Advancing Software for Ecological Forecasting: Agenda

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David LeBauer

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Abstract: Humanity is depending on scientists, engineers, and farmers to provide more food using fewer resources in the 21st century. For emerging technologies to support the engineering of adaptive, resilient, and productive agroecosystems, the underlying research will need to be increasingly data-driven and computationally intensive. This presents new technical challenges as the development of software to use these data streams is a growing component of research. We can reduce this burden by sharing software and data as it enables efficient encoding and exchange of knowledge. Integrating software components with a loosely coupled, modular approach provides a scaffold for integrative science. I will illustrate this method and the integrative science that it enables with two examples from systems that my collaborators and I have designed. The first system, PEcAn, is a framework for the analysis and use of trait-based crop and ecosystem models that integrates data across scales to understand and predict traits, yields, and ecosystem level processes. The second system supports the TERRA REF computing pipeline, a scalable suite of components for high throughput plant phenotyping. Both of these projects are designed to integrate rather than replace the diverse and information-rich software and data being developed by independent research teams. I will conclude by presenting a roadmap of future possibilities that these technologies and methods make possible.

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Abstract: Humans need a secure and sustainable food supply, and science can help. We have an opportunity to transform agriculture by combining knowledge of organisms and ecosystems to engineer ecosystems that sustainably produce food, fuel, and other services. The challenge is that the information we have. Measurements, theories, and laws found in publications, notebooks, measurements, software, and human brains are difficult to combine. We homogenize, encode, and automate the synthesis of data and mechanistic understanding in a way that links understanding at different scales and across domains. This allows extrapolation, prediction, and assessment. Reusable components allow automated construction of new knowledge that can be used to assess, predict, and optimize agro-ecosystems. Developing reusable software and open-access databases is hard, and examples will illustrate how we use the Predictive Ecosystem Analyzer (PEcAn, pecanproject.org), the Biofuel Ecophysiological Traits and Yields database (BETYdb, betydb.org), and ecophysiological crop models to predict crop yield, decide which crops to plant, and which traits can be selected for the next generation of data driven crop improvement. A next step is to automate the use of sensors mounted on robots, drones, and tractors to assess plants in the field. The TERRA Reference Phenotyping Platform (TERRA-Ref, terraref.github.io) will provide an open access database and computing platform on which researchers can use and develop tools that use sensor data to assess and manage agricultural and other terrestrial ecosystems. TERRA-Ref will adopt existing standards and develop modular software components and common interfaces, in collaboration with researchers from iPlant, NEON, AgMIP, USDA, rOpenSci, ARPA-E, many scientists and industry partners. Our goal is to advance science by enabling efficient use, reuse, exchange, and creation of knowledge. --- Invited talk for the "Informatics for Reproducibility in Earth and Environmental Science Research" session at the American Geophysical Union Fall Meeting, Dec 17 2015.

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