Weed Suppression and Component Crops Response in Maize/Pumpkin Intercropping Systems in Zimbabwe
Bibliographic record
Abstract
Intercropping is a common practice in the smallholder sector of Zimbabwe with potential contribution to weed management. The proper combination of plant population, composition of the component species and frequency of weeding which lead to weed suppression are still unknown and that prompted this investigation. The experiment was set up as a factorial experiment in a randomised complete block design with three factors: cropping systems (sole maize, sole pumpkins and maize/pumpkin intercrop), weeding regimes (weeding at 3 weeks after planting (WAP) and at 3 and 8 WAP) and pumpkin population (16.5% and 33%) of maize population. Results showed no significant effect of cropping system, pumpkin population and weeding regime on maize yield, pumpkin yield, pumpkin leaf number and weed density. Weed biomass was significantly higher (P=0.000) at weeding regimes of 3 WAP than at 3 and 8 WAP. Pumpkin population of 16.5% had higher weed biomass compared to 33%. Themaize/pumpkin intercrop had significantly (P=0.002) lower weed biomass compared to sole crops. There were significant interactions of weeding regime and pumpkin population and cropping system and pumpkin population (P=0.035). The results indicate that intercrops with 33% pumpkin population and weeded at 3 and 8 WAP are superior in terms of weed biomass suppression. Intercropping done at correct crop combination and weeding at the right time therefore shifts crop-weed competition in favour of the crop as it reduces dry matter accumulation of the weeds without affecting yield of component crops. Intercropping can therefore be used as a component in integrated weed management in the smallholder sector.
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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".