Soil organic matter (SOM) plays an important role in agricultural production and arable land quality improvement. Hyperspectral technology enables frequent surveys over large areas. In this study, we explored the spectral heterogeneity of differences in soil types and SOM content, and proposed a method for measuring SOM content in large areas using spectroscopy. The results indicate regional variations in factors affecting soil spectral absorption peaks, with noticeable latitudinal disparities. The first-order differential partial-least-squares method provided the best prediction for the SOM inversion. The coefficient of determination (R²) for the SOM inversion model was 0.93, and the root mean square error (RMSE) was 3.42, with an 8.49 g/kg difference in the SOM content. When the difference in SOM content fell between 8 and 15 g/kg, the inversion effect model performed best. The optimal model R² exceeded 0.95, and the RMSE was less than 5. The comprehensive analysis showed that the organic matter content was an important factor affecting the SOM content estimate and must be considered in the real process. In addition, it is crucial to categorize soil samples on the basis of distinct soil types while maintaining a consistent range of SOM content within the same soil type, ideally between 8 and 15 g/kg. Subsequently, the first-order differential partial least squares method is applicable. These results are expected to contribute to the acquisition of high-quality information on variations in the SOM of complex large-scale areas.

In order to improve the existing phosphorus index assessment methods, using the interactive evaluation index (IEI) as an auxiliary variable, the geographically weighted regression (GWR) was adopted as prediction means. A method of regional soil phosphorus risk assessment was constructed by modifying phosphorus index model (MPIM). The GWR-IEI method more accurately predicted available phosphorus (AP) and soil organic matter (SOM), and the prediction precision and goodness of fit were high. Compared with the ordinary least square (OLS) method, the relative improvement of the root mean squared errors (RMSE) with the GWR-IEI method reached 28.95% for available phosphorus predicted, while that of SOM was 21.24%. The phosphorus loss risk of most of the study area (95.29%) was moderate to low. The areas featuring an extremely high phosphorus loss accounted for merely 0.33% of the total research area. Phosphorus loss depends on the effects of many factors. Areas which have strong source or transfer factors are not necessarily high-risk areas for phosphorus loss. Only the co-occurrence of transfer and source factors leads to high risk and greater potential for phosphorus loss. The GWR-IEI-MPIM method accurately reflected the degree of risk for phosphorus at the regional scale, which provides a valuable reference for risk assessment of phosphorus. Keywords: high landscape heterogeneity, GWR, county scale, simplified assessment DOI: 10.25165/j.ijabe.20211402.6185 Citation: Ning H R, Liu Q M, Zhang S W, Ye H C, Shen Q, Zhang W T, et al. Simple assessment of farmland soil phosphorus loss risk at county scale with high landscape heterogeneity. Int J Agric & Biol Eng, 2021; 14(2): 126–134.