In New Project, Millions of Farmers Will Help to Enhance Insurance Against Climate Catastrophes
Daniel Osgood, NSF grant recipient. Credit: Francesco Fiondella
Megafires, extreme weather condition, locust swarms, pandemics: These are simply a few of the many natural disasters that have devastated farmers over the last few years, leaving and ruining livelihoods cravings in their wake. Between 2008 and 2018, catastrophes cost the farming sectors of establishing countries over $108 billion in harmed or lost crop and livestock production, according to a current report from the Food and Agriculture Organization of the United Nations.
To handle the risks of disasters, developing world farmers increasingly purchase index insurance coverage, which pays out benefits according to a predetermined “index” or design, such as seasonal rains volume. The smallest mistakes in choice of satellite information can skew outputs and toss the payment system out of synch with the farmers real needs. Ultimately, poorly designed index insurance can lead entire neighborhoods and countries into an incorrect sense of security and result in both grave monetary shortfalls and food shortages when environment disasters strike.
A National Science Foundation (NSF) grant to Columbia researchers Daniel Osgood, Eugene Wu and Lydia Chilton, announced September 1, comes simply in time. The NSF designated $600,000 to Osgood, of Columbia Climate Schools International Research Institute for Climate and Society, and Wu and Chilton, from Columbias Computer Science department, to help them design a set of scalable personalized open-source tools that can collect agricultural catastrophe threat data from millions of individual farmers living in some remote parts of the world. Throughout the pandemic, Osgood, Wu and Chilton had already started utilizing their minimal coding knowledge to cobble together small web-based tools to collect information directly from thousands of farmers about the climate catastrophe threats they face. Previously, most index insurance coverage products depended on notified guesswork and satellite information, however did not have the capacity to tap the knowledge of crowds of farmers themselves. The NSFs financial support will enable them to react to that need with population-scale tools that are developed to last.
State of the Planet talked to Osgood about the NSF-funded project in addition to his career path. The following interview had been modified for length and clearness.
How did you pertain to work at the intersection of financial instrument design, farming risk, and climate catastrophe?
Its type of a crazy journey in hindsight, how I collected this odd mix of abilities. As an undergraduate, I studied economics and engineering in a double degree program and began working as a researcher on satellite telescope jobs that look at the beginning of deep space. But it seemed like I was simply having and playing enjoyable. I desired to do something that was more linked to my roots.
I was born upon an appointment in Arizona– Im foreign American but my moms and dads operated in healthcare on the appointment– and matured in New Mexico, so I was always interested in things like environment and water and land. My teachers at UC Berkeley said, “Try agricultural economics,” so I began my PhD in agricultural economics while I was still working in the labs building telescopes. Then as a PhD trainee, I supported the structure of a great deal of the early water markets in the American West, along with climate and water market details systems for farmers that might assist them prepare water use.
You developed a talent for talking with farmers about what they require and how they make choices, which has actually become a really critical part of your work. How did you select up this ability?.
The research study objective was not designed to support the research study neighborhood, however to help on cooperative extension projects– to help the farmers. I worked in a NOAA-funded program called CLIMAS where my task was to talk with farmers about climate and science.
Early in your profession, you played a big role in the development of index insurance coverage items for farmers. How did this come about?
In the early 2000s, the World Bank started supporting new index insurance coverage jobs. The personnel thought they required more information to create these products, but they discovered that what they actually required was somebody who might talk with farmers and local decision-makers about science. At the end of the day, they needed to fix up the experience of farmers with their remote-sensing and crop models and environment projections. Thats when the Bank approached Columbias International Research Institute for Climate and Society (IRI) for help, and as the climate economic expert at IRI, I was generated to lead the work.
Farmers at a focus group meeting in Ethiopia. Credit: IRI.
So, the index insurance coverage designs diverged pretty substantially from the farmers direct experience?
Yeah. I indicate, its really simple to get it wrong. Its very simple for farmers to be forced to purchase something they do not comprehend, or to have items not show the beneficiaries that theyre intended for..
Can you provide an example?
Well, for circumstances, in 2015 in Malawi there was this scandal. It was an extremely bad year for agriculture, so individuals in Malawi were anticipating a significant food security payout. The dry spell overwhelmed the federal governments capability to respond, since instead of reserved reserves, they had actually enrolled in an insurance coverage strategy, the Africa Risk Capacity project, to protect against large droughts and it stopped working to come through. The index model utilized by the Africa Risk Capacity task, run by the World Food Program (WFP), wasnt registering enough hardship for a payout. We had a really small task with about five villages in Malawi and we did have a payment, so the WFP commissioned us to write a report to help unload what failed.
We attempted to match satellite data from the start and end of the rainy season with the recollections of the farmers to see if there was contract. What we discovered was that various years and parts of the season were essential to different sets of farmers even within one town, because they were using different ranges of crop. The various losses from these different sets of farmers required to be thought through for the insurance coverage to work.
What are the repercussions of getting the model wrong?.
If we get it wrong, the farmers do not get payouts when they need them. When you go from having a couple hundred farmers in an experiment to tens of thousands of farmers, the stakes go up, too. Were trying to support these farmers to make wise and informed choices about their incomes, their capability to feed their kids and to plan their futures.
How do you ensure that you get good information from the farmers?
We have actually been using video games. One method of gamifying a process is to utilize whats called intrinsic motivation, where were not paying someone money, were offering them badges on their phone or some aspect of fun. At the same time, were getting details, for example, about the satellites or historic environment or the projections; were getting information about what farmers needs are; and then were in fact utilizing this info to create something new together.
We utilized an application called IKON which turns a simple question into a video game. So instead of simply asking farmers straight how good or bad a particular season was, it asks to try to think much better than their next-door neighbors or the satellites how well their farms performed in such and such a duration. There is a series of these and farmers get badges based on how well they think. Its enjoyable enough that individuals play it. The strategy now is to have scientists go to a couple of villages where things are most complex or most representative of a region, and then use these video games to go to a much broader place set of locations.
We can also ask to think which previous years the insurance product that they acquired would have offered them payouts. In this way, we can discover if the farmers actually comprehend whats been offered to them, which has actually been an issue with a lot of these jobs.
Experts dealing with the farmer data in Ethiopia to reconcile it with satellites in among the IRI groups prototype tools. Credit: IRI.
Considering that you started this work, the appeal of index insurance for small farmers has grown rather considerably.
Yes, when I started working on index insurance coverage, there were most likely a couple thousand farmers covered by the instrument, and now there are lots of millions. It continues to grow. One job at Columbia under method now, called the ACToday Columbia World Project, is working to provide insurance to a million smallholder farmers. The benefits for farmers extend beyond just defense versus environment danger– index insurance coverage likewise provides the monetary security to take productive organization dangers.
How did you construct something that could gather info from these farmers at a big scale?
So initially we began prototyping tools to see if we could automate some of our surveys to work with larger numbers of farmers. Throughout COVID, the variety of these models blew up since individuals could not go into the field. There was this Wizard of Oz thing happening, where people collecting the information believed they were using software application, however really, they were typing something into a web user interface at the end of their work day, and thousands of miles away in New York, our team was programming it and examining it overnight so that it would appear in the user interface a day or two later. We were anxiously hard coding this workflow.
The next action was to configure web interfaces that would allow somebody within Malawi or Ethiopia or Senegal to finish the analysis themselves through the web simply as well as or better than we were able to do it in New York. And then the federal government of Zambia said, “Look, we have a national insurance product for millions of farmers throughout all of Zambia, however it was developed individually by a consultant and it isnt tuned to the local reality. Could you set up style tools so we can produce something that will work all across Zambia?” We recognized we were in method over our heads with the technology, which is where the NSF grant comes in.
How will the NSF grant help you move this work forward?
Essentially, we were using tools that we had coded ourselves, but were not coders. These tools, developed for small usage, were currently crashing typically and we could not fix them due to the fact that our capacity was overwhelmed. And now, in the previous year and a half, the tasks have gone to a much larger scale. They need a versatile and appropriate software framework developed by computer researchers, not economic experts and climate researchers who meddle coding. So its ideal timing with the NSF grant, because we have a much greater need for our tools than we had expected.
What is especially novel about the technology that youre constructing today?
The amazing thing is that this kind of bottom-up technique has never ever worked in the past at a massive scale. Theres never ever been a framework that would enable countless residents to drive decision-making on a job created to benefit them, and its still totally based upon science. Its like artificial intelligence, however its in fact human intelligence or neighborhood intelligence. In some methods, its a brand-new kind of democracy. The city government can say yes and no, and make their choices, but the citizens for whom the programs are developed are totally associated with driving the procedure..
Is there a single core concern that stimulates all your work?
I think the greatest question I have is how can the knowledge of each specific help solve a community-wide problem? Im trying to coin this term “crowd core,” that describes crowd-based cooperative research services to private issues. Im hoping crowd core is a door to a more cooperative future.
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Kristen French|October 27, 2021
The NSF assigned $600,000 to Osgood, of Columbia Climate Schools International Research Institute for Climate and Society, and Wu and Chilton, from Columbias Computer Science department, to help them develop a set of scalable customizable open-source tools that can gather farming catastrophe danger information from millions of individual farmers living in some remote parts of the world. When you go from having a couple hundred farmers in an experiment to 10s of thousands of farmers, the stakes go up, too. Yes, when I started working on index insurance, there were probably a couple thousand farmers covered by the instrument, and now there are numerous millions. One task at Columbia under method now, called the ACToday Columbia World Project, is working to offer insurance coverage to a million smallholder farmers. The benefits for farmers extend beyond simply protection against climate risk– index insurance likewise offers them the financial security to take productive organization dangers.
