Coupling proximal sensing, seasonal forecasts and crop modelling to optimize nitrogen variable rate application in durum wheat

Nitrogen (N) fertilization in durum wheat has traditionally been managed based on yield goals without considering temporal and spatial variability of yield potential related to changes in soil properties, weather and crop response to fertilization. In fact, this approach may lead to inefficient N use by the crop, resulting in both economic losses and environmental issues. To overcome these drawbacks, several optical-oriented, site-specific management systems have been developed to consider the effect of the aforementioned sources of variability and modulate N applications to the actual crop nutrient status and requirements. In this study, a novel approach that integrates proximal sensing, seasonal weather forecasts and crop modelling to manage site-specific N fertilization in durum wheat is proposed. This approach is based on four successive steps: (1) optimal N supply is estimated by means of a crop model fed with a mix of observed and forecast weather data; (2) actual crop N uptake is estimated using proximal sensing; (3) N prescription maps are created merging crop model and proximal sensing information; (4) N-Variable Rate Application (N-VRA). The aforementioned approach was implemented in a 13.6-ha field characterized by large soil variability in texture and organic matter content. Results indicated that the system was able to capture spatial variability in crop N uptake and manage N distribution through N-VRA leading to a substantial reduction of the spatial variability in yield and protein content while reducing the total amount of N supplied compared to uniform treatments. However, further advances are necessary to improve model performances.

Automated summary

A novel approach is proposed to manage site-specific nitrogen fertilization in durum wheat by integrating proximal sensing, seasonal weather forecasts, and crop modeling. This approach successfully reduces the amount of nitrogen supplied, improves crop nitrogen use efficiency, and decreases spatial variability in yield and protein content. Further improvements in model performance are necessary.