Research Project: Knowledge Systems and Tools to Increase the Resilience and Sustainability of Western Rangeland Agriculture

Location: Range Management Research

2024 Annual Report

Objectives
Objective 1: Build predictive models of the impacts of land management and climate on western rangeland systems to guide decision making. Sub-objective 1.1: Develop and test models of arid rangeland ecosystem dynamics using field experiments and remote sensing data. Sub-objective 1.2: Apply erosion models to assess interactions among vegetation, climate (e.g., drought), and management changes with land, air, and water resources at multiple scales on western rangelands. Objective 2: Develop strategies to improve environmental and economic outcomes for Southwestern livestock-based agroecosystems. Sub-objective 2.1: Create and test new strategies to supply sustainable beef from western rangelands (LTAR Common Experiment). Sub-objective 2.2: Expand manureshed solutions to recouple animal and cropland systems. Objective 3: Co-develop indicators of sustainability and climate resilience with scientists, partners, and other stakeholders. Sub-objective 3.1: Co-develop sustainability indicator framework and peer-to-peer benchmarking tool to evaluate how agricultural systems perform in relation to sustainability goals at the farm/ranch level. Sub-objective 3.2: Develop new indicators of wind erosion that can support rangeland monitoring programs, land use planning, and management through the National Wind Erosion Research Network. Sub-objective 3.3: Develop methods for establishing quantitative benchmarks to interpret indicators and assess management effectiveness to improve sustainability and climate resilience. Objective 4: Co-develop decision support tools with stakeholders to help producers adapt to changing climate across landscapes modified by water scarcity (and excess), invasive species, erosion, changes in fire frequency, and historic land degradation. Sub-objective 4.1: Build tools that connect land potential information, monitoring, local knowledge, and big data for rangeland systems to improve adaptive decision-making. Sub-objective 4.2: Work with regional partner groups and the USDA Southwest Climate Hub to understand partner decision space and co-develop, test, and refine decision-support applications and concepts. Sub-objective 4.3: Co-develop a framework for “knowledge systems” that allow scientists, producers, managers, and policymakers to easily access and apply databases, knowledge sources, models, and decision support tools to inform land management at local to global scales.

Approach
Arid and semiarid rangelands of the United States and world face accelerating changes in climate, land use, and ecosystem function. Now more than ever, livestock producers and land managers need access to locally relevant, site-specific information and tools to manage change and build resilience to achieve sustainability goals. Available information, however, is insufficient for this task because a) system-level science to predict site-specific ecosystem changes in rangelands does not exist; b) the costs and benefits of new technologies and alternative livestock production systems are unknown; c) indicators for evaluating costs and benefits of available technologies are not comprehensive; and d) stakeholders often do not have the ability to identify, select, access, and apply suitable land management decision-support tools from among the hundreds that are available. The proposed project contributes solutions to these problems through a combination of field research, research co-production with stakeholders, modeling, and tool development. We will develop spatially explicit predictive models of vegetation change, productivity, carbon dynamics, and soil erosion potential in arid rangelands that will enable precision management at fine scales. We will create cost and benefit information for existing beef supply chain options and new technologies and practices (e.g., precision ranching and circular nutrient management). Simultaneously, we will work with the Long-Term Agroecosystem Research Network to develop new, standardized approaches for measuring and using sustainability indicators for comprehensive evaluation of management alternatives. Finally, we will advance the development of knowledge systems that integrate indicator tools and information sources, and we will connect them to the decision-making needs of stakeholders via database integration and use cases in the adaptive management of large rangeland landscapes. We will maintain and leverage century-long datasets, long-term collaborations with diverse partners including the Southwest Climate Hub, and a suite of existing models and computational tools to achieve these objectives.

Progress Report
Progress was made in all objectives. ARS researchers in Las Cruces, New Mexico developed empirical models using machine learning to test cost-effective methods to predict vegetation productivity using digital cameras. We collected, compiled, and harmonized data from eddy-covariance systems, weather stations, and image time series to explore models to predict ecosystem productivity using inexpensive digital cameras at rangeland sites in southern New Mexico. ARS researchers used vegetation transect data in the Landscape Data Commons and horizontal sediment flux data from the National Wind Erosion Research Network (NWERN) to produce estimates of wind erosion in different rangeland types and states across the western U.S. (Objective 1). Progress towards creating and testing new strategies to supply sustainable beef from western rangelands and expand manureshed solutions to recouple animal and cropland systems (LTAR Common Experiment) was made by planning focus groups and summits for New Mexico, Colorado, and Minnesota in 2025 and ongoing efforts to conduct engagement about manureshed barriers and solutions in three states (Objective 2). ARS researchers led the co-development of a sustainability indicator framework via a committee of 17 scientists from across the U.S. to evaluate how agricultural systems perform in relation to sustainability goals at the farm/ranch level and developed new indicators of wind erosion that can support rangeland monitoring programs through NWERN. We calculated Aeolian EROsion (AERO) model estimates for 50,000 locations in the Landscape Data Commons (LDC). Data can be accessed by the LDC Data Portal and through an application programing interface (API) that offers broad and open public access. In addition, we completed a workflow and produced a workshop ‘curriculum’ with the Bureau of Land Management that has been tested through workshops with field offices managing terrestrial and aquatic ecosystems from New Mexico to Alaska to support implementation of quantitative benchmarks to assess management effectiveness to improve climate resilience (Objective 3). ARS researchers built and deployed an API tool that enables users to access data from the Landscape Data Commons while implementing dataset-specific privacy requirements as progress toward connecting land potential information, local knowledge, and big data for rangeland systems to improve adaptive decision-making. We worked with regional partner groups and the USDA Southwest Climate Hub to understand partner decision space and to co-develop, test and refine decision-support applications and concepts. Researchers established a framework for the co-development of “knowledge systems” via workshops that allow scientists, managers and policymakers to easily access and apply databases, knowledge sources, models and decision support tools to inform land management at local to global scales (Objective 4).

Accomplishments
1. Developed performance indicators to systematically evaluate management outcomes. Environmental conditions and economic pressures are highly variable among farms and ranches, limiting a standardized approach to measuring management outcomes. ARS scientists in Las Cruces, New Mexico, collaborated with other scientists in the Long-Term Agroecosystem Research (LTAR) network to develop a set of performance indicators and a methodology to measure the outcomes of farming and ranching approaches systematically with respect to production, environmental, economic, and social outcomes. A rigorous collaborative process was used to develop a unified indicator set that reflects the values of producers and others across the United States. This indicator system will allow the impact of agricultural innovations developed by USDA and others to be integrated and reported at a national scale.

2. Developed and released software tool to enable users to access rangeland monitoring data. ARS scientists in Las Cruces, New Mexico, built and deployed an application programming interface (API) that enables users to access vegetation monitoring data from the Landscape Data Commons while implementing dataset-specific privacy requirements. This API is publicly available and allows scientists and land managers access to information such as bare ground cover and presence of invasive species, that support research and land management decisions. The API tool directly connects data and indicators to other decision-support tools, such as the Land Treatment Exploration Tool. This direct connection allows indicators to be incorporated into treatment planning processes. This API is being used across the U.S. to enhance land management.

3. Genetic relationships of Raramuri Criollo cattle with other breeds. Low and variable rainfall in the southwestern U.S. translates to low and variable forage production that is projected to become worse in the future. Raramuri Criollo (RC) are a cattle biotype that have adapted to the harsh conditions of the Copper Canyon region of Northern Mexico for nearly five centuries with minimal selection or crossbreeding. Previous research by ARS scientists in Las Cruces, New Mexico, showed RC cows may be more suited to environmental conditions of the southwestern U.S. because they travel farther, use large shrubland areas and are less susceptible to heat stress than conventional breeds. Understanding the genetic makeup and diversity of this heritage biotype is crucial for the preservation of this unique genetic resource. Scientists determined the genotype of the RC cattle herd at the Jornada Experimental Range and compared it to other heritage cattle biotypes with similar origins. These cattle formed a group distinct from other Criollo biotypes (e.g., Texas Longhorn), suggesting this biotype is a valuable genetic resource to facilitate the selection of cattle with traits suited to the harsh climate extremes of the arid Southwest. Measures are underway to conserve this unique gene pool for the long-term.

4. Using precision ranching tools for adaptive management. Precision ranching tools, including virtual fencing, remote sensing tools, and field data collection tools have the potential to improve the ability of ranchers and land managers to make rapid adjustments to management and reduce management costs. Strategies for the use of multiple precision tools in arid rangelands of the Southwest, however, did not exist. ARS scientists in Las Cruces, New Mexico, developed a general strategy and implemented precision management for the 300-square mile Jornada Experimental Range as a demonstration project. Over 100 cattle were outfitted with virtual fencing collars and activity sensors and waters are monitored using water level sensors. Management decisions are based on satellite-based vegetation production data. This demonstration project is being used in outreach to regional ranchers and land managers and has created interest in tool adoption and financial support for precision ranching by land management agencies.

5. Developed empirical models to test cost-effective methods to predict vegetation productivity using digital cameras. The role of rangeland landscapes in carbon storage is uncertain due to unreliable data on carbon uptake and release (or "fluxes"). Measuring CO2 fluxes is expensive and requires a great deal of technical expertise. ARS scientists in Las Cruces, New Mexico, explored the potential for estimating CO2 fluxes with digital cameras called "PhenoCams." These cameras are cheaper and easier to manage than more expensive “Eddy Covariance” systems used by researchers. Results showed that PhenoCams can measure fluxes effectively while reducing costs. Researchers are now enhancing models to understand where and why they work better in some ecosystems than others. Data will be merged with existing networks to develop models to estimate carbon flux on millions of acres across the western U.S.

6. Accessible indicators of wind erosion potential. Wind erosion in rangelands is a critical environmental concern, but useable, precise estimates of erosion potential at broad scales have been limited by a lack of suitable data available to land managers. ARS scientists in Las Cruces, New Mexico, incorporated vegetation transect data and horizontal sediment flux data into the Landscape Data Commons, a database accessible to the public. Standard indicators were calculated and included with other standard monitoring datasets. The Aeolian EROsion (AERO) model was applied to these data to produce wind erosion indicators for 50,000 locations across the U.S. Application Programming Interfaces (APIs) and the Landscape Data Commons data portal were then developed containing raw and calculated wind erosion data that are accessible to land managers and the public.

7. Established Manureshed Action Research Cycle to work directly with producers to meet manure challenges. Manure contains valuable soil nutrient resources and has been shown to increase soil carbon, but it is heavy, waterlogged, and unpleasant to transport in a timely manner from areas of manure generation to areas that could use it productively. Transport must be timed so that it arrives at the appropriate time to be applied to crops while avoiding nutrient losses to air and water. ARS scientists in Las Cruces, New Mexico, initiated systematic engagement, focus groups, and summits to connect actors in three manuresheds, so that they can envision optimal recycling solutions together. The Manureshed Action Network implements the “manureshed” concept to help agricultural producers and stakeholders understand the importance of manure redistribution at the right place, right time, and right amount.

8. Tools for evaluating conservation practice effectiveness at broad scales. The effectiveness of conservation practices supported by land management agencies, such as weed management or restoration seeding, has been difficult to measure due to a lack of monitoring data associated with treatments. ARS scientists in Las Cruces, New Mexico, combined a monitoring dataset from the Landscape Data Commons and treatment data in the Land Treatment Digital Library to evaluate the effects of a variety of practices, including brush control, fuels treatments, post-fire rehabilitation and seeding, in the Great Basin, Colorado Plateau and Chihuahuan Desert. Sites were identified that have detailed information on treatment types and sufficient monitoring data collected by the Bureau of Land Management that would allow for analyses of treatment effects on soil erosion and other indicators of land health. These results are being used by agency land managers and producers to determine the conditions under which treatments can positively impact rangeland health in western rangelands.

9. International coordination on soil information systems. Soil information serves as the foundation for agricultural management globally. Current soil information systems vary widely across the world in both the quality of the data and the extent to which they can be used for decisions. ARS scientists in Las Cruces, New Mexico, completed worked with international partners and the US Department of State to develop a strategy for improving global soil information systems and guided the design and initial implementation of a $30M project with the Food and Agriculture Organization to improve soil information systems in five countries in Central America and Africa. These systems will assist policymakers and producers in identifying locations and methods where agricultural and soil conservation practices have the greatest return on investment. This decision framework is now being applied and promoted globally through the U.S. State Department’s Vision for Adapted Crops and Soils.

Review Publications
Peinetti, R.H., Bestelmeyer, B.T., Chirino, C.C., Florencia, V.L., Kin, A.G. 2023. Thresholds and alternative states in neotropical dry forest in response to fire severity. Ecological Applications. 34(2). Article e2937. https://doi.org/10.1002/eap.2937.
McCord, S.E., Webb, N.P., Bestelmeyer, B.T., Bonefont Flores, K., Brehm, J.R., Brown, J., Courtright, E.M., Dietrich, C., Duniway, M., Edwards, B., Fraser, C.M., Herrick, J.E., Knight, A.C., Metz, L., Van Zee, J.W., Tweedie, C. 2023. The Landscape Data Commons: A system for standardizing, accessing, and applying large environmental datasets for agroecosystem research and management. Agricultural and Environmental Letters. 8(2). Article e20120. https://doi.org/10.1002/ael2.20120.
Herrick, J.E., Maynard, J.M., Bestelmeyer, B.T., Carey, C.J., Salley, S.W., Shepherd, K., Stewart, Z.P., Wills, S.A., Ziadat, F.M. 2023. Practical guidance for deciding whether to account for soil variability when managing for land health, agricultural production and climate resilience. Journal of Soil and Water Conservation. 78(6):125A-133A. https://doi.org/10.2489/jswc.2023.0706A.
Bestelmeyer, B.T., McCord, S.E., Browning, D.M., Burkett, L.M., Elias, E.H., Estell, R.E., Herrick, J.E., James, D.K., Spiegal, S.A., Utsumi, S.A., Webb, N.P., Williamson, J.C. 2024. Fulfilling the promise of digital tools to build rangeland resilience. Frontiers in Ecology and the Environment. 22(5). Article e2736. https://doi.org/10.1002/fee.2736.
Webb, N.P., Edwards, B.L., Heller, A., McCord, S.E., Schallner, J.W., Treminio, R.S., Wheeler, B.E., Stauffer, N.G., Spiegal, S.A., Duniway, M.C., Traynor, A.C., Kachergis, E., Houdeshell, C. 2024. Establishing quantitative benchmarks for soil erosion and ecological monitoring, assessment, and management. Ecological Indicators. 159. Article e111661. https://doi.org/10.1016/j.ecolind.2024.111661.
Treminio, R.S., Webb, N.P., Edwards, B.L., Faist, A., Newingham, B.A., Kachergis, E. 2024. Spatial patterns and controls on wind erosion in the Great Basin. Journal of Geophysical Research-Biogeosciences. 129(1). Article e2023JG007792. https://doi.org/10.1029/2023JG007792.
Nyamuryekung'E, S., Duff, G.C., Utsumi, S.A., Estell, R.E., McIntosh, M.M., Funk, M., Cox, A., Cao, H., Spiegal, S.A., Perea, A., Cibils, A.F. 2023. Real-time monitoring of grazing cattle using LORA-WAN sensors to improve precision in detecting animal welfare implications via daily distance walked metrics. Animals. 13(16). Article 2641. https://doi.org/10.3390/ani13162641.
Saeedimoghaddam, M., Nearing, G., Goodrich, D.C., Hernandez, M., Guertin, D., Metz, L., Wei, H., Ponce-Campos, G., Burns, I., McCord, S.E., Nearing, M., Williams, C.J., Houdeshell, C., Rahman, M., Meles, M.B., Barker, S. 2024. An artificial neural network to estimate the foliar and ground cover input variables of the Rangeland Hydrology and Erosion Model. Journal of Hydrology. 631. Article 130835. https://doi.org/10.1016/j.jhydrol.2024.130835.
Wojcikiewicz, R.R., Webb, N.P., Edwards, B.L., Van Zee, J.W., Courtright, E.M., Cooper, B.F., Hanan, N.P. 2023. Aeolian sediment transport responses to vegetation cover change: Effects of sampling error on model uncertainty. Journal of Geophysical Research. 128(12). Article e2023JF007319. https://doi.org/10.1029/2023JF007319.
Young, K.E., Reed, S.C., Morton, M., Bowker, M.A. 2024. Inoculated biocrust cover and functions diverged over a gradient of soil textures and water availability. Restoration Ecology. Article e14125. https://doi.org/10.1111/rec.14125.
Dashbal, B., Bestelmeyer, B.T., Densambuu, B., Ulambayar, B., Sainnemekh, S., Van Zee, J.W., Williamson, J.C., Battur, A., Tseelie, E. 2023. Implementing a resilience-based management system in Mongolia's rangelands. Ecosphere. 14 (10). Article e4665. https://doi.org/10.1002/ecs2.4665.
Martinez, P., Brehm, J.R., Nafus, A.M., Laurence-Traynor, A., Salley, S., McCord, S.E. 2024. Leveraging ecological monitoring programs to collect soil and geomorphology data across the western United States. Journal of Soil and Water Conservation. 79(3):132-144. https://doi.org/10.2489/jswc.2024.00068.
Sonnier, G., Augustine, D.J., Paudel, S., Porensky, L.M., Silveira, M., Toledo, D.N., Azad, S., Boughton, R., Browning, D.M., Clark, P., Fay, P.A., Kaplan, N.E., Thibault, K., Swain, H.M., Veum, K.S., Boughton, E. 2024. Impact of plant diversity and management intensity on magnitude and stability of productivity in North American grazing lands. Applied Vegetation Science. 27(2). Article e12776. https://doi.org/10.1111/avsc.12776.
Spaeth, K., Rutherford, W.A., Houdeshell, C., Williams, C.J., Simpson, B., Green, S., Toledo, D.N., Suffridge, E., McCord, S.E. 2024. Insights from the USDA Grazing Land National Resources Inventory and field studies. Journal of Soil and Water Conservation. 79(3):37A-42A. https://doi.org/10.2489/jswc.2024.0107A.
Lassaletta, L., Sanz-Cobeña, A., Pinsard, C., Ma, L., Spiegal, S.A., Reidsma, P. 2024. Special issue opening editorial: Reducing nitrogen waste through crop and livestock reconnection. Agricultural Systems. 214. Article e103816. https://doi.org/10.1016/j.agsy.2023.103816.
Winkler, Z., Boucheron, L.E., Utsumi, S.A., Nyamuryekung'e, S., McIntosh, M.M., Estell, R.E. 2024. Effects of dataset curation on body condition score (BCS) determination with a vision transformer (ViT) applied to RGB+Depth images. Computers and Electronics in Agriculture. 8. Article 100482. https://doi.org/10.1016/j.atech.2024.100482.
Langevin, A.E., Boggess, L.M., Harrison, G.R., Madritch, M.D. 2024. Cliff nesting birds provide nutrient inputs to cliff ecosystems. Basic and Applied Ecology. 79:74-83. https://doi.org/10.1016/j.baae.2024.06.001.
Humphreys Jr, J.M., Shults, P.T., Velazquez Salinas, L., Bertram, M.R., Pelzel-McCluskey, A.M., Peters, D.C., Rodriguez, L.L., Pauszek, S.J. 2024. Interrogating genomes and geography to unravel multiyear vesicular stomatitis epizootics. Viruses. 16(7). Article 1118. https://doi.org/10.3390/v16071118.
Chappell, A., Webb, N.P., Hennen, M., Zender, C.S., Ciais, P., Schepanski, K., Edwards, B.L., Ziegler, N.P., Balkanski, Y., Tong, D., Leys, J.F., Heidenreich, S., Hynes, R., Fuchs, D., Zeng, Z., Baddock, M.C., Lee, J.A., Kandakji, T. 2023. Elucidating hidden and enduring weaknesses in dust emission modeling. Journal of Geophysical Research Atmospheres. 128(17). Article e2023JD038584. https://doi.org/10.1029/2023JD038584.
Bestelmeyer, B.T. 2023. Sustainability through culture and innovation: Three perspectives from the 75th Annual Meeting of the Society for Range Management. Rangelands. 45(4):51-52. https://doi.org/10.1016/j.rala.2023.07.002.
Christensen, E.M., James, D.K., Randall, R.M., Bestelmeyer, B.T. 2023. Abrupt transitions in a southwest USA desert grassland related to the Pacific Decadal Oscillation. Ecology. 104(7). Article e4065. https://doi.org/10.1002/ecy.4065.
Carter, S.K., Haby, T., Meineke, J.K., Foster, A.C., McCall, L.E., Espy, L.D., Gilbert, M.A., Herrick, J.E., Prentice, K.L. 2023. Prioritizing science efforts to inform decision-making on public lands. Frontiers in Ecology and the Environment. 21(10):453-460. https://doi.org/10.1002/fee.2672.
Hurst, Z.M., Spiegal, S.A. 2023. Design thinking for responsible Agriculture 4.0 innovations in rangelands. Rangelands. 45(4):68-78. https://doi.org/10.1016/j.rala.2023.03.003.
Bestelmeyer, B.T., Utsumi, S., McCord, S.E., Browning, D.M., Burkett, L.M., Elias, E.H., Estell, R.E., Herrick, J.E., James, D.K., Spiegal, S.A., Webb, N.P., Williamson, J.C. 2023. Managing an arid ranch in the 21st century: New technologies for novel ecosystems. Rangelands. 45(4):60-67 https://doi.org/10.1016/j.rala.2023.05.002.
Tyree, G., Chappell, A., Villareal, M., Dhital, S., Duniway, M., Edwards, B., Faist, A., Nauman, T., Webb, N.P. 2024. Oil and gas development influences potential for dust emission from the Upper Colorado River Basin, USA. Earth Surface Processes and Landforms. 49(11):3292-3307. https://doi.org/10.1002/esp.5887.
Dhital, S., Webb, N.P., Chappell, A., Kaplan, M.L., Nauman, T.W., Tyree, G.L., Duniway, M.C., Edwards, B., LeGrand, S.L., Letcher, T.W., McKenzie, S.S., Naple, P., Chaney, N.W., Cai, J. 2024. Synoptic analysis and WRF-Chem Model simulation of dust events in the Southwestern United States. Journal of Geophysical Research Atmospheres. 129(13). Article e2023JD040650. https://doi.org/10.1029/2023JD040650.
Wheeler, B., Webb, N.P., Williams, C.J., Faist, A., Edwards, B., Herrick, J.E., Lepak, N., Kachergis, E., Mccord, S.E., Newingham, B.A., Pietrasiak, N., Toledo, D.N. 2024. Integrating erosion models into land health assessments to better understand landscape condition. Rangeland Ecology and Management. 96:32-46. https://doi.org/10.1016/j.rama.2024.05.003.
Miri, A., Webb, N.P. 2024. The impact of a multiple-row Tamarix windbreak on grain size parameters of aeolian sand flux. Soil Science Society of America Journal. 88(2):482-497. https://doi.org/10.1002/saj2.20623.
Myers, E.B., Browning, D.M., Burkett, L.M., James, D.K., Bestelmeyer, B.T. 2024. Novel use of image time series to distinguish dryland vegetation responses to wet and dry years. Journal of Remote Sensing. 1. Article 0190. https://doi.org/10.34133/remotesensing.0190.
Pi, H., Zhang, X., Li, S., Webb, N.P. 2024. Influence of crop rotation, irrigation, fertilization, and tillage on the aggregate property and soil wind erosion potential in the floodplain of the Yellow River. Aeolian Research. 67-69. Article e100925. https://doi.org/10.1016/j.aeolia.2024.100925.
Nyamuryekung'E, S., Cox, A., Perea, A., Estell, R.E., Cibils, A.F., Holland, J.P., Waterhouse, T., Duff, G., Funk, M., McIntosh, M.M., Spiegal, S.A., Bestelmeyer, B.T., Utsumi, S. 2023. Behavioral adaptations of nursing Brangus cows to virtual fencing: Insights from a training deployment phase. Animals. 13(22). Article e3558. https://doi.org/10.3390/ani13223558.