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Macadamia Crop Forecasting 2023 - 2025

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Mayer, D. G. (2025) Macadamia Crop Forecasting 2023 - 2025. Project Report. Hort Innovation.

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Publisher URL: https://www.horticulture.com.au/globalassets/laserfiche/assets/project-reports/mc22001/final-report-mc22001.pdf

Abstract

The ‘Macadamia Crop Forecasting’ project delivered –

• a climate-adjusted macadamia crop forecast for the 2023, 2024 and 2025 seasons, and
• longer-term forecasts (out to 10 years) for the expected production of the Australian macadamia industry.
This information is important to inform processors and producers, and to assist in decision-making regarding industry logistics, export contracts, and future industry expansion. Each year, targeted data were successfully sourced and analysed to create the annual and long-term crop forecasts. The Australian Macadamia Society (AMS) collated historical production on a regional basis. The relevant meteorological variables were obtained from the Australian Bureau of Meteorology, and other important data (including price history and satellite imagery) were collated. Production patterns were fitted by cross-matching actual production for each region against tree numbers. These models form the basis of the expected production for the long-term forecasts. Adjustments for climate and other proven effects were then made for each year’s final forecast in February. These forecasts were developed using both ‘more traditional’ statistical models along with some of the more promising machine-learning algorithms. In parallel, an industry-wide survey of flower ratings was collated by the Macadamia Benchmarking Project team, to identify any potential problems. The results and crop forecasts were drawn together in a full report that was forwarded to Hort Innovation and AMS each year. This report constitutes the key outcome of this project. The project’s Reference Group, including key AMS and industry personnel, met annually.

Whilst past results have been quite acceptable, in the future forecasting accuracy could well be improved by adopting some of the more-promising developing methodologies.

Item Type:Monograph (Project Report)
Corporate Creators:Department of Primary Industries, Queensland
Business groups:Animal Science
Keywords:Final report; Macadamia; crop forecast; climate effects; statistical models; machine learning
Subjects:Science > Botany > Genetics
Agriculture > Agriculture (General) > Methods and systems of culture. Cropping systems
Plant culture > Food crops
Plant culture > Fruit and fruit culture > Nuts
Live Archive:17 Jul 2025 03:03
Last Modified:17 Jul 2025 03:03

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