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Prediction of Grain Yield in Wheat by CHAID and MARS Algorithms Analyses

cris.lastimport.scopus2025-10-23T06:55:26Z
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dc.abstract.enGenetic 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.
dc.affiliationWydział Inżynierii Środowiska i Inżynierii Mechanicznej
dc.affiliationWydział Rolnictwa, Ogrodnictwa i Biotechnologii
dc.affiliation.instituteKatedra Inżynierii Biosystemów
dc.affiliation.instituteKatedra Metod Matematycznych i Statystycznych
dc.contributor.authorDemirel, Fatih
dc.contributor.authorEren, Baris
dc.contributor.authorYilmaz, Abdurrahim
dc.contributor.authorTürkoğlu, Aras
dc.contributor.authorHaliloğlu, Kamil
dc.contributor.authorNiedbała, Gniewko
dc.contributor.authorBujak, Henryk
dc.contributor.authorJamshidi, Bita
dc.contributor.authorPour-Aboughadareh, Alireza
dc.contributor.authorBocianowski, Jan
dc.contributor.authorNowosad, Kamila
dc.date.access2025-05-27
dc.date.accessioned2025-08-29T07:49:34Z
dc.date.available2025-08-29T07:49:34Z
dc.date.copyright2023-05-23
dc.date.issued2023
dc.description.abstract<jats:p>Genetic 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.</jats:p>
dc.description.accesstimeat_publication
dc.description.bibliographyil., bibliogr.
dc.description.financepublication_nocost
dc.description.financecost0,00
dc.description.if3,3
dc.description.number6
dc.description.points100
dc.description.versionfinal_published
dc.description.volume13
dc.identifier.doi10.3390/agronomy13061438
dc.identifier.issn2073-4395
dc.identifier.urihttps://sciencerep.up.poznan.pl/handle/item/4504
dc.identifier.weblinkhttp://www.mdpi.com/2073-4395/13/6/1438
dc.languageen
dc.relation.ispartofAgronomy
dc.relation.pagesart. 1438
dc.rightsCC-BY
dc.sciencecloudnosend
dc.share.typeOPEN_JOURNAL
dc.subject.enmorphological characterization
dc.subject.enplant breeding
dc.subject.enprediction
dc.subject.enselection
dc.titlePrediction of Grain Yield in Wheat by CHAID and MARS Algorithms Analyses
dc.title.volumeSpecial Issue Predictions and Estimations in Agricultural Production under a Changing Climate
dc.typeJournalArticle
dspace.entity.typePublication
oaire.citation.issue6
oaire.citation.volume13