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Biblioteca (s) : |
INIA Las Brujas. |
Fecha : |
18/08/2023 |
Actualizado : |
18/08/2023 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Autor : |
CAPELESSO, A.; FARIÑA, S.; CAJARVILLE, C.; MENDOZA, A. |
Afiliación : |
A. CAPELESSO, Facultad de Veterinaria, Universidad de la República, Uruguay; SANTIAGO FARIÑA, INIA (Instituto Nacional de Investigación Agropecuaria); C. CAJARVILLE, Facultad de Veterinaria, Universidad de la República, Uruguay; ALEJANDRO FRANCISCO MENDOZA AGUIAR, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
Título : |
O32 (not presented). Effect of feeding strategy and cow genotype on feed efficiency in pasture-based feeding systems. [conference abstract]. |
Fecha de publicación : |
2023 |
Fuente / Imprenta : |
Animal - science proceedings, July 2023, Volume 14, Issue 4, Pages 566-567. https://doi.org/10.1016/j.anscip.2023.04.033 -- OPEN ACCESS. |
ISSN : |
2772-283X |
DOI : |
10.1016/j.anscip.2023.04.033 |
Idioma : |
Inglés |
Notas : |
Article history: Available online 4 August 2023, Version of Record 4 August 2023. -- Corresponding author: A. Capelesso. ascapelesso@gmail.com --
Part of special issue: 11th International Symposium on the Nutrition of Herbivores (ISNH 2023). 4-8 June 2023, Florianópolis, Brazil. (https://www.sciencedirect.com/journal/animal-science-proceedings/vol/14/issue/4 ) -- |
Contenido : |
Feed efficiency is a most common method to determine biological efficiency of milk production. One way to increase feed efficiency is to balance rations and make more strategic use of supplement. Admore, it is known that cows selected for milk yield have a higher feed efficiency when fed well-balanced diets ad libitum, but it is not clear how it is affected as pasture is included in the diet. Thereby, the aim of this study was to quantify the effect of feeding strategy (FS) and cow genotype (G) on individual animal performance and feed efficiency in pasture-based feeding systems during spring. The experiment was a randomized complete block design, with a 2?×?2 factorial arrangement of treatments, combining feeding strategies and Holstein Friesian cow genotypes. |
Palabras claves : |
Concentrate; Dairy cows; Grazing; Total mixed ration. |
Asunto categoría : |
L02 Alimentación animal |
URL : |
https://www.sciencedirect.com/science/article/pii/S2772283X23008099/pdf
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Marc : |
LEADER 01942nam a2200229 a 4500 001 1064297 005 2023-08-18 008 2023 bl uuuu u01u1 u #d 022 $a2772-283X 024 7 $a10.1016/j.anscip.2023.04.033$2DOI 100 1 $aCAPELESSO, A. 245 $aO32 (not presented). Effect of feeding strategy and cow genotype on feed efficiency in pasture-based feeding systems. [conference abstract].$h[electronic resource] 260 $aAnimal - science proceedings, July 2023, Volume 14, Issue 4, Pages 566-567. https://doi.org/10.1016/j.anscip.2023.04.033 -- OPEN ACCESS.$c2023 500 $aArticle history: Available online 4 August 2023, Version of Record 4 August 2023. -- Corresponding author: A. Capelesso. ascapelesso@gmail.com -- Part of special issue: 11th International Symposium on the Nutrition of Herbivores (ISNH 2023). 4-8 June 2023, Florianópolis, Brazil. (https://www.sciencedirect.com/journal/animal-science-proceedings/vol/14/issue/4 ) -- 520 $aFeed efficiency is a most common method to determine biological efficiency of milk production. One way to increase feed efficiency is to balance rations and make more strategic use of supplement. Admore, it is known that cows selected for milk yield have a higher feed efficiency when fed well-balanced diets ad libitum, but it is not clear how it is affected as pasture is included in the diet. Thereby, the aim of this study was to quantify the effect of feeding strategy (FS) and cow genotype (G) on individual animal performance and feed efficiency in pasture-based feeding systems during spring. The experiment was a randomized complete block design, with a 2?×?2 factorial arrangement of treatments, combining feeding strategies and Holstein Friesian cow genotypes. 653 $aConcentrate 653 $aDairy cows 653 $aGrazing 653 $aTotal mixed ration 700 1 $aFARIÑA, S. 700 1 $aCAJARVILLE, C. 700 1 $aMENDOZA, A.
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INIA Las Brujas (LB) |
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Biblioteca (s) : |
INIA La Estanzuela. |
Fecha actual : |
06/06/2019 |
Actualizado : |
14/08/2019 |
Tipo de producción científica : |
Poster |
Autor : |
PEZARD, J.; FERNANDEZ, P.; PEREYRA, S.; QUINCKE, M.; SAINT-PIERRE, C.; SINGH, P.K.; AZZIMONTI, G. |
Afiliación : |
AgroParisTech, Paris, France.; PETER DENNIS FERNANDEZ GRAF, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; SILVIA ANTONIA PEREYRA CORREA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MARTIN CONRADO QUINCKE WALDEN, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; CAROLINA SAINT-PIERRE, International Maize and Wheat Improvement Center (CIMMYT), El Batán, México.; PAWAN K. SINGH, International Maize and Wheat Improvement Center (CIMMYT), El Batán, México.; GUSTAVO AZZIMONTI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
Título : |
Adapting automated image analysis to breeding programs constraints for the characterization of the resistance to leaf rust and other diseases. |
Fecha de publicación : |
2018 |
Fuente / Imprenta : |
In: Proceedings of the International Cereal Rusts and Powdery Mildew Conference (ICRPMC): Skukuza, South Africa, 23-26 september 2018. |
Idioma : |
Inglés |
Contenido : |
Description:
Disease phenotyping methods used in breeding programs to characterize the level of resistance of breeding materials usually consist on visual scores (VS) of disease symptoms determined in field trials. VS are considered as high time-consuming and rely on experienced operators. Nevertheless, up to date, it is the only method that has an efficient time/effort relationship considering breeding constrains. The objective was to develop a phenotyping methodology based on automated image analysis (AIA) for leaf diseases, adapted to the constraints of a breeding program. 410 wheat lines from 5 different breeding programs were sowed in three field trials, as part of the materials tested in 2017 at the multi-disease phenotyping platform INIA-CIMMYT, Uruguay. One trial was inoculated with Puccinia triticina isolates the second with Zymoseptoria tritici isolates and the third had natural infection of P. striiformis f. sp. tritici. Six flag leaves per genotype were cut and scanned with a flatbed scanner. A script was developed in the ImageJ software to autonomously recognize and measure the leaf diseased surface. Disease recognition and surface measurements were based on the different threshold color patterns of each disease. Host response was also determined for leaf and stripe rust, measuring the ratio of necrosis-chlorosis/sporulation area of lesions. AIA recognized the different diseases (error<5%). The diseased surfaces obtained by AIA correlated significantly and positively with the VS measured for the three diseases. Host responses estimated by AIA were the same as determined visually, (error<5%). AIA was fast, a mean of 214 leaves/hour analyzed, taking into account the adjustments of color thresholds and the validation of AIA. However, the time to prepare and scan the leaves was higher than the VS: a mean of 205 lines could be scanned per person/day while a mean of 402 lines per person/day could be visually scored. Adjustments to the scan methodology are being carried out to enhance the speed at this step. Nevertheless, AIA can be a performing alternative to VS in limited panels or mapping populations that undergo QTL analysis, where precise measurements of quantitative resistance variables are required to detect QTL with moderate effects and QTL interactions. MenosDescription:
Disease phenotyping methods used in breeding programs to characterize the level of resistance of breeding materials usually consist on visual scores (VS) of disease symptoms determined in field trials. VS are considered as high time-consuming and rely on experienced operators. Nevertheless, up to date, it is the only method that has an efficient time/effort relationship considering breeding constrains. The objective was to develop a phenotyping methodology based on automated image analysis (AIA) for leaf diseases, adapted to the constraints of a breeding program. 410 wheat lines from 5 different breeding programs were sowed in three field trials, as part of the materials tested in 2017 at the multi-disease phenotyping platform INIA-CIMMYT, Uruguay. One trial was inoculated with Puccinia triticina isolates the second with Zymoseptoria tritici isolates and the third had natural infection of P. striiformis f. sp. tritici. Six flag leaves per genotype were cut and scanned with a flatbed scanner. A script was developed in the ImageJ software to autonomously recognize and measure the leaf diseased surface. Disease recognition and surface measurements were based on the different threshold color patterns of each disease. Host response was also determined for leaf and stripe rust, measuring the ratio of necrosis-chlorosis/sporulation area of lesions. AIA recognized the different diseases (error<5%). The diseased surfaces obtained by AIA correlated significantly and posit... Presentar Todo |
Palabras claves : |
INIA-CIMMYT; PLATAFORMA FENOTIPADO DE TRIGO; RUST DISEASE; WHEAT. |
Thesagro : |
ENFERMEDADES DE LAS PLANTAS; Trigo. |
Asunto categoría : |
H20 Enfermedades de las plantas |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/13100/1/PosterPezardetalICRPMC2018.pdf
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Marc : |
LEADER 03183nam a2200253 a 4500 001 1059824 005 2019-08-14 008 2018 bl uuuu u00u1 u #d 100 1 $aPEZARD, J. 245 $aAdapting automated image analysis to breeding programs constraints for the characterization of the resistance to leaf rust and other diseases.$h[electronic resource] 260 $aIn: Proceedings of the International Cereal Rusts and Powdery Mildew Conference (ICRPMC): Skukuza, South Africa, 23-26 september 2018.$c2018 520 $aDescription: Disease phenotyping methods used in breeding programs to characterize the level of resistance of breeding materials usually consist on visual scores (VS) of disease symptoms determined in field trials. VS are considered as high time-consuming and rely on experienced operators. Nevertheless, up to date, it is the only method that has an efficient time/effort relationship considering breeding constrains. The objective was to develop a phenotyping methodology based on automated image analysis (AIA) for leaf diseases, adapted to the constraints of a breeding program. 410 wheat lines from 5 different breeding programs were sowed in three field trials, as part of the materials tested in 2017 at the multi-disease phenotyping platform INIA-CIMMYT, Uruguay. One trial was inoculated with Puccinia triticina isolates the second with Zymoseptoria tritici isolates and the third had natural infection of P. striiformis f. sp. tritici. Six flag leaves per genotype were cut and scanned with a flatbed scanner. A script was developed in the ImageJ software to autonomously recognize and measure the leaf diseased surface. Disease recognition and surface measurements were based on the different threshold color patterns of each disease. Host response was also determined for leaf and stripe rust, measuring the ratio of necrosis-chlorosis/sporulation area of lesions. AIA recognized the different diseases (error<5%). The diseased surfaces obtained by AIA correlated significantly and positively with the VS measured for the three diseases. Host responses estimated by AIA were the same as determined visually, (error<5%). AIA was fast, a mean of 214 leaves/hour analyzed, taking into account the adjustments of color thresholds and the validation of AIA. However, the time to prepare and scan the leaves was higher than the VS: a mean of 205 lines could be scanned per person/day while a mean of 402 lines per person/day could be visually scored. Adjustments to the scan methodology are being carried out to enhance the speed at this step. Nevertheless, AIA can be a performing alternative to VS in limited panels or mapping populations that undergo QTL analysis, where precise measurements of quantitative resistance variables are required to detect QTL with moderate effects and QTL interactions. 650 $aENFERMEDADES DE LAS PLANTAS 650 $aTrigo 653 $aINIA-CIMMYT 653 $aPLATAFORMA FENOTIPADO DE TRIGO 653 $aRUST DISEASE 653 $aWHEAT 700 1 $aFERNANDEZ, P. 700 1 $aPEREYRA, S. 700 1 $aQUINCKE, M. 700 1 $aSAINT-PIERRE, C. 700 1 $aSINGH, P.K. 700 1 $aAZZIMONTI, G.
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