Interpretation of the random effect variance in lmer Sorry, I am new to linear mixed models. I run a linear mixed model and get the following Output:

I researched the yield and LAI of Sweetcorn. Yield and LAI of [A] pic was measured in ground-base using portable machine. In case of [B] pic , all data was recorded by UAV (Same field). So, the No. of data is different because of the measuring method.
Anyway, i run a linear mixed model using lmer in R and got the result sheet as above.
I am looking forward to report this result including random effect variance, however, it is too hard to understand the meaning of random effect variance exactly. My question is, what exactly does the random effects tells me (the variance is 53.92 and 52.59 in [A], 5.525 and 816.310 in [B])? How do i interpret the result of random effect?
Thanks in advance.

Without knowing exactly what your variables measure (continous, centered, etc.), I can only speak to what I see. It appears that you have an intercepts-only model without random slopes. From what I can see, the random effects intercept here (53.92) is the variance between plots, whereas the residual random effects variance (52.59) is how much they vary within plots.This can also be interpreted as how much error there is in your model estimation of random effects.
As this model does not include random slopes, we cannot say for certain what the relationship is between each slope or if it is correlated with each intercept.
