Natural steppe grasslands are the principal food resource for sheep in the Patagonia region, reared for meat and wool. However, there is currently a concern about the relationship between ruminant livestock and climate change due to its contribution to anthropogenic greenhouse gas (GHG) emissions. The objective of this study was to determine the carbon footprints (CF) of sheep meat (lamb) and wool on a range of farms using empirical data collected on farm and then upscaled to the regional scale using models that use topographic, climatic, and vegetation indices as independent variables. At the regional level, the total CF of lamb and wool (the combination of emissions produced on farm, via transport, and via industrial processing) varied from 10.64 to 41.32 kg CO2-eq/kg for lamb meat (carcass) and from 7.83 to 18.70 kg CO2-eq/kg for fine-grade wool. For both, the predominant contribution was from primary production on-farm (75–90%), followed by industrial processing (2–15%), and transportation. We used multiple regression models to produce maps of lamb and wool CF at farm gate across Santa Cruz province. The model for variation of lamb CF explained 95% of the variance on the data and the most significant predictor variables were temperature seasonality and normalized difference vegetation index (NDVI, dimensionless). The most important variables for the model of CF of greasy wool production at farm gate were isothermality, temperature seasonality, and NDVI explained 98%. The lowest CF values of both products (lamb and wool) were located in more productive grasslands. The successful management of livestock GHG emissions becomes an important challenge to the scientific, commercial, and policy communities. The results of CF for lamb and wool production found in the present work assist in characterizing the greenhouse gas emissions profile of livestock products in Southern Patagonia by providing a baseline against which mitigation actions can be planned and progress monitored.

Forest biodiversity conservation and species distribution modeling greatly benefit from broad-scale forest maps depicting tree species or forest types rather than just presence and absence of forest, or coarse classifications. Ideally, such maps would stem from satellite image classification based on abundant field data for both model training and accuracy assessments, but such field data do not exist in many parts of the globe. However, different forest types and tree species differ in their vegetation phenology, offering an opportunity to map and characterize forests based on the seasonal dynamic of vegetation indices and auxiliary data. Our goal was to map and characterize forests based on both land surface phenology and climate patterns, defined here as forest phenoclusters. We applied our methodology in Argentina (2.8 million km 2 ), which has a wide variety of forests, from rainforests to cold-temperate forests. We calculated phenology measures after fitting a harmonic curve of the enhanced vegetation index (EVI) time series derived from 30-m Sentinel 2 and Landsat 8 data from 2018-2019. For climate, we calculated land surface temperature (LST) from Band 10 of the thermal infrared sensor (TIRS) of Landsat 8, and precipitation from Worldclim (BIO12). We performed stratified X-means cluster classifications followed by hierarchical clustering. The resulting clusters separated well into 54 forest phenoclusters with unique combinations of vegetation phenology and climate characteristics. The EVI 90th percentile was more important than our climate and other phenology measures in providing separability among different forest phenoclusters. Our results highlight the potential of combining remotely sensed phenology measures and climate data to improve broad-scale forest mapping for different management and conservation goals, capturing functional rather than structural or compositional characteristics between and within tree species. Our approach results in classifications that go beyond simple forest-nonforest in areas where the lack of detailed ecological field data precludes tree species-level classifications, yet

One reason for the decline of natural forest is that many ecosystem services (ESs) are usually not priced and values were only considered provisioning services. Argentina enacted the National Law 26,331/07, which regulates protection, enrichment, restoration and management of native forests and its environmental services. The objective is to determine the ecological and sociopolitical factors that influence the dynamics of forest cover loss before and after the law implementation and discuss the effectiveness of conservation and forest management policies. Satellite images, national ordination, forest regions maps and other variables were combined in GIS with national databases (social, agriculture, industry) to determine the evolution of potential drivers of forest changes. The main potential drivers were: (i) population

Producción diferencial de biomasa en plántulas de Nothofagus pumilio bajo gradientes de luz y humedad del suelo Differential biomass productivity of Nothofagus pumilio seedlings under light and soil moisture gradients