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RESEARCH ARTICLE

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

Abstract

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.


References

Buchan G (1989) Applicability of the simple lognormal model to particle-size distribution in soils. Soil Science 147, 155–161.
Applicability of the simple lognormal model to particle-size distribution in soils.CrossRef | open url image1

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.
Combining remote sensing-simulation modeling and genetic algorithm optimization to explore water management options in irrigated agriculture.CrossRef | open url image1

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.
Prediction of soil–water characteristic curve using genetic programming.CrossRef | open url image1

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
Soil moisture estimation in a semiarid watershed using RADARSAT-1 satellite imagery and genetic programming.CrossRef | open url image1

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.
The australian soil texture boomerang: a comparison of the australian and USDA/FAO soil particle-size classification systems.CrossRef | open url image1

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.
Comparison of different approaches to the development of pedotransfer functions for water-retention curves.CrossRef | open url image1

Parasuraman K, Elshorbagy A, Carey SK (2007a) Modelling the dynamics of the evapotranspiration process using genetic programming. Hydrological Sciences Journal 52, 563–578.
Modelling the dynamics of the evapotranspiration process using genetic programming.CrossRef | open url image1

Parasuraman K, Elshorbagy A, Si BC (2007b) Estimating saturated hydraulic conductivity using genetic programming. Soil Science Society of America Journal 71, 1676–1684.
Estimating saturated hydraulic conductivity using genetic programming.CrossRef | open url image1

Rousseva S (1997) Data transformations between soil texture schemes. European Journal of Soil Science 48, 749–758.
Data transformations between soil texture schemes.CrossRef | open url image1

Selle B, Muttil N (2011) Testing the structure of a hydrological model using genetic programming. Journal of Hydrology 397, 1–9.
Testing the structure of a hydrological model using genetic programming.CrossRef | open url image1

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.
Fuzzy goal programming based genetic algorithm approach to nutrient management for rice crop planning.CrossRef | open url image1

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.
A unifying quantitative analysis of soil texture: improvement of precision and extension of scale.CrossRef | open url image1

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)



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