Prediction of grain yield in wheat by CHAID and MARS algorithms analyses

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dc.authorid0000-0002-6846-8422en_US
dc.authorid0000-0002-3852-6476en_US
dc.authorid0000-0001-9991-1792en_US
dc.authorid0000-0002-4014-491Xen_US
dc.authorid0000-0003-3721-6473en_US
dc.contributor.authorDemirel, Fatih
dc.contributor.authorEren, Barış
dc.contributor.authorYılmaz, Abdurrahim
dc.contributor.authorTürkoğlu, Aras
dc.contributor.authorHaliloğlu, Kamil
dc.contributor.authorNiedbala, Gniewko
dc.date.accessioned2023-09-22T11:12:32Z
dc.date.available2023-09-22T11:12:32Z
dc.date.issued2023en_US
dc.departmentBAİBÜ, Ziraat Fakültesi, Tarla Bitkileri Bölümüen_US
dc.description.abstractGenetic information obtained from ancestral species of wheat and other registered wheat has brought about critical research, especially in wheat breeding, and shown great potential for the development of advanced breeding techniques. The purpose of this study was to determine correlations between some morphological traits of various wheat (Triticum spp.) species and to demonstrate the application of MARS and CHAID algorithms to wheat-derived data sets. Relationships among several morphological traits of wheat were investigated using a total of 26 different wheat genotypes. MARS and CHAID data mining methods were compared for grain yield prediction from different traits using cross-validation. In addition, an optimal CHAID tree structure with minimum RMSE was obtained and cross-validated with nine terminal nodes. Based on the smallest RMSE of the cross-validation, the eight-element MARS model was found to be the best model for grain yield prediction. The MARS algorithm proved superior to CHAID in grain yield prediction and accounted for 95.7% of the variation in grain yield among wheats. CHAID and MARS analyses on wheat grain yield were performed for the first time in this research. In this context, we showed how MARS and CHAID algorithms can help wheat breeders describe complex interaction effects more precisely. With the data mining methodology demonstrated in this study, breeders can predict which wheat traits are beneficial for increasing grain yield. The adaption of MARS and CHAID algorithms should benefit breeding research.en_US
dc.identifier.citationDemirel, F., Eren, B., Yilmaz, A., Türkoğlu, A., Haliloğlu, K., Niedbała, G., ... & Nowosad, K. (2023). Prediction of Grain Yield in Wheat by CHAID and MARS Algorithms Analyses. Agronomy, 13(6), 1438.en_US
dc.identifier.doi10.3390/agronomy13061438
dc.identifier.endpage13en_US
dc.identifier.issn2073-4395
dc.identifier.issue6en_US
dc.identifier.scopus2-s2.0-85163997319en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1en_US
dc.identifier.urihttp://dx.doi.org/10.3390/agronomy13061438
dc.identifier.urihttps://hdl.handle.net/20.500.12491/11728
dc.identifier.volume13en_US
dc.identifier.wosWOS:001013796400001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorYılmaz, Abdurrahim
dc.language.isoenen_US
dc.publisherMDPIen_US
dc.relation.ispartofAgronomy-Baselen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMorphological Characterizationen_US
dc.subjectPlant Breedingen_US
dc.subjectPredictionen_US
dc.subjectSelectionen_US
dc.subjectRegression Splines MARSen_US
dc.subjectTraitsen_US
dc.titlePrediction of grain yield in wheat by CHAID and MARS algorithms analysesen_US
dc.typeArticleen_US

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