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Record W3084440410 · doi:10.5539/jas.v12n10p316

Ferralsol Porosity and Density After Mechanical Scarification and Crop Rotation Systems

2020· article· en· W3084440410 on OpenAlexvenueno aff
Monica Carolina Sustakowski, Edleusa Pereira Seidel, Renata F. Barabasz, Rayssa H. da Silva, Willian dos Reis, Juliano Zimmermann, Maria do Carmo Lana

Bibliographic record

VenueJournal of Agricultural Science · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Management and Crop Yield
Canadian institutionsnot available
FundersUniversidade Estadual do Oeste do Paraná
KeywordsScarificationBulk densityAgronomyPorosityTillageCover cropCrop rotationEnvironmental scienceCropMathematicsMaterials scienceSoil waterBiologySoil scienceComposite material

Abstract

fetched live from OpenAlex

With intensive cultivation and the lack of crop rotation, the no-tillage system has been showing compacted areas. For its reduction, mechanical scarification and crop rotation has been recommended. Thus, the present study aimed to evaluate in a no-tillage system the effect of mechanical scarification and crop rotation systems on soil porosity and density after 18 months. The experimental design used was randomized blocks in a split-plot scheme, with four replications. The main plots consisted of maintaining the no-tillage system and minimal soil preparation with a chisel, carried out before the cultivation of cover plants. The subplots were composed of different plant species grown in winter: wheat, consortium of forage pea + black oat and consortium of black oat + forage turnip. Both were succeeded by cultivation of soy and lupin. Macroporosity, microporosity, total porosity and bulk density were evaluated. After 18 months, the scarified soil showed a higher bulk density (0.10-0.15 m layer) when compared to the no-tillage system, demonstrating that this isolated decompression alternative does not improve the physical properties of the soil. The area in succession to oat + turnip/soybean/lupin showed lower bulk density.

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 score0.964
Threshold uncertainty score0.278

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 communication0.0000.001
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 head0.022
GPT teacher head0.209
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

Citations0
Published2020
Admission routes1
Has abstractyes

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