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An incorporative statistic and neural approach for crop yield modelling and forecasting

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Abstract

An incorporative framework is proposed in this study for crop yield modelling and forecasting. It is a complementary approach to traditional time series analysis on modelling and forecasting by treating crop yield and associated factors as a non-temporal collection. Statistics are used to identify the highly related factor(s) among many associates to crop yield and then play a key role in data cleaning and a supporting role in data expansion, if necessary, for neural network training and testing. Wheat yield and associated plantation area, rainfall and temperature in Queensland of Australia over 100 years are used to test this incorporative approach. The results show that well-trained multilayer perceptron models can simulate the wheat production through given plantation areas with a mean absolute error (MAE) of ~2%, whereas the third-order polynomial correlation returns an MAE of ~20%. However, statistical analysis plays a key role in identifying the most related factor, detecting outliers, determining the general trend of wheat yield with respect to plantation area and supporting data expansion for neural network training and testing. The combination of these two methods provides both meaningful qualitative and accurate quantitative data analysis and forecasting. This incorporative approach can also be useful in data modelling and forecasting in other applications due to its generic nature.

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References

  1. Jame YW, Cutforth HW (1996) Crop growth models for decision support systems. Can J Plant Sci 76:9–19

    Article  Google Scholar 

  2. Yun JI (2003) Predicting regional rice production in South Korea using spatial data and crop-growth modelling. Agric Syst 77:23–38

    Article  Google Scholar 

  3. Tao F, Yokozawa M, Zhang Z, Xu Y, Hayashi Y (2005) Remote sensing of crop production in China by production efficiency models: Models comparisons, estimates and uncertainties. Ecol Model 183:385–396

    Article  Google Scholar 

  4. Box G, Jenkins G (1970) Time series analysis: Forecasting and control. Holden Day, San Francisco

    MATH  Google Scholar 

  5. Shephard N (1995) Generalized linear autoregression. Economics working paper 8, Nuffield College, Oxford

  6. Adya M, Collopy F (1998) How effective are neural networks at forecasting and prediction? A review and evaluation. J Forecast 17:481–495

    Article  Google Scholar 

  7. Liang F (2005) Bayesian neural networks for nonlinear time series forecasting. Stat Comput 15:13–29

    Article  MathSciNet  Google Scholar 

  8. Zhang GP, Kline D (2007) Quarterly time-series forecasting with neural networks. IEEE Trans Neural Netw 18:1800–1814

    Article  Google Scholar 

  9. Zhang GP (2003) Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50:159–175

    Article  MATH  Google Scholar 

  10. Taskaya-Temizel T, Casey M (2005) A comparative study of autoregressive neural network hybrids. Neural Netw 18:781–789

    Article  Google Scholar 

  11. Yu L, Wang S, Lai K (2005) A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates. Comput Oper Res 32:2523–2541

    Article  MATH  Google Scholar 

  12. Hoogenboom G (2000) Contribution of agrometeorology to the simulation of crop production and its applications. Agric Forest Meteorol 103:137–157

    Article  Google Scholar 

  13. He Y, Zhang Y, Zhang S, Fang H (2005) Application of artificial neural network on relationship analysis between wheat yield and soil nutrients. in: IEEE 27th annual conference on engineering in medicine and biology. Shanghai, China

  14. Green TR, Salas JD, Martinez A, Erskine RH (2007) Relating crop yield to topographic attributes using spatial analysis neural networks and regression. Geoderma 139:23–37

    Article  Google Scholar 

  15. de Jong E, Rennie DA (1969) Effect of soil profile type and fertilizer on moisture use by wheat grown on fallow or stubble land. Can J Soil Sci 49:189–197

    Article  Google Scholar 

  16. Campbell CA, Zentner RP, Johnson PJ (1988) Effect of crop rotation and fertilization on the quantitative relationship between spring wheat yield and moisture use in southwest Saskatchewan. Can J Soil Sci 68:1–16

    Article  Google Scholar 

  17. Australian Bureau of Statistics (2008) Agricultural commodities—historical data. Commonwealth of Australia, Canberra

    Google Scholar 

  18. Gately E (1996) Neural networks for financial forecasting. Wiley, London

    Google Scholar 

  19. Yale K (1997) Preparing the right data diet for training neural networks. IEEE Spectr 3:64–66

    Article  Google Scholar 

  20. Windeatt Y, Dias K (2008) Ensemble approaches to facial action unit classification. Lect Notes Comput Sci 5197:551–559

    Article  Google Scholar 

  21. Hornik K, Stinchcomb M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2:359–366

    Article  Google Scholar 

  22. White H (1989) Some asymptotic results for learning in single hidden layer feedforward network models. J Am Stat Assoc 84:1008–1013

    Article  Google Scholar 

  23. Garcia-Crespo A, Ruiz-Mezcua B, Fernandez-Fdz D, Zaera R (2007) Prediction of the response under impact of steel armours using a multilayer perceptron. Neural Comput Appl 16:147–154

    Article  Google Scholar 

  24. Li M, Guo W, Verma B, Tickle K, O’Connor J (2009) Intelligent methods for solving inverse problems of backscattering spectra with noise: A comparison between neural networks and simulated annealing. Neural Comput Appl 18:423–430

    Article  MATH  Google Scholar 

  25. Kurtulus DF (2009) Ability to forecast unsteady aerodynamic forces of flapping airfoils by artificial neural network. Neural Comput Appl 18:359–368

    Article  Google Scholar 

  26. Guo WW (2010) Incorporating statistical and neural network approaches for student course satisfaction analysis and prediction. Expert Syst Appl 37:3358–3365

    Article  Google Scholar 

  27. Marquardt D (1963) An algorithm for least-squares estimation of nonlinear parameters. SIAM J Appl Math 11:431–441

    Article  MathSciNet  MATH  Google Scholar 

  28. Hagan M, Menhaj M (1994) Training feedforward networks with the Marquardt algorithm. IEEE Trans Neural Netw 5:989–993

    Article  Google Scholar 

  29. Hagan M, Demuth H, Beale MH (1996) Neural network design. PWS Publishing, Boston

    Google Scholar 

  30. Demuth H, Beale M (2004) Neural network toolbox for use with Matlab. The MathWorks, Massachusetts

    Google Scholar 

  31. Demuth H, Beale M, Hagan M (2007) Neural network toolbox 5. The MathWorks, Massachusetts

    Google Scholar 

Download references

Acknowledgments

L. Li and G. Whymark are thanked for their comments on the early draft of this paper. We appreciate the reviewers for their constructive feedback that brought significant improvement to this work.

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Authors and Affiliations

  1. School of Information and Communication Technology, Central Queensland University, Rockhampton, QLD, 4702, Australia

    William W. Guo

  2. College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, 010018, Inner Mongolia, China

    Heru Xue

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  1. William W. Guo
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  2. Heru Xue
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Correspondence to William W. Guo.

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Guo, W.W., Xue, H. An incorporative statistic and neural approach for crop yield modelling and forecasting. Neural Comput & Applic 21, 109–117 (2012). https://doi.org/10.1007/s00521-011-0636-0

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  • DOI: https://doi.org/10.1007/s00521-011-0636-0

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