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This research is based on the research gaps in the climate sector and their impacts on rice productivity. From this question, this research aims to identify the main topics and best scenarios to achieve climate mitigation and optimal productivity. The method used is Systematic Literature Review (SLR), searching and reviewing Scopus journals with the keywords: climate, agriculture, and sumatra. Filtering journals using this method has resulted in 52 indexed journals. The result of this study is the discovery of gaps in climate research, especially the correlation of climate phenomena such as El Nino to rice productivity in several regions such as West Sumatra. This region, classified as the west coast of Sumatra Island, contains a minimum of related research topics. Therefore, researching this topic is highly recommended for climate mitigation and maximum productivity achievement.

Whenever there is a relationship between the explanatory variables (X_S). This relationship causes multicollinearity which in turn leads to inaccurate and bias estimations of the model parameters. Therefore, this results in high discrepancy that influences the next phase of the statistical inference where (OLS), method loses its features having the lowest variance. Consequently, this paper concerns itself with figuring out methods that can be applied by researchers and those who are interested in this field to overcome this problem using (Ridge) method. Moreover, the paper seeks to solve other problems such as the loss of normal distribution property or abnormalility by means of methodical means including (Ridge) and (Robust Ridge). However this study is applied through simulation experiments aim at producing the data of the model. Based on these experiments and tests, the research has come up with the result that (Robust Ridge) is the best method that might be employed to solve the problem of has both normal and abnormal data for the estimation of the parameters of the Logistic Regression Model.