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Using multi-objective classification to model communities of soil microarthropods
Authors:Damjan Demšar  Sašo D?eroski  Thomas Larsen  Jan Struyf  Jørgen Axelsen  Marianne Bruus Pedersen  Paul Henning Krogh
Institution:1. Department of Knowledge Technologies, Jo?ef Stefan Institute, Jamova Ljubljana, Slovenia;2. Department of Terrestrial Ecology, National Environmental Research Institute, Roskilde, Denmark;3. Department of Computer Science, Katholieke Universiteit Leuven, Leuven, Belgium
Abstract:In agricultural soil, a suite of anthropogenic events shape the ecosystem processes and populations. However, the impact from anthropogenic sources on the soil environment is almost exclusively assessed for chemicals, although other factors like crop and tillage practices have an important impact as well. Thus, the farming system as a whole should be evaluated and ranked according to its environmental benefits and impacts. Our starting point is a data set describing agricultural events and soil biological parameters. Using machine learning methods for inducing regression and model trees, we produce empirical models able to predict the soil quality from agricultural measures in terms of quantities describing the soil microarthropod community. We are also interested in discovering additional higher level knowledge. In particular, we have identified the most important factors influencing the population densities of springtails and mites and their biodiversity. We also identify to which agricultural actions different microarthropods react distinctly. To obtain this higher level knowledge, we employ multi-objective regression trees.
Keywords:Multi-objective classification  Modelling  Soil microarthropods
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