Publications by authors named "E F Viglizzo"

We quantified and analyzed the effect that the domestic and international demand for beef and soybean proteins had on carbon emissions in Argentina during the first two decades of the twenty-first century. We also analyzed the influence of both factors on the national deforestation rates. Principal component analysis and simple regression analyses were in turn used to detect components that maximize data variance, and to quantify relevant relationships.

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Based on international guidelines, the elaboration of national carbon (C) budgets in many countries has tended to set aside the capacity of grazing lands to sequester C as soil organic carbon (SOC). A widely applied simple method assumes a steady state for SOC stocks in grasslands and a long-term equilibrium between annual C gains and losses. This article presents a theoretical method based on the annual conversion of belowground biomass into SOC to include the capacity of grazing-land soils to sequester C in greenhouse gases (GHG) calculations.

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Assuming a steady state between carbon (C) gains and losses, greenhouse gases (GHG) inventories that follow a widely used simplified procedure (IPCC Tier 1) tend to underestimate the capacity of soils in grazing-land to sequester C. In this study we compared the C balance reported by (i) national inventories that followed the simplified method (Tier 1) of IPCC (1996/2006), with (ii) an alternative estimation derived from the meta-analysis of science-based, peer-reviewed data. We used the global databases (i) EDGAR 4.

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Our knowledge about the functional foundations of ecosystem service (ES) provision is still limited and more research is needed to elucidate key functional mechanisms. Using a simplified eco-hydrological scheme, in this work we analyzed how land-use decisions modify the partition of some essential regulatory ES by altering basic relationships between biomass stocks and water flows. A comprehensive meta-analysis and review was conducted based on global, regional and local data from peer-reviewed publications.

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