Maize Yield and Nitrate Loss Prediction with Machine Learning Algorithms

Thumbnail Image
Supplemental Files
Martinez-Feria, Rafael
Archontoulis, Sotirios
Major Professor
Committee Member
Journal Title
Journal ISSN
Volume Title
Shahhosseini, Mohsen
Doctorate Student / Research Assistant
Hu, Guiping
Affiliate Associate Professor
Research Projects
Organizational Units
Organizational Unit

The Department of Agronomy seeks to teach the study of the farm-field, its crops, and its science and management. It originally consisted of three sub-departments to do this: Soils, Farm-Crops, and Agricultural Engineering (which became its own department in 1907). Today, the department teaches crop sciences and breeding, soil sciences, meteorology, agroecology, and biotechnology.

The Department of Agronomy was formed in 1902. From 1917 to 1935 it was known as the Department of Farm Crops and Soils.

Dates of Existence

Historical Names

  • Department of Farm Crops and Soils (1917–1935)

Related Units

Organizational Unit
Industrial and Manufacturing Systems Engineering
The Department of Industrial and Manufacturing Systems Engineering teaches the design, analysis, and improvement of the systems and processes in manufacturing, consulting, and service industries by application of the principles of engineering. The Department of General Engineering was formed in 1929. In 1956 its name changed to Department of Industrial Engineering. In 1989 its name changed to the Department of Industrial and Manufacturing Systems Engineering.
Journal Issue
Is Version Of

Pre-season prediction of crop production outcomes such as grain yields and N losses can provide insights to stakeholders when making decisions. Simulation models can assist in scenario planning, but their use is limited because of data requirements and long run times. Thus, there is a need for more computationally expedient approaches to scale up predictions. We evaluated the potential of five machine learning (ML) algorithms 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 3 million genotype, environment and management scenarios. Random forests most accurately predicted maize yield and N loss at planting time, with a RRMSE of 14% and 55%, respectively. ML meta-models reasonably reproduced simulated maize yields 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. Averaged across all ML models, weather conditions, soil properties, management information and initial conditions were roughly equally important when predicting yields. 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.


This article is published as Shahhosseini, Mohsen, Rafael A. Martinez-Feria, Guiping Hu, and Sotirios V. Archontoulis. "Maize yield and nitrate loss prediction with machine learning algorithms." Environmental Research Letters 14, no. 12 (2019): 124026. DOI: 10.1088/1748-9326/ab5268. Posted with permission.

Tue Jan 01 00:00:00 UTC 2019