Spatial variation is a recurrent issue in field trials and can cause obstacles in terms of genetic selection. Analyses that account for spatial variation within location can lead breeders to predict genetic values accurately across locations in multi‐environment trials (METs). The present study aims to fit spatial models for analyzing soybean [Glycine max (L.) Merr.] seed composition traits using a two‐stage analysis pipeline and to assess its efficiency relative to a single‐stage analysis setting. Seed protein content (SPC), seed oil content (SOC), and seed storage protein content (SSP) data were collected from 283 soybean genotypes tested in four environments (C1, C2, V1, and V2). In Stage 1 of the two‐stage analysis, a randomized complete block (RCB) design model as well as four two‐dimensional first‐order (AR1 ⊗ AR1) spatial models were fit in each dataset to determine the most suitable model for genetic prediction. Predicted genetic values were used as input data for Stage 2. The most used spatial model [5] in Stage 1 of this study had accommodated local and global residuals. The autocorrelation estimates depicted spatial trends, especially in terms of rows, while column autocorrelation coefficients were low for C1 and C2 because of the limited number of blocks and their short length. Broad‐sense heritability, mean accuracy, and selection gains were greater for all traits in the two‐stage analysis than in the single‐stage analysis. The two‐stage analysis leveraged the spatial model fitting in the Stage 1 and proved to be advantageous for soybean seed composition breeding.

中文翻译：

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模拟空间趋势并增强遗传选择：大豆种子成分育种的一种方法

空间变异是田间试验中经常出现的问题，可能在遗传选择方面造成障碍。分析位置内空间变化的分析可以使育种者在多环境试验（METs）中准确预测各个位置的遗传值。本研究旨在拟合用于分析大豆的空间模型[ Glycine max[L.）Merr。]使用两阶段分析管道并评估其相对于单阶段分析设置的效率来分析种子组成特征。种子蛋白含量（SPC），种子油含量（SOC）和种子贮藏蛋白含量（SSP）数据是从在四种环境（C1，C2，V1和V2）中测试的283种大豆基因型收集的。在两阶段分析的第1阶段中，将一个随机完全块（RCB）设计模型以及四个二维一阶（AR1⊗AR1）空间模型拟合到每个数据集中，以确定最适合遗传预测的模型。将预测的遗传值用作第2阶段的输入数据。本研究第1阶段最常用的空间模型[5]已适应了局部和全局残差。自相关估计描述了空间趋势，尤其是在行方面，C1和C2的列自相关系数很低，因为块的数量有限且长度较短。两阶段分析中所有性状的广义遗传力，平均准确性和选择增益均高于单阶段分析。两阶段分析利用了阶段1中的空间模型拟合，并证明对大豆种子成分育种有利。