Whether industrial or community-based, agriculture is a complex system of plants, people, and technology
How might we design stronger relationships within this system?
The Approach
I began by exploring agricultural system dynamics to identify the loops that reinforce their effects. Then I created a new multi-user centric model which opened the possibility of designing for relationships rather than just users. It also helped frame a post-human-centered approach to users and stakeholders.
The Industrial Insight
I began by sketching the dominant agriculture model to analyze its inner workings. Profit is the primary motivation for industrial systems generally and agriculture is no different. This causes knowledge to be viewed as a competitive advantage that must be protected through technological and legal means.
Profit and R&D are the strongest drivers in the system and creators of strong, self-reinforcing negative effects for health, food culture, biodiversity, and the environment.
The Community Insight
Community agriculture is one response to industrial agriculture. In this model profit and R&D are replaced by personhours and community knowledge, but these assets can be in short supply, particularly in the communities most affected by food justice issues. The difficulty of scaling knowledge effectively is directly related to difficulties scaling community agriculture overall.
Principled Direction
Based on our system dynamics analysis, we believe community agriculture is an overall positive response to food justice issues compared to the industrial system, so we should explore a system intervention that strengthens learning and knowledge.
Flipping the Orthodoxy with a Multi User Model
To build human capital in community agriculture, a human-centered approach might lead to the creation of education and training programs. Instead, we took a broader approach. I created a multi-user model based on the fundamental components of agriculture: humans, plants, and technology. Considering multiple coequal users broadened our approach by acknowledging the agency of non-humans like plants and technology. Instead of focusing on a single entity, this new multi-user model allows us to pick a coordinate in between these entities and design for that relational space rather than just a human, plant, or tech-centered approach.
The Idea
What if plants could simply tell you what they needed when they needed it? Embedding plant knowledge could allow for new garden models where no one has ownership or full responsibility of plants. It also requires less learning and education time from time-pressed people.
The Prototype
I assembled and coded an Arduino-based IOT device to measure soil moisture, light, and temperature and display a message from the plant to ask humans for care if needed. I tested this in a busy studio space and observed human behaviors and interactions. The plant thrived over 5 weeks despite having no caretakers with direct responsibility.
I also experimented with a Twitter-enabled version, but it was not as effective as the embedded screen in this context.
Designing Technology for Plants & People
The prototype validated our hypothesis that a talking plant could thrive in an environment where no one had direct responsibility for its care. I learned that while there are technology pieces specifically designed for plants, the knowledge on what plants actually need as far as light and water have not been translated into technical specifications.
Other future considerations included anthropomorphizing the plant and the technology further - would human response be better if the plant was funny or cute? How much agency should a plant/tech hybrid have? Within a community of people who don't "own" the plant, who should benefit from the produce?
