Abstract. Soil moisture in deep soil layers is a relatively stable water resource for vegetation growth in the semi-arid Loess Plateau of China. Characterizing the spatial variations of deep soil moisture and its influencing factors at a moderate watershed scale is important to ensure the sustainability of vegetation restoration efforts. In this study, we focused on analyzing the spatial variation and factors influencing soil moisture content (SMC) in (0–500 cm) soil layers based on a soil moisture survey of the Ansai watershed, Yanan, Shannxi province. Our results can be divided into four main findings. (1) At the watershed scale, the higher spatial variation of deep SMC occurred at 0–20 cm, 120–140 cm and 480–500 cm in the vertical direction. At a comparable depth but in the horizontal direction, the spatial variation of deep SMC under native vegetation was much lower than that in human-managed vegetation and introduced vegetation. (2) The deep SMC in native vegetation and human-managed vegetation was significantly higher than that of introduced vegetation, and different degrees of soil desiccation occurred under all introduced vegetation types. (3) Taking the SMC condition of native vegetation as a reference for local control, soil could be divided into four layers: I) shallow rapid change layer (0–60 cm); II) main rainfall infiltration layer (60–220 cm); III) transition layer (220–400 cm); and IV) stable layer (400–500 cm). Positive and significant correlations existed between SMC at layers II, III and IV, and the correlations of the neighboring layer ranges were clearly stronger than that of nonadjacent depth ranges, although the SMC at shallow rapid change layer I showed a disconnect (i.e., no correlations) with those at the three other soil depth layers. (4) The influencing factors of deep SMC at the watershed scale varied with land management types. The main local controls of SMC variation were soil particle composition and annual average rainfall; human agricultural management measures can alter soil buck density, which contributes to higher deep SMC. In introduced vegetation, plant growth conditions, planting density, and litter water holding traits showed significant relationships with deep SMC. The results of this study are of practical significance for vegetation restoration strategies and the sustainability of restored ecosystems.
Cultivation techniques have an important influence on grain yield of maize. This experiment investigated the effect of stover return (SR) and different nitrogen (N) application rate on soil organic carbon (SOC) composition, soil nutrient and maize yield.• Different nitrogen application rate 100 (N100), 150 (N150), 200 (N200), 250 (N250) or 300 (N300) kg ha À1 applied to the maize field with stover return and without stover return traditional planting (TP) method.• Nitrogen application rate and stover return affected the SOC, labile organic carbon (LOC), microbial biomass (MBC), NO 3 À -N, NH 4 + -N and maize yield. Soil N, soil carbon content and maize yield of SR were all higher than TP. The SOC content of SR and TP were 9.67 and 9.19 g kg À1 , respectively. Nitrogen application was significantly and positively correlated with soil MBC, LOC, SOC, NO 3 À -N, NH 4 + -N and yield. The maximum values of SOC composition, soil nutrients and maize yield were reached at SR with 250 kg ha À1 .• Stover return with application of N 250 kg ha À1 significantly increased the growth attribute and maize yield in subtropical region compared with traditional planting.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.