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Record W3023255941 · doi:10.5539/jmsr.v9n2p46

Adoption of NPS Fertilizer on Sorghum Crop Production by Smallholder Farmers in Gemechis and Mieso Districts of West Hararghe Zone, Oromia Regional State, Ethiopia

2020· article· en· W3023255941 on OpenAlexvenueno aff
Muhammed Shako Hiko

Bibliographic record

VenueJournal of Materials Science Research · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Innovations and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsSorghumFertilizerAgricultureLivestockDescriptive statisticsGovernment (linguistics)Agricultural scienceProductivityProduction (economics)Agricultural productivityAgricultural economicsHousehold incomeLocal government areaGeographyBusinessEconomicsEnvironmental scienceMathematicsLocal governmentStatisticsEconomic growthAgronomyForestry

Abstract

fetched live from OpenAlex

The adoption of inorganic fertilizer such as NPS which is concerned by development clients and government is different from one farmer to another farmer and this makes productivity of agricultural crops to vary from one plot to another plot due to socio-economic, institutional and other factors. Therefore, this study was intended to know the socio-economic factors that significantly affect utilization of inorganic fertilizer NPS. Primary data was collected from 201 sampled households of selected districts. Secondary data was collected from stakeholders related with production of sorghum and inorganic fertilizer NPS in the study areas. In the sampling procedure, two stage simple random sampling was used. In the first stage, kebeles were randomly taken from total kebeles in the two districts. In the second stage, households were randomly selected from the selected kebeles. Data was analyzed using descriptive, inferential statistics and econometric models methods of data analysis. In econometric models Double Hurdle model was use to know factors affect adoption decision of inorganic fertilizer NPS and intensity use of inorganic fertilizer NPS. Double Hurdle model result confirms that district of the household, education level, family size, extension visit, expectation of the coming rainfall by the household, number of farm plot owned, total farm land owned and off/non-farm income earned by the household significantly affect adoption decision inorganic fertilizer NPS. Double hurdle model result also reveals that, district of the household, livestock holding, number of farm plot owned, participation on agricultural training by the household significantly affect intensity use of inorganic fertilizer NPS. Government and concerned stakeholders should give attention on these significant socio-economic factors so that utilization inorganic fertilizer can be improved to sorghum crop productivity.

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)0.001
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 score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.132
GPT teacher head0.342
Teacher spread0.210 · 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".

Quick stats

Citations2
Published2020
Admission routes1
Has abstractyes

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