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Record W4321446864 · doi:10.5558/tfc2023-001

An economic analysis and seed yield assessment of alternative breeding strategies in a white spruce tree improvement program

2023· article· en· W4321446864 on OpenAlexafffundvenueabout
Esteban Galeano, Simon W. Bockstette, Barb R. Thomas

Bibliographic record

VenueThe Forestry Chronicle · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta-Pacific Forest IndustriesGovernment of Alberta
KeywordsSeed orchardGenetic gainBiologyOpen pollinationTree breedingProgeny testingGerminationSeedlingYield (engineering)Breeding programHorticultureSelection (genetic algorithm)AgronomyPollinationCultivarBotanyGenetic variationWoody plantPollen

Abstract

fetched live from OpenAlex

Tree improvement programs in Alberta have been primarily based on selections from open-pollinated wild stand trees, without systematic breeding of elite parents through controlled pollination. This strategy has resulted in relatively slow advancements in genetic gain in most programs, given their age. Our objective was to compare seed yield, genetic parameters, and economic implications of three different breeding strategies to advance Alberta’s white spruce (Picea glauca (Moench) Voss) improvement programs. Eighteen genotypes, identified as ‘elite’ based on height breeding values (BV), were selected from a first-generation white spruce orchard and controlled crosses (CC) and controlled polymix (PM) crosses performed to compare seed yield and genetic parameters with open-pollinated (OP) seedlots from the same parent. Results show, that on average, OP seedlots had 29 seeds/cone and weighed 2.7 grams/1000 seeds, significantly larger than the CC seedlots with a mean of 10 seeds/cone and 2.2 g/1000 seeds. Polymix crosses produced intermediate mean values with 19 seeds/cone and 2.5 g/1000 seeds and were not significantly different from the other two breeding strategies. No statistical differences were found for cone length or germination percentage among the CC, PM, and OP breeding strategies. Significant phenotypic variability was found for seeds/cone (yield) and seed weight among families from CC, and these traits had moderate narrow-sense heritabilities of 0.49 (±0.11) and 0.29 (±0.13), respectively. There was no significant correlation between the general combining ability (GCA) of seed yield and BV for seedling growth, but females F132, F138, and F927 and males M966, M1002, and M1045 showed the best performance in seed yield and growth and would be good candidates to include in future controlled crosses. If CC breeding is considered a strategy for operational seed production, as conducted elsewhere, the highest land expectation value (LEV) was $141 CAD/ha at age 60, and the best net present values (NPV) were for both discount rates (2%, 4%) with log prices ($90 CAD/m3, $200 CAD/m3) tested. However, this advantage of CC vs PM and OP only occurred on the highest quality sites (i.e., Site Index (SI) 13 and 18). On the low-productivity site (SI = 6), with the various scenarios, improved material did not show any financial benefit. Our results show that seeds produced from the controlled crosses breeding strategy were inferior to those produced from the controlled polymix and open-pollinated strategies. However, with sufficient investment and company support, the CC breeding strategy could be used as a cost-effective method to increase genetic gain and advance white spruce breeding programs in Alberta.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.087
Threshold uncertainty score0.174

Distilled classifier scores by category (both heads)

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 head0.012
GPT teacher head0.280
Teacher spread0.267 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2023
Admission routes4
Has abstractyes

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