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Record W1995995512 · doi:10.5344/ajev.2012.12002

Effects of Converting from Sprinkler to Drip Irrigation on Water Conservation and the Performance of Merlot Grown on a Loamy Sand

2012· article· en· W1995995512 on OpenAlexaff
Pat Bowen, Carl Bogdanoff, Brad Estergaard

Bibliographic record

VenueAmerican Journal of Enology and Viticulture · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicHorticultural and Viticultural Research
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsDrip irrigationLoamIrrigationAgronomyEnvironmental scienceWater contentWater-use efficiencyCropSoil waterHorticultureBiologySoil scienceGeology

Abstract

fetched live from OpenAlex

Effects of converting from overhead sprinkler to drip irrigation on the growth, leaf gas exchange, and fruit production of Merlot grapevines with and without cluster thinning were determined over four years. Drip or sprinkler irrigation was applied to the loamy sand soil when in-row soil moisture was depleted to <8%. Irrigation frequency averaged 50% higher (27 compared with 18 times per year) under drip than sprinkler irrigation, but 64% less water (574 compared with 1580 L per vine per yr) was applied on average under drip. Maps of moisture in the soil profile revealed differences in moisture distribution and dry-down dynamics in response to irrigation method. Increasing soil dry-down rates over years indicated that roots proliferated within the drip-irrigated soil volume. Converting to drip reduced the growth and survival of floor vegetation. Vine vigor, leaf gas exchange, and crop yield were reduced but crop yield recovered in the second year and vigor recovered by the fourth year. Stomatal conductance and leaf gas exchange remained lower under drip irrigation. Transpirational water-use efficiency was higher under drip than sprinkler irrigation in the first three years. Input water use efficiency averaged 2.5 times higher under drip irrigation over the four years. Fruit maturation was advanced by drip compared with sprinkler irrigation each year and was associated with increased cluster exposure and higher ambient temperatures. Cluster thinning also advanced fruit maturation but reduced crop yield substantially each year and had only minor interactions with irrigation method.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.463
Threshold uncertainty score0.160

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 head0.010
GPT teacher head0.229
Teacher spread0.219 · 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 designBench or experimental
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

Citations15
Published2012
Admission routes1
Has abstractyes

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