# Crop yield probability density forecasting via quantile random forest and Epanechnikov Kernel function

## TL;DR

This paper introduces a novel probabilistic crop yield forecasting model using quantile random forest and Epanechnikov kernel, demonstrating high accuracy and reliability in predicting yields and their uncertainties for groundnut and millet in Ghana.

## Contribution

The paper presents the first application of a probability density forecasting model combining quantile random forest and Epanechnikov kernel for crop yields, enhancing uncertainty quantification.

## Key findings

- Prediction intervals have high coverage probability.
- The model accurately captures yield distributions.
- Feature importance identifies key weather variables.

## Abstract

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.