Location: Northwest Watershed Research Center
Project Number: 2052-13610-014-00-D
Project Type: In-House Appropriated
Start Date: Mar 25, 2019
End Date: Mar 24, 2024
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.