limma, edgeR and DESeq2 use different ways to estimate the read counts that they are using for the DE analysis as well as for the log2FC.
The basic steps of the DE tools are:
- normalization for differences in sequencing depth
- turning the very few read counts per gene per condition (usually only 2 or 3!) into values that will work with downstream statistical tests
- apply the statistical tests
The tools have different solutions for all three steps, but while edgeR and DESeq2 are (nowadays) somewhat similar, limma uses the voom transformation for step 2, which actually aims at changing a lot of the annoying properties of read count data (their paper is very informative). Therefore, it is not too surprising that the logFC values differ since the values that each tool ends up using for the statistical test are not the actual raw read counts.
As far as what you can trust more -- there's no good answer to that. All three values are estimates that have been shaped by the specific assumptions the tools make. Personally, I would not be concerned as long as the trends are the same, and after a quick glance at your example above it seems like the direction is generally the same.
Just make sure to (i) note which logFC value you're going with and (ii) to use this type of value for all your samples and comparisons.