Vulnerability Assessment of Wheat Yield Under Warming Climate in Northern India Using Multi-model Projections

被引:5
|
作者
Patel, Shubhi [1 ,2 ]
Mall, R. K. [1 ]
Jaiswal, Rohit [1 ]
Singh, Rakesh [1 ,2 ]
Chand, Ramesh [1 ,3 ]
机构
[1] Banaras Hindu Univ, DST Mahamana Ctr Excellence Climate Change Res, Inst Environm & Sustainable Dev, Varanasi, Uttar Pradesh, India
[2] Banaras Hindu Univ, Inst Agr Sci, Dept Agr Econ, Varanasi, Uttar Pradesh, India
[3] Banaras Hindu Univ, Inst Agr Sci, Dept Mycol & Plant Pathol, Varanasi, Uttar Pradesh, India
关键词
Wheat; Climate change; Multi-model projection; CERES-wheat; Model uncertainty; Impact assessment; DIVERSE AGROCLIMATIC ZONES; CERES-WHEAT; CHANGE IMPACT; FUTURE CLIMATE; FOOD SECURITY; UTTAR-PRADESH; RICE YIELD; MODEL; CROP; UNCERTAINTY;
D O I
10.1007/s42106-022-00208-1
中图分类号
S3 [农学(农艺学)];
学科分类号
0901 ;
摘要
Climate change impact on crop production using different climate model projections varies considerably and it is challenging to choose a suitable climate scenario for risk assessment. This study aims to assess the climate change impact on the wheat crop in nine agro-climatic zones (ACZs) of Uttar Pradesh (UP) in Northern India using the CERES-Wheat crop model, driven by high resolution projected climate from different regional climate models (RCMs). The results show that the vegetative growth period would be shortened across the ACZs and scenarios where higher reductions will be witnessed under RCP 8.5 viz., up to 10 days in the 2050s (2040-2069), and 14 days in the 2080s (2070-2099). Also, in the 2080s shortening up to 17 days will be observed in the total growth period under RCP 8.5. When elevated CO2 concentration was not considered the wheat yields were found to reduce up to 20.5 and 30% under RCP 4.5 and RCP 8.5, respectively, in the 2050s. In the 2080s, the losses will be more pronounced reaching up to 41.5% under RCP 8.5. With the consideration of CO2, the yield reductions will be up to 14 and 18% under RCP 4.5 and RCP 8.5 respectively in the 2080s. Uncertainty associated with climate model revealed that ACCESS 1-0 and MPI-ESM-LR predicted higher mean yield reductions while CNRM-CM5 has shown a mild effect. Present study concluded that eastern UP is a vulnerable region for wheat production in the 21st century. The results suggest that there is an urgent need for developing suitable adaptation strategies to ameliorate the adverse effects on wheat production in UP through regional policy planning.
引用
收藏
页码:611 / 626
页数:16
相关论文
共 50 条
  • [21] Multi-model projections of tree species performance in Quebec, Canada under future climate change
    Boulanger, Yan
    Pascual, Jesus
    Bouchard, Mathieu
    D'Orangeville, Loic
    Perie, Catherine
    Girardin, Martin P.
    GLOBAL CHANGE BIOLOGY, 2022, 28 (05) : 1884 - 1902
  • [22] A multi-model assessment of inequality and climate change
    Emmerling, Johannes
    Andreoni, Pietro
    Charalampidis, Ioannis
    Dasgupta, Shouro
    Dennig, Francis
    Feindt, Simon
    Fragkiadakis, Dimitris
    Fragkos, Panagiotis
    Fujimori, Shinichiro
    Gilli, Martino
    Grottera, Carolina
    Guivarch, Celine
    Kornek, Ulrike
    Kriegler, Elmar
    Malerba, Daniele
    Marangoni, Giacomo
    Mejean, Aurelie
    Nijsse, Femke
    Piontek, Franziska
    Simsek, Yeliz
    Soergel, Bjoern
    Taconet, Nicolas
    Vandyck, Toon
    Young-Brun, Marie
    Zhao, Shiya
    Zheng, Yu
    Tavoni, Massimo
    NATURE CLIMATE CHANGE, 2024, 14 (12) : 1254 - 1260
  • [23] Oxygen and indicators of stress for marine life in multi-model global warming projections
    Cocco, V.
    Joos, F.
    Steinacher, M.
    Froelicher, T. L.
    Bopp, L.
    Dunne, J.
    Gehlen, M.
    Heinze, C.
    Orr, J.
    Oschlies, A.
    Schneider, B.
    Segschneider, J.
    Tjiputra, J.
    BIOGEOSCIENCES, 2013, 10 (03) : 1849 - 1868
  • [24] Climate change impact assessment on hydropower generation using multi-model climate ensemble
    Chilkoti, Vinod
    Bolisetti, Tirupati
    Balachandar, Ram
    RENEWABLE ENERGY, 2017, 109 : 510 - 517
  • [25] Statistical multi-model climate projections of surface ocean waves in Europe
    Perez, Jorge
    Menendez, Melisa
    Camus, Paula
    Mendez, Fernando J.
    Losada, Inigo J.
    OCEAN MODELLING, 2015, 96 : 161 - 170
  • [26] Impacts of Climate Change on Peanut Yield in China Simulated by CMIP5 Multi-Model Ensemble Projections
    Xu, Hanqing
    Tian, Zhan
    Zhong, Honglin
    Fan, Dongli
    Shi, Runhe
    Niu, Yilong
    He, Xiaogang
    Chen, Maosi
    REMOTE SENSING AND MODELING OF ECOSYSTEMS FOR SUSTAINABILITY XIV, 2017, 10405
  • [27] Quantifying climate change impacts on hydropower production under CMIP6 multi-model ensemble projections using SWAT model
    Yalcin, Emrah
    HYDROLOGICAL SCIENCES JOURNAL, 2023, 68 (13) : 1915 - 1936
  • [28] Climate change projections for Switzerland based on a Bayesian multi-model approach
    Fischer, A. M.
    Weigel, A. P.
    Buser, C. M.
    Knutti, R.
    Kuensch, H. R.
    Liniger, M. A.
    Schaer, C.
    Appenzeller, C.
    INTERNATIONAL JOURNAL OF CLIMATOLOGY, 2012, 32 (15) : 2348 - 2371
  • [29] Bayesian multi-model projections of climate: generalization and application to ENSEMBLES results
    Buser, C. M.
    Kuensch, H. R.
    Schaer, C.
    CLIMATE RESEARCH, 2010, 44 (2-3) : 227 - 241
  • [30] Climate change projections of medicanes with a large multi-model ensemble of regional climate models
    Romera, Raquel
    Angel Gaertner, Miguel
    Sanchez, Enrique
    Dominguez, Marta
    Jesus Gonzalez-Aleman, Juan
    Miglietta, Mario Marcello
    GLOBAL AND PLANETARY CHANGE, 2017, 151 : 134 - 143