ArticleScientific reports2021
Coupling machine learning and crop modeling improves crop yield prediction in the US Corn Belt.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
47 citing papers in PubMed.
- Sequential Soybean Phenotypes for Grain Yield Prediction in Reciprocal Two-Season Validation.Plants (Basel, Switzerland) · 2026Article
- Harnessing artificial intelligence in plant breeding: innovations in digital phenotyping and breeding methodologies.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Review
- Biochar and zeolite as carriers of PGPB strains: impact on the physiology, productivity and quality characteristics of industrial tomato.Scientific reports · 2026Article
- Sustainable regional corn yield prediction for the United States through interpretable machine learning approach.Scientific reports · 2026Article
- A Robust Lemuria Framework for efficient crop prediction.Scientific reports · 2026Article
- A machine learning-coupled APSIM model pipeline for projected oil palm yield in Surat Thani, Thailand.PloS one · 2026Article
- Article
- Deployment-oriented evaluation of stage-specific information for rice yield prediction across years, sites and site-year environments in southern China.Frontiers in plant science · 2026Article
- Recent Trends in Machine Learning, Deep Learning, Ensemble Learning, and Explainable Artificial Intelligence Techniques for Evaluating Crop Yields Under Abnormal Climate Conditions.Plants (Basel, Switzerland) · 2025Review
- Article
- Gaps and strategies for accurate simulation of waterlogging impacts on crop productivity.Nature food · 2025Review
- Genomic selection: Essence, applications, and prospects.The plant genome · 2025Review
- Enhancing Genomic Prediction Accuracy of Reproduction Traits in Rongchang Pigs Through Machine Learning.Animals : an open access journal from MDPI · 2025Article
- Optimizing chickpea yield prediction under wilt disease through synergistic integration of biophysical and image parameters using machine learning models.Scientific reports · 2025Article
- Comparative assessment of empirical and hybrid machine learning models for estimating daily reference evapotranspiration in sub-humid and semi-arid climates.Scientific reports · 2025Article
- A data-driven crop model for biomass sorghum growth process simulation.Frontiers in plant science · 2025Article
- Article
- Analyses of crop yield dynamics and the development of a multimodal neural network prediction model with G×E×M interactions.Frontiers in plant science · 2025Article
- Landscape structure, climate variability, and soil quality shape crop biomass patterns in agricultural ecosystems of Bavaria.Frontiers in plant science · 2025Article
- A bi-stage data-driven process-based model for sorghum breeding and yield prediction: coupling explainable artificial intelligence and crop modeling.Frontiers in plant science · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
This study investigates whether coupling crop modeling and machine learning (ML) improves corn yield predictions in the US Corn Belt. The main objectives are to explore whether a hybrid approach (crop modeling + ML) would result in better predictions, investigate which combinations of hybrid models provide the most accurate predictions, and determine the features from the crop modeling that are most effective to be integrated with ML for corn yield prediction. Five ML models (linear regression, LASSO, LightGBM, random forest, and XGBoost) and six ensemble models have been designed to address the research question. The results suggest that adding simulation crop model variables (APSIM) as input features to ML models can decrease yield prediction root mean squared error (RMSE) from 7 to 20%. Furthermore, we investigated partial inclusion of APSIM features in the ML prediction models and we found soil moisture related APSIM variables are most influential on the ML predictions followed by crop-related and phenology-related variables. Finally, based on feature importance measure, it has been observed that simulated APSIM average drought stress and average water table depth during the growing season are the most important APSIM inputs to ML. This result indicates that weather information alone is not sufficient and ML models need more hydrological inputs to make improved yield predictions.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.