Yield and Production Gaps in Rainfed Wheat, Barley, and Canola in Alberta
Bibliographic record
Abstract
Improving crop yields are essential to meet the increasing pressure of global food demands. The loss of high quality land, the slowing in annual yield increases of major cereals, increasing fertilizer use, and the effect of this on the environment all indicate that we need to develop new strategies to increase grain yields with less impact on the environment. One strategy that could help address this concern is by narrowing the yield gaps of major crops using improved genetics and management. The objective of this study was to determine wheat (Triticum spp. L.), barley (Hordeum vulgare L.), and canola (Brassica napus L.) yields and production gaps in Alberta. We used 10 years of data (2005-2014) to understand yield variability and input efficiency at a farmers' specified level of management, and the yield potential under optimal management to suggest appropriate pathways for closing yield gaps. Significant management gaps were observed between attainable and actual yields of rainfed wheat (24%), barley (25%), and canola (30%). In addition, genetic gaps (i.e., gaps due to genetic selection) in wheat, barley, and canola were 18, 12, and 5%, respectively. Genetic selection with optimal crop management could increase yields of wheat, barley, and canola significantly, with estimated yield gains of 3.42, 1.92, and 1.65 million tons, respectively, each year under rainfed conditions in Alberta. This paper identifies yield gaps and offers suggestions to improve efficiency in crop production.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".