, researchers, extension workers and farmers.

The objectives of the study were: 1) To build statistical models integrating weather and disease predictors into a single equation to forecast final angular leaf spot (incidence, severity and defoliation) at 10 weeks after planting, area under disease progress curves (incidence, severity and defoliation), seed yield and seed infection by Pseudocercospora griseola. 2) To identify evaluation periods associated with weather and disease predictors in the models. 3) To formulate for users short and long term decisions from the relations established between predictors and dependent variables participating in the models.

The hypotheses tested were: a) Weather and disease predictors can be associated to build statistical models to forecast final angular leaf spot disease (incidence, severity and defoliation) at 10 weeks after planting, area under disease (incidence,

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severity and defoliation) progress curves, seed yield and seed infection by Pseudocercospora griseola. b) There is an association between evaluation periods, weather and disease predictors. c) Short and long term decisions can be formulated from the relations established between predictors and dependent variables.

7.2 Materials and methods 7.2.1 Conceptual method A conceptual model was formulated based on the epidemiology of the pathogen. Angular leaf spot significant spread, development and yield loss occur under a combination of conditions of moderate temperature, prolonged periods of wet weather or high humidity (Celetti et al., 2005). Angular leaf spot increase will occur 8 - 12 days after inoculation (Saettler, 1994). Infection and disease occur under conditions of moderate temperatures (16 - 28ºC) and high relative humidity (86 - 96 %), humidity and precipitation being more important than temperature (Saettler, 1994; Sindham and Bose, 1980a). Between 9 and 12 days after inoculation, sporulation occurs after 24 - 48 hours of continuous humid and moist conditions and the spores produced cause secondary disease spread. Those spores remain on foliage until late flowering or early pod set (Ferraz, 1980). As reported by Neergaard (1988), cool weather conditions in the period of flowering and seed development are conducive to seed infection.

7.2.2 Calibration Dependent variables recorded at UZ Farm and ARDA Muzarabani were for the disease

[final leaf incidence, severity and defoliation collected at 10 WAP (li10, ls10, rrdef10); area under disease progress curve for incidence (dcn), severity (dcv) and defoliation (dcl); harvested seed infection level (iv)] and host [yield (yd)]. Disease predictors recorded at the start of the disease were: number of days to the start of the disease (ddis); disease incidence, severity and defoliation (lidis, lsdis and dfdis), which might vary between planting dates. Disease predictors recorded during the growing period were: leaf incidence, severity and defoliation from 4 to 10 WAP [(li4, li6, li8, li10), (ls4, ls6, ls8, ls10),

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(rrdef4, rrdef6, rrdef8, rrdef10)], pod incidence and severity (oi, os). Weather predictors were: duration of water, temperature and humidity (dw, dt, dh); daily mean water, temperature and humidity (wi, ti, hi).

7.2.3 Models development and selection Hypothesis testing was done by studying the relationship between the dependent and each independent variable, to examine if the relationship was positive or negative, to evaluate the range of each variable and the magnitude of the relationship (small, medium or large). Data were plotted to ascertain if the relationship was linear or not. In the case where it was not linear, transformation (square root: sr) was done to bring the relation to linearity. Daily mean humidity variables and all temperature variables, and the variables dcl, yd, ddis, lidis and dt were not transformed. All the remaining variables were square root (sr) transformed. The graphical exploration was also used to examine the plots for functional forms and outliers’ patterns. Variables participating in the multiple-points regression analysis were screened with forward selection (step-up method) and backward elimination (step-down method) using increasing R-squared, Cp Mallow statistics and mean squares (s). The Cp Mallow statistic was used to select the best combination of variables. As presented by Draper and Smith (1981), a plot of the Cp versus p (predictors) will show up the adequate model as points fairly close to the Cp = p line. Equations with considerable lack of fit, will give rise to points above the Cp = p lines. Cp is closely related to R-squared statistics: as terms are added to the model to reduce the residual sum of squares for a model containing p parameters including the constant, Cp usually increases. The smallest model has a smaller Cp value, but the Cp of the largest model (which has a large value of p) is closer to its p (Draper and Smith, 1981). From the primary multiple regression equations and their characteristics combining all the predictors selected, final equations were obtained by eliminating variables on the basis of statistical significance (P > 0.10) (Yaffee, 1998). When the constant was not significant as was the case of the equation describing seed infection (-0.2274, P = 0.339), the multiple regression analysis was performed without incorporating the intercept. It is important to note that for regression analysis, the dependent and independent variables need not to be both normally distributed. The normality of data is only applicable to the

132 dependent variable (Butt and Royle, 1974). The normality of standardized residuals was evaluated by exploring their boxplot, stem-and-leaf and normal plot as well as applying Kolmogorov-Smirnov normality test. Heteroscedasticity was tested by White’s General Specification Test. Independence between observations was depicted by exploring the plot of standardized residuals versus order. Influential outliers were diagnosed by examining standardized residuals, leverages and Cook’s distances of different observations against their critical values (standardized residuals: -3.5 to 3.5; leverage: value associated with α, n (sample size) and p (number of predictors); and Cook’s distance: 50- percentile values of F distribution (n = sample size, k = number of predictors) (Kleinbaum et al., 1988; Yaffee, 1998), and by exploring their boxplots and stem-and-leaf plots. Multicollinearity was depicted by examining the residuals correlation matrix and the Variance Inflation Factor (VIF) (to be concerned with any value larger than 10) (Passoa, 2004). Centring was used to reduce the correlation between predictors

[hi5 and sr(pd)3] and the constant while modeling seed yield. Calculations needed prior to statistical analyses were performed using excel spreadsheets. Forward selection, backward elimination and primary selection of parameters were done in Genstat 14. Other tests were carried out using Genstat 14 and Minitab 16.

7.2.4 Models validation The validity of each multiple regression combining data for both years was assessed by testing the model and the parameters obtained from the statistical analysis (constant, partial regression coefficients and R2). The predicted values were regressed on the actual observed values. Linear regression analysis of observations against results predicted from a model should yield a slope not significantly different from one and an intercept not significantly different from 0 (Draper and Smith, 1981). t-test was run to test if the gradient of the regression line was different from one and the intercept different from zero. But Chuang and Jeger (1987) reported that a slope of unity is enough to indicate a good relationship between predicted and observed values. To evaluate the applicability of the models, the pooled data was separated by years (Steyerberg and Harrell, 2002). The predicted values for each year, using the model, were regressed on the actual values observed (Chuang and Jeger, 1987). A form of data splitting “Leave out more than one

133 observation” approach was also used to validate the selected models (Draper and Smith, 1981; Steyerberg and Harrell, 2002). Three sets of four observations were removed randomly from each year using the random numbers method, one for each planting date, and the remaining observations were used to develop new regression equations to predict the removed values. Coefficients of determination (R2s) and standard deviations of the residuals (Ss) of different models were compared. A paired t-test was used to compare the predicted and observed values.

7.3 Results 7.3.1 Disease and weather predictors associated with final leaf disease characteristics at 10 WAP Information related to the multiple regression equations is presented in Table 7.1. From the total 43 predictors, 33 predictors were selected by forward selection and backward elimination. Then 20 predictors were included in the final modeling process for disease incidence and severity, and19 for defoliation at 10 WAP.

For the final leaf disease incidence multiple regression, the constant and all the partial regression coefficients were significant (P < 0.001). The final equation comprised six weather predictors [one humidity: sr(dh6), five water: sr(dw4), sr(dw6), sr(dw8), sr(dw10), sr(wi4)] and two disease predictors including one at the beginning of the infection (lidis) and one disease incidence predictor [sr(li4)].

For the final leaf disease severity multiple regression, the constant and all the partial regression coefficients were significant (P < 0.001). The final equation comprised four weather predictors [one humidity: hi6, three water: sr(dw6), sr(dw8), sr(dw10)] and three disease predictors including two at the beginning of the infection [lidis, sr(lsdis)] and one incidence predictor [sr(li4)].

For the final leaf disease defoliation multiple regression, the constant and all the partial regression coefficients were significant (P < 0.001). The final equation comprised five weather predictors [one humidity: sr(dh6), four water: sr(dw6), sr(dw10), sr(wi4), sr(wi6)]

134 and three disease predictors including one at the beginning of the infection [sr(lsdis)], one incidence [sr(li8)] and one severity predictor [sr(ls4)].

7.3.2 Disease and weather predictors associated with area under disease progress curves Information related to the multiple regression equations is presented in Table 7.1. From the total 43 predictors, 33 were selected by forward selection and backward elimination. Then 20 predictors were included in each of the final modeling process for area under disease progress curve incidence, severity and defoliation models.

For the area under leaf disease incidence progress curve multiple regression, the constant and all the partial regression coefficients were significant (P < 0.001). The equation comprised five weather predictors [one humidity: sr(dh6), four water: sr(dw4), sr(dw6), sr(dw8), sr(wi6)] and five disease predictors including two at the beginning of the infection [lidis, sr(lsdis)], two incidence [sr(li4), sr(li10)] and one severity predictor

[sr(ls10)].

For the area under leaf disease severity progress curve multiple regression, the constant and all the partial regression coefficients were significant (P < 0.001). The equation comprised four weather predictors [one humidity: sr(dh6), three water: sr(dw4), sr(dw6), sr(wi6)] and five disease predictors including two at the beginning of the infection [lidis, sr(lsdis)], one incidence [sr(li10)] and two severity predictors [sr(ls4), sr(ls10)].

For the area under leaf disease defoliation multiple regression, the constant and all the partial regression coefficients were significant (P < 0.001). The equation comprised four weather predictors [one humidity: sr(dh4), three water: sr(dw6), sr(dw8), s(wi6)] and four disease predictors including one at the beginning of the infection [sr(lsdis)], one incidence [sr(li4)] and two severity predictors [sr(ls6), sr(ls10)].

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7.3.3 Disease and weather predictors associated with seed yield and seed infection Information related to the multiple regression equations is presented in Table 7.1. From the total 54 predictors, 36 were selected by forward selection and backward elimination methods. Then 20 predictors were included in each final modeling process.

For seed yield multiple regression the constant and all the partial regression coefficients were significant (P < 0.05). The equation comprised three weather predictors [one humidity: hi10, two water: sr(dw6), sr(dwmat)] and four disease predictors including two incidence [sr(li10), sr(oi)] and two severity predictors [sr(ls4), sr(os)].

For seed infection multiple regression all the partial regression coefficients were significant (P < 0.05). The equation comprised four water predictors [sr(dw)4, sr(dw)6, sr(dw)8, sr(dw)mat] and five disease predictors including two at the beginning of the infection [lidis, sr(lsdis)] and three severity predictors [sr(ls4), sr(ls10), sr(os)].

7.3.4 Validation of the models 7.3.4.1 Comparison of different models obtained by data splitting and removal Coefficients of determination (R2s) and standard deviations of the residuals (Ss) of different models developed are presented in Table 7.2. The R2s of all the models were significant (P < 0.001), indicating a similarity of those belonging to sister models. Compared to R2s and Ss of both years combined data, the improvement of the two parameters was year specific. In the first year, the lack of those two parameters improvement was observed only in seed yield (yd) models. In the second year, 2 improvement of R s and Ss was observed in final disease incidence [sr(li10)], final disease defoliation [sr(rrdef10)], area under disease progress curve defoliation (dcl) and (yd) models. The lack of improvement of both parameters was observed in [sr(ls10)] and [sr(dcn)] models. There was no agreement between those two parameters in [sr(dcv)] and [sr(iv)] models.

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Table 7.1: Relationship between dependent and independent variables from the common bean trial conducted at UZ Farm and ARDA Muzarabani in 2002/3 and 2003/4 with the percentage calculated from the number of dependent variables in which the independent variable is significant.

Predictor/ sr(li 10 ) sr(ls 10 ) sr(rrdef 10 ) sr(dcn) sr(dcv) dcl Seed Seed General Parameter yield infection Range N 228 237 238 256 234 241 256 204 204 - 256 Cp Mallow plot Equations 9 10 9 6 5 3 14 15 3 - 15 Variables (Vs) 14 - 19 14 - 19 14 - 18 16 - 19 17 - 19 18 - 19 11-19 11 - 19 11 - 19 Vs best equation 14 14 14 18 19 18 11 11 11 - 19

Forward/backward 33 33 33 33 33 33 36 36 33 - 36

Primary selection 20 20 19 20 20 20 20 20 19 - 20 Final selection 8 7 8 10 9 8 7 9 7 - 10 Percentage Constant -5.9459*** -11.7951*** 0.5508*** 7.933*** 1.1478*** -0.1697*** 2383.4*** 87.5 lidis 0.0501*** -0.0068*** 0.0477*** 0.0046*** 0.0021* 62.5 sr(lsdis) 0.7321*** 0.0265*** -1.0445*** -0.3783*** -0.0125*** -0.1179*** 75.0 hi 6 0.1958*** 12.5 hi 10 -21.738*** 12.5 sr(dh 4 ) 0.0691*** 12.5 sr(dh 6 ) 1.3007*** 0.0281*** 2.5971*** -0.3299*** 50.0 sr(li 4 ) -0.2174*** 0.2352*** 1.1950*** 0.0175*** 50.0 sr(li 8 ) 0.0099*** 12.5 sr(li 10 ) 0.5604*** 0.0383*** -90.37*** 37.5 sr(ls 4 ) -0.01675*** 2.1026*** -231.97*** 0.2934*** 50.0 sr(ls 6 ) 0.0948*** 12.5 sr(ls 10 ) 0.9248*** 1.3096*** 0.0340*** 0.1069*** 50.0 sr(oi) 48.23*** 12.5 sr(os) -98.82*** 0.2463*** 25.0 sr(dw 4 ) -6.4097*** -3.9074*** -0.8127*** -0.1487* 50.0 sr(dw 6 ) 2.6101*** -0.7452*** -0.0302*** 7.0477*** 0.8416*** 0.0284*** -1337.92*** -1.1238*** 100.0 sr(dw 8 ) 2.1559*** -3.2172*** -3.7453*** -0.0350*** 1.4178*** 62.5 sr(dw 10 ) -2.5363*** 3.6797*** 0.0173*** 37.5 sr(dw mat ) -113.32* -0.0965* 25.0 sr(wi 4 ) 6.6739*** 0.0328*** 25.0 sr(wi 6 ) 0.0196*** -4.8255*** -0.8178*** -0.0277*** 50.0 ***: significant at P < 0.001, **: significant at P < 0.01, *: significant at P < 0.05 sr: square root, lidis: disease incidence at the start of the disease, lsdis: disease severity at the start of the disease, li4 to li10: leaf incidence from 4 to 10 WAP, ls4 to ls10: leaf severity from 4 to 10 WAP, rrdef10: leaf disease defoliation 10 WAP, dcn: area under disease progress curve incidence, dcv: area under disease progress curve severity, dcl: area under disease progress curve defoliation, dw4 to dwmat: duration of water from 4 WAP to maturity, dh4 to dh6: duration of relative humidity from 4 to 6 WAP, hi6 to hi10: daily mean relative humidity from 6 to 10 WAP, wi4 to wi6: daily mean water from 4 to 10 WAP

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Table 7.2: Coefficients of determination (R2s) (significant at P < 0.001) and standard deviations of the residuals (Ss) (between brackets) of the models developed with combined data for both years, individual data for each year with data splitting and removal.

Dependent Non removal Removal 2002/3 Removal 2003/4 variable Both 2002/3 2003/4 First Second Third First Second Third sr(li10) 96.4 99.1 99.5 99.1 99.3 99.2 99.5 99.5 99.6 (0.7828) (0.3745) (0.3217) (0.3788) (0.3507) (0.3709) (0.3233) (0.3212) (0.2719) sr(ls10) 95.4 96.9 93.5 96.7 96.8 96.8 93.7 93.6 93.7 (0.5337) (0.4425) (0.5936) (0.4456) (0.4457) (0.4467) (0.5809) (0.5883) (0.5822) sr(rrdef10) 95.1 97.8 97.2 97.7 97.8 97.8 97.2 97.2 97.2 (0.0161) (0.0110) (0.0123) (0.0113) (0.0110) (0.0110) (0.0122) (0.0123) (0.0123) sr(dcn) 97.6 99.8 96.5 99.8 99.8 99.8 96.4 96.6 96.5 (1.4370) (0.4295) (1.7400) (0.4396) (0.4356) (0.4383) (1.7540) (1.7010) (1.7410) sr(dcv) 99.2 99.4 99.2 99.5 99.4 99.4 99.2 99.2 99.2 (0.3522) (0.3032) (0.3537) (0.2860) (0.3067) (0.2961) (0.3574) (0.3460) (0.3566) dcl 99.4 99.6 99.5 99.6 99.6 99.6 99.5 99.5 99.5 (0.0190) (0.0166) (0.0181) (0.0166) (0.0169) (0.0166) (0.0181) (0.0179) (0.0178) yd 77.2 70.3 87.8 70.9 69.6 69.7 87.9 86.7 87.9 (411.3) (456.3) (318.3) (451.3) (459.3) (454.2) (310.9) (326.0) (314.3) sr(iv) 89.0 94.8 90.2 93.9 95.5 94.6 87.5 87.7 88.9 (0.2577) (0.1689) (0.2818) (0.1693) (0.1575) (0.1704) (0.2946) (0.2722) (0.2826) sr: square root, li10: leaf incidence 10 WAP, ls10: leaf severity 10 WAP, rrdef10: leaf disease defoliation 10 WAP, dcn: area under disease progress curve incidence, dcv: area under disease progress curve severity, dcl: area under disease progress curve defoliation, yd: seed yield, iv: seed infection

7.3.4.2 Regression between responses and fitted values Graphical representation of the relationship between responses and estimated values are presented in Figures 7.1 to 7.4. The multiple linear regression equations built were appropriate, since all the gradients were not significantly different from one. But, the models developed were less suited for low values for disease incidence [sr(li10)] and for high values for seed yield and seed infection for individual years observations (2002/3 and 2003/4) and data combining both years. Other remaining models were suited for low, middle and high values. The models in which the intercepts were not significantly different from zero were those built for the dependent variables AUDPCs severity and defoliation [sr(dcv) and dcl] for observations combining both years; disease incidence, defoliation, AUDPCs incidence, severity and defoliation [sr(li10), sr(rrdef10), sr(dcn), sr(dcv) and dcl] for individual 2002/3 and 2003/4 data; disease severity and seed infection [sr(ls10) and sr(iv)] for 2002/3 only. Except those cited, other models have their intercepts significantly different from zero (P < 0.05).

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7.3.4.3 “Leave out more than one observation” approach Summerized data from paired t-test analysis are presented in Tables 7.3 to 7.5. In all the models there was no significant difference between the predicted and observed mean values for each year, and their differences were falling into the 95 % confidence intervals.

7.3.5 Assumptions and considerations The number of predictors estimated by the Cp Mallow plot was between the predictions kept by primary and final selection for all the models. Forecasting was achieved by reducing to one the number of equations predicted by Cp Mallow plot for each dependent variable [3 dcl - 15 sr(iv)]. It was also achieved by reducing the number of variables in the best fitted equation produced by Cp Mallow plot {11 [yd and sr(iv)] - 19 sr (dcv)} to {7 [sr(ls10) and yd] - 10 sr(dcn)}. The multiple regression equations were appropriate since the simple equations lines representing the relationships between responses and fits did not deviate significantly from the 1:1 lines. The gradients of different equations developed were not significantly different from one for individual years and combined data for both years (Draper and Smith, 1981). All the slopes were close to one and varied from 0.77026 (yd 2002/3) to 0.9981 [sr(dcn) 2002/3)]. But, there was a variation in the intercepts obtained from the models with some being significantly different (P < 0.05) and others not different from 0. The predicted values by different models built after data removals were not significantly different from actual values.

The dependent variables sr(li10), sr(ls10) and sr(rrdef10) were more dependent upon weather predictors with the respective frequencies in each one of 75.0, 57.1 and 62.5 %. The dependent variables sr(dcv), yd and sr(iv) were more dependent upon disease predictors with the respective frequencies in each one of 55.6, 57.1 and 55.6 %. Weather and disease predictors influenced equally sr(dcn) and dcl. The frequencies (%) obtained were respectively 53.0 for weather and 47.0 for disease predictors. This made weather predictors the most represented. Water predictors (42.4 %) were the most represented weather predictors and humidity predictors (10.6 %) the least represented. The most

139

9 ns ** 14 y = 0.9502 x + 0.1773 y = 0.9644nsx + 0.2547* 8 12 7

10 6

8 5 4 6 3 4 2 2 1 0 0 0 2 4 6 8 10 12 0 2 4 6 8 10

R espo nse R espo nse

Disease incidence both years Disease severity both years

12 9 ns ns y = 0.9685nsx + 0.1197ns y = 0.9914 x + 0.0627 8 10 7

8 6

5 6 4

4 3

2 2 1

0 0 0 2 4 6 8 10 12 0 2 4 6 8 10 R esp onse R espo nse

Disease incidence 2002/3 Disease severity 2002/3

12 8 ns * ns ns y = 0.9353 x + 0.2130 y = 0.9948 x + 0.0362 7 10 6 8 5

6 4

3 4 2 2 1

0 0 0 2 4 6 8 10 12 0 2 4 6 8 R espo nse R espo nse

Disease incidence 2003/4 Disease severity 2003/4

Figure 7. 1: Regression analysis between leaf disease incidence and severity responses and fitted values by multiple regression with disease and weather variables in 2002/3 and 2003/4 (solid squares are model-data pairs, the broken line is 1:1 and the solid line is the regression line). ns: gradient and intercept not significantly different from 1 and 0 respectively using t-test, *: intercept significantly different from 0 at 0.05 level using t-test, **: intercept significantly different from 0 at 0.01 level using t- test.

140

0.95 ns ** 30 y = 0.9509 x + 0.0398 y = 0.9762nsx + 0.3269* 0.9 25

0.85 20

0.8 15

0.75 10

0.7 5

0.65 0 0.65 0.7 0.75 0.8 0.85 0.9 0.95 0 5 10 15 20 25 30 R esp onse R esp onse

Disease defoliation both years AUDPC incidence both years

0.95 ns ns 25 y = 0.9784 x + 0.0176 y = 0.9981nsx + 0.0274ns 0.90 20

0.85 15

0.80 10

0.75 5

0.70 0 0.7 0.75 0.8 0.85 0.9 0.95 0 5 10 15 20 25 R esp onse R esp onse

Disease defoliation 2002/3 AUDPC incidence 2002/3

ns ns 0.95 y = 0.9720nsx + 0.0226ns 30 y = 0.9651 x + 0.4679 25 0.90

20 0.85 15

0.80 10

0.75 5

0.70 0 0.7 0.75 0.8 0.85 0.9 0.95 0 5 10 15 20 25 30

R espo nse R espo nse

Disease defoliation 2003/4 AUDPC incidence 2003/4

Figure 7. 2: Regression analysis between leaf disease defoliation and area under disease progress curve incidence responses and fitted values by multiple regression with disease and weather variables in 2002/3 and 2003/4 (solid squares are model-data pairs, the broken line is 1:1 and the solid line is the regression line). ns: gradient and intercept not significantly different from 1 and 0 respectively using t-test. *: intercept significantly different from 0 at 0.05 level using t-test, **: intercept significantly different from 0 at 0.01 level using t-test.

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16 0.9 y = 0.9920nsx + 0.0395ns y = 0.9943nsx + 0.0014ns 14 0.8 0.7 12 0.6 10 0.5 8 0.4 6 0.3

4 0.2

2 0.1

0 0 0 5 10 15 0.0 0.2 0.4 0.6 0.8 1.0 R esp onse R espo nse

AUDPC severity both years AUDPC defoliation both years

14 0.8 y = 0.9944nsx + 0.0308ns y = 0.9960ns x + 0.0011ns 0.7 12 0.6 10 0.5 8 0.4

6 0.3

4 0.2

0.1 2 0 0 0.00 0.20 0.40 0.60 0.80 0 5 10 15 -0.1 R esp onse R esp onse

AUDPC severity 2002/3 AUDPC defoliation 2002/3

16 ns ns 0.9 y = 0.9946 x + 0.0012 ns ns 14 y = 0.9919 x + 0.0355 0.8

12 0.7

10 0.6

0.5 8 0.4 Fitted value Fitted 6 0.3 4 0.2

2 0.1

0 0 0 2 4 6 8 10 12 14 0.0 0.2 0.4 0.6 0.8 1.0 -0.1 Response

R espo nse

AUDPC severity 2003/4 AUDPC defoliation 2003/4

Figure 7. 3: Regression analysis between areas under disease progress curves (AUDPCs) severity and defoliation responses and fitted values by multiple regression with disease and weather variables in 2002/3 and 2003/4 (solid squares are model-data pairs, the broken line is 1:1 and the solid line is the regression line). ns: gradient and intercept not significantly different from 1 and 0 respectively using t-test.

142

4500 5 y = 0.7718nsx + 300.34*** n *** 4000 4.5 y = 0.8919 sx + 0.0999

3500 4 3.5 3000 3 2500 2.5 2000 2 Fitted value Fitted vakue 1500 1.5 1000 1 500 0.5

0 0 0 500 1000 1500 2000 2500 3000 3500 4000 4500 0 1 2 3 4 5 Response Response

Seed yield both years Seed infection both years

4000 5 ns *** ns ns 3500 y = 0.7026 x + 377.71 4.5 y = 0.9495 x + 0.0426 4 3000 3.5 2500 3

2000 2.5

1500 2 1.5 1000 1 500 0.5

0 0 0 1000 2000 3000 4000 0 1 2 3 4 5 R espo nse R espo nse

Seed yield 2002/3 Seed infection 2002/3

4500 5 ns ** ns * 4000 y = 0.8776 x + 166.73 4.5 y = 0.9005 x + 0.0977

3500 4 3.5 3000 3 2500 2.5 2000 2 1500 1.5 1000 1 500 0.5 0 0 0 1000 2000 3000 4000 5000 0 1 2 3 4 5 R espo nse R esp onse

Seed yield 2003/4 Seed infection 2003/4

Figure 7. 4: Regression analysis between seed yield and seed infection responses and fitted values by multiple regression with disease and weather variables in 2002/3 and 2003/4 (solid squares are model-data pairs, the broken line is 1:1 and the solid line is the regression line). ns: gradient and intercept not significantly different from 1 and 0 respectively using t-test, *: intercept not significantly different from 0 at 0.05 level using t-test, **: intercept not significantly different from 0 at 0.01 level using t-test, ***: intercept not significantly different from 0 at 0.001 level using t-test.

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Table 7.3: Comparison made in each year (2002/3 and 2003/4) of responses and estimated values of final disease incidence [sr(li10)], severity [sr(ls10)] and defoliation [sr(rrdef10)] by multiple regression equations by paired t-test after removal of one observation in each planting date repeated three times.

Source sr(li10) sr(ls10) sr(rrdef10) 2002/3 2003/4 2002/3 2003/4 2002/3 2003/4 Response 7.21 7.23 4.066 3.455 0.8066 0.8052 Estimated 7.08 7.46 4.060 3.519 0.8108 0.8036 Difference 0.134 -0.233 0.0059 -0.063 -0.00423 0.00153 T value 0.70 -1.43 0.08 -0.28 -1.44 0.36 P 0.501 0.180 0.937 0.785 0.178 0.729 95 % CI LL -0.290 -0.591 -0.1550 -0.561 -0.01069 -0.00795 95 % CI UL 0.559 0.125 0.1668 0.434 0.00224 0.01101 P: probability, 95 % CI LL: 95 % confidence interval lower limit, 95 % CI UL: 95 % confidence interval upper limit

Table 7.4: Comparison made in each year (2002/3 and 2003/4) of responses and estimated values of final area under disease progress curve incidence [sr(dcn)], severity [sr(dcv)] and defoliation (dcl) by multiple regression equations by paired t-test after removal of one observation in each planting date repeated three times.

Source sr(dcn) sr(dcv) dcl 2002/3 2003/4 2002/3 2003/4 2002/3 2003/4 Response 13.91 13.64 5.45 4.95 0.2908 0.2408 Estimated 13.86 13.63 5.51 4.92 0.2838 0.2366 Difference 0.045 0.010 -0.060 0.036 0.0070 0.0042 T value 0.63 0.02 -0.46 0.30 1.50 0.63 P 0.541 0.984 0.655 0.766 0.161 0.544 95 % CI LL -0.1121 -1.089 -0.349 -0.221 -0.0032 -0.0106 95 % CI UL 0.2022 1.110 0.229 0.292 0.0172 0.0191 P: probability, 95 % CI LL: 95 % confidence interval lower limit, 95 % CI UL: 95 % confidence interval upper limit

Table 7.5: Comparison made in each year (2002/3 and 2003/4) of responses and estimated values of final seed yield (yd) and seed infection [sr(iv)] by multiple regression equations by paired t-test after removal of one observation in each planting date repeated three times.

Source yd sr(iv) 2002/3 2003/4 2002/3 2003/4 Response 1458 1465 1.324 1.754 Estimated 1362 1455 1.377 1.522 Difference 96 11 -0.0526 0.231 T value 0.70 0.10 -0.65 2.15 P 0.501 0.926 0.527 0.055 95 % CI LL -208 -234 -0.2301 -0.006 95 % CI UL 400 255 0.1248 0.468 P: probability, 95 % CI LL: 95 % confidence interval lower limit, 95 % CI UL: 95 % confidence interval upper limit

144 important water predictor was [sr(dw)6] which participated in all eight multiple regression equations developed. It was followed by sr(dw)8 (62.5 %), sr(dh)6, sr(dw)4 and sr(wi)6 (50.0 %). Other predictors were present in less than four equations out of eight developed, and some were specific to a particular equation. No humidity predictor was associated with sr(iv). Water duration at maturity [sr(dwmat)] participated in seed yield and infection equations. Disease severity predictors (25.8 %) were the most represented disease predictors and disease incidence predictors (21.2 %) the least represented. The most important disease predictor was sr(lsdis) present in 75.0 % of the eight multiple regression equations. It was followed by lidis (62.5 %), sr(li4), sr(ls4) and sr(ls10) (50.0 %). Pod incidence and severity participated both in seed yield equation, and pod severity alone participated in the seed infection equation. No disease severity predictor was associated with sr(li10) and sr(ls10), no disease predictor at the start of the disease was associated with yd, and no disease incidence predictor was associated with sr(iv).

Taking into account the frequencies (%) of different group of predictors at different growth periods (4 WAP: 22.7, 6 WAP: 27.3, 8 WAP: 9.1, 10 WAP: 16.7, Pod filling: 4.5, maturity: 3.0), the most important stage was 6 WAP. It was followed by 4 and 10 WAP.

7.4 Discussion The most important stages associated with disease characteristics, seed yield and seed infections were 6, 4 and 10 WAP. Concerning the importance of evaluation periods, Burleigh et al. (1972), in his attempt of identifying the regression equations for the prediction of grain weight losses caused by leaf rust (Puccinia recondita f. sp. tritici) on wheat, selected disease severity per tiller recorded at the boot stage, disease severity on the flag leaf at early berry and early dough as independent variables with significant partial regressions coefficients, and developed several equations with a maximum R2 of 79.0 %. Disease infection has a detrimental effect on yield and Navas-Cortés et al. (2000) reported chickpea seed yield decrease with increasing final disease intensity index. With regard to the predictors at the start of the disease, the variable ddis did not participate in any equation, but Analytis (1973), identified the date on which the disease was first recorded on the leaf as a significant predictor for disease severity on individual leaves. In

145 the same context, Navas-Cortés et al. (2000) observed chickpea seed yield increase with the delay in time to Fusarium wilt initial symptoms. The variables sr(dcn), sr(dcv) and dcl as predictors did not participate in any equation, but the same author reported chickpea seed yield decrease with increasing standardized area under disease intensity progress curve.

Water was an important predictor in the models developed and is a key factor in disease epidemiology. In our study most of the water (68.9 to 81.1 %) was provided by rainfall at UZ Farm, and at ARDA Mz all the water was applied through irrigation. Payne et al. (2001) predicted wheat and pea yield well with multiple regression equations using monthly rain with respective R2 of 62.0 and 65.0 %. Rainfall averaged weekly, and average weekly morning and evening relative humidity exhibited positive effect on rice yield (Sarmah and Handique, 2001). Si and Walton (2004) identified also that canola seed yield was improved by post-anthesis rainfall increase. Mengistu and Heatherly (2006) reported that the incidence of Phomopsis longicola Hobbs on soybean was associated with total rainfall and rainfall frequencies for each year, and that conducive moisture environment overrode the effects of irrigation. Potato late blight intensity enhancing factors include number of days with precipitation (Zwankuizen and Zadocks, 2002). Kranz (1968), in a collective study of 59 fungal diseases of weed and crop hosts in tropical Guinea observed that the variation in the percentage of infected plants was related to rainfall and integers describing growth stage. But Talboys and Wilson (1970), found rainfall, its square and interaction with temperature non significant, while trying to study their association with the severity of hop wilt (Verticillium albo-atrum Reinke and Berthold), and explain that such results were due to the small number of occasions when high rainfall was recorded. Royle and Thomas (1972) found that equations that included rainfall duration and quantity as significant variables gave the best prediction of the percentage of leaves of hop gardens infected by downy mildew each year. In the case of humidity, Snow et al. (1968), identified the number of hours in which relative humidity was greater than 97 % during slash pine seedling exposure to fusiform rust as a consistent significant variable in each year. Moschini and Pérez (1999) predicted wheat leaf rust severity as function of days with relative humidity > 70 % without precipitation. Ahmed

146 and Kulkarni (1968) reported that infections of the pathogen Taphrina maculans E.J. Butler on turmeric were favoured by 80 % atmospheric humidity. The association of humidity and rain was reported by Arnon (1972) who identified the two weather factors as main factors affecting the time and extent of disease development in irrigated crops. Moschini (1996) found the variable made of the number of two day-intervals with rainfall (> 81 %) the first day, and relative humidity (≥ 78 %) the second day, strongly associated with the incidence of wheat head blight caused by Fusarium graminearum Schwabe (R2 = 81 %).

Defoliation, AUDPC and temperature variables did not participate in the prediction equations. They were eliminated in the modeling process. They might have minor importance in ALS epidemiology or indirect effects through other predictors. The correlation analysis showed that defoliation and AUDPC variables were related to all disease and most weather characteristics. The results obtained with temperature are in agreement with the findings of Sindham and Bose (1980a) who noted that relative humidity and precipitation were more important than temperature in ALS epidemiology. As this regards, Talboys and Wilson (1970) observed an interaction temperature x rainfall, and Hannusch and Boland (1996), an interaction relative humidity x temperature. Djurle et al. (1996) found that almost 50 % of the variation in leaf wetness duration can be explained by maximum and minimum temperatures, rainfall and hours with relative humidity above 90 % on a daily basis.

7.5 Conclusion

The variables sr(li10), sr(ls10) and sr(rrdef10) were more dependent upon weather predictors; and sr(dcv), yd and sr(iv) upon disease predictors. But weather and disease independent variables were equally important in the prediction of sr(dcn) and dcl. Weather independent variables were more represented than the disease predictors. Water and disease severity were the most represented weather and disease predictors. The most important disease predictor was sr(lsdis). It was followed by lidis, sr(li4), sr(ls4) and sr(ls10). The most important weather predictor was sr(dw6). It was followed by sr(dw8),

147 sr(dh6), sr(dw4) and sr(wi6). The most important stage was 6 WAP. It was followed by 4 WAP and 10 WAP.

For general disease control the predictors and stages to consider might be: at the start of the disease [sr(lsdis), lidis)], at 4 WAP [sr(ls4), sr(li4), sr(dw4)], at 6 WAP [sr(dw6), sr(wi6), sr(dh6)], 8 WAP [sr(dw8)], pod filling [sr(oi), sr(os)], maturity [sr(dwmat)]. Specifically for seed yield production, the predictors to consider might be: disease incidence 4 WAP to pod filling [sr(li10), sr(oi)], disease severity 4 WAP to pod filling

[sr(ls)4, sr(os)], water 6 WAP to maturity [sr(dw6), sr(dwmat)], and humidity at 10 WAP

(hi10). For clean seed production, the predictors to consider might be: at the start of the disease [sr(lsdis), lidis], disease severity 4 WAP to pod filling [sr(ls4), sr(ls10), sr(os)], water 4 WAP to maturity [sr(dw4), sr(dw6), sr(dw8), sr(dwmat)].

The multiple regression equations were appropriate since the simple equations lines representing the relationships between responses and fits did not deviate significantly from the 1:1 lines. The gradients of different equations developed were not significantly different from one for individual years and combined data for both years. In all the models developed by removing randomly three times four observations from each year, one from each planting date, there was no significant difference between the predicted and observed mean values for each year, and their differences were falling into the 95 % confidence intervals. Generally, compared to the models combining both years’ data, the coefficients of determination of sister models obtained by data splitting and removal in each year were similar, and the standard deviations of the residuals low. For each one of the dependent variables, one equation was developed and the number of predictors reduced to 7 - 10. The equations developed associate disease incidence and severity, mean daily humidity and water, duration of humidity and water with specific evaluation periods. Since disease incidence and severity at the start of the disease were part of disease and seed infection predictors, decisions can be made early enough to permit disease control measures and warn farmers.

148

This study might direct us to different decisions: 1) The use of tolerant/resistant cultivars, which can be sown in all planting dates without disease control. 2) The use of early planting date in summer and winter crop in lowlands with warmer winter in association with sprinkler or furrow irrigation for disease control. 3) Disease characteristics prevailing from the start of the infection to pod filling are conducive to high disease pressure at the end of the season, high seed infection and low seed yield, and an early start of the disease (2 - 4 WAP) implies the use of control measures to limit disease damage. 4) Clean seed can be obtained by selecting clean pods in the field since pod severity was related to seed infection. 5) Water applied and humidity conditions are most indicative of the start of disease infection, high disease pressure and progression. Farmers should be warned as soon as those conditions are recorded early in the season, and control measures applied when necessary. Generally the critical periods were 6, 4 and 10 WAP. 6) Pyramiding resistance of genes from Andean and Mesoamerican gene pools to develop varieties tolerant/resistant not carrier of the pathogen through seed that can be used in integrated crop production strategies might be an effective and durable disease management.

149

CHAPTER 8: GENERAL DISCUSSION AND RECOMMENDATIONS

8.1 An assessment of quality and health of field bean seeds home-saved by smallholder farmers Sweet bean was the most preferred type by the smallholder farmers. This was supported by Kutywayo (2000) who reported that the cultivar Natal Sugar was the most preferred by smallholder farmers in the area. As also reported by Chiduza (1994) and Chinwada (1994), most farmers used home-saved seed (73.2 % in this study), which in general had different levels of seedborne fungal infection. The percentage of farmers using merchant and seed from other farmers was respectively 22.0 and 3.7 %. As presented by David and Oliver (2002), farmers look for seed from other sources because they have either lost most or all of their seed as a result of drought, heavy rains, disease, pests or poor storage; have eaten or sold all their seed; want to cultivate more land but do not have enough seed to plant on the new land; or want to try a new variety.

As reported by Kutywayo (2000), diseases caused by Ascochyta phaseolorum, Cercospora canescens, Macrophomina phaseolina, Colletotrichum lindemuthianum and Fusarium solani were the most frequent under smallholder conditions in Chinyika Resettlement Area (CRA). Angular leaf spot caused by Pseudocercospora griseola was reported under smallholder farmers (Manyangarirwa, 2001), but was not frequent in CRA (Kutywayo, 2000), and the pathogen was not identified on seed collected from the area. This might suggest that, disease pressure was not enough to allow pathogen transmission to the seed, or weather conditions especially during the reproductive phase were not conducive to seed infection. Since some of the farmers were able to produce good quality seed, the production of improved seed by farmers could be promoted. Farmers, if trained, can complement the effort made by seed companies and institutions to produce good quality seed (David and Oliver, 2002). Production of good quality seed by farmers can be achieved if they follow steps recommended for growing bean seed including; selection of varieties, seed, site, appropriate season and planting period, suitable irrigation system, removal of unwanted plants, and control of weeds, diseases and pests. Therefore, farmers

150 may be exposed to the advantages of using clean and graded seed. The preferred sweet bean type was susceptible to other seedborne fungi, mainly F. oxysporum, A. alternata and C. lindemuthianum, and its resistance might be improved.

8.2 Studies on sources of inoculum of Pseudocercospora griseola The major sources of inoculum for Pseudocercospora griseola identified in this study were infected seed, concomitant infected plants in the field, the soil at the end of the growing period and the air. These sources were also identified by several researchers. Infected seed, as a source of inoculum, was identified by Bolkan et al. (1976), Sengooba and Mukiibi (1986), CMI (1986), Neergaard (1988), Richardson (1990), Saettler (1994), Kutywayo (2000) and Manyangarirwa (2001). Sengooba and Mukiibi (1986), Inglis et al. (1988), Allorent and Savary (2005) and Wagara et al. (2007) recognized infected plants in the field as a source of inoculum. The importance of alternative hosts was pointed out by CMI (1986), Sengooba and Mukiibi (1986) and Sartorato et al. (2005). Plant debris and soil were recognized as sources of inoculum by Sengooba and Mukiibi (1986), Correa and Saettler (1987) and Celetti et al. (2005). Regarding the air as a source of inoculum for P. griseola as confirmed by Sartorato et al. (2005), it is known that the pathogen is transported by dry air currents (Smith et al., 1992), and disseminated by water splashes (Gupta and Mathew, 1999) and wind-blown particles (Cardona-Alvarez and Walker, 1956; Gupta and Mathew, 1999; Allorent and Savary, 2005). Information on trapping P. griseola in the air or catching the pathogen in rainfall and irrigation water is scarce. The increase of the pathogen dispersal was observed by Allorent et al. (2005) during the rainy season, and the pathogen was identified as waterborne in the Kali stream of Uttara Kannada region in India. It was collected on leaf litter from water by Ramesh and Vijaykumar in 2005.

There was an increase in the number of spores towards the end of the season which might be associated with increase in disease severity, plant defoliation, pods and seeds infection. This might result in reduction in seed yield, quality and health. Consequently, staggered planting will result in the spread of inoculum from early to mid and late planted

151 crop. The increase of inoculum in the soil due to defoliation is detrimental to the following crop if no control measures are applied.

8.3 Effect of cultural practices on common bean angular leaf spot disease characteristics 8.3.1 Weather variables The site ARDA Mz had lower relative humidity (59.09 %) than UZ Farm (80.10 %) (P < 0.001). There was a negative relationship between relative humidity recordings from the two sites. This difference might affect the development of ALS epidemics in the two sites.

The most important feature of the study was the triple interaction year (YR)*planting date (PD)*evaluation period (EP), significant (P < 0.001) for all the variables studied. This might result in different mean performance and variability between evaluation periods within planting dates, planting dates within years and finally years. The combination of evaluation period high and low values and their variability should be related to the start of the disease, its development and spread. The variation of weather factors was reported with years (Sindham and Bose, 1980a; Rodriguez et al., 1999; Jesus et al., 2001), planting dates (Rodriguez et al., 1999; Nath et al., 2001) and evaluation periods (Last et al., 1969; Sindham and Bose, 1980a; Nath et al., 2001). Planting date was identified as the principal source of weather variation, and year the second. Year 1 had longer duration of temperature and water, lower temperature and relative humidity daily means than year 2. Compared to medium and late planting dates, early and winter were less favourable to the disease. Those conditions included for winter planting date; more intermediate and low values, and for EPD more intermediate values. Early and winter plantings were the least discriminating, providing little information on evaluation periods in duration and daily mean variables in both years. There was a variation between EPs with regard to their performance and variability. Weather conditions at maturity were favourable for good quality seed harvest for winter and late planting dates. The conditions of high humidity and water prevailing at maturity for the early and medium planting dates might interfere negatively with harvesting operations and seed quality.

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8.3.2 Leaf characteristics Considering disease incidence, severity and defoliation at UZ Farm; planting date was the most important factor, as it was significant (P < 0.05) in most cases and participated in different significant interactions with other factors studied. Generally, the early planted crop was the least attacked, with the disease starting late between 4 and 6 WAP, in addition to a slow disease progression. Reduction of disease infection due to appropriate planting period was obtained by different researchers. Planting early reduced the intensity of Fusarium head blight in spring wheat (Subedi et al., 2007), angular leaf spot in French bean (Sindham and Bose, 1980a), and the severity of Fusarium head blight in spring wheat (Subedi et al., 2007), powdery mildew in coriander (Kalra et al., 2000), and Phaeosphaeria leaf spot in maize (Pegoraro et al., 2001). Navas-Cortés et al. (1998) also reported that planting date delayed the onset of Fusarium wilt epidemic in chickpea, slowed its rate of development, and reduced the final amount of disease. Defoliation was also related to disease severity in common bean (Willocquet et al., 2004), and was observed late in the season in French bean (Sindham and Bose, 1980a). In this study, the winter crop did not have any disease on leaves and pods, and there was no defoliation due to the disease. This absence of disease symptoms might be explained by weather variables which were less favourable for disease infection and development compared to summer medium and late plantings. Therefore, winter and summer early plantings can be adopted in order to grow a better crop that is less attacked by ALS.

Sprinkler and furrow irrigation at UZ Farm had similar effects on disease incidence, severity and defoliation. At ARDA Muzarabani there was no disease infection under both irrigation systems. That equivalence between irrigation methods was due to similar weather conditions. In this regard, both winter and early plantings can be associated with any irrigation method.

Year one had more disease incidence, severity and defoliation than year 2 at UZ Farm, while at ARDA Muzarabani, both years did not have any disease infection. Variations in disease infection with years have been observed by other researchers (Navas-Cortés et al., 2000; Pron’czuk et al., 2004; Subedi et al., 2007). These differences between years

153 were related to variations in weather conditions such as temperature, relative humidity, and increase in inoculum and pathogen populations. In this study, the conditions prevailing in year 1 were favourable for disease development. Generally, the results obtained may be explained by weather conditions which differed in terms of favourable conditions for the pathogen/disease between planting dates within years and evaluation periods within planting dates. The management of ALS may, thus, be based on choosing cultural practices (summer early and winter planting dates associated with either sprinkler or furrow irrigation) which might delay disease infection and decrease disease incidence, severity and defoliation variables by acting on weather conditions favouring disease infection and spread.

8.3.3 Seed yield Planting date was the most important factor for seed yield and it participated in different significant interactions with other factors studied (P < 0.05). The best planting dates were winter and early planting date in summer. Good seed yield was associated with the absence of the disease and low disease infection. These findings are similar to reports done by other researchers on different crops. Low disease infection and yield increase were observed in coriander (Kalra et al., 2000), maize (Pegoraro et al., 2001), winter triticale (Schwarte et al., 2006) and bread wheat (Ahari et al., 2003) with early planting dates. These results can be explained by the creation of favourable weather conditions affecting yield such as rainfall, temperature and air moisture (Fisher, 1984; Squire, 1990). Hence, early and winter planting dates might be adopted to achieve good seed yield. However, the analysis of weather data showed that humid conditions prevailed at the end of the growing season for early planting date; a situation that might affect the seed quality. This implies that harvesting is done at physiological maturity to avoid the prolonged humid conditions, and improved facilities for drying plants/pods after harvest and seed after processing are put in place.

Generally, there were no significant differences between sprinkler and furrow irrigation in most variables studied when used to supplement rainfall for the summer crop. However, for the winter crop, where water was completely supplied by irrigation, seed

154 yield was higher under sprinkler than furrow irrigation. Varying results have been reported by different researchers on the effect of sprinkler and furrow irrigation on yield in different crops (Robertson and Frazier, 1982; White and Singh, 1991; Boldt et al., 1996; Cetin and Bilgel, 2002; Turkington et al., 2004; Trout et al., 2008; Firouzabadi, 2012). To achieve greater yields, early planting date might be associated with any irrigation method, and winter planting with sprinkler irrigation. The use of other planting dates supplemented by irrigation methods in summer could be accompanied with appropriate control measures for the disease.

8.3.4 Seed health The infection of seed planted was low in both years at UZ Farm and ARDA Mz with a maximum of 0.2 %. These results were in agreement with laboratory findings showing the absence of the pathogen in home-saved seed from Chinyika Resettlement Area, and results obtained by Kutywayo (2000) and Buruchara (1990). This low pathogen infection in planted seed might be a consequence of low disease pressure in the field during the growing period, especially during the reproductive phase.

The general trend was that seed harvested in year 1 was less infected than the one harvested the following year. Seed from winter and early planting dates was the least infected, while seed from medium and late planting dates had the highest infections. These differences in the percentage of seed infected amongst the planting dates and years were caused by varying environmental conditions and are in agreement with the findings by Duvnjak et al. (2005) for soybean mildew. Considering irrigation systems, seed infection of plants after sprinkler and furrow irrigation was equivalent at UZ Farm, and there was no seed infection at ARDA Muzarabani. Therefore, to achieve low seed infection by P. griseola, any irrigation method can be used with winter and summer early planting dates. Production of seed free of P. griseola infection can be achieved by combining good quality seed, cultural practices and environmental conditions to minimize disease inoculum levels and spread in the field.

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8.4 Correlation The variables estimated at the start of the disease and leaf disease variables from 4 to 8 WAP [incidence, severity and defoliation], pod disease variables [incidence and severity], and weather variables from 4 to 10 WAP [duration of humidity and temperature, and daily mean humidity) were positively correlated with the main disease variables (incidence, severity and defoliation at 10 WAP, their respective area under disease progress curves, and seed infection) and negatively correlated with yield. The variable estimated at the start of disease (days to first disease infection) and the weather variable (daily mean temperature) were correlated negatively with the main disease variables and positively with yield. The variables (duration of water and daily mean water) were the most variable across evaluation periods, and correlations were specific to evaluation periods. Most of the disease and some weather variables measured in this study were therefore, negatively correlated with yield. This indicated that ALS was detrimental to yield. These results are in agreement with reports by other researchers who reported a negative correlation between disease variables and yield (Pegoraro et al., 2001; Wiatrack et al., 2004).

Disease infection, development and spread are associated with weather factors. Disease variables are associated with humidity (Sharma, 1986; Gautam, 1987; Sen, 1987; Chakraborty and Ballard, 1995; Duggal, 2001; Olanya et al., 2001; Chandel, 2003; Godoy et al., 2003; Bhardwaj et al., 2005), temperature (Sen, 1987; Duggal, 2001; Kaushal, 2003; Godoy et al., 2003; Stone et al., 2007;) and rainfall ( Sen, 1978; Prashar, 1986; Sharma, 1986; Chakraborty and Ballard, 1995; Duggal, 2001; Olanya et al., 2001; Chandel, 2003; Kaushal, 2003; Bhardwaj et al., 2005).

8.5 Prediction of angular leaf spot effect in common bean using weather and disease predictors The disease dependent variables collected at 10 WAP (incidence, severity and defoliation) were more dependent upon weather predictors; and AUDPC severity, seed yield and seed infection upon disease predictors. But weather and disease predictors influenced AUDPCs incidence and defoliation equally. Disease variables, seed yield and

156 seed health depended upon meteorological and biological factors. Disease incidence depends upon rainfall quantity (Royle and Thomas, 1972; Butt and Royle, 1974; NECTAR, 1998; Bhardwaj et al., 2005; Mengistu and Heatherly, 2006), duration (Royle and Thomas, 1972; NECTAR, 1998) and intensity (NECTAR, 1998). Disease infection, development and spread depend upon relative humidity (Ahmed and Kulkarni, 1968; Godoy et al., 2003; Bhardwaj et al., 2005). Specifically, relative humidity duration can be used in the prediction of disease events (Godoy et al., 2003) such as severity (Moschini and Pérez, 1999). Yield depends upon rainfall quantity (Payne et al., 2001; Sarmah and Handique, 2001; Si and Walton, 2004), relative humidity (Sarmah and Handique, 2001) and disease characteristics such as intensity (Navas-Cortés et al., 2000). Seed health depends upon weather factors such as rainfall quantity and duration (Yeates et al., 2000).

Biological variables which participated in different equations developed were for the start of the disease [lidis, sr(lsdis], disease incidence [sr(li4), sr(li8), sr(li10), sr(oi)] and disease severity [sr(ls4), sr(ls6), sr(ls10), sr(os)]. The importance of disease incidence in modeling was demonstrated by Chuang and Jeger (1987) who predicted the relative rate of black sigatoka by an equation including the percentage of leaf infected at a given time. In the case of disease severity, Dirks and Romig (1970) identified out of five biological variables, two severity predictors for rust number of uredospores for stem and leaf wheat. These variables identified were a measure of disease severity at the day of forecasting and a measure of either age or rate of the epidemic at that time. Seed yield was predicted by final disease intensity index in chickpea (Navas-Cortés et al., 2000), and in wheat by disease severity recorded per tiller at the boot stage, and on the flag leaf at early berry and early dough (Burleigh et al., 1972).

Weather predictors were more represented than the disease ones in the prediction equations. Water and disease severity were the most represented weather and disease predictors. The most important disease predictor was disease severity at the start of the infection. It was followed by disease incidence at the start of the infection, disease incidence and severity at 4 weeks after planting (WAP), and disease severity at 10 WAP.

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Pod incidence and severity participated both in seed yield equation, and pod severity alone participated in the seed infection one. The most important weather predictor was duration of water at 6 WAP. It was followed by duration of water at 8 WAP, duration of humidity at 6 WAP, duration of water at 4 WAP, and daily mean water at 6 WAP. Water duration at maturity participated in seed yield and seed infection equations. The most important stage was 6 WAP. It was followed by 4 WAP and 10 WAP.

For each one of the dependent variables, one equation was developed and the number of predictors reduced to 7 - 10. Equations developed associated disease incidence and severity, mean daily humidity and water, duration of humidity and water with specific evaluation periods. Since disease incidence and severity at the start of the disease were part of disease and seed infection predictors, decisions can be made early enough to permit disease control measures and warn farmers. Farmers should be warned as soon as the conditions of water received and humidity favourable to the disease are recorded early in the season, and control measures should be applied when necessary. The critical periods were 4, 6 and 10 WAP.

8.6 Recommendations  The results from the survey indicated that some farmers were able to produce good quality seed without any seedborne fungus infection. Then, it might be advantageous to expose farmers to the advantages of using clean and graded seed, and to train them in seed production starting with those who have already the skill to produce good seed.

 The sweet bean seed collected covered a range of seed sizes with varying weights. There is a need to describe the sweet bean type in order to differentiate the principal cultivars.

 It appeared that sweet bean type was sensitive to different seedborne fungi such as F. oxysporum, A. alternata, and C. lindemuthianum, and had a low level of off- types which included seeds from natural outcrossing. Therefore, breeding strategies

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might be adopted to incorporate resistance to diseases in sweet bean, and to develop new varieties from outcrosses.

 It is also imperative to promote farmer friendly protocols that-test bean seed for seedborne fungi, to minimize their frequency. In addition to the three main fungi identified, sweet bean was attacked by minor fungi M. phaseolina, F. solani, C. canescens, P. exigua and R. solani. This gives an idea of a wider evaluation of bean varieties and identification of seedborne fungi in all agro-ecological zones in relation to growing seasons and planting periods. .  The most important feature in the study of weather variables was the significant triple interaction year*planting date*evaluation period for all the variables studied. Consequently, the analysis of weather factors might be done on many years of observation to get more information on their variation across years, planting dates and evaluation periods. Information is lacking on water loss due to irrigation systems in local conditions, and water availability in the soil between irrigation days, implying a need for further studies to quantify water loss due to sprinkler and furrow irrigation, and soil water depression between irrigation intervals.

 More research is needed to quantify P. griseola inoculum present in the air, clarify its behavior in the atmosphere, and to study the environmental factors associated with the presence of the pathogen in the airspora in local conditions.

 The understanding of the importance of different sources of inoculum might come from the monitoring of airborne inoculum and field conditions.

 A global approach can be undertaken for studying the airspora associating fungi of medical and agricultural importance.

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 Investigation of the microclimate conditions associated with the effect of sprinkler and furrow irrigation on bean disease characteristics in local conditions will assist in understanding their equivalent effect. The effect of defoliation on disease pressure and progression need to be quantified, since defoliation is an important factor in ALS infection.

 The difference in seed yield between irrigation systems varied with the mode of water application as full (sprinkler more yielding than furrow) or supplemental to rainfall (sprinkler equivalent to furrow). It stands out that the effect of irrigation systems (sprinkler and furrow), planting date (early, medium, late and winter), and years of evaluation on common bean seed yield needs elucidation under local conditions in areas representing varying weather conditions and different angular leaf spot disease pressures.

 Weather conditions at maturity were favourable for clean seed harvest for winter and late planting dates, but not for medium and early planting dates, which experienced high humidity that can interfere negatively with harvest operations and seed quality. For these two planting dates, an economic study might be undertaken to compare the advantage of an early-planted with a cost for artificial drying, and a late-planted one with a cost of disease control but under natural drying conditions. In addition, the variability of dry spells occurring in summer need to be understood.

 Visual selection reduces seed infection, increases seed weight and germination, and is efficient if done in healthy seed with less than 1 % seed infection by Pseudocercospora griseola (Icishahayo et al., 2007). Hence, there is a need of assessing the impact of visual selection of seed for planting on infection by the pathogen after cycles of harvesting. Breeding for resistance for P. griseola is possible, and sources of resistance have been identified (Garcia et al., 2006). Therefore, the evaluation of the level of infection by the pathogen of seed from varieties under screening for ALS resistance with disease high pressure will help to

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identify varieties not carrier of the disease. The results on seed infection obtained in this study combined both external and internal contamination by the pathogen. Further research might be carried out to identify the location of the inoculum in seed parts (hilum, seed coat, cotyledons, embryo).

 In the present study, the internal validation was done to evaluate the applicability of the models. It will be necessary to use the external validity to check the adaptation of statistical models developed to various diseases and environments. This study has mostly focused on the growing period and little was done about the periods before sowing and the start of the disease. Studying those two periods will permit to generate more information about the effect of the remote sources of inoculum. Some weather and disease predictors, and associated evaluation periods did not participate in the prediction equations. Thus, their behaviour in the modeling process needs to be more investigated using varied datasets. In the actual study, as it appears in the regression plots between responses and fitted values, some observations don’t have complete data with individual year or both years together. It appears that the research of new competitive predictors and the reduction of the interval between data observations to less than two weeks might provide more data and cover the gaps. This is also an indication that such kinds of models need varied data from different environments (locations, years, seasons, planting dates and evaluation periods). Coefficients of determination for different models developed varied from 69.6*** (yd second four data removal) in 2002/3 to 99.8*** [sr(dcn) all the models] in the same year. Although these were relatively high values, this indicated that some important factors might still not be accounted for, probably more sensitive assessments of available inoculum, radiation intercepted by healthy leaf area, host dry matter, seed yield components and transpired water might be useful (Chuang and Jeger, 1987; Waggoner and Berger, 1987). Since there was a variation in the participation of different weather and disease predictors in equations developed, an attempt can be made to classify them according to their importance in the modeling process.

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APPENDICES

Appendix 1: Studies on sources of inoculum of Pseudocercospora griseola

Table 1.1: Analysis of variance of disease severity (transformed by square root) of 25 varieties to compare weeks after crop emergence in 2009/10 summer season

Source DF SS MS F P Replication 2 0.2670 0.1335 1.04 WACE 5 126.3549 25.2710 197.44 < 0.001 Residual 442 56.5738 0.1280 Total 449 183.1958

Table 1.2: Analysis of variance of disease severity of Natal Sugar planted in the border area to compare weeks after crop emergence in 2009/10 summer season

Source DF SS MS F P WACE 6 629.1640 104.8607 454.75 < 0.001 Residual 182 41.9674 0.2306 Total 188 671.1314

Table 1.3: Analysis of variance of the quadratic regression of disease severity (transformed by square root and centred by dividing each observation by the standard deviation = 0.5681) as dependent variable and weeks after crop emergence (centred by substracting from each observation the mean = 7.5) as predictor obtained from data collected in 2009/10 on 25 varieties

Source DF SS MS F P Regression 2 4.9244 2.4622 87.41 0.002 Residual Error 3 0.0845 0.0282 Total 5 5.0089 S = 0.1678 R-Sq = 98.3 % R-Sq(adj) = 97.2 %

Table 1.4: Characteristics of different parameters of the quadratic regression between disease severity and weeks after crop emergence obtained from data collected in 2009/10 on 25 varieties

Predictor Coefficient StDev t P Constant 2.5056 0.1054 23.77 0.000 x 0.50152 0.04012 12.5 0.001 x2 0.11832 0.02747 4.31 0.023 x: Weeks after crop emergence

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Table 1.5: Analysis of variance of the polynomial regression of disease severity as dependent variable and weeks after crop emergence as predictor obtained from data collected in 2009/10 on Natal Sugar planted in the border area

Source DF SS MS F P Regression 3 22.9624 7.6541 138.17 0.001 Residual 3 0.1662 0.0554 Total 6 23.1286 S = 0.2354 R-Sq = 99.3 % R-Sq(adj) = 98.6 %

Table 1.6: Characteristics of different parameters of the polynomial regression between disease severity and weeks after crop emergence obtained from data collected in 2009/10 on Natal Sugar planted in the border area.

Predictor Coefficient StDev t P Constant 33.038 7.472 4.42 0.021 x -14.633 2.991 -4.89 0.016 x2 2.142 0.385 5.56 0.011 x3 -0.094 0.016 -5.90 0.010 x: Weeks after crop emergence

Table 1.7: Analysis of variance of seed infection (transformed by square root) of 14 varieties obtained in 2010/11 summer season

Source DF SS MS F P Period 1 3.299 3.299 3.05 0.156 Residual 4 4.327 1.082 0.96 Variety 2 10.560 5.280 4.69 0.045 Period.Variety 2 0.528 0.264 0.23 0.796 Residual 8 9.012 1.126 Total 17 27.726

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500

400

300

200 Frequency

100

0 100 200 300 400 Wind direction (degrees)

Figure 1.1: General histogramme of wind direction (º) collected in March-April 2009/10 summer season at Crop Science Department site

300

200

Frequency 100

0 100 200 300 400 Wind direction (degrees)

Figure 1.2: General Histogrammes of wind direction (o) collected in March-April 2010/11 summer season at Crop Science Department site

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Appendix 2: General analyses of weather data presenting the degrees of freedom (DF) and the probabilities related to F-test.

Source DF dt sr(dh) sr(dw) ti hi sr(wi) YR 1 < 0.001 0.243 0.015 0.006 0.018 0.921 IR 1 0.961 0.827 0.033 0.965 0.961 0.859 PD 3 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 EP 5 0.086 0.637 < 0.001 0.423 0.725 < 0.001 YR*IR 1 0.990 0.915 0.592 0.985 0.997 0.658 YR*PD 3 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 0.140 YR*EP 5 0.260 0.443 0.295 0.558 0.506 0.290 IR*PD 3 1.000 0.970 0.565 1.000 1.000 0.791 IR*EP 5 1.000 1.000 0.991 1.000 1.000 0.924 PD*EP 15 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 YR*IR*EP 5 1.000 1.000 0.992 1.000 1.000 0.951 YR*IR*PD 3 1.000 0.993 0.978 1.000 1.000 0.906 YR*PD*EP 15 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 IR*PD*EP 15 1.000 1.000 1.000 1.000 1.000 0.993 YR: year, PD: planting date, EP: evaluation period

Appendix 3: Analysis of ALS disease incidence characteristics observed at UZ Farm in 2002/3 and 2003/4

Table 3.1: Friedman test of the number of days to the start of disease infection in 2002/3

Adjustment for ties DF S P Non adjusted 5 13.75 0.017 Adjusted 5 11.85 0.001

Table 3.2: Percentage of plants attacked by ALS at 6 weeks after planting at UZ Farm in 2002/3 transformed by square root

Source of variation DF SS MS F P Block 3 1.0112 0.3371 1.49 IR 1 0.123 0.123 0.54 0.515 Residual 3 0.6807 0.2269 0.61 PD 2 1715.9985 857.9993 2291.22 < 0.001 IR.PD 2 0.246 0.123 0.33 0.721 Residual 84 31.4556 0.3745 Total 95 1749.515

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Appendix 4: Analysis of ALS disease severity characteristics observed at UZ Farm in 2002/3 and 2003/4

Table 4.1: Analysis of variance of disease leaf severity at the start of the infection at UZ Farm in 2002/3 transformed by square root

Source of variation DF SS MS F P Block 3 3.2388 1.0796 2.54 IR 1 12.0715 12.0715 28.38 0.013 Residual 3 1.276 0.4253 0.52 PD 2 65.7369 32.8685 40.53 < 0.001 IR.PD 2 18.8925 9.4463 11.65 < 0.001 Residual 84 68.1275 0.811 Total 95 169.3433

Table 4.2: Percentage of leaf area attacked by ALS at 4 weeks at UZ Farm in 2003/4, during late planting, data transformed by square root

Source of variation DF SS MS F P Block 3 8.0734 2.6911 6.15 IR 1 38.5477 38.5477 88.12 < 0.001 Residual 27 11.8115 0.4375 Total 31 58.4326

Table 4.3: Combined analysis over years of the percentage of leaf area attacked by ALS at 10 weeks after planting at UZ Farm in 2002/3 and 2003/4 transformed by square root

Source of variation DF SS MS F P YR 1 13.8829 13.8829 6.11 0.048 Residual 6 13.6304 2.2717 2.75 IR 1 58.8533 58.8533 71.21 < 0.001 YR.IR 1 0.1747 0.1747 0.21 0.662 Residual 6 4.9591 0.8265 1.23 PD 2 262.269 131.1345 194.85 < 0.001 YR.PD 2 150.9883 75.4941 112.18 < 0.001 IR.PD 2 12.5805 6.2903 9.35 < 0.001 YR.IR.PD 2 21.991 10.9955 16.34 < 0.001 Residual 168 113.0616 0.673 Total 191 652.3908

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Appendix 5: Analysis of ALS disease defoliation characteristics observed at UZ Farm in 2002/3 and 2003/4

Table 5.1: Combined analysis of variance over years of the relative rate of defoliation at the start of the infection obtained at UZ Farm in 2002/3 and 2003/4 transformed by square root

Source of variation DF SS MS F P YR 1 0.050584 0.050584 24.88 0.002 Residual 6 0.0121978 0.002033 2.45 IR 1 0.056472 0.056472 68.15 < 0.001 YR.IR 1 0.0081749 0.0081749 9.87 0.02 Residual 6 0.0049716 0.0008286 0.92 PD 2 0.051604 0.025802 28.66 < 0.001 YR.PD 2 0.0377129 0.0188565 20.94 < 0.001 IR.PD 2 0.0255371 0.0127686 14.18 < 0.001 YR.IR.PD 2 0.0172788 0.0086394 9.6 < 0.001 Residual 168 0.1512538 0.0009003 Total 191 0.4157869

Table 5.2: Analysis of variance of the rate of defoliation due to ALS infection at 10 weeks after planting at UZ Farm in 2002/3 transformed by square root

Source of variation DF SS MS F P Block 3 0.0011686 0.0003895 0.6 IR 1 0.0196839 0.0196839 30.28 0.012 Residual 3 0.0019504 0.0006501 1.02 PD 2 0.0714917 0.0357459 55.91 < 0.001 IR.PD 2 0.0151974 0.0075987 11.89 < 0.001 Residual 84 0.0537053 0.0006393 Total 95 0.1631973

Table 5.3: Analysis of variance of the rate of defoliation due to ALSinfection at 10 weeks after planting at UZ Farm in 2003/4 transformed by square root

Source of variation DF SS MS F P Block 3 0.020721 0.006907 2.12 IR 1 0.12913 0.12913 39.69 0.008 Residual 3 0.009761 0.003254 1.69 PD 2 0.658393 0.329196 171.48 < 0.001 IR.PD 2 0.090389 0.045194 23.54 < 0.001 Residual 84 0.161254 0.00192 Total 95 1.069647

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Appendix 6: Analysis of seed yield (kg/ha) and seed infection (%) recorded at UZ Farm and ARDA Mz in 2002/3 and 2003/4

Table 6.1: Analysis of seed yield (kg/ha) obtained at UZ Farm in 2002/3

Source of variation DF SS MS F P Block 3 988583 329528 5.04 IR 1 93112 93112 1.42 0.319 Residual 3 196251 65417 0.51 PD 2 20449949 10224975 80.07 < 0.001 IR.PD 2 409597 204798 1.60 0.207 Residual 84 10727468 127708 Total 95 32864959

Table 6.2: Analysis of seed yield (kg/ha) obtained at UZ Farm in 2003/4

Source of variation DF SS MS F P Block 3 302004 100668 0.70 IR 1 39364 39364 0.27 0.637 Residual 3 430421 143474 3.23 PD 2 8739673 4369836 98.50 < 0.001 IR.PD 2 973549 486775 10.97 < 0.001 Residual 84 3726738 44366 Total 95 14211750

Table 6.3: Combined analysis over years of seed yield (kg/ha) obtained at ARDA Mz in 2002/3 and 2003/4

Source of variation DF SS MS F P YR 1 6386349 6386349 52.46 < 0.001 Residual 6 730401 121733 0.64 IR 1 22720118 22720118 111.66 < 0.001 YR.IR 1 2065947 2065947 10.88 0.002 Residual 54 10253405 189878 Total 63 42156220

Table 6.4: Friedman test of the percentage of seed harvested at UZ Farm in 2002/3 infected by Pseudocercospora griseola

Adjustment for ties DF S P Non adjusted 5 8.93 0.112 Adjusted 5 17.86 0.003

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Appendix 7: Multiple regression of leaf severity at 10 weeks after planting with disease and weather predictors

Table 7.1: Leaf severity at 10 weeks after planting forward selection analysis of variance

Change DF SS MS F P

+ sr(rrdef10) 1 1304.35363 1304.35363 49034.83 < 0.001

+ sr(li10) 1 42.17578 42.17578 1585.52 < 0.001

+ sr(dw4) 1 4.7762 4.7762 179.55 < 0.001 + sr(dcv) 1 3.82987 3.82987 143.98 < 0.001

+ sr(ls6) 1 7.89053 7.89053 296.63 < 0.001

+ sr(ls8) 1 4.54697 4.54697 170.94 < 0.001 + sr(lsdis) 1 2.77173 2.77173 104.2 < 0.001

+ sr(wi6) 1 1.24224 1.24224 46.7 < 0.001 + ddis 1 1.02613 1.02613 38.58 < 0.001

+ sr(rrdef6) 1 0.92157 0.92157 34.64 < 0.001

+ sr(dw8) 1 0.87775 0.87775 33 < 0.001

+ sr(wi8) 1 0.44494 0.44494 16.73 < 0.001

+ sr(dh8) 1 0.38996 0.38996 14.66 < 0.001

+ sr(li4) 1 0.32827 0.32827 12.34 < 0.001

+ hi8 1 0.12308 0.12308 4.63 0.032

+ hi10 1 0.12727 0.12727 4.78 0.03

+ sr(dh6) 1 0.0736 0.0736 2.77 0.098 + lidis 1 0.054 0.054 2.03 0.156 Residual 237 6.30433 0.0266 Total 255 1382.25786 5.42062 Final model: Constant + sr(rrdef10) + sr(li10) + sr(dw4) + sr(dcv) + sr(ls6) + sr(ls8) + sr(lsdis) + sr(wi6) + ddis + sr(rrdef6) + sr(dw8) + sr(wi8) + sr(dh8) + sr(li4) + hi8 + hi10 + sr(dh6) + lidis

Table 7.2: Leaf severity at 10 weeks after planting backward elimination analysis of variance presenting [+ sr(wi)] as the last case added

Change DF SS MS F P

+ sr(wi8) 33 1377.53781 41.74357 1963.34 < 0.001 residual 222 4.72005 0.02126

- sr(rrdef6) -1 -0.00025 0.00025 0.01 0.914

- sr(wi8) -1 -0.00029 0.00029 0.01 0.907

- sr(rrdef10) -1 -0.00136 0.00136 0.06 0.801

- sr(li4) -1 -0.00470 0.00470 0.22 0.639

- sr(rrdef4) -1 -0.00915 0.00915 0.43 0.512 Total 255 1382.25786 5.42062 Final model: Constant + dcl + ddis + hi4 + hi6 + hi8 + hi10 + lidis + sr(dcn) + sr(dcv) + sr(dfdis) + sr(dh4) + sr(dh6) + sr(dh8) + sr(dh10) + sr(li6) + sr(li8) + sr(li10) + sr(ls4) + sr(ls6) + sr(ls8) + sr(lsdis) + sr(dw4) + sr(dw6) + sr(dw8) + sr(dw10) + sr(rrdef8) + sr(wi4) + sr(wi6)

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Table 7.3: Characteristics of primarily selected variables from those obtained by forward selection and backward elimination using (VIF < 10) and F-test (P < 0.10) in Genstat 14 for the multiple regression leaf severity at 10 weeks after planting with disease and weather predictors

Variable R2 R2(adj) Cp s 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 Name Number hi6 1 94.4 94.3 724.9 0.5539 X 1 64.5 64.4 5894.1 1.3894 X lidis 2 97.4 97.4 197.8 0.3758 X X 2 95.8 95.8 470.5 0.4767 X X sr(dfdis) 3 97.8 97.7 140.0 0.3505 X X X 3 97.7 97.7 148.9 0.3545 X X X sr(dh4) 4 98.0 98.0 99.3 0.3313 X X X X 4 98.0 98.0 103.0 0.3331 X X X X sr(dh8) 5 98.2 98.2 65.8 0.3144 X X X X X 5 98.2 98.1 72.6 0.3179 X X X X X sr(li4) 6 98.3 98.2 56.4 0.3092 X X X X X X 6 98.3 98.2 57.2 0.3096 X X X X X X sr(li6) 7 98.3 98.3 46.7 0.3037 X X X X X X X 7 98.3 98.3 48.3 0.3045 X X X X X X X sr(li8) 8 98.5 98.4 30.4 0.2944 X X X X X X X X 8 98.4 98.4 34.0 0.2964 X X X X X X X X sr(li10) 9 98.5 98.4 24.7 0.2908 X X X X X X X X X 9 98.5 98.4 24.8 0.2908 X X X X X X X X X sr(ls4) 10 98.5 98.5 20.6 0.2879 X X X X X X X X X X 10 98.5 98.5 23.0 0.2893 X X X X X X X X X X sr(ls6) 11 98.6 98.5 11.1 0.2865 X X X X X X X X X X X 11 98.5 98.5 12.2 0.2872 X X X X X X X X X X X sr(ls8) 12 98.6 98.5 11.2 0.2860 X X X X X X X X X X X X 12 98.6 98.5 11.3 0.2861 X X X X X X X X X X X X sr(lsdis) 13 98.6 98.5 16.2 0.2838 X X X X X X X X X X X X X 13 98.6 98.5 17.2 0.2843 X X X X X X X X X X X X X sr(dw4) 14 98.6 98.5 11.5 0.2804 X X X X X X X X X X X X X X 14 98.6 98.5 13.9 0.2818 X X X X X X X X X X X X X X sr(dw6) 15 98.6 98.6 12.1 0.2801 X X X X X X X X X X X X X X X 15 98.6 98.6 12.4 0.2803 X X X X X X X X X X X X X X X sr(dw8) 16 98.6 98.5 13.6 0.2804 X X X X X X X X X X X X X X X X 16 98.6 98.5 13.8 0.2805 X X X X X X X X X X X X X X X X sr(dw10) 17 98.6 98.5 15.3 0.2808 X X X X X X X X X X X X X X X X X 17 98.6 98.5 15.4 0.2809 X X X X X X X X X X X X X X X X X sr(rrdef10) 18 98.6 98.5 17.2 0.2814 X X X X X X X X X X X X X X X X X X 18 98.6 98.5 17.2 0.2814 X X X X X X X X X X X X X X X X X X sr(wi4) 19 98.6 98.5 11.1 0.2819 X X X X X X X X X X X X X X X X X X X 19 98.6 98.5 11.2 0.2820 X X X X X X X X X X X X X X X X X X X sr(wi6) 20 98.6 98.5 21.0 0.2825 X X X X X X X X X X X X X X X X X X X X

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17 Cp

9 9 11 13 15 17 19 21 23 p

Figure 7.1: Cp plot for leaf severity at 10 weeks after planting

Table 7.4: Matrix of correlation coefficients between residuals of ALS leaf severity at 10 weeks after planting multiple regression with disease and weather predictors

Estimate Reference Correlations constant 1 1 hi6 2 -0.70 1 lidis 3 0.36 -0.63 1 sr(li4) 4 0.35 -0.22 -0.06 1 sr(lsdis) 5 -0.46 0.42 -0.73 -0.12 1 sr(dw6) 6 0.02 -0.60 0.53 -0.19 -0.20 1 sr(dw8) 7 -0.45 0.13 -0.27 -0.07 0.33 -0.04 1 sr(dw10) 8 -0.07 0.29 -0.01 0.00 -0.10 -0.36 -0.68 1 2 3 4 5 6 7

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-3 -2 -1 0 1 2 3 Standardized residual

Figure 7.2: Schematic plot of standardized residuals for leaf severity at 10 weeks after planting multiple regression (range: -3.10 to 3.06, critical value: -3.5 to 3.5) obtained from data collected at UZ Farm and ARDA Mz in 2002/3 and 2003/4

3 -3 000 4 -2 5 12 -2 43322000 14 -1 96 30 -1 4433222211111000 57 -0 999999998877776666666655555 117 -0 444444444444444444443333333322222211111111111111111111100000 (63) 0 000011111111111111111111122222233333333333333333333333344444444 57 0 555556667777778889999 36 1 000000111122233344444 15 1 5566888899 5 2 002 2 2 5 1 3 0

Figure 7.3: Stem-and-leaf diagram of standardized residuals (N: 237, leaf unit: 0.10, range: - 3.10 to 3.06, critical value: -3.5 to 3.5) for leaf severity at 10 weeks after planting multiple regression obtained from data collected at UZ Farm and ARDA Mz in 2002/3 and 2003/4

199

Frequency 20

-4 -3 -2 -1 0 1 2 3 Standardized Residual

Figure 7.4: Histogram of standardized residuals for leaf severity at 10 weeks after planting multiple regression [(range: -3.10 to 3.06, critical value: -3.5 to 3.5) obtained from data collected at UZ Farm and ARDA Mz in 2002/3 and 2003/4

3 2 1 0 -1

Residual Standardized -2

-3

50 100 150 200 Observation Order

Figure 7.5: Plot of standardized residuals versus data order for leaf severity at 10 weeks after planting multiple regression obtained from data collected at UZ Farm and ARDA Mz in 2002/3 and 2003/4

200

1 0

-1

Standardized Residual Standardized -2

-3 0 1 2 3 4 5 6 7 8 Fitted Value

Figure 7.6: Plot of standardized residuals versus fits for leaf severity at 10 weeks after planting multiple regression obtained from data collected at UZ Farm and ARDA Mz data in 2002/3 and 2003/4 (White’s general test: 94.56, df: 35, Chi-square (0.05,35): 49.80, probability: 0.000)

0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10 0.11 Leverage

Figure 7.7: Boxplot of leverages {range: 0.014 to 0.108, critical value [P(0.01): 0.149, P(0.05): 0.133]} for the predictor model of leaf severity at 10 weeks after planting obtained from data collected at UZ Farm and ARDA Mz in 2002/3 and 2003/4

201

34 1 4444444444444444466666666666666668 104 2 23333333344555555555555666666666666666667777777788888888888888888+ (79) 3 00000000000000001111122333334444444444444444445555555566667777777+ 54 4 0000111111124455666888899999 26 5 112233344466779 11 6 0235689 4 7 169 1 8 1 9 1 10 8

Figure 7.8: Stem-and-leaf of leverages {range: 0.014 to 0.108, critical value [P(0.01): 0.149, P(0.05): 0.133]} of leaf severity at 10 weeks after planting multiple regression (N: 237, leaf unit: 0.0010) obtained from data collected at UZ Farm and ARDA Mz in 2002/3 and 2003/4

.999 .99 .95 .80 .50 .20

Probability .05 .01 .001

-3 -2 -1 0 1 2 3 Standardized residual Av erage: -0.0007350 Kolmogorov -Smirnov Normality Test StDev : 1.00562 D+: 0.082 D-: 0.085 D : 0.085 N: 237 Approximate P-Value < 0.01

Figure 7.9: Kolmogorov-Smirnov normal probability plot for the normality test of leaf severity at 10 weeks after planting multiple regression

0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 Cook's distance

Figure 7.10: Boxplot of Cook’s distance (range: 0.000 to 0.085, critical value: 0.93) for the predictor model of leaf severity at 10 weeks after planting obtained from data collected at UZ Farm and ARDA Mz in 2002/3 and 2003/4

202

(177) 0 00000000000000000000000000000000000000000000000000000000000000000+ 60 0 555555556666667777888899 36 1 00111222444 25 1 555667789 16 2 2344 12 2 55599 7 3 13 5 3 8 4 4 2 3 4 3 5 3 5 3 6 3 6 3 7 1 2 7 2 8 1 1 8 5

Figure 7.11: Stem-and-leaf of Cook’s distance (range: 0.000 to 0.085, critical value: 0.93) of the multiple regression of leaf severity at 10 weeks after planting (N: 237, leaf unit: 0.0010) obtained from data collected at UZ Farm and ARDA Mz in 2002/3 and 2003/4

Table 7.5: Analysis of variance of the multiple regression of leaf severity at 10 weeks after planting with disease and weather predictors

Source DF SS MS F P Regression 7 1243.78 177.68 623.83 0.000 Residual Error 229 65.23 0.28 Total 236 1309.01 S = 0.5337 R2 = 95.0 % R2(adj) = 94.9 %

Table 7.6: Characteristics of different parameters of the leaf severity at 10 weeks after planting multiple regression with disease and weather predictors

Predictor Coefficient StDev t P VIF constant -11.7951 0.5158 -22.87 0.000 hi6 0.195759 0.006198 31.58 0.000 2.8 lidis -0.00681 0.001847 -3.69 0.000 4.0 sr(li4) 0.23524 0.013 18.1 0.000 1.4 sr(lsdis) 0.73206 0.0472 15.51 0.000 2.6 sr(dw6) -0.7452 0.1752 -4.25 0.000 2.4 sr(dw8) -3.2172 0.1953 -16.47 0.000 2.7 sr(dw10) 3.6797 0.1635 22.51 0.000 3.0

203

Table 7.7: Analysis of variance of the regression between leaf severity at 10 weeks after planting response and fitted value with multiple regression using disease and weather predictors

Source DF SS MS F P Regression 1 1181.8 1181.8 4481.22 0.000 Residual Error 235 62.0 0.3 Total 236 1243.8

Tabe 7.8: Test of different parameters of the regression analysis between leaf severity at 10 weeks after planting response and fitted value with multiple regression using disease and weather predictors

Predictor Coefficient StDev t P Constant 0.17732 0.06053 2.93 0.004 sr(ls10) 0.95017 0.01419 66.94 0.000

204

Appendix 8: Regression of fitted values and responses for leaf disease severity in 2002/3

Table 8.1: Analysis of variance of the multiple regression between disease severity collected at 10 WAP in 2002/3 with weather predictors

Source DF SS MS F P Regression 7 699.161 99.88 510.02 0.000 Residual Error 116 22.717 0.196 Total 123 721.878 S = 0.4425 R2 = 96.9 % R2(adj) = 96.7 %

Table 8.2: Characteristics of different parameters of the multiple regression analysis

Predictor Coefficient StDev t P Constant -13.213 1.643 -8.04 0.000 hi6 0.21379 0.02865 7.46 0.000 lidis -0.010443 0.003981 -2.62 0.010 sr(li4) 0.0923 0.1314 0.70 0.484 sr(lsdis) 0.87008 0.05819 14.95 0.000 sr(dw6) -0.104 1.039 -0.10 0.920 sr(dw8) -3.491 0.5621 -6.21 0.000 sr(dw10) 3.441 0.8867 3.88 0.000

Table 8.3: Analysis of variance of the regression of fitted values and responses for disease severity obtained at 10 WAP in 2002/3

Source DF SS MS F P Regression 1 677.16 677.16 3754.78 0.000 Residual Error 122 22 0.18 Total 123 699.16

Table 8.4: Test of different parameters of the regression of fitted values and responses for 2002/3

Predictor Coefficient StDev t P Constant 0.11968 0.07119 1.68 0.095 Response 0.96853 0.01581 61.28 0.000

205

Appendix 9: Regression of fitted values and responses for disease leaf severity in 2003/4

Table 9.1: Analysis of variance of the multiple regression between disease severity collected at 10 WAP in 2003/4 with weather predictors

Source DF SS MS F P Regression 7 534.572 76.367 216.71 0.000 Residual Error 105 37.001 0.352 Total 112 571.573 S = 0.5936 R2 = 93.5 % R2(adj) = 93.1 %

Table 9.2: Characteristics of different parameters of the multiple regression analysis

Predictor Coefficient StDev t P Constant -11.0621 0.8268 -13.38 0.000 hi6 0.19699 0.01077 18.30 0.000 lidis -0.00219 0.003225 -0.68 0.499 sr(li4) 0.15616 0.04873 3.20 0.002 sr(lsdis) 0.4401 0.1105 3.98 0.000 sr(dw6) -2.753 1.075 -2.56 0.012 sr(dw8) 0.14 1.464 0.10 0.924 sr(dw10) 1.8543 0.742 2.50 0.014

Table 9.3: Analysis of variance of the regression of fitted values and responses for disease severity obtained at 10 WAP in 2003/4

Source DF SS MS F P Regression 1 499.97 499.97 1603.65 0.000 Residual Error 111 34.61 0.31 Total 112 534.57

Table 9.4: Test of different parameters of the regression of fitted values and responses for 2003/4

Predictor Coefficient StDev t P Constant 0.21299 0.09308 2.29 0.024 Response 0.93526 0.02335 40.05 0.000

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Appendix 10: Removal of observations from 2002/3 for leaf disease severity

Table 10.1: Analysis of variance of the multiple regression of disease severity with disease and weather predictors for the first data removal

Source DF SS MS F P Regression 5 671.93 134.39 676.81 0.000 Residual Error 114 22.64 0.2 Total 119 694.57 S = 0.4456 R2 = 96.7 % R2(adj) = 96.6 %

Table 10.2: Characteristics of different parameters of the multiple regression of disease severity with disease and weather predictors for the first data removal

Predictor Coefficient StDev t P Constant -14.1942 0.7837 -18.11 0.000 lidis -0.012436 0.001992 -6.24 0.000 sr(lsdis) 0.8908 0.05469 16.29 0.000 hi6 0.235923 0.006254 37.72 0.000 sr(dw8) -3.2026 0.2586 -12.38 0.000 sr(dw10) 2.891 0.2814 10.27 0.000

Table 10.3: Analysis of variance of the multiple regression of disease severity with disease and weather predictors for the second data removal

Source DF SS MS F P Regression 5 680.51 136.1 685.14 0.000 Residual Error 114 22.65 0.2 Total 119 703.16 S = 0.4457 R2 = 96.8 % R2(adj) = 96.6 %

Table 10.4: Characteristics of different parameters of the multiple regression of disease severity with disease and weather predictors for the second data removal

Predictor Coefficient StDev t P Constant -14.2302 0.7791 -18.27 0.000 lidis -0.012748 0.002002 -6.37 0.000 sr(lsdis) 0.89187 0.05383 16.57 0.000 hi6 0.237164 0.006282 37.75 0.000 sr(dw8) -3.1832 0.2644 -12.04 0.000 sr(dw10) 2.8532 0.2831 10.08 0.000

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Table 10.5: Analysis of variance of the multiple regression of disease severity with disease and weather predictors for the third data removal

Source DF SS MS F P Regression 5 679.38 135.88 681.01 0.000 Residual Error 114 22.75 0.2 Total 119 702.13 S = 0.4467 R2 = 96.8 % R2(adj) = 96.6 %

Table 10.6: Characteristics of different parameters of the multiple regression of disease severity with disease and weather predictors for the third data removal

Predictor Coefficient StDev t P Constant -14.1704 0.7743 -18.3 0.000 lidis -0.012554 0.001994 -6.3 0.000 sr(lsdis) 0.89131 0.05493 16.23 0.000 hi6 0.236527 0.006252 37.83 0.000 sr(dw8) -3.2453 0.2648 -12.25 0.000 sr(dw10) 2.905 0.285 10.19 0.000

Table 10.7: Removed and estimated data by disease severity multiple regression equations

Removal Planting date Response Estimated 1 Early 5.244 5.3509 1 Medium 7.5895 7.1879 1 Late 4.9092 4.5887 1 Winter 0.7071 0.7558 2 Early 3.577 3.2504 2 Medium 6.6858 6.8392 2 Late 2.9326 3.2964 2 Winter 0.7071 0.7603 3 Early 5.2058 5.3214 3 Medium 6.6633 7.0148 3 Late 3.8601 3.717 3 Winter 0.7071 0.6351

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