The Open Ag Technology and Systems Center
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Many of the most promising avenues for sustainable food-ag system
improvements involve novel applications of sensing, networking, and
computation to big data science, visualization, and analytics. Powerful
data sets and models continue to be developed at the plot, watershed,
and even regional level from these research efforts. However, there are
fundamental issues impeding progress in data-driven sustainability and
preventing translation of research into practice:
- Issue 1: The full value in data is only realized when it can be integrated from multiple, context-rich sources in ways that adhere to trust requirements of its owners.
- Issue 2: The data and algorithms produced by publicly-funded food/ag research are not easily obtainable for verification, extension, or translation to practice.
- Issue 3: The rapid rise of data-driven agriculture has left many stakeholders short of the proper data analytics, software development, and computational thinking skills necessary to thrive.
- Issue 4: The information technologies used in the food/ag industry and in university research lag behind the state of the art due in part to the absence of thriving open source communities that have propelled progress in the broader technology sector.
These issues are solvable by open source data and algorithm exchange paradigms, so much so that we believe data exchange among systems, people, and projects is the most critical component for achieving data-driven sustainability goals. To harness the data revolution in agriculture we must therefore address the key attributes of data flow to empower agriculture which are
- automatable data exchange, and
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