Improving Rural Livelihoods: CIAT's Medium-Term Plan 2002-2004 6-7 December 2001
Notice bibliographique
Résumé
intemational organizations.To promote sustainable rurallivelihoods , CIAT will cultivate five core scientific competencies:Agrobiodi versity a nd genetics.Access to high-quality germplasm-for staple c rops like cassava, beans, and rice, as well as for forages and altemative high-income cropsremains a high priority for s mall farmer s.Genetic research, applied to conserved a nd characterized agrobiodiversity, leads to higher crop productivity, improved plant and soil health, and better human nutrition.Advances in molecular biology and genetic transformation have markedly improved our understanding of agrobiodiversity, thereby creating new opportunities for unlocking the potential of the vast genetic diversity found in the wild ancestors and clase relatives of cultivated crops. Ecology and manageme nt of pests and d iseases.Crop damage by bacteria, fungi, viruses, insects, and other pests is a perennial risk in farming and candeal a knockout blow to rura l livelihoods.In response, farmers all too frequently apply pesticides excessively, both damaging the environment and the health of farm families and consumers, while often failing to effectively control pests.Safer, more effective altematives to pest management, based on better understanding of agro-ecologies, can combine crop varieties with genetic resistance to pests and pathogens; biological control to fight pests with their natural enemies; and better farm management practices, including judicious use of agro-chemicals.Soil ecology and i mprovement.Healthy, fertile soil is vital to overall agroecosystem health and agricultura!competitiveness.Soil quality needs to be enhanced, especially where degradation is already a problem.The soil is also a public "ecological servicen: a regulator of water quality and supply, a way to break down contaminants, and even a carbon sink to slow greenhouse warming.Thus, how tropical farmers manage soil is relevant not only to their livelihoods but also to the survival of all terrestriallife.We view soil holistically , as a complex living system.Emphasis is put on managing fertility based o n better understanding of factors such as nutrient flows through plants and soil organisms.Spatial analysis .Spatial information can help produce more food with fewer environmental risks.Land use decision makers, whether local farm communities or national govemment agencies, need appropriate tools to analyze trade-offs.Advances in geographic infonnation systems (GIS) and modeling, combined with participatory data collection, offer majar opportunities for better land management.However, more user-friendly interfaces need to be designed.Decision-support tools can analyze farming systems and scale up farm behavior to the watershed level to better understand the effects of farmer decisions on resource degradation or improvement. Socioeconomic analysis and participatory research.Understanding farmer and community decision making is crucial to the success of new technologies for improving rural livelihoods.Socio-economic analy sis generates insights and empirically validated princ ipies for designing people-centered solutions, relying heavily but not exclusively on participatory methods.Other important tools and outputs are models, databases, and policy recommendations.Finally, a key contribu tion of socioeconomic analysis will be to monitor and evaluate CIAT research outputs and assess their impact, focusing more on issues of sustain ability and poverty reductio n rather than j ust productivity.This combina tion of five competencies h as distin ct s trengths.Each a rea of competence brings together related d isciplines that h ave significant scope to contribute to and benefit from scientific advance ment.An d each can help CIAT and its partners to achieve a direct.positive, and lasting impact on rural livelihoods in the tropics.Furthermore, these core competen cies are highly complementary , a llowing for integrated approaches to problem solving.Together, they will fo rm an enduring and stable institutlonal framework.while at the same tim e gwing C IAT the flexibiliry to respond to an evolving research agenda.As sctence
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,001 |
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 tête enseignante, 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 ».