Development of a scheduling irrigation application for Hass avocado crops in the Valle del Cauca

dc.contributor.advisorEcheverri Sanchez, Andrés Fernando
dc.contributor.authorErazo Mesa, Osvaldo Edwin
dc.contributor.educationalvalidatorTafur Hermann, Harold
dc.contributor.researchgroupGrupo de Investigación REGARspa
dc.contributor.subjectmatterexpertRamírez Gil, Joaquín Guillermo
dc.contributor.subjectmatterexpertHincapié Gómez, Edgar
dc.contributor.subjectmatterexpertMurillo Sandoval, Paulo José
dc.coverage.regionValle del Cauca - Colombia
dc.date.accessioned2022-08-11T20:02:18Z
dc.date.available2022-08-11T20:02:18Z
dc.date.issued2022-06-10
dc.descriptionTablas, ilustracionesspa
dc.description.abstractOne of the first actions to reach an environmental and social equilibrium in the Colombian hillslope zones cropped progressively with Hass avocado is efficiently managing the water. This study aims to develop a digital tool to schedule the Hass avocado irrigation in the Valle del Cauca (Colombia). The monthly crop irrigation requirement (IR) was computed in the Colombian current and potential production area using global and local climate databases, and it was estimated the possible influence of the Intertropical Convergence Zone (ITCZ) on the monthly IR dynamics. Furthermore, the soil matric potential was monitored during a year in three Hass avocado orchards located in the department of Valle del Cauca to model the soil water dynamics and determine whether the surface soil water content (SSWC) can be used as an indicator of the crop irrigation scheduling. Additionally, Water Cloud Model was calibrated from Sentinel-1 images, and the web application IS-SAR was developed to schedule the crop irrigation, using three irrigation scenarios. Results show that 99.8% of the current and potential area cropped with Hass avocado in Colombia needs irrigation for at least one month. Moreover, it was found that SSWC at 5-10 cm depth range for the three farms can be used as an indicator of Hass avocado irrigation scheduling. IS-SAR simulations in the three evaluated plots resulted in applying irrigation events of up to 107 L tree−1 for 3.4 h. Finally, Hass avocado growers in the Valle del Cauca have a new digital tool based on remote sensing and field data to schedule irrigation in their orchards.eng
dc.description.abstractUna de las primeras medidas para alcanzar un equilibrio ambiental y social en las laderas colombianas cultivadas cada vez más con aguacate cv. Hass es manejar eficientemente el agua. El objetivo de este estudio fue desarrollar una herramienta para programar el riego en el cultivo de aguacate cv. Hass en el Valle del Cauca (Colombia). Se calculó el requerimiento de riego (RR) mensual del cultivo en el área de producción actual y potencial en Colombia utilizando bases de datos de clima globales y locales, así como se estimó la posible influencia de la zona de convergencia intertropical (ZCIT) sobre la dinámica mensual del RR. Además, se monitoreó el potencial mátrico del suelo durante un año en tres fincas cultivadas con aguacate Hass en el Valle del Cauca para modelar la dinámica de agua en el suelo y determinar la viabilidad de usar la humedad superficial del suelo (HSS) como indicador del riego en el cultivo. En complemento, se calibró el modelo Water Cloud Model a partir de imágenes Sentinel-1 y se desarrolló la aplicación web IS-SAR para programar el riego en el cultivo a partir de tres escenarios de riego. Los resultados indican que un 99.8% del área actual y potencial cultivada con aguacate Hass en Colombia requiere riego en al menos un mes al año. Además, se determinó que HSS en el rango de profundidad de 5-10 cm en las tres fincas se puede utilizar como indicador de la programación del riego en el cultivo de aguacate Hass. Una simulación usando IS-SAR en los tres lotes evaluados resultó en aplicar eventos de riego de hasta 107 L árbol−1 durante 3.4 h. En conclusión, los agricultores de aguacate Hass en el Valle del Cauca cuentan con una nueva herramienta digital basada en datos de sensores remotos y de campo para programar el riego del cultivo en sus fincas.spa
dc.description.degreelevelDoctoradospa
dc.description.degreenameDoctor en Ciencias Agrariasspa
dc.description.methodsSe calculó el requerimiento de riego (RR) mensual del cultivo en el área de producción actual y potencial en Colombia utilizando bases de datos de clima globales y locales, así como se estimó la posible influencia de la zona de convergencia intertropical (ZCIT) sobre la dinámica mensual del RR. Además, se monitoreó el potencial mátrico del suelo durante un año en tres fincas cultivadas con aguacate Hass en el Valle del Cauca para modelar la dinámica de agua en el suelo y determinar la viabilidad de usar la humedad superficial del suelo (HSS) como indicador del riego en el cultivo. En complemento, se calibró el modelo Water Cloud Model a partir de imágenes Sentinel-1 y se desarrolló la aplicación web IS-SAR para programar el riego en el cultivo a partir de tres escenarios de riego. Los resultados indican que un 99.8% del área actual y potencial cultivada con aguacate Hass en Colombia requiere riego en al menos un mes al año. Además, se determinó que HSS en el rango de profundidad de 5-10 cm en las tres fincas se puede utilizar como indicador de la programación del riego en el cultivo de aguacate Hass. Una simulación usando IS-SAR en los tres lotes evaluados resultó en aplicar eventos de riego de hasta 107 L árbol 1 durante 3.4 hspa
dc.description.researchareaUso eficiente del agua en agriculturaspa
dc.description.sponsorshipEsta tesis doctorado fue financiada con recursos propios y del Grupo de Investigación REGAR, Escuela EIDENAR - Facultad de Ingeniería de la Universidad del Vallespa
dc.format.extentxviii, 133 páginas + anexosspa
dc.format.mimetypeapplication/pdfspa
dc.identifier.instnameUniversidad Nacional de Colombiaspa
dc.identifier.reponameRepositorio Institucional Universidad Nacional de Colombiaspa
dc.identifier.repourlhttps://repositorio.unal.edu.co/spa
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/81854
dc.language.isoengspa
dc.publisherUniversidad Nacional de Colombiaspa
dc.publisher.branchUniversidad Nacional de Colombia - Sede Palmiraspa
dc.publisher.departmentDoctorado en Ciencias Agrariasspa
dc.publisher.facultyFacultad de Ciencias Agropecuariasspa
dc.publisher.placePalmira, Colombiaspa
dc.publisher.programPalmira - Ciencias Agropecuarias - Doctorado en Ciencias Agrariasspa
dc.relation.referencesAbdullah, F. A., & Samah, B. A. (2013). Factors impinging farmers' use of agriculture technology. Asian Social Science, 9(3), 120–124. https://doi.org/10.5539/ass.v9n3p120spa
dc.relation.referencesAbioye, E. A., Abidin, M. S. Z., Mahmud, M. S. A., Buyamin, S., Ishak, M. H. I., Rahman, M. K. I. A., Otuoze, A. O., Onotu, P., & Ramli, M. S. A. (2020). A review on monitoring and advanced control strategies for precision irrigation. Computers and Electronics in Agriculture, 173(April), 105441. https://doi.org/10.1016/j.compag.2020.105441spa
dc.relation.referencesAcosta-Rangel, A., Li, R., Mauk, P., Santiago, L., & Lovatt, C. J. (2021). Effects of temperature, soil moisture and light intensity on the temporal pattern of floral gene expression and flowering of avocado buds (Persea americana cv. Hass). Scientia Horticulturae, 280(January), 109940. https://doi.org/10.1016/j.scienta.2021.109940spa
dc.relation.referencesAcosta-Rangel, A. M., Li, R., Celis, N., Suarez, D. L., Santiago, L. S., Arpaia, M. L., & Mauk, P. A. (2019). The physiological response of 'Hass' avocado to salinity as influenced by rootstock. Scientia Horticulturae, 256(July), 108629. https://doi.org/10.1016/j.scienta.2019.108629spa
dc.relation.referencesAdam, O., Bischoff, T., & Schneider, T. (2016). Seasonal and interannual variations of the energy flux equator and ITCZ. Part I: Zonally averaged ITCZ position. Journal of Climate, 29(9), 3219–3230. https://doi.org/10.1175/JCLI-D-15-0512.1spa
dc.relation.referencesAgresti, A. (2007). Contingency Tables. In An introduction to categorical data analysis (2nd ed., pp. 21–64). John Wiley & Sons.spa
dc.relation.referencesAli, M. (2010a). Crop Water Requirement and Irrigation Scheduling. In Fundamentals of Irrigation and On-Farm Water Management (pp. 399–452). Springer. https://doi.org/10.1007/978-1-4419-6335-2spa
dc.relation.referencesAli, M. (2010b). Field Water Balance. In Fundamentals of Irrigation and On-Farm Water Management (1st ed., Vol. 1, pp. 331–372). Springer. https://doi.org/10.1007/978-1-4419-6335-2spa
dc.relation.referencesAllen, R., Pereira, L. S., Raes, D., & Smith, M. (1998). FAO Irrigation and Drainage Paper No. 56: Crop Evapotranspiration (guidelines for computing water requirements). Food and Agriculture Organization of the United Nations. https://doi.org/10.1016/S0141-1187(05)80058-6spa
dc.relation.referencesAllen, R., & Pereira, L. (2009). Estimating crop coefficients from fraction of ground cover and height. Irrigation Science, 28(1), 17–34. https://doi.org/10.1007/s00271-009-0182-zspa
dc.relation.referencesAllen, R., Pereira, L. S., Howell, T. A., & Jensen, M. E. (2011). Evapotranspiration information reporting: I. Factors governing measurement accuracy. Agricultural Water Management, 98(6), 899–920. https://doi.org/10.1016/j.agwat.2010.12.015spa
dc.relation.referencesAltendorf, S. (2017). Global Prospect for major Tropical Fruits: Short-term outlook, challenges and opportunities in a vibrant global marketplace. Special Feature. http://www.fao.org/fileadmin/templates/est/COMM_MARKETS_MONITORING/Tropical_Fruits/Documents/Tropical_Fruits_Special_Feature.pdfspa
dc.relation.referencesAndales, A., Chavez, J., & Bauder, T. (2011). Irrigation Scheduling: The Water Balance Approach. In Colorado State University Extension Crop Series Irrigation. https://extension.colostate.edu/topic-areas/agriculture/irrigation-scheduling-the-water-balance-approach-4-707/spa
dc.relation.referencesArpaia, M. L., Boreham, D., & Hofshi, R. (2001). Development of a New Method for Measuring Minimum Maturity of Avocados. California Avocado Society Yearbook, 85, 153–178. https://ucanr.edu/datastoreFiles/234-2677.pdfspa
dc.relation.referencesArpaia, M. L., Collin, S., Sievert, J., & Obenland, D. (2018). 'Hass' avocado quality as influenced by temperature and ethylene prior to and during final ripening. Postharvest Biology and Technology, 140(February), 76–84. https://doi.org/10.1016/j.postharvbio.2018.02.015spa
dc.relation.referencesASABE. (2006). ASAE EP505 APR2004 Measurement and Reporting Practices for Automatic Agricultural Weather Stations (pp. 51–61). American Society of Agricultural Engineers.spa
dc.relation.referencesAsher, J. Ben, Yosef, B. B., & Volinsky, R. (2013). Ground-based remote sensing system for irrigation scheduling. Biosystems Engineering, 114(4), 444–453. https://doi.org/10.1016/j.biosystemseng.2012.09.002spa
dc.relation.referencesAttema, E.P.W., Ulaby, F.T., 1978. Vegetation modeled as a water cloud. Radio Sci. 13, 357–364. https://doi.org/10.1029/RS013i002p00357spa
dc.relation.referencesAwada, H., Ciraolo, G., Maltese, A., Provenzano, G., Moreno Hidalgo, M. A., & Còrcoles, J. I. (2019). Assessing the performance of a large-scale irrigation system by estimations of actual evapotranspiration obtained by Landsat satellite images resampled with cubic convolution. International Journal of Applied Earth Observation and Geoinformation, 75(June 2018), 96–105. https://doi.org/10.1016/j.jag.2018.10.016spa
dc.relation.referencesBabaeian, E., Sadeghi, M., Jones, S. B., Montzka, C., Vereecken, H., & Tuller, M. (2019). Ground, Proximal, and Satellite Remote Sensing of Soil Moisture. Reviews of Geophysics, 57(2), 530–616. https://doi.org/10.1029/2018RG000618spa
dc.relation.referencesBabaeian, E., Paheding, S., Siddique, N., Devabhaktuni, V.K., Tuller, M., 2021. Estimation of root zone soil moisture from ground and remotely sensed soil information with multisensor data fusion and automated machine learning. Remote Sens. Environ. 260, 112434. https://doi.org/10.1016/j.rse.2021.112434spa
dc.relation.referencesBaghdadi, N., Hajj, M. El, Zribi, M., Bousbih, S., 2017. Calibration of the Water Cloud Model at C-Band for winter crop fields and grasslands. Remote Sens. 9, 1–13. https://doi.org/10.3390/rs9090969spa
dc.relation.referencesBakker, W. H., Feringa, W., Gieske, A. S. M., Gorte, B. G. H., Grabmaeir, K. A., Hecker, C. A., Horn, J. A., Huurneman, G. C., Janssen, L. L. F., Kerle, N., van der Meer, F. D., Parodi, G. N., Pohl, C., Reeves, C. V., van Ruitenbeek, F. J., Schetselaar, E. M., Tempfli, K., Weir, M. J. C., Westiga, E., & Woldai, T. (2009a). Active sensors. In K. Tempfli, N. Kerle, G. C. Huurneman, & L. L. F. Janssen (Eds.), Principles of Remote Sensing: An Introductory Textbook (pp. 345–409). The International Institute for Geo-Information Science and Earth Observation (ITC).spa
dc.relation.referencesBakker, W. H., Feringa, W., Gieske, A. S. M., Gorte, B. G. H., Grabmaeir, K. A., Hecker, C. A., Horn, J. A., Huurneman, G. C., Janssen, L. L. F., Kerle, N., van der Meer, F. D., Parodi, G. N., Pohl, C., Reeves, C. V., van Ruitenbeek, F. J., Schetselaar, E. M., Tempfli, K., Weir, M. J. C., Westiga, E., & Woldai, T. (2009b). Electromagnetic energy and remote sensing. In K. Tempfli, N. Kerle, G. C. Huurneman, & L. L. F. Janssen (Eds.), Principles of Remote Sensing: An Introductory Textbook (pp. 53–109). The International Institute for Geo-Information Science and Earth Observation (ITC).spa
dc.relation.referencesBakker, W. H., Feringa, W., Gieske, A. S. M., Gorte, B. G. H., Grabmaeir, K. A., Hecker, C. A., Horn, J. A., Huurneman, G. C., Janssen, L. L. F., Kerle, N., van der Meer, F. D., Parodi, G. N., Pohl, C., Reeves, C. V., van Ruitenbeek, F. J., Schetselaar, E. M., Tempfli, K., Weir, M. J. C., Westiga, E., & Woldai, T. (2009c). Introduction of Earth Observation by Remote Sensing. In K. Tempfli, N. Kerle, G. C. Huurneman, & L. L. F. Janssen (Eds.), Principles of Remote Sensing: An Introductory Textbook (pp. 37–50). The International Institute for Geo-Information Science and Earth Observation (ITC).spa
dc.relation.referencesBakker, W. H., Feringa, W., Gieske, A. S. M., Gorte, B. G. H., Grabmaeir, K. A., Hecker, C. A., Horn, J. A., Huurneman, G. C., Janssen, L. L. F., Kerle, N., van der Meer, F. D., Parodi, G. N., Pohl, C., Reeves, C. V., van Ruitenbeek, F. J., Schetselaar, E. M., Tempfli, K., Weir, M. J. C., Westiga, E., & Woldai, T. (2009d). Principles of Remote Sensing: An Introductory Textbook (K. Tempfli, N. Kerle, G. C. Huurneman, & L. L. F. Janssen (Eds.); 4th ed.). The International Institute for Geo-Information Science and Earth Observation (ITC). https://webapps.itc.utwente.nlspa
dc.relation.referencesBallabio, C., Borrelli, P., Spinoni, J., Meusburger, K., Michaelides, S., Beguería, S., Klik, A., Petan, S., Janeček, M., Olsen, P., Aalto, J., Lakatos, M., Rymszewicz, A., Dumitrescu, A., Tadić, M. P., Diodato, N., Kostalova, J., Rousseva, S., Banasik, K., … Panagos, P. (2017). Mapping monthly rainfall erosivity in Europe. Science of the Total Environment, 579(November 2016), 1298–1315. https://doi.org/10.1016/j.scitotenv.2016.11.123spa
dc.relation.referencesBane, D., Bar-Tal, A., Levy, G., Lukyanov, V., Tarchitzky, J., Paudel, I., & Cohen, S. (2020). Mitigating negative effects of long-term treated wastewater application via soil and irrigation manipulations: Sap flow and water relations of avocado trees (Persea americana Mill.). Agricultural Water Management, 237(April), 106178. https://doi.org/10.1016/j.agwat.2020.106178spa
dc.relation.referencesBarker, J. B., Heeren, D. M., Neale, C. M. U., & Rudnick, D. R. (2018). Evaluation of variable rate irrigation using a remote-sensing-based model. Agricultural Water Management, 203(February 2018), 63–74. https://doi.org/10.1016/j.agwat.2018.02.022spa
dc.relation.referencesBarrios-Perez, C., Okada, K., Varón, G. G., Ramirez-Villegas, J., Rebolledo, M. C., & Prager, S. D. (2021). How does El Niño Southern Oscillation affect rice-producing environments in central Colombia? Agricultural and Forest Meteorology, 306(June 2020). https://doi.org/10.1016/j.agrformet.2021.108443spa
dc.relation.referencesBazzi, H., Baghdadi, N., Fayad, I., Charron, F., Zribi, M., & Belhouchette, H. (2020). Irrigation events detection over intensively irrigated grassland plots using sentinel-1 data. Remote Sensing, 12(24), 1–22. https://doi.org/10.3390/rs12244058spa
dc.relation.referencesBenninga, H. J. F., van der Velde, R., & Su, Z. (2020). Sentinel-1 soil moisture content and its uncertainty over sparsely vegetated fields. Journal of Hydrology X, 9(August), 100066. https://doi.org/10.1016/j.hydroa.2020.100066spa
dc.relation.referencesBerbel, J., & Esteban, E. (2019). Droughts as a catalyst for water policy change. Analysis of Spain, Australia (MDB), and California. Global Environmental Change, 58, 101969. https://doi.org/10.1016/j.gloenvcha.2019.1019697spa
dc.relation.referencesBernal, J. A., & Díaz, C. A. (2020). Actualización tecnológica y buenas prácticas agrícolas (BPA) en el cultivo de aguacate (2nd ed.). Corporación Colombiana de Investigación Agropecuaria (Agrosavia). https://doi.org/10.21930/agrosavia.manual.7403831spa
dc.relation.referencesBernal-Estrada, J.A., Tamayo-Vélez, A.D.J., Díaz-Diez, C.A., 2020. Dynamics of leaf, flower and fruit abscission in avocado cv. Hass in Antioquia, Colombia. Rev. Colomb. Ciencias Hortícolas 14, 324–333. https://doi.org/10.17584/rcch.2020v14i3.10850spa
dc.relation.referencesBeyer, C. P., Cuneo, I. F., Alvaro, J. E., & Pedreschi, R. (2021). Evaluation of aerial and root plant growth behavior, water and nutrient use efficiency and carbohydrate dynamics for Hass avocado grown in a soilless and protected growing system. Scientia Horticulturae, 277(November 2020), 109830. https://doi.org/10.1016/j.scienta.2020.109830spa
dc.relation.referencesBhatti, S., Heeren, D. M., Barker, J. B., Neale, C. M. U., Woldt, W. E., Maguire, M. S., & Rudnick, D. R. (2020). Site-specific irrigation management in a sub-humid climate using a spatial evapotranspiration model with satellite and airborne imagery. Agricultural Water Management, 230(May 2019), 105950. https://doi.org/10.1016/j.agwat.2019.105950spa
dc.relation.referencesBos, M., Kselik, R., Allen, R., & Molden, D. (2009). Water Requirements for Irrigation and the Environment. Springer. https://doi.org/10.1007/978-1-4020-8948-0spa
dc.relation.referencesBousbih, S., Zribi, M., Hajj, M. El, Baghdadi, N., Lili-Chabaane, Z., Gao, Q., & Fanise, P. (2018). Soil moisture and irrigation mapping in a semi-arid region, based on the synergetic use of Sentinel-1 and Sentinel-2 data. Remote Sensing, 10(12), 1–22. https://doi.org/10.3390/rs10121953spa
dc.relation.referencesBousbih, S., Zribi, M., Lili-Chabaane, Z., Baghdadi, N., El Hajj, M., Gao, Q., Mougenot, B., 2017. Potential of sentinel-1 radar data for the assessment of soil and cereal cover parameters. Sensors (Switzerland) 17. https://doi.org/10.3390/s17112617spa
dc.relation.referencesBower, J. P. (1979). Water Relations of Phytophthora Infected Fuerte Trees and Their Influence on Management. In South African Avocado Growers. http://avocadosource.com/Journals/SAAGA/SAAGA_1979/SAAGA_1979_PG_25-27.pdfspa
dc.relation.referencesBower, J. P. (1985). Some aspects of water relations on Avocado Persea americana (Mill.) tree and fruit physiology [University of Natal]. http://www.avocadosource.com/papers/SouthAfrica_Papers/TESIS_BowerJohn1985.pdfspa
dc.relation.referencesBraden, H. (1985). Ein energiehaushalts-und verdunstungsmodell for wasser und stoffhaushaltsuntersuchungen landwirtschaftlich genutzer einzugsgebiete. Mittelungen Deutsche Bodenkundliche Geselschaft, 42(S), 294–299. https://scholar.google.com/scholar_lookup?title=Ein Energiehaushalts- und Verdunstungsmodell for Wasser und Stoffhaushaltsuntersuchungen landwirtschaftlich genutzer Einzugsgebiete&publication_year=1985&author=H. Bradenspa
dc.relation.referencesBretreger, D., Yeo, I. Y., Hancock, G., & Willgoose, G. (2020). Monitoring irrigation using landsat observations and climate data over regional scales in the Murray-Darling Basin. Journal of Hydrology, 590(August), 125356. https://doi.org/10.1016/j.jhydrol.2020.125356spa
dc.relation.referencesBrinkhoff, J., Hornbuckle, J., Ballester Lurbe, C., 2019. Soil moisture forecasting for irrigation recommendation. IFAC-PapersOnLine 52, 385–390. https://doi.org/10.1016/j.ifacol.2019.12.586spa
dc.relation.referencesBrocca, L., Tarpanelli, A., Filippucci, P., Dorigo, W., Zaussinger, F., Gruber, A., & Fernández-Prieto, D. (2018). How much water is used for irrigation? A new approach exploiting coarse resolution satellite soil moisture products. International Journal of Applied Earth Observation and Geoinformation, 73(May), 752–766. https://doi.org/10.1016/j.jag.2018.08.023spa
dc.relation.referencesBrown, H. E., Jamieson, P. D., Hedley, C., Maley, S., George, M. J., Michel, A. J., & Gillespie, R. N. (2020). Using infrared thermometry to improve irrigation scheduling on variable soils. Agricultural and Forest Meteorology. https://doi.org/10.1016/j.agrformet.2020.108033spa
dc.relation.referencesBuiles, S., & Duque, M. (2020). Socio-economic and technological typology of avocado cv. Hass farms from Antioquia (Colombia). Ciencia Rural, 50(7), 1–17. https://doi.org/10.1590/0103-8478cr20190188spa
dc.relation.referencesByrne, M., Pendergrass, A., Rapp, A., & Wodzicki, K. (2018). Response of the Intertropical Convergence Zone to Climate Change: Location, Width, and Strength. Current Climate Change Reports, 4(4), 355–370. https://doi.org/10.1007/s40641-018-0110-5spa
dc.relation.referencesCalera, A., Campos, I., Osann, A., D'Urso, G., & Menenti, M. (2017). Remote sensing for crop water management: From ET modelling to services for the end users. Sensors (Switzerland), 17(5), 1–25. https://doi.org/10.3390/s17051104spa
dc.relation.referencesCaro, D., Alessandrini, A., Sporchia, F., & Borghesi, S. (2021). Global virtual water trade of avocado. Journal of Cleaner Production, 285, 124917. https://doi.org/10.1016/j.jclepro.2020.124917spa
dc.relation.referencesCarr, M. K. V. (2013). The water relations and irrigation requirements of avocado (Persea americana Mill.): A review. Experimental Agriculture, 49(2), 256–278. https://doi.org/10.1017/s0014479712001317spa
dc.relation.referencesCastillo-Argaez, R., Schaffer, B., Vazquez, A., & Sternberg, L. D. S. L. (2020). Leaf gas exchange and stable carbon isotope composition of redbay and avocado trees in response to laurel wilt or drought stress. Environmental and Experimental Botany, 171(October 2019). https://doi.org/10.1016/j.envexpbot.2019.103948spa
dc.relation.referencesCGIAR. (2021). SRTM 90m Digital Elevation Database. https://bigdata.cgiar.org/srtm-90m-digital-elevation-database/spa
dc.relation.referencesChauhan, Y. S., Wright, G. C., Holzworth, D., Rachaputi, R. C. N., & Payero, J. O. (2013). AQUAMAN: A web-based decision support system for irrigation scheduling in peanuts. Irrigation Science, 31(3), 271–283. https://doi.org/10.1007/s00271-011-0296-yspa
dc.relation.referencesChavarria, G., & dos Santos, H. P. (2012). Plant Water Relations: Absorption, Transport and Control Mechanisms. In G. Montanaro (Ed.), Advances in Selected Plant Physiology Aspects (pp. 105–132). https://doi.org/10.5772/33478spa
dc.relation.referencesChiaraviglio, L., Blefari-Melazzi, N., Liu, W., Gutierrez, J. A., Van De Beek, J., Birke, R., Chen, L., Idzikowski, F., Kilper, D., Monti, J. P., & Wu, J. (2017). 5G in rural and low-income areas: Are we ready? Proceedings of the 2016 ITU Kaleidoscope Academic Conference: ICTs for a Sustainable World, ITU WT 2016. https://doi.org/10.1109/ITU-WT.2016.7805720spa
dc.relation.referencesCho, K., Goldstein, B., Gounaridis, D., & Newell, J. P. (2021). Where does your guacamole come from? Detecting deforestation associated with the exports of avocados from Mexico to the United States. Journal of Environmental Management, 278(P1), 111482. https://doi.org/10.1016/j.jenvman.2020.111482spa
dc.relation.referencesChoker, M., Baghdadi, N., Zribi, M., El Hajj, M., Paloscia, S., Verhoest, N. E. C., Lievens, H., & Mattia, F. (2017). Evaluation of the Oh, Dubois and IEM backscatter models using a large dataset of SAR data and experimental soil measurements. Water (Switzerland), 9(1). https://doi.org/10.3390/w9010038spa
dc.relation.referencesChopart, J., Mézo, L., & Mézino, M. (2009). PROBE-w (Water Balance PROgram): A software application for water balance modeling in a cultivated soil. Presentation and User Manual (1.0.156; p. 18). CIRAD. https://agritrop.cirad.fr/549850/1/document_549850.pdfspa
dc.relation.referencesCoelho, E. F., Santos, D. B., & Azevedo, C. A. V. De. (2007). Sensor placement for soil water monitoring in lemon irrigated by micro sprinkler. Revista Brasileira de Engenharia Agrícola e Ambiental, 11(1), 46–52. https://doi.org/10.1590/S1415-43662007000100006spa
dc.relation.referencesCorpoica, Colciencias, & MADR. (2016). Plan Estratégico de Ciencia, Tecnología e Innovación del Sector Agropecuario Colombiano (2017-2027). http://www.colombiacompetitiva.gov.co/sncei/Documents/pectia-terminado.pdfspa
dc.relation.referencesCosta, J. de O., Coelho, R. D., Wolff, W., José, J. V., Folegatti, M. V., & Ferraz, S. F. de B. (2019). Spatial variability of coffee plant water consumption based on the SEBAL algorithm. Scientia Agricola, 76(2), 93–101. https://doi.org/10.1590/1678-992x-2017-0158spa
dc.relation.referencesCrowley, D., & Escalera, J. (2013). Optimizing Avocado Irrigation Practices Through Soil Water Monitoring. http://www.avocadosource.com/CAS_Yearbooks/CAS_96_2013/CAS_2013_V96_PG_055-065.pdfspa
dc.relation.referencesĆulibrk, D., Vukobratovic, D., Minic, V., Alonso Fernandez, M., Alvarez Osuna, J., & Crnojevic, V. (2014). Sensing Technologies For Precision Irrigation. Springer. https://doi.org/10.1007/978-1-4614-8329-8spa
dc.relation.referencesCunha, C. R., Peres, E., Morais, R., Oliveira, A. A., Matos, S. G., Fernandes, M. A., Ferreira, P. J. S. G., & Reis, M. J. C. S. (2010). The use of mobile devices with multi-tag technologies for an overall contextualized vineyard management. Computers and Electronics in Agriculture, 73(2), 154–164. https://doi.org/10.1016/j.compag.2010.05.007spa
dc.relation.referencesCutting, J. G., Bower, J. P., & Wolstenholme, B. N. (1986). Stress , Delayed Harvest and Fruit Quality in Fuerte Avocado Fruit. South African Avocado Growers' Association Yearbook, 9, 39–42.spa
dc.relation.referencesCVC, & IGAC. (2017). Levantamiento Semidetallado de Suelos escala 1:25.000 de las cuencas priorizadas por la Corporación Autónoma Regional del Valle del Cauca - CVC.spa
dc.relation.referencesCVC, 2021. Boletín Actos Administrativos [WWW Document]. URL https://www.cvc.gov.co/documentos/normatividad/boletin-actos-administrativos-ambientales/actos-administrativos-2021?page=0 (accessed 1.3.22).spa
dc.relation.referencesCWR. (2021). The California Irrigation Management Information System (CIMIS). https://cimis.water.ca.gov/spa
dc.relation.referencesda Silva, A. O., da Silva, B. A., Souza, C. F., de Azevedo, B. M., Bassoi, L. H., Vasconcelos, D. V., do Bonfim, G. V., Juarez, J. M., Felipe dos, A., & Carneiro, F. M. (2020). Irrigation in the age of agriculture 4.0: management, monitoring and precision. Revista Ciencia Agronomica, 51(5), 1–17. https://doi.org/10.5935/1806-6690.20200090spa
dc.relation.referencesDabach, S., Shani, U., & Lazarovitch, N. (2016). The influence of water uptake on matric head variability in a drip-irrigated root zone. Soil and Tillage Research, 155, 216–224. https://doi.org/10.1016/j.still.2015.08.012spa
dc.relation.referencesDANE. (2020). Exportaciones - Históricos. https://www.dane.gov.co/index.php/estadisticas-por-tema/comercio-internacional/exportaciones/exportaciones-historicosspa
dc.relation.referencesDari, J., Quintana-Seguí, P., Escorihuela, M. J., Stefan, V., Brocca, L., & Morbidelli, R. (2021). Detecting and mapping irrigated areas in a Mediterranean environment by using remote sensing soil moisture and a land surface model. Journal of Hydrology, 596(December 2020). https://doi.org/10.1016/j.jhydrol.2021.126129spa
dc.relation.referencesDatta, S., Das, P., Dutta, D., & Giri, R. K. (2020). Estimation of Surface Moisture Content using Sentinel-1 C-band SAR Data Through Machine Learning Models. Journal of the Indian Society of Remote Sensing, 0123456789. https://doi.org/10.1007/s12524-020-01261-xspa
dc.relation.referencesDavis Instruments. (2020). WeatherLink Computer Software. https://www.davisinstruments.com/product/weatherlink-computer-software/spa
dc.relation.referencesDeb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197. https://doi.org/10.1109/4235.996017spa
dc.relation.referencesDehnen-Schmutz, K., Foster, G. L., Owen, L., & Persello, S. (2016). Exploring the role of smartphone technology for citizen science in agriculture. Agronomy for Sustainable Development, 36(2), 1–9. https://doi.org/10.1007/s13593-016-0359-9spa
dc.relation.referencesDíaz, L., Hurtado, J.J., Charry, A., Jäger, M., 2021. Brechas tecnológicas de la cadena productiva del aguacate Hass en el Valle del Cauca y descripción del estado del arte. Universidad Nacional de Colombia, Bogotá D.C., Colombia.spa
dc.relation.referencesDirwai, T. L., Mabhaudhi, T., Kanda, E. K., & Senzanje, A. (2021). Moistube irrigation technology development, adoption and future prospects: A systematic scoping review. Heliyon, 7(2), e06213. https://doi.org/10.1016/j.heliyon.2021.e06213spa
dc.relation.referencesDjaman, K., Irmak, S., Sall, M., Sow, A., & Kabenge, I. (2018). Comparison of sum-of-hourly and daily time step standardized ASCE Penman-Monteith reference evapotranspiration. Theoretical and Applied Climatology, 134(1–2), 533–543. https://doi.org/10.1007/s00704-017-2291-6spa
dc.relation.referencesDomínguez-Niño, J. M., Oliver-Manera, J., Girona, J., & Casadesús, J. (2020). Differential irrigation scheduling by an automated algorithm of water balance tuned by capacitance-type soil moisture sensors. Agricultural Water Management, 228(November 2019), 105880. https://doi.org/10.1016/j.agwat.2019.105880spa
dc.relation.referencesDorigo, W. A., Gruber, A., De Jeu, R. A. M., Wagner, W., Stacke, T., Loew, A., Albergel, C., Brocca, L., Chung, D., Parinussa, R. M., & Kidd, R. (2015). Evaluation of the ESA CCI soil moisture product using ground-based observations. Remote Sensing of Environment, 162, 380–395. https://doi.org/10.1016/j.rse.2014.07.023spa
dc.relation.referencesDoupis, G., Kavroulakis, N., Psarras, G., & Papadakis, I. (2017). Growth, photosynthetic performance and antioxidative response of 'Hass' and 'Fuerte' avocado (Persea americana Mill.) plants grown under high soil moisture. Photosynthetica, 55(4), 655–663. https://doi.org/10.1007/s11099-016-0679-7spa
dc.relation.referencesdu Plessis, S. (1991). Factors Important for Optimal Irrigation Scheduling of Avocado Orchards. South African Avocado Growers' Association Yearbook, 14, 91–93. http://www.avocadosource.com/journals/saaga/saaga_1991/saaga_1991_pg_91-93.pdfspa
dc.relation.referencesDunkerley, D. L. (2021). Light and low-intensity rainfalls: A review of their classification, occurrence, and importance in landsurface, ecological and environmental processes. Earth-Science Reviews, 214(June 2020), 103529. https://doi.org/10.1016/j.earscirev.2021.103529spa
dc.relation.referencesEisenhauer, D.E., Martin, D.L., Heeren, D.M., Hoffman, G.J., 2021. Irrigation Systems Management. American Society of Agricultural and Biological Engineers, St. Joseph, MI. https://doi.org/10.13031/ISM.2021spa
dc.relation.referencesEl Hajj, M., Baghdadi, N., Zribi, M., Bazzi, H., 2017. Synergic use of Sentinel-1 and Sentinel-2 images for operational soil moisture mapping at high spatial resolution over agricultural areas. Remote Sens. 9, 1–28. https://doi.org/10.3390/rs9121292spa
dc.relation.referencesElnashar, A., Wang, L., Wu, B., Zhu, W., & Zeng, H. (2021). Synthesis of global actual evapotranspiration from 1982 to 2019. Earth System Science Data, 13(2), 447–480. https://doi.org/10.5194/essd-13-447-2021spa
dc.relation.referencesErazo-Mesa, E., Ramírez-Gil, J. G., & Echeverri, A. S. (2021). Avocado cv . Hass Needs Water Irrigation in Tropical Precipitation Regime: Evidence from Colombia. Water (Switzerland), 13(14). https://doi.org/10.3390/w13141942spa
dc.relation.referencesErazo-Mesa, E., Echeverri-Sánchez, A., Ramírez-Gil, J.G., 2022. Advances in Hass avocado irrigation scheduling under digital agriculture approach. Rev. Colomb. Ciencias Hortícolas 16, e13456. https://doi.org/10.17584/rcch.2022v16i1.13456spa
dc.relation.referencesEr-Raki, S., Ezzahar, J., Merlin, O., Amazirh, A., Hssaine, B. A., Kharrou, M. H., Khabba, S., & Chehbouni, A. (2021). Performance of the HYDRUS-1D model for water balance components assessment of irrigated winter wheat under different water managements in semi-arid region of Morocco. Agricultural Water Management, 244(October 2020), 106546. https://doi.org/10.1016/j.agwat.2020.106546spa
dc.relation.referencesESA. (2021). Sentinel-1 Observation Scenario. The European Space Agency. https://sentinel.esa.int/web/sentinel/missions/sentinel-1/observation-scenariospa
dc.relation.referencesEtchanchu, J., Rivalland, V., Faroux, S., Brut, A., & Boulet, G. (2019). On the use of high resolution satellite imagery to estimate irrigation volumes and its impact in land surface modeling. Hydrology and Earth System Sciences Discussions, April, 1–32. https://doi.org/10.5194/hess-2019-126spa
dc.relation.referencesFang, H., & He, Y. (2008). A Pocket PC based field information fast collection system. Computers and Electronics in Agriculture, 61(2), 254–260. https://doi.org/10.1016/j.compag.2007.11.005spa
dc.relation.referencesFAO. (2020). The State of Food and Agriculture 2020. Overcoming water challenges in agriculture. Food and Agriculture Organization of the United Nations. https://doi.org/10.4060/cb1447enspa
dc.relation.referencesFAO, 2021. Major Tropical Fruits. Rome, Italy.spa
dc.relation.referencesFAOSTAT. (2021). Food and Agriculture Data. http://www.fao.org/faostat/en/#homespa
dc.relation.referencesFeddes, R. A., Kabat, P., Van Bakel, P. J. T., Bronswijk, J. J. B., & Halbertsma, J. (1988). Modelling soil water dynamics in the unsaturated zone - State of the art. Journal of Hydrology, 100(1–3), 69–111. https://doi.org/10.1016/0022-1694(88)90182-5spa
dc.relation.referencesFernández, I., Lecina, S., Ruiz-Sánchez, C., Vera, J., Conejero, W., Conesa, M., Domínguez, A., Pardo, J., Léllis, B., & Montesinos, P. (2020). Trends and challenges in irrigation scheduling in the semi-arid area of Spain. Water (Switzerland), 12(3), 1–26. https://doi.org/10.3390/w12030785spa
dc.relation.referencesFernández, J. (2017). Plant-based methods for irrigation scheduling of woody crops. Horticulturae, 3(2), 1–37. https://doi.org/10.3390/horticulturae3020035spa
dc.relation.referencesFernández, J. E., Romero, R., Montaño, J. C., Diaz-Espejo, A., Muriel, J. L., Cuevas, M. V., Moreno, F., Girón, I. F., & Palomo, M. J. (2008). Design and testing of an automatic irrigation controller for fruit tree orchards, based on sap flow measurements. Australian Journal of Agricultural Research, 59(7), 589–598. https://doi.org/10.1071/AR07312spa
dc.relation.referencesFerreira, L. B., da Cunha, F. F., de Oliveira, R. A., & Rodrigues, T. F. (2020). A smartphone APP for weatherbased irrigation scheduling using artificial neural networks. Pesquisa Agropecuaria Brasileira, 55, 1–10. https://doi.org/10.1590/S1678-3921.PAB2020.V55.01839spa
dc.relation.referencesFessehazion, M.K., Annandale, J.G., Everson, C.S., Stirzaker, R.J., van der Laan, M., Truter, W.F., Abraha, A.B., 2014. Performance of simple irrigation scheduling calendars based on average weather data for annual ryegrass. African J. Range Forage Sci. 31, 221–228. https://doi.org/10.2989/10220119.2014.906504spa
dc.relation.referencesFick, S. E., & Hijmans, R. J. (2017). WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37(12), 4302–4315. https://doi.org/10.1002/joc.5086spa
dc.relation.referencesFischer, G., Ramírez, F., & Casierra-Posada, F. (2016). Ecophysiological aspects of fruit crops in the era of climate change. A review. Agronomia Colombiana, 34(2), 190–199. https://doi.org/10.15446/agron.colomb.v34n2.56799spa
dc.relation.referencesFontanet, M., Fernàndez-Garcia, D., & Ferrer, F. (2018). The value of satellite remote sensing soil moisture data and the DISPATCH algorithm in irrigation fields. Hydrology and Earth System Sciences, 22(11), 5889–5900. https://doi.org/10.5194/hess-22-5889-2018spa
dc.relation.referencesFreebairn, D., Ghahramani, A., Robinson, J., & McClymont, D. (2018). A tool for monitoring soil water using modelling, on-farm data, and mobile technology. Environmental Modelling and Software, 104, 55–63. https://doi.org/10.1016/j.envsoft.2018.03.010spa
dc.relation.referencesFresh Plaza. (2021). Colombia is currently Europe's main supplier of Hass avocado. https://www.freshplaza.com/article/9291112/colombia-is-currently-europe-s-main-supplier-of-hass-avocado/spa
dc.relation.referencesFriedman, S. P., Communar, G., & Gamliel, A. (2016). DIDAS - User-friendly software package for assisting drip irrigation design and scheduling. Computers and Electronics in Agriculture, 120, 36–52. https://doi.org/10.1016/j.compag.2015.11.007spa
dc.relation.referencesFritsch, S., Guenther, F., Wright, M., Suling, M., & Mueller, S. (2019). Package "neuralnet": Training of Neural Networks (R package version 1.44.2; pp. 1–15). https://cran.r-project.org/web/packages/neuralnet/neuralnet.pdfspa
dc.relation.referencesGao, Y., Marpu, P., & Morales, L. M. (2014). Object based image analysis for the classification of the growth stages of Avocado crop, in Michoacán State, Mexico. Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications V, 9263(November 2014), 1–5. https://doi.org/10.1117/12.2068966spa
dc.relation.referencesGalushkin, A. (2007). Neural Networks Theory. Springer. https://doi.org/10.1007/978-3-540-48125-6spa
dc.relation.referencesGarcía, L., Parra, L., Jimenez, J., Lloret, J., & Lorenz, P. (2020). IoT-based smart irrigation systems: An overview on the recent trends on sensors and iot systems for irrigation in precision agriculture. Sensors (Switzerland), 20(4), 1–48. https://doi.org/10.3390/s20041042spa
dc.relation.referencesGarrido‑Rubio, J., Sanz, D., González‑Piqueras, J., & Calera, A. (2019). Application of a remote sensing‑based soil water balance for the accounting of groundwater abstractions in large irrigation areas. Irrigation Science, 1–16. https://doi.org/10.1007%2Fs00271-019-00629-3spa
dc.relation.referencesGil, P. M., Gurovich, L., Schaffer, B., Alcayaga, J., & Iturriaga, R. (2011). Electrical signal measurements in avocado trees: A potential tool for monitoring physiological responses to soil water content? Acta Horticulturae, 889(978), 371–378. https://doi.org/10.17660/ActaHortic.2011.889.45spa
dc.relation.referencesGonzález, R., Fernández, I., Arroyo, M., Rodríguez, J. A., Camacho, E., & Montesinos, P. (2017). Multiplatform application for precision irrigation scheduling in strawberries. Agricultural Water Management, 183, 194–201. https://doi.org/10.1016/j.agwat.2016.07.017spa
dc.relation.referencesGonzález-Estudillo, J. C., González-Campos, J. B., Nápoles-Rivera, F., Ponce-Ortega, J. M., & El-Halwagi, M. M. (2017). Optimal Planning for Sustainable Production of Avocado in Mexico. Process Integration and Optimization for Sustainability, 1(2), 109–120. https://doi.org/10.1007/s41660-017-0008-zspa
dc.relation.referencesGonzález-Orozco, C. E., Porcel, M., Alzate Velásquez, D. F., & Orduz-Rodríguez, J. O. (2020). Extreme climate variability weakens a major tropical agricultural hub. Ecological Indicators, 111(December 2019), 106015. https://doi.org/10.1016/j.ecolind.2019.106015spa
dc.relation.referencesGoodall, G. (1986). Tensiometer : Irrigationist's Best Friend. California Growers, X(7), 1–3. http://www.avocadosource.com/papers/research_articles/goodallgeorge1986.pdfspa
dc.relation.referencesGoogle Inc. (2021). Earth Engine Data Catalog | Google Developers. https://developers.google.com/earth-engine/datasets/catalogspa
dc.relation.referencesGorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27. https://doi.org/10.1016/j.rse.2017.06.031spa
dc.relation.referencesGrajales, L. (2017). Uso racional del agua de riego en cultivo de aguacate Hass (Persea Americana) en tres zonas productoras de Colombia [Universidad Nacional de Colombia Sede Palmira]. http://bdigital.unal.edu.co/60821/1/2017-Luis_Carlos_Grajales_Guzman.pdfspa
dc.relation.referencesGu, Z., Qi, Z., Burghate, R., Yuan, S., Jiao, X., & Xu, J. (2020). Irrigation Scheduling Approaches and Applications: A Review. Journal of Irrigation and Drainage Engineering, 146(6), 1–15. https://doi.org/10.1061/(asce)ir.1943-4774.0001464spa
dc.relation.referencesGuimberteau, M., Laval, K., Perrier, A., & Polcher, J. (2012). Global effect of irrigation and its impact on the onset of the Indian summer monsoon. Climate Dynamics, 39(6), 1329–1348. https://doi.org/10.1007/s00382-011-1252-5spa
dc.relation.referencesGuo, D., & Peterson, T. (2020). Package Evapotranspiration: Modelling Actual, Potential and Reference Crop Evapotranspiration (1.15; p. 75). R Foundation for Statistical Computing. https://cran.r-project.org/web/packages/Evapotranspiration/Evapotranspiration.pdfspa
dc.relation.referencesGustafson, C. D. (1961). Avocado irrigation and tensiometers. California Avocado Society Yearbook, 45(80), 19–22. http://www.avocadosource.com/CAS_Yearbooks/CAS_45_1961/CAS_1961_PG_19-22.pdf.spa
dc.relation.referencesGustafson, C. D., Marsh, A. W., & Branson, R. L. (1972). Drip irrigation experiments in Avocados in San Diego. California Agriculture, 26(7), 12–14. http://www.avocadosource.com/Journals/CA/CA_1972_V26_N7_PG_12_14.pdfspa
dc.relation.referencesGustafson, C. D., Marsh, A. W., Branson, R. L., & Davis, S. (1979). Drip Irrigation on Avocados. California Avocado Society 1979 Yearbook, 63, 95–134. http://209.143.153.251/CAS_Yearbooks/CAS_63_1979/CAS_1979_PG_095-134.pdfspa
dc.relation.referencesGuzmán, D., Ruíz, J. F., & Cadena, M. (2014). Regionalización de Colombia según la estacionalidad de la precipitación media mensual, a través análisis de componentes principales (ACP). IDEAM. http://www.ideam.gov.co/documents/21021/21141/Regionalizacion+de+la+Precipitacion+Media+Mensual/spa
dc.relation.referencesHallikainen, M. T., Ulabz, F. T., Dobson, M. C., El-Rayes, M. A., & Wu, L. K. (1985). Microwave Dielectric Behavior of Wet Soil-Part I: Empirical Models and Experimental Observations. IEEE Transactions on Geoscience and Remote Sensing, GE-23(1), 25–34. https://doi.org/10.1109/TGRS.1985.289497spa
dc.relation.referencesHamad, M. A. A., Eltahir, M. E. S., Ali, A. E. M., & Hamdan, A. M. (2018). Efficiency of Using Smart-Mobile Phones in Accessing Agricultural Information by Smallholder Farmers in North Kordofan – Sudan. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3240758spa
dc.relation.referencesHan, D., Wang, P., Tansey, K., Zhou, X., Zhang, S., Tian, H., Zhang, J., Li, H., 2020. Linking an agro-meteorological model and a water cloud model for estimating soil water content over wheat fields. Comput. Electron. Agric. 179, 105833. https://doi.org/10.1016/j.compag.2020.105833spa
dc.relation.referencesHan, M., Zhang, H., Chávez, J. L., Ma, L., Trout, T. J., & DeJonge, K. C. (2018). Improved soil water deficit estimation through the integration of canopy temperature measurements into a soil water balance model. Irrigation Science, 36(3), 187–201. https://doi.org/10.1007/s00271-018-0574-zspa
dc.relation.referencesHandwert, B. (2017). Holy Guacamole: How the Hass avocado Conquered the World. Smithsonian Magazine. https://www.smithsonianmag.com/science-nature/holy-guacamole-how-hass-avocado-conquered-world-180964250/spa
dc.relation.referencesHernández, I., Fuentealba, C., Olaeta, J. A., Lurie, S., Defilippi, B. G., Campos-Vargas, R., & Pedreschi, R. (2016). Factors associated with postharvest ripening heterogeneity of "Hass" avocados (Persea americana Mill). Fruits, 71(5), 259–268. https://doi.org/10.1051/fruits/2016016spa
dc.relation.referencesHill, R.W., Allen, R.G., 1996. Simple Irrigation Scheduling Calendars. J. Irrig. Drain. Eng. 122, 107–111. https://doi.org/10.1061/(ASCE)0733-9437(1996)122:2(107)spa
dc.relation.referencesHillel, D. (2014). Water Flow in Unsaturated Soil. In Introduction to Environmental Soil Physics (pp. 149–166). Academic Press.spa
dc.relation.referencesHoeben, R., Troch, P. A., Su, Z., Mancini, M., & Chen, K. S. (1997). Sensitivity of radar backscattering to soil surface parameters: A comparison between theoretical analysis and experimental evidence. International Geoscience and Remote Sensing Symposium (IGARSS), 3(September), 1368–1370. https://doi.org/10.1109/igarss.1997.606449spa
dc.relation.referencesHoffman, J. E., & du Plessis, S. (1999). Seasonal Water Requirements of Avocado Trees Grown Under Subtropical Conditions. Revista Chapingo Serie Horticultura, 5, 191–194. https://www.avocadosource.com/WAC4/WAC4_p191.pdfspa
dc.relation.referencesHolzapfel, E., de Souza, J. A., Jara, J., & Guerra, H. C. (2017). Responses of avocado production to variation in irrigation levels. Irrigation Science, 35(3), 205–215. https://doi.org/10.1007/s00271-017-0533-0spa
dc.relation.referencesHornbuckle, J., Vleeshouwer, J., Ballester, C., Montgomery, J., Hoogers, R., & Bridgart, R. (2016). IrriSAT Technical Reference. https://irrisat-cloud.appspot.com/doc/IrriSAT_Technical_Reference.pdfspa
dc.relation.referencesHornbuckle, J. W., Christen, E. W., & Faulkner, R. D. (2006). Development of a Pocket PC Surface Irrigation Decision Support System. Computers in Agriculture and Natural Resources, 433–438. https://doi.org/10.13031/2013.21913spa
dc.relation.referencesHou, L., Zhou, Y., Bao, H., & Wenninger, J. (2017). Simulation of maize (Zea mays L.) water use with the HYDRUS-1D model in the semi-arid Hailiutu River catchment, Northwest China. Hydrological Sciences Journal, 62(1), 93–103. https://doi.org/10.1080/02626667.2016.1170130spa
dc.relation.referencesHuang, Y., Chen, Z. xin, Yu, T., Huang, X. zhi, & Gu, X. fa. (2018). Agricultural remote sensing big data: Management and applications. Journal of Integrative Agriculture, 17(9), 1915–1931. https://doi.org/10.1016/S2095-3119(17)61859-8spa
dc.relation.referencesHuang, Z., Liu, X., Sun, S., Tang, Y., Yuan, X., & Tang, Q. (2021). Global assessment of future sectoral water scarcity under adaptive inner-basin water allocation measures. Science of the Total Environment, 783, 146973. https://doi.org/10.1016/j.scitotenv.2021.146973spa
dc.relation.referencesIDEAM. (2019). Consulta y Descarga de Datos Hidrometeorológicos. http://dhime.ideam.gov.co/atencionciudadano/spa
dc.relation.referencesIDEAM. (2021). Boletín Hidroclimatológico Mensual. http://www.ideam.gov.co/web/tiempo-y-clima/climatologico-mensualspa
dc.relation.referencesImbert, E. (2020). El aguacate en el mundo. In A. Namesny, C. Conesa, I. Hormaza, & G. Lobo (Eds.), Cultivo, poscosecha y procesado del aguacate (pp. 3–18). SPE3 - Especialistes en Serveis per a la Producció Editorial. https://industry.nzavocado.co.nz/world-avocado-market/spa
dc.relation.referencesIrrometer. (2021). Irrometer Reading Tools. https://www.irrometer.com/loggers.htmlspa
dc.relation.referencesIslam, N., & Want, R. (2014). Smartphones: Past, Present, and Future. IEEE Pervasive Computing, 13(4), 89–92. https://doi.org/10.1109/MPRV.2014.74spa
dc.relation.referencesIVFL. (2021). eo4water – Earth observation for water resource management. https://eo4water.com/spa
dc.relation.referencesJalilvand, E., Tajrishy, M., Ghazi, S., & Brocca, L. (2019). Quantification of irrigation water using remote sensing of soil moisture in a semi-arid region. Remote Sensing of Environment, 231(August 2018), 111226. https://doi.org/10.1016/j.rse.2019.111226spa
dc.relation.referencesJobbágy, J., Beloev, H., Kristof, K., & Findura, P. (2016). The Benefits and Efficiency of Precision Irrigation. International Scientific Journal "Mechanization in Agriculture," 43(2), 35–43. https://stumejournals.com/journals/am/2016/2/35spa
dc.relation.referencesJones, H. (2004). Irrigation scheduling: Advantages and pitfalls of plant-based methods. Journal of Experimental Botany, 55(407), 2427–2436. https://doi.org/10.1093/jxb/erh213spa
dc.relation.referencesJulich, S., Mwangi, H., & Feger, K.-H. (2016). Forest Hydrology in the Tropics. In L. Pancel & M. Köhl (Eds.), Tropical Forestry Handbook (2nd ed., pp. 1917–1939). Springer. https://doi.org/10.1007/978-3-642-54601-3_152spa
dc.relation.referencesJung, J., Maeda, M., Chang, A., Bhandari, M., Ashapure, A., & Landivar-Bowles, J. (2021). The potential of remote sensing and artificial intelligence as tools to improve the resilience of agriculture production systems. Current Opinion in Biotechnology, 70, 15–22. https://doi.org/10.1016/j.copbio.2020.09.003spa
dc.relation.referencesKadbhane, S. J., & Manekar, V. L. (2021). Grape production assessment using surface and subsurface drip irrigation methods. Journal of Water and Land Development, 49(IV–VI), 169–178. https://doi.org/10.24425/jwld.2021.137109spa
dc.relation.referencesKaewmard, N., & Saiyod, S. (2014). Sensor data collection and irrigation control on vegetable crop using smart phone and wireless sensor networks for smart farm. ICWiSe 2014 - 2014 IEEE Conference on Wireless Sensors, 106–112. https://doi.org/10.1109/ICWISE.2014.7042670spa
dc.relation.referencesKalmar, D., & Lahav, E. (1977). Water requirements of avocado in Israel. I. Tree and soil parameters†. Australian Journal of Agricultural Research, 28(5), 859–868. https://doi.org/10.1071/AR9770859spa
dc.relation.referencesKang, S., Shin, Y., & Xie, S.-P. (2018). Extratropical forcing and tropical rainfall distribution: energetics framework and ocean Ekman advection. Climate and Atmospheric Science, 1(1), 1–10. https://doi.org/10.1038/s41612-017-0004-6spa
dc.relation.referencesKarthikeyan, L., Pan, M., Wanders, N., Kumar, D. N., & Wood, E. F. (2017). Four decades of microwave satellite soil moisture observations: Part 1. A review of retrieval algorithms. Advances in Water Resources, 109, 106–120. https://doi.org/10.1016/j.advwatres.2017.09.006spa
dc.relation.referencesKhabba, S., Jarlan, L., Er-Raki, S., Le Page, M., Ezzahar, J., Boulet, G., Simonneaux, V., Kharrou, M. H., Hanich, L., & Chehbouni, G. (2013). The SudMed Program and the Joint International Laboratory TREMA: A Decade of Water Transfer Study in the Soil-plant-atmosphere System over Irrigated Crops in Semi-arid Area. Procedia Environmental Sciences, 19, 524–533. https://doi.org/10.1016/j.proenv.2013.06.059spa
dc.relation.referencesKhanna, A., & Kaur, S. (2019). Evolution of Internet of Things (IoT) and its significant impact in the field of Precision Agriculture. Computers and Electronics in Agriculture, 157(November 2018), 218–231. https://doi.org/10.1016/j.compag.2018.12.039spa
dc.relation.referencesKiggundu, N., & Migliaccio, K. (2012). Water savings , nutrient leaching , and fruit yield in a young avocado orchard as affected by irrigation and nutrient management. Irrigation Science, 30, 275–286. https://doi.org/10.1007/s00271-011-0280-6spa
dc.relation.referencesKisi, O. (2011). Modeling Reference Evapotranspiration Using Evolutionary Neural Networks. Journal of Irrigation and Drainage Engineering, 137(10), 636–643. https://doi.org/10.1061/(asce)ir.1943-4774.0000333spa
dc.relation.referencesKnipper, K., Kustas, W., Anderson, M., Alfieri, J., Prueger, J., Hain, C., Gao, F., Yang, Y., McKee, L., Nieto, H., Hipps, L., Alsina, M., & Sanchez, L. (2019). Evapotranspiration estimates derived using thermal-based satellite remote sensing and data fusion for irrigation management in California vineyards. Irrigation Science, 37(3), 431–449. https://doi.org/10.1007/s00271-018-0591-yspa
dc.relation.referencesKramer, P. (1983a). Water: Its Functions and Properties. In Water Relations of Plants (pp. 1–22). Academic Press. https://doi.org/10.1016/b978-0-12-425040-6.50004-7spa
dc.relation.referencesKramer, P. (1983b). Water Movement in the Soil-Plant-Atmosphere Continuum. In Water Relations of Plants (pp. 187–214). Academic Press.spa
dc.relation.referencesKumar, K., Hari Prasad, K.S., Arora, M.K., 2012. Estimation of water cloud model vegetation parameters using a genetic algorithm. Hydrol. Sci. J. 57, 776–789. https://doi.org/10.1080/02626667.2012.678583spa
dc.relation.referencesKweon, S.-K., & Oh, Y. (2015). A modified water-cloud model with leaf angle parameters for microwave backscattering from agricultural fields. IEEE Transactions on Geoscience and Remote Sensing, 53(5), 2802–2809. https://doi.org/10.1109/TGRS.2014.2364914spa
dc.relation.referencesLahav, E., & Kalmar, D. (1977). Water requirements of avocado in Israel. II.* Influence on yield, fruit growth and oil content†. Australian Journal of Agricultural Research, 28(5), 869–877. https://doi.org/10.1071/AR9770869spa
dc.relation.referencesLahav, E., & Kalmar, D. (1983). Determination of the irrigation regimen for an avocado plantation in spring and autumn. Australian Journal of Agricultural Research, 34(6), 717–724. https://doi.org/10.1071/AR9830717spa
dc.relation.referencesLahav, E., & Whiley, A. W. (2002). Irrigation and Mineral Nutrition. In A. W. Whiley, B. Schaffer, & B. N. Wolstenholme (Eds.), The Avocado: Botany, Production and Uses (pp. 259–297). CAB International. https://doi.org/10.1079/9780851993577.0259spa
dc.relation.referencesLawston, P. M., Santanello, J. A., & Kumar, S. V. (2017). Irrigation Signals Detected From SMAP Soil Moisture Retrievals. Geophysical Research Letters, 44(23), 11,860-11,867. https://doi.org/10.1002/2017GL075733spa
dc.relation.referencesLe Page, M., Jarlan, L., El Hajj, M. M., Zribi, M., Baghdadi, N., & Boone, A. (2020). Potential for the detection of irrigation events on maize plots using Sentinel-1 soil moisture products. Remote Sensing, 12(10), 1–22. https://doi.org/10.3390/rs12101621spa
dc.relation.referencesLee, H.-T., & Program NOAA CDR. (2020). NOAA Climate Data Record (CDR) of Monthly Outgoing Longwave Radiation (OLR), Version 2.2-1. NOAA National Climatic Data Center. https://doi.org/10.7289/V5222RQPspa
dc.relation.referencesLi, B., Wang, Y., Hill, R. L., & Li, Z. (2019). Effects of apple orchards converted from farmlands on soil water balance in the deep loess deposits based on HYDRUS-1D model. Agriculture, Ecosystems and Environment, 285(August), 106645. https://doi.org/10.1016/j.agee.2019.106645spa
dc.relation.referencesLi, J., & Roy, D. P. (2017). A global analysis of Sentinel-2a, Sentinel-2b and Landsat-8 data revisit intervals and implications for terrestrial monitoring. Remote Sensing, 9(9). https://doi.org/10.3390/rs9090902spa
dc.relation.referencesLi, W., Awais, M., Ru, W., Shi, W., Ajmal, M., Uddin, S., & Liu, C. (2020). Review of Sensor Network-Based Irrigation Systems Using IoT and Remote Sensing. Advances in Meteorology, 2020, 1–14. https://doi.org/10.1155/2020/8396164spa
dc.relation.referencesLi, Z.L., Leng, P., Zhou, C., Chen, K.S., Zhou, F.C., Shang, G.F., 2021. Soil moisture retrieval from remote sensing measurements: Current knowledge and directions for the future. Earth-Science Rev. 218, 103673. https://doi.org/10.1016/j.earscirev.2021.103673spa
dc.relation.referencesLinker, R., & Sylaios, G. (2016). Efficient model-based sub-optimal irrigation scheduling using imperfect weather forecasts. Computers and Electronics in Agriculture, 130, 118–127. https://doi.org/10.1016/j.compag.2016.10.004spa
dc.relation.referencesLinker, Raphael. (2021). Model‑based optimal delineation of drip irrigation management zones. Precision Agriculture, 22, 287–305. https://doi.org/10.1007/s11119-020-09743-1spa
dc.relation.referencesLorite, I. J., Santos, C., Testi, L., & Fereres, E. (2012). Diseño y construcción de un lisímetro de pesada en una plantación de almendros. Spanish Journal of Agricultural Research, 10(1), 238–250. https://doi.org/10.5424/sjar/2012101-243-11spa
dc.relation.referencesLozac, L., Bazzi, H., Baghdadi, N., El Hajj, M., Zribi, M., & Cresson, R. (2020). Sentinel-1 / Sentinel-2-Derived soil moisture product at plot scale (S2MP). IEEE Geoscience and Remote Sensing Society (M2GARSS 2020). https://doi.org/10.1109/M2GARSS47143.2020.9105210spa
dc.relation.referencesLP Laboratories. (2019). Chloe Irrigation Systems - Apps on Google Play. https://play.google.com/store/apps/details?id=com.chloeirrigation.chloe&hl=en&gl=USspa
dc.relation.referencesLynks Ingeniería. (2016). Manual LYNKBOX-Meteo. Monitoreo de variables ambientales y de suelos V 1.0 (p. 23). Lynks Ingeniería. http://lynks.com.co/wp-content/uploads/2017/05/manual-Lynkbox_METEO-v2.0-RES.pdfspa
dc.relation.referencesMa, Y., Liu, S., Song, L., Xu, Z., Liu, Y., Xu, T., & Zhu, Z. (2018). Estimation of daily evapotranspiration and irrigation water efficiency at a Landsat-like scale for an arid irrigation area using multi-source remote sensing data. Remote Sensing of Environment, 216(December 2016), 715–734. https://doi.org/10.1016/j.rse.2018.07.019spa
dc.relation.referencesMADR. (2018). Cadena de Aguacate: Indicadores e Instrumentos Diciembre 2018. In Ministerio de Agricultura y Desarrollo Rural. https://sioc.minagricultura.gov.co/Aguacate/Documentos/2018-12-30%20Cifras%20Sectoriales.pdfspa
dc.relation.referencesMADR. (2019). Estrategia de Ordenamiento de la Producción Agropecuaria Pesquera y Acuícola. https://sioc.minagricultura.gov.co/Documentos/Estrategia Ordenamiento de la producción.PDFspa
dc.relation.referencesMADR, 2020. Cadena productiva Aguacate. Primer trimestre de 2020. Bogotá D.C., Colombia.spa
dc.relation.referencesMadry, S. (2017). Introduction and History of Space Remote Sensing. In J. N. Pelton, S. Madry, & S. Camacho-Lara (Eds.), Handbook of Satellite Applications (2nd ed., pp. 823–832). Springer International Publishing. https://doi.org/10.1007/978-3-319-23386-4spa
dc.relation.referencesMainuddin, M., Kirby, M., Chowdhury, R. A. R., & Shah-Newaz, S. M. (2015). Spatial and temporal variations of, and the impact of climate change on, the dry season crop irrigation requirements in Bangladesh. Irrigation Science, 33(2), 107–120. https://doi.org/10.1007/s00271-014-0451-3spa
dc.relation.referencesMamalakis, A., & Foufoula-Georgiou, E. (2018). A Multivariate Probabilistic Framework for Tracking the Intertropical Convergence Zone: Analysis of Recent Climatology and Past Trends. Geophysical Research Letters, 45(23), 13,080-13,089. https://doi.org/10.1029/2018GL079865spa
dc.relation.referencesMarsh, A. W., & Gustafson, C. D. (1958). Orchard Irrigation. California Avocado Society 1958 Yearbook, 42, 30–33. http://www.avocadosource.com/CAS_Yearbooks/CAS_42_1958/CAS_1958_PG_030-033.pdfspa
dc.relation.referencesMbabazi, D., Migliaccio, K. W., Crane, J. H., Fraisse, C., Zotarelli, L., Morgan, K. T., & Kiggundu, N. (2017). An irrigation schedule testing model for optimization of the Smartirrigation avocado app. Agricultural Water Management, 179, 390–400. https://doi.org/10.1016/j.agwat.2016.09.006spa
dc.relation.referencesMendes, W. R., Araújo, F. M. U., Dutta, R., & Heeren, D. M. (2019). Fuzzy control system for variable rate irrigation using remote sensing. Expert Systems with Applications, 124, 13–24. https://doi.org/10.1016/j.eswa.2019.01.043spa
dc.relation.referencesMekonnen, M. M., & Hoekstra, A. Y. (2011). The green, blue and grey water footprint of crops and derived crop products. Hydrology and Earth System Sciences, 15(5), 1577–1600. https://doi.org/10.5194/hess-15-1577-2011spa
dc.relation.referencesMesa-Sánchez, Ó. J., & Rojo-Hernández, J. D. (2020). On the general circulation of the atmosphere around Colombia. Revista de La Academia Colombiana de Ciencias Exactas, Fisicas y Naturales, 44(172), 857–875. https://doi.org/10.18257/RACCEFYN.899spa
dc.relation.referencesMeza, F. (2007). Use of ENSO-Driven Climatic Information for Optimum Irrigation under Drought Conditions: Preliminary Assessment Based on Model Results for the Maipo River Basin, Chile. In M. Sivakumar & J. Hansen (Eds.), Climate Prediction and Agriculture: Advances and Challenges (pp. 79–88). Springer.spa
dc.relation.referencesMigliaccio, K., Morgan, K., Vellidis, G., Zotarelli, L., Fraisse, C., Zurweller, B., Andreis, J., Crane, J., & Rowland, D. (2016). Smartphone Apps for Irrigation Scheduling. Transactions of the ASABE, 59(1), 291–301. https://doi.org/10.13031/trans.59.11158spa
dc.relation.referencesMiller, L., Vellidis, G., Mohawesh, O., & Coolong, T. (2018). Comparing a smartphone irrigation scheduling application with water balance and soil moisture-based irrigation methods: Part I—plasticulture-grown tomato. HortTechnology, 28(3), 354–361. https://doi.org/10.21273/HORTTECH04010-18spa
dc.relation.referencesMiyazaki, T. (2005). Soil and Water. In Water Flow in Soils (2nd ed., pp. 1–17). Taylor & Francis. https://doi.org/10.1016/B978-0-444-88080-2.50009-0spa
dc.relation.referencesMolina-Martínez, J. M., & Ruiz-Canales, A. (2009). Pocket PC software to evaluate drip irrigation lateral diameters with on-line emitters. Computers and Electronics in Agriculture, 69(1), 112–115. https://doi.org/10.1016/j.compag.2009.06.006spa
dc.relation.referencesMontesinos, O., Montesinos, A., Crossa, J., 2022. Fundamentals of Artificial Neural Networks and Deep Learning, in: Multivariate Statistical Machine Learning Methods for Genomic Prediction. Springer International Publishing, Cham, pp. 379–425. https://doi.org/10.1007/978-3-030-89010-0_10spa
dc.relation.referencesMontgomery, J., Hornbuckle, J., Hume, I., Vleeshouwer, J., 2015. IrriSAT – weather based scheduling and benchmarking technology, in: Proceedings of the 17th ASA Conference. Building Productive, Diverse and Sustainable Landscapes. Australian Society of Agronomy Inc., Hobart, Australia, pp. 1015–1018.spa
dc.relation.referencesMora, H., Albis, N., García, J., Sandra, Z., Mejía, L., Portilla, D., & Rubiano, A. (2017). Usabilidad De Tic Y Consumo Digital En El Sector Agropecuario Colombiano. XVII Congreso Latino-Iberoamericano de Gestión Tecnológica, 1–16. http://www.uam.mx/altec2017/pdfs/ALTEC_2017_paper_299.pdfspa
dc.relation.referencesMoran, M. S., Inoue, Y., & Barnes, E. M. (1997). Opportunities and limitations for image-based remote sensing in precision crop management. Remote Sensing of Environment, 61(3), 319–346. https://doi.org/10.1016/S0034-4257(97)00045-Xspa
dc.relation.referencesMoreno-Ortega, G., Pliego, C., Sarmiento, D., Barceló, A., & Martínez-Ferri, E. (2019). Yield and fruit quality of avocado trees under different regimes of water supply in the subtropical coast of Spain. Agricultural Water Management, 221(December 2018), 192–201. https://doi.org/10.1016/j.agwat.2019.05.001spa
dc.relation.referencesMottaleb, K. (2018). Perception and adoption of a new agricultural technology: Evidence from a developing country. Technology in Society, 55(April), 126–135. https://doi.org/10.1016/j.techsoc.2018.07.007spa
dc.relation.referencesMualem, Y. (1976). A new model for predicting the hydraulic conductivity of unsaturated porous media. Water Resources Research, 12(3), 513–522. https://doi.org/10.1029/WR012i003p00513spa
dc.relation.referencesMulderij, R. (2018). Overview Global Avocado Market. https://www.freshplaza.com/article/2196118/overview-global-avocado-market/spa
dc.relation.referencesMullissa, A., Vollrath, A., Odongo-Braun, C., Slagter, B., Balling, J., Gou, Y., Gorelick, N., Reiche, J., 2021. Sentinel-1 sar backscatter analysis ready data preparation in google earth engine. Remote Sens. 13, 1954. https://doi.org/10.3390/rs13101954spa
dc.relation.referencesMuthoni, F. K., Odongo, V. O., Ochieng, J., Mugalavai, E. M., Mourice, S. K., Hoesche-Zeledon, I., Mwila, M., & Bekunda, M. (2019). Long-term spatial-temporal trends and variability of rainfall over Eastern and Southern Africa. Theoretical and Applied Climatology, 137(3–4), 1869–1882. https://doi.org/10.1007/s00704-018-2712-1spa
dc.relation.referencesNawandar, N., & Satpute, V. (2019). IoT based low cost and intelligent module for smart irrigation system. Computers and Electronics in Agriculture, 162(April), 979–990. https://doi.org/10.1016/j.compag.2019.05.027spa
dc.relation.referencesNg Cheong, L. R., & Teeluck, M. (2018). Development of an Irrigation Scheduling Software for Sugarcane. Sugar Tech, 20(1), 36–39. https://doi.org/10.1007/s12355-017-0517-7spa
dc.relation.referencesNgongondo, C., Xu, C. Y., Gottschalk, L., & Alemaw, B. (2011). Evaluation of spatial and temporal characteristics of rainfall in Malawi: A case of data scarce region. Theoretical and Applied Climatology, 106(1–2), 79–93. https://doi.org/10.1007/s00704-011-0413-0spa
dc.relation.referencesNhamo, L., Ebrahim, G. Y., Mabhaudhi, T., Mpandeli, S., Magombeyi, M., Chitakira, M., Magidi, J., & Sibanda, M. (2020). An assessment of groundwater use in irrigated agriculture using multi-spectral remote sensing. Physics and Chemistry of the Earth, 115(March 2019), 102810. https://doi.org/10.1016/j.pce.2019.102810spa
dc.relation.referencesNikolaou, G., Neocleous, D., Christou, A., Kitta, E., & Katsoulas, N. (2020). Implementing Sustainable Irrigation in Water-Scarce Regions under the Impact of Climate Change. Agronomy, 10(8), 1–33. https://doi.org/10.3390/agronomy10081120spa
dc.relation.referencesNOAA. (2020). NOAA Interpolated Outgoing Longwave Radiation (OLR). https://psl.noaa.gov/data/gridded/data.interp_OLR.htmlspa
dc.relation.referencesNOAA. (2021). Historical El Nino / La Nina episodes (1950-present). Cold & Warm Episodes by Season. https://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_v5.phpspa
dc.relation.referencesNovoa, V., Ahumada-Rudolph, R., Rojas, O., Sáez, K., de la Barrera, F., & Arumí, J. L. (2019). Understanding agricultural water footprint variability to improve water management in Chile. Science of the Total Environment, 670, 188–199. https://doi.org/10.1016/j.scitotenv.2019.03.127spa
dc.relation.referencesNRC. (1997). Precision Agriculture in the 21st Century. National Academy Press. http://www.nap.edu/catalog.php?record_id=5491spa
dc.relation.referencesNRCS. (1997). Water Requirements. In National Engineering Handbook: Irrigation Guide. Part 652 (p. 754). United States Department of Agriculture. https://directives.sc.egov.usda.gov/OpenNonWebContent.aspx?content=17837.wbaspa
dc.relation.referencesOliver, M.A., Webster, R., 2015. Basic Steps in Geostatistics:The Variogram and Kriging. Springer, Cham. https://doi.org/10.1007/978-3-319-15865-5spa
dc.relation.referencesOlmedo, G., & de la Fuente-Saiz, D. (2018). Surface Energy Balance using METRIC model and water package: 2. advanced procedure. https://cran.r-project.org/web/packages/water/vignettes/METRIC_advanced.htmlspa
dc.relation.referencesOuaadi, N., Jarlan, L., Ezzahar, J., Zribi, M., Khabba, S., Bouras, E., Bousbih, S., Frison, P.L., 2020. Monitoring of wheat crops using the backscattering coefficient and the interferometric coherence derived from Sentinel-1 in semi-arid areas. Remote Sens. Environ. 251, 112050. https://doi.org/10.1016/j.rse.2020.112050spa
dc.relation.referencesOyarce, P., & Gurovich, L. (2010). Electrical signals in avocado trees responses to light and water availability conditions. Plant Signaling and Behavior, 5(1), 34–41. https://doi.org/10.4161/psb.5.1.10157spa
dc.relation.referencesParikh, H., Patel, S., & Patel, V. (2020). Classification of SAR and PolSAR images using deep learning: a review. International Journal of Image and Data Fusion, 11(1), 1–32. https://doi.org/10.1080/19479832.2019.1655489spa
dc.relation.referencesPaudel, I., Cohen, S., Shaviv, A., Bar-Tal, A., Bernstein, N., Heuer, B., & Ephrath, J. (2016). Impact of treated wastewater on growth, respiration and hydraulic conductivity of citrus root systems in light and heavy soils. Tree Physiology, 36(6), 770–785. https://doi.org/10.1093/treephys/tpw013spa
dc.relation.referencesPaull, R., & Duarte, O. (2012). Tropical Fruits. In Crop Production Science in Horticulture Series (2nd ed., Vol. 1). CAB International.spa
dc.relation.referencesPelton, J. N., Madry, S., & Camacho-Lara, S. (2017). Satellite Applications Handbook: The Complete Guide to Satellite Communications, Remote Sensing, Navigation, and Meteorology. In J. N. Pelton, S. Madry, & S. Camacho-Lara (Eds.), Handbook of Satellite Applications (pp. 4–19). Springer International Publishing. https://doi.org/10.1007/978-3-319-23386-4spa
dc.relation.referencesPeng, J., Albergel, C., Balenzano, A., Brocca, L., Cartus, O., Cosh, M. H., Crow, W. T., Dabrowska-Zielinska, K., Dadson, S., Davidson, M. W. J., de Rosnay, P., Dorigo, W., Gruber, A., Hagemann, S., Hirschi, M., Kerr, Y. H., Lovergine, F., Mahecha, M. D., Marzahn, P., … Loew, A. (2021). A roadmap for high-resolution satellite soil moisture applications – confronting product characteristics with user requirements. Remote Sensing of Environment, 252, 112162. https://doi.org/10.1016/j.rse.2020.112162spa
dc.relation.referencesPerry, C., Steduto, P., Allen, R. G., & Burt, C. M. (2009). Increasing productivity in irrigated agriculture: Agronomic constraints and hydrological realities. Agricultural Water Management, 96(11), 1517–1524. https://doi.org/10.1016/j.agwat.2009.05.005spa
dc.relation.referencesPicoli, M. C. A., Machado, P. G., Duft, D. G., Scarpare, F. V., Corrêa, S. T. R., Hernandes, T. A. D., & Rocha, J. V. (2019). Sugarcane drought detection through spectral indices derived modeling by remote-sensing techniques. Modeling Earth Systems and Environment, 5(4), 1679–1688. https://doi.org/10.1007/s40808-019-00619-6spa
dc.relation.referencesPiedelobo, L., Ortega-Terol, D., del Pozo, S., Hernández-López, D., Ballesteros, R., Moreno, M., Molina, J., & González-Aguilera, D. (2018). HidroMap: A new tool for irrigation monitoring and management using free satellite imagery. ISPRS International Journal of Geo-Information, 7(6), 1–19. https://doi.org/10.3390/ijgi7060220spa
dc.relation.referencesPierce, F. J. (2010). Precision Irrigation. Landbauforschung Völkenrode, 340, 45–56. https://literatur.thuenen.de/digbib_extern/dn046667.pdfspa
dc.relation.referencesPleguezuelo, C. R. R., Zuazo, V. H. D., Martínez, J. R. F., Fernández, J. L. M., & Tarifa, D. F. (2011). Monitoring the pollution risk and water use in orchard terraces with mango and cherimoya trees by drainage lysimeters. Irrigation and Drainage Systems, 25(2), 61–79. https://doi.org/10.1007/s10795-011-9112-3spa
dc.relation.referencesPongnumkul, S., Chaovalit, P., & Surasvadi, N. (2015). Applications of smartphone-based sensors in agriculture: A systematic review of research. Journal of Sensors, 2015, 1–18. https://doi.org/10.1155/2015/195308spa
dc.relation.referencesPrévot, L., Champion, I., Guyot, G., 1993. Estimating surface soil moisture and leaf area index of a wheat canopy using a dual-frequency (C and X bands) scatterometer. Remote Sens. Environ. 46, 331–339. https://doi.org/10.1016/0034-4257(93)90053-Zspa
dc.relation.referencesPROCOLOMBIA. (2018, August 13). Colombia es el nuevo proveedor "estrella" de aguacate hass para el mundo. PROCOLOMBIA Noticias, 3. http://www.procolombia.co/noticias/colombia-es-el-nuevo-proveedor-estrella-de-aguacate-hass-para-el-mundospa
dc.relation.referencesPrudente, V. H. R., Martins, V. S., Vieira, D. C., Silva, N. R. de F. e., Adami, M., & Sanches, I. D. A. (2020). Limitations of cloud cover for optical remote sensing of agricultural areas across South America. Remote Sensing Applications: Society and Environment, 20(May), 100414. https://doi.org/10.1016/j.rsase.2020.100414spa
dc.relation.referencesPuértolas, J., Johnson, D., Dodd, I. C., & Rothwell, S. A. (2019). Can we water crops with our phones? Smartphone technology application to infrared thermography for use in irrigation management. Acta Horticulturae, 1253, 443–448. https://doi.org/10.17660/ActaHortic.2019.1253.58spa
dc.relation.referencesQGIS Development Team. (2020). QGIS Geographic Information System (3.14). Open Source Geospatial Foundation. http://qgis.osgeo.orgspa
dc.relation.referencesQu, J., Gao, W., Kafatos, M., Murphy, R., & Salomonson, V. (Eds.). (2006). Earth Science Satellite Remote Sensing Vol. 2: Data, computational processing, and tools. Springer. https://doi.org/10.1007/978-3-540-37294-3spa
dc.relation.referencesQuebrajo, L., Perez-Ruiz, M., Pérez-Urrestarazu, L., Martínez, G., & Egea, G. (2018). Linking thermal imaging and soil remote sensing to enhance irrigation management of sugar beet. Biosystems Engineering, 165, 77–87. https://doi.org/10.1016/j.biosystemseng.2017.08.013spa
dc.relation.referencesR Core Team. (2020). R: A Language and Environment for Statistical Computing (4.0.3 (2020-10-01)). R Foundation for Statistical Computing. https://www.r-project.org/spa
dc.relation.referencesRaes, D. (2002). BUDGET: A soil water and salt balance model. Reference Manual. (5.0; p. 88). Interuniversity Programme in Water Resources Engineering. https://iupware.be/wp-content/uploads/2016/03/BUDGET64bit.zipspa
dc.relation.referencesRamírez-Gil, J. G. (2017). Calidad del fruto de aguacate con aplicaciones de ANA, boro, nitrógeno, sacarosa y anillado. Agronomía Mesoamericana, 28(3), 591. https://doi.org/10.15517/ma.v28i3.23688spa
dc.relation.referencesRamírez-Gil, J. G. (2018). Avocado wilt complex disease, implications and management in Colombia. Revista Facultad Nacional de Agronomía, 71(2), 8525–8541. https://doi.org/10.15446/rfna.v71n2.66465spa
dc.relation.referencesRamírez-Gil, J. G., Morales, J. G., & Peterson, A. T. (2018). Potential geography and productivity of "Hass" avocado crops in Colombia estimated by ecological niche modeling. Scientia Horticulturae, 237(April), 287–295. https://doi.org/10.1016/j.scienta.2018.04.021spa
dc.relation.referencesRamírez-Gil, J. G., Cobos, M. E., Jiménez-García, D., Morales-Osorio, J. G., & Peterson, A. T. (2019). Current and potential future distributions of Hass avocados in the face of climate change across the Americas. Crop and Pasture Science, 70(8), 694–708. https://doi.org/10.1071/CP19094spa
dc.relation.referencesRamírez-Gil, J. G., & Henao-Rojas, J. C. (2020). Mitigation of the Adverse Effects of the El Niño (El Niño, La Niña) Southern Oscillation (ENSO) Phenomenon and the Most Important Diseases in Avocado cv. Hass Crops. Plants, 9(6), 1–21. https://doi.org/10.3390/plants9060790spa
dc.relation.referencesRamírez-Gil, J. G., López, J. H., & Henao-Rojas, J. C. (2020). Causes of hass avocado fruit rejection in preharvest, harvest, and packinghouse: Economic losses and associated variables. Agronomy, 10(1), 1–13. https://doi.org/10.3390/agronomy10010008spa
dc.relation.referencesRamírez-Gil, J. G., Morales, J. G., & Peterson, A. T. (2018). Potential geography and productivity of "Hass" avocado crops in Colombia estimated by ecological niche modeling. Scientia Horticulturae, 237(April), 287–295. https://doi.org/10.1016/j.scienta.2018.04.021spa
dc.relation.referencesRanjan, R., Chandel, A. K., Khot, L. R., Bahlol, H. Y., Zhou, J., Boydston, R. A., & Miklas, P. N. (2019). Irrigated pinto bean crop stress and yield assessment using ground based low altitude remote sensing technology. Information Processing in Agriculture, 6(4), 502–514. https://doi.org/10.1016/j.inpa.2019.01.005spa
dc.relation.referencesReddy, G. P. O. (2018). Satellite Remote Sensing Sensors: Principles and Applications. In G. P. O. Reddy & S. K. Singh (Eds.), Geospatial Technologies in Land Resources Mapping, Monitoring and Management. Geotechnologies and the Environment Volume 21 (pp. 21–43). Springer International Publishing. https://doi.org/10.1007/978-3-319-78711-4_2spa
dc.relation.referencesRichards, S. J., Warneke, J. E., & Bingham, F. . T. (1962). Avocado Tree Growth Response to Irrigation. California Avocado Society, 46, 83–87.spa
dc.relation.referencesRichter, M. (2016). Precipitation in the Tropics. In L. Pancel & M. Köhl (Eds.), Tropical Forestry Handbook (2nd ed., pp. 363–390). Springer. https://doi.org/10.1007/978-3-642-54601-3spa
dc.relation.referencesRitsema, C., Oostindie, K., & Stolte, J. (1996). Evaluation of vertical and lateral flow through agricultural loessial hillslopes using a two-dimensional computer simulation model. Hydrological Processes, 10(8), 1091–1105. https://doi.org/10.1002/(SICI)1099-1085(199608)10:8<1091::AID-HYP414>3.0.CO;2-Jspa
dc.relation.referencesRobson, A., Rahman, M. M., & Muir, J. (2017). Using worldview satellite imagery to map yield in avocado (Persea americana): A case study in Bundaberg, Australia. Remote Sensing, 9(12), 1–18. https://doi.org/10.3390/rs9121223spa
dc.relation.referencesRodríguez, C., Francia, R., García, I., Gálvez, B., Franco, D., & Durán, V. (2018). Avocado (Persea americana Mill.) Trends in Water-Saving Strategies and Production Potential in a Mediterranean Climate , the Study Case of SE Spain : A Review. In I. García & V. Durán (Eds.), Water Scarcity and Sustainable Agriculture in Semiarid Environment (First, pp. 317–346). Elsevier Inc. https://doi.org/10.1016/B978-0-12-813164-0.00014-4spa
dc.relation.referencesRoets, N., Cronje, R., Schoeman, S., Murovhi, N., & Ratlapane, I. (2013). Calibrating avocado irrigation through the use of continuous soil moisture monitoring and plant physiological parameters. South African Avocado Growers' Association Yearbok, 36, 36–41. http://avocadosource.com/Journals/SAAGA/SAAGA_2013/SAAGA_2013_36_PG_36.pdfspa
dc.relation.referencesRomán-Paoli, E., Román-Pérez, F., & Zamora-Echevarría, J. (2009). Evaluation of microirrigation levels for growth and productivity of avocado trees. The Journal of Agriculture of the University of Puerto Rico, 93(3–4), 173–186. https://doi.org/10.46429/jaupr.v93i3-4.5465spa
dc.relation.referencesRuiz-Pérez, J. (2017). Presente y futuro de la industria del aguacate en Colombia. In S. Salazar-García & A. F. Barrientos-Priego (Eds.), Memorias del V Congreso Latinoamericano del Aguacate (pp. 473–482). Asociación de Productores Exportadores de Jalisco, A. C. https://issuu.com/horticulturaposcosecha/docs/memorias_vcla_2017?e=8490508/54350354spa
dc.relation.referencesSalas, J. D., Govindaraju, R. S., Anderson, M., Arabi, M., Francés, F., Suarez, W., Lavado-Casimiro, W., & Green, T. R. (2014). Introduction to Hydrology. In L. K. Wang & C. T. Yang (Eds.), Handbook of Environmental Engineering, Volume 15: Modern Water Resources Engineering (pp. 1–126). Springer Science+Business Media. https://doi.org/10.1007/978-1-62703-595-8_1spa
dc.relation.referencesSalazar-Garcia, S., & Cortés-Flores, J. I. (1986). Root Distribution of Mature Avocado Trees Growing in Soils of Different Texture. California Avocado Society Yearbook, 70, 165–174. http://www.avocadosource.com/cas_yearbooks/cas_70_1986/cas_1986_pg_165-174.pdfspa
dc.relation.referencesSales, A., Vasconcelos, M., Dimitry, I., & Kamienski, C. (2020). The SWAMP Farmer App for IoT-based Smart Water Status Monitoring and Irrigation Control. 2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), 109–113. https://doi.org/10.1109/MetroAgriFor50201.2020.9277588spa
dc.relation.referencesSalgado, E., Cautín, R., 2008. Avocado root distribution in fine and coarse-textured soils under drip and microsprinkler irrigation. Agric. Water Manag. 95, 817–824. https://doi.org/10.1016/j.agwat.2008.02.005spa
dc.relation.referencesSamek, W., Montavon, G., Vedaldi, A., Hansen, L.K., Müller, K.-R. (Eds.), 2019. Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, Lecture Notes in Computer Science. Springer International Publishing, Cham. https://doi.org/10.1007/978-3-030-28954-6spa
dc.relation.referencesSapna, Trivedi, A., & Kumar Pattanaik, K. (2020). A Sensor-Actor Coordination protocol for Variable Rate Irrigation. 2020 IEEE 14th International Conference on Application of Information and Communication Technologies (AICT), 1–6. https://doi.org/10.1109/aict50176.2020.9368742spa
dc.relation.referencesSatizábal, H., & Pérez-Uribe, A. (2007). Relevance Metrics to Reduce Input Dimensions in Artificial Neural Networks. In J. Marques de Sá, L. Alexandre, W. Duch, & D. Mandic (Eds.), Artificial Neural Networks - ICANN 2007 (pp. 39–48). Springer-Verlag. https://doi.org/10.1007/978-3-540-74690-4spa
dc.relation.referencesScanlon, B., Andraski, B., & Bilskie, J. (2002). Miscellaneous Methods for Measuring Matric or Water Potential. In J. Dane & C. Topp (Eds.), Methods of Soil Analysis: Part 4 Physical Methods (pp. 643–670). Soil Science Society of America. https://doi.org/10.2136/sssabookser5.4.c23spa
dc.relation.referencesSchaffer, B., Wolstenholme, N., & Whiley, W. (Eds.). (2013). The Avocado: Botany, Production and Uses (2nd ed.). CAB International.spa
dc.relation.referencesSchowengerdt, R. A. (2007). Remote Sensing (3rd ed.). Elsevier Inc. https://doi.org/10.1016/B978-0-12-369407-2.X5000-1spa
dc.relation.referencesSchulz, S., Becker, R., Richard‐Cerda, J. C., Usman, M., aus der Beek, T., Merz, R., & Schüth, C. (2021). Estimating water balance components in irrigated agriculture using a combined approach of soil moisture and energy balance monitoring, and numerical modelling. Hydrological Processes, 35(3), 1–14. https://doi.org/10.1002/hyp.14077spa
dc.relation.referencesSentek. (2019). IrriMAX Software Desktop (10.1; p. 2). Sentek. https://sentektechnologies.com/download/irrimax-desktop/spa
dc.relation.referencesSerrano, A., & Brooks, A. (2019). Who is left behind in global food systems? Local farmers failed by Colombia's avocado boom. Environment and Planning E: Nature and Space, 2(2), 348–367. https://doi.org/10.1177/2514848619838195spa
dc.relation.referencesShock, C. C., Barnum, J. M., & Seddigh, M. (1998). Calibration of Watermark Soil Moisture Sensors for Irrigation Management. Proceedings of the International Irrigation Show, September, 139–146. https://www.researchgate.net/publication/228762944spa
dc.relation.referencesSigua, G. C., Stone, K. C., Bauer, P. J., Szogi, A. A., & Shumaker, P. D. (2017). Impacts of irrigation scheduling on pore water nitrate and phosphate in coastal plain region of the United States. Agricultural Water Management, 186, 75–85. https://doi.org/10.1016/j.agwat.2017.02.016spa
dc.relation.referencesSilber, A., Israeli, Y., Levi, M., Keinan, A., Chudi, G., Golan, A., Noy, M., Levkovitch, I., Narkis, K., Naor, A., & Assouline, S. (2013). The roles of fruit sink in the regulation of gas exchange and water uptake : A case study for avocado. Agricultural Water Management, 116, 21–28. https://doi.org/10.1016/j.agwat.2012.10.006spa
dc.relation.referencesSilber, A., Israeli, Y., Levi, M., Keinan, A., Shapira, O., Chudi, G., Golan, A., Noy, M., Levkovitch, I., & Assouline, S. (2012). Response of "Hass" avocado trees to irrigation management and root constraint. Agricultural Water Management, 104, 95–103. https://doi.org/10.1016/j.agwat.2011.12.003spa
dc.relation.referencesSilber, A., Naor, A., Cohen, H., Bar-Noy, Y., Yechieli, N., Levi, M., Noy, M., Peres, M., Duari, D., Narkis, K., Assouline, S., Cohen, Y., Bar-Noy, H. ., Yechieli, N., Levi, M., Peres, M., Duari, D., Narkis, K., & Assouline, S. (2019). Irrigation of 'Hass' avocado: effects of constant vs. temporary water stress. Irrigation Science, 37(4), 451–460. https://doi.org/10.1007/s00271-019-00622-wspa
dc.relation.referencesSilber, A., Naor, A., Cohen, H., Yechieli, N., Levi, M., Noy, M., Peres, M., Duari, D., Narkis, K., & Assouline, S. (2018). Avocado fertilization : Matching the periodic demand for nutrients. Scientia Horticulturae, 241(February), 231–240. https://doi.org/10.1016/j.scienta.2018.06.094spa
dc.relation.referencesSilber, A., Naor, A., Israeli, Y., & Assouline, S. (2013). Combined effect of irrigation regime and fruit load on the patterns of trunk-diameter variation of "Hass" avocado at different phenological periods. Agricultural Water Management, 129, 87–94. https://doi.org/10.1016/j.agwat.2013.07.015spa
dc.relation.referencesSilva, A. M., da Silva, R. M., & Santos, C. A. G. (2019). Automated surface energy balance algorithm for land (ASEBAL) based on automating endmember pixel selection for evapotranspiration calculation in MODIS orbital images. International Journal of Applied Earth Observation and Geoinformation, 79(February), 1–11. https://doi.org/10.1016/j.jag.2019.02.012spa
dc.relation.referencesSimionesei, L., Ramos, T. B., Palma, J., Oliveira, A. R., & Neves, R. (2020). IrrigaSys: A web-based irrigation decision support system based on open source data and technology. Computers and Electronics in Agriculture, 178(August), 105822. https://doi.org/10.1016/j.compag.2020.105822spa
dc.relation.referencesŠimůnek, J., Van Genuchten, M. T., & Šejna, M. (2012). Hydrus: Model use, calibration, and validation. Transactions of the ASABE, 55(4), 1261–1274.spa
dc.relation.referencesŠimůnek, Jiří, van Genuchten, M. T., & Šejna, M. (2008). Development and Applications of the HYDRUS and STANMOD Software Packages and Related Codes. Vadose Zone Journal, 7(2), 587–600. https://doi.org/10.2136/vzj2007.0077spa
dc.relation.referencesSingh, G., Singh, A., & Kaur, G. (2021). Role of Artificial Intelligence and the Internet of Things in Agriculture. In Artificial Intelligence to Solve Pervasive Internet of Things Issues (pp. 317–330). Elsevier. https://doi.org/10.1016/b978-0-12-818576-6.00016-2spa
dc.relation.referencesSingh, U., Praharaj, C. S., Gurjar, D. S., & Kumar, R. (2019). Precision irrigation management : concepts and applications for higher use efficiency in field crops (Issue February).spa
dc.relation.referencesSinghroy, V. (2017). Operational Applications of Radar Images. In J. N. Pelton, S. Madry, & S. Camacho-Lara (Eds.), Handbook of Satellite Applications (2nd ed., pp. 911–928). Springer International Publishing. https://doi.org/10.1007/978-3-319-23386-4spa
dc.relation.referencesSinha, S., Santra, A., Sharma, L., Jeganathan, C., Nathawat, M. S., Das, A. K., & Mohan, S. (2018). Multi-polarized Radarsat-2 satellite sensor in assessing forest vigor from above ground biomass. Journal of Forestry Research, 29(4), 1139–1145. https://doi.org/10.1007/s11676-017-0511-7spa
dc.relation.referencesSishodia, R. P., Ray, R. L., & Singh, S. K. (2020). Applications of remote sensing in precision agriculture: A review. Remote Sensing, 12(19), 1–31. https://doi.org/10.3390/rs12193136spa
dc.relation.referencesSmith, M. (1992). CROPWAT: A computer program for irrigation planning and management. FAO Irrigation and Drainage Paper 46. In FAO Irrigation and Drainage Paper 46 (Issue 46). Food and Agriculture Organization of the United Nations. https://archive.org/details/bub_gb_p9tB2ht47NAC/page/n1spa
dc.relation.referencesSommaruga, R., Eldridge, H.M., 2020. Avocado Production: Water Footprint and Socio- economic Implications. EuroChoices 0, 1–6. https://doi.org/10.1111/1746-692X.12289spa
dc.relation.referencesStafford, J. (2000). Implementing precision agriculture in the 21st century. Journal of Agricultural and Engineering Research, 76(3), 267–275. https://doi.org/10.1006/jaer.2000.0577spa
dc.relation.referencesSteele, D. D., Thoreson, B. P., Hopkins, D. G., Clark, B. A., Tuscherer, S. R., & Gautam, R. (2015). Spatial mapping of evapotranspiration over Devils Lake Basin with SEBAL: Application to flood mitigation via irrigation of agricultural crops. Irrigation Science, 33(1), 15–29. https://doi.org/10.1007/s00271-014-0445-1spa
dc.relation.referencesStephens, G. L., Smalley, M. A., & Lebsock, M. D. (2019). The Cloudy Nature of Tropical Rains. Journal of Geophysical Research: Atmospheres, 124(1), 171–188. https://doi.org/10.1029/2018JD029394spa
dc.relation.referencesTaiz, L., & Zeiger, E. (2002a). Photosynthesis: The Light Reactions. In Plant Physiology (3rd ed., p. 690). Sinauer Associates. https://doi.org/10.1093/aob/mcg079spa
dc.relation.referencesTaiz, L., & Zeiger, E. (2002b). Stress Physiology. In Plant Physiology (3rd ed., pp. 591–623). Sinauer Associates. https://doi.org/10.1093/aob/mcg079spa
dc.relation.referencesTakahashi, K., & Battisti, D. S. (2007). Processes controlling the mean tropical pacific precipitation pattern. Part I: The Andes and the eastern Pacific ITCZ. Journal of Climate, 20(14), 3434–3451. https://doi.org/10.1175/JCLI4198.1spa
dc.relation.referencesTamiminia, H., Salehi, B., Mahdianpari, M., Quackenbush, L., Adeli, S., & Brisco, B. (2020). Google Earth Engine for geo-big data applications: A meta-analysis and systematic review. ISPRS Journal of Photogrammetry and Remote Sensing, 164(January), 152–170. https://doi.org/10.1016/j.isprsjprs.2020.04.001spa
dc.relation.referencesTolomio, M., & Casa, R. (2020). Dynamic crop models and remote sensing irrigation decision support systems: A review of water stress concepts for improved estimation of water requirements. Remote Sensing, 12(23), 1–34. https://doi.org/10.3390/rs12233945spa
dc.relation.referencesTorres, J. S. (1998). A simple visual aid for sugarcane irrigation scheduling. Agricultural Water Management, 38(1), 77–83. https://doi.org/10.1016/S0378-3774(98)00043-2spa
dc.relation.referencesTrabucco, A., & Zomer, R. (2019). Global High-Resolution Soil-Water Balance. https://doi.org/10.6084/m9.figshare.7707605.v3spa
dc.relation.referencesTsou, C.-S., 2013. Elitist Non-dominated Sorting Genetic Algorithm based on R.spa
dc.relation.referencesTu, A., Xie, S., Mo, M., Song, Y., & Li, Y. (2021). Water budget components estimation for a mature citrus orchard of southern China based on HYDRUS-1D model. Agricultural Water Management, 243(August 2020), 106426. https://doi.org/10.1016/j.agwat.2020.106426spa
dc.relation.referencesTurner, D. W., Neuhaus, A., Colmer, T., Blight, A., & Whiley, A. (2001). Turning Water Into Oil - Physiology and Efficiency. In C. Scotney (Ed.), Talking Avocados (pp. 1–12). http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.588.9293&rep=rep1&type=pdfspa
dc.relation.referencesTzatzani, T. T., Kavroulakis, N., Doupis, G., Psarras, G., & Papadakis, I. E. (2020). Nutritional status of 'Hass' and 'Fuerte' avocado (Persea americana Mill.) plants subjected to high soil moisture. Journal of Plant Nutrition, 43(3), 327–334. https://doi.org/10.1080/01904167.2019.1683192spa
dc.relation.referencesUN. (2016). International Decade for Action, "Water for Sustainable Development", 2018 2028. Resolution A/RES/71/222 (p. 6). United Nations. https://digitallibrary.un.org/record/849767spa
dc.relation.referencesUnal, & CIAT. (2018). Incremento de la competitividad sostenible en la agricultura de ladera en todo el departamento del Valle del Cauca , Occidente.spa
dc.relation.referencesUNL. (2019). Crop Water App. https://ianr.unl.edu/crop-water-appspa
dc.relation.referencesUPRA. (2018). Zonificación de aptitud para el cultivo comercial de aguacate Hass en Colombia, a escala 1:100.000. Ministerio de Agricultura y Desarrollo Rural. www.upra.gov.co/documents/10184/13821/20180821_aguacate_hass_opt/3624cf6d-755d-4580-a085-75fabb866d86spa
dc.relation.referencesvan Genuchten, M. T. (1980). A Closed-form Equation for Predicting the Hydraulic Conductivity of Unsaturated Soils. Soil Science Society of America Journal, 44(5), 892–898. https://doi.org/10.2136/sssaj1980.03615995004400050002xspa
dc.relation.referencesVan Pelt, S., & Wierenga, P. (2001). Temporal stability of spatially measured soil matric potential probability density function. Soil Science Society of America Journal, 65(3), 668–677. https://doi.org/10.2136/sssaj2001.653668xspa
dc.relation.referencesVellidis, G., Liakos, V., Andreis, J. H., Perry, C. D., Porter, W. M., Barnes, E. M., Morgan, K. T., Fraisse, C., & Migliaccio, K. W. (2016). Development and assessment of a smartphone application for irrigation scheduling in cotton. Computers and Electronics in Agriculture, 127, 249–259. https://doi.org/10.1016/j.compag.2016.06.021spa
dc.relation.referencesVeysi, S., Naseri, A. A., Hamzeh, S., & Bartholomeus, H. (2017). A satellite based crop water stress index for irrigation scheduling in sugarcane fields. Agricultural Water Management, 189(July), 70–86. https://doi.org/10.1016/j.agwat.2017.04.016spa
dc.relation.referencesVollrath, A., Mullissa, A., & Reiche, J. (2020). Angular-based radiometric slope correction for Sentinel-1 on google earth engine. Remote Sensing, 12(11), 1–14. https://doi.org/10.3390/rs121118677spa
dc.relation.referencesVon Hoyningen-Hüne, J. (1983). Die Interception des Niederschlags in landwirtschaftlichen Beständen. Schriftenreihe des DVWK 57. http://wiki.bluemodel.org/images/9/9e/DVWK_57_I.pdfspa
dc.relation.referencesVuthapanich, S., Hofman, P. J., Whiley, A., Klieber, A., & Simons, D. (1995). Effects of irrigation and foliar Cultar on fruit yield and quality of "Hass" avocado fruit. In The Congress (Ed.), Proceedings of the Word Avocado Congress III (pp. 311–315). www.avocadosource.comspa
dc.relation.referencesWaller, P., & Yitayew, M. (2016). Irrigation and drainage engineering. In Irrigation and Drainage Engineering. Springer. https://doi.org/10.1007/978-3-319-05699-9spa
dc.relation.referencesWani, S. P., Rockström, J., & Oweis, T. (2009). Rainfed Agriculture: Unlocking the Potential. In The Indian Economic Journal. CAB International. https://doi.org/10.1177/0019466220090204spa
dc.relation.referencesWeiss, M., Jacob, F., & Duveiller, G. (2020). Remote sensing for agricultural applications: A meta-review. Remote Sensing of Environment, 236(August 2019), 111402. https://doi.org/10.1016/j.rse.2019.111402spa
dc.relation.referencesWhiley, A. (1994). Ecophysiological studies and tree manipulation for maximisation of yield potential in avocado (Persea americana Mill.) [University of Natal]. http://www.avocadosource.com/papers/SouthAfrica_Papers/WhileyAnthony1994/Whiley_THESIS_TOC Table.htmspa
dc.relation.referencesWiner, L., & Zachs, I. (2007). Daily trunk contraction in relation to a base line as an improved criterion for irrigation in avocado. Proceedings VI World Avocado Congress, 1–7. http://www.avocadosource.com/wac6/en/Extenso/3b-109.pdfspa
dc.relation.referencesWorldClim. (2020). Historical climate data. https://www.worldclim.org/data/worldclim21.htmlspa
dc.relation.referencesZhuang, W., Shi, H., Ma, X., Cleverly, J., Beringer, J., Zhang, Y., He, J., Eamus, D., & Yu, Q. (2020). Improving Estimation of Seasonal Evapotranspiration in Australian Tropical Savannas using a Flexible Drought Index. Agricultural and Forest Meteorology, 295(August), 108203. https://doi.org/10.1016/j.agrformet.2020.108203spa
dc.relation.referencesWu, D., Johansen, K., Phinn, S., Robson, A., & Tu, Y.-H. (2020). Inter-comparison of remote sensing platforms for height estimation of mango and avocado tree crowns. International Journal of Applied Earth Observation and Geoinformation, 89(February), 102091. https://doi.org/10.1016/j.jag.2020.102091spa
dc.relation.referencesXie, Y., Lark, T. J., Brown, J. F., & Gibbs, H. K. (2019). Mapping irrigated cropland extent across the conterminous United States at 30 m resolution using a semi-automatic training approach on Google Earth Engine. ISPRS Journal of Photogrammetry and Remote Sensing, 155(February), 136–149. https://doi.org/10.1016/j.isprsjprs.2019.07.005spa
dc.relation.referencesXu, L., Chen, N., Zhang, X., Moradkhani, H., Zhang, C., & Hu, C. (2021). In-situ and triple-collocation based evaluations of eight global root zone soil moisture products. Remote Sensing of Environment, 254(December 2020), 112248. https://doi.org/10.1016/j.rse.2020.112248spa
dc.relation.referencesXue, J., Bali, K. M., Light, S., Hessels, T., & Kisekka, I. (2020). Evaluation of remote sensing-based evapotranspiration models against surface renewal in almonds, tomatoes and maize. Agricultural Water Management, 238(April), 106228. https://doi.org/10.1016/j.agwat.2020.106228spa
dc.relation.referencesYandún, F. J., Salvo del Pedregal, J., Prieto, P. A., Torres-Torriti, M., & Auat, F. A. (2016). LiDAR and thermal images fusion for ground-based 3D characterisation of fruit trees. Biosystems Engineering, 151, 479–494. https://doi.org/10.1016/j.biosystemseng.2016.10.012spa
dc.relation.referencesYang, G., Liu, L., Guo, P., & Li, M. (2017). A flexible decision support system for irrigation scheduling in an irrigation district in China. Agricultural Water Management, 179, 378–389. https://doi.org/10.1016/j.agwat.2016.07.019spa
dc.relation.referencesYang, T., Li, D., Clothier, B., Wang, Y., Duan, J., Di, N., Li, G., Li, X., Jia, L., Xi, B., & Hu, W. (2019). Where to monitor the soil-water potential for scheduling drip irrigation in Populus tomentosa plantations located on the North China Plain ? Forest Ecology and Management, 437(October 2018), 99–112. https://doi.org/10.1016/j.foreco.2019.01.036spa
dc.relation.referencesYohannes, D., Ritsema, C. J., Eyasu, Y., Solomon, H., van Dam, J. C., Froebrich, J., Ritzema, H. P., & Meressa, A. (2019). A participatory and practical irrigation scheduling in semiarid areas: the case of Gumselassa irrigation scheme in Northern Ethiopia. Agricultural Water Management, 218(April), 102–114. https://doi.org/10.1016/j.agwat.2019.03.036spa
dc.relation.referencesZhou, Z., Majeed, Y., Diverres, G., & Gambacorta, E. (2021). Assessment for crop water stress with infrared thermal imagery in precision agriculture: A review and future prospects for deep learning applications. Computers and Electronics in Agriculture, 182(February). https://doi.org/10.1016/j.compag.2021.106019spa
dc.relation.referencesZinkernagel, J., Maestre-Valero, J. F., Seresti, S. Y., & Intrigliolo, D. S. (2020). New technologies and practical approaches to improve irrigation management of open field vegetable crops. Agricultural Water Management, 242(February), 106404. https://doi.org/10.1016/j.agwat.2020.106404spa
dc.relation.referencesZohaib, M., Kim, H., & Choi, M. (2019). Detecting global irrigated areas by using satellite and reanalysis products. Science of the Total Environment, 677, 679–691. https://doi.org/10.1016/j.scitotenv.2019.04.365spa
dc.rights.accessrightsinfo:eu-repo/semantics/restrictedAccessspa
dc.rights.licenseAtribución-NoComercial-SinDerivadas 4.0 Internacionalspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/spa
dc.subject.agrovocRiego
dc.subject.agrovocAgua de riego
dc.subject.agrovocPersea americana Hass
dc.subject.agrovocPotencial matricial
dc.subject.agrovocmatric potential
dc.subject.agrovocsoil
dc.subject.ddc630 - Agricultura y tecnologías relacionadasspa
dc.subject.proposalRequerimiento de riegospa
dc.subject.proposalPotencial mátrico del suelospa
dc.subject.proposalHumedad superficial del suelospa
dc.subject.proposalCoeficiente de dispersiónspa
dc.subject.proposalImágenes SARspa
dc.subject.proposalIrrigation requirementeng
dc.subject.proposalSoil matric potentialeng
dc.subject.proposalSurface soil water contenteng
dc.subject.proposalBackscattering coefficienteng
dc.subject.proposalSAR imageseng
dc.titleDevelopment of a scheduling irrigation application for Hass avocado crops in the Valle del Caucaeng
dc.title.translatedDesarrollo de una herramienta para la programación del riego en el cultivo de aguacate cv. Hass en el Valle del Caucaspa
dc.typeTrabajo de grado - Doctoradospa
dc.type.coarhttp://purl.org/coar/resource_type/c_db06spa
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/doctoralThesisspa
dc.type.redcolhttp://purl.org/redcol/resource_type/TDspa
dc.type.versioninfo:eu-repo/semantics/acceptedVersionspa
dcterms.audience.professionaldevelopmentEstudiantesspa
dcterms.audience.professionaldevelopmentInvestigadoresspa
oaire.accessrightshttp://purl.org/coar/access_right/c_16ecspa
oaire.fundernameUniversidad del Vallespa

Archivos

Bloque original

Mostrando 1 - 1 de 1
No hay miniatura disponible
Nombre:
94043757.2022.pdf
Tamaño:
352.12 KB
Formato:
Adobe Portable Document Format
Descripción:
Documento de tesis de doctorado

Bloque de licencias

Mostrando 1 - 1 de 1
No hay miniatura disponible
Nombre:
license.txt
Tamaño:
3.98 KB
Formato:
Item-specific license agreed upon to submission
Descripción: