Tuesday, June 9, 2015

Effect of warming temperatures on US wheat yields (Guest post by Jesse Tack)

This post discusses research from a paper coauthored with Andrew Barkley and Lanier Nalley in the Proceedings of the National Academy of Sciences. The paper can be found here. We utilize Kansas field-trial data for dryland winter wheat yields. A major strength of this data is that we were able to match yield data with daily temperature observations across eleven locations for the years 1985-2013.

So, there is a lot of variation in the data, and we can accurately measure local temperature exposure. Max, Sol, Wolfram, and Adam Sobel have a nice paper on the importance of such accuracy here, and Wolfram has blogged on the importance of daily versus more aggregate (e.g. monthly) measures here.

Although not the main focus of our paper, we find that the frequency at which temperature exposures are measured has a large impact on simulated warming impacts (see the supplementary information here). Any stats geek – myself included – will tell you that accurate identification requires sufficient variation, and the more variation the better! Mike and Wolfram have some great posts on constructing temperature measures here and here.

We follow their prescribed method for interpolating temperature exposures and constructing degree days. However, it is still common in many empirical analyses to use minimum and maximum temperatures to construct a measure of average temperature and call it a day. Don’t do this! You are missing so much important variation in temperature exposure that can be measured using the interpolation approach outlined by Mike and Wolfram.

Another consideration not often taken into account in climate change impact studies is that warming temperatures can have both positive and negative yield impacts. Extreme temperatures on both the low (cold) and high (heat) end of the temperature distribution are typically bad for crops. So if we think of warming as a shifting of the distribution to the right, the result is fewer of the former (positive effect) and more of the latter (negative effect).

So what? Well, we find that the net warming impact is negative for winter wheat in Kansas (more heat trumps less freeze), but omitting the beneficial effects of freeze reduction leads to vastly overestimated impacts (Figure 1).

Figure 1. Predicted warming impacts under alternative uniform temperature changes across the entire Fall-Winter-Spring growing season. Impacts are reported as the percentage change in yield relative to historical climate. The preferred model includes the effects from a reduction in freezing temperatures, while the alternative holds freeze effects at zero. Bars show 95% confidence intervals using standard errors clustered by year and variety.

The upshot here is that an accurate identification of warming impacts for winter wheat requires accounting for both ends of the temperature distribution. It would be interesting to know if this finding applies to other crops as well.

An additional strength of our data is that we observe 268 wheat varieties in-sample, which allows us to estimate heterogeneous heat resistance. As with other crops, winter wheat has experienced a steady increase in yields over time due to successful breeding efforts. Much of this increase is driven by a lengthened grain-filling stage, which increases yield potential under ideal weather conditions but introduces additional susceptibility to high temperature exposure during this critical period. David has some great posts on evolving weather sensitivities here, here, and here.

Essentially, if this line of reasoning holds we should expect to see a tradeoff between average yields and heat resistance across varieties. We group varieties by the year in which they were released to the public and allow the effect of extreme heat to vary across this grouping. [Aside: there are practical reasons why we group by release year that are discussed in the paper, we are experimenting with other grouping schemes in on-going projects].

We find that there does indeed exist a tradeoff between heat resistance and average yield, with higher yielding varieties less able to resist temperatures above 34°C (Figure 2). If the least resistant variety is switched to the most resistant variety, average yield is reduced by 6.6% and heat resistance is increased by 17.1%. We also find that newer varieties are less heat resistant than older varieties. Linear regressions using estimates for the 268 varieties indicate that these relationships are statistically significant (P-values < 0.05).

Figure 2. Mean (average) yields and heat resistance are summarized by release year. Heat resistance is measured as the percentage impact on mean yield from an additional degree day above 34°C. The smaller the number in absolute value the more heat resistant the variety is.

These findings point to a need for future breeding efforts to focus on heat resistance, and there is currently much work being done in this area. Check out the Kansas State University Wheat Genetics Resource Center (WGRC) and the International Maize and Wheat Improvement Center (CIMMYT) here and here.

From a historical perspective, our results indicate that such advancements will likely come at the expense of higher average yields. However, there is potentially a huge upside to developing a new variety that combines high yields with improved heat resistance. Under such a scenario, reduced freeze exposure could outweigh increased heat, leading to a net positive warming effect.

In the absence of such a silver bullet variety, the average-yield/heat-resistance tradeoff presents an interesting challenge for producer adaptation, which will ultimately be driven by some economic decision-making process. Producers are individuals, or families, and as such they have a certain tolerance for exposing themselves to risk. Much work has been done showing that farmers enjoy smoothing their consumption over time, which is akin to reducing profit variation. Farrell Jensen and Rulon Pope have a nice paper on this here.

So from a climate change adaptation perspective, it is important ask whether producers prefer a variety that offers high average yield but low heat resistance, or a variety with lower average yields coupled with high resistance? Are there important risk preference differences across producers, or are they a fairly homogeneous group? Currently, we don’t have a firm answer for these pertinent questions.

There has been much work in the agricultural economics literature on risk preference heterogeneity and the extent to which producers will trade off average yield for a reduction in yield variance. However, yield variance captures deviations both above and below the average, which might not be the relevant measure of risk under a warming climate since we are largely concerned with negative (i.e. downside) yield effects.

Martin Weitzman refers to this as fat-tailed uncertainty, and has done some really interesting work in this area (e.g. here). Jean Paul Chavas and John Antle are agricultural economists that seem to be working in this direction using the partial moments framework that John developed, see here, here, and here.

Knowledge about the willingness of producers to trade off yield for risk reduction should clearly be an important focus of future breeding efforts. Historically, plant physiologists and geneticists have worked independent of agricultural economists, but this should change as climate change presents a clear need for well-conceived interdisciplinary research.

In closing, it is worth pointing out that public policy will also likely have a strong effect on the welfare implications for producers under warming. Direct funding support for research provides one linkage, but another often overlooked linkage arrives in the form of subsidized agricultural production. For example, do policies that protect producers against large-scale crop losses provide a disincentive to adopt heat resistant varieties? Wolfram and Francis Annan have looked at this issue here and find that U.S. corn and soybean producers’ adaptation potential is skewed by government programs, in turn implying that producers will choose subsidized yield guarantees over costly adaptation measures.

Thus, even if we come to know what the optimal adaptation path is, it is not clear how we will get there. Economists love to talk of the unintended consequences of public policy. Sometimes it seems that every good policy has a dark side. It’s called the dismal science for a reason ;-)   

Monday, May 11, 2015

Introducing SCYM

In the hopes of figuring out how to raise crop yields or farmer incomes around the world, it would be really nice if we had a quick and accurate way of actually measuring yields for individual fields. That has motivated a lot of work over the years on using satellite data, and we have a paper out this week describing another step in that direction.

As I see it there are three main ingredients needed for yield remote sensing to be successful on a meaningful scale. One is the raw data. As Marshall’s recent post explained, there are several new satellite data providers that are really transforming our ability to track individual fields, even in smallholder areas.

Second is the ability to process the data at scale. Five years ago, for example, I would have to hire a research assistant to download imagery, make sure it was geometrically and radiometrically calibrated (i.e. properly lined up and in meaningful units), and then apply whatever algorithms we had. That just didn’t scale very well, in terms of labor or on-site data storage or processing. When a collaborator would ask “could you produce yield estimates for my study area,” I would have to think about how many weeks or months of work that would entail. But a couple of years ago I was introduced to Google’s Earth Engine, which is “a planetary-scale platform for environmental data & analysis.” In practical terms, it means that they have a lot of geospatial data (including all historical Landsat imagery), a lot of built-in algorithms for processing, and an interface to run your own code on the data and visualize or save the output. Part of why it works is that data providers, like the USGS for Landsat, have gotten better at providing well calibrated data. Earth Engine is very cool, and the more I’ve worked with it, the more I can see how this transforms our ability to extract value out of data already collected.

Third, and arguably the rate-limiting step nowadays, is to have algorithms that can translate satellite data into accurate yield estimates. It’s easy enough to do this if you have lots of ground data to calibrate to for a particular site, but that’s generally not scalable (unless people get clever about crowdsourcing ground “truth”). What seemed to be lacking was a very generic, scalable algorithm. So in the last 8 months or so we’ve been working to develop and test one idea about how to do this. I’m calling it a scalable satellite-based crop yield mapper (SCYM, pronounced “skim”), and a description of it has just been published in Remote Sensing of Environment. Conveniently, SCYM also stands for Steph Curry’s Your MVP.

The basic idea is that if you don’t have lots of ground data to calibrate a model, why not generate lots of fake ground data? Then for whatever combination of observations you actually have (say, for instance, satellite images on 2 or 3 specific days, and measures of daily weather), you can look into your fake data to see what the best fit model is to predict the desired variable (“yield”) from the measured predictors. The paper provides more detail, which I won’t bore readers with here. But to give a sense of the type of output, below shows an animation of our maize yield estimates over part of Iowa for 2008-2013. Red are high yields, blue are low.

The figure below shows a comparison between these and ground “truth” estimates for maize, which we take from the dataset described in a previous post.

The cool thing about this is that it’s quite generic. To illustrate that, we reran the model for soybeans, with results nearly as good as for maize.

Hopefully this type of thing will help make faster progress on understanding yields and farm productivity, and figuring out what actually works for improving them. One general lesson out of this for me is that sometimes making something really scalable requires scrapping an old approach. We had been previously running crop models for specific sites and years, but that wasn't possible within the Earth Engine system. I think SCYM (which trains a regression using simulations over lots of sites and years) is more robust than what we had, and along with the new satellite data and Earth Engine-type systems, it might just provide a way to do yield mapping at scale.

Wednesday, April 22, 2015

The inconsistent farmer

One function of this blog, other than to raise the level of guilt in Sol’s life (we are still waiting for his March post -- from 2013), is to help us work through ideas that are possibly wrong, possibly unoriginal, or very likely both at the same time. So here’s one idea I’d welcome feedback on. Let’s call it the “inconsistent farmer” problem. 

In the early days of work on climate change and agriculture, the notion that modelers were being too pessimistic in how they treated farmer’s ability to adapt was captured by the phrase “dumb farmer.” Of course, the idea was not that farmers are actually dumb, but that modelers were treating them as such. Modelers would simulate a “reference” farmer without climate change, and then that same farmer with the same exact practices and crops but with climate change. The idea of calling this a “dumb farmer” is that a real (i.e. smart) farmer would notice the change in climate and adjust. Obviously, a lot of work since has added simulations with hypothetical adjustments.

But let’s revisit the basic setup, in terms of the “reference” farmer. Generally speaking these were meant to characterize the current crops and practices at the time of the study. But the farmer in question was being exposed to some future climate, say of 2050 or 2080. So even the reference farmer was in some sense “dumb” or “backwards” in that 50 years had passed and they were still using the cropping systems of circa 2000.

All this is probably old hat for anyone who has read the literature. But what seems to go less noticed is that the impact models that then use the yield impacts derived from crop models are generally assuming some exogenous yield trend. For example, the recent AgMIP papers have some scenarios out to 2050, with the assumed yield increases summarized below in the table from Nelson et al.

So on the one hand the crop models assume current farmers, and on the other hand the economic impact models assume the more sophisticated future farmer. A farmer can’t be both things at the same time, so we have the “inconsistent farmer.”

Now why would anyone possibly care about this? I’d say for two big reasons. The first is that future technologies could have a very different sensitivity to climate than current ones. This is the idea behind previous posts here and here and here, so I won’t spend much time on it here. But there is some new evidence along these lines, such as the studies on soybean here and here that show modern cultivars are more sensitive to hot weather, like what we saw for wheat. For example, below is one plot for soybean from Rincker et al. showing genetic yield gains (comparing newer vs. older varieties) as a function of the favorability of the environment. The stronger yield trends in good conditions means that the difference between good and bad growing conditions is bigger for the newer cultivars, at least in absolute terms.

Second, and maybe more important, is that there is potentially a lot of double counting going on when people examine adaptation. Or put differently, there’s a lot of overlap between the types of things that explain “exogenous” yield trends, and the types of things that crop modelers use as “adaptation” in their models. For example, I was recently in Eastern India looking at various strategies to get wheat sown earlier. When you ask farmers what the benefits of sowing early are, they generally tell you it's because wheat doesn't like the hot spring so yields are higher if you sow earlier. If you ask them whether the spring weather has been changing, they generally say it's getting warmer. But if you ask them if they are doing anything different because of that warming, they generally say no, they just get lower yields. They don't view the earlier sowing as a benefit specific to climate change. It's a change that would help them anyway.

This idea of double counting is similar to the notion of adaptation illusions that I wrote about earlier. But it depends on the degree to which these “adaptive” measures are already part of the baseline “exogenous” yield trends. To get at that, it’s important to really understand not just the types of things being considered as adaptations but also the source of recent yield growth and the likely drivers of future yield growth. And if the latter are going to be a big part of the “exogenous” trend, they should probably be out of bounds for modelers to incorporate as adaptations.  

I realize that a lot of this seems like semantic details. But I don’t think it is. My sense is that there are real risks of understating the climate change impacts. Either because we are specifying a reference scenario that uses cropping systems that are less sensitive to climate than their future descendants will be, or by allowing technologies to be called on to reduce impacts (i.e. adapt) when in fact they would have already been deployed in the “exogenous” reference. I suppose the first factor could also go the other way, in which we would be overstating impacts because future crops will be less sensitive (i.e. irrigated).

For the double counting, you can look at the types of adaptations that modelers employ and simply ask whether you really think these aren’t part of what will drive the “exogenous” yield trend. Drought tolerance, shifting sow dates, more irrigation and fertilizer – these are all things that have been important sources of recent yield growth and will continue to play a role in future trends. Below is a quick schematic to try to explain this point. The reference farmer is generally assumed to continue on a trajectory of yield growth, shown here as linear to keep it simple (green line). Climate changes then can affect this trajectory, and often impacts are calculated both without and with adaptation (red and blue line). But if one lists the types of things that are implicit in the "exogenous" trend, and then the things generally invoked as adaptations, there is a lot of overlap. These are good things, but in scoping out the prospects for future supply, we shouldn't count them twice.

Wednesday, April 1, 2015

Discounting Climate Change Under Secular Stagnation

Ben Bernanke, recent former Chair of the Federal Reserve, has a new blog.  And he's writing about low interest rates and so-called secular stagnation, a pre-WWII phrase recently resurrected by Larry Summers.

The topic is dismal--hey, they're economists! But for those in the field it's a real hoot to see these titans of economic thought relieved of their official government duties and able to write openly about what they really think.

These two share many views, but Ben has a less dismal outlook than Larry.  Larry thinks we're stuck in a low-growth equilibrium, and low or even negative interest rates are here to stay without large, persistent fiscal stimulus.  Ben thinks this situation is temporary, if long lived.  He writes:
I generally agree with the recent critique of secular stagnation by Jim Hamilton, Ethan Harris, Jan Hatzius, and Kenneth West. In particular, they take issue with Larry’s claim that we have never seen full employment during the past several decades without the presence of a financial bubble. They note that the bubble in tech stocks came very late in the boom of the 1990s, and they provide estimates to show that the positive effects of the housing bubble of the 2000’s on consumer demand were largely offset by other special factors, including the negative effects of the sharp increase in world oil prices and the drain on demand created by a trade deficit equal to 6 percent of US output. They argue that recent slow growth is likely due less to secular stagnation than to temporary “headwinds” that are already in the process of dissipating. During my time as Fed chairman I frequently cited the economic headwinds arising from the aftermath of the financial crisis on credit conditions; the slow recovery of housing; and restrictive fiscal policies at both the federal and the state and local levels (for example, see my August and November 2012 speeches.)
These are good points. But then Larry has a compelling response, too.  I particularly agree with Larry about the basic economic plausibility of  persistent equilibrium real interest rates that are well below zero.  He writes:
Do Real Rates below Zero Make Economic Sense? Ben suggests not– citing my uncle Paul Samuelson’s famous observation that at a permanently zero or subzero real interest rate it would make sense to invest any amount to level a hill for the resulting saving in transportation costs.  Ben grudgingly acknowledges that there are many theoretical mechanisms that could give rise to zero rates. To name a few: credit markets do not work perfectly, property rights are not secure over infinite horizons, property taxes that are explicit or implicit, liquidity service yields on debt, and investors with finite horizons.
Institutional uncertainty seems like a big deal that can't be ignored when thinking about long-run growth and real interest rates (these are closely connected).  People are pessimistic about growth these days, for seemingly pretty good reasons.  Institutional collapse may be unlikely, but far from impossible.  Look at history.  If we think negative growth is possible, savings are concentrated at the top of the wealth distribution, and people are loss averse, it's not hard to get negative interest rates.

Still, I kind of think we'd snap out of this if we had a bit more fiscal stimulus throughout the developed world, combined with a slightly higher inflation target--say 3 or 4 percent.  But keep in mind I'm just an armchair macro guy.

The point I want to make is that these low interest rates, and the possibility of secular stagnation, greatly affects the calculus surrounding optimal investments to curb climate change.  The titans of environmental economics--Weitzman, Nordhaus and Pindyck--have been arguing about the discount rate we should use to weigh distant future benefits against near-future costs of abating greenhouse gas emissions.  They're arguing about this because the right price for emissions is all about the discount rate.  Everything else is chump change by comparison.

Nordhaus and Pindyck argue that we should use a higher discount rate and have a low price on greenhouse gas emissions.  Basically, they claim that curbing greenhouse gas emissions involves a huge transfer of wealth from current, relatively poor people to future supremely rich people.  And a lot of that conclusion comes from assuming 2%+ baseline growth forever. Weitzman counters that there's a small chance that climate change will be truly devastating, causing losses so great that the future may not be as well off as we expect.  Paul Krugman has a great summary of this debate.

Anyway, it always bothered me that Nordhaus and Pindyck had so much optimism built into baseline projections.  Today's low interest rates and the secular stagnation hypothesis paint a different picture.  Quite aside from climate change, growth and real rates look lower than the 2% baseline many assume, and a lot more uncertain.  And that means Weitzman-like discount rates (near zero) make sense even without fat-tailed uncertainty about climate change impacts.

Tuesday, March 10, 2015

Buying Conservation--For the Right Price

Erica Goode has an inspiring article about the benefits of conservation tillage, which has been gaining favor among farmers.  No-till farming can improve yields, lower costs, and improve the environment.  Just the kind of thing we all want to hear--everybody wins!

One important thing Goode doesn't mention: USDA has been subsidizing conservation tillage, and these subsidies have probably played an important role in spreading adoption.

Subsidizing conservation practices like no-till can be a little tricky.  After all, while this kind of thing has positive externalities, farmers presumably reap rewards too.  There are costs involved with undertaking something new. But once the practice is adopted and proven, there would seem to be little need for further subsidies.  The problem is that it can be difficult to take subsidies away once they've been established.

In practice, the costs and benefits of no till and other conservation practices vary.  Some of this has to do with the type of land.  No-till can be excellent for land in the Midwest with thick topsoil.  In the South, where topsoil is thin, maybe no so much.  So, for some farmers conservation practices are worthwhile; for others, the hassle may not be worth the illusive future benefits.  Ideally, policy would provide subsidies to the later, not the former.  But how do policy makers differentiate?  In practice, they don't; everybody gets the subsidies.

Can we do better? Together with some old colleagues at USDA, I've been thinking about this question for a long time, and we recently released a report (PDF) summarizing some of the most essential ideas (here's the Amber Waves short take).

In short, yes, we can do better.  The basic idea involves a form of price discrimination, implemented using augmented signup rules.  Sign ups for conservation programs operate like an auction: farmers submit offers for enrollment, offers are ranked nationally, and the best offers are selected.  The problem is that, when farmers compete on a national scale, farmers happy to do no-till conservation without any subsidies at all are pitted against farmers for whom the private benefits conservation tillage are dubious.  A lot of the subsides probably end up going to farmers who would do it anyway.

Alternatively, signups could impose a degree of local competition, such that the worst offers for any set of observable characteristics--say farms in a crop district with land of similar quality--would be rejected regardless of their national-level standing.  This kind of local competition would garner more competitive offers from no-till farmers who would use the practice even without subsidies.

It's difficult to tell how much more conservation we buy for the tax payer buck using these techniques.  We can't really know without testing the mechanisms on real signups. This is where real policy experiments could have a lot of added value.  Will USDA give it a try? Only time will tell...

Friday, February 20, 2015

For your weekend viewing pleasure

In case you somehow happened to miss yesterday's "Hadoop World Conference" in San Jose, we bring you the following video of GFEED's own Sol Hsiang -- who despite looking very sharp in his suit + earpiece/headset thingy, conspicuously fails to use the word "hadoop", and conspicuously fails to get introduced by Obama, as the speaker before him did.

Since Sol no longer posts, hopefully this video will at least partially sate loyal GFEED readers' desire for Hsiang-related blog activity.  Have a good weekend!

Monday, February 16, 2015

Siestas and climate change

Lately I’ve been thinking about siestas. And not just because of the extremely warm summer – I mean February – we are having in California. Taking an afternoon break is probably one of the oldest and most widespread adaptations humans have in the face of hot weather. Better to work hard in the morning and late afternoon when the heat is not too bad, and take a break when you would otherwise be unproductive. For example, I was never so willing to wake up at 4am as when I was doing field work in Sonora, Mexico in July – it meant I could get most of the day’s work done by lunch and not face the afternoon sun and heat. I can only guess that a similar strategy explains Max’s daily naps in his office, though Berkeley isn’t typically all that hot.

Although humans have widely adopted this practice, we don’t typically think of it as a strategy for how we grow crops. But various studies seem to suggest that the best way to adapt to extreme heat may be to have crops take a break. This can be done on two different time scales. In a day, we can select for crops that slow down growth during hours that are the hottest, and have the highest vapor pressure deficits. This avoids using water at the times when the efficiency of using water would be lowest. A nice recent review by Vincent Vadez and colleagues discusses some of the ways crop scientists are selecting for this trait. As I’ve mentioned in past posts, it’s a strategy we are trying to evaluate as a possible adaptation to climate change. It probably helps a little, but it’s not clear how much.

At longer time scales, it’s possible to just scrap the idea of growing a crop during the peak of the summer, and instead try to fit in two crops that straddle this period. This is the idea behind a recent paper we have led by my student, Chris Seifert, on double cropping trends and potentials in the US. Part of the challenge of double cropping is that it pushes the late summer crop (typically soybean) into greater risks of frost damage at the end of the season. But with warming temperatures, crops develop more quickly and the frost dates recede. This idea has been around for a while, but the idea of the paper was to simulate how much more viable the practice is actually becoming and will continue to become. 

The basic approach was to develop a realistic model of phenology for a wheat-soy double crop, and see how often the two crops can be squeezed in between the last frost of spring and the first frost of fall. Below is a summary of the projected suitability (% years of survival) for current and future conditions under two emission scenarios (top is low emissions, RCP4.5, and bottom is high emission, RCP8.5). The current simulated limits match pretty well where existence of double cropping extends in reality (roughly to the south of Illinois). But by mid-century the area suitable has shifted at least a full state northward, and by 2100 is roughly double or triple the current suitable area depending on emission. It’s also slightly interesting that really warm scenarios actually make this particular system less suitable in the South, because wheat can’t vernalize properly.

The other thing the paper looks at is whether warming has already encouraged a spread of double cropping. The data to look at double-cropping are pretty coarse in spatial resolution and short in time, but the trends do seem consistent with the simulated increase in suitability. For example, the figure below shows the fraction of reported double-cropping (red line) in states that historically have had frost limitations vs. the simulated suitability in those states for three different thresholds of survival (survival = soy maturing before first frost).

Just to be clear, this isn’t a definite gain relative to sticking with a single summer crop of corn or soybean. That would depend on prices and yields, both of which will also depend on what climate is doing. And even if it’s a gain, it could well still be worse than what could be achieved in the current climate with a single crop (for reasons we get into in the paper). Just like taking a siesta may make you more productive than if you didn’t, but not more productive than you’d be without the hot weather. But if nothing else it is an intriguing option that could make sense in a lot more places than it used to. And for many places it falls in the category of things that wouldn’t make much sense without climate change, because of the frost constraint, which I think is a key criterion for a truly effective adaptation.

Overall, maybe the lesson here is the corollary of what Marshall blogged about a few months ago. If we are learning that people’s response to heat is empirically not that different from crops, maybe it follows that strategies people have adopted to deal with heat can give us an idea of what might work well with crops. And maybe with all the heat lately – at least in the Western U.S. – people should start taking more siestas too. Max must just be testing out his adaptation strategy.

Wednesday, February 11, 2015

Measuring yields from space

[This post is co-written with Florence Kondylis at the World Bank, and a similar version was also posted over at the World Bank's Development Impact blog.]

One morning last August a number of economists, engineers, Silicon Valley players, donors, and policymakers met on the UC-Berkeley campus to discuss frontier topics in measuring development outcomes. The idea behind the event was not that economists could ask experts to create measurement tools they need, but instead that measurement scientists could tell economists about what was going on at the frontier of measuring development-related outcomes.  One topic that generated a lot of excitement -- likely due to David Lobell's charm at the podium -- was the potential for a new crop of satellites to remote-sense (i.e. measure) important development outcomes.

Why satellite-based remote sensing?
The potential ability to use satellites to measure common development outcomes of interest excites researchers and practitioners for a number of reasons, chief among them the amount of time and money we typically have to spend to measure these outcomes the “traditional” way (e.g. conducting surveys of households or firms).  Instead of writing large grants, spending days traveling to remote field sites, hiring and training enumerators, and dealing with inevitable survey hiccups, what if instead you could sit at home in your pajamas and, with a few clicks of a mouse, download the data you needed to study the impacts of a particular program or intervention?

The vision of this “remote-sensing” based approach to research is clearly intoxicating, and is being bolstered by the vast amount of high-resolution satellite imagery that is now being acquired and made available.  The recent rise of “nano-“ or “micro”-satellite technology – basically, fleets of cheap, small satellites that image the earth in high temporal and spatial resolution, such as those being deployed by our partner Skybox – could hold particular promise for measuring the types of outcomes that development folks often care about.  This is perhaps most obviously true in agriculture, where unlike in the manufacturing sector, most production takes place outside.

How does it work?
For most agricultural crops – particularly the staple crops grown by African smallholders, such as maize – pretty much anyone can look at a field and see the basic difference between a healthy highly productive crop and low-yielding crop that is nutrient or moisture stressed.  One main clue is color: healthy vegetation reflects and absorbs different wavelengths of light than less-healthy vegetation, which is why leaves on healthy maize plants look deep green and leaves on stressed or dead plants look brown.  Sensors on satellites can also discern these differences in the visual wavelengths, but they also measure differences at other wavelengths, and this turns out to be particularly useful for agriculture.  Healthy vegetation, in turns out, absorbs light in the visible spectrum and reflects strongly in the near infrared (which the human eye can’t see), and simple ratios of reflectance at these two wavelengths form the basis of most satellite-based measures of vegetative vigor – e.g. the familiar Normalized Difference Vegetation Index, or NDVI.   High ratios basically tell you that you’re looking at plants with a lot of big, healthy leaves.

The trick is then to be able to map these satellite-derived vegetation indices into measures of crop yields.  There are two basic approaches (see David's nice review article for more detail).  The first combines satellite vegetation indicies with on-farm yield observations as collected from the typical household or agricultural surveys.  By regressing the “true” survey-based yield measure on the satellite-based vegetation index, you get an estimated relationship between the two that can then be applied to other agricultural plots that you observe in the satellite data but did not survey on the ground.  The second approach combines the satellite data with pre-existing estimates of the relationship between final yield and vegetative vigor under various growing conditions (often as derived from a crop simulation model, which you can think of as an agronomist’s version of a structural model). Applying satellite reflectance measures to these relationships can then be used to estimate yield on a given plot.  A nice feature of this second approach is that it is often straightforward to account for the role of other time-varying factors (e.g. weather) that also affect the relationship between vegetation and final yield.

How well does it work?
These approaches have mainly been applied to larger farm plots in the developed and developing world, at least in part because until very recently the available satellite imagery was generally too coarse to resolve the very small plot sizes (e.g. less than half an acre) common in much of Africa.  For instance, the resolution of the MODIS sensor is 250m x 250m, meaning one pixel would cover more than 15 one-acre plots. Nevertheless, these approaches have been shown to work surprisingly well on these larger fields.  Below are two plots, both again from David and co-author's work, showing the relationship between predicted and observed yields for wheat in Northern Mexico, and maize (aka “corn”, for Americans) in the US Great Plains.   Average plot sizes in both cases are > 20 hectares, equivalent to at least 50 one-acre plots.

Top plot:  wheat yields in northern Mexico, from Lobell et al 2005.  Bottom plot:  corn yields in the US Great Plains, from Sibley et al 2014

Although success is somewhat in the eyes of the beholder here, the fit between observed and satellite-predicted yields is pretty good in both of these cases, with overall R2s of 0.63 in the US case and 0.78 in the Mexico case.  And, at least in both of these cases, the “ground truth” yield data was not actually used to construct the yield prediction – i.e. they are using the second approach described above.  This was possible in this setting because these were crops and growing conditions for which scientists have a good mechanistic understanding of how final yield relates to vegetative vigor.

From rich to poor, big to small
Applying these approaches much of the developing world (e.g. smallholder plots in Africa) has been harder.  This is not only because of the much smaller plot sizes, and thus the difficulty (impossibility, often) of resolving them in existing satellite imagery, but also because of a lack of either (i) ground truth data to develop the satellite-based predictions, and/or (ii) a satisfactory mechanistic understanding in these environments of how to map yields to reflectance measures.

New data sources from both the ground and sky are starting to make this possible.  Sensors on the new micro-satellites mentioned above often have sub-meter resolutions, meaning smallholder plots are now visible from space (a half-acre plot would be covered by over 2000 pixels).  Furthermore, this imagery is being acquired often enough to ensure at least a few cloud-free images during the growing season -- not a small problem in the rainy tropics.

Working with David and some collaborators in Kenya, Uganda, and Rwanda, we are linking this new imagery with ground-based yield data we are collecting to understand whether the satellite data can capably predict yields on heterogeneous smallholder plots.  Below is a map of some of the smallholder maize fields we have mapped and are tracking in Western Kenya, as part of an ongoing experiment with smallholder farmers in the region.

Locations of some plots we are tracking in Western Kenya

Some of the long run goals of this work are to (i) allow researchers who have already have information on plot boundaries and crop choice to use satellite images to estimate yields, and (ii) to allow researchers who do not have plot boundaries but who are interested in broader-scale agricultural performance (eg. at the village or district level) a way to track yields at that scale. This work is ongoing, but given the experience in developed countries, we are hopeful.

Some challenges.
Nevertheless, there are clear challenges to making this approach work at scale, and clear limitations (at least in the near term) to what this technology can provide.   Here are a few of the main challenges:

  1. Which boundaries and which crops. To measure outcomes at the level of the individual farm plot, satellite-based measures will be most easily employable if the researcher already knows the plot boundaries and knows what crop is being grown.   As satellite imagery improves and as computer vision algorithms are developed to remotely identify plot boundaries, both of these constraints will likely be relaxed, but the researcher will still need some ground information on which plots belong to whom. 
  2. Measurement error. Even with plot boundaries in hand, the fact that satellite imagery will not be able to perfectly predict yields means that using satellite-predicted yields as an outcome will likely reduce statistical power (although it’s not immediately clear how much noisier satellite estimates will be, given that survey based measures of these outcomes – e.g. from farmer self reports – are likely also measured with error.)  This almost certainly means that this technology will not be equipped to discern small effects in the smaller-sized ag RCTs that often get run.
  3. Moving beyond yield.  Finally, even with plot boundaries in hand and well-powered study, satellites are going to have a hard time measuring many of the other outcomes we care about – things like profits or consumption expenditure.  Satellites might in the near term be able to get at related outcomes such as assets (something we’re also working on), but it’s clearly going to be hard to observe most household expenditures directly. 

Putting these difficulties together, should we just abandon this whole satellite thing?  We think not, for two reasons.  The first reason is that as we (hopefully) improve our ability to accurately measure smallholder yields from space, this ability would provide a clear compliment to existing surveys.  For instance, if yields are a primary outcome, imagine just being able to do a baseline survey (where field boundaries are mapped) and then measure your outcome at follow-up from the sky.  This will make an entire study both faster and cheaper, which should allow for larger sample sizes, which will in turn help deal with the measurement error issue above.

Second, we still have a surprisingly poor understanding of why some farms, and some farmers, appear to be so much more productive than others.  Is it the case that relatively time-invariant factors like soil type and farmer ability explain most of the observed variation, or are time-varying factors like weather more important?  Satellite data might be particularly useful for this question (David's review paper, and his earlier G-FEED post, gives some really nice examples), because you can assemble huge samples of farm plots that can then be easily followed over time.  Satellite data in this setting therefore might afford more power, and you can do it all in your pajamas.

Tuesday, February 10, 2015

Crop Insurance and the Disincentive to Adapt to Extreme Heat

Francis Annan and I have a new paper that looks at US crop insurance and whether it gives farmers a disincentive to adapt to extreme heat.  The increased frequency of extreme heat under climate change is predicted to lower corn and soybean yields going forward.  Reducing the sensitivity to extreme heat would go a long way in avoiding some of the predicted damages, but there has been no observable progress in relative heat sensitivity over the last six decades. One possible explanation is that government policies bail out farmers and give them a disincentive to adapt.

The United States Federal Crop Insurance Program (FCIP) has gained tremendous momentum in recent decades and has become one of the major agricultural support program in the United States. Created in the 1930s, the crop insurance programs have undergone many developments through the revisions in various federal farm bills, acts, and assistance programs. The 1994 "Crop Insurance Reform Act" had the goal to eliminate annual disaster programs. It made participation in the insurance program mandatory to be eligible for disaster payments. This act implemented catastrophic (CAT) coverage to protect producers against major losses at no cost to producers. In recent years, legislative mandates have further increased the premium subsidy, in addition to the introduction of new insurance products. As a result the program has experienced tremendous growth in participation, especially following the 1994 act (Annan, Tack, Harri and Coble).

The fraction of total crop insurance premiums that is subsidized by the government greatly increased between 1981-2013, especially following the 1994 Crop Insurance Reform Act as shown in the following graph. Subsidy ratios increased from 20% in the early 1980s to 60% in recent years for both corn and soybeans.

The following figures show total planted area for corn and soybeans as solid lines, which has remained rather constant between 1981-2013.  The dashed line shows the planted area that is insured under the federal crop insurance program.  There is a large increase over time and by now almost all planted area is insured.

Did the increase in insured area increase the sensitivity to extreme heat?  We run a regression where we link log yields in a county on four weather variables: moderate heat, extreme heat and a quadratic in season-total precipitation (April-September). We also include county fixed effect, year fixed effects to pick up shifts in overall price levels or regulatory changes and county-specific quadratic time trends as there have been different warming trends across the US (Burke and Emerick). Finally, we interact the weather measures with the fraction of the area that is insured.  The results are given in the following table.

Columns (a) replicate the standard weather-yield relationship. We find that extreme heat has a harmful effect that is an order of magnitude larger than the beneficial effect of moderate heat.  The results also indicate am inverted-U-shape for precipitation that is comparable to previous results and implies an optimal amount for precipitation that is comparable to agronomic estimates. Columns (1b) and (2b) interact the weather-sensitivity of yields with the fraction of the area planted that is insured. The weather-sensitivity of an uninsured field is the coefficients on the weather variable, while the sensitivity of an insured field is the sum of the coefficient on the weather variable and the interaction term. We find that for both corn and soybeans, insured areas exhibit the same sensitivity to beneficial moderate heat, but are more sensitive to fluctuations in extreme heat.  The sensitivity to extreme heat is 67% larger for insured corn than for uninsured corn and 43% larger for insured soybeans than uninsured soybeans.

We conduct various sensitivity checks: The results are comparable whether we use a common quadratic time trend for all counties, a state-specific quadratic time trend, or the county-specific quadratic time trends.  Most importantly, the results are consistent whether we estimate them for the years 1981-2013, 1981-1996, or 1997-2013. Average subsidy rates in the first half are 27% but increase to 55% in the second half as the insured planted area increase from roughly one-fourth to three-fourth while the total planted area remains constant.  While insurance coverage is endogenous, our coefficients of interest interact it with exogenous weather variation. The interacted coefficients correctly identify the sensitivity of the insured field, however, it could be because insured fields are different (e.g., marginal land that is ensured has higher sensitivities), or because crop insurance gives a disincentive to reduce the damaging effects. We believe it is the latter: the heterogeneity story seems unlikely given that we account for county fixed effects, year fixed effects as well as county-specific time trends during a time period when insurance coverage greatly increases. The marginal decision on which areas to insure hence greatly changes over our study period.

Wednesday, January 28, 2015

Food Waste Delusions

A couple months ago the New York Times convened a conference "Food for Tomorrow: Farm Better. Eat Better. Feed the World."  Keynotes predictably included Mark Bittman and Michael Pollan.  It featured many food movement activists, famous chefs, and a whole lot of journalists. Folks talked about how we need to farm more sustainably, waste less food, eat more healthfully and get policies in place that stop subsidizing unhealthy food and instead subsidize healthy food like broccoli.

Sounds good, yes? If you're reading this, I gather you're familiar with the usual refrain of the food movement.  They rail against GMOs, large farms, processed foods, horrid conditions in confined livestock operations, and so on.  They rally in favor of small local farms who grow food organically, free-range antibiotic free livestock, diversified farms, etc.  These are yuppies who, like me, like to shop at Whole Foods and frequent farmers' markets.  

This has been a remarkably successful movement.  I love how easy it has become to find healthy good eats, bread with whole grains and less sugar, and the incredible variety and quality of fresh herbs, fruits, vegetables and meat.  Whole Paycheck Foods Market has proliferated and profited wildly.  Even Walmart is getting into the organic business, putting some competitive pressure on Whole Foods. (Shhhh! --organic isn't necessarily what people might think it is.)

This is all great stuff for rich people like us. And, of course, profits.  It's good for Bittman's and Pollan's book sales and speaking engagements.  But is any of this really helping to change the way food is produced and consumed by the world's 99%?  Is it making the world greener or more sustainable?  Will any of it help to feed the world in the face of climate change?

Um, no.  

Sadly, there were few experts in attendance that could shed scientific or pragmatic light on the issues.  And not a single economist or true policy wonk in sight. Come on guys, couldn't you have at least invited Ezra Klein or Brad Plummer?  These foodie journalists at least have some sense of incentives and policy. Better, of course, would be to have some real agricultural economists who actually know something about large-scale food production and policies around the world. Yeah, I know: BORING!

About agricultural polices: there are a lot of really bad ones, and replacing them with good policies might help.  But a lot less than you might think from listening to foodies.  And, um, we do subsidize broccoli and other vegetables, fruits, and nuts.  Just look at the water projects in the West. 

Let me briefly take on one issue du jour: food waste.  We throw away a heck of a lot of food in this country, even more than in other developed countries.  Why?  I'd argue that it's because food is incredibly cheap in this country relative to our incomes.  We are the world's bread basket.  No place can match California productivity in fruit, vegetables and nuts.  And no place can match the Midwest's productivity in grains and legumes.  All of this comes from remarkable coincidence of climate, geography and soils, combined with sophisticated technology and gigantic (subsidized) canal and irrigation systems in the West.  

Oh, we're fairly rich too.  

Put these two things together and, despite our waste, we consume more while spending less on food than any other country.  Isn't that a good thing?  Europeans presumably waste (a little) less because food is more scarce there, so people are more careful and less picky about what they eat. Maybe it isn't a coincidence that they're skinnier, too.

What to do? 

First, it's important to realize that there are benefits to food waste.  It basically means we get to eat very high quality food and can almost always find what we want where and when we want it.  That quality and convenience comes at a cost of waste.  That's what people are willing to pay for.  

If anything, the foodism probably accentuates preference for high quality, which in turn probably increases waste.  The food I see Mark Bittman prepare is absolutely lovely, and that's what I want.  Don't you?

Second, let's suppose we implemented a policy that would somehow eliminate a large portion of the waste.  What would happen?  Well, this would increase the supply of food even more.  And sinse we have so much already, and demand for food is very inelastic, prices would fall even lower than they are already.  And the temptation to substitute toward higher quality--and thus waste more food--would be greater still.  

Could the right policies help?  Well, maybe.  A little. The important thing here is to have a goal besides simply eliminating waste.  Waste itself isn't problem. It's not an externality like pollution.  That goal might be providing food for homeless or low income families.  Modest incentive payments plus tax breaks might entice more restaurants, grocery stores and others to give food that might be thrown out to people would benefit from it.  This kind of thing happens already and it probably could be done on a larger scale. Even so, we're still going to have a lot of waste, and that's not all bad. 

What about correcting the bad policies already in place?  Well, water projects in the West are mainly sunk costs.  That happened a long time ago, and water rights, as twisted as they may be, are more or less cemented in the complex legal history.   Today, traditional commodity program support mostly takes the form of subsidized crop insurance, which is likely causing some problems.  The biggest distortions could likely be corrected with simple, thoughtful policy tweaks, like charging higher insurance premiums to farmers who plant corn after corn instead of corn after soybeans.  But mostly it just hands cash (unjustly, perhaps) to farmers and landowners.  The odds that politicians will stop handing cash to farmers is about as likely as Senator James Inhofe embracing a huge carbon tax.  Not gonna happen.

But don't worry too much.  If food really does get scarce and prices spike, waste will diminish, because poorer hungry people will be less picky about what they eat.

Sorry for being so hard on the foodies.  While hearts and forks are in the right places, obviously I think most everything they say and write is naive.  Still, I think the movement might actually do some good.  I like to see people interested in food and paying more attention to agriculture.  Of course I like all the good eats.  And I think there are some almost reasonable things being said about what's healthy and not (sugar and too much red meat are bad), even if what's healthy has little to do with any coherent strategy for improving environmental quality or feeding the world.  

But perhaps the way to change things is to first get everyones' attention, and I think foodies are doing that better than I ever could.