Explore the vast range of titles available.

Accelerated soil erosion occurs when anthropogenic processes modify soil, vegetation, or climatic conditions causing erosion rates at a location to exceed their natural variability. Identifying where and when accelerated erosion occurs is a critical first step toward its effective management. Here we explored how erosion assessments structured in the context of ecological sites (a land classification based on soils, landscape setting, and ecological potential) and their vegetation states (plant assemblages that may change due to management) can inform systems for reducing accelerated soil erosion in rangelands. We evaluated aeolian horizontal sediment flux and fluvial sediment erosion rates for five ecological sites in southern New Mexico, USA, using monitoring data and rangeland-specific wind and water erosion models. Across the ecological sites, plots in shrub-encroached and shrub-dominated vegetation states were consistently susceptible to aeolian sediment flux and fluvial sediment erosion. Both processes were found to be highly variable for grassland and grass-succulent states across the ecological sites at the plot scale (0.25 ha). We identified vegetation thresholds that define cover levels below which rapid (exponential) increases in aeolian sediment flux and fluvial sediment erosion occur across the ecological sites and vegetation states. Aeolian sediment flux and fluvial erosion in the study area could be effectively controlled when bare ground cover was <20% of a site or the cover of canopy interspaces >100 cm in length was less than ∼35%. Land use and management activities that alter cover levels such that they cross thresholds, and/or drive vegetation state changes, may increase the susceptibility of areas to erosion. Land use impacts that are constrained within the range of natural variability should not result in accelerated soil erosion. Evaluating land condition against the erosion thresholds identified here will enable identification of areas susceptible to accelerated soil erosion and the development of practical management solutions.

Drylands are characterised by patchy vegetation, erodible surfaces and erosive aeolian processes. Empirical and modelling studies have shown that vegetation elements provide drag on the overlying airflow, thus affecting wind velocity profiles and altering erosive dynamics on desert surfaces. However, these dynamics are significantly complicated by a variety of factors, including turbulence, and vegetation porosity and pliability effects. This has resulted in some uncertainty about the effect of vegetation on sediment transport in drylands. Here, we review recent progress in our understanding of the effects of dryland vegetation on wind flow and aeolian sediment transport processes. In particular, wind transport models have played a key role in simplifying aeolian processes in partly vegetated landscapes, but a number of key uncertainties and challenges remain. We identify potential future avenues for research that would help to elucidate the roles of vegetation distribution, geometry and scale in shaping the entrainment, transport and redistribution of wind-blown material at multiple scales. Gaps in our collective knowledge must be addressed through a combination of rigorous field, wind tunnel and modelling experiments.

Anthropogenic land use and land cover change, including local environmental disturbances, moderate rates of wind-driven soil erosion and dust emission. These human-dust cycle interactions impact ecosystems and agricultural production, air quality, human health, biogeochemical cycles, and climate. While the impacts of land use activities and land management on aeolian processes can be profound, the interactions are often complex and assessments of anthropogenic dust loads at all scales remain highly uncertain. Here, we critically review the drivers of anthropogenic dust emission and current evaluation approaches. We then identify and describe opportunities to: (1) develop new conceptual frameworks and interdisciplinary approaches that draw on ecological state-and-transition models to improve the accuracy and relevance of assessments of anthropogenic dust emissions; (2) improve model fidelity and capacity for change detection to quantify anthropogenic impacts on aeolian processes; and (3) enhance field research and monitoring networks to support dust model applications to evaluate the impacts of disturbance processes on local to global-scale wind erosion and dust emissions.

Understanding where, when, and why agroecosystems are changing requires quality information about ecosystems that span land tenure, ecological processes, and spatial scales. Over the past two decades, land management agencies and research groups have adopted a suite of standardized methods for monitoring rangelands, which have been implemented at over 85,000 monitoring locations globally. However, the ability to use these data to understand agroecosystem dynamics and change across scales and across land ownership has been limited because, until now, these data have not been available in a harmonized, accessible format for analyses, modeling, and decision‐support tools. We present the Landscape Data Commons, a cyberinfrastructure platform that harmonizes and aggregates standardized agroecosystem data, enables linkages to models, and facilitates analysis and interpretation of data within decision‐support tools. The Landscape Data Commons provides a community platform for users to contribute data and develop next‐generation tools to support agroecosystem management through the 21st century. Core Ideas Managers and researchers need monitoring data that are directly connected to models and decision‐support tools. Standardized monitoring protocols present an opportunity to understand cross‐scale agroecosystem dynamics. The Landscape Data Commons provides data harmonization, data access, and model connections. Standardized data and modeled indicators enable managers to leverage quantitative data in decision‐support tools. Shared data and model infrastructure can support collaborative adaptive management on agroecosystems globally. The Landscape Data Commons enables researchers and managers to better understand agroecosystem dynamics by harmonizing and aggregating standardized monitoring data, facilitating connections to models, making data and model outputs available to users through a data portal and API, and providing links to analysis and decision support tools.

Land degradation and climate change pose enormous risks to global food security. Land degradation increases the vulnerability of agroecological systems to climate change and reduces the effectiveness of adaptation options. Yet these interactions have largely been omitted from climate impact assessments and adaptation planning. We examine how land degradation can influence climate-change impacts and the adaptive capacity of crop and livestock producers across agroecological systems. We then present novel strategies for climate-resilient agriculture that support opportunities to integrate responses to these challenges. Forward-looking, climate-resilient agriculture requires: (1) incorporation of land degradation processes, and their linkages with adaptive capacity, into adaptation planning; (2) identification of key vulnerabilities to prioritize adaptation responses; (3) improved knowledge exchange across local to global scales to support strategies for developing the adaptive capacity of producers; and (4) innovative management and policy options that provide multiple \"wins\" for land, climate, and biodiversity, thus enabling global development and food security goals to be achieved.

Rangelands are extensive ecosystems, providing important ecosystem services while undergoing continuous change. As a result, improved monitoring technologies can help better characterize vegetation change. Satellite remote sensing has proven effective in this regard, tracking vegetation dynamics at broad and fine scales. We leveraged the spatial, spectral, and temporal resolution of Sentinel-2 satellites to estimate fractional cover and canopy gap across rangelands of the western United States. We produced annual, 10 m spatial resolution estimates of fractional cover and canopy gap size class for years 2018 to 2024. Fractional cover estimates include that of common plant functional types (annual forb and grass, bareground, littler, perennial forb and grass, shrub, tree) and select genera (including invasive annual grass species, pinyon-juniper species, and sagebrush species); canopy gap size classes include gap sizes 25 to 50, 51 to 100, 101 to 200, and greater than 200 cm. We make these data available as Cloud Optimized GeoTIFFs, organized as 75 × 75 km tiles covering the 17 western states of the United States.

Transitions from semiarid grassland to shrubland states are among the most widely recognized examples of regime shifts in terrestrial ecosystems. Nonetheless, the processes causing grassland–shrubland transitions and their consequences are incompletely understood. We challenge several misconceptions about these transitions in desert grasslands, including that (a) they are currently controlled by local livestock grazing and drought events, (b) they represent severe land degradation, and (c) restoration of grassland states is impossible. Grassland–shrubland transitions are the products of multiple drivers and feedback systems, both ecological and social, interacting at multiple scales of space and time. Grass recovery within shrubland states—with and without shrub removal—produces novel ecosystems that are dissimilar from historical grasslands but that provide important ecosystem services. Projected increases in climate variability are likely to promote the further displacement of perennial grasses by xerophytic shrubs. This article offers guidelines for managing grassland–shrubland transitions in the face of changing biophysical and socioeconomic circumstances.

Ecological studies require quality data to describe the nature of ecological processes and to advance understanding of ecosystem change. Increasing access to big data has magnified both the burden and the complexity of ensuring quality data. The costs of errors in ecology include low use of data, increased time spent cleaning data, and poor reproducibility that can result in a misunderstanding of ecosystem processes and dynamics, all of which can erode the efficacy of and trust in ecological research. Although conceptual and technological advances have improved ecological data access and management, a cultural shift is needed to embed data quality as a cultural practice. We present a comprehensive data quality framework to evoke this cultural shift. The data quality framework flexibly supports different collaboration models, supports all types of ecological data, and can be used to describe data quality within both short- and long-term ecological studies.

Southwest and Southern Plains Air Quality and Production Agriculture Science and Applications Workshop What: Nearly 60 professionals from agricultural, environmental, and health sectors met to identify knowledge gaps and progress barriers within the agriculture–air quality–climate change nexus for the Southwest and southern Plains regions. Short presentations detailed the national assessment results, current mitigation options and resources available for the agricultural sector, current USDA Natural Resources Conservation Service (NRCS) air quality priorities, evolving indicators and models for emissions, and current measurement networks. Raising awareness of air pollutant sources and impacts across agricultural areas can prepare these communities with the information needed to plan with agricultural advisors, make changes to their operations, or advocate for air quality needs. Five major areas of air quality and agriculture guide the organization of this roadmap: * Role of drought and land-use change on dust generation and management * Ammonia emissions from feed yards and dairy operations * Impacts of ozone on agriculture * Mitigation options * Early warning:

Roughness features (e.g., rocks, vegetation, furrows) that shelter or attenuate wind flow over the soil surface can considerably affect the magnitude and spatial distribution of sediment transport in active aeolian environments. Existing dust and sediment transport models often rely on vegetation attributes derived from static land use datasets or remotely sensed greenness indicators to incorporate sheltering effects on simulated particle mobilization. However, these overly simplistic approaches do not represent the three-dimensional nature or spatiotemporal changes of roughness element sheltering. They also ignore the sheltering contribution of non-vegetation roughness features and photosynthetically inactive (i.e., brown) vegetation common to dryland environments. Here, we explore the use of a novel albedo-based sheltering parameterization in a dust transport modeling application of the Weather Research and Forecasting model with Chemistry (WRF-Chem). The albedo method estimates sheltering effects on surface wind friction speeds and dust entrainment from the shadows cast by subgrid-scale roughness elements. For this study, we applied the albedo-derived drag partition to the Air Force Weather Agency (AFWA) dust emission module and conducted a sensitivity study on simulated PM10 concentrations using the Georgia Institute of Technology–Goddard Global Ozone Chemistry Aerosol Radiation and Transport (GOCART) model as implemented in WRF-Chem v4.1. Our analysis focused on a convective dust event case study from 3–4 July 2014 for the southwestern United States desert region discussed by other published works. Previous studies have found that WRF-Chem simulations grossly overestimated the dust transport associated with this event. Our results show that removing the default erodibility map and adding the drag parameterization to the AFWA dust module markedly improved the overall magnitude and spatial pattern of simulated dust conditions for this event. Simulated PM10 values near the leading edge of the storm substantially decreased in magnitude (e.g., maximum PM10 values were reduced from 17 151 to 8539 µg m-3), bringing the simulated results into alignment with the observed PM10 measurements. Furthermore, the addition of the drag partition restricted the erroneous widespread dust emission of the original model configuration. We also show that similar model improvements can be achieved by replacing the wind friction speed parameter in the original dust emission module with globally scaled surface wind speeds, suggesting that a well-tuned constant could be used as a substitute for the albedo-based product for short-duration simulations in which surface roughness is not expected to change and for landscapes wherein roughness is constant over years to months. Though this alternative scaling method requires less processing, knowing how to best tune the model winds a priori could be a considerable challenge. Overall, our results demonstrate how dust transport simulation and forecasting with the AFWA dust module can be improved in vegetated drylands by calculating the dust emission flux with surface wind friction speed from a drag partition treatment.