Abstract
AbstractCrop growth models (CGMs) consist of multiple equations that represent physiological processes of plants and simulate crop growth dynamically given environmental inputs. Because parameters of CGMs are often genotype-specific, gene effects can be related to environmental inputs through CGMs. Thus, CGMs are attractive tools for predicting genotype by environment (G×E) interactions. This chapter reviews CGMs, genetic analyses using these models, and the status of studies that integrate genomic prediction with CGMs. Examples of CGM analyses are also provided.
Reference102 articles.
1. Technow F, Messina CD, Totir LR, Cooper M (2015) Integrating crop growth models with whole genome prediction through approximate Bayesian computation. PLoS One 10:e0130855
2. Onogi A, Watanabe M, Mochizuki T, Hayashi T, Nakagawa H, Hasegawa T, Iwata H (2016) Toward integration of genomic selection with crop modelling: the development of an integrated approach to predicting rice heading dates. Theor Appl Genet 129:805–817
3. Monsi M, Saeki T (1953) Über den lichtfaktor in den pflanzengesellschaften und seine Bedeutung für die stoffproduktion. Jpn J Bot 14:22–52
4. de Wit CT, Brouwer R, Penning FWT (1970) The simulation of photosynthetic systems. Proceedings of the IBP/PP technical meeting, Trebon, 14-21 September 1969, 47-70
5. Bouman BAM, van Keulen H, van Laar HH, Rabbinge R (1996) The “school de Wit” crop growth simulation models: a pedigree and historical overview. Agric Syst 52:171–198
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