Broccoli yield and dry matter partitioning in response to application of sediment from post-harvest washing of mussels
Bibliographic record
Abstract
The response of broccoli (Brassica oleracea L., ‘Arcadia’) yield to application rates of mussel sediment (MS) from post-harvest washing as source of nitrogen (N) was evaluated in a 2 yr (2011–2012) study in Bouctouche, NB, Canada. Treatments in 2011 included a control (no amendment or fertilizer), three rates of MS (28 000, 42 000, and 56 000 L·ha −1 equivalent to 14.7, 22.0, and 29.4 kg N·ha −1 ) and an inorganic N fertilizer (135 kg N·ha −1 ). In 2012, all plots were split, with just half of each plot receiving a repeat treatment application. Fertilizer and MS increased total fresh yields by 115% and 29%, respectively, compared with the control, with no significant differences between MS application rates. Total dry matter yield followed the same order as total fresh yield, but only at P < 0.10 in either years. Marketable yield was not affected by treatments in 2011 or by their residual effect in 2012, while it was greater in fertilizer compared with other treatments after 2 yr repeated application. The greatest head compactness and the lowest yellow-eye were measured in fertilizer treatment in both years, while no differences among MS treatments and the control were observed. The effect of treatments on total dry biomass and its partitioning in the broccoli plant was significant (P < 0.05) in both years. Yield and biomass data revealed that 42 000 L MS·ha −1 application rate plus supplemental N sources can be recommended under the soil and climate conditions of the Canadian Maritimes. The concentration of salts in MS is an application rate limiting factor.
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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 | 0.001 | 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".