Low‐Input Management and Mature Conservation Tillage: Agronomic Potential in a Cool, Humid Climate
Bibliographic record
Abstract
Combining low‐input systems with conservation tillage may be feasible for field crops under northeastern conditions. This study compared the effects of herbicide‐free (HF), organic (ORG), conventional (CONV), and herbicide‐tolerant (GM) cropping systems applied to three 20 yr‐old tillage treatments (MP, moldboard plow; CP, chisel plow; NT, no‐till) on weed biomass and crop productivity in a 4‐yr barley (Hordeum vulgare L.)–red clover (Trifolium pratense L.)–corn (Zea mays L.)–soybean [Glycine max (L.) Merr.] rotation. Barley yield (4.5 Mg ha–1), and red clover forage yield (two cuts: 5.3 Mg ha–1) were similar across treatments. With MP and CP tillage, silage corn yield for CONV and GM systems (15 Mg ha–1) was 25% greater than for HF and ORG (11 Mg ha–1), whereas HF‐NT and ORG‐NT systems produced no harvestable yield. Soybean yield for HF‐MP and ORG‐MP systems was similar to that for CONV and GM (2.4 Mg ha–1), whereas yield in for the HF and ORG systems with CP and NT was half or less than for other treatments. Some form of primary tillage (CP or MP) was needed in corn and soybean to achieve adequate weed control and yield in the ORG and HF systems. Midseason weed proportion of total biomass was greater in the HF and ORG systems with CP and NT, and provided good yield prediction in corn (R2 = 0.74) and soybean (R2 = 0.84). Nutrient availability appeared adequate in corn following N2–fixing red clover but limiting in NT and CP for soybean following corn. Improving crop sequence, fertilization, and weed management will be key to the adoption of low‐input systems using conservation tillage practices in cool, humid climates.
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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) | 0.001 | 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".