While we can learn a lot from this, it's quite obvious that the real world isn't like this. In the wild, temperatures vary just as the daylight varies, the changes in the length of the day correspond to the age of the plant (as it ages over spring/summer), the amount of water available varies wildly. This paper has nonetheless, tried to track changes in gene expression of plants in the wild. They used principal component analysis, which broadly speaking involves sort large numbers of possible genes into set of genes - to find groups of genes that corresponded to different conditions. In the wild at least, there was a limited number of conditions they could track, water levels and temperature - you can't separate the length of daylight/age of plant factors, they're to closely intertwined. Given what has been found in the lab, they were able to correlate a few of the changes in gene expression with groups of genes.
Even better though is that some of the changes in the expression of the groups of genes they tracked, were correlated with multiple different conditions. Notice I say correlated here. In the wild everything is sufficiently messy that you can only identify correlation, not causation. Here's the bit that I'm not sure people will recognize as being so extraordinarily cool though. We can identify various components of the control mechanisms in the lab. In the wild, these control mechanisms interact with each other and the environment in ways that we can't predict yet. The complexity of these systems is immense. And this is just in plants. Which if you think about it, are pretty uniform from one end to the other - there's differences, just nowhere near the differences you get in animals. And when you get that extra layer of complexity ... how freaking cool is that. There is literally so much to learn here that it boggles the mind.