Sheep Grazing Enhances Coarse Relative to Microbial Organic Carbon in Dryland Cropping Systems
Bibliographic record
Abstract
Sheep (Ovis aries L.) grazing, a cost-effective method of weed control compared with herbicide application and tillage, may influence soil C fractions by consuming crop residue and weeds and returning C through feces and urine to the soil. We examined the effect of three weed management practices (sheep grazing, herbicide application, and tillage) and two cropping sequences (continuous spring wheat [Triticum aestivum L.] [CSW] and spring wheat-pea [Pisum sativum L.]/barley [Hordeum vulgare L.] mixture hay-fallow [W-P/B-F]) on soil microbial biomass C (MBC), potential C mineralization (PCM), and particulate organic C (POC) in relation to soil organic C (SOC) at the 0- to 30-cm depth from 2009 to 2011 in southwestern Montana. The MBC at 0 to 5 cm was greater with tillage on CSW than tillage on W-P/B-F in 2009 and 2011, but was greater with herbicide application on CSW than tillage on CSW in 2010. The POC at 0 to 5 cm and 15 to 30 cm was greater with sheep grazing than herbicide application on CSW and W-P/B-F, but at 5 to 15 cm was greater with grazing on CSW. The MBC, PCM, and POC at all depths decreased from 2009 to 2011. Crop residue incorporation into the soil increased MBC with tillage on CSW. Lower proportions of labile than nonlabile organic matter through feces and urine probably reduced MBC at the soil surface, but increased POC with sheep grazing compared with herbicide application on CSW and W-P/B-F. Sheep grazing may increase coarse soil organic matter compared with microbial biomass in dryland cropping systems.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".