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Record W7161769665 · doi:10.82308/4987

Nitrogen transformations and loss over winter in manure-amended soils with cover crops

2019· dissertation· en· W7161769665 on OpenAlexaboutno aff
Leanne Ejack

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsCover cropVicia villosaGreen manureManureTillageCyclingSoil waterNitrogenCrop yield

Abstract

fetched live from OpenAlex

Many farmers apply manure in the fall (autumn season), but without an actively growing crop in the ground, the nitrogen (N) in the manure is susceptible to over-winter losses. Periods of freeze-thaw cycling can exacerbate N losses by stimulating soil microbes to transform reactive substrates like soil mineral N into nitrous oxide (N2O), a potent greenhouse gas. The uptake of reactive N from fall-applied manure by a fall-sown cover crop may reduce over-winter N losses. The objective of my research was to investigate the effect of combining fall manure application with cover cropping on soil N dynamics over winter and during periods of freeze-thaw cycling under field and laboratory conditions. I also examined the relationship between N2O production and reactive soil substrate concentrations. The field experiment was a full factorial in a randomized complete block design with three manure treatments (none, liquid, solid) and four cover crop types (no cover crop, 100% ryegrass [Lolium multiflorum Lam.], a 75% ryegrass/25% hairy vetch [Vicia villosa Roth] mixture and a 50% ryegrass/50% vetch mixture). The experiment was established at two field sites in Québec, Canada. A partial N mass balance (g N m-2) was calculated in fall (sum of the fall soil N stock to 0.15 m depth, N in fall-applied manure, and N in cover crop biomass) and in spring (sum of the spring soil N stock to 0.15 m depth and N in the winter-killed cover crop) for each treatment combination. After terminating the cover crop, spring wheat (Triticum aestivum L.) was planted, and each main plot was split into two subplots that received either 100 kg N ha-1 urea fertilizer or no fertilizer. Wheat samples were taken at tillering, flowering, and maturity to determine N content. Final yield was also measured. Cover crops were not effective at retaining manure N (≤7% uptake) and there was no difference in the fall and spring N balance among the manure and non-manure plots. Residual N was not supplied from fall-applied manure to the spring wheat in the next growing season, and average wheat yields were 11–14% less in the subplots that received no spring N fertilizer than those that received 100 kg N ha-1. In the laboratory, pots with 280–285 g soil received four N fertilizer treatments (none, liquid manure, solid manure, urea), with or without an annual ryegrass cover crop. The pots were exposed to 0, 1, 2, or 3 simulated freeze-thaw cycles (FTCs) at -4 to +4°C. The N2O production was measured at 0, 3, 6 and 9 h for each FTC, then pots were destructively sampled to determine the soil mineral N concentration. There was no difference in N2O production among the treatment combinations across all FTCs, but the pots that received urea or liquid manure had the highest soil mineral N concentration. The cover crop had minimal effect on the soil mineral N concentration. Soil mineral N explained approximately 14% of the variation in N2O production. Pots that underwent FTCs had a remarkable 937–1000% increase in N2O production compared to unfrozen pots. This suggests that N2O-producing microbial activity occurred in the frozen soils at -4oC, causing N2O to accumulate under ice and be released when the soils thawed at 4oC, mostly within the first 3 h. The results of both the field and laboratory studies suggests that microbial N transformations do not stop during the winter months, leading to substantial losses of N in fertilized soils during the non-growing season in cold humid temperate regions

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.141
Threshold uncertainty score0.280

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.007
GPT teacher head0.213
Teacher spread0.207 · 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 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

Citations0
Published2019
Admission routes1
Has abstractyes

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