Current interest in soil-conserving tillage in China has developed from the concern that Chinese agricultural land loses 73·8 Mg C annually. Previous research has shown that changing from conventional tillage to conservation tillage field management increases soil C sequestration. The aim of this study is to determine if no tillage with stubble retention can reduce soil carbon loss and erosion compared with conventional tillage for a cornfield in northern China. We found that soil organic C storage (kg m À2 ) under conservation tillage in the form of no postharvest tillage with stubble retention increased from 28% to 62% in the soil depths of 0-30 cm (p < 0·01) compared with the conventional tillage. Retaining post-harvest stubble with a height of 30 cm and incorporating the stubble into the soil before seeding the next spring increased soil organic carbon the most. Carbon storage (kg ha À1 ) in aboveground and belowground biomass of the corn plants in seedling and harvest stages was significantly greater (p < 0·01) with stubble retention treatments than with conventional tillage. Carbon content in root biomass in all treatments with stubble retention was significantly greater than that with conventional tillage. Soil erosion estimates in the study area under conservation tillage with stubble retention was significantly lower than that under conventional tillage during the monitoring period. Given the complexities of agricultural systems, it is unlikely that one ideal farming practice is suitable to all soils or different climate conditions, but stubble retention during harvesting and incorporation of the stubble into soil in the next spring appears to be the best choice in the dry northern China where farmlands suffer serious wind erosion.

The rapid desertification of grasslands in Inner Mongolia of China poses a significant ecological threaten to northern China. The combined effects of anthropogenic disturbances (e.g., overgrazing) and biophysical processes (e.g., soil erosion) have led to vegetation degradation and the consequent acceleration of regional desertification. Thus, mitigating the accelerated wind erosion, a cause and effect of grassland desertification, is critical for the sustainable management of grasslands. Here, a combination of mobile wind tunnel experiments and wind erosion model was used to explore the effects of different levels of vegetation coverage, soil moisture and wind speed on wind erosion at different positions of a slope inside an enclosed desert steppe in the Xilamuren grassland of Inner Mongolia. The results indicated a significant spatial difference in wind erosion intensities depending on the vegetation coverage, with a strong decreasing trend from the top to the base of the slope. Increasing vegetation coverage resulted in a rapid decrease in wind erosion as explained by a power function correlation. Vegetation coverage was found to be a dominant control on wind erosion by increasing the surface roughness and by lowering the threshold wind velocity for erosion. The critical vegetation coverage required for effectively controlling wind erosion was found to be higher than 60%. Further, the wind erosion rates were negatively correlated with surface soil moisture and the mass flux in aeolian sand transport increased with increasing wind speed. We developed a mathematical model of wind erosion based on the results of an orthogonal array design. The results from the model simulation indicated that the standardized regression coefficients of the main effects of the three factors (vegetation coverage, soil moisture and wind speed) on the mass flux in aeolian sand transport were in the following order: wind speed>vegetation coverage>soil moisture. These three factors had different levels of interactive effects on the mass flux in aeolian sand transport. Our results will improve the understanding of the interactive effects of wind speed, vegetation coverage and soil moisture in controlling wind erosion in desert steppes, and will be helpful for the design of desertification control programs in future.

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This paper presents the multi-objective optimization of the hard milling process of AISI H13 steel under minimum quality lubricant with graphite nanoparticle. The cutting speed, feed per tooth, depth of cut, and hardness of workpiece were taken as the process parameters, while surface roughness, cutting energy, cutting temperature, and material removal rate were considered as technological responses. Response surface or Kriging approximate models were applied to generate the mathematical regression models showing the relationship between machining inputs and outputs obtained by physical experiments. Then, multi-objective particle swarm optimization algorithm in conjunction with the Pareto approach and engineering data mining was adopted to figure out the feasible solutions. The research results show that cutting energy can be reduced up to around 14% compared to the worst case. Based on the Pareto plot, the appropriate selection of machining parameters can help the machine tool operator to increase machining productivity and energy efficiency.

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