Urban foresters must be able to accurately assess costs associated with planting trees in the built environment, especially since resources to perform community forest management are limited. Red oak (Quercus rubra) and swamp white oak (Q. bicolor) (n = 48) that were produced using four different nursery production systems—balled and burlapped (BNB), bare root (BR), pot-in-pot container grown (PIP), and in-ground fabric (IGF)—were evaluated to determine costs of planting in the urban environment. Costs associated with digging holes, moving the trees to the holes, and planting the trees were combined to determine the mean cost per tree: BNB trees cost $11.01 to plant, on average, which was significantly greater than PIP ($6.52), IGF ($5.38), and BR ($4.38) trees. Mean costs for BR trees were significantly lower than all other types of trees; IGF trees were less expensive to plant (by $1.14) than PIP trees, but this difference was not statistically significant (P = 0.058). Probabilities that cost per tree are less than specific values also are calculated. For example, the probabilities that IGF and BR can be planted for less than $8.00 per tree are 1.00. The probability that a PIP can be planted for less than $8.00 is 0.86, whereas the probability for a BNB tree is just 0.01. This study demonstrates that the cost of planting urban trees may be affected significantly in accordance with their respective nursery production method.
Benjamin L. Green, Richard W. Harper, and Daniel A. Lass
L.W. Lass, R.H. Callihan, and D.O. Everson
Predicting sweet corn (Zea mays var. rugosa Bonaf.) harvest dates based on simple linear regression has failed to provide planting schedules that result in the uniform delivery of raw product to processing plants. Adjusting for the date that the field was at 80% silk in one model improved the forecast accuracy if year, field location, cultivar, soil albedo, herbicide family used, kernel moisture, and planting date were used as independent variables. Among predictive models, forecasting the Julian harvest date had the highest correlation with independent variables (R2 = 0.943) and the lowest coefficient of variation (cv = 1.31%). In a model predicting growing-degree days between planting date and harvest, R2 (coefficient of determination) = 0.85 and cv = 2.79%. In the model predicting sunlight hours between planting and harvest, R2 = 0.88 and cv = 6.41%. Predicting the Julian harvest date using several independent variables was more accurate than other models using a simple linear regression based on growing-degree days when compared to actual harvest time.