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Record W3180981926 · doi:10.1016/j.gfs.2021.100559

Contributions of integrated aflatoxin management strategies to achieve the sustainable development goals in various African countries

2021· article· en· W3180981926 on OpenAlexfundno aff
Alejandro Ortega‐Beltran, Ranajit Bandyopadhyay

Bibliographic record

VenueGlobal Food Security · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMycotoxins in Agriculture and Food
Canadian institutionsnot available
FundersBundesministerium für Wirtschaftliche Zusammenarbeit und EntwicklungUnited States Agency for International DevelopmentDepartment of Foreign Affairs and Trade, Australian GovernmentDepartment for International Development, UK GovernmentAgence Française de DéveloppementGlobal Affairs CanadaBill and Melinda Gates Foundation
KeywordsBusinessAflatoxinSustainable developmentEnvironmental planningEnvironmental resource managementNatural resource economicsBiotechnologyGeographyEconomicsBiologyEcology

Abstract

fetched live from OpenAlex

In 2015, all United Nations Member States adopted the 2030 Agenda for Sustainable Development to achieve peace and prosperity for all people in the planet. Meeting that ambitious agenda depends on fulfilling all objectives of 17 Sustainable Development Goals (SDGs). Multiple approaches by diverse actors, many of them interconnected, will allow achieving each SDG. However, with compromised food security and food safety, many SDGs will not be realized. In sub-Saharan Africa (SSA), maize and groundnut are two staple crops frequently contaminated with aflatoxins , which threaten food security and food safety. Aflatoxins are extremely dangerous compounds produced primarily by the fungus Aspergillus flavus . Even at minute concentrations, aflatoxins negatively influence health, income, and trade sectors. Farmers, traders, industries, and consumers become affected. However, practical solutions exist. Non-aflatoxin producing isolates (referred to as atoxigenic) of A. flavus can decrease crop aflatoxin content when used in biocontrol formulations to competitively displace aflatoxin producers during crop development. Typically, treated crops contain 80%–100% less aflatoxin than non-treated crops. The technology was developed by USDA-ARS for use in the US and has been adapted and improved for use in SSA where several products under the tradename Aflasafe are available. There are biocontrol products registered for use in 10 SSA countries and more are being developed. On the other hand, although highly effective, biocontrol is not a panacea. Less aflatoxin occurs across value chains when biocontrol is combined with other practices. In this review, we discuss how i) aflatoxin biocontrol products are developed, manufactured, licensed, and commercialized, ii) aflatoxin management strategies are designed, and iii) integrated aflatoxin management is or will soon be contributing to achieve, in several countries, many targets of most SDGs. We present integrated aflatoxin management as a model intervention contributing to tackle several challenges impeding prosperity and peace in SSA.

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)0.004
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.006
GPT teacher head0.222
Teacher spread0.216 · 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

Citations57
Published2021
Admission routes1
Has abstractyes

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