AI: A tool for climate resilience and farming


The changing climate poses a serious threat to tomato growers in central India. The region has experienced many droughts in the past decade, which have ruined the crops and reduced the farmers' income.

But a Silicon Valley startup called ClimateAi uses artificial intelligence to help farmers cope with the warming temperatures. The startup has created a platform to assess any place's climate, water, and soil conditions and forecast its suitability for growing crops in the next 20 years.

AI at forecasting and running simulations

As CNN reports, the platform was first tested in Maharashtra, India, in 2021. Farmers could use the ClimateAi app to enter their seeds' information and planting locations.

The platform ran simulations and revealed that the tomato output in the region would decrease by about 30% in the next two decades due to extreme heat and drought. It suggested the farmers to change their strategy.

The farmers followed the suggestion and changed their business plans. They chose more climate-resilient seeds and altered the timing of their planting. This helped them quickly find new and better locations for growing tomatoes and save money. Himanshu Gupta, a co-founder of ClimateAi who grew up in India, said that AI was a powerful tool to speed up and improve the solutions for climate change.

AI is helping farmers and tackling the climate crisis in many other ways. AI is a technology that can perform complex tasks that humans might not be able to do. The technology can process and connect huge amounts of data quickly.

This makes AI very good at things like forecasting and running simulations. And unlike traditional computer programs, AI tools can keep learning over time as new data is available or the systems get further feedback about the quality of their outputs.

While scientific discovery used to depend on humans’ ability to collect, observe, and analyze evidence, computers can now handle large datasets, find patterns, and run digital experiments in a fraction of the time that human researchers would need.

According to Fengqi You, a chair professor at Cornell University’s engineering school, climate models involve solving complex equations that describe the interaction of atmosphere models, which can take a significant amount of time.

Similarly, research on new energy-conduction materials, such as those used in solar panels, used to require extensive testing that relied heavily on trial and error. However, with the help of AI, this process can now be expedited. AI-powered technology can work continuously without the need for breaks, which can be beneficial in accelerating the discovery process.

AI probably won’t replace the need for humans in the climate change fight. But it could make their work faster and more effective.

Researchers who want to restore coastlines by replanting seagrass, for example, are using AI to model the best locations to target those replanting efforts, said Dan Keeler, chief communication officer at impact investing firm Newday, which is involved in charitable efforts to support the coastal restoration.

An AI algorithm trained to address the issue could consider everything from toxins in the water or disruptive shipping routes to how replanting efforts could affect nearby sea life or even coastal tourism.

“It’s very difficult to put all those together into a single model with conventional methods, but AI actually makes that much more possible,” Keeler said.

AI is also ‘doing the dirty work’ in climate research

AI is also ‘doing the dirty work’ in climate research. Scientists have found that the Arctic is warming four times faster than the rest of the planet. Rising temperatures are melting sea ice, thawing permafrost, and sparking wildfires in what should be one of the coldest regions on Earth.

Climate experts have said what happens in the Arctic is a sign for the rest of the world. But climate models – which scientists use to predict long-term change – are not capturing how fast it’s warming.

With the help of AI, Anna Liljedahl, a scientist at the Woodwell Climate Research Center, can make permafrost forecasts on a seasonal timescale instead of on the typical 100-year timescale, giving her and other researchers a better picture of how fast the Arctic is melting.

According to Liljedahl as told to CNN, AI serves as an initial tool in the process, but it is not flawless. Therefore, humans need to verify the accuracy of the AI-generated suggestions and explore further to ensure the validity of the results.

In addition to its various applications, AI technology can also provide solutions to pressing issues. For instance, a recent Google DeepMind project utilized an AI model trained on weather forecasts and historical wind turbine data to predict the availability of wind power. This method significantly increased the value of renewable energy sources for wind farmers. Additionally, AI can predict peak energy demand and identify the most efficient locations for renewable energy sources. This optimization ensures that the energy supply meets demand while minimizing waste. Keeler emphasized the importance of this approach, stating that it avoids unnecessary production and consumption of power, which is a significant problem.

According to Gupta of ClimateAi, the challenge lies in integrating renewable capacity into the current fossil fuel-based grid. AI can play a crucial role in identifying available renewable sources in real-time, and optimizing supply and demand for renewable energy.

AI is a technology that can help us fight climate change in many ways. But AI also has a downside. The technology depends on data centers that use a lot of energy and emit greenhouse gases. Experts say that software engineers and climate scientists need to work together to find a balance.

Kara Lamb, an associate research scientist at Columbia University’s Earth and Environmental Engineering Department, said there was a trade-off between using AI and its ecological impact. But she said the benefits of using AI for climate change outweighed the costs.

Originally published on Interesting Engineering : Original article

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