Intercropping lablab with maize increases grain production and soil cover, and reduces pest pressure in Tanzania
Bibliographic record
Abstract
East African farmers and researchers are sharing a renewed interest in Lablab purpureus (L.) Sweet, a multipurpose leguminous cover crop. Lablab can produce food and fodder, fix nitrogen, scavenge resources, protect the soil, and tolerate drought. To capitalize on its potential, lablab requires more research into its basic agronomy, particularly under intercropping situations in which it is usually grown by small-holder farmers in East Africa. The study aimed to understand the effects of lablab-maize intercropping through three measured ecosystem services: 1) grain production of lablab and maize, assessed in yield and land equivalent ratio; 2) late season soil cover of living plant material; and 3) major lablab insect pests: pod boring caterpillars ( Maruca vitrata, Helicoverpa armigera, Etiella zinckenella ) and pod sucking coreid bugs ( Riptortus pedestris, Clavigralla tomentosicollis ). In this study, lablab was intercropped with maize across two agro-ecozones in northern Tanzania over three years. Yield data was collected from both crop species, while late season soil cover and insect pressure focused on lablab. A simple economic analysis examined net return of sole and intercropping as a response to the costs of seed and harvesting. Over the six environments, intercropping reduced lablab grain yields by 35 % (p = 0.009); intercropping reduced maize yields marginally though this was not found to be significant (p = 0.087). The land equivalent ratio of maize and lablab, ranged from 1.36 to 1.96 across environments. Under adequate moisture conditions lablab grown with maize produced 58 % more late season ground cover than when lablab was sole cropped (p = 0.039), whereas in the driest environments, the opposite trend was observed (p = 0.011). The number of pod boring caterpillars (p = 0.052) and pod sucking coreid bugs (p < 0.001) were reduced by 34 % and 57 % respectively by intercropping. Intercropping produced a higher net return than sole cropping lablab or maize. These results demonstrate the diverse benefits of growing maize with lablab allowing for greater food production, increased soil protection, and reduced pest pressure. Of particular importance was the negligible effect of lablab grown with maize, on maize grain yield, highlighting that the detractions of intercropping in smallholder agriculture are outweighed by the advantages. Continued lablab research to identify best agronomic practices and new cultivars will encourage its adoption and help East African farmers diversify and strengthen their cropping systems.
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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".