Shade trees improve coffee healthwithout reducing coffee potential yield
in agroforestry systems in Murang’a, Kenya
of diseases, Coffee Berry Disease (CBD),Coffee Leaf Rust (CLR), and of Coffee potentialyield have been assessed over 5 branches on 50georeferenced Coffee bushes in each plot.
Shade trees improve coffee healthwithout reducing coffee potential yield
in agroforestry systems in Murang’a, Kenya
Karim BARKAOUI, John NYAGA,Fabrice PINARD, Philippe VAAST, Nathalie LAMANDA1CIRAD, Montpellier, FRANCE2ICRAF, Nairobi, KENYA
Gradient of tree shade intensitydue to the diversity of shade tree structure
Agroforestry is increasingly recognized as an efficient wayto reduce diseases and pests. However, controversialresults have been found regarding the effects ofshade trees on coffee disease regluation. Murang’aregion (Kenya) has a bi-modal rainy season and twodistinct coffee fruiting periods, making coffee busheshighly sensitive to cryptogamic diseases: may shadetrees improve health and productivity of coffee?
Assessing tree shade15 coffee agroforestry plots have been studied in thehilly region of Murang’a, Kenya. Shade tree species havebeen assessed and georeferenced. Shade patterns havebeen estimated using ShadeMotion® software.
1CBD intensity declineswith increasing tree shade
2CLR intensity declineswith increasing tree shade
3Yield was not affectedby increasing tree shade
Mean of coffee yield was 2,35 kg/ bush.A high within-plot variability was found(mean of CV = 124 %) but it was notexplained by tree shade.
Mean CLR intensity was 21.86. The effectof shade was significant and strong (0.36),and with lower uncertainty than for CBD.Variations between plots were also lowerthan for CBD.
Mean CBD intensity was 13.35. The effectof shade was significant but weak (0.05),and with high uncertainty. Variationsbetween plots were important, probablydue to topography and altitude differences.
CBD
CLR
Yield
% of infected berries% surface infected
% of infected leaves% surface infected
Nb of berriesWeight of berries
Materials & Methods
Results*
* Data were sqrt-transformed (linearized) before analysis* Color points represent individual observations for each plot * Color lines represent predictions of effect of shade for each individual plot using a linear mixed model.
Wald-χ²= 215.62 ***
Wald-χ²= 1.219 ns
Wald-χ²= 27,55*
Problem Statement
Assessing coffee diseases and yieldIntensity of diseases, Coffee Berry Disease (CBD),Coffee Leaf Rust (CLR), and of Coffee potentialyield have been assessed over 5 branches on 50georeferenced Coffee bushes in each plot.
Shade trees improve coffee healthwithout reducing coffee potential yield
in agroforestry systems in Murang’a, Kenya
Karim BARKAOUI, John NYAGA,Fabrice PINARD, Philippe VAAST, Nathalie LAMANDA1CIRAD, Montpellier, FRANCE2ICRAF, Nairobi, KENYA
Gradient of tree shade intensitydue to the diversity of shade tree structure
Agroforestry is increasingly recognized as an efficient wayto reduce diseases and pests. However, controversialresults have been found regarding the effects ofshade trees on coffee disease regluation. Murang’aregion (Kenya) has a bi-modal rainy season and twodistinct coffee fruiting periods, making coffee busheshighly sensitive to cryptogamic diseases: may shadetrees improve health and productivity of coffee?
Assessing tree shade15 coffee agroforestry plots have been studied in thehilly region of Murang’a, Kenya. Shade tree species havebeen assessed and georeferenced. Shade patterns havebeen estimated using ShadeMotion® software.
1CBD intensity declineswith increasing tree shade
2CLR intensity declineswith increasing tree shade
3Yield was not affectedby increasing tree shade
Mean of coffee yield was 2,35 kg/ bush.A high within-plot variability was found(mean of CV = 124 %) but it was notexplained by tree shade.
Mean CLR intensity was 21.86. The effectof shade was significant and strong (0.36),and with lower uncertainty than for CBD.Variations between plots were also lowerthan for CBD.
Mean CBD intensity was 13.35. The effectof shade was significant but weak (0.05),and with high uncertainty. Variationsbetween plots were important, probablydue to topography and altitude differences.
CBD
CLR
Yield
% of infected berries% surface infected
% of infected leaves% surface infected
Nb of berriesWeight of berries
Materials & Methods
Results*
* Data were sqrt-transformed (linearized) before analysis* Color points represent individual observations for each plot * Color lines represent predictions of effect of shade for each individual plot using a linear mixed model.
Wald-χ²= 215.62 ***
Wald-χ²= 1.219 ns
Wald-χ²= 27,55*
Problem Statement
Assessing coffee diseases and yieldIntensity of diseases, Coffee Berry Disease (CBD),Coffee Leaf Rust (CLR), and of Coffee potentialyield have been assessed over 5 branches on 50georeferenced Coffee bushes in each plot.
Shade trees improve coffee healthwithout reducing coffee potential yield
in agroforestry systems in Murang’a, Kenya
Karim BARKAOUI, John NYAGA,Fabrice PINARD, Philippe VAAST, Nathalie LAMANDA1CIRAD, Montpellier, FRANCE2ICRAF, Nairobi, KENYA
Gradient of tree shade intensitydue to the diversity of shade tree structure
Agroforestry is increasingly recognized as an efficient wayto reduce diseases and pests. However, controversialresults have been found regarding the effects ofshade trees on coffee disease regluation. Murang’aregion (Kenya) has a bi-modal rainy season and twodistinct coffee fruiting periods, making coffee busheshighly sensitive to cryptogamic diseases: may shadetrees improve health and productivity of coffee?
Assessing tree shade15 coffee agroforestry plots have been studied in thehilly region of Murang’a, Kenya. Shade tree species havebeen assessed and georeferenced. Shade patterns havebeen estimated using ShadeMotion® software.
1CBD intensity declineswith increasing tree shade
2CLR intensity declineswith increasing tree shade
3Yield was not affectedby increasing tree shade
Mean of coffee yield was 2,35 kg/ bush.A high within-plot variability was found(mean of CV = 124 %) but it was notexplained by tree shade.
Mean CLR intensity was 21.86. The effectof shade was significant and strong (0.36),and with lower uncertainty than for CBD.Variations between plots were also lowerthan for CBD.
Mean CBD intensity was 13.35. The effectof shade was significant but weak (0.05),and with high uncertainty. Variationsbetween plots were important, probablydue to topography and altitude differences.
CBD
CLR
Yield
% of infected berries% surface infected
% of infected leaves% surface infected
Nb of berriesWeight of berries
Materials & Methods
Results*
* Data were sqrt-transformed (linearized) before analysis* Color points represent individual observations for each plot * Color lines represent predictions of effect of shade for each individual plot using a linear mixed model.
Wald-χ²= 215.62 ***
Wald-χ²= 1.219 ns
Wald-χ²= 27,55*
Problem Statement
Assessing coffee diseases and yieldIntensity of diseases, Coffee Berry Disease (CBD),Coffee Leaf Rust (CLR), and of Coffee potentialyield have been assessed over 5 branches on 50georeferenced Coffee bushes in each plot.
Shade trees improve coffee healthwithout reducing coffee potential yield
in agroforestry systems in Murang’a, Kenya
Karim BARKAOUI, John NYAGA,Fabrice PINARD, Philippe VAAST, Nathalie LAMANDA1CIRAD, Montpellier, FRANCE2ICRAF, Nairobi, KENYA
Gradient of tree shade intensitydue to the diversity of shade tree structure
Agroforestry is increasingly recognized as an efficient wayto reduce diseases and pests. However, controversialresults have been found regarding the effects ofshade trees on coffee disease regluation. Murang’aregion (Kenya) has a bi-modal rainy season and twodistinct coffee fruiting periods, making coffee busheshighly sensitive to cryptogamic diseases: may shadetrees improve health and productivity of coffee?
Assessing tree shade15 coffee agroforestry plots have been studied in thehilly region of Murang’a, Kenya. Shade tree species havebeen assessed and georeferenced. Shade patterns havebeen estimated using ShadeMotion® software.
1CBD intensity declineswith increasing tree shade
2CLR intensity declineswith increasing tree shade
3Yield was not affectedby increasing tree shade
Mean of coffee yield was 2,35 kg/ bush.A high within-plot variability was found(mean of CV = 124 %) but it was notexplained by tree shade.
Mean CLR intensity was 21.86. The effectof shade was significant and strong (0.36),and with lower uncertainty than for CBD.Variations between plots were also lowerthan for CBD.
Mean CBD intensity was 13.35. The effectof shade was significant but weak (0.05),and with high uncertainty. Variationsbetween plots were important, probablydue to topography and altitude differences.
CBD
CLR
Yield
% of infected berries% surface infected
% of infected leaves% surface infected
Nb of berriesWeight of berries
Materials & Methods
Results*
* Data were sqrt-transformed (linearized) before analysis* Color points represent individual observations for each plot * Color lines represent predictions of effect of shade for each individual plot using a linear mixed model.
Wald-χ²= 215.62 ***
Wald-χ²= 1.219 ns
Wald-χ²= 27,55*
Problem Statement
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