At initial breeding stages, using a replicated check design is a viable alternative to reduce experimental field area as well as financial and operational costs. In this situation, spatial modeling could act to increase prediction accuracy of plant genotypic values. The objectives of this study were to demonstrate how spatially adjusted models can be used to reduce experimental error and how to compare statistical models in order to identify the best model for accurate genotype selection. For this purpose, we assessed 200 F_3:4 tomato families for their resistance to Phytophthora infestans isolates. NC1CELBR, NC25P, and the cultivar Santa Clara were used as checks. Under field conditions, plants were inoculated with P. infestans isolates and scored according to their level of disease severity. Nine statistical models were adjusted to estimate family genotypic values. The selection of the fittest model was based on residual variance values, accuracy, Akaike and Bayesian information criteria, and the maximum likelihood ratio test. We observed spatial patterns within the experimental field area. Spatial modeling decreased error, which is indicated by the better experimental variation distribution. Residual variance decreased, while genotypic variance increased ∼10% when spatial analysis was used. Spatial analysis improved selection accuracy by 19% compared with the traditional analysis. Therefore, we recommend incorporating spatial modeling into data analysis in breeding trials for disease resistance because it can provide higher gains from selection compared with traditional modeling approaches, depending on the experimental condition.

中文翻译：

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空间建模提高了致病疫霉抗性番茄基因型选择的准确性

在最初的育种阶段，使用重复检查设计是减少试验田面积以及财务和运营成本的可行替代方案。在这种情况下，空间建模可以提高植物基因型值的预测准确性。本研究的目的是展示如何使用空间调整模型来减少实验误差，以及如何比较统计模型以确定准确基因型选择的最佳模型。为此，我们评估了 200 个 F _3:4番茄家族对致病疫霉分离株的抗性。NC1CELBR、NC25P 和栽培品种 Santa Clara 被用作检查。在田间条件下，用致病疫霉接种植物分离并根据其疾病严重程度进行评分。调整了九个统计模型以估计家族基因型值。适者模型的选择基于残差方差值、准确性、Akaike 和贝叶斯信息标准以及最大似然比检验。我们观察了实验区域内的空间模式。空间建模减少了误差，这由更好的实验变化分布表明。当使用空间分析时，残余方差减少，而基因型方差增加约 10%。与传统分析相比，空间分析将选择精度提高了 19%。所以，