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Record W2037386225 · doi:10.2134/agronj2008.0170x

Soil Properties and Crop Yields in Response to Mixed Paper Mill Sludges, Dairy Cattle Manure, and Inorganic Fertilizer Application

2009· article· en· W2037386225 on OpenAlexaff
Adrien N’Dayegamiye

Bibliographic record

VenueAgronomy Journal · 2009
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsInstitut de Recherche et de Développement en Agroenvironnement
Fundersnot available
KeywordsLoamAgronomyFertilizerManureSoil waterCropSoil fertilityCrop yieldEnvironmental scienceChemistrySoil scienceBiology

Abstract

fetched live from OpenAlex

The contribution of organic wastes to crop yields and soil fertility may be influenced by their composition and the soil type. This 6‐yr study (2001–2006) evaluated the effects of repeated additions of mineral fertilizers (MF), mixed paper mill sludges (PMS) (18, 36, and 54 Mg ha −1 ), dairy cattle manure (DCM) (36 Mg ha −1 ) alone or with reduced mineral fertilizer (60% NPK) (RMF) and a control, on soil properties and corn ( Zea mays L.), barley ( Hordeum vulgaris L.), and soybean ( Glycine max L. Merr.) yields in a clay loam and sandy loam. The applications of PMS and DCM increased mostly N mineralization and crop yields in the sandy loam than in the clay loam. However, increases of soil C contents, water‐stable aggregates and MWD following their application were higher in the clay loam than in the sandy loam. The DCM effects on the soil property changes were of less magnitude than those of PMS. Except in the first year, the PMS applications at rates of 36 and 54 Mg ha −1 without NPK, and PMS applied at a rate of 18 Mg ha −1 with 60% NPK, produced highest crop yields in both soils and were comparable to those obtained with MF. The increase in yield following DCM additions (36 Mg ha −1 ) was lower than that obtained with PMS. Annual MF applications increased crop yields in both soil without significant changes on soil properties. The benefits of PMS and DCM on soil properties and crop yields varied depending on organic wastes and soil type.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.941
Threshold uncertainty score0.191

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 head0.016
GPT teacher head0.203
Teacher spread0.188 · 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

Citations25
Published2009
Admission routes1
Has abstractyes

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