Determination of Photoperiod-Sensitive Phase in Chickpea (Cicer arietinum L.)

Daba, Ketema; Warkentin, Thomas D.; Bueckert, Rosalind; Todd, Christopher D.; Tar’an, Bunyamin

doi:10.3389/fpls.2016.00478

Cited by 21 publications

(22 citation statements)

References 37 publications

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“…This expression data was collected from the plants with first visible floral buds appeared at 15 days after sowing in SD and 13 days in LD (Ridge et al, 2017 ), thus providing the two days difference in floral bud initiation time between SD and LD. This two days difference diverges from previous measurements showing no difference in this time in ICCV 96029 (19 days from seeding ± 0.0) (Daba et al, 2016 ), but it qualitatively matches with the observed difference in expression.…”

Section: Discussionsupporting

confidence: 57%

“…We concentrate on two chickpea cultivars in this study, CDC Frontier and ICCV 96029. CDC Frontier is a photoperiod-sensitive kabuli chickpea cultivar developed at the University of Saskatchewan (Warkentin et al, 2005 ), exhibiting relatively late flowering (Daba et al, 2016 ; Ridge et al, 2017 ). The reference genome sequence was obtained for this cultivar (Varshney et al, 2013 ).…”

Section: Introductionmentioning

confidence: 99%

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Dynamical Modeling of the Core Gene Network Controlling Flowering Suggests Cumulative Activation From the FLOWERING LOCUS T Gene Homologs in Chickpea

et al. 2018

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Initiation of flowering moves plants from vegetative to reproductive development. The time when this transition happens (flowering time), an important indicator of productivity, depends on both endogenous and environmental factors. The core genetic regulatory network canalizing the flowering signals to the decision to flower has been studied extensively in the model plant Arabidopsis thaliana and has been shown to preserve its main regulatory blocks in other species. It integrates activation from the FLOWERING LOCUS T (FT) gene or its homologs to the flowering decision expressed as high expression of the meristem identity genes, including AP1. We elaborated a dynamical model of this flowering gene regulatory network and applied it to the previously published expression data from two cultivars of domesticated chickpea (Cicer arietinum), obtained for two photoperiod durations. Due to a large number of free parameters in the model, we used an ensemble approach analyzing the model solutions at many parameter sets that provide equally good fit to data. Testing several alternative hypotheses about regulatory roles of the five FT homologs present in chickpea revealed no preference in segregating individual FT copies as singled-out activators with their own regulatory parameters, thus favoring the hypothesis that the five genes possess similar regulatory properties and provide cumulative activation in the network. The analysis reveals that different levels of activation from AP1 can explain a small difference observed in the expression of the two homologs of the repressor gene TFL1. Finally, the model predicts highly reduced activation between LFY and AP1, thus suggesting that this regulatory block is not conserved in chickpea and needs other mechanisms. Overall, this study provides the first attempt to quantitatively test the flowering time gene network in chickpea based on data-driven modeling.

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Section: Discussionsupporting

confidence: 57%

Section: Introductionmentioning

confidence: 99%

Dynamical Modeling of the Core Gene Network Controlling Flowering Suggests Cumulative Activation From the FLOWERING LOCUS T Gene Homologs in Chickpea

et al. 2018

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“…With increasing global temperature, faster accumulation of GDDs will take place. In addition to temperature, photoperiod is also a major environmental factor determining time to flower initiation and first flower appearance in plants (Daba et al, 2016), and flowering in wheat is sensitive to photoperiod. Thus, plant development is a function of temperature and daylength (Slafer and Rawson, 1994;Cao and Moss, 1997), and both should be included in predictive algorithms.…”

Section: Introductionmentioning

confidence: 99%

Can Growing Degree Days and Photoperiod Predict Spring Wheat Phenology?

Aslam

Ahmed

Stöckle

et al. 2017

Front. Environ. Sci.

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Wheat (Triticum aestivum) production in the rainfed area of Pothwar Pakistan is extremely vulnerable to high temperature. The expected increase in temperature due to global warming should result in shorter crop life cycles, and thus lower biomass and grain yield. Two major factors control wheat phenological development: temperature and photoperiod. To evaluate wheat development in response to these factors, we conducted experiments that created diverse temperature and daylength conditions by adjusting the crop sowing time. The study was conducted during 2013-14 and 2014-15 using five spring wheat genotypes, four sowing times, at three sites under rainfed management in Pothwar, Pakistan. Wheat crops experienced more cold days with early sowing, but later sowing dates resulted in higher temperatures, especially from anthesis to maturity. These treatments produced large differences in phenology, biomass production, and yield. To investigate whether growing degree days (GDD) and photoperiod algorithms could predict wheat phenology under these changing conditions, GDD was calculated based on the method proposed by Wang and Engel while photoperiod followed the approach introduced in the APSIM crop growth model. GDD was calculated separately and in combination with photoperiod from germination to anthesis. For the grain filling period, only GDD was calculated. The observed and predicted number of days to anthesis and maturity were in good agreement, showing that the combination of GDD and photoperiod algorithms provided good estimations of spring wheat phenology under variable temperature and daylength conditions.

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“…The effect of f (P) in SPA is therefore greater for photoperiods close to Pcrt for long photoperiods compared to a linear formalism. Popt and Pcrt have been adopted from Setiyono et al (2007), but it would be desirable to re-evaluate these thresholds with experiments such as the one presented by Daba et al (2016). The optimization of the photoperiod sensitivity coefficient (S) made it possible to highlight differences in sensitivity within the same maturity group, which have the same Popt and Pcrt.…”

Section: Simple Algorithm Of Phenologymentioning

confidence: 99%

Combining Simple Phenotyping and Photothermal Algorithm for the Prediction of Soybean Phenology: Application to a Range of Common Cultivars Grown in Europe

et al. 2020

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Developing new cropping strategies (very early sowing, crop expansion at higher latitudes, double cropping) to improve soybean production in Europe under climate change needs a good prediction of phenology under different temperature and photoperiod conditions. For that purpose, a simple phenology algorithm (SPA) was developed and parameterized for 10 contrasting soybean cultivars (maturity group 000 to II). Two experiments were carried out at INRA Toulouse (France) for parameterization: 1) Phenological monitoring of plants in pots on an outdoor platform with 6 planting dates. 2) Response of seed germination to temperature in controlled conditions. Multi-location field trials including 5 sites, 4 years, 2 sowing dates, and 10 cultivars were used to evaluate the SPA phenology predictions. Mean cardinal temperatures (minimum, optimum, and maximum) for germination were ca. 2, 30, and 40°C, respectively with significant differences among cultivars. The photoperiod sensitivity coefficient varied among cultivars when fixing Popt and Pcrt, optimal and critical photoperiods respectively, by maturity group. The parameterized algorithm showed an RMSE of less than 6 days for the prediction of crop cycle duration (i.e. cotyledons stage to physiological maturity) in the field trials including 75 data points. Flowering (R1 stage), and beginning of grain filling (R5 stage) dates were satisfactorily predicted with RMSEs of 8.2 and 9.4 days respectively. Because SPA can be also parameterized using data from field experiments, it can be useful as a plant selection tool across environments. The algorithm can be readily applied to species other than soybean, and its incorporation into cropping systems models would enhance the assessment of the performance of crop cultivars under climate change scenarios.

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Determination of Photoperiod-Sensitive Phase in Chickpea (Cicer arietinum L.)

Cited by 21 publications

References 37 publications

Dynamical Modeling of the Core Gene Network Controlling Flowering Suggests Cumulative Activation From the FLOWERING LOCUS T Gene Homologs in Chickpea

Dynamical Modeling of the Core Gene Network Controlling Flowering Suggests Cumulative Activation From the FLOWERING LOCUS T Gene Homologs in Chickpea

Can Growing Degree Days and Photoperiod Predict Spring Wheat Phenology?

Combining Simple Phenotyping and Photothermal Algorithm for the Prediction of Soybean Phenology: Application to a Range of Common Cultivars Grown in Europe

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