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Record W7110086816 · doi:10.5539/jas.v18n1p52

Aftereffects of Cultural Intercroppings of Maize or Cassava in Immature Palm Groves on the Production of Fresh Fruit Bunches

2025· article· W7110086816 on OpenAlexvenueno aff

Bibliographic record

VenueJournal of Agricultural Science · 2025
Typearticle
Language
FieldEnvironmental Science
TopicOil Palm Production and Sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsIntercroppingPalmPalm oilContext (archaeology)Elaeis guineensisCropProductivitySorghum

Abstract

fetched live from OpenAlex

In palm cultivation, failure to comply with technical itineraries has disadvantages on the development of oil palm. It is in this context that the present study aimed to evaluate the influence of peasant practices intercropping the cultivation of oil palm at the immature stage with those of maize and cassava on the production of oil palm at the adult stage. To do this, a reasonable choice of palm groves constituted the experimental design. The plots chosen were those aged 16 years which, during their immature phase, were intercropped either with crops of maize (Treatment T1) or with those of cassava (Treatment T2) or without food crops (T0: Control). The results showed that peasant practices of crop intercropping of maize and cassava negatively influenced the productivity of oil palm in palm bunches. The comparison of the results showed that cassava cultivation has more detrimental effects on the productivity of oil palm in palm bunches, compared to that of maize. During the immature phase of the palm groves, it would therefore be wise to dissociate the cultivation of oil palm from those of cassava and maize to avoid, in the long term, any possible harm linked to this cultural association on the production of oil palm. Otherwise, it would be advisable to restore to the soils of immature palm groves, by fertilization, the mineral nutrients exported through the cassava and maize harvests or to reduce the densities of these food crops to reduce competition for mineral nutrients.

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.

How this classification was reachedexpand

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)0.003
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.652
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.003
Science and technology studies0.0000.003
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.254
Teacher spread0.243 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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