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

• Published in: Precision agriculture Vol. 22; no. 1; pp. 75 - 98
• Main Authors: Morari, F., Zanella, V., Gobbo, S., Bindi, M., Sartori, L., Pasqui, M., Mosca, G., Ferrise, R.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.02.2021; Springer Nature B.V
• Subjects: Agricultural production; Agriculture; Atmospheric Sciences; Biomedical and Life Sciences; Chemistry and Earth Sciences; Computer Science; Crops; Economic impact; Fertilization; Life Sciences; Management systems; Meteorological data; Nitrogen; Nutrient requirements; Nutrient sources; Nutrient status; Optimization; Organic matter; Physics; Remote Sensing/Photogrammetry; Soil properties; Soil Science & Conservation; Soils; Statistics for Engineering; Triticum durum; Variability; Weather forecasting; Wheat; Variable rate; Durum wheat; Fertilization; Nitrogen; Proximal Sensing
• ISSN: 1385-2256; 1573-1618
• DOI: 10.1007/s11119-020-09730-6

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.