Lucrecia Lezana
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Lucrecia Lezana has a total of 18 co-authors. Here are the top co-authors by number of shared publications:
- Carlos de la Peña
Instituto Nacional de Tecnología Agropecuaria (INTA), Buenos Aires, Argentina
1 shared publication - Dardo Rubén López
Instituto Nacional de Tecnología Agropecuaria (INTA), Buenos Aires, Argentina
1 shared publication - Emmanuel Gabioud
Instituto Nacional de Tecnología Agropecuaria (INTA), Buenos Aires, Argentina
1 shared publication - Fabio Daniel Trinco
Instituto Nacional de Tecnología Agropecuaria), Unidad Ejecutora IFAB UEDD (INTA–CONICET), San Carlos de Bariloche, Argentina
1 shared publication - Fernando Javier Umaña
Instituto Nacional de Tecnología Agropecuaria (INTA), Buenos Aires, Argentina
1 shared publication - Fernando Raffo
Instituto Nacional de Tecnología Agropecuaria (INTA), Buenos Aires, Argentina
1 shared publication
Latest Publications
- Open Access
- 24 July 2025
- Published by Pensoft Publishers in Vegetation Classification and Survey
- Vol. 6, 163-179
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.
- 24 July 2025
- Published by Pensoft Publishers in Vegetation Classification and Survey
- Vol. 6, 163-179
Open AccessEnvironmental 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.