Modeling Genetic Traits of Five Common Bean (Phaseolus vulgaris) Genotypes in Multi-Location Trials.

Predicting the phenotype from the genotype of higher plants remains a challenging aspect of plant biology. With the availability of large datasets of genetic and phenotypic data for most crop species, our ability to translate basic science to improve agricultural production depends on the development of robust computational tools. Dynamic crop simulation models have the power to harness biological, environmental and management data to predict crop growth and development. Current crop models rely heavily on genotype-specific parameters (GSPs) estimated from field data, which can be expensive and time consuming to generate. Hence, the need arises to adapt existing crop models to utilize genetic information (genes, QTLs) to facilitate the estimation of GSPs, and help bridge the genotype to phenotype prediction gap. To address this question, field and computational experiments were conducted using Phaseolus vulgaris, the common bean. Experiments are based on the study of a recombinant inbred population (n=180, F_11:14) derived from the cross between two contrasting genotypes: Jamapa, a Mesoamerican land race, and Calima, an Andean cultivar. Phenology and time-series growth data were collected from these five genotypes grown at five sites (North Dakota, Florida, Puerto Rico and two sites in Colombia) and analyzed using the CROPGRO-BEAN model. An iterative, staged approach was used to calibrate model GSPs sequentially against targeted growth variables, starting with early-season phenology parameters and ending with those affecting vegetative and reproductive growth. Optimized parameter values reflected the genetic differences between the genotypes studied, and yielded satisfactory phenotypic growth and development predictions across sites. Furthermore, time-series predictions revealed genotypes that attain comparable endpoint biomass and pod weights, but that reach those weights with different growth trajectories, highlighting the importance of conducting detailed time-series growth analysis. The implications of using crop models to simulate mapping populations of the common bean are further discussed.