ABSTRACT:The climate and its variability is the main risk factor for the success of soybean crop in southern Brazil. Such aspect becomes even more important under the future climate scenarios, in which global warming is expected. Based on that, the objectives of this study were to identify the impacts of raising temperatures on soybean yields in southern Brazil and how management strategies represented by changing sowing dates could be able to mitigate them. The soybean yields for the present and future scenarios were estimated by the crop simulation model CROPGRO-Soybean calibrated for southern Brazil. The simulations were done for 13 locations distributed in the states of Paraná, Santa Catarina and Rio Grande do Sul, considering climatic series of 31 years. The future climate scenarios were built based on downscaling temperature changes from ETA and PRECIS models for the periods: 2013-2043 (D25) and 2041-2071 (D55), without considering the increase in the atmospheric CO 2 concentration. The soybean potential (Y p ) and attainable (Y a ) yields and their relationship (Y a /Y p ) were estimated for all scenarios and used to determine the climatic risk for the crop. In order to investigate how to mitigate the global warming impacts on soybean yields, different sowing dates, two earlier and two later than the present recommendation, were simulated in the crop model and resulting yields were compared. In general, raising temperature will lead soybean crop in southern Brazil to lower yields, for both Y p and Y a , with higher impact on Y a , independently of the climate model used. Also, the climatic risk for the crop will increase in the future climates scenarios. The strategy of changing the sowing date showed to be feasible to reduce the impacts of raising temperatures on soybean yields, but only when it is delayed in relation to the sowing period presently recommended for this crop in the region.

RESUMOO Brasil enfrenta problemas de infraestrutura de armazenagem de grãos. A soja, principal commodity agrícola brasileira, ganha destaque diante da necessidade energética mundial. O objetivo deste estudo foi analisar a produção de soja e a capacidade estática da armazenagem a granel no Estado de Mato Grosso. Por meio da análise exploratória, os dados oficiais de produção, área plantada e capacidade estática, foram espacializados através de um Sistema de Informação Geográfica. O déficit de armazenagem foi analisado como um indicador imprescindível na busca racional das movimentações das safras e da implantação de políticas e tecnologias de armazenagem. Concluiu-se que a cultura da soja está consolidada com participação ativa na produção de grãos no estado de Mato Grosso. Todavia, faltam investimentos, planejamento e implantação de políticas públicas que criem racionalização nos processos e fluxos de pós-colheita, pois os indicadores demonstraram que diante de uma expansão planejada de armazéns a granel será possível minimizar as perdas no escoamento da produção. Isso hoje não é possível em relação à insuficiência de armazéns estáticos a granel para atendimento da produção atual, em especial ao potencial crescimento na produção deste importante estado agrícola brasileiro.Palavras-chave: Armazéns. Escoamento. Produção agrícola. JEL: O10 ABSTRACTBrazil has faced problems related to grain storage infrastructure. Soybean, the main Brazilian agricultural commodity, is highlighted due to the worldwide energy demand. This study was aimed at analyzing the soybean production and the static capacity for bulk storage in the state of Mato Grosso. Through exploratory analysis, the official data of production, acreage and static capacity were spatialized using a Geographic Information System. The storage deficit

Brazil requires a fully representative weather network station; it is common to use data observed in locations distant from the region of interest. However, few studies have evaluated the efficiency and precision associated with the use of climate data, either estimated or interpolated, from stations far from the agricultural area of interest. Hence, this study aimed to demonstrate the impacts of spatial variability of the main meteorological elements on the regional estimate of soybean productivity. Regression analysis was used to compare data recorded at three weather stations located throughout Londrina, PR, Brazil. The water balance of the soybean crop was calculated at 10-day periods and grain productivity losses estimated using the Agro-Ecological Zones (AEZ) methodology. Temperatures at the three locations were similar, while the relative air humidity, and particularly, the rainfall data, were less correlated. A high degree of caution is recommended in the use and choice of a single weather station to represent a municipality or region, particularly in countries, such as Brazil, with multiple regions of agricultural and environmental importance. Models and crop season estimates that do not consider such a recommendation are vulnerable to errors in their forecasts. The volumetric and temporal variability in the spatial rainfall distribution resulted in soybean yield discrepancies, estimated at the municipal level. The consistency of the data series, the location of weather stations and their distance to the location of interest determine the ability of crop models to accurately estimate soybean production based on meteorological data, particularly the rainfall data. This study contributes to future regional research using climate data, and highlights the importance of a weather station network throughout Brazil, demonstrating the urgent need to increase the number of weather stations, particularly for recording rainfall data. Ferreira, R. C. et al escassos estudos que avaliem a eficácia e precisão da utilização de dados climáticos estimados ou interpolados a partir de estações distantes da área agrícola de interesse. Assim, este estudo teve como objetivo demonstrar os impactos da variabilidade espacial dos principais elementos meteorológicos sobre a estimativa regional da produtividade de grãos de soja. Utilizaram-se dados observados em três estações meteorológicas em diferentes locais de Londrina, comparados por meio de análise de regressão. Calculou-se o balanço hídrico decendial para soja e estimaram-se as perdas de produtividade de grãos pelo método Zona Agroecológica. As temperaturas nos diferentes locais apresentaram semelhanças, enquanto a umidade relativa do ar e, principalmente, precipitação pluvial foram mais discrepantes. Recomenda-se muita cautela no uso e na escolha de uma única estação meteorológica para representar um município ou região, como acontece em várias regiões de importância agrícola e ambiental no Brasil. Modelos e resultados de estimativas de safras que não consideram tal...

Despite the several studies comparing methods for evapotranspiration (ETo) estimation, scientific reports demonstrating their use and evaluation when coupled to agrometeorological models have not been analyzed, particularly in regard to the comparison of the estimated results obtained in the modeling with the real production data experimentally obtained in the field. The present study evaluated nine alternative methods to calculate ETo for estimating soybean yield, associated with actual yields obtained in irrigated and non-irrigated fields, at three sowing periods during the 2013/14 crop season in Southern Brazil. All methods were evaluated in relation to the standard Penman-Monteith method. Their performance was measured through regression analysis and statistical coefficients submitted to the Tukey test. ETo values obtained through the alternative methods were used to calculate water balances for soybean, considering irrigated and non-irrigated environments. Theoretical and real potential yields were higher in later sowings. The Priestley-Taylor method was the best to estimate daily ETo alternatively to that recommended by FAO (Penman-Monteith). On the other hand, the alternative method of Thornthwaite-Camargo was the best for estimations at 10-day periods, in all sowing dates. Furthermore, the methods of Thornthwaite-Camargo, Benevides-Lopez, Camargo, and Thornthwaite showed the smallest deviations to estimate ETo (10-day periods) for calculating actual yields (Ya).

Brazil requires a fully representative weather network station; it is common to use data observed in locations distant from the region of interest. However, few studies have evaluated the efficiency and precision associated with the use of climate data, either estimated or interpolated, from stations far from the agricultural area of interest. Hence, this study aimed to demonstrate the impacts of spatial variability of the main meteorological elements on the regional estimate of soybean productivity. Regression analysis was used to compare data recorded at three weather stations located throughout Londrina, PR, Brazil. The water balance of the soybean crop was calculated at 10-day periods and grain productivity losses estimated using the Agro-Ecological Zones (AEZ) methodology. Temperatures at the three locations were similar, while the relative air humidity, and particularly, the rainfall data, were less correlated. A high degree of caution is recommended in the use and choice of a single weather station to represent a municipality or region, particularly in countries, such as Brazil, with multiple regions of agricultural and environmental importance. Models and crop season estimates that do not consider such a recommendation are vulnerable to errors in their forecasts. The volumetric and temporal variability in the spatial rainfall distribution resulted in soybean yield discrepancies, estimated at the municipal level. The consistency of the data series, the location of weather stations and their distance to the location of interest determine the ability of crop models to accurately estimate soybean production based on meteorological data, particularly the rainfall data. This study contributes to future regional research using climate data, and highlights the importance of a weather station network throughout Brazil, demonstrating the urgent need to increase the number of weather stations, particularly for recording rainfall data.