Plant Growth and Nutritional Quality Attributes of Basella alba Applied with Variable Rates of Nitrogen Fertilizer at Different Planting Dates under Canadian Maritime Climatic Conditions
Bibliographic record
Abstract
Nitrogen (N) fertilization at critical planting time is important to optimize productivity and reduce nitrate accumulation in edible portions of green leafy vegetable plants. A field experiment was performed to determine the effects of variations in N rate and planting time on plant growth, yield, and nutritional quality attributes of Basella alba under Atlantic maritime climatic conditions. The N rates were 0 (control), 40 (low), 80 (medium), and 120 kg ha−1 (high) at planting times 15 June–3 August (early season), 6 July–20 August (mid-season), and 4 August–8 September (late season). Plant height, number of branches, and stem girth were increased after 45 days after sowing in early and mid-season plantings, but leaf length decreased during the same time by 32.8% in the late planting. The average yield obtained in early, mid-, and late plantings were 171, 464, and 328 g plant−1, respectively. Low N gave the highest yield in early planting while medium N gave higher yields in mid- and late plantings. However, the medium N increased nitrate accumulation in B. alba by 7% compared to the high N rate. In general, there was no significant effect of N on B. alba total phenolic and total carotenoid contents. Overall, the highest yield was obtained during the warmest summer months of mid- and late plantings. Therefore, there is a potential to grow B. alba as a summer vegetable under Canadian Atlantic maritime conditions. However, it is recommended to reduce the rate of N fertilizer application during high-temperature conditions. Future studies are required to investigate phosphorus and potassium fertilization and nitrate accumulation in B. alba and potential health risks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".