Why may consider rainfall space-time variability in Precision Agriculture?

Abstract

Abstract Brazil is one of the largest soybean producers in the world, however, there are still yield gaps in crops, mainly linked to weather conditions. Based on it, this paper quantifies the spatial variability of rainfall based on two dense networks of rain gauges and analyzes the influence on the attainable productivity (Ya) of the soybean crop. The study was carried out in Piracicaba, SP. For the first rain gauge network a measuring campaign was conducted from 1993 to 1994, with 10 gauges distributed in 1,000.0 ha. The second rain gauge network measuring campaign was conducted from 2016 to 2018, with 9 gauges sampling 36.0 ha. To evaluate the influence of rainfall spatial variability on soybean yield a multi-model (FAO, DSSAT, and MONICA) simulation was used. The relative production loss (Ygrel) caused by water deficiency was simulated for 3 sowing dates and each rainfall sampling point. The results showed that the spatial variability of precipitation has a direct influence on attainable productivity (Ya). However, the magnitude of rainfall variability is not directly replicated in yield. The temporal variability, between the different sowing times, had a major influence on soybean yield.