نوع مقاله : مقاله پژوهشی

نویسندگان

1 عضو هیات علمی

2 عضو هیئت علمی

چکیده

ظرفیت تبادل کاتیونی (CEC) یکی از مشخصه مهم خاک در جذب و رهاسازی عناصر غذایی مورد نیاز گیاه و برآورد پتانسیل خطر فلزات سنگین و برخی آلاینده‌های آلی است. آگاهی از چگونگی الگوی پراکنش مکانی CEC به منظور مدیریت پایدار اکوسیستم، دارای اهمیت ویژه‌ای است. هدف از این پژوهش، تعیین مناسب‌ترین روش‌ میانیابی برای پهنه‌بندی CEC در خاک‌های زراعی بخشی از استان گیلان بود. بدین منظور، 153 نمونه از عمق صفر تا 15 سانتی‌متری خاک برداشت و میزان رس، کربن آلی و CEC خاک اندازه‌گیری شد. روش‌های میان‌یابی شامل کریجینگ، کوکریجینگ، رگرسیون کریجینگ و فازی کریجینگ با استفاده از سیستم اطلاعات جغرافیایی انجام شد. نتایج نشان داد که روش‌های هیبریدی نظیر روش رگرسیون کریجینگ و فازی کریجینگ در مقایسه با سایر تخمین‌ها، (به ترتیب با RMSE 02/1 و 2/1) دقت بیشتری داشت. در مقایسه بین دو روش فوق، نیز روش رگرسیون کریجینگ با توجه به خطای کمتر توانایی بیشتری در برآورد الگوی پراکنش CEC از خود نشان داد. همچنین، نتایج نشان داد که افزایش تعداد داده‌ها با استفاده از توابع تبدیلی مناسب (ایجاد شده با استفاده از ANFIS)، سبب افزایش دقت تخمین گردید. در کل، نتایج این پژوهش نشان داد که استفاده از توابع تبدیلی مناسب و ادغام آن با روش پهنه‌بندی مطلوب، کمک موثری در برآورد دقیق‌تر CEC در منطقه مورد مطالعه نمود.

کلیدواژه‌ها

عنوان مقاله [English]

Suitable geostatistical methods and incorporation of them with pedotransfer functions for mapping cation exchange capacity

چکیده [English]

Cation Exchange Capacity (CEC) is an important characteristic of soil in absorption and desorption of nutrient and potential of heavy metals hazard and some of organic contamination. Knowing spatial patterns of CEC for sustainable management of ecosystems, has special importance. hence, the main objectives of this research was to evaluate, recognize and introduce the best interpolation methods for prediction of CEC in some of Guilan province soils. 153 points of surface soil in depth 0-15 cm were sampled and were measured clay, Organic carbon and CEC. Interpolation methods including kriging, cokriging, fuzzy kriging and regression kriging have been done using GIS. Results showed that the hybrid methods including regression kriging and fuzzy kriging, respectively with RMSE values of 1.02 and 1.2, significantly reduced the error of prediction compared with the other methods. In addition, between these two mentioned methods, the regression kriging had more efficiency in prediction of CEC. The results also revealed that increasing the number of data by means of the best pedotransfer functions (created by ANFIS) will enhance the accuracy of prediction. In general, combination of the best pedotransfer functions with the best interpolation method increased the accuracy of CEC prediction in the study area.
ssss sssss

کلیدواژه‌ها [English]

  • Fuzzy kriging
  • Interpolation
  • Regression Kriging
  • Soil Pedotransfer Function
Amini M., Khademi H., and Fathianpour N. 2002. A comparison of kriging and cokriging techniques in estimating Cl concentration in soil. Iranian Journal Agriculture Science, 33(4): 741-748. (In Persian)
Ayobi Sh., Mohammad Zamani S., and Khormali F. 2007. Prediction total N by organic matter content using some geostatistic approaches in part of farm land of Sorkhankalateh, Golestan Province. Journal Agriculture Sciences and Natural Resource, 14(4): 1-10. (In Persian)
Bower C.A., Reitmeir R.F., and Fireman M. 1952. Exchangeable cation analysis of saline and alkali soils. Soil Sconce, 73: 251-261.
Burrough P., and McDonnell R. 1998. Principles of Geographical Information Systems. Oxford University Press, Oxford, 333p.
Eldeiry A., and Garcia L.A. 2009. Comparison of regression kriging and cokriging techniques to estimate soil salinity using LANDSAT images. Hydrology Days, pp. 27-37.
Gee G.W., and Bauder J.W. 1986. Particle-size analysis. In: Klute A. (Eds.), Methods of Soil Analysis. Part 1. Agronomy Handbook No 9, American Society of Agronomy and Soil Science Society of America. Madison, WI, pp. 383-411.
Hesse P.R. 1971. A text Book of Soil Chemical Analysis. John Marry Ltd., London, 555p.
Hegedus, F.M. (2006). Applying fuzzy logic and neural networks to database evaluation in precision agriculture. Ph. D. dissertation, University of West-Hungary, 90p.
Hengl, T., Heuvelinkb, G.B.M. and Rossiter, D.G. (2007). About regression- kriging: From equations to case studies. Computers & Geosciences, 33: 1301–1315.
Kilic, K., Kilic, S. and Kociyigit, R. 2012. Assessment of spatial variability of soil properties in areas under different land use. Bulgarian Journal of Agricultural Science, 18 (5): 722-732.
Lark, R.M. and Beckett, P.H.T. (1998). A geostastistical descriptor of the spatial distribution of soil classes and its use in predicting the purity of possible soil map units. Geoderma, 83: 243-267.
Liu, Z., Zhou, W., Shen, J., He, P., Lei, Q. and Liang, G. (2014). A simple assessment on spatial variability of rice yield and selected soil chemical properties of paddy fields in South China. Geoderma, 235: 39-47.
Manrique, L.A., Jones, C.A., and Dyke, P.T. (1991). Predicting cation exchange capacity from soil physical and chemical properties. Soil. Sci. Soc. Am. J, 55:787-794.
Miao, Y., Mulla, D.J., and Robert, P.C. (2006). Spatial variability of soil properties, corn quality and yield in two Illinois, USA fields: implications for precision corn management. Precision Agriculture, 7: 5-20.
Mohammadi, J. (2006). Pedometrics (Vol 2: Spatial statistic). Pelk press. Tehran, 435p. (in Persian).
Nelson, D.W. and Sommers, L.P. (1986). Total carbon, organic carbon and organic matter. PP.539–579. In: A. L. Page (Ed.), Methods of Soil Analysis. Part 2, Agronomy Handbook No 9, American Society of Agronomy and Soil Sci. Soc. Amer. Madison, WI.
Odeh, I.O.A., McBratney, A.B. and Chittleborough, D.J. (1995). Further results on prediction of soil properties from terrain attributes: Heterotypic cokriging and regression-kriging. Geoderma, 67: 215–226.
Pekey, H. (2006). The distribution and sources of heavy metals in Izmit Bay surface sediments affected by a polluted stream. Marine Pollut. Bull, 52: 1197–1208.
Rahimi Bandarabadi, S. and Saghfian, B. (2007). Estimation of spatial distribution of rainfall using fuzzy package theory. Investigations of Water Resources of Iran, 3(2): 73-85. (in Persian)
Robinson, T.P. and Metternicht, G. (2006). Testing the performance of spatial interpolation techniques for mapping soil properties. Computer and Electronics in Agriculture, 50: 97-108.
Sumfleth, K. and Duttmann, R. (2008). Prediction of soil property distribution in paddy soil landscapes using terrain data and satellite information as indicators. Ecological Indicators, 8:485–501.
Santra, Y., Chopra, U.K. and Chakraborty, D. (2008). Spatial variability of soil properties and its application in predicting surface map of hydraulic parameters in agriculture farm. Current Science, 95: 473-482.
Shi, J., Wang, H., Xu, J., Wu, J., Liu, X., Zhu, H. and Yu, C. (2007). Spatial distribution of heavy metals in soils: A case study of Changxing, China. Environmental Geology, 52: 1-10.
Sumfleth, K. and Duttmann, R. (2008). Prediction of soil property distribution in paddy soil landscapes using terrain data and satellite information as indicators. Ecological indicators, 8: 485–501.
Tarr, A.B., Moor, K.J., Bullock, D.G. and Dixon, P.M. (2005). Improving Map Accuracy of Soil Variables Using Soil Electrical Conductivity as a Covariate. Precision Agriculture, 6: 255–270.
Wang, Y., Feng, N., Zhang, X. and Liao, G. (2008). Spatial variability of soil cation exchange capacity in hilly tea plantation Soils under different sampling scales. Agricultural Sciences in China, 7(1):96-103.