Optical sensors to predict sugarbeet yield, quality, and fertilizer nitrogen application rate
Bibliographic record
Abstract
Nitrogen (N) management is critical for sugarbeet (Beta vulgaris L.) because N inversely influences root yield and recoverable white sucrose per tonne (RWST). In Ontario, from 2015 to 2017, the use of optical sensors (e.g., a soil plant analysis development (SPAD) chlorophyll meter, GreenSeeker handheld crop sensor) was evaluated as a method to guide N application and harvest date (late-September, late-October) selection by predicting root yield RWST and partial profit margins. In a commercial field, 4 to 5 fertilizer N rates, and 8 to 12 cultivars were tested in a split block design experiment with three replications and two harvest dates. In all years, few cultivars (≤2) had a root yield response to applied N, which was attributed to high inherent soil fertility, and limited our evaluation of optical sensors to adjust in-season N applications. The optimal N rate to maximize RWST and profits was 0 to 45 kg N·ha −1 and confirmed their negative relationship to applied N. Optical sensor readings correlated negatively with RWST across the majority (>60%) of cultivars tested in mid-August and September. Across all cultivars, the regression model of optical sensors to predict RWST at early harvest was strongest (R 2 = 0.48 for SPAD; 0.24 for GreenSeeker) when readings were taken in early September. Although future research to refine this relationship is needed, we recommend the use of optical sensors, particularly the SPAD meter, in early September to guide harvest selection to maximize RWST.
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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".