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Abstract: Empirical models have been developed to describe the soil water retention curve (SWRC). Applications of the fractal theory may provide a useful tool to fill the gap between the use of empirical models and physical interpretation of their parameters. Especially a more generalized model for the SWRC has been developed based on the pore–solid fractal (PSF) distribution. The PSF model covers several existing models as its special cases and theoretically provides a direct way to estimate the SWRC fractal dimension from particle size distributions (PSDs). The first objective of this study was to evaluate the SWRC with the PSF distribution using more than 400 data sets of PSD and SWRC. The second objective was to establish a relationship between the fractal dimension and soil texture. Analyses of the data sets showed that the fractal dimension values estimated using the PSD data were consistently smaller than those estimated using the SWRC data. Therefore, using the fractal dimension from PSD to predict the SWRC will result in underestimation. To resolve the problem, the soil data sets of PSD and SWRC were used to establish relationships between the fractal dimensions in the PSF model of SWRC and soil clay content as well as soil texture class. Independent SWRC data sets were used to test the methods. Predicted results using the PSF model of SWRC with the fractal dimension estimated using clay content or soil texture class were compared with the measured soil water retention data. Linear regressions of the predicted and measured SWRC showed good agreement for relatively fine texture soils with the coefficients of determination (r 2) of 0.940. [Copyright &y& Elsevier]

Biochar is proposed as a soil amendment to improve soil physical-chemical properties and crop productivity. However, the effect of biochar on crop yield is not consistent and the mechanisms affecting plant growth are still not well-understood. In this paper, field experiments were carried out to investigate the impact of biochar addition on soil properties, water and fertilizer productivity of tomato in semi-arid area of Inner Mongolia, China. The tested biochar was made from maize straw by slow pyrolysis. Four biochar treatments were conducted including addition of 10, 20, 40 and 60 t ha −1 and no biochar as a control. Addition of biochar reduced the bulk density and increased the porosity of soil. Electrical conductivity of soil increased with increasing biochar application rate. Soil pH was not affected by addition of biochar in one growing season. Yield, crop water productivity and partial factor productivity for fertilizer were nonlinearly (quadratic) proportional to application rate of biochar. Highest water and fertilizer productivity obtained from the 40 t ha −1 biochar treatment. Comprehensive analysis of biochar impact on yield, water and nutrient productivity, and cost-benefit for the grower indicate that addition of approximate 30 t ha −1 is an appropriate rate for tomato production in the study area. This suggestion is based on the relatively short term effect and local market conditions. A prolonged experiment is desirable to understand the long term response of soil and crop to biochar addition. [ABSTRACT FROM AUTHOR]

Soil salinization is one of the most common land degradation processes that impacts agricultural production, environmental health, and economic welfare. The first task in managing soil salinity is to understand its spatiotemporal characteristics, causal factors and influences, both over large regions and within individual fields. In this study, a typical irrigated agro-ecosystem located in the upper Yellow River basin, suffering from long-term soil salinization problems, was selected as the case study area. Field sampling experiments and investigations related to soil layer salt content, groundwater, irrigation and ground elevation were carried out at three scales (field scale, canal scale and regional scale) covering both croplands and natural vegetation patches during 2016 and 2017. Spatiotemporal characteristics of soil salinity were analyzed for the three scales based on sampling data, remote sensing, geostatistical analysis and model simulation. Results showed that, at the regional scale, the soil salinity distribution was dominated by topography and groundwater depth; at the canal scale, it was dominated by irrigation and cropping pattern; at the field scale, it was dominated by micro-topography and the uniformity of field irrigation. Scale-dependent soil salinity management strategies should be adopted to maintain agricultural production and the sustainability of natural ecosystems. It can be concluded that using integrated approaches including multi scale soil sampling, model simulation and other modern techniques is an effective way for thorough understanding and management of soil salinity. • Soil sampling, GIS/RS and dynamic model were integrated for salinity analysis. • Salinity characteristics and their causal factors were scale dependent. • The proportion of salt-affected soil increased by 14% during the growing season. • Multiple modern techniques are needed for salt prediction and management. [ABSTRACT FROM AUTHOR]