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. 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.
    2. 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.
    3. 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.
    4. 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.
    5. Francis X. Diebold & Jose A. Lopez, 1995. "Forecast evaluation and combination," Research Paper 9525, Federal Reserve Bank of New York.
    6. 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.
    7. 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.
    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. M. Hashem Pesaran & Paolo Zaffaroni, 2004. "Model Averaging and Value-at-Risk Based Evaluation of Large Multi Asset Volatility Models for Risk Management," CESifo Working Paper Series 1358, CESifo.
    3. 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.
    4. Rodney W. Strachan & Herman K. Van Dijk, 2013. "Evidence On Features Of A Dsge Business Cycle Model From Bayesian Model Averaging," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 54(1), pages 385-402, February.
    5. Antoine Mandel & Amir Sani, 2017. "A Machine Learning Approach to the Forecast Combination Puzzle," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01317974, HAL.
    6. Manuel Gebetsberger & Jakob W. Messner & Georg J. Mayr & Achim Zeileis, 2017. "Estimation methods for non-homogeneous regression models: Minimum continuous ranked probability score vs. maximum likelihood," Working Papers 2017-23, Faculty of Economics and Statistics, Universität Innsbruck.
    7. 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.
    8. Arie Preminger & Uri Ben-zion & David Wettstein, 2007. "The extended switching regression model: allowing for multiple latent state variables," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(7), pages 457-473.
    9. Lamont, Owen A., 2001. "Economic tracking portfolios," Journal of Econometrics, Elsevier, vol. 105(1), pages 161-184, November.
    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. Barbara Rossi & Tatevik Sekhposyan, 2016. "Forecast Rationality Tests in the Presence of Instabilities, with Applications to Federal Reserve and Survey Forecasts," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(3), pages 507-532, April.
    12. Stefania D'Amico, 2004. "Density Estimation and Combination under Model Ambiguity," Computing in Economics and Finance 2004 273, Society for Computational Economics.
    13. Michael P. Clements & Ana Beatriz Galvão, 2007. "Macroeconomic Forecasting with Mixed Frequency Data: Forecasting US Output Growth," Working Papers 616, Queen Mary University of London, School of Economics and Finance.
    14. Marian Vavra, 2015. "On a Bootstrap Test for Forecast Evaluations," Working and Discussion Papers WP 5/2015, Research Department, National Bank of Slovakia.
    15. Öğünç, Fethi & Akdoğan, Kurmaş & Başer, Selen & Chadwick, Meltem Gülenay & Ertuğ, Dilara & Hülagü, Timur & Kösem, Sevim & Özmen, Mustafa Utku & Tekatlı, Necati, 2013. "Short-term inflation forecasting models for Turkey and a forecast combination analysis," Economic Modelling, Elsevier, vol. 33(C), pages 312-325.
    16. Ulrich Heilemann & Susanne Schnorr-Bäcker, 2016. "Could The Start Of The German Recession 2008-2009 Have Been Foreseen? Evidence From Real-Time Data," Working Papers 2016-003, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    17. Huiyu Huang & Tae-Hwy Lee, 2010. "To Combine Forecasts or to Combine Information?," Econometric Reviews, Taylor & Francis Journals, vol. 29(5-6), pages 534-570.
    18. 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.
    19. Bofinger, Peter & Schmidt, Robert, 2004. "Should One Rely on Professional Exchange Rate Forecasts? An Empirical Analysis of Professional Forecasts for the ?/US$ Rate," CEPR Discussion Papers 4235, C.E.P.R. Discussion Papers.
    20. Rodríguez-Vargas, Adolfo, 2020. "Forecasting Costa Rican inflation with machine learning methods," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 1(1).

    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.