A simple approach to predict growth stages in winter wheat (Triticum aestivum L.) combining prediction of a crop model and marker based prediction of the deviation to a reference cultivar: A case study in France

Bogard, Matthieu; Pierre, Jean-Baptiste; Huguenin-Bizot, Bertrand; Hourcade, Delphine; Paux, Etienne; Bris, Xavier Le; Gouache, David

doi:10.1016/j.eja.2015.04.007

Cited by 10 publications

(12 citation statements)

References 55 publications

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“…By contrast, very little information is available on possible correlations between the changeable environmental factors (photoperiod and temperature) and yield components in field sowing experiments. A substantial difference existed in the length of the vegetative phase, expressed indirectly as the time between sowing and first node appearance and also characterized by the cumulative and effective thermal time (Tottman and Makepeace, 1979;Bogard et al, 2015). As the yield is fundamentally determined by the quantity of assimilates produced by the plant and their distribution among the plant organs, it is obvious that the relative lengths of the various developmental phases have a decisive influence on the yield components (Slafer and Rawson, 1996;Araus et al, 2002;González et al, 2005;McMaster, 2005;Chen et al, 2009;Foulkes et al, 2011;Kiss et al, 2011;Dreccer et al, 2014;González-Navarro et al, 2015, 2016.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, the timing of three distinct developmental phases was also recorded based on the Zadoks scale (Tottman and Makepeace, 1979) as Z31 (first node appearance at the base of the main stem), Z49 (spike located in the upper part of the flag-leaf sheath), and Z59 (spike fully emerged from the flag-leaf sheath). Instead of using the standard thermal time protocol, the effective thermal time (ETT) was calculated based on the method of Bogard et al (2015) for all developmental phases. The ETT is the sum of the daily average thermal time, modified with the daylength value and the saturation level of the average vernalization requirement of the plants: ETT = S(TT ´ FV ´ FP), where TT is daily average thermal time, FV is the vernalization factor (being 0 before the saturation of the vernalization requirement), and FP is the photoperiod factor (being <1 under a 12-h photoperiod, proportional to the actual daylength).…”

Section: Phenotypic Descriptionsmentioning

confidence: 99%

“…The application of thermal instead of chronological time can make the comparison of experimental results more accurate over different years and environmental conditions (Weir et al, 1984;Porter, 1993;Gouache et al, 2012;Bogard et al, 2015). However, in species having genotypes with varying vernalization requirements and photoperiod sensitivity, such as wheat, the calculation of standard thermal time (summation of the daily average temperatures above the base temperature of growth) is not accurate enough when large numbers of genotypes are involved in the analyses.…”

Section: Phenotypic Descriptionsmentioning

confidence: 99%

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Comparative Study of the Developmental Traits and Yield Components of Bread Wheat under Field Conditions in Several Years of Multi‐Sowing Time Experiments

et al. 2019

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The main aim of the experiments was to demonstrate the possible correlation between developmental and morphological traits and yield components under variable climatic conditions. For this purpose, a collection of 188 wheat (Triticum aestivum L.) genotypes with a heterogeneous gene pool was included in a 3‐yr field experiment applying normal and late sowing dates each year. Under these conditions, almost all the traits were significantly influenced by the genotype, which had the greatest effect on the morphological traits and yield components (explaining 20–58 and 50–60% of the phenotypic variance for the two trait groups, respectively). In the case of plant development, however, the year effect, particularly in the late sowing treatment, was more significant than that of the genotype. Sowing date had the strongest effect on the early developmental phases, explaining 50% of the phenotypic variance, whereas the year had a significant influence on the late developmental phases, being responsible for 37 to 53% of the phenotypic variance. The turning point between the two factors was during the first phase of rapid stem elongation. The environmental driven variation in developmental patterns led to significant variation in yield‐related traits, which ranged from 4.6 (average thousand‐kernel weight) to 16.3% (average seed number) with normal sowing, and from 2.9 (average seed weight) to 29.4% (average thousand‐kernel weight) under late sowing conditions. The significance of the two intervals, from sowing to the start of rapid (intensive) stem elongation and from the start of stem elongation to the boot stage (Z49), was most apparent in the case of yield‐related traits.

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Section: Discussionmentioning

confidence: 99%

Section: Phenotypic Descriptionsmentioning

confidence: 99%

Section: Phenotypic Descriptionsmentioning

confidence: 99%

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Comparative Study of the Developmental Traits and Yield Components of Bread Wheat under Field Conditions in Several Years of Multi‐Sowing Time Experiments

et al. 2019

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“…(, ); Bogard et al . (, )). The genotype analysis can be extended to consider gene effects: Zheng et al .…”

Section: Introductionmentioning

confidence: 99%

“…vernalization and photoperiod) and allow testing of multiple genotype-environmentmanagement combinations (e.g. Chapman et al (2002); Hammer et al (2006); Chenu et al (2009Chenu et al ( , 2011; Bogard et al (2014Bogard et al ( , 2015). The genotype analysis can be extended to consider gene effects: Zheng et al (2013) proposed a model based on known effects of the major vernalization (VRN1) and photoperiod (Ppd-D1) genes that allow accurate prediction of heading time across a wide range of environments and genotypes (residual mean squared error (RSME) of 4.3 days for 4475 observations across the Australian wheatbelt).…”

Section: Introductionmentioning

confidence: 99%

Velocity of temperature and flowering time in wheat – assisting breeders to keep pace with climate change

Zheng

Chenu

Chapman

2016

Global Change Biology

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By accelerating crop development, warming climates may result in mismatches between key sensitive growth stages and extreme climate events, with severe consequences for crop yield and food security. Using recent estimates of gene responses to vernalization and photoperiod in wheat, we modelled the flowering times of all 'potential' genotypes as influenced by the velocity of climate change across the Australian wheatbelt. In the period 1957-2010, seasonal increases in temperature of 0.012 °C yr(-1) were recorded and changed flowering time of a mid-season wheat genotype by an average -0.074 day yr(-1) , with flowering 'velocity' of up to 0.95 km yr(-1) towards the coastal edges of the wheatbelt; this is an estimate of how quickly the given genotype would have to be 'moved' across the landscape to maintain its original flowering time. By 2030, these national changes are projected to accelerate by up to 3-fold for seasonal temperature and by up to 5-fold for flowering time between now and 2030, with average national shifts in flowering time of 0.33 and 0.41 day yr(-1) between baseline and the worst climate scenario tested for 2030 and 2050, respectively. Without new flowering alleles in commercial germplasm, the life cycle of wheat crops is predicted to shorten by 2 weeks by 2030 across the wheatbelt for the most pessimistic climate scenario. While current cultivars may be otherwise suitable for future conditions, they will flower earlier due to warmer temperatures. To allow earlier sowing to escape frost, heat and terminal drought, and to maintain current growing period of early-sown wheat crops in the future, breeders will need to develop and/or introduce new genetic sources for later flowering, more so in the eastern part of the wheatbelt.

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Assessment of the genetic diversity, population structure and allele distribution of major plant development genes in bread wheat cultivars using DArT and gene-specific markers

Kiss

Balla

Cseh

et al. 2021

CEREAL RESEARCH COMMUNICATIONS

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Knowledge of the degree of genetic diversity can provide fundamental information to breeders for use in various breeding programmes, for instance for the selection of lines with better adaptability. The genetic diversity analysis of 188 winter wheat genotypes demonstrated that this group of cultivars could be divided into four clusters based primarily on geographical origin. The first group contained mostly American and Asian cultivars, while cluster 2 consisted of Central European cultivars, cluster 3 of Hungarian and South European cultivars and cluster 4 mainly of cultivars from Western Europe. Cultivars used in breeding programmes in Central and South East European breeding programmes were found in all four clusters. Wheat genotypes originating from this region of Europe proved to have greater genetic variability than lines from Western and Northern Europe. Among the four clusters, there were also differencies in the frequencies of winter–spring alleles in Vrn-A1, Vrn-B1, Vrn-D1 vernalisation response genes and in the frequencies of sensitive–insensitive alleles in Ppd-B1 and Ppd-D1 photoperiod response genes, which explained the differences in heading date of the four clusters as well.

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A simple approach to predict growth stages in winter wheat (Triticum aestivum L.) combining prediction of a crop model and marker based prediction of the deviation to a reference cultivar: A case study in France

Cited by 10 publications

References 55 publications

Comparative Study of the Developmental Traits and Yield Components of Bread Wheat under Field Conditions in Several Years of Multi‐Sowing Time Experiments

Comparative Study of the Developmental Traits and Yield Components of Bread Wheat under Field Conditions in Several Years of Multi‐Sowing Time Experiments

Velocity of temperature and flowering time in wheat – assisting breeders to keep pace with climate change

Assessment of the genetic diversity, population structure and allele distribution of major plant development genes in bread wheat cultivars using DArT and gene-specific markers

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