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Mulch Inoculation and Placement Influenced Barley (Hordeum Vulgare) Growth and Soil Nitrate Levels

2013· article· en· W2184136559 on OpenAlexaff
Jacinta M. Kimiti, Andrew M. Gordon

Bibliographic record

VenueGreener Journal of Agricultural Sciences · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgronomic Practices and Intercropping Systems
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsHordeum vulgareMulchInoculationAgronomyNitrateBiologyPoaceaeHorticultureEcology

Abstract

fetched live from OpenAlex

We investigated the effect of point of mulch placement and use of leaf mulches from plants inoculated with rhizobia on growth, nitrogen concentration and content of barley, and soil nitrate and pH changes. Mulches placed on soil surface enhanced barley heights and vegetative biomass in all leaf types used. However, nitrogen concentration was relatively higher in both barley vegetative plants and ears of barley grown in mixed mulches of both inoculated and uninoculated leaves. Mixing mulch types with soil caused a quick nitrate release within the first four weeks, which sharply dropped before week 6. Placing mulches on the soil surface resulted to a gradual nitrate release over the study period. Further, soil pH in all mulch treatments decreased within the first four weeks. Results from this study indicated that point of mulch placement was more effective than rhizobia inoculation of mulch on barley growth, nitrogen concentration and content, soil nitrate and pH changes. The results for nitrate levels revealed that it might be necessary for farmers to understand nitrogen requirements of crop so as to know where to place mulches. Results on pH revealed that care should be taken when mulching crops that are sensitive to small changes in pH.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.904
Threshold uncertainty score0.442

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 communication0.0000.002
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 head0.025
GPT teacher head0.225
Teacher spread0.201 · 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

Citations1
Published2013
Admission routes1
Has abstractyes

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