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Record W4385981948 · doi:10.3390/agronomy13082168

Phosphorus Mobility in Heavily Manured and Waterlogged Soil Cultivated with Ryegrass (Lolium multiflorum)

2023· article· en· W4385981948 on OpenAlexafffund
Thidarat Rupngam, Aimé J. Messiga, Antoine Karam

Bibliographic record

VenueAgronomy · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant responses to water stress
Canadian institutionsUniversité LavalAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food Canada
KeywordsLolium multiflorumChemistryAnimal scienceAgronomySoil waterLeaching (pedology)PhosphorusDry matterOrganic matterWaterlogging (archaeology)Water contentEnvironmental scienceSoil scienceBiologyWetlandEcology

Abstract

fetched live from OpenAlex

Extended waterlogging (WL) conditions in heavily manured soils can change soil phosphorus (P) dynamics. We assessed the effects of soil moisture regimes (field capacity (FC) and WL) and P rates on (i) dry matter (DM) yield and P offtake of ryegrass, (ii) changes in soil Fe3+, Fe2+, and soil P, and (iii) risk of P leaching. The treatments were tested in a four-month greenhouse experiment using intact soil columns and annual ryegrass (Lolium multiflorum). The DM yield and P offtake were lower under WL compared with FC. The concentration of Fe3+ was 1984 mg kg−1 (0–30 cm) under FC, but 1213 mg kg−1 at 0–5 cm and 2024 mg kg−1 at 25–30 cm depth under WL. The concentration of Fe2+ was 244 mg kg−1 (0–30 cm) under FC, but 2897 at 0–5 cm and 687 mg kg−1 at 25–30 cm under WL. The water extractable P (Pw) was 12.7 mg kg−1 at 0–5 cm and 9.5 mg kg−1 at 25–30 cm under FC, but 8.6 mg kg−1 at 0–5 cm and 10.5 mg kg−1 at 25–30 cm under WL. The P saturation index (PSI) was 27.2% at 0–5 cm and 17.4% at 25–30 cm under FC, but averaged 11.9% at 0–30 cm under WL. We can conclude that extended WL associated with flooding creates reducing conditions in the soil, thus decreasing the concentration of Fe3+, but increasing the concentrations of Fe2+ and the solubility of P which can exacerbate the risk of P loss with runoff and leaching.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.138
Threshold uncertainty score0.771

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 head0.012
GPT teacher head0.197
Teacher spread0.184 · 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 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

Citations8
Published2023
Admission routes2
Has abstractyes

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