Author

Sandeep Dhakal and Ajaya Dhungana

Abstract

Accurate prediction of agricultural yield is extremely important to
ensure food security and to cope with the challenges created by climate change and natural disasters. Forecasting agricultural yield is
a challenging task due to the complex nature of variables (fertiliser,
rainfall, temperature and others) that affect agricultural production.
This study employs six supervised machine learning algorithms:
Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Multi-Layer Perceptron (MLP), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN) to build a
predictive model using 49 years of historical data (1973-2021) on
paddy, wheat, and maize. Model performance was evaluated using
Mean Squared Error (MSE), Mean Absolute Error (MAE), Mean
Absolute Percentage Error (MAPE), and Root Mean Squared Error (rMSE). Results show that DT and RF models are the most
precise with MSE 1% to 5%, MAE of 8% to 21%, followed by
SVM and CNN. Key predictors of crop yield include area cultivated,
capital expenditure, banking expansion, rainfall, temperature, and
fertilizers, while irrigation and road network were less significant.
The study recommends that farmers prioritize commercial farming, agricultural equipment, and timely available of fertilizer application. The Government of Nepal (GoN) should redirect subsidies
towards agricultural mechanization, ensure timely supply fertilizer,
and expand banking services in agricultural areas.