Adriana Derejko, Marcin Studnicki, Elzbieta Wójcik-Gront

Impact of genotype-environment interactions on wheat yield and quality: Evidence from multilocation trials

Introduction

Impact of genotype-environment interactions on wheat yield and quality: evidence from multilocation trials. Analyze genotype-environment interactions on wheat yield & quality. Discover key trade-offs between yield and protein content, informing breeding strategies for resilient, high-quality wheat cultivars.

Abstract

Common wheat is a globally important cereal crop, with its winter form essential for milling and baking industries. This study explores the relationships between grain quality traits and yield in wheat, focusing on genetic and environmental influences. Field trials of 55 cultivars across 12 locations over five seasons evaluated grain yield (GY), grain protein content (GPC), thousand-grain weight (TGW), Zeleny sedimentation value (SV), and Hagberg falling number (FN) using linear mixed models and path analysis. Results revealed trade-offs between yield and quality: higher FN and SV correlated with increased GPC, while higher GY and TGW were linked to lower GPC. GY positively correlated with TGW but was negatively influenced by GPC. Path analysis confirmed these relationships, emphasizing genetic and environmental interactions. GGE biplot analysis identified cultivars with stable performance across environments. This study highlights strategies for balancing yield and quality in breeding programs, offering insights for developing resilient cultivars tailored to specific uses and environmental conditions, applicable beyond wheat breeding.

Review

This study provides a comprehensive and valuable analysis of genotype-environment interactions on yield and quality traits in common wheat, a globally critical crop. The authors have undertaken an extensive investigation, evaluating 55 cultivars across 12 diverse locations over five seasons, which provides a robust dataset for drawing meaningful conclusions. By employing advanced statistical techniques such as linear mixed models, path analysis, and GGE biplots, the research effectively dissects the complex relationships and inherent trade-offs between grain yield, grain protein content, and key quality indicators like Zeleny sedimentation value and Hagberg falling number. The clarity with which these interactions are presented forms the core strength of this work. Methodologically, the paper demonstrates a high level of rigor. The application of linear mixed models appropriately accounts for genetic and environmental variances, while path analysis provides crucial insights into the direct and indirect effects governing the observed correlations, such as the positive relationship between GY and TGW but its negative correlation with GPC. The findings confirming that higher baking quality (FN, SV) traits correlate with increased GPC, while higher GY and TGW are linked to lower GPC, are particularly pertinent for breeding programs. Furthermore, the use of GGE biplot analysis to identify cultivars with stable performance across environments is a practical and highly relevant contribution, aiding breeders in selecting adaptable genotypes. The implications of this research are significant, offering actionable strategies for balancing yield and quality in wheat breeding programs. By providing a deeper understanding of the genetic and environmental factors influencing these critical traits, the study paves the way for developing more resilient cultivars specifically tailored to diverse environmental conditions and end-use requirements. The abstract also suggests the applicability of these insights beyond wheat breeding, underscoring the potential for broader impact in crop improvement. This work substantially enhances our understanding of complex trait architecture and provides an excellent foundation for future genetic and breeding efforts in cereals.

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