Journal articles 2013
Documents
Variation in carbon isotope discrimination and its relationship with harvest index in the reference collection of chickpea germplasm
Krishnamurthy L, Kashiwagi J, Tobita S, Ito O, Upadhyaya HD, Gowda CLL, Gaur PM, Sheshshayee MS, Singh S, Vadez V, Varshney RK (2013). Variation in carbon isotope discrimination and its relationship with harvest index in the reference collection of chickpea germplasm. Functional Plant Biology, pp1–12. Published online 2 July 2013. (DOI: http://dx.doi.org/10.1071/FP13088). (G4008.12). Not open access: view online
Krishnamurthy L, Kashiwagi J, Tobita S, Ito O, Upadhyaya HD, Gowda CLL, Gaur PM, Sheshshayee MS, Singh S, Vadez V, Varshney RK (2013). Variation in carbon isotope discrimination and its relationship with harvest index in the reference collection of chickpea germplasm. Functional Plant Biology, pp1–12. Published online 2 July 2013. (DOI: http://dx.doi.org/10.1071/FP13088). (G4008.12). Not open access: view online
Using membrane transporters to improve crops for sustainable food production
Schroeder JI, Delhaize E, Frommer WB, Guerinot ML, Harrison MJ, Herrera-Estrella L, Horie T, Kochian LV, Munns R, Nishizawa NK, Tsay Y-F, Sanders D. 2013. Using membrane transporters to improve crops for sustainable food production. Nature 497(7447): 60–66. (DOI: 10.1038/nature11909). (G7010.03.01). Not open access: view abstract
Schroeder JI, Delhaize E, Frommer WB, Guerinot ML, Harrison MJ, Herrera-Estrella L, Horie T, Kochian LV, Munns R, Nishizawa NK, Tsay Y-F, Sanders D. 2013. Using membrane transporters to improve crops for sustainable food production. Nature 497(7447): 60–66. (DOI: 10.1038/nature11909). (G7010.03.01). Not open access: view abstract
The statistical analysis of multi-environment data: modeling genotype-by-environment interaction and its genetic basis
Malosetti M, Ribaut JM, van Eeuwijk FA (2013). The statistical analysis of multi-environment data: modeling genotype-by-environment interaction and its genetic basis. Frontiers in Plant Physiology 4:44. (DOI: 10.3389/fphys.2013.00044).
Genotype-by-environment interaction (GEI) is an important phenomenon in plant breeding. This paper presents a series of models for describing, exploring, understanding, and predicting GEI. All models depart from a two-way table of genotype by environment means. First, a series of descriptive and explorative models/approaches are presented: Finlay–Wilkinson model, AMMI model, GGE biplot. All of these approaches have in common that they merely try to group genotypes and environments and do not use other information than the two-way table of means. Next, factorial regression is introduced as an approach to explicitly introduce genotypic and environmental covariates for describing and explaining GEI. Finally, QTL modeling is presented as a natural extension of factorial regression, where marker information is translated into genetic predictors. Tests for regression coefficients corresponding to these genetic predictors are tests for main effect QTL expression and QTL by environment interaction (QEI). QTL models for which QEI depends on environmental covariables form an interesting model class for predicting GEI for new genotypes and new environments. For realistic modeling of genotypic differences across multiple environments, sophisticated mixed models are necessary to allow for heterogeneity of genetic variances and correlations across environments. The use and interpretation of all models is illustrated by an example data set from the CIMMYT maize breeding program, containing environments differing in drought and nitrogen stress. To help readers to carry out the statistical analyses, GenStat® programs, 15th Edition and Discovery® version, are presented as “Appendix.”
Malosetti M, Ribaut JM, van Eeuwijk FA (2013). The statistical analysis of multi-environment data: modeling genotype-by-environment interaction and its genetic basis. Frontiers in Plant Physiology 4:44. (DOI: 10.3389/fphys.2013.00044).
Genotype-by-environment interaction (GEI) is an important phenomenon in plant breeding. This paper presents a series of models for describing, exploring, understanding, and predicting GEI. All models depart from a two-way table of genotype by environment means. First, a series of descriptive and explorative models/approaches are presented: Finlay–Wilkinson model, AMMI model, GGE biplot. All of these approaches have in common that they merely try to group genotypes and environments and do not use other information than the two-way table of means. Next, factorial regression is introduced as an approach to explicitly introduce genotypic and environmental covariates for describing and explaining GEI. Finally, QTL modeling is presented as a natural extension of factorial regression, where marker information is translated into genetic predictors. Tests for regression coefficients corresponding to these genetic predictors are tests for main effect QTL expression and QTL by environment interaction (QEI). QTL models for which QEI depends on environmental covariables form an interesting model class for predicting GEI for new genotypes and new environments. For realistic modeling of genotypic differences across multiple environments, sophisticated mixed models are necessary to allow for heterogeneity of genetic variances and correlations across environments. The use and interpretation of all models is illustrated by an example data set from the CIMMYT maize breeding program, containing environments differing in drought and nitrogen stress. To help readers to carry out the statistical analyses, GenStat® programs, 15th Edition and Discovery® version, are presented as “Appendix.”
The repetitive component of the A genome of peanut (Arachis hypogaea) and its role in remodelling intergenic sequence space since its evolutionary divergence from the B genome
Bertioli DJ, Vidigal B, Nielen S, Ratnaparkhe MB, Lee T-H, Leal-Bertioli SCM, Kim C, Guimarães PM, Seijo G, Schwarzacher T, Paterson AH, Heslop-Harrison P and Araujo ACG (2013). The repetitive component of the A genome of peanut (Arachis hypogaea) and its role in remodelling intergenic sequence space since its evolutionary divergence from the B genome. Annals of Botany 112(3):545–559 (DOI: 10.1093/aob/mct128). Not open access; view abstract. (G6010.01)
Bertioli DJ, Vidigal B, Nielen S, Ratnaparkhe MB, Lee T-H, Leal-Bertioli SCM, Kim C, Guimarães PM, Seijo G, Schwarzacher T, Paterson AH, Heslop-Harrison P and Araujo ACG (2013). The repetitive component of the A genome of peanut (Arachis hypogaea) and its role in remodelling intergenic sequence space since its evolutionary divergence from the B genome. Annals of Botany 112(3):545–559 (DOI: 10.1093/aob/mct128). Not open access; view abstract. (G6010.01)
The growths of leaves, shoots, roots and reproductive organs partly share their genetic control in maize plants
Dignat G, Welcker C, Sawkins M, Ribaut JM and Tardieu F (2013). The growths of leaves, shoots, roots and reproductive organs partly share their genetic control in maize plants. Plant, Cell & Environment Printed online 7 January 2013. (DOI: 10.1111/pce.12045). (G3005.15). Not open access: view abstract
Dignat G, Welcker C, Sawkins M, Ribaut JM and Tardieu F (2013). The growths of leaves, shoots, roots and reproductive organs partly share their genetic control in maize plants. Plant, Cell & Environment Printed online 7 January 2013. (DOI: 10.1111/pce.12045). (G3005.15). Not open access: view abstract
Study on identification and using of several high-quality foreign wheat varieties
Li X-R, Chai Y-F, Sun L-H, Zhao Z-Y, Shao X-S, Xi J-L and Zhang J-C (2013). Study on identification and using of several high-quality foreign wheat varieties. Journal of Shanxi Agricultural Sciences 41(4):307–310,316 (DOI: 10.3969/j.issn.1002-2481.2013.04.01). Article in Chinese with abstract in English. Not open access; view journal website. (G7010.02.01)
Li X-R, Chai Y-F, Sun L-H, Zhao Z-Y, Shao X-S, Xi J-L and Zhang J-C (2013). Study on identification and using of several high-quality foreign wheat varieties. Journal of Shanxi Agricultural Sciences 41(4):307–310,316 (DOI: 10.3969/j.issn.1002-2481.2013.04.01). Article in Chinese with abstract in English. Not open access; view journal website. (G7010.02.01)
Spatial analysis to support geographic targeting of genotypes to enviroments
Hyman G, Hodson D, Jones P (2013). Spatial analysis to support geographic targeting of genotypes to environments. Frontiers in Plant Physiology 4:40. (DOI: 10.3389/fphys.2013.00040).
Crop improvement efforts have benefited greatly from advances in available data, computing technology, and methods for targeting genotypes to environments. These advances support the analysis of genotype by environment interactions (GEI) to understand how well a genotype adapts to environmental conditions. This paper reviews the use of spatial analysis to support crop improvement research aimed at matching genotypes to their most appropriate environmental niches. Better data sets are now available on soils, weather and climate, elevation, vegetation, crop distribution, and local conditions where genotypes are tested in experimental trial sites. The improved data are now combined with spatial analysis methods to compare environmental conditions across sites, create agro-ecological region maps, and assess environment change. Climate, elevation, and vegetation data sets are now widely available, supporting analyses that were much more difficult even 5 or 10 years ago. While detailed soil data for many parts of the world remains difficult to acquire for crop improvement studies, new advances in digital soil mapping are likely to improve our capacity. Site analysis and matching and regional targeting methods have advanced in parallel to data and technology improvements. All these developments have increased our capacity to link genotype to phenotype and point to a vast potential to improve crop adaptation efforts.
Hyman G, Hodson D, Jones P (2013). Spatial analysis to support geographic targeting of genotypes to environments. Frontiers in Plant Physiology 4:40. (DOI: 10.3389/fphys.2013.00040).
Crop improvement efforts have benefited greatly from advances in available data, computing technology, and methods for targeting genotypes to environments. These advances support the analysis of genotype by environment interactions (GEI) to understand how well a genotype adapts to environmental conditions. This paper reviews the use of spatial analysis to support crop improvement research aimed at matching genotypes to their most appropriate environmental niches. Better data sets are now available on soils, weather and climate, elevation, vegetation, crop distribution, and local conditions where genotypes are tested in experimental trial sites. The improved data are now combined with spatial analysis methods to compare environmental conditions across sites, create agro-ecological region maps, and assess environment change. Climate, elevation, and vegetation data sets are now widely available, supporting analyses that were much more difficult even 5 or 10 years ago. While detailed soil data for many parts of the world remains difficult to acquire for crop improvement studies, new advances in digital soil mapping are likely to improve our capacity. Site analysis and matching and regional targeting methods have advanced in parallel to data and technology improvements. All these developments have increased our capacity to link genotype to phenotype and point to a vast potential to improve crop adaptation efforts.
Roles of root aerenchyma development and its associated QTL in dry matter production under transient moisture stress in rice
Niones JM, Suralta RR, Inukai Y and Yamauchi A (2013). Roles of root aerenchyma development and its associated QTL in dry matter production under transient moisture stress in rice. Plant Production Science 16(3):205–216. (G3008.06)
Abstract: Enhanced aerenchyma development in rice under transient drought-to-waterlogged (TDW) stress promotes root system development by promoting lateral root production. This study analyzed the quantitative trait loci (QTLs) associated with the plasticity in aerenchyma development under TD-W stress. A mapping population of 60 F2 genotypes of chromosome segment substituted lines (CSSL) derived from CSSL47 and Nipponbare crosses were grown in rootboxes and evaluated for shoot and root growth, and aerenchyma development (expressed as root porosity). The TD-W stress was imposed starting with water saturated soil condition at sowing and then to progressive drought from 0 to 21 days after sowing (DAS) prior to exposure to sudden waterlogging for another 17 days (21 to 38 DAS). We performed simple and composite interval mapping to identify QTLs for aerenchyma development. QTL associated with aerenchyma development was mapped on the short-arm of chromosome 12 and designated as qAER-12. The effect of qAER-12 on the plasticity in aerenchyma development under TD-W was significantly associated with the increase in lateral root elongation and branching. This resulted in greater root system development as expressed in total root length and consequently contributed to higher dry matter production. This qAER-12 is probably the first reported QTL associated with aerenchyma development in rice under TD-W and is a useful trait for the improvement of the adaptive capability under fluctuating soil moisture conditions.
Niones JM, Suralta RR, Inukai Y and Yamauchi A (2013). Roles of root aerenchyma development and its associated QTL in dry matter production under transient moisture stress in rice. Plant Production Science 16(3):205–216. (G3008.06)
Abstract: Enhanced aerenchyma development in rice under transient drought-to-waterlogged (TDW) stress promotes root system development by promoting lateral root production. This study analyzed the quantitative trait loci (QTLs) associated with the plasticity in aerenchyma development under TD-W stress. A mapping population of 60 F2 genotypes of chromosome segment substituted lines (CSSL) derived from CSSL47 and Nipponbare crosses were grown in rootboxes and evaluated for shoot and root growth, and aerenchyma development (expressed as root porosity). The TD-W stress was imposed starting with water saturated soil condition at sowing and then to progressive drought from 0 to 21 days after sowing (DAS) prior to exposure to sudden waterlogging for another 17 days (21 to 38 DAS). We performed simple and composite interval mapping to identify QTLs for aerenchyma development. QTL associated with aerenchyma development was mapped on the short-arm of chromosome 12 and designated as qAER-12. The effect of qAER-12 on the plasticity in aerenchyma development under TD-W was significantly associated with the increase in lateral root elongation and branching. This resulted in greater root system development as expressed in total root length and consequently contributed to higher dry matter production. This qAER-12 is probably the first reported QTL associated with aerenchyma development in rice under TD-W and is a useful trait for the improvement of the adaptive capability under fluctuating soil moisture conditions.
Full article
Restriction of transpiration rate under high vapour pressure deficit and non-limiting water conditions is important for terminal drought tolerance in cowpea
Belko N, Zaman-Allah M, Diop NN, Cisse N, Zombre G, Ehlers JD and Vadez V (2013). Restriction of transpiration rate under high vapour pressure deficit and non-limiting water conditions is important for terminal drought tolerance in cowpea. Plant Biology 15(2):304–316. (DOI:10.1111/j.1438-8677.2012.00642.x) Also published online in 2012. Not open access: view abstract
Belko N, Zaman-Allah M, Diop NN, Cisse N, Zombre G, Ehlers JD and Vadez V (2013). Restriction of transpiration rate under high vapour pressure deficit and non-limiting water conditions is important for terminal drought tolerance in cowpea. Plant Biology 15(2):304–316. (DOI:10.1111/j.1438-8677.2012.00642.x) Also published online in 2012. Not open access: view abstract
Resistance of αAI-1 transgenic chickpea (Cicer arietinum) and cowpea (Vigna unguiculata) dry grains to bruchid beetles (Coleoptera: Chrysomelidae)
Lüthi C, Álvarez-Alfageme F, Ehlers JD, Higgins TJV and Romeis J (2013). Resistance of αAI-1 transgenic chickpea (Cicer arietinum) and cowpea (Vigna unguiculata) dry grains to bruchid beetles (Coleoptera: Chrysomelidae). Bulletin of Entomological Research, available on CJO2013, pp1–9. (DOI: 10.1017/S0007485312000818). (G6010.02/G7010.07.01). Not open access: view online
Lüthi C, Álvarez-Alfageme F, Ehlers JD, Higgins TJV and Romeis J (2013). Resistance of αAI-1 transgenic chickpea (Cicer arietinum) and cowpea (Vigna unguiculata) dry grains to bruchid beetles (Coleoptera: Chrysomelidae). Bulletin of Entomological Research, available on CJO2013, pp1–9. (DOI: 10.1017/S0007485312000818). (G6010.02/G7010.07.01). Not open access: view online
