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

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.

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Journal
Vegetation Classification and Survey
Published
2025-07-24
DOI
https://doi.org/10.3897/vcs.141917
Primary Topic
Wildlife Ecology and Conservation
Type
article
Field-Weighted Citation Impact
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article

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

Rubén D. Coria, Lucrecia Lezana, Flavia R. Barzan, Noelia C. Calamari et al.
Vegetation Classification and Survey
Wildlife Ecology and Conservation
article

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

Rubén D. Coria, Lucrecia Lezana, Flavia R. Barzan, Noelia C. Calamari, Fernando Raffo, Fernando Umaña, Emmanuel Gabioud, Mariana Weigandt, Sonia B. Canavelli, Roxana Paola Eclesia, Dardo R. López, Juan Pablo Diez, Pablo L. Peri, Fabio Daniel Trinco, Marcelo Germán Wilson, Santiago Varela, Hernán Hernández, Carlos Peña, Juan Ignacio Gazzotti
article en

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.

Vegetation Classification and SurveyVol. 6
Consejo Nacional de Investigaciones Científicas y Técnicas (AR), Instituto Nacional de Tecnologia (BR), National Agricultural Technology Institute (AR)
Openalex Percentile: Top 82%
Wildlife Ecology and Conservation
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