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Abstract

Wheat productivity is seriously threatened by prevalence of stripe rust, caused by Puccinia striiformis f. sp. tritici, across the globe. The current study evaluated 53 wheat genotypes under artificial stripe rust pathogen inoculation to investigate disease severity Cobb’s scale and its impact on agronomic traits. Disease intensity was quantified using the modified, while adult-plant resistance (APR) was assessed by coefficient of infection (CI) values. Eighteen genotypes exhibited high APR with low CI scores, signifying notable resistance diversity. Agronomic traits including grain-filling period (GFP), plant height (PH), peduncle length (PL), spike length (SL), and 1000-grain weight (TGW) were significantly influenced by rust stress. Positive correlations between GFP and traits such as PH, TGW, and grain weight/spike (GW/S) suggest that extended grain filling contributes to better yield resilience under disease pressure. Conversely, flag leaf area (FLA) showed negative correlations with several yield related traits, indicating potential susceptibility to pathogen infiltration. Principal component analysis (PCA) reduced trait variability to 5 major components explaining 77.42% of the total variance; with PC1 being most influential for GW/S. Biplot analysis identified genetically divergent genotypes (e.g., G7, G14, G20) and traits contributing most to phenotypic variation under rust stress. Cluster and profile analyses further categorized genotypes into 6 groups, revealing distinct trait associations with disease tolerance. Integration of disease assessment with multivariate statistical analyses provides valuable insights for wheat breeding programs, paving a way for effective selection of genotypes with strong APR, high yield and physiological robustness against stripe rust

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