BACKGROUNDPre‐harvest pest management tools are essential to minimizing crop loss. The development of predictive models using early warning signs of pest abundance to predict imminent crop loss can guide management decisions and enable targeted, well‐calibrated intervention. With sufficient data, in‐season measures of pest abundance can be an important factor in generating accurate predictions of damage. However, sampling plans for tracking insect phenology and those designed to guide informed pest‐management may not be equivalent. Using data from a commercial almond orchard setting, we evaluated five different lure‐trap combinations used for monitoring navel orangeworm (Amyelois transitella; a primary pest of almond) under different management conditions. Using these data, we developed a predictive model of almond damage and evaluated the contribution of in‐season measures of pest abundance towards accurate predictions of end‐of‐season damage.RESULTSMating disruption in orchard management influenced the efficacy of multiple lure‐trap combinations. Despite this effect, there was as strong correlation between measured pest abundance and damage across multiple trap types regardless of management method. While statistically significant, measures of pest abundance did not enhance the accuracy of predictive models and characteristics of almond variety were stronger predictors of damage at harvest.CONCLUSIONRepeated significant correlations between navel orangeworm abundance and almond damage across multiple trap types justifies continued exploration of predictive modeling as a management tool. Lure‐trap combinations exhibiting resistance to external factors are clear candidates for further development. However, generating accurate predictions of damage using these data will likely require calibration or modification of current sampling protocols. © 2024 Society of Chemical Industry. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

中文翻译：

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评估使用季节有害生物丰度指标来预测季末损害：一项对商业杏仁 （Prunus dulcis） 的研究


背景收获前的害虫管理工具对于最大限度地减少作物损失至关重要。利用有害生物丰发的早期预警信号来预测即将发生的作物损失，开发预测模型可以指导管理决策，并实现有针对性的、经过充分校准的干预措施。有了足够的数据，害虫数量的季节测量可以成为准确预测损害的重要因素。然而，用于跟踪昆虫物候的抽样计划与旨在指导知情害虫管理的抽样计划可能不等效。使用来自商业杏仁园环境的数据，我们评估了在不同管理条件下用于监测脐橙虫（Amyelois transitella;杏仁的主要害虫）的五种不同的诱饵-陷阱组合。利用这些数据，我们开发了一个杏仁损害的预测模型，并评估了季节中害虫丰度测量对准确预测季节末损害的贡献。结果果园管理中的交配破坏影响了多种诱饵-诱捕器组合的有效性。尽管有这种影响，但无论采用何种管理方法，测得的害虫数量与多种诱捕器类型的损害之间都存在很强的相关性。虽然具有统计学意义，但有害生物丰度的测量并没有提高预测模型的准确性，杏仁品种的特征是收获时损害的更强预测因子。结论脐橙虫丰度与多种诱捕器类型的杏仁损伤之间存在显著相关性，证明继续探索预测建模作为管理工具是合理的。表现出对外部因素的抵抗力的诱饵-陷阱组合显然是进一步开发的候选者。 然而，使用这些数据生成准确的损伤预测可能需要校准或修改当前的采样协议。© 2024 化工学会.本文由美国政府雇员提供，他们的工作在美国属于公共领域。