Land Equivalent Ratios, Light Interception, and Water Use in Annual Intercrops in the Presence or Absence of In‐Crop Herbicides
Bibliographic record
Abstract
Increased crop production (overyielding) often observed in intercrops compared to sole crops has been attributed to enhanced resource use. The objective of this study was to investigate intercropping complementarity of wheat (Triticum aestivum L.), canola (Brassica napus L.), and field pea (Pisum arvense L.) for light and water use. Sole crop and intercrop combinations were evaluated for effects on land equivalent ratios (LERs), canopy light interception, soil moisture, water use (WU), and water use efficiency (WUE), with or without in‐crop herbicides at two field sites in Manitoba, Canada. The mean LER was 1.1, but LERs varied greatly between site‐years and herbicide treatments, and were significantly greater than one in 22% of the site‐year‐treatment combinations. The wheat‐canola‐pea and canola‐pea intercrops showed the greatest frequency of overyielding for dry matter (50%) and grain yield (38%), respectively. Peak light interception tended to occur earlier with canola than with field pea, thus increasing the potential for light use complementarity between these crops. There was a positive correlation between LER and light interception in half of the site‐years with applied herbicides and a negative correlation between LER and weed biomass at most site‐years without herbicides. Although crop treatments used water differently within the soil profile, there were no differences in WU, but some differences in WUE, between crop treatments; however, WUE generally was not greater in intercrops compared to sole crops. In this study, overyielding in intercrops was inconsistent, and seemed to be related more to light interception than to water utilization.
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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 | 0.000 | 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) | 0.001 | 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".