Advances in Cereal Crops Breeding
This Special Issue on ‘Advances in Cereal Crops Breeding’ comprises 10 papers covering a wide range of subjects, including the expression-level investigation of genes in terms of salinity stress adaptations and their relationships with proteomics in rice, the use of genetic analysis to assess the ge...
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| Year of Publication: | 2021 |
| Language: | English |
| Physical Description: | 1 electronic resource (196 p.) |
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| 100 | 1 | |a Loskutov, Igor G. |4 edt | |
| 245 | 1 | 0 | |a Advances in Cereal Crops Breeding |
| 260 | |a Basel, Switzerland |b MDPI - Multidisciplinary Digital Publishing Institute |c 2021 | ||
| 300 | |a 1 electronic resource (196 p.) | ||
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| 520 | |a This Special Issue on ‘Advances in Cereal Crops Breeding’ comprises 10 papers covering a wide range of subjects, including the expression-level investigation of genes in terms of salinity stress adaptations and their relationships with proteomics in rice, the use of genetic analysis to assess the general combining ability (GCA) and specific combining ability (SCA) in promising hybrids of maize, the use of DNA markers based on PCR in rice, the identification of quantitative trait loci (QTLs) in wheat and simple sequence repeats (SSR) in rice, the use of single-nucleotide polymorphisms (SNP) in a genome-wide association study (GWAS) in cereals, and Nanopore direct RNA sequencing of related with LTR RNA retrotransposon in triticale prior to the genomic selection of heterotic maize hybrids. | ||
| 546 | |a English | ||
| 650 | 7 | |a Research & information: general |2 bicssc | |
| 653 | |a maize | ||
| 653 | |a density tolerance | ||
| 653 | |a combining ability | ||
| 653 | |a gene effects | ||
| 653 | |a genetic diversity | ||
| 653 | |a rice | ||
| 653 | |a salinity | ||
| 653 | |a submergence tolerance | ||
| 653 | |a blast | ||
| 653 | |a SSR markers | ||
| 653 | |a PCR analysis | ||
| 653 | |a long non-coding RNAs | ||
| 653 | |a seed development | ||
| 653 | |a Nanopore sequencing | ||
| 653 | |a retrotransposons | ||
| 653 | |a triticale | ||
| 653 | |a prediction accuracy | ||
| 653 | |a mixed linear and Bayesian models | ||
| 653 | |a machine learning algorithms | ||
| 653 | |a training set size and composition | ||
| 653 | |a parametric and nonparametric models | ||
| 653 | |a drought stress | ||
| 653 | |a dendrogram | ||
| 653 | |a barley | ||
| 653 | |a breeding | ||
| 653 | |a marker-assisted selection | ||
| 653 | |a genes | ||
| 653 | |a genetic resources | ||
| 653 | |a genome editing | ||
| 653 | |a health benefits | ||
| 653 | |a metabolomics | ||
| 653 | |a oat | ||
| 653 | |a QTL | ||
| 653 | |a wheat | ||
| 653 | |a Triticum aestivum L. | ||
| 653 | |a QMrl-7B | ||
| 653 | |a root traits | ||
| 653 | |a grain yield | ||
| 653 | |a nitrogen use efficiency | ||
| 653 | |a GWAS | ||
| 653 | |a salinity tolerance | ||
| 653 | |a Vietnamese landraces | ||
| 653 | |a abiotic stress | ||
| 653 | |a root | ||
| 653 | |a auxin | ||
| 653 | |a YUCCA | ||
| 653 | |a PIN | ||
| 653 | |a proteomics | ||
| 653 | |a mass spectrometry | ||
| 776 | |z 3-0365-2650-1 | ||
| 776 | |z 3-0365-2651-X | ||
| 700 | 1 | |a Loskutov, Igor G. |4 oth | |
| 906 | |a BOOK | ||
| ADM | |b 2023-12-15 05:52:22 Europe/Vienna |f system |c marc21 |a 2022-04-04 09:22:53 Europe/Vienna |g false | ||
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