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

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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.

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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

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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.

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Authors and Affiliations

  1. Instituto Valenciano de Investigaciones Agrarias, Carretera Moncada-Náquera km 4.5, Apdo. Oficial, 46113, Moncada, Valencia, Spain

    J. M. De Paz, C. Albert, F. Visconti & F. Ingelmo

  2. Centro de Investigaciones sobre Desertificación, Carretera Moncada-Naquera km 4.5, Apdo. Oficial, 46113, Moncada, Valencia, Spain

    M. G. Jiménez, F. Ingelmo & M. J. Molina

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  1. J. M. De Paz
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  2. C. Albert
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  3. F. Visconti
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  4. M. G. Jiménez
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  5. F. Ingelmo
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  6. M. J. Molina
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Correspondence to J. M. De Paz.

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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

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