Soybean yield potential estimated in a central region of RS State, Brazil
Lilian Cris Dallagnol, Crislaine Sartori Suzana‐Milan
Abstract
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Lilian Cris Dallagnol, Crislaine Sartori Suzana‐Milan
Abstract
Open-access reader
Despite soybean global economic relevance, its yield levels vary greatly in time and space. The state of Rio Grande do Sul (RS) has a lower average soybean yield than the Brazilian average, except for its Central region, where high yields and the extension of cultivated areas have been highlighted. Thanks to technology, high-quality cultivars, and professionalization of farmers, soybean tends to increase yield after every season. However, adverse weather is one of the factors that limit yield potential. The influence of factors that limit yield can be better understood if the crop yield potential is known. Therefore, the aim of this study was to estimate soybean yield potential in the central region of RS, based on mathematical models using historical climate data. According to the observations, it was concluded that the model has the potential to set optimum sowing times based on agronomic aspects, besides allowing for the estimation of soybean maximum regional production, showing that there is still much to improve and adapt to obtain high yield in crop management.
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Despite soybean global economic relevance, its yield levels vary greatly in time and space. The state of Rio Grande do Sul (RS) has a lower average soybean yield than the Brazilian average, except for its Central region, where high yields and the extension of cultivated areas have been highlighted. Thanks to technology, high-quality cultivars, and professionalization of farmers, soybean tends to increase yield after every season. However, adverse weather is one of the factors that limit yield potential. The influence of factors that limit yield can be better understood if the crop yield potential is known. Therefore, the aim of this study was to estimate soybean yield potential in the central region of RS, based on mathematical models using historical climate data. According to the observations, it was concluded that the model has the potential to set optimum sowing times based on agronomic aspects, besides allowing for the estimation of soybean maximum regional production, showing that there is still much to improve and adapt to obtain high yield in crop management.
Key concepts: Yield (engineering), State (computer science), Environmental science, Agronomy, Mathematics, Biology, Materials science, Algorithm