Assessing the temporal stability of spatial patterns in crop yields using combine yield monitor data
Bibliographic record
Abstract
Using previous years’ yield patterns may be one method of breaking a field into management zones for the purpose of site-specific management. For this method to be useful there must be temporal stability of yield patterns and there must be a sound method of assessing the spatial-temporal stability of yield in a field. To this end, a method was developed to give a non-biased estimate of the within-field spatial-temporal stability of yield. The method determined the probability that the normalized yield for all years available at a given location in the field fit within the accuracy limits of the combine. Combine accuracies of ± 2.5%, 5% and 10% of the field mean yield and gridded data of 3 m, 6 m, and 9 m cell sizes, as well as crop choice were all included in the model to assess its sensitivity to changes in these factors. The resulting spatial-temporal stability maps were well correlated with visual estimations of the spatial yield patterns. The model results were highly influenced by the inputted combine accuracy, but grid size and crop choice had little affect on the proportion of the field or the spatial pattern of temporal stability in the two field sites examined. The sensitivity of the model to changes in the input value for the combine accuracy indicated that a good estimate of this value is required for the determination of the stable patterns in a field. Key words: Spatial-temporal stability, yield
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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 | 0.002 | 0.001 |
| 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 | 0.001 |
| 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".