Abstract
It is well established that the adverse impact of climate change is going to affect every aspect of the ecosystem in the hilly terrain of the Himalayan region. Therefore, it is inevitable to assess the climate change impact on crop yield over the hilly terrain to undertake sustainable adaptation and agricultural practices. In the present study, Sikkim is considered as a study area, and crop simulation for three different crops (rice, wheat and maize) is carried out using calibrated AquaCrop with an available baseline dataset of 17 years (1998–2015). The future projections of different crop yields are obtained by using bias-corrected climate scenarios from four different global climate models (GCMs) under two different emission scenarios. Moreover, the uncertainty associated with the GCM and emission scenario is examined through the possibility theory. The outcomes from the simulations indicate an increase in the mean percentage change in the yield (0.5% to 20% for rice, 2% to 44% for wheat and 10% to 25% for maize) over Sikkim during 2021–2099. The increase in the mean yield can be attributed to the suitable temperature profile, increase in the CO2 concentration, high elevation of the study area and no significant water stress during the growing seasons of different crops and using the possibility approach indicates that during the recent past (2006–2015), the stabilized scenario is prevailing over the high emission scenarios in most of the cases. Our results facilitate the water and agricultural manager for considering proper and robust adaptation measures to ensure sustainability.
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References
Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration-guidelines for computing crop water requirements-FAO irrigation and drainage paper 56. Fao, Rome 300:D05109
Andarzian B, Hoogenboom G, Bannayan M, Shirali M, Andarzian B (2015) Determining optimum sowing date of wheat using CSM-CERES-wheat model. J Saudi Soc Agric Sci 14:189–199. https://doi.org/10.1016/j.jssas.2014.04.004
ASSOCHAM Report (2016) Drought situation to cost Rs 6.5 lakh crore to economy
Azhoni A, Goyal MK (2018) Diagnosing climate change impacts and identifying adaptation strategies by involving key stakeholder organisations and farmers in Sikkim, India: challenges and opportunities. Sci Total Environ 626:468–477. https://doi.org/10.1016/j.scitotenv.2018.01.112
Baker JT, Allen LH (1993) Contrasting crop species responses to CO2 and temperature: rice, soybean and citrus. Vegetatio 104–105:239–260. https://doi.org/10.1007/BF00048156
Basnet B, Avasthe R, Bhutia K (2003) Present status of maize cultivation in Sikkim and future strategies. ENVIS Bull Ecol 11(1):17–25
Bhatt D, Maskey S, Babel MS, Uhlenbrook S, Prasad KC (2014) Climate trends and impacts on crop production in the Koshi River basin of Nepal. Reg Environ Chang 14:1291–1301. https://doi.org/10.1007/s10113-013-0576-6
Biemans H, Speelman LH, Ludwig F, Moors EJ, Wiltshire AJ, Kumar P, Gerten D, Kabat P (2013) Future water resources for food production in five South Asian river basins and potential for adaptation - a modeling study. Sci Total Environ 468–469:S117–S131. https://doi.org/10.1016/j.scitotenv.2013.05.092
Block PJ, Souza Filho FA, Sun L, Kwon H-H (2009) A Streamflow forecasting framework using multiple climate and hydrological models. JAWRA J Am Water Resour Assoc 45:828–843. https://doi.org/10.1111/j.1752-1688.2009.00327.x
Boote KJ, Allen LH, Prasad PVV et al (2005) Elevated temperature and CO2 impacts on pollination, reproductive growth, and yield of several globally important crops. J Agric Meteorol 60:469–474. https://doi.org/10.2480/agrmet.469
Clark MP, Wilby RL, Gutmann ED, Vano JA, Gangopadhyay S, Wood AW, Fowler HJ, Prudhomme C, Arnold JR, Brekke LD (2016) Characterizing uncertainty of the hydrologic impacts of climate change. Curr Clim Chang Rep 2:55–64. https://doi.org/10.1007/s40641-016-0034-x
Confalonieri R, Bellocchi G, Bregaglio S, Donatelli M, Acutis M (2010) Comparison of sensitivity analysis techniques: a case study with the rice model WARM. Ecol Model 221:1897–1906. https://doi.org/10.1016/j.ecolmodel.2010.04.021
Crawford AJ, McLachlan DH, Hetherington AM, Franklin KA (2012) High temperature exposure increases plant cooling capacity. Curr Biol 22:R396–R397. https://doi.org/10.1016/j.cub.2012.03.044
Das J, Jha S, Goyal MK, Surampalli RY (2020) Challenges of sustainability in agricultural management. In: Sustainability. pp 339–356
Das J, Umamahesh NV (2017) Uncertainty and nonstationarity in streamflow extremes under climate change scenarios over a river basin. J Hydrol Eng 22:04017042. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001571
Das J, Umamahesh NV (2018) Assessment of uncertainty in estimating future flood return levels under climate change. Nat Hazards 93:1–16. https://doi.org/10.1007/s11069-018-3291-2
Das J, Treesa A, Umamahesh NV (2018) Modelling impacts of climate change on a river basin: analysis of uncertainty using rea & possibilistic approach. Water Resour Manag. https://doi.org/10.1007/s11269-018-2046-x
Deb P, Kiem AS, Babel MS, Chu ST, Chakma B (2015a) Evaluation of climate change impacts and adaptation strategies for maize cultivation in the Himalayan foothills of India. J Water Clim Chang 6:596–614. https://doi.org/10.2166/wcc.2015.070
Deb P, Shrestha S, Babel MS (2015b) Forecasting climate change impacts and evaluation of adaptation options for maize cropping in the hilly terrain of Himalayas: Sikkim, India. Theor Appl Climatol 121:649–667. https://doi.org/10.1007/s00704-014-1262-4
Dubey SK, Sharma D (2018) Assessment of climate change impact on yield of major crops in the Banas River basin, India. Sci Total Environ 635:10–19. https://doi.org/10.1016/j.scitotenv.2018.03.343
Eyshi Rezaei E, Webber H, Gaiser T, Naab J, Ewert F (2015) Heat stress in cereals: mechanisms and modelling. Eur J Agron 64:98–113. https://doi.org/10.1016/j.eja.2014.10.003
FAO (2016) The state of food and agriculture: climate change, Agriculture and Food Security
Foley JA, Ramankutty N, Brauman KA, Cassidy ES, Gerber JS, Johnston M, Mueller ND, O’Connell C, Ray DK, West PC, Balzer C, Bennett EM, Carpenter SR, Hill J, Monfreda C, Polasky S, Rockström J, Sheehan J, Siebert S, Tilman D, Zaks DPM (2011) Solutions for a cultivated planet. Nature 478:337–342. https://doi.org/10.1038/nature10452
Ghosh S, Mujumdar PP (2009) Climate change impact assessment: uncertainty modeling with imprecise probability. J Geophys Res 114:D18113. https://doi.org/10.1029/2008JD011648
Giorgi F, Mearns LO (2003) Probability of regional climate change based on the reliability ensemble averaging (REA) method. Geophys Res Lett 30:2–5. https://doi.org/10.1029/2003GL017130
Goswami UP, Bhargav K, Hazra B, Goyal MK (2017) Spatiotemporal and joint probability behavior of temperature extremes over the Himalayan region under changing climate. Theor Appl Climatol 134:1–22. https://doi.org/10.1007/s00704-017-2288-1
Government of Sikkim (2012) The Sikkim State Action Plan on Climate Change
Government of Sikkim (2013) Annual Progress Report
Höllermann B, Evers M (2017) Perception and handling of uncertainties in water management—a study of practitioners’ and scientists’ perspectives on uncertainty in their daily decision-making. Environ Sci Pol 71:9–18. https://doi.org/10.1016/j.envsci.2017.02.003
IPCC (2013) Climate change 2013 - the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge
Jamieson PD, Porter JR, Wilson DR (1991) A test of the computer simulation model ARCWHEAT1 on wheat crops grown in New Zealand. Field Crop Res 27:337–350. https://doi.org/10.1016/0378-4290(91)90040-3
Jha S, Das J, Sharma A, Hazra B, Goyal MK (2019) Probabilistic evaluation of vegetation drought likelihood and its implications to resilience across India. Glob Planet Change 176:23–35. https://doi.org/10.1016/j.gloplacha.2019.01.014
Johnson F, Sharma A (2011) Accounting for interannual variability: a comparison of options for water resources climate change impact assessments. Water Resour Res 47. https://doi.org/10.1029/2010WR009272
Kimball BA (1983) Carbon dioxide and agricultural yield: an assemblage and analysis of 430 prior observations 1. Agron J 75:779–788. https://doi.org/10.2134/agronj1983.00021962007500050014x
Kumar KR, Sahai AK, Kumar KK et al (2006) High-resolution climate change scenarios for India for the 21st century. Curr Sci 90:334–345
Lin BB (2011) Resilience in agriculture through crop diversification: adaptive management for environmental change. Bioscience 61:183–193. https://doi.org/10.1525/bio.2011.61.3.4
Lobell DB, Gourdji SM (2012) The influence of climate change on global crop productivity. Plant Physiol 160:1686–1697. https://doi.org/10.1104/pp.112.208298
Lobell DB, Burke MB, Tebaldi C et al (2008) Prioritizing climate change adaptation needs for food security in 2030. Science (80- ) 319:607–610. https://doi.org/10.1126/science.1152339
Lobell DB, Schlenker W, Costa-Roberts J (2011) Climate trends and global crop production since 1980. Science (80- ) 333:616–620. https://doi.org/10.1126/science.1204531
MAFW (Ministry of Agriculture and Farmers’ Welfare) (2016) Pocket book of agriculture statistics 2016, New Delhi
Mall RK, Gupta A, Sonkar G (2017) Effect of climate change on agricultural crops. In: Current Developments in Biotechnology and Bioengineering. Elsevier, pp 23–46
Mearns LO, Rosenzweig C, Goldberg R (1996) The effect of changes in daily and interannual climatic variability on ceres-wheat: a sensitivity study. Clim Chang 32:257–292. https://doi.org/10.1007/BF00142465
Mujumdar PP, Ghosh S (2008) Modeling GCM and scenario uncertainty using a possibilistic approach: application to the Mahanadi River, India. Water Resour Res 44. https://doi.org/10.1029/2007WR006137
Najafi R, Hessami Kermani MR (2017) Uncertainty modeling of statistical downscaling to assess climate change impacts on temperature and precipitation. Water Resour Manag 31:1843–1858. https://doi.org/10.1007/s11269-017-1615-8
New M, Hulme M (2000) Representing uncertainty in climate change scenarios: a Monte-Carlo approach. Integr Assess 1:203–213. https://doi.org/10.1023/A:1019144202120
Peng S, Huang J, Sheehy JE, Laza RC, Visperas RM, Zhong X, Centeno GS, Khush GS, Cassman KG (2004) Rice yields decline with higher night temperature from global warming. Proc Natl Acad Sci 101:9971–9975. https://doi.org/10.1073/pnas.0403720101
Porter JR, Gawith M (1999) Temperatures and the growth and development of wheat: a review. Eur J Agron 10:23–36. https://doi.org/10.1016/S1161-0301(98)00047-1
Prasad PVV, Pisipati SR, Ristic Z, Bukovnik U, Fritz AK (2008) Impact of nighttime temperature on physiology and growth of spring wheat. Crop Sci 48:2372–2380. https://doi.org/10.2135/cropsci2007.12.0717
Raes D, Steduto P, Hsiao TC, Fereres E (2009) AquaCropThe FAO crop model to simulate yield response to water: II. Main algorithms and software description. Agron J 101:438–447. https://doi.org/10.2134/agronj2008.0140s
Ray DK, Mueller ND, West PC, Foley JA (2013) Yield trends are insufficient to double global crop production by 2050. PLoS One 8:e66428. https://doi.org/10.1371/journal.pone.0066428
Riahi K, Rao S, Krey V, Cho C, Chirkov V, Fischer G, Kindermann G, Nakicenovic N, Rafaj P (2011) RCP 8.5—a scenario of comparatively high greenhouse gas emissions. Clim Chang 109:33–57. https://doi.org/10.1007/s10584-011-0149-y
Rojas R, Feyen L, Dosio A, Bavera D (2011) Improving pan-European hydrological simulation of extreme events through statistical bias correction of RCM-driven climate simulations. Hydrol Earth Syst Sci 15:2599–2620. https://doi.org/10.5194/hess-15-2599-2011
Roudier P, Sultan B, Quirion P, Berg A (2011) The impact of future climate change on West African crop yields: what does the recent literature say? Glob Environ Chang 21:1073–1083. https://doi.org/10.1016/j.gloenvcha.2011.04.007
Saltelli A, Tarantola S, Campolongo F (2000) Sensitivity analysis as an ingredient of modeling. Stat Sci 15:377–395. https://doi.org/10.1214/ss/1009213004
Sánchez B, Rasmussen A, Porter JR (2014) Temperatures and the growth and development of maize and rice: a review. Glob Chang Biol 20:408–417. https://doi.org/10.1111/gcb.12389
Shackley S, Young P, Parkinson S, Wynne B (1998) Uncertainty, complexity and concepts of good science in climate change modelling: are GCMs the best tools? Clim Chang 38:159–205. https://doi.org/10.1023/A:1005310109968
Simonovic SP (2017) Bringing future climatic change into water resources management practice today. Water Resour Manag 31:2933–2950. https://doi.org/10.1007/s11269-017-1704-8
Singh D, Tsiang M, Rajaratnam B, Diffenbaugh NS (2014) Observed changes in extreme wet and dry spells during the South Asian summer monsoon season. Nat Clim Chang 4:456–461. https://doi.org/10.1038/nclimate2208
Spott M (1999) A theory of possibility distributions. Fuzzy Sets Syst 102:135–155. https://doi.org/10.1016/S0165-0114(97)00102-4
Srivastava P, Singh R, Tripathi S, Raghubanshi AS (2016) An urgent need for sustainable thinking in agriculture – an Indian scenario. Ecol Indic 67:611–622. https://doi.org/10.1016/j.ecolind.2016.03.015
Steduto P, Albrizio R (2005) Resource use efficiency of field-grown sunflower, sorghum, wheat and chickpea. Agric For Meteorol 130:269–281. https://doi.org/10.1016/j.agrformet.2005.04.003
Steduto P, Hsiao TC, Raes D, Fereres E (2009) AquaCrop—the FAO crop model to simulate yield response to water: I. Concepts and Underlying Principles. Agron J 101:426–437. https://doi.org/10.2134/agronj2008.0139s
Subash N, Singh SS, Priya N (2013) Observed variability and trends in extreme temperature indices and rice–wheat productivity over two districts of Bihar, India—a case study. Theor Appl Climatol 111:235–250. https://doi.org/10.1007/s00704-012-0665-3
Tao F, Yokozawa M, Hayashi Y, Lin E (2003) Future climate change, the agricultural water cycle, and agricultural production in China. Agric Ecosyst Environ 95:203–215. https://doi.org/10.1016/S0167-8809(02)00093-2
Telwala Y, Brook BW, Manish K, Pandit MK (2013) Climate-induced elevational range shifts and increase in plant species richness in a Himalayan biodiversity epicentre. PLoS One 8:e57103. https://doi.org/10.1371/journal.pone.0057103
Teutschbein C, Seibert J (2012) Bias correction of regional climate model simulations for hydrological climate-change impact studies: review and evaluation of different methods. J Hydrol 456–457:12–29. https://doi.org/10.1016/j.jhydrol.2012.05.052
Thomson AM, Calvin KV, Smith SJ, Kyle GP, Volke A, Patel P, Delgado-Arias S, Bond-Lamberty B, Wise MA, Clarke LE, Edmonds JA (2011) RCP4.5: a pathway for stabilization of radiative forcing by 2100. Clim Chang 109:77–94. https://doi.org/10.1007/s10584-011-0151-4
Tubiello FN, Ewert F (2002) Simulating the effects of elevated CO2 on crops: approaches and applications for climate change. Eur J Agron 18:57–74. https://doi.org/10.1016/S1161-0301(02)00097-7
Vanuytrecht E, Raes D, Steduto P, Hsiao TC, Fereres E, Heng LK, Garcia Vila M, Mejias Moreno P (2014a) AquaCrop: FAO’s crop water productivity and yield response model. Environ Model Softw 62:351–360. https://doi.org/10.1016/j.envsoft.2014.08.005
Vanuytrecht E, Raes D, Willems P (2014b) Global sensitivity analysis of yield output from the water productivity model. Environ Model Softw 51:323–332. https://doi.org/10.1016/j.envsoft.2013.10.017
Wang X, Cai J, Jiang D, Liu F, Dai T, Cao W (2011) Pre-anthesis high-temperature acclimation alleviates damage to the flag leaf caused by post-anthesis heat stress in wheat. J Plant Physiol 168:585–593. https://doi.org/10.1016/j.jplph.2010.09.016
Zadeh LA (1999) Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets Syst 100:9–34. https://doi.org/10.1016/S0165-0114(99)80004-9
Zhao C, Liu B, Piao S, Wang X, Lobell DB, Huang Y, Huang M, Yao Y, Bassu S, Ciais P, Durand JL, Elliott J, Ewert F, Janssens IA, Li T, Lin E, Liu Q, Martre P, Müller C, Peng S, Peñuelas J, Ruane AC, Wallach D, Wang T, Wu D, Liu Z, Zhu Y, Zhu Z, Asseng S (2017) Temperature increase reduces global yields of major crops in four independent estimates. Proc Natl Acad Sci 114:9326–9331. https://doi.org/10.1073/pnas.1701762114
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The authors would like to thank the handling editor and three anonymous reviewers for the constructive and insightful comments to improve the manuscript significantly.
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The authors received the financial support provided by the Department of Science & Technology (DST), Government of India under research project DST/CCP/MRDP/98/2017(G).
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Das, J., Poonia, V., Jha, S. et al. Understanding the climate change impact on crop yield over Eastern Himalayan Region: ascertaining GCM and scenario uncertainty. Theor Appl Climatol 142, 467–482 (2020). https://doi.org/10.1007/s00704-020-03332-y
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DOI: https://doi.org/10.1007/s00704-020-03332-y