Determining the most important soil fertility properties affecting rice yield in paddy fields using principal component analysis

Document Type : Research Paper


1 PhD Graduate, Department of Soil Science, College of Agriculture, Isfahan ( Khorasgan) Branch , Islamic Azad University, Isfahan, Iran

2 Assistant Professor, Department of Soil Science, College of Agriculture, Isfahan (Khorasgan) Branch , Islamic Azad University, Isfahan, Iran

3 Associate Professor, Soil and Water Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran

4 Professor of Soil Science, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran

5 Assistant Professor, Department of Agriculture, Shahed University, Tehran, Iran


Multi-variate statistical methods such as principal component analysis (PCA) and regressions could be used to facilitate the interpretation of complex relationships. The objective of this study was to determine the most important soil fertility properties affecting rice yield in the paddy fields. For this purpose, soil samples were taken from the plow layers of 119 points with suitable distribution in the paddy fileds located in Shaft and Fouman cities of Guilan province. Then after, physical and chemical properties of the soil fertility were measured and analysed using descriptive statistics, principal component analysis and regression methods. Results showed that three PCs with eigen values greater than one named as “k and it’s preservation factors”, ”Total N and it’s provider factors” and ”P and Thickness of plow layer” are respectively explained more than 67.4% of the variability in the soil physical and chemical properties and 55% of the yield variability. In addition, the corresponded properties to the PCs explained 80% of the yield variability. Consequently, in order to increase the yield, management practices such as proper fertilizer applications of nitrogen, potassium and phosphorous and proper tillage for creating suitable plow layer are recommended.


Main Subjects

Ayoubi, S., Khormali, F. (2009). Spatial variability of soil surface nutrients using principal component analysis and geostatistics: A case study of appaipally village, Andhra pradesh, India. Journal of Science and Technology of Agriculture and Natural Resources, 12 (46), 609-622. (In Farsi)
Ayoubi, S., Zamani, S. M. and Khormali, F. (2009). Wheat yield prediction through soil properties using principle component analysis. Iranian Journal of Soil and Water Research, 40 (1), 51-57. (In Farsi)
Azizi, J. (2007). Economic evaluation of rice marketing strategies in guilan province. Journal of agricultural sciences, 12(4), 715-728. (In Farsi)
Balasundram, S. K., Husni , M. H. A. and Ahmad, O. H. (2008). Application of geostatistical tools of qualify spatial variability of selected soil chemical properties from a cultivated tropical peat. Journal of Agronomy, 7(1), 82-87.
Bartholomew, D. J., Steele,F., Moustaki,I and Galbaraite, J. I. (2008). Analysis of multivariate social science data (2th ed.). London: Chapman & Hall/CRC.
Bartlett, M. S.(1954). A note on the multiplying factors for various chi square approximations. Journal of the Royal Statistical Society.16, 296–298.
Benites, V. D. M., Moutta, R. D. O., Coutinho, H. L. D. C., and Balieiro, F. D. C. (2010). Análise discriminante de solos sob diferentes usos em área de Mata Atlântica a partir de atributos da matéria orgânica. Revista Árvore, 34(4).
Biabangard, E. (2010). Research methods in psychology and education. Tehran:Doran. (In Farsi)
Bremner, J. M. and Mulvaney. C. S. (1982). Total nitrogen. In: A. L. Page (ed.) Methods of SoilAnalysis. P Part 2: Chemical and microbiological properties (2th ed.). Agron. (No.2). (pp.9595-624). Am. Soc. Argon., Madison, WI, USA.
Cox M. S., Gerard P. D., Wardlaw M. C. and Abshire M. J. (2003). Variability of selected soil properties and their relationship with soybean yield. Soil Science Society of America Journal, 67, 1296–1302.
Davatgar, N. (2010). Prediction of rice yield under water limited conditions using crop growth and yield models at regional scale. Ph. D. dissertation, University of Tabriz, Iran. (In Farsi)
Davatgar, N., Kavoosi, M., Alinia, M. H. and Paykan, M. (2006). Study of potassiun status and effect of physical and chemical properties of soil on it in paddy soils of guilan province. Journal of Water and Soil Science, 9(4), 71-89. (In Farsi)
De Datta, S. K., Buresh, R. J., Samson, M. I.and Wang, K. R. (1988). Nitrogen use efficiency and nitrogen-15 balances in broadcast-seeded flooded and transplanted rice. Soil Science Society of America journal, 52, 849-855.
Doberman, A.and Fairhurst, T. H. (2000). Rice: Nutrient disorders & nutrient management. International Rice Research Institute, Philippines.
Dobermann, A., Oberthur, T. (1997). Fuzzy mapping of soil fertility—a case study on irrigated rice land in the Phillipines. Geoderma, 77, 317–339.
Fageria, N. K., Slaton, N. A,. Baligar, V. C. (2003). Nutrient management for improving lowland rice productivity and sustainability. Advances in Agronomy, 80, 63-152.
Field, A. (2009). Discovering statistics using SPSS (3th ed.). London: Sage.
Ghaemi, M., Astaraei, A. R., Emami, H., Nassiri Mahalati, M., and Sanaeinejad, S. H. (2014a). Determining soil indicators for soil sustainability assessment using principal component analysis of Astan Quds-east of Mashhad-Iran. Journal of soil science and plant nutrition, 14(4), 1005-1020.
Ghaemi, M., Astaraei, A., Nassisi, M. M., Sanaeinejad, S. and Emami, H. (2014b). Evaluation of maize yield variability based on soil properties and principal component analysis. Journal of Water and Soil Science. 28(4), 276-285. (In Farsi)
Gee, G. W., and Bauder J. W. (1986). Particle-size analysis. In: Klute A, editor. Methods of soil analysis,Part 1. (2th ed.). (pp. 383-411).  Madison, WI, ASA/SSSA.
Hair, J. F., Black, B., Babin, B., Anderson, R. E. and Tatham, R. L. (2006). Multivariate data analysis (6th ed.). New Jersy: Prentice Hall. 
Hill, T., Lewicki, P., and Lewicki, P. (2006). Statistics: methods and applications: a comprehensive reference for science, industry, and data mining. Tulsa : StatSoft, Inc.
Jamieson, P. D., Porter, J. R. and Wilson, D. R. (1991). A test of the computer simulation model ARCWHEAT1 on wheat crops grown in New Zealand. Field Crops Research, 27, 337–350.
Jolliffe, L. T. (1986). Principal component analysis. Springer-Verlag, New York, USA.
Kaiser, H. (1974). An index of factorial simplicity. Psychometrika, 39, 31–36.
Karimi, A. M., Kavoosi, M., and Shokri, V. H. (2013). Phosphorus critical concentration in paddy soils of guilan. Water and soil science (agricultural science), 23(1), 123-134. (In Farsi)
Kaspar, T. C., Fenton, D. J., Colvin, T. S, Karleno, D. L., Jaynes, D. B., and Meek, D. W. (2004). Relationships of corn and soybean yield to soil and terrain properties. Agronomy Journal, 96, 700-709.
Kavoosi, M., and Malakouti, M. J. (2006). Determination of potassium critical level with ammonium acetate extractant in Guilan rice fields. Journal of Water and Soil Science, 10(3), 113-123. (In Farsi)
Khush, G. S. (1993). Varietal needs for different environments and breeding strategies. In Muralidharan, K. and E.A. Siddiq (Eds.), New Frontiers in Rice Research. Directorate of Rice Research, Hyderabad, India. (pp. 68-75).
Kline, R. B. (2005). Principles and practice of structural equation modeling (2th ed.). New York: Guilford.
Kravchenko, A. N. and Bullock, D. G. (2000). Correlation of corn soybean grain yield with topography and soil properties. Agronomy Journal, 92, 75-83.
Landau, S. and Everitt, B. S. (2003). A handbook of statistical analyses using SPSS. London: CRC Press Company.
Liu, R. X., Kuang, J., Gong, Q., and Hou, X. L. (2003). Principal component regression analysis with SPSS. Computer methods and programs in biomedicine, 71(2), 141-147.
Mallarino, A. P., Oyarzabal, E. S. and Hinz, P. N. (1999). Interpreting within field relationships between crop yields and soil and plant variables using factor analysis. Precision Agriculture, 1, 15-25.
Nelson, D. W., and Sommers, L. E. (1996). Total carbon, organic carbon, and organic matter. Methods of soil analysis part 3-chemical methods, (methodsofsoilan3). (pp. 961-1010).
Olsen, S. R., and Sommers, L. E. (1982). Phosphorus. In: A. L. Page (ed.). Methods of soil analysis,Agron. (No. 9). (Part 2): Chemical and Microbiological Properties. (2th ed.).  (pp. 403-430). Am. Soc. Agron., Madison, WI, USA.
Ovalles, F. A. and Collins, M. E. (1988). Variability of northwest Florida soils by principal component analysis. Soil Science Society of America Journal, 52(5), 1430-1435.
Pallant, J. (2005). SPSS survival manual: a step by step guide to data analysis using spss. Buckingham: allen & unwin
Ponnamperuma, F. N. (1978). Electrochemical change in submerged soil and the  growth of rice. Soils and rice, 421-441.
Rajasekharan, P., Nair, K. M., Rajasree, G., and Kutty, M. N. (2013). Soil fertility assessment and information management for enhancing crop productivity in Kerala. Kerala State Planning Board.
Rezai, A. (2002). concepts of probability and statistics (3th ed.). mashhad. (In Farsi)
Rhoades, J.D. (1996). Salinity: Electrical conductivity and total dissolved solids. methods of soil analysis Part 3-Chemical Methods, (methodsofsoilan3). ( pp. 417-435).
Sadusky, M. C., Sparks, D. L. Noll, M. R. and Hendricks, G. J. (1987). Kinetics and mechanisms of potassium release from sandy middle Atlantic Coastal plain soils. (pp.1460- 1465). Soil Science Society of America Journa. 51.
Sharma, S. (1996). Applied multivariable techniques. New York : John Wiley and Sons.
Schoning, I., Totsche, K.U. and Kogel-Knabner, I.. (2006). Small Scale spatial variability of organic carbon stocks in litter and solum of a forested luvisol. Geoderma, 136, 631-642.             
Shukla, M. K., Lal, R. and Ebinger, M. (2004). Principal component analysis for predicting corn biomass and grain yields. Soil Science, 169(3), 215-224.
Soil Survey Staff. (2014). Keys to soil taxonomy (12th ed.). U. S. Department of Agriculture, Natural Recourses Conservation Service.
Sumner, M. E., and Miller, W. P. (1996). Cation exchange capacity and exchange coefficients. Methods of Soil Analysis Part 3-Chemical Methods, (methodsofsoilan3). (pp.1201-1229).
Sys C., Van Ranst E., and Debaveye, J. (1991). Land evaluation. Part 1. Principles in land evaluation and crop production calculations. Brussels: University Ghent.
Tabachnick, B. G. and Fidell, L. S. (2001). Using multivariate statistics. Boston: Allyn and Bacon.
Tabi, F. O., Omoko, M., Boukong, A., Mvondo Ze, A. D., Bitondo, D., and Fuh-Che, C. (2012). Evaluation of lowland rice (Oryza sativa) production system and management recommendations for Logone and Chari flood plain–Republic of Cameroon. Agricultural Science Research Journals, 2(5), 261-273.
Thomas, G. W. (1996). Soil pH and soil acidity. Methods of soil analysis Part 3-Chemical Methods, (methodsofsoilan3), (pp.475-490).
Wuttichaikitcharoen, P. and Babel, M. S. (2014). Principal component and multiple regression analyses for the Eestimation of suspended sediment yield in ungauged basins of northern Thailand. Water, 6(8), 2412-2435.
Wilding, L. P. and Dress, L. R. (1983). Spatial variability and pedology. In L.P. Wilding, N.E. Smeckand and G.F. Hall (Eds.), Pedogenesis and Soil Taxonomy I. Concepts and Interactions. Developments in Soil Science A (11). New York: Elsevier. (pp. 83-116)..
Yanai, J., Lee, C. K., Kaho, T., Iida, M., Matsui, T., Umeda, M. and Kosaki, T. (2001). Geostatistical analysis of soil chemical properties and rice yeild in a paddy fields and application to the analysis of yeild- determining factors. Soil Science and Plant Nutrition, 47, 291-301.