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Record W3005062818 · doi:10.1111/1365-2664.13589

Organic farming benefits birds most in regions with more intensive agriculture

2020· article· en· W3005062818 on OpenAlexafffundabout
David Anthony Kirk, Amanda E. Martin, Kathryn E. Lindsay

Bibliographic record

VenueJournal of Applied Ecology · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Environmental Valuation
Canadian institutionsCarleton UniversityEnvironment and Climate Change Canada
FundersEnvironment and Climate Change Canada
KeywordsOrganic farmingAgricultureIntensive farmingAbundance (ecology)BiodiversityEnvironmental scienceEcological farmingIntensity (physics)AgroforestryMixed farmingGeographyEcologyBiology

Abstract

fetched live from OpenAlex

Abstract Organic farming is considered beneficial for biodiversity conservation in agricultural landscapes but the role of agricultural land use intensity (‘agricultural intensity’), particularly at regional scales, has often been neglected. We used breeding season bird abundance estimates from paired organic‐conventional fields in Saskatchewan (31 pairs), Ontario (36) and Québec (15), Canada to test two alternative predictions: that the positive effect of organic farming on bird abundance was (a) smaller when controlling for overall effects of local‐ and landscape‐scale agricultural intensity (accounting for main effects on abundance); or (b) increases with local‐ or landscape‐scale agricultural intensity (an interaction effect of intensity × organic farming). We also evaluated whether positive effects of organic farming were stronger in regions with greater agricultural intensity. The effect of organic farming on the cross‐species abundance of birds was only statistically supported in Ontario when not accounting for agricultural intensity. However, the estimated effect of organic farming was the same whether or not we controlled for agricultural intensity in Saskatchewan (supported positive effect) and Québec (unsupported effect). Little support existed for more positive effects of organic farming on abundance in areas with greater local‐ or landscape‐scale agricultural intensity. In Ontario, there was a trend for more positive effects of organic farming in more intensively farmed landscapes. Our results showed, for the first time in North America, an effect of regional‐scale agricultural intensity on the potential benefit of organic farms. The influence of organic farming on abundance was most positive in the region with the most intensive agriculture (Saskatchewan) and least positive in the region with the least intensive agriculture (Québec). Additionally, we showed that benefits of organic farming can be overestimated if the effects of local‐ and landscape‐scale agricultural intensity are not considered. However, positive effects of organic farming on cross‐species abundance and the abundance of individual species were still detectable when we controlled for agricultural intensity at local‐ and landscape‐scales. Synthesis and applications. When evaluating impacts of organic farming on biodiversity, it is important to consider the intensity of surrounding agricultural practices at local to regional scales. Our cross‐regional comparison showed that organic farming had the most positive effect on abundance of birds in the region with the most intensive agriculture (Saskatchewan) and least positive effect in the region with the least intensive agriculture (Québec). This demonstrates that birds can benefit from organic farming and this effect can be most pronounced in regions with more intensive agriculture, implying that birds may benefit most from expansion of the area of organic farming in regions where farming is most intensive.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.154
Threshold uncertainty score0.385

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.045
GPT teacher head0.188
Teacher spread0.143 · 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 designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations34
Published2020
Admission routes3
Has abstractyes

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