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Record W4402541594 · doi:10.1093/jas/skae234.302

43 Approaches for improving nitrogen utilization efficiency and environmental sustainability of pork production systems

2024· article· en· W4402541594 on OpenAlexaff
G. C. Shurson, Pedro E Urriola, Zhaohui Yang, B. J. Kerr

Bibliographic record

VenueJournal of Animal Science · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicPhosphorus and nutrient management
Canadian institutionsNutrasource
Fundersnot available
KeywordsSustainabilityProduction (economics)BusinessEnvironmental scienceNitrogenSustainable productionChemistryBiologyEconomicsEcology

Abstract

fetched live from OpenAlex

Abstract Nitrogen (N) is an essential element for life, but due to its inefficient utilization, N waste and emissions generated have exceeded the planetary boundaries and ability of the Earth to effectively overcome these environmentally detrimental effects. Global food animal production contributes 29 to 34% of total global N emissions, with pork supply chains contributing about 16% of these emissions. Life cycle N use efficiency (NUE) of U.S. pork production is only about 56%, which is similar to broiler (55%) and egg (50%) production, but greater than beef (10%) and milk (31%) production. However, only about 10 to 44% of dietary N is converted into edible pork products. While genetic improvement has increased NUE associated with improved lean growth rate, improvement in litter size has led to an increased proportion of low-birth-weight pigs that have reduced NUE. Although metabolic modifiers including porcine somatotropin, ractopamine, and immunocastration improve NUE, they are not feasible or acceptable to consumers in many countries. Separate sex feeding and reducing market weight can also improve NUE. Ultimately feeding program design and implementation have the greatest potential to improve NUE because feed is associated with 70% of N emissions in pork production systems. Multi-objective feed formulation using Life Cycle Assessment environmental impact data of feed ingredients, precision feed formulation and feeding practices to overcome variability in digestible nutrient content of feed ingredients and nutritional requirements of pigs fed in groups, feed processing to enhance energy and nutrient digestibility, and minimizing feed wastage offer the greatest opportunity to improve NUE and environmental sustainability. There are substantial differences in greenhouse gas emissions and embedded water and land use associated with growing-finishing feeding programs depending on the dietary proportions of corn, soybean meal, distillers dried grains with solubles (DDGS), and crystalline amino acids among U.S. geographic regions. Furthermore, addition of thermally processed supermarket food waste to corn-soybean meal (CSBM) diets can significantly reduce environmental impacts and diet cost without compromising growth performance and carcass composition compared with current commercial feeding programs. Although growing-finishing feeding programs using CSBM diets with minimal crystalline amino acids result in greater N intake, N retained, and urinary N excretion compared with feeding low protein, crystalline amino acid supplemented (LP) diets, and diets containing 30% DDGS, growth performance and carcass composition are optimized and impacts on climate change, marine and freshwater eutrophication, and fossil resource are reduced by feeding CSBM diets compared with feeding LP or DDGS diets. Sourcing and using feed ingredients with reduced environmental impact metrics along with precision diet formulation and feeding practices to ensure “getting the right amount of energy and nutrients in the right feed fed to the right pigs at the right time” can improve NUE and environmental sustainability of pork production systems.

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 score0.656
Threshold uncertainty score0.245

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.021
GPT teacher head0.237
Teacher spread0.216 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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