An inclination to take the sums and averages of datum is natural, but can lead to misinformed opinions on the cohesiveness of a system's state-space. For instance, tillage is a controlling parameter to soil respiration, yet the averaged effects of tillage at the landscape scale do not necessarily pronounce themselves below at short-term timescales. Thus, an exploratory data analysis of field variables allows the researcher to better understand patterns occurring within a landscape's state-space that may otherwise be missed by traditional summary statistics, providing a valuable research tool to illuminate spatio-temporal trends within data sets. Presented is an analysis of field-scale soil characteristics and soil respiration in an agriculture field following the division of the field into minimum and standard tillage treatments, and the subsequent growth of maize and sunflower. A variety of analytical techniques were employed to explore patterns amongst variables. A simple analysis of variance was used as a starting point to determine independent correlations. Spatial trends within the field were calculated by two methods: variogram models were used for clay content, total C, and bulk density; and median polish was used for analyzing soil respiration and temperature trends across tillage treatments. Lastly, the temporal persistence of soil respiration was calculated using the Spearman rank correlation coefficient and the relative difference from mean values of soil respiration, across the field at a given time, over several sampling events.