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Preparing Colombian coffee production for climate change: Integrated spatial modelling to identify potential robusta coffee (Coffea canephora P.) growing areas

Author

Listed:
  • Carlos E. González-Orozco

    (Corporación Colombiana de Investigación Agropecuaria- Agrosavia. Centro de Investigación La Libertad)

  • Mario Porcel

    (Corporación Colombiana de Investigación Agropecuaria- Agrosavia. Centro de Investigación La Libertad
    Instituto de Investigación y Formación Agraria)

  • Vivekananda Mittahalli Byrareddy

    (University of Southern Queensland)

  • Eric Rahn

    (International Center for Tropical Agriculture (CIAT))

  • William A. Cardona

    (Corporación Colombiana de Investigación Agropecuaria – Agrosavia)

  • Diego A. Salinas Velandia

    (Corporación Colombiana de Investigación Agropecuaria – Agrosavia)

  • Gustavo A. Araujo-Carrillo

    (Corporación Colombiana de Investigación Agropecuaria – Agrosavia)

  • Jarrod Kath

    (University of Southern Queensland
    University of Southern Queensland)

Abstract

Meeting future demand for coffee under climate change is a challenge. Approaches that can inform where coffee may grow best under current and future climate scenarios are needed. Robusta coffee (Coffea canephora P.) is planted in many tropical areas and makes up around 40% of the world’s coffee supply. However, as the climate shifts, current robusta areas may become less productive, while in other areas new growing regions for robusta may emerge. Colombia is one of the world’s most important Arabica coffee producer, famous for its high-quality coffee. Although robusta coffee is not yet a commercial crop in Colombia, it could be one of the future bastions for robusta coffee in South America contributing to meeting the increasing demand, but this remains unexplored. We aimed to identify areas with highest biophysical and socio-economic potential to grow robusta coffee in Colombia. An integrated modelling approach was used, combining climate suitability and crop-yield modelling for current and future climate scenarios, soil constraints, pest risk assessment and socio-economic constraints to identify the regions with the highest potential productivity and the lowest pest and climate change risks with good market access and low security risks which don’t further expand the agricultural frontier. Our results showed that parts of the foothills along the eastern Andean Mountain ranges, the high plains of the Orinoquía region and the wet parts of the Caribbean region are the best candidates for the potential development of robusta coffee plantations in Colombia. The crop-yield model indicated highest yields of green coffee on the foothills of the eastern Andean Mountain range with an estimated average yield of 2.6 t ha−1 (under rain-fed conditions) which is projected to occur at elevations below 600 m avoiding interference with the traditional and established Arabica coffee regions in Colombia. Under a 2 °C global warming scenario climate change is projected to have the largest impacts on the Caribbean region. Therefore, larger scale irrigated production system could be an appropriate option in the Caribbean region, while diversified smallholder robusta coffee agroforestry systems are considered more favourable in the Orinoquía region.

Suggested Citation

  • Carlos E. González-Orozco & Mario Porcel & Vivekananda Mittahalli Byrareddy & Eric Rahn & William A. Cardona & Diego A. Salinas Velandia & Gustavo A. Araujo-Carrillo & Jarrod Kath, 2024. "Preparing Colombian coffee production for climate change: Integrated spatial modelling to identify potential robusta coffee (Coffea canephora P.) growing areas," Climatic Change, Springer, vol. 177(4), pages 1-26, April.
    • Handle: RePEc:spr:climat:v:177:y:2024:i:4:d:10.1007_s10584-024-03717-2
    DOI: 10.1007/s10584-024-03717-2
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    References listed on IDEAS

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    1. Simon N. Wood, 2011. "Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(1), pages 3-36, January.
    2. Gama-Rodrigues, Antonio Carlos & Müller, Manfred Willy & Gama-Rodrigues, Emanuela Forestieri & Mendes, Fernando Antônio Teixeira, 2021. "Cacao-based agroforestry systems in the Atlantic Forest and Amazon Biomes: An ecoregional analysis of land use," Agricultural Systems, Elsevier, vol. 194(C).
    3. Håvard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392, April.
    4. Yue Qin & John T. Abatzoglou & Stefan Siebert & Laurie S. Huning & Amir AghaKouchak & Justin S. Mankin & Chaopeng Hong & Dan Tong & Steven J. Davis & Nathaniel D. Mueller, 2020. "Agricultural risks from changing snowmelt," Nature Climate Change, Nature, vol. 10(5), pages 459-465, May.
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