Comparison of deep learning models for milk production forecasting at national scale

Abstract

Forecasting of agricultural variables at national scale is essential for developing policies aimed at guaranteeing food security and increase the stability of the entire agri-food chain. However, this is often a challenging task due to the fact that data are usually sparse and long records are rarely available. This work aims at evaluating the potentiality of ten deep learning models for predicting monthly milk production in France, Germany, and Italy, using as input, climatic and economic variables from open datasets. The results indicate that deep learning models are a better alternative to traditional statistical approaches, providing robust results without requiring complex model architectures. Furthermore, the prominent autoregressive nature of milk production highlights the inability of environmental variables to capture external processes that influence the milk production (e.g., milk quota). However, the high accuracy achieved by the models lays the ground for possible application of deep learning models to accurately forecast agricultural variables at national scale with potential implications for the development of dairy insurance products, and risk management practices.