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ARS Home » Midwest Area » Ames, Iowa » National Laboratory for Agriculture and The Environment » Soil, Water & Air Resources Research » Research » Publications at this Location » Publication #370943

Title: Forecasting grasshopper populations in north-central Wyoming

Author
item KUMAR, SUNIL - Animal And Plant Health Inspection Service (APHIS)
item Kistner-Thomas, Erica
item WOLLER, DEREK - Animal And Plant Health Inspection Service (APHIS)
item JECH, LARRY - Retired Non ARS Employee

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 12/4/2019
Publication Date: 2/4/2020
Citation: Kumar, S., Kistner-Thomas, E.J., Woller, D., Jech, L. 2020. Forecasting grasshopper populations in north-central Wyoming. [Abstract] Annual Meeting of the National Grasshopper Management Board.

Interpretive Summary:

Technical Abstract: Since the mid-19th century, grasshoppers have been a perennial threat to North American rangelands as well as adjacent crop lands, and have the potential to cost the economy millions of dollars in annual damages. USDA APHIS (Animal Plant Inspection Service), along with federal, state, tribal, and private land managers in the 17 western, contiguous U.S. states containing rangelands, have gone to great lengths to ensure that populations remain below an economic impact threshold. However, current grasshopper forecasting efforts by APHIS are based solely on the previous year’s grasshopper densities and do not take region specific environmental factors (i.e. climate and landscape) into account. In this inter-agency research project, we aim to improve USDA grasshopper pest forecasting and efforts by developing a predictive grasshopper outbreak model using landscape modeling approaches. We utilized geographically referenced grasshopper survey data (2007-2017) in conjunction with high resolution gridded landscape & climate data in order to determine the effects of environmental variables on monthly grasshopper density counts from 56 sites in North Central WY. Precipitation in October and soil moisture in March were among the best predictors of grasshopper density indicating the importance of moisture for egg development. Models that included only the past year's grasshopper densities explained relatively less variation in the current year's grasshopper densities and were improved significantly with the inclusion of climate and landscape variables. The results of this study can be applied to USDA's grasshopper forecasting effects and may ultimately improve the efficacy of federally funded grasshopper chemical control applications at relevant spatial scales.