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Record W3114457592 · doi:10.3390/agronomy11010024

Spatial Variability of Soil Phosphorus Indices under Two Contrasting Grassland Fields in Eastern Canada

2020· article· en· W3114457592 on OpenAlexafffundabout
Jeff D. Nze Memiaghe, Athyna N. Cambouris, Noura Ziadi, Antoine Karam, Isabelle Perron

Bibliographic record

VenueAgronomy · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsUniversité LavalAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food Canada
KeywordsEnvironmental scienceGrasslandGeostatisticsPastureSoil carbonSoil scienceSpatial variabilityAgronomySoil testHydrology (agriculture)Soil waterGeologyMathematics

Abstract

fetched live from OpenAlex

Phosphorus (P) is an essential nutrient for grassland production systems. However, continuous applications of P fertilizers result in soil P accumulations, increasing the risk of P losses in runoff and erosion. This study aims to investigate the field-scale variability of soil-test P (STP) in two contrasting grassland fields using descriptive statistics and geostatistics for accurate recommendations on soil sampling strategy and sustainable approaches to P management. A young grassland (YG; 2 years) and an old grassland (OG; 10 years under permanent pasture) were classified as humo-ferric podzol and received organic fertilizers. Soil samples were collected in 16-m by 16-m triangular grids at two depths (0–5 and 5–20 cm). They were analyzed for available P and other soil elements extracted using the Mehlich-3 method (M3). The agri-environmental P saturation index (P/Al)M3 was calculated. Phosphorus accumulation was observed in OG (0–5 cm) as a result of long-term manure applications. Repeated applications of organic fertilizers can impact the long-term buildup of soil P, thus decreasing soil P va-riability and spatial dependence in permanent grasslands. A soil sampling strategy focusing on the 0–5 cm layer should be retained in permanent grasslands for sustainable P recommendations in Eastern Canada.

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 score0.293
Threshold uncertainty score0.507

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 head0.189
Teacher spread0.181 · 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

Citations12
Published2020
Admission routes3
Has abstractyes

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