Location: Northwest Watershed Research Center
2024 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
This is the final report for project 2052-13610-014-000D, "Assessment and Mitigation of Disturbed Sagebrush-Steppe Ecosystems", which has been replaced by new project 2052-21500-001-000D, "Disturbance Mitigation and Adaptive Restoration of Sagebrush-Steppe Ecosystems".
In support of Objective 1, ARS researchers in Boise, Idaho, installed and maintained phenology cameras (PhenoCams) located at the Nancy Gulch, Lower Sheep Creek, and Reynolds Mountain sites within the 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. Data from these imagery yielded a LTAR network-wide research paper on agroecosystem productivity and phenology. ARS researchers in Boise, Idaho, also co-authored LTAR synthesis research on water use efficiency within diverse agroecosystems. Field data for the RCEW Long-Term Vegetation Research (LTVR) program and the Great Basin LTAR site, headquartered in Boise, were collected, quality checked, and publicly shared as planned, 2019-2024. The data were used in papers on plant community detection and carbon and water dynamics. The field data as well as more than 120 imagery data sets acquired with unmanned aircraft systems (UAS) at RCEW LTVR research sites were used to produce papers on remote sensing of plant functional type cover and biomass, and the data and imagery themselves were published to Ag Data Commons. A workflow for analyzing UAS imagery using open-source software was developed and published as an online tutorial in the USDA Scientific Computing Initiative (SCINet) Geospatial Workbook. Boise ARS researchers led a LTAR cross-site project using artificial intelligence (AI) to detect and remove spatial error from GPS-based animal tracking data sets involving collaborations with scientists in Fort Collins, Colorado (ARS), U.S. Geological Survey (USGS), Moab, Utah, and Utah State University. A Geographic Information System (GIS) tool was developed by these Boise researchers 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 the above AI research. The ARS unit in Boise also received a Post Doctoral Fellowship award from the USDA SCINet AI Center of Excellence to support this AI modeling research. Several original deep-learning approaches were developed for GPS error detection and publication of these applications and workflows are expected in 2025. ARS researchers in Boise, Idaho, also contributed to a SCINet project led by the ARS unit in Dubois, Idaho, in the development of an AI tool for classifying and counting animal species evident in camera trap imagery. ARS researchers in Boise, Idaho; Temple, Texas; Las Cruces, New Mexico; and Woodward, Oklahoma, developed weather and climate tools in support of LTAR modeling and program objectives and the technology shared with the LTAR network at http://webapps.jornada.nmsu.edu/weather/. Tools included access to gridded historical weather datasets automatically formatted to a suite of LTAR-modeling tools (SWAT, IFSM, SHAW, ALMANAC, APEX) and climate scenarios from the CLIGEN weather generator model. These tools were used by members of the LTAR program for rangeland restoration modeling to identify optimal planting scenarios for Great Basin post-fire seeding management, plant production model development, for characterization of regional phenology scenarios, and in support of seasonal hindcast evaluation for assessment of North American Multi-Model Ensemble (NMME) seasonal forecast models. Human dimensions aspects for adoption of innovative weather and climate tools were also evaluated by a survey of management professionals. ARS researchers in Boise, Idaho, in collaboration with rural sociologists at University of Idaho conducted field data collection, participatory mapping, and semi-structure interviews of cattle ranchers, natural resource management professionals, and other rural community members to develop a socio-economic framework for assessing barriers to adoption of adaptive livestock grazing production systems for cheatgrass-dominated rangelands. The data yielded research papers evaluating relationships between social-ecological drivers of change and the impacts to ecosystems and human communities and well-being. This collaboration also resulted in LTAR synthesis papers on ethical principles for human dimensions research partnerships, integrating human dimensions into ecological and agricultural production research, and socioeconomic performance indicators.
In support of Objective 2, ARS researchers in Boise, Idaho, collaborated with beef cattle ranchers and the U.S. Department of the Interior (USDI) Bureau of Land Management (BLM) on a long-term research project contrasting the efficacy and sustainability of a prevailing BLM cattle grazing practice with that of an alternative, prescribed cattle grazing practice, High Intensity Long Frequency (HILF) grazing for promoting ecological recovery of rangelands heavily damaged by wildfire and cheatgrass invasion. This research project represents the Great Basin LTAR site’s contribution to the LTAR Common Experiment (CE) which contrasts prevailing and alternative agricultural practices across the national scope of the network. Field data collection was completed for the first implementation of the nine-year experiment design (2015-2023). These data have been shared with BLM to facilitate their management decision making. Journal publication of results from this first implementation of the CE is expected during the replacing project. An additional study area was selected to broaden the spatial scope of this CE and better address the extensive wildfire/cheatgrass problem. Boise ARS researchers also conducted a five-year, multi-regional experiment (2017-2022) at study areas in Idaho, Oregon, and Nevada to evaluate the efficacy of targeted beef cattle grazing for creating and maintaining fuel breaks between highly-flammable, annual grass-invaded rangelands and the wildland-urban interface (WUI), habitat for sagebrush-obligate wildlife, or other critical natural, social, or cultural resources threatened by wildfire. Results from this experiment received journal publication in 2023 including a success story where a research fuel break in Nevada intercepted three wildfires threatening greater sage-grouse habitat during its first four years of existence. Public press coverage of this targeted grazing research included five ARS stories and press releases, a USDA Climate Hub feature story, four stories by regional newspapers, two blog interviews with industry and conservation organizations, and a documentary video. Boise ARS scientists also co-authored research on grazing livestock behavior (six papers) was well as synthesis papers on invasive annual grasses and climate change, adaptability of pastoralists to climate change, and sustainability of modern pastoralism. Work on Sub-objective 2C was terminated early in the project plan cycle due to a critical staffing vacancy which has yet to be filled.
In support of Objective 3, ARS researchers in Boise, Idaho; Burns, Oregon; Temple, Texas; and Woodward, Oklahoma, used seedbed microclimatic modeling and hydrothermal germination models to assess potential germination response in multiple field-seeding environments to evaluate the timing of seed germination response relative to post-germination/pre-emergence mortality factors. These studies were coordinated with field validation studies and inferences were expanded to model simulations conducted across the Great Basin sagebrush steppe. Specific management recommendations were identified regarding seeding date effects and bet-hedging strategies and additional management recommendations were developed to deal with restoration and other management problems associated with the high weather and climate variability typical of the western United States. Microclimatic modeling was also used to evaluate and map seedbed favorability as a function of elevation, slope and aspect in order to quantify site resistance to weed invasion, and resilience to ecological disturbance. Additionally, ARS scientists in Boise, Idaho, and Woodward, Oklahoma, developed methodology to use gridded weather data products to characterize the seasonal climate limitations for historical rangeland seeding projects in the western United States. They developed a restoration climatology tool that has been used in the previous and current project plan cycles to support the annual BLM rangeland restoration workshop that annually trains 30-35 restoration specialists from USDA and USDI. This group of ARS scientists and collaborators from western regional universities and management agencies have also developed general guidance for implementing weather-centric rangeland restoration planning and management. This guidance includes field studies and model simulation studies in support of seasonal planting date decisions, long-term seeding and other restoration strategies, and the selection of diverse plant materials as a bet-hedging strategy for seedling establishment in a highly variable weather environment. Work on Sub-objective 3C was severely curtailed due to a critical and protracted staffing vacancy. However, despite this vacancy, ARS researchers in Boise, Idaho, published research concerning the Rangeland Hydrology and Erosion Model (RHEM) and its enhancement and application for evaluating factors effecting rangeland soil erosion; runoff and erosion responses for rangelands encroached by invasive tree species; runoff and erosion before and after invasive tree removal, and the long-term effects of tree removal on rangeland hydrology.
Accomplishments
1. Livestock grazing promotes wildfire protection and recovery of fire-damaged resources. Rangeland megafires are becoming much more frequent in the western United States due to invading, highly flammable annual grass species like cheatgrass, which alone now dominates more than 210,000 km^2, an area comparable to the entire state of Idaho in extent. Management tools are critically needed to combat cheatgrass, reduce the wildfire threat posed to human lives and property at the wildland-urban interface, and to promote recovery of fire-damaged ecosystems. ARS researchers at Boise, Idaho, have determined that targeted cattle grazing in spring can effectively create and maintain fuel breaks between cheatgrass-invaded rangeland and critical resources upslope and downwind, and that prescribed cattle grazing in spring and fall can suppress cheatgrass and promote increases in native and/or desirable plant species. Targeted and prescribed cattle grazing are thus validated tools that livestock producers, landowners, and natural resource managers can use to mitigate cheatgrass invasion and associated wildfire threat and damage and do so more cost-effectively and sustainably than mechanical or herbicide alternatives, with applicability across the extensive rangelands of the western United States and similar impacted ecosystems of the world.
2. Long-term assessment of germination variability across multiple species. Previous research by ARS researchers in Boise, Idaho, and multiple collaborators have identified alternative germination syndromes for diverse restoration species that are often planted throughout the western United States after disturbance by wildfire. ARS researchers in Boise, Idaho, Burns, Oregon, Temple, Texas, Woodward, Oklahoma, Logan, Utah, and Fort Collins, Colorado, simulated potential germination response of bottlebrush squirreltail from the Rocky Mountain region, and big squirreltail from southern Idaho over the last 40-year period and identified germination-response traits that conferred specific advantages for initial establishment in their respective habitats. These results support current agency guidelines for matching available plant materials to their target environments, but also provide some guidance to the appropriate selection of plant materials under conditions of future climate change.
3. Unoccupied Aerial Systems (UAS) provide improved monitoring of rangelands. Rangelands occupy 54% of the global terrestrial surface and 268 million ha in the United States alone. Traditional, ground-based rangeland monitoring has been severely challenged by this vast scope, its associated costs and logistics, and thus has inevitably fallen short in effectively providing the complete, timely, and accurate information on rangeland condition and trend which is necessary for proper management and policy making. ARS researchers in Boise, Idaho, evaluated the use of UAS or drone imagery for monitoring rangelands in the Great Basin region. Accurate estimates of vegetation biomass, plant functional type cover, and other critical monitoring parameters were derived from high-resolution UAS imagery using aerial sampling methods which can be readily and repeatedly applied across extensive landscapes and management units. The fine scale, very detailed data provided by UAS imagery allows ranchers and resource managers to extend, augment or fully replace information traditionally acquired through ground-based monitoring and has the potential of cutting personnel, transportation, and logistical costs by 50% or more. Furthermore, these UAS data also enable representative scaling of rangeland health information to even larger extents (e.g., states, regions, and nationally) through coupling with coarser scale, satellite remote sensing products.
4. Enhancing the sustainability of pastoral livelihoods. Drylands occupy 41% of the global land surface, contain nearly one-third of the world’s biodiversity hotspots, and deliver over $1 trillion in ecosystem goods and services to more than 38% of the global human population. Pastoralism is the most common form of agriculture on drylands but is also among the most threatened of human livelihoods. ARS researchers in Boise, Idaho, evaluated the role herding mobility plays in promoting sustainable pastoralism on drylands such as the Mongolian steppes, East African savannas, and diverse rangelands of the western United States. Pastoral strategies with high degrees of herd movement and adaptivity, previously common in traditional pastoralism, tended to be more socio-economically and environmentally sustainable than strategies with reduced mobility, rigid policy constraints, and aid-dependent risk management. Despite on-going global trends toward less open space and increased non-agricultural land use demands, both greater herd mobility and policy flexibility emerged from this research as critical requirements for sustainable pastoral livelihoods and successful achievement of the United Nations Sustainable Development Goals for No Poverty (#1), Zero Hunger (#2), and Life on the Land (#15).
5. Developing a human dimensions research framework for agroecosystems. While the understanding of the environmental tradeoffs of intensified agricultural production continuously improves, the impact of intensification on the prosperity and well-being of rural individuals and communities is quite understudied. ARS researchers in Boise, Idaho, in close collaboration with rural sociologists from University of Idaho, pioneered research examining socio-environmental and socio-agricultural tradeoffs in the Great Basin region, creating a human dimensions research framework which was subsequently adopted by the Long-Term Agroecosystem Research (LTAR) network. This framework provided a common language and guide for designing research that investigates the role of rural communities in supporting human well-being and rural prosperity along with traditional outcomes of ecosystem services and agricultural production. The LTAR network has since used this framework to better combine agroecosystem monitoring with livelihood assessments while engaging producers and landowners as stakeholders to get their input on sense of place, motivations for conservation, and what rural families and communities do to persevere amidst multiple challenges across their working landscapes. This research has not only had national scale impact but potentially informs international researchers and the livelihoods they study throughout the globe from dryland pastoralists to wetland rice farmers.
6. Predicting cattle resource selection responses to environment and disturbance. Distribution patterns of free-ranging livestock can be affected by environmental factors, landscape-scale disturbances, and management actions like predator reintroduction. Yet, we lack the ability to robustly predict how livestock will respond under diverse settings, leading to potentially adverse consequences to productivity metrics like weaning weight and environmental metrics like stream water quality. ARS researchers in Boise, Idaho, collaborated with Long-Term Agroecosystem Research (LTAR) network colleagues at Archbold Biological Station (Venus, Florida), University of Nebraska (Scotts Bluff), New Mexico State University (Las Cruses), and ARS locations in Fort Collins, Colorado, and Las Cruses, New Mexico, to investigate the influence of topography on beef cattle distribution patterns for settings spanning the contiguous United States. These Boise ARS researchers also collaborated with scientists at the Agricultural Research Agency of Sardinia, Italy, to develop robust models for predicting cattle resource selection in Mediterranean environments. ARS researchers from Boise and Dubois, Idaho, along with colleagues at University of Idaho (Salmon) examined the effect of Residual Feed Intake on cattle resource selection patterns and body condition. Boise ARS researchers collaborated with colleagues at Oregon State University, Corvallis and La Grande; University of Idaho, Caldwell; and ten cooperating cattle ranches from Idaho and Oregon to develop and test a model for predicting spatial risk of cattle depredation by re-introduced gray wolves in the northern Rocky Mountains. Ranchers, natural resource managers and policy makers now have robust, well-validated models for predicting cattle resource selection responses to environment, disturbance, and management actions which can be used to improve cattle productivity, conserve environmental health, and mitigate resource conflict on more than 268 million ha in the United States and other grazinglands of the world.
7. Improving runoff and erosion modeling for juniper control efforts. Western juniper/pinyon pine woodlands occupy over 22.8 million hectares of North America and are encroaching on sagebrush steppe at a rate of 4,600 square kilometers per year. While prescribed fire and/mechanical treatments have demonstrated efficacy for controlling tree encroachment, there is a limited ability to predict the runoff, erosion, and other ecohydrologic effects of these treatments. ARS researchers in Boise, Idaho, developed new erosive parameterization for rangelands within the Water Erosion Prediction Project (WEPP) model; estimated hydrologic conductivity using the Rangeland Hydrology and Erosion Model (RHEM); evaluated runoff and erosion in juniper-encroached sagebrush steppe using RHEM; studied vegetation, runoff, and sediment transport following prescribed fire and mechanical treatments for juniper control also using RHEM; and investigated effects of shrub-steppe restoration on vegetation structure, hydrologic, and sediment connectivity. This research provided critical advancements in rangeland runoff and erosion modeling for assessing juniper and pinyon control management treatments. In RHEM, landowners and natural resource managers have been provided with a well-validated model and decision support tool for predicting runoff and erosion responses from tree control projects employing prescribed fire and/or mechanical tree removal and this tool has applicability across a broad scope of the western United States.
Review Publications
Al-hamdan, O., Williams, C.J., Pierson Jr., F.B., Hernandez, M., Nouwakpo, S.K. 2024. Estimating effective hydraulic conductivity (Ke) for the Rangeland Hydrology and Erosion Model (RHEM). Journal of the ASABE. 67(1):141-149. https://doi.org/10.13031/ja.15652.
Bates, J.D., Copeland, S.M., Hardegree, S.P., Moffet, C., Davies, K.W. 2024. Weather effects on herbaceous yields: Wyoming big sagebrush steppe, southeastern Oregon. Western North American Naturalist. 84(1):89-106. https://doi.org/10.3398/064.084.0108.
Bates, J.D., Johnson, D., Davies, K.W., Svejcar, T., Hardegree, S.P. 2023. Effects of annual weather variation on peak herbaceous yield date in sagebrush steppe. Western North American Naturalist. 83(2):220-231. https://doi.org/10.3398/064.083.0207.
Clark, P., Porter, B.A., Pellant, M., Dyer, K., Norton, T. 2023. Evaluating the efficacy of targeted cattle grazing for fuel break creation and maintenance. Rangeland Ecology and Management. 89:69-86. https://doi.org/10.1016/j.rama.2023.02.005.
Young, S.L., Archer, D.W., Blumenthal, D.M., Boyd, C.S., Clark, P., Clements, D.D., Davies, K.W., Derner, J.D., Gaskin, J.F., Hamerlynck, E.P., Hardegree, S.P., Jensen, K.B., Monaco, T.A., Newingham, B.A., Pierson Jr, F.B., Rector, B.G., Sheley, R.L., Toledo, D.N., Vermeire, L.T., Wonkka, C.L. 2023. Invasive annual grasses: re-envisioning approaches in a changing climate. Journal of Soil and Water Conservation. 78(2):95-103. https://doi.org/10.2489/jswc.2023.00074.