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Empirical and modelled data from a model investigation into the consequences of nitrogen (N) deposition and nutrient manipulation on carbon (C) and nutrient cycling in phosphorus (P)-limited grasslands. Empirical data show above-ground biomass C, soil organic C and total soil N from two grassland types at Wardlow Hay Cop in the Peak District national park, UK. Wardlow is a long-term nutrient manipulation experiment (> 25 years) investigating the consequences of N deposition on grassland ecosystems. These data were collected during the summer of 2019 and were combined with total soil P data collected previously to form a dataset for inclusion in a CNP biogeochemical cycling model; N14CP. We use these empirical data to drive and calibrate the N14CP model in order to develop our understanding of the C, N and P dynamics of the two grasslands. Specifically, we investigate how potential differences in organic P cycling between the two grassland types may explain their contrasting responses to long-term N deposition and further experimental treatments. Accordingly, the bulk of this dataset is modelled data derived from the empirical data, and relates to the responses of plant C, soil C, N and P to N deposition and nutrient manipulation. This includes data on the CNP budgets of the modelled grasslands, P-cycling parameters used within the model, comparisons of empirical to modelled data, and changes in CNP pools resulting from N deposition and nutrient manipulation. Full details about this nonGeographicDataset can be found at https://doi.org/10.5285/98b473c7-3ca9-498d-a851-31152b1f1da7
This dataset contains the carbon and nitrogen content of soil organic matter fractions collected along grassland-to-forest conversion chronosequences. Four chronosequences of grassland-to-forest conversion were used across Scotland, from Alyth to Craik. Soil samples were collected in summer 2018. Soil samples were collected using soil corers. In 2020, soil samples were fractionated in the laboratory, and the elemental composition of bulk soil and soil fraction samples was determined. The goal of the study was to determine the changes in the quantity of soil organic carbon and nitrogen, and the form in which these carbon and nitrogen are stored. Full details about this dataset can be found at https://doi.org/10.5285/ad3d4c0a-66d5-4367-a41c-5f3cdff752f7
The dataset contains the radiocarbon age of soil organic matter fractions collected along grassland-to-forest conversion chronosequences across Scotland. Soil samples were collected in summer 2018. In summer 2019, soil samples were fractionated and the radiocarbon age of bulk soil and soil fraction samples determined by accelerator mass spectrometry. Full details about this dataset can be found at https://doi.org/10.5285/0dd45f6f-0536-4ee3-9932-58bac019d2c6
The study is part of the NERC Rural Economy and Land Use (RELU) programme. This project investigated the links between quality food production and biodiversity protection by asking the question: can production systems that use and maintain biodiverse natural grasslands, translate that into a source of additional product value in the production of meat and cheese and therefore benefit rural economies? The aim was to inverse the conventional understanding of landscape or environmental quality as the outcome of well managed farming to explore the idea of natural grassland biodiversity as an input into more sustainable farming and as an integral component of product quality. This dataset consists of the grassland botanical composition and chemical soil analyses resulting from this project. A botanical field survey of a number of sample grazing sites on selected case study farms records the plant species present within a representative area of phytosociologically homogeneous vegetation and the percentage cover that each species vertically projects onto the ground surface. Soil analyses of sample sites determines soil composition, pH and minerals. Land management, consumer opinion and nutritional data from this study are available at the UK Data Archive under study number 6159 (see online resources). Further documentation for this study may be found through the RELU Knowledge Portal and the project's ESRC funding award web page (see online resources).