No abstract

No abstract

Spatial correlations and soil nutrient variations are important for soil nutrient management. They help to reduce the negative impacts of agricultural nonpoint source pollution. Based on the sampled available nitrogen (AN), available phosphorus (AP), and available potassium (AK), soil nutrient data from 2010, the spatial correlation, was analyzed, and the probabilities of the nutrient's abundance or deficiency were discussed. This paper presents a statistical approach to spatial analysis, the spatial correlation analysis (SCA), which was originally developed for describing heterogeneity in the presence of correlated variation and based on ordinary kriging (OK) results. Indicator kriging (IK) was used to assess the susceptibility of excess of soil nutrients based on crop needs. The kriged results showed there was a distinct spatial variability in the concentration of all three soil nutrients. High concentrations of these three soil nutrients were found near Anzhou. As the distance from the center of town increased, the concentration of the soil nutrients gradually decreased. Spatially, the relationship between AN and AP was negative, and the relationship between AP and AK was not clear. The IK results showed that there were few areas with a risk of AN and AP overabundance. However, almost the entire study region was at risk of AK overabundance. Based on the soil nutrient distribution results, it is clear that the spatial variability of the soil nutrients differed throughout the study region. This spatial soil nutrient variability might be caused by different fertilizer types and different fertilizing practices.

The Export Coefficient Model (ECM) has been widely used to estimate nonpoint source (NPS) pollution loads due to its ease of application. Accurate pollution export coefficients are crucial for reducing uncertainties in load estimation. By integrating artificial simulated rainfall experiments with field survey data, we have developed a new method that estimates regional pollution export coefficients. Results showed that the export coefficients calculated using this new method accurately express the regional rainfall-runoff characteristics, as the simulation precision of this method had grown by 30% than the results with traditional ECM and export coefficients which surveyed from the literature. Based on the calculated regional pollutant export coefficient, the annual loads of TN and TP in the plains area of the Baiyangdian basin in 2010 were 25,967.13 t and 4349.29 t, respectively. Among different types of sources, rural livestock had the greatest contribution (over 60%), whereas rural domestic waste represented the smallest contribution (approximately 10%). Of the different sources, pigs contributed almost half of all NPS pollution from livestock, rural residential areas were the main land use pollution source, and rural living garbage was the main source of rural domestic waste. Spatially, NPS was mainly distributed in the Zhulong and Juma watersheds. Other watersheds only contributed approximately 5% NPS per watershed. However, the per-area loads of these lower load watersheds were larger or nearly equal to that of the Zhulong watershed. Therefore, the lower load watersheds should be addressed for the control of NPS pollution.

No abstract