Spatial Variability Analysis of Soil Properties of Tinsukia District, Assam, India View Full Text


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Article Info

DATE

2018-09-06

AUTHORS

S. K. Reza, D. Dutta, S. Bandyopadhyay, S. K. Singh

ABSTRACT

Accurate analysis of spatial variability of soil properties is a key component of the agriculture ecosystem and environment modelling. A systematic study was carried out to explore the spatial variability of pH, organic carbon (OC), available nitrogen (AN), available phosphorus (AP) and available potassium (AK) in soils of Tinsukia district, Assam, India, for site-specific soil management. For this, a total of 3062 soil samples from a 0–25 cm depth (plough layer) at an approximate interval of 1 km were collected and analysed for different physical and chemical properties. Data were analysed both statistically and geostatistically on the basis of semivariogram. The values of soil pH, and OC, AN, AP and AK varied from 3.4 to 8.2, and 0.2–43.4, 1.1–37.3 and 12.5–392.8 mg/kg, respectively, with mean values of 4.6, and 13.8, 9.6 and 98.4 mg/kg, respectively. The largest variability in the soil properties was observed for K (55%), whereas the least variability was found for pH (14%). The semivariogram for pH, OC, AN, and AP was best fitted by the exponential model, whereas AK was best fitted by the Gaussian model. The range of all soil properties varied from 1119 to 3663 m; thus the length of the spatial autocorrelation is much longer than the sampling interval of 1000 m. Therefore, the current sampling design was appropriate for this study. The nugget/sill ratio indicated a moderate spatial dependence for pH, OC, N and P (33–73%) and a weak spatial dependence for K (82%). The generated spatial distribution maps can serve as an effective tool in site specific nutrient management. This is a prerequisite in farming systems in order to optimize the cost of cultivation as well as to address nutrient deficiency. The study also helped to identify and delineate critical nutrient deficiency zones. More... »

PAGES

1-8

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s40003-018-0365-z

DOI

http://dx.doi.org/10.1007/s40003-018-0365-z

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https://app.dimensions.ai/details/publication/pub.1106805046


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