Abstract
In precision Agriculture, geostatistical methods as a predictive tool have been extensively utilized. The approach estimates soil properties spatial variability and dependency. This study was carried out in Ovia north east Local Government Area of Edo State of Nigeria in order to map soil properties (Sand, Clay, pH, OC, P, N and CEC) and redict their spatial variability. Twenty-nine (29) soil samples were collected randomly from Typic Kandiudults soil type under three different land use, teak forest plantation, shrub, and arable farm. The soil samples were air-dried and passed through a 2 mm sieve before being analyzed for pH(CaCl2), SOC, Sand, Clay, Phosphorus, Nitrogen, and CEC. Generated data were statistically and geostatistically computed to explain the spatial variability of soil properties. The traditional method of soil analysis and interpretation are tedious, time-consuming with escalating budgets thus geostatical approach. Available phosphorus yielded large variability with CV=57.08% followed by clay content with CV=49.03%. Spherical, Gaussian, Hole Effect model, Stable, Exponential and Circular models were fitted for all the soil parameters. The result revealed that soil pH, Sand content, TN and CEC were moderate spatially autocorrelated with nugget/sill value of 0.32, 0.21, 0.49 and 0.30 respectively.  SOC also gave a moderate spatially autocorrelated with nugget/sill value of 0.44. And Clay and Available phosphorus were strong spatially autocorrelated with nugget/sill value of 0.15 and 0.13 respectively. Cross-validation of the output maps using the semivariogram showed that the interpolation models are superior to assuming mean for any unsampled area. The output maps will help soil users within the area to proffer best management technology to improve crop, fiber and water production. 
  
Highlights
The soil ecosystem is a complex one which is formed from different weathering process of rock materials
29 samples were collected from three different land use and were used to evaluate soil spatial distribution
The geostatistics models in GIS which were adopted in this study proved essential for evaluation of different land use inventories in various status
Summary
The soil ecosystem is a complex one which is formed from different weathering process of rock materials. It is composed of mineral and organic fractions, yielding specific physical, chemical, mineralogical and biological properties (Esu, 2005, Kingsley et al, 2019, Akpan-Idiok et al, 2012). On the other hand, is the process of gathering, describing, manipulating, classifying and predicting soil properties (Esu,2005). It provides up-to-date information in terms of landforms, terraces, and vegetation (Denton et al, 2017; Brown et al, 1978). These updated soil inventories are reliable in policy and decision making under precision agriculture (Denton et al, 2017)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
