Biodiverse cropping systems in Manitoba: agronomic and economic responses under water-limited conditions
Bibliographic record
Abstract
As climates shift and weather patterns become increasingly unpredictable, improving agroecosystem resiliency and stability is imperative to ensure the sustainability of global food production systems. Increasing cropping system biodiversity has been proposed as one solution to mitigate and adapt to the impacts of climate change on Canadian Prairie agriculture. The objective of the current study was to evaluate the productivity, resource use, and economics of no-till crop rotations with varying levels of spatial and temporal diversity. The study examined one base rotation (Conventional Rotation) and four alternative rotations: a High-Risk High-Reward Rotation, a Biodiverse Rotation, a perennial grain rotation (Kernza Rotation), and an Organic Rotation. Diversity was incorporated into the crop rotations using several methods, including intercropping, fall-seeded cover cropping, winter annual grain cropping, perennial grain cropping, and organic cropping with green manures. Individual questions related to crop diversity were also evaluated (e.g., Intercrops versus monocrops). The research took place in Carman, MB during the drought years of 2020 and 2021. The High-Risk High-Reward Rotation was the most economical rotation in this study, while the Biodiverse Rotation was the most environmentally conscious rotation. The Kernza Rotation produced lower values than the Conventional Rotation for yield, harvest index, and water use efficiency. Intercrops produced varying results between years and intercropping types, although the pea/canola intercrop produced greater or similar yields and net returns than sole canola crops without the use of synthetic fertilizers. The fall rye/cover cropping system produced greater yields and dry matter than other annual crops but was overall less economical than most annual crops and mimicked important features of the perennial grain system. Additionally, thanks to weed suppression and soil moisture conservation offered by a terminated hairy vetch/barley mulch, the organic wheat produced significantly similar yields and water use efficiencies as conventional wheat crops while maintaining significantly higher harvest indices and net returns. Overall, this study indicates that increasing crop rotation diversity over time and space can maintain or improve crop productivity, resource use efficiency, yield stability, and economics.
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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 | 0.000 | 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".