The Application of the Binary Logistic Regression Model in Examining the Effects of Input Factor Allocation on Tangerine Production: Empirical Evidence from Selayar Islands Regency, Indonesia

This study aimed to examine the effects of the input factor allocation on tangerine production in Selayar Islands Regency, South Sulawesi Province. Binary logistic regression was the data analysis method utilized to achieve the research objective. Based on the findings, it was found that the factors of length of education, farmer group membership, farm distance, labor, pesticides, herbicides, urea fertilizer, manure, and NPK fertilizer all had a significant impact on the output of tangerines in farming. Factors such as education duration, participation in farmer groups, labor availability, and the use of herbicides, urea fertilizer, NPK fertilizer, and manure all positively and significantly impact tangerine farm production. However, the distance between farms and insecticide application imposes a substantial negative effect on tangerine yields. These results suggest that the tangerine farmers at the research site should increase their production by learning farming management, joining farmer groups, cultivating additional land, and using more urea, NPK, and manure. Pesticide application optimization can also boost tangerine production. In addition, farmers need a mix of conventional and organic fertilizers and limited pesticide use to stay productive.