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
| id |
UNACIONAL2_edb19f56b08050304142f2bfdbaa6a09 |
|---|---|
| oai_identifier_str |
oai:repositorio.unal.edu.co:unal/75703 |
| network_acronym_str |
UNACIONAL2 |
| network_name_str |
Universidad Nacional de Colombia |
| repository_id_str |
|
| 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 |
| status_str |
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 |
Agrow (2003) Agrochemical sales flat in 2002. Agrow: World Crop Protection News. http://ipm.osu.edu/trans/043_141.htm Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716–723. doi:10.1109/tac.1974.1100705 Alexandratos, N, Bruinsma, J (2012). World agriculture towards 2030/2050: the 2012 revision, ESA Working Papers 288998, Food and Agriculture Organization of the United Nations, Agricultural Development Economics Division (ESA). Andújar, D., Ribeiro, A., Carmona, R., Fernández-Quintanilla, C., & Dorado, J. (2010). An assessment of the accuracy and consistency of human perception of weed cover. Weed Research, 50(6), 638–647. https://doi.org/10.1111/j.1365-3180.2010.00809.x Anselin, L., & Bera, A. (1998). Spatial Dependence in Linear Regression Models with an introduction to spatial econometrics. En Handbook of Applied Economic Studies (pp. 237–289). Anselin, L., Bongiovanni, R., & Lowenberg-Deboer, J. (2004). A spatial econometric approach to the economics of site-specific nitrogen management in corn production. American Journal of Agriculture economics, 86(August), 675–687. Appleby AP, Muller F, Carpy S (2000) Weed control. In: Muller F (ed) Agrochemicals, Wiley, New York, p 687–707 Arbia, G. (2014). A Primer for Spatial Econometrics With Applications in R. London: Palgrave Macmillan. Auld, B., & Tisdell, C. (1988). Influence of spatial distribution of weeds on crop yield loss. Plant Protection Quarterly, 3(January), 81. Begueira, S. (2010). Generating spatially correlated random fields with R. Recuperado de http://santiago.begueria.es/2010/10/generating-spatially-correlated-random-fields-with-r/ Blanco, Y., & Leyva, A. (2007). Las arvenses en el agroecosistema y sus beneficios agroecológicos como hospederas de enemigos naturales. Cultivos tropicales, 28(2),21-28 Bosnic, A., & Swanton, C. (1997). Influence of barnyardgrass ( Echinochloa crus-galli ) time of emergence and density on corn ( Zea mays ). Weed Science, 45(2), 276–282. Brain, P., & Cousens, R. (1990a). The Effect of Weed Distribution on Predictions of Yield Loss. Journal of Applied Ecology, 27(2), 735–742. https://doi.org/10.2307/2404315 Brain, P., & Cousens, R. (1990b). The Effect of Weed Distribution on Predictions of Yield Loss. Journal of Applied Ecology, 27(2), 735–742. https://doi.org/10.2307/2404315 Bridges, D. C., & Chandler, J. M. (1987). Influence of Johnsongrass (Sorghum halepense) Density and Period of Competition on Cotton Yield. Weed Science, 35, 63–67. Calha IM, Sousa E, Gonzalez-Andujar JL (2014). Infestation maps and spatial stability of main weed species in maize culture. Planta Daninha 32: 275-282 Camps-Valls, G., & Bruzzone, L. (2005). Kernel-based methods for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 43(6), 1351–1362. https://doi.org/10.1109/TGRS.2005.846154 Cardina, J., Johnson, G., & Sparrow, D. (1997). The Nature and Consequence of Weed Spatial Distribution. Weed Science, 45(3), 364–373. Cardona, F. (1971), Competencia de malezas en lechuga (Lactuca sativa var. Capitata). Tesis de Maestría. Universidad Nacional de Colombia. Pp 150. Clements, F., Weaver, J., & Hanson, H. (1929). Plant competition: an analysis of community function. Washington, D.C.: Carnegie Institution of Washington. Cousens, R. (1985a). A simple model relating yield loss to weed density. Annals of Applied Biology, 107(2), 239–252. https://doi.org/10.1111/j.1744-7348.1985.tb01567.x Cousens, R. (1985b). An Empirical Model Relating Crop Yield to Weed and Crop Density and A Statistical Comparison with Other Models. The Journal of Agricultural Science, 105(3), 513–521. https://doi.org/10.1017/S0021859600059396 Cousens, R., Brain, P., O’Donovan, J., & O’Sullivan, P. (1987). The use of biologically realistic equations to describe the effects of weed density and relative time of emergence on crop yield. Weed Science, 35(5), 720–725. Cressie, N. (1993). Statistics for spatial data. Hoboken, New Jersey: John Wiley & Sons. Dale, M. R. T., Dixon, P., Legendre, P., Myers, D. E., & Rosenberg, M. S. (2002). Conceptual and mathematical relationships among methods for spatial analysis. Ecography, 25(5), 558–577. https://doi.org/10.1034/j.1600-0587.2002.250506.x Deen, W., Cousens, R., Warringa, J., Bastiaans, L., Carberry, P., Rebel, K., … Wang, E. (2003). An evaluation of four crop: Weed competition models using a common data set. Weed Research, 43(2), 116–129. https://doi.org/10.1046/j.1365-3180.2003.00323.x Dew, D. A. (1972). An Index of Competition for estimating Crop Loss Due to Weeds. Canadian Journal of Plant Science, 52, 921–927. https://doi.org/10.4141/cjps72-159 El Sharif, H., Wang, J., & Georgakakos, A. P. (2015). Modeling Regional Crop Yield and Irrigation Demand Using SMAP Type of Soil Moisture Data. Journal of Hydrometeorology, 16(2), 904–916. https://doi.org/10.1175/jhm-d-14-0034.1 Elhorst, J. P. (2010). Applied Spatial Econometrics: Raising the Bar. Spatial Economic Analysis, 5(1), 9–28. https://doi.org/10.1080/17421770903541772 Florax, R. J. G. M., Voortman, R. L., & Brouwer, J. (2002). Spatial dimensions of precision agriculture: A spatial econometric analysis of millet yield on Sahelian coversands. Agricultural Economics, 27(3), 425–443. https://doi.org/10.1016/S0169-5150(02)00068-3 Fuentes, C. & Romero, C. Una visión del problema de las malezas en Colombia. Agronomía Colombiana. 1991. 8(2), 364 - 378 Galon, l., Forte, C. T., Giacomini, j. P., Reichert Jr, f. W., Scariot, M. A., David, F. A., & Perin, G. F. (2016). Competitive Ability of Lettuce with Ryegrass. Planta Daninha, 34(2), 239–248. https://doi.org/10.1590/S0100-83582016340200005 Gherekhloo, J., Noroozi, S., Mazaheri, D., Ghanbari, A., Ghannadha, M., Vidal, R., & De Prado, R. (2010). Multispecies weed competition and their economic threshold on the wheat crop. Planta Daninha, 28(2), 239–246. Godfray HC et al., (2010) Food security: The challenge of feeding 9 billion people. Science 327, 812–818 Gomez, A., & Gomez, K. (1984). Statistical procedures for agricultural research. Statistical procedures for agricultural research, 6, 680. González-Andújar, J. L., Chantre, G. R., Morvillo, C., Blanco, A. M., & Forcella, F. (2016). Predicting field weed emergence with empirical models and soft computing techniques. Weed Research, 56(6), 415–423. https://doi.org/10.1111/wre.12223 González-Andújar, J. L., Fernández-Quintanilla, C., Bastida, F., Calvo, R., Izquierdo, J., & Lezaun, J. (2011). Assessment of a decision support system for chemical control of annual ryegrass (Lolium rigidum) in winter cereals. Weed Research, 51(3), 304–309. https://doi.org/10.1111/j.1365-3180.2011.00842.x González-Andújar, J. L., Fernández-Quintanilla, C., & Torner, C. (1993). Competencia entre la avena loca (Avena sterilis) y el trigo de invierno: comparación de modelos empíricos. Investigación Agraria: Producción y Protección Vegetales, 8(3), 425–430. González-Andújar, J. L., & Navarrete, L. (1995). Aplicación del índice de distancias t-cuadrado al estudio de las distribución espacial de las malas hierbas. Investigación Agraria Producción y protección vegetales, 10(2), 295–299. González-Andújar, J. L., & Saavedra, M. (2003). Spatial distribution of annual grass weed populations in winter cereals. Crop Protection, 22(4), 629–633. https://doi.org/10.1016/S0261-2194(02)00247-8 González-Díaz, L., Blanco-Moreno, J. M., & González-Andújar, J. L. (2015). Spatially explicit bioeconomic model for weed management in cereals: Validation and evaluation of management strategies. Journal of Applied Ecology, 52(1), 240–249. https://doi.org/10.1111/1365-2664.12359 Goslee, S. C. (2006). Behavior of vegetation sampling methods in the presence of spatial autocorrelation. Plant Ecology, 187(2), 203–212. https://doi.org/10.1007/s11258-005-3495-x Gräler, B., Pebesma, E., & Heuvelink, G. (2016). Spatio-Temporal Interpolation using gstat. The R Journal, 8(1), 204–218. Grinstead, C. M., & Snell, J. L. (1997). Introduction to Probability: Second Revised Edition. American Mathematical Society, 1–520. Recuperado de http://www.dartmouth.edu/~chance/teaching_aids/books_articles/probability_book/book.html Harvey, R., & Wagner, C. (1994). Using Estimates of Weed Pressure to Establish Crop Yield Loss Equations. Weed Technology, 8(1), 114–118. https://doi.org/doi:10.1017/S0890037X00039294 Heege, H. J. (2013). Precision in Crop Farming. Springer Science. https://doi.org/10.1007/978-94-007-6760-7 Heijting, S. (2007). Spatial analysis of weed patterns. Tesis de Doctorado.Universidad de Wageningen. Recuperado de http://library.wur.nl/wda/dissertations/dis4308.pdf Heisel, T., Ersbøll, A. K., & Andreasen, C. (1999). Weed Mapping with Co-Kriging Using Soil Properties. Precision Agriculture, 1(1), 39–52. https://doi.org/10.1023/A:1009921718225 Holst, N., Rasmussen, I., & Bastiaans, L. (2007). Field weed population dynamics : a review of model approaches and applications. Weed Research, 47, 1–14. Holzner, W., & Numata, M. (1982). Biology and ecology of weeds. (Springer, Ed.). The Hague. Hughes, G. (1996). Incorporating spatial pattern of harmful organisms into crop loss models. Crop Protection, 15(5), 407–421. https://doi.org/10.1016/0261-2194(96)00003-8 Jadhav, B. D., & Patil, P. M. (2014). Hyperspectral Remote Sensing For Agricultural Management: A Survey. International Journal of Computer Applications, 106(7), 975–8887. Jamaica-Tenjo, D. A., & González-Andújar, J. L. (2019). Modelos empíricos de competencia cultivo-mala hierba, Revisión bibliográfica. ITEA-Información Técnica Económica Agraria, xx, 1–18. https://doi.org/https://doi.org/10.12706/itea.2019.007 Jamaica, D. (2013). Dinámica espacial y temporal de poblaciones de malezas en cultivos de papa, espinaca y caña de azúcar y su relación con propiedades del suelo en dos localidades de Colombia. Tesis de Maestría. Facultad de Ciencias Agrarias. Universidad Nacional de Colombia. Pp 82 Jamaica, D., & Plaza, G. (2014). Evaluation of various conventional methods for sampling weeds in potato and spinach crops. Agronomia Colombiana, 32(1). pp.36-43. ISSN 0120-9965. http://dx.doi.org/10.15446/agron.colomb.v32n1.39613. Jordan, N, Schut, M, Graham, S, Barney, JN, Childs, DZ, Christensen, S, Cousens, RD, Davis, AS, Eizenberg, H, Ervin, DE, Fernandez-Quintanilla, C, Harrison, LJ, Harsch, MA, Heijting, S, Liebman, M, Loddo, D, Mirsky, SB, Riemens, M, Neve, P, Peltzer, DA, Renton, M, Williams, M, Recasens, J, Sonderskov, M (2016). Transdisciplinary weed research: new leverage on challenging weed problems? Weed Research 56 345–358. Jurado-Expósito, M., López-Granados, F., García, L., & García-Ferrer, A. (2003). Multi-Species Weed Spatial Variability and Site-Specific Management Maps in Cultivated Sunflower. Weed Science, 51(3), 319–328. https://doi.org/10.1614/0043-1745(2003)051 Kamilaris, A., & Prenafeta-Boldú, F. X. (2018). Deep learning in agriculture: A survey. Computers and Electronics in Agriculture, 147, 70–90. https://doi.org/10.1016/j.compag.2018.02.016 Keller, M. 2014. Effects of weeds on yield and determination of economic thresholds for site-specific weed control using sensor technology. PhD Dissertation. University of Hohenheim. Pp 54. Khazaei, I., Salehi, R., Abdolkarim, K., & Mirjalili, S. (2013). Improvement of lettuce growth and yield with spacing, mulching and organic fertilizer. International Journal of Agriculture and Crop Sciences, 6(16), 1137–1143. Kropff, M., & Spitters, C. (1991). A simple model of crop loss by weed competition from early observations on relative leal area of the weeds. Weed Research, 31, 97–105. Kropff, M., & van Laar, H. (1993). Modelling Crop-Weed Interactions. Modelling Crop-Weed Interactions. Wallingford: CAB INTERNATIONAL. Labrada, R., Caseley, J., & Parker, C. (1996). Manejo de Malezas para Países en Desarrollo. Estudio FAO Producción y Portección vegetal - 120. Rome: Organización de las Naciones Unidas para la Agricultura y la Alimentación. Legendre, P., Dale, M. R. T., Fortin, M. J., Gurevitch, J., Hohn, M., & Myers, D. (2002). The consequences of spatial structure for the design and analysis of ecological field surveys. Ecography, 25(5), 601–615. https://doi.org/10.1034/j.1600-0587.2002.250508.x Lottes, P., Khanna, R., Pfeifer, J., Siegwart, R., & Stachniss, C. (2017). UAV-based crop and weed classification for smart farming. Proceedings - IEEE International Conference on Robotics and Automation, 3024–3031. https://doi.org/10.1109/ICRA.2017.7989347 Lutman PJ. Wand Miller PCH (2007) Spatially variable herbicide application technology; opportunities for herbicide minimisation and protection of beneficial weeds. Research Review No. 62, Home-Grown Cereals Authority (HGCA), UK Manning, W. G., & Mullahy, J. (1999). Estimating log models: to transform or not to transform? (No. 246). Cambridge. Marshall, E. (1988). Field-Scale Estimates of Grass Weed Populations in Arable Land. Weed Research, 28(3), 191–198. https://doi.org/10.1111/j.1365-3180.1988.tb01606.x Matheron, G. (1963). Principles of geostatistics. Economic Geology, 58(8), 1246–1266. https://doi.org/10.2113/gsecongeo.58.8.1246 Mcmaster, G. S., & Wilhelm, W. (1997). Growing degree-days: one equation, two interpretation. Agricultural and Forest Meteorology, 87, 291–300. https://doi.org/10.1016/j.rhum.2013.05.004 Meyer, G. E., Neto, J. C., Jones, D. D., & Hindman, T. W. (2004). Intensified fuzzy clusters for classifying plant, soil, and residue regions of interest from color images. Computers and Electronics in Agriculture, 42(3), 161–180. https://doi.org/10.1016/j.compag.2003.08.002 Montgomery, D. (2013). Design and Analysis of Experiments (8a ed.). John Wiley & Sons. Ngouajio, M., Leroux, G., & Lemieux, C. (1999). A flexible sigmoidal model relating crop yield to weed relative leaf cover and its comparison with nested models. Weed Research, 39(4), 329–343. https://doi.org/10.1046/j.1365-3180.1999.00150.x Nkoa, R., Owen, M. D. K., & Swanton, C. J. (2015). Weed Abundance, Distribution, Diversity, and Community Analyses. Weed Science, 63(sp1), 64–90. https://doi.org/10.1614/WS-D-13-00075.1 Norris, R., Elmore, C., Rejmánek, M., & Akey, W. C. (2001). Spatial arrangement, density, and competition between barnyardgrass and tomato: I. Crop growth and yield. Weed Science, 49(1), 61–68. https://doi.org/10.1614/0043-1745 O’Donovan, J., de St. Remy, E., O’Sullivan, P., Dew, D., & Sharma, A. (1985). Influence of the relative time of emergence of wild oat (Avena fatua) on yield loss of barley (Hordeum vulgare) and wheat (Triticum aesitvum). Weed Science, 33, 498–503. Oerke, E. (2006). Crop losses to pests. Journal of Agricultural Science, 144, 31–43. https://doi.org/10.1017/S0021859605005708 Osborn, S., Panayot, V., & Villa, U. (2017). A multilevel hierarchical sampling technique for spatially correlated random fields. SIAM Journal on Scientific computing, 39(5), S543–S562. https://doi.org/10.1137/090750688 Pérez-Ortiz, M., Peña-Barragán, J. M., Gutiérrez, P. A. A., Torres-Sánchez, J., Hervás-Martínez, C., López-Granados, F., … López-Granados, F. (2015). A semi-supervised system for weed mapping in sunflower crops using unmanned aerial vehicles and a crop row detection method. Applied Soft Computing, 37, 533–544. https://doi.org/10.1016/j.asoc.2015.08.027 Puerta, P., Ciannelli, L., & Johnson, B. (2019). A simulation framework for evaluating multi-stage sampling designs in populations with spatially structured traits. PeerJ, 7, e6471. https://doi.org/10.7717/peerj.6471 Puerto, A. (2017). Clasificación y cuantificación de maleza en cultivos de hortalizas por medio de procesamiento de imágenes digitales. Universidad Nacional de Colombia. Radosevich, S. (1987). Methods to Study Interactions Among Crops and Weeds. Weed Technology, 1, 190–198. Radosevich, S., Holt, J., & Ghersa, C. (2007). Ecology of weeds and invasive plants: Relationship to agriculture and natural resource management. (3 Ed, Ed.). Hoboken, New Jersey. 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). Experimental Methods for Crop – Weed Competition Studies. Weed Science, (2), 2–11. https://doi.org/10.1614/WS-D-13-00062.1 Swanton, C., Weaver, S., Cowan, P., Van Acker, R., Deen, W., & Shreshta, A. (1999). Weed thresholds: theory and applicability. Journal of Crop Production, 2(1), 9–29. https://doi.org/10.1300/J144v02n01 Tang, J. L., Wang, D., Zhang, Z. G., He, L. J., Xin, J., & Xu, Y. (2017). Weed identification based on K-means feature learning combined with convolutional neural network. Computers and Electronics in Agriculture, 135, 63–70. https://doi.org/10.1016/j.compag.2017.01.001 Thorp, K. R., & Tian, L. F. (2004). A review on remote sensing of weeds in agriculture. Precision Agriculture, 5(5), 477–508. https://doi.org/10.1007/s11119-004-5321-1 Torner, C., González-Andújar, J. L., & Fernández-Quintanilla, C. (1991). Wild Oat (Avena-Sterilis L) Competition With Winter Barley - Plant-Density Effects. Weed Research, 31(5), 301–307. Torra, J., Cirujeda, A., Recasens, J., Taberner, A., & Powles, S. B. (2010). PIM (Poppy Integrated Management): A bio-economic decision support model for the management of Papaver rhoeas in rain-fed cropping systems. Weed Research, 50(2), 127–139. https://doi.org/10.1111/j.1365-3180.2010.00761.x Torres-Sánchez, J., López-Granados, F., Peña, J. M., Peña-Barragán, J. M., Peña, J. M., & Peña-Barragán, J. M. (2015). An automatic object-based method for optimal thresholding in UAV images: Application for vegetation detection in herbaceous crops. Computers and Electronics in Agriculture, 114, 43–52. https://doi.org/10.1016/j.compag.2015.03.019 Walter, A., Christensen, S., & Simmelsgaard, S. (2002). Spatial correlation between weed species densities and soil properties. Weed Research, 42(1), 26–38. https://doi.org/10.1046/j.1365-3180.2002.00259.x Webster, R., & Oliver, M. (2007). Geostatistics for Environmental Scientists. 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 |
| dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
| dc.rights.license.spa.fl_str_mv |
Atribución-NoComercial-SinDerivadas 4.0 Internacional |
| dc.rights.uri.spa.fl_str_mv |
http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| dc.rights.accessrights.spa.fl_str_mv |
info:eu-repo/semantics/openAccess |
| rights_invalid_str_mv |
Atribución-NoComercial-SinDerivadas 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ http://purl.org/coar/access_right/c_abf2 |
| eu_rights_str_mv |
openAccess |
| dc.format.extent.spa.fl_str_mv |
xx, 128 páginas |
| dc.format.mimetype.spa.fl_str_mv |
application/pdf |
| dc.publisher.spa.fl_str_mv |
Universidad Nacional de Colombia |
| dc.publisher.program.spa.fl_str_mv |
Bogotá - Ciencias Agrarias - Doctorado en Ciencias Agrarias |
| dc.publisher.department.spa.fl_str_mv |
Escuela de posgrados |
| dc.publisher.faculty.spa.fl_str_mv |
Facultad de Ciencias Agrarias |
| dc.publisher.place.spa.fl_str_mv |
Bogotá, Colombia |
| dc.publisher.branch.spa.fl_str_mv |
Universidad Nacional de Colombia - Sede Bogotá |
| institution |
Universidad Nacional de Colombia |
| bitstream.url.fl_str_mv |
https://repositorio.unal.edu.co/bitstream/unal/75703/3/license_rdf https://repositorio.unal.edu.co/bitstream/unal/75703/1/81721105.2020.pdf https://repositorio.unal.edu.co/bitstream/unal/75703/2/license.txt https://repositorio.unal.edu.co/bitstream/unal/75703/4/81721105.2020.pdf.jpg |
| bitstream.checksum.fl_str_mv |
42fd4ad1e89814f5e4a476b409eb708c 51ffaa5e675c92c9dd16ccb57e10b25a 6f3f13b02594d02ad110b3ad534cd5df f43cd0b74858959c3d5a1a3e688498a9 |
| bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 MD5 |
| repository.name.fl_str_mv |
Repositorio Institucional Universidad Nacional de Colombia |
| repository.mail.fl_str_mv |
repositorio_nal@unal.edu.co |
| _version_ |
1814089585442947072 |
| spelling |
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. Agrow: World Crop Protection News. http://ipm.osu.edu/trans/043_141.htm Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716–723. doi:10.1109/tac.1974.1100705 Alexandratos, N, Bruinsma, J (2012). World agriculture towards 2030/2050: the 2012 revision, ESA Working Papers 288998, Food and Agriculture Organization of the United Nations, Agricultural Development Economics Division (ESA). Andújar, D., Ribeiro, A., Carmona, R., Fernández-Quintanilla, C., & Dorado, J. (2010). An assessment of the accuracy and consistency of human perception of weed cover. Weed Research, 50(6), 638–647. https://doi.org/10.1111/j.1365-3180.2010.00809.x Anselin, L., & Bera, A. (1998). Spatial Dependence in Linear Regression Models with an introduction to spatial econometrics. En Handbook of Applied Economic Studies (pp. 237–289). Anselin, L., Bongiovanni, R., & Lowenberg-Deboer, J. (2004). A spatial econometric approach to the economics of site-specific nitrogen management in corn production. American Journal of Agriculture economics, 86(August), 675–687. Appleby AP, Muller F, Carpy S (2000) Weed control. In: Muller F (ed) Agrochemicals, Wiley, New York, p 687–707 Arbia, G. (2014). A Primer for Spatial Econometrics With Applications in R. London: Palgrave Macmillan. Auld, B., & Tisdell, C. (1988). Influence of spatial distribution of weeds on crop yield loss. Plant Protection Quarterly, 3(January), 81. Begueira, S. (2010). Generating spatially correlated random fields with R. Recuperado de http://santiago.begueria.es/2010/10/generating-spatially-correlated-random-fields-with-r/ Blanco, Y., & Leyva, A. (2007). Las arvenses en el agroecosistema y sus beneficios agroecológicos como hospederas de enemigos naturales. Cultivos tropicales, 28(2),21-28 Bosnic, A., & Swanton, C. (1997). Influence of barnyardgrass ( Echinochloa crus-galli ) time of emergence and density on corn ( Zea mays ). Weed Science, 45(2), 276–282. Brain, P., & Cousens, R. (1990a). The Effect of Weed Distribution on Predictions of Yield Loss. Journal of Applied Ecology, 27(2), 735–742. https://doi.org/10.2307/2404315 Brain, P., & Cousens, R. (1990b). The Effect of Weed Distribution on Predictions of Yield Loss. Journal of Applied Ecology, 27(2), 735–742. https://doi.org/10.2307/2404315 Bridges, D. C., & Chandler, J. M. (1987). Influence of Johnsongrass (Sorghum halepense) Density and Period of Competition on Cotton Yield. Weed Science, 35, 63–67. Calha IM, Sousa E, Gonzalez-Andujar JL (2014). Infestation maps and spatial stability of main weed species in maize culture. Planta Daninha 32: 275-282 Camps-Valls, G., & Bruzzone, L. (2005). Kernel-based methods for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 43(6), 1351–1362. https://doi.org/10.1109/TGRS.2005.846154 Cardina, J., Johnson, G., & Sparrow, D. (1997). The Nature and Consequence of Weed Spatial Distribution. Weed Science, 45(3), 364–373. Cardona, F. (1971), Competencia de malezas en lechuga (Lactuca sativa var. Capitata). Tesis de Maestría. Universidad Nacional de Colombia. Pp 150. Clements, F., Weaver, J., & Hanson, H. (1929). Plant competition: an analysis of community function. Washington, D.C.: Carnegie Institution of Washington. Cousens, R. (1985a). A simple model relating yield loss to weed density. Annals of Applied Biology, 107(2), 239–252. https://doi.org/10.1111/j.1744-7348.1985.tb01567.x Cousens, R. (1985b). An Empirical Model Relating Crop Yield to Weed and Crop Density and A Statistical Comparison with Other Models. The Journal of Agricultural Science, 105(3), 513–521. https://doi.org/10.1017/S0021859600059396 Cousens, R., Brain, P., O’Donovan, J., & O’Sullivan, P. (1987). The use of biologically realistic equations to describe the effects of weed density and relative time of emergence on crop yield. Weed Science, 35(5), 720–725. Cressie, N. (1993). Statistics for spatial data. Hoboken, New Jersey: John Wiley & Sons. Dale, M. R. T., Dixon, P., Legendre, P., Myers, D. E., & Rosenberg, M. S. (2002). Conceptual and mathematical relationships among methods for spatial analysis. Ecography, 25(5), 558–577. https://doi.org/10.1034/j.1600-0587.2002.250506.x Deen, W., Cousens, R., Warringa, J., Bastiaans, L., Carberry, P., Rebel, K., … Wang, E. (2003). An evaluation of four crop: Weed competition models using a common data set. Weed Research, 43(2), 116–129. https://doi.org/10.1046/j.1365-3180.2003.00323.x Dew, D. A. (1972). An Index of Competition for estimating Crop Loss Due to Weeds. Canadian Journal of Plant Science, 52, 921–927. https://doi.org/10.4141/cjps72-159 El Sharif, H., Wang, J., & Georgakakos, A. P. (2015). Modeling Regional Crop Yield and Irrigation Demand Using SMAP Type of Soil Moisture Data. Journal of Hydrometeorology, 16(2), 904–916. https://doi.org/10.1175/jhm-d-14-0034.1 Elhorst, J. P. (2010). Applied Spatial Econometrics: Raising the Bar. Spatial Economic Analysis, 5(1), 9–28. https://doi.org/10.1080/17421770903541772 Florax, R. J. G. M., Voortman, R. L., & Brouwer, J. (2002). Spatial dimensions of precision agriculture: A spatial econometric analysis of millet yield on Sahelian coversands. Agricultural Economics, 27(3), 425–443. https://doi.org/10.1016/S0169-5150(02)00068-3 Fuentes, C. & Romero, C. Una visión del problema de las malezas en Colombia. Agronomía Colombiana. 1991. 8(2), 364 - 378 Galon, l., Forte, C. T., Giacomini, j. P., Reichert Jr, f. W., Scariot, M. A., David, F. A., & Perin, G. F. (2016). Competitive Ability of Lettuce with Ryegrass. Planta Daninha, 34(2), 239–248. https://doi.org/10.1590/S0100-83582016340200005 Gherekhloo, J., Noroozi, S., Mazaheri, D., Ghanbari, A., Ghannadha, M., Vidal, R., & De Prado, R. (2010). Multispecies weed competition and their economic threshold on the wheat crop. Planta Daninha, 28(2), 239–246. Godfray HC et al., (2010) Food security: The challenge of feeding 9 billion people. Science 327, 812–818 Gomez, A., & Gomez, K. (1984). Statistical procedures for agricultural research. Statistical procedures for agricultural research, 6, 680. González-Andújar, J. L., Chantre, G. R., Morvillo, C., Blanco, A. M., & Forcella, F. (2016). Predicting field weed emergence with empirical models and soft computing techniques. Weed Research, 56(6), 415–423. https://doi.org/10.1111/wre.12223 González-Andújar, J. L., Fernández-Quintanilla, C., Bastida, F., Calvo, R., Izquierdo, J., & Lezaun, J. (2011). Assessment of a decision support system for chemical control of annual ryegrass (Lolium rigidum) in winter cereals. Weed Research, 51(3), 304–309. https://doi.org/10.1111/j.1365-3180.2011.00842.x González-Andújar, J. L., Fernández-Quintanilla, C., & Torner, C. (1993). Competencia entre la avena loca (Avena sterilis) y el trigo de invierno: comparación de modelos empíricos. Investigación Agraria: Producción y Protección Vegetales, 8(3), 425–430. González-Andújar, J. L., & Navarrete, L. (1995). Aplicación del índice de distancias t-cuadrado al estudio de las distribución espacial de las malas hierbas. Investigación Agraria Producción y protección vegetales, 10(2), 295–299. González-Andújar, J. L., & Saavedra, M. (2003). Spatial distribution of annual grass weed populations in winter cereals. Crop Protection, 22(4), 629–633. https://doi.org/10.1016/S0261-2194(02)00247-8 González-Díaz, L., Blanco-Moreno, J. M., & González-Andújar, J. L. (2015). Spatially explicit bioeconomic model for weed management in cereals: Validation and evaluation of management strategies. Journal of Applied Ecology, 52(1), 240–249. https://doi.org/10.1111/1365-2664.12359 Goslee, S. C. (2006). Behavior of vegetation sampling methods in the presence of spatial autocorrelation. Plant Ecology, 187(2), 203–212. https://doi.org/10.1007/s11258-005-3495-x Gräler, B., Pebesma, E., & Heuvelink, G. (2016). Spatio-Temporal Interpolation using gstat. The R Journal, 8(1), 204–218. Grinstead, C. M., & Snell, J. L. (1997). Introduction to Probability: Second Revised Edition. American Mathematical Society, 1–520. Recuperado de http://www.dartmouth.edu/~chance/teaching_aids/books_articles/probability_book/book.html Harvey, R., & Wagner, C. (1994). Using Estimates of Weed Pressure to Establish Crop Yield Loss Equations. Weed Technology, 8(1), 114–118. https://doi.org/doi:10.1017/S0890037X00039294 Heege, H. J. (2013). Precision in Crop Farming. Springer Science. https://doi.org/10.1007/978-94-007-6760-7 Heijting, S. (2007). Spatial analysis of weed patterns. Tesis de Doctorado.Universidad de Wageningen. Recuperado de http://library.wur.nl/wda/dissertations/dis4308.pdf Heisel, T., Ersbøll, A. K., & Andreasen, C. (1999). Weed Mapping with Co-Kriging Using Soil Properties. Precision Agriculture, 1(1), 39–52. https://doi.org/10.1023/A:1009921718225 Holst, N., Rasmussen, I., & Bastiaans, L. (2007). Field weed population dynamics : a review of model approaches and applications. Weed Research, 47, 1–14. Holzner, W., & Numata, M. (1982). Biology and ecology of weeds. (Springer, Ed.). The Hague. Hughes, G. (1996). Incorporating spatial pattern of harmful organisms into crop loss models. Crop Protection, 15(5), 407–421. https://doi.org/10.1016/0261-2194(96)00003-8 Jadhav, B. D., & Patil, P. M. (2014). Hyperspectral Remote Sensing For Agricultural Management: A Survey. International Journal of Computer Applications, 106(7), 975–8887. Jamaica-Tenjo, D. A., & González-Andújar, J. L. (2019). Modelos empíricos de competencia cultivo-mala hierba, Revisión bibliográfica. ITEA-Información Técnica Económica Agraria, xx, 1–18. https://doi.org/https://doi.org/10.12706/itea.2019.007 Jamaica, D. (2013). Dinámica espacial y temporal de poblaciones de malezas en cultivos de papa, espinaca y caña de azúcar y su relación con propiedades del suelo en dos localidades de Colombia. Tesis de Maestría. Facultad de Ciencias Agrarias. Universidad Nacional de Colombia. Pp 82 Jamaica, D., & Plaza, G. (2014). Evaluation of various conventional methods for sampling weeds in potato and spinach crops. Agronomia Colombiana, 32(1). pp.36-43. ISSN 0120-9965. http://dx.doi.org/10.15446/agron.colomb.v32n1.39613. Jordan, N, Schut, M, Graham, S, Barney, JN, Childs, DZ, Christensen, S, Cousens, RD, Davis, AS, Eizenberg, H, Ervin, DE, Fernandez-Quintanilla, C, Harrison, LJ, Harsch, MA, Heijting, S, Liebman, M, Loddo, D, Mirsky, SB, Riemens, M, Neve, P, Peltzer, DA, Renton, M, Williams, M, Recasens, J, Sonderskov, M (2016). Transdisciplinary weed research: new leverage on challenging weed problems? Weed Research 56 345–358. Jurado-Expósito, M., López-Granados, F., García, L., & García-Ferrer, A. (2003). Multi-Species Weed Spatial Variability and Site-Specific Management Maps in Cultivated Sunflower. Weed Science, 51(3), 319–328. https://doi.org/10.1614/0043-1745(2003)051 Kamilaris, A., & Prenafeta-Boldú, F. X. (2018). Deep learning in agriculture: A survey. Computers and Electronics in Agriculture, 147, 70–90. https://doi.org/10.1016/j.compag.2018.02.016 Keller, M. 2014. Effects of weeds on yield and determination of economic thresholds for site-specific weed control using sensor technology. PhD Dissertation. University of Hohenheim. Pp 54. Khazaei, I., Salehi, R., Abdolkarim, K., & Mirjalili, S. (2013). Improvement of lettuce growth and yield with spacing, mulching and organic fertilizer. International Journal of Agriculture and Crop Sciences, 6(16), 1137–1143. Kropff, M., & Spitters, C. (1991). A simple model of crop loss by weed competition from early observations on relative leal area of the weeds. Weed Research, 31, 97–105. Kropff, M., & van Laar, H. (1993). Modelling Crop-Weed Interactions. Modelling Crop-Weed Interactions. Wallingford: CAB INTERNATIONAL. Labrada, R., Caseley, J., & Parker, C. (1996). Manejo de Malezas para Países en Desarrollo. Estudio FAO Producción y Portección vegetal - 120. Rome: Organización de las Naciones Unidas para la Agricultura y la Alimentación. Legendre, P., Dale, M. R. T., Fortin, M. J., Gurevitch, J., Hohn, M., & Myers, D. (2002). The consequences of spatial structure for the design and analysis of ecological field surveys. Ecography, 25(5), 601–615. https://doi.org/10.1034/j.1600-0587.2002.250508.x Lottes, P., Khanna, R., Pfeifer, J., Siegwart, R., & Stachniss, C. (2017). UAV-based crop and weed classification for smart farming. Proceedings - IEEE International Conference on Robotics and Automation, 3024–3031. https://doi.org/10.1109/ICRA.2017.7989347 Lutman PJ. Wand Miller PCH (2007) Spatially variable herbicide application technology; opportunities for herbicide minimisation and protection of beneficial weeds. Research Review No. 62, Home-Grown Cereals Authority (HGCA), UK Manning, W. G., & Mullahy, J. (1999). Estimating log models: to transform or not to transform? (No. 246). Cambridge. Marshall, E. (1988). Field-Scale Estimates of Grass Weed Populations in Arable Land. Weed Research, 28(3), 191–198. https://doi.org/10.1111/j.1365-3180.1988.tb01606.x Matheron, G. (1963). Principles of geostatistics. Economic Geology, 58(8), 1246–1266. https://doi.org/10.2113/gsecongeo.58.8.1246 Mcmaster, G. S., & Wilhelm, W. (1997). Growing degree-days: one equation, two interpretation. Agricultural and Forest Meteorology, 87, 291–300. https://doi.org/10.1016/j.rhum.2013.05.004 Meyer, G. E., Neto, J. C., Jones, D. D., & Hindman, T. W. (2004). Intensified fuzzy clusters for classifying plant, soil, and residue regions of interest from color images. Computers and Electronics in Agriculture, 42(3), 161–180. https://doi.org/10.1016/j.compag.2003.08.002 Montgomery, D. (2013). Design and Analysis of Experiments (8a ed.). John Wiley & Sons. Ngouajio, M., Leroux, G., & Lemieux, C. (1999). A flexible sigmoidal model relating crop yield to weed relative leaf cover and its comparison with nested models. Weed Research, 39(4), 329–343. https://doi.org/10.1046/j.1365-3180.1999.00150.x Nkoa, R., Owen, M. D. K., & Swanton, C. J. (2015). Weed Abundance, Distribution, Diversity, and Community Analyses. Weed Science, 63(sp1), 64–90. https://doi.org/10.1614/WS-D-13-00075.1 Norris, R., Elmore, C., Rejmánek, M., & Akey, W. C. (2001). Spatial arrangement, density, and competition between barnyardgrass and tomato: I. Crop growth and yield. Weed Science, 49(1), 61–68. https://doi.org/10.1614/0043-1745 O’Donovan, J., de St. Remy, E., O’Sullivan, P., Dew, D., & Sharma, A. (1985). Influence of the relative time of emergence of wild oat (Avena fatua) on yield loss of barley (Hordeum vulgare) and wheat (Triticum aesitvum). Weed Science, 33, 498–503. Oerke, E. (2006). Crop losses to pests. Journal of Agricultural Science, 144, 31–43. https://doi.org/10.1017/S0021859605005708 Osborn, S., Panayot, V., & Villa, U. (2017). A multilevel hierarchical sampling technique for spatially correlated random fields. SIAM Journal on Scientific computing, 39(5), S543–S562. https://doi.org/10.1137/090750688 Pérez-Ortiz, M., Peña-Barragán, J. M., Gutiérrez, P. A. A., Torres-Sánchez, J., Hervás-Martínez, C., López-Granados, F., … López-Granados, F. (2015). A semi-supervised system for weed mapping in sunflower crops using unmanned aerial vehicles and a crop row detection method. Applied Soft Computing, 37, 533–544. https://doi.org/10.1016/j.asoc.2015.08.027 Puerta, P., Ciannelli, L., & Johnson, B. (2019). A simulation framework for evaluating multi-stage sampling designs in populations with spatially structured traits. PeerJ, 7, e6471. https://doi.org/10.7717/peerj.6471 Puerto, A. (2017). Clasificación y cuantificación de maleza en cultivos de hortalizas por medio de procesamiento de imágenes digitales. Universidad Nacional de Colombia. Radosevich, S. (1987). Methods to Study Interactions Among Crops and Weeds. Weed Technology, 1, 190–198. Radosevich, S., Holt, J., & Ghersa, C. (2007). Ecology of weeds and invasive plants: Relationship to agriculture and natural resource management. (3 Ed, Ed.). Hoboken, New Jersey. 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). Experimental Methods for Crop – Weed Competition Studies. Weed Science, (2), 2–11. https://doi.org/10.1614/WS-D-13-00062.1 Swanton, C., Weaver, S., Cowan, P., Van Acker, R., Deen, W., & Shreshta, A. (1999). Weed thresholds: theory and applicability. Journal of Crop Production, 2(1), 9–29. https://doi.org/10.1300/J144v02n01 Tang, J. L., Wang, D., Zhang, Z. G., He, L. J., Xin, J., & Xu, Y. (2017). Weed identification based on K-means feature learning combined with convolutional neural network. Computers and Electronics in Agriculture, 135, 63–70. https://doi.org/10.1016/j.compag.2017.01.001 Thorp, K. R., & Tian, L. F. (2004). A review on remote sensing of weeds in agriculture. Precision Agriculture, 5(5), 477–508. https://doi.org/10.1007/s11119-004-5321-1 Torner, C., González-Andújar, J. L., & Fernández-Quintanilla, C. (1991). Wild Oat (Avena-Sterilis L) Competition With Winter Barley - Plant-Density Effects. Weed Research, 31(5), 301–307. Torra, J., Cirujeda, A., Recasens, J., Taberner, A., & Powles, S. B. (2010). PIM (Poppy Integrated Management): A bio-economic decision support model for the management of Papaver rhoeas in rain-fed cropping systems. Weed Research, 50(2), 127–139. https://doi.org/10.1111/j.1365-3180.2010.00761.x Torres-Sánchez, J., López-Granados, F., Peña, J. M., Peña-Barragán, J. M., Peña, J. M., & Peña-Barragán, J. M. (2015). An automatic object-based method for optimal thresholding in UAV images: Application for vegetation detection in herbaceous crops. Computers and Electronics in Agriculture, 114, 43–52. https://doi.org/10.1016/j.compag.2015.03.019 Walter, A., Christensen, S., & Simmelsgaard, S. (2002). Spatial correlation between weed species densities and soil properties. Weed Research, 42(1), 26–38. https://doi.org/10.1046/j.1365-3180.2002.00259.x Webster, R., & Oliver, M. (2007). Geostatistics for Environmental Scientists. 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)90065-9EstudiantesInvestigadoresPúblico generalCC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8701https://repositorio.unal.edu.co/bitstream/unal/75703/3/license_rdf42fd4ad1e89814f5e4a476b409eb708cMD53ORIGINAL81721105.2020.pdf81721105.2020.pdfTesis de Doctorado en Ciencias Agrariasapplication/pdf2088217https://repositorio.unal.edu.co/bitstream/unal/75703/1/81721105.2020.pdf51ffaa5e675c92c9dd16ccb57e10b25aMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-83991https://repositorio.unal.edu.co/bitstream/unal/75703/2/license.txt6f3f13b02594d02ad110b3ad534cd5dfMD52THUMBNAIL81721105.2020.pdf.jpg81721105.2020.pdf.jpgGenerated Thumbnailimage/jpeg5411https://repositorio.unal.edu.co/bitstream/unal/75703/4/81721105.2020.pdf.jpgf43cd0b74858959c3d5a1a3e688498a9MD54unal/75703oai:repositorio.unal.edu.co:unal/757032023-07-11 23:03:35.836Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.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 |