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ForecastModel Project

Developing methods that can be used to improve private sector timber forecasts

Background

The ForecastModel Project is a two year project funded by COFORD, aimed at developing methods that can be used to improve private sector timber forecasts. The project is a collaboration between Teagasc and UCD. The project team is Dr Niall Farrelly, Prof. Maarten Nieuwenhuis and Cian O’Connor.

The private forest sector now makes up a significant area with potential for timber supply (280,000 hectares of land has been afforested since 1987). Much of this is now reaching harvesting age and accurate forecasts of production are needed to assist in infrastructure development. Recently, there have been significant advances in the capacity to forecast private sector timber volumes and the quality of datasets, which have been used to produce a forecast for 2016-2035 (Phillips et al. 2016). This project aims to build on developed methods to produce more accurate forecasts of private sector timber production and incorporate recent advances in yield modelling.

Additional data from forest management plans for private forests exist and can be included to increase accuracy of forecasts. More detailed information on stand attributes, productivity and owner management intentions will also be evaluated. The possibility of using this data to generate more accurate forecasts will be evaluated.

The project will also investigate methods to generate crop attributes such as height and stocking to use directly in yield models such as GROWFOR. It is proposed that measures of site productivity and/or remote sensed data could be used to generate estimates of missing stand parameters. The accuracy of the various methods used will be compared.

Objectives

  • It will be assessed how information from the proposed Forest Management Plan (FMP) template can be incorporated into future forecasts. The management intentions of owners will be assessed in order to determine the proportion that intend on adhering to their forest management plans. A survey of forest owners will be undertaken to capture this. Harvesting may be restricted on some sites due to lack of access and/or challenging terrain. A framework will be developed to address this in future forecasts.
  • FMP data will not be present for all stands. In this case, new methods will be developed to overcome gaps in data. Inventory parameters will be predicted using measures of forest site productivity and/or remote sensed data. These inventory parameters (stocking, basal area and top height) can then be inputted into yield models, such as GROWFOR.
  • The accuracy of these predictive methods will be validated. A cost benefit analysis of FMP, remote sensed and yield prediction data will be conducted to determine their practicality in a national forecast.

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