Abstract
Climate change has had adverse impacts on agriculture, but only a handful of studies exist on this phenomenon in South-East Asia. To help provide better-informed policy interventions, in this study, the Google Earth Engine (GEE) cloud-computing platform was used to assess the temporal and spatial changes of drought conditions and related impacts on crops in the Association of Southeast Asian Nations (ASEAN) region from 1980 to 2019. To assess drought intensity and to identify its impact on irrigated and rain-fed agriculture land, 47,192 grid points with 10 × 10 km (km) resolution were created. It found that the Monsoon Climate Region had more droughts with higher intensity, while the Equatorial Climate Region experienced more wet conditions with a lower intensity of drought conditions in irrigated and rain-fed agriculture lands. Still, about 19.9 million hectares (ha) of croplands in the ASEAN region faced severe drought conditions, while 3.6 million ha of croplands faced wet conditions and possible flood damage. Accordingly, the loss of production of irrigated and rain-fed croplands in Cambodia, Indonesia, the Lao People’s Democratic Republic, Myanmar, Thailand, and Viet Nam was estimated at about 21.9 million tons during 2015–2019. To address drought impacts, four levels of policy interventions for ASEAN are suggested—low, medium, high, and business-as-usual—depending on the level of drought conditions in a particular country.
Keywords
- Google earth engine
- Terra climate
- PDSI
- ASEAN
- Climate change
- Agriculture
This is a preview of subscription content, access via your institution.
Buying options

ASEAN = Association of Southeast Asian Nations, Lao PDR = Lao People’s Democratic Republic, Brunei = Brunei Darussalam.

ASEAN = Association of Southeast Asian Nations, ECR = Equatorial Climate Region, GEE = Google Earth Engine, GFSAD = global food security-support analysis data, km = kilometre, MCR = Monsoon Climate Region, PDSI = Palmer drought severity index.

Note The background is the Palmer Drought Severity Index map showing red as higher drought and blue as low drought or wet conditions.

PDSI = Palmer Drought Severity Index.

Lao PDR = Lao People’s Democratic Republic.

Brunei = Brunei Darussalam.

km = kilometre, Brunei = Brunei Darussalam, Lao PDR = Lao People’s Democratic Republic.

Source Authors

Source Authors

BAU = business as usual.

BAU = business as usual.

BAU = business as usual.

BAU = business as usual.
References
Abatzoglou JT (2013) Development of gridded surface meteorological data for ecological applications and modelling. Int J Climatol 33:121–131. https://doi.org/10.1002/joc.3413
Abatzoglou JT, Dobrowski SZ, Parks SA, Hegewisch KC (2018) Terraclimate: a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Scientific Data 5:170191. https://doi.org/10.1038/sdata.2017.191
Aiguo D, Kevin ET, Taotao Q (2004) A global dataset of palmer drought severity index for 1870–2002: relationship with soil moisture and effects of surface warming. J Hydrometeorol 5:1117–1130. https://doi.org/10.1016/j.molcel.2017.04.015
Anang Z, Padli J, Kamaludin M, Sathasivam S (2017) The effect of climate change on water resources using panel approach: the case of Malaysia. Int J Acad Res Bus Soc Sci 7(11):141–152. https://doi.org/10.6007/ijarbss/v7-i11/3446
Antofie T, Naumann G, Spinoni J, Vogt J (2015) Estimating the water needed to end the drought or reduce the drought severity in the Carpathian region. Hydrol Earth Syst Sci 19:177–193. https://doi.org/10.5194/hess-19-177-2015
Aryal JP, Sapkota TB, Khurana R, Khatri-Chhetri A, Rahut DB (2019) Climate change and agriculture in South Asia: adaptation options in smallholder production systems. Environ Dev Sustain 22:5045–5075. https://doi.org/10.1007/s10668-019-00414-4
Asian Development Bank (2009) Asian development outlook 2009. Asian Development Bank, Philippines, pp 110–116
Asian Development Bank (ADB) (2015) Asian development outlook supplement 2015: growth prospects soften for developing Asia. Manila
Association of Southeast Asian Nations (ASEAN) (2020) ASEAN cooperation on climate change, https://environment.asean.org/asean-working-group-on-climate-change/
Bohra-Mishra P, Oppenheimer M, Cai R, Feng S, Licker R (2016) Climate variability and migration in the Philippines. Popul Environ 38:286–308. https://doi.org/10.1007/s11111-016-0263-x
Campos-Taberner M, Moreno-Martínez A, García-Haro FJ, Camps-Valls G, Robinson NP, Kattge J, Running SW (2018) Global estimation of biophysical variables from google earth engine platform. Remote Sens 10(8):1–17. https://doi.org/10.3390/rs10081167
Centre for Research on the Epidemiology of Disasters (CRED) (2019) EM-DAT: the international disasters database, https://www.emdat.be/
Yee Chan C, Tran N, Chi Dao D, et al (2017) Fish to 2050 in the ASEAN region Work. Pap 1–36
Dai A (2011) Characteristics and Trends in various forms of the palmer drought severity index during 1900–2008. J Geophys Res 116(D12). https://doi.org/10.1029/2010JD015541
Dai A, Zhao T (2017) Uncertainties in historical changes and future projections of drought—part I: estimates of historical drought changes. Clim Change 144:519–533. https://doi.org/10.1007/s10584-016-1705-2
Daryanto S, Wang L, Jacinthe PA (2016) Global synthesis of drought effects on maize and wheat production. PLoS ONE 11:e0156362. https://doi.org/10.1371/journal.pone.0156362
Edossa DC, Woyessa YE, Welderufael WA (2016) Spatiotemporal analysis of droughts using self-calibrating palmer’s drought severity index in the central region of South Africa. Theoret Appl Climatol 126:643–657. https://doi.org/10.1007/s00704-015-1604-x
Food and Agricultural Organization of the United Nations (FAO) (2016) El Nino’ event in Vietnam: agriculture, food security, and livelihood need assessment in response to drought and salt water instrusion. Rome
Food and Agricultural Organization of the United Nations (FAO) (2020) FAOSTAT: food and agriculture data. https://www.fao.org/faostat/en/#home. Accessed 4 Dec 2020
Food and Agricultural Organization of the United Nations (FAO) (2012) World food programme, and International fund for agriculture development. The state of food insecurity in the world 2012: economic growth is necessary but not sufficient to accelerate reduction of hunger and malnutrition. FAO, Rome
GEE (2020a) Google earth engine. https://earthengine.google.com/. Accessed 16 Feb 2020
GEE (2020b) Gridmet drought: conus drought indices. https://developers.google.com/earth-engine/datasets/catalog/GRIDMET_DROUGHT. Accessed 9 Dec 2020
Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D, Moore R (2017) Google earth engine: planetary-scale geospatial analysis for everyone. Remote Sens Environ 202:18–27. https://doi.org/10.1016/j.rse.2017.06.031
Government of the United States, Department of Agriculture (USDA) (2012) Vietnam: record rice production forecast on surge in planting in Mekong Delta. Commodity Intelligence Reports, Washington, DC
Government of the United States, Department of Agriculture (USDA) (2013) Thailand: deficient irrigation supplies reduce dry season rice production. Commodity Intelligence Reports, Washington, DC
Government of the United States, Department of Agriculture (USDA) (2013) Cambodia: seasonal flooding impacts wet season rice production in 2013. Commodity Intelligence Reports, Washington, DC
Guo H, Wang L, Chen F, Liang D (2014) Scientific big data and digital earth. Chin Sci Bull 59:5066–5073. https://doi.org/10.1007/s11434-014-0645-3
International Panel on Climate Change (IPCC). (2018). IPCC special report on the impacts of global warming of 1.5 °C. Geneva
Kumar L, Mutanga O (2018) Google earth engine applications since inception: usage, trends, and potential. Remote Sens 10(10):1509. https://doi.org/10.3390/rs10101509
Lafitte HR, Yongsheng G, Yan S, Li ZK (2007) Whole plant responses, key processes, and adaptation to drought stress: the case of rice. J Exp Bot 58(2):169–175
Lai P et al (2020) Responses of seasonal indicators to extreme droughts in Southwest China. Remote Sensing 12(5):818. https://doi.org/10.3390/rs12050818
Lassa JA, Lai A, Goh T (2016) Climate extremes: an observation and projection of its impacts on food production in ASEAN. Nat Hazards 84:19–33. https://doi.org/10.1007/s11069-015-2081-3
Lesk C, Rowhani P, Ramankutty N (2016) Influence of extreme weather disasters on global crop production. Nature 529:84–87. https://doi.org/10.1038/nature16467
Liu Y, Ren L, Ma M, Yang X, Yuan F, Jiang S (2016) An insight into the palmer drought mechanism-based indices: comprehensive comparison of their strengths and limitations. Stoch Env Res Risk Assess 30:119–136. https://doi.org/10.1007/s00477-015-1042-4
Liu X, Ren L, Yuan F, Xu J, Liu W (2012) Assessing vegetation response to drought in the Laohahe catchment, North China. Hydrol Res 43(1–2):91–101. https://doi.org/10.2166/nh.2011.134
Liu X, Zhu X, Pan Y, Bai J, Li S (2018) Performance of different drought indices for agriculture drought in the North China plain. J Arid Land 10:507–516. https://doi.org/10.1007/s40333-018-0005-2
Mavromatis T (2010) Use of drought indices in climate change impact assessment studies: an application to Greece. Int J Climatol 30(9):1336–1348
Miyan MA (2015) Droughts in Asian least developed countries: vulnerability and sustainability. Weather Clim Extremes 7:8–23. https://doi.org/10.1016/j.wace.2014.06.003
Narasimhan B, Srinivasan R (2005) Development and evaluation of soil moisture deficit index (SMDI) and evapotranspiration deficit index (ETDI) for agricultural drought monitoring. Agric Forest Meteorol 133(1–4):69–88
Othman SB (2011) Adaptation to climate change and reducing natural disaster risk: a study on country practices and lesson between Malaysia and Japan. Asian Disaster Reducation Centre, Kobe
Prakash A (2018) Boiling point. Finan Devel 55(3):22
Roberts MG, Dawe D, Falcon WP, Naylor RL (2009) El Niño-southern oscillation impacts on rice production in Luzon, the Philippines. J Appl Meteorol Climatol 48(8):1718–1724. https://doi.org/10.1175/2008JAMC1628.1
Sutton WR, Srivastava JP, Rosegrant M, Thurlow J, Sebastian L (2019a) Striking a balance: managing El Niño and La Niña in Vietnam’s agriculture. World Bank, Washington, DC
Sutton WR, Srivastava JP, Rosegrant M, Valmonte-Santos R, Ashwill M (2019b) Managing El Niño and La Niña in Philippines’ agriculture. World Bank, Washington, DC
Szép IJ, Mika J, Dunkel Z (2005) Palmer drought severity index as soil moisture indicator: physical interpretation, statistical behaviour and relation to global climate. Phys Chem Earth 30(1–3):231–243. https://doi.org/10.1016/j.pce.2004.08.039
Tangang F et al (2020) Projected future changes in rainfall in southeast Asia based on CORDEX–SEA multi-model simulations. Clim Dyn 55:1247–1267. https://doi.org/10.1007/s00382-020-05322-2
Teluguntla P, Gumma MK, Giri C, et al (2016) Global food security support analysis data at nominal 1km (GFSAD1km) derived from remote sensing in support of food security in the twenty-first century: current achievements and future possibilities. L Resour Monit Model Mapp with Remote Sens 131–159
Tilman D, Balzer C, Hill J, Befort BL (2011) Global food demand and the sustainable intensification of agriculture. PNAS 108:20260–20264. https://doi.org/10.1073/pnas.1116437108
Trenberth KE et al (2014) Global warming and changes in drought. Nat Clim Change 4:17–22. https://doi.org/10.1038/nclimate2067
United Nations Economic and Social Commission for Asia and the Pacific (UNESCAP) (2019) Ready for the dry years: building resilience to drought in South-East Asia, 1st edn. Bangkok
United Nations Economic and Social Commission for Asia and the Pacific (UNESCAP) (2020) Ready for the dry years: building resilience to drought in south-East Asia, 2nd edn. Bangkok
United Nations Office for the Coordination of Humanitarian Affairs (OCHA) (2015) Philippines: El Niño Snapshot (as of 05 October 2015), ReliefWeb, https://reliefweb.int/report/philippines/philippines-el-ni-o-snapshot-05-october-2015. Accessed 8 Dec 2020
Vasiliades L, Loukas A (2009) Hydrological response to meteorological drought using the palmer drought indices in Thessaly, Greece. Desalination 237(1–3):3–21. https://doi.org/10.1016/j.desal.2007.12.019
Venkatappa M, Anantsuksomsri S, Castillo JA, Smith B, Sasaki N (2020a) Mapping the natural distribution of bamboo and related carbon stocks in the tropics using google earth engine, phenological behavior, landsat 8, and sentinel-2. Remote Sens 12(18):3109. https://doi.org/10.3390/rs12183109
Venkatappa M, Sasaki N, Anantsuksomsri S, Smith B (2020b) Applications of the google earth engine and phenology-based threshold classification method for mapping forest cover and carbon stock changes in Siem Reap Province, Cambodia. Remote Sens 12(18):3110. https://doi.org/10.3390/RS12183110
Venkatappa M, Sasaki N, Shrestha RP, Tripathi NK, Ma H (2019) Determination of vegetation thresholds for assessing land use and land use changes in Cambodia using the google earth engine cloud-computing platform. Remote Sens 11(13):1514. https://doi.org/10.3390/rs11131514
Wojtys EM (2020) Striking a balance. Sports Health Multidiscipl Approach 2(1):10–11
World Meteorological Organization (WMO) (2020) The state of the global climate 2020. Geneva
Xulu S, Peerbhay K, Gebreslasie M, Ismail R (2018) Drought influence on forest plantations in Zululand, South Africa, using MODIS time series and climate data. Forests 9(9):1–15. https://doi.org/10.3390/f9090528
Yan D, Shi X, Yang Z, Li Y, Zhao K, Yuan Y (2013) Modified palmer drought severity index based on distributed hydrological simulation. Math Model Resour Environ Syst 8. https://doi.org/10.1155/2013/327374
Zhang J et al (2018) Effect of drought on agronomic traits of rice and wheat: a meta-analysis. Int J Environ Res Publ Health 15(5):839. https://doi.org/10.3390/ijerph15050839
Zhao T, Dai A (2017) Uncertainties in historical changes and future projections of drought part II: model-simulated historical and future drought changes. Clim Change 144:535–548. https://doi.org/10.1007/s10584-016-1742-x
Acknowledgements
This study was carried out under funding from the Economic Research Institute for ASEAN and East Asia (ERIA).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Appendix A
Appendix A
Table 1.5 Drought Intensities during the Crop-Growing Season by Country (Tables 1.6, 1.7, 1.8, 1.9, 1.10, 1.11, 1.12, 1.13 and 1.14).
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Venkatappa, M., Sasaki, N., Huang, J., Phoumin, H. (2021). Impacts of Climate Change on Agriculture in South-East Asia—Drought Conditions and Crop Damage Assessment. In: Phoumin, H., Taghizadeh-Hesary, F., Kimura, F., Arima, J. (eds) Energy Sustainability and Climate Change in ASEAN. Economics, Law, and Institutions in Asia Pacific. Springer, Singapore. https://doi.org/10.1007/978-981-16-2000-3_1
Download citation
DOI: https://doi.org/10.1007/978-981-16-2000-3_1
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-1999-1
Online ISBN: 978-981-16-2000-3
eBook Packages: Economics and FinanceEconomics and Finance (R0)