Location: Northwest Watershed Research Center
2023 Annual Report
Objectives
1) As part of the Long-Term Agroecosystems Research (LTAR) network, and in concert with similar long-term, land-based research infrastructure in the U.S., use the Great Basin LTAR site to improve the observational capabilities and data accessibility of the LTAR network and support research to sustain or enhance agricultural production and environmental quality in agroecosystems characteristic of the Great Basin. Research and data collection are planned and implemented based on the LTAR site application and in accordance with the responsibilities outlined in the LTAR Shared Research Strategy (LTARN, 2015), a living document that serves as a roadmap for LTAR implementation. Participation in the LTAR network includes research and data management in support of the ARS GRACEnet and/or Livestock GRACEnet projects.
1A) Improve the understanding of Great Basin ecosystem function and processes by collecting, analyzing and curating multi-scale data in support of LTAR and national database development efforts.
1B) Develop and evaluate remote-sensing tools and approaches for quantifying fine-scale vegetation and wildland fuel dynamics.
1C) Contribute and utilize weather and climate tool applications through the LTAR Climate Group for national and regional LTAR agricultural and natural resource modeling programs in grazing management, ecosystem monitoring, remote sensing, soil productivity, hydrology and erosion.
1D) Create a framework of dominant socioeconomic metrics for assessing long-term sustainability of livestock production and ecosystem services relevant to rural communities dependent upon Great Basin rangelands.
2) Evaluate the interacting effects of livestock grazing, fire, and invasive plants on rangeland ecosystems through development, testing, and application of new databases, assessment tools, and management strategies.
2A) Determine if strategically targeted cattle grazing is effective for reducing fine fuels, moderating wildfire behavior, providing better initial attack alternatives for wildland fire fighters, and protecting critical resources from wildfire damage.
2B) Assess the efficacy of prescriptive cattle grazing for rehabilitating and/or restoring degraded sagebrush-steppe rangelands currently dominated by invasive annual grasses.
2C) Evaluate impacts of the interaction of fire and annual grass invasion on hillslope ecohydrologic processes.
3) Develop weather, climate and eco-hydrologic tools for agricultural and natural resource management applications.
3A) Evaluate, develop and implement soil, plant and atmospheric modeling tools for evaluating and optimizing planting date effects on seedling establishment success of rangeland restoration plant materials.
3B) Evaluate, develop and implement landscape-scale applications for weather centric rangeland restoration planning and management.
3C) Enhance the applicability of the Rangeland Hydrology and Erosion Model (RHEM) for assessing ecohydrologic impacts of annual grass invasion and altered fire regimes.
Approach
Goal 1A: Improve infrastructure, data acquisition protocols, and database management at the Great Basin LTAR. Install phenology cameras and extend vegetation monitoring of replicated sites in three Great Basin (GB) ecosystems. Hypothesis 1B: Unmanned aircraft systems (UAS) will be effective for quantifying vegetation dynamics and fire severity. We will test efficacy of high-resolution imagery, Structure-from-Motion (SfM), and other UAS-derived products for estimating biomass, cover, fuel continuity, and fire severity in the three GB ecosystems. Goal 1C: Develop methodology for utilizing gridded weather data for agro-ecosystem modeling and risk-assessment applications. The weather/climate toolbox will be expanded to provide forecasting data for the entire U.S. to support the LTAR network and broad research efforts. Goal 1D: Develop a socio-economic framework for assessing barriers to adoption of livestock grazing systems in cheatgrass rangelands. Scoping interviews, surveys, and participatory workshops will be used to assess stakeholder and community perceptions of rangeland issues and changes in those perceptions over time. Hypothesis 2A: Targeted grazing can create fuel breaks which moderate wildfire behavior without impacting ecosystem health. We will apply intensive grazing to cheatgrass rangeland, monitor herbaceous fuel height/load reduction to targeted level, and assess ecosystem response to treatment using augmented indicators and protocols developed for the BLM Assessment, Inventory, and Monitoring (AIM) program. Hypothesis 2B: Prescriptive grazing will promote recovery of desirable plant species within degraded rangelands. We will apply replicates of a combination of spring and dormant season grazing to impact cheatgrass cohorts and monitor ecosystem response using AIM indicators and protocols. Hypothesis 2C: Cheatgrass invasion and associated altered fire regimes will increase runoff and erosion. Runoff and erosion will be assessed in unburned and burned cheatgrass compared to unburned sagebrush-steppe (control) using rainfall and overland-flow field simulators. Hypothesis 3A: Hydrothermal germination response models and weather datasets can characterize seed germination, post-germination mortality, and seedling emergence rates. The SHAW model using historical weather data from gridMet will be used to parameterize hydrothermal germination models to evaluate species sensitivity to planting date, over-wintering conditions, and topo-edaphic conditions. Goal 3B: Develop tools for incorporating weather, climate and microclimatic variability into restoration planning and management. We will enhance existing web-application to provide daily weather parameters and parameterize the SHAW model with SSURGO soils data to thus facilitate modeling of germination success and seedling survival under various climatic and environmental scenarios. Goal 3C: Expand the capabilities of RHEM for conducting hydrologic risk assessment on disturbed rangelands. Develop RHEM equations for cheatgrass systems, test the utility of the enhanced RHEM, and establish guidelines for use of RHEM in combination with soil burn severity mapping for risk assessments.
Progress Report
In support of Objective 1, ARS researchers in Boise, Idaho, maintained existing phenology cameras (phenocams) located at Nancy Gulch, Lower Sheep Creek, and Reynolds Mountain sites within Reynolds Creek Experimental Watershed (RCEW) near Murphy, Idaho. All three automated cameras successfully contributed imagery to the nation-wide Phenocam and Long-Term Agroecosystem Research (LTAR) networks. Field data for the RCEW Long-Term Vegetation Research (LTVR) program and the Great Basin LTAR site were collected as planned. More than 100 imagery data sets previously acquired with unoccupied aerial systems (UAS) at RCEW LTVR research sites were processed for analysis. A paper on remote sensing of plant functional types, based on these LTVR imagery and field data, was published to Ecosphere and the data was published to Ag Data Commons. ARS researchers from Boise, Idaho, led a LTAR cross-site project using artificial intelligence (AI) to detect and remove spatial error from global positioning system (GPS)-based animal tracking data sets. ARS researchers in Boise, Idaho, trained cross-site project collaborators from ARS, Fort Collins, Colorado, U.S. Geological Survey (USGS), Moab, Utah, and Utah State University to use a Geographic Information System (GIS) tool, developed in collaboration with ARS Partnerships in Data Innovations (PDI) and a commercial partner (Environmental Systems Research Institute, Inc. (ESRI), Redland, California), to create human-classified GPS tracking sets as critical inputs to AI models. A Post Doctoral Fellowship award from the USDA Scientific Computing Initiative (SCINet) AI Center of Excellence was received by ARS in Boise, Idaho, to support this AI modeling research. ARS researchers in Boise, Idaho, contributed to a SCINet project led by ARS in Dubois, Idaho, researching AI methods for classifying and counting animals evident in camera trap imagery. Scientists in Boise, Idaho, collaborated with Texas State University, on a proposal to the USDA National Institute of Food and Agriculture (NIFA) to research coupled UAS and ground rover robotics systems for agricultural applications (e.g., animal health monitoring on rangelands). Scientists in Boise, Idaho, also collaborated on recently published LTAR research synthesis papers on water use efficiency and ecological and social performance indicators within diverse agroecosystems.
In support of Objective 2, ARS researchers in Boise, Idaho, and the U.S. Department of the Interior (USDI) Bureau of Land Management (BLM), continued to collaborate on a research project which contrasts the efficacy of High Intensity Long Frequency (HILF) cattle grazing versus nominal BLM-permitted cattle grazing for promoting ecological recovery to rangelands heavily damaged by wildfire and cheatgrass invasion. The research represents the Great Basin LTAR Common Experiment (CE). A field tour and planning meeting was held on the CE project site with the above cooperators in attendance to plan the next nine-year application of the CE. A decision was made to restart the CE on a new study area and cooperation was secured from a local rancher to conduct the CE on his private lands. Data from the original CE were transferred to BLM for use in their management decision making. A paper evaluating the efficacy of targeted cattle grazing for creating and maintaining fuel breaks on threatened rangelands was published to Rangeland Ecology and Management. This targeted grazing work was highlight in a blog post by USDA Climate Hubs. ARS researchers in Boise, Idaho, collaborated with researchers from Oregon State University, Boise State University, and the University of Idaho to collect unmanned aircraft systems (UAS) and field data for a project evaluating the efficacy of dormant season cattle grazing for promoting restoration of cheatgrass-invaded rangelands. Scientists in Boise, Idaho, co-authored synthesis papers on invasive annual grasses and climate change, and adaptability of pastoralists to climate change, as well as a data paper titled, “Mapping rangeland health indicators in East Africa from 2000 to 2022” submitted to Earth System Science Data. ARS researchers in Boise, Idaho, along with researchers from Cornell University, Princeton University, University of Michigan, and Arizona State University, collaborated as senior personnel on a proposal submitted to the National Science Foundation’s Dynamics of Integrated Socio-Environmental Systems (DISES) program to research food security and rangeland sustainability in pastoral dryland agrosystems of the world.
In support of Objective 3, ARS scientists at Boise, Idaho, Burns, Oregon, Woodward, Oklahoma, and Fort Collins, Colorado, in collaboration with scientists at California Polytechnic State University, San Luis Obispo, Utah State University, The Nature Conservancy, Oregon, and USGS in Arizona, and Oregon, modeled seed germination responses of multiple native and non-native grasses at over 5,000 sites in the Intermountain West. This included 10 sites for which long-term field validation existed for seedbed microclimate, and 19 sites on an elevational gradient in the 50,000-acre Boise Front Management Area that were also stratified by 100 slope and aspect categories. ARS scientists in Boise, Idaho, assessed both the probabilities of germination responses and the timing of germination relative to potential post-germination mortality events that are often a bottleneck for successful seedling emergence. These data are contributing to a growing database of probabilistic germination response that can be used to inform management relative to plant materials suitability to different environments, planting date decisions, the mechanistic basis for ecological resilience and resistance, and potential climate-change effects on landscape suitability for both native plants and their weedy competitors. Also in support of Objective 3, ARS scientists at Boise, Idaho, Burns, Oregon, Woodward, Oklahoma, Temple, Texas, and Reno, Nevada, in collaboration with scientists at USGS in Colorado, Oregon, and Idaho, University of Nevada, University of California, Merced, Natural Resource Conservation Service in Oregon, and the District of Columbia, and Bureau of Land Management (BLM) in Nevada, Oregon, Idaho, and Colorado, updated and developed new content for the annual BLM Restoration of Sagebrush Ecosystems Training Course. ARS scientists in Boise, Idaho, and Woodward, Oregon, specifically developed a weather-centric rangeland restoration training module based on gridded historical meteorological data and site climatologies that can be used to characterize the probabilities of having a good restoration year, and to provide a climate-context for interpreting past success or failure at restoration field sites.
Accomplishments
1. Elevation and aspect effects on soil microclimate and seedbed favorability. Millions of acres of rangeland in the Intermountain Western United States have been significantly disturbed by the proliferation of invasive annual grasses after wildfire. The ability to restore these lands depends on inherent features of the landscape that affect ecological resistance to invasion and resilience to disturbance. ARS researchers in Boise, Idaho, Burns, Oregon, Fort Collins, Colorado, and Woodward, Oklahoma, along with research scientists at Boise State University and U.S. Geological Survey, South Dakota, used seedbed microclimate and germination modeling to develop mechanistic models of seedbed favorability that fine-tune the mapping of ecological resilience and resistance over space. Although ecological resistance and resilience are higher in upper-elevation rangelands, this mapping approach can also be used to identify the type of years in which successful restoration may be possible in lower-elevation, dryer rangeland ecosystems in conjunction with seasonal forecast information that is available in the fall planting months. This research potentially improves the application of resistance and resilience metrics for the Bureau of Land Management and U.S. Forest Service which are used to prioritize rangeland restoration activities across millions of acres of disturbed rangelands in the Intermountain Western United States.
2. Seasonal climate forecasting for prediction of rangeland plant production. Rangelands in the western United States exhibit high annual and seasonal variability in climatic conditions that drive annual plant production. Timely and skillful seasonal climate forecasts would significantly improve managers' ability to accurately predict production and make livestock stocking and de-stocking decisions that affect the economic bottom line. ARS researchers in Boise, Idaho, and Burns, Oregon, and scientists at the University of California developed mechanistic models for plant production in the California Annual Grassland and used historical forecasting (hindcast) data from the North American Multi Model Ensemble (NMME) program to determine the potential utility of seasonal forecasting at three field experiment stations in California. These scientists confirmed high climate forecast and production forecast skill throughout the growing season and identified times during the year when forecasting might be useful in informing rangeland management decisions. These procedures provide a user’s guide for creating mechanistic plant production forecasts for other western rangelands and may be applicable across a wide range of other agricultural and hydrologic applications.
Review Publications
Copeland, S.M., Davies, K.W., Hardegree, S.P., Moffet, C., Bates, J.D. 2022. Influence of weather on production dynamics in Wyoming big sagebrush steppe across plant associations. Rangeland Ecology and Management. 85:48-55. https://doi.org/10.1016/j.rama.2022.09.002.
Hardegree, S.P., Boehm, A.R., Glenn, N., Sheley, R.L., Reeves, P.A., Pastick, N., Hojjati, A., Boyte, S., Enterkine, J., Moffet, C., Flerchinger, G.N. 2022. Elevation and aspect effects on soil microclimate and the germination timing of fall-planted seeds. Rangeland Ecology and Management. 85:15-27. https://doi.org/10.1016/j.rama.2022.08.003.
Hoover, D.L., Abendroth, L.J., Browning, D.M., Saha, A., Snyder, K.A., Wagle, P., Witthaus, L.M., Baffaut, C., Biederman, J.A., Bosch, D.D., Bracho, R., Busch, D., Clark, P., Ellsworth, P.Z., Fay, P.A., Flerchinger, G.N., Kearney, S.P., Levers, L.R., Saliendra, N.Z., Schmer, M.R., Schomberg, H.H., Scott, R.L. 2022. Indicators of water use efficiency across diverse agroecosystems and spatiotemporal scales. Science of the Total Environment. 864. Article e160992. https://doi.org/10.1016/j.scitotenv.2022.160992.
Roser, A., Enterkine, J., Requena-Mullor, J., Glenn, N.F., Boehm, A.R., de Graaff, M., Clark, P., Pierson Jr, F.B., Caughlin, T. 2022. Drone imagery protocols to map vegetation are transferable between dryland sites across an elevational gradient. Ecosphere. 13(12). Article e4330. https://doi.org/10.1002/ecs2.4330.
Schantz, M., Hardegree, S.P., James, J., Sheley, R.L., Becchetti, T. 2023. Modeling weather effects on plant production in the California Annual Grassland. Rangeland Ecology and Management. 87:177-184. https://doi.org/10.1016/j.rama.2023.01.002.
Schantz, M., Hardegree, S.P., James, J., Becchetti, T., Abatzoglou, J., Hegewisch, K., Sheley, R.L. 2023. Evaluating multimodel ensemble seasonal climate forecasts on rangeland plant production in the California Annual Grassland. Rangeland Ecology and Management. 88:135-142. https://doi.org/10.1016/j.rama.2023.02.013.
Spiegal, S.A., Webb, N., Boughton, E., Boughton, R., Bentley-Brymer, A., Clark, P., Holifield Collins, C.D., Hoover, D.L., Kaplan, N.E., McCord, S.E., Meredith, G., Porensky, L.M., Toledo, D.N., Wilmer, H.N., Wulfhorst, J.D., Bestelmeyer, B.T. 2022. Measuring the social and ecological performance of agricultural innovations on rangelands: Progress and plans for an indicator framework in the LTAR network. Rangelands. 44:334-344. https://doi.org/10.1016/j.rala.2021.12.005.
Terry, T., Hardegree, S.P., Madsen, M., Roundy, B., St. Clair, S. 2022. Trends in soil microclimate and modeled impacts on germination timing in the sagebrush steppe. Ecosphere. 13(9). Article e4226. https://doi.org/10.1002/ecs2.4226.
Wang, X., Liao, C., Brandhorst, S., Clark, P. 2022. Sedentarization as an adaptation to socio-environmental changes? Everyday herding practices in pastoralist communities in southern Ethiopia. Ecology and Society. 27(3). Article 39. https://doi.org/10.5751/ES-13503-270339.
