CSIRO Publishing blank image blank image blank image blank imageBooksblank image blank image blank image blank imageJournalsblank image blank image blank image blank imageAbout Usblank image blank image blank image blank imageShopping Cartblank image blank image blank image You are here: Journals > Soil Research   
Soil Research
Journal Banner
  Soil, Land Care & Environmental Research
blank image Search
blank image blank image
blank image
  Advanced Search

Journal Home
About the Journal
Editorial Structure
For Advertisers
Online Early
Current Issue
Just Accepted
All Issues
Special Issues
Sample Issue
For Authors
General Information
Submit Article
Author Instructions
Open Access
For Referees
Referee Guidelines
Review an Article
Annual Referee Index
For Subscribers
Subscription Prices
Customer Service
Print Publication Dates

blue arrow e-Alerts
blank image
Subscribe to our Email Alert or RSS feeds for the latest journal papers.

red arrow Connect with us
blank image
facebook twitter LinkedIn

Now Online

Land Resources Surveys


Article     |     Next >>   Contents Vol 50(6)

Using genetic programming to transform from Australian to USDA/FAO soil particle-size classification system

José Padarian A B , Budiman Minasny A and Alex McBratney A

A Faculty of Agriculture and Environment, The University of Sydney, Biomedical Building, 1 Central Avenue, Australian Technology Park, NSW 2015, Australia.
B Corresponding author. Email: jose.padarian@sydney.edu.au

Soil Research 50(6) 443-446 http://dx.doi.org/10.1071/SR12139
Submitted: 24 May 2012  Accepted: 3 August 2012   Published: 18 September 2012

PDF (439 KB) $25
 Export Citation

The difference between the International (adopted by Australia) and the USDA/FAO particle-size classification systems is the limit between silt and sand fractions (20 μm for the International and 50 µm for the USDA/FAO). In order to work with pedotransfer functions generated under the USDA/FAO system with Australian soil survey data, a conversion should be attempted. The aim of this work is to improve prior models using larger datasets and a genetic programming technique, in the form of a symbolic regression. The 2–50 µm fraction was predicted using a USDA dataset which included both particle-size classification systems. The presented model reduced the root mean square error (%) by 14.96 and 23.62% (IGBP-DIS dataset and Australian dataset, respectively), compared with the previous model.

Additional keywords: pedotransfer functions, soil texture, symbolic regression.


Buchan G (1989) Applicability of the simple lognormal model to particle-size distribution in soils. Soil Science 147, 155–161.
CrossRef |

Ines AV, Honda K, Gupta AD, Droogers P, Clemente RS (2006) Combining remote sensing-simulation modeling and genetic algorithm optimization to explore water management options in irrigated agriculture. Agricultural Water Management 83, 221–232.
CrossRef |

Johari A, Habibagahi G, Ghahramani A (2006) Prediction of soil–water characteristic curve using genetic programming. Journal of Geotechnical and Geoenvironmental Engineering 132, 661–665.
CrossRef |

Koza J (1992) ‘Genetic programming: On the programming of computers by means of natural selection.’ (The MIT Press: Cambridge, MA)

Koza J (1994) ‘Genetic programming II: Automatic discovery of reusable subprograms.’ (The MIT Press: Cambridge, MA)

Koza J, Bennett H, Andre D, Keane M (1999) ‘Genetic programming III: darwinian invention and problem solving.’ (Morgan Kaufmann Publishers: Burlington, MA)

Makkeasorn A, Chang N, Beaman M, Wyatt C, Slater C (2006) Soil moisture estimation in a semiarid watershed using RADARSAT-1 satellite imagery and genetic programming. Water Resources Research 42, W09401
CrossRef |

Marshall T (1947) Mechanical composition of soil in relation to field descriptions of texture. Council for Scientific and Industrial Research, Bulletin No. 224. Melbourne, Australia.

Minasny B, McBratney A (2001) The australian soil texture boomerang: a comparison of the australian and USDA/FAO soil particle-size classification systems. Australian Journal of Soil Research 39, 1443–1451.
CrossRef |

Minasny B, McBratney A, Bristow K (1999) Comparison of different approaches to the development of pedotransfer functions for water-retention curves. Geoderma 93, 225–253.
CrossRef |

Parasuraman K, Elshorbagy A, Carey SK (2007a) Modelling the dynamics of the evapotranspiration process using genetic programming. Hydrological Sciences Journal 52, 563–578.
CrossRef |

Parasuraman K, Elshorbagy A, Si BC (2007b) Estimating saturated hydraulic conductivity using genetic programming. Soil Science Society of America Journal 71, 1676–1684.
CrossRef |

Rousseva S (1997) Data transformations between soil texture schemes. European Journal of Soil Science 48, 749–758.
CrossRef |

Selle B, Muttil N (2011) Testing the structure of a hydrological model using genetic programming. Journal of Hydrology 397, 1–9.
CrossRef |

Sharma DK, Jana R (2009) Fuzzy goal programming based genetic algorithm approach to nutrient management for rice crop planning. International Journal of Production Economics 121, 224–232.
CrossRef |

Shirazi M, Boersma L, Hart J (1988) A unifying quantitative analysis of soil texture: improvement of precision and extension of scale. Soil Science Society of America Journal 52, 181–190.
CrossRef |

Soil Survey Staff (1995) ‘Soil characterization and profile description data.’ (Soil Survey Laboratory, Natural Resources Conservation Service, USDA: Lincoln, NE)

Tempel P, Batjes N, van Engelen V (1996) ‘IGBP-DIS soil data set for pedotransfer function development.’ (International Soil Reference and Information Centre: Wageningen, The Netherlands)

Subscriber Login

Legal & Privacy | Contact Us | Help


© CSIRO 1996-2015