Field trials conducted in multiple years across several locations play an essential role in plant breeding and variety testing. Usually, the analysis of the series of field trials is performed using a two-stage approach, where each combination of year and site is treated as environment. In variety testing based on the results from the analysis, the best varieties are recommended for cultivation. Under a Bayesian approach, the variety recommendation process can be treated as a formal decision theoretic problem. In the present study, we describe Bayesian counterparts of two stability measures and compare the varieties in terms of the posterior expected utility. Using the described methodology, we identify the most stable and highest tuber yielding varieties in the Polish potato series of field trials conducted from 2016 to 2018. It is shown that variety Arielle was the highest yielding, the third most stable variety and was the second best variety in terms of the posterior expected utility. In the present work, application of the Bayesian approach allowed us to incorporate the prior knowledge about the tested varieties and offered a possibility of treating the variety recommendation process as a formal decision process.

中文翻译：

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早熟马铃薯品种的产量稳定性：贝叶斯分析

多年来在多个地点进行的田间试验在植物育种和品种测试中发挥着至关重要的作用。通常，对一系列田间试验的分析是使用两阶段的方法进行的，其中年份和地点的每个组合都被视为环境。在基于分析结果的品种测试中，推荐最佳品种进行栽培。在贝叶斯方法下，品种推荐过程可以被视为一个正式的决策理论问题。在本研究中，我们描述了两种稳定性度量的贝叶斯对应物，并根据后验期望效用比较了变体。使用所描述的方法，我们在 2016 年至 2018 年进行的波兰马铃薯系列田间试验中确定了最稳定和最高块茎产量的品种。结果表明，Arielle 品种产量最高，稳定性第三，后验预期效用第二好。在目前的工作中，贝叶斯方法的应用使我们能够整合有关测试品种的先验知识，并提供了将品种推荐过程视为正式决策过程的可能性。