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Title: FROM GENOME TO CROP: INTEGRATION THROUGH SIMULATION MODELING

Author
item HOOGENBOOM, GERRIT - UNIV OF GEORGIA
item White, Jeffrey
item MESSINA, CARLOS - UNIV OF FLORIDA

Submitted to: Field Crops Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/30/2004
Publication Date: 8/15/2004
Citation: Hoogenboom, G., White, J.W., Messina, C.D. 2004. From genome to crop: integration through simulation modeling. Field Crops Research. 90:145-163.

Interpretive Summary: Crop models are computer-based software tools that use equations to predict the growth, development and yield of crops. Information on weather, soil conditions and crop management are input to a model so it can reproduce "real world" conditions. A model is built using the best available information from diverse agronomic disciplines, ranging from plant breeding to soil physics. Models are used in many ways, including to guide basic research, to suggest new ways to breed improved crops, and to improve decisions on field management such as when to irrigate or how much fertilizer to apply. Most crop models represent differences among cultivars (e.g., typical seed size or how long the crop takes to mature) using cultivar-specific parameters. Until recently, there was little relation between these parameters and the genetic makeup of the cultivars. This paper reviews the development of the GeneGro model which simulates the impact of seven genes on common beans (which includes navy, pinto, and kidney types). The paper shows how such a model can improve our understanding of how yield varies with year-to-year variation in temperature and precipiation, including possible effects of global change. The paper also looks at how information from the genomics revolution might best be used to improve models further. Use of genomics linked to models could reduce the time it takes to develop new cultivars and devise new management strategies. The net result should be a suite of cultivars and management options that helps producers to maintain or increase productivity in the context of future global change.

Technical Abstract: Crop models are mathematical equations to simulate growth, development and yield as a function of weather, soil conditions and crop management. Such models integrate scientific knowledge from diverse agronomic disciplines, ranging from plant breeding to soil physics. Most crop models use one or more cultivar-specific parameters to identify differences in performance among cultivars. Until recently, however, there was little relation between cultivar-specific parameters and genotypes. The GeneGro model simulates the impact of seven genes on physiological processes in common bean (Phaseolus vulgaris L.), specifying cultivar differences through the presence or absence of the seven genes. The model was based on the bean model GEANGRO. GeneGro has not been incorporated into the Cropping System Model (CSM<), which can simulate growth and development for more than 20 difference crops, although the CSM-GeneGro version is currently implemented only for common bean and soybean {Glycine max (L.) Merr.}. Gene-based models can provide a well-structured linkage between functional genomics and crop physiology, especially as more genes are identified and their functions are clarified. Incorporating genetic information strengthens underlying physiological assumptions of the model, improving its utility for research in crop improvement, crop management, global change, and other fields. We first briefly review issues related to development of gene-based models, ranging from modeling approaches to data management. The CSM-GeneGro model is then used to show how specific genes can simulate both yield levels and yield variability for three locations in the USA. The model is also used to examine how single genes can affect crop response to global change. Gene-based modeling approaches could significantly enhance our ability to predict how global change will impact agricultural production, but modelers and physiologists will have to be proactive in accessing information and tools being developed in the plan genomics community.