The Center on Food Security and the Environment is a joint effort of the Freeman Spogli Institute for International Studies and the Stanford Woods Institute for the Environment.
COVID-19, combined with the effects of ongoing civil conflicts, hotter and drier weather in many areas, and an unfolding locust invasion in Africa and the Middle East, could cut off access to food for tens of millions of people. The world is “on the brink of a hunger pandemic,” according to World Food Program (WFP) Executive Director David Beasley, who warned the United Nations Security Council recently of the urgent need for action to avert “multiple famines of biblical proportions.”
Understanding how these conditions – alone or in combination – might affect crop harvests and food supply chains is essential to finding solutions, according to David Lobell, the Gloria and Richard Kushel Director of the Center on Food Security and the Environment. Below, Lobell discusses the connection between immigration and U.S. food security, a counter-intuitive effect of COVID-19 and more.
How could COVID-19 affect global food security?
I think the biggest effects will probably be related to lost incomes for many low-income people. Even if food prices don’t change, potentially hundreds of millions could be pushed into a much more precarious food situation. I’d be especially worried about remittances – the money immigrants in wealthy nations send home to developing nations – falling, since these are a surprisingly large source of stability for many poor people. Beyond the income effects, there are definitely prospects for reduced supply of foods, but I think these are secondary, especially because global stocks right now are quite large.
Another counter-intuitive effect is that the drop in gasoline demand due to social distancing may be a big driver of changes in food prices. A lot of corn demand is for use in ethanol fuel, and corn prices can affect the prices of many other crops. The price of corn has dropped by about 20 percent since February.
What are the biggest risks in terms of food supply?
Three things come to mind. First, for crops that require a lot of labor, there are some indications that planting and harvest activities are being affected. Even though these are usually included as essential activities, they often rely on migrant populations that can no longer cross state or national borders. California is going to be a prime case study in this.
Second, some countries, like Russia, have started to restrict food exports in an effort to calm domestic consumers worried about food shortages. Even if there is enough global supply, there is a risk that supply for importing countries could be curtailed. This was a big part of the food price spikes a decade ago. Now, we have the added potential that exports will be limited by a lack of mobility to get products to the port – for instance, there are reports from South America that towns won’t let trucks through for fear of the virus.
Third, COVID-19 could really limit the ability of governments and international groups to address other crises that emerge. Nearly every year there are at least a few surprises around the world affecting food that are usually handled before they make big news. Things like livestock diseases and crop pest outbreaks, for example. But without the ability to deploy people to assess and fix problems, there is more scope for issues to go unchecked. Right now, the biggest example of this is the desert locust outbreak in Eastern Africa.
What current and/or likely future weather conditions might have significant impacts on food production?
As the globe warms, we continue to see more “surprises” in most years in terms of record hot or dry growing seasons. It’s a bit too soon to say if and where those will emerge this year. Since global food stocks are high, we have some ability to cope with a shock, but if governments are already nervous it may take less to induce export bans and all of the negative effects those entail.
Ahead of the summer harvest, what is the prospect for controlling locust swarms in threatened countries, and how might the swarms further complicate the global food security picture?
If not for COVID-19, this would likely be the biggest development related to food this year. My understanding is that they are spreading fast in Africa and the Middle East, and while they haven’t yet had big effects in the main production regions, the next couple of months will be critical. The hope is that the winds change and drive them back toward the desert areas they came from. If not, there are at least 20 million people at risk of major food security impacts in the region.
Could we see locust swarms in the U.S.? What can we do to prevent them?
Locusts can occur anywhere. A few years back there was a major outbreak in Israel. They haven’t been a big issue in the U.S. because control methods are available, such as widespread spraying. But again, in a time of COVID-19, these types of responses are harder.
What does history teach us about the situation we are in with multiple threats to food security, and how to deal with it?
I think it comes down to a combination of investing in science-based solutions to avoid problems to begin with, and then having good social safety nets for when problems arise. At that level, it’s not really any different than dealing with infectious disease. The absence of any problems is our goal. At the same time, that absence always seems to breed complacency and neglect. Hopefully, the experiences of 2020 will help strengthen support for a society based on facts, science and compassion.
David Lobell, Center on Food Security and the Environment: (650) 721-6207; dlobell@stanford.edu
Rob Jordan, Stanford Woods Institute for the Environment: (650) 721-1881; rjordan@stanford.edu
COVID-19 and other looming threats could make it much harder for people to access food. David Lobell, director of Stanford’s Center on Food Security and the Environment, outlines likely scenarios and possible solutions.
Join us for a talk with agricultural and development economist Christopher B. Barrett, this quarter’s visiting scholar with the Center on Food Security and the Environment. Barrett is the Stephen B. and Janice G. Ashley Professor of Applied Economics and Management and an International Professor of Agriculture with Cornell’s Dyson School of Applied Economics and Management.
Professor Barrett will discuss food systems advances over the past 50 years that have promoted unprecedented reduction globally in poverty and hunger, averted considerable deforestation, and broadly improved lives, livelihoods and environments in much of the world. He’ll share perspectives on the reasons why, despite those advances, those systems increasingly fail large communities in environmental, health, and increasingly in economic terms and appear ill-suited to cope with inevitable further changes in climate, incomes, and population over the coming 50 years. Barrett will explore the new generation of innovations underway that must overcome a host of scientific and socioeconomic obstacles.
Also a Professor of Economics in the Department of Economics, Barrett is co-editor in chief of the journal Food Policy, is a faculty fellow with David R. Atkinson Center for a Sustainable Future and serves as the director of the Stimulating Agriculture and Rural Transformation (StART) Initiative housed at the Cornell International Institute for Food, Agriculture and Development.
Accurate automated segmentation of remote sensing data could benefit applications from land cover mapping and agricultural monitoring to urban development surveyal and disaster damage assessment. While convolutional neural networks (CNNs) achieve state-of-the-art accuracy when segmenting natural images with huge labeled datasets, their successful translation to remote sensing tasks has been limited by low quantities of ground truth labels, especially fully segmented ones, in the remote sensing domain. In this work, we perform cropland segmentation using two types of labels commonly found in remote sensing datasets that can be considered sources of “weak supervision”: (1) labels comprised of single geotagged points and (2) image-level labels. We demonstrate that (1) a U-Net trained on a single labeled pixel per image and (2) a U-Net image classifier transferred to segmentation can outperform pixel-level algorithms such as logistic regression, support vector machine, and random forest. While the high performance of neural networks is well-established for large datasets, our experiments indicate that U-Nets trained on weak labels outperform baseline methods with as few as 100 labels. Neural networks, therefore, can combine superior classification performance with efficient label usage, and allow pixel-level labels to be obtained from image labels.
The economic costs of Indonesia’s 2015 forest fires are estimated to exceed US $16 billion, with more than 100,000 premature deaths. On several days the fires emitted more carbon dioxide than the entire United States economy. Here, we combine detailed geospatial data on fire and local climatic conditions with rich administrative data to assess the underlying causes of Indonesia’s forest fires at district and village scales. We find that El Niño events explain most of the year-on-year variation in fire. The creation of new districts increases fire and exacerbates the El Niño impacts on fire. We also find that regional economic growth has gone hand-in-hand with the use of fire in rural districts. We proceed with a 30,000-village case study of the 2015 fire season on Sumatra and Kalimantan and ask which villages, for a given level of spatial fire risk, are more likely to have fire. Villages more likely to burn tend to be more remote, to be considerably less developed, and to have a history of using fire for agriculture. Although central and district level policies and regional economic development have generally contributed to voracious environmental degradation, the close link between poverty and fire at the village level suggests that the current policy push for village development might offer opportunities to reverse this trend.
Eradicating hunger and malnutrition is a key development goal of the twenty first century. This paper addresses the problem of optimally identifying seed varieties to reliably increase crop yield within a risk-sensitive decision making framework. Specifically, a novel hierarchical machine learning mechanism for predicting crop yield (the yield of different seed varieties of the same crop) is introduced. This prediction mechanism is then integrated with a weather forecasting model and three different approaches for decision making under uncertainty to select seed varieties for planting so as to balance yield maximization and risk. The model was applied to the problem of soybean variety selection given in the 2016 Syngenta Crop Challenge. The prediction model achieved a median absolute error of 235 kg/ha and thus provides good estimates for input into the decision models. The decision models identified the selection of soybean varieties that appropriately balance yield and risk as a function of the farmer’s risk aversion level. More generally, the models can support farmers in decision making about which seed varieties to plant.
Large and regular seasonal price fluctuations in local grain markets appear to offer African farmers substantial inter-temporal arbitrage opportunities, but these opportunities remain largely unexploited: small-scale farmers are commonly observed to "sell low and buy high" rather than the reverse. In a field experiment in Kenya, we show that credit market imperfections limit farmers' abilities to move grain inter-temporally. Providing timely access to credit allows farmers to buy at lower prices and sell at higher prices, increasing farm revenues and generating a return on investment of 28%. To understand general equilibrium effects of these changes in behavior, we vary the density of loan offers across locations. We document significant effects of the credit intervention on seasonal price fluctuations in local grain markets, and show that these GE effects shape individual level profitability estimates. In contrast to existing experimental work, the results indicate a setting in which microcredit can improve firm profitability, and suggest that GE effects can substantially shape microcredit's effectiveness. In particular, failure to consider these GE effects could lead to underestimates of the social welfare benefits of microcredit interventions.
