Joint validation of yield and inter-annual consistency and elite lines selection in 52 advanced wheat lines based on a PCA-membership function-TOPSIS collaborative system.

In arid-region wheat breeding, environmental fluctuations are large, and screening based on single traits or single-year data is often inaccurate. To address these challenges, this study constructed a multi-model collaborative evaluation system to accurately screen advanced lines for high yield potential and inter-annual consistency. Based on 10 yield traits evaluated in 52 advanced wheat lines over two growing seasons, we utilized systematic clustering to classify lines and stepwise regression to pinpoint core contributing traits. Subsequently, principal component analysis (PCA), membership functions, and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) were integrated for joint validation, enabling the robust identification of lines with superior and inter-annually consistent yield potential. The results showed that: (1) TD-48 and TD-26 ranked among the top ten across all three evaluation methods in both experimental years, showing strong potential for high yield and inter-annual consistency. (2) Cluster analysis divided all tested lines into six categories: High-yield and inter-annually consistent type, High-yield but inter-annually fluctuating type, High-yield type, Medium-yield type, Low-yield type, and Low-yield sensitive type. TD-48 and TD-26 were classified into the high-yield and inter-annually consistent group. (3) A predictive regression model was established via stepwise regression: D = 0.16 + 0.294GY+0.123GWS+0.064TKW+0.107GNP+0.054SNP+0.018BSSN (R