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Record W7017419836

Analysis of the treated wood market for agricultural and horticultural uses in New Zealand

2020· report· en· W7017419836 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Canterbury Research Repository (University of Canterbury) · 2020
Typereport
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsAgricultureLivestockStock (firearms)Quarter (Canadian coin)Wood industry
DOInot available

Abstract

fetched live from OpenAlex

The forest industry contributed $1.91 billion to New Zealand’s GDP in 2019 (Infoshare, 2019), while the other primary sectors of agriculture and horticulture were also prominent in New Zealand’s economy (Westpac, 2016). Horticulture contributed $2.06 billion, while sheep and beef and other livestock farming contributed $4.21 billion in addition to dairy farming’s contribution of $6.16 billion to New Zealand’s GDP in 2019 (Infoshare, 2019). Although each of these industries has been analysed extensively, little has been published about forestry’s relationship with agriculture and horticulture. Specifically, there is little publicly available information about the treated wood market for agricultural and horticultural uses in New Zealand. Ostensibly, this market is significant, with these primary industries playing a key role in New Zealand’s economy. Roundwood is a term commonly used to define both posts and poles, including other materials such as strainers, half rounds, quarter rounds etc. There is a continuous transition in terms of production of posts to poles, with compliance to building and construction standards such as NZS3605 (2001), AS/NZS4676 (2000) or AS2209 (1994) necessary for the latter (Altaner, 2020). Generally, roundwood post and pole products differ widely across these industries (Manley & Calderon, 1982). This study looked at fence material for stock and crops, kiwifruit pergolas, vineyard posts and other horticultural supporting structures. It did not include treated wood for retaining walls, power poles, decks, sheds and so forth. As the majority of wood used in the agricultural and horticultural industries is in ground contact, there is a risk of fungal decay. Radiata pine (Pinus radiata) is the most commonly used species in these two industries and is the dominant plantation species in New Zealand (Richardson, 1993). However, it is not naturally durable and requires preservative treatments such as chromated copper arsenate (CCA) to be suitable for outdoor applications (New Zealand Timber Industry Federation, 2013). This study used three different ways to estimate the size of the treated wood market for agricultural and horticultural uses. Firstly, the market was estimated directly from an estimate of the stock of treated wood in these industries and an estimate of service life. This method is an approximation and involved reconciliation to increase accuracy. Secondly, the production of treated wood was obtained from data provided by the manufacturers who use treatment chemicals, predominately CCA. Lastly, production at the forest level referring to the volume of harvested wood that makes up the supply of treated wood provided the third estimate. This work is useful for the New Zealand forest industry as it can lead to better utilisation and an increased understanding of markets for the small diameter log resource. When producing posts, smaller diameter logs are used, leading to higher value solid wood products. There appears to be a good opportunity to use commercial thinnings or even harvesting residues for posts (Visser et al., 2018). There is also potential for new markets and products to be found for posts (Altaner, 2020). This research will aid the establishment of a naturally durable hardwood resource (Hedley, 1997) as proposed by the New Zealand Dryland Forests Initiative (NZDFI). The NZDFI project was established in 2008 and proposes eucalypts as a promising alternative to treated radiata pine (NZDFI, 2019). The focus is therefore on the agricultural and horticultural markets (Millen et al., 2018b). Manley and Calderon (1982) also noted that in terms of both the number and volume, the greatest demand in these industries is for relatively small agricultural and horticultural posts.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.474
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.004
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0010.001
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 head0.045
GPT teacher head0.272
Teacher spread0.227 · 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 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
Published2020
Admission routes1
Has abstractyes

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