A wheat phenotyping network to incorporate physiological traits for climate change in South Asia

Pask, A.J.D.; Joshi, Arun Kumar; Manès, Yann; Sharma, Indu; Chatrath, Ravish; Singh, Gyanendra; Sohu, V. S.; Mavi, G. S.; Sakuru, V.S.P.; Kalappanavar, I. K.; Mishra, Vinod Kumar; Arun, Banu K.; Mujahid, Muhammad Yaqub; Hussain, Manzoor; Gautam, Nutan Raj; Barma, N. C. D.; Hakim, Abdul; Hoppitt, William; Trethowan, Richard; Reynolds, M.P.

doi:10.1016/j.fcr.2014.07.004

Cited by 46 publications

(55 citation statements)

References 21 publications

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“…All sites were high radiation environments for which climatic data are summarized in Fig. 4 and Table 1, see also climatic data for CENEB and South Asian sites in Pask et al (2014). Trials were conducted in the spring wheat season to correspond with recommended sowing and harvest times, depending on individual environments and contrasting between northern and southern hemispheres.…”

Section: Evaluation Of Performance At International Sitesmentioning

confidence: 99%

“…From total plot harvest, grain yield and total above-ground biomass (at 'field dry' moisture content of approximately 12%; i.e. 88% dry weight) and thousand kernel weight (TKW) were determined using standard protocols (see Pask et al 2012), and harvest index (HI), kernels per square metre (KNO) and grains per spike calculated.…”

Section: Evaluation Of Performance At International Sitesmentioning

confidence: 99%

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Strategic crossing of biomass and harvest index—source and sink—achieves genetic gains in wheat

et al. 2017

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Section: Evaluation Of Performance At International Sitesmentioning

confidence: 99%

Section: Evaluation Of Performance At International Sitesmentioning

confidence: 99%

Strategic crossing of biomass and harvest index—source and sink—achieves genetic gains in wheat

et al. 2017

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“…Two types of international nurseries were phenotyped; WYCYTs and SATYNs. The WYCYT international nurseries are the result of research conducted to raise the yield potential of spring wheat through the strategic crossing of physiological traits related to source and sink potential in wheat (Reynolds et al, 2009;Parry et al, 2010;Foulkes et al, 2011), whereas SATYN nurseries were bred for stress-adaptive physiological traits (Pask et al, 2014;Reynolds and Langridge 2016 (Pask et al, 2014), which included a total of 136 environments (site-year combinations) in major spring wheat-growing countries such as Bangladesh, China, Egypt, India, Iran, Mexico, Nepal, and Pakistan (Fig. 1, Table 1).…”

Section: Germplasmmentioning

confidence: 99%

Pedigree‐Based Prediction Models with Genotype × Environment Interaction in Multienvironment Trials of CIMMYT Wheat

et al. 2017

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1865RESEARCH M ultienvironment trials are the key evaluation tools that plant breeders use to select high-yielding and stable cultivars for the next selection cycle. Statistical analyses of multienvironment trials that incorporate genotype ´ environment (G ´ E) interaction through linear mixed models are crucial to help the breeder select the best candidates. The main feature of linear mixed models is that they are able to model not only independent observations, but also related cultivars and heterogeneous and correlated variance-covariance structures. In linear mixed models, some effects are assumed to have arisen from a distribution of random effects, and best linear unbiased predictors (BLUPs) (Henderson 1975) are computed to estimate such genetic effects. Linear mixed models make accurate predictions of genotypic performance by using covariance structures that consider correlations between sites, years, and plots in the field, as well as genetic associations between relatives.When individuals inherit copies of the same allele, they tend to show a phenotypic resemblance due to the genetic relationship Pedigree-Based Prediction Models with Genotype ´ Environment Interaction in Multienvironment Trials of CIMMYT WheatSivakumar Sukumaran, Jose Crossa,* Diego Jarquín, and Matthew Reynolds ABSTRACT Genotype ´ environment (G ´ E) interaction can be studied through multienvironment trials used to select wheat (Triticum aestivum L.) lines. We used spring wheat yield data from 136 international environments to evaluate the predictive ability (PA) of different models in diverse environments by modeling G ´ E using the pedigree-derived additive relationship matrix (A matrix). These analyses focused on 109 wheat lines from three Wheat Yield Collaboration Yield Trials (WYCYTs) and 168 lines from four Stress Adapted Trait Yield Nurseries (SATYNs) developed by CIMMYT for yield potential conditions and stress conditions, respectively. The main objectives of this study were to use various pedigree-based reaction norm models to predict sites included in each of the three WYCYT nurseries and each of the four SATYN nurseries (individual population) and to predict environments (site-year combinations) when combining the three WYCYT and four SATYN trials (combined population). Results of the PA for the individual-and combined-population analyses indicated that best predictive Model 6 (E + L + A + AE + e) always included the G ´ E denoted as the interaction between the A matrix and environments. The most predictable sites in WYCYTs were Iran DZ (Dezful) and Pak I (Islamabad), whereas the most predictable sites in SATYNs were India I (Indore), Iran DZ, and Mex CM (Cd. Obregon). Heritability was correlated with PA for individual-population prediction analyses, but not for combined-population prediction analyses. Our results indicate pedigree-based reaction norm models with G ´ E can be useful for predicting the performance of lines and selecting good predictable key sites (or environments) to reduce phenotyping costs.

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“…[1] . The scientific significance of crop characterization techniques lies in accurately and rapidly acquiring phenotypic and genotypic data for the discovery of intrinsic connection between various characteristics, whereas the practical significance is to develop elite cultivars for production [2][3][4][5][6][7][8] .…”

Section: Phenotyping Is An Important Means Of Gaining Insight Into Crmentioning

confidence: 99%

Advances in crop phenotyping and multi-environment trials

Liu¹,

Zhang²,

Ma³

et al. 2015

Front. Agr. Sci. Eng.

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Efficient evaluation of crop phenotypes is a prerequisite for breeding, cultivar adoption, genomics and phenomics study. Plant genotyping is developing rapidly through the use of high-throughput sequencing techniques, while plant phenotyping has lagged far behind and it has become the rate-limiting factor in genetics, large-scale breeding and development of new cultivars. In this paper, we consider crop phenotyping technology under three categories. The first is high-throughput phenotyping techniques in controlled environments such as greenhouses or specifically designed platforms. The second is a phenotypic strengthening test in semi-controlled environments, especially for traits that are difficult to be tested in multi-environment trials (MET), such as lodging, drought and disease resistance. The third is MET in uncontrolled environments, in which crop plants are managed according to farmer's cultural practices. Research and application of these phenotyping techniques are reviewed and methods for MET improvement proposed.

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A wheat phenotyping network to incorporate physiological traits for climate change in South Asia

Cited by 46 publications

References 21 publications

Strategic crossing of biomass and harvest index—source and sink—achieves genetic gains in wheat

Strategic crossing of biomass and harvest index—source and sink—achieves genetic gains in wheat

Pedigree‐Based Prediction Models with Genotype × Environment Interaction in Multienvironment Trials of CIMMYT Wheat

Advances in crop phenotyping and multi-environment trials

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