Estimación de la calidad y cantidad de pasto kikuyo (Cenchrus clandestinum (Hochst. ex Chiov.) Morrone) usando imágenes multiespectrales
The evaluation of grazing lands is essential to improve livestock productivity. Data from multispectral airborne sensors allow calculating vegetation indexes (VI) and relating them to physiological and biophysical characteristics of the pastures. The objective of this study was to evaluate the usefu...
- Autores:
-
Posada Asprilla, William
Medina Sierra, Marisol
Cerón Muñoz, Mario
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2019
- Institución:
- Universidad de Ciencias Aplicadas y Ambientales U.D.C.A
- Repositorio:
- Repositorio Institucional UDCA
- Idioma:
- spa
- OAI Identifier:
- oai:repository.udca.edu.co:11158/2008
- Acceso en línea:
- https://revistas.udca.edu.co/index.php/ruadc/article/view/1195
https://doi.org/10.31910/rudca.v22.n1.2019.1195
- Palabra clave:
- Agricultura de precisión
Calidad nutricional
Ganadería de leche
Índices de vegetación
Proteína bruta
Sensores remotos
Cenchrus
Pennisetum clandestinum
Proteina bruta
- Rights
- openAccess
- License
- Derechos Reservados - Universidad de Ciencias Aplicadas y Ambientales
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Estimación de la calidad y cantidad de pasto kikuyo (Cenchrus clandestinum (Hochst. ex Chiov.) Morrone) usando imágenes multiespectrales |
| dc.title.alternative.spa.fl_str_mv |
Estimation of the quality and quantity of Kikuyo grass (Cenchrus clandestinum (Hochst. Ex Chiov.) Morrone) using multispectral images |
| title |
Estimación de la calidad y cantidad de pasto kikuyo (Cenchrus clandestinum (Hochst. ex Chiov.) Morrone) usando imágenes multiespectrales |
| spellingShingle |
Estimación de la calidad y cantidad de pasto kikuyo (Cenchrus clandestinum (Hochst. ex Chiov.) Morrone) usando imágenes multiespectrales Agricultura de precisión Calidad nutricional Ganadería de leche Índices de vegetación Proteína bruta Sensores remotos Cenchrus Pennisetum clandestinum Proteina bruta |
| title_short |
Estimación de la calidad y cantidad de pasto kikuyo (Cenchrus clandestinum (Hochst. ex Chiov.) Morrone) usando imágenes multiespectrales |
| title_full |
Estimación de la calidad y cantidad de pasto kikuyo (Cenchrus clandestinum (Hochst. ex Chiov.) Morrone) usando imágenes multiespectrales |
| title_fullStr |
Estimación de la calidad y cantidad de pasto kikuyo (Cenchrus clandestinum (Hochst. ex Chiov.) Morrone) usando imágenes multiespectrales |
| title_full_unstemmed |
Estimación de la calidad y cantidad de pasto kikuyo (Cenchrus clandestinum (Hochst. ex Chiov.) Morrone) usando imágenes multiespectrales |
| title_sort |
Estimación de la calidad y cantidad de pasto kikuyo (Cenchrus clandestinum (Hochst. ex Chiov.) Morrone) usando imágenes multiespectrales |
| dc.creator.fl_str_mv |
Posada Asprilla, William Medina Sierra, Marisol Cerón Muñoz, Mario |
| dc.contributor.author.spa.fl_str_mv |
Posada Asprilla, William Medina Sierra, Marisol Cerón Muñoz, Mario |
| dc.subject.proposal.spa.fl_str_mv |
Agricultura de precisión Calidad nutricional Ganadería de leche Índices de vegetación Proteína bruta Sensores remotos |
| topic |
Agricultura de precisión Calidad nutricional Ganadería de leche Índices de vegetación Proteína bruta Sensores remotos Cenchrus Pennisetum clandestinum Proteina bruta |
| dc.subject.agrovoc.spa.fl_str_mv |
Cenchrus Pennisetum clandestinum Proteina bruta |
| description |
The evaluation of grazing lands is essential to improve livestock productivity. Data from multispectral airborne sensors allow calculating vegetation indexes (VI) and relating them to physiological and biophysical characteristics of the pastures. The objective of this study was to evaluate the usefulness of VI to estimate the quantity and quality of Kikuyu grass in dairy farms of northern Antioquia, Colombia. We calculated 10 different VI using 168 samples of Kikuyu grass. The samples were weighted to estimate green biomass (BV) and analyzed by near infrared spectroscopy for the contents of crude protein (PB), neutral detergent fiber (FDN) and acid detergent fiber (ADF). Data were analyzed using principal components (CP) and smoothed generalized additive models. The variables that contributed most to the formation of the first principal component (CP1) were the Normalized Difference Vegetation Index (NDVI), the Simple Vegetation Index (RVI), the Normalized Difference Vegetation Green Index (GNDVI), the Green Chlorophyll Index (Clg) and the BV of Kikuyu grass. The mayor contributors to the second principal component (CP2) were the Normalized Red-Edge Vegetation Index (RNDVI), the Red-Edge Chlorophyll Index (Clrg), and the PB, NDF and FDA of Kikuyu. The NDVI explained the BV, and the RNDVI explained the PB. The FDN and FDA estimations in Kikuyu were not precise. |
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2019 |
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2019-08-23T16:07:31Z |
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2019-08-23T16:07:31Z |
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2019-01 |
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Artículo de revista |
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http://purl.org/coar/resource_type/c_6501 |
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info:eu-repo/semantics/article |
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info:eu-repo/semantics/publishedVersion |
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Text |
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0123-4226 |
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https://revistas.udca.edu.co/index.php/ruadc/article/view/1195 |
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https://doi.org/10.31910/rudca.v22.n1.2019.1195 |
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307567 |
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https://revistas.udca.edu.co/index.php/ruadc/article/view/1195 https://doi.org/10.31910/rudca.v22.n1.2019.1195 |
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spa |
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spa |
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Revista UDCA : Actualidad & Divulgación Científica (Bogotá). -- Vol. 22, No. 1 (Ene.-Jun. 2019). -- páginas 13-22 |
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Agricultura |
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Derechos Reservados - Universidad de Ciencias Aplicadas y Ambientales |
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Atribución-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0) |
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Derechos Reservados - Universidad de Ciencias Aplicadas y Ambientales https://creativecommons.org/licenses/by-nc-sa/4.0/ Atribución-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0) http://purl.org/coar/access_right/c_abf2 |
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Bogotá : Universidad de Ciencias Aplicadas y Ambientales, 2019 |
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Posada Asprilla, WilliamMedina Sierra, MarisolCerón Muñoz, Mario2019-08-23T16:07:31Z2019-08-23T16:07:31Z2019-010123-4226https://revistas.udca.edu.co/index.php/ruadc/article/view/1195https://doi.org/10.31910/rudca.v22.n1.2019.1195307567The evaluation of grazing lands is essential to improve livestock productivity. Data from multispectral airborne sensors allow calculating vegetation indexes (VI) and relating them to physiological and biophysical characteristics of the pastures. The objective of this study was to evaluate the usefulness of VI to estimate the quantity and quality of Kikuyu grass in dairy farms of northern Antioquia, Colombia. We calculated 10 different VI using 168 samples of Kikuyu grass. The samples were weighted to estimate green biomass (BV) and analyzed by near infrared spectroscopy for the contents of crude protein (PB), neutral detergent fiber (FDN) and acid detergent fiber (ADF). Data were analyzed using principal components (CP) and smoothed generalized additive models. The variables that contributed most to the formation of the first principal component (CP1) were the Normalized Difference Vegetation Index (NDVI), the Simple Vegetation Index (RVI), the Normalized Difference Vegetation Green Index (GNDVI), the Green Chlorophyll Index (Clg) and the BV of Kikuyu grass. The mayor contributors to the second principal component (CP2) were the Normalized Red-Edge Vegetation Index (RNDVI), the Red-Edge Chlorophyll Index (Clrg), and the PB, NDF and FDA of Kikuyu. The NDVI explained the BV, and the RNDVI explained the PB. The FDN and FDA estimations in Kikuyu were not precise.La evaluación de las praderas destinadas a ganadería es esencial para la productividad de los animales. Los datos de sensores multiespectrales remotos aerotransportados (SM) permiten construir índices de vegetación (VI, por sus siglas en idioma inglés) y relacionarlos con características fisiológicas y biofísicas de las pasturas. El objetivo fue evaluar VI para la estimación de la cantidad y calidad de pasto kikuyo en sistemas lecheros, del norte de Antioquia, Colombia. Se calcularon 10 diferentes VI, con 168 muestras de pasto kikuyo. Las muestras fueron pesadas, para estimar la biomasa verde (BV) y analizadas por espectroscopia del infrarrojo cercano, para los contenidos de proteína bruta (PB), fibra en detergente neutro (FDN) y fibra en detergente ácido (FDA). Los datos, se analizaron usando componentes principales (CP) y modelos aditivos generalizados suavizados. Las variables que más contribuyeron a la formación de la primera componente principal (CP1) fueron el índice de vegetación de diferencia normalizada (NDVI), el índice de vegetación simple (RVI), el índice de vegetación de diferencia normalizada verde (GNDVI), el índice clorofílico verde (Clg) y la BV del pasto kikuyo. Para la segunda componente principal (CP2) fueron el índice de vegetación de diferencia normalizada borde del rojo (RNDVI), el índice borde del rojo de clorofila (Clrg) y PB, FDN y FDA del pasto kikuyo. La BV fue explicada por el NDVI y PB por el RNDVI. La estimación obtenida para FDN y FDA del pasto kikuyo no fueron precisas.Incluye referencias bibliográficasapplication/pdfspaBogotá : Universidad de Ciencias Aplicadas y Ambientales, 2019Revista UDCA : Actualidad & Divulgación Científica (Bogotá). -- Vol. 22, No. 1 (Ene.-Jun. 2019). -- páginas 13-22AgriculturaDerechos Reservados - Universidad de Ciencias Aplicadas y Ambientaleshttps://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccessAtribución-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0)http://purl.org/coar/access_right/c_abf2Estimación de la calidad y cantidad de pasto kikuyo (Cenchrus clandestinum (Hochst. ex Chiov.) Morrone) usando imágenes multiespectralesEstimation of the quality and quantity of Kikuyo grass (Cenchrus clandestinum (Hochst. Ex Chiov.) Morrone) using multispectral imagesArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionTexthttp://purl.org/redcol/resource_type/ARThttp://purl.org/coar/version/c_970fb48d4fbd8a85Agricultura de precisiónCalidad nutricionalGanadería de lecheÍndices de vegetaciónProteína brutaSensores remotosCenchrusPennisetum clandestinumProteina brutaPublicationORIGINAL1195-Texto del artículo-7274-2-10-20190728.pdf1195-Texto del artículo-7274-2-10-20190728.pdfapplication/pdf1694211https://repository.udca.edu.co/bitstreams/9a6ec4ee-c71a-460a-9690-6d679d0c7650/downloada36b29334f21b71138855593f547bbe0MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-814775https://repository.udca.edu.co/bitstreams/9584c2bc-832f-4985-878e-eff68628e4c3/downloadf661acf14bedbf9f5d13897a0387e751MD52TEXT1195-Texto del artículo-7274-2-10-20190728.pdf.txt1195-Texto del 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 nombre de la parte o las partes que el Autor Original y/o el Licenciante hubieren designado para la atribución (v.g., un instituto patrocinador, editorial, publicación) en la información de los derechos de autor del Licenciante, términos de servicios o de otras formas razonables; el título de la Obra si está provisto; en la medida de lo razonablemente factible y, si está provisto, el Identificador Uniforme de Recursos (Uniform Resource Identifier) que el Licenciante especifica para ser asociado con la Obra, salvo que tal URI no se refiera a la nota sobre los derechos de autor o a la información sobre el licenciamiento de la Obra; y en el caso de una Obra Derivada, atribuir el crédito identificando el uso de la Obra en la Obra Derivada (v.g., "Traducción Francesa de la Obra del Autor Original," o "Guión Cinematográfico basado en la Obra original del Autor Original"). Tal crédito puede ser implementado de cualquier forma razonable; en el caso, sin embargo, de Obras Derivadas u Obras Colectivas, tal crédito aparecerá, como mínimo, donde aparece el crédito de cualquier otro autor comparable y de una manera, al menos, tan destacada como el crédito de otro autor comparable.
d.	Para evitar toda confusión, el Licenciante aclara que, cuando la obra es una composición musical:
i.	Regalías por interpretación y ejecución bajo licencias generales. El Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública o la ejecución pública digital de la obra y de recolectar, sea individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, SAYCO), las regalías por la ejecución pública o por la ejecución pública digital de la obra (por ejemplo Webcast) licenciada bajo licencias generales, si la interpretación o ejecución de la obra está primordialmente orientada por o dirigida a la obtención de una ventaja comercial o una compensación monetaria privada.
ii.	Regalías por Fonogramas. El Licenciante se reserva el derecho exclusivo de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, SAYCO), una agencia de derechos musicales o algún agente designado, las regalías por cualquier fonograma que Usted cree a partir de la obra (“versión cover”) y distribuya, en los términos del régimen de derechos de autor, si la creación o distribución de esa versión cover está primordialmente destinada o dirigida a obtener una ventaja comercial o una compensación monetaria privada.
e.	Gestión de Derechos de Autor sobre Interpretaciones y Ejecuciones Digitales (WebCasting). Para evitar toda confusión, el Licenciante aclara que, cuando la obra sea un fonograma, el Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública digital de la obra (por ejemplo, webcast) y de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, Acinpro), las regalías por la ejecución pública digital de la obra (por ejemplo, webcast), sujeta a las disposiciones aplicables del régimen de Derecho de Autor, si esta ejecución pública digital está primordialmente dirigida a obtener una ventaja comercial o una compensación monetaria privada.
5. Representaciones, Garantías y Limitaciones de Responsabilidad.
A MENOS QUE LAS PARTES LO ACORDARAN DE OTRA FORMA POR ESCRITO, EL LICENCIANTE OFRECE LA OBRA (EN EL ESTADO EN EL QUE SE ENCUENTRA) “TAL CUAL”, SIN BRINDAR GARANTÍAS DE CLASE ALGUNA RESPECTO DE LA OBRA, YA SEA EXPRESA, IMPLÍCITA, LEGAL O CUALQUIERA OTRA, INCLUYENDO, SIN LIMITARSE A ELLAS, GARANTÍAS DE TITULARIDAD, COMERCIABILIDAD, ADAPTABILIDAD O ADECUACIÓN A PROPÓSITO DETERMINADO, AUSENCIA DE INFRACCIÓN, DE AUSENCIA DE DEFECTOS LATENTES O DE OTRO TIPO, O LA PRESENCIA O AUSENCIA DE ERRORES, SEAN O NO DESCUBRIBLES (PUEDAN O NO SER ESTOS DESCUBIERTOS). ALGUNAS JURISDICCIONES NO PERMITEN LA EXCLUSIÓN DE GARANTÍAS IMPLÍCITAS, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.
6. Limitación de responsabilidad.
A MENOS QUE LO EXIJA EXPRESAMENTE LA LEY APLICABLE, EL LICENCIANTE NO SERÁ RESPONSABLE ANTE USTED POR DAÑO ALGUNO, SEA POR RESPONSABILIDAD EXTRACONTRACTUAL, PRECONTRACTUAL O CONTRACTUAL, OBJETIVA O SUBJETIVA, SE TRATE DE DAÑOS MORALES O PATRIMONIALES, DIRECTOS O INDIRECTOS, PREVISTOS O IMPREVISTOS PRODUCIDOS POR EL USO DE ESTA LICENCIA O DE LA OBRA, AUN CUANDO EL LICENCIANTE HAYA SIDO ADVERTIDO DE LA POSIBILIDAD DE DICHOS DAÑOS. ALGUNAS LEYES NO PERMITEN LA EXCLUSIÓN DE CIERTA RESPONSABILIDAD, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.
7. Término.
a.	Esta Licencia y los derechos otorgados en virtud de ella terminarán automáticamente si Usted infringe alguna condición establecida en ella. Sin embargo, los individuos o entidades que han recibido Obras Derivadas o Colectivas de Usted de conformidad con esta Licencia, no verán terminadas sus licencias, siempre que estos individuos o entidades sigan cumpliendo íntegramente las condiciones de estas licencias. Las Secciones 1, 2, 5, 6, 7, y 8 subsistirán a cualquier terminación de esta Licencia.
b.	Sujeta a las condiciones y términos anteriores, la licencia otorgada aquí es perpetua (durante el período de vigencia de los derechos de autor de la obra). No obstante lo anterior, el Licenciante se reserva el derecho a publicar y/o estrenar la Obra bajo condiciones de licencia diferentes o a dejar de distribuirla en los términos de esta Licencia en cualquier momento; en el entendido, sin embargo, que esa elección no servirá para revocar esta licencia o que deba ser otorgada , bajo los términos de esta licencia), y esta licencia continuará en pleno vigor y efecto a menos que sea terminada como se expresa atrás. La Licencia revocada continuará siendo plenamente vigente y efectiva si no se le da término en las condiciones indicadas anteriormente.
8. Varios.
a.	Cada vez que Usted distribuya o ponga a disposición pública la Obra o una Obra Colectiva, el Licenciante ofrecerá al destinatario una licencia en los mismos términos y condiciones que la licencia otorgada a Usted bajo esta Licencia.
b.	Si alguna disposición de esta Licencia resulta invalidada o no exigible, según la legislación vigente, esto no afectará ni la validez ni la aplicabilidad del resto de condiciones de esta Licencia y, sin acción adicional por parte de los sujetos de este acuerdo, aquélla se entenderá reformada lo mínimo necesario para hacer que dicha disposición sea válida y exigible.
c.	Ningún término o disposición de esta Licencia se estimará renunciada y ninguna violación de ella será consentida a menos que esa renuncia o consentimiento sea otorgado por escrito y firmado por la parte que renuncie o consienta.
d.	Esta Licencia refleja el acuerdo pleno entre las partes respecto a la Obra aquí licenciada. No hay arreglos, acuerdos o declaraciones respecto a la Obra que no estén especificados en este documento. El Licenciante no se verá limitado por ninguna disposición adicional que pueda surgir en alguna comunicación emanada de Usted. Esta Licencia no puede ser modificada sin el consentimiento mutuo por escrito del Licenciante y Usted.

 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