Estimation of Sugar Beet Yield and its Dry Matter Partitioning Under Different Irrigation and Nitrogen Levels
Bibliographic record
Abstract
In this study, a simple logistic model was developed for estimating total dry matter of sugar beet under different irrigation and nitrogen levels. The experiment was conducted using line source sprinkler irrigation in 2013 and furrow irrigation in 2014. Irrigation treatments were from 44% to 130% of full irrigation and applied nitrogen treatments ranged from 0 to 240 kg N ha-1. Results showed that the model was more accurate in predicting total dry matter at harvest date with the Normalized Root Mean Square Error (NRMSE) amounting to almost 10 percent. After total dry matter estimation, a model was needed for dry matter partitioning between different organs of sugar beet. To achieve this goal, another logistic model was developed and was compared with three revised models. Finally, white sugar content of root dry matter was estimated using a quadratic equation as a function of applied water and nitrogen. Validation results indicated that total and root dry matters, and white sugar yield were estimated fairly well. Results showed that excessive water had negative effects on total dry matter and root dry matter. Also, excessive nitrogen affected root dry matter negatively too, but even the excess had positive effects on total dry matter. In contrast to common belief, our results showed that drought stress reduced both ratios of root to leaf, and root to shoot dry matter.
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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 | 0.000 |
| 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".