Assessment of spatial variability of soil properties using geospatial techniques for farm level nutrient management

Vasu, Duraisamy; Singh, Surendra; Sahu, Nisha; Tiwary, P.; Chandran, P.; Duraisami, V.P.; Ramamurthy, V. V.; Lalitha, M.; Kalaiselvi, B.

doi:10.1016/j.still.2017.01.006

Cited by 140 publications

(51 citation statements)

References 36 publications

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“…According to this classification, OM, K, S and pH showed а strong spatial dependence; and N and P exhibited weak degree of spatial dependence ( Based on the results of the present study we may conclude that moderate and weak spatial dependence of soil fertility parameters can be usually attributed to soil and crop management practices [24]. These results are in line with the International Journal of Geosciences observations reported by Vasu et al [38].…”

Section: Analysis Of Spatial Dependence Of Soil Fertility Parameterssupporting

confidence: 91%

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Assessment of Spatial Variability of Soil Fertility Parameters Using Geospatial Techniques in Temperate Himalayas

Ramzan¹,

Wani²,

Bhat³

2017

IJG

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Section: Analysis Of Spatial Dependence Of Soil Fertility Parameterssupporting

confidence: 91%

“…These high soil test values may not always be an outlier but a form of natural or management induced variation [38].…”

Section: Descriptive Statisticsmentioning

confidence: 99%

Assessment of Spatial Variability of Soil Fertility Parameters Using Geospatial Techniques in Temperate Himalayas

Ramzan¹,

Wani²,

Bhat³

2017

IJG

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“…The EF and Igeo are based on relative assessment of heavy metals in polluted and unpolluted soil conditions Sakram, Machender, Dhakate, Saxena, & Prasad, 2015). Various researchers have done the spatial distribution of heavy metals by GISbased geostatistical techniques (Ander et al, 2013;Petrik, Thiombane, Albanese, Lima, & De Vivo, 2018;Tóth, Hermann, Szatmári, & Pásztor, 2016;Ungureanu, Iancu, Pintilei, & Chicoș, 2016;Vasu et al, 2017;Zou et al, 2015). The objective was to elucidate the causes for high heavy metal contents and to recognize the main regions for further evaluation.…”

Section: Introductionmentioning

confidence: 99%

Spatial distribution and potential ecological risk assessment of heavy metals in agricultural soils of Northeastern Iran

Keshavarzi

Kumar

2019

Geology, Ecology, and Landscapes

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Illustrating the spatial distribution and potential sources of soil properties, and heavy metals, viz., Fe, Zn, Mn, and Cu, are the vital prerequisites for decreasing their pollution. The 68 composite agricultural soil samples in triplicates were collected by employing grid method to evaluate the concentration of pH, sand, silt, clay, organic carbon, P, K, and heavy metals. Multivariate techniques (Pearson's correlation, heatmap, principal component analysis, and nonmetric multidimensional scaling), geostatistcal techniques, and contamination indices were employed. The contents of Fe, Zn, Mn, and Cu were lower than the limits for Iran EPA guidelines and Earth's crust. The results of contamination factor and potential ecological risk index (RI) showed that agricultural soils have less contamination and low ecological risks. The enrichment factor (EF), geoaccumulation index (Igeo) and modified ecological risk index (MRI) indicated that 99%, 86.7% and 52.4% agricultural soil samples showed very high enrichment and ecological risks of heavy metals. Both anthropogenic activities and natural factors were responsible for heavy metal contents. The results of geostatistcal analysis revealed that Zn is accumulated more in Central regions, whereas Cu and Mn accumulated more in South and Northeastern regions of the studied area for EF, Igeo, and modified potential ecological RI. ARTICLE HISTORY

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“…Many layers have been used for stratification, including farmer observations (Taylor et al, 2007), electrical conductivity measurements (Peralta & Costa, 2013), previous years' yields (Flowers, Weisz, & White, 2005), and remotely sensed spectral measurements (Song et al, 2009). A full review of MZ literature is out of the scope of this paper, yet we note that the results for performance of MZs are mixed depending on the data layers used, agronomic history, and biophysical setting (Flowers et al, 2005;Mzuku et al, 2005;Sawchik & Mallarino, 2007;Schepers et al, 2004;Vasu et al, 2017).…”

Section: Design-based Approachmentioning

confidence: 99%

Guiding soil sampling strategies using classical and spatial statistics: A review

Lawrence¹,

Roper

Morris

et al. 2020

Agronomy Journal

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Soil analysis is a key practice to increase the efficiency of nutrient management in agriculture. Since the early 20th century, increasingly sophisticated methods have been developed to describe and manipulate the inherent spatial variability in soil chemical properties within the realms of classical and spatial statistics. In this paper, we reviewed design‐based (classical) and model‐based (geostatistical) sampling to suggest field‐scale sampling strategies consistent with common agronomic management goals in annual crop production systems. To assess the relevance of common sampling methods in relation to practice, current extension recommendations across the United States were compared with results from peer‐reviewed literature. Despite decades of research, specific recommendations for sample sizes, sampling depths, numbers of soil cores, and layouts were highly variable for classical and geostatistical approaches. Mobile nutrients, such as NO3, are frequently lacking in spatial structure and rarely are recommended for site‐specific management. Nonmobile nutrients, such as P, are more spatially dependent and exhibit nested spatial structures that are inconsistent across fields. For these reasons, we recommend design‐based sampling in most situations for simplicity, cost, and objectivity. The common design‐based sampling protocol prescribes collection of individual cores in a zig‐zag pattern that are combined to produce a composite sample. This protocol should be amended because it is not sufficiently randomized and is inadequate for log‐normally distributed variables. To facilitate site‐specific management, we recommend structured approaches for delineating management zones or strata and for researchers to systematically enumerate confounding variables while explicitly defining the scope of inference for future soil sampling studies.

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Assessment of spatial variability of soil properties using geospatial techniques for farm level nutrient management

Cited by 140 publications

References 36 publications

Assessment of Spatial Variability of Soil Fertility Parameters Using Geospatial Techniques in Temperate Himalayas

Assessment of Spatial Variability of Soil Fertility Parameters Using Geospatial Techniques in Temperate Himalayas

Spatial distribution and potential ecological risk assessment of heavy metals in agricultural soils of Northeastern Iran

Guiding soil sampling strategies using classical and spatial statistics: A review

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