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Record W2204691731

Good practices for biosecurity in the pig sector : issues and options in developing and transition countries

2010· book· en· W2204691731 on OpenAlexfundno aff
François Madec, Daniel Hurnik, Vincent Porphyre, Éric Cardinale

Bibliographic record

VenueAgritrop (Cirad) · 2010
Typebook
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Disease Management and Epidemiology
Canadian institutionsnot available
FundersCenters for Disease Control and PreventionCentre de Coopération Internationale en Recherche Agronomique pour le DéveloppementCanadian Food Inspection Agency
KeywordsBiosecurityBusinessTransition (genetics)Transition countriesEconomic growthEconomicsInternational economicsBiologyEcology
DOInot available

Abstract

fetched live from OpenAlex

Pig production systemsIn most countries, a variety of different pig production systems exist, from the simplest, with minimal investment, to large-scale market-oriented enterprises.This paper groups pig production systems into four categories, based on the size of herds, the production goals and husbandry management:scavenging pigs is the most basic traditional system of keeping pigs and the one most commonly reported in both urban and rural areas of developing countries.In this free-range system, pigs roam freely around the household and surrounding area, scavenging and feeding in the street, from garbage dumps or from neighbouring land or forests around villages.Few arrangements are made to provide the pigs with housing.Depending on the local situation, pigs may be free-ranging for most of the year and penned during the rainy season.They may be housed at night in a small shelter, to protect them against theft and predators.Keeping scavenging pigs requires minimal inputs and low investment of labour, with no or limited money invested in concentrated feed or vaccines.Small-scale confined pig production is common in developing and transition countries.Pigs are confined to a shelter, which can range from a simple pen made with local materials to more modern housing.The pigs are completely dependent on their keeper for feed, and receive tree branches, leaves, crop residues, agricultural by-products or prepared feed.Smallholders raise pigs for both subsistence and commercial reasons.Pork is supplied to local markets and to more distant urban markets, through a complex marketing and transport system.Within this system, the financial risks for the producer can be high and there is limited support from organizations and professional bodies for technical inputs or services such as insurance.large-scale confined pig production vary in size, but are generally significantly larger than farms in the previously described categories.Because consumers seek to purchase food at the lowest price, but the price of inputs is rising, the profit margin per pig is decreasing.Producers participating in global commodity pork markets must continually reduce the cost of production per pig to be profitable.Production can be on one site only or on several sites that are all part of the same structure.The major cost reduction measures that can be implemented when moving from small-scale to large-scale confined production are through increased farm size, specialization of farming activities, consolidation of the different steps of pig production, and adoption of an "all-in-all-out" production flow at each site, with implementation of some or even extensive biosecurity protocols.Large pig farms may be family-owned, affiliated to companies or corporately owned.large-scale outdoor pig production, animals are confined by fencing, but are mainly outdoors; there is therefore less need for investment in bricks and mortar facilities.These farms can brand and sell pork for higher prices, and will often have a larger portfolio of activities, including agro-tourism or hunting for example. BiosecurityIn this paper, biosecurity is defined as the implementation of measures that reduce the risk of disease agents being introduced and spread.It requires that people adopt a set of Formaldehyde Cidal Cidal Cidal Cidal Cidal

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.806
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.051
GPT teacher head0.285
Teacher spread0.233 · 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 designTheoretical or conceptual
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

Citations93
Published2010
Admission routes1
Has abstractyes

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