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Spatial decision support for coffee pests and diseases in Costa Rican agroforestry systems.
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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
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
Study area, sampling design, shade and disease descriptors
AFS – August 23 - 29 - Nairobi - Kenya
Costa Rican coffee growing regions
Central Valley
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
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
Coffee Blight (CB) (Phoma costarricensis)
Lesion on leaf
4/13
Coffee Leaf Rust (CLR) (Hemileia vastatrix)
Rust lesion with uredosporesInfected leaf with
coalescent lesions
A coffee plantation before and after a severe Coffee Leaf Rust attack
AFS – August 23 - 29 - Nairobi - Kenya5/13
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 (Pi for the class i ; n classes) as a function
of categorized environmental predictors
Calculation of a synthetic weighted variable, the score S
S = WiPi where Wi = i-1/n-1The 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 - Kenya6/13
Raw environmental data fromWorldClim and SRTM
Disease driving environmental factors generated for the study region: rainfall (1 km resolution); slope % and aspect, elevation (90 m resolution)
Spatial analyses and statistics (cont.)
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
Comparing score predictions with high certainty
AFS – August 23 - 29 - Nairobi - Kenya7/13
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 intensityWi = i-1/n-1
n: number of classes
Score = WiPi
AFS – August 23 - 29 - Nairobi - Kenya8/13
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 - Kenya9/13
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
0
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Prediction made with shade model
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4 behaviours :
2. Similar scores with high and low shade cover: no effect of shade
1
23. Higher scores with low shade cover : sun exposure is favourable to disease development
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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
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0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8
Prediction made with shade model
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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
Group 3 Group 4
Rainfall June to August (mm)
1034 986
Rainfall August to December (mm)
1209 1155
Elevation (m) 1155 1109
Slope inclination (%) 9.4 9.5
Slope aspect (% of points with East or South
orientation)3 63
Significant differences, P < 0.05
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 - Kenya11/13
Comparison of score predictions for Coffee Leaf Rust and Coffee Blight
with high shade (15 - 65%) and low shade (0 -15 %) cover
0.0
0.2
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1.0
0.0 0.2 0.4 0.6 0.8 1.0
Prediction made with shade model
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0.0 0.2 0.4 0.6 0.8 1.0Prediction made with shade model
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Coffee Leaf Rust Coffee Blight
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
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