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Separating out the influence of climatic trend, fluctuations, and extreme events on crop yield: a case study in Hunan Province, China

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Abstract

Separating out the influence of climatic trend, fluctuations and extreme events on crop yield is of paramount importance to climate change adaptation, resilience, and mitigation. Previous studies lack systematic and explicit assessment of these three fundamental aspects of climate change on crop yield. This research attempts to separate out the impacts on rice yields of climatic trend (linear trend change related to mean value), fluctuations (variability surpassing the “fluctuation threshold” which defined as one standard deviation (1 SD) of the residual between the original data series and the linear trend value for each climatic variable), and extreme events (identified by absolute criterion for each kind of extreme events related to crop yield). The main idea of the research method was to construct climate scenarios combined with crop system simulation model. Comparable climate scenarios were designed to express the impact of each climate change component and, were input to the crop system model (CERES-Rice), which calculated the related simulated yield gap to quantify the percentage impacts of climatic trend, fluctuations, and extreme events. Six Agro-Meteorological Stations (AMS) in Hunan province were selected to study the quantitatively impact of climatic trend, fluctuations and extreme events involving climatic variables (air temperature, precipitation, and sunshine duration) on early rice yield during 1981–2012. The results showed that extreme events were found to have the greatest impact on early rice yield (−2.59 to −15.89%). Followed by climatic fluctuations with a range of −2.60 to −4.46%, and then the climatic trend (4.91–2.12%). Furthermore, the influence of climatic trend on early rice yield presented “trade-offs” among various climate variables and AMS. Climatic trend and extreme events associated with air temperature showed larger effects on early rice yield than other climatic variables, particularly for high-temperature events (−2.11 to −12.99%). Finally, the methodology use to separate out the influences of the climatic trend, fluctuations, and extreme events on crop yield was proved to be feasible and robust. Designing different climate scenarios and feeding them into a crop system model is a potential way to evaluate the quantitative impact of each climate variable.

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Acknowledgements

We are thankful for the comments of anonymous reviewers and the editors. This study was financially supported by the Foundation for Innovative Research Groups of the National Natural Science Foundation of China (Grant No. 41321001), the State Key Laboratory of Earth Surface Processes and Resource Ecology and the Faculty of Geographical Science of Beijing Normal University.

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Authors and Affiliations

  1. State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, 100875, China

    Zhu Wang, Peijun Shi, Zhao Zhang, Yongchang Meng, Yibo Luan & Jiwei Wang

  2. Academy of Disaster Reduction and Emergency Management, Beijing Normal University, Beijing, 100875, China

    Zhu Wang, Peijun Shi, Zhao Zhang, Yongchang Meng, Yibo Luan & Jiwei Wang

  3. Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China

    Zhu Wang, Peijun Shi, Zhao Zhang, Yongchang Meng, Yibo Luan & Jiwei Wang

Authors
  1. Zhu Wang
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  2. Peijun Shi
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Correspondence to Peijun Shi.

Additional information

This paper is a contribution to the special issue on East Asian Climate under Global Warming: Understanding and Projection, consisting of papers from the East Asian Climate (EAC) community and the 13th EAC International Workshop in Beijing, China on 24–25 March 2016, and coordinated by Jianping Li, Huang-Hsiung Hsu, Wei-Chyung Wang, Kyung-Ja Ha, Tim Li, and Akio Kitoh.

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Appendices

Appendix 1: Description of genetic coefficients for rice cultivar in the CERES-Rice model

Index

Description

Unit

P1

Growing degree days in the basic vegetative phase

°C days

P2R

Extent to which phasic development leading to panicle initiation is delayed for each hour increase in photoperiod above P2O

°C days

P5

Growing degree days in the grain filling duration

°C days

P2O

the longest day length (in hours) at which the development occurs at a maximum rate

h

G1

Potential spikelet number coefficient

–

G2

Single grain weight under ideal growing conditions

g

G3

Tillering coefficient under ideal conditions

–

G4

Temperature tolerance coefficient

–

Appendix 2: 1 SD for each climate variable in different early rice-growth stage

Climate variable

Station

Sowing stage

Emergence stage

Transplanting stage

Green stage

Tillering stage

Booting stage

Heading stage

Maturity stage

Temperature (℃)

Nanxian

2.5

1.5

2.9

1.6

1.5

1.3

1.6

1.6

Wuling

3.1

1.6

2.6

1.5

1.6

1.3

1.8

1.7

Changsha

2.2

1.4

3.2

2

1.5

1.2

1.7

1.2

Wugang

3.5

1.1

2.6

1.8

1.6

1.1

1.5

1.0

Hengnan

1.9

1.4

3.3

1.6

1.7

1.2

1.5

1.3

Zixing

4.5

1.3

2.9

1.7

1.6

1.1

1.3

0.9

Precipitation (mm)

Nanxian

21.1

79.4

17.2

61.7

47.6

57.6

75.4

65.2

Wuling

25.9

101.1

36.1

51.8

47.9

74.7

76.1

48.4

Changsha

22.3

75.4

22.4

52.7

94.5

89.2

82.2

66.7

Wugang

20.2

43.5

32.5

45.3

48.2

64.7

49.7

79

Hengnan

29.8

64.1

25.1

51.3

54.5

57.6

57.4

54

Zixing

28.1

82.8

21.6

45.9

79.3

57.6

51.7

77.7

Sunshine duration (h)

Nanxian

11.1

28.4

15.7

34

26.5

35.5

27

29.2

Wuling

12.6

37.1

14.4

21.3

26.4

24.2

26.4

33.2

Changsha

12.7

30.7

12.7

20.9

28.4

27.7

23.2

41.5

Wugang

9.7

30.2

14.5

22.1

23.4

38.8

22.4

33.6

Hengnan

7.9

31.8

12.7

25.3

28.5

27.6

28.1

29.4

Zixing

6.2

22.5

8.0

19.2

23.4

29.9

21.4

29.3

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Wang, Z., Shi, P., Zhang, Z. et al. Separating out the influence of climatic trend, fluctuations, and extreme events on crop yield: a case study in Hunan Province, China. Clim Dyn 51, 4469–4487 (2018). https://doi.org/10.1007/s00382-017-3831-6

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