## 1 Introduction  
China is one of the world's three largest cotton-producing countries [1]. As the primary cotton-growing region in China, Xinjiang accounts for over 80% of the nation's annual cotton output. However, this substantial production is accompanied by a widespread reliance on excessive nitrogen fertilizer application, aimed at enhancing yield. Concurrently, Xinjiang faces significant challenges related to the degradation of soil quality, including a decline in organic matter content and increased salinization, resulting from years of intensive cultivation [2]. The overuse of nitrogen fertilizers may further exacerbate potential impacts on soil health and groundwater quality [3] while also contributing to agricultural losses in the region [4]. Therefore, improving yield while enhancing nitrogen use efficiency is a critical concern for cotton cultivation in Xinjiang. Traditionally, optimal fertilization schemes are determined through field experiments [5, 6]. However, due to the time-consuming and inefficient nature of real-world farm experiments, crop models are becoming important tools for assessing the impacts of climate change and management practices on crop yields [7]. Several crop simulation models have been developed for major crops so far [8, 9]. According to research in [10], APSIM and DSSAT are the two most widely used crop models. Specifically in cotton research,DSSAT has demonstrated exceptional capabilities in simulating cotton phenology and yield under drip irrigation systems in southern Xinjiang [11], optimizing irrigation schedules through multi-year meteorological data analysis [12], and evaluating climate change impacts on production potential [13]. Recent applications have extended to predicting cotton responses under different climate circulation patterns [14]. Furthermore, combining field experiments with crop model simulations has been widely employed to determine fertilization plans or improve efficiency [12; 15; 16]. The Decision Support System for Agrotechnology Transfer (DSSAT) has been widely applied as a research and decision-making tool for crop production and resource management [17; 18; 19]. Ref. [12] applied the DSSAT-SUBSTOR-Potato model to evaluate the impact of supplementary irrigation and fertilization strategies on potato yield and water productivity, identifying the optimal irrigation and fertilization scheme combination under drip irrigation scenarios for potatoes. Ref. [15] effectively simulated the growth and development of maize under drip irrigation and rain in Changchun City using the DSSAT model. Subsequently, they explored the optimal nitrogen application scheme for the central region of Jilin Province, forming a real-time method for field management optimization and scheduling decisions. Ref. [16] predicted the growth of wheat using the CERES-Wheat model, calibrated and validated losses through furrow irrigation and nitrate through furrow irrigation pipes, and analyzed the impact of wheat as a winter cover crop on soil water and nitrogen budgets and the growth of cash crops. Ref. [20] assimilated plant nitrogen concentration (PNC) obtained from unmanned aerial vehicles into the CERES-Rice model using the PSO algorithm, comparing the performance of yield estimation models at each growth stage after data assimilation. Ref. [17] calibrated a cotton growth model in Xinjiang using field experiment data and optimized its management decisions. To address the aforementioned issues, this study utilized measured data to calibrate and evaluate the model, assessing the suitability of DSSAT for simulating cotton growth in Xinjiang fields. Building upon model calibration, the study integrated the model with genetic algorithms to optimize nitrogen fertilization strategies for cotton fields in Xinjiang. This integration represents an innovative approach, as previous agricultural optimizations using genetic algorithms primarily focused on water resource allocation [21; 22], with limited application in fertilizer management through crop model coupling.