New selection strategies for determining the traits contributing to increased grain yield in wheat (Triticum aestivum L.) under aluminum stress

Abstract

Finding the suitable traits that mediate grain yield under stress condition represent an important challenge to plant breeders in order to improve production. Meaningful progress in computational analysis enables us to solve this challenge in a more efficient way. In this frame of study, our objective was to determine the most important traits associated with wheat yield improvement under aluminum stress using attribute weighting as well as supervised algorithms. To meet this goal, we studied, under normal and stress conditions, 167 bread wheat recombinant inbred lines. The lines were obtained from a cross of two semi-dwarf spring wheat varieties featured with considerable yield potential; Seri M82 and Babax. A total of 50 different traits including phenological, morphological, agronomic, physiological and biochemical traits were recorded. Ranking traits by attribute weighting algorithms indicated that among the 50 traits, five and eight traits were respectively the most probable candidates under normal and stress conditions, as highlighted by at least six weighting algorithms. According to the performance of decision tree algorithms employed, traits that were pinpointed traits by attribute weighting can be utilized to efficiently discriminate high-, medium-, and low- yield lines with up to 74.92% and 81.47% under normal and stress conditions, respectively. Moreover, valuable basis can be extrapolated from our decision tree models to guide plant breeders for selecting high yield lines going by the key traits highlighted herein. In addition, having an early prediction of yield helps farmers to make informed decision and take efficient steps to improve grain yield.