Maize yield and nitrate loss prediction with machine learning algorithms

Bibliographic Data

ID15545971
AuthorsMohsen Shahhosseini (0000-0001-9588-1143, Iowa State University), Rafael A Martinez‐Feria (0000-0002-4230-5684, Iowa State University), Guiping Hu (0000-0003-0258-3986, Iowa State University, corresponding author), Sotirios V Archontoulis (0000-0001-7595-8107, Iowa State University)
Year2019
Volume14
Issue12
Pages124026-124026
Publication date2019-10-29
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueEnvironmental Research Letters (JOURNAL)
Journal identifiersISSN: 1748-9326 • E-ISSN: 1748-9326
PublisherIOP Publishing (PUBLISHER • GB)
DOI10.1088/1748-9326/ab5268
OpenAlexW2982418982
LanguageEN
Citations received9
References cited50

Pre-growing season prediction of crop production outcomes such as grain yields and nitrogen (N) losses can provide insights to farmers and agronomists to make decisions. Simulation crop models can assist in scenario planning, but their use is limited because of data requirements and long runtimes. Thus, there is a need for more computationally expedient approaches to scale up predictions. We evaluated the potential of four machine learning (ML) algorithms (LASSO Regression, Ridge Regression, random forests, Extreme Gradient Boosting, and their ensembles) as meta-models for a cropping systems simulator (APSIM) to inform future decision support tool development. We asked: (1) How well do ML meta-models predict maize yield and N losses using pre-season information? (2) How many data are needed to train ML algorithms to achieve acceptable predictions? (3) Which input data variables are most important for accurate prediction? And (4) do ensembles of ML meta-models improve prediction? The simulated dataset included more than three million data including genotype, environment and management scenarios. XGBoost was the most accurate ML model in predicting yields with a relative mean square error (RRMSE) of 13.5%, and Random forests most accurately predicted N loss at planting time, with a RRMSE of 54%. ML meta-models reasonably reproduced simulated maize yields using the information available at planting, but not N loss. They also differed in their sensitivities to the size of the training dataset. Across all ML models, yield prediction error decreased by 10%–40% as the training dataset increased from 0.5 to 1.8 million data points, whereas N loss prediction error showed no consistent pattern. ML models also differed in their sensitivities to input variables (weather, soil properties, management, initial conditions), thus depending on the data availability researchers may use a different ML model. Modest prediction improvements resulted from ML ensembles. These results can help accelerate progress in coupling simulation models and ML toward developing dynamic decision support tools for pre-season management

Agronomy · Algorithm · Crop simulation model · Crop yield · Cropping · Machine learning · Predictive modelling · Random forest · Scale (ratio · Statistics · Yield (engineering · Climate change impacts on agriculture · Computer Science · Crop Yield and Soil Fertility · Genetics and Plant Breeding · Mathematics · Artificial Intelligence

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