Comparative evaluation of linear and nonlinear weather-based models for coconut yield prediction in the west coast of India.

Abstract

Coconut is a major plantation crop of coastal India. Accurate prediction of its yield is helpful for the farmers, industries and policymakers. Weather has profound impact on coconut fruit setting, and therefore, it greatly affects the yield. Annual coconut yield and monthly weather data for 2000-2015 were compiled for fourteen districts of the west coast of India. Weather indices were generated using monthly cumulative value for rainfall and monthly average value for other parameters like maximum and minimum temperature, relative humidity, wind speed and solar radiation. Different linear models like stepwise multiple linear regression (SMLR), principal component analysis together with SMLR (PCA-SMLR), least absolute shrinkage and selection operator (LASSO) and elastic net (ELNET) with nonlinear models namely artificial neural network (ANN) and PCA-ANN were employed to model the coconut yield using the monthly weather indices as inputs. The model's performance was evaluated using R2, root mean square error (RMSE) and absolute percentage error (APE). The R2 and RMSE of the models ranged between 0.45-0.99 and 18-3624 nutsha-1 respectively during calibration while during validation the APE varied between 0.12 and 58.21. The overall average ranking of the models based these performance statistics were in the order of ELNET > LASSO > ANN > SMLR > PCA-SMLR > PCA-ANN. Results indicated that the ELNET model could be used for prediction of coconut yield for the region.