The CROBAS family consists of dynamic models combining tree and stand structure and function at different spatial and temporal scales and forming a modular, hierarchical structure.
The core model describes the resource aquisition, allocation and growth of individual trees in a stand during one tree generation (CROBAS, Mäkelä 1986, 1997, 1999). A further development of CROBAS is the “Bridging Model” (Valentine and Mäkelä 2005).
The fast environmental drivers of canopy processes are studied with short-time-step modules (SPP, Oker-Blom et al. 1989, Mäkelä et al. 2006; PRELUED, Mäkelä et al. 2008), and the structure of stems is described in 3 dimensions in a high-resolution structure module (STEMS, Mäkelä and Mäkinen 2003). The high-resolution modules interact with the core module through specified link variables, making the system transparent and efficient.
Applications of this model system to specific questions include predictions of timber yield and quality (PipeQual, Mäkelä 2002, Mäkelä et al. 2000, Mäkelä and Mäkinen 2003, Kantola et al. 2007), growth of heterogeneous stands (ACROBAS, Kokkila et al. 2002, 2006), large-scale effects of climate and weather on productivity (Mäkelä et al. 2008), and a visual stand simulator for research and teaching (PuMe, Vanninen et al. 2006). The models have been utilised in economic analyses of forest management options (Hyytiäinen et al. 2004), contributing to the national recommendations of good forest management practice (Tapio 2006).
Mäkelä A., Pulkkinen M., Kolari P., Lagergren F., Berbigier B., Lindroth A., Loustau D., Nikinmaa E., Vesala T., Hari P. 2008. Developing an empirical model of stand GPP with the LUE approach: analysis of eddy covariance data at five contrasting conifer sites in Europe. Global Change Biology 14: 98- 108.
Kantola A., Mäkinen H. and Mäkelä A. 2007. Stem form and branchiness of Norway spruce as sawn timber – predicted by a process-based model. For Ecol. Manage 241:209-222.
Vanninen P., Härkönen S., Enkenberg J. and Mäkelä A. 2006. PuMe – Interactive learning environment employing the PipeQual model for forest growth and wood quality. New Zealand Journal of Forestry Science 36
Kokkila, T., Mäkelä, A. and Franc, A. 2006. Comparison of distance dependent and distance independent stand growth models – is perfect aggregation possible? Forest Science 52: 623-635
Mäkelä A., Kolari P., Karimäki J., Nikinmaa E., Perämäki M. and Hari P. 2006. Modelling five years of weather-driven variation of GPP in a boreal forest. Agriculture and Forest Meteorology 139:382-398.
Hyytiäinen K., Hari, P. Kokkila T., Mäkelä A., Tahvonen O. and Taipale J. 2004. Connecting a process-based forest growth model to stand-level economic optimization. Canadian Journal of Forest Research 34:2060-2073
Mäkelä A and Mäkinen H. (2003) Generating 3D sawlogs with a process-based growth model. Forest Ecology and Management 184:337-354
Kokkila, T., Mäkelä, A. and Nikinmaa, E. (2002) Describing the spatial distribution of a young tree stand for growth modelling purposes. Silva Fennica 36:265-277
Mäkelä, A. (2002). Derivation of stem taper from the pipe theory in a carbon balance framework. Tree Physiology 22: 891–905
Mäkelä, A., Sievänen, R., Lindner, M. and Lasch, P. (2000) Application of volume growth and survival graphs in the evaluation of four process-based growth models. Tree Physiology 20:347-356
Mäkelä, A. (1999). Acclimation in dynamic models based on structural relationships. Functional Ecology 13:145-156.
Mäkelä, A., Vanninen, P. and Ikonen, V.-P. (1997). An application of process-based modelling to the development of branchiness in Scots pine. Silva Fennica 31:369-380
Mäkelä, A. (1997) A carbon balance model of growth and self-pruning in trees based on structural relationships. Forest Science 43(1):7-24
Mäkelä, A. (1986). Implications of the pipe model theory on dry matter partitioning and height growth in trees. Journal of Theoretical Biology 123, 103-120.