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Record W2614298050 · doi:10.5539/jas.v9n6p188

Sowing Dates and Soybean Cultivars Influence Seed Yield, Oil and Protein Contents in Subtropical Environment

2017· article· en· W2614298050 on OpenAlexvenueno aff
Clovis Pierozan, Jackson Kawakami, Kélin Schwarz, Renan Caldas Umburanas, Murilo Viotto Del Conte, Marcelo Marques Lopes Müller

Bibliographic record

VenueJournal of Agricultural Science · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoybean genetics and cultivation
Canadian institutionsnot available
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsSowingCultivarAgronomyYield (engineering)SubtropicsBiologyCropGrain yieldGrowing seasonHumid subtropical climateHorticultureMaterials scienceMedicine

Abstract

fetched live from OpenAlex

The sowing date is the crop management practice with higher interference on soybean grain yield, although this interference is dependent on the genotype by environment interaction. This study was carried out to evaluate how soybean genotypes behave at different sowing times in a subtropical environment. We evaluated the grain yield, yield components, plant morphology and grain oil and protein content of four soybean cultivars (FPS Urano RR, BMX Apolo RR, BMX Energia RR and BRS 284) at three sowing dates (early, mid, and late sowing in 2010/2011 season) in Mid-South of Parana State, Brazil. Early cultivars at late sowing may have problems with mechanical harvest. The number of pods and grains differed among cultivars, and the cultivar “BRS 284” showed the best results. As an exception, the cultivar “BRS 284” showed yield stability among sowing dates. Higher amount of grain protein content occurs in middle and late sowing dates. The grain yield decreased with delay of sowing due to the reduction of the grains mass and in Southern Brazil is very important to assure high mass of seed to obtain high grain yields.

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 score0.848
Threshold uncertainty score0.394

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 studies0.0010.000
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 head0.219
Teacher spread0.197 · 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

Citations16
Published2017
Admission routes1
Has abstractyes

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