Economic-environmental indices in beef production. Results in livestock models from Corrientes, Argentina

Gustavo Daniel Gimenez; Mariana Calvi; Gustavo Daniel Gimenez; Mariana Calvi

doi:10.3934/aas.2025002

AIMS Animal Science

2025, Volume 1, Issue 1: 3-19. doi: 10.3934/aas.2025002

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Research article

Economic-environmental indices in beef production. Results in livestock models from Corrientes, Argentina

Gustavo Daniel Gimenez ^{1,2
,
,},
Mariana Calvi ^{3
,
,}

1.
National Institute of Agricultural Technology (INTA). Roldan Rural Extension Agency. Catamarca # 861, S2134 ANY, Roldan (Santa Fe) Argentina
2.
National University of Rosario (UNR). Faculty of Veterinary Sciences. Bv. Ovidio Lagos and RN #33, S2170HGJ, Casilda (Santa Fe) Argentina
3.
National Institute of Agricultural Technology (INTA). Mercedes Agricultural and Livestock Experimental Station

Received: 17 December 2024 Revised: 30 April 2025 Accepted: 04 June 2025 Published: 19 June 2025

Consumer markets increasingly demand beef produced under environmentally sustainable conditions. In Argentina, cattle production on grasslands focuses on reducing greenhouse gas (GHG) emissions while achieving economic yields that support long-term sustainability and rural livelihoods. This study aimed to select productive alternatives with improved economic and environmental performance. The analysis was based on a traditional livestock model from the central-southern region of Corrientes, characterized by purebred cattle raised on natural grasslands. This baseline model was compared with four alternative models incorporating additional production phases (growing and fattening) and enhanced management practices (pasture improvement and strategic supplementation). Economic performance was assessed through gross margin analysis, while environmental performance was evaluated by estimating the carbon footprint using the Cool Farm Tool. Higher absolute carbon footprints were observed in full-cycle models (breeding, growing, and fattening). However, when expressed per kilogram of live weight produced, the relative carbon footprint did not follow the same trend. Systems that integrated improved pastures (with higher dry matter yields and better digestibility) and targeted supplementation, as well as additional production stages, achieved greater economic efficiency per unit of GHG emissions. Further assessment under variable conditions is recommended to enhance the robustness of the analysis.
- carbon footprint,
- gross margin,
- cattle
Citation: Gustavo Daniel Gimenez, Mariana Calvi. Economic-environmental indices in beef production. Results in livestock models from Corrientes, Argentina[J]. AIMS Animal Science, 2025, 1(1): 3-19. doi: 10.3934/aas.2025002

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Abstract

Consumer markets increasingly demand beef produced under environmentally sustainable conditions. In Argentina, cattle production on grasslands focuses on reducing greenhouse gas (GHG) emissions while achieving economic yields that support long-term sustainability and rural livelihoods. This study aimed to select productive alternatives with improved economic and environmental performance. The analysis was based on a traditional livestock model from the central-southern region of Corrientes, characterized by purebred cattle raised on natural grasslands. This baseline model was compared with four alternative models incorporating additional production phases (growing and fattening) and enhanced management practices (pasture improvement and strategic supplementation). Economic performance was assessed through gross margin analysis, while environmental performance was evaluated by estimating the carbon footprint using the Cool Farm Tool. Higher absolute carbon footprints were observed in full-cycle models (breeding, growing, and fattening). However, when expressed per kilogram of live weight produced, the relative carbon footprint did not follow the same trend. Systems that integrated improved pastures (with higher dry matter yields and better digestibility) and targeted supplementation, as well as additional production stages, achieved greater economic efficiency per unit of GHG emissions. Further assessment under variable conditions is recommended to enhance the robustness of the analysis.

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