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Abstract Morphological factors significantly impact maize stalk strength, but no study has fully characterized the impact of maize stalk shape on stalk strength. This study uses a data-driven and machine-learning modeling approach to characterize these relationships through a comprehensive sensitivity analysis of model inputs. Using 3D parameterized maize stalk models gave a higher level of control than previous studies by adding more parameters, but with the increase of the dimensionality. The large dimensionality of the models was greatly reduced via principal component analysis (PCA). Analysis revealed that model flexural stiffness, failure strength, and biomass were primarily determined by the first principal component. Material sensitivity analysis was also conducted on the models, and its results were consistent with past studies. The results of this study improve researchers’ understanding of the parameters that influence 3D parameterized maize stalk models. Statistical analysis indicates a strong relationship between the first principal component and section modulus, which further validates the 3D parameterized maize stalk model. Results also show that maize stalk morphology is primarily controlled by only one factor: the first principal component. This may limit researchers’ ability to increase stalk strength without increasing mass. 

Maize is the most grown feed crop in the United States. Due to wind storms and other factors, 5% of maize falls over annually. The longitudinal shear modulus of maize stalk tissues is currently unreported and may have a signifi- cant influence on stalk failure. To better understand the causes of this phenomenon, maize stalk material properties need to be measured so that they can be used as material constants in computational models that provide detailed analysis of maize stalk failure. This study reports longitudinal shear modulus of maize stalk tissue through repeated tor- sion testing of dry and fully mature maize stalks. Measurements were focused on the two tissues found in maize stalks: the hard outer rind and the soft inner pith. Uncertainty analysis and comparison of multiple methodologies indicated that all measurements are subject to low error and bias. The results of this study will allow researchers to better under- stand maize stalk failure modes through computational modeling. This will allow researchers to prevent annual maize loss through later studies. This study also provides a methodology that could be used or adapted in the measurement of tissues from other plants such as sorghum, sugarcane, etc 

Background Modern computational modeling could provide the key to obtaining new insights into the mechanisms of maize stalk failure as well as suggesting new ways to improve stalk strength. However, a complete set of mechanical properties of maize tissues is required to enable computational modeling of maize stems. This study developed two compression test methods for obtaining the longitudinal modulus of elasticity of both rind and pith tissues, assessed the influence of water content on tissue properties, and investigated the relationship between rind modulus and pith modulus. These methods involved uniform 5–7 cm segments of maize stems which were scanned using a flatbed scanner then tested in compression using a universal testing machine in both intact and dissected (rind-only and pith-only) states. Results The modulus of elasticity of pith tissues was highest for fully turgid specimens and decreased as water was removed from the specimens. Water content was negatively correlated with the modulus of elasticity of the rind. Rind and pith tissues were found to be weakly correlated. The median ratio of rind modulus to pith modulus was found to be 17. Of the two methods investigated, the pith-only specimen preparation was found to be simple reliable while the rind-only method was found to be adversely affected by lateral bowing of the specimen. Conclusions Researchers can use the information in this paper to improve computational models of maize stems in three ways: (1) by incorporating realistic values of the longitudinal modulus of elasticity of pith and rind tissues; (2) by selecting pith and rind properties that match empirically observed ratios; and (3) by incorporating appropriate dependencies between these material properties and water content. From an experimental perspective, the intact/pith-only experimental method outlined in this paper is simpler than previously reported methods and provides reliable estimates of both pith and rind modulus of elasticity values. Further research using this measurement method is recommended to more clearly understand the influence of water content and turgor pressure on tissue properties. 

Maize stalk lodging is the structural failure of the stalk prior to harvest and is a major problem for maize (corn) producers and plant breeders. To address this problem, it is critical to understand precisely how geometric and material parameters of the maize stalk influence stalk strength. Computational models could be a powerful tool in such investigations, but current methods of creating computational models are costly, time-consuming and, most importantly, do not provide parameterized control of the maize stalk parameters. The purpose of this study was to develop and validate a parameterized 3D model of the maize stalk. The parameterized model provides independent control over all aspects of the maize stalk geometry and material properties. The model accurately captures the shape of actual maize stalks and is predictive of maize stalk stiffness and strength. The model was validated using stochastic sampling of material properties to account for uncertainty in the values and influence of mechanical tissue properties. Results indicated that buckling is influenced by material properties to a greater extent that flexural stiffness. Finally, we demonstrate that this model can be used to create an unlimited number of synthetic stalks from within the parameter space. This model will enable the future implementation of parameter sweep studies, sensitivity analysis and optimization studies, and can be used to create computational models of maize stalks with any desired combination of geometric and material properties. 

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. 

Stalk lodging is the event of failure just below the ear or node. The most common failure mode is Brazier (localized) buckling, which occurs consistently near the node. Although maize stalk lodging has been a subject of study for many years, relatively little is known about the process and progression of stalk failure. Of particular interest is the issue of failure initiation. An understanding of failure initiation could be beneficial talks that are less susceptible to failure. The purpose of this study was to characterize the tissue-level failure patterns of maize stalks. Various techniques were used to examine the failure region, including imaging (scanning electron microscope, X-ray computed tomography, photographs of the failure progression), experimentation (surface strain measurements, quantification of cross-sectional ovalization). We found that ovalization occurs prior to stalk failure and that ovalization is generally correlated with the onset of buckling. However, ovalization was predictive of failure. Tissue-level analysis revealed that buckling occurs at many different scales, including organ (specifically the stalk) level, tissue level, cellular level, and at the level of the cell wall. Based on our observations, we propose a new conceptual model for stalk failure that makes sense of the mixed data on this topic. This model states that the probability of tissue and buckling failure rise together in a highly correlated fashion and that when one failure mode occurs, it immediately initiates the corresponding mode. This information provides new insights into maize stalk failure and suggests that efforts to improve stalk strength will need to address both tissue strength and buckling resistance simultaneously.