An early study on the potential of landscape and geographical variables to reduce bias in forest forecast planning in Ireland : a view on the value of data mining in an industry and era rich in information

Abstract: At a time when data are an integral part of many industries and the world as a whole, driven by a digital dominant era, there is an emerging discussion surrounding the level of value captured. Using data accumulated in the forest management system and the surrounding databases of the forestry semi state Coillte, my aim was to research if there was any type of data that could potentially be used to identify and understand inaccuracy in long- and short-term forecasting. The Company’s Tactical and Strategic forecast volumes for 2018 were used in conjunction with the actual harvest volume from weigh-bridge measurements and roadside stocks in order to understand the current extent of over- and under-estimation. To achieve this, the methods of linear and stepwise backwards logistic regressions were used. The linear regressions based on the percentage of difference of the forecast volumes towards the actual harvested volumes were inconclusive. The logistic regressions were produced using eight binary response variables based on over-and under-estimation. For each forecast type they included, a dataset with all species and product volumes, a dataset with only the dominant species volume (Sitka Spruce/Picea Sitkensis), and datasets using the most valuable product (large sawlog) with total volumes and volumes of the primary species only. The predictors consisted of landscape and geographic variables; namely elevation, slope, aspect, country segment, distance from coast, latitude, soil type and roughness. The results showed that over-estimation is the most common form of forecast bias with the tactical forecast models being the most accurate using the predictor variables, Elevation, Roughness and Soil Type. The variables, Aspect, Segment within country and Slope were shown to be the least valuable for prediction. Thus, these aspects should be taken into account when researching forecast bias at the planning level.