Wallace Samuel T, Nelson Natalie G, Reisig Dominic D, Huseth Anders S
Department of Biological and Agricultural Engineering, North Carolina State University, Raleigh, NC, USA.
Center for Geospatial Analytics, North Carolina State University, Raleigh, NC, USA.
Environ Entomol. 2025 Apr 17;54(2):378-385. doi: 10.1093/ee/nvaf011.
Corn earworm, Helicoverpa zea Boddie (Lepidoptera: Noctuidae), is a common herbivore that causes economic damage to agronomic and specialty crops across North America. The interannual abundance of H. zea is closely linked to climactic variables that influence overwintering survival, as well as within-season host plant availability that drives generational population increases. Although the abiotic and biotic drivers of H. zea populations have been well documented, prior temporal H. zea modeling studies have largely focused on mechanistic/simulation approaches, long term distribution characterization, or degree day-based phenology within the growing season. While these modeling approaches provide insight into H. zea population ecology, growers remain interested in approaches that forecast the interannual magnitude of moth flights which is a key knowledge gap limiting early warning before crops are planted. Our study used trap data from 48 site-by-year combinations distributed across North Carolina between 2008 and 2021 to forecast H. zea abundance in advance of the growing season. To do this, meteorological data from weather stations were combined with crop and soil data to create predictor variables for a random forest H. zea forecasting model. Overall model performance was strong (R2 = 0.92, RMSE = 350) and demonstrates a first step toward development of contemporary model-based forecasting tools that enable proactive approaches in support of integrated pest management plans. Similar methods could be applied at a larger spatial extent by leveraging national gridded climate and crop data paired with trap counts to expand forecasting models throughout the H. zea overwintering range.
玉米穗虫,即棉铃虫(Helicoverpa zea Boddie,鳞翅目:夜蛾科),是一种常见的食草动物,会对北美各地的农作物和特色作物造成经济损失。棉铃虫的年际丰度与影响越冬存活率的气候变量以及驱动世代种群增长的季节内寄主植物可利用性密切相关。尽管棉铃虫种群的非生物和生物驱动因素已有充分记录,但之前关于棉铃虫的时间建模研究主要集中在机理/模拟方法、长期分布特征或生长季节内基于度日的物候学上。虽然这些建模方法有助于深入了解棉铃虫的种群生态学,但种植者仍对预测蛾类迁飞年际规模的方法感兴趣,这是一个关键的知识空白,限制了作物种植前的早期预警。我们的研究使用了2008年至2021年间分布在北卡罗来纳州的48个地点逐年组合的诱捕数据,以在生长季节之前预测棉铃虫的丰度。为此,将气象站的气象数据与作物和土壤数据相结合,为随机森林棉铃虫预测模型创建预测变量。总体模型性能良好(R2 = 0.92,RMSE = 350),展示了朝着开发基于当代模型的预测工具迈出的第一步,这些工具能够采取积极主动的方法来支持综合虫害管理计划。通过利用国家网格化气候和作物数据以及诱捕计数,可以在更大的空间范围内应用类似方法,以扩展整个棉铃虫越冬范围内的预测模型。