Abstract Stalk lodging in maize results in substantial yield losses worldwide. These losses could be prevented through genetic improvement. However, breeding efforts and genetics studies are hindered by lack of a robust and economical phenotyping method for assessing stalk lodging resistance. A field-based phenotyping platform that induces failure patterns consistent with natural stalk lodging events and measures stalk bending strength in field-grown plants was recently developed. Here we examine the association between data gathered from this new phenotyping platform with counts of stalk lodging incidence on 47 maize hybrids representing a subset of genetic diversity. For comparative purposes, we examine four additional predictive phenotypes commonly assumed to be related to stalk lodging resistance; namely, rind penetrometer resistance (a.k.a. rind puncture resistance), cellulose, hemicellulose, and lignin. Lodging incidence data were gathered on 47 hybrids, grown in 98 distinct environments, spanning four years and 41 unique geographical locations in North America. Using Bayesian generalized linear mixed effects models, we show that stalk lodging incidence is associated with each of the five predictive phenotypes. Further, based on a joint analysis we demonstrate that, among the phenotypes considered, stalk bending strength measured by the new phenotyping platform is the strongest predictive phenotype of naturally occurring stalk lodging incidence in maize, followed by rind penetrometer resistance and cellulose content. This study demonstrates that field-based measurements of stalk bending strength provide a reliable estimate of stalk lodging incidence. The stalk bending strength data acquired from the new phenotyping platform will be valuable for phenotypic selection in breeding programs and for generating mechanistic insights into the genetic regulation of stalk lodging resistance.

中文翻译：

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秸秆弯曲强度与多种环境下玉米秸秆倒伏发生率密切相关

摘要 玉米秸秆倒伏导致世界范围内的大量产量损失。这些损失可以通过遗传改良来预防。然而，育种工作和遗传学研究因缺乏用于评估茎秆倒伏抗性的稳健且经济的表型分析方法而受到阻碍。最近开发了一种基于田间表型的平台，该平台可诱导与自然秸秆倒伏事件一致的故障模式，并测量田间种植植物的秸秆弯曲强度。在这里，我们研究了从这个新的表型平台收集的数据与代表遗传多样性子集的 47 个玉米杂交种的茎倒伏发生率之间的关联。出于比较的目的，我们检查了通常被认为与茎秆抗倒伏性相关的四种额外的预测表型；即，外皮针入度电阻（又名 皮耐穿刺性）、纤维素、半纤维素和木质素。收集了 47 个杂交种的倒伏发生率数据，这些杂交种在北美 98 个不同的环境中生长，跨越四年，分布在 41 个独特的地理位置。使用贝叶斯广义线性混合效应模型，我们表明茎倒伏发生率与五种预测表型中的每一种有关。此外，基于联合分析，我们证明，在考虑的表型中，新表型平台测量的茎弯曲强度是玉米中自然发生的茎倒伏发生率的最强预测表型，其次是果皮渗透计电阻和纤维素含量。这项研究表明，基于现场的茎秆弯曲强度测量提供了对茎秆倒伏发生率的可靠估计。