Agricultural Information Transfer among Tribal Farmers of Malkangiri Using Machine Learning
DOI:
https://doi.org/10.48165/IJEE.2026.6225Keywords:
Agricultural information transfer, Machine learning, Random forest, SHAP values, Tribal farmersAbstract
The study was carried out in 2026 to examine the extent of agricultural information transfer and the determinants of agricultural information transfer among tribal farmers of Malkangiri district of Odisha. Four blocks were selected purposively, and 30 farmers were randomly selected from each block through a multistage random sampling technique, constituting a total sample of 120 respondents. Primary data were collected using a pre-tested and structured interview schedule. Data were analysed using descriptive statistics random forest regression algorithm in Google Colaboratory, with SHapley Additive explanations values computed for explainable feature-level attribution. The agricultural information transfer was low to moderate across all five channels, ranging from the highest mean score for personal locality contact (1.96) to the lowest for private sector engagement (1.18; gap = 60.76%). Experience on farms and age were the most significant predictors, together explaining 66.77 per cent of the model’s feature importance, with education having little influence. The training performance of the random forest model was good (R² = 0.9159), while it acknowledges the 0.4877 gap and attributes it to sample size and RF’s sensitivity.
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