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    Infrastructural, performative and feral: Understanding agri-environmental data relations in Aotearoa New Zealand
    (John Wileyand Sons Australia, Ltd on behalf of New Zealand Geographical Society, 2025-02-17) Edwards S; Henry M; Rosin C
    In agri-environmental decision-making, data are essential to defining problems and informing solutions. Nevertheless, it is important to consider not only how data can be used but also what data does in agri-environmental contexts. We argue that the liveliness of data relations can be explored by attending to their infrastructural, performative and feral qualities. Taking digital agriculture and climate change adaptation as examples, we also consider how these interconnected theses could be applied. This critical approach to data relations and their generative effects will help avoid unintended consequences and shape alternative agri-environmental futures.
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    Development and Deployment of a Framework to Prioritize Environmental Contamination Issues
    (MDPI (Basel, Switzerland), 11/11/2020) Kim ND; Taylor MD; Caldwell J; Rumsby A; Champeau O; Tremblay LA
    Management and regulatory agencies face a wide range of environmental issues globally. The challenge is to identify and select the issues to assist the allocation of research and policy resources to achieve maximum environmental gain. A framework was developed to prioritize environmental contamination issues in a sustainable management policy context using a nine-factor ranking model to rank the significance of diffuse sources of stressors. It focuses on contamination issues that involve large geographic scales (e.g., all pastoral soils), significant population exposures (e.g., urban air quality), and multiple outputs from same source on receiving environmental compartments comprising air, surface water, groundwater, and sediment. Factor scores are allocated using a scoring scale and weighted following defined rules. Results are ranked enabling the rational comparison of dissimilar and complex issues. Advantages of this model include flexibility, transparency, ability to prioritize new issues as they arise, and ability to identify which issues are comparatively trivial and which present a more serious challenge to sustainability policy goals. This model integrates well as a planning tool and has been used to inform regional policy development.