## 5 Results and Discussion
### Crop Yield Correlation Results Obtained by Three Different Methods
\begin{table}
\begin{tabular}{l l l l l l l} \hline
**District** & **SHAP** & **PCA** & **Empirical** & **District** & **SHAP** & **PCA** & **Empirical** \\ \hline Agacoren & 0.008 & \(-\)0.039 & \(-\)0.011 & Guneysnir & 0.182 & 0.120 & 0.080 \\ Akoren & 0.439 * & 0.417 * & 0.438 * & Guzelyurt & 0.340 & \(-\)0.056 & 0.036 \\ Askary\_Merkez & 0.681 * & 0.636 * & 0.644 * & Huyuk & 0.689 * & 0.723 * & 0.708 * \\
**Akeshir** & 0.583 * & 0.319 & 0.389 * & **Igin** & 0.794 * & 0.759 * & 0.705 * \\
**Altinkein** & 0.547 * & 0.353 & 0.347 & Kadinhani & 0.829 * & 0.752 * & 0.740 * \\
**Altinwhisar** & 0.738 * & 0.719 * & 0.716 * & Karaman\_Merkez & 0.729 * & 0.549 * & 0.556 * \\ Ayranci & 0.269 & 0.008 & 0.008 & Karapinar & 0.635 * & 0.453 * & 0.454 * \\
**Beysehir** & 0.623 * & 0.591 * & 0.614 * & Karatay & 0.571 * & 0.445 * & 0.468 * \\
**Bor** & 0.644 * & 0.574 * & 0.565 * & Kulu & 0.331 & 0.402 & 0.430 * \\
**Camardi** & 0.504 * & 0.320 & 0.305 & Meram & 0.456 * & 0.365 & 0.385 \\ Celtik & 0.701 * & 0.487 * & 0.493 * & Nigde\_Merkez & 0.547 * & 0.438 * & 0.441 * \\
**Ciftlik** & 0.408 & 0.475 * & 0.483 * & Ortakoy & 0.565 * & 0.444 * & 0.516 * \\
**Chianbeyli** & 0.762 * & 0.689 * & 0.692 * & Sarayoun & 0.377 & 0.311 & 0.287 \\ Curna & 0.558 * & 0.498 * & 0.480 * & Saraynisi & 0.600 * & 0.580 * & 0.538 * \\ Derbent & 0.573 * & 0.304 & 0.367 & **Selcuklu** & 0.665 * & 0.603 * & 0.576 * \\ Doganhisar & 0.520 * & 0.477 * & 0.417 * & Seydisehir & 0.002 & \(-\)0.110 & \(-\)0.114 \\ Emirgazi & 0.800 * & 0.701 * & 0.713 * & Tuzlukcu & 0.558 * & 0.505 * & 0.550 * \\ Eregli & 0.254 & 0.155 & 0.167 & Ulukisla & 0.672 * & 0.592 * & 0.618 * \\ Eskil & 0.505 * & 0.268 & 0.332 & Yalihuyuk & 0.128 & 0.075 & 0.180 \\ Gulagac & 0.735 * & 0.600 * & 0.662 * & Yunak & 0.641 * & 0.639 * & 0.627 * \\ \hline \end{tabular} In bold are test set districts; * \(p\) < 0.05.  
\end{table} 
Table 3: Correlation results for different CDI weight calculation methods. To assess the temporal relationship between the drought indices and crop yields for the districts in the test set, time series of the indices and crop yield are presented in Figure 6. The standardized crop yields, along with CDI and SPEI indices which belong to the same classification, are placed on the left axis, while the scPDSI values are placed on the right axis for visual comparison. To avoid clutter, only the SPEI value with the highest correlation at the district level was used, rather than all SPEI indices. Overall, a good alignment between CDI and SPEI with crop yield was observed. In contrast, scPDSI failed to capture crop yield trends in certain years, a conclusion that can be quickly drawn from the visual inspection. In all districts, the relationship between all indices and crop yield reached its peak during the wet year of 2015. Between 2004 and 2009, scPDSI deviated from the crop yield trends, showing a smooth movement without significant fluctuations, whereas CDI and SPEI responded promptly to crop yield anomalies. Figure 4: Box plots of correlation coefficients between drought indices and crop yield: (**a**) train set, (**b**) test set. Figure 5: Spatial distribution of correlation results between drought indices and crop yield.