Assessment of soil quality has historically had a tendency to revolve around agricultural production. One example of that is The US land Capability Classification system, as a system to rank soils according to their agricultural quality. Nevertheless, soil quality is more than just agricultural quality and is a subject of subjectivity, dependent on the current land use. This thesis sets out to build a method for evaluation for soil quality in three perspectives, being (water)environment, climate and agriculture. The method for evaluation is to be based upon an easy data collection, cost effective in relation to economy and time as well as low uncertainty. For each perspective, soil-physical quality parameters are defined. Quality parameters for agriculture are chosen to be Plant-Available-Water (PAW) and the relative gas diffusion coefficient (D_p/D_0 ), for environment it is chosen to be saturated hydraulic conductivity (K_s) and D_p/D_0 , and for climate it is chosen to be D_p/D_0 alone. Specific boundaries are set for the quality parameters, dividing them in an ideal scenario (Green zone), bad scenario (Red zone) and a scenario in between (Yellow zone). The boundaries are in the table below.

Zones/Quality parameters             Green           Yellow              Red
K_s (cm/s 10^-4)                          < 10            10 – 50            > 50
PAW (cm^3 Water)/(cm^3 soil)     > 0.15        0.10 – 0.15        < 0.10
D_p/D_0 (cm air)/(cm soil)           > 0.02       0.005 – 0.02       < 0.005

The method for evaluation revolves around predicting these three quality parameters in a way that fits the overall objectives, just presented as easy data collection, cost effectiveness and low uncertainty. Generally, two measurement strategies are chosen to fit the overall objectives and are expected to be possible to eventually predict the quality parameters. These strategies are air-dry water content at relative humidity of 31.8 % (θ_r ) and Visible-Near Infrared Spectroscopy (NIR). θ_r and NIR cannot directly predict the quality parameters but typically only static parameters as clay, organic matter and particle density. From here on the real goal is to find relationships as well as use existing relationships, between a range of soil-physical parameters to eventually get a prediction of the quality parameters. Three datasets are used for modelling. Two of these handed over from Department of agroecology, Aarhus University, being 45 topsoil samples that represents a gradient in clay (Estrup Field – 55°29´09.96´´N, 9°04´09.37´´E) and 20 topsoil samples that represents a gradient in organic matter (Ø Bakker Field – 9°37'53.278"N, 56°27'31.46"E). Last dataset in use is the Danish soil database of Hansen (1976). Generally seen, most of the prediction models for the static parameters are considered as being applicable with various advantages and disadvantages for the inclusion of the whole or a subpart of the dataset and also for measurement strategy. A computation example of a method of evaluation of soil quality for the Estrup Field dataset, showed that most of the samples had a K_s in the yellow zone, where the remaining samples were defined as being in the green zone. For D_p/D_0 , all of the samples were defined as being in the yellow zone and for PAW almost all of the samples were defined as being green, with a single sample being defined as yellow. Because of D_p/D_0 being defined as a quality parameter for all three perspectives used in this thesis, the soil quality evaluation criteria in all three perspectives are viewed as being partly fulfilled (yellow). This applies because, as in order for a specific soil sample to be defined as green, all soil quality parameters must also be green. The computation example should though be viewed with cation, as a sensitivity analysis based upon only a subset of the prediction models, showed an uncertainty of the predictions, that exceeds the chosen boundaries for the quality parameters. Hence, more studies are needed to lower the uncertainty of evaluation methods for soil quality, based upon θ_r and NIR spectroscopy alone.