Native forests around the world are widely used for livestock grazing as they offer different sources of forage. Nevertheless, in heterogeneous forested landscapes, forage productivity drivers are still unclear, hindering precise predictions of field receptivity. Our aim is to relate landscape variables with forage productivity in forested landscapes using satellite and ground-based data. To accomplish this, we harvested 36 enclosures in two Patagonian valleys during a three-year period. The location of the enclosures encompassed a gradient of altitude and mean annual rainfall, across three vegetation types commonly used for cattle raising. We estimated five generalized linear models to predict forage productivity using different remote sensing and ground (field) data as predictors. The most important variables for predicting forage productivity were five of remote sensing type and two of field type. The highest goodness of fit was obtained when all variables were included (D² = 0.71). When ground-based information was combined with remote sensing data, the goodness of fit was higher (D² = 0.65) compared with models that only used remote data as predictors (D² = 0.49). Models obtained based on remote data serve to support situations where field information is not available. High forage productivity levels can be obtained in high forests or scrubs with varying values of canopy openness, without removing the forest. The models generated in this work improve livestock stocking rates adjustment in NW Patagonia forests, and may be also re-estimated with new data in other regions used for cattle raising worldwide, contributing to the sustainable use of native 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.

Living Labs (LLs) are often described as top-down, formally designed innovation spaces, and literature about LL research highlights scarce reports of LLs in Latin America. However, grassroots territorial-innovation spaces exist in Latin America, and we hypothesized that some of them probably agree with the LL framework. The Comarca Andina del Paralelo 42 (CAP42), located in north Patagonia, Argentina, has already seen the emergence of agroecology and organic farming in the 1960s. We considered the CAP42 as a case study for our research. We applied a multidisciplinary approach combining socio-cultural and environmental assessment at the landscape level. We revealed that interacting institutional and cultural factors have shaped the region into a site of agroecological co-innovation, and that agroecological innovation and knowledge co-creation have emerged as bottom-up, non-formalized, long-term process. The assessment of environmental indicators (forest cover loss, land use-related changes of soil properties, etc.) provided evidence for the sustainability of the CAP42 landscape. Forest cover loss observed in the last two decades (2000–2023) was mainly linked to fires rather than agricultural expansion or intensive livestock production. The CAP42 covers the three-layer network approach and place defined in the LL framework, as it is a real-world learning and innovation space enabling multi-actor co-development of context-specific solutions, through multiple forms of knowledge co-creation that align with current LL definitions. It also illustrates that sustainable, long-lasting LLs may result from historically constructed conditions, providing key insights for the development of future LLs.