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Record W2180612023 · doi:10.4141/cjps2011-071

Changes in kernel characteristics during grain filling in silage-specific and dual-purpose corn hybrids

2012· article· en· W2180612023 on OpenAlexafffundvenue
Regan L Bailey, L. M. Dwyer

Bibliographic record

VenueCanadian Journal of Plant Science · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCrop Yield and Soil Fertility
Canadian institutionsAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food Canada
KeywordsSilageHybridKernel (algebra)AgronomyPopulationMathematicsLeafyDry matterDry weightAnimal scienceBiology

Abstract

fetched live from OpenAlex

Ma, B. L. and Dwyer, L. M. 2012. Changes in kernel characteristics during grain filling in silage-specific and dual-purpose corn hybrids. Can. J. Plant Sci. 92: 427–439. Silage-specific corn (Zea mays L.) hybrids have been gaining in popularity in recent years, but there is a lack of information about the difference in kernel characterization between Leafy silage-specific and dual-purpose hybrids. A field study was conducted for 4 yr to determine the changes in kernel moisture content, kernel dry weight, kernel nitrogen (N) and phosphorus (P) concentrations over the period of grain filling for Leafy silage-specific and dual-purpose hybrids at two plant population densities. In both types of hybrids, kernel dry weight content increased rapidly from approximately 2 wk after silking and reached a maximum at about 50 to 68 d after silking. While some differences among hybrids in kernel maximum dry weight were noted (P<0.05), there was no distinction between hybrid types. The coefficients of kernel moisture change differed greatly between the two types with lower rates of decline of kernel moisture in some of the Leafy silage-specific hybrids (down-hill slope b<0.0136±0.00044 g kg −1 d −1 ) than the dual-purpose or silage hybrids without the Leafy characteristic (b≥0.0164±0.00039 g kg −1 d −1 ). Higher plant population density had a negligible effect on kernel moisture decline and N and P concentrations, but reduced kernel dry weight by up to 7% in one year. Kernel P was linearly positively correlated with N content (r>0.79, P<0.01), and there was a linear relationship between kernel moisture and the whole-plant silage moisture content (R 2 ≥0.70). Our data also showed that one of the Leafy silage-specific hybrids had higher rates of kernel dry weight accumulation and softer kernel hardness (P<0.05) than the dual-purpose or non-Leafy silage hybrids. Our findings on the distinct characteristics of hybrid types are important considerations for corn producers to choose suitable hybrids and make decisions on silage harvest dates.

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 score0.146
Threshold uncertainty score0.922

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.025
GPT teacher head0.199
Teacher spread0.174 · 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

Citations14
Published2012
Admission routes3
Has abstractyes

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