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Record W4212802219 · doi:10.3390/agronomy12020415

Evaluation of Agrobiodiversity and Cover Crop Adoption in Southern Ontario Field Crops

2022· article· en· W4212802219 on OpenAlexafffundabout
Katherine Shirriff, Krishna Bahadur KC, Aaron Berg

Bibliographic record

VenueAgronomy · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsUniversity of Guelph
FundersUniversity of Guelph
KeywordsCover cropAgricultural biodiversityDominance (genetics)CroppingAgroforestryAgronomyCropCrop rotationGeographyField cornAgricultureEnvironmental scienceZea maysBiologyArchaeology

Abstract

fetched live from OpenAlex

Incorporating cover crops into corn and soybean operations across Southern Ontario is essential for maintaining yields under environmental stressors. Unfortunately, amongst the literature, there is a concern about the low adoption rate of cover crops in the northern Corn Belt due to a shift toward low agrobiodiversity and dominance of more profitable corn and soybean cropping systems, encouraged by extensive use of fertilizers, herbicides, and pesticides. This study examines whether Southern Ontario is following suit in decreasing agrobiodiversity trends, at the county level, and examines the adoption of cover crops within corn and soybean operations across Southern Ontario using digital imagery from 2013 to 2018. Results reveal that Southern Ontario is indeed shifting from systems characterized by higher agrobiodiversity to systems dominated with corn, soybean, and hay. Despite the benefits of cover crops, this study reveals that most of the current corn and soybean operations are not incorporating cover crops into the rotation. More significantly, the low adoption of cover crops is most apparent in southwestern Ontario, and increases in adoption occur toward the north.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.999

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)0.0020.000

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 head0.025
GPT teacher head0.205
Teacher spread0.180 · 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.

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

Citations6
Published2022
Admission routes3
Has abstractyes

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