Redesigning of soil landscape models within the Rangitikei district for the inclusion of groundwater information (e.g., nitrogen and phosphorus) : a thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Earth Science at Massey University, Manawatu, New Zealand
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Date
2024
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Massey University
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Abstract
Soil models used within the agricultural and sustainable land use sectors rarely address groundwater nutrient influences, particularly the interaction with soil profiles and the role of soils in nutrient mitigation. This study redevelops soil landscape models to include nutrient information from groundwater analysis. A Rangitikei farm in the Manawatu-Whanganui region was identified to have 14 soil types (~95% of these soils being Brown and Pallic) influenced by 6 geological members. The geological and soil information resulted in 3 soil landscape models being created, each with specific landscape, geological, and soil features. LRI and LUC maps were created identifying 10 LRI units and 6 LUC units each with different management procedures across the study area, including individual maps of each LRI component. The instillation of 6 piezometers across the study area at locations that intersected drainage zones with distinctive landform features and geologies, allowed for an overview of the drainage across the study area, with the aim to provide supportive evidence of the denitrifying properties of the soil system in terms of groundwater nitrogen. Groundwater samples from the piezometers were analysed for dissolved reactive phosphorus (DRP), dissolved oxygen (DO), pH, oxidation-reduction potential (ORP) and N-NO₃. The majority of the DRP results from the piezometers and surface waters were above accepted levels, while the majority of the N-NO₃ results were below accepted levels. The DO results primarily showed the support of N reduction within the subsurface system, while N reduction was not supported from the ORP results seen from the piezometers. Piezometer water levels showed a primary response to rainfall prior to sampling. LiDAR data at 1m resolution was utilised for the creating of a landform element map that identified valleys, ridges, spurs, and hollows across the study area.
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