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.

Varieties for droughts meet testing and doubts

There have been lots of interesting papers in the literature lately. More than I can keep up with for reading, much less to blog about. For example, some very nice work looking at land saving effects of productivity, an improved understanding of rainfall changes in the Sahel, and work on farmer perceptions of climate change. Plus the AgMIP efforts are starting to generate publications, such as this comparison of 27 wheat models.

But two I wanted to highlight concern the evidence on whether new “drought tolerant” varieties are outperforming other varieties. They especially caught my attention because we are currently analyzing some datasets for the US, partially to look at this issue. One of the papers, by Jason Roth and colleagues in Agronomy Journal, field tested some of DuPont Pioneer’s “Aquamax” varieties vs. other Pioneer hybrids without the special drought genes. The tests were done in Indiana for 2011, a pretty normal year in this location, and 2012, which was very dry and hot. They found no statistical difference between grain yields (GY), and also very little difference in terms of other outcomes like photosynthesis rates or transpiration. So their basic message is that the label of “drought tolerant” did not translate to any real differences in performance, although they emphasize that “Conclusions regarding the lack of superiority of drought-tolerant hybrids during the drought year are pertinent only to the specific environmental conditions encountered in the particular location tested.”

To me, there are a couple of possible ways to interpret this. One is that the newer varieties being marketed by companies are not really much better in general. Or these results might indicate that the types of droughts the newer varieties were designed for are somehow different than the type of droughts they were exposed to in this experiment. In particular, as we’ve discussed in prior posts, 2012 was a drought characterized by very high temperatures and vapor pressure deficits, the kind of droughts that one expects more of with climate change.

How much does the type of drought matter? Another interesting new study by Cairns et al. in Crop Science sheds some light on that question. They tested varieties developed for drought tolerance in eastern and southern Africa across multiple sites around the world. What was particularly novel was that they tested them not only in “drought”, but also in “heat” treatments (usually by planting late), and also for combined “drought+heat” treatments. The table below summarizes their results by showing the correlation between variety performance in different treatments (they also include a “well-watered” treatment intended to look at yield potential).

What’s really interesting is how remarkably low the correlation between performance in “drought” and “drought+heat” is (0.08). Like with the Roth study, it’s important not to extrapolate this too quickly beyond the particular sites and kind of treatments used in the study. But it certainly does provide support for the notion that very hot droughts require a different kind of variety. (What exactly that variety should look like is something I plan to focus on over the next year, including (I hope) a stint in Australia.)

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.  

Blame it on the rain?

One of the things I’ve been puzzling over lately is why so many agronomists and others in agriculture seem to have the mantra that July rainfall makes the corn crop. These are generally smart people whose opinion I respect. For example, I’m a fan of Scott Irwin and Darrel Good’s blog over at Farmdoc daily, where they recently discussed the prospects for next July’s rainfall. They are definitely not the only ones to focus on rainfall. Whenever I present empirical work on weather and yield to a group of agronomists, at least one will invariably argue that the strong temperature effects are just an artifact of temperature tending to be high when rainfall is low. One problem with that argument is that a lot of the recent empirical work shows temperature as being important, and not all of it uses datasets with high correlations between temperature and rainfall.

Usually I try to explain the various mechanisms that link temperature to yields. And these discussions can often lead to interesting studies about which mechanisms matter more, such as one paper we have coming out soon. But I never really delved into the reason that rainfall is given so much credit for good or bad years. One likely reason is it’s just a lot easier to see whether or not it rains than to detect a shift in temperatures. So people will tend to remember dry years more than hot years. But there’s also some analysis that appears to support the rainfall hypothesis.

A lot of the work on US corn and weather traces back to Louis M. Thompson’s work 30 years ago. These were basically time series models at the state level, looking for instance at how Illinois yields changed over time in relation to weather. More recent work has updated this type of analysis, and emphasizes the role of July rainfall and temperature, but with a bigger role for rainfall. To recreate that type of analysis, I plot below detrended corn yields for Illinois (detrended by fitting a linear slope to represent gradual technology change, and then adjusting all years to 2006 technology) versus July rainfall (prec) and average maximum temperature (Tmax). I also plot prec and Tmax against each other. (Correlation coefficients are given in the bottom panels). Thanks to Wolfram for providing updated data.

You can see that yields are clearly low at low levels of rainfall, and that yields are also low at high Tmax. But you can also see that Tmax and rainfall have a strong negative correlation, which makes it hard to say whether Tmax, rainfall, or some combination (or neither) is the actual cause of yield loss. I also show three recent years in color (red = 2009, green = 2010, blue = 2011). What’s mildly interesting about these years is they don’t follow the normal correlation between Tmax and rainfall. 2009 was especially cool but with medium rainfall, and 2010 and 2011 were both unusually warm for the given amount of rainfall.

The main point, though, is that the colinearity issue cuts both ways. You can’t just decide it’s rainfall and say that temperatures are less important, no more than you can decide it’s all temperature. Many empirical studies try to move beyond these simple time series specifically because of the colinearity problem. One way to reduce colinearity between Tmax and rainfall is to restrict yourself to looking over a narrow range of one of the variables, so that it is essentially being held fixed while the other one is varying. This is hard to do with a time series that is about 50 years long in total, because you quickly run into problems with small sample sizes.

But it’s easier to do this if we look at time series from lots of counties at the same time, or a so-called panel analysis. As a simple illustration, the plot below shows all points for 1950-2011 for counties in the “three I” states: Illinois, Iowa, and Indiana. The left-hand plot just shows the same scatter as before between Tmax and rainfall. Notice there is still a strong negative correlation. But we can now select only points that are within a narrow range of rainfall (shown as green points) or a narrow range of temperature (shown as red). Then we can take the red points and see how rainfall matters when holding temperature constant (middle panel). Or we can take the green points and see how temperature matters when holding precipitation constant (right panel). The black lines in the right two panels show local polynomial fits to the data (using loess in R).

What do we see? Well, at least for these particular places and values of Tmax and rainfall, there does not appear to be much effect of changing rainfall when July Tmax is constant (at around 30C). But temperature changes do appear to be important when rainfall is held constant (at around 100mm).

Now, there are obviously lots of other combinations we could try, and the point isn’t that rainfall is always and completely unimportant. For example, if we hold Tmax constant at a higher level, where I’d expect rainfall to be more critical, we do in fact see a bigger effect of rainfall at low levels (see below).  But if nothing else, it should be clear that a lot of the credit given to July rainfall for US corn is not necessarily well deserved. Coincidentally, the singers of “blame it on the rain” also got a lot of underserved credit!

In future posts, I’ll try to get more into the reasons that temperature can dominate the effects of rainfall, even in a rainfed system. 

The summer of 2013

Last week at AGU I gave a talk about the lessons of the US corn harvest in 2011 and 2012, both of which were below trend line (see figure). That got me thinking a little more about what to look for in 2013. The obvious point is that it is likely to be better than 2012, because it can’t get much worse. But that’s not too insightful, it’s like saying that Cal’s football team will be better next year, since they were so bad this year (By the way, welcome to Max Aufhammer, our newest blogger! With Wolfram’s move to Berkeley that brings our Cal contingent up to 3. I sure hope I don’t say anything to offend them.)

As we’ve talked about in other posts, the summer of 2012 might be considered the normal in a few decades, but not now. And some recent work from Justin Sheffield and colleagues in Nature argues that drought trends globally, and in North America, are not significantly positive if calculated properly (which contradicts some earlier work). We can leave aside for now the question of whether soil moisture trends are the best measure of drought exposure if one cares about corn yields (though a good topic for a future post), and simply say that conditions in 2012 were well below trend.

This means we’d expect next year to be closer to the trend, and that seems to be the overriding sentiment of markets. As Darrell Good over at farmdoc daily explains “In the past five decades, extreme drought conditions in the U.S., like those experienced in 2012, have been followed by generally favorable growing conditions and yields near trend values.”

But two things work against this tendency to revert to the mean. First, the drought still persists throughout much of the country, as seen at UNL’s drought monitor site.  As Good goes on to say, “current dry soil moisture conditions in much of the U.S. and some recent forecasts that drought conditions could persist well into next year have raised concerns that such a rebound in yields may not occur in 2013.” In other words, if the Corn Belt does not get a wet winter and/or spring, expect prices to start climbing again.

Second, though, is that good initial moisture does not eliminate the chance of drought during the season. There’s an interesting piece by folks at the National Climate Data Center (NCDC) in the AGU newsletter I got today (it was actually published Nov. 20, but it takes about 3 weeks for me to get it!). They note that the 2012 was not like previous droughts in the 1930’s  and 1950’s, or even 1988, in that it was very much driven by high temperatures rather than low starting moisture. As they say:

“For example, at the end of February in both 2011 and 2012 the national PDSI (calculated using the observed monthly mean temperature and precipitation averaged across the contiguous United States) was 1.2 (mildly wet) and –2.5 (moderate drought), respectively, compared to 1934 and 1954 of –5.7 and –4.6, respectively.”

This is also shown pretty effectively in an animation by climate central. So the high temperatures in recent years have made drought come on much more quickly than usual. As the NCDC piece says “By the end of September, every month since June 2011 had above normal average temperatures, a record that is unprecedented.” That's 16 straight months of above normal temperatures!

So my seat-of-the-pants guess is that next year’s yields are likely to still be below trend line (which would be at around 160 bushels/acre). Obviously lots of things could push it above trend line (including changing the definition of the trend!), and it’s way too early to have much confidence about how 2013 will end up. But following Sol’s lead on the Sandy damage prediction, I’ll go out on a limb (his mean was too low by a factor of two, but the true damage was within the confidence interval!). And to pair a risky bet with a safe one, I’ll also predict Stanford wins big at the Rose Bowl.

Chicago Council's Drought Series

I was asked to comment on the 2012 drought, by the Chicago Council on Global Affairs. They have been active in food issues the past few years as part of their Global Agricultural Development Initiative. I especially like their weekly newsletter (despite its title "Food for Thought", which I suspect tops the list of most over-used phrases in the world). It's a great a way to follow different stories in the media, especially on the status of production and policies in other countries. you can sign up for it here.

You can see my post here and the rest of the drought series here.

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)

Predicting food prices and conflict

I am generally a stickler for the peer review process. As a scientist I know the peer review process isn’t perfect, but it is a very effective way of weeding out nonsense. And on the topic of food, there is no shortage of nonsense out there.

That’s partly why I have been intrigued by some of the work coming out of the New England Complex Systems Institute (NECSI). Over the past couple of years, they have had reports on how food prices lead to climate and how speculation and ethanol can explain nearly all of recent price movements. And they have recently released a report on the effects of the current droughton food prices. 

These are all complex topics (which I’m guessing is what drew the Institute’s attention in the first place), and quantitative analysis can be very difficult -- the type of situation where reviews by peers can be especially useful. But they clearly have a different mode of doing science than I do, since they appear to not use the peer review process at all. Instead, they self issue reports, and do press releases on these reports that get wide media coverage.  It’s possible some versions of these reports are in peer-review somewhere, but I don’t see any mention of it on their website.

As I said, the peer review process isn’t perfect. It does not always help, and it is almost always slow. So I can understand reluctance to use it, especially when working on such topical issues. But it raises the question of how credible their work is. For example, I have gotten several questions from colleagues asking what I think of their work, questions that likely would not have occurred if the work had been peer reviewed. 

But in the case of NECSI, I think they have come up with a pretty satisfying solution – making testable predictions about the next year. For example, the figure below is from their most recent report, claiming that the drought should drive FAO’s food price index to about 240 by the end of the year. And they are already on record as saying these levels of food prices lead to large scale social unrest (they state a threshold of 210, so 240 is actually well above that). 

So by the end of the year, surely before most peer review processes would have been completed, they will have a clear test of their model. Now, it’s possible they could get things right for the wrong reasons – a broken clock is right twice a day. But they are going out on a limb, which is a way to establish credibility. Hopefully, they will be as honest and diligent in reporting their failures as their successes.

Speaking of which, FAO just released the new food price index for July 2012, which is 213. That’s 3 points above the threshold!

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? .....

World Supply Estimates

USDA’s monthly report is out today. A lot of attention is going to the new corn estimates, which put forecasted yields at 123 bu/acre. Trend yield for 2012 is about 160 bu/acre, so that would mean a 23% drop from trend. That’s still not quite as bad as 1988, which was closer to 30% belowtrend. As Wolfram showed in a previous post, the heat this year has been about as bad as ever, the rainfall not quite as bad as 1988. So overall I don’t think the downward revisions by USDA should come as much of a surprise.

What I hadn’t been paying as much attention to was the situation in other crops. Lost in the news was that USDA actually downgraded the forecast of global production not just for corn and soy, but also wheat and rice. Wheat downgrades are mainly related to the former soviet union, with Russia and Kazakhstan seeing “July heat and dryness across most of the spring wheat growing areas.” For rice, there have been lots of stories about the late monsoon in India, although conditions seem to be improving there a lot in the last week.

Overall, the production forecasts for wheat, rice, and coarse grains are all lower than what production was last year. This is not so unusual in a historical sense. For example, I plot below the global production for these three since 1961 (all points up to 2010 are from FAO, last 2 are estimates/forecasts from the latest USDA numbers). Gray lines show years where production of all three was down from previous year. Since 1961 there have been 7 other years where all three crops dropped, including three since 2000. 

Even if it’s not unusual, it’s a little surprising to me that it would occur in a year that had such high prices to begin with. A lot of economists argue that yields are very price responsive, for example farmers will put more fertilizer or labor into a crop if prices are high. Others say that yields and production are not very responsive in the short term, but over the long term production will respond (mainly because of expanding area). I’m not sure yet what to make of the recent data, but it certainly seems like a good test of theory. Hopefully somebody out there is calculating what production changes over the past 3-4 seasons, when prices have been high, can tell us about the likely value of supply elasticity.

The New Normal? Part Deux

Following on my earlier post, another way of thinking about normal is not just the conditions on the supply side, but on the demand side. The last big drought we had, in 1988, caused a 25% drop in supply, but barely a blip in corn prices. 

Today the prospects of a drop as big as 1988 have already sent prices about 50% higher since June. The difference of course is the markets are much more sensitive now to supply shocks. I'm not an economist (my standard excuse) but from what I can tell it has a lot to do with low stocks, which in turn has a lot to do with ethanol and other inelastic sources of demand. So the question about a new normal is also partly about whether the market situation is the new normal. I'll leave it to the real economists to answer that one.

The New Normal?

Talking to journalists over the past week about the drought, I think the most common question I get is whether, because of climate change, what we are seeing should be viewed as the new normal. My answer is no and yes. At that point I can almost hear them mumbling "can't an academic ever give a straight answer?" So let me explain.

The no part relates to rainfall. There's been really low amounts of rainfall in most parts since the spring, in many places 50% or less of normal. Below is a summary from NOAA.

The nice plots that Wolfram posted show that this year is not quite the most extreme in the past 50 years, but it is close. From climate model projections, we know that even the most extreme models don't get a change in average rainfall by 2020 or even 2030 that is even close enough to bring average rainfall to the historic lows. In fact, it's very rare to see changes that big even by 2100. For example, the figure below from IPCC shows each climate model’s projection of precipitation change by the end of the century. Only one or two show changes of more than 30% in the Corn Belt, and that’s by the end of the century.

Or if projections aren't your thing, just look at observed trends. There is almost nowhere in the world where precipitation trends are bigger than what one can explain just by natural variability. We highlighted this in a Science paper last year focused on agricultural areas. So the bottom line is this is an unusually low rainfall year, and it would still be unusually low if it happened 20 years from now.

Temperature is a different story. Most agricultural areas have seen trends since 1980 of about two standard deviations. That means that an average year now is like a very hot year back in 1980. The US had been an exception to this rule (again see paper), but that was most likely natural variability masking the trend, and there's no reason to think that will continue. For example, a nice analysis by Gerald Meehl and colleagues shows that climate model simulations that reproduce the "warming hole" show just as rapid warming in the 21st century as other models.

This year so far is very warm, but not completely unprecedented in terms of extreme heat, at least not yet (as Wolfram’s plot showed). Without any detailed calculations, I would guess this year is a little warmer than what we should normally expect over the next decade, but not that much. Maybe it’s the new normal for 10 or 20 years from now.

So the best I can say is that a year like this will be more common moving forward. If I'm interpreting "normal" literally, as in an average year, then i don’t think it’s fair to say this is the new normal because rainfall is so low. But years with such high heat and low soil moisture like this won’t be so rare, and we shouldn’t be surprised to see them more often. Especially now that the "mask" of climate variability seems to have been taken off.