Effects of the texture and organic matter values in the estimation of the soil water content at a regional scale

  1. Pedro Pérez Cutillas 12
  2. Gonzalo G. Barberá 1
  3. Carmelo Conesa García 2
  1. 1 Centro de Edafología y Biología aplicada del Segura
    info

    Centro de Edafología y Biología aplicada del Segura

    Murcia, España

    ROR https://ror.org/01fah6g03

  2. 2 Universidad de Murcia
    info

    Universidad de Murcia

    Murcia, España

    ROR https://ror.org/03p3aeb86

Revista:
Cuadernos de investigación geográfica: Geographical Research Letters
  1. Latron, J. (ed. lit.)
  2. Lana-Renault Monreal, Noemí (ed. lit.)

ISSN: 0211-6820,  1697-9540

Ano de publicación: 2018

Volume: 44

Número: 2

Páxinas: 697-718

Tipo: Artigo

DOI: 10.18172/CIG.3484 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

Outras publicacións en: Cuadernos de investigación geográfica: Geographical Research Letters

Obxectivos de Desenvolvemento Sustentable

Resumo

This study compares two methods for the estimation of hydraulic properties of the soil at the regional scale. Soil water content (θ) values was estimated at two fixed soil matric potential values), associated with the field capacity (θfc) and wilting point (θwp). The first method is carried out directly using (θ) values of analytical determinations, by modeling them as a function of environmental variables. The second method employed texture and organic matter (OM) information to obtain (θ) values by pedotransfer functions (PTFs). The comparison of both methods allows evaluating the effect of the textures and OM, of which a significant effect of these variables is produced, suggested that there is a considerable level of consistency between the two methods, despite some differences induced by coarse textures (sand) and OM.

Referencias bibliográficas

  • Akaike, H. 1974. A new look at the statistical model identification. IEEE Transactions on Automatic Control 19, 716-723. https://doi.org/10.1109/TAC.1974.1100705.
  • Arruda, F.B., Zullo Jr, J., Oliveira, J.B. 1987. Parâmetros de solo para o cálculo da água disponível com base na textura do solo. Revista Brasileira de Ciência do Solo 11, 11-15.
  • Batjes, N.H. 1996. Development of a world data set of soil water retention properties using pedotransfer rules. Geoderma 71, 31-52. https://doi.org/10.1016/0016-7061(95)00089-5.
  • Bouma, J. 1989. Using soil survey data for quantitative land evaluation. Advances in Soil Science 9, 177-213. https://doi.org/10.1007/978-1-4612-3532-3_4.
  • Børgesen, C.D., Iversen, B.V., Jacobsen, O.H., Schaap, M.G. 2008. Pedotransfer functions estimating soil hydraulic properties using different soil parameters. Hydrological Processes 22 (11), 1630-1639. https://doi.org/10.1002/hyp.6731.
  • Brady, N.C. 1984. The nature and properties of soils. MacMillan Publishing Company. New York.
  • Dijkerman, J.C. 1988. An Ustult-Aquult-Tropept Catena in Sierra Leone, West Africa, II. Land Qualities and Land. Geoderma 42, 29-49. https://doi.org/10.1016/0016-7061(88)90021-3
  • Hutson, J.L., Cass, A. 1987. A retentivity function for use in soil-water simulation models. Journal of Soil Science 38 (1), 105-113. https://doi.org/10.1111/j.1365-2389.1987.tb02128.x.
  • ICONA. 1986. Proyecto LUCDEME (Lucha contra la Desertificacion del Mediterraneo). Mapa de suelos, escala 1:100.000. ICONA - Ministerio de Agricultura, Pesca y Alimentación, Madrid.
  • Klute, A. 1986. Water retention: Laboratory methods. In: A. Klute (Ed.), Methods of Soil Analysis. Part 1: Physical and Mineralogical Methods. Agranomy Monograph 9, ASA, Madison, WI, pp. 635-662. https://doi.org/10.2136/sssabookser5.1.2ed.c26.
  • Kreye, P., Meon, G. 2016. Subgrid spatial variability of soil hydraulic functions for hydrological modelling. Hydrology and Earth System Sciences 20, 2557-2571. https://doi.org/10.5194/hess-20-2557-2016.
  • Lal, R. 1979. Physical properties and moisture retention characteristics of some Nigerian soils. Geoderma 21, 209-223. https://doi.org/10.1016/0016-7061(78)90028-9.
  • Lal, R., Mahboubi, A.A., Fausey, N.R. 1994. Long-term tillage and rotation effects on properties of a central Ohio soil. Soil Science Society of America Journal 58, 517-522. https://doi.org/10.2136/sssaj1994.03615995005800020038x.
  • Martínez Fernández, J. 1996. Variabilidad especial de las propiedades físicas e hídricas de los suelos en medio semiárido mediterráneo. Tesis Doctoral, Universidad de Murcia, 191 pp.
  • Malik, R.S., Butter, B.S., Analauf, R., Richter, J. 1987. Water penetration into soils with different textures and initial soil contents. Soil Science 144 (6), 389-393. https://doi.org/10.1097/00010694-198712000-00001.
  • Masutti, M.M. 1997. Caracterização da água disponível a partir de parâmetros físico-hídricos em solos da zona da mata do Estado de Pernambuco. Tesis Doctoral, Universidade Federal Rural de Pernambuco, 69 pp., Recife. (Tesis Doctoral)
  • Nelson, D.W., Sommers, L.E. 1982. Total Carbon Organic Carbon and Organic Matter. In: A.L. Page, R.H. Miller, D.R. Keeny (Eds.), Methods of Soil Analysis, Part 2-Chemical and Microbiological Properties, second ed., 9, Part 2. Agronomy Monograph, Madison, WI, pp. 539-579.
  • Nemes, A., Rawls, W.J., 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. https://doi.org/10.2136/sssaj2005.0128.
  • Patil, N.G., Singh, S.K. 2016. Pedotransfer Functions for Estimating Soil Hydraulic Properties: A Review. Pedosphere 26 (4), 417-430. https://doi.org/10.1016/S1002-0160 (15)60054-6.
  • Peraza, J.E.S. 2003. Retenção de água e pedofunções para solos do Rio Grande do Sul. Tesis Doctoral, Universidade Federal de Santa Maria, 118 pp., Santa Maria.
  • Pérez Cutillas, P. 2013. Modelización de propiedades físicas del suelo a escala regional. Casos de estudios en el Sureste Ibérico. Tesis Doctoral, Universidad de Murcia, ISBN: 978-84-697-0660-2.
  • Pérez Cutillas, P., Barberá, G.G., Conesa García, C. 2015. Estimación de la humedad del suelo a niveles de capacidad de campo y punto de marchitez mediante modelos predictivos a escala regional. Boletín de la Asociación de Geógrafos Españoles 68, 523-529.
  • Pérez Cutillas, P., Barberá, G.G., Conesa García, C. 2017. Efectos de las variables ambientales en la estimación de materia orgánica del suelo a escala regional. Boletín de la Asociación de Geógrafos Españoles 75, 175-191.
  • Pidgeon, J.D. 1972. The measurement and prediction of available water capacity of Ferrallitic soils in Uganda. Journal Soil Science, 23, 431-441. https://doi.org/10.1111/j.1365-2389.1972.tb01674.x.
  • Pribyl, D.W. 2010. A critical review of the conventional SOC to SOM conversion factor. Geoderma 156, 75-83. https://doi.org/10.1016/j.geoderma.2010.02.003.
  • Rawls, W.J., Brakensiek, D.L., Saxton, K.E. 1982. Estimation of soil water properties. Transactions of the ASAE 108, 1316-1320. https://doi.org/10.13031/2013.33720.
  • Rawls, W.J., Pachepsky, Y.A., Ritchie, J.E., Sobecki, T.M., Bloodworth, H, 2003. Effect of soil organic carbon on soil water retention. Geoderma 116, 61-76. https://doi.org/10.1016/S0016-7061(03)00094-6.
  • Rawls, W.J., Nemes, A., Pachepskl, Y. 2004. Effect of soil organic carbon on soil hydraulic properties. Developments in soil science 30. https://doi.org/10.1016/S0166-2481(04)30006-1
  • Richards, L.A. 1947. Pressure membrane apparatus: construction and use. Agricultural Engineering 28, 451-454.
  • Santanello, J., Peters-Lidard, C., Garcia, M., Mocko, D., Tischler, M., Moran, M., Thomas, D. 2007. Using remotely sensed estimates of soil moisture to infer spatially distributed soil hydraulic properties. Remote Sensing of Environment 110, 79-97. https://doi.org/10.1016/j.rse.2007.02.007.
  • Schaap, M.G., Leij, F.J., Van Genuchten, M.Th. 2001. Rosetta: A computer program for estimating soil hydraulic parameters with hierarchical pedotransfer functions. Journal of Hydrology, 251 (3-4), 163-176. https://doi.org/10.1016/S0022-1694(01)00466-8.
  • Schuh, W.M., Cline, R.L. 1990. Effect of soil properties on unsaturated hydraulic conductivity pore- interaction factors. Soil Science Society of America Journal 54 (6), 1509-1519. https://doi.org/10.2136/sssaj1990.03615995005400060001.x.
  • Šimunek, J., Van Genuchten, M.Th., Šejna, M. 2008. Development and applications of the HYDRUS and STANMOD software packages and related codes. Vadose Zone Journal 7, 587-600. https://doi.org/10.2136/vzj2007.0077.
  • Twarakavi, N.K.C, Šimunek, J., Schaap, M.G. 2010. Can texture-based classification optimally classify soils with respect to soil hydraulics? Water Resources Research 46 (1), W01501. https://doi.org/10.1029/2009WR007939.
  • Van Genuchten, M.Th. 1980. Closed-form equation for predicting the hydraulic conductivity of unsaturated soil. Soil Science Society of America Journal 44, 1147-1152. https://doi.org/10.2136/sssaj1980.03615995004400050002x.
  • Van Beers, W.F.J. 1980. Soils and soil properties. In: Drainage Principles and Applications.Vol. I. ILRI. Wageningen.
  • Wang, K., Zhang, C., Li, W. 2013. Predictive mapping of soil total nitrogen at a regional scale: A comparison between geographically weighted regression and cokriging. Applied Geography 42, 73-85. https://doi.org/10.1016/j.apgeo.2013.04.002.
  • Wettschereck, D., Aha, D.W., Mohri, T. 1997. A Review and Empirical Evaluation of Feature Weighting Methods for a Class of Lazy Learning Algorithms. Artificial Intelligence Review 11 (1-5), 273-314. https://doi.org/10.1023/A:1006593614256.
  • Wosten, J.H.M., Van Genuchten, M.T. 1988. Using texture and other soil properties to predict the unsaturated soil hydraulic functions. Soil Science Society of America Journal 52 (6), 1762-1770. https://doi.org/10.2136/sssaj1988.03615995005200060045x.
Fonte de datos: Dialnet