Location: Rangeland Resources & Systems Research
Project Number: 3012-21610-003-087-S
Project Type: Non-Assistance Cooperative Agreement
Start Date: Sep 1, 2024
End Date: Dec 31, 2027
Objective:
Objective 1 - Using on-ranch research with virtual fencing, learn how cattle distribute themselves on the landscape in relation to available resources, including forage quality, quantity, and plant species composition.
Objective 2 - Advance tools for near real-time monitoring and prediction of rangeland and livestock condition to facilitate precision livestock management and climate change adaptation.
Objective 3 - Evaluate the long-term impacts of experimentally increased precipitation variability and added wintertime moisture on forage production and quality, grass-shrub competition, and resilience capacity of dominant perennial grasses.
Approach:
The Cooperator and ARS will collaborate in:
1) A co-produced, on-ranch precision livestock management project. We will continue to collaborate with a ranch in the Thunder Basin Ecoregion (TBE) to implement adaptive management of both a cow-calf herd and a yearling steer herd via virtual fencing. In 2022 and 2023, three Vence base stations were installed for pilot testing of the system. An additional base station will be installed in 2024 to extend communication capacity, and we will collect animal locations from branding (June) to weaning (December or January) at 5-minute intervals. Goals for this on-ranch project were co-produced with the rancher. In addition to understanding livestock foraging behavior, they include more even utilization of vegetation in large paddocks, avoiding overuse of preferred grazing areas, targeted grazing of invasive plants, and maintaining animal condition. To assess progress towards these goals, we will monitor vegetation utilization rates and collect plant composition data in areas selected for targeted grazing or restoration. We will also quantify livestock growth rates and diet quality.
2) Development of precision livestock management tools for near real-time monitoring of rangeland conditions. First, we will identify conditions under which satellite-derived standing herbaceous biomass and forage quality modeling approaches from shortgrass steppe can be applied to the TBE. Prior efforts show that, given sufficient ground training data, standing biomass and forage quality can be estimated accurately using satellite data. We will evaluate the performance of models fit to TBE-specific ground data and determine how site-specific factors like shrubs and soil types affect model performance. Second, we will improve monitoring and management of prairie dogs using remote sensing. In an initial study, we used deep learning to detect prairie dog burrows and delineate colonies based on remotely sensed imagery. We will evaluate the utility of this approach in the more heterogeneous rangeland setting of TBE. We will also explore the utility of remotely sensed metrics for identifying when prairie dog colonies become unoccupied due to plague, which is critical for timely and cost-effective management.
3) An experiment that tests precipitation treatments relevant to future climate regimes: 1) increased interannual precipitation variation, 2) increased winter precipitation, and 3) the combination of 1 and 2. We have implemented ten replications of each treatment since 2018 and have monitored soil moisture, plant phenology, plant species composition, forage production, forage quality, plant demography, root biomass, soil nutrients, and soil microbial communities. We propose to continue the experiment through year 8 (2025). In 2026, we will not implement treatments, but will assess legacy effects of the previous years’ treatments by comparing all plots under the same current-year weather conditions. In addition to standard measurements, we will quantify the drought resistance and resilience capacity of the two dominant perennial grasses (stem densities, fecundity, species-level nutrient content, forage quality, and bud banks).
