Author(s): Shagun Sharma and Monika Sohlot

Abstract: The concept of applications of fertilizers based on soil testing gives a measure of the availability of nutrients to the immediate crops. Identifying major constraints and building up an integrated nutrient management (INM) model for the farmer in soil improvement and crop yield, an INM model was implemented on the major rabi crops wheat, mustard, cluster bean and pearl millet during the years 2021-23. On farm trials were conducted at different locations; Khaliyawas (28.1993° N, 76.7668° E), Dungarwas (28.1955° N, 76.7195° E) and Nikhri (28.1862° N, 76.7300° E) villages with application of inorganic fertilizers and vermicompost with inoculation of biofertilizers. Results obtained from the study revealed that integrated nutrient management on soil test values gives the highest yield percentage increased by (37.20%) in cluster bean as compared to control, followed by increased percentage in pearl millet (36.40%), wheat (20%) and mustard (17.52%). Furthermore, an artificial intelligence-based random forest prediction was also considered to predict the correlation between agri-inputs with the observed crop productivity. The model predicted the positive strong and moderate correlation of inputs and have validated the observations from experimental fields for crop improvement. Hence, the beneficial response of the INM model has been successfully achieved with AI-driven model in view of productivity and sustainable crop development.

DOI: 10.22271/27889289.2025.v5.i2d.224

Pages: 315-319 | Views: 82 | Downloads: 40

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