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<br />.'000045 <br /> <br />Field 4. <br /> <br />Both the salinity and SAR levels were fairly high in this field. Additionally, <br />both tended to increase with depth. Like the other fields, the boron levels were <br />quite low. This field displayed the most soil texture (SP) variability, and the <br />highest salinity / SAR correlation (0.99). Both the boron and SP data were <br />also highly correlated with the salinity data. <br /> <br />In general, the salinity and SAR data were always highly correlated in each field. This <br />suggests that the soil chemistry in each field was rather static, even though the soil texture <br />appeared to be quite variable in 3 of the 4 fields. Additionally, 3 of the 4 fields showed some <br />degree of positive correlation between salinity and soil texture. The boron levels in all of the <br />fields were quite low, and the relative % water content was not usually correlated with the other <br />chemical data. <br /> <br />In the absence of a crop, high positive correlation between water content and salinity <br />typically indicates water logging (and hence a drainage problem), while high negative correlation <br />typically indicates poor irrigation and/or infiltration uniformity. Neither of these situations <br />appeared to be occurring in any of the four fields discussed above. High positive correlation <br />between salinity and soil texture is not uncommon even in well managed fields. Such a <br />relationship can simply occur due to difficulties with the leaching of salts from heavier textured <br />soils. Hence, the moderate correlation between the salinity and SP data in fields I, 3, and 4 may <br />simply reflect long term leaching rates. The pH levels in all 4 fields were between 7.5 and 8.0, <br />which are not indicative of a sodicity problem. However, some of the soil SAR levels in fields 2 <br />and 4 were high enough to be classified as sodic. <br /> <br />Field Salinity Modeling and Estimation <br /> <br />Figure 5 shows the general agreement between the depth specific predicted and observed <br />soil salinity levels for fields 1,2 & 3, and 4. The regression models used for each field were <br />estimated using the ESAP software package. In fields 1 and 4, both EM-38 and in line four <br />insertion probe data were used as survey input information. In fields 2 & 3 only EM-38 data was <br />used, since the soil surface was especially dry and hence the insertion probe data was found to be <br />unreliable at about one forth of the survey sites. Note that in all cases the regression models <br />appear to produce reasonable predictions. Note also that field 2 & 3 have been grouped together <br />for prediction purposes, since these two field were adjacently located and managed in similar <br />manners. <br /> <br />Tables 5, 6, and 7 show the predicted median salinity levels by depth in fields 1,2 & 3, <br />and 4, respectively. The percent areas of each field within pre-specified salinity range intervals are <br />also given in these tables. The estimated salinity maps for these fields are shown in figures 6a, 6b, <br />7a, 7b, 8a, and 8b. Note that figures 6a, 7a, and 8a display the bulk average salinity patterns <br />(averaged across all four sampling depths), while figures 6b, 7b, and 8b display the depth specific <br />spatial salinity patterns. <br />