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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha : |
12/12/2016 |
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Actualizado : |
12/12/2016 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Autor : |
MITTON, G. A.; QUINTANA, S.; GIMÉNEZ MARTÍNEZ, P.; MENDOZA, Y.; RAMALLO, G.; BRASESCO, C.; VILLALBA, A.; EGUARAS, M. J.; MAGGI, M. D.; RUFFINENGO, S. R. |
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Afiliación : |
GIULIA A. MITTON, Universidad Nacional de Mar del Plata, Mar del Plata, Argentina; CONICET, Consejo Nacional de Investigaciones Científicas y Técnicas, Bs.As., Argentina; SILVINA QUINTANA, Universidad Nacional de Mar del Plata, Mar del Plata, Argentina; Fares Taie Instituto de Ana´lisis, Mar del Plata, Argentina; PABLO GIMÉNEZ MARTÍNEZ, Universidad Nacional de Mar del Plata, Mar del Plata, Argentina; Agencia Nacional de Promoción Científica y Tecnológica, Bs.As, Argentina; YAMANDU MENDOZA SPINA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; GUSTAVO NOEL RAMALLO MEDINA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; CONSTANZA BRASESCO, Universidad Nacional de Mar del Plata, Mar del Plata, Argentina; CONICET, Consejo Nacional de Investigaciones Científicas y Técnicas, Bs.As., Argentina; AGUSTINA VILLALBA, Universidad Nacional de Mar del Plata, Mar del Plata, Argentina; MARTÍN J. EGUARAS, Universidad Nacional de Mar del Plata, Mar del Plata, Argentina; CONICET, Consejo Nacional de Investigaciones Científicas y Técnicas, Bs.As., Argentina; MATÍAS D. MAGGI, Universidad Nacional de Mar del Plata, Mar del Plata, Argentina; CONICET, Consejo Nacional de Investigaciones Científicas y Técnicas, Bs.As., Argentina; SERGIO R. RUFFINENGO, Universidad Nacional de Mar del Plata, Mar del Plata, Argentina; Universidad Nacional de Mar del Plata (UNMdP), Balcarce, Argentina. |
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Título : |
First record of resistance to flumethrin in a varroa population from Uruguay. [Primer registro de resistencia a flumetrina en una población de Varroa destructor en Uruguay] |
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Fecha de publicación : |
2016 |
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Fuente / Imprenta : |
Journal of Apicultural Research, 2016, Pages 1-6 |
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DOI : |
10.1080/00218839.2016.1257238 |
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Idioma : |
Inglés |
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Contenido : |
ABSTRACT.
Varroa destructor is the most serious parasitic mite that infests the honey bee Apis mellifera. Different treatments with systemic acaricides are applied to control, but due to the intensive use of these chemicals, resistance to organophosphates and pyrethroids has developed worldwide. Most of the resistance episodes have been registered for fluvalinate, and only in a few cases has flumethrin resistance been reported. In Uruguay, no studies on V. destructor resistance to flumethrin have yet been recorded. High infestation levels of V. destructor are continuously detected in colonies of A. mellifera after treatments with flumethrin. Hence, this study aimed to estimate the possible resistance to flumethrin of a V. destructor population from the Colonia Department. Furthermore, the LC50 baseline levels for flumethrin in two Uruguayan populations were determinated. The LC50 for flumethrin for the population from Colonia Department was 3.8 μg/Petri dish, which means an increase of 34.5 fold when compared to the corresponding baseline, suggesting the development of resistance. These results are the first report of resistance to flumethrin in V. destructor in Uruguay, and extend the knowledge of acaricide resistance in the country.
© 2016 National Scientific and Technical Research Council (CONICET) |
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Palabras claves : |
BIOASSAY; FLUMETHRIN; RESISTANCE. |
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Thesagro : |
APICULTURA; APIS MELLIFERA; URUGUAY; VARROA DESTRUCTOR. |
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Asunto categoría : |
-- |
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Marc : |
LEADER 02321naa a2200325 a 4500
001 1056253
005 2016-12-12
008 2016 bl uuuu u00u1 u #d
024 7 $a10.1080/00218839.2016.1257238$2DOI
100 1 $aMITTON, G. A.
245 $aFirst record of resistance to flumethrin in a varroa population from Uruguay. [Primer registro de resistencia a flumetrina en una población de Varroa destructor en Uruguay]$h[electronic resource]
260 $c2016
520 $aABSTRACT. Varroa destructor is the most serious parasitic mite that infests the honey bee Apis mellifera. Different treatments with systemic acaricides are applied to control, but due to the intensive use of these chemicals, resistance to organophosphates and pyrethroids has developed worldwide. Most of the resistance episodes have been registered for fluvalinate, and only in a few cases has flumethrin resistance been reported. In Uruguay, no studies on V. destructor resistance to flumethrin have yet been recorded. High infestation levels of V. destructor are continuously detected in colonies of A. mellifera after treatments with flumethrin. Hence, this study aimed to estimate the possible resistance to flumethrin of a V. destructor population from the Colonia Department. Furthermore, the LC50 baseline levels for flumethrin in two Uruguayan populations were determinated. The LC50 for flumethrin for the population from Colonia Department was 3.8 μg/Petri dish, which means an increase of 34.5 fold when compared to the corresponding baseline, suggesting the development of resistance. These results are the first report of resistance to flumethrin in V. destructor in Uruguay, and extend the knowledge of acaricide resistance in the country. © 2016 National Scientific and Technical Research Council (CONICET)
650 $aAPICULTURA
650 $aAPIS MELLIFERA
650 $aURUGUAY
650 $aVARROA DESTRUCTOR
653 $aBIOASSAY
653 $aFLUMETHRIN
653 $aRESISTANCE
700 1 $aQUINTANA, S.
700 1 $aGIMÉNEZ MARTÍNEZ, P.
700 1 $aMENDOZA, Y.
700 1 $aRAMALLO, G.
700 1 $aBRASESCO, C.
700 1 $aVILLALBA, A.
700 1 $aEGUARAS, M. J.
700 1 $aMAGGI, M. D.
700 1 $aRUFFINENGO, S. R.
773 $tJournal of Apicultural Research, 2016, Pages 1-6
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INIA Las Brujas (LB) |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
16/04/2024 |
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Actualizado : |
18/04/2024 |
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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 : |
MACEDO, I.; PITTELKOW, C.M.; TERRA, J.A.; CASTILLO, J.; ROEL, A. |
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Afiliación : |
IGNACIO MACEDO YAPOR, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; Department of Plant Sciences, Univ. of California, Davis, CA, USA; CAMERON M. PITTELKOW, Department of Plant Sciences, Univ. of California, Davis, CA, USA; JOSÉ ALFREDO TERRA FERNÁNDEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; EMILSE JESUS CASTILLO VELAZQUEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; ALVARO ROEL DELLAZOPPA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
The power of on-farm data for improved agronomy. |
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Fecha de publicación : |
2024 |
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Fuente / Imprenta : |
Global Food Security. 2024, Volume 40, 100752. https://doi.org/10.1016/j.gfs.2024.100752 -- OPEN ACCESS. |
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ISSN : |
2211-9124 |
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DOI : |
10.1016/j.gfs.2024.100752 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 24 November 2023, Revised 27 February 2024, Accepted 3 March 2024, Available online 16 March 2024, Version of Record 16 March 2024. -- Correspondence: Macedo, I.; Department of Plant Sciences, Univ. of California, Davis, CA, United States; email:imacedo@inia.org.uy -- Document type: Article Hybrid Gold Open Access. -- Incluye Appendix A. Supplementary data -- Data availability:
Data will be made available on request. -- License: Under Creative Commons license http://creativecommons.org/licenses/by-nc-nd/4.0/ -- |
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Contenido : |
ABSTRACT.- Advances in technology and analytics to support data-driven agriculture has important implications for global food security and environmental sustainability. However, relatively few studies have investigated the potential to leverage the power of on-farm data for improved agronomy at scale using geospatial machine learning methods. Working in high-yielding rice systems of Uruguay, we developed a geospatial framework to identify yield-limiting factors across 55,000 ha annually of cropland over four seasons (2018?2021 harvest years), while also testing for tradeoffs in the environmental footprint related to nitrogen (N) fertilizer use. Our application of geographically-weighted random forest models showed that crop management decisions influenced rice yield more than variation in soil properties, highlighting the potential for improved agronomy to boost crop production by 1.4-1.8 Mg ha-1 across regions. Seeding date, variety, P rate, and K rate were the most important variables controlling yield, but with significant variation across fields. When these factors were optimized by farmers, the risk of environmental N losses or soil N mining did not increase, highlighting the potential for sustainable intensification by improving N use efficiency. These findings present a pathway for harnessing the benefits of increasingly available on-farm data to identify yield-limiting factors while minimizing negative environmental externalities at the field-level. To enable the development of such geospatial frameworks in other regions, new partnerships are required to engage stakeholders and promote data sharing and collaboration among farmers, researchers, and industry, helping guide regional extension programs and orient future investments in agricultural research. © 2024 The Authors MenosABSTRACT.- Advances in technology and analytics to support data-driven agriculture has important implications for global food security and environmental sustainability. However, relatively few studies have investigated the potential to leverage the power of on-farm data for improved agronomy at scale using geospatial machine learning methods. Working in high-yielding rice systems of Uruguay, we developed a geospatial framework to identify yield-limiting factors across 55,000 ha annually of cropland over four seasons (2018?2021 harvest years), while also testing for tradeoffs in the environmental footprint related to nitrogen (N) fertilizer use. Our application of geographically-weighted random forest models showed that crop management decisions influenced rice yield more than variation in soil properties, highlighting the potential for improved agronomy to boost crop production by 1.4-1.8 Mg ha-1 across regions. Seeding date, variety, P rate, and K rate were the most important variables controlling yield, but with significant variation across fields. When these factors were optimized by farmers, the risk of environmental N losses or soil N mining did not increase, highlighting the potential for sustainable intensification by improving N use efficiency. These findings present a pathway for harnessing the benefits of increasingly available on-farm data to identify yield-limiting factors while minimizing negative environmental externalities at the field-level. To enable the dev... Presentar Todo |
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Palabras claves : |
Data-driven research; Decent work and economic growth - Goal 8; Geospatial data; Industry, innovation and infrastructure - Goal 9; Life on land - Goal 15; Nitrogen balance; Partnership for the goals - Goal 17; Responsible consumption and production - Goal 12; Rice; SISTEMA ARROZ-GANADERÍA - INIA; Sustainability; Sustainable Development Goals (SDGs); Zero hunger - Goal 2. |
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Asunto categoría : |
F01 Cultivo |
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URL : |
https://www.sciencedirect.com/science/article/pii/S2211912424000142/pdf
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Marc : |
LEADER 03526naa a2200361 a 4500
001 1064590
005 2024-04-18
008 2024 bl uuuu u00u1 u #d
022 $a2211-9124
024 7 $a10.1016/j.gfs.2024.100752$2DOI
100 1 $aMACEDO, I.
245 $aThe power of on-farm data for improved agronomy.$h[electronic resource]
260 $c2024
500 $aArticle history: Received 24 November 2023, Revised 27 February 2024, Accepted 3 March 2024, Available online 16 March 2024, Version of Record 16 March 2024. -- Correspondence: Macedo, I.; Department of Plant Sciences, Univ. of California, Davis, CA, United States; email:imacedo@inia.org.uy -- Document type: Article Hybrid Gold Open Access. -- Incluye Appendix A. Supplementary data -- Data availability: Data will be made available on request. -- License: Under Creative Commons license http://creativecommons.org/licenses/by-nc-nd/4.0/ --
520 $aABSTRACT.- Advances in technology and analytics to support data-driven agriculture has important implications for global food security and environmental sustainability. However, relatively few studies have investigated the potential to leverage the power of on-farm data for improved agronomy at scale using geospatial machine learning methods. Working in high-yielding rice systems of Uruguay, we developed a geospatial framework to identify yield-limiting factors across 55,000 ha annually of cropland over four seasons (2018?2021 harvest years), while also testing for tradeoffs in the environmental footprint related to nitrogen (N) fertilizer use. Our application of geographically-weighted random forest models showed that crop management decisions influenced rice yield more than variation in soil properties, highlighting the potential for improved agronomy to boost crop production by 1.4-1.8 Mg ha-1 across regions. Seeding date, variety, P rate, and K rate were the most important variables controlling yield, but with significant variation across fields. When these factors were optimized by farmers, the risk of environmental N losses or soil N mining did not increase, highlighting the potential for sustainable intensification by improving N use efficiency. These findings present a pathway for harnessing the benefits of increasingly available on-farm data to identify yield-limiting factors while minimizing negative environmental externalities at the field-level. To enable the development of such geospatial frameworks in other regions, new partnerships are required to engage stakeholders and promote data sharing and collaboration among farmers, researchers, and industry, helping guide regional extension programs and orient future investments in agricultural research. © 2024 The Authors
653 $aData-driven research
653 $aDecent work and economic growth - Goal 8
653 $aGeospatial data
653 $aIndustry, innovation and infrastructure - Goal 9
653 $aLife on land - Goal 15
653 $aNitrogen balance
653 $aPartnership for the goals - Goal 17
653 $aResponsible consumption and production - Goal 12
653 $aRice
653 $aSISTEMA ARROZ-GANADERÍA - INIA
653 $aSustainability
653 $aSustainable Development Goals (SDGs)
653 $aZero hunger - Goal 2
700 1 $aPITTELKOW, C.M.
700 1 $aTERRA, J.A.
700 1 $aCASTILLO, J.
700 1 $aROEL, A.
773 $tGlobal Food Security. 2024, Volume 40, 100752. https://doi.org/10.1016/j.gfs.2024.100752 -- OPEN ACCESS.
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