Can Computer Science Help Farmers Explore Alternative Crops and Sustainable Farming Methods?
Half of the Earth’s arable land has been redesigned specifically for cultivating only eight food staples—corn, soy, wheat, rice, cassava, sorghum, sweet potato, and potato. They’re responsible for the lion’s share of the world’s caloric intake. There is increasing demand to increase output in response to a growing global population.
Many agricultural experts believe that increasing synthetic fertilizers, chemical pesticides, and high-yield crops is not the best strategy for feeding a rapidly rising global population. Farmers and scientists alike feel imprisoned in a system that, in their opinion, isn’t environmentally or economically viable.
In what ways can nations create a food supply that not only provides for its people but also improves their health and diversity? Implementing organic farming on a large scale as an alternative to industrial agriculture has been challenging.
We looked at this issue from both the computer science and agricultural science points of view in a recent paper. Along with our coworkers Bryan Runck, Adam Streed, Diane R. Wang, and Patrick M. Ewing, we proposed a way to rethink the design and implementation of agricultural systems by borrowing a central idea from computer science: abstraction. This idea summarizes data and concepts and organizes them computationally to analyze and act upon without constantly examining their internal details.
Significant results and effects
The mid-20th century saw a rapid expansion of modern agriculture, yet this period is hardly noticeable in the grand scheme. Synthetic fertilizer and statistical approaches that aided plant breeding were two examples of technological advancements that paved the way.
These developments allowed farms to produce much more food but at the price of the natural world. By converting once-vibrant ecosystems into monoculture agricultural fields, industrial agriculture has exacerbated global warming, contaminated lakes, and bays with fertilizer runoff, and hastened the extinction of countless species.
Many American farmers and agricultural scientists are interested in diversifying their crop options and adopting more environmentally friendly practices. However, it is challenging for them to identify promising new systems, particularly in the face of climate change. Knowledge of plants, weather and climate modeling, geology, other disciplines, and extensive local knowledge is usually required for low-impact farming methods.
This is where the new method we’ve developed comes in.
Spaces of the state as farms
State spaces are a common tool computer scientists use when considering difficult situations. All feasible configurations of a system are represented mathematically by this method. Choosing a path through the environment affects the system positively or negatively.
Take the classic board game of chess as an illustration. Every possible game state corresponds to a unique board configuration at a given instant. Each new game state is the result of a player’s action.
A game’s “state space” is the set of all potential game states that may be reached by taking legal actions. All players are always on the lookout for advantageous game circumstances.
An agricultural system may be seen as a state space inside a given environment. One state in this area is a farm and its current arrangement of plant species. The farmer is looking for favorable states to visit and avoiding those he knows to be undesirable.
The farm goes through transitions from human intervention and natural forces. A farmer’s day might consist of tasks, including tilling, planting, weeding, harvesting, and applying fertilizer. State changes are caused by nature on a small scale, such as when plants develop or when it rains, and on a large scale, during natural catastrophes like floods or wildfires.
Taking a spatial perspective on agriculture gives farmers more leeway than is now available.
Farmers cannot afford to spend years of their lives learning from mistakes they make on their property. To assist farmers in figuring out what’s best for their land, a computer system may draw on agricultural knowledge from various contexts and schools of thinking, like a chess player negotiating with nature.
Farmers practicing conventional agriculture have limited options regarding plant species, farming techniques, and agricultural inputs. With our framework, you may think about more advanced tactics, such as rotating crops or discovering the optimal management approach for a certain plot of land. Users may conduct a state-space search to contemplate the range of possible approaches to achieving their aims, including the role of certain species and geographical locations.
For instance, there are 721 possible crop rotations if a researcher is interested in testing five crop rotations (planting a series of crops on the same land over many years). Longitudinal ecological studies might be included in our method to identify the most promising test systems.
Intercropping, where diverse plants are grown in a combination or near together, is one area where we see enormous promise. It’s been known for a long time that many different plant combinations thrive together, with one plant benefiting the others.
Native American farmers are credited with developing many staple crops, the most well-known of which is the “three sisters”: maize, squash, and beans. Bean vines may climb corn stalks for support, and the squash leaves can keep the ground wet and weed-free. Nitrogen, a vital nutrient, is produced by bacteria on the roots of the bean plants and transferred to the other two plants.
Intercropping methods that pair complementary crops like turmeric with mango or millet, cowpea, and Ziziphus, often known as red date, have been valued by many cultures throughout human history. New research on agrivoltaics demonstrates that installing solar panels on farms may be very beneficial: crops grown underneath the panels benefit from partial shading, and farmers can supplement their income by creating sustainable electricity.
Simulating Variable Farming Practices
We’re working on a software implementation of our framework so that others may use it to create state-space models of farms. The end objective is to reduce the need for the time-consuming and money-draining trial-and-error method now used to try out new ideas in farming by encouraging users to examine alternative designs based on their intuition.
Current methods generally focus on modeling and pursuing improvements of preexisting agriculture systems, which are frequently unsustainable. Our framework may discover new agricultural systems and existing ones can be optimized.
Users will also be able to instruct an AI-powered agent to search the farm’s state space to achieve their goals, much as a chess computer would search the board’s state space to choose the best possible moves.
More plant species are available to modern cultures, and more is known about the interplay between organisms and their habitats than a century ago. In our opinion, agricultural systems aren’t doing enough to use this wealth of information. A more productive, healthier, and more sustainable agricultural system in a constantly changing environment may be attainable via its computational combination.