English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The aim of plant breeding trials is often to identify germplasms that arewell adapt to target environments. These germplasms are identified throughgenomic prediction from the analysis of multi-environmental field trial (MET)using linear mixed models. The occurrence of outliers in MET are common andknown to adversely impact accuracy of genomic prediction yet the detection ofoutliers, and subsequently its treatment, are often neglected. A number ofreasons stand for this - complex data such as MET give rise to distinct levelsof residuals and thus offers additional challenges of an outlier detectionmethod and many linear mixed model software are ill-equipped for robustprediction. We present outlier detection methods using a holistic approach thatborrows the strength across trials. We furthermore evaluate a simple robustgenomic prediction that is applicable to any linear mixed model software. Theseare demonstrated using simulation based on two real bread wheat yield METs witha partially replicated design and an alpha lattice design.

Simple robust genomic prediction and outlier detection for a multi-environmental field trial