Intercropping Spring Wheat with Cereal Grains, Legumes, and Oilseeds Fails to Improve Productivity under Organic Management
Bibliographic record
Abstract
The success of organic wheat (Triticum aestivum L.) production can be severely inhibited by weed and disease pressures. This study sought to determine the effectiveness of wheat intercrop mixtures in suppressing weeds and diseases and increasing grain yield and net return. Field experiments were conducted on organically managed land in 2004 and 2005 and three representative intercrop systems were tested: wheat with other cereals [oats (Avena sativa L.), barley (Hordeum vulgare L.), and spring rye (Secale cereale L.)]; wheat and noncereal seed crops (flax [Linum usitatissimum L.], field pea [Pisum sativum L.], oriental mustard [Brassica juncea L.]); and wheat and cover crops (red clover [Trifolium pratense L.], hairy vetch [Vicia villosa L.], annual ryegrass [Lolium multiflorum Lam.]). The cereal intercrop systems provided no consistent yield benefit over wheat monocultures. Results from noncereal‐wheat intercrops were variable. Wheat‐flax reduced the wheat crop to unacceptable levels but was capable of reducing wheat flag leaf disease levels. Wheat‐field pea resulted in the lowest disease levels, yet had inconsistent yields, and more weeds than wheat monoculture. Wheat‐mustard did not reduce weeds or diseases, but it was capable of high grain yields and net returns, though usually hampered by flea beetle (Phyllotreta cruciferae) attack. The effect of cover crops on wheat was affected by environment. Wheat‐red clover and wheat‐hairy vetch did demonstrate the ability to maintain high wheat grain yield in certain site‐years. In conclusion, wheat intercrop mixtures provided little short‐term benefit over monoculture wheat in this study.
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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".