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Record W2184077043 · doi:10.21273/hortsci.43.7.2060

Modeling Chilling Requirement and Diurnal Temperature Differences on Flowering and Yield Performance in Strawberry Crown Production

2008· article· en· W2184077043 on OpenAlexaffabout
Karen Tanino, Ruojing Wang

Bibliographic record

VenueHortScience · 2008
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBerry genetics and cultivation research
Canadian institutionsUniversity of SaskatchewanCanadian Food Inspection Agency
Fundersnot available
KeywordsYield (engineering)Crown (dentistry)HorticultureBiologyAgronomy

Abstract

fetched live from OpenAlex

In North America, over 800 million strawberry crowns are produced by nurseries each year for the strawberry fruit industry. A modeling approach is a quantifiable method to help nurseries predict optimal crown harvest date and potential fruit yield associated with the annual strawberry crown growing environment. Most available models that quantify growth conditions, e.g., chilling effects, use controlled environment chambers and target prediction of time of strawberry flowering, not fruit yield. This study used commercial field fruit yield data over a 6-year period and five geographically distinct locations to construct models to predict the effects of chilling, diurnal temperature difference, and their interaction with daylength on fruit yield and time to flower. Accumulative chilling unit (ACU) was estimated by using nonweighted (simple, M0) and weighted [Mu (Utah Model), M1, M2] accumulation of effective temperature units. The results showed that flowering time correlated with accumulative chilling hours using either a simple (M0) accumulation model or a weighted accumulation model (Mu, M1, M2). The best correlation of flowering time with ACU was a quadratic function (y = 82.27 − 0.049x + 1.74e −5 x 2 , where y = flowering time, x = ACU) and effective temperatures were from –2 to 15 °C. By contrast, fruit yield was only correlated with ACU using specific weighted accumulation models. The correlation was influenced by weighting factors and effective or inhibitive temperatures involved in the model. Therefore, temperatures have differential effects on fruit yield and on flowering time. When pooled across regions and years, fruit yield could be predicted only by the weighted accumulation Model 2 (M2), a quadratic function (y = –72.15 + 0.98x + 0.0022x 2 ) of the ACU accumulated from 45 d before crown harvest. Fruit yield response to ACU had an optimal level with yield reduction at other values. By contrast, fruit yield linearly increased with increasing difference in diurnal temperature across years and locations. However, the days to first flower were affected interactively by the diurnal temperature difference and daylength when geographically distinct locations are compared. The greater the difference in diurnal temperature at 2 to 3 months before crown harvest, the higher the subsequent fruit yield and the shorter the flowering time. An accumulative diurnal temperature unit of 180 degree-days resulted in 30% yield enhancement of Saskatchewan-grown crowns over California-sourced crowns. The greater diurnal temperature difference may be the major contributor to the Northern Vigour ® response of strawberry crowns produced in northern latitudes such as Saskatchewan.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.069
GPT teacher head0.236
Teacher spread0.166 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations30
Published2008
Admission routes2
Has abstractyes

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