scispace - formally typeset
M

Mary Harty

Researcher at University College Dublin

Publications -  19
Citations -  357

Mary Harty is an academic researcher from University College Dublin. The author has contributed to research in topics: Urea & Calcium ammonium nitrate. The author has an hindex of 7, co-authored 12 publications receiving 247 citations. Previous affiliations of Mary Harty include Queen's University & Teagasc.

Papers
More filters
Journal ArticleDOI

Reducing nitrous oxide emissions by changing N fertiliser use from calcium ammonium nitrate (CAN) to urea based formulations.

TL;DR: Switching from CAN to stabilised urea formulations was found to be an effective strategy to reduce N2O emissions, particularly in wet, temperate grassland.
Journal ArticleDOI

Gross nitrogen transformations in grassland soil react differently to urea stabilisers under laboratory and field conditions

TL;DR: In this paper, the effects of urea in combination with N process inhibitors such as the urease inhibitor N-(butyl) thiophosphoric triamide (NBPT) and/or the nitrification inhibitor dicyandiamide (DCD) on soil N dynamics were investigated.
Journal ArticleDOI

Microbial community structure during fluoranthene degradation in the presence of plants.

TL;DR: To investigate bacterial and fungal community structure during degradation of varying concentrations (0–5000 mg kg−1) of the polycyclic aromatic hydrocarbon (PAH) fluoranthene in the presence or absence of tomato plants, Na6(CO3)(SO4)2, Na3SO4, Na2SO4 and Na2CO3 are measured.
Journal ArticleDOI

Assessing the performance of three frequently used biogeochemical models when simulating N2O emissions from a range of soil types and fertiliser treatments

TL;DR: In this article, the performance of the three semi-mechanistic models, DailyDayCent (DayCent), DeNitrification-DeComposition (DNDC 9.4 and 9.5), and ECOSSE when simulating N2O fluxes from two different land uses (simulated grazing and spring barley) under a range of fertiliser types and application rates was assessed using linear regression analysis, root mean square error (RMSE), and relative error.