Taxonomic remarks on Triticum L. and ×Triticosecale Wittm.

Hammer, Karl; Filatenko, A. A.; Pistrick, Klaus

doi:10.1007/s10722-010-9590-4

Cited by 57 publications

(15 citation statements)

References 29 publications

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“…Spelt (Triticum spelta (L.)) is a hexaploid wheat that is closely related to common wheat (Triticum aestivum (L.)), whereas tetraploid emmer (Triticum dicoccon (Schrank)) is a close relative of durum wheat (T. durum (L.)) (Hammer et al 2011). Spelt and emmer have hulled, non-threshable grains, which are characterized by high nutritional value (Galterio et al 2003).…”

Section: Introductionmentioning

confidence: 99%

Spelt (Triticum spelta) and Emmer (T. dicoccon) Chaff Used as a Renewable Source of Energy

et al. 2017

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The energy values of spelt (Triticum spelta) and emmer (T. dicoccon) chaff were determined and compared to wheat and barley straw. The thermophysical parameters (moisture content, lower calorific value, gross calorific value, content of fixed carbon, volatile matter, and ash) and elemental composition (content of C, H, S, N, and O) of spelt and emmer chaff were determined. Spelt and emmer chaff were characterized by higher gross calorific value (18.75 GJ/Mg vs. 18.31 GJ/Mg), higher lower calorific value (16.74 GJ/Mg vs. 16.35 GJ/Mg), noticeably lower ash content (3.79% vs. 6.16%) and lower content of volatile matter (70.3% vs. 74.9%) than wheat and barley straw. Emmer chaff contained noticeably more sulfur (0.148%), nitrogen (2.20%) and hydrogen (7.50%) than both types of straw (0.064%, 0.66% and 5.55% on average, respectively) and spelt chaff (0.071%, 0.80%, and 7.06%, respectively). Despite the fact that the relatively high sulfur content in emmer chaff is not desirable, the results of this study indicate that the chaff of hulled wheat had considerable energy potential and that hulled wheats could be effectively used as renewable sources of energy in the region of their production.

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Section: Introductionmentioning

confidence: 99%

Spelt (Triticum spelta) and Emmer (T. dicoccon) Chaff Used as a Renewable Source of Energy

et al. 2017

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“…Jakubz., T. compactum Host, T. petropavlovskyi Udacz. et Migusch., and T. sphaerococcum Percival (Hammer et al 2011). In China, several species have the AABBDD genomic composition, such as Xinjiang rice wheat (T. petropavlovskyi), T. aestivum subsp.…”

Section: Introductionmentioning

confidence: 99%

Genetic analysis of wheat (Triticum aestivum L.) and related species with SSR markers

Wang

Zhang

et al. 2012

Genet Resour Crop Evol

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Genetic diversity among 19 Triticum aestivum accessions and 73 accessions of closely related species was analyzed using simple sequence repeat (SSR) markers. Forty-four out of 497 SSR markers were polymorphic. In total 274 alleles were detected (mean 6.32 alleles per locus). The polymorphic information content (PIC) of the loci ranged from 0.3589 to 0.8854 (mean 0.7538). The D genome contained the highest mean number of alleles (6.32) followed by the A and B genomes (6.13 and 5.94, respectively). The correlation between PIC and allele number was significant in all genome groups (0.7540, 0.7361 and 0.7482 for A, B and D genomes, respectively). Among the seven homologous chromosome groups, genetic diversity was lowest in group 7 and highest in group 5. In cluster and principal component analyses, all accessions grouped according to their genomes were consistent with their taxonomic classification. Accessions with the A and D genomes were clustered into two distinct groups, and AABB accessions showed abundant genetic diversity and a close relationship. Triticum durum and T. turgidum were clustered together, consistent with their morphological similarity. Cluster analysis indicated emmer is closely related to hexaploid wheat. Compared with common wheat, higher genetic variation was detected in spelt, T. aestivum subsp. yunnanense and subsp. tibetanum. In addition, a close genetic relationship between T. polonicum and T. macha was observed. The results of the clustering and principal component analyses were essentially consistent, but the latter method more explicitly displayed the relationships among wheat and closely related species.

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“…To estimate the characteristics of spike shape, we selected the most significant parameters according to their predictive capacity for spike density estimation. The parameters were ranked according to their significance based on the averaged results of eight different approaches: (1) linear regression coefficients (Linear Regression); (2) regression coefficients with lasso regularization (Lasso); (3) regression coefficients with ridge regularization (Ridge); (4) selection of parameters according to stability criterion [34] using RandomizedLasso, sklearn library (Stability); (5) ranking according to recursive feature elimination (RFE) [35]; (6) ranking based on average entropy reduction computed when constructing the trees of solutions by random forest method (RF) using RandomForestRegressor class, sklearn.ensemble software package; (7) ranking based on the correlation between regressor and target parameters (Corr) using f_regression class in sklearn.feature_selection package; and (8) ranking based on the maximal information coefficient (MIC), which takes into account nonlinear dependences between parameters [36] using the MINE (maximal information-based nonparametric exploration) statistics of minepy package. The estimate for a parameter in all these strategies varies in the range of 0 to 1.…”

Section: Methods For Predicting Spike Characteristicsmentioning

confidence: 99%

Morphometry of the Wheat Spike by Analyzing 2D Images

Genaev¹,

Komyshev²,

Smirnov³

et al. 2019

Preprint

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Spike shape and morphometric characteristics are among the key characteristics of cultivated cereals associated with their productivity. Identification of the genes controlling these traits requires morphometric data harvesting and analysis for numerous plants, which is automatable using technologies of digital image analysis. A method for wheat spike morphometry utilizing 2D image analysis is proposed. Digital images are acquired in two variants: a spike on a table (one projection) or fixed with a clip (four projections). The method identifies spike and awns in the image and estimates their quantitative characteristics (area in image, length, width, circularity, etc.). Models of sections, quadrilaterals, and radial model are proposed for describing spike shape. Parameters of these models are used to predict spike shape type (spelt, normal, or compact) by machine learning. The mean error in spike density prediction for the images in one projection is 4.61 (~18%) versus 3.33 (~13%) for the parameters obtained using four projections. F1 measure in automated spike classification into three types is 0.78 using logistic regression (one projection) and 0.85 using random forest method (four projections). The proposed method is implemented in Java; examples of images and user guide are available at http://wheatdb.org/werecognizer.

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Taxonomic remarks on Triticum L. and ×Triticosecale Wittm.

Cited by 57 publications

References 29 publications

Spelt (Triticum spelta) and Emmer (T. dicoccon) Chaff Used as a Renewable Source of Energy

Spelt (Triticum spelta) and Emmer (T. dicoccon) Chaff Used as a Renewable Source of Energy

Genetic analysis of wheat (Triticum aestivum L.) and related species with SSR markers

Morphometry of the Wheat Spike by Analyzing 2D Images

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