Nutrient losses from sloping farmland in karst areas lead to the decline in land productivity and nonpoint source pollution. A specially tailored steel channel with an adjustable slope and underground hole fissures was used to simulate the microenvironment of the "dual structure" of the surface and underground of sloping farmland in a karst area. The artificial rainfall simulation method was used to explore the surface and underground runoff characteristics and nutrient losses from sloping farmland under different rainfall intensities. The effect of rainfall intensity on the nutrient loss of farmland on karst sloping land was clarified. The results showed that the surface was the main route of runoff and nutrient loss during the rainy season on sloping farmland in karst areas. The influence of rainfall intensity on the nutrients in surface runoff was more substantial than that on underground runoff nutrients. Nutrient loss was more likely to occur underground than on the surface. The losses of total nitrogen, total phosphorus, and total potassium in surface and underground runoff initially increased and then gradually stabilized with the extension of rainfall duration and increased with increasing rainfall intensity and the amount of nutrient runoff. The output of nutrients through surface runoff accounted for a high proportion of the total, and underground runoff was responsible for a low proportion. Although the amount of nutrients output by underground runoff was small, it could directly cause groundwater pollution. The research results provide a theoretical reference for controlling land source pollution from sloping farming in karst areas.
The rainfall intensity, slope, and underground pore density are major factors affecting the soil erosion of maize‐covered karst slopes. We studied the erosion process of maize‐covered karst slopes and the influencing mechanism of soil erosion on these slopes under different rainfall intensities, slopes, and underground pore density via simulated rainfall tests. The results are as follows: (1) No runoff was observed on the slope surface under light rainfall (30 mm hr−1); instead, subsurface flow was predominant. However, runoff from the surface and subsurface flows under moderate rainfall (60 mm hr−1) increased with the rainfall intensity. The average surface runoff under extreme rainfall (90 mm hr−1) was 2.3‐times that under moderate rainfall. In addition, the average subsurface runoff under extreme rainfall was 1.6‐ and 3.5‐times that under moderate rainfall and light rainfall, respectively. The total amounts of surface and subsurface soil losses increased as the rainfall intensity increased. The surface and subsurface soil losses from light to moderate and extreme rainfall events increased by 258% and 151%, respectively. The soil loss from the surface was 5.3‐times greater than that from the subsurface, indicating that the erosion of maize‐covered karst slopes mainly occurs on the surface and that the subsurface loss is relatively small. (2) As the slope angle increased, the runoff, sediment yield, and proportion of erosion on the surface increased, and vice versa. (3) The subsurface runoff and sediment yield increased as the underground pore density increased, while the opposite occurred on the surface. Multiple regression analysis showed that the rainfall intensity is the most critical factor affecting the soil erosion of maize ‐covered slopes in karst areas, followed by the slope, while the influence of the underground pore density is small.
The ability to quickly and non-destructively monitor the cadmium (Cd) content in agricultural crops is the basic premise of effective prevention and control of Cd contamination in agricultural products. Hyperspectral technology provides a solution for this issue. The potential capability for the spectral prediction of the Cd content in the leaves of pepper and eggplant in the field was explored, and a spectral prediction model of the Cd content in these leaves was established. In this study, based on the indoor spectrum, the sensitive wavebands for predicting the Cd content in leaves were determined preliminarily by correlation analysis. Partial least squares regression (PLSR) and support vector machine regression (SVMR) were used to establish spectral prediction models, and the final sensitive wavebands were determined by the size of the model index. The results show that the SVMR model exhibited higher prediction accuracy than the PLSR model. The RPDp (relative percent different of prediction set) values of the best SVMR prediction models for the pepper leaves and the eggplant leaves were 1.82 and 1.49, respectively. The values of Rp2 (coefficient of determination of prediction set), which can quantitatively estimate the Cd content in leaves, were 0.897 (p < 0.01) and 0.726 (p < 0.01), respectively. This study demonstrated that the leaf spectra of pepper and eggplant in the field can be used to predict the Cd content in leaves, providing a reference for monitoring the Cd content in the fruits of pepper and eggplant in the future.
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