Determination of cotton and wheat yield using the standard precipitation evaporation index in Pakistan

Abstract

This study presents an efficient approach to predict the Rabi and Kharif crop yield using a relatively new and robust machine learning (ML) model named random forest (RF). The standard precipitation evaporation index (SPEI) with different time lags (e.g., 1 to 12 months) are utilized as predictive variables. The SPEI was estimated using the climate prediction center (CPC) precipitation, and temperature dataset for the period 1981–2015 are employed. The feasibility of the RF model is validated against some other well-known ML models such as support vector regression (SVR), k-nearest neighbors (K-NN), and bagged CART models. The results showed a significant relationship between crop yields and the SPEI. The RF model showed the highest performance with the minimum values of absolute error measures (e.g., root mean square error (RMSE) and mean absolute error (MAE)) in the testing phase (0.1826–0.1383) and (0.1234–0.1092) for cotton and wheat production, respectively. Cotton yield prediction accuracy using the RF model improved compared to the SVR, K-NN, bagged CART, and ANN in terms of RMSE, and MAE indices are 12–10.79%, 12.33–10.79%, and 5.7–0.17%, respectively. Overall, the RF model provided a reliable alternative ML-based strategy for the cotton and wheat yield prediction over the Pakistan region.