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This episode is brought to you by Deal. What if your HR platform was actually built for how teams work? Today, most platforms are clunky and compliance feels like a minefield. Deal changes the game. It's one global platform where you can hire, pay and manage anyone, anywhere. Need to hire someone in Berlin? Pay contractors in Sao Paulo? Equip a new hire in Singapore? Deal handles it pay in more than 100 currencies, manage devices globally, and 2,000 local experts in 130 countries have your back. Whether you're five people or 50,000, deal scales with you. And here's the kicker, you'll actually enjoy using it. Visit de e l.comted to book a demo. This episode is brought to you by Pura. After the holidays are over and you start to settle back into your usual routine, you might find yourself just craving less less clutter, less noise, less work. Pura helps you reset your space with premium smart home fragrance Diffus that are completely customizable without requiring a complicated setup. Right now, you can get a free Pura home diffuser when you subscribe to $0.02 for 12 months, set schedules, adjust intensity and come home to the calming effects of your favorite fragrance with just a few taps in the Pura app. Get your free diffuser while the offer lasts@pura.com this episode is brought to you by Planet Visionaries in partnership with the Rolex Perpetual Planet Initiative. I often think about the big ideas in the future that we're building together, and honestly, climate news feels heavy. But here's the thing. There are people out there doing incredible work that actually gives me hope. And that's why I want to tell you about Planet Visionaries, hosted by Alex Honnold. Yes, the free solo climber who is turning his focus to the biggest challenge of all, protecting the only planet we've got. Alex brings his signature curiosity to conversations with the people reshaping our planet's future. In one episode, he talks to Mark Ruffalo, conservationist and actor, about how he has leveraged storytelling to galvanize community and how we can rethink energy and spark real change. These aren't doom and gloom conversations. From Arctic scientists to explorers and activists, every episode reminds us that optimism isn't wishful thinking, it's a strategy. And it's working in partnership with the Rolex Perpetual Planet Initiative. This is Planet Visionaries Listen or watch on Apple, Spotify, YouTube or wherever, you're listening to this podcast. 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 Hu. 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 technology and real world action that's keeping millions of farmers vulnerable and what we need to do to break this cycle.
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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 trucks were dispatched to Karamoja on September 26, 2015. Exactly 10 years. This which marked the first time the office used satellite data to trigger an emergency response. Following this, I helped design a program that would proactively release 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 data sets to produce information at unprecedented scales 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 in 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 get 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 Iringa, Tanzania. But her story could easily be from Uganda, Madagascar or Senegal, or any other country where smallholders face similar challenges. Today's reality is this for Mary and her neighbors. They plant 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 800kg 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 at 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 harvests 3,000kg. 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 her 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 together 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 on the ground. For Mary, it's as if all our technology disappears into a black hole. And in my experience, drop predictions do not deliver pumps to the ground. They produce bulletins. Add to 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 for 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 presenting our information in a way that is accessible to 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 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 technology is 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, improve seeds, irrigation infrastructure, et cetera. 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 uplifts 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.
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That was Catherine Nakalembe 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.ted.com if you're curious about Ted's curation, find out more@ted.com curationguidelines and that's it for today. TED Talks Daily is part of the TED Audio Collective. This talk was fact checked by the TED Research team and produced and edited by our team, Martha Estefanos, Oliver Friedman, Brian Greene, Lucy Little and Tansika Songmarneevong. This episode was mixed by Christopher Faizy Bogan. Additional support from Emma Tobner and Daniela Balarazo. I'm Elise Hu. I'll be back tomorrow with a fresh idea for your feed. Thanks for listening. This episode is brought to you by Deal what if your HR platform was actually built for how teams work today? Most platforms are clunky and compliance feels like a minefield. Deal changes the game. It's one global platform where you can hire, pay and manage anyone, anywhere. Need to hire someone in Berlin? Pay contractors in Sao Paulo? Equip a new hire in Singapore? Deal handles it pay in more than 100 currencies, manage devices globally and 2,000 local experts in 130 countries have your back. Whether you're five people or 50,000, deal scales with you. And here's the kicker, you'll actually enjoy using it. VisiT-E-E-L.com ted to book a demo with.
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Podcast: TED Talks Daily
Episode Title: Why can't we better prepare for extreme weather?
Speaker: Catherine Nakalembe (Food Security Specialist, TED Fellow)
Air Date: January 30, 2026
Catherine Nakalembe explores why, despite extraordinary advances in climate prediction, AI, and satellite technology, farming communities across the world continue to face recurring devastation from droughts, floods, and other extreme weather events. She argues that the real barrier is not prediction, but translation—a critical, missing link needed to transform data-rich insights into meaningful, on-the-ground solutions for vulnerable communities.
We Can Predict, But Can't Prevent
“This is obviously not a prediction problem. It’s a translation problem.” ([03:39])
The Karamoja Experience
“If we could mobilize emergency response within 24 hours, why couldn’t we prevent this predictable crisis from unfolding?” ([05:01])
Current Capabilities
Mary’s Plight Mirrors Millions
A Vision of What's Possible
Not Science Fiction
“This is not science fiction. All the tools, all the technology... together, that extra income exists today.” ([07:56])
The ‘Messy Middle’ Barrier
From Innovation to Translation
Filling Critical Data Gaps
Shifting Financing Approaches
Connecting Policymakers to Technology
Local People as Accelerators
"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..." ([10:27])
“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.” ([10:54])
On the Core Issue:
“This is obviously not a prediction problem. It’s a translation problem.”
— Catherine Nakalembe ([03:39])
Challenge to Status Quo:
“If we could mobilize emergency response within 24 hours, why couldn’t we prevent this predictable crisis from unfolding?”
— Catherine Nakalembe ([05:01])
On the Reality for Farmers:
“For Mary, it’s as if all our technology disappears into a black hole."
— Catherine Nakalembe ([08:13])
On Prioritizing Real-World Action:
“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.”
— Catherine Nakalembe ([09:06])
Ultimate Measure of 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 uplifts her to become a resilient household.”
— Catherine Nakalembe ([10:27])
| Timestamp | Topic/Quote | |-----------|-----------------------------------------------------------------------------------| | 03:25 | Technology enables drought/flood prediction, but crises persist | | 04:23 | Mobilizing emergency food aid in Karamoja in 24 hours | | 05:01 | "Why couldn't we prevent this predictable crisis from unfolding?" | | 07:33 | Hypothetical: If Mary had access to prediction, resources, markets | | 08:13 | Technology’s failure in the “messy middle” | | 09:06 | "80% accurate with impact > 90% accurate with no action" | | 10:27 | Impact measurement: “extra income to Mary,” not just tech stats | | 10:54 | Call to action: “bridge the translation gap... move from prediction to prevention”|
Catherine Nakalembe’s TED Talk confronts the urgent question: Why, with so much technological capability, do farming communities keep suffering the same predictable, devastating outcomes from extreme weather? She powerfully argues that the missing link is translation: transforming data into direct, actionable resources for people on the ground. Highlighting her own experience in East Africa and the fictionalized but representative story of “Mary,” Nakalembe shows how the persistent “messy middle” can be overcome—not by ever-brighter dashboards or detailed bulletins, but by connecting predictive capabilities with real-world actors, smarter financing, and by using human-centric measures of success.
The talk is an impassioned call for a shift from pure technological innovation to practical implementation, evaluating success by increased resilience and livelihoods rather than the sophistication of predictions.