L. Agostini , E.K. Bünemann , C. Jakobsen , T. Salo , L. Wester-Larsen , S. Symanczik
{"title":"利用化学萃取法预测新型生物基肥料的氮矿化度","authors":"L. Agostini , E.K. Bünemann , C. Jakobsen , T. Salo , L. Wester-Larsen , S. Symanczik","doi":"10.1016/j.eti.2024.103781","DOIUrl":null,"url":null,"abstract":"<div><p>Bio-based fertilizers (BBFs) are an increasingly important source of nutrients in agriculture, promoted by the new EU fertilizer regulation aiming to enable a circular bioeconomy. Predicting the mineralization-dependent nutrient release of BBFs is critical for their appropriate use and to minimize environmental losses. We assessed mineralizable nitrogen (N) and carbon (C) of a representative selection of 32 BBFs and evaluated a set of chemical extraction methods to predict their N mineralization dynamics. In 84-day aerobic incubations, cumulative mineral N release varied between −13 and 100 % of amended N. Mineralized C ranged from 10 % to 117 % of amended C. Based on the dynamics of N and C mineralization, BBFs were classified into five significantly different groups. Among the tested chemical indicators of N mineralization from BBFs, cold and hot water presented the lowest extraction intensities, followed by hot potassium chloride and hot sulfuric acid extractions, while C:N ratio is based on total contents. Mineral N released almost immediately was best predicted by cold water extractable N, while hot sulfuric acid extractable N and C:N ratio predicted N released after the first two weeks and after 84 days, respectively. The combination of these three indicators was able to discriminate BBFs into four out of five mineralization classes. Such a cost-effective yet accurate estimation of N mineralization dynamics from BBFs can therefore be used as a basis to inform farmers on suitable timing and amount of BBF application, improving the synchrony between N release from BBFs and crop N demand.</p></div>","PeriodicalId":11725,"journal":{"name":"Environmental Technology & Innovation","volume":"36 ","pages":"Article 103781"},"PeriodicalIF":6.7000,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352186424002578/pdfft?md5=15607a1b393a2a9f3694a23e674a5f62&pid=1-s2.0-S2352186424002578-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Prediction of nitrogen mineralization from novel bio-based fertilizers using chemical extractions\",\"authors\":\"L. Agostini , E.K. Bünemann , C. Jakobsen , T. Salo , L. Wester-Larsen , S. Symanczik\",\"doi\":\"10.1016/j.eti.2024.103781\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Bio-based fertilizers (BBFs) are an increasingly important source of nutrients in agriculture, promoted by the new EU fertilizer regulation aiming to enable a circular bioeconomy. Predicting the mineralization-dependent nutrient release of BBFs is critical for their appropriate use and to minimize environmental losses. We assessed mineralizable nitrogen (N) and carbon (C) of a representative selection of 32 BBFs and evaluated a set of chemical extraction methods to predict their N mineralization dynamics. In 84-day aerobic incubations, cumulative mineral N release varied between −13 and 100 % of amended N. Mineralized C ranged from 10 % to 117 % of amended C. Based on the dynamics of N and C mineralization, BBFs were classified into five significantly different groups. Among the tested chemical indicators of N mineralization from BBFs, cold and hot water presented the lowest extraction intensities, followed by hot potassium chloride and hot sulfuric acid extractions, while C:N ratio is based on total contents. Mineral N released almost immediately was best predicted by cold water extractable N, while hot sulfuric acid extractable N and C:N ratio predicted N released after the first two weeks and after 84 days, respectively. The combination of these three indicators was able to discriminate BBFs into four out of five mineralization classes. Such a cost-effective yet accurate estimation of N mineralization dynamics from BBFs can therefore be used as a basis to inform farmers on suitable timing and amount of BBF application, improving the synchrony between N release from BBFs and crop N demand.</p></div>\",\"PeriodicalId\":11725,\"journal\":{\"name\":\"Environmental Technology & Innovation\",\"volume\":\"36 \",\"pages\":\"Article 103781\"},\"PeriodicalIF\":6.7000,\"publicationDate\":\"2024-08-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2352186424002578/pdfft?md5=15607a1b393a2a9f3694a23e674a5f62&pid=1-s2.0-S2352186424002578-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Environmental Technology & Innovation\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2352186424002578\",\"RegionNum\":2,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BIOTECHNOLOGY & APPLIED MICROBIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Environmental Technology & Innovation","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2352186424002578","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOTECHNOLOGY & APPLIED MICROBIOLOGY","Score":null,"Total":0}
Prediction of nitrogen mineralization from novel bio-based fertilizers using chemical extractions
Bio-based fertilizers (BBFs) are an increasingly important source of nutrients in agriculture, promoted by the new EU fertilizer regulation aiming to enable a circular bioeconomy. Predicting the mineralization-dependent nutrient release of BBFs is critical for their appropriate use and to minimize environmental losses. We assessed mineralizable nitrogen (N) and carbon (C) of a representative selection of 32 BBFs and evaluated a set of chemical extraction methods to predict their N mineralization dynamics. In 84-day aerobic incubations, cumulative mineral N release varied between −13 and 100 % of amended N. Mineralized C ranged from 10 % to 117 % of amended C. Based on the dynamics of N and C mineralization, BBFs were classified into five significantly different groups. Among the tested chemical indicators of N mineralization from BBFs, cold and hot water presented the lowest extraction intensities, followed by hot potassium chloride and hot sulfuric acid extractions, while C:N ratio is based on total contents. Mineral N released almost immediately was best predicted by cold water extractable N, while hot sulfuric acid extractable N and C:N ratio predicted N released after the first two weeks and after 84 days, respectively. The combination of these three indicators was able to discriminate BBFs into four out of five mineralization classes. Such a cost-effective yet accurate estimation of N mineralization dynamics from BBFs can therefore be used as a basis to inform farmers on suitable timing and amount of BBF application, improving the synchrony between N release from BBFs and crop N demand.
期刊介绍:
Environmental Technology & Innovation adopts a challenge-oriented approach to solutions by integrating natural sciences to promote a sustainable future. The journal aims to foster the creation and development of innovative products, technologies, and ideas that enhance the environment, with impacts across soil, air, water, and food in rural and urban areas.
As a platform for disseminating scientific evidence for environmental protection and sustainable development, the journal emphasizes fundamental science, methodologies, tools, techniques, and policy considerations. It emphasizes the importance of science and technology in environmental benefits, including smarter, cleaner technologies for environmental protection, more efficient resource processing methods, and the evidence supporting their effectiveness.