Future Climate Change Impacts on Malta’s Agriculture, Based on Multi-model Results from WCRP’s CMIP5

  • Charles GaldiesEmail author
  • Kimberly Vella
Part of the Climate Change Management book series (CCM)


Based on the World Climate Research Program’s (WCRP) predicted changes in the magnitude and distribution of regional precipitation and temperature, this study assesses the future viability of agriculture in the Maltese islands, which are situated in the central Mediterranean region considered by many as a climate change hotspot. The analysis uses the latest results from an ensemble of 11 Coupled Model Intercomparison Project phase 5 (CMIP5) models addressing IPCC’s four Representative Concentration Pathways (RCPs) for the years 2050 and 2070 as provided by WorldClim database. Using statistical, empirical crop- and livestock-modeling techniques, this unique study shows that future climate change is likely to negatively affect Malta’s natural freshwater supplies, livestock and crop survival. As a consequence, the distribution of the already stressed local arable land will change, modifying production patterns and economics. The analysis of multi-model predictions provided a more robust evaluation of the likely impacts of physical and bioclimatic factors that are of relevance to local agriculture. Irrespective of which RCP scenario is considered, we find that the expected losses in productivity and food quality will be significant.


Malta Agriculture Climate change impacts CMIP5 Evapotranspiration Thermal-humidity index Model clustering Heat stress 


  1. Ahmadalipour A, Rana A, Moradkhani H, and Sharma A (2015) Multi-criteria evaluation of CMIP5 GCMs for climate change impact analysis. Theor Appl Climatol pp 1–44. Accessed 28 Apr 2017CrossRefGoogle Scholar
  2. Arnell NW, Lloyd-Hughes B (2014) The global-scale impacts of climate change on water resources and flooding under new climate and socio-economic scenarios. Clim Change 122(1):127–140. Scholar
  3. Baldacchino G, Galdies C (2015) Global environmental change: economic and labour market implications for small island territories. Xjenza Online 3:81–85Google Scholar
  4. Bohmanova J, Misztal I, Cole JB (2007) Temperature-humidity indices as indicators of milk production losses due to heat stress. J Dairy Sci 90:1947–1956. Scholar
  5. Briley L, Browna D, Kalafatis SE (2015) Overcoming barriers during the co-production of climate information for decision-making. Clim Risk Manage 9:41–49CrossRefGoogle Scholar
  6. Carter TR et al (2007) New Assessment methods and the characterisation of future conditions. climate change 2007: impacts, adaptation and vulnerability. In: Parry ML, Canziani OF, Palutik of JP, van der Linden PJ, Hanson CE (eds) Contribution of working group II to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, UKGoogle Scholar
  7. Cervigni R, Valentini R, Sartini M (eds) (2013) Toward climate-resilient development in nigeria. international bank for reconstruction and development. The World Bank. ISBN (electronic): 978-0-8213-9924-8Google Scholar
  8. Dawson TP, Perryman AH, Osborne TM (2016) Modelling impacts of climate change on global food security. Clim Change 134:429. Scholar
  9. De Martonne E (1926) Une nouvelle function climatologique: L’indice d’aridité. La Meteorologie, pp 449–458Google Scholar
  10. Downing TE, Nishioka S, Parikh KS, Parmesan C, Schneider SH, Toth F, Yohe G (2001) Methods and tools. In McCarthy JJ et al (eds) Climate Change 2001: impacts, adaptation and vulnerability, Cambridge University Press, 105–143 Accessed 28 Apr 2017.
  11. FAO (2009) ETo calculator version 3.1, issued in January 2009. Land and water digital media series No 36. Available via Accessed 28 Apr 2017
  12. FAO (2017) Chapter 1—Introduction to evapotranspiration. Accessed 28 Apr 2017
  13. Feller U (2016) Drought stress and carbon assimilation in a warming climate: reversible and irreversible impacts. J Plant Physiol 20(203):84–94. Scholar
  14. Galdies C (2011) The climate of malta: statistics, trends and analysis, 1951–2010. National Statistics Office, Malta. ISBN 9789995729196Google Scholar
  15. Galdies C (2015) Potential future climatic conditions on tourists: a case study focusing on malta and venice. Xjenza Online 3:6–25Google Scholar
  16. Galdies C, Said A, Camilleri L, Caruana M (2016) Climate change trends in Malta and related beliefs, concerns and attitudes toward adaptation among Gozitan farmers. Eur J Agron 74:18–28CrossRefGoogle Scholar
  17. Gleick PH (1987) Regional hydrologic consequences of increases in atmospheric CO2 and other trace gases. Clim Change 10:137–160. Scholar
  18. Global Warming Focus (2016) Climate research; new climate research study findings have been reported from University of Malta (An analysis of teleconnections in the Mediterranean region using RegCM4), Atlanta, 124Google Scholar
  19. Government of Malta (2010) National climate change adaptation strategy. Climate change committee for adaptation, Malta. Consultation Report, 143 ppGoogle Scholar
  20. Government of Malta (2014) The third, fourth, fifth and sixth national communication of malta under the United Nations framework convention on climate change, 195 ppGoogle Scholar
  21. Hayhoe K et al (2004) Emissions pathways, climate change, and impacts on California. In: Proceedings of the National Academy of Sciences of the USA, 101: 12 422–12 427. Scholar
  22. Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25:1965–1978CrossRefGoogle Scholar
  23. IPCC (2014) Climate Change 2014: Impacts, Adaptation, and Vulnerability.
  24. Kim TK (2015) T-test as a parametric statistic. Korean J Anesthesiol 68(6):540–546. Scholar
  25. Knutti R, Furrer R, Tebaldi C, Cermak J, Meehl GA (2010) Challenges in combining projections from multiple climate models. J. Climate 23:2739–2758. Scholar
  26. Knutti R, Masson D, Gettelman A (2013) Climate model genealogy: generation CMIP5 and how we got there. Geophys Res Lett 40(6):1194–1199. Scholar
  27. Lajinian S, Hudson S, Applewhite L, Feldman J, Minkoff HL (1997) An association between the heat-humidity index and preterm labor and delivery: a preliminary analysis. Am J Public Health 87:1205–1207CrossRefGoogle Scholar
  28. Liuzzo L, Viola F, Noto LV (2016) Wind speed and temperature trends impacts on reference evapotranspiration in Southern Italy. Theor Appl Climatol 123:43–62. Scholar
  29. Mader TL, Davis MS, Brown-Brandl (2006) Environmental factors influencing heat stress in feedlot cattle. Faculty Papers and Publications in Animal Science. Paper 608.Google Scholar
  30. Maliva R, Missimer T (2012) Arid lands water evaluation and management, environmental science and engineering. Springer-Verlag, Berlin HeidelbergCrossRefGoogle Scholar
  31. Maslin M, Austin P (2012) Climate models at their limit? Nature 486:183–184. Scholar
  32. McSweeney CF, Jones RG, Lee RW, Rowell DP (2014) Selecting CMIP5 GCMs for downscaling over multiple regions. Clim Dyn 44:3237–3260. Scholar
  33. National Statistics Office (2012) Census of agriculture 2010: Results-news release. at: Accessed 28 Apr 2017
  34. National Statistics Office (2016) Agriculture and fisheries 2014. National Statistics Office, Malta, p 152Google Scholar
  35. Norušis MJ (2011) Cluster Analysis in: IBM SPSS statistics 19 advanced statistical procedures companion (Chapter 17). Prentice Hall, pp 375–404. Retrieved from
  36. Scerri E (1982) The radiation climate of Malta. Sol Energy 31(1):129–133CrossRefGoogle Scholar
  37. Schoen C (2005) A new empirical model of the temperature–humidity index. J Appl Meteorol 44:1413–1420CrossRefGoogle Scholar
  38. Tan PN, Steinbach M, Kumar V (2006) Introduction to data mining, vol 1. Pearson Addison Wesley, Boston. ISBN-13: 978-0321321367Google Scholar
  39. Taylor KE, Stouffer RJ, and Meehl GA (2012) An overview of CMIP5 and the experiment design. American Meteorology Society April 2012: 485–498. Scholar
  40. Tebaldi C, Knutti R (2007) The use of the multi-model ensemble in probabilistic climate projections. Philos Trans Roy Soc Lond A365:2053–2075. Scholar
  41. Teng J, Chiew FH, Vaze J (2012) Will CMIP5 GCMs reduce or increase uncertainty in future runoff projections? American Geophysical Union—Fall Meeting, 3–7 December 2012, San Francisco, USAGoogle Scholar
  42. The World Bank (2018) Data. Last accessed 14 November 2018
  43. van Vuuren DP, Edmonds J, Kainuma M et al (2012) Clim Change 109:5. Scholar
  44. Walton K (1969) The arid zone. Aldine Publishing Co, Chicago, ILGoogle Scholar
  45. Watling JI, Romanach SS, Bucklin DN, Speroterra C, Brandt LA, Pearlstine LG, Mazzotti FJ (2012) Do bioclimate variables improve performance of climate envelope models? Ecol Model 246:79–85CrossRefGoogle Scholar
  46. Yousef MK (1985) Stress physiology in livestock. CRC Press, Boca Raton, FLGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Environmental Management and Planning Division, Institute of Earth SystemsUniversity of MaltaMsida MsdMalta

Personalised recommendations