Abstract
Model simulation is an important way to study the effects of climate change on agriculture. Such assessment is subject to a range of uncertainties because of either incomplete knowledge or model technical uncertainties, impeding effective decision-making to climate change. On the basis of uncertainties in the impact assessment at different levels, this article systematically summarizes the sources and propagation of uncertainty in the assessment of the effect of climate change on agriculture in terms of the climate projection, the assessment process, and the crop models linking to climate models. Meanwhile, techniques and methods focusing on different levels and sources of uncertainty and uncertainty propagation are introduced, and shortcomings and insufficiencies in uncertainty processing are pointed out. Finally, in terms of how to accurately assess the effect of climate change on agriculture, improvements to further decrease potential uncertainty are suggested.
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References
Wang S W. The global warming debate. Chinese Sci Bull, 2010, 55: 1961–1962
Ge Q S, Wang S W, Fang X Q. An uncertainty analysis of understanding on climate change (in Chinese). Geogr Res, 2010, 29: 191–203
Parry M, Rosenzweig C, Iglesias A, et al. Climate change and world food security: A new assessment. Glob Environ Change, 1999, 9(Suppl 1): S51–S67
Yao F M, Xu Y L, Lin E D, et al. Assessing the impacts of climate change on rice yields in the main rice areas of China. Clim Change, 2007, 80: 395–409
Tubiello F N, Soussana J F, Howden S M. Crop and pasture response to climate change. Proc Natl Acad Sci USA, 2007, 104: 19686–19690
IPCC. 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: Cambridge University Press, 2007
Yin C H, Yan X D, Shi Z G, et al. Simulation of the climatic effects of natural forcings during the pre-industrial era. Chinese Sci Bull, 2007, 52: 1545–1558
Murphy J M, Sexton D M H, Barnett D N, et al. Quantification of modelling uncertainties in a large ensemble of climate change simulations. Nature, 2004, 430: 768–772
Dubrovsky M, Zalud Z, Stastna M. Sensitivity of ceres-maize yields to statistical structure of daily weather series. Clim Change, 2000, 46: 447–472
Challinor A J, Wheeler T R, Slingo J M, et al. Quantification of physical and biological uncertainty in the simulation of the yield of a tropical crop using present-day and doubled CO2 climates. Phil Trans R Soc B, 2005, 360: 2085–2094
Walker W E, Harremoës P, Rotmans J, et al. Defining uncertainty: A conceptual basis for uncertainty management in model-based decision support. Integr Assess, 2004, 4: 5–17
Chen P Q, Cheng B B, Wang F, et al. Discrimination on several key Issues of global climate change (in Chinese). Adv Earth Sci, 2010, 25: 69–75
Yin Y Y, Wang G X. Climate Change Impact Assessment: Methods and Applications (in Chinese). Beijing: Higher Education Press, 2004. 1–311
Lin E D, Liu Y J. Advance in new scenarios of greenhouse gas emission and climate change (in Chinese). Sci Agr Sin, 2008, 41: 1700–1707
Zhang X Q, Peng L L, Lin Z H. Progress on the projections of future climate change with various emission scenarios (in Chinese). Adv Earth Sci, 2008, 23: 174–185
Moss R H, Edmonds J A, Hibbard K A, et al. The next generation of scenarios for climate change research and assessment. Nature, 2010, 463: 747–756
Luo Y. Uncertainty in the science of climate change and futuristic priority research directions (in Chinese). Recent Develop World Seismol, 1997, 17: 2–6
Qin D H, Chen Z L, Luo Y, et al. Updated understanding of climate change sciences (in Chinese). Adv Clim Change Res, 2007, 3: 63–73
Du J. Present situation and prospects of ensemble numerical prediction (in Chinese). Quart J Appl Meteorl, 2002, 13: 16–28
Sun N. Application of crop growth modeling in assessing climate change impact on crop productivity (in Chinese). Earth Sci Front, 2002, 9: 232
Lin E D, Xiong W, Ju H, et al. Climate change impacts on crop yield and quality with CO2 fertilization in China. Phil Trans R Soc B, 2005, 360: 2149–2154
Long S P, Ainsworth E A, Leakey A D B, et al. Food for thought: Lower-than-expected crop yield stimulation with rising CO2 concentrations. Science, 2006, 312: 1918–1921
Tubiello F N, Amthor J S, Boote K J, et al. 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, 2007, 26: 215–223
Leakey A D B, Ainsworth E A, Bernacchi C J, et al. Elevated CO2 effects on plant carbon, nitrogen, and water relations: Six important lessons from FACE. J Exp Bot, 2009, 60: 2859–2876
Zhang T, Zhu J, Yang X. Non-stationary thermal time accumulation reduces the predictability of climate change effects on agriculture. Agric For Meteorol, 2008, 148: 1412–1418
Todorovic M, Albrizio R, Zivotic L, et al. Assessment of AquaCrop, CropSyst, and WOFOST Models in the simulation of sunflower growth under different water regimes. Agron J, 2009, 101: 509–521
Aggarwal P K, Mall R K. Climate change and rice yields in diverse agro-environments of India. II. Effect of uncertainties in scenarios and crop models on impact assessment. Clim Change, 2002, 52: 331–343
Bachelet D, Gay C A. The impacts of climate change on rice yield—comparison of 4 model performances. Ecol Model, 1993, 65: 71–93
Challinor A J, Wheeler T R. Crop yield reduction in the tropics under climate change: Processes and uncertainties. Agric For Meteorol, 2008, 148: 343–356
Ewert F, Rodriguez D, Jamieson P, et al. Effects of elevated CO2 and drought on wheat: Testing crop simulation models for different experimental and climatic conditions. Agric Ecosyst Environ, 2002, 93: 249–266
Matthews R, Wassmann R. Modelling the impacts of climate change and methane emission reductions on rice production: A review. Eur J Agron, 2003, 19: 573–598
Mearns L O, Mavromatis T, Tsvetsinskaya E, et al. Comparative responses of EPIC and CERES crop models to high and low spatial resolution climate change scenarios. J Geophys Res Atmos, 1999, 104: 6623–6646
Cipra B. Revealing uncertainties in computer models. Science, 2000, 287: 960–961
Sadras V O, Calviño P A, Quantification of grain yield response to soil depth in soybean, maize, sunflower, and wheat. Agron J, 2001: 577–583
Wang J, Wang E L, Luo Q Y, et al. Modelling the sensitivity of wheat growth and water balance to climate change in Southeast Australia. Clim Change, 2009, 96: 79–96
Easterling W E, Chen X F, Hays C, et al. Improving the validation of model-simulated crop yield response to climate change: An application to the EPIC model. Clim Res, 1996,: 263–273
Carbone G J, Mearns L O, Mavromatis T, et al. Evaluating CROPGRO-Soybean performance for use in climate impact studies. Agron J, 2003, 95: 537–544
Lenz-Wiedemann V I S, Klar C W, Schneider K. Development and test of a crop growth model for application within a global change decision support system. Ecol Model, 2010, 221: 314–329
Weiss A, Wilhelm W. The circuitous path to the comparison of simulated values from crop models with field observations. J Agric Sci, 2006, 144: 475–488
Gregory P J, Johnson S N, Newton A C, et al. Integrating pests and pathogens into the climate change/food security debate. J Exp Bot, 2009, 60: 2827–2838
White J W, Boote K J, Hoogenboom G, et al. Regression-based evaluation of ecophysiological models. Agron J, 2007, 99: 419–427
Lobell D B, Asner G P. Climate and management contributions to recent trends in US agricultural yields. Science, 2003, 299: 1032–1032
Wang Y, Fang X Q, Xu T. A method for calculating the climatic yield of grain under climate change (in Chinese). J Nat Resour, 2004, 19: 531–536
Aggarwal P K. Uncertainties in crop, soil and weather inputs used in growth-models—Implications for simulated outputs and their applications. Agric Sys, 1995, 48: 361–384
Niu X, Easterling W, Hays C J, et al. Reliability and input-data induced uncertainty of the EPIC model to estimate climate change impact on sorghum yields in the U.S. Great Plains. Agric Ecosyst Environ, 2009, 129: 268–276
Baron C, Sultan B, Balme M, et al. From GCM grid cell to agricultural plot: Scale issues affecting modelling of climate impact. Phil Trans R Soc B, 2005, 360: 2095–2108
Xiong W, Yang J. Advances in linking crop models with climate models (in Chinese). Chin J Eco-Agric, 2008, 16: 249–252
Challinor A J, Ewert F, Arnold S, et al. Crops and climate change: Progress, trends, and challenges in simulating impacts and informing adaptation. J Exp Bot, 2009, 60: 2775–2789
Bakker M M, Govers G, Ewert F, et al. Variability in regional wheat yields as a function of climate, soil and economic variables: Assessing the risk of confounding. Agric Ecosyst Environ, 2005, 110: 195–209
Challinor A J, Osborne T, Morse A, et al. Methods and resources for climate impacts research: Achieving synergy. Bull Am Meteorol Soc, 2009, 90: 836–848
Challinor A J, Wheeler T R, Craufurd P Q, et al. Design and optimisation of a large-area process-based model for annual crops. Agric For Meteorol, 2004, 124: 99–120
Osborne T M, Lawrence D M, Challinor A J, et al. Development and assessment of a coupled crop-climate model. Glob Change Biol, 2007, 13: 169–183
Tao F, Yokozawa M, Zhang Z. Modelling the impacts of weather and climate variability on crop productivity over a large area: A new process-based model development, optimization, and uncertainties analysis. Agric For Meteorol, 2009, 149: 831–850
Iizumi T, Yokozawa M, Nishimori M. Parameter estimation and uncertainty analysis of a large-scale crop model for paddy rice: Application of a Bayesian approach. Agric For Meteorol, 2009, 149: 333–348
Masutomi Y, Takahashi K, Harasawa H, et al. Impact assessment of climate change on rice production in Asia in comprehensive consideration of process/parameter uncertainty in general circulation models. Agric Ecosyst Environ, 2009, 131: 281–291
Reidsma P, Ewert F, Boogaard H, et al. Regional crop modelling in Europe: The impact of climatic conditions and farm characteristics on maize yields. Agric Sys, 2009, 100: 51–60
Challinor A J, Wheeler T R. Use of a crop model ensemble to quantify CO2 stimulation of water-stressed and well-watered crops. Agric For Meteorol, 2008, 148: 1062–1077
Shlyakhter A, James L, Valverde A, et al. Integrated risk analysis of global climate change. Chemosphere, 1995, 30: 1585–1618
New M, Hulme M. Representing uncertainty in climate change scenarios: A Monte-carlo approach. Integr Assess, 2000, 1: 203–213
Tebaldi C, Lobell D B. Towards probabilistic projections of climate change impacts on global crop yields. Geophys Res Lett, 2008, 35: L08705
Lobell D B, Burke M B. Why are agricultural impacts of climate change so uncertain? The importance of temperature relative to precipitation. Environ Res Lett, 2008, 3: 1–8
Zhao Z C. Lastest advances in global climate projections (in Chinese). Adv Clim Change Res, 2006, 2: 68–70, 97
Cui S H, Li F Y, Huang J, et al. Review of sensitivity research on the context of global change (in Chinese). Adv Earth Sci, 2009, 24: 1033–1041
Jones R N. Analysing the risk of climate change using an irrigation demand model. Clim Res, 2000, 14: 89–100
Dessai S, Hulme M. Assessing the robustness of adaptation decisions to climate change uncertainties: A case study on water resources management in the East of England. Glob Environ Change, 2007, 17: 59–72
Katz R W. Techniques for estimating uncertainty in climate change scenarios and impact studies. Clim Res, 2002, 20: 167–185
Makowski D, Naud C, Jeuffroy M H, et al. Global sensitivity analysis for calculating the contribution of genetic parameters to the variance of crop model prediction. Reliab Eng Syst Safe, 2006, 91: 1142–1147
Wu J, Yu F S, Chen Z X, et al. Global sensitivity analysis of growth simulation parameters of winter wheat based on EPIC model(in Chinese). Trans Chin Soc Agric Eng, 2008, 25: 136–142
Lamboni M, Makowski D, Lehuger S, et al. Multivariate global sensitivity analysis for dynamic crop models. Field Crops Res, 2009, 113: 312–320
Refsgaard J C, van der Sluijs J P, Brown J, et al. A framework for dealing with uncertainty due to model structure error. Adv Water Res, 2006, 29: 1586–1597
Peng S B, Huang J L, Sheehy J E, et al. Rice yields decline with higher night temperature from global warming. Proc Natl Acad Sci USA, 2004, 101: 9971–9975
Sheehy J E, Mitchell P L, Ferrer A B. Decline in rice grain yields with temperature: Models and correlations can give different estimates. Field Crops Res, 2006, 98: 151–156
Lobell D B, Ortiz-Monasterio J I. Impacts of day versus night temperatures on spring wheat yields: A comparison of empirical and CERES model predictions in three locations. Agron J, 2007, 99: 469–477
Collins M. Ensembles and probabilities: A new era in the prediction of climate change. Phil Trans R Soc A, 2007, 365: 1957–1970
Lobell D B, Burke M B, Tebaldi C, et al. Prioritizing climate change adaptation needs for food security in 2030. Science, 2008, 319: 607–610
Tao F L, Zhang Z, Liu J Y, et al. Modelling the impacts of weather and climate variability on crop productivity over a large area: A new super-ensemble-based probabilistic projection. Agric For Meteorol, 2009, 149: 1266–1278
Challinor A J, Wheeler T, Hemming D, et al. Ensemble yield simulations: crop and climate uncertainties, sensitivity to temperature and genotypic adaptation to climate change. Clim Res, 2009, 38: 117–127
Naylor R L, Battisti D S, Vimont D J, et al. Assessing risks of climate variability and climate change for Indonesian rice agriculture. Proc Natl Acad Sci USA, 2007, 104: 7752–7757
New M, Lopez A, Dessai S, et al. Challenges in using probabilistic climate change information for impact assessments: An example from the water sector. Phil Trans R Soc A, 2007, 365: 2117–2131
Jones R N. An environmental risk assessment/management framework for climate change impact assessments. Nat Hazards, 2001, 23: 197–230
Luo Q Y, Bellotti W, Williams M, et al. Risk analysis of possible impacts of climate change on South Australian wheat production. Clim Change, 2007, 85: 89–101
Hanson J W, Jones J W. Scaling-up crop models for climate variability application. Agric Sys, 2000, 65: 43–72
Mavromatis T, Boote K J, Jones J W, et al. Developing genetic coefficients from crop simulation models using data from crop performance trials. Crop Sci, 2001, 41: 40–51
Challinor A J, Wheeler T R, Slingo J M, et al. Design and optimisation of a large-area process-based model for annual crops. Agric For Meteorol, 2004, 24: 199–120
de Wit A J W, van Diepen C A. Crop model data assimilation with the Ensemble Kalman filter for improving regional crop yield forecasts. Agric For Meteorol, 2007, 146: 38–56
Ines A V M, Hansen J W. Bias correction of daily GCM rainfall for crop simulation studies. Agric For Meteorol, 2006, 138: 44–53
Baigorria G A, Jones J W, O’Brien J J. Potential predictability of crop yield using an ensemble climate forecast by a regional circulation model. Agric For Meteorol, 2008, 148: 1353–1361
Beven K. Towards a coherent philosophy for modelling the environment. Proc R Soc A, 2002, 458: 2465–2484
Mo X, Beven K. Multi-objective parameter conditioning of a three-source wheat canopy model. Agric For Meteorol, 2004, 122: 39–63
Blasone R-S, Vrugt J A, Madsen H, et al. Generalized likelihood uncertainty estimation (GLUE) using adaptive Markov Chain Monte Carlo sampling. Adv Water Res, 2008, 31: 630–648
He J Q, Jones J W, Graham W D, et al. Influence of likelihood function choice for estimating crop model parameters using the generalized likelihood uncertainty estimation method. Agric Sys, 2010, 103: 256–264
Zhang J H, Yao F M, Zheng L Y, et al. Evaluation of grassland dynamics in the Northern Tibetan Plateau of China using remote sensing and climate data. Sensor, 2007, 7: 3312–3328
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Yao, F., Qin, P., Zhang, J. et al. Uncertainties in assessing the effect of climate change on agriculture using model simulation and uncertainty processing methods. Chin. Sci. Bull. 56, 729–737 (2011). https://doi.org/10.1007/s11434-011-4374-6
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DOI: https://doi.org/10.1007/s11434-011-4374-6