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Record W2278239374 · doi:10.2135/cropsci2015.04.0227

Is There a Role for Sink Size in Understanding Maize Population–Yield Relationships?

2015· article· en· W2278239374 on OpenAlexaboutno aff
D. B. Egli

Bibliographic record

VenueCrop Science · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCrop Yield and Soil Fertility
Canadian institutionsnot available
Fundersnot available
KeywordsSink (geography)BiologyHybridPopulationAgronomyGrain yieldPopulation sizeYield (engineering)Zea maysDemographyGeography

Abstract

fetched live from OpenAlex

ABSTRACT The most consistent change in management practices associated with the increase in maize ( Zea mays L.) yield in the United States since 1930 has been a steady increase in plant population. Populations increased from roughly 30,000 plants ha –1 at the beginning of the hybrid era to >75,000 plants ha –1 in recent years. The purpose of this review was to evaluate the potential role of sink size in historic maize yield–population relationships. The steady increase in yield (∼4×) during the hybrid era required an increase in source activity since there was no change in harvest index. The increase in grain yield was associated with an increase in kernels per unit area, so there was a concomitant increase in both source and sink. Data collected from the literature and evaluations of hybrids from different eras demonstrate that ear size (kernels per ear) of U.S. and Canadian hybrids did not increase during the hybrid era. Ears per plant did not increase above an average of one. Since ear size and prolificacy did not change, higher plant populations were needed to increase sink size (ears and kernels per unit area). This approach to analyzing population–yield relationships suggests that higher populations didn't necessarily contribute directly to higher productivity; rather, the increase in sink size avoided a sink limitation and allowed the higher productivity of the source to be translated into grain yield. Future yield increases will, therefore, require even higher populations which could create new management challenges.

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)0.001
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 score0.047
Threshold uncertainty score0.322

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0000.000
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 head0.202
GPT teacher head0.296
Teacher spread0.094 · 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

Citations38
Published2015
Admission routes1
Has abstractyes

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