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Agregação de solo em pastagens sob diferentes índices de qualidade e sua influência na infiltração de água

2021· article· pt· W3025580620 on OpenAlexaboutno aff
Igor Crabi De Freitas, Eliane Guimarães Pereira Melloni, Rogério Melloni

Bibliographic record

VenueRevista Brasileira de Geografia Física · 2021
Typearticle
Languagept
FieldAgricultural and Biological Sciences
TopicSoil Management and Crop Yield
Canadian institutionsnot available
Fundersnot available
KeywordsPhysicsEnvironmental scienceMathematicsAnimal scienceForestryGeographyBiology

Abstract

fetched live from OpenAlex

No Brasil predomina a pecuária extensiva, muitas vezes em solos de baixo potencial agrícola e passíveis de degradação, principalmente quanto aos aspectos físicos e de infiltração de água. Assim, este trabalho visa analisar o impacto causado pelo processo de sucessão em solo de diferentes áreas de pastagem, com ênfase à sua qualidade física e características físico-hídricas, relacionando-as com o papel desempenhado pelos serviços ecossistêmicos naturalmente observados. Para isso, foram selecionadas 4 áreas de pastagem, em ARGISSOLO VERMELHO distrófico, em diferentes níveis de sucessão natural: pastagem em boas condições visuais (PB); pastagem sob capoeira baixa (CB); pastagem sob capoeira alta (CA); pastagem sob capoeirão (CAO), na microbacia do Ribeirão José Pereira (Itajubá - MG). Dos atributos físicos foram determinados: densidade do solo, porosidade, tamanho e estabilidade de agregados, assim como sua classificação quanto à via de formação. A análise de infiltração foi feita com auxílio de permeâmetro de Guelph e a de resistência à penetração mecânica do solo com penetrômetro de impacto. Para análise dos resultados, foi empregada a análise de variância e teste Tukey a 5%, correlação de Pearson e estatística multivariada de componentes principais. A sucessão natural em pastagens abandonadas melhora a qualidade física do solo, com produção de agregados maiores, mais estáveis e de formação biogênica ao longo do tempo. As áreas puderam ser classificadas em relação à sua qualidade, na seguinte ordem crescente: PB < CB < CA < CAO, a qual respeitou a ação da sucessão natural e, por conseguinte, dos serviços ecossistêmicos. Soil aggregation in pastures under different quality indexes and their influence on water infiltration ABSTRACTIn Brazil, extensive livestock farming predominates, often in soils with low agricultural potential and susceptible to degradation, mainly in terms of physical aspects and water infiltration. Thus, this work aims to analyze the impact caused by the succession process in soil of different pasture areas, with an emphasis on their physical quality and physical-hydric characteristics, relating them to the role played by naturally observed ecosystem services. For this, 4 grazing areas were selected, in Red Ultisol, under different levels of natural succession: grazing in good visual conditions (PB); pasture under low scrub (CB); pasture under high scrub (CA); pasture under “capoeirão” (CAO), in the Ribeirão José Pereira microbasin (Itajubá - MG). The physical attributes were determined: soil density, porosity, size and stability of aggregates, as well as their classification as to the formation path. The infiltration analysis was carried out with the aid of a Guelph permeameter and the resistance to mechanical penetration of the soil with an impact penetrometer. For analysis of the results, analysis of variance and Tukey test at 5%, Pearson's correlation and multivariate statistics of main components were used. The natural succession in abandoned pastures improves the physical quality of the soil, with the production of larger, more stable aggregates and of biogenic formation over time. The areas could be classified according to their quality, in the following increasing order: PB < CB < CA < CAO, which respected the action of natural succession and, therefore, of ecosystem services.Keywords: natural succession, soil quality, ecosystem services, infiltration of water.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.267
Teacher spread0.240 · 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 source (direct Gemma or distilled Codex), 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".

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Citations1
Published2021
Admission routes1
Has abstractyes

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