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Record W2920499484 · doi:10.5539/jas.v11n4p307

Doses and Application Seasons of Potassium in the Soybean-Corn Succession in Soil With Improved Fertility in the Southwest of Goiás

2019· article· en· W2920499484 on OpenAlexvenueno aff
Warlles Domingos Xavier, Leandro Flávio Carneiro, Claudinei Martins Guimarães, João Vitor de Souza Silva, Flávio Araújo Pinto, Diego Oliveira Ribeiro, Vinícius Silva Sousa, Á. V. de Resende

Bibliographic record

VenueJournal of Agricultural Science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Management and Crop Yield
Canadian institutionsnot available
FundersUniversidade Federal de GoiásCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsSowingHuman fertilizationAgronomySoil fertilityPotassiumEcological successionAnimal scienceBiologyPotashMathematicsSoil waterEnvironmental scienceFertilizerChemistryBotanyEcology

Abstract

fetched live from OpenAlex

Soils with improved fertility indicate opportunities for more rational use of fertilizers. The objective of this study was to evaluate the management of potassium fertilization in the succession of soybean-corn in soil with improved fertility, in the southwestern region of the state, Goiás. The experiment was set in 5×3 factorial scheme, arranged in randomized blocks with four repetitions. The treatments consisted of the combination of potassium doses (0, 40, 80, 120 and 160 kg ha-1 of K2O) and seasons of application (100% of the dose in pre-planting, 100% of the dose in coverage and in installments with 50% of the dose in pre-planting + 50% in coverage). The best performance of soybean, considering grain yield, was obtained with the parceled application of 80 kg ha-1 of K2O, with production of 3.6 Mg ha-1. The highest corn production was obtained with the anticipated application of 160 kg ha-1 of K2O in soybean. In the management of potassium fertilization in improved fertility soil in the soybean-corn succession, the parceled application of 120 kg ha-1 of K2O kept the available K reserve in the soil constant when compared to its initial content.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.296
Threshold uncertainty score0.290

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 studies0.0000.000
Scholarly communication0.0000.000
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 head0.008
GPT teacher head0.211
Teacher spread0.203 · 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
Published2019
Admission routes1
Has abstractyes

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