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Record W2990929643 · doi:10.14207/ejsd.2019.v8n3p475

Evaluation Of Soja Productivity (Imperial Variete And Canada) In Association With Maize (Bamboo Variete) In The Ecological Conditions Of Sustainable Use Of Sake / Nord Kivu

2019· article· en· W2990929643 on OpenAlexaboutno aff
Marcel Bwama Meyi, Viviane Musungay, Grégoire Mashala Bitwakamba, Romeo Ciminello

Bibliographic record

VenueEuropean Journal of Sustainable Development · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Rural Development Research
Canadian institutionsnot available
Fundersnot available
KeywordsBambooHectareProductivityPoint of deliveryBiomass (ecology)AgronomyCropBiologyMathematicsTonneAgricultural scienceGeographyAgricultureAgroforestryEcologyEconomics

Abstract

fetched live from OpenAlex

With a view to using land capital in an efficient and effective manner with a view to encouraging sustainable development in this ecological area of North Kivu; and to help minimize land-use conflicts, we have thought of establishing a combination of crops (soybean and maize) as a technique that can help maximize yields / ha and household income, while maintaining the fertility gradient of the soil. The effectiveness of Soybean-maize associations offers interesting prospects for improving the sustainability of production systems.The trial was arranged in randomized randomized blocks with 5 treatments and 3 replicates. For this study we used 2 soybean varieties that are: imperial and canada, with a variety of corn (bamboo).The arrangement of the association was of the type MBILI-MOJA which means two Soya lines intercepted by a corn line. The parameters studied were the phenological and productive parameters which are: survey rate, neck diameter, height at flowering, number of leaves per foot, average number of pods per plant for soybean, average number of seeds of soybean pod, percentage of pod burst, soybean biomass removal, number of ears per plant, weight of 100 soybean and corn seeds, plot production (in kg), yield per tonne per hectare and the LER.The GENSTAT descovery software allowed us to analyze the variance of our data and the averages were separated using the PPDS at the 5% threshold.The performance of the associations was as follows:Imperial: 1,6 L.E.R =, L.E.R> 1 (there is a productive advantage in this association with corn bamboo); Canada: 2,33 LER =, LER> 1 (there is a productive advantage in this association with bamboo maize.) In both cases, there is maintenance of biodiversity and soil fertility.with maize as the imperial variety, that of 2.33 by the performance of LER. Fertility of the soil has been maintained from this technique which promotes soil and atmospheric nitrogen which crops need for their growth and productivity. Keywords: Soybean, maize, Association, Development sustainability, LER, SAKE / North Kivu

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.221
Threshold uncertainty score0.439

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
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 head0.019
GPT teacher head0.216
Teacher spread0.197 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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".

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Citations1
Published2019
Admission routes1
Has abstractyes

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