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Record W2430264777 · doi:10.1139/cjss-2015-0044

Biochar as soil amendment: Impact on chemical properties and corn nutrient uptake in a Podzol

2016· article· en· W2430264777 on OpenAlexvenueno aff
A. B. Syuhada, J. Shamshuddin, Che Fauziah Ishak, A. B. Rosenani, A. Arifin

Bibliographic record

VenueCanadian Journal of Soil Science · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsnot available
FundersUniversiti Putra Malaysia
KeywordsBiocharAmendmentPodzolAgronomyChemistryNutrientFertilizerNitrificationSoil waterAnimal scienceEnvironmental scienceNitrogenSoil scienceBiology

Abstract

fetched live from OpenAlex

A study was conducted to investigate the impact of biochar amendment on chemical properties and corn nutrient uptake in a sandy Podzol soil. Four rates of biochar (0, 5, 10, and 15 g kg −1 ) and two rates of inorganic fertilizer (0 and local recommendation rate for corn) were randomly applied to a completely randomized design with four replicates. Corn was grown for 45 d in a glasshouse using sandy Podzol. The increase in pH of the soil was concomitant with a decrease of exchangeable Al. The fertilized soil significantly increased total N, with a concomitant decrease in soil pH due to a N nitrification. Positive changes did occur in the soil due to biochar application, leading to a significant increase in dry matter yield and corn height. Corn N and K uptakes were significantly increased by the addition of biochar, but the same was not true for Ca and Mg. However, it was found that the concentrations of N, Ca, and Mg in the corn tissue were still lower than their critical level. Our results demonstrate that application of biochar alone is not able to supply enough nutrients for the healthy growth of corn.

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 score0.558
Threshold uncertainty score0.993

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 studies0.0000.001
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 head0.212
Teacher spread0.191 · 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

Citations96
Published2016
Admission routes1
Has abstractyes

Explore more

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