Impacts of conservation agriculture on soil C and N stocks and organic matter fractions: comparing commercial producer fields with a long-term small-plot experiment in Brown Chernozems of Saskatchewan
Bibliographic record
Abstract
Conservation agriculture (CA) is increasingly promoted to build soil organic matter (SOM) based on findings from predominantly small-plot long-term agroecosystem experiments (LTAEs), with minimal on-farm data. Using commercial producer fields ( n = 20) in the Brown Chernozemic soil zones of Saskatchewan, Canada, which were sampled before (1996) and after (2018) adopting direct-seeding and continuous cropping (1997), we examined changes in soil organic carbon (SOC) and soil total nitrogen (STN) stocks, along with C and N stocks in particulate (POM) and mineral-associated organic matter (MAOM), and compared them to an LTAE in the same soil zone. After 21 years, SOC and STN stocks (0–30 cm depth) increased by 13% and 21%, respectively, in commercial producer fields, and were more pronounced in finer- than coarser-textured soils. Conversely, there were no significant changes (0–30 cm depth) after 18 years (1998–2016) with CA (continuous wheat and pulse-wheat under no-tillage (PW-NT)) in the LTAE, except that STN stock for PW-NT decreased by 7.7%. The estimated rate of change to 30 cm depth was similar between the commercial fields and LTAE for SOC (0.28 and 0.16 Mg C ha −1 year −1 , respectively), but not STN (0.04 and −0.03 Mg N ha −1 year −1 , respectively). Changes were more evident in the MAOM than POM fraction in both cases. Although the impact of CA may be similar, as observed for SOC, actual on-farm changes will depend on site-specific factors, and specific CA practice. Therefore, on-farm monitoring studies are needed for more accurate assessments of SOM changes and C sequestration potentials.
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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 | 0.000 |
| 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".