ArkValley Irrigation Grant Final Report

• Project Type: Ag/Muni Grant
• Applicant: Colorado State University Cooperative Extensions
• Project Name: Improvement of Irrigation Technology in Arkansas River Valley
• Title: Demonstrations of Irrigation Technology to Improve Crop Yields, Returns and Water Quality in the Arkansas River Valley of Colorado Summary and Conclusions
• County: Larimer
• Water Conservation - Doc Type: Final Report

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Field 4.

Both the salinity and SAR levels were fairly high in this field, Additionally,
both tended to increase with depth. Like the other fields, the boron levels were
quite low. This field displayed the most soil texture (SP) variability, and the
highest salinity I SAR correlation (0.99). Both the boron and SP data were
also highly correlated with the salinity data.

In general, the salinity and SAR data were always highly correlated in each field, This
suggests that the soil chemistry in each field was rather static, even though the soil texture
appeared to be quite variable in 3 of the 4 fields. Additionally, 3 of the 4 fields showed some
degree of positive correlation between salinity and soil texture. The boron levels in all of the
fields were quite low, and the relative % water content was not usually correlated with the other
chemical data.

In the absence of a crop, high positive correlation between water content and salinity
typically indicates water logging (and hence a drainage problem), while high negative correlation
typically indicates poor irrigation and/or infiltration uniformity. Neither of these situations
appeared to be occurring in any of the four fields discussed above. High positive correlation
between salinity and soil texture is not uncommon even in well managed fields. Such a
relationship can simply occur due to difficulties with the leaching of salts from heavier textured
soils. Hence, the moderate correlation between the salinity and SP data in fields 1, 3, and 4 may
simply reflect long term leaching rates. The pH levels in all 4 fields were between 7.5 and 8.0,
which are not indicative ofa sodicity problem. However, some of the soil SAR levels in fields 2
and 4 were high enough to be classified as sodic.

Field Salinity Modeling and Estimation

Figure 5 shows the general agreement between the depth specific predicted and observed
soil salinity levels for fields 1,2 & 3, and 4. The regression models used for each field were
estimated using the ESAP software package. In fields I and 4, both EM-38 and in line four
insertion probe data were used as survey input information. In fields 2 & 3 only EM-38 data was
used, since the soil surface was especially dry and hence the insertion probe data was found to be
unreliable at about one forth of the survey sites. Note that in all cases the regression models
appear to produce reasonable predictions. Note also that field 2 & 3 have been grouped together
for prediction purposes, since these two field were adjacently located and managed in similar
manners.

Tables 5,6, and 7 show the predicted median salinity levels by depth in fields I, 2 & 3,
and 4, respectively. The percent areas of each field within pre-specified salinity range intervals are
also given in these tables. The estimated salinity maps for these fields are shown in figures 6a, 6b,
7a, 7b, 8a, and 8b. Note that figures 6a, 7a, and 8a display the bulk average salinity patterns
(averaged across all four sampling depths), while figures 6b, 7b, and 8b display the depth specific
spatial salinity patterns.