In the sprawling fields of agriculture, where the sun beats down on rows of crops, a silent revolution is underway. It’s not about the plants themselves, but about the data that describes them. Sabina Leonelli, from the Exeter Centre for the Study of the Life Sciences (Egenis) at the University of Exeter, has been delving into the intricate world of plant data semantics, and her findings could reshape how we approach agriculture and even the energy sector.
Leonelli’s work, published in ‘Philosophy, Theory, and Practice in Biology’ (or ‘Filosofia, Teoria e Pratica in Biologia’ in English), focuses on how we classify and describe plant traits. It’s a complex task, fraught with biological, cultural, scientific, and semantic diversity. “The efforts required to share data place in sharp relief the forms of diversity characterizing the systems used to capture the biological and environmental characteristics of plant variants,” Leonelli explains. This diversity can make it challenging to link and analyze data across different locations and research projects.
The crux of Leonelli’s argument is the need for a “process-sensitive approach” to naming plant traits. Instead of focusing on the biological products, like the color of a leaf or the size of a fruit, she suggests we should document the environmental processes that lead to these traits. This shift could foster more reliable data linkage and robust re-use, making it easier for data scientists, plant researchers, breeders, and farmers to collaborate.
This approach could have significant implications for the energy sector. As the world shifts towards more sustainable energy sources, biofuels derived from crops like cassava are becoming increasingly important. By improving the way we collect and share data about these crops, we can enhance breeding programs and agricultural interventions, leading to more efficient and sustainable biofuel production.
Leonelli’s work with the Crop Ontology, a system that explicitly recognizes and negotiates diversity, shows promise. “I claim that this approach can foster reliable linkage and robust re-use of plant data, while at the same time facilitating dialogue between data scientists, plant researchers, breeders, and other relevant experts in ways that crucially inform agricultural interventions,” she says.
As we look to the future, Leonelli’s research could shape how we think about data semantics and related descriptors in plant science. By articulating semantic differences among plant varieties and methods of data processing, we can develop more inclusive ways to communicate biological knowledge. This could defy existing understandings of systematization and hierarchies of expertise in biology, bolstering the extent to which plant science can support biodiversity and sustainable agriculture.
In an era where data is king, Leonelli’s work reminds us that the way we describe and classify our world can have profound impacts on our future. As we strive for more sustainable and efficient agricultural practices, her insights could guide us towards a more data-driven, collaborative, and inclusive approach to plant science.