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Enregistrement W2959270077

Contributions of biodiversity to the sustainable intensification of food production – Thematic study for The State of the World’s Biodiversity for Food and Agriculture

2019· report· en· W2959270077 sur OpenAlexaff
Ian K. Dawson, Simon Attwood, S.E. Park, Ramni Jamnadass, W. Powell, Trey Sunderland, Roeland Kindt, Stepha McMullin, Peter N. Hoebe, J. A. Baddeley, Charles Staver, Vincent Vadez, Sammy Carsan, James M. Roshetko, Ahmed Amri, E. Karamura, D. Karamura, Md. Emran Hossain, Michael J. Phillips, Amit Kumar, J-P.B. Lillesø, John Benzie, Gerhard Sabastian, Beatrice Ekesa, Walter Ocimati, Lars Graudal

Notice bibliographique

RevueCGSPace A Repository of Agricultural Research Outputs (Consultative Group for International Agricultural Research) · 2019
Typereport
Langueen
DomaineAgricultural and Biological Sciences
ThématiquePlant and animal studies
Établissements canadiensUniversity of British Columbia
Organismes subventionnairesConsortium of International Agricultural Research CentersBoettcher Foundation
Mots-clésBiodiversityAgricultureAgroforestrySustainabilityFood processingLivestockSustainable agricultureAgricultural biodiversityGeographyBusinessEcologyNatural resource economicsBiology
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Biodiversity supports sustainable food production, although recognition of its roles has been relatively neglected in the sustainable intensification literature. In the current study, the roles of biodiversity in sustainable food production are considered, assessing how these roles can be measured, the current state of knowledge and opportunities for intervention. The trajectory of global food production, and the challenges and opportunities this presents for the roles of biodiversity in production, are also considered, as well as how biodiversitybased interventions fit within wider considerations for sustainable food systems. The positive interactions between a diverse array of organisms, including annual crops, animal pollinators, trees, micro-organisms, livestock and aquatic animals, support food production globally. To support these interactions, a range of interventions related to access to materials and practices are required. For annual crops, major interventions include breeding crops for more positive crop–crop interactions, and the integration of a wider range of crops into production systems. For animal pollinators, major interventions include the introduction of pollinator populations into production landscapes and the protection and improvement of pollinator habitat. For trees, a major required intervention is the greater integration of perennial legumes into farmland. For micro-organisms, the implementation of agronomic practices that support beneficial crop-microbe interactions is crucial. For livestock production, breed and crop feedstock diversification are essential, and the implementation of improved methods for manure incorporation into cropland. Finally, in the case of aquatic production, it is essential to support the wider adoption of multi-trophic production systems and to diversify crop- and animal-based feed resources. These and other interventions, and the research needs around them, are discussed. Looking to the future, understanding the drivers behind trends in food systems is essential for determining the options for biodiversity in supporting sustainable food production. The increased dominance of a narrow selection of foods globally indicates that efforts to more sustainably produce these foods are crucial. From a biodiversity perspective, this means placing a strong emphasis on breeding for resource use efficiency and adaptation to climate change. It also means challenging the dominance of these foods through focusing on productivity improvements for other crop, livestock and aquaculture species, so that they can compete successfully and find space within production systems. New biodiversity-based models that support food production need not only to be productive but to be profitable. Thus, as well as describing appropriate production system management practices that enhance production and support the environment, the labour, knowledge, time required to operationalize, and other costs of new production approaches, must be considered and minimized. To support the future roles of biodiversity in sustainable food production, we recommend that particular attention be given to the longitudinal analysis of food sectors to determine how the diversity of foods consumed from these sectors has changed over time. Analysis is already available for crops, but related research is needed for livestock and aquaculture sectors. This analysis will then support more optimal cross-sectoral interactions, in terms of the contributions each sector provides to supplying the different components of human diets. Additional meta-analyses and synthetic reviews of case studies are required as an evidence base for biodiversity-based food production system interventions, but future studies should pay more attention to articulating the potential biases in case study compilation (the problem of ‘cherry picking’ positive examples) and the measures that have been taken to minimize such effects.

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 machine sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,002
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,009
Score d'incertitude au seuil0,024

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0030,004
Études des sciences et des technologies0,0010,002
Communication savante0,0040,004
Science ouverte0,0010,004
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0060,000

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.

Tête enseignante Opus0,110
Tête enseignante GPT0,328
Écart entre enseignants0,218 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreSynthèse

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations11
Publié2019
Routes d'admission1
Résumé présentoui

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