High-throughput phenotyping technologies allow accurate selection of stay-green

Rebetzke, G. J.; Berni, J.A.J.; Bovill, William D.; Deery, David M.; James, Richard A.

doi:10.1093/jxb/erw301

Cited by 76 publications

(69 citation statements)

References 19 publications

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“…However, the leaf greenness reflects both functional (underlying photosynthetic capacity) and non-functional (cosmetic) characteristics [56], although these two characteristics are seldom phenotyped separately. Nevertheless, leaf greenness contributes significantly to grain yield, when associated with photosynthetic capacity and remobilization of stem reserve to grains [57]. Therefore, the five markers (Xgwm294a, Xfbb238b, Xfbb189b, Xwmc0388a, and Xbarc68) associated with QTLs for three traits (WSC, stem reserve mobilization, and chlorophyll content) may also prove useful for MAS leading to yield improvement under drought conditions.…”

Section: Biparental Interval Mapping For Physiological Traitsmentioning

confidence: 99%

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QTL Analysis for Drought Tolerance in Wheat: Present Status and Future Possibilities

2017

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Abstract:In recent years, with climate change, drought stress has been witnessed in many parts of the world. In many irrigated regions also, shortage of water supply allows only limited irrigation. These conditions have an adverse effect on the productivity of many crops including cereals such as wheat. Therefore, genetics of drought/water stress tolerance in different crops has become a priority area of research. This research mainly involves use of quantitative trait locus (QTL) analysis (involving both interval mapping and association mapping) for traits that are related to water-use efficiency. In this article, we briefly review the available literature on QTL analyses in wheat for traits, which respond to drought/water stress. The outlook for future research in this area and the possible approaches for utilizing the available information on genetics of drought tolerance for wheat breeding are also discussed.

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Section: Biparental Interval Mapping For Physiological Traitsmentioning

confidence: 99%

“…However, the cost of HTFP platforms is rather high (cost $100,000 [96]), although recently, cheaper platforms such as "Phenocart" (cost $12,000) have also become available [97]. These platforms will be increasingly used in future for the phenotyping of traits that are relevant to drought tolerance [57].…”

Section: High Throughput Phenotypingmentioning

confidence: 99%

QTL Analysis for Drought Tolerance in Wheat: Present Status and Future Possibilities

2017

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“…The devices used for phenotyping include multispectral, hyperspectral, fluorescence, thermal sensors and imagers (RGB colour cameras). Extensive literature is available with detailed information regarding the use of these devices and advancements in field phenotyping (Araus and Cairns, 2014; Araus et al , 2018; Deery et al , 2016; Fahlgren et al , 2015; Pauli et al , 2016; Rebetzke et al , 2016). Recently, Walter and co‐workers divided phenotyping into four categories: (i) Imaging (RGB: red‐green‐blue): for measuring size, morphology, growth or architecture of the plant or their canopies; (ii) Thermal imaging: based on the indicators such as stomatal transpiration or water status; (iii) Spectral reflectant/fluorescence: for investigating leaves pigments, biochemical and biophysical processes; (iv) Root phenotyping: architecture and physiology of the root system.…”

Section: Phenomics: the Virtue Of Plant Phenotypingmentioning

confidence: 99%

PANOMICS meets germplasm

Weckwerth

Ghatak

Bellaire

et al. 2020

Plant Biotechnology Journal

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Summary Genotyping‐by‐sequencing has enabled approaches for genomic selection to improve yield, stress resistance and nutritional value. More and more resource studies are emerging providing 1000 and more genotypes and millions of SNPs for one species covering a hitherto inaccessible intraspecific genetic variation. The larger the databases are growing, the better statistical approaches for genomic selection will be available. However, there are clear limitations on the statistical but also on the biological part. Intraspecific genetic variation is able to explain a high proportion of the phenotypes, but a large part of phenotypic plasticity also stems from environmentally driven transcriptional, post‐transcriptional, translational, post‐translational, epigenetic and metabolic regulation. Moreover, regulation of the same gene can have different phenotypic outputs in different environments. Consequently, to explain and understand environment‐dependent phenotypic plasticity based on the available genotype variation we have to integrate the analysis of further molecular levels reflecting the complete information flow from the gene to metabolism to phenotype. Interestingly, metabolomics platforms are already more cost‐effective than NGS platforms and are decisive for the prediction of nutritional value or stress resistance. Here, we propose three fundamental pillars for future breeding strategies in the framework of Green Systems Biology: (i) combining genome selection with environment‐dependent PANOMICS analysis and deep learning to improve prediction accuracy for marker‐dependent trait performance; (ii) PANOMICS resolution at subtissue, cellular and subcellular level provides information about fundamental functions of selected markers; (iii) combining PANOMICS with genome editing and speed breeding tools to accelerate and enhance large‐scale functional validation of trait‐specific precision breeding.

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“…Vehicle-mounted phenotypic and omics platforms are simple to operate and can obtain high-resolution and continuous spectral data from closeup observations. Sensors are mounted at different locations and heights of the platform depending on the crop type and growth period [99]. Related accessories include power systems, data acquisition terminals, GPS receivers, and encoders to ensure the running work of the vehicle.…”

Section: Real-time Variable Fertilization Systems Based On Crop Phenomentioning

confidence: 99%

Progress and development on biological information of crop phenotype research applied to real-time variable-rate fertilization

et al. 2020

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Background: Variable-rate fertilization is crucial in the implementation of precision agriculture and for ensuring reasonable and efficient fertilizer application and nutrient management that is tailored to local conditions. The overall goal of these technologies is to maximize grain output and minimize fertilizer input and, thus, achieve the optimal input-output production ratio. As the main form of variable-rate fertilization, real-time variable-rate control technology adjusts fertilizer application according to the growth status and nutrient information of crops and, as such, its effective application relies on the stable and accurate acquisition of crop phenotypic information. Results: Due to the relationship between crop phenotype and real-time fertilizer demand, phenotypic information has been increasingly applied in these contexts in recent years. Here, the establishment and characteristics of inversion models between crop phenotypic information and nutritional status are reviewed. The principles of real-time monitoring applications, the key technologies relating to crop phenotypic biological parameters, and the existing challenges for real-time variable-rate fertilization technology are also evaluated. Future research directions are then discussed in the specific context of the need for sustainable development of modern agriculture in China. Conclusion: This paper provides a theoretical reference for the construction of scientific management technology systems aimed at reducing fertilizer application and maximizing output, and for the development of relevant technologies in the specific context of China.

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High-throughput phenotyping technologies allow accurate selection of stay-green

Cited by 76 publications

References 19 publications

QTL Analysis for Drought Tolerance in Wheat: Present Status and Future Possibilities

QTL Analysis for Drought Tolerance in Wheat: Present Status and Future Possibilities

PANOMICS meets germplasm

Progress and development on biological information of crop phenotype research applied to real-time variable-rate fertilization

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