Register      Login
Soil Research Soil Research Society
Soil, land care and environmental research
RESEARCH ARTICLE

Modelling of soil texture and its verification with related soil properties

M. Shahadat Hossain A , G. K. M. Mustafizur Rahman B D , M. Saiful Alam B , M. Mizanur Rahman B , A. R. M. Solaiman B and M. A. Baset Mia C
+ Author Affiliations
- Author Affiliations

A Department of Soil Science, Sylhet Agricultural University, Sylhet, Bangladesh.

B Department of Soil Science, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur, Bangladesh.

C Department of Crop Botany, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur, Bangladesh.

D Corresponding author. Email: mustafiz@bsmrau.edu.bd

Soil Research 56(4) 421-428 https://doi.org/10.1071/SR17252
Submitted: 23 September 2017  Accepted: 25 January 2018   Published: 19 April 2018

Abstract

Soil texture is an independent and innate soil property and other dynamic soil properties such as electrical conductivity (EC), organic carbon (OC) content and cation exchange capacity (CEC) are mostly dependent on it. An attempt was made to develop a model for numerically simulating soil texture and also to construct relationships of the developed model with other soil properties. Hypothetical data of particle size distribution and our data were used to justify and validate the newly defined indices. Scatter diagrams showed good association between the indices and hypothetical data of soil separates. Moreover, similar trends were observed between the line charts of USDA soil textural class codes and the indices. Strong correlations (r = 0.78–0.96) were found between the indices and soil separates (sand, silt and clay) for our data. However, the indices demonstrated moderate correlations (r = –0.34 to –0.55) with EC and OC of the soils. Strong nonlinear relationships were found between CEC and the three indices (R2 = 0.699, R2 = 0.732 and R2 = 0.672 (all P < 0.001). Furthermore, the variability of EC, OC and CEC within a single USDA textural class and customised textural index groups were described using the developed model. The developed indices showed excellent fitness for simulation of soil texture and demonstrated an extended applicability in terms of their relationships with soil properties related to soil texture, which will help in constructing digital soil maps.

Additional keywords: index, justification, relationship, simulation, soil characteristics, validation.


References

Bouyoucos GJ (1962) Hydrometer method improved for making particle size analysis of soils. Agronomy Journal 54, 464–465.
Hydrometer method improved for making particle size analysis of soils.Crossref | GoogleScholarGoogle Scholar |

Brady NC, Weil RR (2002) ‘The Nature and Properties of Soils.’ 13th Edn. (Pearson Education: New York, USA)

Cotching WE, Oliver G, Downie M, Corkrey R, Doyle RB (2013) Land use and management influences on surface soil organic carbon in Tasmania. Soil Research 51, 615–630.
Land use and management influences on surface soil organic carbon in Tasmania.Crossref | GoogleScholarGoogle Scholar |

Davis ROE, Bennett HH (1927) Grouping of soils on the basis of mechanical analysis. USDA Department Circular 419. USA.

Domsch H, Giebel A (2004) Estimation of soil textural feature from soil electrical conductivity recorded using the EM38. Precision Agriculture 5, 389–409.
Estimation of soil textural feature from soil electrical conductivity recorded using the EM38.Crossref | GoogleScholarGoogle Scholar |

Ersahin S, Gunal H, Kutlu T, Yetgin B, Coban S (2006) Estimating specific surface area and cation exchange capacity in soils using fractal dimension of particle-size distribution. Geoderma 136, 588–597.
Estimating specific surface area and cation exchange capacity in soils using fractal dimension of particle-size distribution.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD28Xhtlaktr7E&md5=a55a5ef12843522fada039b379c5fef5CAS |

Franzen D (2003) Managing saline soils in North Dakota. North Dakota State University Extension Service. Fargo. Available online at http://www.ag.ndsu.edu/ (Accessed on 25 April 2016)

Greve Greve Greve Greve (2012) Quantifying the ability of environmental parameters to predict soil texture fractions using regression-tree model with GIS and LADAR data: The case study of Denmark. Ecological Indicator 18, 1–10.

Inboonchuay T, Suddhiprakarn A, Kheoruenromne I, Anusontpornperm S, Gilkes RJ (2016) Amounts and associations of heavy metals in paddy soils of the Khorat Basin, Thailand. Geoderma Regional 7, 120–131.
Amounts and associations of heavy metals in paddy soils of the Khorat Basin, Thailand.Crossref | GoogleScholarGoogle Scholar |

Jackson ML (1973) ‘Soil Chemical Analysis.’ (Prentice Hall of India Pvt. Ltd.: New Delhi, India)

Jin Z, Dong YS, Qi YC, Liu WG, An ZS (2013) Characterizing variations in soil particle size distribution along a grass-desert shrub transition in the Ordos Plateau of inner Mongolia, China. Land Degradation & Development 24, 141–146.
Characterizing variations in soil particle size distribution along a grass-desert shrub transition in the Ordos Plateau of inner Mongolia, China.Crossref | GoogleScholarGoogle Scholar |

Khan SR, Abbasi MK, Ul Husan A (2012) Effect of induced soil compaction on changes in soil properties and wheat productivity under sandy loam and sandy clay loam soils: a greenhouse experiment. Communications in Soil Science and Plant Analysis 43, 2550–2563.
Effect of induced soil compaction on changes in soil properties and wheat productivity under sandy loam and sandy clay loam soils: a greenhouse experiment.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC38XhtFaku7nE&md5=f2b7343e35de8b6dcbf41de0c674098bCAS |

Kleinbaum DG, Kupper LL, Muller KE (1988) ‘Applied regression analysis and other multivariate methods.’ 2nd Edn. (Duxbury Press: Belmont, California, USA)

Murthy VRK (2002) ‘Basic Principles of Agricultural Meteorology.’ (Book Syndicate Publishers: Hyderabad, India)

Nelson DW, Sommers LE (1982) Total carbon, organic carbon, and organic matter, In ‘Methods of Soil Analysis’. (Ed. AL Page) pp. 539–579. (Agronomy Monograph: ASA. and SSSA, Madison, WI)

Plante AF, Conant RT, Stewart CE, Paustian K, Six J (2006) Impact of soil texture on the distribution of soil organic matter in physical and chemical fractions. Soil Science Society of America Journal 70, 287–296.
Impact of soil texture on the distribution of soil organic matter in physical and chemical fractions.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD28Xht1WnsrY%3D&md5=a5b0eb628f75197ae2a41e0a541c11c1CAS |

Shein EV (2015) Physically based mathematical models in soil science: history, current state, problems, and outlook (analytical review). Eurasian Soil Science 48, 712–718.
Physically based mathematical models in soil science: history, current state, problems, and outlook (analytical review).Crossref | GoogleScholarGoogle Scholar |

Silva M, Poly F, Guillaumaud N, van Elsas JD, Salles JF (2012) Fluctuations in ammonia oxidizing communities across agricultural soils are driven by soil structure and pH. Frontiers in Microbiology 3,
Fluctuations in ammonia oxidizing communities across agricultural soils are driven by soil structure and pH.Crossref | GoogleScholarGoogle Scholar |

Soil Survey Staff (1951) ‘Soil Survey Manual. USDA Agric. Hand Book 18.’ (Govt. Printing Office: Washington DC, USA)

Sonmez S, Buyuktas D, Okturen F, Citak S (2008) Assessment of different soil to water ratios (1 : 1, 1 : 2.5, 1 : 5) in soil salinity studies. Geoderma 144, 361–369.
Assessment of different soil to water ratios (1 : 1, 1 : 2.5, 1 : 5) in soil salinity studies.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1cXitl2jtrY%3D&md5=a656f105f040a2b1241a9e9cca1c658fCAS |

Sultan K (2006) Clay mineralogy of central Victorian (Creswick) soils: clay mineral contents as a possible tool of environmental indicator. Soil and Sediment Contamination 15, 339–356.
Clay mineralogy of central Victorian (Creswick) soils: clay mineral contents as a possible tool of environmental indicator.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD28XmtlGnt70%3D&md5=f3d8c5814e44383b36350b3ccf85961aCAS |

van Capelle C, Schrader S, Brunotte J (2012) Tillage-induced changes in the functional diversity of soil biota- A review with a focus on German data. European Journal of Soil Biology 50, 165–181.
Tillage-induced changes in the functional diversity of soil biota- A review with a focus on German data.Crossref | GoogleScholarGoogle Scholar |

Vereecken H, Schnepf A, Hopmans JW, Javaux M, Or D, Roose T, Vanderborght J, Young MH, Amelung W, Aitkenhead M, Allison SD, Assouiine S, Baveye P, Berli M, Brüggemann N, Finke P, Flury M, Gaiser T, Govers G, Ghezzehei T, Hallett P, Hendricks Franssen JH, Heppell J, Horn R, Huisman JA, Jacques D, Jonard F, Kollet S, Lafolie F, Lamorski K, Leitner D, McBratney A, Minasny B, Montzka C, Nowak W, Pachepsky Y, Padarian J, Romano N, Roth K, Rothfuss Y, Rowe EC, Schwen A, Šimůnek J, Tiktak A, Van Dam J, van der Zee SEATM, Vogel HJ, Vrugt JA, Wöhling T, Young IM (2016) Modeling soil processes: review, key challenges and new perspective. Vadose Zone Journal 15, 1–57.
Modeling soil processes: review, key challenges and new perspective.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC2sXhs1agtbbE&md5=a6dea13bf10f77a4180103e30c04e311CAS |

Whitney M (1911) ‘The Use of Soils East of the Great Plains Region. USDA Bureau of Soils Bulletin No. 78.’ (Govt. Printing Office: Washington DC, USA)