A Systematic Review of Analytical and Modelling Tools to Assess Climate Change Impacts and Adaptation on Coffee Agrosystems

Muhammad Faraz, Valentina Mereu, Donatella Spano, Antonio Trabucco, Serena Marras and Daniel El Chami ()
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Muhammad Faraz: Department of Agriculture Science, University of Sassari, Viale Italia 39/A, I-07100 Sassari, Italy
Valentina Mereu: Euro-Mediterranean Centre on Climate Change (CMCC) Foundation, Impacts on Agriculture, Forests and Ecosystem Services (IAFES) Division, Via De Nicola 9, I-07100 Sassari, Italy
Donatella Spano: Department of Agriculture Science, University of Sassari, Viale Italia 39/A, I-07100 Sassari, Italy
Antonio Trabucco: Department of Agriculture Science, University of Sassari, Viale Italia 39/A, I-07100 Sassari, Italy
Serena Marras: Department of Agriculture Science, University of Sassari, Viale Italia 39/A, I-07100 Sassari, Italy
Daniel El Chami: TIMAC AGRO Italia S.p.A., S.P.13, Località Ca’ Nova, I-26010 Ripalta Arpina, Italy

Sustainability, 2023, vol. 15, issue 19, 1-19

Abstract: Several modelling tools reported the climate change impact on the coffee agrosystems. This article has adopted a systematic approach to searching out information from the literature about different modelling approaches to assess climate change impacts or/and adaptation on coffee crops worldwide. The review included all scientific publications from the date of the first relevant article until the end of 2022 and screened 60 relevant articles. Most results report research conducted in America, followed by Africa. The models assessed in the literature generally incorporate Intergovernmental Panel on Climate Change (IPCC) emission scenarios (80% of manuscripts), particularly Representative Concentration Pathways (RCP) and Special Report on Emission Scenarios (SRES), with the most common projection periods until 2050 (50% of documents). The selected manuscripts contain qualitative and quantitative modelling tools to simulate climate impact on crop suitability (55% of results), crop productivity (25% of studies), and pests and diseases (20% of the results). According to the analysed literature, MaxEnt is the leading machine learning model to assess the climate suitability of coffee agrosystems. The most authentic and reliable model in pest distribution is the Insect Life Cycle Modelling Software (ILCYM) (version 4.0). Scientific evidence shows a lack of adaptation modelling, especially in shading and irrigation practices, which crop models can assess. Therefore, it is recommended to fill this scientific gap by generating modelling tools to understand better coffee crop phenology and its adaptation under different climate scenarios to support adaptation strategies in coffee-producing countries, especially for the Robusta coffee species, where a lack of studies is reported (6% of the results), even though this species represents 40% of the total coffee production.

Keywords: coffee agrosystems; climate change (CC); impact; adaptation; modelling; IPCC scenarios (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
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