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Record W7009455181

Establishment of drip-irrigated poplar in semi arid British Columbia

2019· article· en· W7009455181 on OpenAlexaboutno aff

Bibliographic record

VenueArca (British Columbia Electronic Library Network) · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBioenergy crop production and management
Canadian institutionsnot available
Fundersnot available
KeywordsExclosureVegetation (pathology)Work (physics)Acacia mearnsii
DOInot available

Abstract

fetched live from OpenAlex

The need for research into systems that augment diminishing wood fibre supplies is underscored by escalating pressures on primary forests. In semi-arid forest plantations, small-scale site preparation can reduce competition from non-crop species, but interplanting amongst existing vegetation may create favorable establishment conditions including altered soil water content and hindered deer browsing. The objectives of this thesis are to determine: (1) the impact of mechanical site preparation versus interplanting on the survival and initial growth of Populus cuttings, (2) differences in survival and initial growth between four different selectively-bred Populus clones and (3) whether there is an interaction between the establishment treatments and the different clones in an intensively managed plantation. In 2016, a drip irrigation system was constructed to deliver approximately 2.5 l day-1 of water to cuttings of 4 selectively-bred poplar (Populus deltoides x petrowskyana (P. laurifolia x P. nigra)) clone types (Green Giant, Griffin, Hill, Walker) planted across 6 blocks in a 10-hectare plantation on Skeetchestn Reserve west of Kamloops, BC. Before planting, 50% of each block was treated with mechanical site preparation, and 50% was not mechanically site prepared to allow for interplanting of the cuttings with existing vegetation. Watering took place from July-September 2016 and from May-September 2017. In 2016, trees were watered for 48 days, totaling 1.06 x 106 l. In 2017, trees were watered for 96 days, totaling 2.12 x 106 l. At the end of each growing season, non random sampling including measurements of basal diameter, total height and length of longest stems as well as counts of tree survival were conducted. Generalized linear models were constructed to investigate responses of survival, diameter, height and volume index to establishment treatment, clone type and initial cutting size predictors. Significant differences in tree survival, basal diameter, basal diameter increment, total tree height, total tree height increment, volume index and volume index increment were found between clones (p<0.05) but not between establishment treatments after the first (2016) and second growing season (2017). No significant treatment*clone interactions were detected. By fall 2017, the best performing clone was Green Giant with 75% survival, 15.6 mm basal diameter, 0.6 mm two-year diameter increment, 43.6 cm height, 14.3 cm two-year height increment, 0.10 dm3 volume index and 0.06 dm3 two-year volume index increment. The worst performing clone was Griffin with 32% survival, 12.0 mm basal diameter, 0.2 mm two-year diameter increment, 38.7 cm height, 4.9 cm two-year height increment, 0.06 dm3 volume index and 0.03 dm3 two-year volume index increment. Cuttings with larger initial diameters exhibited significantly better (p<0.05) survival, diameter increment, height increment and volume index increment in the first year after planting. With first year survival being paramount to establishment success, cuttings planted in similar conditions should be selected for large basal diameters. The mechanical site preparation establishment treatment showed consistent improvement of establishment and growth parameters over the interplanting treatment, but mixed significant (p<0.05) and non-significant (p>0.05) results did not support recommending it for future use.

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 categoriesScholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.631
Threshold uncertainty score0.999

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 communication0.0020.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.1390.000

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 head0.003
GPT teacher head0.149
Teacher spread0.146 · 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.

Study designNot applicable
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
Published2019
Admission routes1
Has abstractyes

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