10.3.3. Loss rate per factor including stratification on the operation size

10.3.3. Loss rate per factor including stratification on the operation size

The loss calculations and confidence intervals described above can be used as a means to identify risk factors for colony loss, by looking for confidence intervals that do not overlap each other. Total loss of operations reporting or not reporting a particular management type (e.g. transport of colonies) can be compared using the chi-square test (as in VanEngelsdorp et al., 2010, 2011, for example). The loss rates of operations grouped by factors presumed to be involved in colony mortality (starvation, high varroa infestation etc.) can also be compared. Of course, this analysis does not give any information on, or account for, interdependencies of different factors, for which model fitting is needed (as described below).

To account for known or obvious differences among beekeeping operations, a first stratification, for example on operation size, can be accomplished, by classifying operations as hobby, side-line or commercial. Alternatively the number of colonies per beekeeper can be used as a basis for stratification.

Depending on the size of the survey and cultural differences between the target populations, beekeeping operations can be split into three operation size classes, for example

  • small operations (≤50 colonies),
  • intermediate operations (51-500 colonies),
  • large scale operations (> 500 colonies).

If the scale of beekeeping in the survey population is limited mainly to small and intermediate operations, the classes can be split further as:

  • small hobbyist beekeepers (≤15 colonies),
  • large hobbyist beekeepers (16-50 colonies),
  • small-commercial beekeepers (51-150 colonies),
  • larger-commercial beekeepers (150-500 colonies).

When comparing several operation size classes, a chi-square test can be used first to compare all size classes, and if the result of this is significant, it can be followed up by pairwise multiple comparisons, again using the chi -square test or a z-test of the difference in two proportions. In each such pairwise test, the significance level to reject the null hypothesis should be Bonferroni adjusted (i.e. divided by the number of tests being conducted) to reduce the rate of false rejections of the null hypothesis that operations of different sizes have equal rates of loss. It should be borne in mind that the chi-squared test and z-test assume independent observations and therefore have their limitations.