Usually, increasing agricultural productivity depends on adding something, such as fertilizer or water. A new Stanford University-led study reveals that removing one thing in particular – a common air pollutant – could lead to dramatic gains in crop yields. The analysis, published June 1 in Science Advances, uses satellite images to reveal for the first time how nitrogen oxides – gases found in car exhaust and industrial emissions – affect crop productivity. Its findings have important implications for increasing agricultural output and analyzing climate change mitigation costs and benefits around the world.

“Nitrogen oxides are invisible to humans, but new satellites have been able to map them with incredibly high precision. Since we can also measure crop production from space, this opened up the chance to rapidly improve our knowledge of how these gases affect agriculture in different regions,” said study lead author David Lobell, the Gloria and Richard Kushel Director of Stanford’s Center on Food Security and the Environment.

A NOx-ious problem

Nitrogen oxides, or NOx, are among the most widely emitted pollutants in the world. These gases can directly damage crop cells and indirectly affect them through their role as precursors to formation of ozone, an airborne toxin known to reduce crop yields, and particulate matter aerosols that can absorb and scatter sunlight away from crops.

While scientists have long had a general understanding of nitrogen oxides’ potential for damage, little is known about their actual impacts on agricultural productivity. Past research has been limited by a lack of overlap between air monitoring stations and agricultural areas, and confounding effects of different pollutants, among other challenges to ground-based analysis.

To avoid these limitations, Lobell and his colleagues combined satellite measures of crop greenness and nitrogen dioxide levels for 2018-2020. Nitrogen dioxide is the primary form of NOx and a good measure of total NOx. Although NOx is invisible to humans, nitrogen dioxide has a distinct interaction with ultraviolet light that has enabled satellite measurements of the gas at a much higher spatial and temporal resolution than for any other air pollutant.

“In addition to being more easily measured than other pollutants, nitrogen dioxide has the nice feature of being a primary pollutant, meaning it is directly emitted rather than formed in the atmosphere,” said study co-author Jennifer Burney, an associate professor of environmental science at the University of California, San Diego. “That means relating emissions to impacts is much more straightforward than for other pollutants.”

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Calculating crop impacts

Based on their observations, the researchers estimated that reducing NOx emissions by about half in each region would improve yields by about 25% for winter crops and 15% for summer crops in China, nearly 10% for both winter and summer crops in Western Europe, and roughly 8% for summer crops and 6% for winter crops in India. North and South America generally had the lowest NOx exposures. Overall, the effects seemed most negative in seasons and locations where NOx likely drives ozone formation.

“The actions you would take to reduce NOx, such as vehicle electrification, overlap closely with the types of energy transformations needed to slow climate change and improve local air quality for human health,” said Burney. “The main take-home from this study is that the agricultural benefits of these actions could be really substantial, enough to help ease the challenge of feeding a growing population.”

Previous research by Lobell and Burney estimated reductions in ozone, particulate matter, nitrogen dioxide, and sulfur dioxide between 1999 and 2019 contributed to about 20% of the increase in U.S. corn and soybean yield gains during that period – an amount worth about $5 billion per year.

Future analysis could incorporate other satellite observations, including photosynthetic activity measured through solar-induced fluorescence, to better understand nitrogen dioxide’s effects on crops’ varying degrees of sensitivity to the gas throughout the growing season, according to the researchers. Similarly, more detailed examination of other pollutants, such as sulfur dioxide and ammonia, as well as meteorological variables, such as drought and heat, could help to explain why nitrogen dioxide affects crops differently across different regions, years, and seasons.

“It’s really exciting how many different things can be measured from satellites now, much of it coming from new European satellites,” said study coauthor Stefania Di Tommaso, a research data analyst at Stanford’s Center on Food Security and the Environment. “As the data keep improving, it really drives us to be more ambitious and creative as scientists in the types of questions we ask.”
 

Lobell is also a professor of Earth system science in Stanford’s School of Earth, Energy & Environmental Sciences, the William Wrigley Senior Fellow at the Stanford Woods Institute for the Environment, and a senior fellow at the Freeman Spogli Institute for International Studies and the Stanford Institute for Economic Policy Research. Burney also holds the Marshall Saunders Chancellor’s Endowed Chair in Global Climate Policy and Research at UC San Diego and is a research affiliate at UC San Diego’s Policy Design and Evaluation Laboratory, a fellow at the Stanford Center on Food Security and the Environment, and head of the Science Policy Fellows Program at UC San Diego.

In combating poverty, like any fight, it’s good to know the locations of your targets.

That’s why Stanford scholars Marshall Burke, David Lobell and Stefano Ermon have spent the past five years leading a team of researchers to home in on an efficient way to find and track impoverished zones across Africa.

The powerful tool they’ve developed combines free, publicly accessible satellite imagery with artificial intelligence to estimate the level of poverty across African villages and changes in their development over time. By analyzing past and current data, the measurement tool could provide helpful information to organizations, government agencies and businesses that deliver services and necessities to the poor.

Details of their undertaking were unveiled in the May 22 issue of Nature Communications.

“Our big motivation is to better develop tools and technologies that allow us to make progress on really important economic issues. And progress is constrained by a lack of ability to measure outcomes,” said Burke, a faculty fellow at the Stanford Institute for Economic Policy Research (SIEPR) and an assistant professor of earth system science in the School of Earth, Energy & Environmental Sciences (Stanford Earth). “Here’s a tool that we think can help.”

Lobell, a senior fellow at SIEPR and a professor of Earth system science at Stanford Earth, says looking back is critical to identifying trends and factors to help people escape from poverty.

“Amazingly, there hasn’t really been any good way to understand how poverty is changing at a local level in Africa,” said Lobell, who is also the director of the Center on Food Security and the Environment and the William Wrigley Fellow at the Stanford Woods Institute for the Environment. “Censuses aren’t frequent enough, and door-to-door surveys rarely return to the same people. If satellites can help us reconstruct a history of poverty, it could open up a lot of room to better understand and alleviate poverty on the continent.”

The measurement tool uses satellite imagery both from the nighttime and daytime. At night, lights are an indicator of development, and during the day, images of human infrastructure such as roads, agriculture, roofing materials, housing structures and waterways, provide characteristics correlated with development.

Then the tool applies the technology of deep learning – computing algorithms that constantly train themselves to detect patterns – to create a model that analyzes the imagery data and forms an index for asset wealth, an economic component commonly used by surveyors to measure household wealth in developing nations.

The researchers tested the measuring tool’s accuracy for about 20,000 African villages that had existing asset wealth data from surveys, dating back to 2009. They found that it performed well in gauging the poverty levels of villages over different periods of time, according to their study.

Here, Burke – who is also a center fellow at the Stanford Woods Institute for the Environment and the Freeman Spogli Institute for International Studies – discusses the making of the tool and its potential to help improve the well-being of the world’s poor.

Why are you excited about this new technological resource?

For the first time, this tool demonstrates that we can measure economic progress and understand poverty interventions at both a local level and a broad scale. It works across Africa, across a lot of different years. It works pretty darn well, and it works in a lot of very different types of countries.

Can you give examples of how this new tool would be used?

If we want to understand the effectiveness of an anti-poverty program, or if an NGO wants to target a specific product to specific types of individuals, or if a business wants to understand where a market’s growing – all of those require data on economic outcomes. In many parts of the world, we just don’t have those data. Now we’re using data from across sub-Saharan Africa and training these models to take in all the data to measure for specific outcomes.

How does this new study build upon your previous work?

Our initial poverty-mapping work, published in 2016, was on five countries using one year of data. It relied on costly, high-resolution imagery at a much smaller, pilot scale. Now this work covers about two dozen countries – about half of the countries in Africa – using many more years of high-dimensional data. This provided underlying training datasets to develop the measurement models and allowed us to validate whether the models are making good poverty estimates.

We’re confident we can apply this technology and this approach to get reliable estimates for all the countries in Africa.

A key difference compared to the earlier work is now we’re using completely publicly available satellite imagery that goes back in time – and it’s free, which I think democratizes this technology. And we’re doing it at a comprehensive, massive spatial scale.

How do you use satellite imagery to get poverty estimates?

We’re building on rapid developments in the field of computer science – of deep learning – that have happened in the last five years and that have really transformed how we extract information from images. We’re not telling the machine what to look for in images; instead, we’re just telling it, “Here’s a rich place. Here is a poor place. Figure it out.”

The computer is clearly picking out urban areas, agricultural areas, roads, waterways – features in the landscape that you might think would have some predictive power in being able to separate rich areas from poor areas. The computer says, ‘I found this pattern’ and we can then assign semantic meaning to it.

These broader characteristics, examined at the village level, turn out to be highly related to the average wealth of the households in that region.

What’s next?

Now that we have these data, we want to use them to try to learn something about economic development. This tool enables us to address questions we were unable to ask a year ago because now we have local-level measurements of key economic outcomes at broad, spatial scale and over time.

We can evaluate why some places are doing better than other places. We can ask: What do patterns of growth in livelihoods look like? Is most of the variation between countries or within countries? If there’s variation within a country, that already tells us something important about the determinants of growth. It’s probably something going on locally.

I’m an economist, so those are the sorts of questions that get me excited. The technological development is not an end in itself. It’s an enabler for the social science that we want to do.

Media Contacts

Adam Gorlick, Stanford Institute for Economic Policy Research: (650) 724-0614, agorlick@stanford.edu

The rising level of carbon dioxide in the atmosphere means that crops are becoming less nutritious, and that change could lead to higher rates of malnutrition that predispose people to various diseases.

That conclusion comes from an analysis published Tuesday in the journal PLOS Medicine, which also examined how the risk could be alleviated. In the end, cutting emissions, and not public health initiatives, may be the best response, according to the paper's authors.

Research has already shown that crops like wheat and rice produce lower levels of essential nutrients when exposed to higher levels of carbon dioxide, thanks to experiments that artificially increased CO2 concentrations in agricultural fields. While plants grew bigger, they also had lower concentrations of minerals like iron and zinc.

Read the entire story at NPR.