Evaluation of the Causes of On‐Farm Harvest Losses in Canola in the Northern Great Plains
Bibliographic record
Abstract
Canola ( Brassica napus L.) is the main oilseed crop grown in the northern Great Plains (Canada). This species, however, also is associated with significant seed losses before and during harvest. To determine the factors that contribute to on‐farm harvest losses in B. napus , an extensive on‐farm survey was conducted in four regions across the northern Great Plains in 2010, 2011, and 2012. In addition to measuring on‐farm harvest losses on 310 fields, a survey questionnaire was used to collect agronomic data for each field and wind data from the nearest local weather station was used to determine wind speed during the harvest season. This study showed that total on‐farm harvest losses in canola are a complex phenomenon. This study identified that managing harvest losses in B. napus begins at the time of planting. Management factors that contributed to increased yield were linked to lower proportional B. napus harvest losses. Other factors that contributed to reduced proportional harvest losses included a fungicide application at flowering, earlier windrowing and harvest dates, lower combine harvester ground speed, and reduced windrower width. Factors considered by producers as important, such as combine manufacturer or B. napus variety did not contribute significantly to total harvest losses in this crop. Nevertheless, clear management practices were identified that can be employed to minimize on‐farm harvest losses in B. napus . A better understanding of the contributions of environmental variables to harvest losses in this species is required, particularly as interest in direct‐harvesting B. napus continues to increase in western Canada.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".