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The data were collected to evaluate whether quantitative visual observations (as part of Visual Soil Evaluation) can be used to assess soil functions that determine the soil quality. Quantitative visual observations were collected at five dairy farms in the Netherlands (that participated in the ongoing project ‘Koeien & Kansen’), on 25 sampling sites in total.

Quantitative visual observations were collected for two studies on dairy farms in the Netherlands. Data were collected following the same methodology (Van Leeuwen et al., 2018) based on Visual Soil Assessment of Shepherd (2009).

Visual evaluation of soil structure (VESS) is used for assessing arable management impact on soil quality. When used on pastures, operators have identified limitations because VESS does not consider a surface root-mat typical of managed grassland. The structure of the root-mat may be indicative of nutrient use efficiency, pollution potential and subsurface compaction. The objectives of this research were to develop GrassVESS for grassland soil management, to compare it with VESS and quantitative physical indicators and to assess its utility for soil management. GrassVESS maintained the methodological strengths of VESS, but uses a flow chart, grassland images and a new root-mat score. A focus group found GrassVESS to be quicker, dealt better with technical information and made root-mat evaluation easier. The range of structural quality scores assigned by the focus group for a site was less for GrassVESS than VESS, suggesting the procedure is more reproducible, thus suitable for use by a range of stakeholders. GrassVESS was also deployed at 30 grassland sites across Ireland. Results indicated that GrassVESS generated the same overall diagnoses as VESS, but the GrassVESS root-mat structural quality score was better related to bulk density, total porosity at 5–10 cm and a visual estimation of damaged sward area. It was concluded that GrassVESS has improved the VESS method for the specific assessment of grassland soil structural quality and could be used in real-time farm management decision support.