A new growing degree-day phenology model for wheat stem sawfly (Hymenoptera: Cephidae) in Colorado wheat fields

Authors: VIEIRA, HENRIQUE - Colorado State University; BRADFORD, BENJAMIN - University Of Wisconsin; OSTERHOLZER, ADAM - Colorado State University; Peirce, Erika; COCKRELL, DARREN - Colorado State University; PEAIRS, FRANK - Colorado State University; GROVES, RUSSELL - University Of Wisconsin; NACHAPPA, PUNYA - Colorado State University

Submitted to: PLOS ONE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/11/2025
Publication Date: N/A
Citation: N/A

Interpretive Summary: Wheat stem sawfly (WSS), a key pest in North America, damages wheat by boring into and cutting stems, leading to significant yield loss. Current control methods, such as planting solid stem varieties and using insecticides, are not fully effective. To improve management, this study examined WSS emergence patterns and population sizes in Colorado wheat fields from 2011 to 2023. Using growing degree-days (GDD) as a measure, we developed a model that predicts adult WSS emergence and population peaks. Key findings showed that WSS begins emerging at 148 GDD, peaks at 224 GDD, and concludes at 354 GDD. Additionally, higher WSS populations were linked to longer emergence periods, lower precipitation, and milder temperatures. This phenology model can help forecast WSS activity and improve pest management strategies for farmers.

Technical Abstract: Wheat stem sawfly (WSS), Cephus cinctus (Hymenoptera: Cephidae), is a native grass-feeding insect and one of the most important pests of wheat in North America. Yield losses from WSS can be due to stem boring and/or stem cutting which causes plants to lodge. Current methods such as solid stem varieties and insecticides do not effectively control WSS. A better understanding of WSS emergence and population size and the related environmental factors is key to building efficient and effective integrated pest management (IPM) strategies for this pest. In this study, wheat fields were sampled for adult WSS from mid-April to end of June between 2011 and 2023 in several field sites in two locations in Colorado. This multi-year data was used to create a phenology model that predicts adult WSS emergence and adult population peak based on growing degree-days (GDD). The inter-annual variability in emergence timing based on calendar date was substantially reduced when using a GDD model with a base temperature of 10°C, an upper threshold of 30°C, and a biofix of Jan 1. The model predicted initial WSS emergence at 148 DD, population peak at 224 DD and decline at 354 DD. We also modelled the effects of environmental factors on mean WSS populations at each field site, finding that higher WSS populations are associated with longer emergence periods, less precipitation prior to the start of emergence, milder temperatures during emergence, and milder maximum temperatures before and during emergence. By analyzing multiple years of comprehensive phenology data, we provide robust models to guide adult WSS forecasting and monitoring, for the first time. Further, this data will aid in decision making related to timely and effective management strategies to suppress populations of WSS.