Development of Gene-Based Crop Simulation Models to Predict Crop Phenotypes Using Genotype and Environmental Data

Vallejos, Carlos Eduardo

Date: Tuesday, January 17, 2012
Time: 5:05 PM

Room: Pacific Salon 1

Carlos Eduardo Vallejos , Dept. Horticultural Sciences, University of Florida, Gainesville, FL

James Jones , Dept. Agricultural and Biological Engineering, University of Florida, Gainesville, FL

Kenneth Boote , Agronomy Department, University of Florida, Gainesville, FL

Rongling Wu , Penn State University, Hershey, PA

Melanie Correll , Dept. Agricultural and Biological Engineering, University of Florida, Gainesville, FL

Wei Hou , Department of Biostatistics, University of Florida, Gainesville, FL

Salvador Gezan , University of Florida, Gainesville, FL

Raveendra Patil , Dept. Agricultural and Biological Engineering, University of Florida, Gainesville, FL

Mehul Bhakta , Dept. Horticultural Sciences, University of Florida, Gainesville, FL

Jose Clavijo , Agronomy Department, University of Florida, Gainesville, FL

Li Zhang , Dept. Agricultural and Biological Engineering, University of Florida, Gainesville, FL

Computer crop simulation models can be developed into valuable plant breeding tools. Such tools can be used for the design of crops that can convert available resources into a harvestable product with the highest efficiency under a particular set of environmental and managerial conditions, and inputs. These models comprise a set of dynamic mathematical equations that describe growth and developmental processes of a crop interacting with its environment. Prediction of cultivar performance under a range of environments is made possible by the estimation of cultivar-specific parameters via Markov Chain Monte Carlo simulations. As such, these parameters contain genetic information. Our goal is to identify the genetic determinants of these parameters to convert them into mathematical functions of the genes that control them. Towards this goal, we have chosen as our model system the progeny of a wide cross of the common bean segregating for multiple growth and developmental traits. We are using DNA marker and sequencing technologies to identify genes associated with specific model parameters through a combination of standard and functional mapping approaches to QTL analysis. Gene-based crop simulation models could be used eventually as expert systems to search the gene space and identify the best gene combination for a particular set of environment-input combinations.