Breeder friendly phenotyping

Reynolds, M.P.; Chapman, Scott C.; Crespo‐Herrera, Leonardo; Molero, Gemma; Mondal, Suchismita; Pequeno, Diego Noleto Luz; Pinto, Francisco; Piñera-Chávez, Francisco J; Poland, Jesse; Rivera‐Amado, Carolina; Pierre, Carolina Saint; Sukumaran, Sivakumar

doi:10.1016/j.plantsci.2019.110396

Cited by 169 publications

(133 citation statements)

References 184 publications

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“…Simko and Pechenick (2010) proposed to use rank aggregation methods to combine heterogenous data from independent plant breeding trials. Simko and Linacre (2010) demonstrated how the Rasch model (Rasch 1960) can be used to combine heterogeneous data. The Rasch model is, in principle, very similar to the Luce model (Rasch 1960;Luce 1959).…”

Section: Rank Aggregation Methodsmentioning

confidence: 99%

“…Simko and Linacre (2010) demonstrated how the Rasch model (Rasch 1960) can be used to combine heterogeneous data. The Rasch model is, in principle, very similar to the Luce model (Rasch 1960;Luce 1959). Simko and Linacre (2010) presented four different real datasets as case examples; for brevity, we only focus on one of the datasets, containing data of potato chip quality evaluations.…”

Section: Rank Aggregation Methodsmentioning

confidence: 99%

“…As a data-driven type of research, crop variety evaluation can benefit from multiple revolutions occurring in several fields such as genomics, phenomics, big data and machine learning (Bolger et al 2019;Esposito et al 2020;van Etten et al 2017;Tardieu et al 2017). These revolutions are driven by increased data storage and computing capacity, the availability of sensors, improved DNA sequencing technologies and new field data collection approaches, such as high-throughput and high-precision field phenotyping and crowdsourcing (Tardieu et al 2017;Esposito et al 2020;Chawade et al 2019;Reynolds et al 2020;Van Etten et al 2016). This has caused not only a quantitative leap in data volumes but also a shift to 'big data' approaches that move beyond small-sample statistics to data analysis based on machine learning (Breiman 2001;Thessen 2016;van Etten et al 2017;Ersoz et al 2020).…”

Section: Introductionmentioning

confidence: 99%

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Data synthesis for crop variety evaluation. A review

Brown

Bergh

Bruin

et al. 2020

Agron. Sustain. Dev.

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Crop varieties should fulfill multiple requirements, including agronomic performance and product quality. Variety evaluations depend on data generated from field trials and sensory analyses, performed with different levels of participation from farmers and consumers. Such multi-faceted variety evaluation is expensive and time-consuming; hence, any use of these data should be optimized. Data synthesis can help to take advantage of existing and new data, combining data from different sources and combining it with expert knowledge to produce new information and understanding that supports decision-making. Data synthesis for crop variety evaluation can partly build on extant experiences and methods, but it also requires methodological innovation. We review the elements required to achieve data synthesis for crop variety evaluation, including (1) data types required for crop variety evaluation, (2) main challenges in data management and integration, (3) main global initiatives aiming to solve those challenges, (4) current statistical approaches to combine data for crop variety evaluation and (5) existing data synthesis methods used in evaluation of varieties to combine different datasets from multiple data sources. We conclude that currently available methods have the potential to overcome existing barriers to data synthesis and could set in motion a virtuous cycle that will encourage researchers to share data and collaborate on data-driven research.

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Section: Rank Aggregation Methodsmentioning

confidence: 99%

Section: Rank Aggregation Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Data synthesis for crop variety evaluation. A review

Brown

Bergh

Bruin

et al. 2020

Agron. Sustain. Dev.

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“…In this section, we will elaborate and discuss important traits that confer adaptation and tolerance to chickpea in response to abiotic stresses with emphasis on heat, cold and drought [132]. To avoid skepticism and reluctance to adopt HTP methods due to their complexity, cost and sometimes unproven reliability, we only recommend 'breeder friendly' HTP approaches that can quantitatively measure these traits on a large number of genetic resources or progeny experimental units [133], which are detailed in Table 4.…”

Section: Common Adaptive Traits and High Throughput Phenotyping Appromentioning

confidence: 99%

Breeding for Abiotic Stress Adaptation in Chickpea (Cicer arietinum L.): A Comprehensive Review

2020

Crop Breed Genet Genom.

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Chickpea is an important legume crop, providing a protein rich diet for humans and animal feed. Globally, chickpea is grown in over 56 countries, occupying a production area of approximately 17.8 million ha. The crop is grown mainly in arid and semi-arid regions under rainfed conditions, where it is highly vulnerable to abiotic stresses such as heat, frost and drought at various growth stages during the season. Severe yield losses due to abiotic stresses have been recorded, especially when the crop is exposed to adverse conditions during the reproductive phase, causing instability in chickpea production worldwide. Breeding for tolerant chickpea that is widely adaptable to various growth conditions and diverse growing regions is the best strategic approach but requires a finetuned combination of advanced phenotyping and genotyping methods. However, breeding and selection of suitable chickpea genotypes is complicated by its narrow genetic base which limits the sources of novel alleles, and its indeterminate growth habit that at times allows it to recover, flower, set pods and yield following stressful events if subsequent conditions are favorable. This manuscript provides an insight into common abiotic stresses affecting chickpea production worldwide with an emphasis on heat, frost and drought. We will elaborate on breeding approaches and application of advanced genotyping and phenotyping tools commonly used to develop tolerant chickpea varieties. Finally, key crop tolerance traits that can be easily screened for by using genotypic and phenotypic technologies will be discussed.

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“…Recent developments in high-throughput phenotyping have already made significant contributions to physiological breeding 18,38,39 and breeding programmes 40 . Assessment of photosynthetic related traits using high-throughput surrogates based on spectral profiles has allowed the identification of genetic variation in wheat for photosynthetic capacity and efficiency 24 and respiration 20 at leaf level along with pigmentation composition and water content at canopy level allowing the identification of QTLs associated with spectral indices 41,42 .…”

Section: Using High Density Genotyping and High-throughput Phenotypinmentioning

confidence: 99%

Uncovering candidate genes involved in photosynthetic capacity using unexplored genetic variation in Spring Wheat

Joynson

Molero

Coombes

et al. 2020

Preprint

Self Cite

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To feed an ever-increasing population we must leverage advances in genomics and phenotyping to harness the variation in wheat breeding populations for traits like photosynthetic capacity which remains unoptimized. Here we survey a diverse set of wheat germplasm containing elite, introgression and synthetic derivative lines uncovering previously uncharacterised variation. We demonstrate how strategic integration of exotic material alleviates the D genome genetic bottleneck in wheat, increasing SNP rate by 62% largely due to Ae. tauschii synthetic wheat donors. Across the panel, 67% of the Ae.tauschii donor genome is represented as introgressions in elite backgrounds. We show how observed genetic variation together with hyperspectral reflectance data can be used to identify candidate genes for traits relating to photosynthetic capacity using association analysis. This demonstrates the value of genomic methods in uncovering hidden variation in wheat and how that variation can assist breeding efforts and increase our understanding of complex traits.

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Breeder friendly phenotyping

Cited by 169 publications

References 184 publications

Data synthesis for crop variety evaluation. A review

Data synthesis for crop variety evaluation. A review

Breeding for Abiotic Stress Adaptation in Chickpea (Cicer arietinum L.): A Comprehensive Review

Uncovering candidate genes involved in photosynthetic capacity using unexplored genetic variation in Spring Wheat

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