Assessing farm-scale spatial variability of soil nutrients in central India for site-specific nutrient management
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Title |
Assessing farm-scale spatial variability of soil nutrients in central India for site-specific nutrient management
Not Available |
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Creator |
Sanjib Kumar Behera
Arvind Kumar Shukla Ashok Kumar Patra Chandra Prakash Ajay Tripathi Suresh Kumar Chaudhari Ch. Srinivasa Rao |
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Subject |
Spatial distribution
Vertisol Site-specific nutrient management Geostatistics |
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Description |
Not Available
Proper understanding about spatial variability of phyto-available soil nutrients and their associated properties is important for precise soil nutrient management in order to obtain sustainable crop production. Soil properties namely soil organic carbon (SOC), pH, and electrical conductivity (EC) influence phyto-availability of soil nutrients. We, therefore, carried out the present study to evaluate spatial variability of phyto-available nutrients (available nitrogen (AN), available phosphorus (AP), available potassium (AK), exchangeable Ca (Ex. Ca), exchangeable Mg (Ex. Mg), available sulfur (AS), available zinc (AZn), available copper (ACu), available iron (AFe), available manganese (AMn) and available boron (AB)), and associated soil properties (soil pH, EC and SOC) of the research farm of Indian Council of Agricultural Research-Indian Institute of Soil Science, Bhopal, Madhya Pradesh, India, using ordinary kriging (OK) method for prediction mapping. The experimental semivariograms were obtained and used for generating maps. Soil pH (7.22–8.66), EC (0.10–0.26 dS m−1), and SOC (0.19–0.98%) and concentrations of phyto-available nutrients (AN 76.8–224 kg ha−1, AP 8.20–61.0 kg ha−1, AK 140–693 kg ha−1, Ex. Ca 6779–9803 mg kg−1, Ex. Mg 365–1596 mg kg−1, AS 1.12–37.0 mg kg−1, AZn 0.27–1.64 mg kg−1, ACu 0.69–1.88 mg kg−1, AFe 4.64–19.8 mg kg−1, AMn 3.31–27.2 mg kg−1, AB 0.20–5.00 mg kg−1) in farm soils varied widely with CV values of 2.38–47.3%. Pearson’s correlation coefficient analysis revealed positive and negative significant correlations among the studied soil parameters. The principal component analysis resulted in five principal components (PC), with eigenvalue > 1, which accounted for > 59% of variability. Geostatistical analysis with OK interpolation revealed strong (AN, Ex. Ca, Ex. Mg, AS, AZn and AB), moderate (pH, EC, AP, AK, ACu, AFe and AMn), and weak (SOC) spatial dependence for soil parameters. The spatial distribution maps of soil properties and phyto-available nutrients could be used for location-specific nutrients management strategies and finding the appropriate locations for initiating nutrient experiments in the farm. Not Available |
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Date |
2024-01-04T09:11:14Z
2024-01-04T09:11:14Z 2022-04-25 |
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Type |
Research Paper
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Identifier |
Not Available
Not Available http://krishi.icar.gov.in/jspui/handle/123456789/81101 |
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Language |
English
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Relation |
Not Available;
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Publisher |
SpringerLink
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