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Record W2066723598 · doi:10.3126/hn.v11i1.7221

Biochar Improves Plant Growth and Reduces Nutrient Leaching in Red Clay Loam and Sandy Loam

2012· article· en· W2066723598 on OpenAlexaff
Krishna Pudasaini, Nanjappa Ashwath, Kerry B. Walsh, Thakur Bhattarai

Bibliographic record

VenueHydro Nepal Journal of Water Energy and Environment · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsCentre in Green Chemistry and Catalysis
FundersSwansea University
KeywordsBiocharLoamSoil waterLeaching (pedology)AgronomyEnvironmental scienceSoil classificationNutrientFactorial experimentChemistrySoil scienceMathematicsPyrolysisBiology

Abstract

fetched live from OpenAlex

A factorial pot experiment was conducted using two types of soils (sandy loam and red clay loam) that are commonly used for commercial vegetable production in Bundaberg, region of Central Queensl and Australia. The soils were amended with 0, 25, 50 and 75 t/ha of green waste biochar and minimum doses of N, P and K (30 kg/ha, 30 kg/ha and 40 kg/ha respectively). After two weeks of plant establishment, the pots were leached with 1.5 litres of deionised water at week intervals, and cation concentrations of the leachate were determined. In 25 t/ha biochar treatment, there was a significant (P<0.05) reduction in K and Ca leaching by 40% and 26% respectively from sandy loam, and of Ca by 23% from the red clay loam. Soil water holding capacity and soil organic carbon were also increased in both biochar treated soils. After 12 weeks of growth, shoot weight was signifi cantly (P<0.05) higher in 25 t/ha biochar-treated sandy loam and red clay loam (32% and 31% respectively). These results clearly demonstrated that a higher yield of capsicum can be achieved from green waste biochar application in sandy loam and red clay loam at 25 t/ha biochar.DOI: http://dx.doi.org/10.3126/hn.v11i1.7221 Hydro Nepal Special Issue: Conference Proceedings 2012 pp.86-90

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.194
Threshold uncertainty score0.195

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 head0.008
GPT teacher head0.168
Teacher spread0.161 · 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

Citations15
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueHydro Nepal Journal of Water Energy and EnvironmentSame topicSoil Carbon and Nitrogen DynamicsFrench-language works237,207