Germination experiments are becoming increasingly complex and they are now routinely involving several experimental factors. Recently, a two-step approach utilizing meta-analysis methodology has been proposed for the estimation of hierarchical models suitable for describing data from such complex experiments. Step 1 involves fitting models to data from each sub-experiment, whereas Step 2 involves combination estimates from all model fits obtained in Step 1. However, one shortcoming of this approach was that visualization of resulting fitted germination curves was difficult. Here, we describe in detail an improved two-step analysis that allows visualization of cumulated data together with fitted curves and confidence bands. Also, we demonstrate in detail, through two examples, how to carry out the statistical analysis in practice.

The members of the genus Cuscuta (common name: dodder) are obligate holoparasitic plants that are found throughout the agricultural regions of the world. Of all of the species of dodder, field dodder (Cuscuta campestris) causes the most damage to crops. This species parasitizes the shoots of broadleaf plant crops and weeds. We conducted a series of field dodder seed germination tests in controlled-temperature chambers, in order to describe the effect of temperature on field dodder germination and develop a germination model based on the obtained data. The best fit was obtained when temperature data and time were transformed to thermal time using the beta-function model. The field dodder germination model can serve as a tool for knowledge-based predictions of germination and emergence timing, to allow for the implementation of effective mechanical and chemical management measures.