Skip to main content

A new methodology to assess the maximum irrigation rates at catchment scale using geostatistics and GIS

Abstract

Soil hydraulic parameters are important for irrigation scheduling. In the domain of “precision irrigation”, knowledge of the spatial distribution of these parameters is useful in determining the maximum irrigation rate for each field in a catchment. This study focuses on the development of a new methodology to assess the spatial distribution of the maximum irrigation rate depending on the available soil water holding capacity (ASWHC). This methodology combines geostatistical techniques with geographical information system (GIS) tools. A pilot zone of 12 400 ha in a Spanish Mediterranean area was selected to develop this methodology. The linear coregionalization model (LMCR), considering the percentage of sand, carbonates, and ASWHC at others soil depths as covariates, was the best option to model the ASWHC. Other required soil parameters were also spatially modeled. The percent of coarse fragments was modeled by regression kriging considering the soil map as an auxiliary variable. The bulk density was spatially modeled by LMCR, and extended to the rooting depth by linear regression. The spatial distributions modeled were implemented in a GIS with other spatial information layers of irrigation management parameters, such as the maximum allowable depletion of soil water content, the percent of wetted soil and the irrigation depth. The combination of these layers in the GIS was used to estimate the maximum irrigation rates for each field. A propagation error analysis was performed to know the uncertainties in the maximum irrigation rate estimation. Based on this information, the irrigation managers could optimize the irrigation rates for each field.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Abbreviations

a:

Semivariogram range

ASWHC:

Available soil water holding capacity

BDfine :

Soil bulk density of the fine earth (<2 mm Ø)

C:

Semivariogram sill

CF:

Percentage of coarse fragments (>2 mm Ø)

Co :

Semivariogram nugget effect

CV:

Coefficient of variation

d:

Soil wetted depth

DI:

Dependency index

FC:

Soil water content at field capacity

GIS:

Geographical information system

IRmax :

Maximum irrigation rate

KSMD:

Kriging combined with soil map delineation

KS:

Kolmogorov–Smirnov test

LMCR:

Linear coregionalization model

MAD:

Maximum allowable depletion of the soil water content

OK:

Ordinary kriging

P:

Percentage of wetted soil surface

PWP:

Soil water content at permanent wilting point

RK:

Regression kriging

RMSE:

Root mean square error

RMSSE:

Root mean standardized square error

SE BD :

Standard error of the estimation of the BDfine

SE CF :

Standard error of the estimation of the CF

SE FC :

Standard error of the estimation of the FC

SE(IR max):

Standard error of the estimation of the IRmax

SE PWP :

Standard error of the estimation of the PWP

WUE:

Water use efficiency

References

  1. Abouatallah, A., Salghi, R., El Fadl, A., Hammouti, B., Zarrouk, A., Atraoui, A., et al. (2012). Shading nets usefulness for water saving on citrus orchards under different irrigation doses. Current World Environment, 7(1), 13–22. Retrieved March 5, 2015 from http://www.cwejournal.org/?p=1709.

  2. Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop evapotranspiration: Guidelines for computing crop requirements: Irrigation and Drainage Paper No. 56. FAO, Rome, Italy.

  3. Bouyoucos, G. J. (1927). The hydrometer as a new method for the mechanical analysis of soil. Soil Science, 23, 343–353. doi:10.1097/00010694-192705000-00002.

    CAS  Article  Google Scholar 

  4. Burrough, P. A., & McDonnell, R. A. (1998). Error propagation in numerical modelling. In Burrough & McDonnell (Eds.), Principles of geographic information systems (pp. 241–264). Oxford: Oxford University Press.

    Google Scholar 

  5. Cambardella, C. A., Moorman, T. B., Novak, J. M., Parkin, T. B., Karlen, D. L., Turco, R. F., et al. (1994). Field-scale variability of soil properties in Central Iowa soils. Soil Science Society American Journal, 58, 1501–1511.

    Article  Google Scholar 

  6. De Paz, J. M., Visconti, F., & Rubio, J. L. (2011). Spatial evaluation of soil salinity using the WET sensor in the irrigated area of the Segura river lowland. Journal of Plant Nutrition and Soil Science, 174, 103–112. doi:10.1002/jpln.200900221.

    Article  Google Scholar 

  7. Demolon, A., & Leroux, D. (1952). Guide pour l’etude expérimental du sol. Paris: Gauthier-Villars.

    Google Scholar 

  8. Derrel, L. M. Gilley, J. R., & Baumer, O. W. (1993). Irrigation Water Requirements, Chap. 2. In SCS National Engineering Handbook. Part 623. SCS, USDA.

  9. Evans, R. G., Iversen, W. M., & Kim, Y. (2012). Integrated decision support, sensor networks, and adaptive control for wireless site-specific sprinkler irrigation. Applied Engineering in Agriculture, 28(3), 377–387.

    Article  Google Scholar 

  10. Folhes, M. T., Renno, C. D., & Soares, J. V. (2009). Remote sensing for irrigation water management in the semi-arid Northeast of Brazil. Agricultural Water Management, 96(10), 1398–1408.

    Article  Google Scholar 

  11. Goovaerts, P. (1997). Geostatistics for natural resources evaluation., Applied geostatistics series New York, Oxford: Oxford University Press.

    Google Scholar 

  12. Gowda, P. H., Chavez, J. L., Colaizzi, P. D., Evett, S. R., Howell, T. A., & Tolk, J. A. (2008). ET mapping for agricultural water management: Present status and challenges. Irrigation Science, 26(3), 223–237.

    Article  Google Scholar 

  13. Hedley, C. B., & Yule, I. J. (2009). A method for spatial prediction of daily soil water status for precise irrigation scheduling. Agricultural Water Management, 96, 1737–1745.

    Article  Google Scholar 

  14. Hernández, J. M. (1997). Volumen de suelo mojado. In J. Rodrigo, J. M. Hernández, A. Pérez, J. F González (Eds.), Riego localizado, (pp. 159–169). Mundi-prensa, Ministerio de Agricultura, Pesca y Alimentación. Madrid. (In Spanish).

  15. Heuvelink, G. B. M. (2000). Error propagation with local GIS operations: Theory. In Heuvelink (Ed.), Error propagation in environmental modelling with GIS (pp. 33–49). London: Taylor & Francis.

    Google Scholar 

  16. Hollander, M., Wolfe, D. A., & Chicken, E. (2014). Nonparametric statistical methods. Hoboken, NJ: Wiley.

    Google Scholar 

  17. Hsiao, T. C., Steduto, P., & Fereres, E. (2007). A systematic and quantitative approach to improve water use efficiency in agriculture. Irrigation Science, 25, 209–231.

    Article  Google Scholar 

  18. International Atomic Energy Agency. (2008). Field estimation of soil water content: A practical guide to methods, instrumentation and sensor technology. Vienna: International Atomic Energy Agency.

    Google Scholar 

  19. IUSS Working Group WRB. (2006). World Reference Base for soil resources 2006. 2nd Ed. World Soil Resources Reports No. 103. FAO, Rome.

  20. Jiménez-Bello, M. A., Ruiz, L. A., Hermosilla, T., Recio, J., & Intrigliolo, D. S. (2012). Use of remote sensing and Geographic information Tools for irrigation Management of citrus trees. Options Mediterranéennes B, 67, 65–75.

    Google Scholar 

  21. Keller, J. (1978). Trickle irrigation. In Soil Conservation Service, National Engineer Handbook Section 15-7. USDA.

  22. Keller, J., & Karmeli, D. (1974). Trickle irrigation design. Rain bird. California: Glendora.

    Google Scholar 

  23. Kern, J. S. (1995). Geographic patterns of soil water-holding capacity in the contiguous United States. Soil Science Society American Journal, 59(4), 1129–1133.

    Google Scholar 

  24. Lea-Cox, J. D. (2012). Using wireless sensors networks for precision irrigation scheduling. In M. Kumar (Ed.), Problems, perspectives and challenges of agricultural water management (pp. 233–258). Rijeka: Intech.

    Google Scholar 

  25. Li, L., Nielsen, D. C., Yu, Q., Ma, L., & Ahuja, L. R. (2010). Evaluating the crop water stress index and its correlation with latent heat and CO2 fluxes over winter wheat and maize in the North China plain. Agricultural Water Management, 97, 1146–1155.

    Article  Google Scholar 

  26. Melton, F. S., Johnson, L. F., Lund, C. P., Pierce, L. L., Michaelis, A. R., Hiatt, S. H., et al. (2012). Satellite irrigation management support with the terrestrial observation and prediction system: A framework for integration of satellite and surface observations to support improvements in agricultural water resource management. IEEE Journal of Selected Topics in applied Earth Observations and Remote Sensing, 5, 1709–1721.

    Article  Google Scholar 

  27. Neale, C. M. U., Geli, H. M. E., Kustas, W. P., Alfieri, J. G., Gowda, P. H., Evett, S. R., et al. (2012). Soil water content estimation using a remote sensing based hybrid evapotranspiration modeling approach. Advances in Water Resources, 50, 152–161.

    Article  Google Scholar 

  28. Orton, T. G., Pringle, M. J., Page, K. L., Dalal, R. C., & Bishop, T. F. A. (2014). Spatial prediction of soil organic carbon stock using a linear model of coregionalisation. Geoderma, 230–231, 119–130.

    Article  Google Scholar 

  29. Pebesma, E. J., & Wesseling, C. G. (1998). Gstat, a program for geostatistical modelling, prediction and simulation. Computers and Geosciences, 24(1), 17–31.

    Article  Google Scholar 

  30. PNOA 2010. Plan nacional de ortofotografia aérea. Centro nacional de información geográfica (CNIG), (Spain). Retrieved March 5, 2015 from http://centrodedescargas.cnig.es/CentroDescargas/index.jsp.

  31. Poesen, J., & Lavee, H. (1994). Rock fragments in top soils: Significance and processes. Catena, 23, 1–28.

    Article  Google Scholar 

  32. Poggio, L., Gimona, A., Brown, I., & Castellazzi, M. (2010). Soil available water capacity interpolation and spatial uncertainty modelling at multiple geographical extents. Geoderma, 160, 175–188.

    Article  Google Scholar 

  33. Reynolds, C. A., Jackson, T. J., & Rawls, W. J. (2000). Estimating soil water-holding capacities by linking the Food and Agriculture Organization Soil Map of the World with global pedon databases and continuous pedotransfer functions. Water Resources Research, 36(12), 3653–3662.

    Article  Google Scholar 

  34. Richards, L. A. (1947). Pressure-membrane apparatus construction and use. Agricultural Engineering, 28, 451–454.

    Google Scholar 

  35. Rubio, J. L., Sánchez, J., & Forteza, J. (1995). Mapa de suelos de la Comunidad Valenciana: Sagunto (668). Generalitat Valenciana (Ed.), Valencia (Spain). (In Spanish).

  36. Santa Olalla, J. M., & Juan, J. A. (1993). La aplicación del agua con el riego y su evaluación. In J. M. Santa Olalla & J. A. de Juan (Eds.), Agronomía del Riego. (pp. 613–667). Mundiprensa, Madrid (In Spanish).

  37. Santra, P., Chopra, U. K., & Chakraborty, D. (2008). Spatial variability of soil properties and its application in predicting surface map of hydraulic parameters in an agricultural farm. Current Science, 95, 937–945. Retrieved March 5, 2015 from http://www.currentscience.ac.in/Downloads/article_id_095_07_0937_0945_0.pdf.

  38. Selle, B., Morgen, R., & Huwe, B. (2006). Regionalising the available water capacity from readily available data. Geoderma, 132, 391–405.

    Article  Google Scholar 

  39. Shah, N. G., & Das, I. (2012). Precision irrigation: Sensor network based irrigation. In M. Kumar (Ed.), Problems, perspectives and challenges of agricultural water management (pp. 217–232). Rijeka: Intech.

    Google Scholar 

  40. Singh, R. K., & Irmak, A. (2009). Estimation of crop coefficients using satellite remote sensing. Journal of Irrigation and Drainage Engineering-ASCE, 135(5), 597–608.

    Article  Google Scholar 

  41. Smith, R. J., & Baillie, J. N. (2009). Defining precision irrigation: A new approach to irrigation management. Proceedings of Irrigation and Drainage Conference, Irrigation Australia Ltd., Swan Hill, VIC.

  42. Smith R. J., Baillie J. N., McCarthy, A. C., Raine, S. R., & Baillie, C. P. (2010). Review of Precision Irrigation Technologies and their Application. A Report for National Program for Sustainable Irrigation, Publication 1003017/1, University South Queensland, Toowoomb.

  43. Sven, G. J. (2012). The influence of physical soil properties on the water supply of irrigated orchards-some examples from Val Venosta (South Tyrol/Northern Italy). Journal of Environmental Biology, 33, 417–424. Retrieved March 5, 2015 from http://www.jeb.co.in/journal_issues/201204_apr12_supp/paper_17.pdf.

  44. Taylor, J. R. (1997). An introduction to error analysis: The study of uncertainties in physical measurements (p. 327)., Propagation of uncertainties Sausalito: University Science Books.

    Google Scholar 

  45. Taylor, J. R., & Burrough, P. A. (1986). Multiscale sources of spatial variation in soil. III. Improved methods for fitting the nested model to one-dimensional semivariograms. Mathematical Geology, 18, 811–821.

    Article  Google Scholar 

  46. Ten-Lin, L., Kai-Wei, J., & Dar-Yuan, L. (2006). Interpolating soil properties using kriging combined with categorical information of soil maps. Soil Science Society American Journal, 70, 1200–1209.

    Article  Google Scholar 

  47. Thorup-Kristensen, K. (2001). Root growth and soil nitrogen depletion by onion, lettuce, early cabbage and carrot. Acta Hort. (ISHS) 563, 201–206. Retrieved March 15, 2015 from http://www.actahort.org/books/563/563_25.htm.

  48. Torri, D., Poesen, J., Monaci, F., & Busoni, E. (1994). Rock fragment content and fine soil bulk density. Catena, 23, 65–71.

    Article  Google Scholar 

  49. Veihmeyer, F. J., & Hendrickson, A. H. (1927). The relation of soil moisture to cultivation and plant growth. Proceedings 1st International Congress Soil Science 3, 498–513.

  50. Visconti, F., de Paz, J. M., Martínez, D., & Molina, M. J. (2014). Laboratory and field assessment of the capacitance sensors Decagon 10HS and 5TE for estimating the water content of irrigated soils. Agricultural Water Management, 113, 111–119.

    Article  Google Scholar 

  51. Webster, R., & Oliver, M. A. (2007). Geostatistics for environmental scientists. Statistics in practice. Chichester: Wiley. 315 p.

    Book  Google Scholar 

  52. Wünsche, J. N., Lakso, A. N., & Robinson, T. L. (1995). Comparison of four methods for estimating total light interception by apple trees of varying forms. HortScience, 30, 272–276. Retrieved March 5, 2015 from http://hortsci.ashspublications.org/content/30/2/272.full.pdf.

  53. Young, M. H., Caldwell, T. G., Meadows, D. G., & Fenstermaker, L. F. (2009). Variability of soil physical and hydraulic properties at the Mojave Global Change Facility, Nevada: Implications for water budget and evapotranspiration. Journal of Arid Environments, 73, 733–744.

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to acknowledge the “Ministerio de Economia y Competitividad” from the Government of Spain for funding the Projects CGL2012-39725-C02-01and CGL2012-39725-C02-02 and the anonymous reviewers for the helpful suggestions to improve the manuscript.

Author information

Affiliations

Authors

Corresponding author

Correspondence to J. M. De Paz.

Electronic supplementary material

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

De Paz, J.M., Albert, C., Visconti, F. et al. A new methodology to assess the maximum irrigation rates at catchment scale using geostatistics and GIS. Precision Agric 16, 505–531 (2015). https://doi.org/10.1007/s11119-015-9392-y

Download citation

Keywords

  • Precision irrigation
  • Geostatistics
  • GIS
  • Irrigation rate
  • Soil water holding capacity