Optimizing the Productivity and Resiliency of Cropping Systems in the Major Ecozones on the Canadian Prairies
Bibliographic record
Abstract
Agriculture faces grand challenges of meeting growing food demands and increasing profitability while reducing environmental impacts. A systems approach is required to design and manage cropping systems to meet the goal of agricultural sustainability under climate change. A 4-year rotation study was established in 2018 at seven sites across the Canadian Prairies, including Beaverlodge, Lacombe, and Lethbridge, AB; Melfort, Scott, and Swift Current, SK; and Carman MB. The objective of this project is to develop resilient cropping systems for different ecozones on the Canadian Prairies. This study tested six cropping systems consisting of 1) conventional cropping system (Control), 2) pulse- or oilseed-intensified cropping system (POS), 3) diversified cropping system (DS), 4) market-driven cropping system (MS), 5) high risk and high reward cropping system (HRHRS), and 6) green-manure incorporated soil-health focused cropping system (GMS). Each cropping system varies slightly among experimental sites (ecozones) to mimic local farming practices. Cropping system indicators such as yield, resource use efficiency, soil health, profitability, environmental impact, resiliency, and sustainability will be fully assessed at the end of rotations. The preliminary results from the first 2 years indicated that there was no single cropping system suitable for all study ecozones although POS had an above-average yield and stability among the six cropping systems. We suggest that the optimal cropping system will maximize yield in the high-yielding ecozones and stabilize yields in the low-yielding ecozones. Link to Video Presentation: https://youtu.be/VsK4RNIzaZI
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".