Estimation of moisture characteristic curve parameters using physical, geophysical and mechanical properties of soil

Document Type : Original Article

Authors

1 Shahrekord university

2 Professor of Soil Science, Department of Soil Science, Faculty of Agriculture, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran

3 shahrood university

4 soil science department of Shahrekord University

Abstract

Nowadays, the use of new methods to estimate hydraulic parameters such as soil moisture characteristic curve is considered. The aim of this study was to determine the effective factors in modeling of moisture characteristic curve parameters, from conveniently available, by the decision tree and error estimator cross validation and resub stitution were used. In this study, 72 soil samples were collected from six different tissues from the village of Margalomk and Shahrekord city. Conveniently available soil properties were introduced into software in 2 scenarios (the first scenario %sand, %clay, OM%, CaCO3, BD, pH, EC, mean weight diameter of dry aggregate (MWD dry), mean weight diameter of wet aggregate (MWD wet) and θs, the second scenario %sand, %clay, OM%, CaCO3, BD, %gravel, electrical resistivity, dielectric constant, root penetration resistivity). The results showed that the correlation coefficient for the PWP target variables in the first scenario is 0.88 and in the second scenario the maximum value for the FC target variable is 0.93. By replacing, geophysical and mechanical properties in the second scenario, the correlation coefficient for the variables FC and α Which are affected by the structure and texture of the soil increased, and decreased for PWP, n and m variables, which are more affected by soil texture. %RMSE was also slightly lower for the FC and α variables in the second scenario than in the first scenario, but in general according to% RMSE, modeling for all variables was successful in both scenarios.

Keywords

References

Amir-Abedi H. Asghari Sh.A., Mesri-Gandshamin T., Keivanbehjo F. 2013. Estimating of field capacity, permanent wilting and available water content in Ardabil plain soils using regression and artificial neural network models. Applied Soil Research. 1 (1): 60-72. (In Persian)
Bouyoucos G.J. 1962. Hydrometer method improved for making particle size analysis of soils. Agronomy Journal. 54: 464-465.
Farzadmehr M., Chahmani M., Khayaki Siouki AS. 2018. Comparing decision tree and instance-based learning models to estimate soil saturated hydraulic conductivity. Journal of Soil and Water Conservation Research. 25 (5): 167-184. (In Persian)
Hengle T., and Husnjak S. 2006. Evaluation adequacy and usability of soil maps in Croatia. Soil Science Society of America Journal, 70:920-929.
Hutson J.L., and Cass A. 1987. A retentivity function for use in soil-water simulation models. Journal of Soil Science. 38: 105–113.
Kemper W.D., and RoseNau R.C. 1986. Aggregate stability and size distribution. In Sparks D.L. (Ed.) Methods of Soil Analysis. American society of agronomy, Madison. pp: 425-442. ‏
Khashei Siuki A., Jalali Moakhar V.R., Noferesti A.M., and Ramazani Y. 2015. Comparing nonparametric k-nearest neighbor technique with ANN model for predicting soil saturated hydraulic conductivity. Journal of Soil Management and Sustainable Production. 5: 3. 81-95. (In Persian)
Klute A., and Dirksen C. 1986. Hydraulic conductivity and diffusivity: Laboratory methods. In: A. Klute (Eds). Method of Soil Analysis, Part1: Agronomy Soil Science Society of America Madison.W.I. 687-734.
Meshkani A.S., and Nazemi AS. 2009. Introduction to Data Mining. Ferdowsi University Press, Mashhad. 456P.
 Mesri S., Ghorbanidashtaki Sh., Shirani H., Kamkarrohani A., Motaghian H.M., and Bahrami H.A. 2020. Hydraulic conductivity estimation using different decision tree modeling scenarios. Iranian journal of soil research. (34): 143-154.doi.org/10.22092/ijsr.2020.122159
Mesri S., Ghorbanidashtaki Sh., Shirani H., Kamkarrohani A., and Motaghian H.M. 2020. Evaluating the accuracy of some field methods for measuring soil moisture. Journal of water research in agriculture. 34 :93- 105.doi.org/10.22092/jwra.2020.121920
Minasny B. 2007. Prediction soil properties. Jurnal Ilmu Tanah dan Lingkungan. 7: 54-67.
Moncada M.P., Gabriels D., and Cornelis W.M. 2014. Data-driven analysis of soil quality indicators using limited data. Geoderma. 235: 271-278.
Nemes A., Rawls W.J., and Pachepsky Y.A. 2006. Use of the nonparametric nearest neighbor approach to estimate soil hydraulic properties. Soil Science Society of America Journal. 70: 2. 327-336.
Page 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. Soil Science Society of America Journal. 51: 1460-1465.
Ramezani M., Ganbarian B., Liaghat A.M., and Salehi Khoshkroudi Sh. 2011. Developing pedotransfer functions for saline and saline- alkali soils. Journal of Irrigation and Water Management. 1(1): 99-110. (In Persian with English Summary)
Shahrabi c. 2011. Data Mining 2. First edition, Amir Kabir University Industrial Jihad Press, Tehran. 300p.
Shirani. H. 2017. Artificial Neural Networks with an Application in Agricultural and Natural Resource Sciences. Rafsanjan University Press. 320p. (In Persian)
Walkley A., and 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 Journal. 37: 29-38.
Wenner F. 1916. A method of measuring earth resistivity; Scientific Paper, Report No. 258; National Bureau of Standards; Gaithersburg, MD, USA; V (12), pp. 469-482.

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History

  • Receive Date: 09 October 2020
  • Revise Date: 09 July 2021
  • Accept Date: 31 July 2021

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