This paper is concerned with the generation of designs for early generation, plant‐breeding experiments that use limited experimental resources as efficiently as possible to maximize the realized genetic gain (RGG) resulting from the selection of lines. A number of authors have demonstrated that partially replicated (p‐rep) designs for such experiments, in which the percentage of lines that are duplicated is p, are likely to be more efficient than grid‐plot designs. Therefore, our aim is to obtain the most efficient p‐rep design for an experiment using one of two distinctly different criteria and employing widely or readily available statistical software packages to search for an optimal design. However, this can be difficult because knowledge of the sources of variation and their magnitudes is required and is often unavailable. To overcome this impediment, a comprehensive simulation experiment was conducted to investigate whether designs that are robust to a wide range of experimental situations can be identified. Designs with p set to 20% and for different experimental situations are generated and the performance of each tested for 24 different variation scenarios. We concluded that for large experiments, the RGG obtained with various optimal designs is indeed not affected by the different variation scenarios and that resolved designs for fixed genetic effects should be generated for robustness. On the other hand, the design assumptions affect the RGG for small p‐rep designs. Even so, an overall recommendation is made.

中文翻译：

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在早期植物育种实验中使用错误指定的统计模型生成p-rep设计对遗传增益的影响

本文关注的是早期植物育种设计的产生，这些设计尽可能有效地利用有限的实验资源，以最大化因选择品系而产生的实际遗传增益（RGG）。许多作者已经证明，针对此类实验的部分复制（p- rep）设计（其中重复行的百分比为p）可能比网格图设计更有效。因此，我们的目标是获得最有效的p使用两个截然不同的标准之一进行实验的重复设计，并采用广泛使用或易于获得的统计软件包来搜索最佳设计。但是，这可能很困难，因为需要了解变化的来源及其大小，而且通常是不可用的。为了克服这一障碍，进行了全面的仿真实验，以调查是否可以确定对各种实验情况都可靠的设计。用p设计设置为20％，并针对不同的实验情况生成了该文件，并针对24种不同的变化情况测试了每种产品的性能。我们得出的结论是，对于大型实验，采用各种最佳设计获得的RGG确实不受不同变异方案的影响，并且应生成针对固定遗传效应的解析设计以提高鲁棒性。另一方面，设计假设会影响小型p- rep设计的RGG 。即使这样，还是提出了总体建议。