Research Article

Geostatistical investigation and GIS-based spatial distribution of some micronutrients in Wukari soils, Taraba State, Nigeria

Waizah, Y.1*, Adashu Tanko Gani2
1 Department of Soil Science and Land Resources Management, Faculty of Agriculture and Life Sciences, Federal University Wukari. Taraba State, Nigeria
2 Federal University Wukari, Taraba State, Nigeria
* Corresponding author: waizahyakub@gmail.com
Published: Apr, 2025
DOI: 10.67042/njss.2025.ciwm5d59
Pages: 11-16
Views: 528
Downloads: 665
Cited by: 1

Abstract

Mapping soil fertility is important for site-specific nutrient management, it gives an insight into soil nutrient content, which is crucial for land use planning, fertilizer recommendations, and sustainable management of soil resources. Micronutrient deficiency in the soil is a serious limitation for crop production. Evaluation and mapping the soil micronutrients of a particular area can be a possible solution to mitigating its deficiencies, as they will serve as a guide for nutrient management. A study was conducted in Wukari to quantify and visualize the distribution of pH, manganese, iron, and zinc within the study area. Soil samples were collected from 98 locations. The samples were analyzed for soil reaction (pH), manganese (Mn), iron (Fe), and zinc (Zn). A spatial map for pH, Mn, Fe, and Zn was developed through geostatistical analysis using an ordinary Kriging interpolator. The soil pH is slightly acidic to slightly alkaline (5.3 to 7.6), available manganese (0.01 to 13.97 mg/kg) with mean value of (3.00 mg/kg), available iron (0.43 to 21.20 mg/kg), with a mean value of 11.15 mg/kg, and available zinc content (0.04 to 15.27 mg kg1) has an average of 1.96 mg/kg. Based on the soil test results, the spatial variation of pH, Mn, Fe, and Zn was prepared. The spatial correlations (ranges) of soil properties varied from 8933.50 m for zinc to 16349.81 m for iron. The nugget/sill ratio of the semi-variogram model showed moderate spatial dependence for pH, manganese, and zinc (0.51, 0.59, 0.47), and weak spatial dependence for iron (0.85).

Keywords

Geospatial Semi-variogram Micronutrient Kriging Spatial dependence

References

  1. Arnot-Gyel P., C. H. Qulm S. G.K. Adliua, S. N. A. Codjoe, A. J. Dougj, R. Lamball, D. B. K. Dovia. (2010). Perceived stressors of climate vulnerability across scales in the Savannah zone of Ghana: a participatory approach. Regional Environmental Change, 17 (2016), pp. 213-227.
  2. Brady, N. C., & Weil, R. H. (2008). The nature and properties of soils. 14th edition. Prentice Hall. Upper Saddle River, NJ. 13: 652-710.
  3. Burrough, P.A., Rachael A. McDonnell and Christopher D. Lloyd (1989). Principles of Geographical Information Systems: 2nd Edition (Spatial Information System). Oxford publications.
  4. Burrough, P. A., McDonnell, R., McDonnell, R. A., & Lloyd, C. D. 2015. Principles of geographical information systems. Oxford University Press.
  5. Cambardella, C.A., Moorman, T.B., Novak, J.M., Parkin, T.B., Karlen, D.L., Turco, R.F. and Konopka, A.E., 1994. Field scale variability of soil properties in Central Iowa soils. Journal of Soil Science Society of America 58:1500-1511.
  6. Crete, N. 1992. Smoothing Regional Maps Using Empirical Bayes Predictors. Geographical Analysis 24: 75-86. [https://doi.org/10.1111/j.1538-4632.1992.tb00253.x](https://www.google.com/search?q=https://doi.org/10.1111/j.1538-4632.1992.tb00253.x)
  7. Eu, W., Tunney, H. and Zhang, C. 2010. Spatial variation of soil nutrient in a dairy farm and its implication for site specific for fertilizer application. Soil Tillage Research 106:185-193.
  8. Gour, S. B., Pravir, K. S. and Rabindranath, C. 2018. Assessment of spatial variability of soil properties using geostatistical approach of laterite soil West Bengal, India. Asian of Agrarian Science, 16:438-443.
  9. Jaiswal, P.C. (2000). Soil, Plant and Water Analysis Kalyani Publishers, Ludhiana, New Delhi, Nurdlahyldswah, India 1-396 p.
  10. Johnson, B. G. and Moer, J. 1998. Patterns in species associations in plant communities: the importance of scale. Journal of Vegetation Science, 9(3): 321 332.
  11. Khanka, D., Sunil, L., Rita, A., Sushila, J., Shree P. V., Kunal, S., and Neha, H. G. 2019. Soil fertility assessment and mapping spatial distribution of Agricultural Research Station.
  12. Krige, D.G. 1951. A Statistical Approaches to Some Basic Mine Valuation Problems on the Witwatersrand. Journal of the Chemical, Metallurgical and Mining Society of South Africa. 52: 119-139.
  13. Lindsay W.L. Norvel, W.A 1978 Development of DTPA soil test for zinc, iron, manganese and copper. Soil Science Society America Journal, 42: 421-428.
  14. Mahesh K., Panwar N.R., Pandey C.B. and Janav M.K. 2015. Spatial distribution of available nutrients in the potato growing areas of Banaskantha district of Gujarat. Potato Journal, 42. (2): 130-136.
  15. Omran R. 2012. Improving the Prediction Accuracy of Soil Mapping through Geostatistics International Journal of Geosciences 3(1): 574-590.
  16. Roslee R., Jamaluddin T.A. and Talip M.A. 2012. Integration of GIS using Geostatistical Interpolation Techniques (kriging) (GEOSTAINT-K) in determine model for: landslide susceptibility analysis (LSA) at Kota Kinabalu, Sabah, Malaysia. Journal of Geography Geology 4(1), 18-32.
  17. Unet A., Lopez, T. and Diez, M. 2000. A comparison of soil maps, Kriging and a combined methods for spatial prediction of bulk density and field capacity of ferralsols in the Havana Matanzas plain. Geoderma, 96: 199-213.
  18. Walah Yakub A. M., Sadikq and A. Salem 2022. Fertility capability classification of soils of Wukari, Taraba State, Nigeria. FUW Journal of agriculture and life sciences, Vol No 1 pp 163-178 ISSN: 2545 5141, e-ISSN: 2545 515X.
  19. Walah Yakub and Gana Clement Alex 2024. Effect of cow dung and urea on some soil properties and growth of Amaranthus in Benueyun flood plain in Wukari. International Journal of Sustainable Agricultural Research 11(1) ISSN(e): 2312-6477 ISSN(pl): 2313 0393.
  20. Wang, J. P., Raman, H., Zhang, G. P., Mendham, N., & Zhou, M. X. 2006. Aluminium tolerance in barley (Hordeum vulgare L.): physiological mechanisms, genetics and screening methods. Journal of Zhejiang University Science B, 7(10): 769-787.
  21. Yaser, S., Isa, E. H., Aritvan, K., Mohammad, H. S. and Monen, B. B. 2013. Mapping soil texture using geostatistical methods (A case study of the shaleshord plain central Iran). Arabasdi Journal of Geosciences, 6 (8): 3331-33.

Cited By (1)

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How to Cite

Y., W., & Gani, A. T. (2025). Geostatistical investigation and GIS-based spatial distribution of some micronutrients in Wukari soils, Taraba State, Nigeria. Nigerian Journal of Soil Science, 34(1), 11-16. https://doi.org/10.67042/njss.2025.ciwm5d59

W. Y., and A. T. Gani, "Geostatistical investigation and GIS-based spatial distribution of some micronutrients in Wukari soils, Taraba State, Nigeria," Nigerian Journal of Soil Science, vol. 34, no. 1, pp. 11-16, April 2025. doi: 10.67042/njss.2025.ciwm5d59

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