Establishing quantitative benchmarks for soil erosion and ecological monitoring, assessment, and management
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Abstract
Soil erosion can have a multitude of negative impacts on agroecosystems and society and there remains an urgent need for tools to support its management. Quantitative benchmarks based on holistic understanding of erosion processes, ecosystem function, and land use objectives can be used with monitoring data and models to inform assessments and make objective and actionable decisions about erosion management. However, managers currently lack a framework for establishing benchmarks. Here, we present a framework and evaluation of different approaches to establishing quantitative benchmarks for soil erosion and ecological monitoring and assessment that can inform land management decisions. We use monitoring data collected across Chihuahuan Desert ecosystems in the United States and an aeolian sediment transport model to illustrate how benchmarks can be established. Approaches include establishing benchmarks from relationships between soil erosion indicators, reference states and land potential, including state-and-transition models, and desired conditions from existing monitoring data. We discuss the benefits and caveats of the different approaches and show how combining different benchmarking approaches can help users ensure that benchmarks appropriately reflect thresholds for soil erosion and achievable management outcomes. We finish by identifying future research needs to support establishment and application of erosion benchmarks across agroecosystems and recognize the opportunity to extend the benchmarking approaches to management of other agroecosystem processes and services.
Publication type | Article |
---|---|
Publication Subtype | Journal Article |
Title | Establishing quantitative benchmarks for soil erosion and ecological monitoring, assessment, and management |
Series title | Ecological Indicators |
DOI | 10.1016/j.ecolind.2024.111661 |
Volume | 159 |
Year Published | 2024 |
Language | English |
Publisher | Elsevier |
Contributing office(s) | Southwest Biological Science Center |
Description | 111661, 16 p. |
Google Analytic Metrics | Metrics page |