Why should space-time variability of rainfall be considered in precision agriculture for soybean in Piracicaba, São Paulo state?

Brazil ranks among the world's largest soybean producers; however, significant gaps in crop yield still exist, primarily linked to weather conditions. This study quantifies rainfall spatial variability using two dense networks of rain gauges and examines the impact of this variability on soybean crops’ attainable productivity. The study was carried out in Piracicaba, São Paulo state, Brazil. The first rain gauge network measuring campaign was conducted from 1993 to 1994, featuring ten gauges distributed in 1000 ha. The second rain gauge network was active from 2016 to 2018, comprising nine gauges covering 36 ha. A multi-model simulation was employed to assess the effect of rainfall spatial variability on soybean yield. The relative yield loss (Yg) due to water deficiency was simulated for three different sowing dates and across each rainfall sampling point. The findings indicate that the rainfall spatial variability directly influences attainable productivity. The extent of rainfall variability does not translate directly into yield outcomes; however, temporal variability associated with different sowing times significantly impacts soybean yield.

Keywords:
agro-climatic modeling; soybean yield; spatial variability; water management