Optimal seeding rate for organic production of lentil in the northern Great Plains
Bibliographic record
Abstract
Organic lentil (Lens culinaris Medik.) producers must rely upon the recommended rate for conventional production of 130 plants m-2, but this seeding rate may not be suitable, as organic and conventional production systems differ in management and inputs. The objective of this study was to determine an optimal seeding rate for organic production of lentil considering a number of factors, including yield, weed suppression, soil nitrogen and phosphorus concentrations, plant uptake of phosphorus, and economic return. A field experiment was conducted for 4 site-years at locations near Saskatoon, SK. Treatments included seeding rates of 15, 38, 94, 235 and 375 seeds m-2. Seed yield increased with increasing seeding rate up to 1290 kg ha-1. Weed biomass was reduced by 59% at the highest seeding rate as compared with the lowest seeding rate. Post-harvest soil phosphorus and nitrogen levels were similar between seeding rate treatments. Economic return was maximized at $952 ha-1 at the highest density of 229 plants m-2, achieved with a seeding rate of 375 seeds m-2. Organic farmers should increase the seeding rate of lentil to achieve a plant density of 229 plants m-2 to increase profitability and provide better weed suppression.Key words: Lentil, organic, seeding rate, weed suppression, economic return
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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 | 0.001 |
| 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".