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Research paper

Understanding the impact of cultivar characteristics and environmental conditions on grain protein content and yield in wheat

DOI: https://doi.org/10.24326/as.2026.5529
Submitted: April 15, 2025
Published: 16.02.2026

Abstract

In the face of changing climatic conditions, there is a growing need to better understand the mechanisms influencing wheat yield and grain quality, particularly grain protein content (GPC). While genotype-by-environment interactions (GEI) have been widely studied, few investigations have focused on how specific environmental and varietal traits contribute to these interactions.

In this study, we applied the classification and regression tree (CART) method and a linear mixed model (LMM) to analyze field trial data across different wheat cultivars and varying environmental conditions. The analysis included factors such as soil nutrient content, rainfall distribution during the growing season, and varietal characteristics including plant height and growth duration. Our results revealed that GPC was primarily determined by rainfall during the grain-filling phase and the level of available nitrogen in the soil, while grain yield (GY) was strongly influenced by total rainfall during stem elongation and certain morphological traits. The variable “falling number” was included in the initial analysis but was excluded by the model due to its lack of predictive significance.

This study provides detailed insights into which environmental and varietal traits are most influential in shaping GEI effects on GPC and GY. The use of CART modelling enabled the identification of key predictors affecting cultivar responses under diverse growing conditions. These findings can support breeding and agronomic decision-making by offering predictive tools to select cultivars with improved stability in yield and grain quality under variable climatic conditions.

Our research fills a gap in existing studies by providing new insights into GEI interactions and their impact on yield and grain quality. CART-based models can serve as predictive tools, helping breeders forecast how varieties will respond to future climatic changes and environmental conditions. This approach can significantly contribute to optimizing breeding practices and improving yield stability and quality, supporting sustainable agricultural development.

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