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Record W2080440134 · doi:10.1890/1540-9295-10.9.455

Can intensive farming save nature?

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

Bibliographic record

VenueFrontiers in Ecology and the Environment · 2012
Typereview
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsMcGill University
Fundersnot available
KeywordsBiodiversityAgroforestryAgricultureGeographyLand useAgricultural landLand managementEcosystem servicesSustainabilityPopulationNatural resource economicsEnvironmental resource managementEcologyEcosystemEnvironmental scienceBiologyEconomics

Abstract

fetched live from 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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.960
Threshold uncertainty score0.801

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.204
Teacher spread0.193 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

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Citations47
Published2012
Admission routes1
Has abstractyes

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