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Record W2107692015 · doi:10.1002/jpln.200900243

Effect of long‐term cattle grazing on seasonal nitrogen and phosphorus concentrations in range forage species in the fescue grassland of southwestern Alberta

2010· article· en· W2107692015 on OpenAlexafffundabout
Chunli Li, Xiying Hao, Walter D. Willms, Mengli Zhao, Guodong Han

Bibliographic record

VenueJournal of Plant Nutrition and Soil Science · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food Canada
KeywordsGrazingForageGrasslandAgronomyPoa pratensisPhosphorusFodderBiologyPhenologyAnimal sciencePoaceaeChemistry

Abstract

fetched live from OpenAlex

Abstract Numerous studies have examined the nutritive quality of fodder plants in different seasons but few have related this seasonal response to long‐term grazing intensity. Our objective was to examine the effect of long‐term grazing on the concentrations of total nitrogen, δ 15 N, and total phosphorus in selected forage species from the fescue grassland near Stavely, Alberta. Plants were selected from paddocks that had been stocked at 0 (control), 2.4 (moderate grazing), and 4.8 (heavy grazing) animal unit months ha –1 for 58 years. Plant material from ten species was sampled and analyzed at monthly intervals from May to September in 2007. Total N and P concentrations were not ( p > 0.05) affected by grazing for most species, but total N and P concentrations in Poa. pratensis L. were higher ( p < 0.05) in grazed treatments than in the control. These results reflect an altered plant phenology through defoliation and illustrate delayed phenology in P. pratensis when grazed. The higher δ 15 N concentration for most species in the grazed treatments than the control is an indication of accelerated nitrogen cycling through dung and urine deposition.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.215
Threshold uncertainty score0.169

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 head0.012
GPT teacher head0.235
Teacher spread0.223 · 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

Citations16
Published2010
Admission routes3
Has abstractyes

Explore more

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