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Record W4391074408 · doi:10.1016/j.apsoil.2024.105271

Adding ligneous litter to cultivated organic soil changes the soil micro-food web and alters soil nitrogen availability

2024· article· en· W4391074408 on OpenAlexaff
Krisztina Mosdossy, Cynthia M. Kallenbach, Benjamin Mimee

Bibliographic record

VenueApplied Soil Ecology · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicNematode management and characterization studies
Canadian institutionsMcGill UniversityAgriculture and Agri-Food Canada
Fundersnot available
KeywordsLitterSoil food webPlant litterAgronomySoil carbonSoil biologySoil waterEnvironmental scienceBiologyNitrogen cycleEcosystemNitrogenEcologyChemistry

Abstract

fetched live from OpenAlex

Organic soils are major carbon (C) sinks that when drained for cultivation, become large sources of CO2-C to the atmosphere and are susceptible to high rates of wind erosion. Mulching organic soils with ligneous litter is one strategy for reducing erosion, however, this could shift a bacteria-dominated soil to a fungal-dominated soil, affecting the microbivorous nematode populations and C and nitrogen (N) cycling. We conducted two, six-week greenhouse experiments, with soil sourced in 2020 and then in 2021, to determine how the micro-food web responds to different ligneous litter inputs and placement. We also examined the coinciding effects on bioavailable N and gene abundances encoding for decomposition enzymes to assess how changes in the micro-food web potentially alter C and N dynamics. We compared four different litters (larch, miscanthus, ash, or willow) applied to soil separately and ranging in lignin:N between 65 and 339. To test the effect of litter placement, each litter type was left on top or incorporated into soil planted with lettuce. Starting soil nematode, fungal and bacterial community compositions differed between 2020 and in 2021. Litter additions changed the nematode and fungal community compositions, but the bacteria community was unaffected by litter inputs. We found that litter additions generally increased fungal and fungivore abundances, along with Cephalobidae (cp-2 nematodes) that are typical of low-resource availability. However, this effect was highly dependent on litter type, placement, and year. For instance, the increase in fungal abundance relative to no litter additions only occurred when litter was incorporated into the soil, and this was especially true for our mid-range lignin:N litters, miscanthus and ash (p < 0.001). Regardless of litter type, we saw little evidence for litter inputs reducing bioavailable N compared to the no-litter control. Incorporated miscanthus and ash litter also increased root biomass (but not lettuce biomass) relative to no litter inputs (p < 0.05). These litters had the lowest (miscanthus) and highest (ash) amount of lignin and were also the only litter to increase in decomposition gene abundance, suggesting that microbial function is sensitive to lignin content, but not lignin:N. We suspect that the higher abundance of nematodes with ligneous additions is elevating microbivory, helping to maintain soil bioavailable N. However, the variability we see between 2020 and 2021 suggests that the micro-food web response to litter additions depends on the starting field soil and its biological community. Our results show that ligneous litter amendments select for a fungal micro-food web, but do not necessarily lead to reduced bioavailable N within a 6-week period, despite the high lignin and C:N of the litters. Thus, it appears that soil mineral N levels are maintained with litter incorporation and that this is associated with changes to the micro-food web, particularly increases in fungal and nematode abundances.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.665
Threshold uncertainty score0.530

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 head0.013
GPT teacher head0.200
Teacher spread0.187 · 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 designBench or experimental
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

Citations10
Published2024
Admission routes1
Has abstractyes

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