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Record W3112944863 · doi:10.5539/jas.v13n1p30

Awareness and Application of Existing Agroecological Practices by Small Holder Farmers in Mvomero and Masasi Districts-Tanzania

2020· article· en· W3112944863 on OpenAlexvenueno aff
J. Robert Constantine, K. P. Sibuga, Mawazo J. Shitindi, Angelika Hilberk

Bibliographic record

VenueJournal of Agricultural Science · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Innovations and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsAgroecologyTanzaniaAgricultureDiversification (marketing strategy)BusinessLivestockGovernment (linguistics)Agricultural scienceGeographyAgroforestryAgricultural economicsEnvironmental planningMarketingForestryEconomics

Abstract

fetched live from OpenAlex

A survey study was conducted to assess the level of awareness and application of existing agro-ecological practices by small holder farmers in Mvomero and Masasi districts in Tanzania. The selection of farmers to interview and the villages in the respective districts was based on their long history of producing cassava and maize. A structured questionnaire was used to identify the type of agro-ecological practices, agricultural information sources accessed by farmers, training on agro-ecological practices, type of crops grown in the study areas and kind of livestock kept. Results indicated that the most applied agro-ecological practices were diversification (80.5%), the use of farmer saved seeds (78.2%) followed by intercropping (72.9%) and lastly, agro-forestry (3.2%). The highest percentage of farmers (30.4%) reported to receive information on ecological organic agriculture from non-governmental organisations (NGOs) (SAT, SWISS AID), 27.5% were using own farming experience, 21% reported to receive the information from government extension officers, 13% from friends or neighbours, 4.3% from government institutions (SUA, Agricultural Training Institutes) and 3.6% received information from agricultural input suppliers. Generally, 50% of farmers had received training on agroecological practices indicating the level of awareness. Lack of knowledge among farmers was one of the key factors that hamper the wide application of agroecological practices. There was a need for farmer’s capacity building through training to enhance wider application of agroecological practices hence progressive agricultural production increase.

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)0.001
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 score0.895
Threshold uncertainty score0.195

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.077
GPT teacher head0.302
Teacher spread0.225 · 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.

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".

Quick stats

Citations15
Published2020
Admission routes1
Has abstractyes

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