Good weed management relies on the proper timing of weed control practices in relation to weed emergence dynamics. Therefore, the development of models that predict the timing of emergence may help provide growers with tools to make better weed management decisions. The aim of this study was to validate and compare two previously published predictive empirical thermal time models of the emergence of Abutilon theophrasti growing in maize with data sets from the USA and Europe, and test the hypothesis that a robust and general weed emergence model can be developed for this species. Previously developed Weibull and Logistic models were validated against new data sets collected from 11 site‐years, using four measures of validation. Our results indicated that predictions made with the Weibull model were more reliable than those made with the Logistic model. However, Weibull model results still contained appreciable biases that prevent its use as a general model of A. theophrasti emergence. Our findings highlight the need to develop more accurate models if the ultimate goal is to make more precise predictions of weed seedling emergence globally to provide growers with universally consistent tools to make better weed management decisions.

中文翻译：

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基于洲际数据的Abutilon theophrasti Medik预测性经验性杂草出苗模型的验证

良好的杂草管理取决于杂草出苗动态的适当时机。因此，预测出苗时间的模型的开发可能有助于为种植者提供制定更好的杂草管理决策的工具。本研究的目的是验证和比较两个先前发表的关于util麻神菌出现的预测性经验热时间模型。利用来自美国和欧洲的数据集在玉米上生长，并验证了可以为该物种开发强大而通用的杂草出苗模型的假设。使用四种验证方法，针对从11个站点年度收集的新数据集对先前开发的Weibull和Logistic模型进行了验证。我们的结果表明，使用Weibull模型进行的预测比使用Logistic模型进行的预测更可靠。然而，Weibull模型的结果仍然包含明显的偏差，从而阻止了将其用作嗜热气单胞菌出现的一般模型。 我们的发现强调，如果最终目标是对全球杂草苗出苗情况进行更精确的预测，以便为种植者提供普遍一致的工具以制定更好的杂草管理决策，则需要开发更准确的模型。