水稻籽粒镉积累模型

通过文献调研与分析,建立了水稻籽粒镉含量(CdR,mg/kg)与土壤全镉含量(CdT,mg/kg)、pH值及有机质含量(SOM,g/kg)关系的统计模型:CdR=0.560 exp0.995 log(CdT)-0.173 pH 0.018 SOM](决定系数R2=0.62,P＜0.001,n=156).相关分析和残差分析结果表明,该模型具有较好的解释性,可用于水稻籽粒镉含量的估算.

New statistical model for quantifying Cd content in brown rice grains

The objective of this paper is to display the statistical model for quantifying the soil content of the actual Cd in quantity and its effect on the growing quality of the brown rice grain. As is well recognized in the agricultural research, heavy metals in cropland soils are likely to influence the quality of agricultural produce, which is closely associated with human health. However, while the dependence of crop Cd content in soil Cd concentration has been well confirmed and theoretically documented, less attention has been paid to the quantitative relationship between the Cd content in the rice grain and the soil's actual Cd concentration. Therefore, for our research purpose, we searched and retreated large amounts of data sets from 50 articles and/or degree papers in the given field. Furthermore, necessary correlation analysis was done on the mass data sets, which enable us to find that the concentration of soil exchangeable Cd is positively related to the soil total Cd concentration (Cd-T) and negatively correlated with soil pH. Further regression analysis suggests that the concentration of exchangeable Cd in soil could be quantitatively determined by a linear combination of Cd-T, soil pH and soil organic matters (SOM) concerned. Based on the above results, a statistical model was developed for testing and quantifying the dependence of Cd content in brown rice grain (Cd-R, mg/kg) on the total Cd concentration in the soils (Cd-T, mg/kg), soil pH and organic matters (SOM, g/kg). The model can be mathematically described by Cd-R=0.560 exp(0.995 log(Cd-T)-0.173 pH + 0.018 SOM) (R+2=0.62, P<0.001, n=156). At the same time, the regression and residue analysis prove that this model enjoys a perfect explanation on the observed data sets, and thus is qualified to estimate the Cd content in brown rice grains.