Comparing economics and nitrogen fertilizer costs between diversified and intensified cropping systems in western Canada
Bibliographic record
Abstract
Understanding the effects of different cropping systems is essential to maximizing profitability. This study assessed the net returns (NRs) and nitrogen (N) fertilizer application over 5 years (2018–2022) of six 4-year crop sequences. Crop sequences included a high-risk system (High-Risk); a market-driven system (MD); a soil health enhanced system (SH); a pulse or oilseed-intensified system (Intensified); a cereal and alternative crop-diversified system (Diversified); and a common cropping system grown in the specific location (Control). Crop sequences were located in different regions within Saskatchewan, Alberta, and Manitoba, Canada. NR—the response variable to compare the effectiveness of each sequence—was defined as total revenue minus total costs. Results indicated that NR was affected by different crop sequences, and location influenced crop sequence performance, as diversified did the same or better than MD in two locations and similar to intensified and control in most locations. Across sites, NR was highest for MD, averaging at $400 ha −1 year −1 based on average 12-year (2012–2023) prices. However, MD received the greatest N fertilizer. The diversified sequences received only half the N fertilizer and produced comparable NRs to the intensified and control sequences, which had more frequencies of canola and wheat. While results remained mostly the same with different sensitivity analyses, diversified becomes a better choice than the other sequences when fertilizer prices increase while the costs of the other resources are maintained. In conclusion, while MD produced a higher NR, diversified performed similarly to intensified and control but received much less N fertilizer.
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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 | 0.001 | 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 | 0.001 |
| 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".