Optimization of Nitrogen Fertilization Strategies for Drip Irrigation of Cotton in Large Fields by DSSAT Combined with a Genetic Algorithm

Optimization of Nitrogen Fertilization Strategies for Drip Irrigation of Cotton in Large Fields by DSSAT Combined with a Genetic Algorithm

This study presents a hybrid modeling framework synergizing process-based crop modeling with evolutionary optimization to reconcile yield sustainability with nitrogen management in arid cotton systems. Building upon the DSSAT-CROPGRO model’s demonstrated superiority over pure machine learning approa...

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Bibliographic Details
Main Authors: Zhuo Yu, Weiguo Fu
Format: Article
Language:English
Published: MDPI AG 2025-03-01
Series:Applied Sciences
Subjects:
nitrogen fertilizer
cotton production
DSSAT-CROPGRO
genetic algorithm
Online Access:https://www.mdpi.com/2076-3417/15/7/3580
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Summary:This study presents a hybrid modeling framework synergizing process-based crop modeling with evolutionary optimization to reconcile yield sustainability with nitrogen management in arid cotton systems. Building upon the DSSAT-CROPGRO model’s demonstrated superiority over pure machine learning approaches in simulating nitrogen–crop interactions (calibrated with multi-year phenological datasets), we develop a genetic algorithm-embedded decision system that simultaneously optimizes nitrogen use efficiency (NUE) and economic returns. Field validations across contrasting growing seasons demonstrate the framework’s capacity to reduce nitrogen inputs by 15–20% while increasing profitability by 8–12% compared to conventional practices, without compromising yield stability. The tight coupling of mechanistic understanding with multi-objective optimization advances precision agriculture through two key innovations: (1) dynamic adaptation of fertilization strategies to both biophysical processes and economic constraints and (2) closed-loop integration of crop physiology simulations with evolutionary computation. This paradigm-shifting methodology establishes a new template for developing environmentally intelligent decision-support systems in water-limited agroecosystems.
ISSN:2076-3417

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