Uncertainty-based auto-calibration for crop yield – the EPIC+ procedure for a case study in Sub-Saharan Africa

摘要: Process-based crop models are increasingly used to assess the effects of different agricultural management practices on yield. However, calibration historic yield is a challenging and time-consuming task due data limitation lack adaptive auto-calibration tools compatible with model be calibrated spatial temporal scales. In this study we linked general procedure SUFI-2 (Sequential Uncertainty Fitting Procedure) EPIC (Environmental Policy Integrated Climate) calibrate maize in Sub-Saharan African (SSA) countries. This resulted creation user-friendly software, EPIC+, for at levels grid continent. EPIC+ greatly speeds up process quantification parameter ranges prediction uncertainty. SSA application, three sets parameters referred as Planting Date (PD), Operation (e.g., fertilizer planting density), Model Harvest index, biomass-energy ratio, water stress harvest SCS curve number) steps avoid interaction identifiability problems. first step, by adjusting PD parameters, simulated results improved Western Central next were individual countries resulting better performance more than 40% many third significant improvements all an average 50%. We also found that less socio-political volatility benefited most from calibration. For where production had trends, suggest improving applying linear de-trending transformations, which will explore detail subsequent study.