Enhancing In-crop Diversity in Common Bean by Planting Cultivar Mixtures and Its Effect on Productivity
Bibliographic record
Abstract
Common bean (Phaseolus vulgaris L.) is the most important food legume crop worldwide. Canadian beans, especially large seeded cultivars of Andean origin, have relatively narrow genetic diversities. Establishing crops with mixtures of cultivars instead of pure lines is a simple, cost effective way to increase genetic diversity in the field. A number of studies have demonstrated the benefits of mixture cropping over monocropping in controlling disease, increasing water use efficiency and increasing yield stability. The objective of this study was to determine the effects of increasing in-field diversity, by using mixtures of bean cultivars instead of monocultures, on productivity. The feasibility of growing bean cultivar mixtures in southern Ontario environments was confirmed with a small pilot study that was conducted with four bean cultivars and restricted number of mixtures at two locations in 2017. Mixture performance experiments were performed with seven diverse bean genotypes at two Ontario locations [Woodstock and Elora (two planting dates) research stations] as pure stands and all possible binary mixtures (planted in alternate rows or as completely random mixtures) in 2018. Conventional plot-based above ground crop data were collected. Mixing efficiencies were calculated from the yield data using a Relative yield of the mixture (RYM) index. Diallel analysis was used to identify general mixing ability of cultivars and specific mixing abilities of mixtures. Significant differences among seven bean cultivars and their mixtures were identified in all three environments for all analyzed traits. The results indicated multiple benefits of planting mixtures compared to monocultures A number of mixtures overyielded component cultivars grown in pure stands; they had higher yields, RYM index values greater than one and positive specific mixing abilities (for yield) in both types of biblends. The research has the potential to provide a theoretical basis for the use of precision agriculture tools to plant fields with mixtures instead of monocultures. It could lead to greater in-field diversity in the crop and in the above and below ground ecosystems that might provide greater buffering capacity and resiliency to the cropping system as well as increased ecosystem services.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".