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Record W2898126058 · doi:10.21273/hortsci10099-18

Above- and Belowground Responses to Shifts in Soil Moisture in Bearing Apple Trees

2018· article· en· W2898126058 on OpenAlexaff
David M. Eissenstat, D. Neilsen, Gerry Neilsen, Thomas S. Adams

Bibliographic record

VenueHortScience · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Physiology and Cultivation Studies
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsOrchardIrrigationMulchEvapotranspirationMalusDeficit irrigationAgronomyEnvironmental scienceGrowing seasonFruit treeWater contentDNS root zoneMoistureWater-use efficiencyDrip irrigationHorticultureMathematicsBiologyIrrigation managementGeographyEcology

Abstract

fetched live from OpenAlex

Limiting irrigation to increase fruit quality as well as conserve limited water resources is of increasing importance. We examined the links of aboveground growth and physiology to root growth and distribution under cultural practices associated with restricted irrigation and mulching in an apple (‘Gala/M.9’; Malus × domestica Borkh.) orchard in a semiarid climate. Trees were either mulched to maintain a 10-cm depth or left unmulched. After orchard establishment, half the trees (partially irrigated) received daily drip irrigation only sufficient to meet 50% of daily evapotranspiration for 45 days before fruit harvest using one emitter per tree. The other trees continued to receive 100% irrigation using two emitters. Over three growing seasons, fruit yield was strongly affected in 2 of 3 years by partial irrigation if trees were unmulched but not if mulched. Fruit size and other quality parameters were minimally affected by partial irrigation. Total fine root length in fully irrigated trees was nearly double that of partially irrigated trees. Our results suggest that increases in soil moisture associated with mulching enabled mulched apple trees to tolerate deficit irrigation with minimal consequences for production and quality of apples, but with an overall less extensive and smaller root system.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.481
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.026
GPT teacher head0.248
Teacher spread0.223 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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