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Spatiotemporal variability of soil moisture under different soil groups in Etsako West Local Government Area, Edo State, Nigeria

Journal of the Saudi Society of Agricultural Sciences, ISSN: 1658-077X, Vol: 21, Issue: 2, Page: 125-147
2022
  • 2
    Citations
  • 0
    Usage
  • 36
    Captures
  • 0
    Mentions
  • 31
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    2
    • Citation Indexes
      2
  • Captures
    36
  • Social Media
    31
    • Shares, Likes & Comments
      31
      • Facebook
        31

Article Description

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 m 3 /m 3, Lixisols (Ugieda) recorded 9.0574 m 3 /m 3 whereas Acrisols (Idegun) was 9.1307 m 3 /m 3. September emerged as month with peak soil moisture of 0.9 m 3 /m 3 in Lixisols and Acrisols while January and February were the months with the lowest value of 0.5 m 3 /m 3 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 m 3 /m 3 in Nitisols, 0.002 m 3 /m 3 in Lixisols and 0.001 m 3 /m 3 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.

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