An ecosystem approach to assess soil quality in organically and conventionally managed farms in Iceland and Austria
Bibliographic record
Abstract
Abstract. Intensive agricultural production can be an important driver for the loss of long-term soil quality. For this reason, the European Critical Zone Observatory (CZO) network adopted four pairs of agricultural CZO sites that differ in their management: conventional or organic. The CZO sites include two pairs of grassland farms in Iceland and two pairs of arable farms in Austria. Conventional fields differed from the organic fields in the use of artificial fertilisers and pesticides. Soils of these eight farms were analysed in terms of their physical, chemical, and biological properties, including soil aggregate size distribution, soil organic matter contents, abundance of soil microbes and soil fauna, and taxonomic diversity of soil microarthropods. In Icelandic grasslands, organically farmed soils had larger mean weight diameters of soil aggregates than the conventional farms, while there were no differences on the Austrian farms. Organic farming did not systematically influence organic matter contents or composition, nor soil carbon and nitrogen contents. Also, soil food web structures, in terms of presence of trophic groups of soil organisms, were highly similar among all farms, indicating a low sensitivity of trophic structure to land use or climate. However, soil organism biomass, especially of bacteria and nematodes, was consistently higher on organic farms than on conventional farms. Within the microarthropods, taxonomic diversity was systematically higher in the organic farms compared to the conventional farms. This difference was found across countries and farm, crop, and soil types. The results do not show systematic differences in physical and chemical properties between organic and conventional farms, but confirm that organic farming can enhance soil biomass and that microarthropod diversity is a sensitive and consistent indicator for land management.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".