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Record W4409718284 · doi:10.1016/j.fcr.2025.109916

Intercropping lablab with maize increases grain production and soil cover, and reduces pest pressure in Tanzania

2025· article· en· W4409718284 on OpenAlex
Sasha Loewen, Neil R. Miller, Michelle Carkner, Wilfred Mariki, Martin H. Entz

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueField Crops Research · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgronomic Practices and Intercropping Systems
Canadian institutionsBank of CanadaUniversity of Manitoba
FundersJarislowsky FoundationNelson Mandela African Institution of Science and Technology
KeywordsIntercroppingAgronomyTanzaniaCover cropAgroforestryEnvironmental scienceProduction (economics)Cover (algebra)PEST analysisBiologyHorticulture

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.443
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.033
GPT teacher head0.302
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it