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Record W1633219923 · doi:10.5539/sar.v4n4p27

Effects of Short-term Tillage of a Long-term No-Till Land on Available N and P in Two Contrasting Soil Types

2015· article· en· W1633219923 on OpenAlexaffvenueabout
Miles Dyck, S. S. Malhi, M. Nyborg, Dick Puurveen

Bibliographic record

VenueSustainable Agriculture Research · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of Alberta
Fundersnot available
KeywordsStrawLoamTillageAgronomyChernozemFertilizerAnimal scienceCalcareousNitrateChemistryAmmonium nitrateAmmoniumNo-till farmingEnvironmental scienceSoil waterSoil fertilityBiologySoil scienceBotany

Abstract

fetched live from OpenAlex

The effects of short-term (4 years) tillage (hereafter called reverse tillage [RT]) of land previously under long-term (29 or 30 years) no-till (NT), with straw management (straw removed [SRem] and straw retained [SRet]) and N fertilizer rate (0, 50 and 100 kg N ha-1 in SRet, and 0 kg N ha-1 in SRem plots) were determined in autumn 2011 on ammonium-N, nitrate-N and extractable P in the 0-7.5, 7.5-15 and 15-20 cm soil layers at Breton (Gray Luvisol [Typic Cryoboralf] loam) and Ellerslie (Black Chernozem [Albic Argicryoll] loam), Alberta, Canada. There was no significant effect of RT and straw on ammonium-N, nitrate-N and extractable P in soil. Ammonium-N in soil increased significantly (but small) with N rate in many cases at both sites. Nitrate-N in soil increased with increasing N rate from 0 to 100 kg N ha-1 rate at Ellerslie, and up to 50 kg N ha-1 rate at Breton. Etractable P in soil decreased markedly with increasing N rate up to 100 kg N ha-1 at Breton and up to 50 kg N ha-1 at Ellerslie. In summary, increased N fertilizer rates were usually associated with decreased extractable P and increased nitrate-N in soil, but RT and straw had no effect on these nutrients in soil.

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)0.001
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 score0.171
Threshold uncertainty score0.247

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.027
GPT teacher head0.294
Teacher spread0.268 · 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

Citations4
Published2015
Admission routes3
Has abstractyes

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