Drink more coffee and run faster! Get fit in only 9 seconds! A rant on NYTimes exercise coverage.

As a fairly half-assed exerciser, I am always on the lookout for quick/easy/delicious ways to get in shape without having to really do anything.  Thus a lot of the exercise articles on NYTimes's "Well" blog are irresistible clickbait for me -- as they are for the presumably millions of other NYT-reading half-assed exercisers who ensure that these articles consistently top of the NYT most-read lists.  Drink more caffeine and run faster!  Take a hot bath and run faster!  Get all the exercise you need in only seven minutes!  Scratch that -- only four!!  Scratch that -- only one!!!

image stolen from NYT article

Here's one from this week:  Hot weather workout?  Try a hot bath beforehand.  Triple clickbait for me, as this week is really hot in California, I was hoping to get in some runs, and I certainly enjoy hot baths (+long walks on the beach, white wine, etc).  Original study here (can't tell if that's paywalled), published in the Journal of Strength and Conditioning Research.  This study takes n=9 (!) people, 8 dudes and 1 woman, and first has them run 5k on a treadmill in a lab where they cranked the temperature to 90F.  Then all subjects underwent about a week of heat acclimation, where they pedaled a stationary bike in the hot lab for five 90-minute sessions.  Then, finally, they ran the 5k again in the hot lab. Low and behold, times had improved!  The now-heat acclimated participants shaved an average of about a minute and a half off their 5k times (~ 6% reduction) when running in hot conditions for the second time.

But wait a sec.  You took some mildly fit runners (5k time of 25min is not exactly East African pace), had them put in almost 8 hours of training, and then tested to see whether they got faster??  As a lazy, mildly-fit runner myself, 8 hours of training constitutes a good month for me, so I would be PISSED if I didn't get faster with that much training.  Now, you might say, they were pedaling a bike and not running, and this is indeed what the paper tries to argue in some hard-to-understand phrasing ("Cycling training controlled for performance that could arise from increased training volume were participants to acclimate through running").  But to me it seems pretty unlikely that a lot of biking is going to give you no running benefit at all, and some quick googling found about 1000 blog posts by runner/bikers that claim that it does (and also 1000 that claim that is doesn't, go figure).  In any case, clearly this is not anywhere close to the optimal research design you'd want for figuring out the causal effect of training-when-hot on performing-when-hot.   Strangely, the "hot bath" part does not appear in the paper at all, but just in an off-hand comment by a research quoted by the NYT.  So that's weird too.

Or take the one-minute-of-exercise-is-all-you-need study (original study; NYT clickbait).   This study takes n=25 "sedentary" men and divides them into three groups:  a control group (n=6), a group that does moderate-intensity stationary-bike pedaling for 45min at a time (n=10), and a group that does high-intensity ("all out") stationary-bike pedaling for 3x 20 seconds (n=9).  Only 60 total seconds!  This happens three times a week for 12 weeks, after which researchers compare various measures of fitness, including how much oxygen you can take up at your peak (i.e. VO2-max).  Both the moderate (VO2 = 3.2) and intense groups (VO2=3.0) had improved significantly upon the control group (VO2 = 2.5), but the post-training VO2max levels in the moderate and intense groups were not statistically different from each other.  Hence the paper's exciting title:  "Twelve Weeks of Sprint Interval Training Improves Indices of Cardiometabolic Health Similar to Traditional Endurance Training despite a Five-Fold Lower Exercise Volume and Time Commitment", and the NYT clickbait translation:  "1 Minute of All-Out Exercise May Have Benefits of 45 Minutes of Moderate Exertion".

But wait a sec.  Sample size is n=19 in these treatment groups combined!  A quick calculation suggests that, at this sample size, the study is DRAMATICALLY underpowered to find an effect. That is, the study has a high chance of failing to find an effect when there actually is one.  I calculate that to detect a significant difference in means of 0.2 between these two groups (on a control group standard deviation of 0.7, given in the table), the study would have needed 388 participants, or about 20x what they had! (This assumes the researchers would want an 80% chance of correctly rejecting the null that the two groups had the same mean;  in Stata, if I did it right:   power twomeans 3.2 3.0, sd(0.7)).  Even reliably detecting a 20% increase in VO2 max between the two treated groups would have needed 46 participants, more than twice the number they had.  Put more simply:  with sample sizes this small, you are very unlikely to find significant differences between two groups, even when differences actually exist.  So maybe the moderate exercise group actually did do better, or maybe they didn't, but either way this study can't tell us.  The same thing appears to be true with the 4-min-exercise paper (n=26) -- it's way underpowered.  And I haven't looked systematically, but my guess is this is true of a lot of the studies they cover that find no effect.  Andrew Gelman is always grumping about studies with large effects, but we should probably be just as cautious believing small-N studies that find no effect.

So should the NYT stop covering these underpowered or poorly designed studies?  There's not a lot at stake here I guess, so one reasonable reaction is, "who gives a sh*t"?  But surely this sort of coverage  crowds out coverage of other higher-quality science, such as why your roller-bag wheels wobble so much when you're running to catch a flight, or why eggs are egg-shaped.  And cutting down on this crappy exercise coverage will save me the roughly 20min/week of self-loathing I feel when I click on yet another too-good-to-be-true NYT exercise article, look up the article it actually references, and find my self not very convinced.  Twenty minutes I could have spent exercising!!  That's like 20 1-minute workouts...

Trump's climate gift to Russia

Trump's recent announcement that the US would withdraw from the Paris Accords was hailed as a monumental political, environmental, and economic mistake.  But given all the theater surrounding the announcement, others also saw it as an effort to distract the public from the ongoing investigation of the Administration's ties to Russia.

It's hard to see how this latter claim could actually be evaluated.  But it got me thinking:  what are the benefits to Russia of the US withdrawing from the Paris accords?  Was the US withdrawal a climate gift to Russia?

Now, I'm guessing Trump has not read our paper showing that warming temperatures will have unequal economic effects around the world (unlike Obama, to repeat my shameless self promotion from last week).  In that paper, and consistent with a huge microeconomic literature, we see clear evidence in the historical data that cold high-latitude countries tend to experience higher GDP growth when temperatures warm, with the reverse being true in most of the rest of the world where average temperatures are already warmer (the US included).  Basically, if you're currently cold, you do better when it warms up;  if you're already warm, additional warming hurts you.  Pretty intuitive, and also shows up very clearly in the half century of data we have on economic growth from around the world.

Here's the original plot from our paper, with the figure on the left showing the historical relationship between temperature and GDP growth for all countries in the world.  If you're average temperature is below about 13C, historically your economy grows faster when annual temperatures warm.  If your at or above 13C, growth slows as temperatures warm.  The US has a population-weighted annual average temperature of just over 13C.  Russia has a population-weighted average temperature of just under 5C.  Russia is cold!

Figure 2 from Burke, Hsiang, Miguel 2015 Nature. Effect of annual average temperature on economic production. a, Global non-linear relationship between annual average temperature and change in log gross domestic product (GDP) per capita (thick black line, relative to optimum) during 1960–2010 with 90% confidence interval (blue, clustered by country, N= 6,584). Model includes country fixed effects, flexible trends, and precipitation controls. Vertical lines indicate average temperature for selected countries. Histograms show global distribution of temperature exposure (red), population (grey), and income (black). b, Comparing rich (above median, red) and poor (below median, blue) countries. Blue shaded region is 90% confidence interval for poor countries. Histograms show distribution of country–year observations. c, Same as b but for early (1960– 1989) and late (1990–2010) subsamples (all countries). d, Same as b but for agricultural income. e, Same as b but for non-agricultural income.

Last week we calculated the potential harm done to the economy of withdrawing from Paris.  The idea was this:  withdrawing from the Paris accords would make global temperatures rise relative to what they would have been if the US had met its Paris obligations (an additional +0.3C by 2100, according to these guys).  For reasons already stated, warming temperatures are bad for overall economic output in the US. So we can then calculate, what's the difference in output between now and 2100 that would occur in a withdrawal versus a non-withdrawal world?  For the US, the effects were pretty big:  I calculated that, in present value (i.e. discounting future losses at 3%), the US economy would lose about $8 trillion between now and 2100 due to the extra temperature increase induced by withdrawing from Paris.

What about Russia?  Again, Russia is cold, so extra warming is likely to help the Russian economy, all else equal.  You can actually see this really clearly in the Russian data.  Below is the plot of Russian GDP growth rates versus Russian temperatures, using data 1990-2010 (1990 being the first post-Soviet-collapse year that "Russia" shows up in the national accounts data).  Specifically, these are growth deviations from trend versus temperature deviations from trend;  we are detrending the data since you don't want to conflate trends in temperature that could be correlated with other trending factors that also affect growth.

This is just 20 data points, but the estimated effects are HUGE.  Basically, Russian GDP growth is multiple percentage points higher when temperatures warm by a degree C.  And the Russia-specific estimate is even higher than what we would predict the effect would be for Russia using the global response function pictured in blue above.

Anyway...  Basically what I did is to re-do the same calculation we did last week for the US, but now focusing on effects on the Russian economy and calculating what happens to Russian GDP in the scenario where the US withdraws from Paris versus the scenario where the US stays in.  To be conservative, I use estimates from the global response function, not the Russia-specific mega-response just noted.

Here's the main finding:  Trump's decision to withdraw the US from Paris is a $2.2 trillion dollar gift to Russia (paid out over the next 85 years).  Below is the figure showing what happens to Russian GDP under a withdrawal versus a no-withdrawal scenario (left), and the annual gains in GDP in each year (to 2100).  By 2100, Russia is ~10% richer than it would have been otherwise, and the (discounted) sum of these GDP gains is about $2.2 trillion dollars.

Given that there's no evidence that Trump has read our paper, I don't think we can claim that this climatic gift to Russia was purposeful.  But it's sadly ironic that an announcement that might have been meant to distract us from Russian meddling was simultaneously a monumental economic gift to that country.

The cost of Paris withdrawal

Lots of discussion today about the potential ramifications of the US withdrawing from the Paris Accords.  Folks have already done some nice calculations looking at the climate consequences of US withdrawal, but there's a lot of interest in the potential economic consequences and I hadn't seen anyone take a heroic stab at that yet.  So.....

The clearest picture I (read: google) could find on the climate implications was this nice website here from ClimateInteractive.org, where they find that a Paris-minus-US world and a Paris-including US world is the difference between +3.6C of warming and +3.3C of warming by 2100.  There are clearly a lot of assumptions that go into this calculation (e.g., what the hell happens after 2030 when the INDCs run out, what happens if Trump's successor (Kamala Harris?  Zuck?  Steph Curry?) re-signs us up, etc etc), but let's take this calculation as God's truth.  Withdrawal gives the world +0.3C of additional warming.

So I wanted to figure out: what is the cost to the US in terms of additional damages that are wrought by this extra warming that withdrawal would bring about?  A difference of +0.3C might not sound like much, but we've got this paper (cited by Obama, so it must be right) that suggests that changes in temperature can affect the growth rate of GDP in rich and poor countries alike.  So the current administration might be right that meeting the US's Paris commitments would have economic costs, but these need to be weight against the benefits of the reduced warming that we would get.  So what are those benefits?

So I took the basic setup we had in that paper, and ran the world forward to 2100 under +3.3C warming versus +3.6C warming, and I looked at what our results in that paper said would happen to US GDP in those different worlds.  See here for more info on how we do these sorts of calculations in this framework.  Basically, we have a function that tells us how growth rates change as temperatures change, derived from historical data.  Then we walk countries along this function as you crank up the temperature to your desired level. To get GDP in levels, you apply these changes in the growth rate to some baseline growth scenario, which we take off-the-shelf from the Shared Socioeconomic Pathways (SSPs, see here).  We also need population numbers, and take those from the SSPs as well.

Below is what I get when I run the US forward under +3.3C warming versus +3.6C warming.  Under SSP5 (the baseline scenario we use), the US clips along at an average per capita growth rate of above 2%/year.  Once you crank up the temperature by 3.5C or so, historical data tells us that we should shave between about 0.5-1 percentage point off of annual US growth.  So this means by 2100, instead of growing at 2%/year under a no warming scenario, the US would be growing at less than 1.5%/year in this much warmer world.  The effects earlier in the century are smaller, of course.

Our comparison is between +3.3C and +3.6C, and the effects on the growth rate are of course smaller.  But even small effects on the growth rate can add up to big cumulative effects on GDP over time.  The left plot below compares total US GDP in the "no withdrawal" world versus the "withdrawal" world, and the right plot gives you the amount of GDP that's lost in each year from withdrawing.  To be crystal clear, we're again only thinking about the differences brought about by the change in temperature between the two scenarios -- we're not thinking about what it would cost the US to meet its Paris commitments.

If my calculations are right, the numbers are large.  By 2100, withdrawing from Paris makes us (i.e. people in the US) about 5% poorer than we would have been otherwise.  The cumulative US GDP losses over time from withdrawal, discounted back to 2010 at 3%, are also impressive - I calculate them to be $8.2 trillion dollars (right plot above).  That is, withdrawing from the Paris agreement costs the US economy $8.2 trillion dollars in present discounted value.  That is a large pile of money.  Even if I'm off by a factor of 5, we're still talking low trillions.  

And to be clear, my calculations do not take into account many other near-term benefits of reducing our own emissions, such as the 'co-benefits' of better health outcomes from cleaner air.  These could be quite big as well.

The key policy question for the Trump administration is:  do we think the costs of meeting our obligations under Paris are going to run more than $8 trillion?  Put another way, are they going to amount to almost half of current US GDP?

To generate $8 trillion in costs between now and 2030 (after discounting at 3%), annual costs would have to be somewhere around $750 billion.  The compliance cost estimates for the Clean Power Plan that I've seen are about two orders of magnitude smaller than that, so even if the CPP only got us 10% of the way to our Paris commitment (which is very conservative), these costs do not come close to the overall benefits -- even if other reductions are many times as expensive as the CPP.  (Hopefully somebody can correct me if I'm way off on these cost numbers -- definitely not my specialty).

With benefits this big, withdrawal seems like bad policy.  

Chat with Nature editor Michael White about publishing, interdisciplinary research, and the state of climate economics

I recently had a long discussion with Dr. Michael White (the editor at Nature who handles climate science and economics) about a whole bunch of issues that probably interest g-feeders: working in an interdisciplinary space, deciding when to publish in econ vs. general interest journals, what we're all doing in climate econ these days, and the things he thought were funny (as a non-economist) about the AEA meetings. (There's also a few stories about my oddball childhood in there for Marshall to laugh at.)

You can listen here (1 hr).

He thought he was interviewing me for his podcast, but really I was interviewing him for our blog;) The session was an episode of Michael's podcast Forecast, which he produces to cover climate science.  Michael has been a regular attendee at our climate economics lunch and it's been helpful to get his take on the recent developments in our field.

Are the curious trends in despair and diets related?

There’s a new working paper out by Anne Case and Angus Deaton on one of the most curious (and saddest) trends in America – mortality rates for whites have been rising. There have been various stories about these trends, such as this one that first turned me onto it. It’s clear that the proximate causes are an increase in “deaths of despair,” namely suicides, drugs, and alcohol. It’s also clear that self-reported mental health has been declining in this group. But why?

Any explanation has to account for the fact that the same trends aren’t seen in other racial groups, even though many of them have lower incomes (see figure below from Case and Deaton):

It should also account for the fact that the same trend isn’t seen in other predominantly white countries:

One explanation that seems to have gained most traction is that whites “lost the narrative of their lives.” That is, maybe rising economic inequality and other economic trends have affected all groups, but whites expected more. To me this seems plausible but not really convincing.

Ok, so let me offer another theory. I’ll first say that I think it’s a bit off the wall, but I don’t think I’m going crazy (despite Marshall’s frequent hinting that I am). The idea basically stems from another body of literature that I’ve recently been exploring, mainly because I was interested in allergies. Yeah, allergies. Specifically, a bunch of people I know have sworn that their allergies were fixed by eliminating certain foods, and given that some people in my family have bad seasonal allergies, I decided to look into it.

It turns out that wheat is thought by many to trigger inflammation and allergies. But what’s relevant here is that it’s also thought to affect mental health. More than that, there are actually clinical studies like this one showing that depression increases with gluten intake. There are only 22 subjects in that study, which seems low to me but obviously I don’t do that sort of work. A good summary of scientific and plenty of non-scientific views on the topic can be found in this article. Incredibly, there was even a study in the 1960s showing how hospital admission rates for schizophrenia varied up and down with gluten grain rations during World War 2.

So what’s the connection to the trends in deaths of despair? Well the striking thing to me is that wheat effects are generally only seen in white non-hispanics. Celiac disease, for instance, is much lower in other racial groups. Second, it’s apparently known that celiac has been rising over time, which is thought to indicate increased exposure (of all people) to gluten early in life. And the trends are most apparent in whites, such as seen in the figure below from this paper.

Just to be clear, I realize this is mostly speculation. Not only is this not my area of expertise, but I don’t have any data on the regional trends in gluten or wheat intake in the U.S. to compare to the regional trends in death. I’m not even sure that such data exist. It seems that studies like this one looking at trends in gluten consumption just assume the gluten content of foods is fixed, but it also seems a lot of products now have gluten added to make them rise quicker and better. (Some blame the obsession with whole grain foods, which don't rise as quickly.) If anyone knows of good data on trends in consumption, let me know. It would also be interesting to know if they add less gluten in other countries, where mortality rates haven’t risen.

(As an aside: there’s also a recent study looking at wheat and obesity in cross-section. Apparently country obesity rates are related to wheat availability, but not much else.)

Also to be clear, I still like wheat. Maybe having spent most of my career studying wheat producing systems has made me sympathetic. Or maybe it’s the fact that it has sustained civilization since the dawn of agriculture. But I think it’s possible that we recently have gone overboard in how much is eaten or, more specifically, in how much gluten is added to processed food in this country. And even if there’s only a small chance it’s partly behind the trends of despair (which aren’t just causing mortality, but all sorts of other damage), it’s worth looking into.

Better than the real thing

Why read a stack of research papers when you can watch a 3 min cartoon?

Thanks to Eric Roston of Bloomberg for putting this together.

Targeting poverty with satellites

[This post is co-authored with Matt Davis, co-author and RA extraordinaire...]

About six months ago, our Stanford SustainLab crew had a paper in Science showing that you can make pretty good predictions about local-level economic wellbeing in Africa by combining satellite imagery with fancy tools from machine learning.  To us this was (and is) a promising finding, as it suggests a way to address the fundamental lack of data on economic outcomes in much of the developing world.   As has been widely acknowledged, these data gaps inhibit our ability to both evaluate what interventions reduce poverty and to target assistance to those who need it most.

A natural question that comes up (e.g. here) is: are these satellite-based estimates good enough to actually be useful for either evaluation or targeting?  Our original paper didn't really answer that question.  We're in the process of putting together a follow-up paper that looks at this question on an expanded country set and with an improved machine learning pipeline, but in the meantime we [by which I mean "we", meaning Matt and Neal] wanted to use some of the data from our original paper to more quantitatively explore this question.

Folks have been thinking for decades about how whether using geographic information to inform the targeting of anti-poverty programs could improve their efficiency.  The standard thought experiment goes like this.  Imagine you're a policymaker who has a fixed budget F that she can distribute as cash transfers to anyone in the country (this sort of cash transfer program happens all the time these days, it turns out).  Lets say in particular that the poverty metric that this policymakers cares about is the squared poverty gap (SPG), a common poverty measure that takes into account the distance of individuals from the poverty line.  [If you're having trouble sleeping at night:  For a given poverty line P, an individual with income Y<P has a poverty gap of P-Y and an SPG of (P-Y)^2.  The SPG in a region is the average over all individuals in that region, where anyone with Y>=P has a SPG==0.  So this measure gives a lot of weight to people far below the poverty line].  If you're goal is to reduce the SPG, you do best by giving money to the poorest person you can find until they're equal to the next poorest, giving them both enough money until they're equal to the third poorest, and so-on.

So how should the policymaker distribute the cash?  If she knows nothing about where poor people are, a naive approach would be to distribute money uniformly -- i.e. to just give each of n constituents F/n dollars.  Clearly this could be pretty inefficient, since people already above the poverty line will get money and this won't reduce the SPG.

An alternate approach, now a few decades old in the economics literature, has been to construct "small area estimates" (SAE) of poverty by combining a detailed household survey with a less detailed but more geographically-comprehensive census.  The idea is that while only the household survey measures your outcome of interest (typically consumption expenditure), there are a small set of questions common to both the detailed household survey and the census (call these X).  These are typically questions about respondent age, gender, education, and perhaps a few basic questions on assets.  So using the household survey you can fit a model Y = f(X), which tells you how the Xs map into consumption expenditure (your outcome of interest), and then using the same Xs in the census and your model f(X) to predict consumption expenditure for everyone in the census.  Then you can aggregate these to any level of interest (e.g. village or district), and use them to potentially inform your cash transfers.  This has been explored in a number of papers, e.g. here and here, and apparently has been used to inform policy in a number of settings.

Our purpose here is to compare a targeting approach that uses our satellite-based estimates to either the naive (uniform) transfer or a transfer that's informed by SAE estimates.   To actually evaluate these approaches against each other, we are going to just use the household survey data, aggregated to the cluster (village) level.  In particular, we estimate both the SAE and the satellite-based model on a subset of our household survey data in each country, make predictions for the remainder of the data that the models have not seen, and in this holdout sample, evaluate for a fixed budget the reduction in the SPG you'd get if you allocated using either the naive, SAE, or satellite-based model.  The allocation rule for SAE and satellites is the one described above:   giving money to the poorest village until equal to the next poorest, giving them both enough money until they're equal to the third poorest, and so-on.

Below is what we get, with the figure showing how much reduction in SPG you get from each targeting scheme under increasing program budgets.  The table summarizes the results, showing the cross-validated R2 for the SAE and satellite-features models (the goodness-of-fits from the models that we then use to make predictions that are then used in targeting), and the amount of money each approach saves relative to the uniform transfer to achieve a 50% decline in SPG.

	R-squared		% Reduction in budget to achieve 50% decline in SPG
Country	SAE	Features	SAE	Features
Malawi	0.45	0.44	6.8	8
Nigeria	0.38	0.4	30.7	25.9
Tanzania	0.62	0.54	36	18.3
Uganda	0.64	0.44	39.1	20.7

What do we learn from these simulations?  First, geographical targeting appears to save you money relative to the naive transfer.  You achieve a 50% reduction in SPG for 7-40% less budget than a naive transfer when you use either SAE or satellites to target.  Second, and not surprisingly, when the satellite model and the SAE model fit the data roughly equally well (e.g. Malawi, Nigeria), they deliver similar savings relative to a uniform transfer.  But the amount of budget that you save by using SAE or satellites to target transfers can differ even for similar R2.  Compare Malawi to Nigeria:  the targeted approaches help a lot more in Nigeria than in Malawi, which is consistent with Malawi having poor people all over the place (e.g. see the maps we produced for Malawi and Nigeria)  including in somewhat better-off vilages, which in turn makes targeting on the village mean not as helpful.  Third, SAE leads to more efficient targeting in the two countries where the SAE model has more predictive power -- Tanzania and Uganda.

We're somewhat biased of course, but this to us is fairly promising from a satellite perspective.   First, these SAE estimates are probably and upper bound on actual SAE performance, since it's very rarely the case that you have a household survey and a census in the same year, and we've been generous in the variables we included to calculate the SAE (some of which are not be available in many censuses).  Second, since many countries lack either a census or a household survey, it's not clear whether we can use SAE in these countries, whereas in our Science paper we showed decent out-of-country fits for the satellite-based approach.  Third, we're working on improvements to the satellite-based estimates and anticipate meaningfully higher R2 relative to these benchmarks.  And finally, and perhaps most importantly, the satellite-based approach is going to be incredibly cheap to implement relative to SAE in areas where surveys don't already exist.  So you might be willing to trade off some loss in targeting performance given the low expense of developing the targeting tool. 

So our tentative conclusion is that satellites might have something to offer here.  They're probably going to be even more useful when combined with other approaches and data -- something that we are exploring in ongoing work. 

Some scintillating satellite studies

We’ve had a couple of papers come out that might be of interest to readers of the blog. Both relate to using satellites to measure crop yields for individual fields. This type of data is very hard to come by from ground-based sources. In many places, especially poor ones, farmers simply don’t keep records on production on a field by field basis. In other places, like the U.S., individual farmers often have data, and government agencies or private companies often have data for lots of individuals, but it’s typically not made available to public researchers.

All of this is bad for science. The less data you have on yields, the less we can understand how to improve yields or reduce environmental impacts of crop production. We now know exactly how much a catcher’s glove moves each time they frame a pitch, exactly how well every NBA player shoots from every spot on the floor, and exactly how fast LeBron James flops to ground when he is touched by an opposing player. And all of this knowledge has helped improve team strategies and player performance.

But for agriculture, with all the talk of “big data”, we are still often shooting in the dark. And in some cases it’s getting worse, such as this recent post at farmdoc daily about how the farmer response rate to area and production surveys is fading faster than my hairline (but not quite as fast as Sol’s):

As we’ve discussed before on this blog (e.g., here and here), satellites offer a potential workaround. They won’t be perfect, but anyone who has done phone or field surveys, or even crop cuts within fields, knows that they have problems too. Plus, satellites are getting cheaper and better, and it’s also becoming easier to work with past satellite data thanks to platforms like Google Earth Engine.

Now to the studies. Both employ what we are calling SCYM, which stands either for Scalable Crop Yield Mapper or Steph Curry’s Your MVP, depending on the context. In the first context, the basic idea is to eliminate any reliance on ground calibration by using simulations to calibrate regression models. 

The first study (joint with George Azzari) applied SCYM to Landsat data in the Corn Belt in the U.S. for 2000-2015, and then analyzed the resulting patterns and trends for maize and soybean yields. Here’s the link to the paper, and a brief animation we had made to accompany the story. Also, we’ve made the maps of average yields available for people to visualize and inspect here. Below is a figure that summarizes what I thought was the most interesting finding: that maize yield distributions are getting wider over time.

Overall maize yields are still increasing, but mainly this is driven by growth in high yielding areas. This is true at the scale of counties, and also within individual fields. The paper discusses reasons why (e.g. uptake of precision ag) and possible implications. Maybe I’ll return to it in a future post. A short side note is that since that paper was written we’ve expanded the dataset to a nine state area, and made some useful tweaks to SCYM to improve performance.

The second paper (joint with Marshall) looks at yield mapping with some of the newer high-res sensors in smallholder systems. In this case, we looked at two years of data in Western Kenya, using detailed surveys with hundreds of farmers to test our yield estimates, which were derived using 1m resolution imagery from Terra Bella. The results were pretty good, especially when you consider only fields above half an acre, where problems with self-reported yields are not as big. Just like in the US, the satellites reveal a lot of yield heterogeneity both within and between fields (the top image shows the true-color image, the bottom shows the derived yield estimates for maize fields):

One interesting result is that in many ways it looks like the satellite estimates are no less accurate than the (expensive) field surveys. For example, when we correlate yields with inputs that we think should affect yields, like fertilizer or seed density, we see roughly equal correlations when using satellite or self-report yields.

None of this is to say that satellite-based yields are perfect. But that was never the goal. The goal is to get insights into what factors are (or aren’t) important for yields in different settings. Both studies show how satellites can provide insights that match or even go beyond what is possible with traditional yield measures. We hope to continue to test and apply these approaches in new settings, and plan to make the datasets available to researchers, probably at this site.