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ARS Home » Plains Area » Las Cruces, New Mexico » Range Management Research » Research » Research Project #435564

Research Project: Science and Technologies for the Sustainable Management of Western Rangeland Systems

Location: Range Management Research

2023 Annual Report


Objectives
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.


Approach
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 Report
Progress was made in all objectives. Raramuri Criollo (RC) cattle were evaluated for their suitability for low-input production systems in arid southwestern rangelands (Objective 1A). This biotype exhibits behaviors that are well matched to extensive rangeland conditions. Over this project, we showed that they travel farther from water, explore larger areas and exert a lower footprint on rangelands than their Angus crossbred counterparts. Oryx, a South African antelope game species that was introduced to the region decades ago has expanded its range across southern New Mexico. Their interactions with cattle were examined this year using game cameras and numbers were substantially lower when cattle were present in study pastures and appear to be lower with RC than Brangus. Conservation practices for restoration of desert grasslands were evaluated (Objective 1B). Grassland restoration in eroded arid shrublands is considered nearly impossible. However, spontaneous recovery of grasses in an eroded shrubland following a sequence of high rainfall years suggests that restoration potential has been underestimated. Experiments were conducted that couple shrub removal with novel devices to reduce sediment transport and enhance grass establishment (connectivity modifiers) for grassland restoration. The combination of shrub control and connectivity modifiers enhanced grass recovery rates. A new long-term experiment was established building on these results to test the efficacy of a combination of restoration approaches including connectivity modifiers, chemical treatments to induce shrub mortality, addition of composted manure, and exclosures to protect establishing grasses from native herbivorous mammals. The long-term experiment will provide insight into how combinations of restoration activities perform in terms of grass recovery in degraded southwestern rangelands. Efforts continued to use big data to understand relationships among management practices, ecological processes, and climatic variation (Objective 2A). ARS scientists in Las Cruces, New Mexico, used a big data-model integration approach to create preictive models of Vesicular Stomatitis outbreaks as to analyze variations in grassland production before, during, and after the “Dust Bowl” drought of the 1930s in the Central Great Plains Region of Iowa and Nebraska. Before this severe drought, grass production could be predicted by climate and soil factors, but other land use factors and soil erosion were related to grass production during and after drought. This modeling approach provides a method to predict resilience to extreme drought. National Wind Erosion Research Network (NWERN) data collected by researchers across the U.S. (Objective 2B) were used to simulate wind erosion on U.S. grazing lands using the newly developed Aeolian EROsion (AERO) model. This model has been used at thousands of locations by partner agencies, especially the Bureau of Land Management (BLM) and Natural Resources Conservation Service (NRCS) in rangeland monitoring programs. Wind erosion risk was assessed across U.S. grazing lands to determine hotspots for wind erosion. Patterns of wind erosion and dust emission were analyzed across rangeland ecosystems with respect to disturbance factors such as wildfire, invasive annual grasses, oil and gas development, and shrub encroachment. Information about key erosion processes and thresholds were used to establish quantitative benchmarks adopted by the BLM which are being used to support land health assessments and land use and management plans on millions of acres nationwide. Models were developed to help forecast climate-driven vegetation changes in Southwestern rangelands using long-term datasets and remotely sensed observations (Objective 3A). Information about the effects of climate and land use on changes in rangeland vegetation change has improved greatly from long-term monitoring, but these data have not yet enabled site-specific forecasts of vegetation change, which limits the ability of producers and land managers to plan for change. Long-term legacy data were extended, curated, and analyzed to develop a new understanding of vegetation responses to climate variables. In addition, a new method was developed to predict vegetation state changes based on remotely sensed transitions in vegetation cover data with the SyncroSim simulation model. The model showed that transition from a grassland to shrub-invaded state depended annual rainfall, soil type, and shrub invasion of surrounding areas. The new modelling approach offers land managers the ability to estimate the relative likelihood of vegetation change for management decisions. Remotely sensed satellite and surface cameras can be used to map and monitor plant phenology and productivity and collect long-term data to feed into models (Objective 3B), but information to guide selection of sensors and appropriate timing of use is needed. A metric assessment framework was created based on growing season length and productivity in grazing, cropping, and mixed grazing-cropping agroecosystems to optimize instrument selection to monitor, model, and forecast ecosystem productivity at multiple time scales. This method allows different sources of image and climatological data sets to be integrated to help land managers and producers improve monitoring and forecasting of plant growth and anticipate changes due to weather and climate. Surface energy balance towers were established along a gradient to quantify implications of ecological state change on carbon and water cycles. Remote sensing models incorporating the metric assessment framework indicated higher levels of uncertainty in productivity of grazing systems than in other agro-ecosystems. Development of a database to improve Ecological Site Description (ESD) quality and accessibility was completed (Objective 4). ESDs provide site-specific, science-based guidance for selecting appropriate conservation and climate-smart practices to sustain or restore agricultural systems, but they were not previously in a format that allowed them to be linked with conservation planning databases used to apply practices on the ground. A new web application, the Transition Sandbox, was developed that provides a user-guided interface to build State and Transition Models (STMs) within ESDs. The Transition Sandbox improved linkages of ecological science to the selection of conservation practices to ensure that practices applied are based on the best science available and are accessible to land managers, policy makers and the public. Standardized tools and techniques were developed for multi-scale inventory, monitoring, and assessment of rangelands (Objective 5A). A collection of papers was assembled in a special issue describing the latest advances for monitoring tools and techniques for rangelands and creating a framework for teaching the next generation of scientists how to design and implement high quality monitoring programs. A framework and toolset for standardizing agroecosystem indicators of rangeland health was developed which describes the process for harmonizing standardized rangeland monitoring data to support data-driven decision making. These indicators have been used in numerous decisions, reports, and papers by BLM and NRCS and their science partners. Also, ARS scientists compiled the Landscape Data Commons (a system that improves accessibility of monitoring data) which contains decision support tools to help land managers and policy makers make meaningful decisions on management and conservation of millions of acres of rangeland. The Land-Potential Knowledge System (LandPKS) application development was completed (Objective 5B). A “Toolbox” function was added, allowing users to access a number of tools, including soil color and soil texture determinations. This mobile application allows land managers to rapidly identify information about their soil and vegetation and provides an efficient system for assessing land potential and improving land management decisions globally. The Long-Term Agroecosystem Research (LTAR) 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 7). Network initiatives led over the project period included manureshed (nutrient transfers from animal production to cropland operations), wind erosion, and phenology. This year, the first phase of a new initiative was completed that focused on indicators to measure the performance of agricultural systems being examined across the LTAR network. A survey was conducted and four major types of innovations were identified (farm/ranch practices, manureshed management, phenology monitoring systems, and knowledge co-production), resulting in the development of a Sustainability Indicator System designed for widespread use by ranchers and farmers across the U.S. and Canada. Four hundred network scientists provided feedback on a “menu” of indicators that measure attributes of sustainable farm/ranch systems. Measurable, general, and robust indicators that reflect a full range of interests in U.S. agricultural production systems will improve communication and speed adoption of useful agricultural innovations.


Accomplishments
1. Wind erosion network implementation to support a national assessment. Rangeland and cropland wind erosion reduces soil productivity, impacts water resources, 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, analyzed Aeolian EROsion (AERO) model runs across approximately 70,000 locations (2011-2020) sampled by partner agencies’ rangeland monitoring programs. Analyses established spatial patterns and controls on wind erosion and dust emission across rangeland ecosystems, including interactive effects of disturbance processes such as wildfire, invasive annual grasses, oil and gas development, and shrub encroachment. AERO model runs were linked to ecological site descriptions (ESDs) and state-and-transition models (STMs), and fuzzy clustering analyses of monitoring datasets, to establish how wind erosion responds to and influences ecosystem state changes across western rangelands. The research produced quantitative keys and identified functional thresholds for wind erosion that have informed establishment of quantitative benchmarks adopted by the BLM to support land health assessments and efforts to assess the efficacy of land use and management plans.

2. Mobile applications to improve land management decisions. Land managers in the U.S. currently lack an efficient system for accessing and sharing knowledge about land management that is relevant to the potential of their land. Because land potential depends on soil, topography and climate, the identification of appropriate management systems begins by matching areas with similar conditions. ARS scientists in Las Cruces, New Mexico, successfully completed development of the current LandPKS (Land Potential Knowledge System) mobile app on iOS and Android phones and tablets. The LandPKS mobile app allows for collecting agricultural data and connecting field data to cloud-based data and computation to provide value-added information. A “Toolbox” function was added, allowing users to access a number of tools, including soil color and soil texture determinations, without creating a plot. Collaborations were initiated with a technology-for-good non-governmental organization based in the Silicon Valley, on the development of the next generation of LandPKS apps and expanded collaboration with the UDSA National Resources Conservation Service. LandPKS allows managers to rapidly identify their soil and access soil survey information (LandInfo) and monitor vegetation (Vegetation), both of which are necessary to support outcome-based rangeland management.

3. Synthesis of advances in rangeland health monitoring. 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, published a framework and toolset for standardizing agroecosystem vegetation and soil indicators which describe the process ARS developed for harmonizing standardized rangeland monitoring data for use in data-support decision making. This framework provides the conceptual basis for an R software package terradactyl which is now used by both Bureau of Land Management (BLM) and Natural Resources Conservation Service (NRCS) to calculate standard indicators of rangeland health. User guides were published online and workshops were given to teach BLM and NRCS employees how to use this software package. These indicators have been used by partnering agencies (BLM and NRCS) and their collaborators in over 30 decisions, reports, and papers. Finally, ARS scientists compiled a description of the Landscape Data Commons (a system that improves data access and leverages standardized monitoring data to assist with decision making) to assist with decision support tools. These products contributed to ARS developed erosion models and decision support tools and are being used by land managers and policy makers on millions of acres of rangelands globally.

4. Selecting conservation and climate-smart practices based on ecological processes. Ecological Site Descriptions (ESDs) provide site-specific, science-based guidance for evaluating resource concerns and selecting appropriate conservation and climate-smart practices to sustain or restore agricultural systems. Consistent application of scientific knowledge in ESDs, however, has not been possible due to the narrative format of state and transition models (STMs) within ESDs, thereby precluding connections to conservation planning databases used to apply practices on the ground. ARS scientists in Las Cruces, New Mexico developed a new web application, the Transition Sandbox, that provides a user-guided interface to build STMs consistently and that allows STM information to be rapidly developed. The Transition Sandbox provides an efficient method to link ecological science to the selection of conservation practices and can help ensure that practices applied to rangelands worldwide are based on the best science available.

5. Choosing the right sensors to track agricultural productivity. Technology offers many options for tracking rangeland and cropland production. Users need information to guide the selection of sensors and when to use them to meet management and production needs. ARS scientists in Las Cruces, New Mexico, established three new instrumented surface energy balance towers coupled to Phenocams along a gradient to quantify implications of ecological state change on carbon (C) and water cycles. The “metric assessment framework” devised the previous year indicated higher levels of uncertainty in remote sensing models of ecosystem productivity in grazing systems than in other agro-ecosystems. Preliminary data from the new towers show that subtle differences in ecosystem condition can produce important variations in CO2 uptake, offering new insights for ecosystem services from grazinglands. Our results indicate that land managers may be able to measure C sequestration changes over time and spatial differences in rangelands using relatively inexpensive Phenocams.

6. A new long-term experiment to understand restoration constraints in highly degraded Chihuahuan Desert ecosystems. Grassland restoration in eroded shrublands has been considered nearly impossible, so restoration attempts in eroded shrublands of the Chihuahuan Desert region are generally avoided. Spontaneous recovery of grasses in an eroded shrubland following a sequence of high rainfall years suggests that restoration potential has been underestimated. ARS scientists in Las Cruces, New Mexico, have initiated a pilot project to test the efficacy of a combination of restoration approaches including connectivity modifiers (simple devices that reduce sediment transport and enhance grass establishment), chemical treatments to induce shrub mortality, addition of composted manure, and exclosures to protect establishing grasses from native herbivorous mammals. The long-term experiment will evaluate how combinations of restoration activities perform with respect to annual weather variations and provide data on restoration options to land managers who seek to maximize ecosystem services in the region’s most degraded ecosystems.

7. A new method to forecast vegetation state changes. While knowledge of the effects of climate and land use on rangeland vegetation change has improved greatly with long-term monitoring, these data have not yet enabled site-specific forecasts of vegetation change into the future. ARS scientists in Las Cruces, New Mexico, used observations of transitions in remotely sensed long-term rangeland vegetation cover data with the SyncroSim simulation model, which is a spatially-explicit landscape model for forecasting changes in discrete states. The model showed that the probability of a transition from a grassland to shrub-invaded state depended on the number of adjacent pixels that were shrub-invaded, yearly rainfall, and soil type and can produce maps of transition likelihood under different climate scenarios. The new modelling approach linking remote sensing data to SyncroSim can provide land managers the ability to estimate the relative likelihood of vegetation change as a basis for management decisions.

8. Regional variation in grassland resilience to drought. Severe drought events have variable effects on grasslands at a regional to continental scale, but the causes of these variations are poorly understood. ARS scientists in Las Cruces, New Mexico, used a big data-model integration approach to analyze variations in grassland production before, during, and after the “Dust Bowl” drought of the 1930s in the Central Great Plains Region of Iowa and Nebraska. Climate and soil factors predicted grass production prior to the drought, yet additional land use and erosion factors were needed to explain production in both drought and post-drought. Tallgrass prairie exhibited stronger production responses to precipitation and temperature compared with northern and southern mixed-grass prairies during all periods. Tallgrass prairie counties along the boundary with mixed-grass prairies were more negatively impacted by soil loss from abandoned cropland during the drought compared with other tallgrass prairie counties. The modeling approach provides a method to allow land managers to predict broad-scale variations in resilience to extreme drought in response to climate, soils, and land management.

9. Comprehensive indicators to evaluate agricultural innovations. ARS scientists in Las Cruces, New Mexico led an initiative on indicators that measure the performance of agricultural systems under investigation across the Long-Term Agroecosystem Research Network. The Sustainability Indicator System is being designed for widespread use by ranchers and farmers across the U.S. and Canada. A survey was conducted of 400 network scientists to request feedback on the 2022 “menu” of indicators that measure the status of six attributes of sustainable farm/ranch systems. Results of the survey are being used to form a new framework of network indicators with democratic and transparent network buy-in. Measurable, general, and robust indicators that reflect a full range of interests in U.S. agricultural production systems will improve communication and speed adoption of useful agricultural innovations.

10. Low input livestock production strategies. Raramuri Criollo (RC) are a cattle biotype that has undergone approximately 500 years of natural selection and adaptation to harsh rangeland conditions. A long-term experiment was set up to compare this biotype to desert-adapted Brangus cattle. South African oryx (a species of large antelope) were introduced to the area as a game species and their range is expanding across New Mexico. Their impact on cattle behavior and habitat use was examined using cameras to collect image data of antelope presence and location. Oryx presence was not distributed uniformly across study pastures and numbers were substantially lower when cattle were present in study pastures. Preliminary evidence suggests oryx numbers were lower in pastures containing Criollo than Brangus. Results suggest oryx concentrations could have implications in terms of their interactions with cattle. The ability of Criollo to exclude oryx from grazing areas provides information on a poorly understood, additional benefit of Criollo for consideration in producer adoption decisions.


Review Publications
Buenemann, M., Coetzee, M.E., Kutuahupira, J., Maynard, J.J., Herrick, J.E. 2023. Errors in soil maps: The need for better on-site estimates and soil map predictions. PLOS ONE. 18(1). Article e0270176. https://doi.org/10.1371/journal.pone.0270176.
Ifekharul Islam, K., Elias, E.H., Brown, C.P., James, D.K., Heimel, S. 2022. A statistical approach to using remote sensing data to discern streamflow variable influence in the snow melt dominated Upper Rio Grande Basin. Remote Sensing. 14(23). Article 6076. https://doi.org/10.3390/rs14236076.
Elias, E.H., Savoy, H.M., Swanson, D.A., Cohnstaedt, L.W., Peters, D.C., Derner, J.D., Pelzel-McClusky, A., Drolet, B.S., Rodriguez, L.L. 2022. Landscape dynamics of a vector-borne disease in the Western US: How vector-habitat relationships inform disease hotspots. Ecosphere. 13(11). Article e4267. https://doi.org/10.1002/ecs2.4267.
Chappell, A., Webb, N.P., Hennen, M., Schepanski, K., Ciais, P., Balkanski, Y., Zender, C., Tegen, I., Zeng, Z., Tong, D., Baker, B., Eckstrom, M., Baddock, M., Eckardt, F., Kandaki, T., Lee, J., Nobakht, M., Von Holdt, J., Leys, J. 2023. Satellites reveal Earth's seasonally shifting dust emission sources. Science of the Total Environment. 883. Article 163452. https://doi.org/10.1016/j.scitotenv.2023.163452.
Gifford, C., Taylor, K., Spiegal, S.A., Duff, G., Aney, S., Elias, E.H., Steiner, J., Estell, R.E., MacFarlane, Z.D., Schohr, T.K., DeAtley, K.L., Banwarth, M.R. 2023. Bull selection and management in extensive rangeland production systems of New Mexico: A producer survey. Translational Animal Science. 7(1):1-13. https://doi.org/10.1093/tas/txac167.
Elias, E.H., Fuchs, B., Lisonbee, J., Bernadt, B., Martinez, V., Haigh, T. 2023. Evolution of the Southwest Drought Learning Network: Collective response to exceptional drought. Bulletin of the American Meteorological Society. 4(5):E935-E942. https://doi.org/10.1175/BAMS-D-22-0017.1.
Cibils, A.F., Estell, R.E., Spiegal, S.A., Nuamuryekung'E, S., Mcintosh, M.M., Duni, D.M., Herrera-Conegliano, O.A., Rodriguez Almeida, F.A., Roacho, E.O., Blanco, L.J., Duniway, M.C., Utsumi, S.A., Gonzalez, A.L. 2022. Adapting to climate change on desert rangelands: A multi-site comparison of grazing behavior plasticity of heritage and improved beef cattle. Journal of Arid Environments. 209. Article 104886. https://doi.org/10.1016/j.jaridenv.2022.104886.
Ziegler, N., Webb, N., Gillies, J., Edwards, B., Nikolich, G., Van Zee, J.W., Cooper, B., Browning, D.M., Courtright, E.M., Legrand, S. 2023. Plant phenology drives seasonal changes in shear stress partitioning in a semi-arid rangeland. Agricultural and Forest Meteorology. 330. Article 109295. https://doi.org/10.1016/j.agrformet.2022.109295.
Mccord, S.E., Brehm, J.R., Burnett, S.H., Dietrich, C., Edwards, B., Metz, L.J., Hernandez-Narvaez, M., Pierson Jr, F.B., Ramirez, K., Stauffer, N.G., Webb, N.P., Tweedie, C. 2022. A framework and toolset for standardizing agroecosystem indicators. Ecological Indicators. 144. Article 109511. https://doi.org/10.1016/j.ecolind.2022.109511.
Kleinhesslink, A.R., Kachergis, E., McCord, S.E., Shirly, J., Hupp, N.R., Carlson, J.C., Morford, S.L., Jones, M.O., Smith, J.T., Allred, B.W., Naugle, D.E. 2022. Long-term trends in vegetation on Bureau of Land Management rangelands in the Western United States. Rangeland Ecology and Management. 87:1-12. https://doi.org/10.1016/j.rama.2022.11.004.
Torell, G.L., Torell, L.A., Enyinnaya, J., Spiegal, S.A., Estell, R.E., Cibils, A.F., Anderson, D.M., Gonzalez, A.L. 2023. Economics of Raramuri Criollo and British crossbred cattle production in the Chihuahuan desert: Effects of foraging distribution and finishing strategy. Journal of Arid Environments. 211. Article 104922. https://doi.org/10.1016/j.jaridenv.2022.104922.
McIntosh, M.M., Spiegal, S.A., McIntosh, S.Z., Sanchez, C.J., Estell, R.E., Steele, C.M., Elias, E.H., Bailey, D.W., Brown, J.R., Cibils, A.F. 2023. Matching beef cattle breeds to the environment for desired outcomes in a changing climate: A systematic review. Journal of Arid Environments. 211. Article 104905. https://doi.org/10.1016/j.jaridenv.2022.104905.
Bittman, S., Worth, D., Hunt, D.E., Spiegal, S.A., Kleinman, P.J., Vendramini, J., Silveira, M., Flynn, K.C., Reid, K., Martin, T., Vanderzaag, A., Javorek, S., Nanayakkara, S. 2023. Distribution of livestock sectors in Canada: Implications for manureshed management. Journal of Environmental Quality. 52(3):596-609. https://doi.org/10.1002/jeq2.20457.
Roacho, E.O., Rodriguez, A.F., Utsumi, S.A., Fredrickson, E.L., Bezanilla Enriquez, G.A., Estell, R.E., Gonzalez, A.L., Cibils, A.F. 2023. Foraging behavior of Raramuri Criollo vs. commercial crossbred cows on rangelands of the southwestern United States and Northern Mexico. Journal of Arid Environments. 212. Article 104943. https://doi.org/10.1016/j.jaridenv.2023.104943.
Hennen, M., Chappell, A., Webb, N. 2022. Modelled direct causes of dust emission change (2001-2020) in southwestern USA and implications for management. Aeolian Research. 60. Article 100852. https://doi.org/10.1016/j.aeolia.2022.100852.
Maynard, J.J., Maniak, S., Hamrick, L., Peacock, G., McCord, S.E., Herrick, J.E. 2022. LandPKS Toolbox: Open-source mobile app tools for sustainable land management. Journal of Soil and Water Conservation. 77(6):91A-97A. https://doi.org/10.2489/jswc.2022.0927A.
Nightingale Lasche, S., Schroeder, R.W., McIntosh, M.M., Lucero, J.E., Spiegal, S.A., Funk, M.P., Beck, R.F., Holechek, J.L., Faist, A.M. 2023. Long-term growing season aridity and grazing seasonality effects on perennial grass biomass in a Chihuahuan Desert rangeland. Journal of Arid Environments. 209. Article 104902. https://doi.org/10.1016/j.jaridenv.2022.104902.
LeGrand, S.L., Letcher, T.W., Okin, G.S., Webb, N.P., Gallagher, A.R., Dhital, S., Hodgdon, T.S., Ziegler, N.P., Michaels, M.L., Chappell, A. 2023. Application of a satellite-retrieved sheltering parameterization (v1.0) for dust event simulation with WRF-Chem v4.1. Geoscientific Model Development. 16(3):1009–1038. https://doi.org/10.5194/gmd-16-1009-2023.
Snyder, K.A., Richardson, W., Browning, D.M., Lieurance, W., Stringham, T.K. 2023. Plant phenology of high-elevation meadows: Assessing spectral responses of grazed meadows. Rangeland Ecology and Management. 87:69–82. https://doi.org/10.1016/j.rama.2022.12.001.
Hoover, D.L., Abendroth, L.J., Browning, D.M., Saha, A., Snyder, K.A., Wagle, P., Witthaus, L.M., Baffaut, C., Biederman, J.A., Bosch, D.D., Bracho, R., Busch, D., Clark, P., Ellsworth, P.Z., Fay, P.A., Flerchinger, G.N., Kearney, S.P., Levers, L.R., Saliendra, N.Z., Schmer, M.R., Schomberg, H.H., Scott, R.L. 2022. Indicators of water use efficiency across diverse agroecosystems and spatiotemporal scales. Science of the Total Environment. 864. Article e160992. https://doi.org/10.1016/j.scitotenv.2022.160992.
Christensen, E.M., James, D.K., Randall, R., Bestelmeyer, B.T. 2023. Abrupt dryland transitions related to the Pacific Decadal Oscillation in the 20th century. Ecology. Article e4065. https://doi.org/10.1002/ecy.4065.
Spiegal, S.A., Estell, R.E., Cibils, A., Armstrong, E., Blanco, L., Bestelmeyer, B.T. 2023. Can heritage Criollo cattle promote sustainability in a changing world? Journal of Arid Environments. 216. Article 104980. https://doi.org/10.1016/j.jaridenv.2023.104980.
Elias, E.H., Tsegaye, T.D., Hapeman, C.J., Mankin, K.R., Kleinman, P.J., Cosh, M.H., Peck, D.E., Coffin, A.W., Archer, D.W., Alfieri, J.G., Anderson, M.C., Baffaut, C., Baker, J.M., Bingner, R.L., Bjorneberg, D.L., Bryant, R.B., Gao, F.N., Gao, S., Heilman, P., Knipper, K.R., Kustas, W.P., Leytem, A.B., Locke, M.A., McCarty, G.W., McElrone, A.J., Moglen, G.E., Moriasi, D.N., O'Shaughnessy, S.A., Reba, M.L., Rice, P.J., Silber-Coats, N., Wang, D., White, M.J., Dobrowolski, J.P. 2023. A vision for integrated, collaborative solutions to critical water and food challenges. Journal of Soil and Water Conservation. 78(3):63A-68A. https://doi.org/10.2489/jswc.2023.1220A.
Burruss, N., Peters, D.C., Huang, H. 2023. The resistance and resilience of Great Plains ecoregion boundaries to the 1930s drought as a lens to future dynamics. Ecosphere. 14(5). Article e4538. https://doi.org/10.1002/ecs2.4538.
Saha, A., Saha, G., Cibin, R., Spiegal, S.A., Kleinman, P.J., Veith, T.L., White, C., Drohan, P., Tsegaye, T.D. 2023. Evaluating water quality benefits of manureshed management in the Susquehanna River Basin. Journal of Environmental Quality. 52(2):328-340. https://doi.org/10.1002/jeq2.20429.
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