Virtual Plants Modeling Plant Architecture In Changing Environments
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Virtual Plants: Modeling Plant Architecture in Changing Environments
Plant architecture is a major determinant of the resource use efficiency of crops. The architecture of a plant shows ontogenetic structural changes which are modified by multiple environmental factors: Plant canopies are exposed to natural fluctuations in light quantity and the dynamically changing canopy architecture induces local variations in light quality. Changing temperature conditions or water availability during growth additionally affect plant architecture and thus crop productivity, because plants have various options to adapt their architecture to the available resources. Meeting the challenge of ensuring food security we must understand the plant’s mechanisms for integrating and responding to an orchestra of environmental factors. ‘Virtual plants’ describe plant architecture in silico. Virtual plants have the potential to help us understanding the complex feedback processes between canopy architecture, multiple environmental factors and crop productivity. As a research tool, they have become increasingly popular within the last decade due to their great power of realistically visualizing the plant’s architecture. This Research Topic highlights current research carried out on modeling plant architecture in changing environments.
Viticulture and Winemaking under Climate Change
The importance of viticulture and the winemaking socio-economic sector is acknowledged worldwide. The most renowned winemaking regions show very specific environmental characteristics, where climate usually plays a central role. Considering the strong influence of weather and climatic factors on grapevine yields and berry quality attributes, climate change may indeed significantly impact this crop. Recent trends already point to a pronounced increase in growing season mean temperatures, as well as changes in precipitation regimes, which have been influencing wine typicity across some of the most renowned winemaking regions worldwide. Moreover, several climate scenarios give evidence of enhanced stress conditions for grapevine growth until the end of the century. Although grapevines have high resilience, the clear evidence for significant climate change in the upcoming decades urges adaptation and mitigation measures to be taken by sector stakeholders. To provide hints on the abovementioned issues, we have edited a Special Issue entitled “Viticulture and Winemaking under Climate Change”. Contributions from different fields were considered, including crop and climate modeling, and potential adaptation measures against these threats. The current Special Issue allows for the expansion of scientific knowledge in these particular fields of research, as well as providing a path for future research.
Mathematical Modeling and Simulation
Learn to use modeling and simulation methods to attack real-world problems, from physics to engineering, from life sciences to process engineering Reviews of the first edition (2009): "Perfectly fits introductory modeling courses [...] and is an enjoyable reading in the first place. Highly recommended [...]" —Zentralblatt MATH, European Mathematical Society, 2009 "This book differs from almost all other available modeling books in that [the authors address] both mechanistic and statistical models as well as 'hybrid' models. [...] The modeling range is enormous." —SIAM Society of Industrial and Applied Mathematics, USA, 2011 This completely revised and substantially extended second edition answers the most important questions in the field of modeling: What is a mathematical model? What types of models do exist? Which model is appropriate for a particular problem? What are simulation, parameter estimation, and validation? What kind of mathematical problems appear and how can these be efficiently solved using professional free of charge open source software? The book addresses undergraduates and practitioners alike. Although only basic knowledge of calculus and linear algebra is required, the most important mathematical structures are discussed in sufficient detail, ranging from statistical models to partial differential equations and accompanied by examples from biology, ecology, economics, medicine, agricultural, chemical, electrical, mechanical, and process engineering. About 200 pages of additional material include a unique chapter on virtualization, Crash Courses on the data analysis and programming languages R and Python and on the computer algebra language Maxima, many new methods and examples scattered throughout the book and an update of all software-related procedures and a comprehensive book software providing templates for typical modeling tasks in thousands of code lines. The book software includes GmLinux, an operating system specifically designed for this book providing preconfigured and ready-to-use installations of OpenFOAM, Salome, FreeCAD/CfdOF workbench, ParaView, R, Maxima/wxMaxima, Python, Rstudio, Quarto/Markdown and other free of charge open source software used in the book.