scholarly journals Root and canopy traits and adaptability genes explain drought tolerance mechanism in winter wheat

2020 ◽  
Author(s):  
A.S. Nehe ◽  
M. J. Foulkes ◽  
I. Ozturk ◽  
A. Rasheed ◽  
L. York ◽  
...  
Keyword(s):  
Drought Tolerance ◽  
Grain Yield ◽  
High Throughput ◽  
Vegetation Index ◽  
Root Traits ◽  
Wheat Cultivars ◽  
Drought Tolerant ◽  
Green Area ◽  
High Throughput Phenotyping ◽  
Semiarid Conditions

AbstractBread wheat (Triticum aestivum L) is one of main staple food crops worldwide contributing 20% calories in human diet. Drought stress is the main factor limiting yields and threatening to food security, with climate change resulting in more frequent and intense drought. Developing drought-tolerant wheat cultivars is a promising way forward. The use of a holistic approaches that include high-throughput phenotyping and genetic makers in selection could help in accelerating genetic gains. Fifty advanced breeding lines were selected from the CIMMYT Turkey winter wheat breeding program and studied under irrigated and semiarid conditions for two years. High-throughput phenotyping were done for wheat crown root traits using shovelomics techniques and canopy green area and senescence dynamics using vegetation indices (green area using RGB images and Normalized Difference Vegetation Index using spectral reflectance). In addition, genotyping by KASP markers for adaptability genes was done. Overall, under semiarid conditions compared to irrigated conditions yield reduced by 3.09 t ha−1 (−46.8%). Significant difference between the treatment and genotype was observed for grain yield and senescence traits. Genotypes responded differently under drought stress. Root traits including shallower nodal root angle under irrigated conditions and root number per shoot under semiarid conditions were associated with increased grain yield. RGB based vegetation index measuring canopy green area at anthesis was more strongly associated with GY than NDVI under drought. Five established functional genes (PRR73.A1 – flowering time, TEF-7A – grain size and weight, TaCwi.4A - yield under drought, Dreb1-drought tolerance, and ISBW11.GY.QTL.CANDIDATE- grain yield) were associated with different drought-tolerance traits in this experiment. We conclude that a combination of high-throughput phenotyping and selection for genetic markers can help to develop drought-tolerant wheat cultivars.

scholarly journals Root and canopy traits and adaptability genes explain drought tolerance responses in winter wheat

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0242472
Author(s):  
A. S. Nehe ◽  
M. J. Foulkes ◽  
I. Ozturk ◽  
A. Rasheed ◽  
L. York ◽  
...  
Keyword(s):  
Drought Tolerance ◽  
Vegetation Index ◽  
Root Traits ◽  
Wheat Breeding ◽  
Root Number ◽  
Breeding Lines ◽  
Drought Tolerant ◽  
Green Area ◽  
High Throughput Phenotyping ◽  
Semiarid Conditions

Bread wheat (Triticum aestivum L) is one of the three main staple crops worldwide contributing 20% calories in the human diet. Drought stress is the main factor limiting yields and threatening food security, with climate change resulting in more frequent and intense drought. Developing drought-tolerant wheat cultivars is a promising way forward. The use of holistic approaches that include high-throughput phenotyping and genetic markers in selection could help in accelerating genetic gains. Fifty advanced breeding lines were selected from the CIMMYT Turkey winter wheat breeding program and studied under irrigated and semiarid conditions in two years. High-throughput phenotyping was done for wheat crown root traits and canopy senescence dynamics using vegetation indices (green area using RGB images and Normalized Difference Vegetation Index using spectral reflectance). In addition, genotyping by KASP markers for adaptability genes was done. Overall, under semiarid conditions yield reduced by 3.09 t ha-1 (-46.8%) compared to irrigated conditions. Genotypes responded differently under drought stress and genotypes 39 (VORONA/HD24-12//GUN/7/VEE#8//…/8/ALTAY), 18 (BiII98) and 29 (NIKIFOR//KROSHKA) were the most drought tolerant. Root traits including shallow nodal root angle under irrigated conditions and root number per shoot under semiarid conditions were correlated with increased grain yield. RGB based vegetation index measuring canopy green area at anthesis was better correlated with GY than NDVI was with GY under drought. The markers for five established functional genes (PRR73.A1 –flowering time, TEF-7A –grain size and weight, TaCwi.4A - yield under drought, Dreb1- drought tolerance, and ISBW11.GY.QTL.CANDIDATE- grain yield) were associated with different drought-tolerance traits in this experiment. We conclude that–genotypes 39, 18 and 29 could be used for drought tolerance breeding. The trait combinations of canopy green area at anthesis, and root number per shoot along with key drought adaptability makers (TaCwi.4A and Dreb1) could be used in screening drought tolerance wheat breeding lines.

scholarly journals High-throughput phenotyping platform for analyzing drought tolerance in rice

Planta ◽  
2020 ◽  
Vol 252 (3) ◽  
Author(s):  
Song Lim Kim ◽  
Nyunhee Kim ◽  
Hongseok Lee ◽  
Eungyeong Lee ◽  
Kyeong-Seong Cheon ◽  
...  
Keyword(s):  
Drought Tolerance ◽  
High Throughput ◽  
Crop Production ◽  
Near Infrared ◽  
New Technologies ◽  
Infrared Imaging ◽  
Imaging Data ◽  
Drought Tolerant ◽  
Breeding Cycles ◽  
High Throughput Phenotyping

Abstract Main conclusion A new imaging platform was constructed to analyze drought-tolerant traits of rice. Rice was used to quantify drought phenotypes through image-based parameters and analyzing tools. Abstract Climate change has increased the frequency and severity of drought, which limits crop production worldwide. Developing new cultivars with increased drought tolerance and short breeding cycles is critical. However, achieving this goal requires phenotyping a large number of breeding populations in a short time and in an accurate manner. Novel cutting-edge technologies such as those based on remote sensors are being applied to solve this problem. In this study, new technologies were applied to obtain and analyze imaging data and establish efficient screening platforms for drought tolerance in rice using the drought-tolerant mutant osphyb. Red–Green–Blue images were used to predict plant area, color, and compactness. Near-infrared imaging was used to determine the water content of rice, infrared was used to assess plant temperature, and fluorescence was used to examine photosynthesis efficiency. DroughtSpotter technology was used to determine water use efficiency, plant water loss rate, and transpiration rate. The results indicate that these methods can detect the difference between tolerant and susceptible plants, suggesting their value as high-throughput phenotyping methods for short breeding cycles as well as for functional genetic studies of tolerance to drought stress.

scholarly journals High-throughput phenotyping (HTP) identifies seedling root traits linked to variation in seed yield and nutrient capture in field-grown oilseed rape (Brassica napusL.)

2016 ◽  
Vol 118 (4) ◽  
pp. 655-665 ◽  
Author(s):  
C. L. Thomas ◽  
N. S. Graham ◽  
R. Hayden ◽  
M. C. Meacham ◽  
K. Neugebauer ◽  
...  
Keyword(s):  
Oilseed Rape ◽  
Seed Yield ◽  
High Throughput ◽  
Root Traits ◽  
Seedling Root ◽  
High Throughput Phenotyping

scholarly journals Validation of UAV-based alfalfa biomass predictability using photogrammetry with fully automatic plot segmentation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhou Tang ◽  
Atit Parajuli ◽  
Chunpeng James Chen ◽  
Yang Hu ◽  
Samuel Revolinski ◽  
...  
Keyword(s):  
Prediction Model ◽  
High Throughput ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Selection Process ◽  
Forage Legume ◽  
Breeding Programs ◽  
High Throughput Phenotyping ◽  
Fully Automatic ◽  
Uav Images

AbstractAlfalfa is the most widely cultivated forage legume, with approximately 30 million hectares planted worldwide. Genetic improvements in alfalfa have been highly successful in developing cultivars with exceptional winter hardiness and disease resistance traits. However, genetic improvements have been limited for complex economically important traits such as biomass. One of the major bottlenecks is the labor-intensive phenotyping burden for biomass selection. In this study, we employed two alfalfa fields to pave a path to overcome the challenge by using UAV images with fully automatic field plot segmentation for high-throughput phenotyping. The first field was used to develop the prediction model and the second field to validate the predictions. The first and second fields had 808 and 1025 plots, respectively. The first field had three harvests with biomass measured in May, July, and September of 2019. The second had one harvest with biomass measured in September of 2019. These two fields were imaged one day before harvesting with a DJI Phantom 4 pro UAV carrying an additional Sentera multispectral camera. Alfalfa plot images were extracted by GRID software to quantify vegetative area based on the Normalized Difference Vegetation Index. The prediction model developed from the first field explained 50–70% (R Square) of biomass variation in the second field by incorporating four features from UAV images: vegetative area, plant height, Normalized Green–Red Difference Index, and Normalized Difference Red Edge Index. This result suggests that UAV-based, high-throughput phenotyping could be used to improve the efficiency of the biomass selection process in alfalfa breeding programs.

scholarly journals Contribution of Ascorbate and Glutathione in Endobacteria Bacillus subtilis-Mediated Drought Tolerance in Two Triticum aestivum L. Genotypes Contrasting in Drought Sensitivity

Plants ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 2557
Author(s):  
Dilara Maslennikova ◽  
Oksana Lastochkina
Keyword(s):  
Triticum Aestivum ◽  
Bacillus Subtilis ◽  
Drought Tolerance ◽  
Triticum Aestivum L ◽  
Subtilis Strain ◽  
Wheat Cultivars ◽  
Stress Markers ◽  
Drought Tolerant ◽  
Adaptation To Drought ◽  
Significant Activation

We evaluated the effect of endobacteria Bacillus subtilis (strain 10–4) as a co-inoculant for promoting plant growth and redox metabolism in two contrasting genotypes of Triticum aestivum L. (wheat): Ekada70 (drought tolerant (DT)) and Salavat Yulaev (drought susceptible (DS)) in early stages of adaptation to drought (12% PEG–6000). Results revealed that drought reduced growth and dramatically augmented oxidative stress markers, i.e., hydrogen peroxide (H2O2) and lipid peroxidation (MDA). Furthermore, the depletion of ascorbate (AsA) and glutathione (GSH), accompanied by a significant activation of ascorbate peroxidase (APX) and glutathione reductase (GR), in both stressed wheat cultivars (which was more pronounced in DS genotype) was found. B. subtilis had a protective effect on growth and antioxidant status, wherein the stabilization of AsA and GSH levels was revealed. This was accompanied by a decrease of drought-caused APX and GR activation in DS plants, while in DT plants additional antioxidant accumulation and GR activation were observed. H2O2 and MDA were considerably reduced in both drought-stressed wheat genotypes because of the application of B. subtilis. Thus, the findings suggest the key roles in B. subtilis-mediated drought tolerance in DS cv. Salavat Yulaev and DT cv. Ekada70 played are AsA and GSH, respectively; which, in both cases, resulted in reduced cell oxidative damage and improved growth in seedlings under drought.

scholarly journals ChronoRoot: High-throughput phenotyping by deep segmentation networks reveals novel temporal parameters of plant root system architecture

2020 ◽  
Author(s):  
Nicolás Gaggion ◽  
Federico Ariel ◽  
Vladimir Daric ◽  
Éric Lambert ◽  
Simon Legendre ◽  
...  
Keyword(s):  
Deep Learning ◽  
Root System ◽  
High Throughput ◽  
System Architecture ◽  
Root Traits ◽  
Root System Architecture ◽  
Machine Intelligence ◽  
Reconstruction Process ◽  
High Throughput Phenotyping ◽  
3D Printed

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.

High-yielding NERICA mutant rice for upland areas and hope for Bangladeshi farmers.

2021 ◽  
pp. 53-64
Author(s):  
Mirza Mofazzal Islam ◽  
Shamsun Nahar Begum ◽  
Rigyan Gupta
Keyword(s):  
Drought Tolerance ◽  
Grain Yield ◽  
Crop Production ◽  
Upland Rice ◽  
Vital Role ◽  
High Yield ◽  
Rice Varieties ◽  
Commercial Cultivation ◽  
Drought Tolerant ◽  
Different Doses

Abstract Drought is an important stress phenomenon in Bangladesh that greatly hampers crop production. So, it is imperative to develop drought-tolerant rice varieties. Low-yielding, non-uniform flowering and late-maturing Africa rice - New Rice for Africa (NERICA), viz. NERICA-1, NERICA-4 and NERICA-10 varieties - were irradiated with different doses of gamma-rays (250, 300 and 350 Gy) in 2010. M1 plants were grown and M2 plants were selected based on earliness and higher grain yield. The desired mutants along with other mutants were grown as the M3 generation during 2011. A total of 37 mutants from NERICA-1, NERICA-4 and NERICA-10 were selected on the basis of plant height, short duration, drought tolerance and high yield in the M4 generation. In the M5 generation, six mutants were selected for drought tolerance, earliness, grain quality and higher yield. With respect to days to maturity and grain yield (t/ha), the mutant N1/250/P-2-6-1 of NERICA-1 matured earlier (108 days) and had higher grain yield (5.1 t/ha) than the parent. The mutant N4/350/P-4(5) of NERICA-4 also showed a higher grain yield (6.2 t/ha) than its parent and other mutants. On the other hand, NERICA-10 mutant N10/350/P-5-4 matured earlier and had a higher yield (4.5 t/ha) than its parent. Finally, based on agronomic performance and drought tolerance, the two mutants N4/350/P-4(5) and N10/350/P-5-4 were selected and were evaluated in drought-prone and upland areas during 2016 and 2017. These two mutants performed well with higher grain yield than the released upland rice varieties. They will be released soon for commercial cultivation and are anticipated to play a vital role in food security in Bangladesh.

High-throughput phenotyping platforms enhance genomic selection for wheat grain yield across populations and cycles in early stage

2019 ◽  
Vol 132 (6) ◽  
pp. 1705-1720 ◽  
Author(s):  
Jin Sun ◽  
Jesse A. Poland ◽  
Suchismita Mondal ◽  
José Crossa ◽  
Philomin Juliana ◽  
...  
Keyword(s):  
Grain Yield ◽  
Genomic Selection ◽  
High Throughput ◽  
Early Stage ◽  
Wheat Grain ◽  
High Throughput Phenotyping ◽  
Selection For

scholarly journals Advancing High-Throughput Phenotyping of Wheat in Early Selection Cycles

2020 ◽  
Vol 12 (3) ◽  
pp. 574 ◽  
Author(s):  
Yuncai Hu ◽  
Samuel Knapp ◽  
Urs Schmidhalter
Keyword(s):  
Grain Yield ◽  
Plant Breeding ◽  
High Throughput ◽  
Complex Traits ◽  
New Technologies ◽  
Early Selection ◽  
Spectral Indices ◽  
Wheat Genotypes ◽  
High Throughput Phenotyping ◽  
Spectral Sensing

Enhancing plant breeding to ensure global food security requires new technologies. For wheat phenotyping, only limited seeds and resources are available in early selection cycles. This forces breeders to use small plots with single or multiple row plots in order to include the maximum number of genotypes/lines for their assessment. High-throughput phenotyping through remote sensing may meet the requirements for the phenotyping of thousands of genotypes grown in small plots in early selection cycles. Therefore, the aim of this study was to compare the performance of an unmanned aerial vehicle (UAV) for assessing the grain yield of wheat genotypes in different row numbers per plot in the early selection cycles with ground-based spectral sensing. A field experiment consisting of 32 wheat genotypes with four plot designs (1, 2, 3, and 12 rows per plot) was conducted. Near infrared (NIR)-based spectral indices showed significant correlations (p < 0.01) with the grain yield at flowering to grain filling, regardless of row numbers, indicating the potential of spectral indices as indirect selection traits for the wheat grain yield. Compared with terrestrial sensing, aerial-based sensing from UAV showed consistently higher levels of association with the grain yield, indicating that an increased precision may be obtained and is expected to increase the efficiency of high-throughput phenotyping in large-scale plant breeding programs. Our results suggest that high-throughput sensing from UAV may become a convenient and efficient tool for breeders to promote a more efficient selection of improved genotypes in early selection cycles. Such new information may support the calibration of genomic information by providing additional information on other complex traits, which can be ascertained by spectral sensing.

scholarly journals Phenotyping Root Systems in a Set of Japonica Rice Accessions: Can Structural Traits Predict the Response to Drought?

Rice ◽  
2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Paulo Henrique Ramos Guimarães ◽  
Isabela Pereira de Lima ◽  
Adriano Pereira de Castro ◽  
Anna Cristina Lanna ◽  
Patrícia Guimarães Santos Melo ◽  
...  
Keyword(s):  
Root System ◽  
Water Deficit ◽  
High Throughput ◽  
Root Traits ◽  
Root Systems ◽  
Japonica Rice ◽  
Plastic Response ◽  
Reproductive Phase ◽  
High Throughput Phenotyping ◽  
Structural Traits

Abstract Background The root system plays a major role in plant growth and development and root system architecture is reported to be the main trait related to plant adaptation to drought. However, phenotyping root systems in situ is not suited to high-throughput methods, leading to the development of non-destructive methods for evaluations in more or less controlled root environments. This study used a root phenotyping platform with a panel of 20 japonica rice accessions in order to: (i) assess their genetic diversity for a set of structural and morphological root traits and classify the different types; (ii) analyze the plastic response of their root system to a water deficit at reproductive phase and (iii) explore the ability of the platform for high-throughput phenotyping of root structure and morphology. Results High variability for the studied root traits was found in the reduced set of accessions. Using eight selected traits under irrigated conditions, five root clusters were found that differed in root thickness, branching index and the pattern of fine and thick root distribution along the profile. When water deficit occurred at reproductive phase, some accessions significantly reduced root growth compared to the irrigated treatment, while others stimulated it. It was found that root cluster, as defined under irrigated conditions, could not predict the plastic response of roots under drought. Conclusions This study revealed the possibility of reconstructing the structure of root systems from scanned images. It was thus possible to significantly class root systems according to simple structural traits, opening up the way for using such a platform for medium to high-throughput phenotyping. The study also highlighted the uncoupling between root structures under non-limiting water conditions and their response to drought.

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