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Title Effects of Grassland Community Composition and Digestate Fertiliser on Nitrous Oxide Emissions from Soil
Description The dataset originates from a two-year field experiment (2022–2024) at Teagasc Johnstown Castle, Wexford, Ireland. It captures nitrous oxide (N₂O) emissions, emission intensities, and emission factors from grassland systems fertilised with either digestate (from an anaerobic digestion plant) or calcium ammonium nitrate (CAN). Four grassland community compositions were tested: perennial ryegrass monoculture, ryegrass with white clover, ryegrass with red clover, and a six-species mixture including grasses, legumes, and herbs. The dataset therefore integrates greenhouse gas fluxes, soil characteristics, climatic records (temperature, rainfall, air pressure), soil moisture, and agronomic performance data (yields, nitrogen uptake) across fertiliser treatments and grassland types
License CC-BY-NC
Teagasc Department Crops Research
Teagasc Programme Crops, Environment and Land Use
Language English
Principal Investigator (PI) Dr John Finn
Principal Investigator (PI) email john.finn@teagasc.ie
Principal Investigator (PI) ORCID https://orcid.org/0000-0003-3616-5563
Data creator(s)
  1. Ali Sultan Khan
  2. Alexandre B. De Menezes
  3. Dominika J. Krol
Geographic coverage Teagasc Johnstown Castle Environmental Research Centre, Wexford, Ireland (52°17′44″N, 6°30′48″W)
Digital Object Identifier (DOI) doi.org/10.82253/pvfj-0c27
Citation Khan, Ali Sultan and Finn, John A. and De Menezes, Alexandre B. and Krol, Dominika J., (2025) Effects of Grassland Community Composition and Digestate Fertiliser on Nitrous Oxide Emissions from Soil [Dataset]. Teagasc Open Data Platform. https://doi.org/10.82253/pvfj-0c27
Rights notes This dataset is made available under a Creative Commons Attribution–NonCommercial (CC BY-NC) license. This license enables reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use. If you remix, adapt, or build upon the material, you must license the modified material under identical terms. CC BY-SA includes the following elements: BY: credit must be given to the creator. SA: Adaptations must be shared under the same terms.
Related resources
  1. Khan, Ali Sultan and Finn, John A. and De Menezes, Alexandre B. and Krol, Dominika J., (2025) Effects of Grassland Community Composition and Digestate Fertiliser on Nitrous Oxide Emissions from Soil. Available at SSRN: https://ssrn.com/abstract=5201198 or http://dx.doi.org/10.2139/ssrn.5201198
Equipment used Gas fluxes were measured using the static chamber method. Stainless steel chamber bases (40 × 40 × 10 cm) were inserted into the soil and used with detachable chambers for gas sampling. Headspace samples were collected at intervals (0, 20, and 40 minutes after closure) and analysed following protocols adapted from Murphy et al. (2022). Soil moisture was recorded with a HH2 moisture meter and WET sensor (Delta-T Devices, Cambridge, UK). Meteorological data (air pressure, rainfall, and temperature) were sourced from the nearby Met Éireann weather station. Fertiliser applications involved calibrated spreaders, and digestate composition (dry matter, C, N, NH₄⁺-N) was determined by laboratory chemical analysis prior to application
Provenance information Data collection involved high-resolution N₂O sampling following each fertiliser application: four times per week in the first two weeks, twice weekly in the next two weeks, and weekly thereafter until the next application. In winter months (November–March), sampling frequency was reduced to monthly. Cumulative N₂O emissions were calculated by trapezoidal integration of daily fluxes. Emission intensities were scaled to nitrogen yield, and emission factors estimated using IPCC and Global Research Alliance guidelines. In year one, digestate N application was unintentionally lower due to delays in nutrient analysis, but this was corrected in year two by pre-application C and N testing. The dataset was further refined through statistical analyses (using RStudio), with data transformations applied where needed to meet assumptions of normality and variance
Supplementary data Please refer to README file for data dictionary