Producers are continually being exposed to new technologies and the Computer Age. Site specific management in agriculture has been gaining acceptance and use in some areas of the country and continues to grow across the Cotton Belt. Global Positioning Systems (GPS) and Geographic Information System (GIS) make it possible to geo-reference fields which become the framework for multi-layered data that can be used to describe relationships or events occurring in the field or a specific management zone. This study on 15-acre field at the Delta Research and Extension Center was initiated in 1998 in an effort to evaluate the spatial variability of corn and cotton yields as wells the spatial variability of topsoil and subsoil soil characteristics measured in the same areas. Corn grown in 1998 had yields which ranged from a low 132 bu/A to a high of 186 bu/A and a field average of 156 bu/A. In 1998, the same area had corn yields which ranged from 151 bu/A to a high of 222 bu/A and a field average of 182 bu/A. The area was rotated to cotton in 1999 with total lint yields ranging from a low of 949 lb/A to a high of 1508 lb/A and a average across the field of 1248 lb/A. Regression analysis was used was used to examine the the soil characteristics with respect to corn yields. When considering a single factor in 1998, the highest correlation occurred between yield and subsoil P (r ² = 0.2460) followed by topsoil P (r ² = 0.2394). When two factors were considered in the model, the highest correlation occurred with subsoil P + subsoil K (r ² = 0.2725). Adding the second factor did not greatly increase the correlation. With the second corn crop in 2000, subsoil P was again the factor with the highest correlation to yield (r ² = 0.0751). The correlation was poorer in 2000 than had been observed in 1998 indicating that other factors were exhibiting a stronger influence than before. The soil pH became more of a factor in 2000 with the second highest single factor correlation (r ² = 0.0657). These two factors together increased the correlation to r ² = 0.1344. When evaluating factors that influenced cotton lint yields, soil characteristics such as topsoil exchangeable acidity (r ² =
0.0979) and both subsoil CEC (r ² = 0.0971) and topsoil CEC (r ² = 0.0960) had a stronger influence than P. Crop response to P was different depending upon the crop grown. While corn yields increased with increasing P levels, cotton yields decreased. With the data collected and analyzed to date, it is apparent that the yield controlling factors are complex and are also not consistent from crop -to-crop or year-to-year. While the technology is new and exciting, many questions and concerns are yet to be answered. The new technology does provide many useful tools for examining the variations occurring in the field