Research graph
References from Sequential Soybean Phenotypes for Grain Yield Prediction in Reciprocal Two-Season Validation. Local targets link to admitted publications; unresolved targets remain external evidence.
Effects of the El Niño Southern Oscillation phenomenon and sowing dates on soybean yield and on the occurrence of extreme weather events in southern Brazil
10.1016/j.agrformet.2020.108038 · 2020 · External reference
Soybean maturity groups and sowing dates to minimize ENSO and extreme weather events effects on yield variability in the Southeastern US
10.1016/j.agrformet.2022.109104 · 2022 · External reference
Soybean yield and water productivity gaps associate with ENSO-dependent effects of fungicide, sowing date and maturity group
10.1016/j.eja.2024.127133 · 2024 · External reference
Impact assessment of soybean yield and water productivity in Brazil due to climate change
10.1016/j.eja.2021.126329 · 2021 · External reference
Defining soybean maturity group options for contrasting weather scenarios in the American Southern Cone
10.1016/j.fcr.2022.108676 · 2022 · External reference
Soybean yield and crop stage response to planting date and cultivar maturity in Iowa, USA
10.1002/agj2.20053 · 2020 · External reference
Genotypic and phenotypic parameters associated with early maturity in soybean
10.1590/s1678-3921.pab2022.v57.02545 · 2022 · External reference
Genotype x environment interaction analysis of soybean (Glycine max (L.) Merrill) grain yield across production environments in Southern Africa
10.1016/j.fcr.2020.107922 · 2020 · External reference
Assessment of sowing dates and plant densities using CSM-CROPGRO-Soybean for soybean maturity groups in low latitude
10.1017/s0021859621000204 · 2020 · External reference
10.3390/atmos17070701
10.3390/atmos17070701 · External reference
Are soybean models ready for climate change food impact assessments?
10.1016/j.eja.2022.126482 · 2022 · External reference
Maximizing soybean yield by understanding planting date, maturity group, and seeding rate interactions in North Carolina
10.1002/csc2.20603 · 2021 · External reference
10.3390/agronomy16141396
10.3390/agronomy16141396 · External reference
Soybean yield response to management practices (4–40 years) and soil health parameters
10.1016/j.fcr.2025.109959 · 2025 · External reference
Key management practices driving soybean yield variability in lowland fields of southern Brazil
10.1002/agj2.70334 · 2026 · External reference
10.3389/fpls.2020.624273
10.3389/fpls.2020.624273 · External reference
10.3390/rs13224632
10.3390/rs13224632 · External reference
10.3390/rs15174286
10.3390/rs15174286 · External reference
UAV-based aerial phenotyping to assess key morphophysiological traits and yield in soybean
10.1016/j.atech.2025.101276 · 2025 · External reference
10.3390/rs17040690
10.3390/rs17040690 · External reference
10.1186/s12870-022-03559-z
10.1186/s12870-022-03559-z · External reference
10.3389/fpls.2021.719706
10.3389/fpls.2021.719706 · External reference
High-throughput field phenotyping of soybean: Spotting an ideotype
10.1016/j.rse.2021.112797 · 2022 · External reference
Evaluating differences among crop models in simulating soybean in-season growth
10.1016/j.fcr.2024.109306 · 2024 · External reference
Soybean photosynthesis and crop yield are improved by accelerating recovery from photoprotection
10.1126/science.adc9831 · 2022 · External reference
Leaf area reduction during the pod set period changes the photomorphogenic light balance and increases the pod number and yield in soybean canopies
10.1016/j.fcr.2023.109148 · 2023 · External reference
Redefining soybean critical period for yield determination
10.1016/j.fcr.2024.109662 · 2025 · External reference
Reductions in leaf area index, pod production, seed size, and harvest index drive yield loss to high temperatures in soybean
10.1093/jxb/erac503 · 2023 · External reference
Morpho-physiological traits associated with drought responses in soybean
10.1002/csc2.20314 · 2021 · External reference
Quantifying the physiological, yield, and quality plasticity of Southern USA soybeans under heat stress
10.1016/j.stress.2023.100195 · 2023 · External reference
10.3389/fphgy.2025.1591146
10.3389/fphgy.2025.1591146 · External reference
10.3390/rs13050977
10.3390/rs13050977 · External reference
10.3390/rs15010007
10.3390/rs15010007 · External reference
End-to-end 3D CNN for plot-scale soybean yield prediction using multitemporal UAV-based RGB images
10.1007/s11119-023-10096-8 · 2024 · External reference
Beyond assimilation of leaf area index: Leveraging additional spectral information using machine learning for site-specific soybean yield prediction
10.1016/j.agrformet.2024.110022 · 2024 · External reference
Satellite-based soybean yield forecast: Integrating machine learning and weather data for improving crop yield prediction in southern Brazil
10.1016/j.agrformet.2019.107886 · 2020 · External reference
Machine learning for soybean yield forecasting in Brazil
10.1016/j.agrformet.2023.109670 · 2023 · External reference
Satellite-based soybean yield prediction in Argentina: A comparison between panel regression and deep learning methods
10.1016/j.compag.2024.108978 · 2024 · External reference
Yield prediction by machine learning from UAS-based multi-sensor data fusion in soybean
10.1186/s13007-020-00620-6 · 2020 · External reference
10.3390/rs14092256
10.3390/rs14092256 · External reference
Bridging the gap between crop breeding and GeoAI: Soybean yield prediction from multispectral UAV images with transfer learning
10.1016/j.isprsjprs.2024.03.015 · 2024 · External reference
10.3389/fpls.2021.791256
10.3389/fpls.2021.791256 · External reference
Simultaneous corn and soybean yield prediction from remote sensing data using deep transfer learning
10.1038/s41598-021-89779-z · 2021 · External reference
From rows to yields: How foundation models for tabular data simplify crop yield prediction
10.1038/s41598-026-50338-z · 2026 · External reference
Crop yield prediction using machine learning: A systematic literature review
10.1016/j.compag.2020.105709 · 2020 · External reference
Coupling machine learning and crop modeling improves crop yield prediction in the US Corn Belt
10.1038/s41598-020-80820-1 · 2021 · External reference
A surrogate model based on feature selection techniques and regression learners to improve soybean yield prediction in southern France
10.1016/j.compag.2021.106578 · 2022 · External reference
Critical evaluation of the effects of a cross-validation strategy and machine learning optimization on the prediction accuracy and transferability of a soybean yield prediction model using UAV-based remote sensing
2024 · External reference
Winter wheat yield prediction using convolutional neural networks from environmental and phenological data
10.1038/s41598-022-06249-w · 2022 · External reference
Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure
10.1111/ecog.02881 · 2017 · External reference
Spatial validation reveals poor predictive performance of large-scale ecological mapping models
10.1038/s41467-020-18321-y · 2020 · External reference
Cross validation for model selection: A review with examples from ecology
10.1002/ecm.1557 · 2023 · External reference
Cross-validation pitfalls when selecting and assessing regression and classification models
10.1186/1758-2946-6-10 · 2014 · External reference
Leakage and the reproducibility crisis in machine-learning-based science
10.1016/j.patter.2023.100804 · 2023 · External reference
Improving generalisability and transferability of machine-learning-based maize yield prediction model through domain adaptation
10.1016/j.agrformet.2023.109652 · 2023 · External reference
Improving spatial transferability of deep learning models for small-field crop yield prediction
10.1016/j.ophoto.2024.100064 · 2024 · External reference
Predictive drivers and transferability of multi-scale machine learning based crop yield prediction under drought across European and Asian climates
10.1016/j.agwat.2026.110254 · 2026 · External reference
10.1186/s12916-019-1466-7
10.1186/s12916-019-1466-7 · External reference
Assessing the Performance of Prediction Models
10.1097/ede.0b013e3181c30fb2 · 2010 · External reference
Minimum sample size for developing a multivariable prediction model: Part I—Continuous outcomes
10.1002/sim.7993 · 2019 · External reference
Evaluation of clinical prediction models (part 2): How to undertake an external validation study
10.1136/bmj-2023-074820 · 2024 · External reference
A data-driven simulation platform to predict cultivars’ performances under uncertain weather conditions
10.1038/s41467-020-18480-y · 2020 · External reference
Proposal and extensive test of a calibration protocol for crop phenology models
10.1007/s13593-023-00900-0 · 2023 · External reference
Reproductive stage superiority in irrigation scheduling: UAV spectral mechanisms validated by field canopy architecture for soybean yield prediction
10.1016/j.fcr.2025.110230 · 2026 · External reference
10.3390/agriculture16141548
10.3390/agriculture16141548 · External reference
Unresolved reference
External reference
Regularization paths for generalized linear models via coordinate descent
10.18637/jss.v033.i01 · 2010 · External reference
Regularization and Variable Selection Via the Elastic Net
10.1111/j.1467-9868.2005.00503.x · 2005 · External reference
PLS-regression: A basic tool of chemometrics
10.1016/s0169-7439(01)00155-1 · 2001 · External reference
A concordance correlation coefficient to evaluate reproducibility
10.2307/2532051 · 1989 · External reference
Soybean yield and crop stage response to planting date and cultivar maturity in Iowa, USA
10.1002/agj2.20053 · ExternalCitation · doi-reference
Key management practices driving soybean yield variability in lowland fields of southern Brazil
10.1002/agj2.70334 · ExternalCitation · doi-reference
Morpho-physiological traits associated with drought responses in soybean
10.1002/csc2.20314 · ExternalCitation · doi-reference
Maximizing soybean yield by understanding planting date, maturity group, and seeding rate interactions in North Carolina
10.1002/csc2.20603 · ExternalCitation · doi-reference
Cross validation for model selection: A review with examples from ecology
10.1002/ecm.1557 · ExternalCitation · doi-reference
Minimum sample size for developing a multivariable prediction model: Part I—Continuous outcomes
10.1002/sim.7993 · ExternalCitation · doi-reference
End-to-end 3D CNN for plot-scale soybean yield prediction using multitemporal UAV-based RGB images
10.1007/s11119-023-10096-8 · ExternalCitation · doi-reference
Proposal and extensive test of a calibration protocol for crop phenology models
10.1007/s13593-023-00900-0 · ExternalCitation · doi-reference
Satellite-based soybean yield forecast: Integrating machine learning and weather data for improving crop yield prediction in southern Brazil
10.1016/j.agrformet.2019.107886 · ExternalCitation · doi-reference
Effects of the El Niño Southern Oscillation phenomenon and sowing dates on soybean yield and on the occurrence of extreme weather events in southern Brazil
10.1016/j.agrformet.2020.108038 · ExternalCitation · doi-reference
Soybean maturity groups and sowing dates to minimize ENSO and extreme weather events effects on yield variability in the Southeastern US
10.1016/j.agrformet.2022.109104 · ExternalCitation · doi-reference
Improving generalisability and transferability of machine-learning-based maize yield prediction model through domain adaptation
10.1016/j.agrformet.2023.109652 · ExternalCitation · doi-reference
Machine learning for soybean yield forecasting in Brazil
10.1016/j.agrformet.2023.109670 · ExternalCitation · doi-reference
Beyond assimilation of leaf area index: Leveraging additional spectral information using machine learning for site-specific soybean yield prediction
10.1016/j.agrformet.2024.110022 · ExternalCitation · doi-reference
Predictive drivers and transferability of multi-scale machine learning based crop yield prediction under drought across European and Asian climates
10.1016/j.agwat.2026.110254 · ExternalCitation · doi-reference
UAV-based aerial phenotyping to assess key morphophysiological traits and yield in soybean
10.1016/j.atech.2025.101276 · ExternalCitation · doi-reference
Crop yield prediction using machine learning: A systematic literature review
10.1016/j.compag.2020.105709 · ExternalCitation · doi-reference
A surrogate model based on feature selection techniques and regression learners to improve soybean yield prediction in southern France
10.1016/j.compag.2021.106578 · ExternalCitation · doi-reference
Satellite-based soybean yield prediction in Argentina: A comparison between panel regression and deep learning methods
10.1016/j.compag.2024.108978 · ExternalCitation · doi-reference
Impact assessment of soybean yield and water productivity in Brazil due to climate change
10.1016/j.eja.2021.126329 · ExternalCitation · doi-reference
Are soybean models ready for climate change food impact assessments?
10.1016/j.eja.2022.126482 · ExternalCitation · doi-reference
Soybean yield and water productivity gaps associate with ENSO-dependent effects of fungicide, sowing date and maturity group
10.1016/j.eja.2024.127133 · ExternalCitation · doi-reference
Genotype x environment interaction analysis of soybean (Glycine max (L.) Merrill) grain yield across production environments in Southern Africa
10.1016/j.fcr.2020.107922 · ExternalCitation · doi-reference
Defining soybean maturity group options for contrasting weather scenarios in the American Southern Cone
10.1016/j.fcr.2022.108676 · ExternalCitation · doi-reference
Leaf area reduction during the pod set period changes the photomorphogenic light balance and increases the pod number and yield in soybean canopies
10.1016/j.fcr.2023.109148 · ExternalCitation · doi-reference
Evaluating differences among crop models in simulating soybean in-season growth
10.1016/j.fcr.2024.109306 · ExternalCitation · doi-reference
Redefining soybean critical period for yield determination
10.1016/j.fcr.2024.109662 · ExternalCitation · doi-reference
Soybean yield response to management practices (4–40 years) and soil health parameters
10.1016/j.fcr.2025.109959 · ExternalCitation · doi-reference
Reproductive stage superiority in irrigation scheduling: UAV spectral mechanisms validated by field canopy architecture for soybean yield prediction
10.1016/j.fcr.2025.110230 · ExternalCitation · doi-reference
Bridging the gap between crop breeding and GeoAI: Soybean yield prediction from multispectral UAV images with transfer learning
10.1016/j.isprsjprs.2024.03.015 · ExternalCitation · doi-reference
Improving spatial transferability of deep learning models for small-field crop yield prediction
10.1016/j.ophoto.2024.100064 · ExternalCitation · doi-reference
Leakage and the reproducibility crisis in machine-learning-based science
10.1016/j.patter.2023.100804 · ExternalCitation · doi-reference
High-throughput field phenotyping of soybean: Spotting an ideotype
10.1016/j.rse.2021.112797 · ExternalCitation · doi-reference
Quantifying the physiological, yield, and quality plasticity of Southern USA soybeans under heat stress
10.1016/j.stress.2023.100195 · ExternalCitation · doi-reference
PLS-regression: A basic tool of chemometrics
10.1016/s0169-7439(01)00155-1 · ExternalCitation · doi-reference
Assessment of sowing dates and plant densities using CSM-CROPGRO-Soybean for soybean maturity groups in low latitude
10.1017/s0021859621000204 · ExternalCitation · doi-reference
Spatial validation reveals poor predictive performance of large-scale ecological mapping models
10.1038/s41467-020-18321-y · ExternalCitation · doi-reference
A data-driven simulation platform to predict cultivars’ performances under uncertain weather conditions
10.1038/s41467-020-18480-y · ExternalCitation · doi-reference
Coupling machine learning and crop modeling improves crop yield prediction in the US Corn Belt
10.1038/s41598-020-80820-1 · ExternalCitation · doi-reference
Simultaneous corn and soybean yield prediction from remote sensing data using deep transfer learning
10.1038/s41598-021-89779-z · ExternalCitation · doi-reference
Winter wheat yield prediction using convolutional neural networks from environmental and phenological data
10.1038/s41598-022-06249-w · ExternalCitation · doi-reference
From rows to yields: How foundation models for tabular data simplify crop yield prediction
10.1038/s41598-026-50338-z · ExternalCitation · doi-reference
Reductions in leaf area index, pod production, seed size, and harvest index drive yield loss to high temperatures in soybean
10.1093/jxb/erac503 · ExternalCitation · doi-reference
Assessing the Performance of Prediction Models
10.1097/ede.0b013e3181c30fb2 · ExternalCitation · doi-reference
Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure
10.1111/ecog.02881 · ExternalCitation · doi-reference
Regularization and Variable Selection Via the Elastic Net
10.1111/j.1467-9868.2005.00503.x · ExternalCitation · doi-reference
Soybean photosynthesis and crop yield are improved by accelerating recovery from photoprotection
10.1126/science.adc9831 · ExternalCitation · doi-reference
Evaluation of clinical prediction models (part 2): How to undertake an external validation study
10.1136/bmj-2023-074820 · ExternalCitation · doi-reference
Cross-validation pitfalls when selecting and assessing regression and classification models
10.1186/1758-2946-6-10 · ExternalCitation · doi-reference
10.1186/s12870-022-03559-z
10.1186/s12870-022-03559-z · ExternalCitation · doi-reference
10.1186/s12916-019-1466-7
10.1186/s12916-019-1466-7 · ExternalCitation · doi-reference
Yield prediction by machine learning from UAS-based multi-sensor data fusion in soybean
10.1186/s13007-020-00620-6 · ExternalCitation · doi-reference
Genotypic and phenotypic parameters associated with early maturity in soybean
10.1590/s1678-3921.pab2022.v57.02545 · ExternalCitation · doi-reference
Regularization paths for generalized linear models via coordinate descent
10.18637/jss.v033.i01 · ExternalCitation · doi-reference
A concordance correlation coefficient to evaluate reproducibility
10.2307/2532051 · ExternalCitation · doi-reference
10.3389/fphgy.2025.1591146
10.3389/fphgy.2025.1591146 · ExternalCitation · doi-reference
10.3389/fpls.2020.624273
10.3389/fpls.2020.624273 · ExternalCitation · doi-reference
10.3389/fpls.2021.719706
10.3389/fpls.2021.719706 · ExternalCitation · doi-reference
10.3389/fpls.2021.791256
10.3389/fpls.2021.791256 · ExternalCitation · doi-reference
10.3390/agriculture16141548
10.3390/agriculture16141548 · ExternalCitation · doi-reference
10.3390/agronomy16141396
10.3390/agronomy16141396 · ExternalCitation · doi-reference
10.3390/atmos17070701
10.3390/atmos17070701 · ExternalCitation · doi-reference
10.3390/rs13050977
10.3390/rs13050977 · ExternalCitation · doi-reference
10.3390/rs13224632
10.3390/rs13224632 · ExternalCitation · doi-reference
10.3390/rs14092256
10.3390/rs14092256 · ExternalCitation · doi-reference
10.3390/rs15010007
10.3390/rs15010007 · ExternalCitation · doi-reference
10.3390/rs15174286
10.3390/rs15174286 · ExternalCitation · doi-reference
10.3390/rs17040690
10.3390/rs17040690 · ExternalCitation · doi-reference
