## 5 Results and Discussion
### Future Directions, Recommendations, and Practical Applications  
Although the CDI developed in this study was created using innovative algorithms, it has certain limitations. The winter wheat yield values used in the study are limited to 20 years. Developing models with more extensive data is encouraged, noting that remote sensing time series are often limited in duration. Additionally, it has not been investigated whether the remote sensing data used in the study area require any additional corrections; the observations used are derived from established global algorithms. Researchers are strongly advised to explore this aspect and incorporate the need for corrections between the remote sensing data and ground observations in their study areas. In addition to improving temporal resolution, enhancements in spatial resolution could help with yield modeling  
Figure 13: Dependences plots obtained after SHAP analysis of single indices used to calculate CDI.  
in individual fields. The present study's XGBoost hyperparameters optimization was challenging, and grid search algorithm addressed this challenge. Nevertheless, future research should investigate the potential of advanced heuristic methods. Given that XGBoost was employed for the construction of predictive models, TreeSHAP was utilized. In the context of larger datasets and broader regions, it is possible that deep learning models and the KernelSHAP method may yield superior results.  
The model inputs include remote sensing datasets containing information on temperature, precipitation, soil moisture, vegetation condition, plant water status, and primary productivity. However, the study did not use evapotranspiration (ET) values. Future studies could benefit from incorporating the Evaporative Stress Index [118; 119], which represents the ratio of potential ET to actual ET. Moreover, variables representing global ocean-atmosphere interactions, such as El Nino and La Nina, could further improve CDI calculations if they include independent information from the indices explored here.  
Our study focused on one of the largest grain-producing regions in Turkiye. The primary motivation for this work was the fact that the area predominantly practices rainfed agriculture, as crops grown in rain-fed areas tend to respond more sensitively to meteorological variables. In regions where irrigation data and the genetic characteristics of the crops used are available, it is recommended that these factors be included in the study to develop even more advanced indices.  
Just as drought affects all sectors, the sector where its direct impacts are often most clearly seen is agriculture [120]. Among the most important stakeholders in drought studies are companies operating in the agricultural sector, local producers, and traders involved in the sale and marketing of agricultural products [121]. We hope that this study will contribute to the development of agricultural drought monitoring systems at the local level to enable decision-makers to address complex drought monitoring processes in greater detail and accelerate effective decision making.  
In the USA, a country known for its advanced drought monitoring systems, CDIs are being developed using similar applications [122]. Various CDIs can be generated using different combinations of inputs. Countries like Turkiye, where agricultural products constitute a significant share of the gross national product, could improve decision-making processes by utilizing CDI-like indices. Since all the inputs used in creating CDI indices are obtained from free sources and are continuously updated, this study is valuable for the development of online drought monitoring systems that provide local-scale information to end-users.