دورية أكاديمية
Assessing water quality for cropping management practices: A new approach for dissolved inorganic nitrogen discharged to the Great Barrier Reef.
| العنوان: | Assessing water quality for cropping management practices: A new approach for dissolved inorganic nitrogen discharged to the Great Barrier Reef. |
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| المؤلفون: | Thorburn PJ; CSIRO Agriculture and Food, 306 Carmody Rd, St Lucia, Qld, 4067, Australia. Electronic address: Peter.Thorburn@csiro.au., Biggs JS; CSIRO Agriculture and Food, 306 Carmody Rd, St Lucia, Qld, 4067, Australia., McCosker K; Queensland Department Agriculture and Fisheries, 25 Yeppoon Road, Parkhurst, Qld, 4700, Australia., Northey A; Queensland Department Agriculture and Fisheries, 25 Yeppoon Road, Parkhurst, Qld, 4700, Australia. |
| المصدر: | Journal of environmental management [J Environ Manage] 2022 Nov 01; Vol. 321, pp. 115932. Date of Electronic Publication: 2022 Aug 13. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Academic Press Country of Publication: England NLM ID: 0401664 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1095-8630 (Electronic) Linking ISSN: 03014797 NLM ISO Abbreviation: J Environ Manage Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: London ; New York, Academic Press. |
| مواضيع طبية MeSH: | Nitrogen*/analysis , Saccharum*, Agriculture/methods ; Crops, Agricultural ; Ecosystem ; Fertilizers ; Water Quality |
| مستخلص: | 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. Competing Interests: Declaration of competing interest The authors have no competing interests to declare. (Crown Copyright © 2022. Published by Elsevier Ltd. All rights reserved.) |
| فهرسة مساهمة: | Keywords: Farmer survey; Fertiliser; Machine learning; Optimum nitrogen; Sugarcane; Water quality policy |
| المشرفين على المادة: | 0 (Fertilizers) N762921K75 (Nitrogen) |
| تواريخ الأحداث: | Date Created: 20220816 Date Completed: 20220914 Latest Revision: 20220914 |
| رمز التحديث: | 20231215 |
| DOI: | 10.1016/j.jenvman.2022.115932 |
| PMID: | 35973290 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 1095-8630 |
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| DOI: | 10.1016/j.jenvman.2022.115932 |