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Record W4392545724 · doi:10.1002/saj2.20648

Effectiveness of wood ash and paper sludge as liming and nutrient sources for annual ryegrass grown in podzolic soils of Newfoundland

2024· article· en· W4392545724 on OpenAlexafffundabout
Bilal Javed, Yeukai Katanda, Muhammad Nadeem, Thilini Wickremasinghe, Muhammad M. Farhain, Raymond Thomas, Lakshman Galagedara, Xiaobin Guo, Mumtaz Cheema

Bibliographic record

VenueSoil Science Society of America Journal · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicComposting and Vermicomposting Techniques
Canadian institutionsWestern UniversityGovernment of Newfoundland and LabradorMemorial University of Newfoundland
FundersMemorial University of Newfoundland
KeywordsSoil waterWood ashEnvironmental scienceNutrientAgronomySoil scienceBiologyEcology

Abstract

fetched live from OpenAlex

Abstract The acidic soils of western Newfoundland require liming for successful production of most crops. Locally sourced paper mill waste wood ash (WA) and paper sludge (SL) have potential as cheaper alternatives to limestone (LIME). Two greenhouse experiments evaluated WA and SL as liming and soil conditioning amendments for annual ryegrass ( Lolium multiflorum ) production. At 55 days after seeding, soil pH in WA (6.2 and 6.3) and wood ash and paper sludge (WASL) (6.0 and 6.3) were not different from that in LIME (6.0 and 6.5) for Experiments 1 and 2, respectively. However, pH in SL was 0.4 and 0.3 points lower than in LIME. Compared to LIME, WA, SL, and WASL produced 31%–52% and 57%–74% greater biomass yield in Experiments 1 and 2, respectively. N uptake was greater in WA (60 and 129 kg N ha −1 ) and WASL (51 and 97 kg N ha −1 ) compared to LIME (40 and 85 kg N ha −1 ), in Experiments 1 and 2, respectively. SL did not differ from LIME in Experiment 1, but reduced N uptake by 57% in Experiment 2. The results show significant potential of WA as an alternative amendment for liming and yield improvement of annual ryegrass grown in NL podzolic soils. However, SL has limited potential due to the risk of increasing N immobilization and residual soil mineral N when growing conditions are limiting. Combining SL with WA or biochar seemed to alleviate these risks. Overall, the adoption of these amendments for field production systems warrants serious consideration, following supplemental field studies to determine optimal application rates and timing.

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)0.002
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 score0.763
Threshold uncertainty score0.361

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.011
GPT teacher head0.259
Teacher spread0.249 · 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

Citations9
Published2024
Admission routes3
Has abstractyes

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