1. Spatiotemporal variability of soil moisture under different soil groups in Etsako West Local Government Area, Edo State, Nigeria
- Author
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Obot Akpan Ibanga, Mamuro Goodluck Omonigho, and Osaretin Friday Idehen
- Subjects
Hydrology (agriculture) ,Environmental change ,Agriculture ,business.industry ,DNS root zone ,Environmental science ,Soil classification ,Spatial variability ,Physical geography ,Precision agriculture ,General Agricultural and Biological Sciences ,business ,Water content - Abstract
Soil moisture has continued to occupy the forefront of several interdisciplinary debates globally, particularly in agriculture, climatology, hydrology and civil engineering for several decades. Climate smart and precision farming depend extensively on knowledge of the volumetric pattern and trend in water content of the soil particularly at the root zone. This study evaluated the temporal and spatial variation of soil moisture (0–40 cm depth) in Etsako West Local Government Area, Edo State, Nigeria. It used time series soil moisture data (1982–2019) sourced from the Famine Early Warning Systems Network (FEWS NET) and Land Data Assimilation System (FLDAS). Descriptive and inferential statistics as well as geographical information system (GIS) were used in data analysis. Results showed that Nitisols (Iyuku) had mean soil moisture of 9.0285 m3/m3, Lixisols (Ugieda) recorded 9.0574 m3/m3 whereas Acrisols (Idegun) was 9.1307 m3/m3. September emerged as month with peak soil moisture of 0.9 m3/m3 in Lixisols and Acrisols while January and February were the months with the lowest value of 0.5 m3/m3 across the three soil classes. Soil moisture in all the soil classes also exhibited a rising trend in the climatic period with annual increase of 0.002 m3/m3 in Nitisols, 0.002 m3/m3 in Lixisols and 0.001 m3/m3 in Acrisols. Temporal pattern of soil moisture was not statistically significance while spatial variation was statistical significant across the soil classes. In a spatial data-driven society, this study has therefore unlocks the potentials of GIS and remote sensing resources in assessing global environmental change indicators in data sparse regions.
- Published
- 2022
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