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Record W4284881811 · doi:10.3389/fsufs.2022.826486

Soil Health and Biodiversity Is Driven by Intensity of Organic Farming in Canada

2022· article· en· W4284881811 on OpenAlexaffabout
Derek H. Lynch

Bibliographic record

VenueFrontiers in Sustainable Food Systems · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsDalhousie University
Fundersnot available
KeywordsAgroecologyOrganic farmingEcological farmingAgricultureAgroforestryCroppingTillageSoil healthBiodiversitySoil carbonEnvironmental scienceMixed farmingIntegrated farmingNutrient managementEcosystem servicesBusinessEcosystemGeographySoil organic matterAgronomyEcologySoil waterBiology

Abstract

fetched live from OpenAlex

Organic farming is continuing to expand in Canada, with close to 6,000 producers farming over 2% of all agricultural land. There is insufficient evidence, however, of a trend toward larger average farm size and increasing specialization by these organic farms. This mini-review postulates that a gradient of intensity of farm management exists within organic farming sectors in Canada, with respect to cropping diversity, and tillage and nutrient utilization, and this gradient of intensity is a key determinant of agroecological outcomes. This variation in management approach and intensity reflects producer's individual perspectives on organic farming principles and practices, irrespective of farm scale. By directly influencing farm crop and vegetative diversity and cover, and farm nutrient status and carbon cycling, management intensity determines soil carbon storage and flux, soil health and biodiversity agroecological and ecosystem services, plus farm agronomic resilience. Demographic trends and perspectives of new entrants in organic farming are encouraging signs of an increasingly inclusive and socio-ecologically complex Canadian organic farming sector, which recognizes the agroecological implications of intensity of organic farm management across all production sectors.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.909
Threshold uncertainty score0.379

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.007
GPT teacher head0.168
Teacher spread0.160 · 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

Citations12
Published2022
Admission routes2
Has abstractyes

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