机器学习预测生物炭老化对农业土壤氧化亚氮排放的影响

IF 2.3 Q1 AGRICULTURE, MULTIDISCIPLINARY ACS agricultural science & technology Pub Date : 2024-08-19 DOI:10.1021/acsagscitech.4c0011410.1021/acsagscitech.4c00114
Shujun Wang, Jie Li, Xiangzhou Yuan, Sachini Supunsala Senadheera, Scott X. Chang, Xiaonan Wang* and Yong Sik Ok*, 
{"title":"机器学习预测生物炭老化对农业土壤氧化亚氮排放的影响","authors":"Shujun Wang,&nbsp;Jie Li,&nbsp;Xiangzhou Yuan,&nbsp;Sachini Supunsala Senadheera,&nbsp;Scott X. Chang,&nbsp;Xiaonan Wang* and Yong Sik Ok*,&nbsp;","doi":"10.1021/acsagscitech.4c0011410.1021/acsagscitech.4c00114","DOIUrl":null,"url":null,"abstract":"<p >Biochar effects on agricultural soils change over time as biochar ages. To better understand the long-term impacts of biochar application on climate change mitigation, the effect of biochar aging on nitrous oxide (N<sub>2</sub>O) emissions has been widely investigated in field experiments. However, the underlying relationship of N<sub>2</sub>O emissions with biochar properties, fertilization practices, soil properties, and weather conditions is poorly understood. We collected data from 30 peer-reviewed publications with 279 observations and used machine learning (ML) to model and explore critical factors affecting daily N<sub>2</sub>O fluxes. We established and compared models constructed using neural networks (NN), support vector regression (SVR), random forest (RF), and extreme gradient boosting (XGB). We found that the gradient boosting regression (GBR) model was the optimal algorithm for predicting daily N<sub>2</sub>O fluxes (<i>R</i><sup>2</sup> &gt; 0.90). The importance of factors driving daily N<sub>2</sub>O fluxes is as follows: fertilization practices (44%) &gt; weather conditions (30%) &gt; soil properties (21%) &gt; biochar properties (5%). In addition, the aging time of biochar, potassium application rate, soil clay fraction, and mean air temperature were critical factors affecting the daily N<sub>2</sub>O fluxes. When biochar is initially applied, it can reduce N<sub>2</sub>O emissions; however, it has no long-term effects in reducing N<sub>2</sub>O emissions. The accurate prediction and insights from the ML model benefit the assessment of the long-term effects of biochar aging on N<sub>2</sub>O emissions from agricultural soils.</p>","PeriodicalId":93846,"journal":{"name":"ACS agricultural science & technology","volume":null,"pages":null},"PeriodicalIF":2.3000,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Machine Learning Predicts Biochar Aging Effects on Nitrous Oxide Emissions from Agricultural Soils\",\"authors\":\"Shujun Wang,&nbsp;Jie Li,&nbsp;Xiangzhou Yuan,&nbsp;Sachini Supunsala Senadheera,&nbsp;Scott X. Chang,&nbsp;Xiaonan Wang* and Yong Sik Ok*,&nbsp;\",\"doi\":\"10.1021/acsagscitech.4c0011410.1021/acsagscitech.4c00114\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p >Biochar effects on agricultural soils change over time as biochar ages. To better understand the long-term impacts of biochar application on climate change mitigation, the effect of biochar aging on nitrous oxide (N<sub>2</sub>O) emissions has been widely investigated in field experiments. However, the underlying relationship of N<sub>2</sub>O emissions with biochar properties, fertilization practices, soil properties, and weather conditions is poorly understood. We collected data from 30 peer-reviewed publications with 279 observations and used machine learning (ML) to model and explore critical factors affecting daily N<sub>2</sub>O fluxes. We established and compared models constructed using neural networks (NN), support vector regression (SVR), random forest (RF), and extreme gradient boosting (XGB). We found that the gradient boosting regression (GBR) model was the optimal algorithm for predicting daily N<sub>2</sub>O fluxes (<i>R</i><sup>2</sup> &gt; 0.90). The importance of factors driving daily N<sub>2</sub>O fluxes is as follows: fertilization practices (44%) &gt; weather conditions (30%) &gt; soil properties (21%) &gt; biochar properties (5%). In addition, the aging time of biochar, potassium application rate, soil clay fraction, and mean air temperature were critical factors affecting the daily N<sub>2</sub>O fluxes. When biochar is initially applied, it can reduce N<sub>2</sub>O emissions; however, it has no long-term effects in reducing N<sub>2</sub>O emissions. The accurate prediction and insights from the ML model benefit the assessment of the long-term effects of biochar aging on N<sub>2</sub>O emissions from agricultural soils.</p>\",\"PeriodicalId\":93846,\"journal\":{\"name\":\"ACS agricultural science & technology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2024-08-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS agricultural science & technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://pubs.acs.org/doi/10.1021/acsagscitech.4c00114\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AGRICULTURE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS agricultural science & technology","FirstCategoryId":"1085","ListUrlMain":"https://pubs.acs.org/doi/10.1021/acsagscitech.4c00114","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

摘要

生物炭对农业土壤的影响会随着生物炭老化时间的推移而变化。为了更好地了解生物炭的应用对减缓气候变化的长期影响,人们在田间试验中广泛研究了生物炭老化对一氧化二氮(N2O)排放的影响。然而,人们对一氧化二氮排放与生物炭特性、施肥方法、土壤特性和天气条件之间的内在联系知之甚少。我们从 30 篇经同行评审的出版物中收集了 279 个观测数据,并使用机器学习(ML)来建模和探索影响每日 N2O 通量的关键因素。我们建立并比较了使用神经网络(NN)、支持向量回归(SVR)、随机森林(RF)和极端梯度提升(XGB)构建的模型。我们发现,梯度提升回归(GBR)模型是预测每日一氧化二氮通量的最佳算法(R2 > 0.90)。影响每日 N2O 通量的重要因素如下:施肥方法(44%);天气条件(30%);土壤性质(21%);生物炭性质(5%)。此外,生物炭的老化时间、钾施用量、土壤粘土成分和平均气温也是影响日 N2O 通量的关键因素。在最初施用生物炭时,生物炭可以减少一氧化二氮的排放;但是,生物炭在减少一氧化二氮排放方面没有长期效果。ML 模型的准确预测和见解有利于评估生物炭老化对农业土壤 N2O 排放的长期影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Machine Learning Predicts Biochar Aging Effects on Nitrous Oxide Emissions from Agricultural Soils

Biochar effects on agricultural soils change over time as biochar ages. To better understand the long-term impacts of biochar application on climate change mitigation, the effect of biochar aging on nitrous oxide (N2O) emissions has been widely investigated in field experiments. However, the underlying relationship of N2O emissions with biochar properties, fertilization practices, soil properties, and weather conditions is poorly understood. We collected data from 30 peer-reviewed publications with 279 observations and used machine learning (ML) to model and explore critical factors affecting daily N2O fluxes. We established and compared models constructed using neural networks (NN), support vector regression (SVR), random forest (RF), and extreme gradient boosting (XGB). We found that the gradient boosting regression (GBR) model was the optimal algorithm for predicting daily N2O fluxes (R2 > 0.90). The importance of factors driving daily N2O fluxes is as follows: fertilization practices (44%) > weather conditions (30%) > soil properties (21%) > biochar properties (5%). In addition, the aging time of biochar, potassium application rate, soil clay fraction, and mean air temperature were critical factors affecting the daily N2O fluxes. When biochar is initially applied, it can reduce N2O emissions; however, it has no long-term effects in reducing N2O emissions. The accurate prediction and insights from the ML model benefit the assessment of the long-term effects of biochar aging on N2O emissions from agricultural soils.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
2.80
自引率
0.00%
发文量
0
期刊最新文献
Issue Publication Information Issue Editorial Masthead Advancing Nanotechnology in Agriculture and Food: A Guide to Writing a Successful Manuscript Soil Potassium Sensor Using a Valinomycin-Decorated Reduced Graphene Oxide (rGO-v)-Based Field-Effect Transistor for Precision Farming Antifungal Activity of Vanillic Acid Grafted Chitosan Derivatives against Plant Pathogenic Fungi, Fusarium sp.
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1