Long-term cropping system impact on quality and productivity of a Dark Brown Chernozem in southern Alberta
Bibliographic record
Abstract
Smith, E. G., Janzen, H. H. and Larney, F. J. 2015. Long-term cropping system impact on quality and productivity of a Dark Brown Chernozem in southern Alberta. Can. J. Soil Sci. 95: 177-186. Long-term cropping system studies offer insights into soil management effects on agricultural sustainability. In 1995, a 6-yr bioassay study was superimposed on a long-term crop rotation study established in 1951 at Lethbridge, Alberta, to determine the impact of past cropping systems on soil quality, crop productivity, grain quality, and the relationship of yield productivity to soil quality. All plots from 13 long-term crop rotations were seeded to wheat (Triticum aestivum L.) in a strip plot design [control, nitrogen (N) fertilizer]. Prior to seeding, soils were sampled to determine soil chemical properties. Total wheat production for the last 4 yr of the study was used as the measure of productivity. The 1995 soil analysis indicated crop rotations with less frequent fallow and with N input had higher soil quality, as indicated by soil organic carbon (SOC) and light fraction carbon (LF-C) and N (LF-N). SOC had a positive relationship to total wheat yield, but was largely masked by the application of N in this bioassay study. Frequent fallow in the previous crop rotation lowered productivity. The concentration of LF-C had a negative relationship, whereas LF-N had a positive relationship to total wheat yield, with and without N fertilization in this bioassay study. Grain N concentration was higher with applied N and when the long-term rotation included the addition of N by fertilizer, livestock manure, annual legume green manure or legume hay. This study determined that long-term imposition of management practices have lasting effects on soil quality and crop productivity.
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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 | 0.001 | 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".