Can fair comparisons between products be made when there are uncertainties in the data?

Yes, they can. But there are some caveats.

Any comparison between products should only be made when the individual assessments have been made with the same method, scope, system boundaries, and assumptions. If the different studies have been made with differences in any of these aspects the comparison may be unfair and unreliable, with the risk of the wrong conclusions being drawn.

There are uncertainties regarding several aspects of the calculations. Most of the uncertainties relate to biological processes, such as nitrous oxide emissions from agricultural fields or methane emissions from livestock. These emissions may differ depending on growing conditions, weather, individual animals in a given population, etc. As a food producer, you typically cannot control or even know exactly what the weather was like where grains were grown, or which animal in a stock ended up in which package, etc. What can be known, however, is what the emissions are on average for different production systems.

Consequently, different products and different production systems may have predictable differences between their expected emissions. With similar uncertainties, there can still be a point in comparing them, as long as the method for the calculations, scope, all system boundaries, and assumptions are consistent.
As an example, say that you have two products with emissions of 0.66 kg CO2e and 0.74 kg CO2e respectively. There may be an uncertainty interval of say ± 0.08 kg CO2e for both. But the first may still cause reliably lower emissions due to known factors, i.e. factors with much lower uncertainty, such as transport or energy-related emissions.

Also, the producers may make an investment that reliably reduces the emissions for the second product by eg. 0.06 kg CO2e. This matters and can be known. The only way to convey this improvement about the product is to communicate the climate footprint with relatively high precision, as the midpoint in the uncertainty interval. The uncertainty is outside of the control and knowledge of the producers and from a probability perspective, can be assumed to affect the two products equally.