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Vegetation Classification and Survey (VCS)
Article . 2025 . Peer-reviewed
License: CC BY
Data sources: Crossref
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Article . 2025
License: CC BY
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CONICET Digital
Article . 2025
License: CC BY
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Environmental spatial assessment at ranch scale: a tool for optimizing livestock management in Argentine forests

Authors: Santiago Agustín Varela; Noelia Cecilia Calamari; Fabio Daniel Trinco; Juan Pablo Diez; Mariana Weigandt; Flavia Romina Barzán; Lucrecia Lezana; +12 Authors

Environmental spatial assessment at ranch scale: a tool for optimizing livestock management in Argentine forests

Abstract

Question: Effective environmental management depends on accurate and efficient assessments of ecological conditions. This study introduces a method that combines remote sensing with a Rapid Assessment Method (RAM) to evaluate the environmental status of land units at the ranch scale. We ask whether this integrated approach can generate spatially explicit information to support the implementation and monitoring of sustainable livestock grazing in native forests. Study area: The study was conducted in three livestock ranches in Argentina. These ranches encompass heterogeneous landscapes characterized by varying soil types, vegetation cover, and topographic features that influence ecological conditions. Methods: Remote sensing imagery was used to delineate Ecological Sites (ESi) based on bio-geomorphic attributes such as soil, topography, and vegetation. These spatial units guided field sampling, where RAM was applied to assess environmental indicators. Field data were integrated into an Environmental Health Index (EHI), summarizing the ecological status of each ESi. Ordinary kriging interpolated EHI values across the landscape, enabling spatial visualization of environmental health patterns and spatial variability. Results: Mapping of EHI values showed that most land units exhibited intermediate to high environmental health, suggesting generally favorable ecological conditions across the ranches. However, some areas had low EHI values associated with signs of disturbance or degradation, including fire, flooding, invasive species, or overgrazing. Conclusions: The integration of remote sensing with RAM provided a detailed overview of ecological conditions at the ranch scale. The results can guide more focused and efficient fieldwork in areas requiring closer monitoring due to potential environmental degradation or changes linked to livestock management. This approach supports the implementation and evaluation of sustainable grazing management in native forests, offering a robust, scalable tool for environmental assessment. Additionally, by optimizing the allocation of sampling effort, it enables more informed decision-making to maintain and enhance ecological health in these production systems. Taxonomic reference: Anton and Zuloaga (2023). Abbreviations: EHI = environmental health index; ESi = ecological site; RAM = rapid assessment methodology; MBGI = silvo pastoral or forest management with integrated livestock.

Country
Argentina
Keywords

Integrated forest and livestock management, environmental degradation, Argentina, vegetation assessment, Environmental health index (EHI), Remote sensing, integrated forest and livestock management, Environmental degradation, Environmental sciences, remote sensing, environmental health index (EHI), https://purl.org/becyt/ford/4.1, https://purl.org/becyt/ford/4, GE1-350

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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