Agriculture is a cornerstone of India’s economy, employing between 40% and 50% of the country’s workforce, while feeding over a billion people. But it is becoming more and more threatened by extreme weather events linked to climate change. Between 2015 and 2021India lost 83.8 million acres (33.9 million hectares) to floods and excess rain, and 86.5 million acres (35 million hectares) to drought.
India’s farmers are predominantly smallholders—but these small farms, fragmented across the country, are heterogeneous and have limited data. This makes it difficult to draw up guidelines that can explain how they are affected by extreme weather events.
Jain’s ground-breaking research combines field interviews with satellite-based mapping tools to find out how farmers are responding and adapting to these increasing pressures.
Her research focuses on how smallholder agricultural production can be made sustainable, productive and more importantly, resilient to unpredictable weather. After working in the fields with farmers, from 2021 to 2023she then used historical data on groundwater availability and insights from working with farmers to determine how cropping patterns change under a warmer climate.
With this, she works to scale up the rich individual accounts from farmers, utilizing satellite and remote sensing tools to understand what is happening on a regional or national scale. She hopes this will provide further information on how we design policies that can future-proof agricultural production in the face of a changing climate.
For his work, Jain has now been awarded the opening ASU Science Award for Transformational Impactwhich recognizes research that not only advances knowledge, but also makes an important contribution to society.
She spoke to Live Science about her ability to connect the people on the ground with actionable solutions to reduce the environmental impact of food systems.
Editor’s note: This interview has been edited and condensed for clarity.
What shaped the kind of research you do today?
I spent a year in India doing research on the ground and spent a lot of time with small farmers. I became very interested in the impact of climate change on agriculture and how people adapt. After seeing how important agriculture was to daily livelihoods, and how uncertain and precarious agriculture had become in these times, it just made me feel very passionate about working on this issue.
Initially, when I started my work, I spent a lot of time asking them how they were affected by climate change and how they adapted. I learned to do remote sensing to use satellite images to scale what I saw, to a regional and national scale. Now, what I’m really interested in doing is thinking about how we can use these satellite data sets to better identify and target interventions to help farmers adapt further.
How does your work on the ground with farmers inform the more quantitative aspect of your work, ie satellite imagery and agricultural datasets?
Our work now focuses on the IGP region (the Indo-Gangetic Plains (IGP), which spans the states of Punjab, Haryana, Uttar Pradesh and Bihar) because it is the main breadbasket. It is there that a large proportion of India’s rice and wheat is produced. We identify which data products are of interest to produce by spending time on the ground.
For example (I heard) many farmers say they increased watering as the temperature warmed. So we decided to understand how big a problem it is, how much of it is happening across India. We then developed satellite datasets to measure irrigation. That’s where we spend time on the ground and use it to inform the datasets we produce in the lab.
How did this fieldwork and day-to-day interactions with farmers help you identify the gaps in your data, or did it complement the data you already had? Is this knowledge transferable across other agricultural regions in the tropics?
The satellite data, while very powerful for understanding patterns at large scales, doesn’t really allow you to understand the decision-making drivers behind the patterns you see. So we really rely on our household surveys – large-scale quantitative data – to understand these options.
While a large part of our work, probably 70% of it, is done in India, we are expanding our work to other countries. We take a similar approach and work with partners in Mexico, Colombia, Zambia and various smallholder farming systems across the tropics.
As climate change is primarily characterized by unpredictability, how does your research work to adapt or mitigate it?
There are two ways. One is that with the satellite data we can get a long-term historical understanding of cropping practices for about 20 years. Then we can put them into our models to understand how, when a particular weather event happened, what did people do in the past? What was the impact?
The other way they can help is with real-time monitoring. We can look at the vegetation growth curves of crops during a season. For example, our work has largely focused on wheat across the IGP. We also have some new work on rice and wheat in central India. We focus largely on cereal crops because they are the main basis for livelihoods and are also easier to map using satellite data.
You have considered data sets that map groundwater availability, climate change and crop patterns. How can this help inform mitigation or adaptation in the face of extreme weather events, heat waves, droughts and floods, particularly those affecting farmers in India and other South Asian countries?
The challenge when using historical data to understand how people adapt is that we can only tell how they have adapted to what has happened in the past. But obviously conditions are changing – extreme events are becoming more frequent. So definitely more work is needed in this space, because maybe taking what we learned historically wouldn’t exactly apply in the future. I think this is an important research question.
How will this research be expanded over the next few years?
The kind of projects I’m really excited about now are projects where we use satellite data to target and inform intervention, which is more action-oriented. To give an example, I have some work where we’re trying to see if we can use satellite data to pick up the lowest gearing fields, and ultimately target interventions (in those regions of India).
