scholarly journals Soil Erosion Influencing Factors in the Semiarid Area of Northern Shaanxi Province, China

2020 ◽  
Author(s):  
Ning Ai ◽  
Qingke Zhu ◽  
Guangquan Liu ◽  
Tianxing Wei
Keyword(s):  
Soil Erosion ◽  
Influencing Factors ◽  
Shaanxi Province ◽  
Semiarid Area ◽  
Northern Shaanxi

scholarly journals Estimating Changes in the Green Water Productivity of Cropping Systems in Northern Shaanxi Province in China’s Loess Plateau

Water ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 1198 ◽  
Author(s):  
Jinping Wang ◽  
Jinzhu Ma ◽  
Afton Clarke-Sather ◽  
Jiansheng Qu
Keyword(s):  
Loess Plateau ◽  
Hydrological Modeling ◽  
Meteorological Data ◽  
Water Productivity ◽  
Experimental Studies ◽  
Northern Region ◽  
Green Water ◽  
Shaanxi Province ◽  
Cropping Patterns ◽  
Northern Shaanxi

Water shortages limit agricultural production in the world’s arid and semi-arid regions. The Northern region of China’s Shaanxi Province, in the Loess Plateau, is a good example. Raising the water productivity of rainfed grain production in this region is essential to increase food production and reduce poverty, thereby improving food security. To support efforts to increase crop water productivity (CWP), we accounted for limitations of most existing studies (experimental studies of specific crops or hydrological modeling approaches) by using actual field data derived from statistical reports of cropping patterns. We estimated the CWPs of nine primary crops grown in four counties in Northern Shaanxi from 1994 to 2008 by combining statistics on the cultivated area and yields with detailed estimates of evapotranspiration based on daily meteorological data. We further calculated both the caloric CWP of water (CCWP) and the CWP of productive water (i.e., water used for transpiration). We found that regional CWP averaged 6.333 kg mm–1 ha–1, the CCWP was 17,683.81 cal mm–1 ha–1, the CWP of productive green water was 8.837 kg mm–1 ha–1, and the CCWP of productive green water was 24,769.07 cal mm–1 ha–1. Corn, sorghum, and buckwheat had the highest CWP, and although potatoes had the largest planted area and relatively high CWP, they had a low CCWP.

scholarly journals Appropriate Management Scale of Farmland and Regional Differences under Different Objectives in Shaanxi Province, China

Land ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 314
Author(s):  
Qianxi Zhang ◽  
Zehui Chen ◽  
Fei Li
Keyword(s):  
Influencing Factors ◽  
Loess Plateau ◽  
Regional Differences ◽  
Agricultural Development ◽  
Shaanxi Province ◽  
Employment Rate ◽  
Grain Production ◽  
The Loess Plateau ◽  
Surplus Labor ◽  
The Government

Agricultural development is facing two problems: insufficient grain production and low profit of farmers. There is a contradiction between the government’s goal of increasing production and the farmer’s goal of increasing profit. Exploring the appropriate management scale of farmland under different objectives is of great significance to alleviate the conflict of interests between the government and farmers. In this study the Cobb-Douglas production function model was used to measure the appropriate management scale of farmland under different objectives in Shaanxi Province and analyze the regional differences. Under the two objectives, the appropriate management scale of the Loess Plateau was the largest in the three regions, followed by Qinba Mountains and Guanzhong Plain. Farmland area and quality were the main influencing factors for the appropriate management scale of farmland under the goal of maximizing the farmland yield, while the nonagricultural employment rate and farmland transfer rate were the main influencing factors under the goal of maximizing farmers’ profits. It is easy for Shaanxi Province to increase farmers’ profits, but more land needed to be transferred to increase farmland yield. These results suggest that in order to balance the goal of increasing yield and profit, the transfer of rural surplus labor should be promoted, and the nonagricultural employment rate should be improved. In Loess Plateau, restoring the ecological environment and enhancing the farmland quality. In Guanzhong Plain, avoiding urban land encroachment on farmland. In Qinba Mountains, developing farming techniques and moderately increasing the intensity of farmland exploit.

scholarly journals Predicting Rainfall-Induced Soil Erosion Based on a Hybridization of Adaptive Differential Evolution and Support Vector Machine Classification

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Tuan Vu Dinh ◽  
Hieu Nguyen ◽  
Xuan-Linh Tran ◽  
Nhat-Duc Hoang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Soil Erosion ◽  
Differential Evolution ◽  
Influencing Factors ◽  
Support Vector ◽  
Adaptive Differential Evolution ◽  
Artificial Neural

Soil erosion induced by rainfall is a critical problem in many regions in the world, particularly in tropical areas where the annual rainfall amount often exceeds 2000 mm. Predicting soil erosion is a challenging task, subjecting to variation of soil characteristics, slope, vegetation cover, land management, and weather condition. Conventional models based on the mechanism of soil erosion processes generally provide good results but are time-consuming due to calibration and validation. The goal of this study is to develop a machine learning model based on support vector machine (SVM) for soil erosion prediction. The SVM serves as the main prediction machinery establishing a nonlinear function that maps considered influencing factors to accurate predictions. In addition, in order to improve the accuracy of the model, the history-based adaptive differential evolution with linear population size reduction and population-wide inertia term (L-SHADE-PWI) is employed to find an optimal set of parameters for SVM. Thus, the proposed method, named L-SHADE-PWI-SVM, is an integration of machine learning and metaheuristic optimization. For the purpose of training and testing the method, a dataset consisting of 236 samples of soil erosion in Northwest Vietnam is collected with 10 influencing factors. The training set includes 90% of the original dataset; the rest of the dataset is reserved for assessing the generalization capability of the model. The experimental results indicate that the newly developed L-SHADE-PWI-SVM method is a competitive soil erosion predictor with superior performance statistics. Most importantly, L-SHADE-PWI-SVM can achieve a high classification accuracy rate of 92%, which is much better than that of backpropagation artificial neural network (87%) and radial basis function artificial neural network (78%).

scholarly journals Characteristics of Soil Erodibility K Value and Its Influencing Factors in the Changyan Watershed, Southwest Hubei, China

Land ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 134
Author(s):  
Xiaofang Huang ◽  
Lirong Lin ◽  
Shuwen Ding ◽  
Zhengchao Tian ◽  
Xinyuan Zhu ◽  
...  
Keyword(s):  
Land Use ◽  
Soil Erosion ◽  
Spatial Variability ◽  
Influencing Factors ◽  
Soil Type ◽  
Soil Survey ◽  
Purple Soil ◽  
Soil Erodibility ◽  
K Value ◽  
K Factor

Soil erodibility K factor is an important parameter for evaluating soil erosion vulnerability and is required for soil erosion prediction models. It is also necessary for soil and water conservation management. In this study, we investigated the spatial variability characteristics of soil erodibility K factor in a watershed (Changyan watershed with an area of 8.59 km2) of Enshi, southwest of Hubei, China, and evaluated its influencing factors. The soil K values were determined by the EPIC model using the soil survey data across the watershed. Spatial K value prediction was conducted by regression-kriging using geographic data. We also assessed the effects of soil type, land use, and topography on the K value variations. The results showed that soil erodibility K values varied between 0.039–0.052 t·hm2·h/(hm2·MJ·mm) in the watershed with a block-like structure of spatial distribution. The soil erodibility, soil texture, and organic matter content all showed positive spatial autocorrelation. The spatial variability of the K value was related to soil type, land use, and topography. The calcareous soil had the greatest K value on average, followed by the paddy soil, the yellow-brown soil (an alfisol), the purple soil (an inceptisol), and the fluvo-aquic soil (an entisol). The soil K factor showed a negative correlation with the sand content but was positively related to soil silt and clay contents. Forest soils had a greater ability to resist to erosion compared to the cultivated soils. The soil K values increased with increasing slope and showed a decreasing trend with increasing altitude.

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