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Climate-related land use policies in Brazil: How much has been achieved with economic incentives in agriculture?

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  • Carauta, Marcelo
  • Troost, Christian
  • Guzman-Bustamante, Ivan
  • Hampf, Anna
  • Libera, Affonso
  • Meurer, Katharina
  • Bönecke, Eric
  • Franko, Uwe
  • Ribeiro Rodrigues, Renato de Aragão
  • Berger, Thomas

Abstract

Until 2019, the Brazilian federal government employed a number of policy measures to fulfill the pledge of reducing greenhouse gas emissions from land use change and agriculture. While its forest law enforcement strategy was partially successful in combating illegal deforestation, the effectiveness of positive incentive measures in agriculture has been less clear. The reason is that emissions reduction from market-based incentives such as the Brazilian Low-Carbon Agriculture Plan cannot be easily verified with current remote sensing monitoring approaches. Farmers have adopted a large variety of integrated land-use systems of crop, livestock and forestry with highly diverse per-hectare carbon balances. Their responses to policy incentives were largely driven by cost and benefit considerations at the farm level and not necessarily aligned with federal environmental objectives. This article analyzes climate-related land-use policies in the state of Mato Grosso, where highly mechanized soybean–cotton and soybean–maize cropping systems prevail. We employ agent-based bioeconomic simulation together with life-cycle assessment to explicitly capture the heterogeneity of farm-level costs, benefits of adoption, and greenhouse gas emissions. Our analysis confirms previous assessments but suggests a smaller farmer policy response when measured as increase in area of integrated systems. In terms of net carbon balances, our simulation results indicate that mitigation effects at the farm level depended heavily on the exact type of livestock and grazing system. The available data were insufficient to rule out even adverse effects. The Brazilian experience thus offers lessons for other land-rich countries that build their climate mitigation policies on economic incentives in agriculture.

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  • Carauta, Marcelo & Troost, Christian & Guzman-Bustamante, Ivan & Hampf, Anna & Libera, Affonso & Meurer, Katharina & Bönecke, Eric & Franko, Uwe & Ribeiro Rodrigues, Renato de Aragão & Berger, Thomas, 2021. "Climate-related land use policies in Brazil: How much has been achieved with economic incentives in agriculture?," Land Use Policy, Elsevier, vol. 109(C).
  • Handle: RePEc:eee:lauspo:v:109:y:2021:i:c:s0264837721003410
    DOI: 10.1016/j.landusepol.2021.105618
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    1. Schielein, Johannes & Börner, Jan, 2018. "Recent transformations of land-use and land-cover dynamics across different deforestation frontiers in the Brazilian Amazon," Land Use Policy, Elsevier, vol. 76(C), pages 81-94.
    2. Schreinemachers, Pepijn & Berger, Thomas & Aune, Jens B., 2007. "Simulating soil fertility and poverty dynamics in Uganda: A bio-economic multi-agent systems approach," Ecological Economics, Elsevier, vol. 64(2), pages 387-401, December.
    3. Grovermann, Christian & Schreinemachers, Pepijn & Riwthong, Suthathip & Berger, Thomas, 2017. "‘Smart’ policies to reduce pesticide use and avoid income trade-offs: An agent-based model applied to Thai agriculture," Ecological Economics, Elsevier, vol. 132(C), pages 91-103.
    4. Tacconi, Luca & Rodrigues, Rafael J. & Maryudi, Ahmad, 2019. "Law enforcement and deforestation: Lessons for Indonesia from Brazil," Forest Policy and Economics, Elsevier, vol. 108(C), pages 1-1.
    5. Utomo, Dhanan Sarwo & Onggo, Bhakti Stephan & Eldridge, Stephen, 2018. "Applications of agent-based modelling and simulation in the agri-food supply chains," European Journal of Operational Research, Elsevier, vol. 269(3), pages 794-805.
    6. Kremmydas, Dimitris & Athanasiadis, Ioannis N. & Rozakis, Stelios, 2018. "A review of Agent Based Modeling for agricultural policy evaluation," Agricultural Systems, Elsevier, vol. 164(C), pages 95-106.
    7. Balmann, Alfons, 1997. "Farm-Based Modelling of Regional Structural Change: A Cellular Automata Approach," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 24(1), pages 85-108.
    8. John M Antle, 2019. "Data, Economics and Computational Agricultural Science," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 101(2), pages 365-382.
    9. Nendel, C. & Berg, M. & Kersebaum, K.C. & Mirschel, W. & Specka, X. & Wegehenkel, M. & Wenkel, K.O. & Wieland, R., 2011. "The MONICA model: Testing predictability for crop growth, soil moisture and nitrogen dynamics," Ecological Modelling, Elsevier, vol. 222(9), pages 1614-1625.
    10. Thomas Berger & Christian Troost, 2014. "Agent-based Modelling of Climate Adaptation and Mitigation Options in Agriculture," Journal of Agricultural Economics, Wiley Blackwell, vol. 65(2), pages 323-348, June.
    11. Thomas Berger & Regina Birner & Nancy Mccarthy & JosÉ DíAz & Heidi Wittmer, 2007. "Capturing the complexity of water uses and water users within a multi-agent framework," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(1), pages 129-148, January.
    12. Berger, Thomas, 2001. "Agent-based spatial models applied to agriculture: a simulation tool for technology diffusion, resource use changes and policy analysis," Agricultural Economics, Blackwell, vol. 25(2-3), pages 245-260, September.
    13. Christian Troost & Thomas Berger, 2015. "Dealing with Uncertainty in Agent-Based Simulation: Farm-Level Modeling of Adaptation to Climate Change in Southwest Germany," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 97(3), pages 833-854.
    14. Stabile, Marcelo C.C. & Guimarães, André L. & Silva, Daniel S. & Ribeiro, Vivian & Macedo, Marcia N. & Coe, Michael T. & Pinto, Erika & Moutinho, Paulo & Alencar, Ane, 2020. "Solving Brazil's land use puzzle: Increasing production and slowing Amazon deforestation," Land Use Policy, Elsevier, vol. 91(C).
    15. Quang, Dang Viet & Schreinemachers, Pepijn & Berger, Thomas, 2014. "Ex-ante assessment of soil conservation methods in the uplands of Vietnam: An agent-based modeling approach," Agricultural Systems, Elsevier, vol. 123(C), pages 108-119.
    16. Thomas Berger & Christian Troost & Tesfamicheal Wossen & Evgeny Latynskiy & Kindie Tesfaye & Sika Gbegbelegbe, 2017. "Can smallholder farmers adapt to climate variability, and how effective are policy interventions? Agent-based simulation results for Ethiopia," Agricultural Economics, International Association of Agricultural Economists, vol. 48(6), pages 693-706, November.
    17. Hampf, Anna C. & Carauta, Marcelo & Latynskiy, Evgeny & Libera, Affonso A.D. & Monteiro, Leonardo & Sentelhas, Paulo & Troost, Christian & Berger, Thomas & Nendel, Claas, 2018. "The biophysical and socio-economic dimension of yield gaps in the southern Amazon – A bio-economic modelling approach," Agricultural Systems, Elsevier, vol. 165(C), pages 1-13.
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    3. Tang, Wenxi & Lu, Zhibo, 2022. "Application of self-organizing map (SOM)-based approach to explore the relationship between land use and water quality in Deqing County, Taihu Lake Basin," Land Use Policy, Elsevier, vol. 119(C).
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    5. Marohn, Carsten & Troost, Christian & Warth, Benjamin & Bateki, Christian & Zijlstra, Mink & Anwar, Faizan & Williams, Benjamin & Descheemaeker, Katrien & Berger, Thomas & Asch, Folkard & Dickhoefer, , 2022. "Coupled biophysical and decision-making processes in grassland systems in East African savannahs – A modelling framework," Ecological Modelling, Elsevier, vol. 474(C).
    6. Tianran Ding & Bernhard Steubing & Wouter Achten, 2022. "Coupling optimization with territorial LCA to support agricultural land-use planning," ULB Institutional Repository 2013/352783, ULB -- Universite Libre de Bruxelles.
    7. Coronese, Matteo & Occelli, Martina & Lamperti, Francesco & Roventini, Andrea, 2023. "AgriLOVE: Agriculture, land-use and technical change in an evolutionary, agent-based model," Ecological Economics, Elsevier, vol. 208(C).
    8. Luis Ramirez Camargo & Gabriel Castro & Katharina Gruber & Jessica Jewell & Michael Klingler & Olga Turkovska & Elisabeth Wetterlund & Johannes Schmidt, 2022. "Pathway to a land-neutral expansion of Brazilian renewable fuel production," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    9. Lili Guo & Sihang Guo & Mengqian Tang & Mengying Su & Houjian Li, 2022. "Financial Support for Agriculture, Chemical Fertilizer Use, and Carbon Emissions from Agricultural Production in China," IJERPH, MDPI, vol. 19(12), pages 1-19, June.
    10. Tianran Ding & Wouter Achten, 2022. "Coupling agent-based modeling with territorial LCA to support agricultural land-use planning," ULB Institutional Repository 2013/352782, ULB -- Universite Libre de Bruxelles.

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