There is a most lively discussion as to whether climate change will have significant negative impacts on US agriculture. There are a number of papers by my co-bloggers (I am not worthy!) showing that extreme heat days will have significant negative impacts on yields for all major crops except for rice. I will talk about rice another day. For the main crop growing regions in the US, climate models project a significant increase in these extreme heat days. This will likely, short of miraculous adaptation, lead to significant yield losses. To put it simply, this part of the literature has shown a sensitivity of yields to extreme temperatures and linked it with projected increases in these extreme temperature events.
On the other hand, there are a number of papers, which argue that climate change will have no significant impacts on US agriculture. Seo, in a recent issue of Climatic Change, essentially argues that the literature projecting big impacts confuses weather ("panel models") and climate ("cross sectional models") and that using weather instead of climate as a source of identification leads to big impacts. As Wolfram Schlenker and I note in a comment this is simply not true for five reasons:
1) Even the very limited number of papers he cites, which use weather as the source of variation to identify a sensitivity, clearly state what this means when interpreting the resulting coefficients. There is no confusion here.
2) He fails to discuss the fact that the bias from adaptation when using weather as a source of variation could go in either direction.
3) It is simply not true that all panel models find big impacts and all Ricardian cross sectional models find small impacts. There are big and small impacts to be found in both camps.
4) There is recent work by Burke and Emerick, which uses the fixed effects identification strategy with climate on the right hand side! I wish I would have thought of that. They can compare their "long differences" (a.k.a. climate) sensitivity results to more traditional weather sensitivity results and find no significant difference between the two. This will either enrage both camps or make them very happy, since it suggests that the difference between sources of variation (weather versus climate) in this setting is not huge.
5) The big differences in studies may finally not be due to differences in sensitivities, but differences in the climate model used. Burke et al. point out that uncertainty over future climate is a major driver of variation in impacts. We refer the reader to this excellent study, which discusses a much broader universe of studies and very carefully discusses the sources of uncertainty in impacts estimates.
We are of the humble opinion, that the most carefully done studies using both identification strategies yield similar estimates for the Eastern United States.