INTEGRATION OF COMPONENTS FOR THE SIMULATION OF BIOTIC AND ABIOTIC STRESSES IN MODEL-BASED YIELD FORECASTING SYSTEMS

The raising global demand for agricultural products and the exacerbated inter-annual fluctuations of food productions due to climate change are increasing world food price volatility and threatening food security in developing countries. In this context, the availability of reliable operational yield forecasting systems would allow policy makers to regulate agricultural markets. However, the reliability of the current approaches (the most sophisticated being based on crop models) is undermined by different sources of uncertainty. In particular, large area simulations can be affected by errors deriving from the uncertainty in input data (e.g., sowing dates, information on cultivar/hybrid grown, management practices) and upscaling assumptions, as well as from the incomplete adequacy of crop models to reproduce the effects of key factors affecting inter-annual yield fluctuations (e.g., extreme weather events, pests, diseases). The aim of this Ph.D. project was to reduce the uncertainty affecting the existing model-based forecasting systems through: (i) the implementation of approaches for the estimation of the impact of biotic and abiotic stressors on crop yields (based on dynamic models and on dedicated agro-climatic indicators), and (ii) the integration of remote sensing information within crop models. Concerning the first objective, the approaches for the simulation of transplanting shock and cold-induced spikelet sterility in rice included in Oryza2000 and WARM models, respectively, were improved, by increasing the model adherence to the underlying systems. Moreover, generic approaches for the simulation of the impacts of extreme weather events on crop yields were developed and evaluated, as well as approaches specific for sugarcane. For the second objective, remote sensing information was used to derive rice-cropped areas and sowing dates varying with time and space, as well as for the assimilation of exogenous leaf area index data using both recalibration and updating techniques (to account for factors not explicitly reproduced by the model within large-area applications). The application of the improved forecasting systems to different crops and agro-climatic contexts worldwide led to marked improvements compared to existing approaches. This was achieved through an increase in the percentage of inter-annual yield variability explained. On the one hand, the simulation of the impact of weather extremes (cold shocks, heat waves, water stress and frost) allowed to reduce the tendency of CGMS (the monitoring and forecasting system of the European Commission) to overestimate cereal yields in case of unfavorable seasons. Moreover, the integration of dynamic crop models and of agro-climatic indicators led to enhance the predicting capacity of available approaches. On the other hand, the integration of remote-sensing information within high resolution simulation chains allowed to decidedly reduce the uncertainty of the standard CGMS-WARM system when applied to the main European rice districts.