数字农科院2.0

Three Bayesian Tracer Models: Which Is Better for Determining Sources of Root Water Uptake Based on Stable Isotopes under Various Soil Water Conditions?

文献类型： 外文期刊

作者： Liu, Junming;Si, Zhuanyun;Li, Shuang;Abubakar, Sunusi Amin;Zhang, Yingying;Wu, Lifeng;Gao, Yang;Duan, Aiwang

作者机构：

关键词： stable isotope;root water uptake;soil water content;winter wheat;Bayesian model

期刊名称： AGRONOMY-BASEL

ISSN：

年卷期： 2023 年 13 卷 3 期

页码：

收录情况： SCIE（2023版）

摘要： Stable hydrogen and oxygen isotopes provide a powerful technique for quantifying the proportion of root water uptake (RWU) from different potential water sources. Although many models coupled with stable isotopes have been developed to estimate plant water source apportionment, inter-comparisons of different methods are still limited, especially their performance under different soil water content (SWC) conditions. In this study, three Bayesian tracer mixing models, which included MixSIAR, MixSIR and SIAR, were tested to evaluate their performances in determining the RWU of winter wheat under various SWC conditions (normal, dry and wet) in the North China Plain (NCP). The proportions of RWU in different soil layers showed significant differences (p < 0.05) among the three Bayesian models, for example, the proportion of 0-20 cm soil layer calculated by MixSIR, MixSIAR and SIAR was 69.7%, 50.1% and 48.3% for the third sampling under the dry condition (p < 0.05), respectively. Furthermore, the average proportion of the 0-20 cm layer under the dry condition was lower than that under normal and wet conditions, being 45.7%, 58.3% and 59.5%, respectively. No significant difference (p > 0.05) was found in the main RWU depth (i.e., 0-20 cm) among the three models, except for individual sampling periods. The performance of three models in determining plant water source allocation varied with SWC conditions: the performance indicators such as coefficient of determination (R-2) and Nash-Sutcliffe efficiency coefficient (NS) in MixSIAR were higher than that in MixSIR and SIAR, showing that MixSIAR performed well under normal and wet conditions. The rank of performance under the dry condition was MixSIR, MixSIAR, and then SIAR. Overall, MixSIAR performed relatively better than other models in predicting RWU under the three different soil moisture conditions.

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