Yield, Protein, and Starch Equilibrium of Indigenous Varieties: An Open Door for Computational Breeding in Enhancing Selection Strategies
Given the increasing demand for wheat and the challenges of climate change, it is essential for breeding programs to adapt their strategies to reach the maximum biological potential of new varieties faster. Our study investigates the relationship between wheat yield, protein content, and starch accu...
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
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| Series: | Agronomy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4395/15/6/1280 |
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| Summary: | Given the increasing demand for wheat and the challenges of climate change, it is essential for breeding programs to adapt their strategies to reach the maximum biological potential of new varieties faster. Our study investigates the relationship between wheat yield, protein content, and starch accumulation over five years of Romanian winter wheat varieties. This study included a total of 25 wheat varieties, comprising 16 newly developed ones and 9 varieties registered and cultivated in Romania. The experiment was conducted in three replications over a period of five years. To monitor the equilibrium pattern, the Glosa variety was used as a reference, known for its optimal balance of yield and protein across Romania, as reported in several studies and farmers’ reports. Our research results indicate an inverse correlation between protein content and yield, whereas starch content exhibits a positive correlation with yield among the wheat varieties analyzed. K-means and Principal Component Analyses (PCA) identified Glosa, Lovrin02, Lovrin08, and Boema as the most balanced varieties regarding yield and grain quality stability. The equilibrium model revealed in the results offers information on trait inheritance and heritability, as similar equilibrium patterns were observed across the 25 analyzed varieties over a five-year testing period. Furthermore, integrating an equilibrium model into computational breeding could provide a framework for enabling breeding programs to optimize yield and grain composition while eliminating low-potential varieties. |
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| ISSN: | 2073-4395 |