Abstract Lodging is a complex phenomenon affecting wheat production worldwide and is a consequence of the interaction of wheat plants with abiotic (wind, rain, etc.) and biotic (e. g. plant disease, etc.) factors. Wheat breeders rely heavily on incidences of natural lodging to select lines with resistance; however, the intermittent nature of lodging events means this approach is unreliable. A model of the lodging process has been published to estimate the lodging susceptibility of plants that uses information from 15 stem and root characteristics of field grown plants that influence lodging. This approach estimates lodging susceptibility in the absence of natural lodging. However, the main shortcoming of this methodology for plant breeders is the amount of time required to measure these traits (100–150 min per plot). This study investigated two strategies to optimise the methods of estimating lodging risk in the absence of natural lodging: i) determining the minimum number of plants that must be measured per experimental plot (sample size) to identify genetic differences and ii) minimizing the number of traits required to assess lodging susceptibility increasing the feasibility to apply the methodology in a breeding context. Spring wheat grown under North West Mexico environmental conditions was established during four crop seasons (2010−11, 2011−12, 2012−13 and 2013−14) for this study. Results indicated an optimum sample size of seven plants per plot as the minimum required to identify genetic differences between cultivars with good statistical power and precision (assuming each treatment plot was replicated 3 times). Cultivar ranking and absolute values for trait dimensions were maintained when compared with larger sample sizes. A reduced number of traits can be used to estimate cultivar lodging susceptibility performances and key traits include: plant height, ear number per plant, ear area, natural frequency, breaking strength, length, diameter and wall width of one basal internode, structural rooting depth and root plate spread. Targeting these key traits, this study established that on a daily basis, 10 (47 min per plot) plots can be assessed by measuring seven plants per plot per person. Moreover, if the screening focuses only on the key traits for leverage/stem/root dimensions, then the daily plot assessment capacity would increase to 25.

中文翻译：

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优化表型分析方法以评估小麦倒伏风险

摘要 倒伏是影响全球小麦生产的复杂现象，是小麦植株与非生物（风、雨等）和生物（如植物病害等）因素相互作用的结果。小麦育种者严重依赖自然倒伏的发生率来选择具有抗性的品系；然而，住宿事件的间歇性意味着这种方法是不可靠的。已经发布了一个倒伏过程模型来估计植物的倒伏易感性，该模型使用来自影响倒伏的田间种植植物的 15 种茎和根特征的信息。这种方法估计在没有自然倒伏的情况下倒伏的敏感性。然而，这种方法对于植物育种者的主要缺点是测量这些性状所需的时间（每块地 100-150 分钟）。本研究调查了两种策略，以优化在没有自然栖息地的情况下估计栖息地风险的方法：i) 确定每个试验地必须测量的最小植物数量（样本大小）以识别遗传差异和 ii) 最小化植物数量评估倒伏易感性所需的特征增加了在育种环境中应用该方法的可行性。本研究在四个作物季节（2010-11、2011-12、2012-13 和 2013-14）中建立了在墨西哥西北部环境条件下生长的春小麦。结果表明，每个地块的最佳样本量为 7 株植物，这是以良好的统计功效和精确度（假设每个处理地块重复 3 次）鉴定品种之间遗传差异所需的最小数量。与较大的样本量相比，品种排名和性状维度的绝对值得以保持。减少数量的性状可用于估计品种的倒伏易感性，关键性状包括：株高、每株穗数、穗面积、自然频率、断裂强度、一个基部节间的长度、直径和壁宽、结构生根深度和根板蔓延。针对这些关键性状，本研究确定每天可以通过测量每人每块地 7 株植物来评估 10 个（每块地 47 分钟）地块。此外，如果筛选只关注杠杆/茎/根维度的关键特征，那么每天的小区评估能力将增加到 25。减少数量的性状可用于估计品种的倒伏易感性，关键性状包括：株高、每株穗数、穗面积、自然频率、断裂强度、一个基部节间的长度、直径和壁宽、结构生根深度和根板蔓延。针对这些关键性状，本研究确定每天可以通过测量每人每块地 7 株植物来评估 10 个（每块地 47 分钟）地块。此外，如果筛选只关注杠杆/茎/根维度的关键特征，那么每天的小区评估能力将增加到 25。减少数量的性状可用于估计品种的倒伏易感性，关键性状包括：株高、每株穗数、穗面积、自然频率、断裂强度、一个基部节间的长度、直径和壁宽、结构生根深度和根板蔓延。针对这些关键性状，本研究确定每天可以通过测量每人每块地 7 株植物来评估 10 个（每块地 47 分钟）地块。此外，如果筛选只关注杠杆/茎/根维度的关键特征，那么每天的小区评估能力将增加到 25。结构生根深度和根板分布。针对这些关键性状，本研究确定每天可以通过测量每人每块地 7 株植物来评估 10 个（每块地 47 分钟）地块。此外，如果筛选只关注杠杆/茎/根维度的关键特征，那么每天的小区评估能力将增加到 25。结构生根深度和根板分布。针对这些关键性状，本研究确定每天可以通过测量每人每块地 7 株植物来评估 10 个（每块地 47 分钟）地块。此外，如果筛选只关注杠杆/茎/根维度的关键特征，那么每天的小区评估能力将增加到 25。