Precipitation and nitrogen management are key drivers of cropping system productivity in the Canadian prairies
Bibliographic record
Abstract
The performance of cropping systems is a function of crop and management practice interaction in a given growing environment. However, the critical factors affecting productivity remains unclear under varying climate conditions. We conducted a 5 year study at six sites in western Canada to identify the critical factors affecting the productivity, standardized as protein-based yield (PBY), and quantify the relationships between yield and critical factors. We tested six crop rotations, including conventional system (Control), pulse- or oilseed-intensified system (Intensified), diversified system (Diversified), market-driven system (Market-driven), high-risk and potentially high reward system (High-risk), and soil-health enhanced system (Soil-health). The importance index and structural equation modeling were used to identify key factors and explore the underlying relationships among them. Results showed that Market-driven and Diversified rotations outperformed the Control by 2%–6% in PBY, while Soil-health and High-risk yielded 23%–26% lower than the Control. Relative to the Control, all rotations showed an increase trend in PBY over time, with Diversified rotations increasing 13%–28% faster than Market-driven and Intensified rotations. Precipitation and nitrogen (N) management are the primary factors affecting cropping system productivity, explaining 25% and 21% yield variations, respectively. Structural equation modeling analysis revealed that precipitation had a significant indirect effect on yield through affecting biological N fixation of pulse crops, in addition to a significant direct effect. Increasing pulse frequency and rotation complexity mitigated PBY loss by 10%–24% during low rainfall seasons. We recommend integrating pulse crops into cropping systems to enhance N management and mitigate yield loss in low precipitation regions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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".