Agovino, M., Casaccia, M., Ciommi, M., Ferrara, M. & Marchesano, K. Agriculture, climate change and sustainability: The case of EU-28. Ecol. Ind. 105, 525–543 (2019).
Google Scholar
Vegro, C. L. R. & de Almeida, L. F. in Coffee Consumption and Industry Strategies in Brazil 3–19 (Elsevier, 2020).
Bunn, C., Läderach, P., Jimenez, J. G. P., Montagnon, C. & Schilling, T. Multiclass classification of agro-ecological zones for Arabica coffee: An improved understanding of the impacts of climate change. PLoS ONE 10, e0140490 (2015).
Google Scholar
Bunn, C., Läderach, P., Rivera, O. O. & Kirschke, D. A bitter cup: climate change profile of global production of Arabica and Robusta coffee. Clim. Change 129, 89–101 (2015).
Google Scholar
Pham, Y., Reardon-Smith, K., Mushtaq, S. & Cockfield, G. The impact of climate change and variability on coffee production: A systematic review. Clim. Change 156, 609–630 (2019).
Google Scholar
Chemura, A., Kutywayo, D., Chidoko, P. & Mahoya, C. Bioclimatic modelling of current and projected climatic suitability of coffee (Coffea arabica) production in Zimbabwe. Reg. Environ. Change 16, 473–485 (2016).
Google Scholar
Laderach, P. et al. in The economic, social and political elements of climate change 703–723 (Springer, 2011).
Baker, P. & Haggar, J. Global warming: Effects on global coffee (SCAA Conference Handout, Long Beach, 2007).
Craparo, A., Van Asten, P. J., Läderach, P., Jassogne, L. T. & Grab, S. Coffea arabica yields decline in Tanzania due to climate change: Global implications. Agric. For. Meteorol. 207, 1–10 (2015).
Google Scholar
Alves, M. C., Carvalho, L. G., Pozza, E. A., Sanches, L. & Maia, J. Ecological zoning of soybean rust, coffee rust and banana sigatoka based on Brazilian climate changes. Earth Syst. Sci. Global Change Clim. People 6, 35–46. https://doi.org/10.1016/j.proenv.2011.05.005 (2011).
Google Scholar
Jaramillo, J., Muchugu, E., Vega, F. E., Davis, A. & Borgemesister, C. Some like it hot: The influence and implications of climate change on coffee berry borer (Hypothenemus hampei) and coffee production in East Africa. PLoS ONE 6, e24528. https://doi.org/10.1371/journal.pone.0024528 (2011).
Google Scholar
Kutywayo, D., Chemura, A., Kusena, W., Chidoko, P. & Mahoya, C. The impact of climate change on the potential distribution of agricultural pests: The case of the coffee white stem borer (Monochamus leuconotus P.) in Zimbabwe. Plos One 8, e73432. https://doi.org/10.1371/journal.pone.0073432 (2013).
Läderach, P. et al. Climate change adaptation of coffee production in space and time. Clim. Change 141, 47–62 (2017).
Google Scholar
Scholz, M. B. d. S., Kitzberger, C. S. G., Prudencio, S. H. & Silva, R. S. d. S. F. d. The typicity of coffees from different terroirs determined by groups of physico-chemical and sensory variables and multiple factor analysis. Food Res. Int. 114, 72–80. https://doi.org/10.1016/j.foodres.2018.07.058 (2018).
Bertrand, B. et al. Comparison of the effectiveness of fatty acids, chlorogenic acids, and elements for the chemometric discrimination of coffee (Coffea arabica L.) varieties and growing origins. J. Agric. Food Chem. 56, 2273–2280 (2008).
Cheng, B., Furtado, A., Smyth, H. E. & Henry, R. J. Influence of genotype and environment on coffee quality. Trends Food Sci. Technol. 57, 20–30 (2016).
Google Scholar
Bote, A. D. & Vos, J. Tree management and environmental conditions affect coffee (Coffea arabica L.) bean quality. NJAS-Wageningen J. Life Sci. 83, 39–46 (2017).
de Carvalho, A. M. et al. Relationship between the sensory attributes and the quality of coffee in different environments. Afr. J. Agric. Res. 11, 3607–3614 (2016).
Google Scholar
Sberveglieri, V. et al. in AIP Conference Proceedings. 86–87 (American Institute of Physics).
Bertrand, B. et al. Climatic factors directly impact the volatile organic compound fingerprint in green Arabica coffee bean as well as coffee beverage quality. Food Chem. 135, 2575–2583 (2012).
Google Scholar
International Trade Centre. The Coffee Exporter’s Guide (World Trade Organization and the United Nations, 2011).
Lambot, C. et al. in The Craft and Science of Coffee (ed Britta Folmer) 17–49 (Academic Press, 2017).
Ahmed, S. & Stepp, J. R. Beyond yields: Climate effects on specialty crop quality and agroecological management. Element. Sci. Anthropocene 4, 92 (2016).
Purba, P., Sukartiko, A. & Ainuri, M. in IOP Conference Series: Earth and Environmental Science. 012021 (IOP Publishing).
Traore, T. M., Wilson, N. L. & Fields, D. What explains specialty coffee quality scores and prices: A case study from the cup of excellence program. J. Agric. Appl. Econ. 50, 349–368 (2018).
Google Scholar
Barjolle, D., Quiñones-Ruiz, X. F., Bagal, M. & Comoé, H. The role of the state for geographical indications of coffee: Case studies from Colombia and Kenya. World Dev. 98, 105–119 (2017).
Google Scholar
Oguamanam, C. & Dagne, T. Geographical indication (GI) options for Ethiopian coffee and Ghanaian cocoa. Innovation and intellectual property: Collaborative dynamics in Africa, 77–108 (2014).
Boaventura, P. S. M., Abdalla, C. C., Araujo, C. L. & Arakelian, J. S. Value co-creation in the specialty coffee value chain: The third-wave coffee movement. Revista de Administração de Empresas 58, 254–266 (2018).
Google Scholar
Lannigan, J. Making a space for taste: Context and discourse in the specialty coffee scene. Int. J. Inf. Manage. 51, 101987 (2020).
Google Scholar
Masters, G., Baker, P. & Flood, J. Climate change and agricultural commodities. CABI Work. Pap. 2, 1–38 (2010).
Rahman, S., Gross, M., Battiste, M. & Gacioch, M. Specialty Coffee Farmers’ Climate Change Concern and Perceived Ability to Adapt. (2016).
Srinivasan, R., Giannikas, V., Kumar, M., Guyot, R. & McFarlane, D. Modelling food sourcing decisions under climate change: A data-driven approach. Comput. Ind. Eng. 128, 911–919 (2019).
Google Scholar
Chemura, A., Schauberger, B. & Gornott, C. Impacts of climate change on agro-climatic suitability of major food crops in Ghana. PLoS ONE 15, e0229881 (2020).
Google Scholar
FAO. (Food Agriculture Organization of the United Nations, Roma, 2012).
Hirons, M. et al. Pursuing climate resilient coffee in Ethiopia: A critical review. Geoforum 91, 108–116 (2018).
Google Scholar
Central Statistical Agency (CSA). Agricultural Sample Survey 2018/19. (2019).
Murken, L. et al. Climate Risk Analysis for Identifying and Weighing Adaptation Strategies in Ethiopia’s Agricultural Sector. (2020).
Ridley, F. The past and future climatic suitability of arabica coffee (Coffea arabica L.) in East Africa, Durham University, (2011).
Putri, S. P., Irifune, T. & Fukusaki, E. GC/MS based metabolite profiling of Indonesian specialty coffee from different species and geographical origin. Metabolomics 15, 126 (2019).
Google Scholar
Mengistie, G. in Extending the Protection of Geographical Indications: Case studies of Agricultural Products of Africa Vol. 15 (eds M Blakeney, T Coulet, Getachew Mengistie, & M.T Mahop) 150 (Routledge, 2011).
Kufa, T., Ayano, A., Yilma, A., Kumela, T. & Tefera, W. The contribution of coffee research for coffee seed development in Ethiopia. J. Agric. Res. Dev. 1, 009–016 (2011).
Moat, J. et al. Resilience potential of the Ethiopian coffee sector under climate change. Nat. Plants 3, 17081 (2017).
Google Scholar
Moat, J., Gole, T. W. & Davis, A. P. Least Concern to Endangered: Applying climate change projections profoundly influences the extinction risk assessment for wild Arabica coffee. Glob. Change Biol. 25, 390–403 (2019).
Google Scholar
Davis, A. P., Gole, T. W., Baena, S. & Moat, J. The impact of climate change on indigenous arabica coffee (Coffea arabica): Predicting future trends and identifying priorities. PLoS ONE 7, e47981. https://doi.org/10.1371/journal.pone.0047981 (2012).
Google Scholar
CIAT. Future Climate Scenarios for Tanzania’s Arabica Coffee Growing Areas. 27 (International Center for Tropical Agriculture, Cali, Colombia: , 2012).
Laderach, P., Jarvis, A. & Ramirez, J. The impact of climate change in coffee-growing regions: The case of 10 municipalities in Nicaragua. 4 (CafeDirect/GTZ, 2006).
Gomes, L. C. et al. Agroforestry systems can mitigate the impacts of climate change on coffee production: A spatially explicit assessment in Brazil. Agr. Ecosyst. Environ. 294, 106858. https://doi.org/10.1016/j.agee.2020.106858 (2020).
Google Scholar
Brown, N. in Daily Coffee News (Roast Magazine, 2018).
Labouisse, J.-P., Bellachew, B., Kotecha, S. & Bertrand, B. Current status of coffee (Coffea arabica L.) genetic resources in Ethiopia: implications for conservation. Genet. Resour. Crop Evol. 55, 1079 (2008).
MFA. Coffee production in Ethiopia. The 4th World Coffee Conference in Addis Ababa, Ministry of Foreign Affairs of Ethiopia, Addis Ababa, Ethiopia (2016).
Tolessa, K., D’heer, J., Duchateau, L. & Boeckx, P. Influence of growing altitude, shade and harvest period on quality and biochemical composition of Ethiopian specialty coffee. J. Sci. Food Agric. 97, 2849–2857 (2017).
Chemura, A., Mahoya, C., Chidoko, P. & Kutywayo, D. Effect of soil moisture deficit stress on biomass accumulation of four coffee (Coffea arabica) varieties in Zimbabwe. ISRN Agron. 1–10, 2014. https://doi.org/10.1155/2014/767312 (2014).
Google Scholar
Hannah, L. et al. Climate change, wine, and conservation. Proc. Natl. Acad. Sci. 110, 6907–6912 (2013).
Google Scholar
Impact on variety and origin chemometric determination. Villarreal, D. et al. Genotypic and environmental effects on coffee (Coffea arabica L.) bean fatty acid profile. J. Agric. Food Chem. 57, 11321–11327 (2009).
Google Scholar
Sisay, B. T. in Sustainable agriculture reviews 33 99–113 (Springer, 2018).
DaMatta, F. b. M., Avila, R. T., Cardoso, A. A., Martins, S. C. & Ramalho, J. C. Physiological and agronomic performance of the coffee crop in the context of climate change and global warming: A review. J. Agric. Food Chem. 66, 5264–5274 (2018).
CABI. (2015).
Barbet-Massin, M., Jiguet, F., Albert, C. H. & Thuiller, W. Selecting pseudo-absences for species distribution models: How, where and how many?. Methods Ecol. Evol. 3, 327–338 (2012).
Google Scholar
Liu, C., Newell, G. & White, M. The effect of sample size on the accuracy of species distribution models: Considering both presences and pseudo-absences or background sites. Ecography 42, 535–548 (2019).
Google Scholar
Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978. https://doi.org/10.1002/joc.1276 (2005).
Google Scholar
R Core Team. R: A language and environment for statistical computing. (2019).
Hengl, T. et al. SoilGrids1km—global soil information based on automated mapping. PloS one 9 (2014).
Nair, K. P. P. The Agronomy and Economy of Important Tree Crops of the Developing World. 368 (Elservier, 2010).
Coste, J. Coffee: The plant and the product. (Longman, 1992).
Lin, F.-J. Solving multicollinearity in the process of fitting regression model using the nested estimate procedure. Qual. Quant. 42, 417–426 (2008).
Google Scholar
Dormann, C. F. et al. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 36, 27–46 (2013).
Google Scholar
Breiman, L. Random forests machine learning. 45: 5–32. View Article PubMed/NCBI Google Scholar (2001).
Elith, J., Leathwick, J. R. & Hastie, T. A working guide to boosted regression trees. J. Anim. Ecol. 77, 802–813 (2008).
Google Scholar
Li, X. & Wang, Y. Applying various algorithms for species distribution modelling. Integr. Zool. 8, 124–135 (2013).
Google Scholar
Gobeyn, S. et al. Evolutionary algorithms for species distribution modelling: A review in the context of machine learning. Ecol. Model. 392, 179–195 (2019).
Google Scholar
Vapnik, V. The nature of statistical learning theory. (Springer science & business media, 2013).
Choubin, B., Darabi, H., Rahmati, O., Sajedi-Hosseini, F. & Kløve, B. River suspended sediment modelling using the CART model: A comparative study of machine learning techniques. Sci. Total Environ. 615, 272–281 (2018).
Google Scholar
Pourghasemi, H. R., Yousefi, S., Kornejady, A. & Cerdà, A. Performance assessment of individual and ensemble data-mining techniques for gully erosion modeling. Sci. Total Environ. 609, 764–775 (2017).
Google Scholar
Allouche, O., Tsoar, A. & Kadmon, R. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 43, 1223–1232 (2006).
Google Scholar
Chang, Y. & Bourque, C.P.-A. Relating modelled habitat suitability for Abies balsamea to on-the-ground species structural characteristics in naturally growing forests. Ecol. Ind. 111, 105981 (2020).
Google Scholar
Naimi, B. & Araújo, M. B. sdm: A reproducible and extensible R platform for species distribution modelling. Ecography 39, 368–375 (2016).
Google Scholar
Zurell, D. et al. A standard protocol for reporting species distribution models. Ecography (2020).
ArcGIS Desktop v. 10.2 (Environmental Systems Research Institute, Redlands, CA, Redlands, 2012).
WorldClim. Global climate and weather data. https://www.worldclim.org/data/cmip6/cmip6_clim2.5m.html ( 2020).
Navarro-Racines, C., Tarapues, J., Thornton, P., Jarvis, A. & Ramirez-Villegas, J. High-resolution and bias-corrected CMIP5 projections for climate change impact assessments. Sci. Data 7, 1–14 (2020).
Google Scholar
van Vuuren, D. P. et al. A new scenario framework for Climate Change Research: scenario matrix architecture. Clim. Change 122, 373–386. https://doi.org/10.1007/s10584-013-0906-1 (2014).
Google Scholar
Popp, A. et al. Land-use futures in the shared socio-economic pathways. Glob. Environ. Chang. 42, 331–345. https://doi.org/10.1016/j.gloenvcha.2016.10.002 (2017).
Google Scholar
O’Neill, B. C. et al. The roads ahead: Narratives for shared socioeconomic pathways describing world futures in the 21st century. Glob. Environ. Chang. 42, 169–180 (2017).
Google Scholar
Doelman, J. C. et al. Exploring SSP land-use dynamics using the IMAGE model: Regional and gridded scenarios of land-use change and land-based climate change mitigation. Glob. Environ. Chang. 48, 119–135 (2018).
Google Scholar
Source: Ecology - nature.com