Modelización de la interferencia cultivo-malezas, mediante modelos autorregresivos espaciales, con validación en un cultivo de lechuga
ilustraciones, gráficas, tablas
- Autores:
-
Jamaica Tenjo, David Alejandro
- Tipo de recurso:
- Doctoral thesis
- Fecha de publicación:
- 2019
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/75703
- Palabra clave:
- 630 - Agricultura y tecnologías relacionadas::631 - Técnicas específicas, aparatos, equipos, materiales
Lettuce - weed control
Image processing
Geology - statistical methods
Procesamiento de imágenes
Geología - Modelos estadísticos
Spatial regression
Autoregressive
Weed crop competition
Simulation
In silico research
Remote sensing
Image processing
Regresión espacial
Autorregresivos
Competencia cultivo malezas
Simulación
Investigación in silico
Sensores remotos
Procesamiento de imágenes
- Rights
- openAccess
- License
- Atribución-NoComercial-SinDerivadas 4.0 Internacional
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| dc.title.spa.fl_str_mv |
Modelización de la interferencia cultivo-malezas, mediante modelos autorregresivos espaciales, con validación en un cultivo de lechuga |
| dc.title.translated.eng.fl_str_mv |
Modeling weed-crop interference, using spatial autoregressive models, with validation in a lettuce crop |
| title |
Modelización de la interferencia cultivo-malezas, mediante modelos autorregresivos espaciales, con validación en un cultivo de lechuga |
| spellingShingle |
Modelización de la interferencia cultivo-malezas, mediante modelos autorregresivos espaciales, con validación en un cultivo de lechuga 630 - Agricultura y tecnologías relacionadas::631 - Técnicas específicas, aparatos, equipos, materiales Lettuce - weed control Image processing Geology - statistical methods Procesamiento de imágenes Geología - Modelos estadísticos Spatial regression Autoregressive Weed crop competition Simulation In silico research Remote sensing Image processing Regresión espacial Autorregresivos Competencia cultivo malezas Simulación Investigación in silico Sensores remotos Procesamiento de imágenes |
| title_short |
Modelización de la interferencia cultivo-malezas, mediante modelos autorregresivos espaciales, con validación en un cultivo de lechuga |
| title_full |
Modelización de la interferencia cultivo-malezas, mediante modelos autorregresivos espaciales, con validación en un cultivo de lechuga |
| title_fullStr |
Modelización de la interferencia cultivo-malezas, mediante modelos autorregresivos espaciales, con validación en un cultivo de lechuga |
| title_full_unstemmed |
Modelización de la interferencia cultivo-malezas, mediante modelos autorregresivos espaciales, con validación en un cultivo de lechuga |
| title_sort |
Modelización de la interferencia cultivo-malezas, mediante modelos autorregresivos espaciales, con validación en un cultivo de lechuga |
| dc.creator.fl_str_mv |
Jamaica Tenjo, David Alejandro |
| dc.contributor.advisor.spa.fl_str_mv |
Darghan Contreras, Aquiles Enrique González Andújar, José Luis |
| dc.contributor.author.spa.fl_str_mv |
Jamaica Tenjo, David Alejandro |
| dc.subject.ddc.spa.fl_str_mv |
630 - Agricultura y tecnologías relacionadas::631 - Técnicas específicas, aparatos, equipos, materiales |
| topic |
630 - Agricultura y tecnologías relacionadas::631 - Técnicas específicas, aparatos, equipos, materiales Lettuce - weed control Image processing Geology - statistical methods Procesamiento de imágenes Geología - Modelos estadísticos Spatial regression Autoregressive Weed crop competition Simulation In silico research Remote sensing Image processing Regresión espacial Autorregresivos Competencia cultivo malezas Simulación Investigación in silico Sensores remotos Procesamiento de imágenes |
| dc.subject.lemb.eng.fl_str_mv |
Lettuce - weed control Image processing Geology - statistical methods |
| dc.subject.lemb.spa.fl_str_mv |
Procesamiento de imágenes Geología - Modelos estadísticos |
| dc.subject.proposal.eng.fl_str_mv |
Spatial regression Autoregressive Weed crop competition Simulation In silico research Remote sensing Image processing |
| dc.subject.proposal.spa.fl_str_mv |
Regresión espacial Autorregresivos Competencia cultivo malezas Simulación Investigación in silico Sensores remotos Procesamiento de imágenes |
| description |
ilustraciones, gráficas, tablas |
| publishDate |
2019 |
| dc.date.issued.spa.fl_str_mv |
2019-10-25 |
| dc.date.accessioned.spa.fl_str_mv |
2020-02-24T18:47:04Z |
| dc.date.available.spa.fl_str_mv |
2020-02-24T18:47:04Z |
| dc.type.spa.fl_str_mv |
Trabajo de grado - Doctorado |
| dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
| dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
| dc.type.coar.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_db06 |
| dc.type.content.spa.fl_str_mv |
Text |
| dc.type.redcol.spa.fl_str_mv |
http://purl.org/redcol/resource_type/TD |
| format |
http://purl.org/coar/resource_type/c_db06 |
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acceptedVersion |
| dc.identifier.uri.none.fl_str_mv |
https://repositorio.unal.edu.co/handle/unal/75703 |
| dc.identifier.instname.spa.fl_str_mv |
Universidad Nacional de Colombia |
| dc.identifier.reponame.spa.fl_str_mv |
Repositorio Institucional Universidad Nacional de Colombia |
| dc.identifier.repourl.none.fl_str_mv |
https://repositorio.unal.edu.co/ |
| url |
https://repositorio.unal.edu.co/handle/unal/75703 https://repositorio.unal.edu.co/ |
| identifier_str_mv |
Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
| dc.language.iso.spa.fl_str_mv |
spa |
| language |
spa |
| dc.relation.references.spa.fl_str_mv |
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Rejmánek, M., Robinson, G., & Rejmánková, E. (1989). Weed-Crop Competition: Experimental Designs and Models for Data Analysis. Weed Science, 37(2), 276–284. Rendon-Aguilar, B., Bernal-Ramirez, L., & Sánchez-Reyes, GA. (2017). Las plantas arvenses: más que hierbas del campo. Oikos=. Instituto de Ecología. Universidad Nacional Autónoma de México. Renton, M., & Chauhan, B. (2017). Modelling crop-weed competition: Why, what, how and what lies ahead? Crop Protection, 95, 101–108. https://doi.org/10.1016/j.cropro.2016.09.003 Rodríguez Albarrcín, H. S., Darghan Contreras, A. E., & Henao, M. C. (2019). Spatial regression modeling of soils with high cadmium content in a cocoa producing area of Central Colombia. Geoderma Regional, 16, e00214. https://doi.org/10.1016/j.geodrs.2019.e00214 Rodríguez, M., Plaza, G., Gil, R., & Chaves, B. (2008). Reconocimiento y fluctuación poblacional arvense en el cultivo de espinaca ( Spinacea oleracea L .) para el municipio de Cota , Cundinamarca Recognition and population fluctuation of weeds in spinach crop ( Spinacea oleracea L .) in the municipality of Co. Ronchi, C., & Silva, A. (2006). Effects of weed species competition on the growth of young coffee plants. Planta Daninha, 24(3), 415–423. https://doi.org/10.1590/S0100-83582006000300001 Shukla, G., & Subrahmanyam, G. (1999). A Note on an Exact Test and Confidence Interval for Competition and Overlap Effects. Biometrics, 55(March), 273–276. Smith, R. J. (1993). Logarithmic transformation bias in allometry. American Journal of Physical Anthropology, 90(2), 215–228. https://doi.org/10.1002/ajpa.1330900208 Spitters, C. (1983). An alternative approach to the analysis of mixed cropping experiments. Netherland Journal of Agricultural Science, 31, 1–11. Swanton, C., Nkoa, R., & Blackshaw, R. E. (2015). 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Wiley. https://doi.org/10.2136/vzj2002.0321 Weiner, J., Griepentrog, H.-W., & Kristensen, L. (2001). Suppression of weeds by spring wheat. Journal of Applied Ecology, 38, 784–790. Williams II, M. M., & Boydston, R. A. (2013). Intraspecific and interspecific competition in sweet corn. Agronomy Journal, 105(2), 503–508. https://doi.org/10.2134/agronj2012.0381 Yordanova, M., & Nikolov, A. (2017). Influence of plant density and mulching on weed infestation in lettuce (Lactuca sativa var . romana Hort .). Journal of Agriculture and Veterinary Science, 10(10), 71–76. https://doi.org/10.9790/2380-1010017176 Zanin, G., Berti, A., & Riello, L. (1998). Incorporation of weed spatial variability into the weed control decision-making process. Weed Research, 38(2), 107–118. https://doi.org/10.1046/j.1365-3180.1998.00074.x Zeng, W. S., Zeng, W. S., & Tang, S. Z. (2011). Bias Correction in Logarithmic Regression and Comparison with Weighted Regression for Nonlinear Models. Nature Precedings, 1–11. https://doi.org/10.1038/npre.2011.6708 Zimdahl, R. (2004). Weed-Crop Competition. A Review (Second Edi). Blackwell Publishing. Zimdahl, R. (2007). Fundamentals of weed science. Elsevier. https://doi.org/10.1016/0378-4290(95)90065-9 |
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Atribución-NoComercial-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Darghan Contreras, Aquiles Enrique6daa0bb8-67e1-4bfc-9811-173313945dc1González Andújar, José Luis5d36ef94-646e-4193-8074-1291f688b746Jamaica Tenjo, David Alejandrob93b9321-4131-4f2a-8f6c-046b744cdc0f2020-02-24T18:47:04Z2020-02-24T18:47:04Z2019-10-25https://repositorio.unal.edu.co/handle/unal/75703Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, gráficas, tablasLa modelización de la competencia maleza-cultivo en general no contempla la heterogeneidad y la dependencia espacial tanto de las malezas como del cultivo, llevando a decisiones sesgadas en la toma de decisiones de manejo. Por esta razón, en esta investigación se propuso y se evaluaron variantes de un modelo autorregresivo espacial que incorpora los supuestos de heterogeneidad y dependencia. Para evaluar estas variantes del modelo en diversos escenarios de abundancia, agregación, distribución, dependencia espacial y la capacidad de competencia de las malezas y el cultivo, se construyó un simulador de campos Gaussianos continuos, herramienta de la geoestadística que en este caso, permite generar datos de malezas y cultivo que, asignando a cada pixel un área determinada, imitan la forma real en la que pueden aparecer en el campo. Luego, para la validación del modelo en un cultivo de lechuga y debido a la gran cantidad de información necesaria, se desarrolló un software que procesa imágenes multiespectrales, el cual permitió calcular la cobertura de malezas con gran precisión en una resolución espacial inferior a 1 mm2. Finalmente, en la validación del modelo, se incorporó una matriz de pesos que proviene de la distancia y de la cobertura de las malezas presentes entre cada planta de cultivo con sus vecinas más cercanas. Los parámetros de dependencia espacial de las malezas, del cultivo y del error fueron altamente significativos, lo que implica, para las condiciones evaluadas, que el uso de modelos autorregresivos espaciales es justificado y necesario para evaluar la competencia cultivo-malezas. (Texto tomado de la fuente).The modeling of weed-crop competition, in general, does not contemplate the heterogeneity and spatial dependence of both weeds and cultivation, leading to biased decisions in management decision making. For this reason, this research proposed and evaluated variants of a spatial autoregressive model that incorporates the assumptions of heterogeneity and dependence. To evaluate these variants of the model in various scenarios of abundance, aggregation, distribution, spatial dependence and the competence capacity of weeds and the crop, a simulator of continuous Gaussian fields was built, a tool of geostatistics that in this case, allows to generate weed and crop data that, by assigning a given coverage area to each pixel, mimic the real way in which they can appear in the field. Then, for the validation of the model in a lettuce crop and due to a large amount of information needed, the software was developed that processes multispectral images, which allowed weed coverage to be calculated with great precision at a spatial resolution of fewer than 1 mm2. Finally, in the validation of the model, a matrix of weights was incorporated that comes from the distance and coverage of the weeds present between each crop plant with its closest neighbors. The parameters of spatial dependence of weeds, crop, and error were highly significant, which implies, for the conditions evaluated, that the use of spatial autoregressive models is justified and necessary to assess the crop-weed competition.DoctoradoDoctor en Ciencias AgrariasMalherbologíaCiencias Agronómicasxx, 128 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ciencias Agrarias - Doctorado en Ciencias AgrariasEscuela de posgradosFacultad de Ciencias AgrariasBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá630 - Agricultura y tecnologías relacionadas::631 - Técnicas específicas, aparatos, equipos, materialesLettuce - weed controlImage processingGeology - statistical methodsProcesamiento de imágenesGeología - Modelos estadísticosSpatial regressionAutoregressiveWeed crop competitionSimulationIn silico researchRemote sensingImage processingRegresión espacialAutorregresivosCompetencia cultivo malezasSimulaciónInvestigación in silicoSensores remotosProcesamiento de imágenesModelización de la interferencia cultivo-malezas, mediante modelos autorregresivos espaciales, con validación en un cultivo de lechugaModeling weed-crop interference, using spatial autoregressive models, with validation in a lettuce cropTrabajo de grado - Doctoradoinfo:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_db06Texthttp://purl.org/redcol/resource_type/TDAgrow (2003) Agrochemical sales flat in 2002. 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