Topic A.1: Modeling of forest and agricultural raw material flows 2010 – 2020 considering forecasts of calamities and disruptive factors (example: Lower Saxony)

Lower Saxony has a forest area of almost 1,2 million hectares of hard and softwood, corresponding to 24% of its land area and 10% of the whole of Germany. Based on preliminary work on the wood supply modeling (Müller et al., 2004; BMELV, 2005; FNR, 2010; Carus et al., 2010), scenario corridors are predicted for domestic flows of raw wood. The confinement to the Lower Saxony sample allows a higher data accuracy. A methodical challenge is the mapping of calamities and other disruptive factors up to 2020, analyzed in model simulation scenarios (Rüther et al., 2007). The general framework conditions to be observed are, for instance, the requirements of Nature Conservation (e.g., relinquishment of utilization of circa 10% of the local forest area). The future demand for firewood will also have an effect on the availability of raw materials for industrial use and must be taken into consideration in the estimation of the material flows. Export and import flows of finished and semi-finished wood products cannot be significantly affected by domestic economic decisions and are thus not the focus of this topic. They are only considered on a “random basis” in selected case studies that are product-oriented regarding selected timber industries.

As a result, scenarios of mass flows of soft wood logs, hardwood logs, industrial soft wood and industrial hard wood are expected, taking into account defined disruptive factors (exploitation rights, calamities, change in the timber industry structures, demand for certain wood types, etc.). From this, knowledge can be gained of their effects on the mass flows of the renewable resources under consideration. In conjunction with topics B.1, B.5 and B.8, the obtained data can be transferred to the applicable data bases. Requirements for the databases are also defined in topics B.3, B.4 and B.6.

In keeping with the good data structure of the timber industry domain, and taking the preliminary work into account (FNR, 2007; Deimling et al., 2007; Kopetz et al., 2007; Nusser et al., 2007; Thrän, 2007; Musiol et al., 2009; Peters et al., 2010; Thrän et al., 2010), similar forecasts are developed for the agricultural commodities domain.

This creates the methodological basis to improve the interface between the forestry sector and the timber industry, as well as between the agricultural sector and the industry purchasing the raw materials, specifically in the logistics area (see also topic B.1). Special attention is paid to the derivation of the by-product flows from the model scenarios of renewable resources’ mass flows.