A review of intercropping systems in Western Canada
Bibliographic record
Abstract
Abstract Intercropping is gaining interest from Western Canadian producers who are looking for information on how to incorporate intercrops into their production systems. This review summarizes agronomic research on intercropping from the last 40 years in Western Canada and discusses the potential challenges of integrating intercropping into existing crop rotations while identifying challenges and possible solutions. Reviewed literature indicates that several intercrop combinations have been tested in small plots involving up to four crops simultaneously grown, with over 60% comprising pulse–oilseed combinations followed by pulse–cereal combinations at ∼30%. The land equivalent ratio (LER) for pulse–oilseed and pulse–cereal averaged 1.11 and 1.13, respectively. Key agronomic factors that influenced LER in different intercrop combinations have been summarized, and the relationship of N and seeding rate with crop grain LER and partial land equivalent ratio has been assessed. While the relationship between N rate and LER was unclear in pulse–oilseed combinations such as pea (Pisum sativum L.)–canola (Brassica napus L.), LER decreased linearly with increasing N rate (p < 0.004) in pea–barley (Hordeum vulgare L.) intercrop. We highlighted that incorporating intercrops into current rotations will decrease crop rotation lengths with possible implications for disease management. There are many logistical challenges to intercropping but new technology may help producers to adapt.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".