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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha : |
27/04/2021 |
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Actualizado : |
27/04/2021 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Autor : |
SIMEONE, M.; GÓMEZ, C.; BERTALMIO, A.; RUIZ, E.; HAUTEVILLE, C.; GODOY, L.; TITO, B.; GARCÍA, M.L. |
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Afiliación : |
MELINA SIMEONE, Instituto de Biotecnología y Biología Molecular, CCT-La Plata CONICET-UNLP Facultad de Ciencias Exactas, UNLP, La Plata, Argentina; CLAUDIO GÓMEZ, Estación Experimental Agropecuaria Concordia, Instituto Nacional de Tecnología Agropecuaria (INTA), Entre Ríos, Argentina; ANA MARIA BERTALMIO CASARIEGO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ESPERANZA RUIZ, Laboratorio de Investigación y Desarrollo de Bioactivos, Facultad de Ciencias Exactas, UNLP, La Plata, Argentina; CLAUDIA HAUTEVILLE, Estación Experimental Agropecuaria Concordia, Instituto Nacional de Tecnología Agropecuaria (INTA), Entre Ríos, Argentina; LAURA GODOY SUÁREZ, Estación Experimental Agropecuaria Concordia, Instituto Nacional de Tecnología Agropecuaria (INTA), Entre Ríos, Argentina; BLAS TITO, Estación Experimental Agropecuaria Concordia, Instituto Nacional de Tecnología Agropecuaria (INTA), Entre Ríos, Argentina; MARÍA L. GARCÍA, Instituto de Biotecnología y Biología Molecular, CCT?La Plata CONICET?UNLP Facultad de Ciencias Exactas, UNLP, La Plata, Argentina. |
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Título : |
Detection of citrus psorosis virus by RT‐qPCR validated by diagnostic parameters. |
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Fecha de publicación : |
2021 |
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Fuente / Imprenta : |
Plant Pathology, May 2021, Volume 70, Issue 4, Pages 980-986. Doi: https://doi.org/10.1111/ppa.13341 |
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ISSN : |
0032-0862 |
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DOI : |
10.1111/ppa.13341 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received, 8 September 2020; Accepted, 28 December 2020, First published, 18 January 2021.
This work was supported by Agencia Nacional de Promoción Científica y Tecnológica (ANPCYT) PICT 2014‐1007 and PICT Start UP 2014‐3762, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) Proyectos de Investigación de Unidades Ejecutoras?(IBBM), Universidad Nacional de La Plata (UNLP) X‐692, and Instituto Nacional de Tecnología Agropecuaria (INTA) (PNFRU‐1172; 11721; ERIOS‐630081, PD I081 and RIST I091). M.S. was supported by ANPCyT and CONICET. M.L.G. belongs to CONICET and Departamento de Ciencias Biológicas, Facultad de Ciencias Exactas, UNLP. We thank Beatriz Stein and Julia Figueroa from the Estación Experimental Agroindustrial Obispo Colombres (EEOC), Tucumán, for providing samples from their collection and Magalí Gabrielli for technical assistance in the total RNA extractions. We thank Pedro Moreno for helpful discussion and critical reading of the manuscript. |
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Contenido : |
ABSTRACT.
Citrus psorosis virus (CPsV) is the causal agent of psorosis, an important disease of citrus. Sanitary and certification programmes helped reduce disease damage caused by psorosis and other graft‐transmissible diseases in many citrus‐growing regions. For quarantine and certification programmes, most of these diseases are currently diagnosed using biological indexing (BI) on sensitive indicator plants. In the case of citrus psorosis, CPsV can be detected by molecular methods such as quantitative reverse transcription PCR (RT‐qPCR), which is cheaper and faster than BI, but sensitivity, reliability, and reproducibility of both procedures have not been compared so far. In this work, 128 plants from Argentina and Uruguay were analysed using BI and CPsV detection by the RT‐qPCR assay. Almost perfect agreement between both diagnostic procedures and sensitivity, specificity, and estimated likelihood ratios indicate that RT‐qPCR is equivalent to BI for citrus psorosis diagnosis, thus providing confidence in the quick diagnostic procedure to monitor the sanitary status of citrus trees.
© 2021 British Society for Plant Pathology |
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Palabras claves : |
Citrus psorosis virus; Diagnostic parameters; RT-qPCR. |
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Asunto categoría : |
H20 Enfermedades de las plantas |
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Marc : |
LEADER 03027naa a2200277 a 4500
001 1061995
005 2021-04-27
008 2021 bl uuuu u00u1 u #d
022 $a0032-0862
024 7 $a10.1111/ppa.13341$2DOI
100 1 $aSIMEONE, M.
245 $aDetection of citrus psorosis virus by RT‐qPCR validated by diagnostic parameters.$h[electronic resource]
260 $c2021
500 $aArticle history: Received, 8 September 2020; Accepted, 28 December 2020, First published, 18 January 2021. This work was supported by Agencia Nacional de Promoción Científica y Tecnológica (ANPCYT) PICT 2014‐1007 and PICT Start UP 2014‐3762, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) Proyectos de Investigación de Unidades Ejecutoras?(IBBM), Universidad Nacional de La Plata (UNLP) X‐692, and Instituto Nacional de Tecnología Agropecuaria (INTA) (PNFRU‐1172; 11721; ERIOS‐630081, PD I081 and RIST I091). M.S. was supported by ANPCyT and CONICET. M.L.G. belongs to CONICET and Departamento de Ciencias Biológicas, Facultad de Ciencias Exactas, UNLP. We thank Beatriz Stein and Julia Figueroa from the Estación Experimental Agroindustrial Obispo Colombres (EEOC), Tucumán, for providing samples from their collection and Magalí Gabrielli for technical assistance in the total RNA extractions. We thank Pedro Moreno for helpful discussion and critical reading of the manuscript.
520 $aABSTRACT. Citrus psorosis virus (CPsV) is the causal agent of psorosis, an important disease of citrus. Sanitary and certification programmes helped reduce disease damage caused by psorosis and other graft‐transmissible diseases in many citrus‐growing regions. For quarantine and certification programmes, most of these diseases are currently diagnosed using biological indexing (BI) on sensitive indicator plants. In the case of citrus psorosis, CPsV can be detected by molecular methods such as quantitative reverse transcription PCR (RT‐qPCR), which is cheaper and faster than BI, but sensitivity, reliability, and reproducibility of both procedures have not been compared so far. In this work, 128 plants from Argentina and Uruguay were analysed using BI and CPsV detection by the RT‐qPCR assay. Almost perfect agreement between both diagnostic procedures and sensitivity, specificity, and estimated likelihood ratios indicate that RT‐qPCR is equivalent to BI for citrus psorosis diagnosis, thus providing confidence in the quick diagnostic procedure to monitor the sanitary status of citrus trees. © 2021 British Society for Plant Pathology
653 $aCitrus psorosis virus
653 $aDiagnostic parameters
653 $aRT-qPCR
700 1 $aGÓMEZ, C.
700 1 $aBERTALMIO, A.
700 1 $aRUIZ, E.
700 1 $aHAUTEVILLE, C.
700 1 $aGODOY, L.
700 1 $aTITO, B.
700 1 $aGARCÍA, M.L.
773 $tPlant Pathology, May 2021, Volume 70, Issue 4, Pages 980-986. Doi: https://doi.org/10.1111/ppa.13341
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas; INIA Treinta y Tres. |
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Fecha actual : |
25/01/2019 |
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Actualizado : |
22/12/2020 |
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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 : |
PRAVIA, V.; KEMANIAN, A. R.; TERRA, J.A.; SHI, Y.; MACEDO, I.; GOSLEE, S. |
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Afiliación : |
MARIA VIRGINIA PRAVIA NIN, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ARMEN R. KEMANIAN, Department of Plant Science, The Pennsylvania State University, USA.; JOSÉ ALFREDO TERRA FERNÁNDEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; YUNING SHI, Department of Ecosystem Science and Management, The Pennsylvania State University, USA.; IGNACIO MACEDO YAPOR, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; SARAH GOSLEE, Pasture Systems and Watershed Management Research Unit, USDA-ARS, USA. |
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Título : |
Soil carbon saturation, productivity, and carbon and nitrogen cycling in crop-pasture rotations. |
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Fecha de publicación : |
2019 |
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Fuente / Imprenta : |
Agricultural Systems, May 2019, volume 171, pages 13-22. |
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ISSN : |
0308-521X |
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DOI : |
10.1016/j.agsy.2018.11.001 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 30 December 2017 // Received in revised form 2 November 2018 // Accepted 2 November 2018.
Funding for this work was provided by the Instituto Nacional de Investigación Agropecuaria (INIA-Uruguay) and the USDA-ARS Research Agreement Contract #58-1902-1-165 (Modeling of multispecies pasture growth and management). Appendices. |
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Contenido : |
ABSTRACT.
Agricultural systems integrating perennial grass-legume pastures in rotation with grain crops sustain high crop yields while preserving soil organic carbon (Cs) with low nitrogen (N) fertilizer inputs. We hypothesize that Cs saturation in the topsoil may explain the favorable C and N cycling in these systems. We tested this hypothesis by evaluating and simulating three contrasting crop and pasture rotational systems from a 20-year no-till experiment in Treinta y Tres, Uruguay. The systems were: 1) Continuous annual cropping (CC); 2) crop-pasture rotation with two years of crops and four years of pastures (CP); and 3) perennial pasture (PP). Using the Cycles agroecosystems model, we evaluated the inclusion or exclusion of a Cs saturation algorithm. The model simulated forage, soybean, and sorghum grain yields correctly, with low root mean square error (RMSE) of 1.5, 0.7 and 1.0 Mg ha−1, respectively. Measurements show Cs accretion and Cs decline for the first and second half of the experiment, respectively. The Cs accretion rate was highest for PP, while the Cs decline was highest for CC (1.3 vs −0.6 Mg ha−1 y−1 of C). The model captured this Cs dynamics and performed better when using the Cs saturation algorithm than when excluding it (RMSE 4.7 vs 6.8 Mg C ha−1 and relative RMSE of 14% and 21% for the top 15-cm). The model with saturation simulated subsoil Cs distribution with depth well, and simulated faster N turnover and greater N availability for the subsequent grain crop in CP vs CC. The results suggest that Cs saturation, and by extension soil organic N saturation, underpin the sustainability of crop-pasture rotations, and that modeling Cs saturation dynamics can be critical to reliably simulate complex crop-pasture rotational systems.
© 2018 Elsevier Ltd MenosABSTRACT.
Agricultural systems integrating perennial grass-legume pastures in rotation with grain crops sustain high crop yields while preserving soil organic carbon (Cs) with low nitrogen (N) fertilizer inputs. We hypothesize that Cs saturation in the topsoil may explain the favorable C and N cycling in these systems. We tested this hypothesis by evaluating and simulating three contrasting crop and pasture rotational systems from a 20-year no-till experiment in Treinta y Tres, Uruguay. The systems were: 1) Continuous annual cropping (CC); 2) crop-pasture rotation with two years of crops and four years of pastures (CP); and 3) perennial pasture (PP). Using the Cycles agroecosystems model, we evaluated the inclusion or exclusion of a Cs saturation algorithm. The model simulated forage, soybean, and sorghum grain yields correctly, with low root mean square error (RMSE) of 1.5, 0.7 and 1.0 Mg ha−1, respectively. Measurements show Cs accretion and Cs decline for the first and second half of the experiment, respectively. The Cs accretion rate was highest for PP, while the Cs decline was highest for CC (1.3 vs −0.6 Mg ha−1 y−1 of C). The model captured this Cs dynamics and performed better when using the Cs saturation algorithm than when excluding it (RMSE 4.7 vs 6.8 Mg C ha−1 and relative RMSE of 14% and 21% for the top 15-cm). The model with saturation simulated subsoil Cs distribution with depth well, and simulated faster N turnover and greater N a... Presentar Todo |
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Palabras claves : |
AGROECOSYSTEM MODELING; CROP PASTURE INTERSEEDNG; LONG-TERM EXPERIMENTS; SOIL ORGANIC MATTER. |
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Thesagro : |
CARBONO ORGANICO DEL SUELO. |
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Asunto categoría : |
--
P34 Biología del suelo |
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Marc : |
LEADER 03007naa a2200277 a 4500
001 1059451
005 2020-12-22
008 2019 bl uuuu u00u1 u #d
022 $a0308-521X
024 7 $a10.1016/j.agsy.2018.11.001$2DOI
100 1 $aPRAVIA, V.
245 $aSoil carbon saturation, productivity, and carbon and nitrogen cycling in crop-pasture rotations.$h[electronic resource]
260 $c2019
500 $aArticle history: Received 30 December 2017 // Received in revised form 2 November 2018 // Accepted 2 November 2018. Funding for this work was provided by the Instituto Nacional de Investigación Agropecuaria (INIA-Uruguay) and the USDA-ARS Research Agreement Contract #58-1902-1-165 (Modeling of multispecies pasture growth and management). Appendices.
520 $aABSTRACT. Agricultural systems integrating perennial grass-legume pastures in rotation with grain crops sustain high crop yields while preserving soil organic carbon (Cs) with low nitrogen (N) fertilizer inputs. We hypothesize that Cs saturation in the topsoil may explain the favorable C and N cycling in these systems. We tested this hypothesis by evaluating and simulating three contrasting crop and pasture rotational systems from a 20-year no-till experiment in Treinta y Tres, Uruguay. The systems were: 1) Continuous annual cropping (CC); 2) crop-pasture rotation with two years of crops and four years of pastures (CP); and 3) perennial pasture (PP). Using the Cycles agroecosystems model, we evaluated the inclusion or exclusion of a Cs saturation algorithm. The model simulated forage, soybean, and sorghum grain yields correctly, with low root mean square error (RMSE) of 1.5, 0.7 and 1.0 Mg ha−1, respectively. Measurements show Cs accretion and Cs decline for the first and second half of the experiment, respectively. The Cs accretion rate was highest for PP, while the Cs decline was highest for CC (1.3 vs −0.6 Mg ha−1 y−1 of C). The model captured this Cs dynamics and performed better when using the Cs saturation algorithm than when excluding it (RMSE 4.7 vs 6.8 Mg C ha−1 and relative RMSE of 14% and 21% for the top 15-cm). The model with saturation simulated subsoil Cs distribution with depth well, and simulated faster N turnover and greater N availability for the subsequent grain crop in CP vs CC. The results suggest that Cs saturation, and by extension soil organic N saturation, underpin the sustainability of crop-pasture rotations, and that modeling Cs saturation dynamics can be critical to reliably simulate complex crop-pasture rotational systems. © 2018 Elsevier Ltd
650 $aCARBONO ORGANICO DEL SUELO
653 $aAGROECOSYSTEM MODELING
653 $aCROP PASTURE INTERSEEDNG
653 $aLONG-TERM EXPERIMENTS
653 $aSOIL ORGANIC MATTER
700 1 $aKEMANIAN, A. R.
700 1 $aTERRA, J.A.
700 1 $aSHI, Y.
700 1 $aMACEDO, I.
700 1 $aGOSLEE, S.
773 $tAgricultural Systems, May 2019, volume 171, pages 13-22.
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