Accuracy of Multi‐Environmental Trials in Predicting New Environments Using Different Approaches Based on Environmental Covariates: A Case in Barley (Hordeum vulgare L.) Breeding DOI Creative Commons
Diriba Tadese, Hans‐Peter Piepho, Girma F. Dinsa

и другие.

Plant Breeding, Год журнала: 2025, Номер unknown

Опубликована: Март 2, 2025

ABSTRACT One of the current innovations in predicting genotype performances a target population environments is integrating environmental covariates (ECs) into multi‐environment trial (MET) data analysis. In this study, MET set barley ( Hordeum vulgare L.) breeding program years 2016 and 2017 was used. We evaluated compared different approaches using ECs new environments. The comparison done mean squared error predicted differences (MSEPD) under linear mixed models. MSEPD computed for cross‐validation mechanism that drops out one environment at time. Our results show models with resulted smaller model without ECs. Among approaches, reduced rank regression approach component smallest followed by fitting both first second synthetic extended Finlay–Wilkinson regression. Overall, there potential gain predictive accuracy plant programs.

Язык: Английский

Accuracy of Multi‐Environmental Trials in Predicting New Environments Using Different Approaches Based on Environmental Covariates: A Case in Barley (Hordeum vulgare L.) Breeding DOI Creative Commons
Diriba Tadese, Hans‐Peter Piepho, Girma F. Dinsa

и другие.

Plant Breeding, Год журнала: 2025, Номер unknown

Опубликована: Март 2, 2025

ABSTRACT One of the current innovations in predicting genotype performances a target population environments is integrating environmental covariates (ECs) into multi‐environment trial (MET) data analysis. In this study, MET set barley ( Hordeum vulgare L.) breeding program years 2016 and 2017 was used. We evaluated compared different approaches using ECs new environments. The comparison done mean squared error predicted differences (MSEPD) under linear mixed models. MSEPD computed for cross‐validation mechanism that drops out one environment at time. Our results show models with resulted smaller model without ECs. Among approaches, reduced rank regression approach component smallest followed by fitting both first second synthetic extended Finlay–Wilkinson regression. Overall, there potential gain predictive accuracy plant programs.

Язык: Английский

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