Optimum seeding rates for three annual green manure crops: the effect of weed competition
Bibliographic record
Abstract
Green manure crops are critical to maintaining soil fertility in organic cropping systems. However, little research has been conducted to address their contribution to weed control. Indian Head black lentil (Lens culinaris), AC Green Fix chickling vetch (Lathyrus sativus), and Trapper field pea (Pisum sativum) are legumes developed for use as annual green manure crops in Saskatchewan. Currently, the recommended seeding rates for these crops are based on those developed for seed production of similar genotypes under weed-free conditions. Thus, our objective was to determine the optimal seeding rates for these three annual green manure crops under weedy and weed free conditions. The competitive ability of the green manure crops was assessed based on their ability to maintain crop biomass and reduce weed biomass. Each green manure crop was grown at five plant population densities (10, 26, 64, 160, and 400 plants m2) with weedy and weed free treatments. Wild oat (Avena fatua) and wild mustard (Brassica kaber) were planted in the weedy treatment to supplement the natural weed community. Biomass samples were taken at early bud and full bloom stages to simulate when these crops would be terminated. Trapper field pea produced the most crop biomass while Indian Head black lentil and AC Green Fix chickling vetch produced comparable amounts of crop biomass under both weedy and weed-free conditions. While Trapper field pea produced more crop biomass than AC Green Fix chickling vetch, weed biomass was similar for both species. Weed biomass was highest for Indian Head black lentil. Total biomass increased between the early bud and full bloom biomass dates for all species. However, weed biomass as a proportion of total biomass increased more than crop biomass between early bud and full bloom in weedy \ntreatments. Recommended seeding rates developed from this experiment will help organic producers take advantage of the weed control offered by a competitive annual green manure while still achieving their objectives of improved soil nutrient status and soil quality.
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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".