The effect of four tillage systems on agronomic properties and soil health indicators in southern Manitoba
Bibliographic record
Abstract
Soil health encompasses the collective functioning of chemical, physical, and biological properties in soil. The extent to which soil management affects soil health and the links with agronomic outcomes remain unclear. This project aimed to understand the interrelations of tillage systems, soil health, and agronomic properties in Portage la Prairie, MB, Canada. Tillage systems were cultivation, deep tillage, raised beds, and vertical tillage. Soybean ( Glycine max (L.) Merr), corn ( Zea mays L.), and canola ( Brassica napus L.) were all grown in 2020, 2021, and 2022. Crop yield, seed protein content, and seed oil content were measured each year. Soil samples were taken in spring 2021, fall 2021, and fall 2022 and analyzed for nitrate-N, ammonium-N, total N, ACE protein, water extractable organic N, water extractable total N, water extractable ammonium N, soil organic matter, soil organic carbon, calcium carbonate equivalent, CO2 burst, permanganate oxidizable carbon, water extractable organic C, pH, salts, Olsen P, K, S, sand, silt, and clay. Tillage system had a significant impact on agronomic properties in seven crop by sampling combinations. Tillage system effected soil nitrate-N concentration at five crop by sampling combinations, three more than any other soil property. Soybean agronomic properties correlated with soil health indicators more frequently than for corn and canola. This suggests that the utility of soil health indicators may be crop specific. Further research is needed to understand the mechanisms underpinning the ability of soil health indicators to predict agronomic outcomes and to benchmark soil health indicator values with time.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".