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.

Are yields becoming less or more sensitive to temperature?

One of the hopes for adaptation is that farmers and breeders can somehow make crop yields less sensitive to temperature. Given that many agricultural areas have already seen significant amounts of warming, it’s fair to ask whether this is in fact happening. At the same time, it’s worth considering the possibility that things may be going in the opposite direction – that yields may in fact be getting more sensitive to weather. There’s a lot of history on this topic, which I’ll try to summarize briefly here before delving into some recent research summaries.

Before history, though, it’s useful to keep in mind three distinctions. First, yield sensitivity is not the same as yield variability. The latter can change if weather variability changes, even if sensitivity to weather stays the same. Second, an increase in yield sensitivity (or, for that matter, variability) is not necessarily a bad thing from a farmer’s or even a consumer’s point of view. If increased variability comes as a cost of increased average productivity, then both producers and consumers may benefit from the newer, more variable yield levels.

Third, this is potentially a VERY scale dependent question. Yield at a national scale, for example, can change for a lot of reasons apart from a change in yield variability for individual fields. For example, work by PeterHazell 30 years ago showed that changes in national yield variability were often driven by increased correlation of yields across different parts of the country. This could happen if production becomes more concentrated in a given geographic region, if practices or varieties become more uniform, or if weather becomes more correlated. There are lots of interesting questions related to sensitivity of aggregate yields to weather, some of which may be a topic for future posts. But here I want to focus on the evidence for yield sensitivity at the field level.

In thinking about changes in sensitivity, it might be useful to lump things into two bins – things that we would expect to reduce sensitivity to temperature, and things we’d expect to increase it. In the first category are many agronomic changes, particularly a stable supply of irrigation water and pest and disease control measures. It’s also possible that varietal changes could reduce sensitivity, for example if they are more resistant to pest or disease damage (especially if these disease pressures are different from year to year), or if they are more “hardy”, by which breeders often mean the ability to handle extreme moisture or temperature conditions. One example of this would be the flood tolerant rice lines recently developed. Finally, there is increased atmospheric CO₂, which one would expect to lower impacts of droughts by improving water use efficiency.

In the other bin are things that increase sensitivity. Some agronomic changes could increase sensitivity, for example high fertilizer rates can allow crops to be more responsive to good weather conditions. Similarly, new varieties can be more responsive to good conditions. The “responsiveness” of varieties to water and nutrients is in fact one of the main reasons that current varieties are so high yielding on average.

The net change in yield sensitivity of a cropping system is the result of all of these factors pushing in different directions. So which changes matter most in modern cropping systems? I recently came across a very nice volume from an IFPRI meeting held 25 years ago, which included several papers on the topic of yield variability (here’s a link, but beware of large file size). It seems many of their comments are just as relevant today as then. In particular, most of them emphasize the “responsiveness” issue – that well-fertilized modern hybrids are increasingly capable of taking advantage of good years. Especially in rainfed systems, this seems to dominate the other factors.

For example, here’s Donald Duvick (a world renowned corn breeder) on the experience of US corn:

“The yield advantage of the new hybrids is greatest when environmental conditions are most favorable. When environmental factors are severely limiting, as in drought, the new hybrids outyield the old ones, but by a smaller margin. It is likely, therefore, that present-day hybrids introduce the possibility of greater year-to-year variation in U.S. maize yields than used to occur, since they can expand their yields so much further in environmentally favorable seasons. When environmental factors are overwhelmingly limiting, the fallback in yield of the new hybrids is correspondingly greater than it would have been for the older hybrids, even though the new hybrids, under poor conditions, yield more than the old ones.”

He goes on to conclude

“I expect that any changes in U.S. maize farming practices will be made in concert. The tendency for the nation's maize plantings to be handled like one big farm will continue. Reactions to varying climatic conditions will be amplified, and some measure of instability in year-to-year national expectations for maize yields must continue. This may be the price that must be paid for high average yield in the long term.”

Similar conclusions are made by other authors in the volume on tropical maize, temperate and tropical wheat, and other crops. More recent work has made similar points, for example David Connor and others have written about how Australian wheat yields are now more variable than in previous generations, because farmers and cultivars are now so good at taking advantage of high rainfall years, but still face low yields in dry years because of insurmountable water constraints (compare 1920-1940 and 1980-2000 in figure below, taken from here. not shown here are the major recent droughts which would further show the remarkable amount of current variability in the system).

Of course, in some cropping systems things can go the other direction. For example, a recent study of maize yields in France showed a decline in rainfall sensitivity associated with increased irrigation. And another study at the grid-cell level for major maize and soybean regions found both areas of increases and decreases in temperature sensitivity. Although in that study, it seems plausible that a lot of the variation was due to noise, because cells of increasing sensitivity were often adjacent to cells with decreasing sensitivity.

In a recent study, which was a collaboration with CIMMYT (the International maize and wheat improvement center), we attempted to look at how heat sensitivity has changed for one particular set of cropping systems that are of wide interest – irrigated spring wheat. To make a long story very short, we looked at two breeding nurseries run by CIMMYT, and evaluated yield changes for differing temperature conditions. In the main nursery aimed at producing “elite” varieties, there has been steady yield progress at cool temperatures but no significant progress at warmer temperatures (left panel in figure below shows yield trends for 4 bins defined by grain filling temperatures. “climate corrected” trends account for shifts in locations and weather within each temperature bin over time). This seems to be a similar story of cultivars becoming more responsive to good conditions (low temperatures in the case of wheat), with a resulting increase in the sensitivity to bad conditions (high temperatures). Again, this isn't necessarily a bad thing, but does suggest that wheat farmers are getting more not less sensitive to high temperatures. Interestingly, when looking at nurseries targeted at drought stress (right panel), the gains appear more evenly distributed and maybe even higher under worse conditions (albeit with more noise because of a smaller sample size). This is a reminder that it is possible to create more hardy plants. But those won’t necessarily be the ones that farmers choose to grow.

Why heat hurts

In a recent post I wrote about why rainfall is sometimes given too much credit for variations in crop production. Or, put differently, temperature deserves more credit than it often gets. A paper we have out this week delves into the reasons for this. Below is a brief background and summary, but see full paper here.

As background, there are now several studies showing close correlations between crop yields and temperature, in both rainfed or irrigated areas. More specifically, we now see that a lot of these correlations are driven by the tails of the temperature distribution – more very hot days means lower yields. Those of you familiar with our blog will know we often represent this effect by summing up degree days above some threshold, like 29 or 30 °C. See posts here, and here.

But correlations don’t tell us much about mechanisms of causation. And as a result, many people are wary about using correlations to project future impacts. So why might hot days in particular be so important? Agronomists are usually quick to talk about the key time of flowering when some really hot days can spell disaster. This is confirmed by many experimental studies, including one recent one here. But there are other things that could be going on in farmers’ fields in addition to this. One is the fact that hot air can hold more moisture, so that hotter days tend to have higher vapor pressure deficits (VPD). When air has higher VPD, plants lose more water per amount of CO₂ uptake, which lowers their water use efficiency. The response of most plants is to then slow down growth, which avoids losing too much water during the parts of the day where efficiency is lowest. Tom Sinclair, among others, has a boatload of interesting papers on this dynamic and the tradeoffs involved with selecting varieties that slow down more or less under high VPD. 

Now to the study. We wanted to see if the VPD effect could explain the observed importance of hot days for corn in the U.S. So we took an Australian crop model, APSIM, that handles the VPD effect and simulated some long time series of yields at different locations. We then look at whether the model can reproduce the observed relationships, and the match was quite good:

 We then look at some diagnostics in the model to confirm that it’s high VPD causing growth to slow, which causes lower yields. Then, to be sure it is not just that hot days or years tend to happen when rainfall is low, we artificially manipulate temperature or rainfall one at a time, and see how the model responds. The figure below shows the change in water stress (lower values on the y-axis mean higher amounts of water stress) for 3 key months for corn (July being the most important for final yield). This shows that a warming of 2°C causes a much bigger (3x) increase in water stress than a drop of in-season rainfall by 20%.

Why does this matter? First, it suggests that a lot of the effects of extreme heat (at least for this crop, in this region, in today’s climate) are related to drought stress. So when the media refers to the big drought, that is technically correct (at least in this case). But it’s important to be clear what we mean by drought – we don’t necessarily mean low rainfall (a common meteorological definition of drought), or even low soil moisture (a common definition of “agricultural drought”), but we mean a more agronomic definition of drought, such as “not enough water to grow as fast as a plant can”. The key is that water stress is not just about water supply to the plant, but about how much water it has relative to how much water it needs to maintain growth rates. The “needs” or water demand part depends on VPD, and hotter days on average tend to have higher VPD (and extended heat waves tend to have much higher VPD).

Second, it implies that efforts to adapt to warming in U.S. maize should probably, at least for the near future, focus on dealing with water stress associated with high VPD, rather than, say, the direct effects of heat damage during flowering.  

One thing we didn’t have the horsepower to do for this study would be to repeat the analysis with other types of crop models, most of which handle water stress slightly differently than APSIM. That would tell us how many models that have been used to project climate change impacts on U.S. agriculture are actually getting the key process right. It’s the type of model comparison that hopefully AGMIP will tackle – not just comparing projections of different models, but seeing how well they perform in reproducing historical responses to extreme heat.  

A random thought on adaptation

There have been some interesting stories lately about how the new drought tolerant seeds are performing. Anecdotes of farmers marveling over how their crops fare are not exactly evidence, since there may be many more who were disappointed. But it does at least suggest that new seeds are better adapted. Similarly, one often hears stories about how agriculture is expanding into new areas, which is another way that agriculture could adapt to global warming.

I have little doubt that new varieties and migration of crop areas will help with climate change, but the key question is how much. Will it be a 1% or 50% type of effect? A lot of our research is about trying to find and analyze datasets that can answer this question. Typically any one dataset can only tell us so much, so it’s really about trying to piece together a picture from multiple different analyses. Not all of these analyses have to be very sophisticated. For example, a simple plot of average country yields vs. average growing season temperature is shown below (I made this based on the methods and data in this paper from last year. The figure is part of a review that is coming out in Plant Physiology later this year.)

The green blobs each represent a country, with the size proportional to total production. The vertical gray line shows an independent estimate of the optimal season temperature for yields, based on a recent review by Hatfield et al. that was based on experimental studies. It’s not exactly a pretty figure – lots of factors differ between countries other than temperature, which results in a lot of scatter. But I think it illustrates three important points that are sometimes missed:

1. The highest yielding countries are fairly strongly clustered around the gray line, except for barley where they are significantly cooler. (This is a surprisingly good match given that the gray lines were completely independent of this dataset.) Although many countries grow each crop above its optimum, the maximum yields are clearly lower at high temperatures. This casts some doubt on the notion that yields can be maintained as temperatures rise, since the warmer countries should already have incentive to better adapt to their conditions.

2. Large producers span a pretty wide range of temperatures. This is sometimes cited as evidence that agriculture is well adapted to a wide range of climates, but I think it’s more accurate to say that farming is profitable across a wide range of climates. For example, people sometimes like to point out that if we grow corn in Alabama, how can global warming be a concern? The answer is that we grow corn in Alabama, but not nearly as well as if it had the climate of Illinois. The lack of a tight relationship between yields and crop areas indicates that agriculture is not greatly optimized to current climate. To me, this casts doubt on any argument that migration of agriculture will be a major source of adaptation. There are clearly a lot of factors other than climate that enter into a decision about which crop to grow.

3. For maize and wheat, a lot of the bigger producers tend to fall to the right of the optimum. This helps to illustrate why the global production of these crops are typically predicted to be hurt by warming, even if some countries gain.

None of these points are proven by the figure. It’s pretty rare that a simple cross-section like this can prove anything. But sometimes simple plots can really challenge a strong prior belief. If it was common to match crops to their temperature optimum – either by relocating where the crops grow or by changing the crop’s optimum temperature – then I would expect to see a much flatter cross-sectional relationships between yields and temperature, or a much tighter concentration of area around the optimum.

Is temperature variance changing?

People in agriculture often talk about “weather getting more variable.” It’s usually hard to know exactly what they mean – sometimes they are talking about precipitation becoming less frequent and more intense, and sometimes they're talking about hot extremes becoming more frequent. But it’s well known that what was considered “extreme” historically can become more frequent just by shifting the mean of the weather distribution, without any change in variance. The IPCC SREX figure below shows that clearly in the first panel.

We care about variance, though, not just because of its ability to increase the occurrence of historical hot extremes. If variance is increasing, this would mean more uncertainty faced each year by farmers and markets about what the growing season weather will be. Note that we are talking here strictly about weather variance. The variance in production can increase just from a shift in the distribution towards less favorable temperatures, as we showed in a recent study led by Dan Urban.

The question of whether variance per se has been changing (or is projected to change) has received much less attention than whether extremes are becoming more common. This is partly because changes in variance are harder to measure than shifts in means or increases in extreme events. But an interesting analysis by Donat and Alexander in GRL sheds some light directly on the variance question. They looked at the distribution of daily temperature anomalies for two 30-year time periods: 1951-1980 and 1981-2010. The figure below from their paper maps the change between the two time periods for three parameters of the distribution (mean, variance, and skewness), both for minimum (left) and maximum (right) temperature.

Two things seem new to me here. (Certainly the shifts in mean are not new, but it’s interesting to note that the shifts are about equal for minimum and maximum temperature.) First, the variance changes are mixed around the world, and not statistically significant in most places (the significant areas at 10% are shown with hatching). The authors also say that the variance changes depend a lot on what criteria they use to exclude grid cells without enough data.

The second interesting thing is that the skewness has increased in most parts, much more uniformly than the changes in variance. Just to be clear, we are talking about skewness in the statistical sense, not in the way it is sometimes used to mean “distorted” or “biased”. An increase in skewness means that the distribution is now less left-skewed or more right-skewed than before, which would mean that for a given average, there is a higher chance of having warm anomalies and a lower chance of having cool anomalies (see bottom panel of ipcc figure above).  

It’s hard to know what exactly is driving the skewness, but I suspect their paper will spur some more focus on this issue. Maybe it has to do with the shift in rainfall distribution toward heavier events, with less rainfall during moderate events. For now it seems safe to say that temperatures are not clearly becoming more variable for most parts of the world, but they seem to be slightly more skewed toward hotness.

What's the price of corn in your meat? Less than you think.

In my OpEd last week I had a lot of back and fourth with the editor.  I probably had too many statistics and my first draft was just too long.  I also should have provided background information up front for the statistics I wanted to present.

One thing that got dropped in the process was an explanation for why retail food prices will rise so little even though corn prices have increased 60 percent.  So much of our food is ultimately derived from corn, or from other commodities like wheat and soybeans whose prices track corn prices fairly closely.  But it still makes little difference.

Take meat, for example.  There are only 3-5 pounds of corn used to make an additional pound of beef, and between 2 and 3 pounds of corn for a pound of chicken or pork.   The calculation isn't particularly straightforward, but these numbers are probably about right ``on the margin," as economists like to say. This can vary a bit from operation to operation or how it's measured, but feed use efficiency has risen a lot over the last couple decades with the growth of confined animal feeding operations, or CAFOs.

Let's says 5 pounds of corn per pound of meat.  There are 56 pounds of corn in a bushel and since June prices have increased from about $5 to about $8 per bushel.  This means the amount corn in your quarter-pound burger have increased from about 11 cents to about 18 cents.  If there is market power by processing companies or retailers, retail prices would go up by less than this amount (this is basic microeconomics, but I'll save the details for another time).  So, you'll have to squint to see the effect of this year's drought on prices at grocery stores and restaurants.

There are lots of complaints about CAFOs being inhumane for animals.  That may be, but they are also extremely efficient at using resources.  Without CAFOs, you would see bigger prices in all kinds of food, and this year's heat and drought would have caused a larger price spike.  We would also be using more land in crop production globally, and be using more fertilizers that pollute water and all manner of other environmental problems that follow from crop production.  Many environmentalists don't like CAFO's but they may well be doing more good for the environment than eating grass-fed beef, unless the high price of grass fed beef causes you to eat less.  (Granted, grass-fed beef is probably healthier.)

Anyhow, the main point is that commodities are a tiny share of retail prices in developed economies.  Prices of most everything, including food, is made up primarily of labor and capital costs, plus rents to producers and retailers with market power.  The big concern for high commodity prices in the developed world where the commodity share of food expenditures is much, much greater and people spend a much larger share of their income on food.
(cross posted at GGG)

Nonlinearities and exposure to extreme heat: what do we know?

Anomalous dry and hot conditions across the US, as well as recent well-publicized research, have amplified the discussion over how human affairs might be affected by changing patters of exposure to extreme heat.  You hear the word "non-linear" thrown around a lot in these discussions, so we wanted to clarify some of the things we know (and don't know) about non-linearity in this setting.

For the time-constrained or otherwise impatient, here are the take-homes:

1.  Small increases in average temperature can have large effects on extreme heat exposure - so extreme heat exposure tends to increase non-linearly with average temperature.

2.  Many economic outcomes we care about (agriculture, other types of economic output) respond negatively to increased exposure to extreme heat, although there is limited evidence that this response is itself non-linear.

3. Because these economic outcomes respond to extreme heat, and extreme heat responds non-linearly to increases in average temperature, then it follows that these outcomes can respond non-linearly to increases in average temperature.

Boiled down even further:  extreme heat is bad under current climate, a few degrees of extra warming will increase exposure to extreme heat dramatically in many places, and this could have very large negative impacts on outcomes that folks care about.  

Now here's what some of the science says about these points.  

1.  Extreme heat exposure tends to increase non-linearly with average temperature.  This can be pretty easily visualized by looking at some counties in the US.  I pulled data for three corn-growing counties in the US (Sioux County in Iowa, Calhoun County in Georgia, and Clay County in Minnesota), and plotted how exposure to extreme heat would change if temperatures increased anywhere between +1C and +7C.  Extreme heat as defined here is uses the agronomic notion of "growing degree days", and essentially measures the amount of time an area is exposed to temperatures above a given threshold, set here to 29C.  As discussed below, temperatures above 29C have been shown to be particularly harmful for agricultural productivity.

The left panel in each plot shows how much time corn grown in each county spends at different temperatures.  The grey line is the average in current climate (the last three decades), and the grey shaded area is the amount of time spent above the magical 29C threshold.  The colors show what happens as you increase average temperatures by +2C, +4C, or +6C.  Particularly in the places that start out cooler (Clay County in Minnesota), small initial increases in temperature can lead to big increases in exposure to temperatures above 29C.  

How much this exposure increases is quantified in the right panel of each plot.  The dark line shows the increase in exposure to temperatures above 29C as average temperatures rise up to +7C.  The dashed line shows the increase in exposure if every degree of warming was like the first degree of warming - a simple way of looking a looking at linearity.  That these lines diverge shows the non-linearity, and as you can read off the right axis, small increases in average temperature mean very large percentage increase in exposure to extreme heat.  A +1C warming means anywhere between a 43% (Georgia) and a 61% (Minnesota) increase in exposure to extreme heat under this definition.

2. Many outcomes we care about respond very negatively to extreme heat.  This is a very active topic of research, and the other venerable posters on this blog are at the forefront of research on this topic, but let me briefly summarize some findings to date.  

Effects of extreme heat on agricultural outcomes are now increasingly well documented.  Here's a summary plot from Wolfram and Michael's 2009 PNAS piece, showing how the main US field crops respond to hot temperatures.  For both corn and soy, things fall off pretty dramatically above about 29C.

Importantly, though, this is not showing a non-linear response to extreme heat.  The paper shows that modeling the yield response to extreme heat linearly does just fine. But this response is still really negative:  an extra day spent above 29C (relative to spending it at around 29C, where corn and soy are happy) reduces end of season yield by about 1%.  This is huge effect.

David Lobell and colleagues tell a very similar story looking at maize in Africa:  yields respond roughly linearly to extreme heat, and the effect size is almost exactly what Wolfram and Michael find in the US.  

New findings outside agriculture are eerily similar.  Sol shows in a 2010 PNAS paper that non-agricultural economic output in the Caribbean also drops off quickly above 29-30C, and a recent paper by Graff Zivin and Niedell shows a similar dropoff of US labor supply above 29C in industries particularly exposed to climate (construction, agriculture, mining, transportation, etc).  Sol has a nice blog post on this. 

Again, none of these studies appear to document non-linear responses to extreme heat.  Instead, the take-home from all of them is that outcomes respond very negatively (if perhaps linearly) to increased exposure to extreme heat.

3.  Outcomes do appear to respond non-linearly to increases in average temperature.  This is just a logical extension of the above two points.  If extreme heat increases nonlinearly with increases in average temperature, and outcomes respond strongly to changes in extreme heat, then these same outcomes will respond non-linearly to changes in average temperature.  

The appropriate adjective to affix to "non-linear" in this setting ("mildly"? "highly"?) is perhaps in the eyes of the beholder.  Here is a plot of how Wolfram and Michael's 2009 paper predicts that corn yields will respond to increasing amounts of average warming (I just grabbed the coefficients and standard errors from their appendix Table A5):

The dotted grey line again shows the impact trajectory if every additional degree of warming was like the first +1C of warming - the poor man's linearity.  The actual predicted impacts are well below this line starting at about +2C, showing that the responsiveness of US corn yields to temperatures is [fill in preferred adjective] non-linear.  I'd imagine the other findings in (2) above would look about the same.

So:  increases in average temperature lead to non-linear increases in extreme heat, which will do bad things to outcomes we care about.  There isn't a lot of evidence that these outcomes respond non-linearly to extreme heat, but it's not clear how much this matters - a strong negative linear response of these outcomes to extreme heat is enough to generate pretty negative impact projections under future warming.

A final word of caution is probably in order.  These empirical studies do a good job of capturing responses to extremes that we've seen in the past.  Unavoidably, they do less good of a job imagining how outcomes might respond to future extremes that we haven't yet experienced.  It could be that outcomes indeed respond non-linearly to these changes in extremes, instead of responding non-linearly just to changes in averages.  If so, this will probably only make a bad story worse.

Are we coping with extreme heat better than in the past?

I'm live at CNN...

Extreme heat and droughts -- a recipe for world food woes

With extreme heat and the worst drought in half a century continuing to plague the farm states, there are important lessons to be learned for all of us -- farmers, consumers and the world's poorest populations alike -- about the effect of climate change.

The Agriculture Department announced this season's first major crop yield forecasts, and they weren't pretty: a nationwide average of 123.4 bushels of corn per acre, the lowest level since 1995. Soybean yield is expected to be low too, though not as bad as corn.

The United States, which is the world's largest producer and exporter of staple grains, is grappling with the biggest surprise in production shortfalls since the Dust Bowl of the 1930s. Certainly, this July surpassed July 1936 as the hottest month on record. 

So, how will the devastation affect U.S. crop farmers? .....

2012 Weather Anomalies in Eastern US

Following up on an early post about the record setting heat in the United States, below are a few more plots to show the spatial distribution of the heat wave. The weather data has been updated to August 6, 2012. Here is the overall US average (red line is 2012, the grey lines are 1960-2011) for degree days above 29C, the weather variable that best predicts corn yields.

There is considerable spatial heterogeneity in how hot it has been. The next graph shows anomalies (difference to the 1950-2011 historic average) for degree days above 29C for counties in the Eastern United State. The data uses March 1st - August 6th, 2012. For comparison, the historic US average for the entire season (March 1st-August 31) is 34 degree days, so an extra 135 is four times the historic average - and that is on top of the historic average in a given location!

There is even more heterogeneity for rainfall. While places along the Mississippi River seem dry, some Northern and Southern counties actually had above normal rainfall.

This entry is cross-posted here

The World

A nice story on The World about drought and corn. i like the farmer's line of "crops don't like heat, and the plants know it."

Also, an interesting story on how drought tolerant seeds are doing.