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Accurate weed emergence models are valuable tools for scheduling planting, cultivation, and herbicide applications. Multiple models predicting giant ragweed emergence have been developed, but none have been validated in diverse crop rotation and tillage systems, which have the potential to influence weed emergence patterns. This study evaluated the performance of published giant ragweed emergence models across various crop rotations and spring tillage dates in southern Minnesota. Across experiments, the most robust model was a mixed-effects Weibull (flexible sigmoidal function) model predicting emergence in relation to hydrothermal time accumulation with a base temperature of 4.4 C, a base soil matric potential of −2.5 MPa, and two random effects determined by overwinter growing degree days (GDD) (10 C) and precipitation accumulated during seedling recruitment. The deviations in emergence between individual plots and the fixed-effects model were distinguished by the positive association between the lower horizontal asymptote (Drop) and maximum daily soil temperature during seedling recruitment. This finding indicates that crops and management practices that increase soil temperature will have a shorter lag phase at the start of giant ragweed emergence compared with practices promoting cool soil temperatures. Thus, crops with early-season crop canopies such as perennial crops and crops planted in early spring and in narrow rows will likely have a slower progression of giant ragweed emergence. This research provides a valuable assessment of published giant ragweed emergence models and illustrates that accurate emergence models can be used to time field operations and improve giant ragweed control across diverse cropping systems.
IPM requires information about populations of pest and beneficial organisms in managed and constructed habitats. A recurring question is whether potentially injurious pests are abundant enough to warrant intervention, or whether beneficial organisms or other factors are likely to maintain control. Because most managed habitats are too large to be examined completely, practitioners must sample them and draw an inference about the whole.
Sampling plans in IPM can be grouped into three categories, depending on the sampler's goal. First, detection sampling is used in surveillance and regulatory applications, where the critical density of a target organism is effectively zero. Detection plans are designed to control the chance that the organism is erroneously missed. Second, estimation sampling is used where the goal is to quantify abundance, usually with desired levels of precision and reliability. Estimation sampling is used mainly in research, but it can also be used to evaluate IPM implementation and effectiveness. Finally, decision sampling is used where a choice to intervene with one or more management tactics depends on whether abundance has or will soon exceed a threshold density. Rather than estimate density, the goal is more simply to classify the habitat as needing or not needing intervention.
In all three situations, the basic process is the same. A sampler selects a set of sample units from the habitat using a defined procedure, assesses each for presence or abundance of the target organism, and then draws a conclusion based on the results.
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