Mathematical and computational techniques for sustainable agricultural development in Nepal
Year: 2025
Type: article
Abstract: Challenges facing sustainable agriculture in Nepal include limited resources, climate variability, and suboptimal land use. In this work, mathematics and computing are applied to increase agricultural productivity in a sustainable manner. A Linear Programming model was developed for land-water allocation that resulted in a 12.5% increase in crop yield. Machine learning models, especially Random Forest, achieved 92.8% accuracy in crop yield prediction and data-driven decision-making. The seasonal variations of soil moisture has been analyzed using the Soil Moisture Model. The study finally concludes that mathematics and computing serve as a resource optimization tool in building climate resiliency toward ensuring long-term food security in Nepal.
Authors: Ashok Kumar Mahato, Rahul Das, Suresh Kumar Sahani
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: hybrid
OpenAlex ID: W4411733003