Location: Northwest Watershed Research Center2022 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, ARS researchers in Boise, Idaho, maintained existing phenology cameras (phenocam) 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. This data resulted in the publication of a paper titled, “Monitoring agroecosystem productivity and phenology at a national scale: A metric assessment framework” in Ecological Indicators, based on these imagery contributions. Field data for the RCEW Long-Term Vegetation Research program and the Great Basin LTAR site were collected as planned. Plant diversity data were provided to the LTAR Biology Working Group (WG) to develop a network-wide paper on relationships between remotely-sensed gross primary productivity and plant diversity. Plant production data were compiled and submitted to the Agricultural Collaborative Research Outcomes System (AgCROS) to support the LTAR network-wide Legacy Production Data project. Wind erosion monitoring of established sagebrush plant communities continued on a quarterly basis in support of ARS Wind Erosion Network. Unmanned Aircraft System (UAS) imagery continued to be collected along with the corresponding field data from three core research areas at RCEW (Nancy Gulch, Lower Sheep Creek, and Reynolds Mountain). Programming scripts were developed to automate analysis for these UAS imagery using the USDA Scientific Computing Initiative (SCINet) High Performance Computing (HPC) cluster, Atlas. A paper titled, “Communal processes of health and well-being for rangelands research and practice,” was published in a special issue of Rangelands. Also published in this special issue was a paper titled, “Measuring the social and ecological performance of agricultural innovations on rangelands: Progress and plans for an indicator framework in the LTAR network.” Also, in support of Objective 1, ARS scientists in Boise, Idaho, Woodward, Oklahoma, and Burns, Oregon, in collaboration with scientists at Boise State University and the University of California, Merced, developed a spatial database of daily weather parameters (precipitation, air temperature, solar radiation, wind speed, dewpoint) and archetype soil profiles for representative soil types (silt loam, sandy loam, loam, clay loam). ARS researchers have already used this database to model spatial and temporal variability in seedbed temperature and moisture for seedling establishment as a function of soil type, elevation, slope, and aspect for a variety of restoration plant materials. This database can also be used by ARS research personnel in Boise, Idaho, and Tucson, Arizona, for modeling antecedent moisture effects on potential runoff and erosion in the Boise Front watershed system, and for additional investigations on soil microclimate as it relates to ecological resilience and resistance to weed invasion and potential guidance on alternative restoration practices in the western United States. 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 multi-regional evaluation on the efficacy of targeted cattle grazing for creating and maintaining fuel breaks on fire-prone landscapes. Field data for assessing targeted grazing (TG) treatment attainment and ecosystem response were collected as planned at all three existing project areas in Boise, Idaho, Elko, Nevada, and Frenchglen, Oregon. Annual reports summarizing field data were provided to the BLM National Office in fulfillment of the Interagency Agreement (2052-13610-014-08I, "BLM/ARS Targeted Grazing Demonstration Monitoring Project"). A paper describing the TG findings to date (2017-2021) was submitted to a special issue dedicated to wildland fire research in Rangeland Ecology & Management. This TG research was the subject of two USDA blog posts, one popular press article in Under the Microscope, and one ARS press release. Data for ecosystem responses under the Great Basin LTAR Common Experiment (CE), where High Intensity Long Frequency (HILF) cattle grazing was contrasted with nominal BLM permitted grazing, were collected as planned. Summary reports for CE data were provided to cooperating cattle producers and the BLM Boise District, Snake River Birds of Prey National Conservation Area, and Four Rivers Field offices. 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. The researchers submitted a research editorial paper titled, “Invasive annual grasses – re-envisioning approaches in a changing climate” to a special issue of Journal of Soil and Water Conservation, dedicated to exploring conservation challenges associated with directional climate change. They also submitted a paper titled, “Everyday adaptation to socio-environmental changes in Southern Ethiopia” to Ecology and Society, regarding pastoralist mobility responses to environmental disturbance. In support of Objective 3, ARS scientists in Boise, Idaho, Burns, Oregon, Woodward, Oklahoma, and Fort Collins, Colorado, in collaboration with scientists at Cal Poly San Luis Obispo and the University of California, Merced, assessed seedbed microclimatic effects on seed germination and emergence at 15 field sites in Oregon, Nevada and Idaho over a period of 40 years (1979 - 2020). ARS researchers in Boise, Idaho, assessed the probability of successful germination relative to the probability of post-germination mortality from freezing for four principal restoration species as a function of planting date in the fall. This probabilistic description of seed germination, emergence, and survival during the first year after planting can help identify the seasonal weather patterns under which one could expect good seedling survival and parameterize seasonal forecasting models for predicting planting strategies for real-time fall seeding. This approach can also provide a context for understanding the relevance of short-term field studies within the context of long-term site variability in seedbed microclimate. This type of data is essential for interpreting field-study data in such a variable-weather environment, and to justify strategies for optimizing planting date decisions in the Intermountain Western United States. Also, in support of Objective 3, ARS scientists in Boise, Idaho, and Woodward, Oklahoma, in collaboration with scientists at Boise State University and Oklahoma State University, developed web-based tools to extract soil parameters for seedbed and hydrologic modeling from the Natural Resources Conservation Service (NRCS) Soil Survey Geographic Database (SSURGO) database. Soils data and soil-texture/depth data have been directly used in ARS modeling projects conducted by ARS scientists in Boise, Idaho, to model seedbed microclimate in the Boise Front Management Area. These data have also been used to derive soil inputs for restoration-climatology reports that are used by students in the BLM rangeland restoration course to characterize long-term seedbed conditions at multiple sites across the Great Basin and Intermountain Western United States. Parameter estimation algorithms for the infiltration component of Rangeland Hydrology and Erosion Model (RHEM) were assessed and work is underway to improve the representation of disturbed conditions including juniper woodland encroachment and burning.
1. Improved rangeland monitoring with Unmanned Aircraft System (UAS) imagery. Rangelands occupy 54% of land surfaces globally and 268 million ha in the United States alone. Given this vast scope, ground-based monitoring approaches are cost- and logistically-challenged and inevitably fall short in effectively providing rangeland condition and trend data 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, United States. Analyses of UAS imagery provided highly accurate estimates of rangeland vegetation biomass, plant functional type cover, and other critical parameters using aerial sampling methods readily 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 at the local level with the potential of cutting personnel, transportation, and logistical costs in half. These UAS data will 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.
2. Weather impacts on rangeland seedling survival in the sagebrush-steppe region of the Great Basin. Sagebrush-steppe rangelands across the entire Intermountain United States are consistently being converted to invasive annual grasses after wildfire. These areas are inherently difficult to restore given the intense competitive abilities of invasive grasses, the generally dry conditions, and extremely high variability in seasonal and annual weather. ARS researchers in Boise, Idaho, Burns, Oregon, Fort Collins, Colorado, Woodward, Oklahoma, and collaborators at the University of California and Cal Poly San Luis Obispo, measured germination and emergence of key restoration species at 15 sites in Oregon, Nevada and Idaho over two years, and modeled potential seedling emergence over a 40-year period to assess the probabilities of good restoration conditions occurring at these sites, and to evaluate planting date effects on both germination timing and the potential occurrence of post-germination mortality from periodic soil freezing. These studies suggest that late-fall planting can significantly avoid seedling exposure to freezing mortality in the winter. Seeding decisions in the fall, however, would be significantly improved from skillful seasonal climate forecasts of growing season precipitation and winter temperatures.
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