Agriculture Forecasting: Predicting Commodity Producer Prices and Yield

Recently, with the rising population, accurate forecasting of agricultural commodity prices and yield has been vital to policymakers, farmers, and consumers to ensure economic stability and sustainable agricultural practices while making sure that agricultural quotas are met. Several forecasting authorities, such as the Mars Crop Yield Forecasting System (MCYFS), are responsible for maintaining accuracy within these results. However, they rely on a statistical approach rather than machine learning methods. Several advantages are found within the use of machine learning algorithms, including the ability to analyse vast amounts of data and identify patterns, which improves prediction accuracy.

This chapter explores various algorithms like Long Short-Term Memory (LSTM), Extreme Gradient Boosting (XGBoost), Prophet and Random Forest (RF) to predict producer prices and yield for chosen agricultural commodities in the European region. Through the implementation of these algorithms, this chapter aims to achieve a better view of forecasting accuracy and improved decision-making in the agricultural sector.