| dc.description.abstract | A reliable and accurate forecasting method for crop yields is very important for the
farmer, the economy of a country, and the agricultural stakeholders. However, due to
weather extremes and uncertainties as a result of increasing climate change, most crop
yield forecasting models are not reliable and accurate. In this paper, a hybrid crop
yield probability density forecasting method via quantile regression forest and Epanechnikov kernel function (QRF-SJ) is proposed to capture the uncertainties and extremes of
weather in crop yield forecasting. By assigning probability to possible crop yield values,
probability density forecast gives a complete description of the yield of crops. 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 proposed model is
able to capture the nonlinearity between crop yield and the weather variables via random
forest. The values of prediction interval coverage probability and prediction interval normalized average width for the two crops show that the constructed prediction intervals
cover the target values with perfect 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 is the first of its kind in literature | en_US |
