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Statistical forecasting of the Australian macadamia crop

Mayer, D. G. and Stephenson, R. A. (2016) Statistical forecasting of the Australian macadamia crop. Acta Horticulturae, 1109 . pp. 265-270. ISSN 05677572 (ISSN); 9789462611030 (ISBN)

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Article Link(s): http://dx.doi.org/10.17660/ActaHortic.2016.1109.43

Abstract

Three types of forecasts of the total Australian production of macadamia nuts (t nut-in-shell) have been produced early each year since 2001. The first is a long-term forecast, based on the expected production from the tree census data held by the Australian Macadamia Society, suitably scaled up for missing data and assumed new plantings each year. These long-term forecasts range out to 10 years in the future, and form a basis for industry and market planning. Secondly, a statistical adjustment (termed the climate-adjusted forecast) is made annually for the coming crop. As the name suggests, climatic influences are the dominant factors in this adjustment process, however, other terms such as bienniality of bearing, prices and orchard aging are also incorporated. Thirdly, industry personnel are surveyed early each year, with their estimates integrated into a growers and pest-scouts forecast. Initially conducted on a 'whole-country' basis, these models are now constructed separately for the six main production regions of Australia, with these being combined for national totals. Ensembles or suites of step-forward regression models using biologically-relevant variables have been the major statistical method adopted, however, developing methodologies such as nearest-neighbour techniques, general additive models and random forests are continually being evaluated in parallel. The overall error rates average 14% for the climate forecasts, and 12% for the growers' forecasts. These compare with 7.8% for USDA almond forecasts (based on extensive early-crop sampling) and 6.8% for coconut forecasts in Sri Lanka. However, our somewhatdisappointing results were mainly due to a series of poor crops attributed to human reasons, which have now been factored into the models. Notably, the 2012 and 2013 forecasts averaged 7.8 and 4.9% errors, respectively. Future models should also show continuing improvement, as more data-years become available.

Item Type:Article
Business groups:Horticulture and Forestry Science
Keywords:Biennial bearing Climate Ensemble model Nut Step-forward regression
Subjects:Science > Statistics > Statistical data analysis
Deposited On:12 Aug 2016 03:59
Last Modified:15 Aug 2016 06:26

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