## 5 Discussion  
### Key Findings of This Study  
This study confirms that the DSSAT-GA framework fundamentally redefines nitrogen management in arid cotton systems by harmonizing yield sustainability with economic resilience. Field validation demonstrated that the optimized strategy increased nitrogen use efficiency (NUE) by 7% (13.48 vs. 12.59 kg/kg in \(T_{3}\)) while achieving near-maximum experimental yields--a critical advancement given the diminishing returns of conventional fertilization. The sensitivity analysis further substantiated these findings: under nitrogen price fluctuations (\(\pm\)20%) and cotton market volatility (\(\pm\)15%), DSSAT-GA maintained 10.99-11.06% relative profit gains over traditional practices (Table 10), with fiber quality consistently outperforming controls (0.51 vs. 0.45-0.47). These results are mechanistically rooted in the algorithm's fitness function, which explicitly optimizes the yield-to-fertilization ratio rather than isolated yield or cost metrics. By prioritizing thermodynamic efficiency, this approach inherently buffers against input-output price disequilibrium, as evidenced by the stable NUE across market shocks--a previously unreported property of hybrid crop modeling systems.  
The framework's robustness arises from three synergistic innovations. First, precision parameterization of DSSAT using high-resolution soil-weather data minimized epistemic uncertainties in nitrogen demand forecasting, enabling accurate simulation of phenology-driven nutrient uptake patterns. Second, genetic algorithms introduced adaptive optimization capabilities, dynamically reconciling real-time crop requirements with probabilistic climate ensembles to generate Pareto-optimal fertilization schedules. Crucially, the integration of multi-temporal decision horizons allowed the system to anticipate seasonal nitrogen mineralization dynamics, avoiding the myopic over-application typical of heuristic practices. This is particularly impactful in cotton systems, where excessive  
Figure 10: Global sensitivity analysis of input parameters in DSSAT + GA modeling.  
vegetative growth under nitrogen surplus exacerbates bell shedding--a phenomenon our model mitigated through stage-specific allocation aligned with sink strength dynamics.  
The comparative analysis with PPO reinforces our methodological rationale for hybridizing process-based modeling with evolutionary optimization. While reinforcement learning approaches excel in data-rich environments through rapid policy iteration, their black-box nature fundamentally limits extrapolation capacity under novel conditions--a critical constraint in agricultural systems facing climate extremes. During simulated drought scenarios (\(\pm\)40% precipitation reduction), PPO's nitrogen allocation patterns diverged sharply from established crop physiological requirements, over-applying fertilizer during vegetative stages by 22% compared to DSSAT-GA's climate-adaptive schedules. This mechanistic misalignment persisted despite equivalent training episodes, underscoring the value of embedding domain knowledge through DSSAT's biophysical constraints. Our framework achieves superior generalization by explicitly modeling soil-plant nitrogen dynamics while retaining evolutionary optimization's global search capability, thereby balancing data-driven adaptation with process-guided decision-making--a crucial advantage for sustainable intensification in vulnerable arid ecosystems.