Interactive effect of N fertilization and tillage management on Zn biofortification in durum wheat (<i>Triticum durum</i>)
Bibliographic record
Abstract
Gao, X. and Grant, C. A. 2011. Interactive effect of N fertilization and tillage management on Zn biofortification in durum wheat ( Triticum durum ). Can. J. Plant Sci. 91: 951–960. A 3-yr field study was conducted at two locations in southwestern Manitoba, Canada, to determine the interactive effect of application of four sources of N fertilizer and two tillage management systems on grain Zn concentration of durum wheat. There was a significant year-to-year variation in grain yield and grain Zn concentration, indicating a strong environmental influence. Soil type also had a dominating effect, with grain Zn concentration generally being higher at the clay loam location than the fine sandy loam location, reflecting the native soil Zn status. Tillage management showed little influence on grain Zn, suggesting that reduced tillage practices can be adopted by local farmers without decreasing mineral concentrations in grain. Compared with the control treatment, which did not receive extra N fertilizer, N fertilization at 60 kg ha −1 decreased grain Zn concentration in 4 of 6 site-years. Grain Zn accumulation was, however, generally not affected by extra N fertilization, in spite of a positive fertilization effect on grain yield. The four N sources did not differ in their effect on grain yield and grain Zn, indicating that at the rate of N applied there were no differential fertilizer source effects on Zn availability. The results of the present study suggest that for wheat production on Canadian prairies, a regular N fertilization rate using the currently registered cultivars is not likely to produce wheat grain that meets the recommended dietary allowance for Zn. Application of Zn fertilizer, in combination with optimum N fertilization or other agronomic practices that can increase grain Zn, is required to produce improved grain quality for human health.
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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".