Eurasian Journal of Soil Science
Volume 10, Issue 2, Apr 2021, Pages 171 - 178DOI: 10.18393/ejss.863606
Stable URL: http://ejss.fess.org/10.18393/ejss.863606
Copyright © 2021 The authors and Federation of Eurasian Soil Science Societies
Comparison of Fuzzy logic and Boolean methods in mapping nitrogen and phosphorus nutrients
Article first published online: 18 Jan 2021 | How to cite | Additional Information (Show All)
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Hashemimajd, K., Kochakpour, S., Davatgar, N., Sohrabi, E., 2021. Comparison of Fuzzy logic and Boolean methods in mapping nitrogen and phosphorus nutrients. Eurasian J. Soil Sci. 10(2): 171 - 178. DOI : 10.18393/ejss.863606Author information
Kazem Hashemimajd , Department of Soil Science, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, IranShaghayegh Kochakpour , Department of Soil Science, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Iran
Naser Davatgar , Soil and Water Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran
Elham Sohrabi , Department of Soil Science, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Iran Ardabili, Iran
Publication information
Article first published online : 18 Jan 2021Manuscript Accepted : 09 Jan 2021
Manuscript Received: 14 Sep 2019
DOI: 10.18393/ejss.863606
Stable URL: http://ejss.fesss.org/10.18393/ejss.863606
Abstract
One of the approaches for increasing of yield and reduction of rice production costs is precision agriculture. Complete and correct determination of nutrition status of paddy soils is necessary in precise agriculture. To compare fuzz y and Boolean methods and mapping of nutrition status of nitrogen and phosphorus, 370 compound samples were collected from 306 ha of paddy soils of Rice Research Institute in Rasht County from plots with the dimension of 50 × 100 m. Total nitrogen and available phosphorus contents were measured. Results showed that interpolation and mapping by fuzzy logic was more accurate and correct in comparison with Boolean method and had greater distinguishing power to indicate deficiency of nutrients. Evaluation of dependency of paddy soils using of fuzzy function and Boolean method, showed that the southwest of study area have nitrogen and phosphorus deficiency and the other parts have minor limitation for these elements.Keywords
Fuzzy logic, mapping, nutrients, precision agriculture.
Corresponding author
References
Amini, M., Afyuni, M., Fathianpour, N., Khademi, H., Flühler, H., 2005. Continuous soil pollution mapping using fuzzy logic and spatial interpolation. Geoderma 124 (3): 223-233.
Boltz, D.F., Howell, J.A., 1978. Colorimetric determination of nonmetals. John Wiley and Sons, 543p.
Burrogh, P.A., MacMillan, R.A., van deursen, W., 1992. Fuzzy classification methods for determining land suitability from soil profile observations and topography. European Journal of Soil Science 43(2): 193-210.
Dahiya, I.S., Anlauf, R., Kersebaum, K.C., Richter, J., 1985. Spatial variability of some nutrient constituents of an Alfisol from loess II. Geostatistical analysis. Zeitschrift für Pflanzenernährung und Bodenkunde 148(3): 268-277.
Dobermann, A., Oberthür, T., 1997. Fuzzy mapping of soil fertility — a case study on irrigated riceland in the Philippines. Geoderma 77(2-4): 317-339.
Franzen, D.W., Hopkins, D.H., Sweeney, M.D., Ulmer, M.K., Halvorson, A.D., 2002. Evaluation of soil survey scale for zone development of site‐specific nitrogen management. Agronomy Journal 94(2): 381-389.
Freissinet, C., Erlich, M., Vauclin, M., 1998. A fuzzy logic-based approach to assess imprecisions of soil water contamination modelling. Soil and Tillage Research 47(1-2): 11-17.
Hasani Pak, A.A., 1998. Geostatistics. Tehran University Press, Tehran, Iran. 328p. [in Persian].
Heuvlink, G.B.M., Burrough, P.A., 1993. Error propagation in logical cartographic modelling using Boolean methods and continuous classification. International Journal of Geographical Information Systems 7(3): 231-246.
Khodabandeh, N., GhasempourAlamdari, M., 2005. Rice cropping. Daneshgahe Azade Eslami Ghaemshahr Iran. 168p. [in Persian].
Khosla, R., Fleming, K., Delgado, J.A., Shaver, T.M., Westfall, D.G., 2002. Use of site-specific management zones to improve nitrogen management for precision agriculture. Journal of Soil and Water Conservation 57(6): 513-518.
Lund Research Ltd. 2018. Testing for Normality using SPSS Statistics. Available at [Access date: 02.08.2018]: https://statistics.laerd.com/spss-tutorials/testing-for-normality-using-spss-statistics.php
Martin-Clouaire, R.M., Cazemier, D.R., Lagacherie, P., 2000. Representing and processing uncertain soil information for mapping soil hydrological properties. Computers and Electronics in Agriculture 29(1-2): 41-57.
McBratney, A.B., Odeh, I.O.A., 1997. Application of fuzzy sets in soil science: fuzzy logic, fuzzy measurements and fuzzy decisions. Geoderma 77(2-4): 85-113.
McBratney, A.B., Pringle, M.J., 1997. Spatial variability in soil – Implications for precision agriculture. In: Precision Agriculture 1997. Stafford, J.V. (Ed.). Proceedings of the 1st European Conference on Precision Agriculture, Oxford, UK. pp. 639–643.
Murphy, J., Riley, J.P., 1962. A modified single solution method for the determination of phosphate in natural waters. Analytica Chimia Acta 27: 31–36.
Ping, J.L., Dobermann, A., 2003. Creating spatially contiguous yield classes for site‐specific management. Agronomy Journal 95(5): 1121-1131.
Reyniers, M., Maertens, K., Vrindts, E., De Baerdemaeker, J., 2006. Yield variability related to landscape properties of a loamy soil in central Belgium. Soil and Tillage Research 88(1): 262-273.
Rezaee, A.M., 1995. Concepts of statistics and probability. Mashhad publisher, Iran. 446p. [in Persian].
Schepers, A.R., Shanahan, J.F., Liebig, M.A., Schepers, J.S., Johnson, S.H., Luchiari, Jr.A., 2004. Appropriateness of management zones for characterizing spatial variability of soil properties and irrigated corn yields across years. Agronomy Journal 96(1): 195-203.
Schöning, I., Totsche, K.U., Kögel-Knabner, I., 2006. Small scale spatial variability of organic carbon stocks in litter and solum of a forested Luvisol. Geoderma 136(3-4): 631-642.
Sicat, R.S., Carranza, E.J.M., Nidumolu, U.B., 2005. Fuzzy modeling of farmers' knowledge for land suitability classification. Agricultural Systems 83(1): 49-75.
Soil Survey Staff, 2014. Keys to soil taxonomy. 12rd Edition. United States Department of Agriculture (USDA), Natural Resources Conservation Service. Washington DC, USA. 360p. Available at [Access date: 14.09.2019]: https://www.nrcs.usda.gov/wps/PA_NRCSConsumption/download?cid=stelprdb1252094&ext=pdf
Sun, B., Zhou, S., Zhao, Q., 2003. Evaluation of spatial and temporal changes of soil quality based on geostatistical analysis in the hill region of subtropical China. Geoderma 115(1-2): 85-99.
Trangmar, B.B., Yost, R.S., Uehara, G., 1985. Application of geostatistics to spatial studies of soil properties. Advance in Agronomy 38: 45-94.
Wilding, L.P., Dress. L.R., 1983. Spatial variability and pedology. In: Wilding, L.P., Smeck, N.E., Hall, G.F. (Eds.). Pedogensis and Soil Taxonomy. I. Concepts and Interactions. Elsevier, Amsterdam, The Netherlands. pp. 83-116.
Zadeh, L.A., 1965. Fuzzy sets. Information and Control 8 (3): 338-353.
Abstract
One of the approaches for increasing of yield and reduction of rice production costs is precision agriculture. Complete and correct determination of nutrition status of paddy soils is necessary in precise agriculture. To compare fuzz y and Boolean methods and mapping of nutrition status of nitrogen and phosphorus, 370 compound samples were collected from 306 ha of paddy soils of Rice Research Institute in Rasht County from plots with the dimension of 50 × 100 m. Total nitrogen and available phosphorus contents were measured. Results showed that interpolation and mapping by fuzzy logic was more accurate and correct in comparison with Boolean method and had greater distinguishing power to indicate deficiency of nutrients. Evaluation of dependency of paddy soils using of fuzzy function and Boolean method, showed that the southwest of study area have nitrogen and phosphorus deficiency and the other parts have minor limitation for these elements.
Keywords: Fuzzy logic, mapping, nutrients, precision agriculture.
References
Amini, M., Afyuni, M., Fathianpour, N., Khademi, H., Flühler, H., 2005. Continuous soil pollution mapping using fuzzy logic and spatial interpolation. Geoderma 124 (3): 223-233.
Boltz, D.F., Howell, J.A., 1978. Colorimetric determination of nonmetals. John Wiley and Sons, 543p.
Burrogh, P.A., MacMillan, R.A., van deursen, W., 1992. Fuzzy classification methods for determining land suitability from soil profile observations and topography. European Journal of Soil Science 43(2): 193-210.
Dahiya, I.S., Anlauf, R., Kersebaum, K.C., Richter, J., 1985. Spatial variability of some nutrient constituents of an Alfisol from loess II. Geostatistical analysis. Zeitschrift für Pflanzenernährung und Bodenkunde 148(3): 268-277.
Dobermann, A., Oberthür, T., 1997. Fuzzy mapping of soil fertility — a case study on irrigated riceland in the Philippines. Geoderma 77(2-4): 317-339.
Franzen, D.W., Hopkins, D.H., Sweeney, M.D., Ulmer, M.K., Halvorson, A.D., 2002. Evaluation of soil survey scale for zone development of site‐specific nitrogen management. Agronomy Journal 94(2): 381-389.
Freissinet, C., Erlich, M., Vauclin, M., 1998. A fuzzy logic-based approach to assess imprecisions of soil water contamination modelling. Soil and Tillage Research 47(1-2): 11-17.
Hasani Pak, A.A., 1998. Geostatistics. Tehran University Press, Tehran, Iran. 328p. [in Persian].
Heuvlink, G.B.M., Burrough, P.A., 1993. Error propagation in logical cartographic modelling using Boolean methods and continuous classification. International Journal of Geographical Information Systems 7(3): 231-246.
Khodabandeh, N., GhasempourAlamdari, M., 2005. Rice cropping. Daneshgahe Azade Eslami Ghaemshahr Iran. 168p. [in Persian].
Khosla, R., Fleming, K., Delgado, J.A., Shaver, T.M., Westfall, D.G., 2002. Use of site-specific management zones to improve nitrogen management for precision agriculture. Journal of Soil and Water Conservation 57(6): 513-518.
Lund Research Ltd. 2018. Testing for Normality using SPSS Statistics. Available at [Access date: 02.08.2018]: https://statistics.laerd.com/spss-tutorials/testing-for-normality-using-spss-statistics.php
Martin-Clouaire, R.M., Cazemier, D.R., Lagacherie, P., 2000. Representing and processing uncertain soil information for mapping soil hydrological properties. Computers and Electronics in Agriculture 29(1-2): 41-57.
McBratney, A.B., Odeh, I.O.A., 1997. Application of fuzzy sets in soil science: fuzzy logic, fuzzy measurements and fuzzy decisions. Geoderma 77(2-4): 85-113.
McBratney, A.B., Pringle, M.J., 1997. Spatial variability in soil – Implications for precision agriculture. In: Precision Agriculture 1997. Stafford, J.V. (Ed.). Proceedings of the 1st European Conference on Precision Agriculture, Oxford, UK. pp. 639–643.
Murphy, J., Riley, J.P., 1962. A modified single solution method for the determination of phosphate in natural waters. Analytica Chimia Acta 27: 31–36.
Ping, J.L., Dobermann, A., 2003. Creating spatially contiguous yield classes for site‐specific management. Agronomy Journal 95(5): 1121-1131.
Reyniers, M., Maertens, K., Vrindts, E., De Baerdemaeker, J., 2006. Yield variability related to landscape properties of a loamy soil in central Belgium. Soil and Tillage Research 88(1): 262-273.
Rezaee, A.M., 1995. Concepts of statistics and probability. Mashhad publisher, Iran. 446p. [in Persian].
Schepers, A.R., Shanahan, J.F., Liebig, M.A., Schepers, J.S., Johnson, S.H., Luchiari, Jr.A., 2004. Appropriateness of management zones for characterizing spatial variability of soil properties and irrigated corn yields across years. Agronomy Journal 96(1): 195-203.
Schöning, I., Totsche, K.U., Kögel-Knabner, I., 2006. Small scale spatial variability of organic carbon stocks in litter and solum of a forested Luvisol. Geoderma 136(3-4): 631-642.
Sicat, R.S., Carranza, E.J.M., Nidumolu, U.B., 2005. Fuzzy modeling of farmers' knowledge for land suitability classification. Agricultural Systems 83(1): 49-75.
Soil Survey Staff, 2014. Keys to soil taxonomy. 12rd Edition. United States Department of Agriculture (USDA), Natural Resources Conservation Service. Washington DC, USA. 360p. Available at [Access date: 14.09.2019]: https://www.nrcs.usda.gov/wps/PA_NRCSConsumption/download?cid=stelprdb1252094&ext=pdf
Sun, B., Zhou, S., Zhao, Q., 2003. Evaluation of spatial and temporal changes of soil quality based on geostatistical analysis in the hill region of subtropical China. Geoderma 115(1-2): 85-99.
Trangmar, B.B., Yost, R.S., Uehara, G., 1985. Application of geostatistics to spatial studies of soil properties. Advance in Agronomy 38: 45-94.
Wilding, L.P., Dress. L.R., 1983. Spatial variability and pedology. In: Wilding, L.P., Smeck, N.E., Hall, G.F. (Eds.). Pedogensis and Soil Taxonomy. I. Concepts and Interactions. Elsevier, Amsterdam, The Netherlands. pp. 83-116.
Zadeh, L.A., 1965. Fuzzy sets. Information and Control 8 (3): 338-353.