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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha : |
25/04/2018 |
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Actualizado : |
25/04/2018 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Autor : |
GALLINO, J.P.; RUIBAL, C.; CASARETTO, E.; FLEITAS, A.L.; BONNECARRERE, V.; BORSANI, O.; VIDAL, S. |
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Afiliación : |
JUAN P. GALLINO, Universidad de la República (UdelaR)/ Facultad de Ciencias; CECILIA RUIBAL, Universidad de la República (UdelaR)/ Facultad de Ciencias; ESTEBAN CASARETTO, Universidad de la República (UdelaR)/ Facultad de Agronomía; ANDREA L. FLEITAS, Universidad de la República (UdelaR)/ Facultad de Ciencias; MARIA VICTORIA BONNECARRERE MARTINEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; OMAR BORSANI, Universidad de la República (UdelaR)/ Facultad de Agronomía; SABINA VIDAL, Universidad de la República (UdelaR)/ Facultad de Ciencias. |
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Título : |
A dehydration-induced eukaryotic translation initiation factor iso4G identified in a slow wilting soybean cultivar enhances abiotic stress tolerance in Arabidopsis. |
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Fecha de publicación : |
2018 |
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Fuente / Imprenta : |
Frontiers in Plant Science, 2018, v.9, Article number 262. (2 March 2018). OPEN ACCESS |
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DOI : |
10.3389/fpls.2018.00262 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received: 22 December 2017; Accepted: 14 February 2018; Published: 02 March 2018. |
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Contenido : |
ABSTRACT.
Water is usually the main limiting factor for soybean productivity worldwide and yet advances in genetic improvement for drought resistance in this crop are still limited. In the present study, we investigated the physiological and molecular responses to drought in two soybean contrasting genotypes, a slow wilting N7001 and a drought sensitive TJS2049 cultivars. Measurements of stomatal conductance, carbon isotope ratios and accumulated dry matter showed that N7001 responds to drought by employing mechanisms resulting in a more efficient water use than TJS2049. To provide an insight into the molecular mechanisms that these cultivars employ to deal with water stress, their early and late transcriptional responses to drought were analyzed by suppression subtractive hybridization. A number of differentially regulated genes from N7001 were identified and their expression pattern was compared between in this genotype and TJS2049. Overall, the data set indicated that N7001 responds to drought earlier than TJ2049 by up-regulating a larger number of genes, most of them encoding proteins with regulatory and signaling functions. The data supports the idea that at least some of the phenotypic differences between slow wilting and drought sensitive plants may rely on the regulation of the level and timing of expression of specific genes. One of the genes that exhibited a marked N7001-specific drought induction profile encoded a eukaryotic translation initiation factor iso4G (GmeIFiso4G-1a). GmeIFiso4G-1a is one of four members of this protein family in soybean, all of them sharing high sequence identity with each other. In silico analysis of GmeIFiso4G-1 promoter sequences suggested a possible functional specialization between distinct family members, which can attain differences at the transcriptional level. Conditional overexpression of GmeIFiso4G-1a in Arabidopsis conferred the transgenic plants increased tolerance to osmotic, salt, drought and low temperature stress, providing a strong experimental evidence for a direct association between a protein of this class and general abiotic stress tolerance mechanisms. Moreover, the results of this work reinforce the importance of the control of protein synthesis as a central mechanism of stress adaptation and opens up for new strategies for improving crop performance under stress.
© 2018 Gallino, Ruibal, Casaretto, Fleitas, Bonnecarrère, Borsani and Vidal. MenosABSTRACT.
Water is usually the main limiting factor for soybean productivity worldwide and yet advances in genetic improvement for drought resistance in this crop are still limited. In the present study, we investigated the physiological and molecular responses to drought in two soybean contrasting genotypes, a slow wilting N7001 and a drought sensitive TJS2049 cultivars. Measurements of stomatal conductance, carbon isotope ratios and accumulated dry matter showed that N7001 responds to drought by employing mechanisms resulting in a more efficient water use than TJS2049. To provide an insight into the molecular mechanisms that these cultivars employ to deal with water stress, their early and late transcriptional responses to drought were analyzed by suppression subtractive hybridization. A number of differentially regulated genes from N7001 were identified and their expression pattern was compared between in this genotype and TJS2049. Overall, the data set indicated that N7001 responds to drought earlier than TJ2049 by up-regulating a larger number of genes, most of them encoding proteins with regulatory and signaling functions. The data supports the idea that at least some of the phenotypic differences between slow wilting and drought sensitive plants may rely on the regulation of the level and timing of expression of specific genes. One of the genes that exhibited a marked N7001-specific drought induction profile encoded a eukaryotic translation initiation factor iso4G (Gm... Presentar Todo |
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Palabras claves : |
ABIOTIC STRESS; ARABIDOPSIS; DROUGHT; EIFiso4G; SOYBEAN CROP; TRANSLATION INITIATION. |
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Asunto categoría : |
-- |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/9385/1/Frontiers-in-Plant-Science.-2018.fpls-09-00262.pdf
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Marc : |
LEADER 03453naa a2200289 a 4500
001 1058480
005 2018-04-25
008 2018 bl uuuu u00u1 u #d
024 7 $a10.3389/fpls.2018.00262$2DOI
100 1 $aGALLINO, J.P.
245 $aA dehydration-induced eukaryotic translation initiation factor iso4G identified in a slow wilting soybean cultivar enhances abiotic stress tolerance in Arabidopsis.$h[electronic resource]
260 $c2018
500 $aArticle history: Received: 22 December 2017; Accepted: 14 February 2018; Published: 02 March 2018.
520 $aABSTRACT. Water is usually the main limiting factor for soybean productivity worldwide and yet advances in genetic improvement for drought resistance in this crop are still limited. In the present study, we investigated the physiological and molecular responses to drought in two soybean contrasting genotypes, a slow wilting N7001 and a drought sensitive TJS2049 cultivars. Measurements of stomatal conductance, carbon isotope ratios and accumulated dry matter showed that N7001 responds to drought by employing mechanisms resulting in a more efficient water use than TJS2049. To provide an insight into the molecular mechanisms that these cultivars employ to deal with water stress, their early and late transcriptional responses to drought were analyzed by suppression subtractive hybridization. A number of differentially regulated genes from N7001 were identified and their expression pattern was compared between in this genotype and TJS2049. Overall, the data set indicated that N7001 responds to drought earlier than TJ2049 by up-regulating a larger number of genes, most of them encoding proteins with regulatory and signaling functions. The data supports the idea that at least some of the phenotypic differences between slow wilting and drought sensitive plants may rely on the regulation of the level and timing of expression of specific genes. One of the genes that exhibited a marked N7001-specific drought induction profile encoded a eukaryotic translation initiation factor iso4G (GmeIFiso4G-1a). GmeIFiso4G-1a is one of four members of this protein family in soybean, all of them sharing high sequence identity with each other. In silico analysis of GmeIFiso4G-1 promoter sequences suggested a possible functional specialization between distinct family members, which can attain differences at the transcriptional level. Conditional overexpression of GmeIFiso4G-1a in Arabidopsis conferred the transgenic plants increased tolerance to osmotic, salt, drought and low temperature stress, providing a strong experimental evidence for a direct association between a protein of this class and general abiotic stress tolerance mechanisms. Moreover, the results of this work reinforce the importance of the control of protein synthesis as a central mechanism of stress adaptation and opens up for new strategies for improving crop performance under stress. © 2018 Gallino, Ruibal, Casaretto, Fleitas, Bonnecarrère, Borsani and Vidal.
653 $aABIOTIC STRESS
653 $aARABIDOPSIS
653 $aDROUGHT
653 $aEIFiso4G
653 $aSOYBEAN CROP
653 $aTRANSLATION INITIATION
700 1 $aRUIBAL, C.
700 1 $aCASARETTO, E.
700 1 $aFLEITAS, A.L.
700 1 $aBONNECARRERE, V.
700 1 $aBORSANI, O.
700 1 $aVIDAL, S.
773 $tFrontiers in Plant Science, 2018$gv.9, Article number 262. (2 March 2018). OPEN ACCESS
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Registro original : |
INIA Las Brujas (LB) |
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 | Acceso al texto completo restringido a Biblioteca INIA Las Brujas. Por información adicional contacte bibliolb@inia.org.uy. |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
31/01/2020 |
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Actualizado : |
31/01/2020 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Circulación / Nivel : |
Internacional - -- |
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Autor : |
GASO, D.; BERGER, A.; CIGANDA, V. |
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Afiliación : |
DEBORAH VIVIANA GASO MELGAR, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ANDRES GUSTAVO BERGER RICCA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; VERONICA SOLANGE CIGANDA BRASCA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Predicting wheat grain yield and spatial variability at field scale using a simple regression or a crop model in conjunction with Landsat images. |
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Fecha de publicación : |
2019 |
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Fuente / Imprenta : |
Computers and Electronics in Agriculture, April 2019, Volume 159, Pages 75-83. Doi: https://doi.org/10.1016/j.compag.2019.02.026 |
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ISSN : |
0168-1699 |
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DOI : |
10.1016/j.compag.2019.02.026 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 8 February 2018 / Revised 22 February 2019 / Accepted 25 February 2019 / Available online 4 March 2019..
This work was supported by ANII fellowship program and INIA fundings. The authors thank farmers who provided field data. |
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Contenido : |
ABSTRACT.
Early prediction of crop yields has been a challenge frequently resolved through the combination of remote sensing data and crop models. The aim of this study was to evaluate two different methods based on remote sensing data for predicting winter wheat (Triticum aestivum L.) yield at field scale. We compared the accuracy of: (i) a simple regression method between different vegetation indices at anthesis and grain yield, and (ii) a crop model method based on optimization of two parameters (specific leaf nitrogen and initial aboveground-biomass) using time series of vegetation indices. Vegetation indices were derived from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) images acquired for two growing seasons (2013, 2014) across 22 fields in south western Uruguay with an average size of 128 ha. At all sites, leaf area index (LAI) was measured during a field campaign, and grain yield was measured with yield monitors on harvesters. The simple regression method (SRM) achieved higher accuracy than the model-based method (CMM) for the estimation of yield at field scale (RMSE = 966 kg ha −1 and RMSE = 1532 kg ha −1 , respectively). When deviations between observed and estimated yields were evaluated at pixel (30 × 30 m) level, the model-based method was better at detecting existing spatial variability in grain yield and at identifying areas of different yield potential. Even though both methods have limited utility to estimate yield at field scale with very high accuracy due to large RMSE, the methodologies are suitable to predict harvest volumes at large agricultural areas or at country level, and to construct synthetic yield maps reflecting within field variability. Higher temporal resolution of images would improve accuracy in estimating yield and spatial variability at field scale. © 2019 Elsevier B.V. MenosABSTRACT.
Early prediction of crop yields has been a challenge frequently resolved through the combination of remote sensing data and crop models. The aim of this study was to evaluate two different methods based on remote sensing data for predicting winter wheat (Triticum aestivum L.) yield at field scale. We compared the accuracy of: (i) a simple regression method between different vegetation indices at anthesis and grain yield, and (ii) a crop model method based on optimization of two parameters (specific leaf nitrogen and initial aboveground-biomass) using time series of vegetation indices. Vegetation indices were derived from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) images acquired for two growing seasons (2013, 2014) across 22 fields in south western Uruguay with an average size of 128 ha. At all sites, leaf area index (LAI) was measured during a field campaign, and grain yield was measured with yield monitors on harvesters. The simple regression method (SRM) achieved higher accuracy than the model-based method (CMM) for the estimation of yield at field scale (RMSE = 966 kg ha −1 and RMSE = 1532 kg ha −1 , respectively). When deviations between observed and estimated yields were evaluated at pixel (30 × 30 m) level, the model-based method was better at detecting existing spatial variability in grain yield and at identifying areas of different yield potential. Even though both methods have limited utility to ... Presentar Todo |
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Palabras claves : |
Crop growth model; Landsat; Leaf area index; Wheat; Yield. |
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Asunto categoría : |
F01 Cultivo |
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Marc : |
LEADER 02944naa a2200241 a 4500
001 1060735
005 2020-01-31
008 2019 bl uuuu u00u1 u #d
022 $a0168-1699
024 7 $a10.1016/j.compag.2019.02.026$2DOI
100 1 $aGASO, D.
245 $aPredicting wheat grain yield and spatial variability at field scale using a simple regression or a crop model in conjunction with Landsat images.$h[electronic resource]
260 $c2019
500 $aArticle history: Received 8 February 2018 / Revised 22 February 2019 / Accepted 25 February 2019 / Available online 4 March 2019.. This work was supported by ANII fellowship program and INIA fundings. The authors thank farmers who provided field data.
520 $aABSTRACT. Early prediction of crop yields has been a challenge frequently resolved through the combination of remote sensing data and crop models. The aim of this study was to evaluate two different methods based on remote sensing data for predicting winter wheat (Triticum aestivum L.) yield at field scale. We compared the accuracy of: (i) a simple regression method between different vegetation indices at anthesis and grain yield, and (ii) a crop model method based on optimization of two parameters (specific leaf nitrogen and initial aboveground-biomass) using time series of vegetation indices. Vegetation indices were derived from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) images acquired for two growing seasons (2013, 2014) across 22 fields in south western Uruguay with an average size of 128 ha. At all sites, leaf area index (LAI) was measured during a field campaign, and grain yield was measured with yield monitors on harvesters. The simple regression method (SRM) achieved higher accuracy than the model-based method (CMM) for the estimation of yield at field scale (RMSE = 966 kg ha −1 and RMSE = 1532 kg ha −1 , respectively). When deviations between observed and estimated yields were evaluated at pixel (30 × 30 m) level, the model-based method was better at detecting existing spatial variability in grain yield and at identifying areas of different yield potential. Even though both methods have limited utility to estimate yield at field scale with very high accuracy due to large RMSE, the methodologies are suitable to predict harvest volumes at large agricultural areas or at country level, and to construct synthetic yield maps reflecting within field variability. Higher temporal resolution of images would improve accuracy in estimating yield and spatial variability at field scale. © 2019 Elsevier B.V.
653 $aCrop growth model
653 $aLandsat
653 $aLeaf area index
653 $aWheat
653 $aYield
700 1 $aBERGER, A.
700 1 $aCIGANDA, V.
773 $tComputers and Electronics in Agriculture, April 2019, Volume 159, Pages 75-83. Doi: https://doi.org/10.1016/j.compag.2019.02.026
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