Spatial and temporal variability of corn response to nitrogen and seed rates

The quantification of the spatial and temporal variability of crop response to controllable inputs and the relationship with their controlling factors is important for making input prescriptions. In this study, we (1) assessed the within-field spatial variability of corn (Zea mays L.) response to nitrogen (NR) and seed (SR) rates using on-farm precision experiments (OFPE) data and geographically weighted regression (GWR) models; (2) determined the spatial agreement between NR and SR response classes, and their temporal stability; and (3) modeled the effect of weather and site-specific features on the spatial distribution of the responses. Response maps were estimated by fitting GWR models to yields and as-applied NR and SR from 14 OFPE. Responses classified into positive and non-positive response classes were spatially joined to weather, soil, and landscape covariates to train a random forest model. GWR models explained 30%-60% of yield variation. Positive responses to NR and SR represented about 46% and 33% of fields' area, respectively, with large variation across field-year combinations. Temporal agreement was about 50%-60%, for NR and SR, respectively. The spatial agreement between NR and SR response classes suggests that factors controlling the crop response to these inputs are similar. In both cases, weather variables were the most important predictors, followed by landscape and soil attributes. Although weather variables acted at a field level, that is, field-year, the latter reflected the effect of within-field variability on crop response. To reduce this weather uncertainty, future research should consider conducting larger numbers of OFPE across multiple seasons. We detected significant spatial variability of corn response to nitrogen and seed rates using geographically weighted regression and on-farm precision experiments data.The temporal consistency of response zones to same input across years was inconsistent due to weather.Spatial agreement between zones for the same year was higher as they shared the same controlling factors.Seasonal conditions were the most important factors determining the changes to get positive responses to these inputs.