The yield in any given field or management zone is a product of interaction between many soil properties and production inputs. Therefore, multi-year yield maps may give better insight into determining potential management zones. This research was conducted to develop a methodology to delineate yield response zones by using two-state frequency analysis conducted on yield maps for 3 years on two commercial corn fields near Wiggins, Colorado. A zone was identified by the number of years that yield was equal and greater than the average yield in a given year. Classes producing statistically similar yield were combined resulting in three potential yield zones. Results indicated that the variability of yield over time and space could successfully be assessed at the same time without the drawbacks of averaging data from different years. Frequency analysis of multi-year yield data could be an effective way to establish yield response zones. Seventeen percent of the field #1 consistently produced lower yield than the mean while 43% of the field produced yield over the mean. Corresponding values for field #2 were 6% and 42%. The remainder of the fields produced fluctuating yields between years. These spatially and temporally sound yield response maps could be used to identify the yield-limiting factors in zones where yield is either low or fluctuating. Yield response maps could also be helpful to delineate potential management zones with the help of resource zones such as electrical conductivity and soil maps, along with the directed soil sampling results.

Comparing distributions among fields, species, and management practices will help us understand the spatial dynamics of weed seed banks, but analyzing observational data requires nontraditional statistical methods. We used cluster analysis and classification and regression tree analysis (CART) to investigate factors that influence spatial distributions of seed banks. CART is a method for developing predictive models, but it is also used to explain variation in a response variable from a set of possible explanatory variables. With cluster analysis, we identified patterns of variation with direction of the distance over which seed bank density was correlated (range of spatial dependence) with single-species seed banks in corn. Then we predicted patterns of the seed banks with CART using field and species characteristics and seed bank density as explanatory variables. Patterns differed by magnitude of variation in the range of spatial dependence (strength of anisotropy) and direction of the maximum range. Density and type of irrigation explained the most variation in pattern. Long ranges were associated with large seed banks and stronger anisotropy with furrow than center pivot irrigation. Pattern was also explained by seed size and longevity, characteristics for natural dispersal, species, soil texture, and whether the weed was a grass or broadleaf. Significance of these factors depended on density or type of irrigation, and some patterns were predicted for more than one combination of factors. Dispersal was identified as a primary process of spatial dynamics and pattern varied for seed spread by tillage, wind, or natural dispersal. However, demographic characteristics and density were more important in this research than in previous research. Impact of these factors may have been clearer because interactions were modeled. Lack of data will be the greatest obstacle to using comparative studies and CART to understand the spatial dynamics of weed seed banks.

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In zone soil sampling a field is divided into homogenous areas using an easy to measure ancillary attribute (e.g., apparent soil electrical conductivity [ECa]) and a few samples are taken from each zone to estimate the soil characteristics in each zone. This study determined if ECa–directed zone sampling in two fields in northeastern Colorado could correctly predict soil texture and soil organic matter (SOM) patterns of samples taken by a more intensive grid sample method. Each field, which were predominantly Bijou loamy sand (coarse‐loamy, mixed, superactive, mesic Ustic Haplargids), and Valentine sand (mixed, mesic Typic Ustipsamments), was divided into three ECa zones and soil texture and OM content in the top 30 cm of soil were measured. There was a significant difference in the soil texture and SOM in both fields between ECa Zone 1 and Zone 3. Logistic regression showed that in both fields, approximately 80% of the grid sample sites in ECa Zone 1 were correctly predicted. Only 50% of the grid sample sites in ECa Zone 3 were correctly predicted as Zone 3 in one field whereas 77% of the grid sites in ECa Zone 3 were correctly predicted in the other field. However, approximately 80% of the samples in the grid sites ≥10 m from the zone boundaries were classified correctly as compared to the samples that were <10 m from the boundary in which only 50 to 54% were classified correctly. These results support the utilization of ECa‐directed zone sampling as an alternative to grid soil sampling if the transition zones are avoided.

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