IDEAS home Printed from https://ideas.repec.org/a/eee/agisys/v173y2019icp519-523.html
   My bibliography  Save this article

Improved crop forecasts for the Australian macadamia industry from ensemble models

Author

Listed:
  • Mayer, David G.
  • Chandra, Kerri A.
  • Burnett, Jolyon R.

Abstract

Annual forecasts for the Australian macadamia crop have been issued since 2001, with varying (and not always improving) degrees of accuracy. Regression models using climate variables have formed the basis for these forecasts, with general linear model (GLM) ensembles being adopted more recently. This has proven to be a challenging task, as there are only a small number of observations (18) combined with a large number (90+) of independent variables – these being different climate measures for different times of the year (representing ‘key physiological periods for macadamia trees’). Also, these ‘assumedly-independent’ variables contain various degrees of correlation. This study uses cross-validation, with the most recent data for the two dominant production regions of Australia (Lismore and Bundaberg), to investigate the relative performance of alternate modelling methods. These modelling methods were GLMs, partial least squares (PLS) regression and LASSO (least absolute shrinkage and selection operator) penalised regression. Model ensembles, which have been shown to be beneficial in many alternate disciplines, are used to advantage. Both GLMs and PLS produced quite-disappointing results, failing to meet the project's benchmarked accuracy of ±10% error. The optimal LASSO models performed notably better, with a further improvement when ensembles were incorporated. The lowest mean absolute error (MAE) rates here were 9.0% for Lismore and 5.9% for Bundaberg. Hence LASSO ensembles will be adopted for future forecasts of the Australian macadamia crop.

Suggested Citation

  • Mayer, David G. & Chandra, Kerri A. & Burnett, Jolyon R., 2019. "Improved crop forecasts for the Australian macadamia industry from ensemble models," Agricultural Systems, Elsevier, vol. 173(C), pages 519-523.
    • Handle: RePEc:eee:agisys:v:173:y:2019:i:c:p:519-523
    DOI: 10.1016/j.agsy.2019.03.018
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0308521X18314483
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.agsy.2019.03.018?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Deng, Xinan & Luo, Yuzhou & Dong, Suocheng & Yang, Xiusheng, 2005. "Impact of resources and technology on farm production in northwestern China," Agricultural Systems, Elsevier, vol. 84(2), pages 155-169, May.
    2. Mayer, D.G. & Stephenson, R.A. & Jones, K.H. & Wilson, K.J. & Bell, D.J.D. & Wilkie, J. & Lovatt, J.L. & Delaney, K.E., 2006. "Annual forecasting of the Australian macadamia crop - integrating tree census data with statistical climate-adjustment models," Agricultural Systems, Elsevier, vol. 91(3), pages 159-170, December.
    3. Thordis L. Thorarinsdottir & Tilmann Gneiting, 2010. "Probabilistic forecasts of wind speed: ensemble model output statistics by using heteroscedastic censored regression," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(2), pages 371-388, April.
    4. Liang, Tung & Wong, Wilbert P. H. & Uehara, Goro, 1983. "Simulating and mapping agricultural land productivity: An application to Macadamia nut," Agricultural Systems, Elsevier, vol. 11(4), pages 225-253.
    5. Mayer, D. G. & Schoorl, D. & Holt, J. E., 1991. "Modelling export opportunities for the Hong Kong fresh fruit and vegetable market," Agricultural Systems, Elsevier, vol. 36(2), pages 221-230.
    6. Garcia-Paredes, J. D. & Olson, K. R. & Lang, J. M., 2000. "Predicting corn and soybean productivity for Illinois soils," Agricultural Systems, Elsevier, vol. 64(3), pages 151-170, June.
    7. Francis X. Diebold & Jose A. Lopez, 1995. "Forecast evaluation and combination," Research Paper 9525, Federal Reserve Bank of New York.
    8. Mayer, D. G. & Schoorl, D. & Holt, J. E., 1989. "Progressive validation of Queensland horticultural crop forecasts," Agricultural Systems, Elsevier, vol. 29(2), pages 179-187.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mayer, D.G. & Stephenson, R.A. & Jones, K.H. & Wilson, K.J. & Bell, D.J.D. & Wilkie, J. & Lovatt, J.L. & Delaney, K.E., 2006. "Annual forecasting of the Australian macadamia crop - integrating tree census data with statistical climate-adjustment models," Agricultural Systems, Elsevier, vol. 91(3), pages 159-170, December.
    2. Kim, Hyun Hak & Swanson, Norman R., 2018. "Mining big data using parsimonious factor, machine learning, variable selection and shrinkage methods," International Journal of Forecasting, Elsevier, vol. 34(2), pages 339-354.
    3. Ana-Maria Fuertes & Elena Kalotychou, 2004. "Forecasting sovereign default using panel models: A comparative analysis," Computing in Economics and Finance 2004 228, Society for Computational Economics.
    4. Stefania D'Amico, 2004. "Density Estimation and Combination under Model Ambiguity," Computing in Economics and Finance 2004 273, Society for Computational Economics.
    5. Marian Vavra, 2015. "On a Bootstrap Test for Forecast Evaluations," Working and Discussion Papers WP 5/2015, Research Department, National Bank of Slovakia.
    6. Rogelio Ladrón de Guevara Cortés & Salvador Torra Porras & Enric Monte Moreno, 2021. "Statistical and computational techniques for extraction of underlying systematic risk factors: a comparative study in the Mexican Stock Exchange," Revista Finanzas y Politica Economica, Universidad Católica de Colombia, vol. 13(2), pages 513-543, August.
    7. Rodríguez-Vargas, Adolfo, 2020. "Forecasting Costa Rican inflation with machine learning methods," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 1(1).
    8. Jose A. Lopez & Christian Walter, 1997. "Is implied correlation worth calculating? Evidence from foreign exchange options and historical data," Research Paper 9730, Federal Reserve Bank of New York.
    9. Elkin Castaño V. & Luis Fernando Melo Velandia, 1998. "Métodos De Combinación De Pronósticos:Una Aplicación A La Inflación Colombiana," Borradores de Economia 3212, Banco de la Republica.
    10. Lukas Menkhoff & Rafael Rebitzky & Michael Schroder, 2008. "Do dollar forecasters believe too much in PPP?," Applied Economics, Taylor & Francis Journals, vol. 40(3), pages 261-270.
    11. Kunze, Frederik, 2017. "Predicting exchange rates in Asia: New insights on the accuracy of survey forecasts," University of Göttingen Working Papers in Economics 326, University of Goettingen, Department of Economics.
    12. Yudong Wang & Li Liu, 2016. "Crude oil and world stock markets: volatility spillovers, dynamic correlations, and hedging," Empirical Economics, Springer, vol. 50(4), pages 1481-1509, June.
    13. Di Bu & Simone Kelly & Yin Liao & Qing Zhou, 2018. "A hybrid information approach to predict corporate credit risk," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(9), pages 1062-1078, September.
    14. Rianne Legerstee & Philip Hans Franses, 2015. "Does Disagreement Amongst Forecasters Have Predictive Value?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(4), pages 290-302, July.
    15. Paul Bennett & In Sun Geoum & David S. Laster, 1997. "Rational bias in macroeconomic forecasts," Staff Reports 21, Federal Reserve Bank of New York.
    16. Bauer, Gregory H., 2017. "International house price cycles, monetary policy and credit," Journal of International Money and Finance, Elsevier, vol. 74(C), pages 88-114.
    17. Peter F. Christoffersen & Francis X. Diebold & Roberto S. Mariano & Anthony S. Tay & Yiu Kuen Tse, 2006. "Direction-of-Change Forecasts Based on Conditional Variance, Skewness and Kurtosis Dynamics: International Evidence," PIER Working Paper Archive 06-016, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    18. Clements, Michael P. & Harvey, David I., 2011. "Combining probability forecasts," International Journal of Forecasting, Elsevier, vol. 27(2), pages 208-223.
    19. Roberto Leon-Gonzalez & Blessings Majoni, 2023. "Exact Likelihood for Inverse Gamma Stochastic Volatility Models," GRIPS Discussion Papers 23-07, National Graduate Institute for Policy Studies.
    20. Diebold, Francis X. & Shin, Minchul, 2019. "Machine learning for regularized survey forecast combination: Partially-egalitarian LASSO and its derivatives," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1679-1691.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:agisys:v:173:y:2019:i:c:p:519-523. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/agsy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.