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Record W2002540911 · doi:10.1139/x04-069

Belowground to aboveground biomass ratio and vertical root distribution responses of mature<i>Pinus radiata</i>stands to phosphorus fertilization at planting

2004· article· en· W2002540911 on OpenAlexvenueno aff
Ayalsew Zerihun, Kelvin D. Montagu

Bibliographic record

VenueCanadian Journal of Forest Research · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsnot available
Fundersnot available
KeywordsHuman fertilizationBiomass (ecology)Pinus radiataPhosphorusRadiataBiomass partitioningNutrientSowingAgronomyBotanyAllometryBiologyAnimal scienceChemistryVignaEcology

Abstract

fetched live from OpenAlex

We compared the belowground biomass (BGB)/aboveground biomass (AGB) ratio and the vertical root distribution of 40-year-old Pinus radiata D. Don fertilized with 0 or 90 kg P·ha –1 at planting. Root biomass was determined by a combination of coring (fine roots, ϕ &lt; 2 mm; small roots, 2 ≤ ϕ &lt; 15 mm) and excavation (coarse roots, ϕ ≥ 5 mm). Stand-level AGB and coarse root biomass (CRB) were estimated with the use of allometric relations. After 40 years, AGB and CRB of P-fertilized trees were 4.5 times those of unfertilized trees, indicating that CRB scaled isometrically with AGB independently of P supply. By contrast, P fertilization increased the fine and small root biomass (FSRB) pool by only 50%. As a result, the scaling of FSRB to AGB was dependent on P supply. The differential response of the FSRB to P fertilization caused the overall BGB/AGB ratio to decrease from 0.29 in control plots to 0.20 in P-fertilized plots. Phosphorus fertilization also altered the vertical distribution of fine root biomass (FRB). For example, the proportion of FRB in the top 15 cm increased from 41% to 52% with P fertilization. Collectively, the results showed that P added early in the growth phase had a persistent effect on the BGB/AGB ratio in P. radiata. This was primarily brought about by altered biomass partitioning to the nutrient-acquiring FSRB pool.

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)0.001
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 score0.336
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.021
GPT teacher head0.280
Teacher spread0.260 · 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

Citations38
Published2004
Admission routes1
Has abstractyes

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