A hydrothermal model to predict Russian thistle (Salsola tragus) seedling emergence in the dryland of the Pacific Northwest (USA)

Authors: OREJA, FERNANDO - Oregon State University; GENNA, NICHOLAS - Oregon State University; GONZALEZ-ANDUJAR, J - Spanish National Research Council; Wuest, Stewart; BARROSO, JUDIT - Oregon State University

Submitted to: Weed Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/10/2023
Publication Date: 11/17/2023
Citation: Oreja, F.H., Genna, N.G., Gonzalez-Andujar, J.L., Wuest, S.B., Barroso, J. 2023. A hydrothermal model to predict Russian thistle (Salsola tragus) seedling emergence in the dryland of the Pacific Northwest (USA). Weed Science. 72(1):108-112. https://doi.org/10.1017/wsc.2023.67.
DOI: https://doi.org/10.1017/wsc.2023.67

Interpretive Summary: Russian thistle (Salsola tragus L.) is among the most troublesome weeds in cropland and ruderal semiarid areas of the Pacific Northwest (PNW). Predicting S. tragus emergence timing plays a critical role in scheduling weed management measures. The objective of this research was to develop and validate a predictive model of the seedling emergence pattern of S. tragus under field conditions in the PNW that can provide precise information to farmers and crop advisors in order to increase the efficacy of control measures targeting this species. The relationship between cumulative seedling emergence and cumulative hydrothermal time under field conditions was modeled and is the first to use hydrothermal time units (HTT) to predict S. tragus emergence. The model showed a very good fit to the experimental data. Seedling emergence starts at 5 HTT and 50% and 95% emergence is completed at 56 HTT and 177 HTT, respectively. This model appears robust enough to be used as a predictive tool for S. tragus seedling emergence.

Technical Abstract: Russian thistle (Salsola tragus L.) is among the most troublesome weeds in cropland and ruderal semiarid areas of the Pacific Northwest (PNW). Predicting S. tragus emergence timing plays a critical role in scheduling weed management measures. The objective of this research was to develop and validate a predictive model of the seedling emergence pattern of S. tragus under field conditions in the PNW that can provide precise information to farmers and crop advisors in order to increase the efficacy of control measures targeting this species. The relationship between cumulative seedling emergence and cumulative hydrothermal time under field conditions was modeled using the Weibull function. The latter model is the first to use hydrothermal time units (HTT) to predict S. tragus emergence and showed a very good fit to the experimental data. According to this model, seedling emergence starts at 5 HTT and 50% and 95% emergence is completed at 56 HTT and 177 HTT, respectively. For model validation, independent field experiments were carried out. Cumulative seedling emergence was accurately predicted by the model, supporting the idea that this model is robust enough to be used as a predictive tool for S. tragus seedling emergence.