Other

  • Evaluating greenhouse gas (GHG) emissions at farm level is an important tool to mitigate climate change.
    Livestock account for 80% of the total GHG emissions in Uruguay, and beef cow-calf systems are possibly the
    largest contributors. In cow-calf grazing systems, optimizing forage allowance and grazing intensity may
    increase pasture productivity, reproductive performance, beef productivity, and possibly reduce GHG emissions.
    This study estimated GHG emissions per kg of live weight gain (LWG) and per hectare from 20 cow-calf
    systems in Uruguay, with different management practices. The GHG emissions were on average 20.8 kg
    CO2-e.kg LWG-1, ranging from 11.4 to 32.2. Beef productivity and reproductive efficiency were the main
    determinants of GHG emissions. Five farm clusters were identified with different productive and environmental
    efficiency by numerical classification of relevant variables. Improving grazing efficiency by optimizing the
    stocking rate and forage production can increase beef productivity by 22% and reduce GHG emissions per kg
    LWG by 28% compared to “low performance” management. Further improvements in reproductive efficiency
    can increase productivity by 41% and reduce GHG emissions per kg LWG by 23%, resulting in a “carbon smart”
    strategy. However, the most intensified farms with highest stocking rate and beef productivity, did not reduce
    GHG emissions per kg LWG, while increased GHG emissions per ha compared to the carbon smart. This
    analysis showed that it is possible to simultaneously reduce carbon footprint per kg and per ha, by optimizing
    grazing management. This study demonstrated that there is high potential to reduce cow-calf GHG emissions
    through improved grazing management

  • Gonzalo Becoña Instituto Plan Agropecuario, Uruguay

    Laura Astigarraga Facultad de Agronomía, Universidad de la República, Uruguay

    Valentin D. Picasso Facultad de Agronomía, Universidad de la República, Uruguay

    Source:https://bit.ly/3e1UJoF

    Courtesty:https://www.academia.edu

    Copyright:Copyright for this article is retained by the author(s), with first publication rights granted to the journal.
    This is an open-access article distributed under the terms and conditions of the Creative Commons Attribution
    license (http://creativecommons.org/licenses/by/3.0/).

View / Download research paper

©️2022 Deus Labs Ltd | All Rights Reserved

Privacy Policy

Log in with your credentials

or    

Forgot your details?

Create Account