Optimal seeding rate for organic production of field pea in the northern Great Plains
Bibliographic record
Abstract
Seeding rates have not been established for organic production of field pea in the northern Great Plains and producers must rely upon a recommended target stand of 88 plants m-2 for conventional production of this crop. This seeding rate may not be suitable as the two systems differ in the use of inputs and in pest management. The objective of this study was to determine an optimal seeding rate for organic production of field pea considering a number of agronomic factors and profitability. Field sites were established using a randomized complete block design with increasing seeding rates, summerfallow and green manure treatments. Seed yield increased up to 1725 kg ha-1 with increasing seeding rate. Weed biomass decreased with increasing seeding rate by up to 68%. Post-harvest soil phosphorus levels and soil water storage did not change consistently between treatments. Post-harvest soil inorganic nitrogen (N), however, was higher for the summerfallow and green manure treatments than for the seeding rate treatments. Field pea reached a maximum economic return at a seeding rate of 200 seeds m-2 and an actual plant density of 120 plants m-2. Organic farmers should increase the seeding rate of field pea to increase returns and provide better weed suppression. Key words: Pea (field), organic, seeding rate, weed suppression, profit, soil N
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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 | 0.001 |
| 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".