## 4 Results
### Comparative Analysis with Proximal Policy Optimization (PPO)  
To further validate the efficacy of our DSSAT-GA framework, we conducted parallel simulations in the calibrated DSSAT environment for both algorithms, ensuring identical soil-climate initialization conditions. While previous tables presented field-validated results, the DSSAT-GA data herein reflect purely simulated outputs to enable direct comparison with the PPO agent. Minor discrepancies between these simulated values and earlier field measurements (e.g., \(\pm\)2.8% in lint yield) arise from inherent differences between controlled simulations and real-world variability, yet the shared virtual environment ensures methodological parity for comparative evaluation.  
The simulation-to-simulation comparison revealed amplified advantages for DSSAT-GA over the pure reinforcement learning approach (Table 11). PPO exhibited a 12.1% higher nitrogen requirement (\(p<0.001\)) yet produced 5.0% less lint (\(p<0.001\)), translating to a 15.2% deficit in nitrogen use efficiency. Temporal analysis of fertilization decisions  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Scenario** & **Method** & **Net Profit** & \
\begin{tabular}{l} **Absolute Net** \\ **Profit Change (\%)** \\ \end{tabular} &
\begin{tabular}{l} **Relative Net** \\ **Profit Change (\%)** \\ \end{tabular} \\ \hline Baseline & T3 & 9960.5091 & 0 & 0 \\ Baseline & DSSAT + GA & 11,057.956 & 11.02 & 11.02 \\ N cost +20\% & T3 & 9931.7091 & \(-\)0.29 & 0 \\ N cost +20\% & DSSAT + GA & 11,028.1296 & 10.72 & 11.04 \\ N cost \(-\)20\% & T3 & 9989.3091 & 0.29 & 0 \\ N cost \(-\)20\% & DSSAT + GA & 11,087.7824 & 11.32 & 10.99 \\ Cotton price +15\% & T3 & 11,476.18547 & 15.22 & 0 \\ Cotton price +15\% & DSSAT + GA & 12,739.0192 & 27.9 & 11 \\ Cotton price \(-\)15\% & T3 & 8444.832735 & \(-\)15.22 & 0 \\ Cotton price \(-\)15\% & DSSAT + GA & 9376.8928 & \(-\)5.86 & 11.04 \\ Combined stress & T3 & 8416.032735 & \(-\)15.51 & 0 \\ Combined stress & DSSAT + GA & 9347.0664 & \(-\)6.16 & 11.06 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparative net profitability under market volatility scenarios (Unit: USD ha\({}^{-1}\)). The DSSAT + GA framework demonstrates consistent performance improvements across nitrogen price fluctuations (\(\pm\)20%), cotton market variations (\(\pm\)15%), and combined stress conditions, maintaining 10.99–11.06% profit gains over conventional practices (\(T_{3}\)). Absolute Net Profit Change (%) represents the change in net profit for each scenario and each method relative to the baseline (\(T_{3}\)), while Relative Net Profit Change (%) represents the change in net profit for the DSSAT + GA method relative to the \(T_{3}\) method under each scenario.  
showed PPO's delayed response to flowering-stage nutrient demands, applying only 58% of optimal nitrogen during critical boll formation (DAS 80-110) compared to DSSAT-GA's 82% allocation efficiency. This mechanistic misalignment persisted despite PPO's superior short-term reward maximization, achieving 92% of maximum theoretical yield within 15,000 episodes but failing to escape local optima in later training stages.  
Notably, DSSAT-GA's simulated lint yield (4876.3 kg ha\({}^{-1}\)) showed a 3.1% reduction compared to field-validated results (5025.6 kg ha\({}^{-1}\)), attributable to the exclusion of farmer interventions like pest control. However, both algorithms operated under identical simulated constraints, preserving the validity of comparative metrics. The hybrid framework maintained robust performance across environmental perturbations introduced during sensitivity testing (\(\pm\)20% precipitation variation), whereas PPO's economic returns fluctuated excessively (CV = 18.7% vs. DSSAT-GA's CV = 9.3%), demonstrating superior stability from evolutionary optimization's population-based search strategy.