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Stalk lodging in the monocot Zea mays is an important agricultural issue that requires the development of a genome-to-phenome framework, mechanistically linking intermediate and high-level phenotypes. As part of that effort, tools are needed to enable better mechanistic understanding of the microstructure in herbaceous plants. A method was therefore developed to create finite element models using CT scan data for Zea mays. This method represents a pipeline for processing the image stacks and developing the finite element models. 2-dimensional finite element models, 3-dimensional watertight models, and 3-dimensional voxel-based finite element models were developed. The finite element models contain both the cell and cell wall structures that can be tested in silico for phenotypes such as structural stiffness and predicted tissue strength. This approach was shown to be successful, and a number of example analyses were presented to demonstrate its usefulness and versatility. This pipeline is important for two reasons: (1) it helps inform which microstructure phenotypes should be investigated to breed for more lodging-resistant stalks, and (2) represents an essential step in the development of a mechanistic hierarchical framework for the genome-to-phenome modeling of herbaceous plant stalk lodging. 

Stalk lodging contributes to significant crop yield losses. Therefore, understanding the biomechanical strength and structural rigidity of grain stalks can contribute to improving stalk lodging resistance in crops. From the structural constituents of the stalk, the rind provides the principal structure, supporting cells against tension and bending loads. In this work, the biomechanical and viscoelastic behavior of the rind from the internodes of two sweet sorghum varieties (Della and REDforGREEN (RG)), grown in two different growing seasons, were evaluated by three-point micro-bending tests using a dynamic mechanical analyzer (DMA). In addition, the chemical composition of rinds and the microfibril angle (MFA) of the cell wall were determined using XRD. The results revealed that the biomechanical behavior of Della varieties was stiffer and more resistant to loads than that of RG varieties. Two features of the rind biomechanical properties, flexural modulus (FM) and flexural strength (FS), showed a significant reduction for RG. Particularly, a reduction in FS of 16–37% and in FM of 22–41% were detected for RG1. Changes in the stalks’ rind biomechanical properties were attributed to cell wall components. Total lignin and glucan/cellulose contents were positively correlated with the FM and FS of the rind. Subsequently, an increase in the two cell wall components drove an increase in stiffness. Furthermore, the MFA of the rind was also found to influence the rind strength. 

This study sought to better understand how time of day (ToD) or turgor pressure might affect the flexural stiffness of sweet sorghum stalks and potentially regulate stalk lodging resistance. Stalk flexural stiffness measured across a 48 h period in 2019 showed a significant diurnal association with leaf water potential and stalk flexural stiffness. While the correlation between stalk flexural stiffness and this proxy for internal turgor status was statistically significant, it only accounted for roughly 2% of the overall variance in stiffness. Given that turgor status is a dynamic rather than fixed physiological variable like the cellular structure, these data suggest that internal turgor plays a small yet significant role in influencing the flexural stiffness of fully mature stalks prior to a stalk lodging event. The association was assessed at earlier developmental stages across three distinct cultivars and found not to be significant. Panicle weight and stalk basal weight, but not stalk Brix or water content, were found to be better predictors of stalk flexural stiffness than either ToD or turgor status. Observation across three cultivars and four distinct developmental stages ranging from the vegetative to the hard-dough stages suggests that stalk flexural stiffness changes significantly as a function of time. However, neither ToD nor turgor status appear to meaningfully contribute to observed variations in stalk flexural stiffness in either individual stalks or across larger populations. As turgor status was not found to meaningfully influence stalk strength or flexural stiffness at any developmental time point examined in any of the three sweet sorghum cultivars under study, turgor pressure likely offers only inconsequential contributions to the biomechanics underlying sweet sorghum stalk lodging resistance. 

Stalk lodging (structural failure crops prior to harvest) significantly reduces annual yields of vital grain crops. The lack of standardized, high throughput phenotyping methods capable of quantifying biomechanical plant traits prevents comprehensive understanding of the genetic architecture of stalk lodging resistance. A phenotyping pipeline developed to enable higher throughput biomechanical measurements of plant traits related to stalk lodging is presented. The methods were developed using principles from the fields of engineering mechanics and metrology and they enable retention of plant-specific data instead of averaging data across plots as is typical in most phenotyping studies. This pipeline was specifically designed to be implemented in large experimental studies and has been used to phenotype over 40,000 maize stalks. The pipeline includes both lab- and field-based phenotyping methodologies and enables the collection of metadata. Best practices learned by implementing this pipeline over the past three years are presented. The specific instruments (including model numbers and manufacturers) that work well for these methods are presented, however comparable instruments may be used in conjunction with these methods as seen fit. • Efficient methods to measure biomechanical traits and record metadata related to stalk lodging. • Can be used in studies with large sample sizes (i.e., > 1,000). 

Quercus alba L., also known as white oak, eastern white oak, or American white oak, is a quintessential North American species within the white oak section (Quercus) of the genus Quercus, subgenus Quercus. This species plays a vital role as a keystone species in eastern North American forests and plays a significant role in local and regional economies. As a long-lived woody perennial covering an extensive natural range, Q. alba’s biology is shaped by a myriad of adaptations accumulated throughout its natural history. Populations of Q. alba are crucial repositories of genetic, genomic, and evolutionary insights, capturing the essence of successful historical adaptations and ongoing responses to contemporary environmental challenges in the Anthropocene. This intersection offers an exceptional opportunity to integrate genomic knowledge with the discovery of climate-relevant traits, advancing tree improvement, forest ecology, and forest management strategies. This review provides a comprehensive examination of the current understanding of Q. alba’s biology, considering past, present, and future research perspectives. It encompasses aspects such as distribution, phylogeny, population structure, key adaptive traits to cyclical environmental conditions (including water use, reproduction, propagation, and growth), as well as the species’ resilience to biotic and abiotic stressors. Additionally, this review highlights the state-of-the-art research resources available for the Quercus genus, including Q. alba, showcasing developments in genetics, genomics, biotechnology, and phenomics tools. This overview lays the groundwork for exploring and elucidating the principles of longevity in plants, positioning Q. alba as an emerging model tree species, ideally suited for investigating the biology of climate-relevant traits. 

Context: Stalk lodging causes up to 43 % of yield losses in maize (Zea mays L.) worldwide, significantly worsening food and feed shortages. Stalk lodging resistance is a complex trait specified by several structural, material, and geometric phenotypes. However, the identity, relative contribution, and genetic tractability of these intermediate phenotypes remain unknown. Objective: The study is designed to identify and evaluate plant-, organ-, and tissue-level intermediate phenotypes associated with stalk lodging resistance following standardized phenotyping protocols and to understand the variation and genetic tractability of these intermediate phenotypes. Methods: We examined 16 diverse maize hybrids in two environments to identify and evaluate intermediate phenotypes associated with stalk flexural stiffness, a reliable indicator of stalk lodging resistance, at physiological maturity. Engineering-informed and machine learning models were employed to understand relationships among intermediate phenotypes and stalk flexural stiffness. Results: Stalk flexural stiffness showed significant genetic variation and high heritability (0.64) in the evaluated hybrids. Significant genetic variation and comparable heritability for the cross-sectional moment of inertia and Young’s modulus indicated that geometric and material properties are under tight genetic control and play a combinatorial role in determining stalk lodging resistance. Among the twelve internode-level traits measured on the bottom and the ear internode, most traits exhibited significant genetic variation among hybrids, moderate to high heritability, and considerable effect of genotype × environment interaction. The marginal statistical model based on structural engineering beam theory revealed that 74–80 % of the phenotypic variation for flexural stiffness was explained by accounting for the major diameter, minor diameter, and rind thickness of the stalks. The machine learning model explained a relatively modest proportion (58–62 %) of the variation for flexural stiffness. 

Abstract This study presents a methodology for a high-throughput digitization and quantification process of plant cell walls characterization, including the automated development of two-dimensional finite element models. Custom algorithms based on machine learning can also analyze the cellular microstructure for phenotypes such as cell size, cell wall curvature, and cell wall orientation. To demonstrate the utility of these models, a series of compound microscope images of both herbaceous and woody representatives were observed and processed. In addition, parametric analyses were performed on the resulting finite element models. Sensitivity analyses of the structural stiffness of the resulting tissue based on the cell wall elastic modulus and the cell wall thickness; demonstrated that the cell wall thickness has a three-fold larger impact of tissue stiffness than cell wall elastic modulus.