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Identifying the determinant characteristics influencing soil compactibility indices using neural networks and path analysis | ||
| Desert | ||
| مقاله 3، دوره 26، شماره 2، اسفند 2021، صفحه 173-186 اصل مقاله (582.18 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/jdesert.2021.298777.1006768 | ||
| نویسندگان | ||
| H. Shirani* 1؛ M.R. Mosaddeghi2؛ N. Rafienejad3؛ S. Sadr4؛ H. Naghavi5؛ H. Dashti6 | ||
| 11Department of Soil Science, College of AgricultureVali-e-Asr University of Rafsanjan, Iran. | ||
| 2Department of Soil Science, College of Agriculture, Isfahan University of Technology, Isfahan 84156-83111, Iran | ||
| 3vali asr uni. | ||
| 4College of Agriculture, Payame Noor University of Kerman, Iran | ||
| 5Soil science of Kerman agriculture research center | ||
| 6Department of Plant Breeding, College of Agriculture, Vali-e-Asr University of Rafsanjan, Iran | ||
| چکیده | ||
| Soil compactibility can be quantified using different indices such as maximum dry bulk density (BDmax) and critical water content (θcritical) in a compaction test. The objective of this study was to determine soil properties influencing soil compactibility by evaluate pedotranfer functions (PTFs) with respect to their accuracy and usefulness for the prediction of BDmax and θcritical using linear regression and ANN methods. 100 soil samples were collected from arable and virgin lands in southeast Iran. Primary particle size distribution, CaSO4, CaCO3, organic matter (OM) contents and natural bulk density were used as predictors. Two PTFs were developed using linear multiple stepwise regression: a PTF that estimates BDmax using clay and sand contents and natural bulk density as predictors (R2 = 0.45), the other one for the estimation of θcritical using clay and CaSO4 contents as predictors (R2 = 0.51). Furthermore, an attempt was made to construct PTFs for the prediction of the BDmax and θcritical using ANNs. High prediction efficiencies were achieved using the ANN models. Generally, when all of the easily-available soil properties were included as predictors, much more accurate estimates were obtained by the ANN models for the θcritical and BDmax as compared with the linear regression method. Sensitivity analysis showed that the most important variable in BDmax prediction using ANNs is the BDnatural followed by sand and clay, CaCO3 and CaSO4 contents. The θcritical had the highest sensitivity to clay content and the lowest sensitivity to OM content in the studied soils. | ||
| کلیدواژهها | ||
| Pedotransfer functions؛ Linear regression؛ Path analysis؛ Maximum dry bulk density؛ Critical water content؛ Proctor compaction test | ||
| مراجع | ||
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References Abdelbaki AM. 2018. Evaluation of pedotransfer functions for predicting soil bulk density for U.S. soil. Ain Shams Engineering Journal, 9(4); 1611-1619. Asgarzadeh H, Mosaddeghi M, Dexter AR, Mahboubi AA, Neyshabouri MR. 2014. Determination of soil available water for plants: consistency between laboratory and field measurements. Geoderma, 226; 820. ASTM, 1992. Standard Test Method for Heat Visible Smoke Release Rates for Materials Products Using an Oxygen Consumption Calorimeter American Society for Testing Materials, West Conshohocken, PA. Barzegar A, Asoodar M, Ansari M. 2000. Effectiveness of sugarcane residue incorporation at different water contents and the Proctor compaction loads in reducing soil compactibility. Soil Tillage Research, 57; 167-172. Benites VM, Machado PL, Fidalgo EC, Coelho MR, Madari BE. 2007. Pedotransfer functions for estimating soil bulk density from existing soil survey reports in Brazil. Geoderma, 139; 90-97. Besalatpour A, Ayoubi S, Hajabbasi M, Mosaddeghi M, Schulin R. 2013. Estimating wet soil aggregate stability from easily available properties in a highly mountainous watershed. Catena, 111; 72-79. Boivin P, Schäffer B, Temgoua E, Gratier M, Steinman G. 2006. Assessment of soil compaction using soil shrinkage modelling: Experimental data and perspectives. Soil Tillage Research, 88; 65-79. Braunack M, Arvidsson J, Håkansson I. 2006. Effect of harvest traffic position on soil conditions and sugarcane (Saccharum officinarum) response to environmental conditions in Queensland. Australia. Soil Tillage Research, 89; 103-121. Ekwue E, Stone R. 1997. Density-moisture relations of some Trinidadian soils incorporated with sewage sludge. Journal of agricultural engineering research, 40; 317-323. Håkansson I, Stenberg M, Rydberg T. 1998. Long-term experiments with different depths of mouldboard ploughing in Sweden. Soil Tillage Research, 46; 209-223. Hargreaves PR, Baker KL, Graceson A, Bonnett S, Ball BC, Cloy JM. 2019. Soil compaction effects on grassland silage yields and soil structure under different levels of compaction over three years. European Journal of Agronomy, 109; 125916. Heuscher SA, Brandt CC, Jardine PM. 2005. Using soil physical and chemical properties to estimate bulk density. Soil Science Society of America Journal, 69; 51-56. Khaboushan EA, Emami H, Mosaddeghi MR, Astaraei AR. 2018. Estimation of unsaturated shear strength parameters using easily-available soil properties. Soil and Tillage Research, 184; 118-127. Kodikara J, Islam T, Sounthararajah A. 2018. Review of soil compaction: History and recent developments. Transportation Geotechnics, 17; 24-34. Lado M, Ben-Hur M, Shainberg I. 2007. Clay mineralogy, ionic composition, and pH effects on hydraulic properties of depositional seals. Soil Science Society of America Journal, 71; 314-321. Lambe T. 1951. Soil testing for engineers. John Willey, New York. DESERT2021,26(2): 173-186 186 Lleras C. 2005. Path analysis. Encyclopedia of social measurement, 3(1), 25-30 Lozano N, Rolim MM, Oliveira VS, Tavares UE, Pedrosa EMR. 2013. Evaluation of soil compaction by modeling field vehicle traffic with SoilFlex during sugarcane harvest. Soil and Tillage Research, 129; 61-68. Mohammadi M, Fatemi Aghda SM, Talkhablou T, Cheshomi A. 2020. Prediction of the shear strength parameters from easily-available soil properties by means of multivariate regression and artificial neural network methods. Geomechanics and Geoengineering, 1-13. Montgomery D, Peck E, Vining G. 2012. Introduction to linear regression analysis. John Wiley & Sons. Mosaddeghi M, Morshedizad M, Mahboubi A, Dexter A, Schulin R 2009. Laboratory evaluation of a model for soil crumbling for prediction of the optimum soil water content for tillage. Soil Tillage Research, 105; 242-250. Nelson R, Klameth L, Nettleton W. 1978. Determining Soil Gypsum Content and Expressing Properties of Gypsiferous Soils Soil Science Society of America Journal, 42; 659-661. Pal M, Bharati P. 2019. Introduction to correlation and linear regression analysis. In Applications of Regression Techniques(pp. 1-18). Springer, Singapore Proctor R. 1933. Fundamental principles of soil compaction. Engineering News-Record, 111. Reichert J, Suzuki , Dalvan J, Horn R, Håkansson I. 2009. Reference bulk density and critical degree-ofcompactness for no-till crop production in subtropical highly weathered soils. Soil Tillage Research, 102; 242-254. Sarmast M, Farpoor MH, Boroujeni IE. 2016. Comparing Soil Taxonomy (2014) and updated WRB (2015) for describing calcareous and gypsiferous soils, Central Iran. Catena, 145; 83-91. Shirani H. 2017. Artificial neural networks with an application approach in agricultural sciences and natural resources, Valiasr University of Rafsanjan Publications. (In persian). Shirani H, Habibi M, Besalatpour A, Esfandiarpour I. 2015. Determining the features influencing physical quality of calcareous soils in a semiarid region of Iran using a hybrid PSO-DT algorithm. Geoderma, 259; 1-11. Shirani H, Rizabandi E, Mosaddeghi M, Dashti H. 2010. impact of pistachio residues on compactibility, and permeability for water and air of two aridic soils from southeast of Iran. Arid land research management, 24; 365-384. Sims J. 1996. Lime requirement. In: Sparks, D.L., A.L. Page, P.A. Helmke, P.N. Loeppert, P.N. Soltanpour, M.A. Tabatabai, C.T. Johnson, M.E. Sumner(Ed.) Methods of Soil Analysis. Chemical Methods. Madison, WI: Soil Sci. Soc. Am. J. Book Series Number 5. ASA and SSSA, pp. 491-515. Soane B. 1990. The role of organic matter in soil compactibility: a review of some practical aspects. Soil Tillage Research, 16; 179-201. Soane B, Van Ouwerkerk C. 2013. Soil compaction in Crop production. editors. Developments in agricultural engineering. Amsterdam: Elsevier; p. 662. Vaught R, Brye KR, Miller D. 2006. Relationships among coefficient of linear extensibility and clay fractions in expansive, stoney soils. Soil Science Society of America Journal, 70; 1983-1990. Wagner L, Ambe N, Ding D. 1994. Estimating a Proctor density curve from intrinsic soil properties. Transactions of the ASAE, 37; 1121-1125. Walkley A, Black IA. 1934. An examination of the Degtjareff method for determining soil organic matter, and a proposed modification of the chromic acid titration method. Soil science, 37; 29-38. Zhang S, Grip H, Lövdahl L. 2006. Effect of soil compaction on hydraulic properties of two loess soils in China. Soil Tillage Research, 90; 117-125. Zolfaghari Z, Mosaddeghi M, Ayoubi S. 2015. ANN‐based pedotransfer and soil spatial prediction functions for predicting Atterberg consistency limits and indices from easily available properties at the watershed scale in western Iran. Soil use management, 31; 142-154. | ||
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