NITROGEN NUTRITION ON LEAF CHLOROPHYLL, CANOPY REFLECTANCE, GRAIN PROTEIN AND GRAIN YIELD OF WHEAT VARIETIES WITH CONTRASTING GRAIN PROTEIN CONCENTRATION
Bibliographic record
Abstract
Wheat cultivars (‘AC Barrie’, ‘Brook Field’, ‘Hoffman’, and ‘Norwell’) with different protein concentrations were compared under four nitrogen (N) levels (0, 50, 100 and 150 kg ha−1) in an environment-controlled greenhouse, and the same experiment with an additional N level (200 kg N ha−1) was repeated in the field in 2007. In the greenhouse experiment, application of 100 kg N ha−1 resulted in significantly greater grain yield due mainly to higher number of grains per spike and heavier mean grain weight; in the field study, the 150 kg N ha−1 treatment produced the greatest yield (P<0.01) primarily due to more number of grains per spike. Crude grain protein percentage was increased significantly with each increment of N up to the highest level; however, protein yield (kg ha−1) increased significantly with fertilizer up to 150 kg N ha−1. Leaf chlorophyll contents were increased linearly with increment of N levels up to 150 kg ha−1 both in the greenhouse and field trials while leaf area indices continued to increase up to the highest application rate (200 kg N ha−1). Canopy reflectance, expressed as normalized difference vegetation index (NDVI), attained maximum value with 150 kg N ha−1 in the field experiment. Among the varieties tested, “Hoffman” out-yielded other three varieties due to heavier grain weight. Although highest grain and/or plant crude protein content were recorded in ‘AC Barrie’, it was the variety ‘Hoffman’ that produced the highest total protein (kg ha−1) with largest NDVI and leaf area index (LAI) values.
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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) | 0.000 | 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 | 0.001 |
| 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 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".