Location: Range Management Research2021 Annual Report
1: Develop or improve livestock management & restoration practices to promote resilience to climate variability & adaptation to increasingly shrub-dominated environments. 1A: Compare productivity & environmental impacts of Raramuri Criollo cattle to conventional livestock production systems in the arid Southwest (part of LTAR Common Experiment). 1B: Develop collaborative science approaches to test the efficacy of practices to recover & sustain perennial grass cover in the desert grassland region. 2: Leveraging temporal & spatial datasets from the Jornada & surrounding region, design & implement big data-model integration approaches to predict and/or resolve disease outbreaks & other regional agricultural problems. 2A: Develop a strategy & operational framework for a gricultural Grand Challenges that require big data & trans-disciplinary scientific expertise based on spatio-temporal modeling of cross-scale interactions & interactive machine learning. 2B: Develop national wind erosion assessments using big data & models developed through the National Wind Erosion Research Network. 3: Improve understanding of ecological state change in the desert grassland region through synthesis & analysis of long-term climate, vegetation, & livestock data, alongside numerous ongoing short- and long-term experiments, including how gradual & abrupt transitions occur in rangeland agroecosystems & how they can be managed. 3A: Predict alternative states in Western rangelands by integrating multiple lines of evidence including spatiotemporal modeling. 3B: Formulate phenological indicators of gradual & abrupt changes in primary production using integration of remotely-sensed imagery and ground-based observations. 4: Complete development of a new database to improve quality, accessibility, & utility of Ecological Site Description (ESD) information nationwide, & collaborate with NRCS to complete national population of ESD information. 5: Develop tools & techniques for managing & integrating ground-based assessment & monitoring data, remotely sensed & digital spatial data, & connect data to interpretive frameworks & models to develop actionable interpretations for land management. 5A: Develop tools & techniques for managing & integrating ground-based assessment and monitoring data, remotely sensed and digital spatial data, and connect data to interpretive frameworks and models to develop actionable interpretations for land management. 5B: Develop, test, and facilitate adoption of a data collection and decision support system that increases land manager ability to monitor their land, and to access, evaluate, integrate and apply local and scientific knowledge. 6: Develop new tools and information to assist agricultural stakeholders in coping with climate variability through: research, science translation & information synthesis; tool development & technology transfer; stakeholder outreach & education (Southwest Regional Climate Hub). 7: Operate & maintain the Jornada Experimental Range LTAR network site using technolgies & practices agreed upon by LTAR leadership. Contribute to the LTAR working groups & common experiments as resources allow.
1a. We will initiate a grazing experiment to determine whether ecosystem parameters respond differently to grazing of Raramuri Criollo and common British breeds; compare behavioral attributes; and monitor input and output parameters to evaluate productivity of these two types of cattle.1b. We will embed monitoring experiments in public lands brush management treatments to determine the circumstances of successful grassland restoration and test the role of soil properties in grass restoration. 2a. We will use interactive machine learning to improve predictions of Vesicular Stomatitis Virus occurrence. 2b. We will develop a national, standard, public dataset coupled to an improved wind erosion model to develop scalable, precise wind erosion estimates. 3a. We will model spatiotemporal dynamics of ecosystems based on Jornada long-term data to develop a novel approach for spatial predictions of ecosystem processes. 3b. We will use multi-scale measurements of plant phenology to improve estimates of climate impacts on agricultural production. 4a. We will expand an ecological site database (EDIT) to make management-relevant information more widely available. 5a. We will develop training and user support systems to make national rangeland monitoring datasets more useful to the public. 5b. We will expand capabilities of LandPKS mobile apps to provide accurate estimates of soil processes and linkages to other databases. 6. We will use climate science synthesis, web tool development, and collaborative outreach to improve adaptive capacity of Southwestern producers and managers. 7. We will contribute network-level research, local research, and data sharing to advance national LTAR goals.
Progress was made in all six objectives. In an effort to identify cattle biotypes more suited for low-input production systems in arid rangelands (Objective 1A), supplement intake and body condition score (BCS) of Raramuri Criollo (RC) cattle were compared to desert-adapted Brangus cows. Supplement intake was lower for RC cows than Brangus cows, while BCS was similar between breeds at the end of the grazing period, suggesting RC may require fewer inputs to maintain body condition. Identifying biotypes with attributes that match forage and environmental conditions in extensive semiarid ecosystems will benefit ranchers in the region. Evaluations of conservation practices for desert grasslands progressed (Objective 1B). A study was completed on a novel rangeland vegetation type that emerged in a degraded rangeland after an extreme rainfall event. This novel type exhibited several desirable vegetation and soil features, suggesting a “grassy shrubland” could be a viable restoration target for degraded arid rangelands. Progress was made in developing a framework for agricultural Grand Challenges using big data and trans-disciplinary scientific expertise (Objective 2A). Researchers collaborated with other ARS locations and the Animal and Plant Health Inspection Service to integrate and harmonize environmental, vector, host and viral variables with disease occurrence data to predict outbreaks and distribution of vector-borne diseases such as vesicular stomatitis virus under current and future climate scenarios. Occurrence of VS was related to summer and winter precipitation, maximum winter temperature, elevation, fall vegetation biomass, horse density, and proximity to rivers. A new publicly accessible database system was developed to store National Wind Erosion Research Network data collected by researchers across the U.S. (Objective 2B). Standard indicators of wind erosion and ecosystem attributes derived from these aggregated datasets are available to land managers and policy makers to support management decisions and evaluate effects of management practices on wind erosion. New models are being developed to help understand and predict climate-driven vegetation changes in Southwestern rangelands using long-term datasets, sensor and imagery products, and conceptual models (Objective 3A). New datasets were assembled and analyzed to examine the effects of varying sequences of wet and dry periods on production and species richness in an arid rangeland. These analyses showed that the sequential pattern of annual rainfall is an important predictor of rangeland conditions. Remotely sensed satellite and surface cameras (phenocams) were used at multiple sites across the U.S. to evaluate a model (PhenoGrass) to forecast grass production and determine locations where the model successfully predicted grass growth (Objective 3B). Integration of near-surface sensors with satellite imagery enhanced long-term grass production forecasts to support management decisions for livestock, forage and crop production. A new version of the Ecosystem Dynamics Interpretative Tool (EDIT) was released during the past year (Objective 4). This online database, which now contains 12,333 Ecological Site Descriptions (ESDs) and receives 120,000 views monthly, provides land managers with readily accessible ESDs to assist with science-based conservation planning across the United States. Standardized tools and techniques were developed for multi-scale inventory, monitoring, and assessment of rangelands (Objective 5A). The Landscape Data Commons (a geodatabase to store standardized monitoring data and associated indicator calculations) was developed and deployed. This database and associated analytical tools are available online to researchers and land managers for conservation planning and were used by the Bureau of Land Management and Natural Resources Conservation Service (NRCS) assessment programs to produce reports and make management decisions regarding wildlife habitat suitability, evaluate conservation practice effectiveness, and improve grazing management systems on millions of acres of rangelands. Progress was also made on the development of the Land-Potential Knowledge System (LandPKS) application (Objective 5B). The soil health monitoring feature was expanded to include the NRCS Cropland In-Field Soil Health Assessment indicators and methods. The Soil ID function was improved and a Habitat module was added. These additions will improve the ability of land managers to access information and make management decisions to enhance land productivity and sustainability. The Long-Term Agroecosystem Research network, composed of 18 research sites across the U.S., coordinates research activities in a variety of conditions and environments at a national scale (Objective 6). ARS scientists in Las Cruces, New Mexico led a network initiative on rangeland soil erosion to assess wind and water erosion on public and private grazing lands across the U.S. Models were developed and used to estimate potential aeolian sediment transport, dust emission, runoff, water erosion, and sediment yield at over 60,000 locations. These estimates were assimilated into a publicly accessible database for use by stakeholders to access erosion information and other indicators of rangeland health.
1. Wind erosion network implementation to support a national assessment. Rangeland and cropland wind erosion reduces soil productivity and causes highway fatalities, human health problems, and infrastructure damage. Long-term networked research using standardized methodology is needed to accurately measure and model effects of management practices on wind erosion to mitigate this problem. ARS scientists in Las Cruces, New Mexico, developed a new publicly accessible database system as part of the Landscape Data Commons geodatabase to store National Wind Erosion Research Network (NWERN) data. The Landscape Data Commons harmonizes NWERN datasets with other inventory and monitoring datasets collected by the ARS Long-Term Agroecosystem Research Network and Natural Resources Conservation Service and Bureau of Land Management stakeholders, enabling public NWERN data access via application programming interfaces (APIs). Standard indicators of wind erosion and ecosystem attributes have been produced from the aggregated datasets and are available using the Landscape Data Commons APIs. An automated quality control toolbox has been implemented to ensure delivery of quality meteorological data and standard metadata alongside vegetation and sediment transport datasets in the Landscape Data Commons. These databases will enable land managers to consider the impacts of management practices on wind erosion, which to this point has not been possible.
2. Multi-scale Big Data-model integration to improve production and environmental quality on western rangelands. Vector-borne diseases such as vesicular stomatitis virus (VS) have major economic implications for animal agriculture globally. ARS scientists in Las Cruces, New Mexico, collaborated with other ARS research locations and Animal and Plant Health Inspection Service researchers to integrate and harmonize environmental, vector, host, and viral variables with disease occurrence data to predict occurrence and distribution of vector-borne diseases under current and future climate scenarios. The current extent of VS is confined to the western portion of the U.S. and is related to summer and winter precipitation, winter maximum temperature, elevation, fall vegetation biomass, horse density, and proximity to rivers. Climate change scenarios had non-uniform impacts for predicted VS occurrence. We expect that the heterogeneous impacts of climate change across the western U.S. will be exacerbated with additional changes in climate interacting with land use and land cover that affect the hydrological cycles and the ecology of insect vectors involved in VS transmission.
3. Tools and techniques for multi-scale inventory, monitoring, and assessment. Standardized approaches for monitoring rangelands are needed to allow land managers and public land agencies to collect and share data that address numerous rangeland management and policy needs. ARS scientists in Las Cruces, New Mexico, led the development and deployment of a geodatabase (the Landscape Data Commons) to store standardized monitoring data and associated indicator calculations. This geodatabase is freely available to the public. Data in the Landscape Data Commons directly support the Bureau of Land Management (BLM) and Natural Resources Conservation Services (NRCS) assessment programs as well as soil erosion research in the Long-Term Agroecosystem Research Network. R packages were developed and updated to support data harmonization and standardized rangeland indicator development of data stored in the Landscape Data Commons. Additional analytical and computing improvements were added to enable the use of standardized monitoring data in the Landscape Data Commons to develop ecological site concepts and prioritize conservation planning. These tools, datasets, and R packages were used by BLM and NRCS to produce reports and make management decisions regarding wildlife habitat suitability, evaluate conservation practice effectiveness, and improve grazing management systems across 761 million acres of US rangelands. Methods, tools, databases, information resources and training are available on-line and are being used by land managers and policy makers to manage rangelands at local to continental scales over millions of acres of rangelands.
4. Ecological dynamics national database. Ecological Site Descriptions (ESDs) provide the scientific basis for site specific conservation decisions made by planners and land managers, yet this information was not organized such that it could be readily accessed and integrated with other decision tools. A new version of the web-based Ecosystem Dynamics Interpretative Tool (EDIT) developed by ARS scientists in Las Cruces, New Mexico, was released in 2019 based on feedback from across the country. The EDIT database now contains 12,333 ESDs and receives 120,000 views per month to add or access natural resource information from the webpage or Application Programming Interfaces linked to other tools. This year we programmed connections to the Natural Resources Conservation Service’s (NRCS) National Soil Information System database allowing automated updates of ecological site data, developed a state and transition model classifier tool to aid ESD development, and programmed a tabular structure to link ESDs to resource concerns used in NRCS conservation planning. The database dramatically improves access to site specific natural resource information by land managers and the public across the U.S.
5. Remotely sensed phenological indicators of plant production for livestock management. Integration of remote sensing and data acquisition technologies is needed to improve rangeland vegetation monitoring and use of natural resources. ARS scientists in Las Cruces, New Mexico, evaluated a grass forecast model (PhenoGrass) using nearly 500 site-years of grassland data from digital near-surface camera (phenocam) instruments across the U.S. and identified areas where the model performed well to capture grass growth (greenness). For those locations, long-term grassland forecasts can be made using the current model, while low performing areas (including the Southwestern U.S.) need further model development. The PhenoGrass model integrates near-surface camera data to provide an “on the ground” perspective to facilitate interpretation and improve confidence in productivity forecasts compared to products that rely only on broad-scale satellite remote sensing. Improved forecasts for grassland productivity at the pasture scale will help farmers, ranchers, and land managers make informed decisions about resource management and economic viability.
6. Low input livestock production strategies. New world cattle biotypes may help ranchers cope with low and variable forage production that often occurs on western U.S. rangelands. Raramuri Criollo (RC) cattle have undergone approximately 500 years of natural selection and adaptation to harsh rangeland conditions. ARS scientists in Las Cruces, New Mexico, provided evidence that, compared to desert-adapted Brangus cattle, RC cattle exhibit several desirable traits. Preliminary results revealed that RC cows consumed less protein supplement and had similar body condition scores at the end of a three-month study, suggesting RC may require fewer inputs. Identifying biotypes with attributes that match environmental conditions in extensive semiarid ecosystems will benefit ranchers in the Southwest by imposing fewer constraints on movement and optimizing use of available forage.
7. Evaluation of conservation practices for desert grasslands. The ability to restore historical grassland composition in arid southwestern rangelands is often limited and land managers are seek to identify methods to achieve realistic restoration targets. ARS scientists in Las Cruces, New Mexico, completed a study of the ecosystem characteristics of a novel rangeland vegetation type that emerged within a degraded rangeland being rested from grazing following an extreme rainfall event. The rangeland type has several desirable characteristics including comparatively high grass cover, soil carbon and soil health, and soil water retention and low rates of wind erosion that have been sustained for over a decade. This study confirms that a “grassy shrubland” could be a viable and climate resilient restoration target for degraded Southwestern arid rangelands in the future. Public and private land managers now recognize a restoration option that was previous unknown and can devise strategies to manage for this new rangeland vegetation type.
8. Long-Term Agroecosystem Research. The Long-Term Agroecosystem Research (LTAR) network seeks to integrate scientific research across a network of 18 sites via experimental approaches, measurements, and data. ARS scientists in Las Cruces, New Mexico, led a network initiative on rangeland soil erosion that addresses the status of soil erosion on federal (public) and non-federal (private) grazing lands in the U.S. Seven LTAR sites, three cooperating ARS and U.S. Geological Survey sites, and stakeholders at Natural Resources Conservation Service and Bureau of Land Management, collaborated to assess wind and water erosion across U.S. grazing lands. Data models were developed to enable applications of the Aeolian Erosion model and Rangeland Hydrology and Erosion Model to standard inventory and monitoring datasets. The erosion prediction models were then applied to estimate potential aeolian sediment transport, dust emission, runoff, water erosion, and sediment yield at over 60,000 locations. The wind and water erosion estimates were ingested into a publicly accessible database (Landscape Data Commons), which enables stakeholder access to erosion information alongside other indicators of rangeland health. LTAR projects to assess how erosion rates are responding to invasive plant species, wildfire, and conservation practices are in progress and leveraging the erosion simulations. Multiple stakeholders are actively engaged in this effort to deliver erosion prediction tools and analyses to producers and natural resource agencies.
9. Land-Potential Knowledge System (LandPKS) development and implementation. The Long-Term Agroecosystem Research (LTAR) network seeks to integrate scientific research across a network of 18 sites via experimental approaches, measurements, and data. ARS scientists in Las Cruces, New Mexico, led a network initiative on rangeland soil erosion that addresses the status of soil erosion on federal (public) and non-federal (private) grazing lands in the U.S. Seven LTAR sites, three cooperating ARS and U.S. Geological Survey sites, and stakeholders at Natural Resources Conservation Service and Bureau of Land Management, collaborated to assess wind and water erosion across U.S. grazing lands. Data models were developed to enable applications of the Aeolian Erosion model and Rangeland Hydrology and Erosion Model to standard inventory and monitoring datasets. The erosion prediction models were then applied to estimate potential aeolian sediment transport, dust emission, runoff, water erosion, and sediment yield at over 60,000 locations. The wind and water erosion estimates were ingested into a publicly accessible database (Landscape Data Commons), which enables stakeholder access to erosion information alongside other indicators of rangeland health. LTAR projects to assess how erosion rates are responding to invasive plant species, wildfire, and conservation practices are in progress and leveraging the erosion simulations. Multiple stakeholders are actively engaged in this effort to deliver erosion prediction tools and analyses to producers and natural resource agencies.
10. Prediction of climate-driven vegetation state changes. Directional decreases or increases in precipitation are predicted for rangelands in the future. ARS scientists in Las Cruces, New Mexico, are integrating long-term datasets with sensor and imagery products, static and dynamic maps, and conceptual models, to improve understanding and prediction of vegetation responses of drylands to alternative climate scenarios. New gap-filled datasets of climate were developed and analyzed to characterize the effects of sequences of wet and dry climatic periods on production and species richness in an arid landscape; the sequential pattern of annual rainfall was found to be an important predictor of rangeland conditions. These long-term datasets will be used in models to assist land managers in predicting the impacts of climate change on Southwestern rangeland ecosystems.
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