Prettiest pictures of 2014

The next person that says big data, puts a fiver in the "most overused terms in meetings in the year 2014" jar. I am excited about the opportunities of ever larger micro datasets, but even more thrilled by how much thought is going into the visualization of these datasets. One of my favorite macho nerd blogs Gizmodo just put up a number of the 2014 best data visualizations. If you also think that these are of so purdy, come take Sol's class at GSPP where he will teach you how to make graphs worthy of this brave new word.

Source: Gizmodo. 

Predictions, paradigms, and paradoxes

Failure can be good. I don’t mean in the “learn from your mistakes” kind of way. Or in the “fail year after year to even put up a good fight in the Big Game” kind of way. But failure is often the sidekick of innovation and risk-taking. In places with lots of innovation, like Silicon Valley, failure doesn’t have the stigma it has in other places. Because people understand that being new and different is the best way to innovate, but also the best way to fail.

Another area that failure can be really useful is in making predictions. Not that bad predictions are useful in themselves, but they can be useful if they are bad in different ways than other predictions. Then averaging predictions together can result in something quite good. The same way that a chorus can sound good even if each person is singing off key.

We have a paper out today that provides another example of this, in the context of predicting wheat yield responses to warming. By “we” I mean a group of authors that contributed 30 different models (29 “process-based” models and 1 statistical model by yours truly), led by Senthold Asseng at U Florida. I think a few of the results are pretty interesting. For starters, the study used previously unpublished data that includes experiments with artificial heating, so the models had to predict not only average yields but responses to warming when everything else was held constant. Also, the study was designed in phases so that models were first asked to make predictions knowing only the sow date and weather data for each experiment. Then they were allowed to calibrate to phenology (flowering and maturity dates), then to some of the yield data. Not only was the multi-model median better than any individual model, but it didn’t really improve much as the models were allowed to calibrate more closely (although the individual model predictions themselves improved). This suggests that using lots of models makes it much less important to have each model try as hard as they can to be right.

A couple of other aspects were particularly interesting to me. One was how well the one statistical model did relative to the others (but you’ll have to dig in the supplement to see that). Another was that, when the multi-model median was used to look at responses to both past and projected warming at 30 sites around the world, 20 of the 30 sites showed negative impacts of past warming (for 1981-2010, see figure below). That agrees well with our high level statement in the IPCC report that negative impacts of warming have been more common than positive ones. (I actually pushed to add this analysis after I got grilled at the plenary meeting on our statement being based on too few data points. I guess this an example of policy-makers feeding back to science).

Add this recent paper to a bunch of others showing how multi-model medians perform well (like here, here, and here), and I think the paradigm for prediction in the cropping world has shifted to using at least 5 models, probably more. So what’s the paradox part? From my perspective, the main one is that there’s not a whole lot of incentive for modeling groups to participate in these projects. Of course there’s the benefit of access to new datasets, and insights into processes that can be had by comparing models. But they take a lot of time, groups generally are not receiving funds to participate, there is not much intellectual innovation to be found in running a bunch of simulations and handing them off, and the resulting publications have so many authors that most of them get very little credit. In short, I was happy to participate, especially since I had a wheat model ready from a previous paper, but it was a non-trivial amount of work and I don’t think I could advise one of my students to get heavily involved.

So here’s the situation: making accurate predictions is one of the loftiest goals of science, but the incentives to pursue the best way to make predictions (multi-model ensembles) are slim in most fields. The main areas I know of with long-standing examples of predictions using large ensembles are weather (including seasonal forecasts) or political elections. In both cases the individual models are run by agencies or polling groups with specific tasks, not scientists trying to push new research frontiers. In the long-term, I’d guess the benefit of better crop predictions on seasonal to multi-decadal time scales would probably be worth the investment by USDA and their counterparts around the world to operationalize multi-model approaches. But relying on the existing incentives for research scientists doesn’t seem like a sustainable model.

Adapting to extreme heat

Since we are nearing the holidays, I figured I should write something a bit more cheerful and encouraging than my standard line on how we are all going to starve.  My coauthor Michael Roberts and I have emphasized for a while the detrimental effect of extreme heat on corn yields and the implications for a warming planet.  When we looked at the sensitivity to extreme heat over time, we found an improvement (i.e., less susceptibility) roughly around the time hybrids were introduced in the 30s,  but that improvement soon vanished again around the 1960s.   Heat is as susceptible to heat now as it was in 1930.  Our study simply allowed the effect of extreme heat to vary smoothly across time, but wasn't tied to a particular event.

David Popp has been working a lot on innovation and he suggested to look at the effect of hybrid corn adaptation on the sensitivity to extreme heat in more detail.  Richard Sutch had a nice article on how hybrid corn was adopted slowly across states, but fairly quickly within each state.  David and I thought we could use the fairly rapid rollout within state but slow rollout across state as source of identification of the role of extreme heat. Here's a new graph of the rollout by state:

The first step was to extended the daily weather data back to 1901 to take a look at the effect of extreme heat on corn yields over time - we wanted a pre-period to rule out that crappy weather data in the early 1900s results in a lot of attenuation bias but get significant results with comparable coefficients when we use data from the first three decades of the 20th century.

In a second step we interact the weather variables with the fraction of the planted area that is hybrid corn. We find evidence that the introduction of corn is reducing the sensitivity of hybrid corn from -0.53 to -0.33 in the most flexible specification in column (3b) below, which is an almost 40% reduction. Furthermore, the sensitivity to precipitation fluctuations seems to diminish as well. (Disclaimer: these are new results, so they might change a bit once I get rid of my coding errors).

The table regresses state-level yields in 41 states on weather outcomes.  All regressions include state-fixed effects as well as quadratic time trends. Columns (b) furthermore include year fixed effects to pick up common shocks (e.g., global corn prices). Columns (1a)-(1b) replicate the standard regression equation we have been estimating before, columns (2a)-(2b) allow the effect of extreme heat to change with the fraction of hybrid corn that is planted, while columns (3a)-(3b) allow the effect of all four weather variables to change in the fraction of hybrid corn.

In summary: there is evidence that at least for the time period when hybrid corn were adopted that innovation in crop varieties lead to an improvement in heat tolerance, which would be extremely useful as climate change is increasing the frequency of these harmful temperatures.  On that (slightly more upbeat note): happy holidays.

Feeding 9... er... 11 billion people

Demographers have been telling us for a while that global populations will level off to about 9 billion, and this "9 billion" number has indeed become the conventional wisdom -- so much so that one of your trusty G-FEED bloggers actually teaches a (presumably excellent) class called "Feeding Nine Billion".

With the current global population at just over 7 billion, the belief that population might level off at 9 billion has given some solace to folks worried about the "pile of grain" problem, i.e. the general concern that feeding a bunch of extra mouths around the world might prove difficult.  9 billion people by 2100 implies a much slower population growth rate over the coming century than was observed over the last century, and while a scandalous 800 million people in the world continue to go to bed hungry every night, there has been notable success in reducing the proportion of the world population who don't have enough to eat even as populations have skyrocketed.  This success, if you can call it that, has in part to do with the ability of the world's agricultural producers to so far "keep up" with the growing demand for food induced by growing incomes and populations, as evidenced by the general decline in real food prices over the last half century (the large food price spikes in the last 5-7 years notwithstanding).

But a paper last month by Gerland et al in Science (gated version here), straightforwardly titled "World population stabilization unlikely this century", provides some uncomfortable evidence that the magic 9 billion number might be a substantial underestimate of the population we're likely to see by the end of this century.  Turns out that fertility rates have not fallen as fast in Africa as expected:  while the total fertility rate has fallen, the decline has only been about a quarter as fast as what was observed in the 1970s and 80s in Latin America and Asia.  This is apparently due both to slow declines in African families' desired family sizes, as well as a substantial unmet need for contraception. Here's a plot from this paper showing the relatively slow decline in African fertility:

So run the world forward for 85 years taking these slower-than-expected fertility declines into account, and you get population projections much higher than 9 billion.  In fact, the mean estimate in the Gerland et al paper of population in 2100 is 11 billion, with their 95% confidence interval barely scraping 9 billion on the low end and 13 (!) billion on the high end.  In fact, their 95% confidence interval for 2050 barely contains 9 billion. Here's the relevant plot (R users will appreciate the near-unadulterated use of the ggplot defaults):

Figure 1 from Gerland et al 2014, Science

So perhaps David should retitle his class, "11 is the new 9", or, "Feeding 9 billion in the next 20 years", or, "Feeding 11 billion (95% CI, 9 billion to 13 billion)".  In any case, these 2+ billion extra mouths are not entirely welcome news for those worried about the global pile of grain.  These much larger numbers imply that even greater progress needs to be made on improving agricultural yields if we want to (a) keep prices at reasonable levels and (b) not have to massively expand agricultural land use to do it.  Thanks, Gerland et al!  

Estimating the impacts of CO2 Fertilization...

While David is shaking in his boots about Saturday's matchup of the Cal Bears against Stanford (both teams have a shameful 5-5 record so far), I have been spending time perusing NASA's recent explosion of multimedia offerings. The video that caught my attention comes from a recent paper displaying the transport of CO2 across the globe. That got me thinking...

My illustrious co-bloggers have documented extensive evidence that extreme heat is bad for crops. We also know that rainfed crops do not appreciate a lack of or too much rainfall. How do we know this? The G-Feed crowd likes to use econometric methods to estimate dose response functions between yields/output and temperature/precipitation. In order to attach a causal interpretation to the estimated coefficients of these dose response functions, one needs "exogenous" (read roughly random) sources of variation in temperature and rainfall. While we know that the distribution of crops across climate zones is not random, day to day changes in weather can be interpreted as random, if one controls carefully for other confounders. We first made this point in a PNAS paper in 2006 and this has been standard practice subject to a number of well understood caveats.

So we know: Extreme Heat=Bad. Too much or too little water = Bad.

What we do not understand well so far are the impacts of CO2 on crop yields using non experimental data. There are plenty of studies which pump CO2 into open top chambers of fields and measure differences in yields between carbon fertilized and control plots. What we do not have is a good measure of carbon fertilization in a field setting which incorporates farmer behavior. What has prevented me and arguably many others from attacking this problem empirically is the fact that I thought that CO2 mixed roughly uniformly across space and any variation in CO2 is variation over time. This variation is not useful as one cannot empirically separate the impacts of CO2 from other factors that vary over time, such as prices and business cycles.

The video link above makes me want to question that assumption. The model based patterns here show tremendous spatial and temporal variability within a year. This is the type of variation in temperature and precipitation we use to identify their impacts on yield. While I understand that we do not have a great historical dataset of ground level CO2 measurements, I wonder if an interdisciplinary team of rockstars could come up with a meaningful identification strategy to allow us to measure the impacts of CO2 on yields. Not much good will come from global climate change, but we cannot simply measure the bads and ignore the goods. If anyone has any good ideas, I am interested. I got lots of great suggestions on my climate data post, so here's hoping...

The hunger strawman

A few questions are almost guaranteed to come up from an audience whenever I give a public talk, regardless of what I talk about. Probably the most persistent question is something like “Don’t we already produce more than enough food to feed everyone?” or its close relative “Isn’t hunger just a poverty or distribution problem?”

Some students recently pointed me to an op-ed by Mark Bittman in the NY Times called “Don’t ask how to feed the 9 billion” that rehashes this question/argument. It probably caught their attention because I teach a class called “Feeding 9 billion”, and they’re wondering why I’d organize a class around a question they supposedly shouldn’t even be asking. The op-ed has some catchy lines such as “The solution to malnourishment isn’t to produce more food. The solution is to eliminate poverty.” Or “So we should not be asking, ‘How will we feed the world?,’ but ‘How can we help end poverty?’" My first reaction to these kind of statements is usually “Gee, why didn’t anyone think of reducing poverty before -- we should really get some people working on that!” But more seriously, I think it’s really a quite ludicrous and potentially dangerous view for several reasons. Here’s three:

1. To talk about poverty and food production as if they are two separate things is to forget that in most of parts of the world, the poorest people earn their livelihoods in agriculture. Increasing productivity of agriculture is almost always poverty reducing in rural areas. The 2008 World Development Report explains this well. Of course, the poor in urban areas are a different story, but that doesn’t change the critical global link between underperforming agriculture and poverty.
2. Food prices matter, even if they are low enough that many of us barely notice when they change. If you go to a market, you’d of course rather have hundreds of dollars in your pocket than a few bucks. But if you are there with a few bucks, and you’re spending half or more of your income on food, it makes a big difference whether food prices are up or down by, say, 20%. If you could magically eliminate poverty that’d be great, but for a given level of poverty, small changes in prices matter. And if productivity of agriculture slows down, then (all else equal) prices tend to rise.
3. Maybe most importantly, there’s no guarantee that past progress on keeping productivity rising and prices low will continue indefinitely, especially if we lose sight of its importance. There’s a great deal of innovation and hard work that goes into simply maintaining current productivity, much less continuing to improve it. Just because many remain hungry doesn’t mean we should treat past successes as failures, or take past successes for granted. And just because we have the technology and environment to feed 7 billion, it doesn’t mean we have it to feed 9 billion (at least not on the current amount of cropland, with some fraction of land going to bioenergy, etc.).

When Stanford had Andrew Luck, we didn’t go undefeated. The football team still had some weaknesses and ended up losing a couple of games, sometimes because of a key turnover or because we gave up too many points. Nobody in their right mind, though, concluded that “the solution to winning football games isn’t to have a good quarterback, it’s to have a good defense.” That would be the wrong lesson to learn from the Andrew Luck era. In other words, it’s possible for more than one thing to matter at the same time. (Incidentally, this year Stanford football has produced more than enough points to be a great team; they just haven't distributed them evenly across the games.)

Similarly, nobody that I know is actually claiming that the only thing we have to worry about for reducing hunger is increasing crop production. That would be idiotic. So it’s a complete strawman to say that the current strategy to reduce malnourishment is simply to raise yields in agriculture. It’s part of a strategy, and an important part, but not the whole thing.

I’m not sure why this strawman persists. I can think of a few cynical reasons, but I’m not really sure. To paraphrase a joke a student told me the other day: there’s really only one good use for a strawman. To drink, man.

Indian crops and air pollution (Guest Post by Jen Burney)

Greetings, loyal G-FEED Readers,

My colleague (and former postdoctoral mentor!), V. "Ram" Ramanathan and I have a paper out this week in PNAS (here) on the impacts of short-lived climate pollutants (SLCPs) on Indian crop yields over the past 30 years (1980-2010). Anticipating the publication, the G-FEED crew invited me to guest post this week. Thanks, guys! It's a fantastic opportunity to talk about the paper itself as well as what we know more generally about air pollution and agricultural impacts. I'm biased, but I think this topic is going to be increasingly important in the coming years. And it feels fitting to discuss it here, as G-FEEDers have been active contributors to understanding the relationship between air quality and crop yields: this work builds on some papers by Ram and Max (here and here), speaks to current work by Wolfram and co. on ozone impacts in the US, and benefitted from feedback from Marshall and David at various points.

First, some background. When we think about anthropogenic climate change, we've usually got carbon dioxide (CO₂), and maybe methane (CH₄), nitrous oxide (N₂O), and CFCs/HCFCs on our minds. These greenhouse gases are basically homogeneously mixed in the atmosphere, and they have very long atmospheric lifetimes (with the exception of methane, centuries). They're the reason that, even if human emissions ceased instantaneously tomorrow, the earth would still see another degree or so of warming. However, the big boon in climate science in the past decade or so has been our increasing understanding of the role that short-lived species play in the climate system. These compounds are more conventionally considered pollutants, and include things like aerosol particulates, ozone precursor compounds, and short-lived greenhouse gases (e.g., tropospheric [surface] ozone and HFCs). The one thing they have in common is their relatively short atmospheric lifetimes, but -- as their physical and chemical properties vary widely -- their mechanisms of impact on our climate system differ. In some cases we don't yet have a full handle on their impacts: for example, uncertainty in cloud-aerosol interactions represents one of the biggest uncertainties remaining in GCMs. (Take a look at the radiative forcing attribution figure from the IPCC AR5 for fun.)

What we do know is this: black carbon is the second or third largest contributor to present warming (after CO₂ and perhaps methane, depending on how you partition its impact – it is part LLGHG and part SLCP, since it’s also an ozone precursor). And the four main warming SLCPs combined (BC, tropospheric ozone, methane, and HFCs) have contributed around half of present warming (again, it depends on how you partition methane’s impacts). Moreover, two of these – ozone and BC – have some clear pathways of impact on crops beyond their impacts through temperature and precipitation. Ozone is directly toxic to plants, and BC cuts down on surface radiation, which should negatively impact photosynthesis. And both ozone and BC are very spatially heterogeneous – they are not well-mixed, and so local emissions could very well be expected to have local impacts.

So…given these realities we took the unabashedly empirical approach and threw available data at the problem. We ran a panel regression analysis that included growing season and crop-area averaged temperature, precipitation, and pollutant emissions on the RHS, along with various time controls. Our unit of analysis was major rice- and wheat- producing states in India, and we looked at data from 1980-2010. We find that relative yield loss (RYL) for climate and air pollution (weighted average for India) is 36% for wheat. That is, yields in 2010 would have been 36% higher absent climate and pollution trends over the past 3 decades. Moreover, most of that RYL is due to pollution (90%). (Our estimate for rice RYL is 20% but is not statistically significant.) This suggests a few things: first, cost benefit analyses for air pollution mitigation programs will likely look a lot better in a place like India if agricultural benefits are included. Second, in the near term, cleaning up the air could help offset T- and P-related yield losses.

How should we think about these results in comparison to previous work? First, what we find in terms of climate (T & P) impacts is slightly smaller than the coarser-scale findings for S. Asia by David and Wolfram et al (here). (They find -5-6%, we have -3.5%.) These are probably not statistically different, but one way to think about this is that, absent pollution variables on the RHS, we’d expect the coefficients for T and P to include some of the impacts of SLCPs. The same holds for the time controls. Wolfram and others have made the case that these panel analyses should include quadratic time trends of one form or another to account for an empirical leveling off of yields. Biophysical limits alone make this a totally sensible proposition, but it’s also likely that the “leveling off” trend that has been observed (more on that by David, here) includes the previously unexplored impact of pollution.

Some previous studies have explored the impact of individual pollutants on yields. Max et al looked at black carbon indirectly, by examining the impact of surface radiation on yields (here and here). They see the signature of atmospheric brown clouds on precipitation and thus rice yields, but no direct radiative impact. My best guess is that: (1) their metric of total radiation contains a combination of two effects – a reduction in direct radiation from black carbon and an increase in diffuse radiation from scattering aerosols like sulfates. Plants can often use diffuse radiation more effectively for photosynthesis. (2) More important, rainy-season rice is probably the hardest place to see this signal, as precipitation clears particulates out of the air. We put both BC and sulfur dioxide (the main precursor of sulfate aerosols, which are scattering, or net cooling) in our model to account for these two effects independently, and we see significance for BC’s impact on wheat but not rice, as would be expected based on seasonal concentrations.

On the ozone side of things, a handful of papers have used atmospheric chemistry models to estimate surface ozone concentrations at a given point in time. Those concentrations have then been used in conjunction with exposure-response (E-R) relationships from test plots to estimate yield losses. In theory, if you really do have a good handle on surface concentrations and a valid E-R function, this would be the way to go. The preferred method is AOT40 – accumulated hours of exposure over 40ppbv. But this number is extremely sensitive to crop cultivar, estimated concentrations, when/how long you count exposure, and other management factors. So these kinds of estimates have very large error bars / uncertainties. (See here, here, here, and here.) A few of those studies suggest RYL in 2000 to be 15%-28% for India for ozone though – a comforting similarity in magnitude.

For me, the nice part of this analysis was that we really took a physical approach – we put stuff on the right hand side in forms that made physical sense, and it turned out to hold up to all the (sometimes nonsensical) things reviewers asked us to do. Best of all, the results agree at least in magnitude with some other approaches.

In terms of impact, I hope this paper helps bring agricultural benefits into the conversation about air pollution mitigation. On the methods side, we did a lot of work to think about how to detangle air pollution and climate impacts based on what we can actually measure – an exercise fraught with nonlinearity. I hope some of our work can help guide future efforts to estimate pollution (and pollution policy) impacts. Readers of this blog are well familiar with threshold-y temperature and precipitation effects, but the pollution impacts and mitigation landscape is worse. A real heterogeneous treatment effects nightmare, where everything is co-emitted with other stuff that all acts in different directions with different thresholds. (See FAQ 8.2, Figure 1 from the IPCC AR5 below for a hilarious, if depressing, rendition.) 

I mentioned the competing impacts on radiation from absorbing and scattering aerosols above, but another interesting example is in ozone formation. Ozone depends on both the absolute and relative concentrations of NOx and VOCs. We had evidence (satellite and ground) that we had both types of ozone regimes (NOx-sensitive or NOx-saturated) in our study area. We used the VOC/NOx ratio to account for that; it should probably be standard practice in these kinds of econometric analyses if you’re doing anything involving NOx or ozone.

All that horn-tooting aside, our analysis was limited in a few ways, and I’m excited to push it in some other directions as a result. A few thoughts:

First, we use emissions, and not concentrations, of pollutants. The main reason is that there aren’t any long-run records of pollutant concentrations in India (or most places, for that matter), and we need better satellite-aided algorithms for extrapolating station data to get these kind of reliable exposure maps (the main task consuming my time right now). So we use emissions inventories of aerosols and ozone precursors as proxies for concentrations. Of course, these inventories are also just estimates, and in some cases (e.g., here) have been shown to be wayyy off (particularly low for black carbon). So I’m looking forward to using better exposure proxies. Ideally, one would look at both emissions estimates (the policy-relevant variable), and concentrations (the physically-relevant variable) together.

Second, we’re also just statistically limited. Emissions of all pollutants have been going up fairly monotonically, and there’s not a ton of signal there for estimation. Going to smaller scales doesn’t make sense from a physical perspective (then you have to worry about transport). So the best thing to do would be to run this same kind of analysis in different countries. I’m particularly excited to look at different pollution regimes – think biomass dominated versus coal dominated, etc. Hopefully I can convince some G-FEEDers that this would be an awesome collaboration idea (ahem, Sol).

I’ll leave off there, though I’m always happy to discuss this stuff if there are any questions. Thanks to G-FEED for having me, and to all of you for reading this tome.

Don’t forget to vote!

- Jen

Guest posting on G-FEED

We're going to try something new out on G-FEED, which is to invite colleagues for guest posts when they have a new paper that is relevant to the topics we cover. Not only will this help to obscure how infrequently we manage to post, but it will provide some fresh perspectives. And hopefully it's a good chance for people to explain their work in their own words, without having to make a commitment to long-term posting. There is a lot of great work out there, and to paraphrase George Costanza, if you take everything the community has ever done in our entire lives and condense it down into one blog, it looks decent!

So without further ado, first up is Jen Burney, who hails from UCSD and has a new paper in PNAS on ozone and crop yields in India...

Fanning the flames, unnecessarily

This post is a convolution of David's post earlier today and Sol's from a few days ago.  Our debate with Buhaug et al that Sol blogged about has dragged on for a while now, and engendered a range of press coverage, the most recent by a news reporter at Science.  "A debate among scientists over climate change and conflict has turned ugly", the article begins, and goes on to catalog the complaints on either side while doing little to engage with the content.

Perhaps it should be no surprise that press outlets prefer to highlight the mudslinging, but this sort of coverage is not really helpful.  And what was lost in this particular coverage were the many things I think we've actually learned in the protracted debate with Buhaug.

We've been having an ongoing email dialog with ur-blogger and statistician Andrew Gelman, who often takes it upon himself to clarify or adjudicate these sorts of public statistical debates, which is a real public service.  Gelman writes in an email:

In short, one might say that you and Buhang are disagreeing on who has the burden of proof.  From your perspective, you did a reasonable analysis which holds up under reasonable perturbations and you feel it should stand, unless a critic can show that any proposed alternative data inclusion or data analytic choices make a real difference.  From their perspective, you did an analysis with a lot of questionable choices and it’s not worth taking your analysis seriously until all these specifics are resolved.

I'm sympathetic with this summary, and am actually quite sympathetic to Buhaug and colleagues' concern about our variable selection in our original Science article.  Researchers have a lot of choice over how and where to focus their analysis, which is a particular issue in our meta-analysis since there are multiple climate variables to choose from and multiple ways to operationalize them.  Therefore it could be that our original effort to bring each researcher's "preferred" specification into our meta-analysis might have doubly amplified any publication bias -- with researchers of the individual studies we reviewed emphasizing the few significant results, and Sol, Ted, and I picking the most significant one out of those.  Or perhaps the other researchers are not to blame and the problem could have just been with Sol, Ted, and my choices about what to focus on.

How journalism works (or doesn't)

One reason we started this blog was the frustration of being misrepresented in media coverage of topics we work on. It can be hard for people to grasp just how frustrating it can be. You spend time talking to a journalist until they seem to get what you're saying, they go off and write the story, and then only about half the time do they check back to see if the quotes they attribute to you are right.  Having words put in your mouth is often compounded by other issues, like a "he said, she said" tone that can make issues appear much more contentious than they should be (see Sol's last post)

Another case in point - the other day I took a call from a reporter for the Guardian who said she was working on a story about which crops are threatened by climate change. I thought I was pretty clear that when we talk about impacts we are never talking about complete eradication of the crop. But today I see their story is "8 foods you're about to lose due to climate change"!

When she asks about CO2 I say it's absolutely clear that it has a benefit, it's just a question of whether it's enough to counteract the bad stuff that happens with climate change. That turned into:

One major issue is carbon dioxide, or CO2. Plants use the gas to fuel photosynthesis, a fact that has led some analysts to argue that an increase CO2 is a good thing for farming. Lobell disagrees, noting that CO2 is only one of many factors in agriculture. “There’s a point at which adding more and more CO2 doesn’t help,” he says. Other factors – like the availability of water, the increasing occurrence of high and low temperature swings and the impact of stress on plant health – may outweigh the benefits of a CO2 boost.

What happens over time is you learn to be a little more aggressive with reporters, but that only helps so much. And also you learn to stop answering your phone so much, and to stick with the handful of reporters you think do a really good job. It's sad but true.

What's especially annoying, though, is when people see these stories and start attributing everything it says to you, as if you wrote it, picked the headline, etc. (I see some tweets today saying I'm trying to spread fear about climate change.) The irony is when I give talks or speak on panels I'm more often than not accused of being a techno-optimistic, both about climate change and food security in general. I actually am quite optimistic. About food. Just not about journalism.

One effect to rule them all? Our reply to Buhaug et al's climate and conflict commentary

For many years there has been a heated debate about the empirical link between climate and conflict. A year ago, Marshall, Ted Miguel and I published a paper in Science where we reviewed existing quantitative research on this question, reanalyzed numerous studies, synthesized results into generalizable concepts, and conducted a meta-analysis of parameter estimates (watch Ted's TED talk).  At the time, many researchers laid out criticism in the press and blogosphere, which Marshall fielded through G-FEED. In March, Halvard Buhaug posted a comment signed by 26 authors on his website strongly critiquing our analysis, essentially claiming that they had overturned our analysis by replicating it using an unbiased selection of studies and variables. At the time, I explained numerous errors in the comment here on G-FEED. 

The comment by Buhaug et al. was published today in Climatic Change as a commentary (version here), essentially unchanged from the version posted earlier with none of the errors I pointed out addressed. 

You can read our reply to Buhaug et al. here. If you don't want to bother with lengthy details, our abstract is short and direct:

Abstract: A comment by Buhaug et al. attributes disagreement between our recent analyses and their review articles to biased decisions in our meta-analysis and a difference of opinion regarding statistical approaches. The claim is false. Buhaug et al.’s alteration of our meta-analysis misrepresents findings in the literature, makes statistical errors, misclassifies multiple studies, makes coding errors, and suppresses the display of results that are consistent with our original analysis. We correct these mistakes and obtain findings in line with our original results, even when we use the study selection criteria proposed by Buhaug et al. We conclude that there is no evidence in the data supporting the claims raised in Buhaug et al.

Will the dry get drier, and is that the right question?

A “drought” can be defined, it seems, in a million different ways. Webster’s dictionary says it’s “a period of dryness especially when prolonged; specifically:  one that causes extensive damage to crops or prevents their successful growth.” Wikipedia tells me “Drought is an extended period when a region receives a deficiency in its water supply.” The urban dictionary has a different take.

But nearly all definitions share the concepts of dryness and of damage or deficiency. We’ve talked a lot on this blog about drought from an agricultural perspective, and in particular how droughts in agriculture can (or at least should) often be blamed as much on high temperatures and strong evaporative demand as on low rainfall. At the same time, there’s lots of interesting work going on trying to assess drought from a hydrological perspective. Like this recent summary by Trenberth et al.

The latest is a clever study by Greve et al. that tries to pin down whether and where droughts are becoming more or less common. They looked at lots of combinations of possible data sources for rainfall, evapotranspiration (ET) and potential evapotranspiration (ETp). They then chose those combinations that produced a reasonable relationship between E/P and ETp/P, defined as the Budyko curve, and used them to calculate trends in dryness for 1948-2005. The figure below shows their estimate of wet and dry areas and the different instances of wet areas getting wetter, wet getting drier, etc. The main point of their paper and media coverage was that these trends don’t follow the traditional expectation of WWDD (wet get wetter and dry get drier) – the idea that warming increases the water holding capacity of the air and thus amplifies existing patterns of rainfall.

Also clear in the figure is that the biggest exception to the rule appears to be wet areas getting drier. There don’t seem to be many dry areas getting wetter over the last 50 years.

Other than highlighting their nice paper, I wanted to draw attention to something that seems to get lost in all of the back-and-forth in the community looking at trends in dryness and drought, but that I often discuss with agriculture colleagues: it’s not clear how useful any of these traditional measures of drought really are. The main concept of drought is about deficiency, but deficient relative to what? The traditional measures all use a “reference” ET, with the FAO version of penman-monteith (PM) the gold standard for most hydrologists. But it’s sometimes forgotten that PM uses an arbitrary reference vegetation of a standard grass canopy. Here’s a description from the standard FAO reference:

“To avoid problems of local calibration which would require demanding and expensive studies, a hypothetical grass reference has been selected. Difficulties with a living grass reference result from the fact that the grass variety and morphology can significantly affect the evapotranspiration rate, especially during peak water use. Large differences may exist between warm-season and cool season grass types. Cool-season grasses have a lower degree of stomatal control and hence higher rates of evapotranspiration. It may be difficult to grow cool season grasses in some arid, tropical climates. The FAO Expert Consultation on Revision of FAO Methodologies for Crop Water Requirements accepted the following unambiguous definition for the reference surface:
"A hypothetical reference crop with an assumed crop height of 0.12 m, a fixed surface resistance of 70 s m-1 and an albedo of 0.23."
The reference surface closely resembles an extensive surface of green grass of uniform height, actively growing, completely shading the ground and with adequate water."

Of course, there are reasons to have a reference that is fixed in space and time – it makes it easier to compare changes in the physical environment. But if the main concern of drought is about agricultural impacts, then you have to ask yourself how much this reference really represents a modern agricultural crop. And, more generally, how relevant is the concept of a static reference in agriculture, where the crops and practices are continually changing. It’s a bit like when Dr. Evil talks about “millions of dollars” in Austin Powers.  

Here’s a quick example to illustrate the point for those of you still reading. Below is a plot I made for a recent talk that shows USDA reported corn yields for a county of Iowa where we have run crop model simulations. I then use the simulations (not shown) to define the relationship between yields and water requirements. This is a fairly tight relationship since water use and total growth are closely linked, and depends mainly on average maximum temperature. The red line then shows the maximum yield that could be expected (assuming current CO2 levels ) in a dry year, defined as the 5^th percentile of historical annual rainfall. Note that for recent years, this amount of rainfall is almost always deficient and will lead to large amounts of water stress. But 50 years ago the yields were much smaller, and even a dry year provided enough water for typical crop growth (assuming not too much of it was lost to other things like runoff or soil evaporation).  

An alternative to the PM approach is to have the reference ET defined by the potential growth of the vegetation. This was described originally, also by Penman, as a “sink strength” alternative to PM, and is tested in a nice recent paper by Tom Sinclair. It would be interesting to see the community focused on trends try to account for trends in sink strength. That way they’d be looking not just at changes in the dryness part of drought, but also the deficiency part.

As someone interested in climate change, it’s nice to see continued progress on measuring trends in the physical environment. But for someone concerned about whether agriculture needs to prepare for more drought, in the sense of more water limitations to crop growth, then I think the answer in many cases is a clear yes, regardless of what’s happening to climate. As yield potential become higher and higher, the bar for what counts as "enough" water continues to rise.

Agricultural Economics gets Politico

Update: For the record, I'm actually not against Federal crop insurance.  Like Obamacare, I generally favor it.  But the subsidies are surely much larger than they need to be for maximum efficiency.  And I think premiums could likely be better matched to risk, and that such adjustments would be good for both taxpayers and the environment.

Wow.  Frumpy agricultural economics goes Politico!

Actually, it's kind of strange to see a supposedly scandalous article in Politico in which you know almost every person mentioned. 

At issue is the federal crop insurance program.  The program has been around a long time, but its scope and size--the range crops and livestock insurable under the program and the degree to which taxpayers subsidize premiums--have grown tremendously over the last 20 years.  And the latest farm bill expands the program and its subsidies to grand new heights.

Nearly all the agricultural economists I know regard the crop insurance program (aka Obamacare for the corn) as overly subsidized.  But the issue here is not the subsides but the huge contracts received by agricultural economists moonlighting as well-paid consultants for USDA's Risk Management Agency (RMA), to help RMA design and run the insurance program.

For full disclosure: I used to work for USDA in the Economic Research Service and did some research on crop insurance.  Although, strangely, ties between ERS and RMA are thin to nonexistent. I've met and spoke to both Joe Glauber (USDA's Chief Economist) and Bruce Babcock (a leading professor of agricultural economics at Iowa State) a few times, and know and respect their work. And I used to work at NC State as a colleague of Barry Goodwin's.  I also went to Montana State for a master's degree way back, where I took courses from Myles Watts and Joe Attwood, who are mentioned in the article. I know Vince Smith from that time too.

Perhaps most importantly, some of my recent research uses some rich data resources that we obtained from RMA. But I have never received any monies from RMA.  Believe it or not, my interest is in the science, and despite having no vested financial interest in any of it, I have found myself in the cross hairs of agricultural interests who didn't seem to like my research findings.  Anyway, ag econ is a small, small world...

Okay, disclosures out of the way: What's the big deal here?  So ag economists work for RMA, make some nice cash, and then moonlight for the American Enterprise Institute to bash agricultural subsidies.  Yeah, there are are conflicts of interest, but it would seem that there are interests on many sides and the opportunistic ag economists in question seem willing to work for all of them.  They'll help RMA design crop insurance programs, but that doesn't mean they advocate for the programs or the level of subsidies farmers, insurance companies and program managers receive under them.  We observe the opposite.

I've got some sense of the people involved and their politics.  Most of them are pretty hard-core conservative (Babcock may be an exception, not sure), and my sense is that most are unsupportive of agricultural subsidies in general.  But none are going to turn down big pay check to try to make the program as efficient as possible.  I don't see a scandal here.  Really.

Except, I do kind of wonder why all this money is going to Illinois, Texas and Montana when folks at Columbia, Hawai'i, and Stanford could, almost surely, do a much better job for a fraction of  taxpayers' cost.  With all due respect (and requisite academic modesty--tongue in cheek), I know these guy's work, and I'm confident folks here at G-FEED could do a much better job.  I personally don't need a penny (okay, twist my arm and I'll take a month of summer salary). Just fund a few graduate students and let us use the data for good science.

 

People are not so different from crops, turns out

A decade of strong work by my senior colleagues here at G-FEED have taught us that crops don’t like it hot: 

• Wolfram and Mike have the original go-to paper on US ag [ungated copy here], showing that yields for the main US field crops respond very negatively to extreme heat exposure
• David, Wolfram, Mike + coauthors have a nice update in Science using even fancier data for the US, showing that while average corn yields have continued to increase in the US, the sensitivity of corn to high temperatures and moisture deficits has not diminished. 
• And Max et al have a series of nice papers looking at rice in Asia, showing that hot nighttime temperatures are particularly bad for yields.

The results matter a lot for our understanding of the potential impacts of climate change, suggesting that in the absence of substantial adaptation we should expect climate change to exert significant downward pressure on future growth in agricultural productivity.

But we also know that for many countries of the world, agriculture makes up a small share of the economy.  So if we want to say something meaningful about overall effects of climate change on the economies of these countries (and of the world as a whole), we're also going to need to know something about how non-agricultural sectors of the economy might respond to a warmer climate. 

Thankfully there is a growing body of research on non-agricultural effects of climate -- and there is a very nice summary of some of this research (as well as the ag research) just out in this month's Journal of Economic Literature, by heavyweights Dell, Jones, and Olken. [earlier ungated version here].

I thought it would be useful to highlight some of this research here -- some of it already published (and mentioned elsewhere on this blog), but some of it quite new.  The overall take-home from these papers is that non-agricultural sectors are often also surprisingly sensitive to hot temperatures

First here are three papers that are already published:

1. Sol's 2010 PNAS paper was one of the first to look carefully at an array of non-agricultural outcomes (always ahead of the game, Sol...), using a panel of Caribbean countries from 1970-2006. Below is the money plot, showing strong negative responses of a range of non-ag sectors to temperature.  Point estimate for non-ag sectors as a whole was -2.4% per +1C, which was higher than the comparable estimate for the ag sector (-0.1% per 1C).

From Hsiang (2010)

2. Using a country-level panel, Dell Jones and Olken's instaclassic 2012 paper [ungated here] shows that both ag and non-ag output responds negatively to warmer average temperatures -- but only in poor countries. They find, for instance, that growth in industrial output in poor countries falls 2% for every 1C increase in temperature, which is only slightly lower than the -2.7% per 1C decline they find for ag. They find no effects in rich countries. 

3. Graff Zivin and Neidell (2014) use national time use surveys in the US to show that people work a lot less on hot days.  Below is their money fig:  on really hot days (>90F), people in "exposed" industries (which as they define it includes everything from ag to construction to manufacturing) work almost an hour less (left panel).  The right panels show leisure time.  So instead of working, people sit in their air conditioning and watch TV. 

from Graff Zivin and Neidell 2014.  Left panel is labor supply, right two panels are outdoor and indoor leisure time. 

And here are three papers on the topic you might not have seen, all of which are current working papers:

4.  Cachon, Gallino, and Olivares (2012 working paper) show, somewhat surprisingly, that US car manufacturing is substantially affected by the weather.  Using plant-level data from 64 plants, They show that days above 90F reduce output on that day by about 1%, and that production does not catch up in the week following a hot spell (i.e. hot days did not simply displace production).

5. Adhvaryu, Kala, and Nyshadham (2014 working paper)  use very detailed production data from garment manufacturing plants to show that hotter temperatures reduce production efficiency (defined as how much a particular production line produces on a given day, relative to how much engineering estimates say they should have produced given the complexity of the garment they were producing that day).  Not sure if I have the units right, but I think they find about a 0.5% decrease in production efficiency on a day that's +1C hotter.

6. Finally, in a related study, Somanathan et al (2014 working paper) use a nation-wide panel of Indian manufacturing firms and show that output decreases by 2.8% per +1C increase in annual average temperature.  They show that this is almost all coming from increased exposure above 25C, again pointing to a non-linear response of output to temperature.  For a subset of firms, they also collect detailed worker-level daily output data, and show that individual-level productivity suffers when temperatures are high -- but that this link is broken when plants are air conditioned.

So apparently it's not just crops that do badly when it's hot.  Most of the studies just mentioned cite the human physiological effects of heat stress as the likely explanation for why non-agricultural output also falls with increased heat exposure, and this seems both intuitive and plausible -- particularly given how similar the effect sizes are across these different settings.  But what we don't yet know is how these mostly micro-level results might aggregate up to the macro level. Do they matter for the projected overall effect of climate change on economies?  This is something Sol and I have been working on and hope to be able to share results on soon.  In the meantime, I will be setting my thermostat to 68F. 

An open letter to you climate people

Dear Climate People (yes, I mean you IPCC WG1 types):

I am a lowly social scientist. An economist to be precise. I am the type of person who is greatly interested in projecting impacts of climate change on human and natural systems. My friends and I are pretty darn good at figuring out how human and natural systems responded to observed changes in weather and climate. We use fancy statistics, spend tons of time and effort collecting good data on observed weather/climate and outcomes of interest. Crime? Got it. Yields for any crop you can think of? Got your back. Labor productivity? Please. Try harder.

But you know what's a huge pain in the neck for all of us? Trying to get climate model output in a format that is useable by someone without at least a computer science undergraduate degree. While you make a big deal out of having all of your climate model output in a public depository, we (yes the lowly social scientists) do not have the skills to read your terabytes and terabytes of netCDF files into our Macbooks and put them in a format we can use.

What do I mean by that? The vast majority of us use daily data of TMin, Tmax and Precipitation at the surface. That's it. We don't really care what's going on high in the sky. If we get fancy, we use wet bulb temperature and cloud cover. But that's really pushing it. For a current project I am trying to get county level Climate Model Output for the CMIP5 models. All of them. For all 3007 US counties. This should not be hard. But it is. My RA finally got the CMIP5 output from a Swiss server and translated them into a format we can use (yes. ASCII. laugh if you wish. The "a" in ASCII stands for awesome.) We now are slicing and dicing these data into the spatial units we can use. We had to buy a new computer and bang our heads against the wall for weeks.

If you want more people working on impacts in human and natural systems, we need to make climate model output available to them at the spatial and temporal level of resolution they need. For the old climate model output, there was such a tool, which was imperfect, but better than what we have now. I got a preview of the update to this tool, but it chokes on larger requests.

Here's what I'm thinking IPCC WG1: Let's create a data deposit, which makes climate model output available to WG2 types like me in formats I can understand. I bet you the impacts literature would grow much more rapidly. The most recent AR5 points out that the largest gaps in our understanding are in human systems. I am not surprised. If this human system has trouble getting the climate data into a useful format, I am worried about folks doing good work, who are even more computationally challenged than I am.

Call me some time. I am happy to help you figure out what we could do. It could be amazing!

Your friend (really) Max.

Can we measure being a good scientific citizen?

This is a bit trivial, but I was recently on travel, and I often ponder a couple of things when traveling. One is how to use my work time more efficiently. Or more specifically, what fraction of requests to say yes to, and which ones to choose? It’s a question I know a lot of other scientists ask themselves, and it’s a moving target as the number of requests change over time, for talks, reviews, etc.

The other thing is that I usually get a rare chance to sit and watch Sportscenter, and I'm continually amazed by how many statistics are now used to discuss sports. Like “so-and-so has a 56% completion percentage when rolling left on 2^nd down” or “she’s won 42% of points on her second serve when playing at night on points that last less than 8 strokes, and when someone in the crowd sneezes after the 2^nd stroke.” Ok, I might be exaggerating a little, but not by much.

So it gets me wondering why scientists haven’t been more pro-active in using numbers to measure our perpetual time management issues. Take reviews for journals as an example. It would seem fairly simple for journals to report how many reviews different people perform each year, even without revealing who reviewed which papers. I’m pretty sure this doesn’t exist, but could be wrong (The closest thing I’ve seen to this is Nature sends an email at the end of each year saying something like “thanks for your service to our journal family, you have reviewed 8 papers for us this year”).  It would seem that comparing the number of reviews to the number of papers you get reviewed by others (also something journals could easily report) would be a good measure of whether each person is doing their part.

Or more likely you’d want to share the load with your co-authors, but also account for the fact that a single paper usually requires about 3 reviewers. So we can make a simple “science citizen index” or “scindex” that would be

SCINDEX = A / (B x C/D) = A x D / (B x C)

where

A = # of reviews performed

B = # of your submissions that get reviewed (even if the paper ends up rejected)

C = average number of reviews needed per submission (assume = 3)

D = average number of authors per your submitted papers

Note that to keep it simple, none of this counts time spent as an editor of a journal. And it doesn’t adjust for being junior or senior, even though you could argue junior people should do less reviews and make up for it when they are senior. And I’m sure some would complain that measuring this will incentivize people agreeing but then doing lousy reviews. (Of course that never happens now). Anyhow, if this number is equal to 1 than you are pulling your own weight. If it’s more than 1 you are probably not rejecting enough requests. So now I’m curious how I stack up. Luckily I have a folder where I save all reviews and can look at the number saved in a given year. Let’s take 2013. Apparently I wrote 27 reviews, not counting proposal or assessment related reviews. And Google Scholar can quickly tell me how many papers I was an author on in that year (14), and I can calculate the average number of authors per paper (4.2). Let’s also assume that a few of those were first rejected after review elsewhere (I don’t remember precisely, but that’s an educated guess), so that my total submissions were 17. So that makes my scindex 27 x 4.2 / (17 x 3) = 2.2. For 2012 it was 3.3.

Holy cow! I’m doing 2-3 times as many reviews as I should be reasonably expected to. And here I was thinking I had a reasonably good balance of accepting/rejecting requests. It also means that there must be lots of people out there who are not pulling their weight (I’m looking at you, Sol).

It would be nice if the standard citation reports add something like a scindex to the h-index and other standards. Not because I expect scientists to be rewarded for being a good citizen, though that would be nice, or because it would expose the moochers. But because it would help us make more rational decisions about how much of the thankless tasks to take on. Or maybe my logic is completely off here. If so, let me know. I’ll blame it on being tired from too much travel and doing too many reviews!

Commodity Prices: Financialization or Supply and Demand?

I've often panned the idea that commodity prices have been greatly influenced by so-called financialization---the emergence of tradable commodity price indices and growing participation by Wall Street in commodity futures trading. No, Goldman Sachs did not cause the food and oil-price spikes in recent years. I've had good company in this view.   See, for example, Killian, Knittel and Pindyck, Krugman (also here), Hamilton, Irwin and coauthers, and I expect many others.

I don't deny that Wall Street has gotten deeper into the commodity game, a trend that many connect to  Gorton and Rouwenhorst (and much earlier similar findings).  But my sense is that commodity prices derive from more-or-less fundamental factors--supply and demand--and fairly reasonable expectations about future supply and demand.  Bubbles can happen in commodities, but mainly when there is poor information about supply, demand, trade and inventories.  Consider rice, circa 2008.

But most aren't thinking about rice. They're thinking about oil.

The financialization/speculation meme hasn't gone away, and now bigger guns are entering the fray, with some new theorizing and evidence.

Xiong theorizes (also see Cheng and Xiong and Tang and Xiong) that commodity demand might be upward sloping.  A tacit implication is that new speculation of higher prices could feed higher demand, leading to even higher prices, and an upward spiral.  A commodity price "bubble" could arise without accumulation of inventories, as many of us have argued.  Tang and Xiong don't actually write this, but I think some readers may infer it (incorrectly, in my view).

It is an interesting and counter-intuitive result.  After all, The Law of Demand is the first thing everybody learns in Econ 101:  holding all else the same, people buy less as price goes up.  Tang and Xiong get around this by considering how market participants learn about future supply and demand.  Here it's important to realize that commodity consumers are actually businesses that use commodities as inputs into their production processes.  Think of refineries, food processors, or, further down the chain, shipping companies and airlines.  These businesses are trying to read crystal balls about future demand for their final products.  Tang and Xiong suppose that commodity futures tell these businesses something about future demand.  Higher commodity futures may indicate stronger future demand for their finished, so they buy more raw commodities, not less.

There's probably some truth to this view.  However, it's not clear whether or when demand curves would actually bend backwards.  And more pointedly, even if the theory were true, it doesn't really imply any kind of market failure that regulation might ameliorate. Presumably some traders actually have a sense of the factors causing prices to spike: rapidly growing demand in China and other parts of Asia, a bad drought, an oil prospect that doesn't pan out, conflict in the Middle East that might disrupt future oil exports, and so on.  Demand shifting out due to reasonable expectations of higher future demand for finished product is not a market failure or the makings of a bubble.  I think Tang and Xiong know this, but the context of their reasoning seems to suggest they've uncovered a real anomaly, and I don't think they have.  Yes, it would be good to have more and better information about product supply, demand and disposition.  But we already knew that.

What about the new evidence?

One piece of evidence is that commodity prices have become more correlated with each other, and with stock prices, with a big spike around 2008, and much more so for indexed commodities than off-index commodities.

This spike in correlatedness happens to coincide with the overall spike in commodity prices, especially oil and food commodities.  This fact would seem consistent with the idea that aggregate demand growth--real or anticipated--was driving both higher prices and higher correlatedness.  This view isn't contrary to Tang and Xiong's theory, or really contrary to any of the other experts I linked to above.  And none of this really suggests speculation or financialization has anything to do with it.  After all, Wall Street interest in commodities started growing much earlier, between 2004 and 2007, and we don't see much out of the ordinary around that time.

The observation that common demand factors---mainly China growth pre-2008 and the Great Recession since then---have been driving price fluctuations also helps to explain changing hedging profiles and risk premiums noted by Tang and Xiong and others.  When idiosyncratic supply shocks drive commodity price fluctuations (e.g, bad weather), we should expect little correlation with the aggregate economy, and risk premiums should be low, and possibly even negative for critical inputs like oil.  But when large demand shocks drive fluctuations, correlatedness becomes positive and so do risk premiums.

None of this is really contrary to what Tang and Xiong write.  But I'm kind of confused about why they see demand growth from China as an alternative explanation for their findings. It all looks the same to me.  It all looks like good old fashioned fundamentals.

Another critical point about correlatedness that Tang and Xiong overlook is the role of ethanol policy.  Ethanol started to become serious business around 2007 and going into 2008, making a real if modest contribution to our fuel supply, and drawing a huge share of the all-important US corn crop.

During this period, even without subsidies, ethanol was competitive with gasoline.  Moreover, ethanol concentrations hadn't yet hit 10% blend wall, above which ethanol might damage some standard gasoline engines.  So, for a short while, oil and corn were effectively perfect substitutes, and this caused their prices to be highly correlated.  Corn prices, in turn, tend to be highly correlated with soybean and wheat prices, since they are substitutes in both production and consumption.

With ethanol effectively bridging energy and agricultural commodities, we got a big spike in correlatedness.  And it had nothing to do with financialization or speculation.

Note that this link effectively broke shortly thereafter. Once ethanol concentrations hit the blend wall, oil and ethanol went from being nearly perfect substitutes to nearly perfect complements in the production of gasoline.  They still shared some aggregate demand shocks, but oil-specific supply shocks and some speculative shocks started to push corn and oil prices in opposite directions.

Tang and Xiong also present new evidence on the volatility of hedgers positions. Hedgers--presumably commodity sellers who are more invested in commodities and want to their risk onto Wall Street---have highly volatile positions relative to the volatility of actual output.

These are interesting statistics.  But it really seems like a comparison of apples and oranges.  Why should we expect hedger's positions to scale with the volatility of output?  There are two risks for farmers: quantity and price.  For most farmers one is a poor substitute for the other.

After all, very small changes in quantity can cause huge changes in price due to the steep and possibly even backward-bending demand.  And it's not just US output that matters.  US farmers pay close attention to weather and harvest in Brazil, Australia, Russia, China and other places, too.

It also depends a little on which farmers we're talking about, since some farmers have a natural hedge if they are in a region with a high concentration of production (Iowa), while others don't (Georgia).  And farmers also have an ongoing interest in the value of their land that far exceeds the current crop, which they can partially hedge through commodity markets since prices tend to be highly autocorrelated.

Also, today's farmers, especially those engaged in futures markets, may be highly diversified into other non-agricultural investments.  It's not really clear what their best hedging strategy ought to look like.

Anyhow, these are nice papers with a bit of good data to ponder, and a very nice review of past literature.  But I don't see how any of it sheds new light on the effects of commodity financialization. All of it is easy to reconcile with existing frameworks.  I still see no evidence that speculation and Wall Street involvement in commodities is wreaking havoc.