Nitrogen status in maize grown at different row spacings and nitrogen availability
Bibliographic record
Abstract
Barbieri, P. A., Echeverría, H. E., Sainz Rozas, H. R. and Andrade, F. H. 2013. Nitrogen status in maize grown at different row spacings and nitrogen availability. Can. J. Plant Sci. 93: 1049–1058. Improving nitrogen use efficiency (NUE) is imperative to sustainable agriculture. To attain this goal in maize crops (Zea mays L.) there are nitrogen (N) diagnosis methods that enable determination of a crop's nutritional status by analysis of plant parts. Maize planted in narrow rows (NR) can have increased dry matter (DM), grain yield and accumulated N. However, no reports have been found on the effect of NR of N in plant diagnosis methods. An experiment was performed over 3 yr to evaluate NR and N fertilizer rates on the N dilution curve, N concentration in grain and chlorophyll content in maize. Treatments consisted of a factorial combination of row width (70, 52 and 35 cm) and N rate (0 to 180 kg N ha−1). The N dilution curves adjusted for fertilized or control treatments were similar among row spacing. Nitrogen concentration in grain was correlated with relative yield (RY), and similar critical values for N response were similar between row spacings. Leaf chlorophyll content increased with N and NR; however, green index (GI) and N sufficiency index (NSI) values were not different between row spacing when correlated to RY. These results indicate that response thresholds to N fertilization determined on plant tissue for NR treatments were similar among row spacings. Thus, there is no need to adjust the response thresholds to N application based on row spacing, as NR did not cause any changes in physiological efficiency (PE) due to the determined proportional increases, both in accumulated N in DM and grain yield.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| 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".