Optimal time for remote sensing to relate to crop grain yield on the Canadian prairies
Bibliographic record
Abstract
The optimal time to acquire remote sensing imagery to relate to grain yield has not been thoroughly investigated for the Canadian prairies. Remotely sensed data collected when there is the best relationship with yield should provide useful information on the in-field spatial variability of biophysical factors affecting crop productivity relevant to site-specific management. The correlations of normalized difference vegetation index (NDVI) with grain yield for three dates in 2000 at Indian Head and Swift Current, SK, for field pea, canola, and spring wheat were compared. No single date consistently had the highest NDVI-yield correlation for all crops. The period between Jul. 10 to 30 was optimal to obtain NDVI to relate to grain yield for springseeded crops that typically mature in August. Significant NDVI-yield correlations for this period were confirmed in three additional site-years. In a further site-year, however, NDVI-yield correlation was significant for wheat and pea, but not for canola. Occasional problems relating the NDVI to canola yield were attributed to characteristics of the canola canopy, namely, the highly reflective flowers and the dropping of leaves after flowering. In terms of both magnitude and temporal stability of the NDVI-yield correlation, we ranked the crops as: spring wheat, then pea, and then canola. Key words: Remote sensing; grain yield, field pea, canola, wheat, normalized difference vegetation index
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".