Thursday, February 05, 2026

 

Inaugural ASU–Science Prize Recognizes Research that Serves Farmers from the Ground Up



Release author: Meagan Phelan


American Association for the Advancement of Science (AAAS)

Inaugural ASU–Science Prize Recognizes Research that Serves Farmers from the Ground Up 

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Meha Jain doing fieldwork in India.

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Credit: Meha Jain






 

When Meha Jain talks about satellites, she often ends up talking about people.

Years before she became an associate professor at the University of Michigan’s School for Environment and Sustainability, Jain was standing in rural smallholder farming communities – systems projected to be extremely negatively impacted by climate change – trying to understand how environmental change is experienced on the ground. “The more time I spent in the field,” she says, “the more I realized that humans and the environment are completely linked.”

That realization crystallized during a formative year she spent in India in 2007–2008, shortly after graduating from college. Working in rural communities, she saw firsthand the complexity of the decisions that smallholder farmers face—decisions shaped not just by climate or soil, but by policy, infrastructure, and survival. “Spending that year in the field really developed my passion,” she says.

That passion has now earned Jain the inaugural ASU–Science Prize for Transformational Impact, established to recognize innovative work by an early-career researcher that not only advances knowledge; it also demonstrably serves society.

“At its best, research carries impact far beyond the laboratory to improve daily human life,” said ASU President Michael M. Crow. “Meha Jain’s work exemplifies that important reality, demonstrating how new knowledge can create much-needed solutions and lasting benefits for communities around the world. ASU is proud to partner with AAAS and Science to recognize scientific leadership and innovation.”

The prize was part of a partnership announced in 2025 between the American Association for the Advancement of Science (AAAS) and Arizona State University (ASU), developed with input from AAAS’s flagship journal, Science.

“Our partnership with AAAS reflects a shared commitment to reimagine how research can connect to and impact the world,” said Sally C. Morton, executive vice president of ASU’s Knowledge Enterprise. “By supporting early-career researchers whose work is changing lives, the ASU-Science Prize helps to recognize and inspire the next generation of scientists to advance discovery for the benefit of all.”

Jain’s related research, including studies published in 2021 and 2023, uses satellite imagery and machine learning to reveal how smallholder farms, which are essential for food security for millions of people, adapt to climate stress—and how those adaptations can carry hidden costs. Ultimately, these insights inform tools that can be used to increase food production in an environmentally sustainable way, for smallholder farmers.

Scaling up what farmers already know

At first glance, Jain’s work appears highly technical: satellites, algorithms, vast datasets. But the questions behind it almost always originate far from a computer screen. “Most of my research questions are informed by what we or our collaborators see on the ground,” she says.

Jain and her lab routinely speak with farmers, whose management approaches vary widely, and local organizations to understand their most pressing challenges. Those conversations then guide what kinds of satellite datasets they develop and what patterns they look for at scale.

In her work in India, in particular, one recurring theme surprised her. In many places in the country, farmers told her they were increasingly relying on groundwater to cope with changing rainfall and rising temperatures—even though they knew it wasn’t sustainable. “What really struck me,” Jain says, “was that this wasn't a knowledge gap. Farmers understood the long-term consequences of using groundwater for irrigation. It was the circumstances they were in that left them with few alternatives.”

That insight reshaped her research. Rather than asking whether farmers were overusing groundwater, Jain wanted to know how widespread the practice really was, and where it posed the greatest risks.

Using satellite data, her team developed novel methods to detect irrigation practices across entire regions, to understand real-world farm management and its impacts on both crop production and the environment. The results were sobering. In some areas, groundwater depletion was far more severe than previously understood. In others, differences in aquifers or historical practices meant the situation was less dire.

This ability to see nuance at scale has become a defining feature of Jain’s work. Her research doesn’t just document environmental harm; it shows where interventions are likely to succeed, and where they may fall short. Ultimately, it aims to help identify the most effective ways to sustainably increase production at the landscape scale.

"We at Science and Arizona State University worked long and hard on how to articulate to potential applicants the unique characteristics of projects that we hoped to recognize with this new prize,” said Brad Wible, Senior Editor at Science. “That so many strong and creative applications were submitted, and that Dr. Jain's work stood out among them, is immensely rewarding, knowing that we are onto something important, and that we can play a role in elevating such vital work."

From observation to accountability

Jain’s commitment to serving society deepened through conversations not only with farmers, but with the organizations trying to support them. Many of these groups work intensively with hundreds to thousands of farmers, promoting more sustainable practices such as zero tillage or direct-seeded rice. But they often struggle to measure their impact beyond where the interventions took place. “Even if they collect data from targeted communities,” Jain explains, “they may not know what’s happening to the environment at the landscape level.”

Satellite data offered a solution. Working with partners and globally-available satellite data products, Jain helped develop maps that show where sustainable practices are actually being adopted—and how those changes affect yields and environmental outcomes over time. By overlaying these maps with information about where interventions took place, organizations can better understand whether their efforts are making a difference, and under what conditions.

Another beauty of satellite data, Jain explained, is that you can look beyond a single dataset or a single group of farmers. It allows researchers and practitioners alike to ask a wider range of questions about impact—and to answer them with evidence.

That evidence can be uncomfortable. Jain’s work has revealed tradeoffs that complicate easy narratives about climate adaptation. In some cases, practices that help farmers cope with short-term climate stress accelerate long-term groundwater loss. Scaling up local observations, she says, has made the magnitude of these risks impossible to ignore. But by showing the full picture, “we can start to think more proactively about sustainable management strategies for the future,” she said.

Precision for people, not just plots

A central insight of Jain’s research is that agriculture is profoundly heterogeneous—even within a single village. One farmer with the exact same soil conditions may plant a crop weeks earlier than a neighboring farmer. Satellite technology, which has rapidly improved in resolution and frequency, now makes it possible to map such fine-scale differences with unprecedented precision. For solutions for farmers to work, they have to reflect these differences, Jain says.

Jain’s vision is one of precision insight for individual farms. Data should help identify not just what works, but where it works best.

In recent years, that vision has begun to move from analysis toward direct support. Jain and her collaborators are developing a smartphone app designed to deliver satellite-derived insights back to farmers and organizations in usable ways.

When she began her career, she saw satellites primarily as tools for observation. Over time, as sensors improved and partnerships deepened, her sense of obligation evolved. “I became more excited about creating data products that could actually be used,” she says.

Defining success

For Jain, real-world impact is the ultimate measure of success. “Having that real-world interaction is incredibly motivating,” she says. Looking ahead five or ten years, she hopes to be working with partners across many more countries, seeing tangible increases in the adoption of sustainable practices, higher yields, and reduced water use. She also envisions global maps that can guide policymakers and practitioners toward interventions most likely to succeed.

The ASU–Science Prize, she hopes, sends a signal to other scientists—especially those early in their careers—that rigorous science and societal impact are not competing goals. “Follow what motivates you,” she advises. “Don’t worry so much about what you think will be a high-impact paper. Work on the problems that make you want to show up every day.”

She also points to a quieter lesson from her own experience: the willingness of governments, NGOs, and companies to collaborate with academics when the goal is real-world impact. That openness, she says, has been both surprising and deeply encouraging.

Runner-up

Mayank Kejriwal, research associate professor of industrial and systems engineering at the University of Southern California’s Viterbi School of Engineering, is the runner-up for his work to develop an innovative search system that transforms fragmented web data into actionable insights for disrupting sex trafficking. This system, called Domain-specific Insight Graphs (DIG), scales actions that investigators tend to do in real life: searching across the web, painstakingly piecing clues together, and following leads. DIG's methods incorporate frontier artificial intelligence research and were designed to compress processes typically requiring months into days, or even hours.

 

Apes share human ability to imagine



First study to show capacity to pretend not uniquely human



Peer-Reviewed Publication

Johns Hopkins University

Apes Share Human Ability to Imagine /VIDEO 

video: 

In a series of tea party-like experiments, Johns Hopkins University researchers demonstrate for the first time that apes can use their imagination and play pretend, an ability thought to be uniquely human.

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Credit: Johns Hopkins University





In a series of tea party-like experiments, Johns Hopkins University researchers demonstrate for the first time that apes can use their imagination and play pretend, an ability thought to be uniquely human.

Consistently and robustly across three experiments, one bonobo engaged with cups of imaginary juice and bowls of pretend grapes, challenging long-held assumptions about the abilities of animals.

The findings suggest that the capacity to understand pretend objects is within the cognitive potential of, at least, an enculturated ape, and likely dates back 6 to 9 million years, to our common evolutionary ancestors.

“It really is game-changing that their mental lives go beyond the here and now,” said co-author Christopher Krupenye, a Johns Hopkins assistant professor in the Department of Psychological and Brain Sciences who studies how animals think. “Imagination has long been seen as a critical element of what it is to be human but the idea that it may not be exclusive to our species is really transformative.

“Jane Goodall discovered that chimps make tools and that led to a change in the definition of what it means to be human and this, too, really invites us to reconsider what makes us special and what mental life is out there among other creatures.”

The findings are published today in Science.

By age two, human children can engage in pretend scenarios, like tea parties. Even at 15-months-old, infants show measures of surprise when they see a person “drinking” from a cup after pretending to empty it.

There had been no controlled studies of pretense in nonhuman animals, despite several anecdotal reports of animals seemingly engaging in pretending behavior from both the wild and captivity.

For example, in the wild, young female chimpanzees have been observed carrying and playing with sticks, holding them like mothers would hold their infants. And a chimpanzee in captivity seemed to drag imaginary blocks along the floor after playing with real wooden blocks.

Krupenye and co-author Amalia Bastos, a former Johns Hopkins postdoctoral fellow who is now a lecturer at Scotland’s University of St. Andrews, wondered if they could test this capacity to pretend in a controlled environment.

They created experiments very similar to a child’s tea party to test Kanzi, a 43-year-old bonobo living at Ape Initiative, who had been anecdotally reported to engage in pretense and could respond to verbal prompts by pointing.

In each test, an experimenter and Kanzi faced one another, tea party-style, across a table set with either empty pitchers and cups or bowls and jars.

In the first task there were two transparent cups on the table, both empty, alongside an empty transparent pitcher. The experimenter tipped the pitcher to “pour” a little pretend juice into each cup, then pretended to dump the juice out of one cup, shaking it a bit to really get it out. They then asked Kanzi, “Where’s the juice?”

Kanzi pointed to the correct cup that still contained pretend juice most of the time, even when the experimenter changed the location of the cup filled with pretend juice.

In case Kanzi thought there was real juice in the cup, even if he couldn’t see it, the team ran a second experiment. This time there was a cup of real juice alongside the cup of pretend juice. When Kanzi was asked what he wanted, he pointed toward the real juice almost every time.

A third experiment repeated the same concept, except with grapes. An experimenter pretended to sample a grape from an empty container, then placed it inside one of the two jars. They pretend emptied one of the containers and asked Kanzi, “Where’s the grape?” Kanzi again indicated the location of the pretend object.

Kanzi was never perfect, but he was consistently correct.

“It’s extremely striking and very exciting that the data seem to suggest that apes, in their minds, can conceive of things that are not there,” Bastos said. “Kanzi is able to generate an idea of this pretend object and at the same time know it’s not real.”

The findings inspire continued study, especially trying to test whether other apes and other animals can engage in pretend play or track pretend objects. The team also hopes to explore other facets of imagination in apes, perhaps their ability to think about the future or to think about what’s going on in the minds of others.

“Imagination is one of those things that in humans gives us a rich mental life. And if some roots of imagination are shared with apes, that should make people question their assumption that other animals are just living robotic lifestyles constrained to the present,” Krupenye said. “We should be compelled by these findings to care for these creatures with rich and beautiful minds and ensure they continue to exist.”

Apes Share Human Ability to Imagine /PHOTO 1 

Kanzi, a 43-year-old bonobo living at Ape Initiative, who had been anecdotally reported to engage in pretense and could respond to verbal prompts by pointing.

Kanzi, a 43-year-old bonobo living at Ape Initiative, who had been anecdotally reported to engage in pretense and could respond to verbal prompts by pointing.

Apes Share Human Ability to Imagine /PHOTO 3 

test Kanzi, a 43-year-old bonobo living at Ape Initiative, who had been anecdotally reported to engage in pretense and could respond to verbal prompts by pointing.

Credit

Ape Initiative

 

Flexible governance for biological data is needed to reduce AI’s biosecurity risks



Summary author: Walter Beckwith


American Association for the Advancement of Science (AAAS)





In a Policy Forum, Doni Bloomfield and colleagues discuss the need for expanded – yet tailored and flexible – governance for the biological data used to develop powerful artificial intelligence (AI) models. Rapidly advancing AI systems trained on biological data have enabled researchers to design new molecules, predict protein structure and function, and probe vast and highly complex biological datasets for novel insights that could greatly expand our understanding of nature and human health. However, these same tools could also be misused for dangerous purposes, such as designing harmful pathogens or generating genetic sequences that bypass safety checks. Despite these widely recognized risks, current governance is severely lacking, and increasingly powerful models are often released without safety evaluation. Here, Bloomfield et al. discuss how biological data governance could be achieved to both mitigate potential risks of biological AI systems without impeding their research potential. Just as researchers accept limits on access to personal information in genetic datasets in order to protect privacy without halting research, say the authors, similar frameworks could restrict only a narrow class of especially sensitive pathogen data while leaving most scientific data openly available. Such targeted controls would make it harder for malicious actors to obtain the rare and costly datasets needed to train dangerous models, without significantly impeding legitimate research, especially if paired with secure digital research environments. Bloomfield et al. argue that this oversight should, however, remain limited, targeted, and flexible so that governance frameworks can adapt as required to keep up with both technological and scientific advancements. Moreover, to prevent abuse or excessive bureaucratic control, the research community should have the ability to appeal data classifications, and governing agencies should promise to ensure fast, transparent review processes so that much-needed safety measures do not become obstacles to legitimate scientific processes. “Formalizing a system of data access would allow researchers to scrutinize and develop these controls and would give scientists and companies clarity in an environment that is currently somewhat unpredictable,” write the authors. “Starting this work will also allow scientists and governments to learn more about the nature of AI risk and revise data-access controls in light of tangible evidence, rather than guesswork.”