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Record W2091247012 · doi:10.2134/agronj2012.0204

Long‐Term Manure Applications Impact on Irrigated Barley Forage Mineral Concentrations

2013· article· en· W2091247012 on OpenAlexaffabout
Mônica B. Benke, Srimathie P. Indraratne, Xiying Hao

Bibliographic record

VenueAgronomy Journal · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsForageManureAgronomyFeedlotHordeum vulgareNutrientAnimal scienceManure managementLivestockEnvironmental sciencePoaceaeBiologyEcology

Abstract

fetched live from OpenAlex

Long‐term manure use near livestock feedlot operations can result in soil nutrient imbalances. We investigated the impact of continuous (37‐yr) and discontinued (30‐yr continuous + 7 yr without applications) manure applications on soil properties and barley ( Hordeum vulgare L.) yield and nutritive value under semiarid field conditions in southern Alberta, Canada. Feedlot cattle manure annual applications were 0, 60, 120, and 180 Mg ha –1 (wet weight) to an irrigated Chernozemic soil. After 37 yr, barley yields were not affected by manure applications, while forage crude protein (CP), NO 3 –N, P, K, and Zn concentrations increased ( P < 0.01). Forage Ca/P ratios decreased and K/(Ca + Mg) ratios increased with manure applications to critical levels of illness potential in ruminants. Discontinuing manure applications did not affect forage CP content; however, forage P and K concentrations declined ( P < 0.01) with decreases in soil P and K. This resulted in the return of forage Ca/P ratios within the recommended levels. Seven years after manure applications were discontinued, forage Zn concentrations were similar to concentrations from unmanured plots. Although forage nutrient balances for animal nutrition improved after manure applications were discontinued, soil nutrient recovery was small, and soil P, K, and Zn concentrations in the discontinued plots remained greater ( P < 0.01) than concentrations in the unmanured plots and well above those required for optimum crop growth.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.999

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)0.0030.002

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 head0.006
GPT teacher head0.223
Teacher spread0.217 · 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; both teacher heads agree on what is shown here.

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

Citations17
Published2013
Admission routes2
Has abstractyes

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