Search

Alfalfa biomass can be fractionated into leaf and stem components. Leaves comprise a protein-rich and highly digestible portion of biomass for ruminant animals, while stems constitute a high fiber and less digestible fraction, representing 50 to 70% of the biomass. However, little attention has focused on stem-related traits, which are a key aspect in improving the nutritional value and intake potential of alfalfa. This study aimed to identify molecular markers associated with four morphological traits in a panel of five populations of alfalfa generated over two cycles of divergent selection based on 16-h and 96-h in vitro neutral detergent fiber digestibility in stems. Phenotypic traits of stem color, presence of stem pith cells, winter standability, and winter injury were modeled using univariate and multivariate spatial mixed linear models (MLM), and the predicted values were used as response variables in genome-wide association studies (GWAS). The alfalfa panel was genotyped using a 3K DArTag SNP markers for the evaluation of the genetic structure and GWAS. Principal component and population structure analyses revealed differentiations between populations selected for high- and low-digestibility. Thirteen molecular markers were significantly associated with stem traits using either univariate or multivariate MLM. Additionally, support vector machine (SVM) and random forest (RF) algorithms were implemented to determine marker importance scores for stem traits and validate the GWAS results. The top-ranked markers from SVM and RF aligned with GWAS findings for solid stem pith, winter standability, and winter injury. Additionally, SVM identified additional markers with high variable importance for solid stem pith and winter injury. Most molecular markers were located in coding regions. These markers can facilitate marker-assisted selection to expedite breeding programs to increase winter hardiness or stem palatability.

Phenotypic plasticity (PP) is the ability of an organism to produce multiple phenotypes in response to environmental changes. In cultivated species, such as maize (Zea mays L.), the PP of plant architecture traits will play an important role in the adaptation of genotypes to unpredictable scenarios given by climate change, marginal areas, and seeding with variable plant density (D). We bring information to improve the understanding of the environmental modulation of PPs of plant architecture traits of maize, untangling their genetic bases, and testing the hypothesis of independent genetic control of the traits per se and their PPs. The PP of traits related to leaf area, spatial distribution of leaf area and stem architecture [(leaf area, maximum leaf width, maximum leaf length (LL), leaf orientation value, vertical leaf angle, leaf length to the flagging point (LF), LF/LL relationship (LFLL), azimuthal leaf orientation, ear height (EH), plant eight (PH), EH/PH relatioship (EHPH) and stem diameter] were estimated using 160 RILs from the IBM B73 × Mo17 Syn4 population, cultivated under two contrasting D (5 and 10 pl m−2) during two growing seasons that determined different environmental conditions. Data were phenotypically analyzed and quantitative traits loci (QTLs) were mapped. For leaf area and stem architecture related traits, high mean values of traits per se were related with high PPs values at low intraspecific competition while low mean values were observed at high intraspecific competition. The opposite response was found on leaf orientation related traits, with the exception of AZ. Forty-eight QTLs were detected for PP of plant architecture related traits on all chromosomes with exception of chromosome 7. There was no phenotypic correlation and no co-located QTLs for traits per se and their PPs. This independent genetic control for traits per se and their PPs would allow breeders to develop genotypes adapted to specific environments selecting for high or low PP in combination with high or low values for relevant agronomic traits. [ABSTRACT FROM AUTHOR]

Alfalfa biomass can be fractionated into leaf and stem components. Leaves comprise a protein-rich and highly digestible portion of biomass for ruminant animals, while stems constitute a high fiber and less digestible fraction, representing 50 to 70% of the biomass. However, little attention has focused on stem-related traits, which are a key aspect in improving the nutritional value and intake potential of alfalfa. This study aimed to identify molecular markers associated with four morphological traits in a panel of five populations of alfalfa generated over two cycles of divergent selection based on 16-h and 96-h in vitro neutral detergent fiber digestibility in stems. Phenotypic traits of stem color, presence of stem pith cells, winter standability, and winter injury were modeled using univariate and multivariate spatial mixed linear models (MLM), and the predicted values were used as response variables in genome-wide association studies (GWAS). The alfalfa panel was genotyped using a 3K DArTag SNP markers for the evaluation of the genetic structure and GWAS. Principal component and population structure analyses revealed differentiations between populations selected for high- and low-digestibility. Thirteen molecular markers were significantly associated with stem traits using either univariate or multivariate MLM. Additionally, support vector machine (SVM) and random forest (RF) algorithms were implemented to determine marker importance scores for stem traits and validate the GWAS results. The top-ranked markers from SVM and RF aligned with GWAS findings for solid stem pith, winter standability, and winter injury. Additionally, SVM identified additional markers with high variable importance for solid stem pith and winter injury. Most molecular markers were located in coding regions. These markers can facilitate marker-assisted selection to expedite breeding programs to increase winter hardiness or stem palatability., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Medina, Heuschele, Zhao, Lin, Beil, Sheehan and Xu.)

Plant architecture has played an important role in the adaptation of maize (Zea mays L.) hybrids to historical increases in plant density in order to maximize yields per unit area. At high density, a compact plant structure would allow for less interference by light among plants of the row and a deeper penetration of the radiation towards the lowest canopy layers, without compromising the capture of radiation at crop level. The genetic control of plant architecture traits of maize under contrasting plant densities remains poorly understood. In this work, traits related to leaf and stem architecture were phenotypically analyzed and QTLs were mapped using 160 RILs from the IBM B73 × Mo17 Syn4 population cultivated at low density and high density during 2013–2014 and 2014–2015 growing seasons in Buenos Aires province, Argentina. Forty-nine QTLs were detected on chromosomes 1, 3, 4, 5, 9 and 10. Most QTLs of vertical insertion angle of leaves and leaf orientation value (i.e., vertical angle affected by the curvature of leaves) were detected on chromosome 5 at high density and showed a high percentage of co-location. Detected QTLs for plant and ear height, and the relationship between them were concentrated on chromosome 9, with consistent effect under different density × environment combinations. These regions had large-effect QTLs and constitute hot spots that need to be studied in more detail to determine their potential use in breeding programs. [ABSTRACT FROM AUTHOR]

Aim: Plant trait databases often contain traits that are correlated, but for whom direct (undirected statistical dependency) and indirect (mediated by other traits) connections may be confounded. The confounding of correlation and connection hinders our understanding of plant strategies, and how these vary among growth forms and climate zones. We identified the direct and indirect connections across plant traits relevant to competition, resource acquisition and reproductive strategies using a global database and explored whether connections within and between traits from different tissue types vary across climates and growth forms. Location: Global. Major taxa studied: Plants. Time period: Present. Methods: We used probabilistic graphical models and a database of 10 plant traits (leaf area, specific leaf area, mass‐ and area‐based leaf nitrogen and phosphorous content, leaf life span, plant height, stem specific density and seed mass) with 16,281 records to describe direct and indirect connections across woody and non‐woody plants across tropical, temperate, arid, cold and polar regions. Results: Trait networks based on direct connections are sparser than those based on correlations. Land plants had high connectivity across traits within and between tissue types; leaf life span and stem specific density shared direct connections with all other traits. For both growth forms, two groups of traits form modules of more highly connected traits; one related to resource acquisition, the other to plant architecture and reproduction. Woody species had higher trait network modularity in polar compared to temperate and tropical climates, while non‐woody species did not show significant differences in modularity across climate regions. Main conclusions: Plant traits are highly connected both within and across tissue types, yet traits segregate into persistent modules of traits. Variation in the modularity of trait networks suggests that trait connectivity is shaped by prevailing environmental conditions and demonstrates that plants of different growth forms use alternative strategies to cope with local conditions. [ABSTRACT FROM AUTHOR]

With the use of terrestrial laser scanning (TLS) in forest stands, surveys are now equipped to obtain dense point cloud data. However, the data range, i.e., the number of points, often reaches the billions or even higher, exceeding random access memory (RAM) limits on common computers. Moreover, the processing time often also extends beyond acceptable processing lengths. Thus, in this paper, we present a new method of efficiently extracting stem traits from huge point cloud data obtained by TLS, without subdividing or downsampling the point clouds. In this method, each point cloud is converted into a wireframe model by connecting neighboring points on the same continuous surface, and three-dimensional points on stems are resampled as cross-sectional points of the wireframe model in an out-of-core manner. Since the data size of the section points is much smaller than the original point clouds, stem traits can be calculated from the section points on a common computer. With the study method, 1381 tree stems were calculated from 3.6 billion points in ~20 min on a common computer. To evaluate the accuracy of this method, eight targeted trees were cut down and sliced at 1-m intervals; actual stem traits were then compared to those calculated from point clouds. The experimental results showed that the efficiency and accuracy of the proposed method are sufficient for practical use in various fields, including forest management and forest research.

Modern sugarcane cultivars are derived from the interspecific crossing between Saccharum officinarum and wild sugarcane, Saccharum spontaneum. The introgression of valuable characteristics from wild sugarcane is recognized as extremely important, but this process typically requires long-term effort over multiple generations of backcrosses owing to the low sugar content of the initial interspecific hybrids. In this study, we aimed to identify Japanese wild sugarcanes with high juice brix in order to promote effective interspecific crossing of sugarcane. Sixty-four accessions from the Nansei Islands and 70 accessions from the Honshu were evaluated for juice brix. Wild sugarcanes with high juice brix were demonstrated to exist among wild sugarcanes indigenous to the Honshu. A significant difference was observed between the median juice brix values of wild sugarcanes of the Nansei Islands and those of the Honshu. The relationship between juice brix and stem traits was then examined in 20 wild sugarcanes, 10 each from the Nansei Islands and the Honshu. The reproducibility of juice brix value in both experiments was confirmed. In contrast to juice brix, stem traits, such as length, diameter, and volume, were typically smaller in wild sugarcanes from the Honshu. Moreover, a negative correlation was observed between the index of stem volume and juice brix. In this study, we identified outstanding wild sugarcanes with high juice brix. Using germplasms from the identified wild sugarcanes in interspecific crossing could contribute to the increases in both yield and sugar content.

With the use of terrestrial laser scanning (TLS) in forest stands, surveys are now equipped to obtain dense point cloud data. However, the data range, i.e., the number of points, often reaches the billions or even higher, exceeding random access memory (RAM) limits on common computers. Moreover, the processing time often also extends beyond acceptable processing lengths. Thus, in this paper, we present a new method of efficiently extracting stem traits from huge point cloud data obtained by TLS, without subdividing or downsampling the point clouds. In this method, each point cloud is converted into a wireframe model by connecting neighboring points on the same continuous surface, and three-dimensional points on stems are resampled as cross-sectional points of the wireframe model in an out-of-core manner. Since the data size of the section points is much smaller than the original point clouds, stem traits can be calculated from the section points on a common computer. With the study method, 1381 tree stems were calculated from 3.6 billion points in ~20 min on a common computer. To evaluate the accuracy of this method, eight targeted trees were cut down and sliced at 1-m intervals; actual stem traits were then compared to those calculated from point clouds. The experimental results showed that the efficiency and accuracy of the proposed method are sufficient for practical use in various fields, including forest management and forest research. [ABSTRACT FROM AUTHOR]