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Record W3134362173 · doi:10.5194/egusphere-egu21-1876

Silicon fertilizer and biochar effects on plant and soil PhytOC concentration and soil PhytOC stability and fractionation in subtropical bamboo plantations

2021· article· en· W3134362173 on OpenAlexaff
Chengpeng Huang, Li Wang, Xiaoqiang Gong, Zhangting Huang, Miaorong Zhou, Jiong Li, Jiasen Wu, Scott X. Chang, Peikun Jiang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSilicon Effects in Agriculture
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBiocharFertilizerBambooAgronomyBiologyBotanyChemistry

Abstract

fetched live from OpenAlex

<p>The use of exogenous silicon (Si) amendments, such as Si fertilizers and biochar, can effectively increase crop Si uptake and the formation of phytoliths, which are siliceous substances that are abundant in numerous plant species. Phytolith-occluded carbon (C) (PhytOC) accumulation in soil plays an important role in long-term soil organic C (SOC) storage. Nevertheless, the effects of both Si fertilizer and biochar application on PhytOC sequestration in forest plant-soil systems have not been studied. We investigated the impact of Si fertilizer and biochar applications on 1) the PhytOC pool size, the solubility of plant and soil phytoliths, and soil PhytOC in soil physical fractions (light (LFOM) and heavy fractions of organic matter (HFOM)) in Moso bamboo (<em>Phyllostachys pubescens</em>) forests; and 2) the relationships among plant and soil PhytOC concentrations and soil properties. We used a factorial design with three Si fertilizer application rates: 0 (S0), 225 (S1) and 450 (S2) kg Si ha<sup>−1</sup>, and two biochar application rates: 0 (B0) and 10 (B1) t ha<sup>−1</sup>. The concentrations of PhytOC in the bamboo plants and topsoil (0–10 cm) increased with increasing Si fertilizer addition, regardless of biochar application. Biochar addition increased the soil PhytOC pool size, as well as the LFOM- and HFOM-PhytOC fractions, regardless of Si fertilizer application. The Si fertilizer application increased or had no effect on soil phytolith solubility with or without biochar application, respectively. Soil PhytOC was correlated with the concentration of soil organic nitrogen (R<sup>2</sup>=0.32), SOC (R<sup>2</sup>=0.51), pH (R<sup>2</sup>=0.28), and available Si (R<sup>2</sup>=0.23). Furthermore, Si fertilizer application increased plant and soil PhytOC by increasing soil available Si. Moreover, biochar application increased soil PhytOC concentration in LFOM-PhytOC and the unstable fraction of PhytOC. We conclude that Si fertilizer and biochar application promoted PhytOC sequestration in the plant-soil system and changed its distribution in physical fractions in the Moso bamboo plantation in subtropical China.</p>

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.653
Threshold uncertainty score0.292

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 head0.014
GPT teacher head0.222
Teacher spread0.208 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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