Editor’s note: This story has been updated with information regarding sales and acquisitions.

CLEARLAKE, Calif. — The Clearlake Planning Commission this week will consider the city’s plans to sell or acquire several properties.

For the 20th time since 1933, Congress is writing a multiyear farm bill that will shape what kind of food U.S. farmers grow, how they raise it and how it gets to consumers. These measures are large, complex and expensive: The next farm bill is projected to cost taxpayers US$1.5 trillion over 10 years.

Modern farm bills address many things besides food, from rural broadband access to biofuels and even help for small towns to buy police cars. These measures bring out a dizzying range of interest groups with diverse agendas.

Umbrella organizations like the American Farm Bureau Federation and the National Farmers Union typically focus on farm subsidies and crop insurance. The National Sustainable Agriculture Coalition advocates for small farmers and ranchers. Industry-specific groups, such as cattlemen, fruit and vegetable growers and organic producers, all have their own interests.

Environmental and conservation groups seek to influence policies that affect land use and sustainable farming practices. Hunger and nutrition groups target the bill’s sections on food aid. Rural counties, hunters and anglers, bankers and dozens of other organizations have their own wish lists.

As a former Senate aide and senior official at the U.S. Department of Agriculture, I’ve seen this intricate process from all sides. In my view, with the challenges in this round so complex and with critical 2024 elections looming, it could take Congress until 2025 to craft and enact a bill. Here are four key issues shaping the next farm bill, and through it, the future of the U.S. food system.

The price tag

Farm bills always are controversial because of their high cost, but this year the timing is especially tricky. In the past two years, Congress has enacted major bills to provide economic relief from the COVID-19 pandemic, counter inflation, invest in infrastructure and boost domestic manufacturing.

These measures follow unprecedented spending for farm support during the Trump administration. Now legislators are jockeying over raising the debt ceiling, which limits how much the federal government can borrow to pay its bills.

Agriculture Committee leaders and farm groups argue that more money is necessary to strengthen the food and farm sector. If they have their way, the price tag for the next farm bill would increase significantly from current projections.

On the other side, reformers argue for capping payments to farmers, which The Washington Post recently described as an “expensive agricultural safety net,” and restricting payment eligibility. In their view, too much money goes to very large farms that produce commodity crops like wheat, corn, soybeans and rice, while small and medium-size producers receive far less support.

Food aid is the key fight

Many people are surprised to learn that nutrition assistance – mainly through the Supplemental Nutrition Assistance Program, formerly known as food stamps – is where most farm bill money is spent. Back in the 1970s, Congress began including nutrition assistance in the farm bill to secure votes from an increasingly urban nation.

Today, over 42 million Americans depend on SNAP, including nearly 1 in every 4 children. Along with a few smaller programs, SNAP will likely consume 80% of the money in the new farm bill, up from 76% in 2018.

Why have SNAP costs grown? During the pandemic, SNAP benefits were increased on an emergency basis, but that temporary arrangement expired in March 2023. Also, in response to a directive included in the 2018 farm bill, the Department of Agriculture recalculated what it takes to afford a healthy diet, known as the Thrifty Food Plan, and determined that it required an additional $12-$16 per month per recipient, or 40 cents per meal.

Because it’s such a large target, SNAP is where much of the budget battle will play out. Most Republicans typically seek to rein in SNAP; most Democrats usually support expanding it.

Anti-hunger advocates are lobbying to make the increased pandemic benefits permanent and defend the revised Thrifty Food Plan. In contrast, Republicans are calling for SNAP reductions, and are particularly focused on expanding work requirements for recipients.

Debating climate solutions

The 2022 Inflation Reduction Act provided $19.5 billion to the Department of Agriculture for programs that address climate change. Environmentalists and farmers alike applauded this investment, which is intended to help the agriculture sector embrace climate-smart farming practices and move toward markets that reward carbon sequestration and other ecosystem services.

This big pot of money has become a prime target for members of Congress who are looking for more farm bill funding. On the other side, conservation advocates, sustainable farmers and progressive businesses oppose diverting climate funds for other purposes.

There also is growing demand for Congress to require USDA to develop better standards for measuring, reporting and verifying actions designed to protect or increase soil carbon. Interest is rising in “carbon farming” – paying farmers for practices such as no-till agriculture and planting cover crops, which some studies indicate can increase carbon storage in soil.

But without more research and standards, observers worry that investments in climate-smart agriculture will support greenwashing – misleading claims about environmental benefits – rather than a fundamentally different system of production. Mixed research results have raised questions as to whether establishing carbon markets based on such practices is premature.

A complex bill and inexperienced legislators

Understanding farm bills requires highly specialized knowledge about issues ranging from crop insurance to nutrition to forestry. Nearly one-third of current members of Congress were first elected after the 2018 farm bill was enacted, so this is their first farm bill cycle.

I expect that, as often occurs in Congress, new members will follow more senior legislators’ cues and go along with traditional decision making. This will make it easier for entrenched interests, like the American Farm Bureau Federation and major commodity groups, to maintain support for Title I programs, which provide revenue support for major commodity crops like corn, wheat and soybeans. These programs are complex, cost billions of dollars and go mainly to large-scale operations.

Agriculture Secretary Tom Vilsack’s current stump speech spotlights the fact that 89% of U.S. farmers failed to make a livable profit in 2022, even though total farm income set a record at $162 billion. Vilsack asserts that less-profitable operations should be the focus of this farm bill – but when pressed, he appears unwilling to concede that support for large-scale operations should be changed in any way.

When I served as deputy secretary of agriculture from 2009 to 2011, I oversaw the department’s budget process and learned that investing in one thing often requires defunding another. My dream farm bill would invest in three priorities: organic agriculture as a climate solution; infrastructure to support vibrant local and regional markets and shift away from an agricultural economy dependent on exporting low-value crops; and agricultural science and technology research aimed at reducing labor and chemical inputs and providing new solutions for sustainable livestock production.

In my view, it is time for tough policy choices, and it won’t be possible to fund everything. Congress’ response will show whether it supports business as usual in agriculture, or a more diverse and sustainable U.S. farm system.

Kathleen Merrigan, Executive Director, Swette Center for Sustainable Food Systems, Arizona State University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

The famous first image of a black hole just got two times sharper. A research team used artificial intelligence to dramatically improve upon its first image from 2019, which now shows the black hole at the center of the M87 galaxy as darker and bigger than the first image depicted.

I’m an astronomer who studies and has written about cosmology, black holes and exoplanets. Astronomers have been using AI for decades. In fact, in 1990, astronomers from the University of Arizona, where I am a professor, were among the first to use a type of AI called a neural network to study the shapes of galaxies.

Since then, AI has spread into every field of astronomy. As the technology has become more powerful, AI algorithms have begun helping astronomers tame massive data sets and discover new knowledge about the universe.

Better telescopes, more data

As long as astronomy has been a science, it has involved trying to make sense of the multitude of objects in the night sky. That was relatively simple when the only tools were the naked eye or a simple telescope, and all that could be seen were a few thousand stars and a handful of planets.

A hundred years ago, Edwin Hubble used newly built telescopes to show that the universe is filled with not just stars and clouds of gas, but countless galaxies. As telescopes have continued to improve, the sheer number of celestial objects humans can see and the amount of data astronomers need to sort through have both grown exponentially, too.

For example, the soon-to-be-completed Vera Rubin Observatory in Chile will make images so large that it would take 1,500 high-definition TV screens to view each one in its entirety. Over 10 years it is expected to generate 0.5 exabytes of data – about 50,000 times the amount of information held in all of the books contained within the Library of Congress.

There are 20 telescopes with mirrors larger than 20 feet (6 meters) in diameter. AI algorithms are the only way astronomers could ever hope to work through all of the data available to them today. There are a number of ways AI is proving useful in processing this data.

Picking out patterns

Astronomy often involves looking for needles in a haystack. About 99% of the pixels in an astronomical image contain background radiation, light from other sources or the blackness of space – only 1% have the subtle shapes of faint galaxies.

AI algorithms – in particular, neural networks that use many interconnected nodes and are able to learn to recognize patterns – are perfectly suited for picking out the patterns of galaxies. Astronomers began using neural networks to classify galaxies in the early 2010s. Now the algorithms are so effective that they can classify galaxies with an accuracy of 98%.

This story has been repeated in other areas of astronomy. Astronomers working on SETI, the Search for Extraterrestrial Intelligence, use radio telescopes to look for signals from distant civilizations. Early on, radio astronomers scanned charts by eye to look for anomalies that couldn’t be explained. More recently, researchers harnessed 150,000 personal computers and 1.8 million citizen scientists to look for artificial radio signals. Now, researchers are using AI to sift through reams of data much more quickly and thoroughly than people can. This has allowed SETI efforts to cover more ground while also greatly reducing the number of false positive signals.

Another example is the search for exoplanets. Astronomers discovered most of the 5,300 known exoplanets by measuring a dip in the amount of light coming from a star when a planet passes in front of it. AI tools can now pick out the signs of an exoplanet with 96% accuracy.

Making new discoveries

AI has proved itself to be excellent at identifying known objects – like galaxies or exoplanets – that astronomers tell it to look for. But it is also quite powerful at finding objects or phenomena that are theorized but have not yet been discovered in the real world.

Teams have used this approach to detect new exoplanets, learn about the ancestral stars that led to the formation and growth of the Milky Way, and predict the signatures of new types of gravitational waves.

To do this, astronomers first use AI to convert theoretical models into observational signatures – including realistic levels of noise. They then use machine learning to sharpen the ability of AI to detect the predicted phenomena.

Finally, radio astronomers have also been using AI algorithms to sift through signals that don’t correspond to known phenomena. Recently a team from South Africa found a unique object that may be a remnant of the explosive merging of two supermassive black holes. If this proves to be true, the data will allow a new test of general relativity – Albert Einstein’s description of space-time.

Making predictions and plugging holes

As in many areas of life recently, generative AI and large language models like ChatGPT are also making waves in the astronomy world.

The team that created the first image of a black hole in 2019 used a generative AI to produce its new image. To do so, it first taught an AI how to recognize black holes by feeding it simulations of many kinds of black holes. Then, the team used the AI model it had built to fill in gaps in the massive amount of data collected by the radio telescopes on the black hole M87.

Using this simulated data, the team was able to create a new image that is two times sharper than the original and is fully consistent with the predictions of general relativity.

Astronomers are also turning to AI to help tame the complexity of modern research. A team from the Harvard-Smithsonian Center for Astrophysics created a language model called astroBERT to read and organize 15 million scientific papers on astronomy. Another team, based at NASA, has even proposed using AI to prioritize astronomy projects, a process that astronomers engage in every 10 years.

As AI has progressed, it has become an essential tool for astronomers. As telescopes get better, as data sets get larger and as AIs continue to improve, it is likely that this technology will play a central role in future discoveries about the universe.

Chris Impey, University Distinguished Professor of Astronomy, University of Arizona

This article is republished from The Conversation under a Creative Commons license. Read the original article.