Predictive Mapping of Soil Properties for Precision Agriculture Using Geographic Information System (GIS) Based Geostatistics Models

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. 
  

Spatial Distribution of Soil Nutrients in Farmland in a Hilly Region of the Pearl River Delta in China Based on Geostatistics and the Inverse Distance Weighting Method

“…Based on these, a series of quantitative indicators were used to discuss the forecast results of different models. That was an innovative attempt in the thinking of plant nutrients in soil analysis because of many studies focus only on one method in the past [6,9,10,13]. The results showed that TN is moderately enriched and AP is deficient in the study area, which was consistent with the geochemical survey results.…”

“…Spatial variation is generally divided into intrinsic variation and extrinsic variation. Internal changes are caused by structural factors, while external changes are caused by random factors [6,10,33]. The structural factors of internal change include soil texture, soil particle, soil bulk density, etc.…”

“…Secondly, in the selection of research objects, there are few studies on discussing ASi together with AN, AP and AK. In this study [42], the spatial distribution features of these soil nutrients were discussed according to the characteristics of regional natural geography and crop planting in the studied area, which was helpful to fully understand the soil nutrients and make a measure for precision agriculture [3,6].…”

“…Geostatistical methods as a predictive tool have been extensively utilized [6]. There have been many studies of soil nutrient properties in recent years.…”

Experimental Investigation and Mapping of Index and Shear Behavior of Soils a Case in Gimbichu Town, Ethiopia

“…Accordingly, experimental data were recorded and analyzed for required objectives that to determine and to present fine-grained soil behaviors of the town. Laboratory results were mapped to have spatial information on the foundation material/soil using ArcGIS software (18,19) .…”