Publicación:
Modelos alométricos para la medición no destructiva del área foliar del frijol caupí (Vigna unguiculata L. Walp.) sometido a condiciones de estrés

dc.audience
dc.contributor.advisorJarma-Orozco, Alfredo De Jesús
dc.contributor.authorTello Coley, Alberto Jose
dc.contributor.juryBarrera, José Luis
dc.contributor.juryPérez Polo, Dairo Javier
dc.date.accessioned2024-07-10T12:57:31Z
dc.date.available2024-07-10T12:57:31Z
dc.date.issued2024-07-08
dc.description.abstractEl frijol caupí es una especie de importancia económica para la seguridad alimentaria de muchos pueblos alrededor del mundo, puesto que es una planta que resiste condiciones de estrés abiótico en especial el hídrico, salino y manejo agronómico insuficiente, además de ser una importante fuente de proteínas, energía y de otros nutrientes. Las mediciones de área foliar emplean métodos costosos, lentos y poco aplicables no solo en cultivos de frijol caupí si no también en las demás especies importantes para el hombre. Hasta el momento en la especie de estudio no se han realizado mediciones de área foliar que impliquen los efectos del estrés ambiental y mucho menos el desarrollo de un modelo matemático que permita predecir de manera precisa esta variable física. Conocer el área foliar de la planta en ambientes adecuados y estresantes por medios no destructivos puede permitir tomar correctivos a nivel de manejo y puede ser de utilidad en programas de fitomejoramiento. Los datos obtenidos mostraron que el foliolo central de la hoja de frijol no modifica su morfología independientemente de la condición ambiental permitiendo utilizar un modelo predictivo de área foliar de la forma Y=0.63xLA1.01, en cambio el test de identidad sugirió diferencias entre los foliolos laterales entre todos los tratamientos, lo cuales absorbieron el efecto del estrés al cambiar su morfología dependiendo del ambiente lo que produjo tres modelos predictivos de área foliar de la forma Y=0.64xLA1.02, Y=0.67xLA1.02 y Y=0.68xLA1.01, para condiciones óptimas, salinidad y sequía respectivamente.spa
dc.description.abstractCowpea bean is a species of economic importance for the food security of many communities around the world, as it is a plant that withstands abiotic stress conditions, particularly water and saline stress, as well as insufficient agronomic management. Additionally, it is an important source of proteins, energy, and other nutrients. Leaf area measurements employ costly, slow, and impractical methods not only for cowpea bean crops but also for other important species for humans. To date, no leaf area measurements have been conducted on the species under study that consider the effects of environmental stress, much less the development of a mathematical model that accurately predicts this physical variable. Understanding the leaf area of the plant in both suitable and stressful environments through non-destructive means can allow for corrective measures at the management level and can be useful in plant breeding programs. The data obtained showed that the central leaf of the cowpea bean does not alter its morphology regardless of the environmental condition, allowing the use of a predictive model for leaf area in the form Y=0.63xLA1.01. However, the identity test suggested differences among the lateral leafs across all treatments, which absorbed the stress effect by changing their morphology depending on the environment, resulting in three predictive models for leaf area in the form Y=0.64xLA1.02, Y=0.67xLA1.02 , and Y=0.68xLA1.01, for optimal conditions, salinity, and drought, respectivelyeng
dc.description.degreelevelMaestría
dc.description.degreenameMagíster en Ciencias Agronómicas
dc.description.modalityTrabajos de Investigación y/o Extensión
dc.description.tableofcontents1 RESUMEN. .................. 1
dc.description.tableofcontents2 ABSTRACT. ............. 2
dc.description.tableofcontents3 INTRODUCCIÓN. ............... 3
dc.description.tableofcontents4 OBJETIVOS. ................ 6
dc.description.tableofcontents4.1 OBJETIVO GENERAL: ............... 6
dc.description.tableofcontents4.2 OBJETIVOS ESPECÍFICOS: ............ 6
dc.description.tableofcontents5 MARCO TEÓRICO. ............... 7
dc.description.tableofcontents5.1 Importancia de la especie. .......... 7
dc.description.tableofcontents5.1.1 Fríjol Caupí (Vigna unguiculata L. Walp). ........... 7
dc.description.tableofcontents5.1.2 Área sembrada, producción y rendimiento mundial de frijol caupí. .................... 7
dc.description.tableofcontents5.2 Importancia del área foliar. ............. 9
dc.description.tableofcontents5.3 Condiciones de estrés en plantas. .................. 11
dc.description.tableofcontents5.4 Antecedentes de investigación. ..................... 14
dc.description.tableofcontents6 METODOLOGÍA. ................ 19
dc.description.tableofcontents6.1 Ubicación del Experimento. .................... 19
dc.description.tableofcontents6.2 Condiciones Meteorológicas. .................... 19
dc.description.tableofcontents6.3 Material Vegetal. ...................... 19
dc.description.tableofcontents6.4 Variable de respuesta. .................... 19
dc.description.tableofcontents6.5 Diseño de muestreo. .................. 21
dc.description.tableofcontents6.6 Manejo agronómico. ........................ 22
dc.description.tableofcontents6.7 Procedimiento. ........................ 22
dc.description.tableofcontents6.7.1 Medidas de intercambio gaseoso. ............... 22
dc.description.tableofcontents6.7.2 Estímulo de estrés por sequía. ................. 22
dc.description.tableofcontents6.7.3 Estimulo de estrés por salinidad. ...................... 23
dc.description.tableofcontents6.7.4 Área foliar por medio de procesador de imágenes. ................... 23
dc.description.tableofcontents6.7.5 Pruebas de identidad del modelo...................... 24
dc.description.tableofcontents6.7.6 Modelos teóricos. ................ 24
dc.description.tableofcontents6.7.7 Análisis estadístico. .............. 25
dc.description.tableofcontents6.7.8 Modelo de validación. ............ 25
dc.description.tableofcontents7 RESULTADOS. ........................ 26
dc.description.tableofcontents7.1 Análisis de los foliolos central y laterales de frijol caupí (Vigna unguiculata) bajo condiciones normales y de estrés. ............. 28
dc.description.tableofcontents7.1.1 Análisis de los foliolos centrales en función de todos los tratamientos. .............. 28
dc.description.tableofcontents7.1.2 Análisis de los foliolos laterales en función del tratamiento control. .................. 32
dc.description.tableofcontents7.1.3 Análisis de los foliolos laterales en función del tratamiento bajo condiciones de salinidad................. 35
dc.description.tableofcontents7.1.4 Análisis de los foliolos laterales en función del tratamiento bajo condiciones de sequía............ 38
dc.description.tableofcontents7.2 DISCUSIÓN. ................... 41
dc.description.tableofcontents8 CONCLUSIONES. ........... 43
dc.description.tableofcontents9 RECOMENDACIONES. .......... 44
dc.description.tableofcontents10 BIBLIOGRAFÍA ................... 45
dc.format.mimetypeapplication/pdf
dc.identifier.instnameUniversidad de Córdoba
dc.identifier.reponameRepositorio Universidad de Córdoba
dc.identifier.repourlhttps://repositorio.unicordoba.edu.co
dc.identifier.urihttps://repositorio.unicordoba.edu.co/handle/ucordoba/8364
dc.language.isospa
dc.publisherUniversidad de Córdoba
dc.publisher.facultyFacultad de Ciencias Agrícolas
dc.publisher.placeMontería, Córdoba, Colombia
dc.publisher.programMaestría en Ciencias Agronómicas
dc.relation.referencesAbebe, B. & Alemayehu, M. (2022). A review of the nutritional use of cowpea (Vigna unguiculata L. Walp) for human and animal diets. Journal of Agriculture and Food Research, 10, 100383. https://doi.org/10.1016/j.jafr.2022.100383
dc.relation.referencesAhmad, S. Ahmad, R. Ashraf, M., Ashraf, M., & Waraich, E. (2009). Sunflower (Helianthus annuus L.) response to drought stress at germination and seedling growth stages. 41(2), 647-654.
dc.relation.referencesAhmed, I., Nadira, U., Bibi, N., Cao, F., He, X., Zhang, G., & Wu, F. (2015). Secondary metabolism and antioxidants are involved in the tolerance to drought and salinity, separately and combined, in Tibetan wild barley. Environmental and Experimental Botany, 111, 1-12. https://doi.org/10.1016/j.envexpbot.2014.10.003
dc.relation.referencesAlsamadany, H. (2022). Physiological, biochemical and molecular evaluation of mungbean genotypes for agronomical yield under drought and salinity stresses in the presence of humic acid. Saudi Journal of Biological Sciences, 29(9), 103385. https://doi.org/10.1016/j.sjbs.2022.103385
dc.relation.referencesAndrade, K., Rivera, R., & Cuenca Cuenca, E. (2023). Efecto de distintos niveles de fertilización en el comportamiento agronómico del frejol caupí INIAP-463. La Técnica Revista de las Agrociencias ISSN 2477-8982, 13(2), 61-66. https://doi.org/10.33936/latecnica.v13i2.5375
dc.relation.referencesAntunes, W., Pompelli, M., Carretero, D., & DaMatta, F. (2008). Allometric models for non-destructive leaf area estimation in coffee ( Coffea arabica and Coffea canephora ). Annals of Applied Biology, 153(1), 33-40. https://doi.org/10.1111/j.1744-7348.2008.00235.x
dc.relation.referencesAyalew, T., Yoseph, T., Högy, P., & Cadisch, G. (2022). Leaf growth, gas exchange and assimilation performance of cowpea varieties in response to Bradyrhizobium inoculation. Heliyon, 8(1), e08746. https://doi.org/10.1016/j.heliyon.2022.e08746
dc.relation.referencesBarrera, J., Suárez, D., & Melgarejo, L. (2010). Análisis de crecimiento en plantas. En L. M. Melgarejo, Experimientos en fisiología vegetal. Bogotá D.C: Universidad Nacional de Colombia.
dc.relation.referencesBoukhana, M., Ravaglia, J., Hétroy, F., & De Solan, B. (2022). Geometric models for plant leaf area estimation from 3D point clouds: A comparative study. Graphics and Visual Computing, 200057. https://doi.org/10.1016/j.gvc.2022.200057
dc.relation.referencesButtaro, D., Rouphael, Y., Rivera, C., Colla, G., & Gonnella, M. (2015). Simple and accurate allometric model for leaf area estimation in Vitis vinifera L. genotypes. Photosynthetica, 53(3), 342-348. https://doi.org/10.1007/s11099-015-0117-2
dc.relation.referencesBurgos, A., Avanza, M., Balbo, C., Prause, J., & Argüello, J. (2010). Modelos para la estimación no destructiva del área foliara de dos cultivares de mandioca (Manihot esculenta Crantz) en la Argentina. Agriscientia(27), 55-61.
dc.relation.referencesCabezas, M., Peña, F., Duarte, H., Colorado, J., & Lora, R. (2009). Un modelo para la estimación del área foliar en tres especies forestales de forma no destructiva. Revista U.D.C.A Actualidad & Divulgación Científica, 12(1). https://doi.org/10.31910/rudca.v12.n1.2009.648
dc.relation.referencesCalderón, A., & Soto, F. (2009). ESTIMACIÓN DE ÁREA FOLIAR EN POSTURAS DE MANGO (Manguifera indica L.) Y AGUACATERO (Persea spp) EN FASE DE VIVERO A PARTIR DE LAS MEDIDAS LINEALES DE LAS HOJAS. Cultivos tropicales, 30(1), 7.
dc.relation.referencesCampos, G., García, M., & Pérez, D. (2011). RESPUESTA DE 20 VARIEDADES DE CARAOTA (Phaseolus vulgaris L.) ANTE EL ESTRÉS POR NaCl DURANTE LA GERMINACIÓN Y EN FASE PLANTULAR. 11.
dc.relation.referencesCardona, C., Aramendiz, H., & Barrera, C. (2009). ESTIMACIÓN DEL ÁREA FOLIAR DE PAPAYA. Revista U.D.C.A Actualidad & Divulgación Científica, 12(1), 9.
dc.relation.referencesCardona, C., Araméndiz, H., & Barrera, C. (2009). Modelo para Estimación de Área Foliar en Berenjena (Solanum melongena L) Basado en Muestreo no Destructivo. Temas Agrarios, 14(2), 14-22. https://doi.org/10.21897/rta.v14i2.675
dc.relation.referencesCardona, C., Jarma, A., Áramendiz, H., Peña, M., & Vergara, C. (2015). Respuestas fisiológicas y bioquímicas del fríjol caupí (Vigna unguiculata L. Walp.) bajo déficit hídrico. Revista Colombiana de Ciencias Hortícolas, 8(2), 250. https://doi.org/10.17584/rcch.2014v8i2.3218
dc.relation.referencesCarneiro, A., da Costa, D., Lopes, D., Bento, P., Cavalcante, R., & Siviero, A. (2019). Cowpea: A Strategic Legume Species for Food Security and Health. En J. Jimenez & A. Clemente (Eds.), Legume Seed Nutraceutical Research. IntechOpen. https://doi.org/10.5772/intechopen.79006
dc.relation.referencesCarvalho, M., Lino, T., Rosa, E., & Carnide, V. (2017). Cowpea: A legume crop for a challenging environment. Journal of the Science of Food and Agriculture, 97(13), 4273-4284. https://doi.org/10.1002/jsfa.8250
dc.relation.referencesCemek, B., Ünlükara, A., Kurunç, A., & Küçüktopcu, E. (2020). Leaf area modeling of bell pepper (Capsicum annuum L.) grown under different stress conditions by soft computing approaches. Computers and Electronics in Agriculture, 174, 105514. https://doi.org/10.1016/j.compag.2020.105514
dc.relation.referencesCristofori, V., Rouphael, Y., Gyves, E., & Bignami, C. (2007). A simple model for estimating leaf area of hazelnut from linear measurements. Scientia Horticulturae, 113(2), 221-225. https://doi.org/10.1016/j.scienta.2007.02.006
dc.relation.referencesCogliatti, D., Cataldi, M., & Iglesias, F. (2010). Estimación del área de las hojas en plantas de trigo bajo diferentes tipos de estrés abiótico. Agriscientia, 27, 43-53.
dc.relation.referencesDemirsoy, H., Demirsoy, L., Uzun, S., & Ersoy, B. (2004). Non-destructive Leaf Area Estimation in Peach. 3.
dc.relation.referencesDesoky, E., Elrys, A., Mansour, E., Eid, R., Selem, E., Rady, M., Ali, E., Mersal, G., & Semida, W. (2021). Application of biostimulants promotes growth and productivity by fortifying the antioxidant machinery and suppressing oxidative stress in faba bean under various abiotic stresses. Scientia Horticulturae, 288, 110340. https://doi.org/10.1016/j.scienta.2021.110340
dc.relation.referencesDolph, G. (1977). The effect of different calculational techniques on the estimation of leaf area and the construction of leaf size distributions. Bull. torrey Bot. Club, 264(9).
dc.relation.referencesEckert, F., Kandel, H., Johnson, B., Rojas, G., Deplazes, C., Vander, A., & Osorno, J. (2011). Row Spacing and Nitrogen Effects on Upright Pinto Bean Cultivars under Direct Harvest Conditions. Agronomy Journal, 103(5), 1314-1320. https://doi.org/10.2134/agronj2010.0438
dc.relation.referencesEstrada, V., Márquez, C., De La Cruz, E., Osorio, R., & Sánchez, E. (2018). Biofortificación de frijol caupí (Vigna unguiculata L. Walp) con zinc: Efecto en el rendimiento y contenido mineral. Revista Mexicana de Ciencias Agrícolas, 20. https://doi.org/10.29312/remexca.v0i20.986
dc.relation.referencesFascella, G., Darwich, S., & Rouphael, Y. (2013). Validation of a leaf area prediction model proposed for rose. Chilean Journal of Agricultural Research, 73(1), 73-76. https://doi.org/10.4067/S0718-58392013000100011
dc.relation.referencesFlávio, F, & Folegatti, M. (2005). Estimation of leaf area for greenhouse cucumber by linear measurements under salinity and grafting. Scientia Agricola, 62(4), 305-309. https://doi.org/10.1590/S0103-90162005000400001
dc.relation.referencesFreitas, R., Dombroski, J., Freitas, F., Nogueira, N., & Pinto, J. (2017). PHYSIOLOGICAL RESPONSES OF COWPEA UNDER WATER STRESS AND REWATERING IN NO-TILLAGE AND CONVENTIONAL TILLAGE SYSTEMS. Revista Caatinga, 30(3), 559-567. https://doi.org/10.1590/1983-21252017v30n303rc
dc.relation.referencesGao, M., Van, G., Vos, J., Eveleens, B., & Marcelis, L. (2012). Estimation of leaf area for large scale phenotyping and modeling of rose genotypes. Scientia Horticulturae, 138, 227-234. https://doi.org/10.1016/j.scienta.2012.02.014
dc.relation.referencesGonçalves, C., Assis, F., Medeiros, J., Teixeira, M., & Filho, A. (2008). Modelos matemáticos para estimativa de área foliar de feijao caupi. Revista Caatinga, 21(1), 120-127.
dc.relation.referencesGonçalves, M., Ribeiro, R.., & Amorim, L. (2022). Non-destructive models for estimating leaf area of guava cultivars. Bragantia, 81, e2822. https://doi.org/10.1590/1678-4499.20210342
dc.relation.referencesGuerrero, M., Herrera, J., & Camacho, J. (2023). Modelo no destructivo para estimar el área foliar individual mediante parámetros alométricos en gulupa (Passiflora edulis fo. Edulis). Revista U.D.C.A Actualidad & Divulgación Científica, 26(2). https://doi.org/10.31910/rudca.v26.n2.2023.2356
dc.relation.referencesHall, A. (2012). Phenotyping Cowpeas for Adaptation to Drought. Frontiers in Physiology, 3. https://doi.org/10.3389/fphys.2012.00155
dc.relation.referencesHernández, I., Jarma, A., & Pompelli, M. (2021). Allometric models for non-destructive leaf area measurement of stevia: An in depth and complete analysis. Horticultura Brasileira, 39(2), 205-215. https://doi.org/10.1590/s0102-0536-20210212
dc.relation.referencesHorn, L., & Shimelis, H. (2020). Production constraints and breeding approaches for cowpea improvement for drought prone agro-ecologies in Sub-Saharan Africa. Annals of Agricultural Sciences, 65(1), 83-91. https://doi.org/10.1016/j.aoas.2020.03.002
dc.relation.referencesJalal, A., Rauf, K., Iqbal, B., Khalil, R., Mustafa, H., Murad, M., Khalil, F., Khan, S., Oliveira, C., & Filho, M. (2023). Engineering legumes for drought stress tolerance: Constraints, accomplishments, and future prospects. South African Journal of Botany, 159, 482-491. https://doi.org/10.1016/j.sajb.2023.06.028
dc.relation.referencesKeramatlou, I., Sharifani, M., Sabouri, H., Alizadeh, M., & Kamkar, B. (2015). A simple linear model for leaf area estimation in Persian walnut (Juglans regia L.). Scientia Horticulturae, 184, 36-39. https://doi.org/10.1016/j.scienta.2014.12.017
dc.relation.referencesLian, H., Qin, C., Zhao, Q., Begum, N., & Zhang, S. (2022). Exogenous calcium promotes growth of adzuki bean (Vigna angularis Willd.) seedlings under nitrogen limitation through the regulation of nitrogen metabolism. Plant Physiology and Biochemistry, 190, 90-100. https://doi.org/10.1016/j.plaphy.2022.08.028
dc.relation.referencesLima, R., Moreira, A., Vanderlane, A., Castro, L., Souza, L., & Lima, R. (2015). Modelos de Determinação Não Destrutiva de Área Foliar de Feijão Caupi Vigna unguiculata (L.). Global Science and Technology, 8(2), 17-27
dc.relation.referencesLopes, Á., Setubal, I., Costa, V., Zilli, J., Rodrigues, A., & Bonifacio, A. (2022). Synergism of Bradyrhizobium and Azospirillum baldaniorum improves growth and symbiotic performance in lima bean under salinity by positive modulations in leaf nitrogen compounds. Applied Soil Ecology, 180, 104603. https://doi.org/10.1016/j.apsoil.2022.104603
dc.relation.referencesLucero, C., Filippo, M., Vila, H., & Venier, M. (2017). Comparing water deficit and saline stress between 1103P and. Revista de la Facultad de Ciencias Agrarias, 12.
dc.relation.referencesManoj, B., Gupta, M., Iqbal, M., & Gupta, S. (2022). Chitosan augments bioactive properties and drought resilience in drought-induced red kidney beans. Food Research International, 159, 111597. https://doi.org/10.1016/j.foodres.2022.111597
dc.relation.referencesMartinez, A., Tordecilla, L., Grandett, L., Rodríguez, M., & Cordero, C. (2020). Fríjol Caupí (Vigna unguiculata L. Walp): Perspectiva socioeconómica y tecnológica en el Caribe colombiano. Ciencia y Agricultura, 17(2), 12-22. https://doi.org/10.19053/01228420.v17.n2.2020.10644
dc.relation.referencesMathobo, R., Marais, D., & Steyn, J. (2017). The effect of drought stress on yield, leaf gaseous exchange and chlorophyll fluorescence of dry beans (Phaseolus vulgaris L.). Agricultural Water Management, 180, 118-125. https://doi.org/10.1016/j.agwat.2016.11.005
dc.relation.referencesMbuma, N., Gerrano, A., Lebaka, N., & Labuschagne, M. (2022). Interrelationship between grain yield components and nutritional quality traits in cowpea genotypes. South African Journal of Botany, 150, 34-43. https://doi.org/10.1016/j.sajb.2022.07.006
dc.relation.referencesMendoza, E., Rouphael, Y., Cristofori, V., & Mira, F. (2007). A non-destructive, simple and accurate model for estimating the individual leaf area of kiwi ( Actinidia deliciosa). Fruits, 62(3), 171-176. https://doi.org/10.1051/fruits:2007012
dc.relation.referencesMessier, J., McGill, B., & Lechowicz, M. (2010). How do traits vary across ecological scales? A case for trait-based ecology: How do traits vary across ecological scales? Ecology Letters, 13(7), 838-848. https://doi.org/10.1111/j.1461-0248.2010.01476.x
dc.relation.referencesMunns, R. (1993). Physiological processes limiting plant growth in saline soils: Some dogmas and hypotheses. Plant, Cell and Environment, 16(1), 15-24. https://doi.org/10.1111/j.1365-3040.1993.tb00840.x
dc.relation.referencesPeksen, E. (2007). Non-destructive leaf area estimation model for faba bean (Vicia faba L.). Scientia Horticulturae, 113(4), 322-328. https://doi.org/10.1016/j.scienta.2007.04.003
dc.relation.referencesPompelli, M., Antunes, W., Ferreira, D., Cavalcante, P., Wanderley, H., & Endres, L. (2012). Allometric models for non-destructive leaf area estimation of Jatropha curcas. Biomass and Bioenergy, 36, 77-85. https://doi.org/10.1016/j.biombioe.2011.10.010
dc.relation.referencesPompelli, M., Figueirôa, J., & Lozano, F. (2018). Allometric models for non-destructive leaf area estimation in Eugenia uniflora (L.). Peruvian Journal of Agronomy, 2(2), 1. https://doi.org/10.21704/pja.v2i2.1133
dc.relation.referencesPompelli, M., Santos, J., & Santos, M. (2019). Estimating leaf area of Jatropha nana through non-destructive allometric models. AIMS Environmental Science, 6(2), 59-76. https://doi.org/10.3934/environsci.2019.2.59
dc.relation.referencesQueiroga, J., Romano, E., Souza, J., & Miglioranza, É. (2003). Estimativa da área foliar do feijão-vagem (Phaseolus vulgaris L.) por meio da largura máxima do folíolo central. Horticultura Brasileira, 21(1), 64-68. https://doi.org/10.1590/S0102-05362003000100013
dc.relation.referencesQuintana, W., Pinzón, E., & Torres, D. (2016). Evaluación del crecimiento de fríjol (Phaseolus vulgaris L.) cv. Ica Cerinza, bajo estrés salino. Revista U.D.C.A Actualidad & Divulgación Científica, 19(1). https://doi.org/10.31910/rudca.v19.n1.2016.113
dc.relation.referencesRao, G., Khan, B., & Chadha, K. (1978). Comparison of methods of estimating leaf-surface area through leaf characteristics in some cultiv ars of Mangifera indica. Scientia Horticulturae, 8(4), 341-348. https://doi.org/10.1016/0304-4238(78)90056-0
dc.relation.referencesRouphael, Y., Colla, G., Fanasca, S., & Karam, F. (2007). Leaf area estimation of sunflower leaves from simple linear measurements. Photosynthetica, 45(2), 306-308. https://doi.org/10.1007/s11099-007-0051-z
dc.relation.referencesSantos, J., Jarma, A., Antunes, W., Mendes, K., Figueiroa, J., Pessoa, L., & Pompelli, M. (2021). New approaches to predict leaf area in woody tree species from the Atlantic Rainforest, Brazil. Austral Ecology, 46(4), 613-626. https://doi.org/10.1111/aec.13017
dc.relation.referencesSantos, M., Jarma, A., Lozano, F., Santos, J., Rivera, J., Espitia, M., Castillejo, Á., Jarma, B., & Pompelli, M. (2018). Leaf area estimation in Jatropha curcas (L.): An update. AIMS Environmental Science, 5(5), 353-371. https://doi.org/10.3934/environsci.2018.5.353
dc.relation.referencesSpann, T., & Heerema, R. (2010). A simple method for non-destructive estimation of total shoot leaf area in tree fruit crops. Scientia Horticulturae, 125(3), 528-533. https://doi.org/10.1016/j.scienta.2010.04.033
dc.relation.referencesSteel, M., & Penny, D. (2000). Parsimony, Likelihood, and the Role of Models in Molecular Phylogenetics. Molecular Biology and Evolution, 17(6), 839-850. https://doi.org/10.1093/oxfordjournals.molbev.a026364
dc.relation.referencesSuárez, J., Melgarejo, L., Durán, E., Di Rienzo, J., & Casanoves, F. (2018). Non-destructive estimation of the leaf weight and leaf area in cacao ( Theobroma cacao L.). Scientia Horticulturae, 229, 19-24. https://doi.org/10.1016/j.scienta.2017.10.034
dc.relation.referencesTeobaldelli, M., Rouphael, Y., Gonnella, M., Buttaro, D., Rivera, C., Muganu, M., Colla, G., & Basile, B. (2020). Developing a fast and accurate model to estimate allometrically the total shoot leaf area in grapevines. Scientia Horticulturae, 259, 108794. https://doi.org/10.1016/j.scienta.2019.108794
dc.relation.referencesTsialtas, J., Koundouras, S., & Zioziou, E. (2008). Leaf area estimation by simple measurements and evaluation of leaf area prediction models in Cabernet-Sauvignon grapevine leaves. Photosynthetica, 46(3), 452-456. https://doi.org/10.1007/s11099-008-0077-x
dc.relation.referencesTurk, K., Hall, A., & Asbell, C. (1980). Drought Adaptation of Cowpea. I. Influence of Drought on Seed Yield 1. Agronomy Journal, 72(3), 413-420. https://doi.org/10.2134/agronj1980.00021962007200030004x
dc.relation.referencesVadez, V., Kholová, J., Hummel, G., Zhokhavets, U., Gupta, S., & Hash, C. (2015). LeasyScan: A novel concept combining 3D imaging and lysimetry for high-throughput phenotyping of traits controlling plant water budget. Journal of Experimental Botany, 66(18), 5581-5593. https://doi.org/10.1093/jxb/erv251
dc.relation.referencesWalther, B., & Moore, J. (2005). The concepts of bias, precision and accuracy, and their use in testing the performance of species richness estimators, with a literature review of estimator performance. Ecography, 28(6), 815-829. https://doi.org/10.1111/j.2005.0906-7590.04112.x
dc.relation.referencesWang, W., Vinocur, B., & Altman, A. (2003). Plant responses to drought, salinity and extreme temperatures: Towards genetic engineering for stress tolerance. Planta, 218(1), 1-14. https://doi.org/10.1007/s00425-003-1105-5
dc.relation.referencesWilliams, L., & Martinson, T. (2003). Nondestructive leaf area estimation of ‘Niagara’ and ‘DeChaunac’ grapevines. Scientia Horticulturae, 98(4), 493-498. https://doi.org/10.1016/S0304-4238(03)00020-7
dc.relation.referencesYau, W., Ng, O., & Lee, S. (2021). Portable device for contactless, non-destructive and in situ outdoor individual leaf area measurement. Computers and Electronics in Agriculture, 187, 106278. https://doi.org/10.1016/j.compag.2021.106278
dc.relation.referencesZaman, M., Shahid, S., & Heng, L. (2018). Guideline for Salinity Assessment, Mitigation and Adaptation Using Nuclear and Related Techniques. Springer International Publishing. https://doi.org/10.1007/978-3-319-96190-3
dc.rightsCopyright Universidad de Córdoba, 2024
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.rights.coarhttp://purl.org/coar/access_right/c_abf2
dc.rights.licenseAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject.keywordsCowpea bean
dc.subject.keywordsLeaf area
dc.subject.keywordsSaline stress
dc.subject.keywordsWater stress
dc.subject.keywordsAllometry
dc.subject.proposalFrijol caupí
dc.subject.proposalArea foliar
dc.subject.proposalEstrés salino
dc.subject.proposalEstrés hídrico
dc.subject.proposalAlometría
dc.titleModelos alométricos para la medición no destructiva del área foliar del frijol caupí (Vigna unguiculata L. Walp.) sometido a condiciones de estrésspa
dc.typeTrabajo de grado - Maestría
dc.type.coarhttp://purl.org/coar/resource_type/c_bdcc
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.contentText
dc.type.driverinfo:eu-repo/semantics/masterThesis
dc.type.redcolhttp://purl.org/redcol/resource_type/TM
dc.type.versioninfo:eu-repo/semantics/acceptedVersion
dspace.entity.typePublication
Archivos
Bloque original
Mostrando 1 - 2 de 2
Cargando...
Miniatura
Nombre:
tellocoleyalbertojose.pdf
Tamaño:
5.91 MB
Formato:
Adobe Portable Document Format
No hay miniatura disponible
Nombre:
Formato de autorización.pdf
Tamaño:
195.67 KB
Formato:
Adobe Portable Document Format
Bloque de licencias
Mostrando 1 - 1 de 1
No hay miniatura disponible
Nombre:
license.txt
Tamaño:
15.18 KB
Formato:
Item-specific license agreed upon to submission
Descripción:
Colecciones