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Publication
Digitization of Crop Nitrogen Modelling: A Review
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Advisor(s)
Abstract(s)
Applying the correct dose of nitrogen (N) fertilizer to crops is extremely important. The
current predictive models of yield and soil–crop dynamics during the crop growing season currently
combine information about soil, climate, crops, and agricultural practices to predict the N needs
of plants and optimize its application. Recent advances in remote sensing technology have also
contributed to digital modelling of crop N requirements. These sensors provide detailed data,
allowing for real-time adjustments in order to increase nutrient application accuracy. Combining
these with other tools such as geographic information systems, data analysis, and their integration in
modelling with experimental approaches in techniques such as machine learning (ML) and artificial
intelligence, it is possible to develop digital twins for complex agricultural systems. Creating digital
twins from the physical field can simulate the impact of different events and actions. In this article,
we review the state-of-the-art of modelling N needs by crops, starting by exploring N dynamics in the
soil−plant system; we demonstrate different classical approaches to modelling these dynamics so as
to predict the needs and to define the optimal fertilization doses of this nutrient. Therefore, this article
reviews the currently available information from Google Scholar and ScienceDirect, using relevant
studies on N dynamics in agricultural systems, different modelling approaches used to simulate
crop growth and N dynamics, and the application of digital tools and technologies for modelling
proposed crops. The cited articles were selected following the exclusion criteria, resulting in a total
of 66 articles. Finally, we present digital tools and technologies that increase the accuracy of model
estimates and improve the simulation and presentation of estimated results to the manager in order
to facilitate decision-making processes.
Description
Keywords
process simulation Internet of Things data science decision support systems variable rate fertilization
Pedagogical Context
Citation
: Silva, L.; Conceição, L.A.; Lidon, F.C.; Patanita, M.; D’Antonio, P.; Fiorentino, C. Digitization of Crop Nitrogen Modelling: A Review. Agronomy 2023, 13, 1964. https:// doi.org/10.3390/agronomy13081964