Predictive approach to optimize the number of visual graders for indirect selection of high-yielding Urochloa ruziziensis genotypes

Abstract Forage plant breeders often use visual scores to assess agronomic traits because of the costs associated with in-depth phenotyping in the initial stages of breeding cycles. The aim of this study was to investigate the impact of the number of graders on the effectiveness of indirect selection of high-yielding genotypes and determine an optimal number of graders in the early-stage trials of Urochloa ruziziensis. For that purpose, five graders assessed 2.219 U. ruziziensis genotypes in an augmented block design. Biomass production and vigor scores were evaluated in two cuts and were analyzed using a linear mixed model approach. Vigor scores were analyzed considering each grader's score and the combinations of two, three, four, and five graders. Genetic variance was significant for both traits. Visual evaluation was effective in identifying productive genotypes based on the statistical criteria. The optimal number of graders for indirect selection of high-yielding U. ruziziensis genotypes is three.

摘要 饲草作物育种者常在育种周期初期阶段采用视觉评分法评估农艺性状，这是由于该阶段开展深度表型分析（phenotyping）所需成本较高。本研究旨在探究评分员数量对高产基因型间接选择效果的影响，并确定鲁兹臂形草（Urochloa ruziziensis）早期试验中的最优评分员数量。为此，5名评分员采用增广区组设计（augmented block design），对2219份鲁兹臂形草基因型材料开展评价。本研究分别在两次刈割中评估了生物量产量与活力评分，并采用线性混合模型（linear mixed model）方法进行分析。针对活力评分的分析，分别考量了单名评分员的评分结果，以及2名、3名、4名、5名评分员的组合评分结果。两个性状的遗传方差均达显著水平。基于统计标准，视觉评价可有效筛选出高产基因型。针对鲁兹臂形草高产基因型进行间接选择的最优评分员数量为3名。