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Record W7043076468

Response of soil fungal communities associated with different soil fractions to tillage practices and crop rotation

2024· dissertation· en· W7043076468 on OpenAlexaboutno aff

Bibliographic record

VenueMspace (University of Manitoba) · 2024
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsTillageCrop rotationDecomposerCropSoil waterConventional tillageMonocultureSoil testInternal transcribed spacer
DOInot available

Abstract

fetched live from OpenAlex

This thesis investigates the effects of agricultural practices on soil microbial communities, with a specific focus on understanding how different tillage practices and crop rotation impact soil fungal communities within the distinct soil fractions. A two consecutive year study (2021 and 2022) was conducted in three fields located in Manitoba, Canada, each undergoing a different phase a crop rotation cycle that included corn, canola, and soybean. Additionally, the research examined multiple tillage practices that included conventional tillage, deep tillage, vertical tillage, and raised bed practice. To comprehensively assess soil fungal microbial communities, next-generation sequencing of the internal transcribed spacer region within nuclear ribosomal DNA was employed. Our study confirmed that tillage practices and crop rotation shape the structure of the soil fungal communities within the different soil fractions. In both years, Ascomycota and Basidiomycota were recognized as the prevailing fungal phyla in all tillage practices and crop rotations. These two phyla are widely recognized as the primary classical fungal decomposers and pathotrophs in soils. Furthermore, while tillage practices did not exert a significant impact on soil fungal alpha diversity, the highest predicted average fungal richness, as measured by Observed, Shannon, and Simpson alpha diversity indices, was associated with vertical tillage in 2021 and conventional tillage in 2022. Moreover, there was a significant difference in soil fungal alpha diversity among plots within different fields in 2021. Predictions based on both Shannon and Simpson indices indicated that plots in soybean in 2021 and plots in corn in 2022 were anticipated to exhibit the highest average fungal richness. We also noted a significant difference in soil fungal alpha diversity among different soil fractions in both years of our study. The relative abundance of numerous amplicon sequence variants exhibited significant difference in response to both tillage practices and crop rotation. Specifically, soils subjected to conventional tillage practices demonstrated a significantly lower relative abundance of plant pathogens within the phylum Ascomycota and a higher relative abundance of saprotrophic fungi. Our investigation further revealed that the relative abundance of beneficial soil fungi, particularly arbuscular mycorrhizal fungi, was higher in soils under the vertical tillage practice. In plots within the corn field, we also noted a markedly higher relative abundance of arbuscular mycorrhizal fungi. Conversely, plots within canola fields exhibited a higher relative abundance of saprotrophic fungi, including members of the phyla Ascomycota, Basidiomycota, and Mortierellomycota. Our results suggest that tillage and crop rotation influence soil fungal community distribution. This study provides insights into the intricate relationships between agricultural practices and soil fungal communities, shedding light on the dynamics of these critical components of agricultural ecosystems in Manitoba.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.020
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.220
Teacher spread0.200 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2024
Admission routes1
Has abstractyes

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