Modelling and mapping of subsurface nitrate-attenuation index in agricultural landscapes

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dc.citation.volume384
dc.contributor.authorCollins SB
dc.contributor.authorSingh R
dc.contributor.authorMead SR
dc.contributor.authorHorne DJ
dc.contributor.editorZhang L
dc.date.accessioned2025-05-20T03:23:15Z
dc.date.available2025-05-20T03:23:15Z
dc.date.issued2025-06
dc.description.abstractEnvironmental management of nutrient losses from agricultural lands is required to reduce their potential impacts on the quality of groundwater and eutrophication of surface waters in agricultural landscapes. However, accurate accounting and management of nitrogen losses relies on a robust modelling of nitrogen leaching and its potential attenuation – specifically, the reduction of nitrate to gaseous forms of nitrogen – in subsurface flow pathways. Subsurface denitrification is a key process in potential nitrate attenuation, but the spatial and temporal dynamics of where and when it occurs remain poorly understood, especially at catchment-scale. In this paper, a novel Landscape Subsurface Nitrate-Attenuation Index (LSNAI) is developed to map spatially variable subsurface nitrate attenuation potential of diverse landscape units across the Manawatū-Whanganui region of New Zealand. A large data set of groundwater quality across New Zealand was collated and analysed to assess spatial and temporal variability of groundwater redox status (based on dissolved oxygen, nitrate and dissolved manganese) across different hydrogeological settings. The Extreme Gradient Boosting algorithm was used to predict landscape unit subsurface redox status by integrating the nationwide groundwater redox status data set with various landscape characteristics. Applying the hierarchical clustering analysis and unsupervised classification techniques, the LSNAI was then developed to identify and map five landscape subsurface nitrate attenuation classes, varying from very low to very high potential, based on the predicted groundwater redox status probabilities and identified soil drainage and rock type as key influencing landscape characteristics. Accuracy of the LSNAI mapping was further investigated and validated using a set of independent observations of groundwater quality and redox assessments in shallow groundwaters in the study area. This highlights the potential for further research in up-scaling mapping and modelling of landscape subsurface nitrate attenuation index to accurately account for spatial variability in subsurface nitrate attenuation potential in modelling and assessment of water quality management measures at catchment-scale in agricultural landscapes.
dc.description.confidentialfalse
dc.edition.editionJune 2025
dc.identifier.citationCollins SB, Singh R, Mead SR, Horne DJ. (2025). Modelling and mapping of subsurface nitrate-attenuation index in agricultural landscapes. Journal of Environmental Management. 384.
dc.identifier.doi10.1016/j.jenvman.2025.125628
dc.identifier.eissn1095-8630
dc.identifier.elements-typejournal-article
dc.identifier.issn0301-4797
dc.identifier.number125628
dc.identifier.piiS0301479725016044
dc.identifier.urihttps://mro.massey.ac.nz/handle/10179/72916
dc.languageEnglish
dc.publisherElsevier Ltd
dc.publisher.urihttps://www.sciencedirect.com/science/article/pii/S0301479725016044
dc.relation.isPartOfJournal of Environmental Management
dc.rights(c) 2025 The Author/s
dc.rightsCC BY 4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectAgriculture
dc.subjectWater quality
dc.subjectNitrate attenuation
dc.subjectGroundwater redox conditions
dc.subjectMachine learning
dc.subjectExtreme gradient boosting
dc.titleModelling and mapping of subsurface nitrate-attenuation index in agricultural landscapes
dc.typeJournal article
pubs.elements-id500745
pubs.organisational-groupOther
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