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United States Department of Agriculture

Agricultural Research Service

Assessment of Salinity and Irrigation/Drainage Practices
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1 - Abstract
2 - Introduction
3 - Mobile Four-Electrode Sensing System
4 - Mobile Electromagnetic Sensing System
5 - Mapping Theory and Software
6 - Conclusions
7 - References
Mapping Theory and Software
 
Several of the examples given to show the utility of the assessment equipment involved results converted to soil salinity units, as determined using soil samples and conventional laboratory-extraction procedures (ECe). In fact, most effective use of the mobile sensor-systems described above requires a rapid, accurate method for converting ECa measurements to ECe values.
 
We previously showed (Rhoades et al., 1989b, 1990) that ECe can be determined from ECa with sufficient accuracy for practical assessment using knowledge, or reasonably accurate estimates, of the clay and water contents in the soil profile at each ECa measurement site. While this method is suitable when ECa measurements are made by hand, it is impractical for processing the large amounts of data generated with the mobile measurement systems.
 
For this reason we developed a practical methodology based on multiple linear regression (MLR) to estimate soil salinity from
 
  • Extensive ECa survey data
  • Limited ground-truth data of ECe
  • Trend surface parameters
With the assessment system described herein, a series of easily obtained EM, or four-electrode, or both, instrument readings are acquired across a field using a relatively dense, systematic survey scheme. A limited number of soil samples are then acquired from a specially selected, subset of measurement-sites (as explained below) and measured for salinity (the Rapid Field Method of Rhoades et al, 1989a is most practical for this purpose).
 
An MLR equation is subsequently established with the co-located data (Lesch et al,1992).
 
An important requisite of the MLR approach is that the locations of the soil salinity calibration sites must be spatially representative of the entire survey area. This requisite was satisfied by implementing a newly developed spatial sampling algorithm.
 
Theory and tests of appropriateness of both the MLR approach and the calibration sampling/siting algorithm are described in detail elsewhere (Lesch et al, 1993a,b). Software for this approach is available from the U. S. Salinity Laboratory (Lesch, et. al., 1995). Additionally, other software has been developed to process the mobile, four-electrode transect data for the purposes of plotting transect "profiles" and producing salinity maps.
 

An example soil salinity map produced using the above procedures and software

Figure 10: predicted soil salinity survey map
Figure 10. Map of average rootzone (0-1.2 m) soil salinity (ECe basis) in a tile-drained field (silty loam soil) located in the Coachella Valley of California. which shows the spatial pattern (average rootzone basis) of the Coachella Valley field previously discussed
  • The median ECe value of 10-12 dS/m measured within the 0-1 m depth in the field shown in figure 10, is excessive for crop production.
  • Additionally, the type of salinity distribution found within the profile, as previously discussed, implies that the direction of net water within the rootzone varies across the field depending upon the invoked patterns of irrigation and drainage.
  • The net-flow direction is upward over much of the field implying inadequate irrigation and drainage systems and related management.
  • These results are described in more detail in two papers: submitted to the Journal of Irrigation and Drainage, ASCE entitled:
"Improving Irrigation/Drainage/Salinity Management using Spatially Referenced Salinity Measurements" by J D Rhoades, S M Lesch, R D Le Mert, and W J Alves.

"Salt Transport in Cracking Soils: Salt Distributions in Soil Profiles and Fields, and Salt Pickup by Runoff Waters" by J D Rhoades, S M Lesch, S L Burch, J Letey, R D Le Mert, P J Shouse, J D Oster, and T O'Halloran.
 
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Last Modified: 4/7/2006
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