Assessment of spatial variability of soil properties using geostatistical approach of lateritic soil (West Bengal, India)

Bhunia, Gouri Sankar; Shit, Pravat Kumar; Chattopadhyay, R.

doi:10.1016/j.aasci.2018.06.003

Cited by 71 publications

(34 citation statements)

References 23 publications

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“…While the moderate and weak spatial autocorrelation in other soil property (pH, SOC, Sand, nitrogen, and CEC) may have been developed from random factors such as poor land conservation approach, chemical fertilizer application, uncontrolled grazing amongs others. Furthermore, the decrease of the soil heterogeneity across the three different land use may have been influenced by human activities, drainage, fertilization among others, these reduce spatial correlation (Gouri et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

“…Geostatistics techniques estimate the spatial variability using variogram models which predicts the values of soil properties at un-sampled locations (Pravat et al, 2016;Goovaerts, 1998Denton et al, 2016;Gouri et al, 2018 andOfem et al, 2017). These methods are widely used to assess spatial correlation in soils and ascertain the spatial variability in soil properties (physical, chemical and biological) as opined by Gouri et al, 2018. Kriging engages the mean of a known location to determine the property of value in the unsampled area, which will narrow the section of estimation to the highest degree (Penížek & Borůvka, 2006).…”

Section: Introductionmentioning

confidence: 99%

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

John¹,

Lawani²,

Esther³

et al. 2019

MAS

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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.   

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

John¹,

Lawani²,

Esther³

et al. 2019

MAS

View full text Add to dashboard Cite

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“…A higher nugget/ sill ratio indicates that the spatial variability is primarily caused by stochastic factors, such as fertilization, farming measures, cropping systems and other human activities. The lower ratio suggests that structural factors such as climate, parent material, topography, diverse vegetation, proximity to sea and anthropogenic activities play a significant role in spatial variability [ 88 ]. We too have reported moderate spatial dependence for SOC and AN in west-coast region of India [ 2 ].…”

Section: Resultsmentioning

confidence: 99%

Spatial mapping of soil properties in Konkan region of India experiencing anthropogenic onslaught

et al. 2021

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Soils of Indian Konkan region, part of ecologically sensitive Western Ghats have been subjected to anthropogenic activities of late. This has endangered the ecological security through conspicuous losses in topsoil quality. The rationale of the present study was to map the soil properties and create management zones for ensuring food and nutritional security. The study was conducted in South Goa district of the state of Goa located in Konkan region. A total of 258 geo-referenced soil samples were collected and analyzed for pH, EC, SOC, available N, P, K and DTPA extractable micronutrients viz., Zn, Cu, Fe and Mn. Soil pH was found to be in acidic range. A wide variability existed in SOC content ranging from 0.12–5.85%. EC was mostly neutral with mean value 0.08±0.37 dSm-1, while available nitrogen (AN), available phosphorus (AP) and available potassium (AK) varied in range from 56.4–621.6 kg ha-1, 0.5–49.7 kg ha-1 and 31.5–786.2 kg ha-1 with mean values 211.2±76.9, 8.4±8.2 and 202.3±137.6 kg ha-1, respectively. A wide range was exhibited by cationic DTPA extractable Zn, Cu, Fe and Mn with mean values, 0.22±0.30, 0.44±0.60, 7.78±5.98 and 7.86±5.86 mg kg-1, respectively. Soil pH exhibited significant positive correlation with EC, AP AK and Zn and negative correlation with Fe and Cu. SOC exhibited significantly correlated with AN, AP, AK, Zn and Fe. Geo-statistical analysis revealed J-Bessel as best fit semivariogram model for pH, AP and AK; Rational Quadratic for EC, SOC, Zn and Mn; Hole effect for AN; Stable for Cu and K-Bessel for Fe for their spatial mapping. Four principal components showed eigenvalues more than one and cumulative variability of 59.38%. Three distinct soil management zones showing significant variation in soil properties were identified and delineated for wider scale management of soils. Precision nutrient management based on spatial variation and their mapping would enable refined agricultural and environmental management practices in the region.

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“…Cemek, et al [6] examined the spatial variability of soil properties affecting salinity and alkalinity on the Bafra plain of northern Turkey and found that 1) the spatial variability of soil properties in different soil layers is different, and 2) the spatial dependence of soil properties was mainly caused by external factors such as groundwater, drainage, irrigation system, and microtopography. Bhunia et al [25] applied a geostatistical model to analyze the spatial variability of soil properties in lateritic soils of West Bengal, India. They pointed out that land management has a certain impact on soil quality and the geostatistics model is a very effective method to explore the spatial variability of soil properties.…”

Section: Literature Reviewmentioning

confidence: 99%

Spatial Distribution Characteristics of Soil Salinity and Moisture and Its Influence on Agricultural Irrigation in the Ili River Valley, China

Zhang

2019

Sustainability

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Soil salinization is a global problem, which threatens agricultural productivity and sustainability, especially in arid and semi-arid regions. Soil salinity and moisture are important factors affecting agricultural production in arid regions. However, few studies have considered the influence of topographic factors on the spatial distribution patterns of soil salinity and moisture. This research aims to explore the spatial distribution characteristics and its influencing factors of soil salinity and moisture in the oasis farmland of arid areas. In this paper, GIS and geostatistics methods were applied to analyze the spatial distribution characteristics and variability of soil salinity and moisture, and then the corresponding proxy variables were used to quantitatively study the influence factors by using the geographical detector model. The results showed the coefficients of the variation of soil salinity and moisture to be 71.25% and 31.89%, respectively. There was moderate spatial autocorrelation of soil salinity and moisture. Soil salinity in the southwest was higher than in the northeast, and soil moisture in the northwest and southeast were lower than in the center and the northeast edge. The main influencing factors were available phosphorus, roughness of terrain, alkaline nitrogen, available potassium, and elevation. Combined action of topographic factors and soil nutrients has a major influence on the spatial distribution of soil salinity and moisture. Therefore, developing a suitable fertilizer regime under different topographic conditions could be an effective way to promote the sustainability of oasis agriculture in arid areas.

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Assessment of spatial variability of soil properties using geostatistical approach of lateritic soil (West Bengal, India)

Cited by 71 publications

References 23 publications

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

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

Spatial mapping of soil properties in Konkan region of India experiencing anthropogenic onslaught

Spatial Distribution Characteristics of Soil Salinity and Moisture and Its Influence on Agricultural Irrigation in the Ili River Valley, China

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