Characterizing Genotype-Specific Rice Architectural Traits Using Smart Mobile App and Data Modeling

The quantity and quality of light captured by a plant’s canopy control many of its growth and development processes. However, light quality-related processes are not very well represented in most traditional and functional–structural crop models, which has been a major barrier to furthering crop model improvement and to better capturing the genetic control and environment modification of plant growth and development. A main challenge is the difficulty in obtaining dynamic data on plant canopy architectural characteristics. Current approaches on the measurement of 3D traits often relies on technologies that are either costly, excessively complicated, or impractical for field use. This study presents a methodology to estimate plant 3D traits using smart mobile app and data modeling. Leaf architecture data on 16 genotypes of rice were collected during two crop seasons using the smart-app PocketPlant3D. Quadratic Bézier curves were fitted to leaf lamina for estimation of insertion angle, elevation angle, and curve height. Leaf azimuth angle distribution, leaf phyllotaxis, canopy leaf angle distribution, and light extinction coefficients were also analyzed. The results could be used for breeding line selection or for parameterizing or evaluating rice 3D architectural models. The methodology opens new opportunities for strengthening the integration of plant 3D architectural traits in crop modeling, better capturing the genetic control and environment modification of plant growth and development, and for improving ideotype-based plant breeding.

Genome-wide association screening and verification of potential genes associated with root architectural traits in maize (Zea mays L.) at multiple seedling stages

Background Breeding for new maize varieties with propitious root systems has tremendous potential in improving water and nutrients use efficiency and plant adaptation under suboptimal conditions. To date, most of the previously detected root-related trait genes in maize were new without functional verification. In this study, seven seedling root architectural traits were examined at three developmental stages in a recombinant inbred line population (RIL) of 179 RILs and a genome-wide association study (GWAS) panel of 80 elite inbred maize lines through quantitative trait loci (QTL) mapping and genome-wide association study. Results Using inclusive composite interval mapping, 8 QTLs accounting for 6.44–8.83 % of the phenotypic variation in root traits, were detected on chromosomes 1 (qRDWv3-1-1 and qRDW/SDWv3-1-1), 2 (qRBNv1-2-1), 4 (qSUAv1-4-1, qSUAv2-4-1, and qROVv2-4-1), and 10 (qTRLv1-10-1, qRBNv1-10-1). GWAS analysis involved three models (EMMAX, FarmCPU, and MLM) for a set of 1,490,007 high-quality single nucleotide polymorphisms (SNPs) obtained via whole genome next-generation sequencing (NGS). Overall, 53 significant SNPs with a phenotypic contribution rate ranging from 5.10 to 30.2 % and spread all over the ten maize chromosomes exhibited associations with the seven root traits. 17 SNPs were repeatedly detected from at least two growth stages, with several SNPs associated with multiple traits stably identified at all evaluated stages. Within the average linkage disequilibrium (LD) distance of 5.2 kb for the significant SNPs, 46 candidate genes harboring substantial SNPs were identified. Five potential genes viz. Zm00001d038676, Zm00001d015379, Zm00001d018496, Zm00001d050783, and Zm00001d017751 were verified for expression levels using maize accessions with extreme root branching differences from the GWAS panel and the RIL population. The results showed significantly (P < 0.001) different expression levels between the outer materials in both panels and at all considered growth stages. Conclusions This study provides a key reference for uncovering the complex genetic mechanism of root development and genetic enhancement of maize root system architecture, thus supporting the breeding of high-yielding maize varieties with propitious root systems.

Phenotyping Almond Orchards for Architectural Traits Influenced by Rootstock Choice

The cropping potential of almond (Prunus amygdalus (L.) Batsch, syn P. dulcis (Mill.)) cultivars is determined by their adaptation to edaphoclimatic and environmental conditions. The effects of scion–rootstock interactions on vigor have a decisive impact on this cropping success. Intensively planted orchards with smaller less vigorous trees present several potential benefits for increasing orchard profitability. While several studies have examined rootstock effects on tree vigor, it is less clear how rootstocks influence more specific aspects of tree architecture. The objective of this current study was to identify which architectural traits of commercially important scion cultivars are influenced by rootstock and which of these traits can be useful as descriptors of rootstock performance in breeding evaluations. To do this, 6 almond cultivars of commercial significance were grafted onto 5 hybrid rootstocks, resulting in 30 combinations that were measured after their second year of growth. We observed that rootstock choice mainly influenced branch production, but the effects were not consistent across the different scion–rootstock combinations evaluated. This lack of consistency in response highlights the importance of the unique interaction between each rootstock and its respective scion genotype.

Genetic Variation in Root Architectural Traits in Lactuca and Their Roles in Increasing Phosphorus-Use-Efficiency in Response to Low Phosphorus Availability

Low phosphorus (P) bioavailability in the soil and concerns over global P reserves have emphasized the need to cultivate plants that acquire and use P efficiently. Root architecture adaptation to low P can be variable depending on species or even genotypes. To assess the genetic variability of root architectural traits and their responses to low P in the Lactuca genus, we examined fourteen genotypes including wild species, ancient and commercial lettuce cultivars at low (LP, 0.1 mmol. L–1) and high P (HP, 1 mmol. L–1). Plants were grown in cylindrical pots adapted for the excavation and observation of root systems, with an inert substrate. We identified substantial genetic variation in all the investigated root traits, as well as an effect of P availability on these traits, except on the diameter of thinner roots. At low P, the main responses were a decrease in taproot diameter, an increase in taproot dominance over its laterals and an increase in the inter-branch distance. Although the genotype x P treatment effect was limited to root depth, we identified a tradeoff between the capacity to maintain a thick taproot at low P and the dominance of the taproot over its laterals. Regardless of the P level, the phosphorus-use-efficiency (PUE) varied among lettuce genotypes and was significantly correlated with total root biomass regardless of the P level. As taproot depth and maximum apical diameter were the principal determinants of total root biomass, the relative increase in PUE at low P was observed in genotypes that showed the thickest apical diameters and/or those whose maximal apical diameter was not severely decreased at low P availability. This pre-eminence of the taproot in the adaptation of Lactuca genotypes to low P contrasts with other species which rely more on lateral roots to adapt to P stress.

Uncovering genomic regions controlling plant architectural traits in hexaploid wheat using different GWAS models

Wheat is a major food crop worldwide. The plant architecture is a complex trait mostly influenced by plant height, tiller number, and leaf morphology. Plant height plays a crucial role in lodging and thus affects yield and grain quality. In this study, a wheat population was genotyped by using Illumina iSelect 90K single nucleotide polymorphism (SNP) assay and finally 22,905 high-quality SNPs were used to perform a genome-wide association study (GWAS) for plant architectural traits employing four multi-locus GWAS (ML-GWAS) and three single-locus GWAS (SL-GWAS) models. As a result, 174 and 97 significant SNPs controlling plant architectural traits were detected by ML-GWAS and SL-GWAS methods, respectively. Among these SNP makers, 43 SNPs were consistently detected, including seven across multiple environments and 36 across multiple methods. Interestingly, five SNPs (Kukri_c34553_89, RAC875_c8121_1490, wsnp_Ex_rep_c66315_64480362, Ku_c5191_340, and tplb0049a09_1302) consistently detected across multiple environments and methods, played a role in modulating both plant height and flag leaf length. Furthermore, candidate SNPs (BS00068592_51, Kukri_c4750_452 and BS00022127_51) constantly repeated in different years and methods associated with flag leaf width and number of tillers. We also detected several SNPs (Jagger_c6772_80, RAC875_c8121_1490, BS00089954_51, Excalibur_01167_1207, and Ku_c5191_340) having common associations with more than one trait across multiple environments. By further appraising these GWAS methods, the pLARmEB and FarmCPU models outperformed in SNP detection compared to the other ML-GWAS and SL-GWAS methods, respectively. Totally, 152 candidate genes were found to be likely involved in plant growth and development. These finding will be helpful for better understanding of the genetic mechanism of architectural traits in wheat.

Seasonal plasticity more important than population variability in effects on white clover architecture and productivity

Background and Aims The drivers of white clover (Trifolium repens) architecture and productivity are still imperfectly understood. Our aim was to investigate the impact of genetic background, neighbourhood and season on different architectural traits, clover and total biomass yield as well as the relationship between those traits and yield. Methods We grew eight white clover populations in pure stands and in mixed stands with contrasting mixture partners. Over four consecutive regrowth periods within one year, we measured trait sizes and determined clover and total yield amounts. Key Results The size of the architectural traits differed between populations and changed in response to neighbourhood and season. Population did not affect the sign and degree of those changes. Among the tested factors, season was by far the most important driver of white clover architecture, with the seasonal pattern notably differing between architectural traits. Clover and total yield were positively related to the architectural traits leaf area, petiole length, internode length and specific leaf area. Whereas the direction of the relationship was widely unaffected, its magnitude was clearly altered by neighbourhood and season. Conclusions Our results show that seasonal effects are the key for a deeper understanding of architecture of white clover individuals and to improve productivity of white clover communities.

Improving architectural traits of maize inflorescences

The domestication and improvement of maize resulted in radical changes in shoot architecture relative to its wild progenitor teosinte. In particular, critical modifications involved a reduction of branching and an increase in inflorescence size to meet the needs for human consumption and modern agricultural practices. Maize is a major contributor to global agricultural production by providing large and inexpensive quantities of food, animal feed, and ethanol. Maize is also a classic system for studying the genetic regulation of inflorescence formation and its enlarged female inflorescences directly influence seed production and yield. Studies on the molecular and genetic networks regulating meristem proliferation and maintenance, including receptor-ligand interactions, transcription factor regulation, and hormonal control, provide important insights into maize inflorescence development and reveal potential avenues for the targeted modification of specific architectural traits. In this review, we summarize recent findings on the molecular mechanisms controlling inflorescence formation and discuss how this knowledge can be applied to improve maize productivity in the face of present and future environmental challenges.