Hostname: page-component-586b7cd67f-gb8f7 Total loading time: 0 Render date: 2024-11-25T02:58:30.821Z Has data issue: false hasContentIssue false
  • English
  • Français

Use of cluster analysis for classification of Benchmark soil samples from India in different micronutrient availability groups

Published online by Cambridge University Press:  27 March 2009

J. C. Katyal ,
S. P. Doshi  and
P. K. Malhotra
J. C. Katyal
Affiliation:
All India Coordinated Scheme of Micronutrients in Soils and Plants, Punjab Agricultural University, Ludhiana, India
S. P. Doshi
Affiliation:
Indian Agricultural Statistics Research Institute, Library Avenue, New Delhi-110 012, India
P. K. Malhotra
Affiliation:
Indian Agricultural Statistics Research Institute, Library Avenue, New Delhi-110 012, India
Get access Rights & Permissions [Opens in a new window]

Summary

In view of the increase in micronutrient deficiencies in crop plants, ans attempt is made to group 57 Benchmark Indian soils into different micronutrient availability classes (clusters) vis-à-vis some soil characteristics. Since most of the soil characters are expressed in proportions or percentages (a usual practice in soil studies), the data matrix was transformed into log10 values to bring it nearer to normality. The transformed data matrix was used for cluster analysis and subsequently also for discriminant analysis. By following the method of Euclidean cluster analysis, the 57 Benchmark soils could be subdivided into three clusters. The distinctness of clusters was proven by distance-matrix as well as discriminant analysis. All the soils of cluster I and II originated from arid and semi-arid climates and these were alkaline in reaction, low in organic carbon (OC) and high in total lime. On the contrary, cluster III comprised all but a few soils from humid-subhumid regions. These soils were acidic in reaction and relatively high in OC and total lime. The soils of cluster I and II were poorer in diethylene triamine penta-acetic acid (DTPA) extractable micronutrient cations than those of cluster III. This study thus reveals that it is possible to classify diverse soils by statistical means into distinct clusters based upon micronutrient availability and associated soil properties. Furthermore, it suggests that soils of arid and semi-arid climates as a group, because of their low micronutrient availability, are expected to be more prone to deficiencies than those of humid and subhumid zones.

Type
Research Article
Information
The Journal of Agricultural Science , Volume 104 , Issue 2 , April 1985 , pp. 421 - 424
Copyright
Copyright © Cambridge University Press 1985

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Anderberg, M. R. (1973). Cluster Analysis for Applications. Probability and Mathematical Statistics: a series of monographs and textbooks. New York: Academic Press.Google Scholar
Beale, E. M. I. (1969). Euclidean cluster analysis. A paper contributed to 37th session of the International Statistical Institute.Google Scholar
Doshi, S. P., Chaudhry, M. I. & Jain, D. (1981). Users Manual for Fortran Programs in Classifactory Analysis. New Delhi: Indian Agricultural Statistics Research Institute.Google Scholar
Golder, P. A. & Yeomans, K. A. (1973). The use of cluster analysis for stratification. Applied Statistics 22, 213–219.CrossRefGoogle Scholar
Gower, J. C. (1967). A comparison of some methods of cluster analysis. Biometrics 23, 623–637.CrossRefGoogle ScholarPubMed
Katyal, J. C. & Agarwala, S. C. (1982). Micronutrient research in India. Fertilizer News 27 (2), 66–86.Google Scholar
Kawaguchi, K. & Kyuma, K. (1975). Paddy soils in tropical Asia. Part 4, Soil material classification. South East Asian Studies 23, 215–227.Google Scholar
Kendall, M. G. (1980). Multivariate Analysis, 2nd edn.London: Charles Griffin.Google Scholar
Lindsay, W. L. & Norvell, W. A. (1978). Development of a DTPA soil test for zinc, iron, manganese and copper. Soil Science Society of America Journal 42, 421–428.CrossRefGoogle Scholar
Murthy, R. S., Hirekerur, L. R., Deshpande, S. B. & Venkatarao, B. V. (ed.) (1982). Benchmark Soils of India – Morphology Characteristics and Classification for Resource Management. Nagpur, Maharashtra, India: National Bureau of Soil Survey and Land Use Planning (ICAR).Google Scholar
Rao, C. R. (1952). Advanced Statistical Methods in Biometric Research. New York: John Wiley.Google Scholar
Sokal, R. R. & Sneath, P. H. A. (1963). Principles of Numerical Taxonomy. San Francisco: Freeman.Google Scholar