Soybean and maize off-season sowing dates when cultivated in succession: impacts of climate variability on yield and profitability

• Autor(a) principal: Nóia Junior, Rogério de Souza
• Data de Publicação: 2019
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Biblioteca Digital de Teses e Dissertações da USP
• Texto Completo: http://www.teses.usp.br/teses/disponiveis/11/11152/tde-23082019-153442/
• topic: Atingível e real; Attainable and actual crop yield; Crop management; Crop modeling; Déficit hídrico; Manejo de culturas agrícolas; Modelagem de crescimento de cultura; Potential; Produtividade potencial; Water deficit

In the last decade, Brazilian soybean and maize, cultivated in succession, accounted for 23.8 ± 1.9% and 6.9 ± 0.9% of world\'s production, respectively. More than 80% of soybean and maize production in Brazil is under rainfed conditions, which results in a high interannual yield variability and, consequently, increasing the risks for food supply, not only in the country but also around the world. Among the natural phenomena that cause climate and yield variability in Brazil, El Niño Southern Oscillation (ENSO) is the most important. The best way to minimize the impacts of ENSO, mainly those associated to water deficit in rainfed crops, is by defining the most favorable sowing dates, when the probability of crop failure is small. Based on that, this study aimed: to determine the best sowing dates for the soybean-maize production system, based on the economic profitability at national scale; to assess the influence of the ENSO phases (El Niño, La Niña and Neutral) on spatial and temporal soybean and maize off-season yield variabilities for different sowing dates; and to determine the magnitude of the current soybean- maize succession yield gap due to water deficit and crop management in different Brazilian producing regions. To achieve such goals, soybean and maize off-season simulations were performed using three previously calibrated and validated crop simulation models (FAO-AZM, DSSAT and APSIM), in a multi-model approach. Soybean and maize yields were simulated for 29 locations in 12 states, with soybean sowing dates ranging from 21st September to 1st January, for a period of 34 years (1980-2013). Maize sowings were simulated in the same day soybean was harvested. The optimal sowing dates for soybean-maize succession varied according to the Brazilian region, with water deficit, solar radiation and air temperature being the main weather variables that influenced this crop system. ENSO phases affected soybean and maize yields across the country, having, in general, opposite effects during the warm (El Niño) and cold (La Niña) phases, but also depending on the sowing date considered. The yield gap (YG) of soybean-maize succession varied among locations, sowing dates and growing seasons. However, the yield gaps caused by water deficit (YGw) were, on average, higher than those caused by sub-optimal crop management (YGm), which can be explained by the high inter-annual and spatial climate variability observed in the Brazilian territory.