Spatial variability of soil properties using geostatistical method: a case study of lower Brahmaputra plains, India

Abstract

Soil properties like pH, organic carbon (OC), available nitrogen (AN), available phosphorus (AP), and available potassium (AK) vary spatially from a field to a larger region scale and determine the soil fertility. This study addressed the spatial variability of soil properties in Brahmaputra plains, northeastern India using geostatistical method. For this, a total of 767 soil samples from a depth of 0-25 cm at an approximate interval of 1 km were collected over the entire Bongaigaon district of Assam. Data were analyzed both statistically and geostatistically on the basis of semivariogram. Soil properties showed large variability with greatest variation was observed in AP (86 %) where as the smallest variation was in pH (19 %). The semivariogram for all soil properties were best fitted by exponential models and showed a highest (2.7 km) range for OC and lowest (1.2 km) for AP. The nugget/sill ratio indicates a strong dependence for pH (12 %), moderate spatial dependence for available nutrients (53-72 %) and a weak spatial dependence for OC (77 %). Evaluation of spatial maps indicated that except for AN due to high root mean square error (61.8), kriging could successfully interpolate other soil properties. Soil pH highly negatively correlated with OC (−0.330**) and AN (−0.228**) and highly positive correlated with AP (0.334**) and AK (0.164**). A highly significant correlation was also found between OC and AN (0.490 ** ).

Spatialization of Granulometric and Chemical Characteristics in Irrigated Hydromorphic Soil

“…This low variability, particularly for the pH (H2O) attribute, can occur, according to Reza et al (2017), because the values of this attribute are presented in a log scale of the concentration of protons in the soil solution. Polo et al (2010), Reza et al (2016), andShukla et al (2016), in their studies, verified the low CV of the pH (H2O), when compared with those of other soil properties, thereby corroborating this work. From the 0.1-0.2 m depth range analysis, all the attributes showed average variation, except for the attributes, Al, P, K, Cu, and Zn, which varied significantly.…”

Comparison of different interpolation techniques in determining of agricultural soil index on land consolidation projects

“…The nugget-sill ratio of SI in the area was 0.23 (23 %), which showed intense spatial autocorrelations according to Table 2. Geostatistical analysis uses the semivariogram to quantify the spatial variation of a regionalized variable and derives important parameters used for kriging spatial interpolation (Reza et al, 2016). Different semivariogram functions (Circular, Spherical, Tetraspherical, Pentaspherical, Exponential, Gaussian) were evaluated to select the best fit with the data.…”

Variability modeling and mapping of soil properties for improved management in Ethiopia

“…There was SSD on soil pH and OC; moderate spatial dependence (MSD) on Olsen-P (Panday et al, 2018), OC (Costa et al, 2015;Panday et al, 2018), and pH (Panday et al, 2018). Weak spatial dependence on soil Olsen-P (Ozgoz et al, 2013;Costa et al, 2015) and OC (Reza et al, 2016) and SSD on Olsen-P (Kingsley et al, 2019) were also mentioned. According to Wang et al (2009) and Fu et al (2014), a strong and MSD of soil parameters are due to soil internal factors, including parent materials, soil texture, geography, and plants.…”