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Registro completo
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Biblioteca (s) : |
INIA La Estanzuela. |
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Fecha : |
30/11/2020 |
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Actualizado : |
05/09/2022 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Autor : |
WANG, X.; SILVA, P.; BELLO, N.M.; SINGH, D.; EVERS, B.; SINGH, R.P.; POLAND, J. |
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Afiliación : |
XU WANG, Department of Plant Pathology, Kansas State University, Manhattan, KS, United States.; MARIA PAULA SILVA VILLELLA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay.;Department of Plant Pathology, Kansas State University, Manhattan, KS, United States; Interdepartmental Genetics, Kansas State University, Manhattan, KS, United State; NORA M. BELLO, Department of Statistics, Kansas State University, Manhattan, KS, United States,; DALJIT SINGH, Department of Plant Pathology, Kansas State University, Manhattan, KS, United States.; BYRON EVERS, Department of Plant Pathology, Kansas State University, Manhattan, KS, United States.; RAVI P. SINGH, Global Wheat Program, International Maize and Wheat Improvement Center, Mexico City, Mexico.; JESSE POLAND1, Department of Plant Pathology, Kansas State University, Manhattan, KS, United States. |
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Título : |
Improved accuracy of high-throughput phenotyping from unmanned aerial systems by extracting traits directly from orthorectified images. |
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Fecha de publicación : |
2020 |
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Fuente / Imprenta : |
Frontiers in Plant Science, 21 October 2020, Volume 11, Article number 587093. Open Access. Doi: https://doi.org/10.3389/fpls.2020.587093 |
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DOI : |
10.3389/fpls.2020.587093 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received: 27 July 2020/ Accepted: 30 September 2020/Published: 21 October 2020. |
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Contenido : |
The development of high-throughput genotyping and phenotyping has provided access to many tools to accelerate plant breeding programs. Unmanned Aerial Systems (UAS)-based remote sensing is being broadly implemented for field-based highthroughput phenotyping due to its low cost and the capacity to rapidly cover large breeding populations. The Structure-from-Motion photogrammetry processes aerial
images taken from multiple perspectives over a field to an orthomosaic photo of a complete field experiment, allowing spectral or morphological trait extraction from the canopy surface for each individual field plot. However, some phenotypic information observable in each raw aerial image seems to be lost to the orthomosaic photo,probably due to photogrammetry processes such as pixel merging and blending. To formally assess this, we introduced a set of image processing methods to extract phenotypes from orthorectified raw aerial images and compared them to the negative control of extracting the same traits from processed orthomosaic images. We predict that standard measures of accuracy in terms of the broad-sense heritability of the remote sensing spectral traits will be higher using the orthorectified photos than with the orthomosaic image. Using three case studies, we therefore compared the broadsense heritability of phenotypes in wheat breeding nurseries including, (1) canopy temperature from thermal imaging, (2) canopy normalized difference vegetation index (NDVI), and (3) early-stage ground cover from multispectral imaging. We evaluated heritability estimates of these phenotypes extracted from multiple orthorectified aerial images via four statistical models and compared the results with heritability estimates of these phenotypes extracted from a single orthomosaic image. Our results indicate that extracting traits directly from multiple orthorectified aerial images yielded increased estimates of heritability for all three phenotypes through proper modeling, compared to estimation using traits extracted from the orthomosaic image. In summary, the image processing methods demonstrated in this study have the potential to improve the quality of the plant trait extracted from high-throughput imaging. This, in turn, can enable breeders to utilize phenomics technologies more effectively for improved selection. MenosThe development of high-throughput genotyping and phenotyping has provided access to many tools to accelerate plant breeding programs. Unmanned Aerial Systems (UAS)-based remote sensing is being broadly implemented for field-based highthroughput phenotyping due to its low cost and the capacity to rapidly cover large breeding populations. The Structure-from-Motion photogrammetry processes aerial
images taken from multiple perspectives over a field to an orthomosaic photo of a complete field experiment, allowing spectral or morphological trait extraction from the canopy surface for each individual field plot. However, some phenotypic information observable in each raw aerial image seems to be lost to the orthomosaic photo,probably due to photogrammetry processes such as pixel merging and blending. To formally assess this, we introduced a set of image processing methods to extract phenotypes from orthorectified raw aerial images and compared them to the negative control of extracting the same traits from processed orthomosaic images. We predict that standard measures of accuracy in terms of the broad-sense heritability of the remote sensing spectral traits will be higher using the orthorectified photos than with the orthomosaic image. Using three case studies, we therefore compared the broadsense heritability of phenotypes in wheat breeding nurseries including, (1) canopy temperature from thermal imaging, (2) canopy normalized difference vegetation index (NDVI), and (3) early-s... Presentar Todo |
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Palabras claves : |
CANOPY TEMPERATURE; GROUND COVER; HIGH-THROUGHPUT PHENOTYPING; NORMALIZED DIFFERENCE VEGETATION INDEX; UNMANNED AERIAL SYSTEMS; WHEAT. |
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Thesagro : |
TRIGO. |
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Asunto categoría : |
-- |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/16703/1/fpls-11-587093.pdf
https://www.frontiersin.org/articles/10.3389/fpls.2020.587093/pdf
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Marc : |
LEADER 03406nam a2200289 a 4500
001 1061531
005 2022-09-05
008 2020 bl uuuu u0uu1 u #d
024 7 $a10.3389/fpls.2020.587093$2DOI
100 1 $aWANG, X.
245 $aImproved accuracy of high-throughput phenotyping from unmanned aerial systems by extracting traits directly from orthorectified images.$h[electronic resource]
260 $aFrontiers in Plant Science, 21 October 2020, Volume 11, Article number 587093. Open Access. Doi: https://doi.org/10.3389/fpls.2020.587093$c2020
500 $aArticle history: Received: 27 July 2020/ Accepted: 30 September 2020/Published: 21 October 2020.
520 $aThe development of high-throughput genotyping and phenotyping has provided access to many tools to accelerate plant breeding programs. Unmanned Aerial Systems (UAS)-based remote sensing is being broadly implemented for field-based highthroughput phenotyping due to its low cost and the capacity to rapidly cover large breeding populations. The Structure-from-Motion photogrammetry processes aerial images taken from multiple perspectives over a field to an orthomosaic photo of a complete field experiment, allowing spectral or morphological trait extraction from the canopy surface for each individual field plot. However, some phenotypic information observable in each raw aerial image seems to be lost to the orthomosaic photo,probably due to photogrammetry processes such as pixel merging and blending. To formally assess this, we introduced a set of image processing methods to extract phenotypes from orthorectified raw aerial images and compared them to the negative control of extracting the same traits from processed orthomosaic images. We predict that standard measures of accuracy in terms of the broad-sense heritability of the remote sensing spectral traits will be higher using the orthorectified photos than with the orthomosaic image. Using three case studies, we therefore compared the broadsense heritability of phenotypes in wheat breeding nurseries including, (1) canopy temperature from thermal imaging, (2) canopy normalized difference vegetation index (NDVI), and (3) early-stage ground cover from multispectral imaging. We evaluated heritability estimates of these phenotypes extracted from multiple orthorectified aerial images via four statistical models and compared the results with heritability estimates of these phenotypes extracted from a single orthomosaic image. Our results indicate that extracting traits directly from multiple orthorectified aerial images yielded increased estimates of heritability for all three phenotypes through proper modeling, compared to estimation using traits extracted from the orthomosaic image. In summary, the image processing methods demonstrated in this study have the potential to improve the quality of the plant trait extracted from high-throughput imaging. This, in turn, can enable breeders to utilize phenomics technologies more effectively for improved selection.
650 $aTRIGO
653 $aCANOPY TEMPERATURE
653 $aGROUND COVER
653 $aHIGH-THROUGHPUT PHENOTYPING
653 $aNORMALIZED DIFFERENCE VEGETATION INDEX
653 $aUNMANNED AERIAL SYSTEMS
653 $aWHEAT
700 1 $aSILVA, P.
700 1 $aBELLO, N.M.
700 1 $aSINGH, D.
700 1 $aEVERS, B.
700 1 $aSINGH, R.P.
700 1 $aPOLAND, J.
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Registro original : |
INIA La Estanzuela (LE) |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
20/06/2023 |
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Actualizado : |
20/07/2023 |
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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 : |
BALDASSINI, P.; BAETHGEN, W.; CAMBA SANS, G.; QUINCKE, A.; PRAVIA, V.; TERRA, J.A.; MACEDO, F.; PIÑEIRO, G.; PARUELO, J. |
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Afiliación : |
PABLO BALDASSINI, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; Departamento de Métodos Cuantitativos y Sistemas de Información, Facultad de Agronomía, LART IFEVA, Universidad, de Buenos Aires, CONICET, Argentina; WALTER E. BAETHGEN, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; International Research Institute for Climate and Society (IRI), Columbia Climate School, Columbia University, United States; GONZALO HERNÁN CAMBA SANS, Departamento de Métodos Cuantitativos y Sistemas de Información, Facultad de Agronomía, LART IFEVA, Universidad, de Buenos Aires, CONICET, Argentina; JUAN ANDRES QUINCKE WALDEN, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; MARIA VIRGINIA PRAVIA NIN, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; JOSÉ ALFREDO TERRA FERNÁNDEZ, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; FERNANDO LIBER MACEDO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; GERVASIO PIÑEIRO, Cátedra de Ecología, Facultad de Agronomía, LART IFEVA, Universidad, de Buenos Aires, CONICET, Argentina; JOSÉ PARUELO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; Dpto. Métodos Cuantitativos y Sistemas de Información, Fac. Agronomía, LART IFEVA, Univ. Bs.As., CONICET, Bs.As. Argentina; IECA, Fac. Ciencias, IECA, UdelaR, Montevideo, Uruguay. |
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Título : |
Carbon stocks and potential sequestration of Uruguayan soils. A road map to a comprehensive characterization of temporal and spatial changes to assess Carbon footprint. |
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Complemento del título : |
Original research. |
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Fecha de publicación : |
2023 |
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Fuente / Imprenta : |
Frontiers in Sustainable Food Systems. 2023, Volume 7. https://doi.org/10.3389/fsufs.2023.1045734 |
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DOI : |
10.3389/fsufs.2023.1045734 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 16 Sep 2022; Accepted 25 May 2023; Published 20 July 2023. -- Correspondence: Dr. Pablo Baldassini, Instituto Nacional de Investigación Agropecuaria, INIA La Estanzuela, Colonia, Uruguay. -- Edited by: Bruno José Rodrigues Alves, Brazilian Agricultural Research Corporation (EMBRAPA), Brazil. --
Reviewed by: Gerald Moser, University of Giessen; Germany Ernesto Viglizzo, Independent researcher, Santa Rosa, La Pampa, Argentina. --
This article is part of the Research Topic Finding Paths to Net-Zero Carbon in Climate-Smart Food Systems (https://www.frontiersin.org/research-topics/29787/finding-paths-to-net-zero-carbon-in-climate-smart-food-systems#articles ). -- FUNDING: This research was supported by agrant from ANII-CONICETIA_2021_4_04. -- License: This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). -- Supplementary material: The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fsufs.2023.1045734/full#supplementary-material |
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Contenido : |
Carbon net emission is a critical aspect of the environmental footprint in agricultural systems. However, the alternatives to describe soil organic carbon (SOC) changes associated with different agricultural management practices/land uses are limited. Here we provide an overview of carbon (C) stocks of non-forested areas of Uruguay to estimate SOC changes for different soil units affected by accumulated effects of crop and livestock production systems in the last decades. For this, we defined levels based on SOC losses relative to the original (reference) SOC stocks: 25% or less, between 25% and 50%, and 50% or more. We characterized the reference SOC stocks using three approaches: (1) an equation to derive the potential SOC capacity based on the clay and fine silt soil content, (2) the DayCent model to estimate the SOC stocks based on climate, soil texture and C inputs from the natural grasslands of the area, (3) an estimate of SOC using a proxy derived from remote sensing data (i.e., the Ecosystem Services Supply Index) that accounts for differences in C inputs. Depending on the used reference SOC, the soil units had different distributions of SOC losses within the zones defined by the thresholds. As expected, the magnitude of SOC changes observed for the different soil units was related to the relative frequency of annual crops, however, the high variability observed along the gradient of land uses suggests a wide space for increasing SOC with agricultural management practices. The assessment of the C stock preserved (CSP) belowground and the potential for increasing C accumulation or sequestration (CAP) are critical components of the C footprint of a given system. Thus, we propose a methodological road map to derive indicators of CSP and CAP at the farm level combining both, biogeochemical simulation models and conceptual models based on remote sensing data. We recognize at least three critical issues that require scientific and political consensus to implement the use of this propose: (1) how to define reference C stocks, (2) how to estimate current C stocks over large areas and in heterogeneous agricultural landscapes, and (3) what is a reasonable/acceptable threshold of C stocks reduction. Copyright: © 2023 Baldassini, Baethgen, Camba Sans, Quincke, Pravia, Terra, Macedo, Piñeiro and Paruelo. MenosCarbon net emission is a critical aspect of the environmental footprint in agricultural systems. However, the alternatives to describe soil organic carbon (SOC) changes associated with different agricultural management practices/land uses are limited. Here we provide an overview of carbon (C) stocks of non-forested areas of Uruguay to estimate SOC changes for different soil units affected by accumulated effects of crop and livestock production systems in the last decades. For this, we defined levels based on SOC losses relative to the original (reference) SOC stocks: 25% or less, between 25% and 50%, and 50% or more. We characterized the reference SOC stocks using three approaches: (1) an equation to derive the potential SOC capacity based on the clay and fine silt soil content, (2) the DayCent model to estimate the SOC stocks based on climate, soil texture and C inputs from the natural grasslands of the area, (3) an estimate of SOC using a proxy derived from remote sensing data (i.e., the Ecosystem Services Supply Index) that accounts for differences in C inputs. Depending on the used reference SOC, the soil units had different distributions of SOC losses within the zones defined by the thresholds. As expected, the magnitude of SOC changes observed for the different soil units was related to the relative frequency of annual crops, however, the high variability observed along the gradient of land uses suggests a wide space for increasing SOC with agricultural management prac... Presentar Todo |
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Palabras claves : |
Agricultural emissions; Carbon Sequestration; DAYCENT; Ecosystem services; Remote sensing; Soil Organic Carbon. |
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Asunto categoría : |
P01 Conservación de la naturaleza y recursos de La tierra |
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URL : |
https://www.frontiersin.org/articles/10.3389/fsufs.2023.1045734/pdf
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Marc : |
LEADER 04434naa a2200313 a 4500
001 1064201
005 2023-07-20
008 2023 bl uuuu u00u1 u #d
024 7 $a10.3389/fsufs.2023.1045734$2DOI
100 1 $aBALDASSINI, P.
245 $aCarbon stocks and potential sequestration of Uruguayan soils. A road map to a comprehensive characterization of temporal and spatial changes to assess Carbon footprint.$h[electronic resource]
260 $c2023
500 $aArticle history: Received 16 Sep 2022; Accepted 25 May 2023; Published 20 July 2023. -- Correspondence: Dr. Pablo Baldassini, Instituto Nacional de Investigación Agropecuaria, INIA La Estanzuela, Colonia, Uruguay. -- Edited by: Bruno José Rodrigues Alves, Brazilian Agricultural Research Corporation (EMBRAPA), Brazil. -- Reviewed by: Gerald Moser, University of Giessen; Germany Ernesto Viglizzo, Independent researcher, Santa Rosa, La Pampa, Argentina. -- This article is part of the Research Topic Finding Paths to Net-Zero Carbon in Climate-Smart Food Systems (https://www.frontiersin.org/research-topics/29787/finding-paths-to-net-zero-carbon-in-climate-smart-food-systems#articles ). -- FUNDING: This research was supported by agrant from ANII-CONICETIA_2021_4_04. -- License: This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). -- Supplementary material: The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fsufs.2023.1045734/full#supplementary-material
520 $aCarbon net emission is a critical aspect of the environmental footprint in agricultural systems. However, the alternatives to describe soil organic carbon (SOC) changes associated with different agricultural management practices/land uses are limited. Here we provide an overview of carbon (C) stocks of non-forested areas of Uruguay to estimate SOC changes for different soil units affected by accumulated effects of crop and livestock production systems in the last decades. For this, we defined levels based on SOC losses relative to the original (reference) SOC stocks: 25% or less, between 25% and 50%, and 50% or more. We characterized the reference SOC stocks using three approaches: (1) an equation to derive the potential SOC capacity based on the clay and fine silt soil content, (2) the DayCent model to estimate the SOC stocks based on climate, soil texture and C inputs from the natural grasslands of the area, (3) an estimate of SOC using a proxy derived from remote sensing data (i.e., the Ecosystem Services Supply Index) that accounts for differences in C inputs. Depending on the used reference SOC, the soil units had different distributions of SOC losses within the zones defined by the thresholds. As expected, the magnitude of SOC changes observed for the different soil units was related to the relative frequency of annual crops, however, the high variability observed along the gradient of land uses suggests a wide space for increasing SOC with agricultural management practices. The assessment of the C stock preserved (CSP) belowground and the potential for increasing C accumulation or sequestration (CAP) are critical components of the C footprint of a given system. Thus, we propose a methodological road map to derive indicators of CSP and CAP at the farm level combining both, biogeochemical simulation models and conceptual models based on remote sensing data. We recognize at least three critical issues that require scientific and political consensus to implement the use of this propose: (1) how to define reference C stocks, (2) how to estimate current C stocks over large areas and in heterogeneous agricultural landscapes, and (3) what is a reasonable/acceptable threshold of C stocks reduction. Copyright: © 2023 Baldassini, Baethgen, Camba Sans, Quincke, Pravia, Terra, Macedo, Piñeiro and Paruelo.
653 $aAgricultural emissions
653 $aCarbon Sequestration
653 $aDAYCENT
653 $aEcosystem services
653 $aRemote sensing
653 $aSoil Organic Carbon
700 1 $aBAETHGEN, W.
700 1 $aCAMBA SANS, G.
700 1 $aQUINCKE, A.
700 1 $aPRAVIA, V.
700 1 $aTERRA, J.A.
700 1 $aMACEDO, F.
700 1 $aPIÑEIRO, G.
700 1 $aPARUELO, J.
773 $tFrontiers in Sustainable Food Systems. 2023, Volume 7. https://doi.org/10.3389/fsufs.2023.1045734
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