scholarly journals Pulse Root Ideotype for Water Stress in Temperate Cropping System

Plants ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 692
Author(s):  
Shiwangni Rao ◽  
Roger Armstrong ◽  
Viridiana Silva-Perez ◽  
Abeya T. Tefera ◽  
Garry M. Rosewarne

Pulses are a key component of crop production systems in Southern Australia due to their rotational benefits and potential profit margins. However, cultivation in temperate cropping systems such as that of Southern Australia is limited by low soil water availability and subsoil constraints. This limitation of soil water is compounded by the irregular rainfall, resulting in the absence of plant available water at depth. An increase in the productivity of key pulses and expansion into environments and soil types traditionally considered marginal for their growth will require improved use of the limited soil water and adaptation to sub soil constrains. Roots serve as the interface between soil constraints and the whole plant. Changes in root system architecture (RSA) can be utilised as an adaptive strategy in achieving yield potential under limited rainfall, heterogenous distribution of resources and other soil-based constraints. The existing literature has identified a “‘Steep, Deep and Cheap” root ideotype as a preferred RSA. However, this idiotype is not efficient in a temperate system where plant available water is limited at depth. In addition, this root ideotype and other root architectural studies have focused on cereal crops, which have different structures and growth patterns to pulses due to their monocotyledonous nature and determinant growth habit. The paucity of pulse-specific root architectural studies warrants further investigations into pulse RSA, which should be combined with an examination of the existing variability of known genetic traits so as to develop strategies to alleviate production constraints through either tolerance or avoidance mechanisms. This review proposes a new model of root system architecture of “Wide, Shallow and Fine” roots based on pulse roots in temperate cropping systems. The proposed ideotype has, in addition to other root traits, a root density concentrated in the upper soil layers to capture in-season rainfall before it is lost due to evaporation. The review highlights the potential to achieve this in key pulse crops including chickpea, lentil, faba bean, field pea and lupin. Where possible, comparisons to determinate crops such as cereals have also been made. The review identifies the key root traits that have shown a degree of adaptation via tolerance or avoidance to water stress and documents the current known variability that exists in and amongst pulse crops setting priorities for future research.

2020 ◽  
Author(s):  
Nicolás Gaggion ◽  
Federico Ariel ◽  
Vladimir Daric ◽  
Éric Lambert ◽  
Simon Legendre ◽  
...  

ABSTRACTDeep learning methods have outperformed previous techniques in most computer vision tasks, including image-based plant phenotyping. However, massive data collection of root traits and the development of associated artificial intelligence approaches have been hampered by the inaccessibility of the rhizosphere. Here we present ChronoRoot, a system which combines 3D printed open-hardware with deep segmentation networks for high temporal resolution phenotyping of plant roots in agarized medium. We developed a novel deep learning based root extraction method which leverages the latest advances in convolutional neural networks for image segmentation, and incorporates temporal consistency into the root system architecture reconstruction process. Automatic extraction of phenotypic parameters from sequences of images allowed a comprehensive characterization of the root system growth dynamics. Furthermore, novel time-associated parameters emerged from the analysis of spectral features derived from temporal signals. Altogether, our work shows that the combination of machine intelligence methods and a 3D-printed device expands the possibilities of root high-throughput phenotyping for genetics and natural variation studies as well as the screening of clock-related mutants, revealing novel root traits.


Agronomy ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 1328
Author(s):  
Rebecca K. McGrail ◽  
David A. Van Sanford ◽  
David H. McNear

Most of the effort of crop breeding has focused on the expression of aboveground traits with the goals of increasing yield and disease resistance, decreasing height in grains, and improvement of nutritional qualities. The role of roots in supporting these goals has been largely ignored. With the increasing need to produce more food, feed, fiber, and fuel on less land and with fewer inputs, the next advance in plant breeding must include greater consideration of roots. Root traits are an untapped source of phenotypic variation that will prove essential for breeders working to increase yields and the provisioning of ecosystem services. Roots are dynamic, and their structure and the composition of metabolites introduced to the rhizosphere change as the plant develops and in response to environmental, biotic, and edaphic factors. The assessment of physical qualities of root system architecture will allow breeding for desired root placement in the soil profile, such as deeper roots in no-till production systems plagued with drought or shallow roots systems for accessing nutrients. Combining the assessment of physical characteristics with chemical traits, including enzymes and organic acid production, will provide a better understanding of biogeochemical mechanisms by which roots acquire resources. Lastly, information on the structural and elemental composition of the roots will help better predict root decomposition, their contribution to soil organic carbon pools, and the subsequent benefits provided to the following crop. Breeding can no longer continue with a narrow focus on aboveground traits, and breeding for belowground traits cannot only focus on root system architecture. Incorporation of root biogeochemical traits into breeding will permit the creation of germplasm with the required traits to meet production needs in a variety of soil types and projected climate scenarios.


Agronomy ◽  
2019 ◽  
Vol 9 (7) ◽  
pp. 364 ◽  
Author(s):  
Martina Roselló ◽  
Conxita Royo ◽  
Miguel Sanchez-Garcia ◽  
Jose Miguel Soriano

Roots are crucial for adaptation to drought stress. However, phenotyping root systems is a difficult and time-consuming task due to the special feature of the traits in the process of being analyzed. Correlations between root system architecture (RSA) at the early stages of development and in adult plants have been reported. In this study, the seminal RSA was analysed on a collection of 160 durum wheat landraces from 21 Mediterranean countries and 18 modern cultivars. The landraces showed large variability in RSA, and differences in root traits were found between previously identified genetic subpopulations. Landraces from the eastern Mediterranean region, which is the driest and warmest within the Mediterranean Basin, showed the largest seminal root size in terms of root length, surface, and volume and the widest root angle, whereas landraces from eastern Balkan countries showed the lowest values. Correlations were found between RSA and yield-related traits in a very dry environment. The identification of molecular markers linked to the traits of interest detected 233 marker-trait associations for 10 RSA traits and grouped them in 82 genome regions named marker-train association quantitative trait loci (MTA-QTLs). Our results support the use of ancient local germplasm to widen the genetic background for root traits in breeding programs.


2020 ◽  
Vol 2020 ◽  
pp. 1-23 ◽  
Author(s):  
Kevin G. Falk ◽  
Talukder Zaki Jubery ◽  
Jamie A. O’Rourke ◽  
Arti Singh ◽  
Soumik Sarkar ◽  
...  

We report a root system architecture (RSA) traits examination of a larger scale soybean accession set to study trait genetic diversity. Suffering from the limitation of scale, scope, and susceptibility to measurement variation, RSA traits are tedious to phenotype. Combining 35,448 SNPs with an imaging phenotyping platform, 292 accessions (replications=14) were studied for RSA traits to decipher the genetic diversity. Based on literature search for root shape and morphology parameters, we used an ideotype-based approach to develop informative root (iRoot) categories using root traits. The RSA traits displayed genetic variability for root shape, length, number, mass, and angle. Soybean accessions clustered into eight genotype- and phenotype-based clusters and displayed similarity. Genotype-based clusters correlated with geographical origins. SNP profiles indicated that much of US origin genotypes lack genetic diversity for RSA traits, while diverse accession could infuse useful genetic variation for these traits. Shape-based clusters were created by integrating convolution neural net and Fourier transformation methods, enabling trait cataloging for breeding and research applications. The combination of genetic and phenotypic analyses in conjunction with machine learning and mathematical models provides opportunities for targeted root trait breeding efforts to maximize the beneficial genetic diversity for future genetic gains.


2019 ◽  
Author(s):  
E. Adeleke ◽  
R. Millas ◽  
W. McNeal ◽  
J Faris ◽  
A. Taheri

AbstractBackground and aimsRoot system architecture is a vital part of the plant that has been shown to vary between species and within species based on response to genotypic and/or environmental influences. The root traits of wheat seedlings is critical for the establishment and evidently linked to plant height and seed yield. However, plant breeders have not efficiently developed the role of RSA in wheat selection due to the difficulty of studying root traits.MethodsWe set up a root phenotyping platform to characterize RSA in 34 wheat accessions. The phenotyping pipeline consists of the germination paper-based moisture replacement system, image capture units, and root-image processing software. The 34 accessions from two different wheat ploidy levels (hexaploids and tetraploids), were characterized in ten replicates. A total of 19 root traits were quantified from the root architecture generated.ResultsThis pipeline allowed for rapid screening of 340 wheat seedlings within 10days. Also, at least one line from each ploidy (6x and 4x) showed significant differences (P < 0.05) in measured traits except in mean seminal count. Our result also showed strong correlation (0.8) between total root length, maximum depth and convex hull area.ConclusionsThis phenotyping pipeline has the advantage and capacity to increase screening potential at early stages of plant development leading to characterization of wheat seedling traits that can be further examined using QTL analysis in populations generated from the examined accessions.


2019 ◽  
Vol 441 (1-2) ◽  
pp. 33-48 ◽  
Author(s):  
Yilin Fang ◽  
Steven B. Yabusaki ◽  
Amir H. Ahkami ◽  
Xingyuan Chen ◽  
Timothy D. Scheibe

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Houmiao Wang ◽  
Xiao Tang ◽  
Xiaoyi Yang ◽  
Yingying Fan ◽  
Yang Xu ◽  
...  

Abstract Background Root system architecture (RSA), which is determined by the crown root angle (CRA), crown root diameter (CRD), and crown root number (CRN), is an important factor affecting the ability of plants to obtain nutrients and water from the soil. However, the genetic mechanisms regulating crown root traits in the field remain unclear. Methods In this study, the CRA, CRD, and CRN of 316 diverse maize inbred lines were analysed in three field trials. Substantial phenotypic variations were observed for the three crown root traits in all environments. A genome-wide association study was conducted using two single-locus methods (GLM and MLM) and three multi-locus methods (FarmCPU, FASTmrMLM, and FASTmrEMMA) with 140,421 SNP. Results A total of 38 QTL including 126 SNPs were detected for CRA, CRD, and CRN. Additionally, 113 candidate genes within 50 kb of the significant SNPs were identified. Combining the gene annotation information and the expression profiles, 3 genes including GRMZM2G141205 (IAA), GRMZM2G138511 (HSP) and GRMZM2G175910 (cytokinin-O-glucosyltransferase) were selected as potentially candidate genes related to crown root development. Moreover, GRMZM2G141205, encoding an AUX/IAA transcriptional regulator, was resequenced in all tested lines. Five variants were identified as significantly associated with CRN in different environments. Four haplotypes were detected based on these significant variants, and Hap1 has more CRN. Conclusions These findings may be useful for clarifying the genetic basis of maize root system architecture. Furthermore, the identified candidate genes and variants may be relevant for breeding new maize varieties with root traits suitable for diverse environmental conditions.


2020 ◽  
Author(s):  
Yanlin Shao ◽  
Kevin R. Lehner ◽  
Hongzhu Zhou ◽  
Isaiah Taylor ◽  
Chuanzao Mao ◽  
...  

AbstractRoot System Architecture (RSA) is a key factor in the efficiency of nutrient capture and water uptake in plants. Understanding the genetic control of RSA will be useful in minimizing fertilizer and water usage in agricultural cropping systems. Using a hydroponic screen and a gel-based imaging system we identified a rice gene, OsVST1, which plays a key role in controlling RSA. This gene encodes a homolog of the Arabidopsis VAP-RELATED SUPPRESSORS OF TMM (VSTs), a class of proteins that promote signaling in stomata by mediating plasma membrane-endoplasmic reticulum contacts. OsVST1 mutants have shorter primary roots, decreased root meristem size, and a more compact root system architecture. We show that the Arabidopsis VST triple mutants have similar phenotypes, with reduced primary root growth and smaller root meristems. Expression of OsVST1 largely complements the short root length and reduced plant height in the Arabidopsis triple mutant, supporting conservation of function between rice and Arabidopsis VST proteins. In a field trial, mutations in OsVST1 do not adversely affect grain yield, suggesting that modulation of this gene could be used as a way to optimize RSA without an inherent yield penalty.


Sign in / Sign up

Export Citation Format

Share Document