AquaCrop A new model for crop prediction under water deficit conditions - Calibration for potato - Gabriella IZZI gabriella.izzi@fao.org Centro Internacional de la Papa (CIP) La Molina, Lima, Peru 22 September, 2008
Objectives General framework Description of AquaCrop Main issues related to AquaCrop calibration for potato AquaCrop performances Discussion, feed-backs, & contacts
Background Revision of the 1979 FAO I & D Paper no.33, Yield Response to Water Ya ETa 1 - = Ky 1 - Ym ETc Consultative process Separation herbaceous-crops and trees: AquaCrop & Guidelines
Why another model? total Uncertainty parameters structure Complexity
AquaCrop main characteristics AquaCrop differs from other models for its relatively small number of parameters AquaCrop is explicit and mostly intuitive, and maintains an optimum balance between simplicity, accuracy and robustness AquaCrop is aimed at practical end-users: farmers and irrigation associations, extension services, governmental agencies, NGOs, planners, economists, as well as researchers and students. AquaCrop as unique crop response to water model with crop-specific parameters
AquaCrop Network & CGIAR
Evolution from Paper 33 Water-driven model AquaCrop { HI BIOMASS WP daily time-steps Canopy Transpiration E Paper 33 YIELD Ky Crop Evapotranspiration long-term sums
AquaCrop Conceptual Framework Atmosphere Crop Soil Management
AquaCrop Conceptual Framework Atmosphere CLIMATE Rain R S, T, RH, u ET o T ( o C) CO 2
Data needed for calibration Atmosphere Rainfall ETo Tmin & Tmax
AquaCrop Conceptual Framework Crop Phenology Leaf expansion g s Canopy Cover Senescence Rooting depth
AquaCrop Conceptual Framework Crop canopy cover CCo CGC CCx CDC Canopy Cover (CC) follows the exponential growth during the first half of the full development (Eq. 1) and an exponential decay during the second half of the full development (Eq. 2) time to full canopy start of canopy senescence time to harvest time CC CC e CGC t = (1) o CC x x o CGC t = CC (CC CC ) e (2)
Data needed for calibration Crop Density In-season CC Phenology
AquaCrop Conceptual Framework Crop T ( o C) ET o E s T a WP Phenology Leaf expansion g s Canopy Cover Senescence Rooting depth Biomass
AquaCrop Conceptual Framework Crop WP = Biomass T C (g m -2 mm -1 ) WP * = Biomass T C ET O CO 2 (2000) (g m -2 ) Biomass (kg m -2 ) 3 2 1 Sorghum Sunflower Chickpea Wheat Biomass (kg m -2 ) 3 2 1 Sorghum Sunflower Chickpea Wheat 0 0.0 0.3 0.6 0.9 Σ ETc TC (mm x 1000) 0 0 40 80 120 160 Σ(ETc/ETo) ( TC / O )
Data needed for calibration Crop In-season dry biomass
AquaCrop Conceptual Framework Crop T ( o C) ET o E s T a WP Phenology Leaf expansion g s Canopy Cover Senescence Biomass HI Yield Rooting depth
Data needed for calibration Crop In-season yield
AquaCrop conceptual framework Crop Water stress coefficients
AquaCrop conceptual framework Crop Water stress coefficients
Data needed for calibration Crop Different water regimes
AquaCrop Conceptual Framework Soil Rain Irrig. Runoff E s Ks Infiltration Soil water (& salt) balance Redistribution T a Texture 1 Texture 2 Texture Runoff K sat FC PWP deep percolation
Data needed for calibration Soil Soil layers FC, PWP, Ksat
AquaCrop Conceptual Framework Management Water Management Rainfed Irrigation User defined schedule (timing and depth) Model-generated schedule (fixed interval; fixed depth; % of RAW) Irrigation method (drip; sprinkler; surface» basin; border; furrow) Field Management Fertility level (non-limiting; high; moderate; poor) Field-surface practices (mulching; soil bunds)
Data needed for calibration Management Irrigation date Irrigation depth
Data needed for calibration Management Fertility level
AquaCrop performances
Maize Texas Spain Quzhou California Florida
Maize California Treatments: Full Irrigation (I) Rainfed (NI) Irrig. day 55 onward (I55) Biomass Yield Measured Simulated Measured Simulated 24.3 22.7 11.4 10.8 16.8 16.8 5.2 6.2 21.2 22.4 10.3 10.6 I55 (IRR NI I (Full (No on IRR) Day55) treatment Ground Ground Cover Cover I55 NI I treatment biomass GC (%) 100 90 80 70 60 50 40 30 Calc Calc Calc GC GC GC 20 CC CC CC 10 0 0 20 40 60 80 100 120 140 cum DM (g/m2) 3000 2500 2000 1500 1000 500 0 DM Biomass DM Biomass DM Biomass 0 20 40 60 80 100 120 140 DAP DAP
Cotton Texas Spain Greece Syria Turkey
Cotton Spain Treatments: Full Irrigation Deficit Irrigation 14 Sim ulated (t ha -1 ) 12 10 8 6 4 2 Yield Biomass 1:1 0 0 2 4 6 8 10 12 14 Observed (t ha -1 )
Cotton Syria Treatments: Full Irrigation Deficit Irrigation
Wheat Texas Kansas Sardinia Luancheng Quzhou Fengqui
Wheat China Treatments: 98-99 Full Irr. 99-00 Full Irr. 100 Yield Measured Simulated 6.72 6.50 6.22 7.09 40 40 35 35 China Quzhou Wheat 98-99 China Quzhou Wheat 99-00 Min Min and and Max Max Temperature Temperature (oc) (oc) 40 30 20 20 10 10-20 -20 China Quzhou Wheat 98-99 99-00 0 0 1 31 50 61 91 100 121 150 151 181 200 211-10 -10 DAP Canopy Cover (%) 75 50 25 Biomass (t/ha) 30 25 25 20 20 15 15 10 10 0 China Quzhou Wheat 99-00 China Quzhou Wheat 98-99 0 50 100 150 200 250 DAP 55 00 00 50 50 100 150 200 250 DAP
Bolivia Quinoa
Quinoa Bolivia Treatments: Full Irrigation Deficit Irrigation
Conclusions AquaCrop shows first encouraging results, under full, deficit and rainfed conditions AquaCrop needs to be calibrated for potato AquaCrop calibration and validation need solid datasets, under a variety of agro-climatic, water and fertilization conditions
Thank You