Geospatial Approach to Soil Fertility Mapping in Dailekh District, Nepal: A GIS Perspective

Published on 15 March 2025 | AgroEnvironmental Sustainability
Vol. 3 No. 1 (2025): March Issue | DOI: 10.59983/s2025030105
Open Access
Download PDF
Check for updates via Crossmark
AgroEnvironmental Sustainability
Gaurab Dhakal , Sanket Kattel

Abstract

Spatial mapping of the soil gives the distribution patterns of the nutrients, which is crucial for integrated nutrient management, site-specific crop selection, water resource management, and adaptation to climate change for optimizing productivity. This research aims to identify the spatial variability of soil chemical properties in the Dailekh district of Karnali Province, Nepal, by preparing a map in a raster setting. A total of 204 samples were collected using stratified random sampling techniques using Google Earth Pro and were analyzed using IBM SPSS 27.0 and Arc Map 10.2 software. The classical statistical method was used for the descriptive analysis of sampled data. The Quantile Quantile (QQ) plot was made to visualize the distribution pattern, and non-normal data were log-transformed to match the straight line. Before making a map, sampled datasets were examined using the trend analysis feature of Arc Map using second-order polynomials in 3D scattered plots. The widely used interpolation technique, Ordinary kriging of two Exponential and Circular models, was applied to data and cross-validated with minimum estimated errors. Fertility mapping of parameters results in more than 81%, 56 %, and 57% of the areas covered by nitrogen, phosphorus, and potassium, with medium in status. Similarly, organic matter has low content shades in 65% of areas and moderately acidic pH in 49% of areas. This research supports decision-making for nutrient distribution across agricultural fields and sustainable land management for precision farming.

Keywords

ordinary kriging semivariogram spatial dependency trend analysis

References

  1. Augustin, N. H., Sauleau, E., & Wood, S. N. (2012). On quantile quantile plots for generalized linear models. Computational Statistics & Data Analysis, 56(8), 2404–2409. https://doi.org/10.1016/j.csda.2012.01.026 [Google Scholar]
  2. Bhunia, G. S., Shit, P. K., & Maiti, R. (2016). Comparison of GIS-based interpolation methods for spatial distribution of soil organic carbon (SOC). Journal of the Saudi Society of Agricultural Sciences, 17(2), 114–126. https://doi.org/10.1016/j.jssas.2016.02.001 [Google Scholar]
  3. Bremner, J., Mulvaney, C. (1982). Nitrogen—Total. Agronomy Monograph/Agronomy, 1, 595–624. https://doi.org/10.2134/agronmonogr9.2.2ed.c31 [Google Scholar]
  4. Cambardella, C. A., Moorman, T. B., Novak, J. M., Parkin, T. B., Karlen, D. L., Turco, R. F., & Konopka, A. E. (1994). Field‐Scale variability of soil properties in Central Iowa soils. Soil Science Society of America Journal, 58(5), 1501–1511. https://doi.org/10.2136/sssaj1994.03615995005800050033x [Google Scholar]
  5. Chakraborty, D., Sherpa, S.R., Koirala, P., Baral, B.R., Karki, S., Sapkota, S., Gathala, M., & Krupnik, T.J. (2024). Mind the (nutrient supply) gap: an assessment of demand and supply limitations for Nitrogen, Phosphorus and Potassium for major crops in Nepal. The Cereal Systems Initiative for South Asia (CSISA), Nepal Seed and Fertilizer (NSAF) Project, & Transforming Agrifood Systems in South Asia (TAFSSA). Kathmandu, Nepal. https://hdl.handle.net/10883/35104 [Google Scholar]
  6. Chalise, D., Kumar, L., & Kristiansen, P. (2019). Land degradation by soil erosion in Nepal: a review. Soil Systems, 3(1), 12. https://doi.org/10.3390/soilsystems3010012 [Google Scholar]
  7. Eldeiry, A. A., & Garcia, L. A. (2012). Evaluating the performance of ordinary kriging in mapping soil salinity. Journal of Irrigation and Drainage Engineering, 138(12), 1046–1059. https://doi.org/10.1061/(asce)ir.1943-4774.0000517 [Google Scholar]
  8. Ghimire, D., Baral, B. R., & Panday, D. (2018). Assessment of Spatial Variability of Soil Chemical Properties under Small-holder Farmers Field: a Case Study from Kavrepalanchowk District of Nepal. Current Agriculture Research Journal, 6(3), 337–343. https://doi.org/10.12944/carj.6.3.12 [Google Scholar]
  9. Ghimire, P., Shrestha, S., Acharya, A., Wagle, A., & Acharya, T. D. (2024). Soil fertility mapping of a cultivated area in Resunga Municipality, Gulmi, Nepal. PLoS ONE, 19(1), e0292181. https://doi.org/10.1371/journal.pone.0292181 [Google Scholar]
  10. Goovaerts, P. (1997). Geostatistics for natural resources evaluation. Oxford University Press. [Google Scholar]
  11. Jackson, M.L. (1967). Soil chemical analysis. Prentices Hall Inc. Engle Wool, CL, USA. pp. 39. [Google Scholar]
  12. Jena, R. K., Moharana, P. C., Pradhan, U. K., Sharma, G. K., Ray, P., Roy, P. D., & Ghosh, D. (2024b). Soil fertility mapping and applications for site-specific nutrient management: a case study. In Elsevier eBooks (pp. 65–80). https://doi.org/10.1016/b978-0-443-18773-5.00025-9 [Google Scholar]
  13. Karki, R., Talchabhadel, R., Aalto, J., & Baidya, S. K. (2015). New climatic classification of Nepal. Theoretical and Applied Climatology, 125(3–4), 799–808. https://doi.org/10.1007/s00704-015-1549-0 [Google Scholar]
  14. Kaur, H., Kaur, A., Singh, B., & Bhatt, R. (2020). Application of geospatial technology in assessment of spatial variability in soil properties: a review. Current Journal of Applied Science and Technology, 39, 57–71. https://doi.org/10.9734/cjast/2020/v39i3931104 [Google Scholar]
  15. Malla, R., Shrestha, S., & Khadka, D. (2020). Soil Fertility Mapping and Assessment of the Spatial Distribution of Sarlahi District, Nepal. American Journal of Agricultural Science, 7(1), 8-16. [Google Scholar]
  16. Mokarram, M., & Sathyamoorthy, D. (2016). Investigation of the relationship between drinking water quality based on the content of inorganic components and landform classes using fuzzy AHP (case study: south of Firozabad, west of Fars province, Iran). Drinking Water Engineering and Science, 9(2), 57–67. https://doi.org/10.5194/dwes-9-57-2016 [Google Scholar]
  17. Oli, B., Lamichhane, S., & Oli, K. (2020). Use of GIS in soil fertility mapping of Rapti Municipality, Chitwan, Nepal. Journal of Agriculture and Applied Biology, 1(2), 64–73. https://doi.org/10.11594/jaab.01.02.04 [Google Scholar]
  18. Olsen, S.R., Cole, C.V. and Watanabe, F.S. (1954) Estimation of Available Phosphorus in Soils by Extraction with Sodium Bicarbonate. USDA Circular No. 939, US Government Printing Office, Washington DC. Available online: https://ia803207.us.archive.org/21/items/estimationofavai939olse/estimationofavai939olse.pdf (accessed on 10 May 2024). [Google Scholar]
  19. Panday, D., Ojha, R. B., Chalise, D., Das, S., & Twanabasu, B. (2019). Spatial variability of soil properties under different land use in the Dang district of Nepal. Cogent Food & Agriculture, 5(1), 1600460. https://doi.org/10.1080/23311932.2019.1600460 [Google Scholar]
  20. Ramzan, S., Wani, M. A., & Bhat, M. A. (2017). Assessment of spatial variability of soil fertility parameters using geospatial techniques in temperate Himalayas. International Journal of Geosciences, 8(10), 1251–1263. https://doi.org/10.4236/ijg.2017.810072 [Google Scholar]
  21. Tesfahunegn, G. B., Tamene, L., & Vlek, P. L. (2011). Catchment-scale spatial variability of soil properties and implications on site-specific soil management in northern Ethiopia. Soil and Tillage Research, 117, 124–139. https://doi.org/10.1016/j.still.2011.09.005 [Google Scholar]
  22. Trangmar, B., Yost, R., & Uehara, G. (1986). Application of geostatistics to spatial studies of soil properties. In Advances in agronomy (pp. 45–94). https://doi.org/10.1016/s0065-2113(08)60673-2 [Google Scholar]
  23. Walkley, A., & Black, I. A. (1934). An Examination of the Degtjareff method for determining soil organic matter, and a proposed modification of the chromic acid titration method. Soil Science, 37(1), 29–38. https://doi.org/10.1097/00010694-193401000-00003 [Google Scholar]
  24. Yadav, S. K., Kafle, K., Poudel, A., Gelal, R., & Adhikari, B. (2022). Use of GIS for spatial mapping of soil fertility in Dhanusha, Nepal. International Journal of Applied Biology, 6(2), 1–13. https://doi.org/10.20956/ijab.v6i2.18449 [Google Scholar]
  25. Zhang, H., Lu, L., Liu, Y., & Liu, W. (2015). Spatial sampling strategies for the effect of interpolation accuracy. ISPRS International Journal of Geo-Information, 4(4), 2742–2768. https://doi.org/10.3390/ijgi4042742 [Google Scholar]
Article Statistics

Usage statistics will be available soon.

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Dhakal, G., & Kattel, S. (2025). Geospatial Approach to Soil Fertility Mapping in Dailekh District, Nepal: A GIS Perspective. AgroEnvironmental Sustainability, 3(1), 40-48. https://doi.org/10.59983/s2025030105

Search author on: PubMed | Google Scholar

Similar Articles

1-10 of 36

You may also start an advanced similarity search for this article.