WWA’s Climate Adaptation Decisions program began to focus on drought impacts in the range livestock industry (“ranching”) during FY 15/16 as part of a collaborative project with the USDA Northern Plains Climate Hub and the DoI North Central Climate Science Center. The goals of the project, established at retreats among the three organizations, were to combine drought impact calculators developed by the USDA Agricultural Research Service (ARS) with a ranching drought decision model, developed by Jeffrey Tranel, Rod Sharp, and John Deering, economists with the Colorado State University Cooperative Extension Service (available here). With support from NIDIS we then expanded the modeling effort aiming to help ranchers make herd management decisions in extreme drought, given uncertainties about the market, feed prices, and next year’s climate. Finally, in collaboration with CU’s Earth Lab, we added a module that calculates likely pay-off of the USDA Risk Management Agency (RMA) range insurance program. The structure of the combined model is shown below.
A beta version is now available for downloading here,
along with guidance on running the model.
DRIR Sample Runs
The model simulates a five-year period in which a drought is embedded, calculating annual profit and end-of-year net worth for a small set of scenarios of typical Colorado cow/calf ranch sizes, or the ranch parameters (e.g., herd size) can be set by the user. The version downloaded here includes actual rainfall data for the Central Plains Experimental Range (CPER) and insurance indemnification is calculated for the RMA grid cell that encompasses the CPER. The full DRIR model calculates the costs and revenues associated with five drought management options: no adaptation, buy additional feed, truck cow-calf pairs to rented pasture, sell pairs and replace cows, and sell pairs without replacement. The version available here gives results for the first three options, plus a normal, non-drought year, for baseline comparison.
The user can set the starting year, choose a ranch scenario, and set prices received as well as the costs of feed and rental pasture if they wish.
Results for a run starting with a drought in 2002, with and without the USDA insurance show both the costs of adapting and the marked improvement in income with insurance that makes up for extra costs and bring net income from the cow/calf operation up to a normal (no drought) year.
Note that since this is 5-year run based on real climate data it happens that the insurance pays off (based on the precipitation grid in which the ranch resides) in 2003, 2004, and 2006 in addition to the starting drought year (2002). But in all these years the ranch rain gage record, input to the drought calculator, showed more than 80% of normal forage likely for the season, and thus the rancher took no response. BUT the grid cell in which the ranch resides did show sufficient drought, given the policy selections made for this run, to receive an insurance pay out.
The runs below, starting with 2012, another significant drought year in the region, illustrate an unusual outcome of ranch drought decision making and insurance: in 2016 the ranch rain gage does trigger adaptations, but the gridded rainfall product on which the insurance is based does not trigger a payment. In that case it pays not to have purchased the insurance (and not incurring the premium expense), but the rancher cannot know when these decisions are made whether the insurance will pay off. When we do longer runs, over decades with and without insurance we find that overall it pays to buy the insurance, at least for this RMA grid cell and partly because the premium is subsidized; insurance pays off sufficiently frequently to cover the premium costs in the long-run.
The Climate Adaptation Decision Models team includes: Bill Travis, Adam McCurdy, Joseph Tuccillo, Trisha Shrum, Max Roland, Evan Lih, and Travis Williams.
For information about the Ranch Drought project, contact: firstname.lastname@example.org
For information about the R-DRIR simulation tool, contacts: email@example.com