Prediction of winter wheat yield at county level in China using ensemble learning

Abstract

Since increasing food demand and continuous reduction of available farmland, reliable and near-real-time wheat yield forecasts are essential to ensure regional and global food supplies. Although the crop model has been widely used in yield estimation, its applicability in large-scale yield prediction is limited due to the large amount of data required for parameterization. We took the main winter wheat growing areas in China and developed an ensemble learning framework based on seven machine learning algorithms, such as extreme gradient boosting, random forest, and support vector regression. The model used satellite vegetation index time series, climate, soil properties, and elevation data to provide county-level winter wheat yield forecasts from 2001 to 2015. The results showed that the ensemble explained 86% of the yield variability, which outperformed all base learners. By calculating the correlation between the prediction results of the base learners, we believed that the prediction performance of ensemble learning still has the potential for improvement. Soil properties and elevation data effectively improved the performance of the model because they contained information about yield prediction that could not be fully captured by vegetation index and climate data. As the growing season went on, the unique contribution of increasing climate data to yield forecasts was always more than that of vegetation index, especially in the early growing season. Furthermore, we evaluated the model’s ability to perform within-season prediction, and the model achieved satisfactory prediction accuracy 2 months before harvest (R2 = 0.85, RMSE = 480 kg/ha, MAPE = 7.52%). The framework of yield forecast established in this research can be applied to other crop varieties and regions and provide stakeholders with sufficiently accurate yield predictions.

Ensemble learning based on remote sensing data for monitoring agricultural drought in major winter wheat-producing areas of China

“…The ET model can determine the importance of variables based on the contribution of each variable to the model’s prediction (Zhang et al, 2022b). We estimated remotely-sensed drought factors’ importance for predicting 1-, 3-, and 6-month SPEIs by the ET model based on the datasets of all stations and the corresponding stations of each cluster, respectively (Figure 5).…”

“…Therefore, it is crucial to increase the model's robustness to make it suitable for drought prediction in different regions. Stacking is an ensemble approach that combines the strengths of various models and compensates for the shortcomings of just one model (Zhang et al, 2022b). In this study, selected heterogeneous models were integrated as base learners for the Stacking model to learn the advantages of each base learner for better results.…”

Time Series Forecasting and Modeling of Food Demand Supply Chain Based on Regressors Analysis

“…Forecasting the demand for any commodity is one of the most important aspects of preventing wastage in any form. Zhang et al [7] predicted wheat production forecast on a country level using ensemble learning. I. Shah et al [8,9,10,11,12] and N.Bibi et al [13] have suggested many methods to predict the electricity demands and prices for various times, i.e., short term, medium-term, and long-term as well.…”

Estimating Maize Yield from 2001 to 2019 in the North China Plain Using a Satellite-Based Method

“…In recent years, machine learning algorithms have become more popular, particularly deep neural networks, to forecast crop yields. Studies have shown that these algorithms can predict county-level yields up to four months in advance of harvest [18][19][20]. However, it needs a lot of historical data to be trained, and the outcomes of the training on a particular period and region cannot be extended to other times and regions [21,22].…”