Seasonal distribution of runoff and soil loss under four tillage treatments in the upper St. John River valley New Brunswick, Canada
Bibliographic record
Abstract
The effect of four different tillage treatments on surface runoff and soil loss, their seasonal distribution and temporal variation in soil erodibility were examined using runoff-erosion plots (10 m wide × 30 m long), on a Holmesville gravelly loam soil, a major soil type used for potato production in New Brunswick. Fall moldboard plowing, fall chisel plowing, spring moldboard plowing and subsoiling followed by fall moldboard plowing were evaluated under barley and fallow conditions on 8 and 11% slopes between 1989 and 1993. With exception of one year, annual precipitation was lower than normal. However, due to higher rainfall during the cropping season, the calculated erosivities were higher than those typically used for conservation planning in this region. Runoff data revealed that fall moldboard plowing generated the highest runoff. Either performing subsoiling prior to fall moldboard plowing or delaying moldboard plowing until the next spring, reduced runoff by approximately 10%. Chisel plowing, which loosens the soil without inverting it and leaves a large amount of residues on the surface, provided by far the greatest benefit in reducing runoff (20% reduction over fall moldboard plowing). Soil loss from fall moldboard plowing on the 11% slope under fallow was 2.8 and 2.6 times greater than from spring moldboard plowing and fall chisel plowing, respectively. The majority of the difference in soil loss occurred during the summer months. The benefit of spring moldboard and fall chisel plowing was considerably less on the 8% slope in which soil loss from the fall moldboard plowing was only 24 and 19% higher than spring moldboard and fall chisel plowing, respectively, indicating that the benefits are slope dependent and increase with increasing slope from 8 to 11%. When the plots were planted in Chapais barley (Hordeum vulgare L.), soil losses were negligible. Tillage treatments, and particularly cropping practices, play a major role in seasonal distribution of runoff and soil loss. Under fallow, approximately 79% of runoff and 8.1% of soil loss occurred during the non-cropping season whereas 96% of runoff and 68% of soil loss were found when the plots were planted in barley. The soil erodibility factor was two to three times higher during March and April, which coincide with the winter-spring thaw period, than during the rest of the year. This seasonal variation must be considered when using event-based models to predict soil losses. Key words: Moldboard plow, chisel plow, subsoiling, erodibility, erosivity, universal soil loss equation, crop residue
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".