Economics of cropping sequences diversified with specialty crops in the semi-arid Canadian prairies
Bibliographic record
Abstract
The agronomic and environmental benefits of diversified cropping systems have been well documented in the Canadian prairies. However, little is known about the profitability of diversified rotations with oilseeds, cereals, legumes, and specialty crops. This study consisted of two 5-year (2018–2022) experiments carried out at four sites in Saskatchewan and Alberta. Treatments were arranged in a randomized complete block design with four replicates. Net return (NR) was defined as total revenue minus total costs. Results showed diversified sequences with Oriental mustard, red lentil, yellow field pea, and yellow mustard had higher NR than continuous wheat and wheat with chemical fallow sequences. Moreover, sequences diversified with quinoa, yellow mustard, field pea, and wheat showed high NR across all sites. Wheat after chemical fallow in the wheat with chemical fallow sequence (wheat–wheat–chemical fallow–wheat–wheat) had high NR; however, this did not compensate for the loss of NR in the chemical fallow phase, resulting in the lowest NR. The inclusion of industrial, oriental, and yellow mustard in sequences with wheat and field pea decreased nitrogen cost by 30% compared to a continuous wheat sequence, concluding that such sequences not only improved NRs but also showed a significant reduction in nitrogen requirement costs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".