Prediction of Grain Yield in Wheat by CHAID and MARS Algorithms Analyses
Fatih Demirel,
Baris Eren,
Abdurrahim Yilmaz,
Aras Türkoğlu,
Kamil Haliloğlu,
Gniewko Niedbała,
Henryk Bujak,
Bita Jamshidi,
Alireza Pour-Aboughadareh,
Jan Bocianowski,
Kamila Nowosad
Affiliations
Fatih Demirel
Department of Agricultural Biotechnology, Faculty of Agriculture, Igdır University, Igdir 76000, Türkiye
Baris Eren
Department of Agricultural Biotechnology, Faculty of Agriculture, Igdır University, Igdir 76000, Türkiye
Abdurrahim Yilmaz
Department of Field Crops, Faculty of Agriculture, Bolu Abant Izzet Baysal University, Bolu 14030, Türkiye
Aras Türkoğlu
Department of Field Crops, Faculty of Agriculture, Necmettin Erbakan University, Konya 42310, Türkiye
Kamil Haliloğlu
Department of Field Crops, Faculty of Agriculture, Ataturk University, Erzurum 25240, Türkiye
Gniewko Niedbała
Department of Biosystems Engineering, Faculty of Environmental and Mechanical Engineering, Poznań University of Life Sciences, Wojska Polskiego 50, 60-627 Poznań, Poland
Henryk Bujak
Department of Genetics, Plant Breeding and Seed Production, Wrocław University of Environmental and Life Sciences, Plac Grunwaldzki 24A, 53-535 Wrocław, Poland
Bita Jamshidi
Department of Food Security and Public Health, Khabat Technical Institute, Erbil Polytechnic University, Erbil 44001, Iraq
Alireza Pour-Aboughadareh
Seed and Plant Improvement Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj P.O. Box 3158854119, Iran
Jan Bocianowski
Department of Mathematical and Statistical Methods, Poznan University of Life Sciences, Wojska Polskiego 28, 60-637 Poznań, Poland
Kamila Nowosad
Department of Genetics, Plant Breeding and Seed Production, Wrocław University of Environmental and Life Sciences, Plac Grunwaldzki 24A, 53-535 Wrocław, Poland
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.
