Selecting high-performing and stable pea genotypes in multi-environmental trial (MET): Applying AMMI, GGE-biplot, and BLUP procedures

Authors: Daba, Sintayehu; Kiszonas, Alecia; McGee, Rebecca

Submitted to: Plants
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/14/2023
Publication Date: 6/16/2023
Citation: Daba, S.D., Kiszonas, A., McGee, R.J. 2023. Selecting high-performing and stable pea genotypes in multi-environmental trial (MET): Applying AMMI, GGE-biplot, and BLUP procedures. Plants. 12(12). Article 2343. https://doi.org/10.3390/plants12122343.
DOI: https://doi.org/10.3390/plants12122343

Interpretive Summary: Pea is used in a variety of forms, including as a vegetable in which immature seeds (and sometimes pods) are consumed, as whole dry seeds in which physiologically mature seeds are consumed, and as flour and fractionated products (protein isolate and starch fraction) derived from physiologically mature seeds. Breeders target peas for different market segments (yellow pea, green pea, vegetable pea, winter pea, and other market classes). A pea breeding program tailored for protein isolation is now needed due to the growing market for pea protein. The USDA-ARS grain legume breeding program stationed at Pullman, Washington focuses on developing green and yellow pea varieties, as well as winter varieties. The program began with green peas in the 1970s and was later extended to yellow peas in the 1980s, and to autumn-sown food quality peas in 2009. High seed yield, increased protein concentration, resistance to biotic and abiotic stresses, and ease of harvesting have been the main target traits. During the breeding history of any crop, a substantial amount of data on phenotypic traits is accumulated. This data could be used to help breeders enhance their breeding program. Here, we tap into ten years (2012-2021) of yield trial data for three pea market classes (green, yellow, and winter types) from the USDA-ARS grain legume breeding program. Predictive accuracy of models can be assessed using cross-validation by dividing the data into training and validation sets. Six balanced datasets were extracted to test the predictive success of BLUP and AMMI family models. Identification of potential genotypes for upcoming breeding efforts is one of the anticipated results. In this regard, the advantages of various statistical tools can be utilized. It is also crucial to compare the yields of various pea market classes and to account for how weather conditions influence pea seed yield.

Technical Abstract: A large amount of data on various traits is accumulated over the course of a breeding program and can be used to optimize various aspects of the crop improvement pipeline. We leveraged data from advanced yield trials (AYT) of three classes of peas (green, yellow, and winter peas) collected over 10 years (2012-2021) to analyze and test key aspects fundamental to pea breeding. Six balanced datasets were used to test predictive success of BLUP and AMMI family models. Predictive assessment using cross validation indicated that BLUP offered better predictive accuracy as compared to any AMMI family model. However, BLUP may not always identify the best genotype that perform well across environments. AMMI and GGE, two statistical tools used to exploit GE, could fill this gap and aid in understanding how genotypes perform across environments. AMMI's yield by environmental IPCA1, WAASB by yield plot, and GGE biplot were shown to be useful in identifying genotypes for specific or broad adaptability. When compared to the most favorable environment, we observed a yield reduction of 80 87% in the most unfavorable environment. The seed yield variability across environments was caused in part by weather variability. Hotter conditions in June and July as well as low precipitation in May and June affected seed yield negatively. In conclusion, the findings of this study are useful to breeders in the variety selection process and growers in pea production.