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

Yanxia ZHAO; Chunyi WANG; Yi ZHANG

doi:10.1007/s13351-019-8143-9

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

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Corresponding author: Yi ZHANG, zy@cma.gov.cn;

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Supported by the National Natural Science Foundation of China (41505097) and Basic Research and Operation Funds of Chinese Academy of Meteorological Sciences (2017Z004)

DOI: 10.1007/s13351-019-8143-9

Abstract

Abstract

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.
- analysis of variance (ANOVA),
- climate change,
- ensemble simulation,
- maize yield,
- uncertainty

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Fig. 1. The major maize-growing area and locations of the selected study stations in Jilin Province.

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

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

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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.

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

Parameter	Value
β_1,0	–3.3870 (–3.4907, –3.3074)
β_2,0	–3.2981 (–3.3922, –3.2163)
β_3,0	–3.3980 (–3.5077, –3.2909)
β_4,0	–3.3589 (–3.4639, –3.2804)
β_5,0	–3.3565 (–3.4794, –3.2561)
β_6,0	–3.3869 (–3.5036, –3.3013)
β_7,0	–3.3480 (–3.4650, –3.2617)
β_8,0	–3.3075 (–3.3911, –3.2449)
β₁	0.4449 (0.4288, 0.4662)
β₂	–0.0115 (–0.0123, –0.0109)

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Zhao, Y. X., C. Y. Wang, and Y. Zhang, 2019: Uncertainties in the effects of climate change on maize yield simulation in Jilin Province: A case study. J. Meteor. Res., 33(4), 777–783, doi: 10.1007/s13351-019-8143-9.

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Manuscript History

Received: 18 September 2018

Accepted: 19 March 2019

Final form: 31 July 2019

Published online: 25 August 2019

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