In particular, models that consider analysis of yield trials either independently or jointly (thus borrowing strength across trials) are considered.
These are demonstrated using simulation based on two real bread wheat yield METs. Although these methods are not optimal methods in outlier detection, they offer practical value for ease of application in the analysis pipeline of regularly collected data. We demonstrate outlier detection methods that are simple to implement in any linear mixed model software packages and computationally fast. Second, many linear mixed model software packages that cater for complex variance structures needed in the analysis of MET are not well streamlined for diagnostics by practitioners. This complexity offers additional challenges for an outlier detection method. A number of reasons stand for this-first, complex data such as a MET give rise to distinct levels of residuals (eg, at a trial level or individual observation level). The occurrence of outliers in MET is common and known to adversely impact the accuracy of genomic prediction yet the detection of outliers are often neglected. These varieties are identified through genomic prediction from the analysis of multi‐environmental field trial (MET) using linear mixed models. AbstractThe aim of plant breeding trials is often to identify crop variety that are well adapt to target environments.
