Modeling the responses of Coffee (Coffea arabica L.) distribution to current and future climate change in Jimma Zone, Ethiopia

Fedhasa Benti Chalchissa, Girma Mamo Diga, Alemayehu Regassa Tolossa

Abstract

Coffee arabica species have already been affected by climate change, with socioeconomic implications. Smallholder farmers have encountered and will continue to confront issues in maintaining their coffee plants' productivity. This study aimed to determine which bio-climatic characteristics are most beneficial to coffee production in current and future climate change scenarios. The responses of coffee distribution to climatic conditions were studied under the current, moderate representative concentration (RCP4.5), and worst representative concentration (RCP8.5) pathways using a bioclimatic modelling approach or the Maxent model. Multiple regression models (path and response optimizers) were used to parameterize and optimize the logistic outputs of plant distribution. Results showed that climatic factors such as total precipitation, precipitation seasonality, and mean temperature are the most important climatic factors in determining the success of C. arabica farming. Under the current conditions, total precipitation significantly benefits C. arabica whereas precipitation seasonality significantly affects it (P < 0.001). In the current condition, coffee responded neither negatively nor positively to the mean temperature, but positively in RCP4.5 and RCP8.5. It would also respond positively to increased total precipitation under RCP4.5 but negatively to rising precipitation under the RCP8.5. The average five top-optimal multiple responses of C. arabica were 75.8, 77, and 70% for the present, RCP4.5, and RCP8.5, respectively. The positive response of C. arabica to bioclimatic variables in the RCP4.5 scenario is projected to be much bigger than in the present and RCP4.5 scenarios (P < 0.001). As precipitation and temperature-related variables increase, the cultivation of C. arabica will increase by 1.2% under RCP4.5 but decrease by 5.6% under RCP8.5. A limited number of models and environmental factors were used in this study, suggesting that intensive research into other environmental aspects is needed using different models.

Keywords

Climatic factors; MaxEnt Model; Response optimization; Precipitation; Temperature

Full Text:

PDF

References

Abdelaal, M., Fois, M., Fenu, G., & Bacchetta, G. (2019). Using MaxEnt modeling to predict the potential distribution of the endemic plant Rosa arabica Crép. in Egypt. Ecological Informatics, 50, 68-75. https://doi.org/10.1016/j.ecoinf.2019.01.003

Abolmaali, S. M.-R., Tarkesh, M., & Bashari, H. (2018). MaxEnt modeling for predicting suitable habitats and identifying the effects of climate change on a threatened species, Daphne mucronata, in central Iran. Ecological Informatics, 43, 116-123. https://doi.org/10.1016/j.ecoinf.2017.10.002

Aderolu, I. A., Babalola, F. D., Ugioro, O., Anagbogu, C. F., Ndagi, I., Mokwunye, F. C., Mokwunye, I. U., Idrisu, M., & Asogwa, E. U. (2014). Production and marketing of Coffee (Coffea robusta) in Kogi State, Nigeria: Challenges and recommendation for intervention. JOURNAL OF SOCIAL SCIENCE RESEARCH, 3(2), 207-215. https://doi.org/10.24297/jssr.v3i2.3559

Alemayehu, M. (2015). Ethiopian Highlands: Home for Arabica Coffee (Coffea arabica L.). In Tropical lakes in a changing environment: water, land, biology, climate and humans (pp. 58-65). https://www.researchgate.net/profile/Melkamu-Workie/publication/317231826_Ethiopian_Highlands_Home_for_Arabica_Coffee_Coffea_arabica_L/links/592d1b0f0f7e9b9979b3a3da/Ethiopian-Highlands-Home-for-Arabica-Coffee-Coffea-arabica-L.pdf

Amamo, A. A. (2014). Coffee production and marketing in Ethiopia. Eur J Bus Manag, 6(37), 109-122. https://www.iiste.org/Journals/index.php/EJBM/article/view/18697

Angelieri, C. C. S., Adams-Hosking, C., Ferraz, K. M. P. M. d. B., de Souza, M. P., & McAlpine, C. A. (2016). Using Species Distribution Models to Predict Potential Landscape Restoration Effects on Puma Conservation. PLOS ONE, 11(1), e0145232. https://doi.org/10.1371/journal.pone.0145232

Bliss, S. (2017). Coffee Biomes. Geography Bulletin, 49(4), 29-41. https://www.gtansw.org.au/files/geog_bulletin/2017/4_2017/06_GTA%20NSW%20Bulletin_Issue%204_2017_Coffee%20Biomes.pdf

Boria, R. A., Olson, L. E., Goodman, S. M., & Anderson, R. P. (2014). Spatial filtering to reduce sampling bias can improve the performance of ecological niche models. Ecological Modelling, 275, 73-77. https://doi.org/10.1016/j.ecolmodel.2013.12.012

Brázdil, R., Trnka, M., Mikšovský, J., Řezníčková, L., & Dobrovolný, P. (2015). Spring-summer droughts in the Czech Land in 1805–2012 and their forcings. International Journal of Climatology, 35(7), 1405-1421. https://doi.org/10.1002/joc.4065

Brown, J. L., Hill, D. J., Dolan, A. M., Carnaval, A. C., & Haywood, A. M. (2018). PaleoClim, high spatial resolution paleoclimate surfaces for global land areas. Scientific Data, 5(1), 180254. https://doi.org/10.1038/sdata.2018.254

Bunn, C. (2015). Modeling the climate change impacts on global coffee production [Dissertation, Humboldt-Universität zu Berlin, Lebenswissenschaftliche Fakultät]. https://doi.org/10.18452/17349

Camargo, M. B. P. d. (2010). The impact of climatic variability and climate change on arabic coffee crop in Brazil. Bragantia, 69(1), 239-247. https://doi.org/10.1590/S0006-87052010000100030

Chauhan, R., Hooda, M., & Tanga, A. A. (2015). Coffee: the backbone of Ethiopian economy. International Journal of Economic Plants, 1(2), 82-86. http://pphouse.org/upload_article/83447731_6_IJEP_February_2015_Chauhan_et_al.pdf

Chemura, A., Kutywayo, D., Chidoko, P., & Mahoya, C. (2016). Bioclimatic modelling of current and projected climatic suitability of coffee (Coffea arabica) production in Zimbabwe. Regional Environmental Change, 16(2), 473-485. https://doi.org/10.1007/s10113-015-0762-9

Chiou, C.-R., Hsieh, T.-Y., & Chien, C.-C. (2015). Plant bioclimatic models in climate change research. Botanical Studies, 56(1), 26. https://doi.org/10.1186/s40529-015-0104-8

Coffee: World Markets and Trade. (2019). World Agricultural Outlook Board/USDA. https://downloads.usda.library.cornell.edu/usda-esmis/files/m900nt40f/xk81jw68v/kp78gs60d/coffee.pdf

Cuneen, J., & Tobar, D. (2015). Chi-square Tests. In Sport Industry Research and Analysis: An Approach to Informed Decision Making. Routledge, New York. https://doi.org/10.4324/9781315212944-28

Davis, A. P., Gole, T. W., Baena, S., & Moat, J. (2012). The Impact of Climate Change on Indigenous Arabica Coffee (Coffea arabica): Predicting Future Trends and Identifying Priorities. PLOS ONE, 7(11), e47981. https://doi.org/10.1371/journal.pone.0047981

Deribe, H. (2019). Review on Factors which Affect Coffee (Coffea Arabica L.) Quality in South Western, Ethiopia. International Journal of Forestry and Horticulture, 5(1), 12-19. https://doi.org/10.20431/2454-9487.0501003

Diro, S., Erko, B., & Yami, M. (2019). Cost of Production of Coffee in Jimma Zone, Southwest Ethiopia. Ethiopian Journal of Agricultural Sciences, 29(3), 13-28. https://www.ajol.info/index.php/ejas/article/download/189554/178783

Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37(12), 4302-4315. https://doi.org/10.1002/joc.5086

Funk, C. C., Rowland, J., Eilerts, G., Kebebe, E., Biru, N., White, L., & Galu, G. (2012). A climate trend analysis of Ethiopia [Report](2012-3053). (Fact Sheet, Issue. U. S. G. Survey. http://pubs.er.usgs.gov/publication/fs20123053

Gelman, A., Goodrich, B., Gabry, J., & Vehtari, A. (2019). R-squared for Bayesian Regression Models. The American Statistician, 73(3), 307-309. https://doi.org/10.1080/00031305.2018.1549100

Gray, S. B., & Brady, S. M. (2016). Plant developmental responses to climate change. Developmental Biology, 419(1), 64-77. https://doi.org/10.1016/j.ydbio.2016.07.023

Halligan, S., Altman, D. G., & Mallett, S. (2015). Disadvantages of using the area under the receiver operating characteristic curve to assess imaging tests: A discussion and proposal for an alternative approach. European Radiology, 25(4), 932-939. https://doi.org/10.1007/s00330-014-3487-0

Intergovernmental Panel on Climate Change. (2014). Climate Change 2013 – The Physical Science Basis: Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press. https://doi.org/10.1017/CBO9781107415324

Iscaro, J. (2014). The impact of climate change on coffee production in Colombia and Ethiopia. Global Majority E-Journal, 5(1), 33-43. http://bangladeshstudies.org/files/Global_Majority_e_Journal_5_1.pdf#page=33

Karger, D. N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R. W., Zimmermann, N. E., Linder, H. P., & Kessler, M. (2017). Climatologies at high resolution for the earth’s land surface areas. Scientific Data, 4(1), 170122. https://doi.org/10.1038/sdata.2017.122

Kwon, Y., Lee, T., Lang, A., & Burnette, D. (2019). Assessment on latitudinal tree species richness using environmental factors in the southeastern United States. PeerJ, 7, e6781. https://doi.org/10.7717/peerj.6781

Mas, J.-F., Soares Filho, B., Pontius, R. G., Farfán Gutiérrez, M., & Rodrigues, H. (2013). A Suite of Tools for ROC Analysis of Spatial Models. ISPRS International Journal of Geo-Information, 2(3), 869-887. https://doi.org/10.3390/ijgi2030869

McHugh, M. L. (2013). The chi-square test of independence. Biochemia medica, 23(2), 143-149. https://doi.org/10.11613/BM.2013.018

Melesse, S. F. (2014). Covariates and latents in growth modelling [Doctoral dissertation, University of Kwazulu-Natal]. Pietermaritzburg. http://hdl.handle.net/10413/11825

Mighty, M. A. (2015). Site suitability and the analytic hierarchy process: How GIS analysis can improve the competitive advantage of the Jamaican coffee industry. Applied Geography, 58, 84-93. https://doi.org/10.1016/j.apgeog.2015.01.010

Minten, B., Stifel, D., & Tamru, S. (2014). Structural Transformation of Cereal Markets in Ethiopia. The Journal of Development Studies, 50(5), 611-629. https://doi.org/10.1080/00220388.2014.887686

Moat, J., Williams, J., Baena, S., Wilkinson, T., Gole, T. W., Challa, Z. K., Demissew, S., & Davis, A. P. (2017). Resilience potential of the Ethiopian coffee sector under climate change. Nature Plants, 3(7), 17081. https://doi.org/10.1038/nplants.2017.81

Moore, D. S. (2001). The Basic Practice of Statistics. Technometrics, 43(3), 381-381. https://doi.org/10.1198/tech.2001.s639

Moraes, G. A. B. K., Chaves, A. R. M., Martins, S. C. V., Barros, R. S., & DaMatta, F. M. (2010). Why is it better to produce coffee seedlings in full sunlight than in the shade? A morphophysiological approach. Photosynthetica, 48(2), 199-207. https://doi.org/10.1007/s11099-010-0025-4

National Meteorological Services Agency. (2005). Agrometeorological bulletin. Seasonal Agro Meteorological Bulletin, 15(3), 1-19. http://www.wamis.org/countries/ethiopia/Eth20051503.pdf

O'Donnel, M. S., & Ignizio, D. A. (2012). Bioclimatic predictors for supporting ecological applications in the conterminous United States [Report](691). (Data Series, Issue. U. S. G. Survey. http://pubs.er.usgs.gov/publication/ds691

Padalia, H., Srivastava, V., & Kushwaha, S. P. S. (2014). Modeling potential invasion range of alien invasive species, Hyptis suaveolens (L.) Poit. in India: Comparison of MaxEnt and GARP. Ecological Informatics, 22, 36-43. https://doi.org/10.1016/j.ecoinf.2014.04.002

Paquit, J. (2019). Maxent Modeling Training Manual in ArcGIS 10. https://doi.org/10.13140/RG.2.2.21119.97448

Pearson, R. G., & Dawson, T. P. (2003). Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful? Global Ecology and Biogeography, 12(5), 361-371. https://doi.org/10.1046/j.1466-822X.2003.00042.x

Phillips, S. J., Anderson, R. P., Dudík, M., Schapire, R. E., & Blair, M. E. (2017). Opening the black box: an open-source release of Maxent. Ecography, 40(7), 887-893. https://doi.org/10.1111/ecog.03049

Phillips, S. J., & Dudík, M. (2008). Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography, 31(2), 161-175. https://doi.org/10.1111/j.0906-7590.2008.5203.x

Pohlan, H. A. J., & Janssens, M. J. J. (2010). Growth and Production of Coffee. In W. H. Verheye (Ed.), Soils, Plant Growth and Crop Production. UNESCO-Encyclopedia of Life Support System. https://www.eolss.net/ebooklib/bookinfo/soils-plant-growth-crop-production.aspx#

Potopová, V., Štěpánek, P., Možný, M., Türkott, L., & Soukup, J. (2015). Performance of the standardised precipitation evapotranspiration index at various lags for agricultural drought risk assessment in the Czech Republic. Agricultural and Forest Meteorology, 202, 26-38. https://doi.org/10.1016/j.agrformet.2014.11.022

Pramanik, M., Paudel, U., Mondal, B., Chakraborti, S., & Deb, P. (2018). Predicting climate change impacts on the distribution of the threatened Garcinia indica in the Western Ghats, India. Climate Risk Management, 19, 94-105. https://doi.org/https://doi.org/10.1016/j.crm.2017.11.002

Qin, A., Liu, B., Guo, Q., Bussmann, R. W., Ma, F., Jian, Z., Xu, G., & Pei, S. (2017). Maxent modeling for predicting impacts of climate change on the potential distribution of Thuja sutchuenensis Franch., an extremely endangered conifer from southwestern China. Global Ecology and Conservation, 10, 139-146. https://doi.org/https://doi.org/10.1016/j.gecco.2017.02.004

Samper, L. F., & Quiñones-Ruiz, X. F. (2017). Towards a Balanced Sustainability Vision for the Coffee Industry. Resources, 6(2). https://doi.org/10.3390/resources6020017

Somarriba, E., & López Sampson, A. (2018). Coffee and Cocoa Agroforestry Systems: Pathways to Deforestation, Reforestation, and Tree Cover Change. In LEAVES - Leveraging Agricultural Value Chains to Enhance Tropical Tree Cover and Slow Deforestation. International Bank for Reconstruction and Development / The World Bank https://doi.org/10.13140/RG.2.2.29700.78724

Song, B., Niu, S., & Wan, S. (2016). Precipitation regulates plant gas exchange and its long-term response to climate change in a temperate grassland. Journal of Plant Ecology, 9(5), 531-541. https://doi.org/10.1093/jpe/rtw010

Steffen, W., Rice, M., Hughes, L., & Dean, A. (2018). The good, the bad and the ugly: limiting temperature rise to 1.5°C. https://www.climatecouncil.org.au/wp-content/uploads/2018/10/CC-IPCC-report-1.pdf

Svoboda, M. D., & Fuchs, B. A. (2017). Handbook of Drought Indicators and Indices. In D. A. Wilhite & R. S. Pulwarty (Eds.), Drought and Water Crises: Integrating Science, Management, and Policy (2nd ed.). CRC Press. https://doi.org/10.1201/b22009

USDA Foreign Agricultural Service. (2019). Ethiopia Coffee Annual Coffee Annual Report. G. A. I. Network. https://apps.fas.usda.gov/newgainapi/api/report/downloadreportbyfilename?filename=Coffee%20Annual_Addis%20Ababa_Ethiopia_5-29-2019.pdf

Vegro, C. L. R., & de Almeida, L. F. (2020). Chapter 1 - Global coffee market: Socio-economic and cultural dynamics. In L. F. de Almeida & E. E. Spers (Eds.), Coffee Consumption and Industry Strategies in Brazil (pp. 3-19). Woodhead Publishing. https://doi.org/10.1016/B978-0-12-814721-4.00001-9

Wan, P., & Davis, R. A. (2020). Goodness-of-fit testing for time series models via distance covariance. Journal of Econometrics. https://doi.org/10.1016/j.jeconom.2020.05.008

Wang, N., Jassogne, L., van Asten, P. J. A., Mukasa, D., Wanyama, I., Kagezi, G., & Giller, K. E. (2015). Evaluating coffee yield gaps and important biotic, abiotic, and management factors limiting coffee production in Uganda. European Journal of Agronomy, 63, 1-11. https://doi.org/10.1016/j.eja.2014.11.003

Wang, X., Jiang, B., & Liu, J. S. (2017). Generalized R-squared for detecting dependence. Biometrika, 104(1), 129-139. https://doi.org/10.1093/biomet/asw071

Woldesenbet, A. G., Woldeyes, B., & Chandravanshi, B. S. (2015). Wet coffee processing waste management practice in Ethiopia. Asian Journal of Science and Technology, 6(05), 1467-1471. http://www.journalajst.com/sites/default/files/issues-pdf/Dwonload%202205.pdf

Yuvaraj, R. M. (2020). Extents of Predictors for Land Surface Temperature Using Multiple Regression Model. The Scientific World Journal, 2020, 3958589. https://doi.org/10.1155/2020/3958589

DOI: https://doi.org/10.20961/stjssa.v19i1.54885

Refbacks

  • There are currently no refbacks.