Maize (Zea mays L.) is a prominent Brazilian commodity, being the second largest crop produced and fifth exported product by the country. Due to its importance for the agricultural sector, there is a concern about the effect of climate change on the crop. Process-based models are valuable tools to evaluate the effects of climate on crop yields. The Joint UK Land Environment Simulator (JULES) is a land-surface model that can be run with an integrated crop model parameterization. The resulting model (JULES-crop) thus integrates crop physiology principles with the complexity of atmosphere-biosphere coupling. It has been shown to be a valuable tool for large-scale simulations of crop yields as a function of environmental and management variables. In this study, we calibrated JULES-crop using a robust experimental dataset collected for summer and off-season maize fields across Brazil. A targeted local sensitivity analysis was performed to detect parameters of major importance during the calibration process. After calibration, the model was able to satisfactorily simulate both season and off-season cultivars. Modeling efficiency (EF) was high for leaf area index (EF = .73 and .71, respectively, for summer season and off-season datasets), crop height (EF = .89), and grain dry mass (EF = .61 and .89, respectively, for summer season and off-season datasets). The model showed a lower accuracy for simulating leaf dry mass in summer season cultivars (EF = .39) and soil moisture (EF = .44), demonstrating the necessity of further improvements including additional parametrizations of the rainfed conditions.

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RESUMO:O conhecimento da adesão de produtores rurais a sistemas de irrigação, é um relevante aspecto para aferir sobre o nível de manejo agrícola de um local. Objetivou-se avaliar a agricultura irrigada em propriedades rurais da região de Bauru -SP. Para a análise foram utilizados os seguintes parâmetros: técnicas de manejo, culturas irrigadas, fonte de captação da água para irrigação e a importância da produção agrícola na renda mensal do produtor. A coleta dos dados, foi realizada por meio de visitas a 38 produtores com aplicação do questionário elaborado anteriormente, escolhidas de forma aleatória. Foi constatado a baixa adesão dos produtores agrícolas da região em relação a práticas em agricultura irrigada, em que 42 % das propriedades rurais tinham um sistema de irrigação. As oleirícolas foram as principais culturas irrigadas com 57 %, a água utilizada na maioria das propriedades é proveniente de poço artesiano, em 56 % dos casos. Uma das razões apresentadas no trabalho para a pequena expansão da agricultura irrigada na região de Bauru -SP, pode estar atrelada ao fato de que 61 % dos produtores rurais, obtém da atividade agrícola um valor de até 33 % da renda mensal, ocasionando em um menor investimento tecnológicos. PALAVRAS-CHAVES: Irrigação, Produtores rurais, Produtividade PROSPECT OF IRRIGATED AGRICULTURE IN RURAL PROPERTIES FROM THE REGION OF BAURU-SP ABSTRACT:The knowledge of the adhesion of irrigation systems by rural producers is a relevant aspect to gauge about a local agricultural management level.

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Using the process-based JULES-crop model for forecasting off-season maize yield in BrazilMaize (Zea mays L.) is an important Brazilian commodity, being the second most produced crop and the fifth most exported in Brazil. In view of its relevance for many sectors of the economy, studies that deepen the consequences of climatic effects are imperatives in face of a climate change scenario for the next decades. For this purpose, process-based biophysical models has been used to evaluate the weather effects on crop yield. However, there is a gap in the science of models able to perform in large-scale due to limitations in the integration of energy, CO 2 , water and momentum fluxes with crop physiology. In view of this lacuna, the land surface model Joint UK land environment simulator (JULES) was integrated with a parametrization of different crops, among which maize, however, the model was not calibrated and evaluated in Brazil. This thesis brings in two chapters the use of a large-scale model in maize and its application to predict the off-season maize yield in Brazil. In the first chapter, the objective was to calibrate and evaluate the JULES-crop model for maize, obtaining a high performance to simulate leaf area index (LAI), canopy height and grain dry mass both for irrigated or rainfed conditions, in different regions of Brazil and sowing dates. In the second chapter, it was possible to use the calibrated JULES-crop, in addition to agroclimatic indicators such as air temperature, rainfall and diffuse radiation, to develop a large scale yield forecasting model for off-season maize in Brazil. The conjunction of agro-climatic indicators and JULES-crop outputs resulted in high performance predictions for maize yield from the 80 th day of the cycle. Therefore, it is possible to confirm a skillful model to simulate in a large scale, and that it is able to improve the forecasting for maize yield in Brazil.