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Record W4385417819 · doi:10.1111/ejss.13403

Maize straw is more effective than maize straw biochar at improving soil N availability and gross N transformation rate

2023· article· en· W4385417819 on OpenAlexaff
Zunqi Liu, Na Xu, Ting Cao, Zhengfeng An, Yang Xu, Tianyi He, Tingting Yang, Jun Meng

Bibliographic record

VenueEuropean Journal of Soil Science · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsUniversity of Alberta
FundersEarmarked Fund for China Agriculture Research SystemNational Natural Science Foundation of China
KeywordsBiocharAmendmentStrawNitrificationChemistryLeaching (pedology)AgronomySoil waterMineralization (soil science)AmmoniumAnimal scienceNitrogenEnvironmental scienceEnvironmental chemistrySoil scienceBiology

Abstract

fetched live from OpenAlex

Abstract Soil nitrogen (N) transformation is vital in determining farmland N availability. Although many studies have investigated the effect of biochar on N retention and loss via leaching and gaseous emissions, few have determined the dynamics of gross N transformation during crop growth in long‐term biochar‐amended soils and compared the effect of the biochar with that of its feedstock. In this study, we conducted a five‐time field measurement of soil gross N turnover rates via 15 N isotope pool dilution during maize growth in 2021. Three treatments were employed, including no amendment, biochar and straw applied annually at rates of 2.63 and 7.50 t ha −1 , respectively, since 2013. The results showed that biochar did not change the rate of gross N mineralisation when compared with no amendment, but straw increased it by 139% in August, resulting in significantly higher cumulative gross N mineralisation than biochar and no amendment (701 vs 489 and 499 mg kg −1 in 200 d). The inconsistent influence was attributed to the fact that inherent biochar‐N was recalcitrant and could not be mineralized like the straw. The gross nitrification rate was decreased by 72.9% and 77.4% by biochar and straw application, respectively, in June relative to no amendment, but then it increased from July to August in the straw treatment as a result of the elevated gross N mineralisation rate. The decreased nitrification in the biochar treatment was an outcome of the synergetic effect of a low ammonium pool (−59.4%) and a high gross ammonium immobilisation rate (+263%), which was likely due to excessive fertilizer N loss and abiotic adsorption to biochar. Meanwhile, biochar amendment inhibited bacterial 16S and fungal ITS genes, as well as ureC and bacterial and archaea‐ amoA gene copies. In conclusion, straw is more effective than biochar at improving soil N transformation and availability in the long term.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.013
GPT teacher head0.219
Teacher spread0.206 · 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 source (direct Gemma or distilled Codex), 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

Citations13
Published2023
Admission routes1
Has abstractyes

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