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Record W2092544640 · doi:10.4141/s04-017

Contribution of on-farm and industrial composts to soil pH and enrichment in available nutrients and metals

2004· article· en· W2092544640 on OpenAlexvenueno aff
Bernard Gagnon

Bibliographic record

VenueCanadian Journal of Soil Science · 2004
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicComposting and Vermicomposting Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsCompostLoamManureNutrientAgronomyPoultry litterChemistryHumusSoil pHEnvironmental scienceSoil waterBiologySoil science

Abstract

fetched live from OpenAlex

Soil composition following addition of on-farm manure composts should be investigated for better utilization of these products in the field. An experiment was conducted to evaluate the contribution of several on-farm and industrial composts to soil pH and its enrichment in available major nutrients and metals (Cu, Zn, Fe, Mn). Materials were mixed at a rate of 200 mg N kg -1 with an Arago sandy loam (Humo-Ferric Podzol), and incubated in glass jars at 35°C for 13 wk. Composts from poultry litter (PL), vegetable residue (VR), wood shavings and dairy manure were among those that provided the highest levels of available N and P at the end of incubation. The vegetable residue compost also substantially increased the levels of Mehlich-3 Ca and Mg, but it was a poor source of K. Most dairy manure composts (DM) contributed to increase the soil exchangeable K. The industrial yard trimming compost (YT) largely increased soil pH and available N and Ca, but it was a very poor source of P and K. The spent mushroom compost (MU) also increased soil pH and Mehlich-3 Ca. In contrast to the major nutrients, on-farm composts had a limited impact on soil pH and on the available metal contents. This study indicates that the enrichment in major nutrients and metals of this acidic sandy loam and the relative contribution of the studied elements were mainly related to composted material sources and their degree of decomposition. Key words: Composting, farm manure, soil composition

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)0.001
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 score0.418
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.030
GPT teacher head0.238
Teacher spread0.208 · 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

Citations10
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Soil ScienceSame topicComposting and Vermicomposting TechniquesFrench-language works237,207