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Soil salinization typically inhibits the ability of decomposer organisms to utilize soil organic matter, and an increase in soil clay content can mediate the negative effect of salinity on carbon (C) mineralization. However, the interactive effects of soil salt concentrations and properties on C mineralization remain uncertain. In this study, a laboratory experiment was performed to investigate the interactive effects of soil salt content (0.1%, 0.3%, 0.6% and 1.0%) and texture (sandy loam, sandy clay loam and silty clay soil with 6.0%, 23.9% and 40.6% clay content, respectively) on C mineralization and microbial community composition after cotton straw addition. With increasing soil salinity, carbon dioxide (CO2) emissions from the three soils decreased, but the effect of soil salinity on the decomposition of soil organic carbon varied with soil texture. Cumulative CO2 emissions in the coarse-textured (sandy loam and sandy clay loam) soils were more affected by salinity than those in the fine-textured (silty clay) soil. This difference was probably due to the differing responses of labile and resistant organic compounds to salinity across different soil texture. Increased salinity decreased the decomposition of the stable C pool in the coarse-textured soil, by reducing the proportion of fungi to bacteria, whereas it decreased the mineralization of the active C pool in the fine-textured soil through decreasing the Gram-positive bacterial population. Overall, our results suggest that soil texture controlled the negative effect of salinity on C mineralization through regulating the soil microbial community composition.

Soil penetration resistance (SPR) is an important indicator for soil strength which not only affects the growth of crop roots and crop yield but also is crucial in the design and selection of agricultural machinery. The determination of SPR in the laboratory is complex and time-consuming, while measuring SPR on-site shows high uncertainty at different times and locations due to soil heterogeneity. Therefore, this paper investigated the impact of soil parameters on SPR for paddy soils in the plastic state and then established a simple regression model to predict SPR using easy-to-obtain soil physical properties, including clay content, water content and density. Using the combined approaches of central composition rotatable design (CCRD) with response surface methodology (RSM), SPR of 20 soil samples from five paddy fields were measured in the laboratory. The results from the experiments showed that the contribution rate of each single factor to SPR from high to low was soil density, clay content and water content. Statistical analysis for the established equation suggested that the p-value for goodness of fit was significant (p < 0.001) and the p-value for lack of fit was insignificant (p > 0.05); meanwhile, the coefficient of determination (R2) was 0.95, indicating that the model was effective in predicting the SPR. Subsequently, the performance of the regression model was validated by comparing the estimated SPR with in situ field measurements, which showed high accuracy, with percent errors within 10%. Our study successfully proposed a method to estimate SPR using easy-to-measure soil properties that could be obtained from sensors in the soil or field investigations, including soil clay content, water content and wet bulk density.

Soil salinization typically inhibits the ability of decomposer organisms to utilize soil organic matter, and an increase in soil clay content can mediate the negative effect of salinity on carbon (C) mineralization. However, the interactive effects of soil salt concentrations and properties on C mineralization remain uncertain. In this study, a laboratory experiment was performed to investigate the interactive effects of soil salt content (0.1%, 0.3%, 0.6% and 1.0%) and texture (sandy loam, sandy clay loam and silty clay soil with 6.0%, 23.9% and 40.6% clay content, respectively) on C mineralization and microbial community composition after cotton straw addition. With increasing soil salinity, carbon dioxide (CO2) emissions from the three soils decreased, but the effect of soil salinity on the decomposition of soil organic carbon varied with soil texture. Cumulative CO2 emissions in the coarse-textured (sandy loam and sandy clay loam) soils were more affected by salinity than those in the fine-textured (silty clay) soil. This difference was probably due to the differing responses of labile and resistant organic compounds to salinity across different soil texture. Increased salinity decreased the decomposition of the stable C pool in the coarse-textured soil, by reducing the proportion of fungi to bacteria, whereas it decreased the mineralization of the active C pool in the fine-textured soil through decreasing the Gram-positive bacterial population. Overall, our results suggest that soil texture controlled the negative effect of salinity on C mineralization through regulating the soil microbial community composition. [ABSTRACT FROM AUTHOR]

Soil penetration resistance (SPR) is an important indicator for soil strength which not only affects the growth of crop roots and crop yield but also is crucial in the design and selection of agricultural machinery. The determination of SPR in the laboratory is complex and time-consuming, while measuring SPR on-site shows high uncertainty at different times and locations due to soil heterogeneity. Therefore, this paper investigated the impact of soil parameters on SPR for paddy soils in the plastic state and then established a simple regression model to predict SPR using easy-to-obtain soil physical properties, including clay content, water content and density. Using the combined approaches of central composition rotatable design (CCRD) with response surface methodology (RSM), SPR of 20 soil samples from five paddy fields were measured in the laboratory. The results from the experiments showed that the contribution rate of each single factor to SPR from high to low was soil density, clay content and water content. Statistical analysis for the established equation suggested that the p-value for goodness of fit was significant (p < 0.001) and the p-value for lack of fit was insignificant (p > 0.05); meanwhile, the coefficient of determination (R2) was 0.95, indicating that the model was effective in predicting the SPR. Subsequently, the performance of the regression model was validated by comparing the estimated SPR with in situ field measurements, which showed high accuracy, with percent errors within 10%. Our study successfully proposed a method to estimate SPR using easy-to-measure soil properties that could be obtained from sensors in the soil or field investigations, including soil clay content, water content and wet bulk density. [ABSTRACT FROM AUTHOR]