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Each chapter focuses on a particular topic and includes an introduction, a detailed explanation of the available methods, applications of the methods to one or two simple models that are followed throughout the book, real-life examples of the methods from literature, and finally a section detailing implementation of the methods using the R programming language. The consistent use of R makes this book immediately and directly applicable to scientists seeking to develop models quickly and effectively, and the selected examples ensure broad appeal to scientists in various disciplines. Goodreads helps you keep track of books you want to read.

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Refresh and try again. Open Preview See a Problem? Details if other :. Thanks for telling us about the problem. Return to Book Page. David Makowski. James W. Each chapter focuses on a particular topic and includes an introduction, a detailed explanation of the available methods, applications of the m This second edition of Working with Dynamic Crop Models is meant for self-learning by researchers or for use in graduate level courses devoted to methods for working with dynamic models in crop, agricultural, and related sciences.

Get A Copy. Kindle Edition , pages. More Details Other Editions 1. Friend Reviews. During hands-on examples, participants learned how to use the yggdrasil package and user interface to connect two models written in Python and C from package installation to model transformation and execution. Anyone interested in walking through the hackathon materials on their own can do so using the documentation on GitHub. The potential to add significant value to the rapid advances in plant breeding technologies associated with statistical whole genome prediction methods is a new frontier for crop physiology and modelling.

Yield advance by genetic improvement continues to require prediction of phenotype based on genotype, and this remains challenging for complex traits despite recent advances in genotyping and phenotyping.

ISBN 13: 9780123970084

But does this biological reality come with a degree of complexity that restricts applicability in crop improvement? Simple, high speed, parsimonious models are required for dealing with the thousands of genotypes and environment combinations in modern breeding programs. In contrast, it is often considered that greater model complexity is needed to evaluate potential of putative variation in specific traits in target environments as knowledge on their underpinning biology advances.

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Is this a contradiction leading to divergent futures? Here it is argued that biological reality and parsimony do not need to be separable. It is further asserted that the structure needed for the next generation of crop models to be most effective perhaps requires both jointly, along with the capacity to evolve in crop growth and development process algorithms.

Specific examples in modelling photosynthesis from biochemical scale and canopy development from plant scale are used to highlight the concepts presented. His research underpins the development of mathematical models of crop growth, development and yield that enable simulation of consequences of genetic and management manipulation of crops in specific target environments.

Benes works in generative methods for geometry synthesis, and his main focus is in procedural, inverse procedural modeling, and simulation of natural phenomena. Longyun Guo works on the mathematical modeling of secondary metabolism in plants. He obtained his B. John Morgan.

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A dynamic model of lignin biosynthesis was developed by integrating different types of information, which contributes to deeper understanding of underlying regulatory mechanisms, and help to provide a rational design for lignin biosynthesis manipulation. Chris works on bringing genetic information into crop growth models. He is completing his Ph. Melanie J.

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Mao Li is a mathematician who specializes in plant science. Her research focuses on quantitative data analysis, development of mathematical methods such as persistent homology based approaches, and computational algorithms to quantify plant morphology, below and above ground, from microbe to the globe. Stephen Steve P. Long is an environmental plant physiologist who studies how to improve photosynthesis—the process of turning sunlight and carbon dioxide into the sugars that drive yield—to increase the yield of food and biofuel crops. Steve has added to our understanding of the long-term impacts of climate change, such as rising levels of carbon dioxide and ozone on crops.


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During his time at the University of Nebraska, he has collaborated with statisticians, engineers, biochemists, and applied plant breeders resulting in 43 peer-reviewed papers over the past four years.