Farmer managed research to assess legume intercropping in conservation agriculture systems in rural Zimbabwe
Bibliographic record
Abstract
The lack of adequate mulch and crop rotations are major constraints to the implementation of conservation agriculture (CA) for smallholder farmers in sub-Saharan Africa. One possible solution to these constraints is intercropping the main cereal crop with a leguminous cover crop; a technology option that also has the potential to improve the food security and economic productivity of smallholder CA systems. This study used farmer managed research plots to assess the impacts of integrating different grain legumes (cowpeas (Vigna unguiculata), lablab (Lablab purpureus), and pigeon pea (Cajanus cajan) into maize based CA farming systems in two semi-arid regions of Zimbabwe. The results from one cropping cycle (late 2015 to mid-2016) found that while there was a significant increase in total biomass production when an intercrop was added to the standard, mulched, monocropped CA maize crop at one site, there was no difference at the second (drier) site and that the addition of a legume intercrop reduced, but did not eliminate the need to add supplemental mulch to CA based farming systems. However, the addition of the cowpea intercrop in particular significantly increased the economic profitability and food security impacts of the farming system at both sites (an effect that was more pronounced at the drier site). Overall, this study found that intercropping of legumes into CA based systems had the potential to improve sustainability, productivity and profitability, resilience and food security impacts for the farmers involved 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".