Canopy Architecture and Performance Modeling in Sorghum bicolor
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The global population is increasing in size and economic affluence, and sustainable intensification of agriculture is necessary to meet projected fuel and food demands without increasing input of limited resources like water. One approach to sustainably intensify agriculture is to improve the productivity of food and biofuel crops by increasing photosynthetic efficiency through modulation of canopy features. Sorghum is the world's fifth leading cereal crop and a promising bioenergy crop, and represents a useful target for crop improvement. Genetically engineering sorghum canopy architecture ideotypes for target production environments is facilitated by combining mathematical modeling, in silico simulation, field experimentation, and genetic analyses to determine optimal combinations of traits and the genetic networks regulating those traits, and this dissertation describes work towards this end. First, a model to improve calculations of genetic linkage in plant recombinant inbred line populations was developed so that regions of the genome associated with phenotypic variation can be more accurately defined. Second, functional-structural plant modeling was combined with field experimentation and genetic analyses to quantify the influence of leaf angle, a canopy architecture trait, on plant productivity, as well as to identify genomic loci regulating leaf angle. Third, a bioenergy sorghum crop model was parameterized to predict production of lignocellulosic biomass; this model was extended to explore how different canopy transpiration strategies influenced the end productivity of the crop and identify transpiration strategies yielding optimal water use. Continued progress in this work will provide ways to evaluate germplasm performance in silico and guide field testing for crop improvement.
Truong, Sandra Khavi (2017). Canopy Architecture and Performance Modeling in Sorghum bicolor. Doctoral dissertation, Texas A & M University. Available electronically from