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A Simplified Soil Nutrient Information System:Study from the North East Region of India


Affiliations
1 ICAR-Central Marine Fisheries Research Institute, Kochi 682 018, India
2 ICAR-National Bureau of Soil Survey and Land Use Planning, Regional Centre, Kolkata 700 091, India
3 ICAR-National Bureau of Soil Survey and Land Use Planning, Regional Centre, Jorhat 785 004, India
4 ICAR-National Bureau of Soil Survey and Land Use Planning, Amravati Road, Nagpur 440 033, India
 

Soil fertility has direct implications on the agricultural production scenarios of a region. Surface soil samples at 1 km grid were collected to assess the fertility status of Lakhimpur district (Assam) in North East India. Fertility parameters like soil organic carbon, available nitrogen, phosphorus, potassium, iron, manganese, zinc and copper were determined using standard analytical procedure. Spatial distribution maps of the soil parameters were generated using regularized spline method in ArcGIS 10.0. The average soil organic carbon content was 1.05% and the maximum area was under high availability status (78%). In the case of nitrogen, 57% of the area was under low availability status. In the case of available potassium and phosphorus, the areas under low availability status were 48% and 49% respectively. But for micronutrients, in general, the availability status was high except for zinc, which indicated that 40% of the area was under low availability. A methodology was developed to integrate the individual nutrient layers using a set of decision rules to identify the multinutrient deficient zones. The integrated map showed that 24% of the area had multiple nutrient deficiencies and fell under high priority zone that warrant immediate nutrient management interventions to mitigate the situation.

Keywords

Decision Rules, Multinutrient Deficiency, Soil Fertility, Spatial Variability, Spline Interpolation, Soil Information System.
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  • Ray, P. K., Jana, A. K., Maitra, D. N., Saha, M. N., Chaudhury, J., Saha, S. and Saha, A. R., Fertilizer prescriptions on soil test basis for jute, rice and wheat in Typic Ustochrept. J. Indian Soc. Soil Sci., 2000, 48, 79–84.

  • Tandon, H. L. S., Fertilizers in Indian Agriculture – from 20th to 21st Century, FDCO, New Delhi, India, 2004, p. 240.

  • Tandon, H. L. S., Soil nutrient balance sheets in India: importance, status, issues, and concerns, 2007, Better crops – India, accessed 26 June 2016; http://www.ipni.net/publication/bca.nsf/0/FF2C8B8426BB6323852579A4007AD4DC/$FILE/bc-india_Nov07_p15.pdf.

  • Kumar, M. V., Saliha, B. B., Kannan, P. and Mahendran, P. P., Delineation and geographic information system (GIS) mapping of soil nutrient status of sugarcane growing tracts of Theni district, Tamil Nadu. Afr. J. Agric. Res., 2015, 10(33), 3281–3291; doi:10.5897/AJAR2013.7251.

  • Shukla, G., Mishra, G. C. and Singh, S. K., Kriging approach for estimating deficient micronutrients in the soil: a case study. Int. J. Agric. Environ. Biotechnol., 2015, 8(2), 309–314; doi:10.5958/2230-732X.2015.00038.8.

  • Sathisha, G. C. and Ganeshamurthy, A. N., Optimizing soil fertility and foster productivity of mango: an appraisal on soil fertility status and development of nutrient delineation maps of India. Curr. Adv. Agric. Sci., 2015, 7(1), 33–36; doi:10.5958/2394-4471.2015.00006.4.

  • Pandian, R. D. and Haroon, A. R. M., Soil nutrient status mapping through GIS techniques of direct seeding rice cultivating area of Ramnad district. Trends Biosci., 2014, 7(22), 3722–3726.

  • Rajeswaran, R. and Mani, S., Status of available zinc as related to soil characteristics of Madurai, Tamil Nadu Using GIS Techniques. Trends Biosci., 2014, 7(14), 1630–1634.

  • Bhuyan, N., Barua, N. G., Borah, D. K., Bhattacharyya, D. and Basumatari, A., Georeferenced micronutrient status in soils of Lakhimpur district of Assam. J. Indian Soc. Soil Sci., 2014, 62(2), 102–107.

  • Sarmah, M. C., Neog, K., Das, A. and Phukan, J. C. D., Impact of soil fertility and leaf nutrients status on cocoon production of Muga silkworm, Antheraea assamensis (Helfer) in potential muga growing areas of Assam, India. Int. J. Curr. Microbiol. Appl. Sci., 2013, 2(9), 25–38.

  • Baruah, U., Chattopadhyay, T., Dutta, D., Reza, S. K., Bandyopadhyay, S. and Sarkar Dipak, Assessment and mapping of some important soil parameters including macro and micronutrients for Lakhimpur district of Assam state towards optimum land use planning. NBSS Publ. 1041 (J), NBSS&LUP, Nagpur, 2012, p. 17.

  • Soil Survey Staff, Keys to Soil Taxonomy, United States Department of Agriculture (USDA) – Natural Resources Conservation Service, Washington, DC, 2014, 12th edn.

  • Walkley, A. and Black, I. A., An examination of the Degtjareff method for determining soil organic matter and a proposed modification of the chromic acid titration method. Soil Sci., 1934, 37, 29–38.

  • Rich, C. I., Elemental analysis by flame photometry. In Methods of Soil Analysis, Part 2: Chemical and Microbiological Properties (ed. Black, C. A.), American Society of Agronomy, Madison, Wisconson, 1965, pp. 849–864.

  • Subbiah, B. W. and Asija, G. L., A rapid procedure for estimation of available nitrogen in soils. Curr. Sci., 1956, 25, 259–260.

  • Bray, H. R. and Kurtz, L. T., Determination of total organic and available forms of phosphorus in soil. Soil Sci., 1945, 59, 39–45.

  • Lindsay, W. L. and Norvell, W. A., Development of DTPA micro-nutrients soil test for Zn, Fe, Mn and Cu. Soil. Sci. Soc. Am. Proc., 1978, 42, 421–428.

  • Li, J. and Heap, A. D., A Review of Spatial Interpolation Methods for Environmental Scientists, Geoscience Australia, Record 2008/23, 2008, p. 137.

  • Dressler, M., Art of Surface Interpolation, KUNŠTÁT, 2009, pp. 80; accessed on 26 June 2016; http://m.dressler.sweb.cz/AOSIM.pdf.

  • Gao, Y., Gao, J. and Chen, J., Spatial variation of surface soil available phosphorous and its relation with environmental factors in the Chaohu Lake watershed. Int. J. Environ. Res. Public Health 2011, 8, 3299–3317; doi:10.3390/ijerph8083299.

  • Zare-Mehrjardi, M., Taghizadeh-Mehrjardi, R. and Akbarzadeh, A., Evaluation of geostatistical techniques for mapping spatial distribution of soil pH, salinity and plant cover affected by environmental factors in southern Iran. Not. Sci. Biol., 2010, 2, 92–103.

  • Hosseini, E., Gallichand, D. and Marcotte, D., Theoretical and experimental performance of spatial interpolation methods for soil salinity analysis. Trans. Am. Soc. Agric. Eng., 1994, 37, 1799–1807.

  • Meul, M. and van Meirvenne, M., Kriging soil texture under different types of nonstationarity. Geoderma, 2003, 112, 217–233.

  • Robinson, T. P. and Metternicht, G., Testing the performance of spatial interpolation techniques for mapping soil properties. Comput. Electron. Agric., 2006, 50, 97–108.

  • Collins, F. C. and Bolstad, P. V., A comparison of spatial interpolation techniques in temperature estimation. In Proceedings, Third International Conference/Workshop on Integrating GIS and Environmental Modeling, SantaFe, NM. Santa Barbara, CA: National Center for Geographic Information and Analysis, Santa Barbara, 1996.

  • Hutchinson, M. F., Interpolating mean rainfall using thin plate smoothing splines. Int. J. Geogr. Inf. Syst., 1995, 9(4), 385–403.

  • Jernigan, R. W., A Primer on Kriging. US Environmental Protection Agency, Washington DC, 1986, p. 89.

  • Takkar, P. N., Soil fertility, fertilizer and integrated nutrient use. In Hand Book of Agriculture (eds Rai et al.), Indian Council of Agriculture, New Delhi, 2009, 6th revised edn, p. 516.

  • Bandyopadhyay, S., Dutta, D., Reza, S. K., Baruah, U. and Sarkar, D., Land use planning of Diring-Thanglong micro-watershed of Karbi-Anglong and Golaghat Districts of Assam under Hill and Mountain Ecosystem for Integrated Development, Report No. 1048, NBSS & LUP (ICAR), Nagpur, 2013, p. 57.

  • Havlin, J. L., Beaton, J. D., Tisdale, S. L. and Nelson, W. L., Soil Fertility and Fertilizers, Prentice Hall, Upper Saddle River, NJ, 1999, 6th edn, p. 499.

  • Smaling, E. M. A. and Braun, A. R., Soil fertility research in sub-Saharan Africa: new dimensions, new challenges. Comm. Soil Sci. Plant Anal., 1996, 27(3–4), 365–386.

  • Sood, A., Sharma, P. K. Tur, N. S. and Nayyar, V. K., Micronutrient status and their spatial variability in soils of Muktsar district of Punjab – a GIS approach. J. Indian Soc. Soil Sci., 2009, 57(3), 300–306.


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  • A Simplified Soil Nutrient Information System:Study from the North East Region of India

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Authors

Shelton Padua
ICAR-Central Marine Fisheries Research Institute, Kochi 682 018, India
T. Chattopadhyay
ICAR-National Bureau of Soil Survey and Land Use Planning, Regional Centre, Kolkata 700 091, India
S. Bandyopadhyay
ICAR-National Bureau of Soil Survey and Land Use Planning, Regional Centre, Kolkata 700 091, India
S. Ramchandran
ICAR-National Bureau of Soil Survey and Land Use Planning, Regional Centre, Jorhat 785 004, India
R. K. Jena
ICAR-National Bureau of Soil Survey and Land Use Planning, Regional Centre, Jorhat 785 004, India
P. Ray
ICAR-National Bureau of Soil Survey and Land Use Planning, Regional Centre, Jorhat 785 004, India
P. Deb Roy
ICAR-National Bureau of Soil Survey and Land Use Planning, Regional Centre, Jorhat 785 004, India
U. Baruah
ICAR-National Bureau of Soil Survey and Land Use Planning, Regional Centre, Jorhat 785 004, India
K. D. Sah
ICAR-National Bureau of Soil Survey and Land Use Planning, Regional Centre, Jorhat 785 004, India
S. K. Singh
ICAR-National Bureau of Soil Survey and Land Use Planning, Amravati Road, Nagpur 440 033, India
S. K. Ray
ICAR-National Bureau of Soil Survey and Land Use Planning, Regional Centre, Jorhat 785 004, India

Abstract


Soil fertility has direct implications on the agricultural production scenarios of a region. Surface soil samples at 1 km grid were collected to assess the fertility status of Lakhimpur district (Assam) in North East India. Fertility parameters like soil organic carbon, available nitrogen, phosphorus, potassium, iron, manganese, zinc and copper were determined using standard analytical procedure. Spatial distribution maps of the soil parameters were generated using regularized spline method in ArcGIS 10.0. The average soil organic carbon content was 1.05% and the maximum area was under high availability status (78%). In the case of nitrogen, 57% of the area was under low availability status. In the case of available potassium and phosphorus, the areas under low availability status were 48% and 49% respectively. But for micronutrients, in general, the availability status was high except for zinc, which indicated that 40% of the area was under low availability. A methodology was developed to integrate the individual nutrient layers using a set of decision rules to identify the multinutrient deficient zones. The integrated map showed that 24% of the area had multiple nutrient deficiencies and fell under high priority zone that warrant immediate nutrient management interventions to mitigate the situation.

Keywords


Decision Rules, Multinutrient Deficiency, Soil Fertility, Spatial Variability, Spline Interpolation, Soil Information System.

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DOI: https://doi.org/10.18520/cs%2Fv114%2Fi06%2F1241-1249