Assessing water quality for cropping management practices: A new approach for dissolved inorganic nitrogen discharged to the Great Barrier Reef

Journal of Environmental Management(2022)

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摘要
Applications of nitrogen (N) fertiliser to agricultural lands impact many marine and aquatic ecosystems, and improved N fertiliser management is needed to reduce these water quality impacts. Government policies need information on water quality and risk associated with improved practices to evaluate the benefits of their adoption. Policies protecting Great Barrier Reef (GBR) ecosystems are an example of this situation. We developed a simple metric for assessing the risk of N discharge from sugarcane cropping, the biggest contributor of dissolved inorganic N to the GBR. The metric, termed NiLRI, is the ratio of N fertiliser applied to crops and the cane yield achieved (i.e. kg N (t cane)−1). We defined seven classes of water quality risk using NiLRI values derived from first principles reasoning. NiLRI values calculated from (1) results of historical field experiments and (2) survey data on the management of 170,177 ha (or 53%) of commercial sugarcane cropping were compared to the classes. The NiLRI values in both the experiments and commercial crops fell into all seven classes, showing that the classes were both biophysically sensible (c.f. the experiments) and relevant to farmers’ experience. We then used machine learning to explore the association between crop management practices recorded in the surveys and associated NiLRI values. Practices that most influenced NiLRI values had little apparent direct impact on N management. They included improving fallow management and reducing tillage and compaction, practices that have been promoted for production rather than N discharge benefits. The study not only provides a metric for the change in N water quality risk resulting from adoption of improved practices, it also gives the first clear empirical evidence of the agronomic practices that could be promoted to reduce water quality risk while maintaining or improving yields of sugarcane crops grown in catchments adjacent to the GBR. Our approach has relevance to assessing the environmental risk of N fertiliser management in other countries and cropping systems.
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关键词
Water quality policy,Machine learning,Farmer survey,Optimum nitrogen,Sugarcane,Fertiliser
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