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Biblioteca (s) : |
INIA Treinta y Tres. |
Fecha : |
29/10/2019 |
Actualizado : |
29/10/2019 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Autor : |
SAVIAN, J.V.; PRIANO, M.E.; NADIN, L.B.; TIERI, M.P.; MARINHO TRES SCHONS, R.; BASSO, C.; PONTES PRATES, A.; BAYER, C. |
Afiliación : |
JEAN VICTOR SAVIAN, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. Grazing Ecology Research Group, Federal University of Río Grande do Sul, Porto Alege, RS, Brasil.; MARÍA EUGENIA PRIANO, Research Center in Physics and Engineering of the Center of the Province of Buenos Aires; LAURA BEATRIZ NADIN, Veterinary Faculty, National University of the Centre of the Province of Buenos Aires, Tandil, Argentina; MARÍA PAZ TIERI, National Institute of Agricultural Technology, Rafaela, Santa Fé, Argentina; RADAEL MARINHO TRES SCHONS, Grazing Ecology Research Group, Federal University of Río Grande do Sul, Porto Alegre, Brasil.; CATARINE BASSO, Grazing Ecology Research Group, Federal University of Río Grande do Sul, RS, Brasil.; ARTHUR PONTES PRATES, Grazing Ecology Research Group, Federal University of Río Grande do Sul, RS, Brasil.; CIMÉLIO BAYER, Department of Soil Science, Federal University of Río Grande do Sul, Porto Alegre, RS, Brasil. |
Título : |
Effect of sward management on the emissions of CH4 and N2O from faeces of sheep grazing Italian ryegrass pastures. |
Fecha de publicación : |
2019 |
Fuente / Imprenta : |
Small Ruminant Reseach, Sept. 2019, volume 178, Pages 123-128. |
DOI : |
10.1016/j.smallrumres.2019.08.011 |
Idioma : |
Inglés |
Notas : |
History Article: Received 4 April 2019; Received in revised from 21 August 2019; Accepted 22 August 2019. Available online 24 August 2019. |
Contenido : |
Rotatinuous stocking (RN) management is based on animal ingestive behaviour responses, where optimal preand post-grazing sward heights are defined to increase nutrient intake per unit of grazing time. We hypothesized that the optimal sward structure and consequently, a high herbage nutritive value in RN treatment results in a
greater faecal nitrogen (N) excretion by sheep and consequently, a greater faecal greenhouse gas (GHG) emissions compared with the traditional rotational stocking (RT) management, which is based on a maximum herbage accumulation and harvest. Therefore, the aim of this study was to evaluate the effect of two grazing
management strategies (RN and RT) on the amount of dry matter (DM) faecal excretion, faecal N excretion and faecal GHG (CH4 and N2O) emissions from growing sheep grazing Italian ryegrass pastures. In order to evaluate faecal production and N excretion per animal and per ha, a first experiment (1) was carried out: RT - pre and post-grazing sward heights of 25 and 5 cm, respectively and, RN - pre and post-grazing sward heights of 18 and 11 cm, respectively. A second experiment (2) was carried out to measure the CH4 and N2O fluxes from faeces, using the static chamber method. Daily DM faecal and N excretion per animal were higher (P<0.001) in RN compared with RT treatment. However, when considered daily DM faecal and N excretion per ha, results were lower (P<0.001) for the RN than the RT treatment. CH4 and N2O emissions from faeces were higher (P<0.001) in RN compared with RT treatment, both per animal and per hectare. In conclusion, our study showed that the RN grazing management, based on animal behaviour, resulted in a higher daily N excretion per animal and higher CH4 and N2O emissions from faeces of sheep grazing Italian ryegrass pastures. This study contributes to improve GHG national inventories for the subtropical Brazilian climatic conditions, where estimations from CH4 and N2O emissions factors for faeces from growing sheep grazing Italian ryegrass are markedly lower than the values reported by IPCC Default Tier 1. MenosRotatinuous stocking (RN) management is based on animal ingestive behaviour responses, where optimal preand post-grazing sward heights are defined to increase nutrient intake per unit of grazing time. We hypothesized that the optimal sward structure and consequently, a high herbage nutritive value in RN treatment results in a
greater faecal nitrogen (N) excretion by sheep and consequently, a greater faecal greenhouse gas (GHG) emissions compared with the traditional rotational stocking (RT) management, which is based on a maximum herbage accumulation and harvest. Therefore, the aim of this study was to evaluate the effect of two grazing
management strategies (RN and RT) on the amount of dry matter (DM) faecal excretion, faecal N excretion and faecal GHG (CH4 and N2O) emissions from growing sheep grazing Italian ryegrass pastures. In order to evaluate faecal production and N excretion per animal and per ha, a first experiment (1) was carried out: RT - pre and post-grazing sward heights of 25 and 5 cm, respectively and, RN - pre and post-grazing sward heights of 18 and 11 cm, respectively. A second experiment (2) was carried out to measure the CH4 and N2O fluxes from faeces, using the static chamber method. Daily DM faecal and N excretion per animal were higher (P<0.001) in RN compared with RT treatment. However, when considered daily DM faecal and N excretion per ha, results were lower (P<0.001) for the RN than the RT treatment. CH4 and N2O emissions from faeces were higher (... Presentar Todo |
Palabras claves : |
FACTOR DE EMISIÓN FECAL; FAECAL EMISSION FACTOR; GREENHOUSE GASES; GROWING SHEEP; PASTURE MANAGEMENT; SWARD HEIGHT. |
Thesagro : |
GASES DE EFECTO INVERNADERO; MANEJO DE PASTURAS. |
Asunto categoría : |
L01 Ganadería |
Marc : |
LEADER 03219naa a2200325 a 4500 001 1060357 005 2019-10-29 008 2019 bl uuuu u00u1 u #d 024 7 $a10.1016/j.smallrumres.2019.08.011$2DOI 100 1 $aSAVIAN, J.V. 245 $aEffect of sward management on the emissions of CH4 and N2O from faeces of sheep grazing Italian ryegrass pastures.$h[electronic resource] 260 $c2019 500 $aHistory Article: Received 4 April 2019; Received in revised from 21 August 2019; Accepted 22 August 2019. Available online 24 August 2019. 520 $aRotatinuous stocking (RN) management is based on animal ingestive behaviour responses, where optimal preand post-grazing sward heights are defined to increase nutrient intake per unit of grazing time. We hypothesized that the optimal sward structure and consequently, a high herbage nutritive value in RN treatment results in a greater faecal nitrogen (N) excretion by sheep and consequently, a greater faecal greenhouse gas (GHG) emissions compared with the traditional rotational stocking (RT) management, which is based on a maximum herbage accumulation and harvest. Therefore, the aim of this study was to evaluate the effect of two grazing management strategies (RN and RT) on the amount of dry matter (DM) faecal excretion, faecal N excretion and faecal GHG (CH4 and N2O) emissions from growing sheep grazing Italian ryegrass pastures. In order to evaluate faecal production and N excretion per animal and per ha, a first experiment (1) was carried out: RT - pre and post-grazing sward heights of 25 and 5 cm, respectively and, RN - pre and post-grazing sward heights of 18 and 11 cm, respectively. A second experiment (2) was carried out to measure the CH4 and N2O fluxes from faeces, using the static chamber method. Daily DM faecal and N excretion per animal were higher (P<0.001) in RN compared with RT treatment. However, when considered daily DM faecal and N excretion per ha, results were lower (P<0.001) for the RN than the RT treatment. CH4 and N2O emissions from faeces were higher (P<0.001) in RN compared with RT treatment, both per animal and per hectare. In conclusion, our study showed that the RN grazing management, based on animal behaviour, resulted in a higher daily N excretion per animal and higher CH4 and N2O emissions from faeces of sheep grazing Italian ryegrass pastures. This study contributes to improve GHG national inventories for the subtropical Brazilian climatic conditions, where estimations from CH4 and N2O emissions factors for faeces from growing sheep grazing Italian ryegrass are markedly lower than the values reported by IPCC Default Tier 1. 650 $aGASES DE EFECTO INVERNADERO 650 $aMANEJO DE PASTURAS 653 $aFACTOR DE EMISIÓN FECAL 653 $aFAECAL EMISSION FACTOR 653 $aGREENHOUSE GASES 653 $aGROWING SHEEP 653 $aPASTURE MANAGEMENT 653 $aSWARD HEIGHT 700 1 $aPRIANO, M.E. 700 1 $aNADIN, L.B. 700 1 $aTIERI, M.P. 700 1 $aMARINHO TRES SCHONS, R. 700 1 $aBASSO, C. 700 1 $aPONTES PRATES, A. 700 1 $aBAYER, C. 773 $tSmall Ruminant Reseach, Sept. 2019, volume 178, Pages 123-128.
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Registro completo
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Biblioteca (s) : |
INIA La Estanzuela. |
Fecha actual : |
05/11/2020 |
Actualizado : |
05/09/2022 |
Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
Circulación / Nivel : |
Internacional - -- |
Autor : |
TREVISAN, R.; PÉREZ, O.; SCHMITZ, N.; DIERS, B.; MARTIN, N |
Afiliación : |
RODRIGO TREVISAN, Department of Crop Sciences, University of Illinois at Urbana Champaign, Urbana, IL 61801, USA.; OSVALDO MARTIN PEREZ GONZALEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; NATHAN SCHMITZ, GDM Seeds Inc., Gibson City, IL 60936, USA.; BRIAN DIERS, Department of Crop Sciences, University of Illinois at Urbana Champaign, Urbana, IL 61801, USA.; NICOLAS MARTIN, Department of Crop Sciences, University of Illinois at Urbana Champaign, Urbana, IL 61801, USA. |
Título : |
High-throughput phenotyping of soybean maturity using time Series UAV imagery and convolutional neural networks. |
Fecha de publicación : |
2020 |
Fuente / Imprenta : |
Remote Sensing, 2020, 12(21), 3617. OPEN ACCESS. DOI: https://doi.org/10.3390/rs12213617. |
DOI : |
10.3390/rs12213617 |
Idioma : |
Inglés |
Notas : |
Article history: Received: 18 September 2020 / Revised: 28 October 2020 / Accepted: 29 October 2020 / Published: 4 November 2020. |
Contenido : |
Abstract: Soybean maturity is a trait of critical importance for the development of new soybean cultivars, nevertheless, its characterization based on visual ratings has many challenges.Unmanned aerial vehicles (UAVs) imagery-based high-throughput phenotyping methodologies
have been proposed as an alternative to the traditional visual ratings of pod senescence. However, the lack of scalable and accurate methods to extract the desired information from the images remains a significant bottleneck in breeding programs. The objective of this study was to develop an image-based high-throughput phenotyping system for evaluating soybean maturity in breeding programs. Images were acquired twice a week, starting when the earlier lines began maturation until the latest ones were mature. Two complementary convolutional neural networks (CNN) were
developed to predict the maturity date. The first using a single date and the second using the five best image dates identified by the first model. The proposed CNN architecture was validated using more than 15,000 ground truth observations from five trials, including data from three growing seasons and two countries. The trained model showed good generalization capability with a root mean squared error lower than two days in four out of five trials. Four methods of estimating prediction uncertainty showed potential at identifying different sources of errors in the maturity date predictions. The architecture developed solves limitations of previous research and can be used at scale in commercial breeding programs. MenosAbstract: Soybean maturity is a trait of critical importance for the development of new soybean cultivars, nevertheless, its characterization based on visual ratings has many challenges.Unmanned aerial vehicles (UAVs) imagery-based high-throughput phenotyping methodologies
have been proposed as an alternative to the traditional visual ratings of pod senescence. However, the lack of scalable and accurate methods to extract the desired information from the images remains a significant bottleneck in breeding programs. The objective of this study was to develop an image-based high-throughput phenotyping system for evaluating soybean maturity in breeding programs. Images were acquired twice a week, starting when the earlier lines began maturation until the latest ones were mature. Two complementary convolutional neural networks (CNN) were
developed to predict the maturity date. The first using a single date and the second using the five best image dates identified by the first model. The proposed CNN architecture was validated using more than 15,000 ground truth observations from five trials, including data from three growing seasons and two countries. The trained model showed good generalization capability with a root mean squared error lower than two days in four out of five trials. Four methods of estimating prediction uncertainty showed potential at identifying different sources of errors in the maturity date predictions. The architecture developed solves limitations of previ... Presentar Todo |
Palabras claves : |
GLYCINE MAX (L.) MERR; MACHINE LEARNING; PHYSIOLOGICAL MATURITY; PLANT BREEDING; SOYBEAN PHENOLOGY. |
Thesagro : |
MEJORAMIENTO GENETICO DE PLANTAS; SOJA. |
Asunto categoría : |
F30 Genética vegetal y fitomejoramiento |
URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/14789/1/remotesensing-12-03617.pdf
https://www.mdpi.com/2072-4292/12/21/3617/htm#
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Marc : |
LEADER 02555naa a2200277 a 4500 001 1061456 005 2022-09-05 008 2020 bl uuuu u00u1 u #d 024 7 $a10.3390/rs12213617$2DOI 100 1 $aTREVISAN, R. 245 $aHigh-throughput phenotyping of soybean maturity using time Series UAV imagery and convolutional neural networks.$h[electronic resource] 260 $c2020 500 $aArticle history: Received: 18 September 2020 / Revised: 28 October 2020 / Accepted: 29 October 2020 / Published: 4 November 2020. 520 $aAbstract: Soybean maturity is a trait of critical importance for the development of new soybean cultivars, nevertheless, its characterization based on visual ratings has many challenges.Unmanned aerial vehicles (UAVs) imagery-based high-throughput phenotyping methodologies have been proposed as an alternative to the traditional visual ratings of pod senescence. However, the lack of scalable and accurate methods to extract the desired information from the images remains a significant bottleneck in breeding programs. The objective of this study was to develop an image-based high-throughput phenotyping system for evaluating soybean maturity in breeding programs. Images were acquired twice a week, starting when the earlier lines began maturation until the latest ones were mature. Two complementary convolutional neural networks (CNN) were developed to predict the maturity date. The first using a single date and the second using the five best image dates identified by the first model. The proposed CNN architecture was validated using more than 15,000 ground truth observations from five trials, including data from three growing seasons and two countries. The trained model showed good generalization capability with a root mean squared error lower than two days in four out of five trials. Four methods of estimating prediction uncertainty showed potential at identifying different sources of errors in the maturity date predictions. The architecture developed solves limitations of previous research and can be used at scale in commercial breeding programs. 650 $aMEJORAMIENTO GENETICO DE PLANTAS 650 $aSOJA 653 $aGLYCINE MAX (L.) MERR 653 $aMACHINE LEARNING 653 $aPHYSIOLOGICAL MATURITY 653 $aPLANT BREEDING 653 $aSOYBEAN PHENOLOGY 700 1 $aPÉREZ, O. 700 1 $aSCHMITZ, N. 700 1 $aDIERS, B. 700 1 $aMARTIN, N 773 $tRemote Sensing, 2020, 12(21), 3617. OPEN ACCESS. DOI: https://doi.org/10.3390/rs12213617.
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