{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T10:29:57Z","timestamp":1789122597325,"version":"build-2803163510"},"reference-count":32,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T00:00:00Z","timestamp":1789084800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Dicle University Scientific Research Projects Coordination Unit","award":["Z\u0130RAAT.26.034"],"award-info":[{"award-number":["Z\u0130RAAT.26.034"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Plants"],"abstract":"<jats:p>Identifying superior wheat genotypes under rainfed semi-arid conditions and understanding the traits that drive yield remain challenging, and multi-trait selection indices and explainable machine learning are seldom used together. This study combined the Multi-Trait Genotype\u2013Ideotype Distance Index (MGIDI) with explainable machine learning to evaluate 20 bread wheat (Triticum aestivum L.) genotypes for 14 phenological, physiological and yield-component traits in a randomised complete block design in a single rainfed season in Diyarbak\u0131r, T\u00fcrkiye. Broad-sense heritability was high for all traits (H2 = 0.84\u20130.99), and the close agreement between genotypic and phenotypic coefficients of variation indicated a predominant genetic contribution to phenotypic variation. Grain yield, the number of grains per spike, and grain weight per spike showed the highest expected genetic advance (GAM = 17\u201322%). MGIDI-based selection of the top 25% of genotypes increased grain yield by 9.50%, with concurrent gains of 6\u201313% in spike length, spikelets per spike and grain weight per spike, and the relationship between MGIDI and grain yield was negative and significant (R2 = 0.58, p &lt; 0.001). Among five learners evaluated under block-stratified GroupKFold cross-validation, Gradient Boosting performed best (CV R2 = 0.740; training R2 = 0.998; RMSE = 30.55 kg da\u22121; MAE = 24.43 kg da\u22121). SHAP analysis identified grain weight per spike, plant height and days to flowering as the main yield drivers, and these rankings were corroborated by model-independent permutation importance. Combining MGIDI with explainable machine learning allowed genotype selection and yield-determining traits to be evaluated within a single framework, and the selection decisions were biologically consistent.<\/jats:p>","DOI":"10.3390\/plants15182787","type":"journal-article","created":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T09:09:40Z","timestamp":1789117780000},"page":"2787","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MGIDI and Explainable Machine Learning for Multi-Trait Selection and Yield Drivers in Rainfed Bread Wheat"],"prefix":"10.3390","volume":"15","author":[{"given":"Levent","family":"Yorulmaz","sequence":"first","affiliation":[{"name":"Department of Field Crops, Faculty of Agriculture, Dicle University, Diyarbak\u0131r 21280, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-0225-7629","authenticated-orcid":false,"given":"S\u00fcreyya Bet\u00fcl","family":"Rufaio\u011flu","sequence":"additional","affiliation":[{"name":"Department of Soil Science and Plant Nutrition, Faculty of Agriculture, Harran University, \u015eanl\u0131urfa 63300, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-8406-4824","authenticated-orcid":false,"given":"Murat","family":"Tun\u00e7","sequence":"additional","affiliation":[{"name":"Department of Field Crops, Faculty of Agriculture, Harran University, \u015eanl\u0131urfa 63300, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7141-5911","authenticated-orcid":false,"given":"Sibel","family":"\u0130peke\u015fen","sequence":"additional","affiliation":[{"name":"Department of Field Crops, Faculty of Agriculture, Dicle University, Diyarbak\u0131r 21280, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mihriban","family":"Okur","sequence":"additional","affiliation":[{"name":"Department of Agricultural Machinery and Technologies Engineering, Faculty of Agriculture, Dicle University, Diyarbak\u0131r 21280, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Do\u011fan","family":"\u0130peke\u015fen","sequence":"additional","affiliation":[{"name":"Department of Plant Protection, Faculty of Agriculture, Dicle University, Diyarbak\u0131r 21280, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cuma","family":"Ak\u0131nc\u0131","sequence":"additional","affiliation":[{"name":"Department of Field Crops, Faculty of Agriculture, Dicle University, Diyarbak\u0131r 21280, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mehmet","family":"Y\u0131ld\u0131r\u0131m","sequence":"additional","affiliation":[{"name":"Department of Field Crops, Faculty of Agriculture, Dicle University, Diyarbak\u0131r 21280, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,11]]},"reference":[{"key":"ref_1","unstructured":"FAO (2024). FAOSTAT Statistical Database, Food and Agriculture Organization of the United Nations. Available online: https:\/\/www.fao.org\/faostat."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1111\/aab.12108","article-title":"Food security: The challenge of increasing wheat yield and the importance of not compromising food safety","volume":"164","author":"Curtis","year":"2014","journal-title":"Ann. Appl. Biol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1293","DOI":"10.1038\/ncomms2296","article-title":"Recent patterns of crop yield growth and stagnation","volume":"3","author":"Ray","year":"2012","journal-title":"Nat. Commun."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Bezak, N., and Miko\u0161, M. (2020). Changes in the compound drought and extreme heat occurrence in the 1961\u20132018 period at the European scale. Water, 12.","DOI":"10.3390\/w12123543"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Rufaio\u011flu, S.B., Bilgili, A.V., Erdin\u00e7, S., \u0130rfan, \u00d6., Salih, A., Mohamed, I.A., and Matos-Carvalho, J.P. (2025). Sensor-based yield prediction in durum wheat under semi-arid conditions using machine learning across Zadoks growth stages. Remote Sens., 17.","DOI":"10.3390\/rs17142416"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1771","DOI":"10.1002\/joc.962","article-title":"Precipitation changes and variability in T\u00fcrkiye linked to the North Atlantic Oscillation during the period 1930\u20132000","volume":"23","author":"Erlat","year":"2003","journal-title":"Int. J. Climatol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1007\/s00704-025-05533-9","article-title":"Climate change impacts on hydrological and meteorological variables in Diyarbak\u0131r Province: Trend analysis and machine learning-based drought forecasting","volume":"156","author":"Akbas","year":"2025","journal-title":"Theor. Appl. Climatol."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"\u00c7a\u011flak, S., and T\u00fcrke\u015f, M. (2023). Spatial distribution and future projections of thermal comfort conditions during the hot period of the year in Diyarbak\u0131r City, Southeastern Turkey. Sustainability, 15.","DOI":"10.3390\/su151310473"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Gill, H.S., Halder, J., Zhang, J., Brar, N.K., Rai, T.S., Hall, C., and Sehgal, S.K. (2021). Multi-trait multi-environment genomic prediction of agronomic traits in advanced breeding lines of winter wheat. Front. Plant Sci., 12.","DOI":"10.3389\/fpls.2021.709545"},{"key":"ref_10","first-page":"195","article-title":"Screening of drought tolerance level of some wheat cultivars (Triticum durum Desf.) under water stress","volume":"35","author":"Yorulmaz","year":"2025","journal-title":"J. Anim. Plant Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"552","DOI":"10.1016\/j.molp.2024.03.007","article-title":"Genomic selection in plant breeding: Key factors shaping two decades of progress","volume":"17","author":"Alemu","year":"2024","journal-title":"Mol. Plant"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1383","DOI":"10.1093\/bioinformatics\/btaa981","article-title":"MGIDI: Toward an effective multivariate selection in biological experiments","volume":"37","author":"Olivoto","year":"2021","journal-title":"Bioinformatics"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Al-Ashkar, I., Sallam, M., Almutairi, K.F., Shady, M., Ibrahim, A., and Alghamdi, S.S. (2023). Detection of high-performance wheat genotypes and genetic stability to determine complex interplay between genotypes and environments. Agronomy, 13.","DOI":"10.3390\/agronomy13020585"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Chaity, S.S., Islam, M.R., Faruquee, M., Ahmed, J.U., and Islam, A.A. (2026). Identification of elite rice lines with better breeding values using genomic prediction and multi-trait genotype\u2013ideotype distance index (MGIDI) for grain yield under irrigation cropping system. PLoS ONE, 19.","DOI":"10.1371\/journal.pone.0340188"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"887","DOI":"10.1002\/agj2.20991","article-title":"Genetic diversity in tropical wheat germplasm and selection via multi-trait index","volume":"114","author":"Lima","year":"2022","journal-title":"Agron. J."},{"key":"ref_16","first-page":"381","article-title":"Machine learning algorithms A review","volume":"9","author":"Mahesh","year":"2020","journal-title":"Int. J. Sci. Res."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Sandhu, K.S., Patil, S.S., Aoun, M., and Carter, A.H. (2022). Multi-trait multi-environment genomic prediction for end-use quality traits in winter wheat. Front. Genet., 13.","DOI":"10.3389\/fgene.2022.831020"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Samek, W., Montavon, G., Vedaldi, A., Hansen, L.K., and M\u00fcller, K.-R. (2019). Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, Springer.","DOI":"10.1007\/978-3-030-28954-6"},{"key":"ref_19","unstructured":"Lundberg, S.M., and Lee, S.-I. (2017, January 4\u20139). A unified approach to interpreting model predictions. Proceedings of the 31st International Conference on Neural Information Processing Systems (NeurIPS 2017), Long Beach, CA, USA."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Song, M., Zhang, S., Qiu, S., Qin, R., Zhao, C., Wu, Y., Sun, H., Liu, G., and Cui, F. (2025). GRE: A framework for significant SNP identification associated with wheat yield leveraging GWAS\u2013Random Forest joint feature selection and explainable machine learning. Genes, 16.","DOI":"10.3390\/genes16101125"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"109758","DOI":"10.1016\/j.compag.2024.109758","article-title":"An interpretable approach combining Shapley additive explanations and LightGBM based on data augmentation for improving wheat yield estimates","volume":"229","author":"Wang","year":"2025","journal-title":"Comput. Electron. Agric."},{"key":"ref_22","first-page":"100074","article-title":"A novel multi-trait genotype\u2013ideotype distance index (MGIDI) for genotype selection in plant breeding: Application, prospects, and limitations","volume":"3","author":"Debnath","year":"2024","journal-title":"Crop Des."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1111\/j.1365-3180.1974.tb01084.x","article-title":"A decimal code for the growth stages of cereals","volume":"14","author":"Zadoks","year":"1974","journal-title":"Weed Res."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"e67996","DOI":"10.1590\/1983-40632021v5167996","article-title":"Genetic parameters and multiple-trait selection in wheat genotypes","volume":"51","author":"Meier","year":"2021","journal-title":"Pesqui. Agropecu. Trop."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Nardino, M., Perin, E.C., Aranha, B.C., Carpes, S.T., Fontoura, B.H., de Sousa, D.J.P., and de Freitas, D.S. (2022). Understanding drought response mechanisms in wheat and multi-trait selection. PLoS ONE, 17.","DOI":"10.1371\/journal.pone.0266368"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1002\/csc2.20628","article-title":"Multi-trait selection of bread wheat ideotypes for adaptation to early sown condition","volume":"62","author":"Farhad","year":"2022","journal-title":"Crop Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1007\/s42976-020-00080-7","article-title":"Genotype selection for grain yield and quality based on multiple traits of common wheat (Triticum aestivum L.)","volume":"49","author":"Tsenov","year":"2021","journal-title":"Cereal Res. Commun."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.compag.2015.11.018","article-title":"Wheat yield prediction using machine learning and advanced sensing techniques","volume":"121","author":"Pantazi","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Han, J., Zhang, Z., Cao, J., Luo, Y., Zhang, L., Li, Z., and Zhang, J. (2020). Prediction of winter wheat yield based on multi-source data and machine learning in China. Remote Sens., 12.","DOI":"10.3390\/rs12020236"},{"key":"ref_30","first-page":"257","article-title":"Explainable artificial intelligence and interpretable machine learning for agricultural data analysis","volume":"6","author":"Ryo","year":"2022","journal-title":"Artif. Intell. Agric."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"108640","DOI":"10.1016\/j.fcr.2022.108640","article-title":"Interpretable machine learning methods to explain on-farm yield variability of high productivity wheat in Northwest India","volume":"287","author":"Nayak","year":"2022","journal-title":"Field Crops Res."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"126204","DOI":"10.1016\/j.eja.2020.126204","article-title":"Wheat yield predictions at a county and field scale with deep learning, machine learning, and Google Earth Engine","volume":"123","author":"Cao","year":"2021","journal-title":"Eur. J. Agron."}],"container-title":["Plants"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2223-7747\/15\/18\/2787\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T09:29:54Z","timestamp":1789118994000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2223-7747\/15\/18\/2787"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,11]]},"references-count":32,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["plants15182787"],"URL":"https:\/\/doi.org\/10.3390\/plants15182787","relation":{},"ISSN":["2223-7747"],"issn-type":[{"value":"2223-7747","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,11]]}}}