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Investigating the spatial distribution characteristics of the interaction between soil salinity and moisture is crucial in revealing moisture–salinity interaction in semi-arid farmland. The sampling of soil was performed on the second (S1), fifth (S2), eighth (S3), eleventh (S4), and fourteenth (S5) days after the erosive rainfall. The multifractal method was used to analyze spatial distribution parameters of soil moisture and salinity under the different stages. The findings showed that the soil moisture content decreased from 22.44% to 12.73%, while the salinity increased from 0.71 to 1.18 g kg–1 after the rainfall. As the amount of moisture in the soil decreased, the variability in the distribution of moisture initially increased from S1 to S3 and then decreased, while the salinity content also decreased. The spatial distribution of soil moisture and salinity content showed a strong correlation at S3 to S4 (with the relative water content of soil ranging from 0.52 to 0.75), indicating a significant coupling effect in these stages. However, the distribution of soil salinity was not uniform under high moisture content conditions (S1 to S2), as it was leached unevenly by rainfall, and under low moisture content conditions (S5), it precipitated, resulting in a low correlation between the spatial distribution of soil moisture and salinity content. This research has provided insight into the coupling dynamics of soil moisture and salinity content, revealing the mechanisms governing their spatial distribution in dryland agricultural regions. [ABSTRACT FROM AUTHOR]

Soil salinization and water deficits in gully consolidation farmland including excavated farmland (EF) and sediment deposited farmland (SF) have become an increasing concern in the Loess Hilly region, China. However, the responses of different farmland types and tillage‐mulching practices on soil salt content (SSC) and water use efficiency (WUE) have not been widely studied in this region. Thus, we conducted a two‐year field experiment to assess the soil water content (SWC) and SSC and their effect on the spring maize yield and WUE under different tillage‐mulching practices in EF and SF. Eight treatments incorporating flat cropping without mulching (FC), ridge planting without mulching (RP), ridge cropping with straw mulching (SM), and ridge cropping with plastic mulching (PM) were applied to EF and SF plots, respectively. Our results indicated that the yield of spring maize was limited by SWC (p < 0.01). Although EF had a low yield of spring maize compared to SF, it had the highest WUE (p < 0.01), which was mainly due to the lower groundwater consumption. Meanwhile, the average SSC was significantly lower in EF than in SF (p < 0.01). PM had the highest SWC (14.9%) and significantly reduced the accumulation of SSC (−0.09 g kg−1), thereby improving spring maize yield (12,200 kg ha−1) and WUE (1.71 kg m−3) (p < 0.05). These results suggested that EF with PM is optimal for groundwater conservation, decreasing the risk of soil salinization, and improving WUE and maize yield in dryland agricultural areas. [ABSTRACT FROM AUTHOR]

• Multifractal parameters of soil particle size distribution (PSD) can reflect soil moisture content (SMC). • Multifractal parameters describe differences in soil PSD in multiple dimensions. • Explanation of variation in SMC by multifractal parameters increased as the SMC value decreased. • Explanation of variation in SMC by multifractal parameters was higher in the 40–80 cm soil layer. Determining the soil moisture content (SMC) distribution is indispensable for field management, especially in arid and semiarid regions. Effective parameters can contribute to the optimization of SMC models for accurate SMC prediction. Therefore, in this study, the relationships between the SMC and multifractal parameters (D 2 , D v , D s , A v , and F v denote the correlation dimension, property of small probability and large probability, spectrum width, and symmetry of spectrum shape, respectively) of the soil particle size distribution (PSD) were explored in the hilly loess region of China. A grid method was adopted (20 m × 20 m, total = 384 points) to sample the SMC in the 0–40 cm and 40–80 cm soil layers on the second, eighth, and twelfth days after the first rainfall, which were defined as the early sampling (ES), medium sampling (MS), and late sampling (LS), respectively. We found that the variation in the SMC explained by the multifractal parameters increased as SMC decreased, where they accounted for 40.07% and 75.75% of the SMC in the 0–40 cm and 40–80 cm soil layers in LS, respectively. The variation in the SMC in the 0–40 cm explained by the multifractal parameters was lower than that in the 40–80 cm soil layer in all sampling stages. The equations fitted for the whole soil layer were significant in all sampling stages (P < 0.01). A v (31.95%) and D v (13.59%) had negative correlations with SMC, and their relative importance values with respect to SMC were high in all sampling stages (P < 0.05). SMC at higher values had a positive correlation with F v , whereas SMC at lower values had a significant positive correlation with D 2 and negative correlation with D s , and these two parameters in the MS and LS stages explained more than 75% of the variation in the SMC in the 40–80 cm soil layer. These results suggest that the characteristics of the soil PSD can be described in detail by the multifractal parameters, which can directly reflect the SMC. Thus, we conclude that SMC is closely related to the multifractal parameters of the PSD (A v , D v , D s , D 2 , and F v) and it can be applied to optimize SMC models. [ABSTRACT FROM AUTHOR]