Productivity, soil fertility and soil health benefits associated with intercropping small grains and oilseeds with legumes in the semi-arid environment of the Canadian Prairies
Bibliographic record
Abstract
Intercropping is a promising strategy to diversify cropping systems and contribute to improving the sustainability and resiliency of agriculture. However, there is a lack of knowledge about specific combinations of crop species that could be grown in large-scale mechanized systems, and this hinders adoption of this practice. In addition, few studies have looked at intercropping legume crops with small grain and oilseed crops and their comparative benefits on productivity, soil fertility and soil health indicators, simultaneously. Our study addressed this knowledge gap by comparing several intercrop combinations: pea-canola, lentil-wheat, chickpea-flax, faba bean-oat, faba bean-wheat, faba bean-canola, faba bean-flax, and comparing with monoculture controls for each crop. Land equivalent ratios (LERs) suggested that pea-canola, chickpea-flax, and faba bean-flax intercrops were more productive than when grown as monocrops, but only the pea-canola LER was marginally significantly higher than one (p = 0.087). Intercrops of cereals and legumes tended to do poorly; in particular, oat outcompeted faba bean completely in both years. Soil N and P fluxes showed few or no differences between monocrops and intercrop combinations and did not explain the better performance of the pea-canola, chickpea-flax or faba bean-flax intercrops. Soil water-soluble nitrogen trends were related to fertilization. However, both permanganate oxidation carbon (POX-C, also known as active C), and autoclave-citrate extractable (ACE) protein were significantly increased in legume monocrops compared to non-legume monocrops between grain filling and post-harvest, with intercrops showing intermediary values. Overall, our results indicate that intercropping legumes with oilseeds—even in a northern semi-arid region—can produce direct short-term benefits. These early indicators of soil health change bode well for the longer-term success and continued benefits of intercropping systems. • The pea-canola intercrop was more productive than monocrops. • Chickpea-flax and faba bean-flax intercrops are worth further investigation. • Wheat and oat were too competitive in intercrops with pulses. • Differences in soil fertility did not explain the performance of intercrops. • Increases in POX-C and ACE protein are due to legumes rather than species diversity.
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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 | 0.001 | 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 | 0.001 | 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".