Barath Raghavan, an affiliate professor at USC Viterbi, is pioneering “computational agroecology,” a novel strategy to sustainable agriculture that makes use of computational instruments to design numerous, optimum farming ecosystems. The researchers have conceptualized agriculture as a search via a “state space,” which contains all potential configurations of a system, similar to a farm, enabling farmers and researchers to discover, simulate and discover optimum combos of assorted components like crop choice, soil sort, climate circumstances, irrigation, and pest management, doubtlessly revolutionizing farming planning and methods.
A pc science examine presents a revolutionary new method to consider agriculture and its potential advantages for farming.
The world’s inhabitants hit a staggering eight billion on November 15, 2022. As this quantity continues to rise, the looming query is: how can we guarantee everybody has sufficient to eat? The problem is additional amplified by points similar to local weather change, depletion of pure assets, soil degradation, and the environmental impression of fossil fuel-dependent agriculture. There’s a urgent want for change, however the query is, what type ought to this transformation take?
In response to this, Barath Raghavan, an affiliate professor of pc science at USC Viterbi, is popping conventional farming practices on their head. He is spearheading the creation of computational instruments that might doubtlessly revolutionize the way in which farmers conceive, implement, and handle sustainable farming strategies.
Horticulture fanatic and pc scientist Barath Raghavan is rethinking conventional farming practices. Credit: Noe Montes
Raghavan, a member of the California Rare Fruit Growers group, at present grows greater than 150 totally different edible vegetation in his yard. A decade in the past, he began to mix his pursuits by researching how computing might make agriculture extra sustainable.
Raghavan calls this new space of analysis “computational agroecology,” uniting expertise and farming experience to develop numerous agricultural landscapes primarily based on pure ecosystems. From crop choice to planting to irrigation, the strategy permits farmers to discover hundreds of various potential designs to optimize meals manufacturing with out fossil fuel-derived pesticides.
“How can we design an ecosystem that is as productive and sustainable as a natural forest, but instead of producing food for wildlife, it’s producing food for people?” stated Raghavan.
“It’s an incredibly hard problem because designing an ecosystem is a super complex, dynamic, natural system. We’re trying to build computing tools that can figure out how ecosystems work, so we can grow food plentifully and sustainably.”
“A totally new way to think about agriculture”
In a brand new paper lately revealed in PNAS Nexus, Raghavan and his colleagues suggest “a totally new way to think about agriculture and the benefits it can have for research and farming,” stated Raghavan.
In this examine, the researchers reconceptualize agriculture as a search via a “state space,” which represents all potential configurations of a system—on this context, agricultural land.
To higher perceive the idea of a state area, think about a field of blocks: every block could possibly be crimson, blue, or yellow. The state area would include all of the potential methods to rearrange these blocks, similar to all crimson, blue, or inexperienced, or a mixture of the three colours.
In the identical method, a state area for an agricultural system would possibly include all of the potential variables that the system can take—similar to crop or soil sort, climate circumstances, irrigation, fertilization, or pest management.
This permits agricultural researchers and farmers to discover the totally different paths and methods accessible—taking totally different “blocks” or variables and putting them collectively to see what works.
Essentially, an agricultural “sandbox” to find out optimum configurations to extend crop yield, enhance sustainability, and uncover fully new combos of crops that develop effectively collectively.
For occasion, the framework allows analytics and machine learning that could allow researchers to analyze the patterns between crop yield and soil moisture content or simulate growing different types of crops together for biodiversity.
“Once we can conceive of a farm this way, we can then reframe many research questions and farming planning questions as a search through the space of all possible states the farm could possibly end up in, with certain states being more desirable than others,” said Raghavan.
“This allows us to compare and contrast different approaches to farming, explore and combine techniques, and then search the state space in simulation for new farming techniques that have never been tried before and where trial and error in the real world would be far too expensive and time-consuming.”
“Playing a chess game with nature”
For example, in Southern California, farmers have recently discovered that high-quality coffee can grow plentifully between avocado trees. But figuring out the right way to do that, and maybe even add another couple of crops that work well together, is site specific.
“Each farmer doesn’t have the time or ability to do trial and error for years to figure out the right way to grow a half dozen crops on their land,” said Raghavan.
“Instead, with the conceptual framework and eventually software framework of state spaces, a farmer could spell out an objective—such as diversified harvest with high yield and possible high profit for a specific piece of land—and have the system explore the state space and produce possible plant mixtures, placement, and management techniques that meet the farmer’s criteria.”
Raghavan compares the process to “playing a chess game with nature, but one that is both competitive and collaborative.”
“You’re making moves on the chessboard, which is your land, and nature is making moves too. Pests are going to eat one crop; a flood is going to damage another. What we are building is a computational framework that allows you to explore all the different ways that you might ‘play’ this game of chess with nature so that we can come up with the best one for your land.”
The group including Raghavan recently received a grant from the U.S. Department of Agriculture’s National Institute of Food and Agriculture for their research in this area. Now, the team is working through possible use cases with researchers and farmers to incorporate specific use cases and to develop software that can make it easy to simulate and explore state spaces.
Reference: “State spaces for agriculture: A meta-systematic design automation framework” by Bryan Runck, Adam Streed, Diane R Wang, Patrick M Ewing, Michael B Kantar Barath Raghavan, 16 March 2023, PNAS Nexus.
DOI: 10.1093/pnasnexus/pgad084
