Work

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

Year: 2025

Type: article

Abstract: 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.

Source: Applied Sciences

DOI: 10.3390/app15073580

Authors: Zhuo Yu, Weiguo Fu

Institution: Jiangsu University

Language: English

Topic: Irrigation Practices and Water Management

Subfield: Soil Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDG: Zero hunger

Open Access status: gold

Estimated APC paid: $2,896