{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T12:16:13Z","timestamp":1789992973588,"version":"4.0.1"},"reference-count":70,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,19]],"date-time":"2026-09-19T00:00:00Z","timestamp":1789776000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003046","name":"Embrapa Soja, Londrina","doi-asserted-by":"publisher","award":["1293\u2013FAPED\/CNPSO\/TED TRANSFEREGOV\u2013966344\/2024"],"award-info":[{"award-number":["1293\u2013FAPED\/CNPSO\/TED TRANSFEREGOV\u2013966344\/2024"]}],"id":[{"id":"10.13039\/501100003046","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Plants"],"abstract":"<jats:p>Whether sequential crop phenotypes improve grain yield prediction after transfer to another growing season remains poorly quantified. We evaluated soybean (Glycine max (L.) Merr.) at one experimental site over two seasons in a split-plot experiment with five sowing periods, two cultivars, four seeding rates, and four blocks (160 subplots per season). Information gates were cumulative predictor sets available through V4, R2, R5, and R6. They combined subplot means of leaf area, total dry matter, branching, main-stem nodes, and plant height from distinct destructive samples. Grain yield at R8 was the outcome. Elastic-net models estimated phenotypic corrections to development-season means for matching management combinations. Reciprocal whole-season evaluation used development-only grouped tuning and conditional within-season whole-block bootstrap intervals. The management comparator produced root mean squared errors (RMSEs) of 1398 and 1394 kg ha\u22121 and predictive R2 values of 0.282 and \u22120.182 for 2021\/22 \u2192 2022\/23 and the reciprocal direction, respectively. Paired RMSE gains from cumulative phenotypes ranged from \u22120.419% to 0.038% and from \u22120.712% to \u22120.104%, respectively, where positive values denote lower independent-season error. Alternative management comparators yielded similarly small changes. Predictor distributions and calibration differed between transfer directions. These evaluations identify little incremental benefit from the measured phenotypes under the tested conditions, while demonstrating the importance of distinguishing yield ranking, calibration, and additional predictive information. Broader environmental validation is needed to determine when phenotyping improves forecasting beyond management expectations.<\/jats:p>","DOI":"10.3390\/plants15182865","type":"journal-article","created":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T11:05:53Z","timestamp":1789988753000},"page":"2865","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Sequential Soybean Phenotypes for Grain Yield Prediction in Reciprocal Two-Season Validation"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2343-5045","authenticated-orcid":false,"given":"Renan","family":"Falcioni","sequence":"first","affiliation":[{"name":"National Soybean Research Center, Brazilian Agricultural Research Corporation, Rodovia Carlos Jo\u00e3o Strass, s\/n\u00ba, Acesso Orlando Amaral, Distrito de Warta, Londrina 86085-981, PR, Brazil"},{"name":"Graduate Program in Agronomy, State University of Maring\u00e1, Av. Colombo 5790, Maring\u00e1 87020-900, PR, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jos\u00e9 Salvador Simoneti","family":"Foloni","sequence":"additional","affiliation":[{"name":"National Soybean Research Center, Brazilian Agricultural Research Corporation, Rodovia Carlos Jo\u00e3o Strass, s\/n\u00ba, Acesso Orlando Amaral, Distrito de Warta, Londrina 86085-981, PR, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luis Guilherme Teixeira","family":"Crusiol","sequence":"additional","affiliation":[{"name":"National Soybean Research Center, Brazilian Agricultural Research Corporation, Rodovia Carlos Jo\u00e3o Strass, s\/n\u00ba, Acesso Orlando Amaral, Distrito de Warta, Londrina 86085-981, PR, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4854-2661","authenticated-orcid":false,"given":"Marcos Rafael","family":"Nanni","sequence":"additional","affiliation":[{"name":"Graduate Program in Agronomy, State University of Maring\u00e1, Av. Colombo 5790, Maring\u00e1 87020-900, PR, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1898-5042","authenticated-orcid":false,"given":"Jos\u00e9 Renato Bou\u00e7as","family":"Farias","sequence":"additional","affiliation":[{"name":"National Soybean Research Center, Brazilian Agricultural Research Corporation, Rodovia Carlos Jo\u00e3o Strass, s\/n\u00ba, Acesso Orlando Amaral, Distrito de Warta, Londrina 86085-981, PR, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"108038","DOI":"10.1016\/j.agrformet.2020.108038","article-title":"Effects of the El Ni\u00f1o Southern Oscillation phenomenon and sowing dates on soybean yield and on the occurrence of extreme weather events in southern Brazil","volume":"290","author":"Fraisse","year":"2020","journal-title":"Agric. For. Meteorol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"109104","DOI":"10.1016\/j.agrformet.2022.109104","article-title":"Soybean maturity groups and sowing dates to minimize ENSO and extreme weather events effects on yield variability in the Southeastern US","volume":"324","author":"Perondi","year":"2022","journal-title":"Agric. For. Meteorol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"127133","DOI":"10.1016\/j.eja.2024.127133","article-title":"Soybean yield and water productivity gaps associate with ENSO-dependent effects of fungicide, sowing date and maturity group","volume":"155","author":"Caviglia","year":"2024","journal-title":"Eur. J. Agron."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"126329","DOI":"10.1016\/j.eja.2021.126329","article-title":"Impact assessment of soybean yield and water productivity in Brazil due to climate change","volume":"129","author":"Zanon","year":"2021","journal-title":"Eur. J. Agron."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"108676","DOI":"10.1016\/j.fcr.2022.108676","article-title":"Defining soybean maturity group options for contrasting weather scenarios in the American Southern Cone","volume":"287","author":"Parra","year":"2022","journal-title":"Field Crops Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1002\/agj2.20053","article-title":"Soybean yield and crop stage response to planting date and cultivar maturity in Iowa, USA","volume":"112","author":"Kessler","year":"2020","journal-title":"Agron. J."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"e02545","DOI":"10.1590\/s1678-3921.pab2022.v57.02545","article-title":"Genotypic and phenotypic parameters associated with early maturity in soybean","volume":"57","author":"Silva","year":"2022","journal-title":"Pesqui. Agropecu. Bras."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"107922","DOI":"10.1016\/j.fcr.2020.107922","article-title":"Genotype x environment interaction analysis of soybean (Glycine max (L.) Merrill) grain yield across production environments in Southern Africa","volume":"256","author":"Mwiinga","year":"2020","journal-title":"Field Crops Res."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"819","DOI":"10.1017\/S0021859621000204","article-title":"Assessment of sowing dates and plant densities using CSM-CROPGRO-Soybean for soybean maturity groups in low latitude","volume":"158","author":"Sampaio","year":"2020","journal-title":"J. Agric. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Dapper, F.P., Battisti, R., Elli, E.F., Xavier, M.F.N., Moreira, A.N.H., Flores, R.A., Oliveira, H.F.E.D., Silva, M.V.D., Garcia, J.A.Q., and Verjel, M.L.J. (2026). Assessment of sowing dates and plant densities using the CSM-CROPGRO-Soybean model under cerrado climatic conditions. Atmosphere, 17.","DOI":"10.3390\/atmos17070701"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"126482","DOI":"10.1016\/j.eja.2022.126482","article-title":"Are soybean models ready for climate change food impact assessments?","volume":"135","author":"Kothari","year":"2022","journal-title":"Eur. J. Agron."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4365","DOI":"10.1002\/csc2.20603","article-title":"Maximizing soybean yield by understanding planting date, maturity group, and seeding rate interactions in North Carolina","volume":"61","author":"Morris","year":"2021","journal-title":"Crop Sci."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Foloni, J.S.S., Falcioni, R., Crusiol, L.G.T., Nanni, M.R., Pinto, L.F.D., Zucareli, C., and Farias, J.R.B. (2026). Optimizing soybean seeding rate for yield across early to late sowing dates in a humid subtropical environment. Agronomy, 16.","DOI":"10.3390\/agronomy16141396"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"109959","DOI":"10.1016\/j.fcr.2025.109959","article-title":"Soybean yield response to management practices (4\u201340 years) and soil health parameters","volume":"329","author":"Silva","year":"2025","journal-title":"Field Crops Res."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"e70334","DOI":"10.1002\/agj2.70334","article-title":"Key management practices driving soybean yield variability in lowland fields of southern Brazil","volume":"118","author":"Tagliapietra","year":"2026","journal-title":"Agron. J."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Yoosefzadeh-Najafabadi, M., Earl, H.J., Tulpan, D., Sulik, J., and Eskandari, M. (2021). Application of machine learning algorithms in plant breeding: Predicting yield from hyperspectral reflectance in soybean. Front. Plant Sci., 11.","DOI":"10.3389\/fpls.2020.624273"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Teodoro, P.E., Teodoro, L.P.R., Baio, F.H.R., da Silva Junior, C.A., dos Santos, R.G., Ramos, A.P.M., Pinheiro, M.M.F., Osco, L.P., Gon\u00e7alves, W.N., and Carneiro, A.M. (2021). Predicting days to maturity, plant height, and grain yield in soybean: A machine and deep learning approach using multispectral data. Remote Sens., 13.","DOI":"10.3390\/rs13224632"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ren, P., Li, H., Han, S., Chen, R., Yang, G., Yang, H., Feng, H., and Zhao, C. (2023). Estimation of soybean yield by combining maturity group information and unmanned aerial vehicle multi-sensor data using machine learning. Remote Sens., 15.","DOI":"10.3390\/rs15174286"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"101276","DOI":"10.1016\/j.atech.2025.101276","article-title":"UAV-based aerial phenotyping to assess key morphophysiological traits and yield in soybean","volume":"12","author":"Sandifer","year":"2025","journal-title":"Smart Agric. Technol."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Yang, C., Yang, G., Wang, H., Li, S., Zhang, J., Pan, D., Ren, P., Feng, H., and Li, H. (2025). Identifying key traits for screening high-yield soybean varieties by combining UAV-based and field phenotyping. Remote Sens., 17.","DOI":"10.3390\/rs17040690"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Gill, M., Anderson, R., Hu, H., Bennamoun, M., Petereit, J., Valliyodan, B., Nguyen, H.T., Batley, J., Bayer, P.E., and Edwards, D. (2022). Machine learning models outperform deep learning models, provide interpretation and facilitate feature selection for soybean trait prediction. BMC Plant Biol., 22.","DOI":"10.1186\/s12870-022-03559-z"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Vogel, J.T., Liu, W., Olhoft, P., Crafts-Brandner, S.J., Pennycooke, J.C., and Christiansen, N. (2021). Soybean yield formation physiology\u2014A foundation for precision breeding based improvement. Front. Plant Sci., 12.","DOI":"10.3389\/fpls.2021.719706"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"112797","DOI":"10.1016\/j.rse.2021.112797","article-title":"High-throughput field phenotyping of soybean: Spotting an ideotype","volume":"269","author":"Roth","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"109306","DOI":"10.1016\/j.fcr.2024.109306","article-title":"Evaluating differences among crop models in simulating soybean in-season growth","volume":"309","author":"Kothari","year":"2024","journal-title":"Field Crops Res."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"851","DOI":"10.1126\/science.adc9831","article-title":"Soybean photosynthesis and crop yield are improved by accelerating recovery from photoprotection","volume":"377","author":"Burgess","year":"2022","journal-title":"Science"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"109148","DOI":"10.1016\/j.fcr.2023.109148","article-title":"Leaf area reduction during the pod set period changes the photomorphogenic light balance and increases the pod number and yield in soybean canopies","volume":"303","author":"Quijano","year":"2023","journal-title":"Field Crops Res."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"109662","DOI":"10.1016\/j.fcr.2024.109662","article-title":"Redefining soybean critical period for yield determination","volume":"321","author":"Cerrudo","year":"2025","journal-title":"Field Crops Res."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1629","DOI":"10.1093\/jxb\/erac503","article-title":"Reductions in leaf area index, pod production, seed size, and harvest index drive yield loss to high temperatures in soybean","volume":"74","author":"Burroughs","year":"2023","journal-title":"J. Exp. Bot."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"672","DOI":"10.1002\/csc2.20314","article-title":"Morpho-physiological traits associated with drought responses in soybean","volume":"61","author":"Guzzo","year":"2021","journal-title":"Crop Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"100195","DOI":"10.1016\/j.stress.2023.100195","article-title":"Quantifying the physiological, yield, and quality plasticity of Southern USA soybeans under heat stress","volume":"9","author":"Poudel","year":"2023","journal-title":"Plant Stress"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Singh-Bakala, H., Ravelombola, F., Adeva, C., Oliveira, M., Zhang, R., Argenta, J., Shannon, G., and Lin, F. (2025). Harnessing photosynthetic and morpho-physiological traits for drought-resilient soybean: Integrating field phenotyping and predictive approaches. Front. Plant Physiol., 3.","DOI":"10.3389\/fphgy.2025.1591146"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Crusiol, L.G.T., Nanni, M.R., Furlanetto, R.H., Sibaldelli, R.N.R., Cezar, E., Sun, L., Foloni, J.S.S., Mertz-Henning, L.M., Nepomuceno, A.L., and Neumaier, N. (2021). Yield prediction in soybean crop grown under different levels of water availability using reflectance spectroscopy and partial least squares regression. Remote Sens., 13.","DOI":"10.3390\/rs13050977"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Yang, Y., Zhang, Q., Duan, R., Liu, J., Qin, Y., and Wang, X. (2022). Toward multi-stage phenotyping of soybean with multimodal UAV sensor data: A comparison of machine learning approaches for leaf area index estimation. Remote Sens., 15.","DOI":"10.3390\/rs15010007"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1007\/s11119-023-10096-8","article-title":"End-to-end 3D CNN for plot-scale soybean yield prediction using multitemporal UAV-based RGB images","volume":"25","author":"Bhadra","year":"2024","journal-title":"Precis. Agric."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"110022","DOI":"10.1016\/j.agrformet.2024.110022","article-title":"Beyond assimilation of leaf area index: Leveraging additional spectral information using machine learning for site-specific soybean yield prediction","volume":"351","author":"Gaso","year":"2024","journal-title":"Agric. For. Meteorol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"107886","DOI":"10.1016\/j.agrformet.2019.107886","article-title":"Satellite-based soybean yield forecast: Integrating machine learning and weather data for improving crop yield prediction in southern Brazil","volume":"284","author":"Schwalbert","year":"2020","journal-title":"Agric. For. Meteorol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"109670","DOI":"10.1016\/j.agrformet.2023.109670","article-title":"Machine learning for soybean yield forecasting in Brazil","volume":"341","author":"Wangerpohl","year":"2023","journal-title":"Agric. For. Meteorol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"108978","DOI":"10.1016\/j.compag.2024.108978","article-title":"Satellite-based soybean yield prediction in Argentina: A comparison between panel regression and deep learning methods","volume":"221","author":"Wang","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1186\/s13007-020-00620-6","article-title":"Yield prediction by machine learning from UAS-based multi-sensor data fusion in soybean","volume":"16","author":"Rainey","year":"2020","journal-title":"Plant Methods"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Pejak, B., Lugonja, P., Anti\u0107, A., Pani\u0107, M., Pand\u017ei\u0107, M., Alexakis, E., Mavrepis, P., Zhou, N., Marko, O., and Crnojevi\u0107, V. (2022). Soya Yield Prediction on a Within-Field Scale Using Machine Learning Models Trained on Sentinel-2 and Soil Data. Remote Sens., 14.","DOI":"10.3390\/rs14092256"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.isprsjprs.2024.03.015","article-title":"Bridging the gap between crop breeding and GeoAI: Soybean yield prediction from multispectral UAV images with transfer learning","volume":"210","author":"Skobalski","year":"2024","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Lu, W., Du, R., Niu, P., Xing, G., Luo, H., Deng, Y., and Shu, L. (2022). Soybean yield preharvest prediction based on bean pods and leaves image recognition using deep learning neural network combined with GRNN. Front. Plant Sci., 12.","DOI":"10.3389\/fpls.2021.791256"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"11132","DOI":"10.1038\/s41598-021-89779-z","article-title":"Simultaneous corn and soybean yield prediction from remote sensing data using deep transfer learning","volume":"11","author":"Khaki","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"19001","DOI":"10.1038\/s41598-026-50338-z","article-title":"From rows to yields: How foundation models for tabular data simplify crop yield prediction","volume":"16","author":"Sabo","year":"2026","journal-title":"Sci. Rep."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"105709","DOI":"10.1016\/j.compag.2020.105709","article-title":"Crop yield prediction using machine learning: A systematic literature review","volume":"177","author":"Kassahun","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1606","DOI":"10.1038\/s41598-020-80820-1","article-title":"Coupling machine learning and crop modeling improves crop yield prediction in the US Corn Belt","volume":"11","author":"Shahhosseini","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"106578","DOI":"10.1016\/j.compag.2021.106578","article-title":"A surrogate model based on feature selection techniques and regression learners to improve soybean yield prediction in southern France","volume":"192","author":"Corrales","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_48","first-page":"101096","article-title":"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","volume":"16","author":"Habibi","year":"2024","journal-title":"J. Agric. Food Res."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"3215","DOI":"10.1038\/s41598-022-06249-w","article-title":"Winter wheat yield prediction using convolutional neural networks from environmental and phenological data","volume":"12","author":"Srivastava","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"913","DOI":"10.1111\/ecog.02881","article-title":"Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure","volume":"40","author":"Roberts","year":"2017","journal-title":"Ecography"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"4540","DOI":"10.1038\/s41467-020-18321-y","article-title":"Spatial validation reveals poor predictive performance of large-scale ecological mapping models","volume":"11","author":"Ploton","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"e1557","DOI":"10.1002\/ecm.1557","article-title":"Cross validation for model selection: A review with examples from ecology","volume":"93","author":"Yates","year":"2023","journal-title":"Ecol. Monogr."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1186\/1758-2946-6-10","article-title":"Cross-validation pitfalls when selecting and assessing regression and classification models","volume":"6","author":"Krstajic","year":"2014","journal-title":"J. Cheminform."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"100804","DOI":"10.1016\/j.patter.2023.100804","article-title":"Leakage and the reproducibility crisis in machine-learning-based science","volume":"4","author":"Kapoor","year":"2023","journal-title":"Patterns"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"109652","DOI":"10.1016\/j.agrformet.2023.109652","article-title":"Improving generalisability and transferability of machine-learning-based maize yield prediction model through domain adaptation","volume":"341","author":"Priyatikanto","year":"2023","journal-title":"Agric. For. Meteorol."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"100064","DOI":"10.1016\/j.ophoto.2024.100064","article-title":"Improving spatial transferability of deep learning models for small-field crop yield prediction","volume":"12","author":"Stiller","year":"2024","journal-title":"ISPRS Open J. Photogramm. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"110254","DOI":"10.1016\/j.agwat.2026.110254","article-title":"Predictive drivers and transferability of multi-scale machine learning based crop yield prediction under drought across European and Asian climates","volume":"327","author":"Sahu","year":"2026","journal-title":"Agric. Water Manag."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"On behalf of Topic Group \u2018Evaluating diagnostic tests and prediction models\u2019 of the STRATOS initiative, Van Calster, B., McLernon, D.J., van Smeden, M., Wynants, L., and Steyerberg, E.W. (2019). Calibration: The Achilles heel of predictive analytics. BMC Med., 17.","DOI":"10.1186\/s12916-019-1466-7"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1097\/EDE.0b013e3181c30fb2","article-title":"Assessing the Performance of Prediction Models","volume":"21","author":"Steyerberg","year":"2010","journal-title":"Epidemiology"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1262","DOI":"10.1002\/sim.7993","article-title":"Minimum sample size for developing a multivariable prediction model: Part I\u2014Continuous outcomes","volume":"38","author":"Riley","year":"2019","journal-title":"Stat. Med."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"e074820","DOI":"10.1136\/bmj-2023-074820","article-title":"Evaluation of clinical prediction models (part 2): How to undertake an external validation study","volume":"384","author":"Riley","year":"2024","journal-title":"BMJ"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"4876","DOI":"10.1038\/s41467-020-18480-y","article-title":"A data-driven simulation platform to predict cultivars\u2019 performances under uncertain weather conditions","volume":"11","author":"Bogard","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1007\/s13593-023-00900-0","article-title":"Proposal and extensive test of a calibration protocol for crop phenology models","volume":"43","author":"Wallach","year":"2023","journal-title":"Agron. Sustain. Dev."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"110230","DOI":"10.1016\/j.fcr.2025.110230","article-title":"Reproductive stage superiority in irrigation scheduling: UAV spectral mechanisms validated by field canopy architecture for soybean yield prediction","volume":"336","author":"Tang","year":"2026","journal-title":"Field Crops Res."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Cui, D., Fu, J., Li, M., Zhu, S., Zhang, L., Yu, R., Qiu, Q., Chen, X., and Chen, Q. (2026). Multi-Temporal prediction of soybean yield at the plot scale through multi-source UAV sensor fusion and dynamic modeling. Agriculture, 16.","DOI":"10.3390\/agriculture16141548"},{"key":"ref_66","unstructured":"Fehr, W.R., and Caviness, C.E. (1977). Stages of Soybean Development, Iowa Agriculture and Home Economics Experiment Station. Available online: https:\/\/dr.lib.iastate.edu\/bitstreams\/13bd0d8f-66ff-4d0e-a0e3-a70c2c47f6f3\/download."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v033.i01","article-title":"Regularization paths for generalized linear models via coordinate descent","volume":"33","author":"Friedman","year":"2010","journal-title":"J. Stat. Softw."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1111\/j.1467-9868.2005.00503.x","article-title":"Regularization and Variable Selection Via the Elastic Net","volume":"67","author":"Zou","year":"2005","journal-title":"J. R. Stat. Soc. Ser. B Stat. Methodol."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/S0169-7439(01)00155-1","article-title":"PLS-regression: A basic tool of chemometrics","volume":"58","author":"Wold","year":"2001","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"255","DOI":"10.2307\/2532051","article-title":"A concordance correlation coefficient to evaluate reproducibility","volume":"45","author":"Lin","year":"1989","journal-title":"Biometrics"}],"container-title":["Plants"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2223-7747\/15\/18\/2865\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T11:52:01Z","timestamp":1789991521000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2223-7747\/15\/18\/2865"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,19]]},"references-count":70,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["plants15182865"],"URL":"https:\/\/doi.org\/10.3390\/plants15182865","relation":{},"ISSN":["2223-7747"],"issn-type":[{"value":"2223-7747","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,19]]}}}