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The role of climatic variables in winter cereal yields: a retrospective analysis

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Abstract

This study examined the effects of observed climate including [CO2] on winter cereal [winter wheat (Triticum aestivum), barley (Hordeum vulgare) and oat (Avena sativa)] yields by adopting robust statistical analysis/modelling approaches (i.e. autoregressive fractionally integrated moving average, generalised addition model) based on long time series of historical climate data and cereal yield data at three locations (Moree, Dubbo and Wagga Wagga) in New South Wales, Australia. Research results show that (1) growing season rainfall was significantly, positively and non-linearly correlated with crop yield at all locations considered; (2) [CO2] was significantly, positively and non-linearly correlated with crop yields in all cases except wheat and barley yields at Wagga Wagga; (3) growing season maximum temperature was significantly, negatively and non-linearly correlated with crop yields at Dubbo and Moree (except for barley); and (4) radiation was only significantly correlated with oat yield at Wagga Wagga. This information will help to identify appropriate management adaptation options in dealing with the risk and in taking the opportunities of climate change.

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Acknowledgments

The authors would like to thank Dr. Helen Fairweather, who previously worked at NSW DPI, for passing on the digitalised winter cereal production data.

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Authors and Affiliations

  1. University of Technology, Sydney, PO Box 123, Broadway, Sydney, NSW, 2007, Australia

    Qunying Luo

  2. Office of Environment and Heritage, Sydney, NSW, 2000, Australia

    Li Wen

Authors
  1. Qunying Luo
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  2. Li Wen
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Corresponding author

Correspondence to Qunying Luo.

Appendix

Appendix

We generated a hypothetical time series (Y) with 1,000 data point. Y was produced by the equation: Y = yb + 1.5 × x1 + 1.2 × x2 + 1.2 × dum + ε. The time series was made of five components:

  1. 1.

    A random walk base time series (yb) with white noise (a normal variable with zero mean and variance one)

  2. 2.

    A gradually increasing variable (x1) with small normal errors—a high-frequency unidirectional variable

  3. 3.

    A high-frequency stochastic variable (x2) with large normal errors

  4. 4.

    A low-frequency dummy variable (dum) representing periodic “jumps” in Y

  5. 5.

    An error term with 0 mean and 0.01 standard deviation

We filtered the hypothetic time series Y with (1) autoregressive fractionally integrated moving average, ARFIMA (0, d, 0), and (2) autoregressive integrated moving average, ARIMA (0, 1, 0). We then analysed the original and filtered time series using ordinary least square (OLS) regression. Table 5 summarised the results from the three models from one run.

Table 5 Summary of three OLS models (the significant predictors are in bold)
Full size table

A number of conclusions can be draw from our simple exercise:

  1. 1.

    All three methods can be applied to analyse the effects of stochastic variables and the results are similar (i.e. the estimates for x2) if the underlying mechanism for long-term memory (i.e. the dummy) is known.

  2. 2.

    The ordinary ARIMA process removes long-run persistence in time series as well as the potential impacts of high-frequency factors, which have the same trend of the underlying mechanism causing long-term memory.

  3. 3.

    ARFIMA process has the potential to remove the low-frequency long-term memory and preserve the high-frequency variations.

The executable R codes for the simulation:

figure a

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Luo, Q., Wen, L. The role of climatic variables in winter cereal yields: a retrospective analysis. Int J Biometeorol 59, 181–192 (2015). https://doi.org/10.1007/s00484-014-0834-4

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  • DOI: https://doi.org/10.1007/s00484-014-0834-4

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