Speaker
Description
In Germany, cultivars are tested for regional recommendations in federal state cultivar trials, taking the form of multi-environment trials (METs). Their primary objective is to identify cultivars that are best suited for regional production in agro-ecological zones. For perennial ryegrass, current selection decisions are predominantly based on yield. Incorporating additional quality characteristics could improve the selection process. In Bavarian state cultivar trials, additional quality parameters were taken for perennial ryegrass to describe cultivar quality. Due to financial constraints, the number of samples sent to the laboratory for analysis annually is limited. Consequently, single-plot samples were partly mixed across replicates within a site and analysed as mixed or composite samples. The decision which single-plot samples were mixed for mixed samples were made by intuition. The objective of the current study was finding an optimal distribution of single-plot and mixed samples for quality parameters in perennial ryegrass for a constrained number of laboratory samples. Data from METs of perennial ryegrass across three sites in Bavaria during the years 2017–2023 were analysed. The analysis comprises two main steps. First, variance components at each site across trial cycles were estimated. In this context, a general strategy for dealing with different year effect in perennial crops is proposed and applied. The second step involves the simulation of data representing alternative sampling designs. Data of alternative designs were analysed with variance components fixed to the estimates obtained in the first step. The precision measured as power and standard error of treatment differences was assessed for each alternative design. The most efficient design in this framework is an even distribution of two mixed samples per cultivar and cut at each site. Where the residual error variance is high, more samples are worthwhile. The results enable the implementation of quality aspects into the trialling system without exceeding the given limits of workload or financial constraints.
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