Estimating canola (<i>Brassica napus</i> L.) yield potential using an active optical sensor
Bibliographic record
Abstract
Active optical sensors have potential as tools to increase N fertilizer use efficiency in crop production; however, empirical data are required to utilize the sensors for this purpose. Data were compiled from N fertilizer trials at five Canadian locations (2004-2007) to determine the feasibility of using optical sensors during the growing season to estimate the seed yield potential of canola (Brassica napus). The normalized difference vegetation index (NDVI) of each plot in each trial was measured using a hand-held optical sensor several times each season. The NDVI between the six-leaf stage and the beginning of flowering was divided by one of several different heat unit summations to normalize the measurements, and data were combined across locations. Linear and exponential regression analyses were completed for canola seed yield as a function of both the original and normalized NDVI measurements. When data from all locations were combined, NDVI was significantly correlated with canola seed yield (R 2 = 0.35; P < 0.001) and normalizing NDVI did not improve the correlation. Categorizing the locations by soil zone (Brown-Dark Brown and thin-Black-Black) and completing separate regression analyses for each group increased the correlation coefficients for NDVI and seed yield (R 2 = 0.36-0.43). Furthermore, dividing NDVI by the heat unit summations generally improved the correlation when the data were categorized by soil zone. The largest correlation coefficient occurred when NDVI was divided by growing degree days with a base temperature of 5°C (R 2 = 0.53-0.67). Our results show that optical sensors can be used to estimate canola yield potential early enough in the growing season to have potential as an N management tool.Key words: Normalized difference vegetation index, agriculture, precision, nitrogen use efficiency
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".