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Record W3036222267 · doi:10.7939/r3-w2en-7x26

Cultivation and Grazing Impacts on Extracellular Enzyme Activity in Alberta Grasslands

2020· article· en· W3036222267 on OpenAlexaboutno aff
Dauren Kaliaskar

Bibliographic record

VenueUniversity of Alberta Library · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsGrazingGrasslandAgroforestryExtracellularEnvironmental scienceAgronomyEcologyBiology

Abstract

fetched live from OpenAlex

Grasslands cover a quarter of the planet’s terrestrial surface and constitute 70% of the world’s agricultural land area. Grasslands provide clean water, facilitate effective nutrient cycling, and provide necessary habitat and forage for livestock and wildlife. In addition, grasslands have the potential to mitigate greenhouse gas emissions by sequestering carbon (C) and nitrogen (N) in soil. Grazing is one of the most common uses of grasslands and may alter C and nutrient mineralisation. Therefore, understanding the impact of different grazing systems (i.e. continuous and rotational) on C and nutrient cycling, as well as past management practices (cultivation), climate and soil properties, is of significant interest. This study examined the role of grazing systems on soil biogeochemical cycling by measuring extracellular enzyme activity (EEA), which is an indicator of soil biological activity. The activities of six soil extracellular enzymes were analysed that are involved in C (xylosidase, β-glucosidase, cellobiosidase), nitrogen (N) (N-acetyl-β glucosaminidase, urease), and phosphorus (phosphatase) cycling. Soil samples were tested from 12 pairs of field sites (i.e., ranches) with varying grazing practices (i.e., AMP or non-AMP grazing, with divergent stocking rates) for at least five years prior. An information theoretic model selection approach was used to determine those independent variables (disturbance regime, climate, soil) that explained the variability of each EEA. Results showed that a long resting period (mainly present in AMP ranches) increased β-glucosidase activity, while a high stocking rate increased urease activity. In contrast, soils with known previous cultivation had lower xylosidase and phosphatase activities, suggesting a legacy effect of previous cropping. The main environmental factors regulating enzyme activity were available soil N and climatic aridity. Overall, grazing practices, as represented by grazing systems, appear capable of altering C and nutrient cycling, with AMP grazing increasing C mineralisation iniiithese Alberta grasslands. This finding highlights the importance of grazing practices that maintain soil biological activity.

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 score0.170
Threshold uncertainty score0.975

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 head0.012
GPT teacher head0.170
Teacher spread0.158 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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