Soil enzymatic factors expressing the influence of land use, tillage system and texture on soil biochemical quality
Bibliographic record
Abstract
We identified complexes of soil nutrient mineralising enzymes expressing the influence of land use, tillage system and texture on soil biochemical quality in production systems involving corn, soybean, wheat and oat. The activities of dehydrogenase, β-glucosidase, L-glutaminase, urease, alkaline phosphatase, and arylsulphatase were measured in 760 soil samples taken from the A horizon of uncultivated land and cultivated Gleysols and Luvisols cropped with conventional tillage (CT) and reduced tillage (RT) systems between 1994 and 1996.Discriminant analysis showed that an enzymatic decomposition factor captured 96% of the total dispersion in soil enzyme activity responding to type of land use and tillage system. The soil enzymes β-glucosidase, dehydrogenase and L-glutaminase contributed most to this factor and were sensitive indicators for assessing the health of microbial mineralisation processes of the C and N cycles. Two biochemical factors expressed the influence of texture on soil enzyme activity. The first, a soil organic C and N decomposition factor captured 68% of the dispersion in enzyme activities was influenced mostly by β-glucosidase and dehydrogenase. The second factor, which captured 32% of the dispersion in enzyme activity, was influenced mostly by arylsulphatase and denotes the effects of texture on a pool generally considered to be extracellular in nature. Cluster analysis helped define seven levels of soil enzyme activity ranging from very low (mostly in soils cropped with CT) to very high [mostly in uncultivated (UC) land and soils cropped with RT]. The identification of key enzymatic factors and the definition of seven levels of enzyme activity serve as a basis for developing quantitative systems monitoring the impact of crop production systems on soil enzymes having specific ecological functions in agricultural land. Key words: Soil enzymes, tillage, land use, texture, dehydrogenase, β-glucosidase, glutaminase
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".