## 2 Materials and Methods
### CSM-CROPGRO-Cotton Model  
The Decision Support System for Agrotechnology Transfer (DSSAT) is a process-based crop modeling platform that integrates principles of plant ecophysiology, soil biogeochemistry, and micrometeorology to simulate crop developmental processes from germination to maturity through dynamic interactions with environmental and management variables. Central to its predictive capacity are three core computational modules: (1) a meteorological driver requiring daily inputs of solar radiation, precipitation, and temperature to regulate phenological progression and photosynthetic carbon assimilation; (2) a soil-crop interface module that simulates vertical water transport, nutrient diffusion, and root uptake dynamics using stratified soil properties (texture, organic matter, hydraulic characteristics, and initial N-P-K concentrations); and (3) a management module enabling parameterization of fertilization strategies (timing, formulation, dosage) with mechanistic quantification of their impacts on yield formation and nutrient use efficiency. Model outputs encompass critical agronomic metrics (biomass accumulation, yield components), biogeochemical cycling (nitrate leaching, soil residual nitrogen), and environmental footprints (water-fertil productivity), with prediction accuracy contingent upon localized calibration of cultivar-specific genetic coefficients governing photoperiod sensitivity and assimilate partitioning. In our Xinjiang cotton fertilization optimization framework, DSSAT's coupling of soil nitrate spatiotemporal distribution models with cotton nutrient demand functions generated multidimensional response surfaces quantifying yield-environment tradeoffs, thereby enabling multi-objective genetic algorithm optimization. The system's robustness has been empirically validated through global field trials, demonstrating \(<\)15% root mean square error in yield prediction under nitrogen management scenarios [30], establishing it as an indispensable tool for deciphering water-fertilizer synergies and designing sustainable intensification protocols [31; 32]. This study employed the CSM-CROPGRO-Cotton model within DSSAT, a process-based framework rooted in the generic CROPGRO architecture [33; 34]. The model dynamically simulates cotton phenological development, canopy light interception, and nitrogen \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Treatment**} & \multirow{2}{*}{**Number of Plants**} & \multicolumn{2}{c}{**Simulated**} & \multicolumn{1}{c}{**Observed**} \\  & & & **Lint Yield (kg/ha)** & **Lint Yield (kg/ha)** \\ \hline \multirow{8}{*}{2023} & Tr1-1 & 18 & & 2897.523 \\  & Tr1-2 & 20 & 3155.6 & 3624.611 \\  & Tr1-3 & 16 & & 3314.385 \\  & Tr2-1 & 17 & & 2804.882 \\  & Tr2-2 & 15 & 3137.7 & 3628.184 \\  & Tr2-3 & 17 & & 3537.326 \\  & Tr3-1 & 13 & & 3156.506 \\  & Tr3-2 & 21 & 3658.4 & 4356.539 \\  & Tr3-3 & 21 & & 4133.004 \\ \hline \multirow{8}{*}{2024} & Tr1-1 & 21 & & 3642.3 \\  & Tr1-2 & 20 & 3594.2 & 3837.4 \\ \cline{1-1}  & Tr1-3 & 16 & & 3382.1 \\ \cline{1-1}  & Tr2-1 & 18 & & 4943.1 \\ \cline{1-1}  & Tr2-2 & 15 & 4148.6 & 4422.8 \\ \cline{1-1}  & Tr2-3 & 21 & & 4097.6 \\ \cline{1-1}  & Tr3-1 & 19 & & 4617.9 \\ \cline{1-1}  & Tr3-2 & 19 & 4629.4 & 4682.9 \\ \cline{1-1}  & Tr3-3 & 19 & & 4292.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparing simulated values inferred from the model with observed values from actual experiments (Part 2). uptake-translocation processes across the entire growth cycle by integrating cultivar-specific physiological parameters, including photosynthetic capacity, leaf area expansion patterns, and carbon partitioning mechanisms to reproductive organs. Its mechanistic strength lies in coupling spatiotemporal nitrate diffusion models with root absorption kinetics, enabling precise quantification of nitrogen transport efficiency in soil-plant systems and marginal yield responses to fertilization strategies.