Advancing ecohealth in Southeast Asia and China: Lessons from the Field Building Leadership Initiative
Notice bibliographique
Résumé
FBLI country sites Executive summaryIntensification of crop and livestock production can improve food, nutrition, and income security; however, intensification can also lead to increased health risks, environmental degradation, and biodiversity loss.This is especially true in Southeast Asia and China, regions facing rapid economic growth.To address this complex challenge, a better understanding of the interactions between agricultural practices, human health, and ecosystems is required.The Field Building Leadership Initiative (FBLI), supported by the International Development Research Centre (IDRC), has been working to understand and address intensive agricultural practices and associated health risks in Southeast Asia and China.Developed jointly by research centres in China, Indonesia, Thailand and Vietnam, and launched in 2012, this five-year initiative allows researchers and their partners to carry out research, capacity building, and knowledge translation to inform practice and policy. Key messages Intensive agricultural practices can have far-reaching impacts on health and environment Smallholder farmers play an important role in meeting the global demand for food The livelihoods of smallholder farmers are affected both positively and negatively by agricultural intensification Measures which are likely to help to address challenges include:-Creation and dissemination of guidelines for best agricultural practices and monitoring and evaluation of guidelines; -Long-term commitment of partnership initiatives; and -Increased investment in research and policy surrounding agriculture and health Research for developmentThe FBLI team, working with stakeholders from the onset of research for over four years, has achieved progress in improving the health of smallholder farmers.Specifically, the project created new evidence on health risks of agricultural intensification and developed innovative interventions to mitigate health risks and promote sustainable agricultural practices.The integration of FBLI research results into agricultural practices is testimony of the rigorous research efforts and productive engagement of FBLI with relevant stakeholders.Through the initiative, researchers and partners undertook research on a number of issues: Pesticide use and its impact on human health and agricultural ecosystems in China; Human and animal waste management in Vietnam; Rubber plantations and vector-borne diseases in Thailand; and Small-scale dairying in Indonesia.Ecohealth are approaches that recognize that human health and well-being are the result of a complex set of interactions between people, social and economic conditions, culture, and the natural environment.In short, human health is dependent on the health of our ecosystems.A number of achievements were noted so far: Better understanding of health risks of agricultural intensification; Innovative products and interventions to address such health risks; Preliminary changes observed in behaviours and practices of farmers towards more sustainable agricultural development; Increased Ecohealth capacity of senior researchers and new generation of researchers Increased awareness of Ecohealth among researchers and academic institutions; and Involvement of academic institutions, NGOs, ministries, and community members in research activities through networking and engagement. Building capacity and knowledge to actionThe FBLI has been supporting the development of sustainable cohorts of Ecohealth practitioners and researchers.For example, through the FBLI's Global Health True Leader Series, a regional leadership training program, many young professionals from various fields (e.g., agriculture, health, and environment) developed their leadership skills and Ecohealth competencies.This program has reached over 400 participants from ten Asian countries.Ecohealth curricula has also been integrated in four universities in Southeast Asia and China.The FBLI supported policy advocacy, for example, policy alliance groups were formed in each project country to facilitate research knowledge sharing and uptake.These groups consisted of mid-level policy makers, senior FBLI researchers and representatives from other regional networks.FBLI is connected with Ecohealth and One Health networks in the region to promote Ecohealth approaches, including Southeast Asia One Health Network (SEAOHUN), Ecohealth Emerging Infectious Diseases Research Initiative (Eco EID), Economic Development, and Ecosystem Changes, and Emerging Infectious Diseases Risks Evaluation (ECOMORE).The team is working towards raising public awareness on agricultural intensification issues through bulletins, publications, and a growing social media presence. Moving forward and lessons learnedAs FBLI progresses into its final year, the initiative will focus its programming on data analysis and reporting, monitoring outcomes, and knowledge sharing.The next synthesis booklet is expected to be published at the end of 2016.Lessons learned: Despite interest of researchers in using the Ecohealth approach, it is a complex undertaking requiring substantial time and skills.However, the capacity of team members in using the Ecohealth approach increased through experiences. Linking researchers to policy makers and influencing policy decisions have proven to be challenging, but processes such as word-of-mouth can help facilitate the networking.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,016 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,013 | 0,007 |
| Communication savante | 0,007 | 0,004 |
| Science ouverte | 0,002 | 0,013 |
| Intégrité de la recherche | 0,003 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».