Characterizing Variation in Nitrogen Use Efficiency in Wheat Genotypes Using Proximal Canopy Sensing for Sustainable Wheat Production
Bibliographic record
Abstract
Global nitrogen use efficiency (NUE) for cereal production is marginal and is estimated to be about 33%. Remote sensing tools have tremendous potential for improving NUE in crops through efficient nitrogen management as well as the identification of high-NUE genotypes. The objectives of this study were (i) to identify and quantify the variation in NUE across 24 winter wheat genotypes (Triticum aestivum L.) and (ii) to determine if the normalized difference vegetation index (NDVI) could characterize the variability in NUE across wheat genotypes. This study was conducted in 2010 and 2011 in the semi-arid climate of Northeastern Colorado across dryland and irrigated conditions. Our results indicate significant variation in the NUE among genotypes across two irrigation conditions. We observed a strong relationship between the NDVI and NUE—as PFP (partial factor productivity) and PNB (partial nitrogen balance)—across the 24 wheat genotypes under dryland conditions (average R2 for PFP and PNB = 0.84) at Feekes growth stage 11.1, for site year II. However, poor association was observed under irrigated conditions (average R2 for PFP and PNB = 0.29) at Feekes growth stage 3 to 4 for site year II. This study demonstrates the potential and limitations of active canopy sensing to successfully characterize the variability in NUE across wheat genotypes.
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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 | 0.000 | 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 | 0.001 |
| 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".