## 6 Conclusions  
The rising world population and the consequent increase in demand for reliable food supplies and also the impact of climate change on food production have rekindled concerns about food security in many parts of the world. In many areas that lack sufficient water resources, rainfed farms are the main source of food and income, but these farms are being increasingly affected by climate change. Predicting the yields of these farms based on climate and remote sensing data can greatly guide government policies and help farmers and agriculture-related businesses make better decisions regarding resource procurement and allocation. In this study, the yield of rainfed chickpea farms in 11 top chickpea producing counties in Kermanshah province of Iran was predicted using SVR, RF, and KNN. The predictions were then evaluated in terms of multiple measures. Given the sensitivity of rainfed chickpea yield to the time of data, the predictions were made in two scenarios: (1) using the averages of the data of all growing months; and (2) using the data of a combination of months with the highest correlation with the yield. A total of 16 factors that tend to affect plant growth were collected from weather and remote sensing data, and an integrated database of these factors was formed. To improve prediction accuracy, the raster maps of rainfed farms were merged with slope and altitude maps of the province. Overall, RF showed the highest prediction accuracy in terms of all computed validation measures. The obtained predictions were only 7-8% different from the statistics reported by the Statistical Center and the Ministry of Agriculture of Iran, indicating that machine learning methods can indeed be a reliable choice for crop yield modeling. In future studies, it could be worthwhile to examine the effect of genotype and seed type of spring and autumn chickpea cultivated in local farms and also the soil type of these farms on the yield prediction at farm and regional level and also to explore the economic dimension of this discussion. Uncertainties of this study are related to amount of rainfall in the future which has been affected by global warming and environmental issues in the past two decades all over the world specially in Iran. We tried to predict production of chickpea in Kermanshah Province as one of the main producers of this product in Iran. Results of this research could help government policy makers to have an accurate statistic of chickpea. It is essential for import and export too.  
Conceptualization, S.R.; Methodology, S.R.; Software, E.J.; Validation, A.M., M.J.; Formal analysis, E.J.; Investigation, A.M.; Resources, A.I. and M.R.; Data curation, A.I. and M.J.; Writing--original draft preparation, S.R.; Writing--review and editing, S.C. and A.I.; Visualization,M.R.; Supervision, A.M.; Project administration, K.T.; Funding acquisition, S.C. and K.T. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
This research was supported by the Prince of Songkla University from grant number-ENV6402012N.  
The authors declare no conflict of interest.  
Researchers in the future can use different crops for prediction, use other techniques of prediction, and investigate other locations for chickpea and other products.