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ForecastModel Project

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


The ForecastModel Project was 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 built on developed methods to produce more accurate forecasts of private sector timber production and incorporated recent advances in yield modelling.

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

The project also investigated methods to generate crop attributes such as height and stocking to use directly in yield models such as GROWFOR. 


  • Assess how information from the proposed Forest Management Plan (FMP) template can be incorporated into future forecasts. The management intentions of owners was assessed in order to determine the proportion that intend on adhering to their forest management plans. A survey of forest owners was undertaken to capture this. 
  • Where FMP data were not available, methods were developed to overcome gaps in data. Inventory parameters were predicted using measures of forest site productivity and/or remote sensed data. These inventory parameters (stocking, basal area and top height) were then inputted into yield models, such as GROWFOR.
  • The accuracy of these predictive methods was validated. A cost benefit analysis of FMP, remote sensed and yield prediction data was conducted to determine their practicality in a national forecast.