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Record W2797408406 · doi:10.5539/jas.v10n5p276

Phosphorus Availability for Irrigated Rice Cultivated Under No-Tillage and Different Phosphate Sources

2018· article· en· W2797408406 on OpenAlexvenueno aff
Magali De Ávila Fortes, Rogério Oliveira de Sousa, Algenor da Silva Gomes, Fábiana Schmidt, W. B. Scivittaro, Luis Henrique Gularte Ferreira, Juliana Brito da Silva Teixeira, T. A. Veçozzi

Bibliographic record

VenueJournal of Agricultural Science · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicPhosphorus and nutrient management
Canadian institutionsnot available
Fundersnot available
KeywordsAgronomyTillagePhosphorusPhosphateEnvironmental scienceCropHuman fertilizationUpland riceCrop yieldMathematicsOryza sativaChemistryBiology

Abstract

fetched live from OpenAlex

The objective of this work was to evaluate the response of irrigated rice to phosphate fertilization using triple superphosphate (TSP) and Arad phosphate rock (APR), and the phosphate residual effects of upland crops in no-tillage system on the following rice crop. Also, it aimed to evaluate the efficiency of Mehlich-1 and anion exchange resin as soil P extractors. Two experiments were conducted in Albaqualf soil under irrigated conditions in the southern region of Rio Grande do Sul State. The experiments were designed as random blocks with four replications and the treatments were displayed as a 2 × 2 factorial (TSP and APR, with and without annual P application as TSP). No yield responses to phosphate were observed. Phosphate fertilization performed on upland crops (maize and soybeans) presented a residual effect on the rice crop even after four years of consecutive cropping under no-tillage. The extractors Mehlich-1 and AER were equally efficient in the evaluation of P availability for the rice crop. P content values obtained by both methods did show a significant correlation with accumulated plant P. The APR presents a similar performance as the TSP in regard to phosphate nutrition in irrigated rice when rotated with upland crops under no-tillage system.

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 score0.785
Threshold uncertainty score0.441

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.010
GPT teacher head0.222
Teacher spread0.212 · 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
Published2018
Admission routes1
Has abstractyes

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