Incorporating realistic trait physiology into crop growth models to support genetic improvement. Issue 1 (25th January 2021)

• Record Type: Journal Article
• Authors: Boote, K J; Jones, J W; Hoogenboom, G
• Editors: Chenu, Karine; Long, Stephen P
• Is Part Of: In silico plants. Volume 3: Issue 1(2021)
• Publication Date: 2021-01-25
• Subjects: Climate change -- CROPGRO-model -- crop simulation -- crop physiology -- genotype-specific-parameters (GSP) -- genotype by environment interaction -- genetic improvement -- phenotype; Plant physiology -- Periodicals; Botany -- Periodicals; Botany -- Mathematical models -- Periodicals; Crop science -- Periodicals; 580
• DOI: 10.1093/insilicoplants/diab002
• Languages: English
• ISSNs: 2517-5025

Abstract

Abstract: In silico plant modelling is the use of dynamic crop simulation models to evaluate hypothetical plant traits (phenology, processes and plant architecture) that will enhance crop growth and yield for a defined target environment and crop management (weather, soils, limited resource). To be useful for genetic improvement, crop models must realistically simulate the principles of crop physiology responses to the environment and the principles by which genetic variation affects the dynamic crop carbon, water and nutrient processes. Ideally, crop models should have sufficient physiological detail of processes to incorporate the genetic effects on these processes to allow for robust simulations of response outcomes in different environments. Yield, biomass, harvest index, flowering date and maturity are emergent outcomes of many interacting genes and processes rather than being primary traits directly driven by singular genetics. Examples will be given for several grain legumes, using the CSM-CROPGRO model, to illustrate emergent outcomes simulated as a result of single and multiple combinations of genotype-specific parameters and to illustrate genotype by environment interactions that may occur in different target environments. Specific genetically influenced traits can result in G × E interactions on crop growth and yield outcomes as affected by available water, CO2 concentration, temperature, and other factors. An emergent outcome from a given genetic trait may increase yield in one environment but have little or negative effect in another environment. Much work is needed to link genetic effects to the physiological processes for in silico modelling applications, especially for plant breeding under future climate change.