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CONSTRAINTS EXPERIENCED BY MUSHROOM GROWERS IN ADOPTING IMPROVED MUSHROOM PRODUCTION PRACTICES IN PUSA, BIHAR

2021· article· en· W3192893544 on OpenAlexaff
Saloni Sarraf, Dipak Kumar Bose, Jahanara Jahanara

Bibliographic record

VenueInternational Journal of Advances in Agricultural Science and Technology · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicOrganizational Leadership and Management Strategies
Canadian institutionsInstitute of Aging
Fundersnot available
KeywordsMushroomUnavailabilityAgricultureAgricultural scienceBusinessProduction (economics)Agricultural economicsEngineeringEconomicsGeographyBiologyFood science

Abstract

fetched live from OpenAlex

Mushrooms (vegetarian meat/vegetable beef stick) is becoming fast popular because of its short time period between cultivation and harvesting; less initial investment and can be grown with locally available resources. Though more technology is available for boosting mushroom production, the yield so far achieved is not high. Hence, an attempt is made to find out the constraints faced by mushroom growers in adoption of improved mushroom production practices. Samastipur district of Bihar was purposively selected for the study because, Dr. Rajendra Prasad Central Agricultural University, a pioneer in mushroom production technology is located in the study area. 120 respondents from six villages of Pusa, Samastipur, Bihar forms the respondents of the present study. Primary data was collected from the mushroom growers and the responses were subjected to statistical analysis. The results indicated that unavailability of quality spawn, unavailability of skilled labor, absence of technical guidance, high transport cost, unavailability of storage facilities, high cost of spawn and long distance market were the most problematic constraints faced by the mushroom growers. Thus, it can be concluded that adequate extension service should be made available to make mushroom cultivation popular, market and marketing of the products.

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)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.496
Threshold uncertainty score0.408

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.006
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 head0.011
GPT teacher head0.268
Teacher spread0.257 · 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 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
Published2021
Admission routes1
Has abstractyes

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