Impact Assessment of Climate Change on Rice Yield Using a Crop Growth Model and Activities Toward Adaptation: Targeting Three Provinces in Indonesia

  • Yoshiyuki Kinose
  • Yuji Masutomi


In accordance with the Paris Agreement, the assessment of climate change impacts on rice productivity is expected at both the national and local (i.e., province or state) levels in Asian countries, to create and implement adaptation plans for climate change. However, there is limited information on the impact of climate change on local rice production in developing countries, especially at the local level. To this end, we aimed to clarify this impact on the yield of local rice in Indonesia in the next 25 years, using a crop growth model, MATCRO-Rice, in three provinces including North Sumatra, East Java, and Bali. Climate change was predicted to reduce the yield primarily because of increase in air temperature. Furthermore, the simulated yield reductions were different among the districts in each province, indicating the importance of regional adaptation priorities. We discussed several adaptation strategies with local stakeholders in each province from the viewpoints of feasibility and priority. Some strategies, such as change in cultivars to have high tolerance to high air temperature, which was ranked as being highly feasible and high priority, are expected to be future adaptation options.



This study was supported by the Program on Development of Regional Climate Change Adaptation Plans in Indonesia, by the Ministry of the Environment of Japan, and by the Environment Research and Technology Development Fund (S-12) of the Environmental Restoration and Conservation Agency. This study was performed in collaboration with the Indonesian Ministry of National Development Planning (BAPPENAS; Badan Perencanaan Pembangunan Nasional). We are indebted to Dr. Keiichi Hayashi for providing us with experimental data, from studies conducted in IJCRP, which were funded by the Ministry of Agriculture, Forestry, and Fisheries of Japan, regarding tuning the crop growth model. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP. We also thank the climate modeling groups (listed in Table 5.1) for producing and making available their model output. For CMIP, the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provided coordinating support and led the development of software infrastructure in partnership with the Global Organization for Earth System Science Portals.


  1. Aggarwal PK, Mall RK (2002) Climate change and rice yields in diverse agro-environments of India. II. Effect of uncertainties in scenarios and crop models on impact assessment. Clim Chang 52:331–343CrossRefGoogle Scholar
  2. Babel MS, Agarwal A, Swain DK et al (2011) Evaluation of climate change impacts and adaptation measures for rice cultivation in Northeast Thailand. Clim Res 46:137–146CrossRefGoogle Scholar
  3. Bouman BAM, Kropff MJ, Tuong TP et al (2001) In: International Rice Research Institute, and Wageningen: Wageningen University and Research Centre (ed) ORYZA2000: modeling lowland Rice, Los Baños (Philippines)Google Scholar
  4. BPS (Badan Pusat Statistik) (2016) Food crops. Accessed 30 November 2018
  5. Dlugokencky E, Tans P (2018) NOAA/ESRL. Accessed 30 November 2018
  6. FAO (Food and Agriculture Organization of the United Nations) (2016) Global Map of Irrigation Areas (GMIA) 2013, AQUASTAT. Accessed 30 November 2018
  7. FAO (Food and Agriculture Organization of the United Nations), IIASA (International Institute for Applied Systems Analysis), ISRIC (International Soil Reference and Information Centre), ISSCAS (Institute of Soil Science, Chinese Academy of Sciences), JRC (Joint Research Centre) (2012) Harmonized World Soil Database (version 1.2)Google Scholar
  8. Gomez-Garcia M, Ogawada D, Matsumura A et al (2017) Evaluation of the application of the ISI-MIP bias-correction method of future simulations of climate over Indonesia for the implementation of climate change adaptation plans. HESSS4 Tokyo, JapanGoogle Scholar
  9. Horie T (1987) A model for evaluating climatic productivity and water balance of irrigated rice and its application to Southeast Asia. Southeast Asian Stud 25:62–74Google Scholar
  10. IMoA (Indonesian Ministry of Agriculture) (2016) Rencana Strategis Kementerian Pertanian Tahun 2015–2019. Accessed 30 November 2018 (In Indonesian)
  11. Joos F, Spahni R (2008) Rates of change in natural and anthropogenic radiative forcing over the past 20,000 years. Proc Natl Acad Sci USA 105:1425–1430CrossRefGoogle Scholar
  12. Kropff MJ, van Laar HH, Matthews RB (1994) ORYZAl: an ecophysiological model for irrigated rice production. In: SARP research proceedings, AB-DLO, Wageningen, The NetherlandsGoogle Scholar
  13. Laborte AG, Gutierrez MA, Balanza JG et al (2017) RiceAtlas, a spatial database of global rice calendars and production. Sci Data 4:170074CrossRefGoogle Scholar
  14. Li T, Hasegawa T, Yin X et al (2015) Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions. Glob Chang Biol 21:1328–1341CrossRefGoogle Scholar
  15. Lu C, Tian H (2017) Global nitrogen and phosphorus fertilizer use for agriculture production in the past half century: shifted hot spots and nutrient imbalance. Earth Syst Sci Data 9:181–192CrossRefGoogle Scholar
  16. Maclean JL (2013) Rice almanac: source book for the most important economic activity on earth. International Rice Research InstituteGoogle Scholar
  17. Masutomi Y, Ono K, Mano M et al (2016a) A land surface model combined with a crop growth model for paddy rice (MATCRO-Rice v. 1)-part 1: model description. Geosci Model Dev 9:4133–4154CrossRefGoogle Scholar
  18. Masutomi Y, Ono K, Takimoto T et al (2016b) A land surface model combined with a crop growth model for paddy rice (MATCRO-Rice v. 1)-part 2: model validation. Geosci Model Dev 9:4155–4167CrossRefGoogle Scholar
  19. Matthews RB, Kropff MJ, Horie T et al (1997) Simulating the impact of climate change on rice production in Asia and evaluating options for adaptation. Agric Syst 54:399–425CrossRefGoogle Scholar
  20. Meinshausen M, Smith SJ, Calvin K et al (2011) The RCP greenhouse gas concentrations and their extensions from 1765 to 2300. Clim Chang 109:213–241CrossRefGoogle Scholar
  21. Naylor RL, Battisti DS, Vimont DJ et al (2007) Assessing risks of climate variability and climate change for Indonesian rice agriculture. Proc Natl Acad Sci USA 104:7752–7757CrossRefGoogle Scholar
  22. Pachauri RK, Allen MR, Barros VR et al (2014) Climate change 2014: synthesis report. In: Pachauri R, Meyer L (eds) Contribution of working groups I, II and III to the fifth assessment report of the Intergovernmental panel on climate change, IPCC, Geneva, SwitzerlandGoogle Scholar
  23. Parry ML, Rosenzweig C, Iglesias A et al (2004) Effects of climate change on global food production under SRES emissions and socio-economic scenarios. Glob Environ Chang 14:53–67CrossRefGoogle Scholar
  24. Parry ML, Canziani OF, Palutikof JP et al (2007) Climate change 2007: impacts adaptation and vulnerability. Contribution of working group II to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, CambridgeGoogle Scholar
  25. Peng S, Huang J, Sheehy JE et al (2004) Rice yields decline with higher night temperature from global warming. Proc Natl Acad Sci USA 101:9971–9975CrossRefGoogle Scholar
  26. Porter JR, Xie L, Challinor AJ et al. (2014) Food security and food production systems. In: Field CB, Barros VR, Dokken DJ, Mach KJ, Mastrandrea MD, Bilir TE, Chatterjee M, Ebi KL, Estrada YO, Genova RC, Girma B, Kissel ES, Levy AN, MacCracken S, Mastrandrea PR, White LL (eds) Climate change 2014: impacts, adaptation, and vulnerability. Part a: global and sectoral aspects. Contribution of working group II to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge/New York, pp 485–533Google Scholar
  27. Prasad PVV, Boote KJ, Allen LH Jr et al (2006) Species, ecotype and cultivar differences in spikelet fertility and harvest index of rice in response to high temperature stress. Field Crops Res 95:398–411CrossRefGoogle Scholar
  28. Rosenzweig C, Parry ML (1994) Potential impact of climate change on world food supply. Nature 367:133–138CrossRefGoogle Scholar
  29. Rosenzweig C, Elliott J, Deryng D et al (2014) Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. Proc Natl Acad Sci USA 111:3268–3273CrossRefGoogle Scholar
  30. Sacks WJ, Deryng D, Foley JA et al (2010) Crop planting dates: an analysis of global patterns. Glob Ecol Biogeogr 19:607–620. Accessed 30 November 2018
  31. Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull Am Meteor Soc 93:485–498CrossRefGoogle Scholar
  32. Tubiello FN, Amthor JS, Boote KJ et al (2007) Crop response to elevated CO2 and world food supply: a comment on “Food for Thought…” by Long et al., Science 312:1918–1921, 2006. Eur J Agron 26:215–223Google Scholar
  33. UNFCCC (United Nations Framework Convention on Climate Change) (2015) The Paris Agreement. Accessed 30 November 2018
  34. USDA (United States Department of Agriculture) (2010) Gain report. Accessed 30 November 2018
  35. van Vuuren DP, Edmonds J, Kainuma M et al (2011) The representative concentration pathways: an overview. Clim Chang 109:5–31CrossRefGoogle Scholar
  36. von Grebmer K, Bernstein J, Hossain N et al. (2017) 2017 Global hunger index: the inequalities of hunger. Accessed 30 November 2018
  37. Weedon GP, Balsamo G, Bellouin N et al (2014) The WFDEI meteorological forcing data set: WATCH forcing data methodology applied to ERA-interim reanalysis data. Water Resour Res 50:7505–7514CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Yoshiyuki Kinose
    • 1
  • Yuji Masutomi
    • 2
  1. 1.Graduate Faculty of Interdisciplinary ResearchUniversity of YamanashiKofuJapan
  2. 2.College of AgricultureIbaraki UniversityInashikiJapan

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