Modeling Reproductive Growth And Development In The Common Bean Phaseolus Vulgaris L
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Modeling Reproductive Growth and Development in the Common Bean (Phaseolus Vulgaris L.)
Study sites. Optimized parameter values reflected the genetic differences between the genotypes studied, and yielded satisfactory phenotypic growth and development predictions across sites. Time-series predictions revealed genotypes that attain comparable endpoint biomass and pod weights, but that reach those weights with different growth trajectories. Finally, over 12 quantitative trait loci (QTL) were detected that affect the onset and duration of post-anthesis reproductive stages in the RIL population using mixed-effects QTL models. These included two major QTLs, Fin and DiM. 1-22, that affect the days to the onset of stages R3 through R7, and the duration of seed fill (R5-R7), explaining up to 34% of the observed phenotypic variance, respectively. Moreover, this QTL displayed temperature and photoperiod-specific effects.
Crop Systems Biology
The sequencing of genomes has been completed for an increasing number of crop species, and researchers have now succeeded in isolating and characterising many important QTLs/genes. High expectations from genomics, however, are waving back toward the recognition that crop physiology is also important for realistic improvement of crop productivity. Complex processes and networks along various hierarchical levels of crop growth and development can be thoroughly understood with the help of their mathematical description – modelling. The further practical application of these understandings also requires quantitative predictions. In order to better support design, engineering and breeding for new crops and cultivars for improving agricultural production under global warming and climate change, there is an increasing call for an interdisciplinary research approach, which combines modern genetics and genomics, traditional physiology and biochemistry, and advanced bioinformatics and modelling. Such an interdisciplinary approach has been practised in various research groups for many years. However, it does not seem to be fully covered in the format of book publications. We want to initiate a book project on crop systems biology - narrowing the gaps between genotypes and phenotypes and the gaps between crop modelling and genetics/genomics, for publication in 2013/2014. The book will be meant for those scientists and graduate students from fundamental plant biology and applied crop science who are interested in bridging the gap between these two fields. We have invited a group of scientists (who have very good track records in publishing excellent papers in this field or in a closely related area) to contribute chapters to this new book, and they have agreed to do so.
Working with Dynamic Crop Models
Working with Dynamic Crop Models: Methods, Tools and Examples for Agriculture and Environment, 3e, is a complete guide to working with dynamic system models, with emphasis on models in agronomy and environmental science. The introductory section presents the foundational information for the book including the basics of system models, simulation, the R programming language, and the statistical notions necessary for working with system models. The most important methods of working with dynamic system models, namely uncertainty and sensitivity analysis, model calibration (frequentist and Bayesian), model evaluation, and data assimilation are all treated in detail, in individual chapters. New chapters cover the use of multi-model ensembles, the creation of metamodels that emulate the more complex dynamic system models, the combination of genetic and environmental information in gene-based crop models, and the use of dynamic system models to aid in sampling. The book emphasizes both understanding and practical implementation of the methods that are covered. Each chapter simply and clearly explains the underlying principles and assumptions of each method that is presented, with numerous examples and illustrations. R code for applying the methods is given throughout. This code is designed so that it can be adapted relatively easily to new problems. - An expanded introductory section presents the basics of dynamic system modeling, with numerous examples from multiple fields, plus chapters on numerical simulation, statistics for modelers, and the R language - Covers in detail the basic methods: uncertainty and sensitivity analysis, model calibration (both frequentist and Bayesian), model evaluation, and data assimilation - Every method chapter has numerous examples of applications based on real problems, as well as detailed instructions for applying the methods to new problems using R - Each chapter has multiple exercises for self-testing or for classroom use - An R package with much of the code from the book can be freely downloaded from the CRAN package repository