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Record W4283382615 · doi:10.3390/horticulturae8070571

Early-Summer Deficit Irrigation Increases the Dry-Matter Content and Enhances the Quality of Ambrosia™ Apples At- and Post-Harvest

2022· article· en· W4283382615 on OpenAlex
Changwen Lu

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueHorticulturae · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicHorticultural and Viticultural Research
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsIrrigationOrchardDry matterHorticultureDeficit irrigationGrowing seasonIrrigation schedulingAnimal scienceAgronomyEnvironmental scienceBiologyIrrigation management

Abstract

fetched live from OpenAlex

Ambrosia™ is an apple that naturally has limited post-harvest quality retention, which is accompanied by relatively low dry-matter content (DMC). This trial was proposed to improve the DMC of this apple by scheduling deficit irrigation (DI) conducted in a semi-arid orchard in the Similkameen Valley (British Columbia, Canada) in 2018 and 2019. Two irrigation regimes were implemented in the orchard: commercial irrigation (CI) and DI, which was defined as irrigation for 2/5 of the timespan of CI. Five irrigation treatments were conducted: 1—adequate irrigation (AI), which used CI for the whole season; 2—early-summer DI (ED), which used DI from 20 June to 20 July; 3—middle-summer DI (MD), which used DI from 20 July to 20 August; 4—late-summer DI (LD), which used DI from 20 August to 10 days before harvest; and 5—double-period DI (DD), which covered the interval of MD and LD. The DI treatments resulted in a significant decrease from AI −1.0 to −1.5 MPa in stem water potential (SWP), followed by subsequent recovery. Conversely, SWP did not recover, and instead reached a critical low of −2.5 MPa under continued deficit conditions (DD). This, in turn, correlated with significant differences in the DMC among the treatments. Specifically, ED resulted in a rapid and sustained increase in DMC throughout the summer. At the time of harvest, ED resulted in a five-fold increase in the proportion of fruit, with greater than 16% DMC and 15% DMC in 2018 and 2019, respectively, compared to AI. DD resulted in similar levels of DMC elevation compared to ED, but also caused irregular maturation and the increased incidence of soft scald disorder in the post-harvest period. MD and LD had variable effects on DMC, and also increased the incidence of soft scald disorder. Consequently, fruit collected from the ED resulted in the best blush color attributes, higher soluble solid content, and a significant improvement in the post-harvest retention of both fruit firmness and acidity. The ED irrigation model would be recommended as a practical way for Ambrosia™ growers in semi-arid regions to decrease water usage, and to ensure high fruit quality for superior marketing and sustainable production.

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.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.930
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.064
GPT teacher head0.284
Teacher spread0.220 · 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