Campus Units
Agronomy, Industrial and Manufacturing Systems Engineering, Bioeconomy Institute (BEI)
Document Type
Article
Publication Version
Submitted Manuscript
Publication Date
2020
Journal or Book Title
arXiv
Research Focus Area(s)
Operations Research
Abstract
The emerge of new technologies to synthesize and analyze big data with high-performance computing, has increased our capacity to more accurately predict crop yields. Recent research has shown that Machine learning (ML) can provide reasonable predictions, faster, and with higher flexibility compared to simulation crop modeling. The earlier the prediction during the growing season the better, but this has not been thoroughly investigated as previous studies considered all data available to predict yields. This paper provides a machine learning based framework to forecast corn yields in three US Corn Belt states (Illinois, Indiana, and Iowa) considering complete and partial in-season weather knowledge. Several ensemble models are designed using blocked sequential procedure to generate out-of-bag predictions. The forecasts are made in county-level scale and aggregated for agricultural district, and state level scales. Results show that ensemble models based on weighted average of the base learners outperform individual models. Specifically, the proposed ensemble model could achieve best prediction accuracy (RRMSE of 7.8%) and least mean bias error (-6.06 bu/acre) compared to other developed models. Comparing our proposed model forecasts with the literature demonstrates the superiority of forecasts made by our proposed ensemble model. Results from the scenario of having partial in-season weather knowledge reveal that decent yield forecasts can be made as early as June 1st. To find the marginal effect of each input feature on the forecasts made by the proposed ensemble model, a methodology is suggested that is the basis for finding feature importance for the ensemble model. The findings suggest that weather features corresponding to weather in weeks 18-24 (May 1st to June 1st) are the most important input features.
Copyright Owner
The Authors
Copyright Date
2020
Language
en
File Format
application/pdf
Recommended Citation
Shahhosseini, Mohsen; Hu, Guiping; and Archontoulis, Sotirios, "Forecasting Corn Yield with Machine Learning Ensembles" (2020). Industrial and Manufacturing Systems Engineering Publications. 228.
https://lib.dr.iastate.edu/imse_pubs/228
Included in
Agronomy and Crop Sciences Commons, Applied Statistics Commons, Systems Engineering Commons
Comments
This is a pre-print of the article Shahhosseini, Mohsen, Guiping Hu, and Sotirios V. Archontoulis. "Forecasting Corn Yield with Machine Learning Ensembles." arXiv preprint arXiv:2001.09055 (2020). Posted with permission.