Coffee Pests and Diseases in Costa Rica

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Spatial decision support for coffee pests and diseases in Costa Rican agroforestry systems.

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  • Jacques: Anadir fotos enfermedades
  • Laura puedes aqui poner 5 mapas como ejemplo de los factores generados?
  • Puedes hacer las mapas en un foramto similar como el de sitio de estudio. Incluyendo tambien el relieve
  • Puedes hacer las mapas en un foramto similar como el de sitio de estudio. Incluyendo tambien el relieve
  • Coffee Pests and Diseases in Costa Rica

    1. 1. SPATIAL DECISION SUPPORT FOR COFFEE PESTS AND DISEASES RISK MANAGEMENT IN COSTA RICAN AGROFORESTRY SYSTEMS AFS – August 23 - 29 - Nairobi - Kenya AVELINO, Jacques (CIRAD/IICA-PROMECAFE/CATIE) LADERACH, Peter (CIAT) COLLET, Laure (CIAT) BARQUERO, Miguel (ICAFE) CILAS, Christian (CIRAD) 1/13
    2. 2. Justifications and objective AFS – August 23 - 29 - Nairobi - Kenya Objective To show how better decisions and disease risk-adapted agroforestry practices, for coffee growing regions, can be derived, based on spatial decision support tools and ground data. Justifications Patchiness in the distribution of plant pests and diseases due to spatial heterogeneity of the environment and the agronomic management. Environmental information can be combined with spatial analyses to determine potential pests and diseases distribution, make better decisions and improve the risk management. 2/13
    3. 3. Study area, sampling design, shade and disease descriptors AFS – August 23 - 29 - Nairobi - Kenya Disease Descriptors ALSD: Attack intensity index calculated on an attack and defoliation severity scale CLR and CB: Maximal annual % of infected leaves Main Diseases Mycena citricolor : American Leaf Spot Disease (ALSD) Hemileia vastatrix : Coffee Leaf Rust (CLR) Phoma costarricensis : Coffee Blight (CB) Sampling Design Data from a two-year survey on coffee diseases in Costa Rica (Avelino et al., 2007) 27 geo-referenced plots sampled in Central Valley Plot size surveyed: 100 coffee trees Shade Assessment by using a spherical densiometer Shade cover range: 0 - 65 % 3/13 Costa Rican coffee growing regions Central Valley
    4. 4. American Leaf Spot Disease (ALSD) ( Mycena citricolor ) Severe infection Left: asexual fructifications (gemmae) Right: sexual fructifications (carpophore) Lesions on leaf and fruits AFS – August 23 - 29 - Nairobi - Kenya 4/13 Coffee Blight (CB) ( Phoma costarricensis ) Lesion on leaf
    5. 5. Coffee Leaf Rust (CLR) ( Hemileia vastatrix ) Rust lesion with uredospores Infected leaf with coalescent lesions A coffee plantation before and after a severe Coffee Leaf Rust attack AFS – August 23 - 29 - Nairobi - Kenya 5/13
    6. 6. Spatial analyses and statistics Bayesian statistics and spatial analyses To delimitate areas with distinct disease risks as a function of environmental factors under two conditions of shade (below 15 % and above 15 %) 4 main steps in the model building stage 1. Identification of disease driving environmental factors (predictors) from literature 2. Disease driving environmental factors generated for the study region by using the WorldClim climate database (Hijmans et al., 2005) and the Shuttle Radar Topography Mission (SRTM) data (Jarvis, 2004) 3. Probablity prediction for each condition of shade through Bayesian statistics: Prediction per attack intensity class ( P i for the class i ; n classes ) as a function of categorized environmental predictors Calculation of a synthetic weighted variable, the score S S =  W i P i where W i = i-1/n-1 The higher the score, the higher the probability of high attack intensity. 4. Calculation of certainty , a measure of confidence of the prediction depending on the number of observations. The higher the certainty, the higher the confidence of prediction. AFS – August 23 - 29 - Nairobi - Kenya 6/13
    7. 7. Spatial analyses and statistics (cont.) Comparing score predictions with high certainty AFS – August 23 - 29 - Nairobi - Kenya 7/13 Raw environmental data from WorldClim and SRTM Disease driving environmental factors generated for the study region: rainfall (1 km resolution); slope % and aspect, elevation (90 m resolution) Observed geo-referenced disease attack intensities under low shade and high shade conditions Bayesian Statistics (CaNaSTA algorithm, O’Brien 2004) Predicted probability map of disease risk for two shade conditions Low Shade % = 0-15 High Shade % = 15-65
    8. 8. Score prediction for Mycena citricolor attack intensity index with high shade model (15 - 65%) and low shade model (0 -15 %) 15 - 65 % shade cover 0 - 15 % shade cover Pi : Probablity prediction for class i of attack intensity Wi = i-1/n-1 n: number of classes Score =  WiPi AFS – August 23 - 29 - Nairobi - Kenya 8/13
    9. 9. Score value for Mycena citricolor attack intensity index with high shade model (15 - 65%) and low shade model (0 -15 %) Certainty > 0.7 Certainty: a measure of confidence of the prediction depending on the number of observations 15 - 65 % shade cover 0 - 15 % shade cover AFS – August 23 - 29 - Nairobi - Kenya 9/13
    10. 10. Comparison of score predictions for Mycena citricolor attack intensity index with high shade (15 - 65%) and low shade (0 -15 %) cover 1. Low scores with high and low shade cover: environment unfavourable for disease development 4 behaviours : 2. Similar scores with high and low shade cover: no effect of shade 3. Higher scores with low shade cover : sun exposure is favourable to disease development 4. Higher scores with high shade cover : shade is favourable for disease development AFS – August 23 - 29 - Nairobi - Kenya Interactions shade-environment for Mycena citricolor development 10/13 0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 Prediction made with shade model Prediction made with sun model 1 2 3 4
    11. 11. 3. Higher scores with low shade cover : sun exposure is favourable for M. citricolor development 4. Higher scores with high shade cover : shade is favourable for M. citricolor development Comparison of driving environmental factors for groups 3 and 4 In Central Valley, shade could be used for ALSD control on West and North oriented slopes, inadequately exposed to sun (decreased dew on coffee leaves ?), and should be avoided on East and South oriented slopes, well exposed to sun (decreased radiation ?) AFS – August 23 - 29 - Nairobi - Kenya 11/13 3 4 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 Prediction made with shade model Prediction made with sun model 0 Significant differences, P < 0.05 63 3 Slope aspect (% of points with East or South orientation) 9.5 9.4 Slope inclination (%) 1109 1155 Elevation (m) 1155 1209 Rainfall August to December (mm) 986 1034 Rainfall June to August (mm) Group 4 Group 3
    12. 12. Comparison of score predictions for Coffee Leaf Rust and Coffee Blight with high shade (15 - 65%) and low shade (0 -15 %) cover No clear interaction: in Central Valley, shade decreases CLR attacks (due to probable fruit load reduction and decreased leaf susceptibility) AFS – August 23 - 29 - Nairobi - Kenya Interaction shade-altitude in Central Valley: increased CB attacks at high altitudes (reduction of maximum temperatures by shade ?) and decreased CB attacks at lower altitudes (increase of minimum temperatures by shade ?) Altitude= 1399 m Altitude= 1107 m 12/13 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 Prediction made with shade model Prediction made with sun model 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 Prediction made with shade model Prediction made with sun model Coffee Leaf Rust Coffee Blight
    13. 13. AFS – August 23 - 29 - Nairobi - Kenya Conclusions 1. A method to delimitate areas with distinct disease risks based on spatial decision support tools and ground data 2. A method to analyze cropping practices effects, and especially shade effects 3. Evidence of interactions between shade and environment for coffee diseases 4. Need of site specific shade practices according to coffee disease risks and environment characteristics 13/13 Thank you

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