  A reliable and accurate forecasting model for crop yields is of crucial
importance for efficient decision-making process in the agricultural sector.
However, due to weather extremes and uncertainties, most forecasting models for
crop yield are not reliable and accurate. For measuring the uncertainty and
obtaining further information of future crop yields, a probability density
forecasting model based on quantile random forest and Epanechnikov kernel
function (QRF-SJ) is proposed. The nonlinear structure of random forest is
applied to change the quantile regression model for building the probabilistic
forecasting model. Epanechnikov kernel function and solve-the equation plug-in
approach of Sheather and Jones are used in the kernel density estimation. A
case study using the annual crop yield of groundnut and millet in Ghana is
presented to illustrate the efficiency and robustness of the proposed
technique. The values of the prediction interval coverage probability and
prediction interval normalized average width for the two crops show that the
constructed prediction intervals capture the observed yields with high coverage
probability. The probability density curves show that QRF-SJ method has a very
high ability to forecast quality prediction intervals with a higher coverage
probability. The feature importance gave a score of the importance of each
weather variable in building the quantile regression forest model. The farmer
and other stakeholders are able to realize the specific weather variable that
affect the yield of a selected crop through feature importance. The proposed
method and its application on crop yield dataset are the first of its kind in
literature.
