I have a dataset that I want to model with a single response variable (yield of a crop plant). However, I have multiple replicates of my proposed predictor variables, from multiple sampling surveys of visiting insect pollinators for each plant.
Is it possible to model yield without averaging the predictor variables by plant? The only way I see to do this is to have repeated entries of the same response measurement for each plant, in order to match the number of survey repetitions for each plant. However, we were not able to conduct insect surveys on each plant for the same number of times due to logistical reasons.
It seems like I would be losing information if I just averaged the predictor variable values for each plant, but on the other hand, inflating the number of responses to match the number of insect surveys would place more weight on plants with more surveys (I think).
Is there a recommended approach for dealing with this situation? Is this addressed by adding the plant ID as a random effect?