Assessment and mapping of spatial variation of soil physical health in a farm

Amirinejad, Ali; Kamble, Kalpana; Aggarwal, Pramila; Chakraborty, Debashis; Pradhan, Sanatan; Mittal, Raj

doi:10.1016/j.geoderma.2010.09.021

Cited by 52 publications

(25 citation statements)

References 31 publications

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“…Moderate spatial dependence was observed for available soil P and K, whereas low spatial dependence was observed in case of soil pH. Amirinejad et al (2011) reported the similar results.…”

Section: Comparing Models Using Cross-validationsupporting

confidence: 66%

Characterizing spatial variability of soil properties in salt affected coastal India using geostatistics and kriging

et al. 2015

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Soil salinization is a major problem affecting 955 Mha globally and 7 Mha in India. Soil properties vary spatially and knowing the extent of spatial variability of soil physicochemical characteristics is highly essential for management of these soils and crop cultivation. This study was conducted in salt-affected coastal parts of eastern India, with the following objectives: (i) to explore the spatial variability of soil properties (soil electrical conductivity (ECe), soil pH, soil organic carbon (SOC), available soil nitrogen, available soil phosphorus, and available soil potassium) and fitting the semivariogram models; (ii) to estimate the values of soil properties at unsampled locations using geostatistical tools; and (iii) to prepare the spatial maps of soil properties using parameters of best fit semivariogram model and interpolation by ordinary kriging technique. A total of 132 soil samples were collected. Gaussian, exponential, circular, spherical, K-Bessel, and spherical semivariogram models were found to be the best fit for assessing the spatial variability in ECe, soil pH, SOC, available soil nitrogen, available soil phosphorus, and available soil potassium, respectively. The best fit model parameters were used to create the spatial maps for these soil properties by ordinary kriging. It was concluded that geostatistical and kriging tools can be used to estimate the value of soil properties at unsampled locations and ultimately to develop spatial maps for site-specific nutrient management.

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“…Moderate spatial dependence was observed for available soil P and K, whereas low spatial dependence was observed in case of soil pH. Amirinejad et al (2011) reported the similar results.…”

Section: Comparing Models Using Cross-validationsupporting

confidence: 66%

Characterizing spatial variability of soil properties in salt affected coastal India using geostatistics and kriging

et al. 2015

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“…6). The results thus supported earlier findings (Neill, 1979;Pierce et al, 1983;Amirinejad et al, 2011) that good soil physical health is essential for optimum sustained crop production. From the curve, it is clear that Y/Y max is proportional to PI.…”

Section: Resultssupporting

confidence: 90%

Assessment of soil physical health and productivity of Kharkhoda and Gohana blocks of Sonipat district (Haryana), India

Kumar

Aggarwal

Singh

et al. 2014

JANS

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Abstract:In order to assess soil health of Kharkhoda and Gohana blocks of Sonipat district (a part of western Yamuna canal irrigated region), important parameters namely pH, electrical conductivity (EC), texture, bulk density (BD), saturated hydraulic conductivity (HC), soil organic carbon (OC), available water retension capacity (AWRC) and non capillary pores (NCP) were measured by collecting undisturbed soil samples in nearly 66 villages. Soil physical rating index (PI) method was used to compute PI which was an indicator of soil physical health of that region. Results revealed that in Gohana and Kharkhoda blocks, nearly 90% area had pH <8.0 and EC>4 dS m -1 , which indicated that soils were saline. Prediction maps of soil BD showed that 75% of the total area in 15-30 cm soil layer had BD above >1.6 mg m -3 , which indicated the presence of hard pan in subsurface. HC data of subsurface layer also showed that 60% of the area had values<0.5 cm hr -1 which reconfirmed the presence of hard pan. For both surface as well as subsurface soil layers, mostly AWC was >10% which indicated adequate water retention capacity of these soils. However 85% of subsurface had poor soil aeration capacity as indicated NCP range < 10 %. Prediction map of PI for subsurface layer showed that majority of area had PI<0.4 which indicated that expected yield of the crop cannot be more than 70% of the potential yield even under normal or higher levels of fertilizer and water inputs.

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“…Bulk density was not affected by clearing and burning at the beginning of cropping but somewhat higher at the end of two year cropping period (Yemefack, 2005). This latter acted indirectly on crop growth by influencing several other soil properties (permeability, water retention,…) that can individually or collectively impact directly on crop productivity (Douglas et al, 2003;Ali Ashraf Amirinejad et al, 2011). Owing to the poor status of ferrallitic soils, natural regeneration provided by MO from different land covers seems insufficient (Bilong et al, 2017); chemical contributions brought by organic manure (guano, and sheep) according to Biaou et al (2017) improved significantly soil fertility.…”

Section: Introductionmentioning

confidence: 99%

Contributions of soil chemical and physical properties in the dynamics of soil quality in the southern Cameroon plateau shifting agricultural landscape

Nyeck¹,

Mbogba²,

Njilah³

et al. 2018

Int. J. Bio. Chem. Sci

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Soil quality results from combination of chemical, physical and biological characteristics; according to ecosystems, some of these aspects become dominant. This Study was carried out on soils under 08 land cover types in the southern Cameroon ((bare soil with burned vegetation (FR1), bare soil with unburned vegetation (FR), Chromolaena odorata fallow (JC), bush ligneous fallow (JR), secondary forest (FS), primary forest (FC), Gilbertiodendron dewevei forest (FG), and raffia forest (RA) to assess the contributions of soil chemical and physical indicators into global soil quality index (IQSg). Topsoil samples were analyzed for physico-chemical characterizations and IQSg computed. Multiples statistical tests were used to compare the contribution of soil quality indicators and to select those that contributed the most in IQSg. Thus, under FR1, FC, FG and RA, IQSg was relatively high and the main contributors were chemical, estimated at more than 70%. Under FR, IQSg remains high and chemical contribution predominant (65%) on physical (35%). On the contrary, under fallows, IQSg is relatively low; these two types of contributions are nearly equivalents. Thus, the main indicators helping to assess IQSg in lower cost, contributing significantly to global soil quality and representatives of indicators are: organic matter (MO), pHw, and C/N ratio. The difference in contribution is due to the types of cultural practices, quality and quantity of MO brought by land cover types and topographical position.

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Assessment and mapping of spatial variation of soil physical health in a farm

Cited by 52 publications

References 31 publications

Characterizing spatial variability of soil properties in salt affected coastal India using geostatistics and kriging

Characterizing spatial variability of soil properties in salt affected coastal India using geostatistics and kriging

Assessment of soil physical health and productivity of Kharkhoda and Gohana blocks of Sonipat district (Haryana), India

Contributions of soil chemical and physical properties in the dynamics of soil quality in the southern Cameroon plateau shifting agricultural landscape

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