Abstract
The crop simulation model Decision Support System for Agrotechnology Transfer (DSSAT) was applied over the Mekong River Delta (MRD), Southern Vietnam, to assess future (2020–2050) impacts of climate change on rice production. The DSSAT model was driven using observed station data and projected climate data derived through the dynamical downscaling of three global climate models (GCMs) using the Weather Research and Forecasting (WRF) model. The WRF model was simulated at a spatial resolution of 30 km over the study region, and the large-scale driving fields for future climates were taken from the Coupled Model Inter-Comparison Project Phase 3 (CMIP3) global models ECHAM5, CCSM3, and MIROC5 under the A2 emission scenario. Rice growth during two main seasons, namely, the winter-spring (winter) and summer-autumn (summer), were selected to quantify impacts under both irrigated and rain-fed rice cultivation. The results from this climate-crop study suggest that under rain-fed conditions, winter rice yield was likely to experience nearly 24% reduction while summer rice yield was projected to decrease by about 49%. Without irrigation, the annual rice yield was projected to decrease by about 36.5%, and under irrigated conditions, climate change is likely to reduce annual irrigated rice yields by about 1.78%. Winter rice yield was likely to decrease by 4.7% while summer rice yield was projected to marginally increase by about 0.68%. Increasing temperatures and seasonal variations of precipitation are likely to significantly reduce rice yields under rain-fed condition. In addition, (1) a decrease (increase) in the number of rainy days during the dry (wet) season and (2) positive effects of elevated CO2 for rain-fed rice growth under each of the three WRF model realizations would markedly influence rice yields. With Vietnam being one of the largest exporters of rice, these findings have serious implications for the local agricultural sector. This also serves an early warning for the policymakers and stakeholders for effective planning of not only crop production but also water resource management. The findings call for prudent diversification strategy planning by those countries which import rice.
Similar content being viewed by others
References
Balkovič J, Van Der Velde M, Skalský R, Xiong W, Folberth C, Khabarov N, Smirnov A, Mueller ND, Obersteiner M (2014) Global wheat production potentials and management flexibility under the representative concentration pathways. Glob Planet Chang 122:107–121
Challinor A, Wheeler T, Craufurd P, Slingo J, Grimes D (2004) Design and optimisation of a large-area process-based model for annual crops. Agric For Meteorol 124:99–120
Chen F, Dudhia J (2001) Coupling an advanced land surface-hydrology model with the Penn State–NCAR MM5 modeling system. Part I: model implementation and sensitivity. Mon Wea Rev 129:569–585
Chen C, Qian C, Deng A, Zhang W (2012) Progressive and active adaptations of cropping system to climate change in Northeast China. Eur J Agron 38:94–103
Chun JA, Li S, Wang Q, Lee W-S, Lee E-J, Horstmann N, Park H, Veasna T, Vanndy L, Pros K, Vang S (2016) Assessing rice productivity and adaptation strategies for Southeast Asia under climate change through multi-scale crop modeling. Agric Syst 143:14–21
Deryng D, Sacks W, Barford C, Ramankutty N (2011) Simulating the effects of climate and agricultural management practices on global crop yield. Glob Biogeochem Cycles, 25
Elliott J, Kelly D, Chryssanthacopoulos J, Glotter M, Jhunjhnuwala K, Best N, Wilde M, Foster I (2014) The parallel system for integrating impact models and sectors (pSIMS). Environ Model Softw 62:509–516
Erda L, Wei X, Hui J, Yinlong X, Yue L, Liping B, Liyong X (2005) Climate change impacts on crop yield and quality with CO(2) fertilization in China. Phil Trans of the Roy Soc B: Bio Sci 360:2149–2154
Ewert F, Van Ittersum MK, Heckelei T, Therond O, Bezlepkina I, Andersen E (2011) Scale changes and model linking methods for integrated assessment of agri-environmental systems. Agric Ecosyst Environ 142:6–17
FAO (2009) FAO, World Health Organization. Available at the link: http://www.fao.org/docrep/012/i0680e/i0680e00.htm
General Statistics Office of Vietnam (2014) Statistical Documentation and Service Centre. Available at: https://www.gso.gov.vn/Default_en.aspx?tabid=491
Grell GA (1993) Prognostic evaluation of assumptions used by cumulus parameterizations. Mon Wea Rev 121:764–787
Hong SY, Noh Y, Dudhia J (2006) A new vertical diffusion package with an explicit treatment of entrainment processes. Mon Wea Rev. https://doi.org/10.1175/MWR3199.1
IRRI (2008) Annual report. Available at: http://irri.org/resources/publications/annual-reports/annual-report-2008
IRRI (2009) Annual report. Available at: http://irri.org/resources/publications/annual-reports/annual-report-2009
Jones JW, Hoogenboom G, Porter CH, Boote KJ, Batchelor WD, Hunt L, Wilkens PW, Singh U, Gijsman AJ, Ritchie JT (2003) The DSSAT cropping system model. Eur J Agron 18:235–265
Keating BA, Carberry PS, Hammer GL, Probert ME, Robertson MJ, Holzworth D, Huth NI, Hargreaves JN, Meinke H, Hochman Z (2003) An overview of APSIM, a model designed for farming systems simulation. Eur J Agron 18:267–288
Lang NT, Russell R, Ismail AM, NV H, Tam BP, Ha PTT, Binh NHT, Tru PC, Nhien TT, Phuoc NT, Buu BC, Wassman R (2015) Selection of submergence and salinity tolerance rice varieties to support the Vietnamese Mekong Delta. Vietnamese Agriculture Publisher, 377
Li S, Wang Q, Chun JA (2017) Impact assessment of climate change on rice productivity in the Indochinese Peninsula using a regional-scale crop model. Int J Climatol 37:1147–1160. https://doi.org/10.1002/joc.5072
Lobell DB, Schlenker W, Costa-Roberts J (2011) Climate trends and global crop production since 1980. Science 333:616–620
Manzanilla D, Paris T, Vergara G, Ismail A, Pandey S, Labios R, Tatlonghari G, Acda R, Chi T, Duoangsila K (2011) Submergence risks and farmers’ preferences: implications for breeding Sub1 rice in Southeast Asia. Agric Syst 104:335–347
Mlawer EJ, Taubman SJ, Brown PD, Iacono MJ, Clough SA (1997) Radiative transfer for inhomogeneous atmosphere: RRTM, a validated correlated-k model for the longwave. J Geophys Res 102(D14):16663–16682
Naresh Kumar S, Aggarwal P, Saxena R, Rani S, Jain S, Chauhan N (2013) An assessment of regional vulnerability of rice to climate change in India. Clim Chang 118:683–699
Olesen JE, Trnka M, Kersebaum K, Skjelvåg A, Seguin B, Peltonen-Sainio P, Rossi F, Kozyra J, Micale F (2011) Impacts and adaptation of European crop production systems to climate change. Eur J Agron 34:96–112
Osborne TM and Wheeler TR (2013) Evidence for a climate signal in trends of global crop yield variability over the past 50 years, Environ Res Lett, 8 (2), doi: https://doi.org/10.1088/1748
Parry ML (2007) Climate change 2007-impacts, adaptation and vulnerability: working group II contribution to the fourth assessment report of the IPCC, Cambridge University Press
Piao S, Ciais P, Huang Y, Shen Z, Peng S, Li J, Zhou L, Liu H, Ma Y, Ding Y, Friedlingstein P, Liu C, Tan K, Yu Y, Zhang T, Fang J (2010) The impacts of climate change on water resources and agriculture in China. Nature 467:43–51
Raghavan VS, Vu MT, Liong SY (2015) Regional climate simulations over Vietnam using the WRF model. Theor Appl Climatol 126:161–182. https://doi.org/10.1007/s00704-015-1557-0
Tatsumi K, Yamashiki Y, Valmir Da Silva R, Takara K, Matsuoka Y, Takahashi K, Maruyama K, Kawahara N (2011) Estimation of potential changes in cereals production under climate change scenarios. Hydrol Process 25:2715–2725
Thompson G, Rasmussen RM, Manning K (2004) Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part I: Description and sensitivity analysis. Mon Wea Rev 132:519–542
Tsuji GY, Uehara G, Balas S (1994) DSSAT version 3, Citeseer
Van Wart J, Kersebaum KC, Peng S, Milner M, Cassman KG (2013) Estimating crop yield potential at regional to national scales. Field Crop Res 143:34–43
Weiss J (2009) The economics of climate change in Southeast Asia: a regional review. © Asian Development Bank. http://hdl.handle.net/11540/179. License: CC BY 3.0 IGO
Xiong W, Skalský R, Porter CH, Balkovič J, Jones JW, Yang D (2016) Calibration-induced uncertainty of the EPIC model to estimate climate change impact on global maize yield. Journal of Advances in Modeling Earth Systems 8(3):1358–1375
Yao F, Xu Y, Lin E, Yokozawa M, Zhang J (2007) Assessing the impacts of climate change on rice yields in the main rice areas of China. Clim Chang 80:395–409
Acknowledgments
This research was supported by the National Research Foundation Singapore under its Campus for Research Excellence and Technological Enterprise program. The Center for Environmental Sensing and Modeling is an interdisciplinary research group of the Singapore MIT Alliance for Research and Technology.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Jiang, Z., Raghavan, S.V., Hur, J. et al. Future changes in rice yields over the Mekong River Delta due to climate change—Alarming or alerting?. Theor Appl Climatol 137, 545–555 (2019). https://doi.org/10.1007/s00704-018-2617-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00704-018-2617-z