Data, and sample sources thereof, on water quality life cycle impact assessments pertaining to catchment scale acidification and eutrophication potentials and the benefits of on-farm mitigation strategies

A - Papers appearing in refereed journals

McAuliffe, G.A., Zhang, Y. and Collins, A. L. 2022. Data, and sample sources thereof, on water quality life cycle impact assessments pertaining to catchment scale acidification and eutrophication potentials and the benefits of on-farm mitigation strategies. Data in Brief. 44 (108505). https://doi.org/10.1016/j.dib.2022.108505

AuthorsMcAuliffe, G.A., Zhang, Y. and Collins, A. L.
Abstract

Based on recent spatially aggregated June Agriculture Survey data and site-specific environmental data, information from common farm types in the East of England was sourced and collated. These data were subsequently used as key inputs to a mechanistic environmental modelling tool, the Catchment Systems Model, which predicts environmental damage arising from various farm types and their management strategies. The Catchment Systems Model, which utilises real-world agricultural productivity data (samples and appropriate consent provided within the Mendeley Data repository) is designed to assess not only losses to nature such as nitrate, phosphate, sediment and ammonia, but also to predict how on-farm intervention strategies may affect environmental performance. The data reported within this article provides readers with a detailed inventory of inputs such as fertiliser, outputs including nutrient losses, and impacts to nature for 1782 different scenarios which cover both arable and livestock farming systems. These 1782 scenarios include baseline (i.e., no interventions), business-as-usual (i.e., interventions already implemented in the study area) and optimised (i.e., best-case scenarios) data. Further, using the life cycle assessment (LCA) methodology, the dataset reports acidification and eutrophication potentials for each scenario under two (eutrophication) and three (acidification) impact assessments to offer an insight into the importance of impact assessment choice. Finally, the dataset also provides its readers with percentage changes from baseline to best-case scenario for each farm type.

KeywordsAgriculture; System analysis; Geographical information systems; Environment; Big data
Year of Publication2022
JournalData in Brief
Journal citation44 (108505)
Digital Object Identifier (DOI)https://doi.org/10.1016/j.dib.2022.108505
PubMed ID35990923
PubMed Central ID9389191
Web address (URL)https://www.sciencedirect.com/science/article/pii/S2352340922006990
Related Output
Is metadata forAssessing catchment scale water quality of agri-food systems and the scope for reducing unintended consequences using spatial life cycle assessment (LCA)
Open accessPublished as ‘gold’ (paid) open access
FunderBiotechnology and Biological Sciences Research Council
European Union
Funder project or codeS2N - Soil to Nutrition - Work package 2 (WP2) - Adaptive management systems for improved efficiency and nutritional quality
S2N - Soil to Nutrition - Work package 3 (WP3) - Sustainable intensification - optimisation at multiple scales
Publisher's version
Copyright license
CC BY 4.0
Accepted author manuscript
Copyright license
CC BY 4.0
Output statusPublished
Publication dates
Online02 Aug 2022
Publication process dates
Accepted27 Jul 2022
PublisherElsevier
ISSN2352-3409

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