New Damage Functions for the Agricultural Sector – Guest Post by Fran Moore

Last week I had a new paper come out with some fantastic co-authors (Delavane Diaz, Tom Hertel, and Uris Baldos) on new damage functions in the agricultural sector. Since this covers a number of topics of interest to G-FEED readers (climate damages, agricultural impacts, SCC etc), I thought I’d dig a bit into what I see as some of the main contributions.

Firstly what we do. Essentially this is an exercise in trying to link a damage function directly to the scientific consensus on climate impacts as represented by findings in the IPCC. We therefore rely on a database of over 1000 results on the yield response to temperature previously published by Challinor et al. and used to support conclusions in the food security chapter of Working Group 2. We do a bit of reanalysis (using a multi-variate regression, adding soybeans, allowing for more geographic heterogeneity in response) to get yield-temperature response functions that can be extrapolated to a global grid to give productivity shocks at 1, 2, and 3 degrees of warming (available for other researchers to use here).

As readers of G-FEED well know though, getting productivity shocks is only half the battle because what we really want are welfare changes. Therefore, we introduce our productivity shocks into the 140-region GTAP CGE model, which has a particularly rich representation of the agriculture sector and international trade. This then gives us welfare changes due to agricultural impacts at different levels of warming, which is what we need for an IAM damage function. Finally, we take our findings all the way through to the SCC by replacing the agricultural damages from FUND with our new estimates. Our headline result is that improving damages just in the agricultural sector leads the overall SCC to more than double.

There’s lots more fun stuff in the paper and supplementary information including a comparison with results from AgMIP, some interesting findings around adaptation effectiveness, and a sensitivity analysis of GTAP parameters. But here I want to highlight what I see as three main contributions.

Firstly I think a major contribution is to the literature on improving the scientific validity of damages underlying IAM results. The importance of improving the empirical basis of damage functions has been pointed out by numerous people. This is something Delavane and I have worked on previously using empirically-estimated growth-rate damages in DICE, and something that Sol and co-authors have done a lot of work on. I think what’s new here is using the existing biophysical impacts literature and tracing its implications all the way through to the SCC. There is an awful lot of scientific work out there on the effects of climate change relevant to the SCC, but wrangling the results from a large number of dispersed studies into a form that can be used to support a global damage function is not straightforward (something Delavane and I discuss in a recent review paper). In this sense the agriculture sector is probably one of the more straightforward to tackle – we definitely relied on previous work from the lead authors of the IPCC chapter and from the AgMIP team. I do think this paper provides one template for implementing the recommendation of the National Academy of Sciences SCC report around damage functions – that they be based on current and peer-reviewed science, that they report and quantify uncertainty wherever possible, and that calibrations are transparent and well-documented.

Secondly, an important contribution of this paper is on quantifying the importance of general-equilibrium effects in determining welfare changes. Since so much climate impacts work estimates local productivity changes, it raises the question of what these can or can’t tell us about welfare under global climate change. We address this question directly by decomposing our welfare changes into three components: the direct productivity effect (essentially just the local productivity change multiplied by crop value), the terms-of-trade effect, and the allocative efficiency effect (caused by interactions with existing market distortions). The last one is generally pretty small in our work, so regional changes in welfare boil down to a combination of local productivity changes and the interaction between a region’s trade position and global price changes. This breakdown for 3 degrees of warming is shown below.

For these productivity shocks, the terms-of-trade (ToT) effect is regionally pretty important. In a number of cases (notably the US, Australia, Argentina, and South Africa) it reverses the sign of the welfare change of the productivity effect. In other words, a number of regions experience yield losses but welfare gains because the increasing value of exports more than compensates for the productivity losses (with the opposite in South Africa). There are a few rules-of-thumb for thinking about how important the ToT effects are likely to be. Firstly, the ToT effects cancel out at the global level, so if you are only interested in aggregate global damages, you don’t have to worry about this effect. By similar logic, the higher the level of regional aggregation, the less important ToT effects are likely to be, since larger regions will likely contain both importers and exporters. Secondly, the importance of ToT effects depends on the magnitude of relative price changes which in turn depends on the productivity shocks. If the effect of climate change is to redistribute production around the globe rather than to increase or decrease aggregate production, then ToT effects will be smaller. We see this in our AgMIP results, where losses in the tropics are offset to a greater degree by gains in temperate regions, leading to smaller price changes and correspondingly smaller ToT effects.

A final point I’d like to highlight is the quantitative importance of our findings for the SCC. We knew from previous work by Delavane that agriculture is important in driving the SCC in FUND. Nevertheless, such a large increase in the total SCC from updating just one sector, in a model that contains 14 different impact sectors, is striking and begs the question of what would happen with other sectors. Moreover, there are suggestions that other models might also be underestimating agricultural impacts. Neither PAGE nor DICE model the agricultural sector explicitly, but both include agricultural impacts as part of more aggregate damage functions (market impacts in PAGE09 and non-SLR damages in DICE 2013). By coupling damage functions from these models to standardized socio-economic and climate modules, we are able to make an apples-to-apples comparison of our agriculture-sector damages with these damage functions. The graph below didn’t make it into the paper, but I think is informative:

                                                                                                                         

What you see is that our estimated agricultural damages (from the meta-analysis of yield estimates) are actually larger than all market damages included in PAGE. This indicates either that there are very small negative impacts and large off-setting benefits in other market sectors, or that PAGE is substantially under-estimating market damages. Comparing our results to DICE would imply that agricultural impacts constitute 45% of non-SLR damages. This seems high to me, considering this damage function includes both market and non-market (i.e. mortality) damages. For instance, in their analysis of US damages, Sol and co-authors found agricultural impacts to be dwarfed by costs from increased mortality, and to be smaller than effects on labor productivity and energy demand, suggesting to me that agricultural damages might also be currently under-estimated in DICE.

That’s my (excessively-long) take on the paper. Thank you to the GFEEDers for the opportunity. Do get in touch if you have any questions or comments on the paper – fmoore at ucdavis dot edu.

El Niño and Global Inequality (guest post by Kyle Meng)

El Niño is here, and in a big way. Recent sea-surface temperatures in the tropical Pacific Ocean, our main indicator of El Niño intensity, is about as high as they were prior to the winter of 1997/98, our last major El Niño (and one of the biggest in recorded history). Going forward, median climate forecasts suggest this intensity will be sustained over the coming months, and could even end up stronger than the 1997/98 El Niño. This will have important global consequences over the next 12 months not just on where food is produced but also how it is traded around the planet.

First, a quick primer. The El Niño Southern Oscillation (ENSO) is a naturally occurring climatic phenomenon, arguably the most important driver of global annual climate variability. It is characterized by two extreme states: El Niño and La Niña. Warm water piles up along the western tropical Pacific during La Niña. During El Niño, the atmospheric and oceanographic forces that maintain this pool of warm water collapse resulting in a large release of heat into the atmosphere that is propagated around the planet within a relatively short period. ENSO’s impact on local environmental conditions around the planet are known as its “teleconnections.” To a rough first order, El Niño makes much of the tropics (from 30N to 30S latitude, shown in red in figure below) hotter and dryer and the temperate regions (shown in blue in figure below) cooler and wetter.

Countries where the majority of the population experience hotter conditions under El Niño are shown in red. Countries that get cooler under El Niño are shown in blue (reproduced from Hsiang and Meng, American Economic Review, May 2015).

There are two features of El Niño that have important implications on global food markets. First, El Niño creates winners and losers across the planet. Sol Hsiang and I have shown in a paper published in the American Economic Review Papers and Proceedings, that between 1960-2010, country-level cereal output in the tropics drop on average by 3.5% for every degree increase in the winter ENSO index. For a large event like 1997/98, or the one anticipated this winter, we estimate a 7% decrease in cereal output across the tropics. Conversely, the relatively more favorable environmental conditions experienced by temperate countries during the same year results in a 5% increase in cereal output. Interestingly, if you sum up the gains and losses across the world, you end up with a positive number: El Niño actually increases global cereal output. 

Second, El Niño impacts are highly spatially correlated, organizing winners and losers to roughly two spatially contiguous blocks across the planet: temperate and tropical countries (which are also mostly just countries south of 30N because there are few countries south of 30S). This means under El Niño, countries suffering crop failures deep in the tropics are also surrounded by neighbors that are likely experiencing similar food shortages at the same time. Why is the spatial scale of El Niño impacts important? Basic economics tells us that the primary driver for international trade is productivity differences across countries. When El Niño occurs, tropical neighbors that normally engage in bilateral trade experience similar crop losses and thus may be less likely to trade with each other. To find an exporter that experiences bumper yields under El Niño, tropical countries have to source food from temperate countries (i.e. North America, Europe, North Asia), that are much further away, for which the cost of trade is higher. The predicted result is that El Niño has two effects on countries in the tropics: it causes direct crop losses and limits the ability of imports to offset such losses. 

Is this happening? In ongoing work with Sol and Jonathan Dingel, we detect exactly these trade effects. From 1960-2010, when an El Niño occurs, cereal output fall in the tropics with some extra imports arriving. However, these imports do not offset all losses such that countries deep in the tropics experience large spikes in food prices. Stay tuned for that paper.

We think this is important beyond food prices during El Niño. In an article published in Nature in 2011, Sol and I, together with Mark Cane, detected that the likelihood of civil wars breaking out in the tropics doubles during strong El Niño years relative to strong La Niña years. Many have asked us about the mechanisms behind this large effect. We now think that direct crop losses together with the limits of trade during El Niño are important parts of the story.

What can be done? Sol and I recently wrote an op-ed in the Guardian on El Niño and its impact on global equality with some policy prescriptions. In the short-term, we argue that aid agencies, peacekeeping groups, refugee organizations, and other international institutions should be prepared to send food to the tropics as local conditions deteriorate. We also argue, in the long-term, that investments should be made to better integrate global food markets and improved access to other financial instruments such as like crop insurance. 

Finally, the spatial nature of El Niño events has similarities with that of anthropogenic climate change, which we know from Marshall, Sol, and Ted’s work is expected to generate winners and losers across the planet. As such adaptation to climate change will involve not just local investments but also global efforts to improve how markets redistribute the unequal effects of climate change.

This is a guest post by Kyle Meng, an Assistant Professor at UC Santa Barbara.

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 ;-)