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Research Articles

Vol. 12 No. sp3 (2025): Advances in Plant Health Improvement for Sustainable Agriculture

Determination of soil fertility management zones in Veeranam Command for rice production using geostatistical methods, principal component analysis and fuzzy clustering

DOI
https://doi.org/10.14719/pst.8327
Submitted
17 March 2025
Published
14-10-2025

Abstract

Crop productivity can be enhanced while reducing environmental threats from excessive fertilization by fully comprehending the spatial variability of soil properties and delineating management zones (MZs). A field investigation was carried out in Veeranam Command area in Southern India to study the spatial variability of soil properties and the delineation of MZs. Grid wise 240 soil samples collected from the study area were analyzed for pH, available macronutrients and micronutrients. The coefficient of variation of the soils varied from low (6.34 %) to high (87.56 %). Geostatistical analysis showed differed spatial variability patterns for the studied soil properties with spatial dependence ranged from moderate to strong and the ordinary kriging method is used to map the distribution of soil properties. MZs were delineated by performing principal component analysis (PCA) and fuzzy K-means clustering. Four PCs with eigen values more than 1 dominated 52.65 % of the total variance, so they were retained for clustering analysis. Six MZs were delineated based on the two criteria modified partition entropy (MPE) and fuzzy performance index (FPI). The studied soil properties differed significantly among MZs. Thus, the methodology used for MZ delineation could be used effectively for soil site-specific nutrient management for avoiding soil degradation concurrently with maximizing crop production in the study area.

References

  1. 1. Thapa GB, Yila OM. Farmers’ land management practices and status of agricultural land in the Jos Plateau, Nigeria. Land Degrad Dev. 2010;23(3):263-77. https://doi.org/10.1002/ldr.1079
  2. 2. Janssen BH, de Willigen P. Ideal and saturated soil fertility as bench marks in nutrient management:1. Outline of the framework. Agric Ecosyst Environ. 2006;116(1-2):132-46. https://doi.org/10.1016/j.agee.2006.03.014
  3. 3. Brejda JJ, Moorman TB, Smith JL, Karlen DL, Allan DL, Dao TH. Distribution and variability of surface soil properties at a regional scale. Soil Sci Soc Am J. 2000;64(3):974-82. https://doi.org/10.2136/sssaj2000.643974x
  4. 4. Liu Z, Zhou W, Shen J, He P, Lei Q, Liang G. A simple assessment on spatial variability of rice yield and selected soil chemical properties of paddy fields in South China. Geoderma. 2014;235:39-47. https ://doi.org/10.1016/j.geode rma.2014.06.027
  5. 5. Almasri MN, Kaluarachchi JJ. Assessment and management of long-term nitrate pollution of ground water in agriculture dominated watersheds. J Hydrol. 2004;295(1-4):225-45. https://doi.org/10.1016/j.jhydrol.2004.03.013
  6. 6. Ferguson RB, Hergert GW, Schepers JS, Gotway CA, Cahoon, JE, Peterson TA. Site-specific nitrogen management of irrigated maize. Soil Sci Soc Am J. 2002;66(2):544-53. https://doi.org/10.2136/sssaj2002.5440
  7. 7. Shashikumar BN, Kumar S, George KJ, Singh AK. Soil variability mapping and delineation of site-specific management zones using fuzzy clustering analysis in a Mid-Himalayan Watershed, India. Environ Dev Sustain. 2023;25(8):8539-59. https://doi.org/10.1007/s10668-022-02411-6
  8. 8. Daya AA, Bejari H. A comparative study between simple kriging and ordinary kriging for estimating and modeling the Cu concentration in Chehlkureh deposit, SE Iran. Arab J Geosci. 2014;8:6003-20. https://doi.org/10.1007/s12517-014-1618-1
  9. 9. Mueller TG, Hartsock NJ, Stombaugh TS, Shearer SA, Cornelius PL, Barnhisel RI. Soil electrical conductivity map variability in limestone soils overlain by loess. Agron J. 2003;95(3):496-507. https://doi.org/10.2134/agronj2003.4960
  10. 10. Fu W, Tunney H, Zhang C. Spatial variation of soil nutrients in a dairy farm and its implications for site-specific fertilizer application. Soil Tillage Res. 2010;106(2):185-93. https://doi.org/10.1016/j.still.2009.12.001
  11. 11. Shukla AK, Sinha NK, Tiwari PK, Prakash C, Behera SK, Lenka NK, et al. Spatial distribution and management zones for sulphur and micronutrients in Shiwalik Himalayan Region of India. Land Degrad Dev. 2017;28(3):959-69. https://doi.org/10.1002/ldr.2673
  12. 12. Brevik EC, Calzolari C, Miller BA, Pereira P, Kabala C, Baumgarten A, et al. Soil mapping, classification, and pedologic modeling: History and future directions. Geoderma. 2016;264:256-74. https://doi.org/10.1016/j.geoderma.2015.05.017
  13. 13. Li Y, Shi Z, Li F, Li H. Delineation of site-specific management zones using fuzzy clustering analysis in a coastal saline land. Comput Electron Agric. 2007;56(2):174-86. https://doi.org/10.1016/j.compag.2007.01.013
  14. 14. Fridgen JJ, Kitchen NR, Sudduth KA, Drummond ST, Wiebold WJ, Fraisse CW. Management zone analyst (MZA): Software for subfield management zone delineation. Agron J. 2004;96(1):100-8. https://doi.org/10.2134/agronj2004.1000
  15. 15. Brock A, Brouder SM, Blumhoff G, Hofmann BS. Defining yield-based management zones for corn-soybean rotations. Agron J. 2005;97(4):1115-28. https://doi.org/10.2134/agronj2004.0220
  16. 16. Tripathi R, Nayak AK, Shahid M, Lal B, Gautam P, Raja R, et al. Delineation of soil management zones for a rice cultivated area in eastern India using fuzzy clustering. Catena. 2015;133:128-36. https://doi.org/10.1016/j.catena.2015.05.009
  17. 17. Kumar P, Sharma M, Butail NP, Shukla AK, Kumar P. Spatial variability of soil properties and delineation of management zones for Suketi basin, Himachal Himalaya, India. Environ Dev Sustain. 2024;26(6):14113-38. https://doi.org/10.1007/s10668-023-03181-5
  18. 18. Kumar P, Kumar P, Sharma M, Shukla AK, Butail NP. Spatial variability of soil nutrients in apple orchards and agricultural areas in Kinnaur region of cold desert, Trans-Himalaya, India. Environ Monit Assess. 2022;194(4):290. https://doi.org/10.1007/s10661-022-09936-3
  19. 19. Behera SK, Mathur RK, Shukla AK, Suresh K, Prakash C. Spatial variability of soil properties and delineation of soil management zones of oil palm plantations grown in a hot and humid tropical region of southern India. Catena. 2018;165:251-9. https://doi.org/10.1016/j.catena.2018.02.008
  20. 20. Jena RK, Bandyopadhyay S, Pradhan UK, Moharana PC, Kumar N, Sharma GK, et al. Geospatial modelling for delineation of crop management zones using local terrain attributes and soil properties. Remote Sens. 2022;14(9):2101. https://doi.org/10.3390/rs14092101
  21. 21. Jia X, Li X, Li Y. Fractal dimension of soil particle size distribution during the process of vegetation restoration in arid sand dune area. Geogr Res. 2007;26(3):518-25.
  22. 22. Jackson ML. Soil Chemical Analysis. New Delhi (India): Prentice Hall of India; 1973.
  23. 23. Walkley AJ, Black IA. 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(1):29-38. https://doi.org/10.1097/00010694-193401000-00003
  24. 24. Subbiah BV, Asija GL. A rapid procedure for the estimation of available nitrogen in soils. Curr Sci. 1956;25:259-60.
  25. 25. Olsen SR, Cole CV, Watanabe FS. Estimation of available phosphorus in soils by extraction with sodium bicarbonate. In: Circular No. 939, United States Department of Agriculture,Washington DC (USA); 1954. p. 1-19.
  26. 26. Stanford S, English L. Use of flame photometer in rapid soil test of K and Ca. Agron J. 1949;41:446-7. https://doi.org/10.2134/agronj1949.00021962004100090012x
  27. 27. Lindsay WL, Norvell WA. Development of a DTPA soil test for zinc, iron, manganese and copper. Soil Sci Soc Am J. 1978;42:421-48. https://doi.org/10.2136/sssaj1978.03615995004200030009x
  28. 28. Gomez KA, Gomez AA. Statistical procedures for Agricultural Research, 2nd ed.; John Wiley & Sons: New York, (USA); 1984. p. 680.
  29. 29. Krig DG. Lognormal-de Wijsian geostatistics for ore evaluation. Johannesburg( South Africa): Printpak (Cape) Ltd.; 1981.
  30. 30. Schepers AR, Shanaham JF, Liebig MA, Schepers JS, Johnson SH, Luchiari JA. Appropriateness of management zones for characterizing spatial variability of soil properties and irrigated corn yields across years. Agron J. 2004;96(1):195-203.
  31. https://doi.org/10.2134/agronj2004.1950
  32. 31. De Gruijte JJ, Mc Bratney AB. A modified fuzzy K-means for predictive classification. In: Bock HH, editor. Classification and Related Methods of Data Analysis. Amsterdam (The Netherlands): Elsevier Science; 1988. p. 97–104.
  33. 32. Xin-Zhang W, Guo-Shun L, Hong-Chao H, Zhen-Hai W, Qing-Hua L, Xu-Feng L, et al. Determination of management zones for a tobacco field based on soil fertility. Comput Electron Agric. 2009;65(2):168-75. https://doi.org/10.1016/j.compag.2008.08.008
  34. 33. Bezdek JC. Pattern Recognition with Fuzzy Objective Function Algorithms. New York: Plenum Press; 1981. https://doi.org/10.1007/978-1-4757-0450-1
  35. 34. Boydell B, Mc Bratney AB. Identifying potential within field management zones from cotton yield estimates. In: Stafford JV, editor. Precision Agriculture, Proceedings of the 2nd European Conference on Precision Agriculture, Odense, Denmark, 11–15 July 1999; SCI: London, UK, 1999. p. 331-41.
  36. 35. Mc Bratney AB, Moore AW. Application of fuzzy sets to climatic classification. Agric For Meteorol. 1985;35(1-4):165-85. https://doi.org/10.1016/0168-1923(85)90082-6
  37. 36. Corstanje R, Grunwald S, Reddy KR, Osborne TZ, Newman S. Assessment of the spatial distribution of soil properties in a Northern Everglades Marsh. J of Environ Qual. 2006;35(3):938-49. https ://doi.org/10.2134/jeq20 05.0255.
  38. 37. Hu S, Chapin FS, Firestone MK, Field CB, Chiariello NR. Nitrogen limitation of microbial decomposition in a grassland under elevated CO2. Nature. 2001;409(6817):188-91. https://doi.org/10.1038/35051576
  39. 38. Schlesinger WH, Lichter J. Limited carbon storage in soil and litter of experimental forest plots under increased atmospheric CO2. Nature. 2001;411(6836):466-9. https://doi.org/10.1038/35078060
  40. 39. Metwally MS, Sameh Shaddad M, Liu M, Yao RJ, Abdo AI, L iP, et al. Soil properties spatial variability and delineation of site-specific management zones based on soil fertility using fuzzy clustering in a hilly field in Jianyang, Sichuan, China. Sustain. 2019;11(24):70-84. https://doi.org/10.3390/su11247084
  41. 40. Jiang H, Liu G, Wang X, Song W, Zhang R, Zhang R, et al. Delineation of site-specific management zones based on soil properties for a hillside field in central China. Arch Agron Soil Sci. 2012;58(10):1075-90.
  42. https://doi.org/10.1080/03650340.2011.570337
  43. 41. Liu GS, Wang XZ, Zhang ZY, Zhang CH. Spatial variability of soil properties in a tobacco field of central China. Soil Sci. 2008;173(9);659-67. https://doi.org/10.1097/SS.0b013e3181847ea0
  44. 42. Goovaerts P. Geostatistics for natural resources evaluation. New York: Oxford Univ. Press; 1997. https://doi.org/10.1093/oso/9780195115383.001.0001
  45. 43. Cambardella CA, Moorman TB, Novak JM, Parkin TB, Karlen DL, Turco RF, et al. Field-scale variability of soil properties in central Iowa soil. Soil Sci Soc of America J. 1994;58(5):1501-11.
  46. https://doi.org/10.2136/sssaj1994.03615995005800050033x
  47. 44. Davatgar N, Neishabouri MR, Sepaskhah AR. Delineation of site-specific nutrient management zones for a paddy cultivated area based on soil fertility using fuzzy clustering. Geoderma. 2012;173:111-8. https://doi.org/10.1016/j.geoderma.2011.12.005
  48. 45. Mao DH, Wang ZM, Li L, Miao ZH, Ma WH, Song CC, et al. Soil organic carbon in the Sanjiang plain of China: Storage, distribution and controlling factors. Biogeosciences. 2015;12(6):1635-45. https://doi.org/10.5194/bg-12-1635-2015
  49. 46. Maleki S, Karimi A, Mousavi A, Kerry R, Taghizadeh-Mehrjardi R. Delineation of soil management zone maps at the regional scale using machine learning. Agron. 2023;13(2):445. https://doi.org/10.3390/agronomy13020445
  50. 47. Rodríguez-Pérez ÁM, Rodríguez CA, Márquez-Rodríguez A, Mancera JJ. Viability analysis of tidal turbine installation using fuzzy logic: Case study and design considerations. Axioms. 2023;12(8):778. https://doi.org/10.3390/axioms12080778
  51. 48. MatÍAs L, Castro J, Zamora R. Soil-nutrient availability under a global-change scenario in a Mediterranean mountain ecosystem. Glob Chang Biol. 2011;17(4):1646-57. https://doi.org/10.1111/j.1365-2486.2010.02338.x
  52. 49. Delgado-Baquerizo M, Maestre FT, Gallardo A, Bowker MA, Wallenstein MD, Quero JL, et al. Decoupling of soil nutrient cycles as a function of aridity in global drylands. Nature. 2013;502(7473):672-6. https://doi.org/10.1038/nature12670
  53. 50. Ondrase G, Bakic Begic H, Zovko M, Filipovic L, Merino-Gergichevich C, Savic R, et al. Biogeochemistry of soilorganic matter in agroecosystems & environmental implications. Sci Total Environ. 2019;658:1559-73.https://doi.org/10.1016/j.scitotenv.2018.12.243

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