Studies assessing phenotypes of plant populations traditionally place their primary focus on increasing measurement precision and improving accuracy. Phenotyping methods that use imaging, remote sensing, and spectroscopy, continue to increase throughput capacity, but information has been unavailable to assess the tradeoffs between increased throughput and any potential decreases in measurement accuracy. In this simulation study, we compare four levels of measurement accuracy across varying levels of throughput, and discuss how an increased error rate can be compensated for via increased throughput, if experimental resources are allocated appropriately. Under the tested scenarios of increased throughput, the correct values of genotypes were best estimated by increasing the number of environments. Genetic mapping studies should increase population size as well to see improvements over more accurate measurement methods. This simplistic simulation mimics many empirical findings and will be of interest to any researcher interested in assessing how high-throughput phenotyping methods affect decision-making in crop research programs.

中文翻译：

---

在对植物种群进行表型分型时，高通量可以产生比高精度更好的决策

传统上，评估植物种群表型的研究主要关注提高测量精度和提高准确性。使用成像、遥感和光谱学的表型分析方法继续提高吞吐量，但没有信息可用于评估增加的吞吐量与测量精度的任何潜在下降之间的权衡。在本模拟研究中，我们比较了不同吞吐量级别的四个测量精度级别，并讨论了如果实验资源分配适当，如何通过增加吞吐量来补偿增加的错误率。在增加吞吐量的测试场景下，通过增加环境数量来最好地估计基因型的正确值。基因图谱研究也应该增加种群规模，以看到比更准确的测量方法的改进。这种简单的模拟模拟了许多实证结果，任何有兴趣评估高通量表型方法如何影响作物研究计划决策的研究人员都会感兴趣。