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Record W2972625202

Timing of stress and yield determination in maize (Zea Mays L.)

2019· dissertation· en· W2972625202 on OpenAlexaboutno aff
Víctor González-Carrasco

Bibliographic record

VenueThe Atrium (University of Guelph) · 2019
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicCrop Yield and Soil Fertility
Canadian institutionsnot available
Fundersnot available
KeywordsZea maysYield (engineering)AgronomyStress (linguistics)HorticultureBiologyPhysicsLinguisticsPhilosophy
DOInot available

Abstract

fetched live from OpenAlex

Yield loss in maize (Zea mays L.) is well known to be caused by abiotic and biotic stresses. Previous studies have focused, primarily, on the determination of yield loss as a result of stress occurring during the critical reproductive stages, that is, around silking and the grain fill period. No studies have assessed yield loss caused by differing stresses that occur early during the vegetative phase. Studies were conducted during 2012-13 under controlled and field conditions at two locations in Ontario, Canada. Early season stresses (i.e., up to V9 stage of growth) included drought, light quality (red to far red) and early high-plant density while high plant density was considered a season long stress. In this thesis, the hypothesis was tested that if yield is reduced in response to early season stress, then, resource capture and resource utilization will be reduced proportionally. The relationship between plant dry matter and floret number follows the classic relationship with a minimum dry matter level required and a plateau. Not all stresses impacted these relationships in the same manner. These results confirm that floret number in maize is established well in advance of flowering and also suggests that floret number is related to plant dry weight sampled between V7 to V9-10 stage of growth. Yield loss in the drought and early high-plant density stress treatments, was caused by reductions in dry matter accumulation and kernel number. Growth rates around silking were reduced and flowering delayed in response to early season stress which explained reductions in kernel set. While ASI was only lengthened by early high density, HI remained unchanged in response to early season stress. Season-long high plant density stress resulted in reduced plant dry matter, and kernel number. Plants presenting low dry matter accumulation in each stage of growth exhibited lower floret and kernel number. Barren plants at maturity showed very low or no dry matter accumulation during the grain filling period. Overall, early season stress reduced resource capture only, while season long stress defined as high plant density reduced both resource capture and utilization.

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 score0.928
Threshold uncertainty score0.998

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 studies0.0000.000
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 head0.023
GPT teacher head0.216
Teacher spread0.193 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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