Modelling of land nutrient cycles: recent progress and future development.

Faculty reviews Pub Date : 2021-06-02 eCollection Date: 2021-01-01 DOI:10.12703/r/10-53
Ying-Ping Wang, Daniel S Goll
{"title":"Modelling of land nutrient cycles: recent progress and future development.","authors":"Ying-Ping Wang,&nbsp;Daniel S Goll","doi":"10.12703/r/10-53","DOIUrl":null,"url":null,"abstract":"<p><p>While widespread imitation of the productivity of the land biosphere by nutrients, like nitrogen and phosphorus, was demonstrated many decades ago, representation of nutrient cycles in global land models has been relatively recent. Over the last three years, significant progress has been made in understanding some of the key processes and their representation in global land models. They include the significance of plant-microbial interaction in affecting nutrient cycles, inorganic soil phosphorus transformation, and nitrogen release from rocks. As a result, our understanding of the linkages among geology, biology, and climate controlling nutrient cycles is improving. However, progress in modelling nutrient cycles at a global scale is still confronted with large uncertainties in representing key processes owing to lack of data at the relevant scales for evaluating coupled carbon and nutrient cycles. Here we recommend two approaches to advance modelling of land nutrient cycles: the application of machine learning techniques to bridge the gap between global modelling and scattered site-level information and the use of optimality principles to identify key mechanisms driving spatial and temporal patterns of nutrients.</p>","PeriodicalId":73016,"journal":{"name":"Faculty reviews","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8204758/pdf/","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Faculty reviews","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.12703/r/10-53","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2021/1/1 0:00:00","PubModel":"eCollection","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9

Abstract

While widespread imitation of the productivity of the land biosphere by nutrients, like nitrogen and phosphorus, was demonstrated many decades ago, representation of nutrient cycles in global land models has been relatively recent. Over the last three years, significant progress has been made in understanding some of the key processes and their representation in global land models. They include the significance of plant-microbial interaction in affecting nutrient cycles, inorganic soil phosphorus transformation, and nitrogen release from rocks. As a result, our understanding of the linkages among geology, biology, and climate controlling nutrient cycles is improving. However, progress in modelling nutrient cycles at a global scale is still confronted with large uncertainties in representing key processes owing to lack of data at the relevant scales for evaluating coupled carbon and nutrient cycles. Here we recommend two approaches to advance modelling of land nutrient cycles: the application of machine learning techniques to bridge the gap between global modelling and scattered site-level information and the use of optimality principles to identify key mechanisms driving spatial and temporal patterns of nutrients.

Abstract Image

土地养分循环模拟:最新进展和未来发展。
虽然氮和磷等营养物质对陆地生物圈生产力的广泛模仿在几十年前就已得到证明,但全球陆地模式中对营养循环的描述相对较晚。在过去三年中,在理解一些关键过程及其在全球陆地模式中的表现方面取得了重大进展。它们包括植物-微生物相互作用在影响养分循环、无机土壤磷转化和岩石氮释放中的意义。因此,我们对地质、生物和气候之间控制养分循环的联系的理解正在改善。然而,由于缺乏评估碳和养分耦合循环的相关尺度数据,全球尺度养分循环模拟的进展在表示关键过程方面仍然面临很大的不确定性。在这里,我们推荐两种方法来推进土地养分循环的建模:应用机器学习技术来弥合全球建模和分散的站点级信息之间的差距,以及使用最优性原则来确定驱动养分时空模式的关键机制。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信