DEFINITION AND IMPLEMENTATION OF PLANT DISEASE SIMULATION MODELS IN INTERACTION WITH CROP MODELS, AIMING AT FORECASTING THE IMPACT OF CLIMATE CHANGE SCENARIOS ON CROP PRODUCTION

The impacts of a changing climate on the social and economic development of humanity have been increasingly studied in the last decades. According to the Intergovernmental Panel on Climate Change (IPCC), the lack of implementation of effective and adequate measures for contrasting green house gases emissions will lead to increasingly severe and partially irreversible impacts on the environment, and consequently on the society. The estimate of possible impacts on food production, starting from agriculture, is essential to develop strategies to alleviate the consequences of climate change. In this context, the evaluation of the future dynamics of plant diseases plays a key role because they determine actual production levels for many crops in many areas, therefore deeply influencing food availability and security. In order to perform such analyses, process-based simulation modelling offers the capability to capture the high non-linearity characterizing the responses of biophysical processes to boundary conditions. However, such models have been marginally used to estimate scenarios of plant diseases impact on crop production, because of the limited availability of modelling approaches and tools. This work constitutes an attempt to respond to the need of developing a software framework for the simulation of a generic fungal plant airborne disease which can be easiliy coupled with a crop simulator in order to improve the estimation of the levels of crop productions under climate change scenarios. The first section of the work deals with the evaluation of models for the estimation of meteorological data and for the simulation of leaf wetness, driving variable of the infection process of fungal plant pathogens. These assessments were justified by the need of feeding the disease models with high quality data, and by the scarce availability of hourly data in large area databases. The second section presents the implementation and the calibration of the generic fungal plant epidemic framework, and its test via an extensive use of sensitivity analysis techniques. The third section deals with the application of the developed modelling solutions, coupled with crop simulators, for the forecasting of the impact of climate change on crop production in Latin America. In the last section, new criteria and metrics for biophysical model evaluation and analysis are presented, aimed at considering the models performance under heterogeneous climatic conditions such as those explored in climate change and large area application studies.