Machine learning and structural equation modeling for revealing the influence factors and pathways of different water management regimes acting on brown rice cadmium.

IF 8.2 1区 环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES
Science of the Total Environment Pub Date : 2024-12-01 Epub Date: 2024-09-24 DOI:10.1016/j.scitotenv.2024.176033
Yingxia Liu, Jinchuan Ma, Junjie Chu, Wanchun Sun, Qiang Wang, Yangzhi Liu, Ping Zou, Junwei Ma
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引用次数: 0

Abstract

Excessive cadmium (Cd) in brown rice has detrimental effects on rice growth and human health. Water management is a cost-effective, eco-friendly measure to suppress Cd accumulation in rice. However, there is no acknowledged water management regime that reduces Cd accumulation in brown rice without compromising the yield. Meanwhile, the major factors affecting brown rice Cd and the pathways of water management affecting rice Cd are not clear. This study explored major factors affecting brown rice Cd using machine learning (ML) and examined the pathways of water management affecting rice Cd using a structural equation model (SEM). Three water management systems were set up, namely flooding, water-saving, and wetting irrigation. Results showed that water-saving irrigation increased dry matter and reduced Cd content and translocation. Root uptake during the grain filling stage and Cd remobilization before the grain filling stage contributed 36 % and 64 % of the Cd accumulation in brown rice, respectively. ML explained 97 % of the variance, suggesting that crop covariates were the most important (e.g., the brown rice bioconcentration factor (12 %), stem Cd (9 %), root-to-stem translocation factor (7 %)), followed by soil covariates (e.g., reducing substances 12 %) and water management (3 %). All SEM explanatory variables collectively explained 94 % of the variation, with a predictive power of 76 %. Water treatments indirectly affected soil available Fe and Mn (indirect effect coefficient = 0.909), iron plaques (indirect effect coefficient = 0.866), soil available Cd (indirect effect coefficient = -0.671), and Cd intensity of xylem sap (BICd, indirect effect coefficient = -0.664) via pH and reducing substances. BICd significantly positively affected stem Cd (path coefficient = 0.445). These findings provide insight into the agronomic and environmental effects of water management on brown rice Cd and influence pathways in soil-rice systems, suggesting that water-saving irrigation may alleviate Cd contamination in the paddy soil.

通过机器学习和结构方程建模,揭示不同水管理制度对糙米镉的影响因素和途径。
糙米中过量的镉(Cd)会对水稻生长和人类健康造成不利影响。水管理是抑制镉在水稻中积累的一种经济、环保的措施。然而,目前还没有一种公认的水管理制度能在不影响产量的情况下减少镉在糙米中的积累。同时,影响糙米镉积累的主要因素以及水管理影响水稻镉积累的途径尚不明确。本研究利用机器学习(ML)探索了影响糙米镉的主要因素,并利用结构方程模型(SEM)研究了影响水稻镉的水分管理途径。建立了三种水管理制度,即漫灌、节水灌溉和湿润灌溉。结果表明,节水灌溉增加了干物质,减少了镉的含量和转移。灌浆期的根吸收和灌浆期前的镉再移动分别占糙米镉积累的 36% 和 64%。ML解释了97%的方差,表明作物协变量最重要(如糙米生物富集因子(12%)、茎秆镉(9%)、根-茎转位因子(7%)),其次是土壤协变量(如还原物质12%)和水管理(3%)。所有 SEM 解释变量共解释了 94% 的变异,预测能力为 76%。水处理通过 pH 值和还原性物质间接影响土壤可利用的铁和锰(间接影响系数 = 0.909)、铁斑块(间接影响系数 = 0.866)、土壤可利用的镉(间接影响系数 = -0.671)和木质部汁液的镉强度(BICd,间接影响系数 = -0.664)。BICd对茎秆镉有明显的正向影响(路径系数 = 0.445)。这些发现有助于深入了解水管理对糙米镉的农艺和环境影响以及土壤-水稻系统中的影响途径,表明节水灌溉可减轻稻田土壤中的镉污染。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Science of the Total Environment
Science of the Total Environment 环境科学-环境科学
CiteScore
17.60
自引率
10.20%
发文量
8726
审稿时长
2.4 months
期刊介绍: The Science of the Total Environment is an international journal dedicated to scientific research on the environment and its interaction with humanity. It covers a wide range of disciplines and seeks to publish innovative, hypothesis-driven, and impactful research that explores the entire environment, including the atmosphere, lithosphere, hydrosphere, biosphere, and anthroposphere. The journal's updated Aims & Scope emphasizes the importance of interdisciplinary environmental research with broad impact. Priority is given to studies that advance fundamental understanding and explore the interconnectedness of multiple environmental spheres. Field studies are preferred, while laboratory experiments must demonstrate significant methodological advancements or mechanistic insights with direct relevance to the environment.
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