Comparing predicted yield and yield stability of willow and Miscanthus across Denmark

To achieve the goals of energy security and climate change mitigation in Denmark and the EU, an expansion of national production of bioenergy crops is needed. Temporal and spatial variation of yields of willow and Miscanthus is not known for Denmark because of a limited number of field trial data. The semi-mechanistic crop model BioCro was used to simulate the production of both short-rotation coppice (SRC) willow and Miscanthus across Denmark. Predictions were made from high spatial resolution soil data and weather records across this area for 1990–2010. The potential average, rain-fed mean yield was 12.1 Mg DM ha −1  yr −1 for willow and 10.2 Mg DM ha −1  yr −1 for Miscanthus. Coefficient of variation as a measure for yield stability was poorest on the sandy soils of northern and western Jutland, and the year-to-year variation in yield was greatest on these soils. Willow was predicted to outyield Miscanthus on poor, sandy soils, whereas Miscanthus was higher yielding on clay-rich soils. The major driver of yield in both crops was variation in soil moisture, with radiation and precipitation exerting less influence. This is the first time these two major feedstocks for northern Europe have been compared within a single modeling framework and providing an important new tool for decision-making in selection of feedstocks for emerging bioenergy systems. © 2015 The Authors. Global Change Biology Bioenergy Published by John Wiley & Sons Ltd

Student and Staff Perspectives on the Use of Big Data in the Tertiary Education Sector: A Scoping Review and Reflection on the Ethical Issues

© 2020 AERA. While universities routinely use student data to monitor and predict student performance, there has been limited engagement with student and staff views, social and ethical issues, policy development, and ethical guidance. We reviewed peer-reviewed and grey-literature articles of 2007 to 2018 describing the perspectives of staff and students in tertiary education on the use of student-generated data in data analytics, including learning analytics. We used an ethics framework to categorize the findings. There was considerable variation but generally low awareness and understanding amongst students and staff about the nature and extent of data collection, data analytics, and use of predictive analytics. Staff and students identified potential benefits but also expressed concerns about misinterpretation of data, constant surveillance, poor transparency, inadequate support, and potential to impede active learning. This review supports the contention that consideration of ethical issues has failed to keep pace with the development of predictive analytics in the tertiary sector