Location: Northwest Watershed Research Center2020 Annual Report
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.
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.
In support of Objective 1, researchers at Boise, Idaho, maintained existing phenology cameras (Nancy Gulch and Reynolds Mountain) at the Reynolds Creek Experimental Watershed (RCEW) located in Murphy, Idaho. Both automated cameras successfully provided imagery to the nation-wide Long-Term Agroecosystem Research (LTAR) and the Phenocam Networks. Installation of a third phenology camera at Lower Sheep Creek was postponed. Data collections for the RCEW Long-Term Vegetation Research program were completed as planned, but these plans included temporary suspension of point-quadrat data collections for cover and leaf area index (LAI) and shifts to alternative collection methods for vegetation structure. A replicate set of 15 Modified Wilson and Cooke (MWAC) wind erosion sampler masts were installed in vegetation enclosures at the Nancy and Reynolds Mountain core sites to provide an estimate of the horizontal sediment mass flux at each site, enabling reliable comparison of sediment transport rates among the LTAR network sites and estimation of the net wind erosion occurring at each site. Research continued collecting imagery from Unmanned Aircraft Systems (UAS) and corresponding field data from three core research areas at RCEW (Nancy Gulch, Lower Sheep Creek, and Reynolds Mountain). Field and UAS data acquired during the 2019 season were analyzed using Structure-from-Motion (SfM) techniques to develop robust relationships between vegetation biomass and SfM-derived plant height. Results from this global-scale study were submitted for publication and the data set was submitted to the Natural Environment Research Council (NERC) Environmental Information Data Centre. Collaborations with Boise State University (2052-13610-014-10A, "Developing Remote Sensing Tools for Rangeland Vegetation Inventory and Assessment") continued through graduate student thesis work comparing the accuracies of UAS-derived vegetation indices (e.g., Modified Soil-Adjusted Vegetation Index (MSAVI)) for predicting fractional cover of plant functional types. Researchers at Boise, Idaho, in collaboration with the USDA Southwest Climate Hub and other ARS and LTAR scientists, improved the functionality of the LTAR network website to include customized data formats for multiple ARS models including the Simultaneous Heat and Water model (SHAW), Soil and Water Assessment Tool (SWAT), Integrated Farm System Model (IFSM), Wind Erosion Prediction System (WEPS), and CLIGEN (stochastic weather generator). Collaborative efforts continued with the University of Idaho (2052-13610-014-12S, "Socio-economic Assessment of Great Basin Livestock Production Systems") and stakeholders in the Northern Great Basin. Through preliminary fieldwork and discussions with stakeholders in February 2020, it was determined that it was more appropriate to use a methodology that includes ranch visits, semi-structured interviews, and participatory mapping. These qualitative approaches to data collection provide detailed information, perspectives, and experiences that cover the researchers’ topics of interest and allow for the emergence of issues most concerning to interviewees. The discovery of patterns and themes through open conversations with interviewees aids the development of key concepts and indicators to include in a socio-economic framework for future assessments. Audio-recorded interviews were transcribed, and qualitative analysis is on-going. In April 2020, a manuscript describing an empirically based framework for identifying and theorizing relationships between social-ecological drivers of change and impacts to ecosystems and human well-being was published. This integrated framework will guide assessments and comparisons of alternative management interventions (business-as- usual v. aspirational) in the next phase of the project for the Great Basin and is applicable at sites across the LTAR network. In support of Objective 2, the Multi-Regional Targeted Grazing (TG) Experiment continued as a collaboration between ARS researchers at Boise, Idaho, and the U.S. Department of the Interior (USDI) Bureau of Land Management (BLM) to evaluate the efficacy of targeted cattle grazing for creating and maintaining fuel breaks on fire-prone landscapes. Data collections for assessing TG treatment attainment and ecosystem response were completed as planned at all three existing project areas in Boise, Idaho, Elko, Nevada, and Frenchglen, Oregon. Summarizations of these TG data were provided to BLM Washington, DC, office in fulfillment of the Interagency Agreement (2052-13610-014-08I, "BLM/ARS Targeted Grazing Demonstration Monitoring Project"). Data collections for ecosystem responses under the Great Basin LTAR Common Experiment (CE), contrasting High Intensity Long Frequency (HILF) cattle grazing to nominal BLM-permitted grazing, were collected as planned. Summarizations of these CE data were provided to the BLM Boise District, Snake River Birds of Prey National Conservation Area, and Four Rivers Field offices and the cattle producers who are the principal project collaborators. Collaborative research continued with the LTAR Livestock Tracking Working Group to evaluate the effects of topography on cattle foraging behavior in rangeland and pasture settings throughout the United States. For the ecohydrology study of the impacts of cheatgrass invasion, initial sites were identified, and methods and study design have been outlined between collaborators, but field data collection had to be delayed until next year due to the pandemic and use of maximum telework. In support of Objective 3, ARS researchers in Boise, Idaho, in collaboration with ARS scientists at Burns, Oregon, and Woodward, Oklahoma, and collaborators at Boise State University, evaluated slope and aspect effects on seedbed favorability for native grass germination and emergence in the Boise Front Management Area. Topographic position affected the timing of germination of native species and invasive annual grasses, and it was determined that microclimatic metrics associated with topographic position could serve as quantitative surrogates for mapping ecological resilience of native plant populations and inherent resistance to weed invasion. Under Sub-objective 3B, ARS scientists from Boise, Idaho, collaborated with ARS scientists from Burns, Oregon, and Woodward, Oklahoma, as well as researchers from Utah State University and the University of Nevada, to publish a protocol for incorporating weather-centric planning into long-term adaptive management plans for rangeland restoration in the sagebrush-steppe region of the Intermountain United States. This protocol requires a longer-term planning approach than currently supported by regional land management agencies, but could significantly improve long-term success in the restoration of these ecosystems which are currently undergoing type conversion to invasive annual grass cover. A dataset of Rangeland Hydrology Erosion Management (RHEM) effective hydraulic conductivity parameters (Ke) for burned sagebrush steppe rangelands was created from extensive past rainfall simulation studies conducted by ARS Boise, Idaho, scientists. Analysis of the data has been initiated to develop RHEM parameter estimation equations. The effort to develop parameterization equations for RHEM was expanded to include developing rangeland parameterization equations for the ARS developed Water Erosion Prediction Project (WEPP) model and its derivatives such as the Erosion Risk Management Tool (ERMiT) developed by ARS Boise, Idaho, scientists in collaboration with the Forest Service, Rocky Mountain Research Station. A draft paper defining WEPP rangeland parameterization equations has been prepared for collaborator review.
1. Determining best planting date for rangeland plant establishment after wildfire. While rangelands in the western United States exhibit very high variability in seedbed temperature and moisture conditions for establishing grass seedlings after wildfire, certain important patterns still exist that influence the probability of seedling establishment as a function of planting date. ARS researchers in Boise, Idaho; Burns, Oregon; Fort Collins, Colorado; and Woodward, Oklahoma; and collaborators at the University of California; simulated planting date effects on the timing of germination of seven important native grass species at a field site in southern Idaho. The analysis provided two useful management implications: 1) Seeds that are planted later in the fall have a much higher chance of surviving winter mortality to emerging seedlings; and 2) Diversification of the seed mix is key to ensuring that at least some seedlots produce sufficient seedlings to successfully establish in the first year after planting. Consistent application of these two principals could significantly improve the probability of successful seedling establishment by public land management agencies, such as the Bureau of Land Management, over millions of acres of disturbed rangeland in the Intermountain Western United States.
2. New framework supports inclusion of human dimensions into Long-Term Agroecosystem Research (LTAR) national network. A goal of the ARS LTAR network is to balance agricultural production with the conservation of natural resources, while promoting prosperity and well-being for rural communities. This requires tools that guide the coordination of national network science, combine knowledge from diverse scientific topics and research streams, and facilitate stakeholder engagement. By analyzing responses from agricultural and public land stakeholders in the northern Great Basin, researchers from ARS in Boise, Idaho, and the University of Idaho, identified important aspects of community, livelihoods and human well-being previously under-represented in national network science. Their work produced a unique framework that provides a common language and guide for designing research that produces outcomes that emphasize the role of rural communities in supporting human well-being and rural prosperity along with traditional outcomes of ecosystem services and agricultural production. Using such a framework, the LTAR network can improve scientific efforts by combining 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.
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