| dc.description.abstract | Mechanical characterization is an important and now frequently used tool for phenotyping plants for crop improvement, e.g. lodging resistance. Mechanics of materials and structures in response to various external stimuli as well as information of basic building blocks that constitute the plants can be applied to study the mechanical behavior of plant stems. The inherent mechanical properties of plant structures such as the stem are relevant to breeding strategies, aiming to tackle issues such as crop lodging due to stem or root lodging. While empirical tests of breaking strength and stiffness have been applied to plants, few of these studies consider the genetic background of the plants examined. In this study, we report for the first time on the mapping of QTL for mechanical traits in sorghum in three RIL mapping populations from crosses between grain and sweet sorghum parents. The genetic architecture of biomechanical traits in the three RIL populations appear to be quantitative and pleiotropic. Six QTL affecting mechanical and morphological traits were detected; two of these QTL were consistently found in all populations and co-localized with previously cloned dwarfing genes Dw1 and Dw3. These results suggest that dwarfing genes affect the mechanical properties of sorghum and ultimately their lodging resistance while also having a profound impact on the stem’s morphology and geometry. Morpho-anatomical stem properties are major component affecting standability. However, phenotyping these traits is low throughput, and has been restricted by the lack of a high-throughput phenotyping platform that can collect both morphological and anatomical stem properties. X-ray computed tomography (CT) offers a potential solution, but studies using this technology in plants have evaluated limited numbers of genotypes. The platform and image analysis pipeline revealed extensive phenotypic variation for important morpho-anatomical traits in well-characterized sorghum genotypes at suitable repeatability rates. CT estimates were highly predictive of morphological traits and moderately predictive of anatomical traits. The image analysis pipeline also identified genotypes with superior morpho-anatomical traits that there were consistent with ground-truth based classification in previous studies. In addition, stem cross section intensity measured by the CT was highly correlated with stem dry weight density, and can potentially serve as a high-throughput approach to measure stem density in grasses. | en |
