Intercropping chickpea–flax for yield and disease management
Bibliographic record
Abstract
Abstract Ascochyta blight (caused by Ascochyta rabiei) is a primary concern of chickpea production worldwide. Intercropping chickpea with a non‐host crop has the potential to suppress this disease and improve resource use efficiency for enhanced crop yield. This study aimed to evaluate the effects of seeding rate and row configuration of chickpea (Cicer arietinum L.)–flax (Linum usitatissimum L) intercropping on (1) yield and seed quality, (2) disease incidence and severity of Ascochyta blight of chickpea, and (3) land productivity of this intercropping system. Field trials were conducted at the Eastern Agricultural Research Center, Sidney, MT, and the Southern Agricultural Research Center, Huntley, MT, in 2020 and 2021. Chickpea was planted with flax in four intercropping configurations (70% chickpea–30% flax in mixed rows, 50% chickpea–50% flax in alternate rows, 50% chickpea–50% flax in mixed rows, and 30% chickpea–70% flax in mixed rows). Chickpea yield decreased with increased flax proportion in the mixed rows intercrop. Flax displayed higher competitiveness than chickpea, resulting in decreased yield and protein concentration in chickpea but increased yield and protein content in flax. Land equivalent ratio of intercropping was greater than one, showing improved land productivity (2%–23% greater than monocropping). Intercropping reduced Ascochyta blight disease incidence and severity; the 50% chickpea–50% flax and 30% chickpea–70% flax intercropping configurations could reduce the disease severity to 50% (in Huntley) and 67% (in Sidney) of that in the monocropping. These results indicated that seed ratio and planting configurations of chickpea–flax intercropping may be manipulated to increase land use efficiency and reduce Ascochyta blight in chickpea. Canadian Development Center ‘CDC Leader’ yielded greater than Royal in the higher disease pressure environment in Huntley indicated that selection of disease resistant cultivars is important for managing Ascochyta blight in chickpea.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".