Uncertainties in the Effects of Climate Change on Maize Yield Simulation in Jilin Province: A Case Study

+ Author Affiliations + Find other works by these authors
Funds: 
Supported by the National Natural Science Foundation of China (41505097) and Basic Research and Operation Funds of Chinese Academy of Meteorological Sciences (2017Z004)

PDF

  • Measuring the impacts of uncertainties identified from different global climate models (GCMs), representative concentration pathways (RCPs), and parameters of statistical crop models on the projected effects of climate change on crop yields can help to improve the availability of simulation results. The quantification and separation of different sources of uncertainty also help to improve understanding of impacts of uncertainties and provide a theoretical basis for their reduction. In this study, uncertainties of maize yield predictions are evaluated by using 30 sets of parameters from statistical crop models together with eight GCMs with reference to three emission scenarios for Jilin Province of northeastern China. Regression models using replicates based on bootstrap resampling reveal that yields are maximized when the optimum average growing season temperature is 20.1°C for 1990–2009. The results of multi-model ensemble simulations show a maize yield reduction of 11%, with 75% probability for 2040–69 relative to the baseline period of 1976–2005. We decompose the variance so as to understand the relative importance of different sources of uncertainty, such as GCMs, RCPs, and statistical model parameters. The greatest proportion of uncertainty (> 50%) is derived from GCMs, followed by RCPs with a proportion of 28% and statistical crop model parameters with a proportion of 20% of total ensemble uncertainty.
  • Fig.  1.   The major maize-growing area and locations of the selected study stations in Jilin Province.

    Fig.  2.   Estimated yield responses to the average growing season temperature for 1990–2009 in the 30 replications of the panel regression model.

    Fig.  3.   The probability density of simulated maize yield changes for 2040–69 relative to the baseline period of 1976–2005.

    Fig.  4.   The ternary plot showing uncertainty in projected yield changes (%) arising from the GCMs, RCPs, and statistical crop model parameters for the eight sites (denoted by small circles) for the future period of 2040–69.

    Table  1   Ensemble means and ranges of regression coefficients in the 30 replications of the panel model

    ParameterValue
    β1,0–3.3870 (–3.4907, –3.3074)
    β2,0–3.2981 (–3.3922, –3.2163)
    β3,0–3.3980 (–3.5077, –3.2909)
    β4,0–3.3589 (–3.4639, –3.2804)
    β5,0–3.3565 (–3.4794, –3.2561)
    β6,0–3.3869 (–3.5036, –3.3013)
    β7,0–3.3480 (–3.4650, –3.2617)
    β8,0–3.3075 (–3.3911, –3.2449)
    β10.4449 (0.4288, 0.4662)
    β2–0.0115 (–0.0123, –0.0109)
    Download: Download as CSV
  • Aryal, A., S. Shrestha, and M. S. Babel, 2019: Quantifying the sources of uncertainty in an ensemble of hydrological climate-impact projections. Theor. Appl. Climatol., 135, 193–209. doi: 10.1007/s00704-017-2359-3
    Asseng, S., F. Ewert, C. Rosenzweig, et al., 2013: Uncertainty in simulating wheat yields under climate change. Nat. Climate Change, 3, 827–832. doi: 10.1038/nclimate1916
    Asseng, S., F. Ewert, P. Martre, et al., 2015: Rising temperatures reduce global wheat production. Nat. Climate Change, 5, 143–147. doi: 10.1038/nclimate2470
    Bassu, S., N. Brisson, J.-L. Durand, et al., 2014: How do various maize crop models vary in their responses to climate change factors? Glob. Change Biol., 20, 2301–2320. doi: 10.1111/gcb.12520
    Bosshard, T., M. Carambia, K. Goergen, et al., 2013: Quantifying uncertainty sources in an ensemble of hydrological climate-impact projections. Water Resour. Res., 49, 1523–1536. doi: 10.1029/2011wr011533
    Cao T.-H., X.-H. Liang, Y.-J. Liu, et al., 2010: Influence of climate change on meteorological yield of maize in Jilin Province. J. Maize Sci., 18, 142–145. (in Chinese) doi: 10.13597/j.cnki.maize.science.2010.02.034
    Ceglar, A., and L. Kajfež-Bogataj, 2012: Simulation of maize yield in current and changed climatic conditions: Addressing modelling uncertainties and the importance of bias correction in climate model simulations. Eur. J. Agron., 37, 83–95. doi: 10.1016/j.eja.2011.11.005
    Challinor, A. J., J. Watson, D. B. Lobell, et al., 2014: A meta-analysis of crop yield under climate change and adaptation. Nat. Climate Change, 4, 287–291. doi: 10.1038/nclimate2153
    Dosio, A., and P. Paruolo, 2011: Bias correction of the ENSEMBLES high-resolution climate change projections for use by impact models: Evaluation on the present climate. J. Geophys. Res. Atmos., 116, D17110. doi: 10.1029/2011JD015934
    Godfray, H. C. J., J. R. Beddington, I. R. Crute, et al., 2010: Food security: The challenge of feeding 9 billion people. Science, 327, 812–818. doi: 10.1126/science.1185383
    He, D., E. L. Wang, J. Wang, et al., 2017: Uncertainty in canola phenology modelling induced by cultivar parameterization and its impact on simulated yield. Agric. For. Meteor., 232, 163–175. doi: 10.1016/j.agrformet.2016.08.013
    Holzkämper, A., P. Calanca, M. Honti, et al., 2015a: Projecting climate change impacts on grain maize based on three different crop model approaches. Agric. For. Meteor., 214-215, 219–230. doi: 10.1016/j.agrformet.2015.08.263
    Holzkämper, A., T. Klein, R. Seppelt, et al., 2015b: Assessing the propagation of uncertainties in multi-objective optimization for agro-ecosystem adaptation to climate change. Environ. Modell. Softw., 66, 27–35. doi: 10.1016/j.envsoft.2014.12.012
    Li H., F. M. Yao, J. H. Zhang, et al., 2014: Analysis on climatic maize yield and its sensitivity to climate change in northeast China. Chinese J. Agrometeorol., 35, 423–428. (in Chinese) doi: 10.3969/j.issn.1000-6362.2014.04.010
    Li, R. Q., S. H. Lyu, B. Han, et al., 2015: Connections between the South Asian summer monsoon and the tropical sea surface temperature in CMIP5. J. Meteor. Res., 29, 106–118. doi: 10.1007/s13351-014-4031-5
    Li, T., T. Hasegawa, X. Y. Yin, et al., 2015: Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions. Global Change Biol., 21, 1328–1341. doi: 10.1111/gcb.12758
    Liu, B., S. Asseng, C. Müller, et al., 2016: Similar estimates of temperature impacts on global wheat yield by three independent methods. Nat. Climate Change, 6, 1130–1136. doi: 10.1038/nclimate3115
    Liu, D. L., G. J. O’Leary, B. Christy, et al., 2017: Effects of different climate downscaling methods on the assessment of climate change impacts on wheat cropping systems. Climatic Change, 144, 687–701. doi: 10.1007/s10584-017-2054-5
    Lobell, D. B., and M. B. Burke., 2010: On the use of statistical models to predict crop yield responses to climate change. Agric. For. Meteor., 150, 1443–1452. doi: 10.1016/j.agrformet.2010.07.008
    Lobell, D. B., C. B. Field, K. N. Cahill, et al., 2006: Impacts of future climate change on California perennial crop yields: Model projections with climate and crop uncertainties. Agric. For. Meteor., 141, 208–218. doi: 10.1016/j.agrformet.2006.10.006
    Okoro, S. U., U. Schickhoff, J. Boehner, et al., 2017: Climate impacts on palm oil yields in the Nigerian Niger Delta. Eur. J. Agron., 85, 38–50. doi: 10.1016/j.eja.2017.02.002
    Schlenker, W., and D. B. Lobell, 2010: Robust negative impacts of climate change on African agriculture. Environ. Res. Lett., 5, 014010. doi: 10.1088/1748-9326/5/1/014010
    Shi, W. J., F. L. Tao, Z. Zhang, 2012: Identifying contributions of climate change to crop yields based on statistical models: A review. Acta Geogr. Sinica, 67, 1213–1222. (in Chinese) doi: 10.11821/xb201209006
    Tack, J., A. Barkley, and L. L. Nalley, 2015: Effect of warming temperatures on US wheat yields. Proc. Natl. Acad. Sci. USA, 112, 6931–6936. doi: 10.1073/pnas.1415181112
    Tao, F. L., and Z. Zhang, 2013: Climate change, wheat productivity and water use in the North China Plain: A new super-ensemble-based probabilistic projection. Agric. For. Meteor., 170, 146–165. doi: 10.1016/j.agrformet.2011.10.003
    Tao, F. L., Z. Zhang, J. Y. Liu, et al., 2009: Modelling the impacts of weather and climate variability on crop productivity over a large area: A new super-ensemble-based probabilistic projection. Agric. For. Meteor., 149, 1266–1278. doi: 10.1016/j.agrformet.2009.02.015
    Tao, F. L., Z. Zhang, W. J. Shi, et al., 2013: Single rice growth period was prolonged by cultivars shifts, but yield was damaged by climate change during 1981–2009 in China, and late rice was just opposite. Glob. Change Biol., 19, 3200–3209. doi: 10.1111/gcb.12250
    Tao, F. L., R. P. Rötter, T. Palosuo, et al., 2018: Contribution of crop model structure, parameters and climate projections to uncertainty in climate change impact assessments. Glob. Change Biol., 24, 1291–1307. doi: 10.1111/gcb.14019
    Vetter, T., J. Reinhardt, M. Flörke, et al., 2017: Evaluation of sources of uncertainty in projected hydrological changes under climate change in 12 large-scale river basins. Climatic Change, 141, 419–433. doi: 10.1007/s10584-016-1794-y
    Wallach, D., L. O. Mearns, A. C. Ruane, et al., 2016: Lessons from climate modeling on the design and use of ensembles for crop modeling. Climatic Change, 139, 551–564. doi: 10.1007/s10584-016-1803-1
    Wang, B., D. L. Liu, C. Waters, et al., 2018: Quantifying sources of uncertainty in projected wheat yield changes under climate change in eastern Australia. Climatic Change, 151, 259–273. doi: 10.1007/s10584-018-2306-z
    Wang, E. L., P. Martre, Z. G. Zhao, et al., 2017: The uncertainty of crop yield projections is reduced by improved temperature response functions. Nat. Plants, 3, 17102. doi: 10.1038/nplants.2017.102
    Wang, N., J. Wang, E. L. Wang, et al., 2015: Increased uncertainty in simulated maize phenology with more frequent supra-optimal temperature under climate warming. Eur. J. Agron., 71, 19–33. doi: 10.1016/j.eja.2015.08.005
    White, J. W., G. Hoogenboom, B. A. Kimball, et al., 2011: Methodologies for simulating impacts of climate change on crop production. Field Crops Res., 124, 357–368. doi: 10.1016/j.fcr.2011.07.001
    Wilby, R. L., J. Troni, Y. Biot, et al., 2009: A review of climate risk information for adaptation and development planning. Int. J. Climatol., 29, 1193–1215. doi: 10.1002/joc.1839
    Wu, D., Z. H. Jiang, and T. T. Ma, 2016: Projection of summer precipitation over the Yangtze–Huaihe River basin using multimodel statistical downscaling based on canonical correlation analysis. J. Meteor. Res., 30, 867–880. doi: 10.1007/s13351-016-6030-1
    Yang, X., Z. Tian, L. X. Sun, et al., 2017: The impacts of increased heat stress events on wheat yield under climate change in China. Climatic Change, 140, 605–620. doi: 10.1007/s10584-016-1866-z
    Zhang, T. Y., J. Zhu, and R. Wassmann, 2010: Responses of rice yields to recent climate change in China: An empirical assessment based on long-term observations at different spatial scales (1981–2005). Agric. For. Meteor., 150, 1128–1137. doi: 10.1016/j.agrformet.2010.04.013
    Zhang, Y., Y. X. Zhao, S. N. Chen, et al., 2015: Prediction of maize yield response to climate change with climate and crop model uncertainties. J. Appl. Meteor. Climatol., 54, 785–794. doi: 10.1175/jamc-d-14-0147.1
    Zhang, Y., Y. X. Zhao, C. Y. Wang, et al., 2017: Using statistical model to simulate the impact of climate change on maize yield with climate and crop uncertainties. Theor. Appl. Climatol., 130, 1065–1071. doi: 10.1007/s00704-016-1935-2
    Zhou, M. Z., and H. J. Wang, 2015: Potential impact of future climate change on crop yield in northeastern China. Adv. Atmos. Sci., 32, 889–897. doi: 10.1007/s00376-014-4161-9
    Zhou, M. Z., H. J. Wang, and Z. G. Huo, 2017: A new prediction model for grain yield in Northeast China based on spring North Atlantic Oscillation and late-winter Bering Sea ice co-ver. J. Meteor. Res., 31, 409–419. doi: 10.1007/s13351-017-6114-6
  • Related Articles

  • Cited by

    Periodical cited type(5)

    1. Hossein Zare, Tobias K. D. Weber, Joachim Ingwersen, et al. Combining Crop Modeling with Remote Sensing Data Using a Particle Filtering Technique to Produce Real-Time Forecasts of Winter Wheat Yields under Uncertain Boundary Conditions. Remote Sensing, 2022, 14(6): 1360. DOI:10.3390/rs14061360
    2. Tina Karimi, Patrick Reed, Keyvan Malek, et al. Diagnostic Framework for Evaluating How Parametric Uncertainty Influences Agro‐Hydrologic Model Projections of Crop Yields Under Climate Change. Water Resources Research, 2022, 58(6) DOI:10.1029/2021WR031249
    3. Yi Zhang, Yanxia Zhao, Qing Sun. Increasing maize yields in Northeast China are more closely associated with changes in crop timing than with climate warming. Environmental Research Letters, 2021, 16(5): 054052. DOI:10.1088/1748-9326/abe490
    4. K. S. Kasiviswanathan, K. P. Sudheer, Bankaru-Swamy Soundharajan, et al. Implications of uncertainty in inflow forecasting on reservoir operation for irrigation. Paddy and Water Environment, 2021, 19(1): 99. DOI:10.1007/s10333-020-00822-7
    5. Pouya Khalili, Badrul Masud, Budong Qian, et al. Non-stationary response of rain-fed spring wheat yield to future climate change in northern latitudes. Science of The Total Environment, 2021, 772: 145474. DOI:10.1016/j.scitotenv.2021.145474

    Other cited types(0)

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return