Predicting County Level Corn Yields Using Deep Long Short Term Memory Models

TL;DR

This paper introduces the first use of LSTM neural networks for county-level corn yield prediction, leveraging large-scale weather and yield data to improve forecast accuracy over traditional survey methods.

Contribution

It is the first to apply deep LSTM models to county-level corn yield prediction, demonstrating their effectiveness with large, complex datasets.

Findings

LSTM models outperform traditional survey-based methods in predictive accuracy.

Deep learning captures complex temporal relations in weather and yield data.

County-level predictions in Iowa show promising results.

Abstract

Corn yield prediction is beneficial as it provides valuable information about production and prices prior the harvest. Publicly available high-quality corn yield prediction can help address emergent information asymmetry problems and in doing so improve price efficiency in futures markets. This paper is the first to employ Long Short-Term Memory (LSTM), a special form of Recurrent Neural Network (RNN) method to predict corn yields. A cross sectional time series of county-level corn yield and hourly weather data made the sample space large enough to use deep learning technics. LSTM is efficient in time series prediction with complex inner relations, which makes it suitable for this task. The empirical results from county level data in Iowa show promising predictive power relative to existing survey based methods.