Biotehniška fakulteta Univerze v Ljubljani is not a stand-alone classical forestry faculty, but a broad biotechnical faculty. For the F9 cluster, the most relevant units are the Oddelek za gozdarstvo in obnovljive gozdne vire and the Oddelek za lesarstvo. For SEO, the Slovenian terms should be included intentionally: gozdarstvo, obnovljivi gozdni viri, lesarstvo, biotehnika, naravovarstvo, krajinska arhitektura, ekologija, gozdni ekosistemi and lesna tehnologija.
Students and researchers work with data from forestry, ecology, wood technology and biotechnical sciences: tree diameter, tree height, growing stock, biomass, increment, stand structure, forest health, biodiversity, GIS layers, spatial data, climate variables, physical and mechanical wood properties, wood product quality and laboratory testing. This article belongs to the F9 hub: statistical help for forestry faculties, SPSS, ecology, GIS and growth models.
Statistical data analysis for Biotehniška fakulteta Univerze v Ljubljani — forestry and wood science
Biotehniška fakulteta Univerze v Ljubljani is not a stand-alone classical forestry faculty, but a broad biotechnical faculty. For the F9 cluster, the most relevant units are the Oddelek za gozdarstvo in obnovljive gozdne vire and the Oddelek za lesarstvo. For SEO, the Slovenian terms should be included intentionally: gozdarstvo, obnovljivi gozdni viri, lesarstvo, biotehnika, naravovarstvo, krajinska arhitektura, ekologija, gozdni ekosistemi and lesna tehnologija.
Students and researchers work with data from forestry, ecology, wood technology and biotechnical sciences: tree diameter, tree height, growing stock, biomass, increment, stand structure, forest health, biodiversity, GIS layers, spatial data, climate variables, physical and mechanical wood properties, wood product quality and laboratory testing. This article belongs to the F9 hub: statistical help for forestry faculties, SPSS, ecology, GIS and growth models.
Why statistics matters for gozdarstvo and lesarstvo
Forestry and wood science work with natural and technical systems characterized by high variability. Tree growth, increment, growing stock, forest health, biodiversity and wood properties depend on tree species, soil, climate, altitude, exposure, management, pests, diseases and environmental change.
Statistical analysis helps distinguish real patterns from random variability. When sites, tree species, stands, treatments, wood technology processes or GIS indicators are compared, it is necessary to test whether differences are statistically significant and practically meaningful.
Data types
Typical data types include dendrometric data: diameter, height, volume, basal area, growing stock and increment; ecological data: biodiversity, habitats, regeneration and degradation; GIS data: spatial layers, slope, exposure, land cover and fragmentation; climate data: temperature, precipitation and drought periods; wood technology data: density, moisture, strength, hardness, shrinkage and swelling; and survey data about forests, environment, urban greenery and wood products.
Research design
In forestry, sampling design is crucial. Data are often collected by sample plots, stands, locations or time points. If measurements are hierarchically grouped, statistical analysis must account for this structure.
In wood technology, studies often compare wood species, treatments, processing conditions or material properties. In GIS and ecological analysis, spatial information must be linked to quantitative variables.
Important statistical methods
Descriptive statistics include means, medians, standard deviations, minima, maxima and coefficients of variation.
ANOVA is used to compare several groups, such as tree species, locations, treatments or wood processing procedures. After significant results, post-hoc tests such as Tukey or Bonferroni help identify specific group differences.
Correlation analysis examines relationships between tree diameter and height, climate and increment, wood density and strength, or biodiversity and habitat structure.
Regression analysis is used to model volume, biomass, increment, tree growth, damage risk, wood quality or environmental effects.
Growth models are especially important in forestry. Tree and stand growth is often nonlinear, so nonlinear models, predictive models and time-series approaches may be appropriate.
Multivariate methods such as PCA and cluster analysis are useful when many variables are analyzed simultaneously, for example in stand classification, ecological pattern analysis or comparison of wood properties.
SPSS, GIS and interpretation
SPSS can be used for descriptive statistics, group comparisons, ANOVA, regression, correlation and basic multivariate analyses. GIS tools are used for spatial analysis, but GIS-derived indicators often require additional statistical interpretation.
The key point is subject-matter interpretation: results must be explained in the context of forest management, ecology, nature conservation, wood technology and sustainable use of renewable resources.
Workflow
The workflow includes reviewing the topic, dataset and research design, selecting appropriate methods, SPSS analysis, table preparation and interpretation. I prepare statistical results and suggest how to present them in the academic paper. The author remains responsible for the final text and interpretation.
Keywords
statistics Biotehniška fakulteta Ljubljana, gozdarstvo statistics, lesarstvo statistics, Oddelek za gozdarstvo in obnovljive gozdne vire statistics, Oddelek za lesarstvo statistics, SPSS gozdarstvo, GIS forestry, forest growth models, forest ecology statistics, lesna tehnologija statistics
Metatext
Statistical data analysis for Biotehniška fakulteta Univerze v Ljubljani: gozdarstvo, lesarstvo, SPSS, GIS, ecology, growth models, ANOVA, regression and wood technology.
