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Biblioteca (s) : |
INIA La Estanzuela. |
Fecha : |
26/12/2018 |
Actualizado : |
05/07/2019 |
Tipo de producción científica : |
Capítulo en Libro Técnico-Científico |
Autor : |
GUTIERREZ, L.; BORGES, A.; QUERO, G.; GONZALEZ-REYMUNDEZ, A.; BERRO, I.; LADO, B.; CASTRO, A. |
Afiliación : |
Departamento de Biometría, Estadística y Computación, Facultad de Agronomía, Universidad de la República, Garzón 780, CP 12900, Montevideo, Uruguay.; Departamento de Biometría, Estadística y Computación, Facultad de Agronomía, Universidad de la República, Garzón 780, CP 12900, Montevideo, Uruguay.; Departamento de Biología Vegetal, Facultad de Agronomía, Universidad de la República, Garzón 780, CP 12900, Montevideo, Uruguay.; Departamento de Biometría, Estadística y Computación, Facultad de Agronomía, Universidad de la República, Garzón 780, CP 12900, Montevideo, Uruguay.; Departamento de Biometría, Estadística y Computación, Facultad de Agronomía, Universidad de la República, Garzón 780, CP 12900, Montevideo, Uruguay.; BETTINA LADO LINDNER, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay./1 Departamento de Biometría, Estadística y Computación, Facultad de Agronomía, Universidad de la República, Garzón 780, CP 12900, Montevideo, Uruguay.; Departamento de Producción Vegetal, Facultad de Agronomía, Universidad de la República, R3 Km 373, Paysandú, Uruguay. |
Título : |
Biostatistical tools for plant breeding in the genomics era. |
Fecha de publicación : |
2018 |
Fuente / Imprenta : |
In: German, S.; Quincke, M.; Vázquez, D.; Castro, M.; Pereyra, S.; Silva, P.; García, A. (Eds.). Seminario Internacional "1914-2014: Un siglo de mejoramiento de trigo en La Estanzuela". Montevideo (UY): INIA, 2018. |
Páginas : |
p.46-57. |
Serie : |
(INIA Serie Técnica; 241). |
ISBN : |
978-9974-38-406-4 |
ISSN : |
1688-9266 |
DOI : |
http://doi.org/10.35676/INIA/ST.241 |
Idioma : |
Inglés |
Contenido : |
SUMMARY:
Since the advent of agriculture, plant breeding has successfully improved plantsfor human benefit. Modern plant breeding
activities consist in evaluating the genetic merit of lines discerning genetic from environment and noise components. To do
so, modern plant breeding relies on the genetics foundations derived from Mendel?s work and statistical tools (or biometry)
generated afterwards. Plant breeding activities could be grouped in three categories: traditional, marker assisted
(MAS), and genomic selection (GS). Traditional plant breeding uses either per sephenotypic information, or information from
relatives to evaluate the genetic value. MAS on the other hand, involves the identification of markers linked to genes or quantitative
traits loci (QTL) of relevant traits, and then selecting individuals based on their marker scores. Finally, GS involves the prediction
of the genetic merit of individuals based on their marker scores and a statistical model. All of the three strategies require the
evaluation of large number of individuals creating massive amounts of data that needs proper analyses. Our objective was to
present some biostatistical strategies that are successfully being used in plant breeding programs. First, we used novel simulation |
Palabras claves : |
GENOMIC SELECTION; GENOTYPE BY ENVIRONMENT INTERACTION; GWAS; QTL MAPPING. |
Thesagro : |
GENOTIPOS. |
Asunto categoría : |
F01 Cultivo |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/12214/1/st-241-2018.p.46-57-Guitierrez-et-al.pdf
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Marc : |
LEADER 02312naa a2200313 a 4500 001 1059383 005 2019-07-05 008 2018 bl uuuu u00u1 u #d 020 $a978-9974-38-406-4 022 $a1688-9266 024 7 $ahttp://doi.org/10.35676/INIA/ST.241$2DOI 100 1 $aGUTIERREZ, L. 245 $aBiostatistical tools for plant breeding in the genomics era.$h[electronic resource] 260 $c2018 300 $ap.46-57. 490 $a(INIA Serie Técnica; 241). 520 $aSUMMARY: Since the advent of agriculture, plant breeding has successfully improved plantsfor human benefit. Modern plant breeding activities consist in evaluating the genetic merit of lines discerning genetic from environment and noise components. To do so, modern plant breeding relies on the genetics foundations derived from Mendel?s work and statistical tools (or biometry) generated afterwards. Plant breeding activities could be grouped in three categories: traditional, marker assisted (MAS), and genomic selection (GS). Traditional plant breeding uses either per sephenotypic information, or information from relatives to evaluate the genetic value. MAS on the other hand, involves the identification of markers linked to genes or quantitative traits loci (QTL) of relevant traits, and then selecting individuals based on their marker scores. Finally, GS involves the prediction of the genetic merit of individuals based on their marker scores and a statistical model. All of the three strategies require the evaluation of large number of individuals creating massive amounts of data that needs proper analyses. Our objective was to present some biostatistical strategies that are successfully being used in plant breeding programs. First, we used novel simulation 650 $aGENOTIPOS 653 $aGENOMIC SELECTION 653 $aGENOTYPE BY ENVIRONMENT INTERACTION 653 $aGWAS 653 $aQTL MAPPING 700 1 $aBORGES, A. 700 1 $aQUERO, G. 700 1 $aGONZALEZ-REYMUNDEZ, A. 700 1 $aBERRO, I. 700 1 $aLADO, B. 700 1 $aCASTRO, A. 773 $tIn: German, S.; Quincke, M.; Vázquez, D.; Castro, M.; Pereyra, S.; Silva, P.; García, A. (Eds.). Seminario Internacional "1914-2014: Un siglo de mejoramiento de trigo en La Estanzuela". Montevideo (UY): INIA, 2018.
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Registro original : |
INIA La Estanzuela (LE) |
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Registro completo
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Biblioteca (s) : |
INIA Treinta y Tres. |
Fecha actual : |
26/07/2021 |
Actualizado : |
26/07/2021 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Circulación / Nivel : |
-- - -- |
Autor : |
TSENG, C-M.; ROEL, A.; MACEDO, I.; MARELLA, M.; TERRA, J.A.; PITTELKOW, C. M. |
Afiliación : |
MENG-CHUN TSENG, Department of Crop Sciences, University of Illinois, Urbana, IL 61801, USA; ALVARO ROEL DELLAZOPPA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; IGNACIO MACEDO YAPOR, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MUZIO MARELLA, Sociedad Anónima Molinos Arroceros Nacionales (SAMAN), Uruguay; JOSÉ ALFREDO TERRA FERNÁNDEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; CAMERON M. PITTELKOW, Department of Plant Sciences, University of California, Davis, CA 95616, USA. |
Título : |
Synergies and tradeoffs among yield, resource use efficiency, and environmental footprint indicators in rice systems. |
Fecha de publicación : |
2021 |
Fuente / Imprenta : |
Current Research in Environmental Sustainability, 2021, volume 3, 100070. OPEN ACCESS. DOI: https://doi.org/10.1016/j.crsust.2021.100070 |
DOI : |
10.1016/j.crsust.2021.100070 |
Idioma : |
Inglés |
Notas : |
Article history: Received 30 April 2021 / / Revised 12 July 2021 // Accepted 13 July 2021 // Available online 24 July 2021. |
Contenido : |
A major question facing global rice systems is the extent to which yield and resource use efficiency indicators can be simultaneously optimized to sustainably meet future food demand. However, research approaches for evaluating synergies and tradeoffs among multiple indicators have been limited to date. Using the case study of rice production in Uruguay, we quantified five cropping system performance indicators at the farm-level from 2012 to 2017, covering approximately 40% of national rice area. Results suggest that maximizing performance in one
indicator is associated with tradeoffs for other indicators, with no farm simultaneously ranking as a topperformer (defined as top 10% of farms) across all indicators. The gaps between the average and topperforming farms were largest for agrochemical contamination risk (33%) and smallest for yield (11%). Comparing the groups of top-performing farms within each indicator revealed opportunities for improving system-level performance via synergistic effects between yield and resource use efficiencies, but not between carbon footprint, agrochemical contamination risk, and other indicators. Importantly, synergistic effects were more pronounced for farms at lower compared to higher productivity levels, suggesting less room for ustainability improvements at higher yield levels, unless yields can be further increased without elevated inputs. Important factors to improve the aggregated sustainability index included N fertilizer rate and seeding date. With potential application to rice production systems worldwide, this study highlights an integrated research approach for quantifying synergies and tradeoffs among multiple indicators to understand opportunities for increasing crop yields without negatively impacting resource use efficiency and environmental footprint. MenosA major question facing global rice systems is the extent to which yield and resource use efficiency indicators can be simultaneously optimized to sustainably meet future food demand. However, research approaches for evaluating synergies and tradeoffs among multiple indicators have been limited to date. Using the case study of rice production in Uruguay, we quantified five cropping system performance indicators at the farm-level from 2012 to 2017, covering approximately 40% of national rice area. Results suggest that maximizing performance in one
indicator is associated with tradeoffs for other indicators, with no farm simultaneously ranking as a topperformer (defined as top 10% of farms) across all indicators. The gaps between the average and topperforming farms were largest for agrochemical contamination risk (33%) and smallest for yield (11%). Comparing the groups of top-performing farms within each indicator revealed opportunities for improving system-level performance via synergistic effects between yield and resource use efficiencies, but not between carbon footprint, agrochemical contamination risk, and other indicators. Importantly, synergistic effects were more pronounced for farms at lower compared to higher productivity levels, suggesting less room for ustainability improvements at higher yield levels, unless yields can be further increased without elevated inputs. Important factors to improve the aggregated sustainability index included N fertilizer rate and seed... Presentar Todo |
Palabras claves : |
CARBON FOOTPRINT; ENVIRONMENTAL IMPACT; NITROGEN USE EFFICIENCE; RICE; SUSTAINABILITY; TRADEOFFS. |
Asunto categoría : |
P01 Conservación de la naturaleza y recursos de La tierra |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/15908/1/Current-Research-Environmental-Sustainability-2021-3-100070.pdf
https://www.sciencedirect.com/science/article/pii/S2666049021000463
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Marc : |
LEADER 02847naa a2200277 a 4500 001 1062325 005 2021-07-26 008 2021 bl uuuu u00u1 u #d 024 7 $a10.1016/j.crsust.2021.100070$2DOI 100 1 $aTSENG, C-M. 245 $aSynergies and tradeoffs among yield, resource use efficiency, and environmental footprint indicators in rice systems.$h[electronic resource] 260 $c2021 500 $aArticle history: Received 30 April 2021 / / Revised 12 July 2021 // Accepted 13 July 2021 // Available online 24 July 2021. 520 $aA major question facing global rice systems is the extent to which yield and resource use efficiency indicators can be simultaneously optimized to sustainably meet future food demand. However, research approaches for evaluating synergies and tradeoffs among multiple indicators have been limited to date. Using the case study of rice production in Uruguay, we quantified five cropping system performance indicators at the farm-level from 2012 to 2017, covering approximately 40% of national rice area. Results suggest that maximizing performance in one indicator is associated with tradeoffs for other indicators, with no farm simultaneously ranking as a topperformer (defined as top 10% of farms) across all indicators. The gaps between the average and topperforming farms were largest for agrochemical contamination risk (33%) and smallest for yield (11%). Comparing the groups of top-performing farms within each indicator revealed opportunities for improving system-level performance via synergistic effects between yield and resource use efficiencies, but not between carbon footprint, agrochemical contamination risk, and other indicators. Importantly, synergistic effects were more pronounced for farms at lower compared to higher productivity levels, suggesting less room for ustainability improvements at higher yield levels, unless yields can be further increased without elevated inputs. Important factors to improve the aggregated sustainability index included N fertilizer rate and seeding date. With potential application to rice production systems worldwide, this study highlights an integrated research approach for quantifying synergies and tradeoffs among multiple indicators to understand opportunities for increasing crop yields without negatively impacting resource use efficiency and environmental footprint. 653 $aCARBON FOOTPRINT 653 $aENVIRONMENTAL IMPACT 653 $aNITROGEN USE EFFICIENCE 653 $aRICE 653 $aSUSTAINABILITY 653 $aTRADEOFFS 700 1 $aROEL, A. 700 1 $aMACEDO, I. 700 1 $aMARELLA, M. 700 1 $aTERRA, J.A. 700 1 $aPITTELKOW, C. M. 773 $tCurrent Research in Environmental Sustainability, 2021, volume 3, 100070. OPEN ACCESS. DOI: https://doi.org/10.1016/j.crsust.2021.100070
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