MétaCan
Menu
Retour à la cohorte
Enregistrement W2080440134 · doi:10.1890/1540-9295-10.9.455

Can intensive farming save nature?

2012· review· en· W2080440134 sur OpenAlexaff
Navin Ramankutty, Jeanine M. Rhemtulla

Notice bibliographique

RevueFrontiers in Ecology and the Environment · 2012
Typereview
Langueen
DomaineEnvironmental Science
ThématiqueConservation, Biodiversity, and Resource Management
Établissements canadiensMcGill University
Organismes subventionnairesnon disponible
Mots-clésBiodiversityAgroforestryAgricultureGeographyLand useAgricultural landLand managementEcosystem servicesSustainabilityPopulationNatural resource economicsEnvironmental resource managementEcologyEcosystemEnvironmental scienceBiologyEconomics

Résumé

récupéré en direct d'OpenAlex

Last month, Sharpless and Hirshfield (Guest Editorial, 2012; 10[8]: 399) discussed how conservation and food production do not compete in the oceans. In the terrestrial realm, of course, the situation is different; as they pointed out, “On land there is a constant struggle between conservation and food production”. There are roughly 130 million km2 of ice-free land on this planet. Fully one-third of this land is currently used for agriculture (~12% and ~22% devoted to croplands and grazing, respectively). We have converted or modified ~20% of Earth's forests and ~50% of its savannas, grasslands, and shrublands for farming. The pressure to convert land is unrelenting; the expected growth of the world's human population, combined with shifts in diet as people become wealthier, may require a doubling of global food production by 2050. Roughly another 25 million km2 (~20%) of land is estimated to be suitable for farming. While this is a lot of land, most of it lies beneath tropical rainforests, which have high ecosystem-service value. Thus, farming has been and will continue to be the major cause of habitat and biodiversity loss. This realization has recently sparked a lively debate about the biodiversity benefits of land sparing (intensifying farming on existing lands, thus sparing land for nature) versus land sharing (which promotes lower intensity, but more extensive, “wildlife friendly” farming systems). Although the debate is far from settled, empirical studies to date suggest that land sparing may be more effective in protecting biodiversity, especially for forest-dependent species and species with limited ranges. This is not to deny that land sharing can increase the quality of the matrix and facilitate dispersal of organisms between remnant habitat fragments, thereby leading to higher biodiversity as compared with intensive farming systems. But intensifying existing croplands might have a greater benefit for overall biodiversity by minimizing the clearing of intact habitat. Of course, there is a large caveat. A major criticism of land sparing is that there is scant empirical evidence that agricultural intensification actually leads to land sparing in practice. In fact, intensification is often accompanied by further expansion, as neighboring farmers adopt the novel practices. This implies either that the demand for the agricultural product has increased because of intensification or that the land sparing has occurred in another region of the world. Whether intensification can create increased demand of agricultural products is an important question; it depends on whether demand remains elastic as supply increases. Various new uses have been found for corn (as corn syrup, corn starch, and more recently, ethanol), for example, as a result of production beyond the needs of human food and livestock feed. Measuring land sparing through empirical analysis is thus confounded by: (1) the lack of a “control” situation to compare against – perhaps cropland would have expanded even faster had it not been for intensification; and (2) the fact that national-level analysis may fail to account for “leakage” of land sparing to other parts of the world. So where does this leave us? What agricultural policies would maximize conservation? We believe that policies should be tailored to different conditions in different regions: land sparing is better suited to the tropics, whereas land sharing provides more benefits in temperate regions. In the tropics, there remain vast areas of intact forest habitat that are currently threatened by agriculture. This is also where hunger is widespread. Intensification in these regions can protect the rainforest, feed more people, and provide opportunities for economic development. The temperate regions, on the other hand, have experienced a long history of intensive agriculture, have already substantially modified biodiversity, and have relatively little intact natural habitat left. Intensification, rather than extensification, is the major source of environmental degradation (eg depletion of freshwater resources and eutrophication from nutrient runoff). Moreover, agriculture in temperate regions is mainly devoted to animal feed and biofuel production; and obesity, rather than malnutrition, is of greater concern. In temperate regions, then, land sharing is the better alternative. We also need to broaden the debate beyond biodiversity. There are numerous other ecosystem services –including climate regulation, water flow and quality regulation, pollination, soil fertility, and so forth – of interest to conservation. The conversation should therefore be about “environmentally friendly farming” rather than wildlife friendly farming versus land sparing. Furthermore, it is imperative to consider the sustainability and resilience of the agricultural system itself. The bottom line is clear. Finding more environmentally friendly ways to ensure that the projected 10 billion people of this planet have an adequate diet is one of the major challenges of conservation; on land, conservation cannot escape from the reality of agriculture.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut 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 enseignants0,960
Score d'incertitude au seuil0,801

Scores Codex et Gemma par catégorie

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

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 Opus0,011
Tête enseignante GPT0,204
Écart entre enseignants0,193 · 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 tête enseignante, 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

Citations47
Publié2012
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueFrontiers in Ecology and the EnvironmentMême sujetConservation, Biodiversity, and Resource ManagementTravaux en français237 207