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An Optimal Decision Support System Based on Crop Dynamic Model for N-Fertilizer Treatment.

Authors :
Singh AP
Yerudkar A
Liuzza D
Liu Y
Glielmo L
Source :
Sensors (Basel, Switzerland) [Sensors (Basel)] 2022 Oct 08; Vol. 22 (19). Date of Electronic Publication: 2022 Oct 08.
Publication Year :
2022

Abstract

The efficient handling of nitrogen has become a critical issue in modern agriculture, from a financial standpoint, as well as in regard to reducing the environmental impacts of using an excessive amount of nitrogen fertilizer. Manure compost is useful for maintaining or raising soil chemical levels without excessive NO3- accumulation; however, for the best grain yield, it should be combined with N fertilizer. Via this study, we aimed to develop an optimal decision support system that indicates when to initiate fertilization based on nitrogen-limited (N-limited) crop growth dynamics. An optimal nitrogen fertilizer (N-fertilizer) management system increases crop yield while maintaining a balance between fertilizer supply and crop demand. This study used the N-limited crop growth model (LINTUL3) to develop an optimal decision support system. In this work, we formulated and resolved two optimization challenges: (i) maximization of biomass growth; and (ii) maximization of growth with the least cost paid on N-fertilizer and its application. Furthermore, two case studies were developed based on the number of fields: (i) optimization for a single field, and (ii) optimization for multiple fields. In the case of multiple fields, it is hypothesized that a fertilizer treatment for one field can leak to other fields and affect the nitrogen dynamics of different fields. Finally, numerical simulations were carried out supporting the theory developed in the paper. The simulations showed that when the proposed work was employed to achieve the goal of optimal nitrogen management for a crop, a 28% to 53% increase in biomass growth under certain scenarios was attained.

Details

Language :
English
ISSN :
1424-8220
Volume :
22
Issue :
19
Database :
MEDLINE
Journal :
Sensors (Basel, Switzerland)
Publication Type :
Academic Journal
Accession number :
36236710
Full Text :
https://doi.org/10.3390/s22197613