Canola emergence rates vary widely with management practices and field conditions, thus it is difficult to know the specific seeding density required to achieve the optimum plant population for maximizing canola yields. It would be beneficial for growers to know the precise range of emergence rates that can be expected based on their practices as well as the local environmental conditions. Meta-analysis is a powerful analytical tool where many independent data sets can be combined into a single analysis to provide a more accurate, wide-ranging interpretation of a research topic. The greatest benefit of meta-analysis for agronomic research is the greater statistical power that results from increased replication across a greater diversity of environments. The objective of this project was to utilize archived small-plot canola agronomic trial data and corresponding regional weather data to conduct a meta-analysis to examine the relationship between environmental conditions and canola emergence. The combined data set comprised agronomic and environmental data from 12 different projects conducted across a total of 47 site-years in Saskatchewan and Manitoba from 2013-2022. Single-variable regression and two-variable interaction models with mixed effects were used to examine the effects of individual management and environment variables and their interactions on the percent emergence of canola. The overall average percent emergence was 60.7%. The meta-analysis confirmed that the field emergence values frequently observed in canola production in western Canada, in the range of as low as 20-30% to as high as 80-90%, can be explained by measurable management and environmental variables. Seeding density, seeding date, seed-placed fertilizer, and average pre- and post-seeding air temperature all had negative effects on the percent emergence of canola, while pre- and post-seeding precipitation had positive effects on emergence. Significant interactions between non-correlated independent variables indicated that seeding date and average air temperature before and after seeding were the most influential variables, but the effects appeared to be likely related to soil moisture. Interacting effects between management and environmental variables were more likely under more ideal conditions of the most influential variables, specifically earlier seeding dates, lower average temperatures, and higher precipitation. Growers should be able to utilize the results of the meta-analysis to adjust seeding densities under certain conditions to achieve the optimum plant populations.