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Record W4410292546 · doi:10.3168/jds.2024-25853

Simulation of management practices to reduce nitrogen losses to water and air on well-drained grass-based dairy farms in derogation

2025· article· en· W4410292546 on OpenAlexaff
Garima Lakhanpal, N. B. Basu, D. O’Brien, Cathal Buckley, B. Horan, Karl G. Richards, Owen Fenton

Bibliographic record

VenueJournal of Dairy Science · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsUniversity of Waterloo
FundersTeagasc
KeywordsDerogationEnvironmental scienceNitrogenDairy industryAgricultural scienceAgronomyBusinessBiologyChemistryFood science

Abstract

fetched live from OpenAlex

A greater understanding of nitrogen (N) flows between productive use in dairy farms and environmental losses can inform regulation, support policy, and manage expectations around delivery of good water and air quality. Here, we use the €riN-Moorepark dairy system model (MDSM) model that was built to simulate N flows and losses for Irish grass-based dairy farms to quantify N flows and losses for a well-drained dairy farm at derogation (i.e., stocked at the maximum allowable rate of 250 kg N ha −1 ) in Ireland and then propose alternate scenarios that can minimize environmental losses. The model is capable of simulating nitrate (NO 3 − ), ammonia (NH 3 ), nitrous oxide (N 2 O), and dinitrogen (N 2 ) emissions from urine, dung, slurry, dairy-soiled water, and fertilizer N under a range of management practices. Specifically, we propose alternative systems around derogation by varying inorganic (200 or 225 kg N ha −1 ) and organic (170, 230, 268, 340, 430 kg N ha −1 ) fertilizer rates; substituting calcium ammonium nitrate (CAN) with protected urea (i.e., urea + Agrotain) and implementing restrictive grazing for vulnerable time periods when losses are the greatest (e.g., October–February or September–February). The €riN-MDSM estimated that at derogation the farm N surplus was 241.3 kg N ha −1 , leached loads were 46.6 kg N ha −1 (target for good water quality outcomes is 30 kg N ha −1 ), and gaseous emissions were 127.3 kg N ha −1 . Better environmental outcomes were observed by reducing stocking rates to 170 kg N ha −1 and 230 kg N ha −1 , decreasing leaching losses by 21.5% and 6.4%, respectively. Further reductions in inorganic fertilizer to 200 kg N ha −1 decreased leaching by 27.5% and NH 3 emissions by 31%. By substituting CAN fertilizer with protected urea, NH 3 emissions decreased by 5.2% from derogation baseline. Further reductions were possible by varying stocking rates and fertilizer rates together. Restrictive grazing significantly decreased NO 3 − leaching to groundwater, with reductions from derogation equivalent to 38.3% and 28.8% for 170 kg N ha −1 and 230 kg N ha −1 stocking rates, respectively. Further reductions in inorganic fertilizer to 200 kg N ha −1 resulted in a 44.3% decrease in NO 3 − leaching to groundwater (∼30 kg N ha −1 target) and a 24% decrease in NH 3 emissions to air. Future systems need to consider a combination of reduced fertilizer rates, restricted grazing, and use of protected urea to minimize N losses, especially during high rainfall periods. Although the results observed demonstrate opportunities to reduce N losses from grazing systems from a combination of reduced fertilizer rates, restricted grazing, and use of protected urea, the effects of these mitigations must also be considered in terms of economic cost to farms. On that basis, future system trials should monitor flows and losses from the implementation of such mitigations in addition to the economic effects to chart a way forward for better water and air outcomes while maintaining the profitability of Irish dairy farms.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.074
Threshold uncertainty score0.147

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.289
Teacher spread0.277 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations2
Published2025
Admission routes1
Has abstractyes

Explore more

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