TED Talks Daily - Why can't we better prepare for extreme weather? | Catherine Nakalembe

Episode Date: January 30, 2026

Thanks to advanced technology, we can now see droughts and crop failures months before they hit. So why are millions of people still going hungry? TED Fellow Catherine Nakalembe, director of the NASA Harvest program in Africa, exposes the blind spots that keep life-saving climate intelligence from reaching the communities it's designed to protect — and shares how to turn early warning into early action.Learn more about our flagship conference happening this April at attend.ted.com/podcast Hosted on Acast. See acast.com/privacy for more information.

You're listening to TED Talks Daily, where we bring you new ideas and conversations to spark your curiosity every day. I'm your host, Elise Hugh. Despite unprecedented advances in climate prediction and the data we have access to, farming communities around the world face the same devastating crises over and over. In this talk, Food Security Specialist and TED Fellow, Catherine Nakalembe, explores why knowing what will happen isn't enough. She explains what she views as the critical missing link between tech. and real-world action that's keeping millions of farmers vulnerable and what we need to do to break this cycle. We can predict droughts, floods, weeks, even months in advance, yet we still see the same crises unfold. Crop failure, economic and environmental devastation, and displacement. The same crises that have trapped farming communities for generations.

This is obviously not a prediction problem. It's a translation problem. one that I came to realize painfully in 2015. Equipped with the best tools available at the time, including that very expensive fancy drone, I spent August 2015 with my team in Karamoja documenting yet another failed cropping season, one that I predicted months earlier using satellite data. This was part of the worst drought in East Africa in decades

affecting 30 million people in Uganda, Kenya, Somalia, and Ethiopia. After my field work, I did something researchers rarely do. I went straight to the office of the prime minister, and 24 hours after my second presentation to several ministers, food tracks were dispatched to Karmoja on September 26, 2015, exactly 10 years this week, which marked the first time the office used satellite data

to trigger an emergency response. Following this, I helped design a program that would proactive, released financing to support alternative employment for communities affected by drought. This program went on to support 450,000 people over five years, saving the government millions in emergency response, and deploying several projects that included environmental restoration. But what haunted me then, and is still true today, is this. If we could mobilize emergency response within 24 hours, why couldn't we prevent this predictable crisis from unfolding.

This paradox has deepened because today's capabilities make 2015's best look primitive. We have over 8,000 satellites and AI models and computation power that will make predictions using this data with other datasets to produce information at unprecedented

skills and at unprecedented speeds. If you can combine this with advances in crop science, mobile banking, mechanization, the possibilities seem limitless. Yet, just last year in 2024, nearly one and three people were worried about where their next meal will come from. Climate disasters have more than doubled since the 1980s. So, the question is,

why does this keep happening? I would like to tell you a story that will help you bridge the gap between why we have such incredible capabilities and are unable to deliver clear information for a farmer, for example, to increase their yield, save their produce by reducing post-harvest losses, and having alternative income so they can survive through tough times.

We have incredible technology, but we're missing translators to connect our predictions of that drought, for example, to real tangible solutions that can give us. a farmer what they actually need to thrive. I'd like to share the story of Mary, whose experience represents millions of smallholder farmers around the world. Mary is not her real name, but she's a farmer in Rhinga, Tanzania, but her story could easily be from Uganda, Madagascar, or Senegal, any other country where smallholders face similar challenges. Today's reality is this. For Mary and her neighbors, they planned February, March, for June-July harvests.

This year, Mary acquired improved seeds along with fertilizer that she heard about from a radio program. Unfortunately, despite her best hopes, rainfall was irregular, and she only harvested 800 kilograms from her one-acre plot. Her poultry business that used to provide critical backup income recently collapsed, So she does not have any savings, and it's just another year of surviving. Now, imagine that Mary did, in fact, receive seasonal information sometime in January. Not only did it include the drought prediction, it included when and where she could access fertilizer, a recommended planting date, but most critical is that she has access to financing

that she could acquire a water pump so she could irrigate during dry spells. Come July, Mary harvest 3,000 kilograms. She has enough to see her through the next harvest, enough income because she has access to buyers that provide premium prices for her produce, and storage so she can store it until market stabilize. She can send a daughter to school, but most critically, she has extra income so she can revive her poultry business.

This is not science fiction. All the tools, all the technology. to get her that extra income exists today. So why is Mary still stuck? Why does she get set back by very predictable crises? The challenge lies in this messy middle, the complex web of relationship and real-life challenges that stand between our incredibly capable predictions and assessments

and real tangible solutions for Mary underground. For Mary, it's as if all our actual actual actual. disappears into a black hole. And in my experience, drop predictions do not deliver pumps to the ground. They produce bulletins. After this complexity, the fact that Mary has a small, irregular-sized field that doesn't fit our perfect pixels, we are doing a terrible job mapping fields like Mary's.

In addition to this, the basic infrastructure required for us to improve our predictions and really bring them to the ground, are largely missing four regions like where Mary is based. This complex, messy middle is where all the capabilities shrivel, because it requires things that technology alone cannot provide. For example, it would require partnering with an extension agent who not only delivers fertilizer, trains a farmer, and is an excellent data collector, but not replacing them.

It would also require, present a person, presenting our information in a way that is accessible to a bank so that they can invest in a farmer like Mary who needs to plant next month. So what is the path forward? We can either expand this messy middle, this translation gap, by creating more tech-driven silos, or we can use our current capabilities

and venture to connect them to real solutions on the ground. To do this, there are five fundamental shifts that we will need to do. The first is, we need to first is, we need to focus on translating. And this would require that we're emphasizing reliability over perfection. A model that is 80% accurate that delivers a pump to Mary is far better than one that's 90% accurate

that never leaves a research paper or a dashboard. It would require that not only do we fill that critical data gap, so we are better able to predict and assess the conditions in Mary's field, we would need to make sure that our predictions can actually be, evaluated. Number three, it would require shifting how we finance climate response, focusing on predictions that will get proactive responses so that Mary is able to recover her investment. Policies that encourage proactive planning are better than policies that emphasize emergency response. This would also mean we incentivize how we can connect our policymakers and

people on the ground with the real advances our tools and technologies able to provide. The fifth is people. We need to see people on the ground as accelerators, as people who are able to connect the real information that we're providing with real solutions, improved seeds, irrigation infrastructure, etc. And I said five, but I have one more. And it's the most important, is how we evaluate impact. Our combined effort cannot be measured by the number of projects or model accuracies. It should be measured by that extra income

that helps Mary and uplift her to become a resilient household. The technology to feed the world exists. Now we need to bridge this translation gap and move from data to decision and prediction to prevention. Thank you. That was Catherine, not.

Aucaleembe at a TED countdown event in New York in partnership with the Bezos Earth Fund in 2025. The TED Fellows program provides support to a dynamic community of more than 500 visionaries from 100-plus countries. They address the world's most pressing challenges. To learn more, visit fellows.tad.com. If you're curious about Ted's curation, find out more at ted.com slash curation guidelines. And that's it for today. Ted Talks Daily is part of the TED Audio Collect.

This talk was fact-checked by the TED Research Team and produced and edited by our team, Martha Estefanos, Oliver Friedman, Brian Green, Lucy Little, and Tonica, Song Marnivong. This episode was mixed by Christopher Faisi Bogan. Additional support from Emma Tobner and Daniela Balezzo. I'm Elise Hugh. I'll be back tomorrow

with a fresh idea for your feed. Thanks for listening.