Vol.:(0123456789) Precision Agriculture https://doi.org/10.1007/s11119-020-09730-6 1 3 Coupling proximal sensing, seasonal forecasts and crop modelling to optimize nitrogen variable rate application in durum wheat F. Morari 1  · V. Zanella 1  · S. Gobbo 1  · M. Bindi 2  · L. Sartori 3  · M. Pasqui 4  · G. Mosca 1  · R. Ferrise 2 © Springer Science+Business Media, LLC, part of Springer Nature 2020 Abstract 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 inefcient N use by the crop, resulting in both economic losses and environ- mental issues. To overcome these drawbacks, several optical-oriented, site-specifc man- agement systems have been developed to consider the efect of the aforementioned sources of variability and modulate N applications to the actual crop nutrient status and require- ments. In this study, a novel approach that integrates proximal sensing, seasonal weather forecasts and crop modelling to manage site-specifc N fertilization in durum wheat is pro- posed. 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 feld 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 treat- ments. However, further advances are necessary to improve model performances. Electronic supplementary material The online version of this article (https://doi.org/10.1007/s1111 9-020-09730-6) contains supplementary material, which is available to authorized users. * F. Morari francesco.morari@unipd.it 1 Department of Agronomy Food Natural Resources Animal and Environment, University of Padua, viale dell’Università, 16, 35020 Legnaro, Italy 2 Department of Plant, Soil and Environmental Science, University of Florence, Piazzale delle Cascine 18, 50144 Florence, Italy 3 Department of Land Environment Agriculture Forestry, University of Padua, vialedell’Università, 16, 35020 Legnaro, Italy 4 CNR–IBIMET, Rome, Italy