African Journal of Water Conservation and Sustainability ISSN: 2375-0936 Vol. 10 (3), pp. 001-008, March, 2022. © International Scholars Journals

Full Length Research Paper

Predication of soil water storage by spatial dependence in a deposited soil farmland

Pei Zhao1 and Mingan Shao2*

1Key Laboratory of Mountain Environment Evolvement and Regulation, Institute of Mountain Hazards and Environment,

Chinese Academy of Sciences, Chengdu 610041, China.

2Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural

Resources Research, Chinese Academy of Sciences, Beijing100101, China.

Accepted 20 October, 2021


Spatial dependence was widely recognized in field observations and the existence was very helpful to the development of precision agriculture. On the Loess Plateau of China, the soil water contents (SWCs) at a deposited soil farmland (DF) were measured using the neutron tubes on two sampling lines during two years. The objectives of this research were to recognize their spatial dependence to predict soil water storage (SWS) and to divide the DF for future study in such kind farmland. The results showed that the mean SWCs of 0-80 cm soil depth decreased at the prior part and increased at the later part of the DF. The coefficient of variation (CV) decreased exponentially with the mean SWC on all observations. Estimated autocorrelation values began at higher value than critical criteria and gradually decreased towards negative values following the increased lag distance. The Moran’s I and ACF both illustrate the existence of spatial correlation of neighboring points on the silting direction, and the cluster characteristics were used to predicate SWS and divide the DF. The topsoil water contents (10 cm) have a good linear relationship with its SWSs (80 cm) since the deposition characteristics of sediment. Three parts of DF should be divided in the related studies on such land. Future studies should focus on the spatial dependence of more soil variables at the DF to help the development of precision agriculture and manage soil resources.

Key words: Autocorrelation, check-dam, linear regression, soil water content.