effect of shade on temperature mitigation and … · effect of shade on temperature mitigation and...
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EFFECT OF SHADE ON TEMPERATURE MITIGATION AND CANOPY ASSIMILATION OF COFFEE AGROFORESTRY SYSTEMS:A MODELLING APPROACH
VEZY R *, PICART D, CHRISTINA M, SOMA M, GEORGIOU S, CHARBONNIER F, LOUSTAU D, IMBACH PB, DE MELO E., HIDALGO HG, ALFARO EJ, LE MAIRE G, ROUPSARD O
3rd European Agroforestry Conference 2016
IMPACT OF FUTURE CLIMATE ON CROP YIELD
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Rosenzweig, Elliott et al. (2014)
IMPACT OF FUTURE CLIMATE ON CROP YIELD
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Rosenzweig, Elliott et al. (2014)
First reductions will begin around 2020in African semi-arids areas, thenprogressively to south and centralamerica, Mexico, and Asia (IPCC AR5WG2).
IMPACT OF FUTURE CLIMATE ON CROP YIELD
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Rosenzweig, Elliott et al. (2014)
First reductions will begin around 2020in African semi-arids areas, thenprogressively to south and centralamerica, Mexico, and Asia (IPCC AR5WG2).
Adaptative options (short to long term):• Sow/Harvest dates• Agroforestry• Breeding• Crop shift
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Agroforestry may regulate microclimate and crop temperature.• What is the expected reduction on temperature?• Effect of shade tree species, density and management?• How to cope with local climate, elevation, bearing…• Does LUE compensate somehow for light reduction
under shade ?
A TRADE-OFF BETWEEN SHADE AND YIELD ?
Regulation of crop canopytemperature ?
LUE increaseunder shade ?
Crop yield?
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METHODS
Field experiments extremely useful but hard to maintain or extend:• Trees take decades to mature• Huge amount of management/climate options to test
Numeric models
Opportunity for model coupling
Agroforestry is spatialy heterogeneous
Need 3D plant-to-plot models + Accurate light interception and energy balance + process-based model + (half-) hour time step.Model selected here: MAESPA (Duursma et al. 2012)
Crop model, horizontallyuniform
Allocation, growth and yield over full rotation + Plot scale + Fast+ Process-based
MAESPA VALIDATION ON A 15 YEARS-OLD AGROFORESTRY TRIAL IN CATIE RESEARCH CENTER (Haggar et al., 2011)
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All credits to M.Soma
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MAESPA LIGHT INTERCEPTION VALIDATION (1)
Simulated Total Transmittance (Day of Year 76, by hour)
7
MAESPA light interception validation through diffuse transmittance from hemispheric photograp
MAESPA LIGHT INTERCEPTION VALIDATION (2)
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Measured diffuse transmittance (0-1)
Sim
ula
ted
DT
(0
-1)
7
MAESPA light interception validation through diffuse transmittance from hemispheric photograp
MAESPA LIGHT INTERCEPTION VALIDATION (2)
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Sim
ula
ted
CC
T (
°C)
Measured coffee canopy temperature (°C)
FUTURE CLIMATE, SIMULATED TO THE POINT
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Statistical downscaling from 14 GCMs to 5 km definition following Hidalgo et al. (2016, in prep)
Time (Year)
To
tal a
nn
ual
rain
fall
(mm
)M
ean
ann
ual
air
tem
p. (
°C)
Time (Year)
FUTURE CLIMATE, SIMULATED TO THE POINT
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Statistical downscaling from 14 GCMs to 5 km definition following Hidalgo et al. (2016, in prep)
Time (Year)
To
tal a
nn
ual
rain
fall
(mm
)M
ean
ann
ual
air
tem
p. (
°C)
Time (Year)From1979
To 2049
FUTURE CLIMATE, SIMULATED TO THE POINT
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Statistical downscaling from 14 GCMs to 5 km definition following Hidalgo et al. (2016, in prep)
Time (Year)
To
tal a
nn
ual
rain
fall
(mm
)M
ean
ann
ual
air
tem
p. (
°C)
Time (Year)
Aquiares, lowland,2.48°C increase for RCP8.5
FUTURE CLIMATE, SIMULATED TO THE POINT
3rd European Agroforestry Conference 2016 8
Statistical downscaling from 14 GCMs to 5 km definition following Hidalgo et al. (2016, in prep)
Time (Year)
To
tal a
nn
ual
rain
fall
(mm
)M
ean
ann
ual
air
tem
p. (
°C)
Time (Year)
Aquiares, lowland,2.48°C increase for RCP8.5
Tarrazu, mountain, 2.27°C increase for RCP8.5
FUTURE CLIMATE, SIMULATED TO THE POINT
3rd European Agroforestry Conference 2016 8
Statistical downscaling from 14 GCMs to 5 km definition following Hidalgo et al. (2016, in prep)
Time (Year)
To
tal a
nn
ual
rain
fall
(mm
)M
ean
ann
ual
air
tem
p. (
°C)
Time (Year)
Aquiares, lowland,2.48°C increase for RCP8.5
Tarrazu, mountain, 2.27°C increase for RCP8.5
Pratically no change expected for precipitations
SIMULATION OF COFFEE MANAGEMENT SCENARIOS
9
Climate Location Shade (species and density) Plot Age
RCP 4.5
X
Tarrazu
(~1500m high)
X
Full Sun: 0
X
1
2
Erythrina poeppigiana .
RCP 8.5Aquiares
(~1000m high)
Low: 200/250 High: 350/400 .
Cordia alliodora .
Low: 50/75 High: 100/125 35
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SIMULATION OF COFFEE MANAGEMENT SCENARIOS
9
Climate Location Shade (species and density) Plot Age
RCP 4.5
X
Tarrazu
(~1500m high)
X
Full Sun: 0
X
1
2
Erythrina poeppigiana .
RCP 8.5Aquiares
(~1000m high)
Low: 200/250 High: 350/400 .
Cordia alliodora .
Low: 50/75 High: 100/125 35
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Pruned twice a year to optimize coffee light intake (low LAI)
SIMULATION OF COFFEE MANAGEMENT SCENARIOS
9
Climate Location Shade (species and density) Plot Age
RCP 4.5
X
Tarrazu
(~1500m high)
X
Full Sun: 0
X
1
2
Erythrina poeppigiana .
RCP 8.5Aquiares
(~1000m high)
Low: 200/250 High: 350/400 .
Cordia alliodora .
Low: 50/75 High: 100/125 35
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Grow freely, low densities, make high coverage (High LAI)
COFFEE LIGHT USE EFFICIENCY (1979-2050 RCP 8.5, AQUIARES)
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COFFEE LIGHT USE EFFICIENCY (1979-2050 RCP 8.5, AQUIARES)
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High shade tree LAI, Coffee LUE increase
COFFEE LIGHT USE EFFICIENCY (1979-2050 RCP 8.5, AQUIARES)
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High shade tree LAI, Coffee LUE increaseLow tree LAI, no or little increase in LUE
COFFEE DAILY MAXIMUM CANOPY TEMPERATURE (RCP 8.5, AQUIARES)
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-1.96°C***, -2.58°C***-2.71°C*** -3.3°C***
CROP MODEL : Aquiares (Lowland), RCP 4.5, Shade = Cordia alliodora (50 tree ha-1, thinned)
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ShadetreeTransmitt-ance
Coffee LAI
Coffee canopyphotosynt.(GPP)
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Coffee yieldwithshadetrees
CROP MODEL : Aquiares (Lowland), RCP 4.5, Shade = Cordia alliodora (50 tree ha-1, thinned)
Air T (°C)
Infloresce-ncesper node
CONCLUSION
• Coupling a 3D with a crop allocation model succesfully simulates leaf temperature, photosynthesis, light use efficiency and yield over full rotations
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CONCLUSION
• Coupling a 3D with a crop allocation model succesfully simulates leaf temperature, photosynthesis, light use efficiency and yield over full rotations
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• Coffee yield could be effectively sustained under future climate through shade management
CONCLUSION
• Coupling a 3D with a crop allocation model succesfully simulates leaf temperature, photosynthesis, light use efficiency and yield over full rotations
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• Coffee yield could be effectively sustained under future climate through shade management
• Models should allow to optimize shade and yield under various geographic location, management and climate scenarios
CONCLUSION
• Coupling a 3D with a crop allocation model succesfully simulates leaf temperature, photosynthesis, light use efficiency and yield over full rotations
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• Coffee yield could be effectively sustained under future climate through shade management
• Models should allow to optimize shade and yield under various geographic location, management and climate scenarios
• Simulations will be extended to 2100 (no strong RCPs differences until 2049)
CONCLUSION
• Coupling a 3D with a crop allocation model succesfully simulates leaf temperature, photosynthesis, light use efficiency and yield over full rotations
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• Coffee yield could be effectively sustained under future climate through shade management
• Models should allow to optimize shade and yield under various geographic location, management and climate scenarios
• Simulations will be extended to 2100 (no strong RCPs differences until 2049)
• Further analyses will be conducted over all the scenarios
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• ANR MACACC Project
• INRA
• CIRAD
• UMR ECO&SOLS / UMR ISPA
• CATIE
• UCR (University of Costa Rica)
ACKNOWLEDGMENTS
ANNEXES
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NUMERIC MODELS SCHEME
17
Meteorologicalvariables
MAESPA Outputs
Meteorologicalvariables
Allocation model
Parameters:Structure,
physiology, soil…
Metamodel: LUE & 𝐾𝐷𝑖𝑓𝑓𝑢𝑠 &𝐾𝐷𝑖𝑟𝑒𝑐𝑡
Parameters:Structure,
physiology, soil…
Outputs (=management
simulations)
Input parameters (as is or modified)
3rd European Agroforestry Conference 2016
NUMERIC MODELS SCHEME
17
Meteorologicalvariables
MAESPA Outputs
Meteorologicalvariables
Allocation model
Parameters:Structure,
physiology, soil…
Metamodel: LUE & 𝐾𝐷𝑖𝑓𝑓𝑢𝑠 &𝐾𝐷𝑖𝑟𝑒𝑐𝑡
Parameters:Structure,
physiology, soil…
Outputs (=management
simulations)
Input parameters (as is or modified)
3rd European Agroforestry Conference 2016
DOWNSCALING TECHNIQUE
• Statistical downscaling:
– Depends on current empirical relationships
– Low computational demand
– Long and high quality data series
– Hard to apply in complex environments