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基于密集田间采样和人工神经网络建模的贫硒农田富硒玉米种植概率。

Probability of cultivating Se-rich maize in Se-poor farmland based on intensive field sampling and artificial neural network modelling.

机构信息

School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China.

School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China; Key Laboratory of Ecological Geochemistry, Ministry of Natural Resources, Beijing 100037, PR China.

出版信息

Chemosphere. 2022 Dec;309(Pt 1):136690. doi: 10.1016/j.chemosphere.2022.136690. Epub 2022 Oct 3.

Abstract

Selenium (Se) is a necessary micronutrient for humans, and its supplementation from crop grains is important to address the ubiquitous Se deficiency in people worldwide. Se uptake by crops largely depend on soil bioavailable Se rather than soil total Se content, which provides possibilities to explore the Se-rich crops in Se-poor area. Here, the possibility of cultivating Se-rich maize grains in Se-poor farmland was tested based on intensive field sampling and mathematical modelling. Sampling was conducted at county scale, and a total of 7779 topsoil samples and 109 maize samples with paired rhizosphere soils samples were collected. Results showed that although the soil Se content in the study county from southwestern China was at a low level (0.01-2.75 mg kg), 54.1% of the maize grain samples satisfied the standard for Se-rich products (0.02-0.30 mg kg). Soil organic matter, iron oxide, and phosphorus levels were correlated negatively with Se bioconcentration factor (BCF) of maize grain. Compared with the multivariate linear regression model, the artificial neural network (ANN) model was more accurate and reliable in predicting maize Se BCF. Prediction using the ANN model showed that 22.7% of the county's farmland was suitable for cultivating naturally Se-rich maize, which increased 21.3% growing areas than that from cultivation based on simply soil total Se. This study provided a new methodological framework for natural Se-rich maize production and verified the probability of cultivating naturally Se-rich maize in Se-poor farmland.

摘要

硒(Se)是人体必需的微量元素,通过作物谷物补充硒对解决全球范围内普遍存在的硒缺乏问题非常重要。作物对硒的吸收在很大程度上取决于土壤中生物可利用的硒,而不是土壤中的总硒含量,这为在贫硒地区探索富硒作物提供了可能性。本研究基于密集的田间采样和数学建模,测试了在贫硒农田中种植富硒玉米的可能性。采样在县级尺度进行,共采集了 7779 个表层土壤样本和 109 个玉米样本以及 109 个根际土壤样本。结果表明,尽管中国西南部研究县的土壤硒含量较低(0.01-2.75 mg kg),但 54.1%的玉米籽粒样本符合富硒产品标准(0.02-0.30 mg kg)。土壤有机质、氧化铁和磷水平与玉米籽粒硒生物浓缩系数(BCF)呈负相关。与多元线性回归模型相比,人工神经网络(ANN)模型在预测玉米硒 BCF 方面更准确可靠。利用 ANN 模型进行预测表明,该县 22.7%的农田适合种植天然富硒玉米,比单纯基于土壤总硒的种植面积增加了 21.3%。本研究为天然富硒玉米的生产提供了一种新的方法框架,并验证了在贫硒农田中种植天然富硒玉米的可能性。

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