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Record W4283784617 · doi:10.1111/ejss.13279

Effect of biochar on soil properties and infiltration in a light salinized soil: Experiments and simulations

2022· article· en· W4283784617 on OpenAlex
Yi Li, Chuncheng Liu, Zhijie Liang, Xiaofang Wang, Xiangyang Fan, De Li Liu, Asim Biswas

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEuropean Journal of Soil Science · 2022
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsBiocharInfiltration (HVAC)LoamSoil waterEnvironmental scienceSoil salinitySoil scienceTopsoilSoil conditionerSoil carbonChemistryMaterials sciencePyrolysis

Abstract

fetched live from OpenAlex

Abstract The sustainable development of agriculture in Xinjiang Province, China has been threatened by soil salinization. Biochar can be an effective amendment to improve salt‐affected soils. An appropriate amount of biochar application and incorporation depth are key factors for amending performance. However, few studies have investigated the effects of differing biochar application amounts on saline soil properties, including soil water infiltration, using a combination of experiments and simulations. In this study, acidulated biochar was applied at rates of 0, 10, 25, 50 and 100 t ha −1 to a farmland topsoil to investigate the impacts of biochar on Xinjiang saline soil's physical and chemical properties and infiltration characteristics. The soil's physical and chemical properties that were investigated included soil pH, soil organic carbon content, soil salt content, saturated hydraulic conductivity ( K s ), soil water retention curves, and infiltration characteristics including cumulative infiltration (CI) and wetting front ( Z f ). HYDRUS‐1D was applied to predict soil water movement under different biochar incorporation depths. Results showed that the rate of change of soil pH, soil organic carbon content, soil salt content and K s were −0.009, 0.102 g kg −1 , 0.045 g kg −1 and 0.035 cm day −1 , respectively, per ton of biochar applied. Soil water retention curves showed that biochar enhanced soil water retention capacity and available soil water content (AWC) in the silt clay loam soil. The Philip model was a good ( R 2 > 0.80) fit to soil water infiltration and indicated that biochar amendment promoted infiltration rates. The van Genuchten model was good for describing soil hydraulic parameters ( R 2 > 0.99) and could be used for HYDRUS‐1D simulations ( R 2 > 0.99, RRMSE <6.8% and NSE >0.98). The optimum biochar application amount for the light salinised soil in Xinjiang was recommended as 25 t ha −1 incorporated to 30 cm depth based on the AWC, soil salt content and incorporation depths. The study provides a reference for future field experiment design. Highlights Effects of biochar on salt‐affected soil studied using a combination of experiments and simulations. Acidulated biochar amendment of saline soils can reduce pH and increase SOC. Biochar incorporation depth was considered to affect soil water infiltration. The biochar application amount at 25 t ha −1 incorporated to 30 cm depth was recommended

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.

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)0.000
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 score0.106
Threshold uncertainty score0.256

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.016
GPT teacher head0.227
Teacher spread0.211 · 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