EFFECT OF SHADE ON TEMPERATURE MITIGATION AND CANOPY ASSIMILATION OF COFFEE AGROFORESTRY SYSTEMS:

Transcription

1 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 3 rd European Agroforestry Conference 2016

2 IMPACT OF FUTURE CLIMATE ON CROP YIELD Rosenzweig, Elliott et al. (2014) 3rd European Agroforestry Conference

3 IMPACT OF FUTURE CLIMATE ON CROP YIELD First reductions will begin around 2020 in African semi-arids areas, then progressively to south and central america, Mexico, and Asia (IPCC AR5 WG2). Rosenzweig, Elliott et al. (2014) 3rd European Agroforestry Conference

4 IMPACT OF FUTURE CLIMATE ON CROP YIELD First reductions will begin around 2020 in African semi-arids areas, then progressively to south and central america, Mexico, and Asia (IPCC AR5 WG2). Adaptative options (short to long term): Sow/Harvest dates Agroforestry Breeding Crop shift Rosenzweig, Elliott et al. (2014) 3rd European Agroforestry Conference

5 A TRADE-OFF BETWEEN SHADE AND YIELD? 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? Crop yield? LUE increase under shade? Regulation of crop canopy temperature? 3rd European Agroforestry Conference

6 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 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, horizontally uniform Allocation, growth and yield over full rotation + Plot scale + Fast+ Process-based Opportunity for model coupling 3rd European Agroforestry Conference

7 MAESPA VALIDATION ON A 15 YEARS-OLD AGROFORESTRY TRIAL IN CATIE RESEARCH CENTER (Haggar et al., 2011) All credits to M.Soma 3rd European Agroforestry Conference

8 MAESPA LIGHT INTERCEPTION VALIDATION (1) Simulated Total Transmittance (Day of Year 76, by hour) 3rd European Agroforestry Conference

9 Simulated DT (0-1) MAESPA LIGHT INTERCEPTION VALIDATION (2) MAESPA light interception validation through diffuse transmittance from hemispheric photograp Measured diffuse transmittance (0-1) 3rd European Agroforestry Conference

10 Simulated CCT ( C) MAESPA LIGHT INTERCEPTION VALIDATION (2) MAESPA light interception validation through diffuse transmittance from hemispheric photograp Measured coffee canopy temperature ( C) 3rd European Agroforestry Conference

11 FUTURE CLIMATE, SIMULATED TO THE POINT Total annual rainfall (mm) Mean annual air temp. ( C) Time (Year) Time (Year) Statistical downscaling from 14 GCMs to 5 km definition following Hidalgo et al. (2016, in prep) 3rd European Agroforestry Conference

12 FUTURE CLIMATE, SIMULATED TO THE POINT Total annual rainfall (mm) Mean annual air temp. ( C) From 1979 Time (Year) To 2049 Time (Year) Statistical downscaling from 14 GCMs to 5 km definition following Hidalgo et al. (2016, in prep) 3rd European Agroforestry Conference

13 Total annual rainfall (mm) Mean annual air temp. ( C) FUTURE CLIMATE, SIMULATED TO THE POINT Time (Year) Time (Year) Aquiares, lowland, 2.48 C increase for RCP8.5 Statistical downscaling from 14 GCMs to 5 km definition following Hidalgo et al. (2016, in prep) 3rd European Agroforestry Conference

14 Total annual rainfall (mm) Mean annual air temp. ( C) FUTURE CLIMATE, SIMULATED TO THE POINT Time (Year) Time (Year) Aquiares, lowland, 2.48 C increase for RCP8.5 Tarrazu, mountain, 2.27 C increase for RCP8.5 Statistical downscaling from 14 GCMs to 5 km definition following Hidalgo et al. (2016, in prep) 3rd European Agroforestry Conference

15 Total annual rainfall (mm) Mean annual air temp. ( C) FUTURE CLIMATE, SIMULATED TO THE POINT Time (Year) 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 Statistical downscaling from 14 GCMs to 5 km definition following Hidalgo et al. (2016, in prep) 3rd European Agroforestry Conference

16 SIMULATION OF COFFEE MANAGEMENT SCENARIOS Climate Location Shade (species and density) Plot Age RCP 4.5 Tarrazu (~1500m high) Full Sun: 0 Erythrina poeppigiana. 1 2 RCP 8.5 X Aquiares (~1000m high) X Low: 200/250 High: 350/400. Cordia alliodora. Low: 50/75 High: 100/ X 3rd European Agroforestry Conference

17 SIMULATION OF COFFEE MANAGEMENT SCENARIOS Climate Location Shade (species and density) Plot Age RCP 4.5 RCP 8.5 X Tarrazu (~1500m high) Aquiares (~1000m high) X Full Sun: 0 Pruned twice a year to optimize coffee light intake (low LAI) Erythrina poeppigiana. Low: 200/250 High: 350/400. Cordia alliodora. Low: 50/75 High: 100/ X 1 2 3rd European Agroforestry Conference

18 SIMULATION OF COFFEE MANAGEMENT SCENARIOS Climate Location Shade (species and density) Plot Age RCP 4.5 RCP 8.5 X Tarrazu (~1500m high) Aquiares (~1000m high) X Full Sun: 0 Grow freely, low densities, make high coverage (High LAI) Erythrina poeppigiana. Low: 200/250 High: 350/400. Cordia alliodora. Low: 50/75 High: 100/ X 1 2 3rd European Agroforestry Conference

19 COFFEE LIGHT USE EFFICIENCY ( RCP 8.5, AQUIARES) 3rd European Agroforestry Conference

20 COFFEE LIGHT USE EFFICIENCY ( RCP 8.5, AQUIARES) High shade tree LAI, Coffee LUE increase 3rd European Agroforestry Conference

21 COFFEE LIGHT USE EFFICIENCY ( RCP 8.5, AQUIARES) High Low tree shade LAI, tree no LAI, or Coffee little increase LUE increase LUE 3rd European Agroforestry Conference

22 COFFEE DAILY MAXIMUM CANOPY TEMPERATURE (RCP 8.5, AQUIARES) C***, C***-2.71 C*** -3.3 C*** 3rd European Agroforestry Conference

23 CROP MODEL : Aquiares (Lowland), RCP 4.5, Shade = Cordia alliodora (50 tree ha -1, thinned) Shade tree Transmit t-ance Coffee LAI Coffee canopy photosynt. (GPP) 3rd European Agroforestry Conference

24 CROP MODEL : Aquiares (Lowland), RCP 4.5, Shade = Cordia alliodora (50 tree ha -1, thinned) Air T ( C) Infloresc e-nces per node Coffee yield with shade trees 3rd European Agroforestry Conference

25 CONCLUSION Coupling a 3D with a crop allocation model succesfully simulates leaf temperature, photosynthesis, light use efficiency and yield over full rotations 3rd European Agroforestry Conference

26 CONCLUSION Coupling a 3D with a crop allocation model succesfully simulates leaf temperature, photosynthesis, light use efficiency and yield over full rotations Coffee yield could be effectively sustained under future climate through shade management 3rd European Agroforestry Conference

27 CONCLUSION Coupling a 3D with a crop allocation model succesfully simulates leaf temperature, photosynthesis, light use efficiency and yield over full rotations 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 3rd European Agroforestry Conference

28 CONCLUSION Coupling a 3D with a crop allocation model succesfully simulates leaf temperature, photosynthesis, light use efficiency and yield over full rotations 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) 3rd European Agroforestry Conference

29 CONCLUSION Coupling a 3D with a crop allocation model succesfully simulates leaf temperature, photosynthesis, light use efficiency and yield over full rotations 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 3rd European Agroforestry Conference

30 ACKNOWLEDGMENTS ANR MACACC Project INRA CIRAD UMR ECO&SOLS / UMR ISPA CATIE UCR (University of Costa Rica) 3rd European Agroforestry Conference

31 ANNEXES 3rd European Agroforestry Conference

32 NUMERIC MODELS SCHEME Meteorological variables Meteorological variables MAESPA Outputs Metamodel: LUE & K Diffus &K Direct Allocation model Outputs (=management simulations) Parameters: Structure, physiology, soil Input parameters (as is or modified) Parameters: Structure, physiology, soil 3rd European Agroforestry Conference

33 NUMERIC MODELS SCHEME Meteorological variables Meteorological variables MAESPA Outputs Metamodel: LUE & K Diffus &K Direct Allocation model Outputs (=management simulations) Parameters: Structure, physiology, soil Input parameters (as is or modified) Parameters: Structure, physiology, soil 3rd European Agroforestry Conference

34 DOWNSCALING TECHNIQUE Statistical downscaling: Depends on current empirical relationships Low computational demand Long and high quality data series Hard to apply in complex environments