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

Tablas, ilustraciones

Autores:: Erazo Mesa, Osvaldo Edwin

Tipo de recurso:: Doctoral thesis

Fecha de publicación:: 2022

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: eng

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repository_id_str
dc.title.eng.fl_str_mv	Development of a scheduling irrigation application for Hass avocado crops in the Valle del Cauca
dc.title.translated.spa.fl_str_mv	Desarrollo de una herramienta para la programación del riego en el cultivo de aguacate cv. Hass en el Valle del Cauca
title	Development of a scheduling irrigation application for Hass avocado crops in the Valle del Cauca
spellingShingle	Development of a scheduling irrigation application for Hass avocado crops in the Valle del Cauca 630 - Agricultura y tecnologías relacionadas Riego Agua de riego Persea americana Hass Potencial matricial matric potential soil Requerimiento de riego Potencial mátrico del suelo Humedad superficial del suelo Coeficiente de dispersión Imágenes SAR Irrigation requirement Soil matric potential Surface soil water content Backscattering coefficient SAR images
title_short	Development of a scheduling irrigation application for Hass avocado crops in the Valle del Cauca
title_full	Development of a scheduling irrigation application for Hass avocado crops in the Valle del Cauca
title_fullStr	Development of a scheduling irrigation application for Hass avocado crops in the Valle del Cauca
title_full_unstemmed	Development of a scheduling irrigation application for Hass avocado crops in the Valle del Cauca
title_sort	Development of a scheduling irrigation application for Hass avocado crops in the Valle del Cauca
dc.creator.fl_str_mv	Erazo Mesa, Osvaldo Edwin
dc.contributor.advisor.none.fl_str_mv	Echeverri Sanchez, Andrés Fernando
dc.contributor.author.none.fl_str_mv	Erazo Mesa, Osvaldo Edwin
dc.contributor.educationalvalidator.none.fl_str_mv	Tafur Hermann, Harold
dc.contributor.researchgroup.spa.fl_str_mv	Grupo de Investigación REGAR
dc.contributor.subjectmatterexpert.none.fl_str_mv	Ramírez Gil, Joaquín Guillermo Hincapié Gómez, Edgar Murillo Sandoval, Paulo José
dc.subject.ddc.spa.fl_str_mv	630 - Agricultura y tecnologías relacionadas
topic	630 - Agricultura y tecnologías relacionadas Riego Agua de riego Persea americana Hass Potencial matricial matric potential soil Requerimiento de riego Potencial mátrico del suelo Humedad superficial del suelo Coeficiente de dispersión Imágenes SAR Irrigation requirement Soil matric potential Surface soil water content Backscattering coefficient SAR images
dc.subject.agrovoc.none.fl_str_mv	Riego Agua de riego Persea americana Hass Potencial matricial matric potential soil
dc.subject.proposal.spa.fl_str_mv	Requerimiento de riego Potencial mátrico del suelo Humedad superficial del suelo Coeficiente de dispersión Imágenes SAR
dc.subject.proposal.eng.fl_str_mv	Irrigation requirement Soil matric potential Surface soil water content Backscattering coefficient SAR images
description	Tablas, ilustraciones
publishDate	2022
dc.date.accessioned.none.fl_str_mv	2022-08-11T20:02:18Z
dc.date.available.none.fl_str_mv	2022-08-11T20:02:18Z
dc.date.issued.none.fl_str_mv	2022-06-10
dc.type.spa.fl_str_mv	Trabajo de grado - Doctorado
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/doctoralThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.coar.spa.fl_str_mv	http://purl.org/coar/resource_type/c_db06
dc.type.content.spa.fl_str_mv	Text
dc.type.redcol.spa.fl_str_mv	http://purl.org/redcol/resource_type/TD
format	http://purl.org/coar/resource_type/c_db06
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/81854
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/81854 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	eng
language	eng
dc.relation.references.spa.fl_str_mv	Abdullah, 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.v9n3p120 Abioye, 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.2020.105441 Acosta-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.2021.109940 Acosta-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.2019.108629 Adam, 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-15-0512.1 Agresti, A. (2007). Contingency Tables. In An introduction to categorical data analysis (2nd ed., pp. 21–64). John Wiley & Sons. Ali, M. (2010a). Crop Water Requirement and Irrigation Scheduling. In Fundamentals of Irrigation and On-Farm Water Management (pp. 399–452). Springer. https://doi.org/10.1007/978-1-4419-6335-2 Ali, 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/10.1007/978-1-4419-6335-2 Allen, 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)80058-6 Allen, 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-z Allen, 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.2010.12.015 Altendorf, 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.pdf Andales, 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/ Arpaia, 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.pdf Arpaia, 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.2018.02.015 ASABE. (2006). ASAE EP505 APR2004 Measurement and Reporting Practices for Automatic Agricultural Weather Stations (pp. 51–61). American Society of Agricultural Engineers. Asher, 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.2012.09.002 Attema, E.P.W., Ulaby, F.T., 1978. Vegetation modeled as a water cloud. Radio Sci. 13, 357–364. https://doi.org/10.1029/RS013i002p00357 Awada, 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.2018.10.016 Babaeian, 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/10.1029/2018RG000618 Babaeian, 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.2021.112434 Baghdadi, 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/rs9090969 Bakker, 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). Bakker, 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). Bakker, 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). Bakker, 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.nl Ballabio, 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.2016.11.123 Bane, 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.2020.106178 Barker, 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.2018.02.022 Barrios-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.2021.108443 Bazzi, 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/rs12244058 Benninga, 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.2020.100066 Berbel, 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.2019.1019697 Bernal, 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.7403831 Bernal-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.2020v14i3.10850 Beyer, 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.2020.109830 Bhatti, 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.2019.105950 Bos, M., Kselik, R., Allen, R., & Molden, D. (2009). Water Requirements for Irrigation and the Environment. Springer. https://doi.org/10.1007/978-1-4020-8948-0 Bousbih, 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/rs10121953 Bousbih, 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/s17112617 Bower, 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.pdf Bower, 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.pdf Braden, 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. Braden Bretreger, 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.2020.125356 Brinkhoff, 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.2019.12.586 Brocca, 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.2018.08.023 Brown, 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.2020.108033 Builes, 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/10.1590/0103-8478cr20190188 Byrne, 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/s40641-018-0110-5 Calera, 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/s17051104 Caro, 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.2020.124917 Carr, 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/s0014479712001317 Castillo-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.2019.103948 CGIAR. (2021). SRTM 90m Digital Elevation Database. https://bigdata.cgiar.org/srtm-90m-digital-elevation-database/ Chauhan, 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-y Chavarria, 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/10.5772/33478 Chiaraviglio, 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.2016.7805720 Cho, 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.2020.111482 Choker, 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/w9010038 Chopart, 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.pdf Coelho, 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/S1415-43662007000100006 Corpoica, 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.pdf Costa, 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-2017-0158 Crowley, 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.pdf Ćulibrk, D., Vukobratovic, D., Minic, V., Alonso Fernandez, M., Alvarez Osuna, J., & Crnojevic, V. (2014). Sensing Technologies For Precision Irrigation. Springer. https://doi.org/10.1007/978-1-4614-8329-8 Cunha, 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.2010.05.007 Cutting, 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. CVC, & IGAC. (2017). Levantamiento Semidetallado de Suelos escala 1:25.000 de las cuencas priorizadas por la Corporación Autónoma Regional del Valle del Cauca - CVC. CVC, 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). CWR. (2021). The California Irrigation Management Information System (CIMIS). https://cimis.water.ca.gov/ da 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/10.5935/1806-6690.20200090 Dabach, 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.2015.08.012 DANE. (2020). Exportaciones - Históricos. https://www.dane.gov.co/index.php/estadisticas-por-tema/comercio-internacional/exportaciones/exportaciones-historicos Dari, 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.2021.126129 Datta, 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-x Davis Instruments. (2020). WeatherLink Computer Software. https://www.davisinstruments.com/product/weatherlink-computer-software/ Deb, 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/10.1109/4235.996017 Dehnen-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/s13593-016-0359-9 Díaz, 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. Dirwai, 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.e06213 Djaman, 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/s00704-017-2291-6 Domínguez-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.2019.105880 Dorigo, 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.2014.07.023 Doupis, 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/s11099-016-0679-7 du 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.pdf Dunkerley, 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.2021.103529 Eisenhauer, 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.2021 El 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/rs9121292 Elnashar, 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-13-447-2021 Erazo-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/w13141942 Erazo-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.2022v16i1.13456 Er-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.2020.106546 ESA. (2021). Sentinel-1 Observation Scenario. The European Space Agency. https://sentinel.esa.int/web/sentinel/missions/sentinel-1/observation-scenario Etchanchu, 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-2019-126 Fang, 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.2007.11.005 FAO. (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/cb1447en FAO, 2021. Major Tropical Fruits. Rome, Italy. FAOSTAT. (2021). Food and Agriculture Data. http://www.fao.org/faostat/en/#home Feddes, 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)90182-5 Fernández, 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/w12030785 Fernández, J. (2017). Plant-based methods for irrigation scheduling of woody crops. Horticulturae, 3(2), 1–37. https://doi.org/10.3390/horticulturae3020035 Fernández, 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/AR07312 Ferreira, 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.V55.01839 Fessehazion, 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/10.2989/10220119.2014.906504 Fick, 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.5086 Fischer, 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.v34n2.56799 Fontanet, 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-22-5889-2018 Freebairn, 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.2018.03.010 Fresh 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/ Friedman, 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.2015.11.007 Fritsch, 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.pdf Gao, 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/10.1117/12.2068966 Galushkin, A. (2007). Neural Networks Theory. Springer. https://doi.org/10.1007/978-3-540-48125-6 García, 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/s20041042 Garrido‑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%2Fs00271-019-00629-3 Gil, 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.2011.889.45 González, 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.2016.07.017 González-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-z González-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.2019.106015 Goodall, G. (1986). Tensiometer : Irrigationist's Best Friend. California Growers, X(7), 1–3. http://www.avocadosource.com/papers/research_articles/goodallgeorge1986.pdf Google Inc. (2021). Earth Engine Data Catalog \| Google Developers. https://developers.google.com/earth-engine/datasets/catalog Gorelick, 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.2017.06.031 Grajales, 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.pdf Gu, 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.1943-4774.0001464 Guimberteau, 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/s00382-011-1252-5 Guo, 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.pdf Gustafson, 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. Gustafson, 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.pdf Gustafson, 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.pdf Guzmán, 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/ Hallikainen, 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.1985.289497 Hamad, 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.3240758 Han, 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.2020.105833 Han, 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-z Handwert, 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/ Hernández, 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/2016016 Hill, 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) Hillel, D. (2014). Water Flow in Unsaturated Soil. In Introduction to Environmental Soil Physics (pp. 149–166). Academic Press. Hoeben, 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.1997.606449 Hoffman, 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.pdf Holzapfel, 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/s00271-017-0533-0 Hornbuckle, J., Vleeshouwer, J., Ballester, C., Montgomery, J., Hoogers, R., & Bridgart, R. (2016). IrriSAT Technical Reference. https://irrisat-cloud.appspot.com/doc/IrriSAT_Technical_Reference.pdf Hornbuckle, 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/10.13031/2013.21913 Hou, 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/10.1080/02626667.2016.1170130 Huang, 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)61859-8 Huang, 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.2021.146973 IDEAM. (2019). Consulta y Descarga de Datos Hidrometeorológicos. http://dhime.ideam.gov.co/atencionciudadano/ IDEAM. (2021). Boletín Hidroclimatológico Mensual. http://www.ideam.gov.co/web/tiempo-y-clima/climatologico-mensual Imbert, 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/ Irrometer. (2021). Irrometer Reading Tools. https://www.irrometer.com/loggers.html Islam, N., & Want, R. (2014). Smartphones: Past, Present, and Future. IEEE Pervasive Computing, 13(4), 89–92. https://doi.org/10.1109/MPRV.2014.74 IVFL. (2021). eo4water – Earth observation for water resource management. https://eo4water.com/ Jalilvand, 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.2019.111226 Jobbágy, 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/2016/2/35 Jones, 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/erh213 Julich, 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_152 Jung, 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.2020.09.003 Kadbhane, 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.2021.137109 Kaewmard, 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.2014.7042670 Kalmar, 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/AR9770859 Kang, 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/s41612-017-0004-6 Karthikeyan, 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.2017.09.006 Khabba, 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.2013.06.059 Khanna, 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.2018.12.039 Kiggundu, 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/s00271-011-0280-6 Kisi, 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.1943-4774.0000333 Knipper, 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-y Kramer, P. (1983a). Water: Its Functions and Properties. In Water Relations of Plants (pp. 1–22). Academic Press. https://doi.org/10.1016/b978-0-12-425040-6.50004-7 Kramer, P. (1983b). Water Movement in the Soil-Plant-Atmosphere Continuum. In Water Relations of Plants (pp. 187–214). Academic Press. Kumar, 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/10.1080/02626667.2012.678583 Kweon, 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.2014.2364914 Lahav, 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/AR9770869 Lahav, 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/AR9830717 Lahav, 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/10.1079/9780851993577.0259 Lawston, 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/10.1002/2017GL075733 Le 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/rs12101621 Lee, 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/V5222RQP Li, 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.2019.106645 Li, 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/rs9090902 Li, 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/10.1155/2020/8396164 Li, 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.2021.103673 Linker, 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.2016.10.004 Linker, Raphael. (2021). Model‑based optimal delineation of drip irrigation management zones. Precision Agriculture, 22, 287–305. https://doi.org/10.1007/s11119-020-09743-1 Lorite, 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/2012101-243-11 Lozac, 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/M2GARSS47143.2020.9105210 LP Laboratories. (2019). Chloe Irrigation Systems - Apps on Google Play. https://play.google.com/store/apps/details?id=com.chloeirrigation.chloe&hl=en&gl=US Lynks 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.pdf Ma, 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.2018.07.019 MADR. (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.pdf MADR. (2019). Estrategia de Ordenamiento de la Producción Agropecuaria Pesquera y Acuícola. https://sioc.minagricultura.gov.co/Documentos/Estrategia Ordenamiento de la producción.PDF MADR, 2020. Cadena productiva Aguacate. Primer trimestre de 2020. Bogotá D.C., Colombia. Madry, 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/10.1007/978-3-319-23386-4 Mainuddin, 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/s00271-014-0451-3 Mamalakis, 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/10.1029/2018GL079865 Marsh, 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.pdf Mbabazi, 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.2016.09.006 Mendes, 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.2019.01.043 Mekonnen, 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-15-1577-2011 Mesa-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.899 Meza, 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. Migliaccio, 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.59.11158 Miller, 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/HORTTECH04010-18 Miyazaki, T. (2005). Soil and Water. In Water Flow in Soils (2nd ed., pp. 1–17). Taylor & Francis. https://doi.org/10.1016/B978-0-444-88080-2.50009-0 Molina-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.2009.06.006 Montesinos, 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_10 Montgomery, 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. Mora, 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.pdf Moran, 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-X Moreno-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.2019.05.001 Mottaleb, 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.2018.07.007 Mualem, 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/WR012i003p00513 Mulderij, R. (2018). Overview Global Avocado Market. https://www.freshplaza.com/article/2196118/overview-global-avocado-market/ Mullissa, 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/rs13101954 Muthoni, 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/s00704-018-2712-1 Nawandar, 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.2019.05.027 Ng 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/s12355-017-0517-7 Ngongondo, 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/s00704-011-0413-0 Nhamo, 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.2019.102810 Nikolaou, 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/agronomy10081120 NOAA. (2020). NOAA Interpolated Outgoing Longwave Radiation (OLR). https://psl.noaa.gov/data/gridded/data.interp_OLR.html NOAA. (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.php Novoa, 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.2019.03.127 NRC. (1997). Precision Agriculture in the 21st Century. National Academy Press. http://www.nap.edu/catalog.php?record_id=5491 NRCS. (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.wba Oliver, M.A., Webster, R., 2015. Basic Steps in Geostatistics:The Variogram and Kriging. Springer, Cham. https://doi.org/10.1007/978-3-319-15865-5 Olmedo, 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.html Ouaadi, 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.2020.112050 Oyarce, 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.5.1.10157 Parikh, 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/10.1080/19479832.2019.1655489 Paudel, 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/tpw013 Paull, R., & Duarte, O. (2012). Tropical Fruits. In Crop Production Science in Horticulture Series (2nd ed., Vol. 1). CAB International. Pelton, 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/10.1007/978-3-319-23386-4 Peng, 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.2020.112162 Perry, 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.2009.05.005 Picoli, 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/s40808-019-00619-6 Piedelobo, 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/ijgi7060220 Pierce, F. J. (2010). Precision Irrigation. Landbauforschung Völkenrode, 340, 45–56. https://literatur.thuenen.de/digbib_extern/dn046667.pdf Pleguezuelo, 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/s10795-011-9112-3 Pongnumkul, 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/10.1155/2015/195308 Prévot, 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-Z PROCOLOMBIA. (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-mundo Prudente, 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.2020.100414 Puértolas, 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.2019.1253.58 QGIS Development Team. (2020). QGIS Geographic Information System (3.14). Open Source Geospatial Foundation. http://qgis.osgeo.org Qu, 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/10.1007/978-3-540-37294-3 Quebrajo, 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.2017.08.013 R 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/ Raes, 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.zip Ramírez-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.v28i3.23688 Ramírez-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.v71n2.66465 Ramírez-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.2018.04.021 Ramírez-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/CP19094 Ramírez-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/plants9060790 Ramírez-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/agronomy10010008 Ramírez-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.2018.04.021 Ranjan, 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.2019.01.005 Reddy, 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_2 Richards, S. J., Warneke, J. E., & Bingham, F. . T. (1962). Avocado Tree Growth Response to Irrigation. California Avocado Society, 46, 83–87. Richter, M. (2016). Precipitation in the Tropics. In L. Pancel & M. Köhl (Eds.), Tropical Forestry Handbook (2nd ed., pp. 363–390). Springer. https://doi.org/10.1007/978-3-642-54601-3 Ritsema, 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-J Robson, 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/rs9121223 Rodríguez, 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/B978-0-12-813164-0.00014-4 Roets, 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.pdf Román-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.v93i3-4.5465 Ruiz-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=8490508/54350354 Salas, 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_1 Salazar-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.pdf Sales, 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/MetroAgriFor50201.2020.9277588 Salgado, 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.2008.02.005 Samek, 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/10.1007/978-3-030-28954-6 Sapna, 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/aict50176.2020.9368742 Satizábal, 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/10.1007/978-3-540-74690-4 Scanlon, 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.c23 Schaffer, B., Wolstenholme, N., & Whiley, W. (Eds.). (2013). The Avocado: Botany, Production and Uses (2nd ed.). CAB International. Schowengerdt, R. A. (2007). Remote Sensing (3rd ed.). Elsevier Inc. https://doi.org/10.1016/B978-0-12-369407-2.X5000-1 Schulz, 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.14077 Sentek. (2019). IrriMAX Software Desktop (10.1; p. 2). Sentek. https://sentektechnologies.com/download/irrimax-desktop/ Serrano, 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/10.1177/2514848619838195 Shock, 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/228762944 Sigua, 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.2017.02.016 Silber, 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.2012.10.006 Silber, 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.2011.12.003 Silber, 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-w Silber, 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.2018.06.094 Silber, 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.2013.07.015 Silva, 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.2019.02.012 Simionesei, 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.2020.105822 Šimůnek, J., Van Genuchten, M. T., & Šejna, M. (2012). Hydrus: Model use, calibration, and validation. Transactions of the ASABE, 55(4), 1261–1274. Šimůnek, 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/vzj2007.0077 Singh, 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/b978-0-12-818576-6.00016-2 Singh, 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). Singhroy, 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/10.1007/978-3-319-23386-4 Sinha, 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/s11676-017-0511-7 Sishodia, 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/rs12193136 Smith, 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/n1 Sommaruga, R., Eldridge, H.M., 2020. Avocado Production: Water Footprint and Socio- economic Implications. EuroChoices 0, 1–6. https://doi.org/10.1111/1746-692X.12289 Stafford, 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.2000.0577 Steele, 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/s00271-014-0445-1 Stephens, 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/10.1029/2018JD029394 Taiz, L., & Zeiger, E. (2002a). Photosynthesis: The Light Reactions. In Plant Physiology (3rd ed., p. 690). Sinauer Associates. https://doi.org/10.1093/aob/mcg079 Taiz, L., & Zeiger, E. (2002b). Stress Physiology. In Plant Physiology (3rd ed., pp. 591–623). Sinauer Associates. https://doi.org/10.1093/aob/mcg079 Takahashi, 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/JCLI4198.1 Tamiminia, 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.2020.04.001 Tolomio, 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/rs12233945 Torres, 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)00043-2 Trabucco, A., & Zomer, R. (2019). Global High-Resolution Soil-Water Balance. https://doi.org/10.6084/m9.figshare.7707605.v3 Tsou, C.-S., 2013. Elitist Non-dominated Sorting Genetic Algorithm based on R. Tu, 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.2020.106426 Turner, 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=pdf Tzatzani, 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/10.1080/01904167.2019.1683192 UN. (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/849767 Unal, & CIAT. (2018). Incremento de la competitividad sostenible en la agricultura de ladera en todo el departamento del Valle del Cauca , Occidente. UNL. (2019). Crop Water App. https://ianr.unl.edu/crop-water-app UPRA. (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-a085-75fabb866d86 van 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/sssaj1980.03615995004400050002x Van 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/sssaj2001.653668x Vellidis, 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.2016.06.021 Veysi, 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.2017.04.016 Vollrath, 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/rs121118677 Von 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.pdf Vuthapanich, 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.com Waller, P., & Yitayew, M. (2016). Irrigation and drainage engineering. In Irrigation and Drainage Engineering. Springer. https://doi.org/10.1007/978-3-319-05699-9 Wani, S. P., Rockström, J., & Oweis, T. (2009). Rainfed Agriculture: Unlocking the Potential. In The Indian Economic Journal. CAB International. https://doi.org/10.1177/0019466220090204 Weiss, 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.2019.111402 Whiley, 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.htm Winer, 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.pdf WorldClim. (2020). Historical climate data. https://www.worldclim.org/data/worldclim21.html Zhuang, 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.2020.108203 Wu, 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.2020.102091 Xie, 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.2019.07.005 Xu, 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.2020.112248 Xue, 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.2020.106228 Yandún, 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.2016.10.012 Yang, 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.2016.07.019 Yang, 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.2019.01.036 Yohannes, 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.2019.03.036 Zhou, 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.2021.106019 Zinkernagel, 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.2020.106404 Zohaib, 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.2019.04.365
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spelling	Atribución-NoComercial-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/restrictedAccesshttp://purl.org/coar/access_right/c_16ecEcheverri Sanchez, Andrés Fernandoc2fffab8d1332afec44a12484e4d9ba9Erazo Mesa, Osvaldo Edwine31b710c34b5398f256f58364875fa7bTafur Hermann, HaroldGrupo de Investigación REGARRamírez Gil, Joaquín GuillermoHincapié Gómez, EdgarMurillo Sandoval, Paulo José2022-08-11T20:02:18Z2022-08-11T20:02:18Z2022-06-10https://repositorio.unal.edu.co/handle/unal/81854Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/Tablas, ilustracionesOne 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.Una 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.Esta tesis doctorado fue financiada con recursos propios y del Grupo de Investigación REGAR, Escuela EIDENAR - Facultad de Ingeniería de la Universidad del ValleDoctoradoDoctor en Ciencias AgrariasSe 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 hUso eficiente del agua en agriculturaxviii, 133 páginas + anexosapplication/pdfengUniversidad Nacional de ColombiaPalmira - Ciencias Agropecuarias - Doctorado en Ciencias AgrariasDoctorado en Ciencias AgrariasFacultad de Ciencias AgropecuariasPalmira, ColombiaUniversidad Nacional de Colombia - Sede Palmira630 - Agricultura y tecnologías relacionadasRiegoAgua de riegoPersea americana HassPotencial matricialmatric potentialsoilRequerimiento de riegoPotencial mátrico del sueloHumedad superficial del sueloCoeficiente de dispersiónImágenes SARIrrigation requirementSoil matric potentialSurface soil water contentBackscattering coefficientSAR imagesDevelopment of a scheduling irrigation application for Hass avocado crops in the Valle del CaucaDesarrollo de una herramienta para la programación del riego en el cultivo de aguacate cv. Hass en el Valle del CaucaTrabajo de grado - Doctoradoinfo:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_db06Texthttp://purl.org/redcol/resource_type/TDValle del Cauca - ColombiaAbdullah, 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.v9n3p120Abioye, 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.2020.105441Acosta-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.2021.109940Acosta-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.2019.108629Adam, 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-15-0512.1Agresti, A. (2007). Contingency Tables. In An introduction to categorical data analysis (2nd ed., pp. 21–64). John Wiley & Sons.Ali, M. (2010a). Crop Water Requirement and Irrigation Scheduling. In Fundamentals of Irrigation and On-Farm Water Management (pp. 399–452). Springer. https://doi.org/10.1007/978-1-4419-6335-2Ali, 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/10.1007/978-1-4419-6335-2Allen, 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)80058-6Allen, 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-zAllen, 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.2010.12.015Altendorf, 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.pdfAndales, 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/Arpaia, 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.pdfArpaia, 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.2018.02.015ASABE. (2006). ASAE EP505 APR2004 Measurement and Reporting Practices for Automatic Agricultural Weather Stations (pp. 51–61). American Society of Agricultural Engineers.Asher, 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.2012.09.002Attema, E.P.W., Ulaby, F.T., 1978. Vegetation modeled as a water cloud. Radio Sci. 13, 357–364. https://doi.org/10.1029/RS013i002p00357Awada, 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.2018.10.016Babaeian, 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/10.1029/2018RG000618Babaeian, 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.2021.112434Baghdadi, 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/rs9090969Bakker, 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).Bakker, 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).Bakker, 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).Bakker, 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.nlBallabio, 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.2016.11.123Bane, 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.2020.106178Barker, 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.2018.02.022Barrios-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.2021.108443Bazzi, 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/rs12244058Benninga, 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.2020.100066Berbel, 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.2019.1019697Bernal, 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.7403831Bernal-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.2020v14i3.10850Beyer, 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.2020.109830Bhatti, 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.2019.105950Bos, M., Kselik, R., Allen, R., & Molden, D. (2009). Water Requirements for Irrigation and the Environment. Springer. https://doi.org/10.1007/978-1-4020-8948-0Bousbih, 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/rs10121953Bousbih, 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/s17112617Bower, 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.pdfBower, 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.pdfBraden, 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. BradenBretreger, 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.2020.125356Brinkhoff, 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.2019.12.586Brocca, 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.2018.08.023Brown, 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.2020.108033Builes, 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/10.1590/0103-8478cr20190188Byrne, 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/s40641-018-0110-5Calera, 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/s17051104Caro, 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.2020.124917Carr, 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/s0014479712001317Castillo-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.2019.103948CGIAR. (2021). SRTM 90m Digital Elevation Database. https://bigdata.cgiar.org/srtm-90m-digital-elevation-database/Chauhan, 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-yChavarria, 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/10.5772/33478Chiaraviglio, 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.2016.7805720Cho, 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.2020.111482Choker, 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/w9010038Chopart, 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.pdfCoelho, 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/S1415-43662007000100006Corpoica, 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.pdfCosta, 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-2017-0158Crowley, 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.pdfĆulibrk, D., Vukobratovic, D., Minic, V., Alonso Fernandez, M., Alvarez Osuna, J., & Crnojevic, V. (2014). Sensing Technologies For Precision Irrigation. Springer. https://doi.org/10.1007/978-1-4614-8329-8Cunha, 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.2010.05.007Cutting, 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.CVC, & IGAC. (2017). Levantamiento Semidetallado de Suelos escala 1:25.000 de las cuencas priorizadas por la Corporación Autónoma Regional del Valle del Cauca - CVC.CVC, 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).CWR. (2021). The California Irrigation Management Information System (CIMIS). https://cimis.water.ca.gov/da 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/10.5935/1806-6690.20200090Dabach, 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.2015.08.012DANE. (2020). Exportaciones - Históricos. https://www.dane.gov.co/index.php/estadisticas-por-tema/comercio-internacional/exportaciones/exportaciones-historicosDari, 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.2021.126129Datta, 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-xDavis Instruments. (2020). WeatherLink Computer Software. https://www.davisinstruments.com/product/weatherlink-computer-software/Deb, 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/10.1109/4235.996017Dehnen-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/s13593-016-0359-9Díaz, 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.Dirwai, 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.e06213Djaman, 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/s00704-017-2291-6Domínguez-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.2019.105880Dorigo, 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.2014.07.023Doupis, 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/s11099-016-0679-7du 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.pdfDunkerley, 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.2021.103529Eisenhauer, 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.2021El 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/rs9121292Elnashar, 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-13-447-2021Erazo-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/w13141942Erazo-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.2022v16i1.13456Er-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.2020.106546ESA. (2021). Sentinel-1 Observation Scenario. The European Space Agency. https://sentinel.esa.int/web/sentinel/missions/sentinel-1/observation-scenarioEtchanchu, 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-2019-126Fang, 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.2007.11.005FAO. (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/cb1447enFAO, 2021. Major Tropical Fruits. Rome, Italy.FAOSTAT. (2021). Food and Agriculture Data. http://www.fao.org/faostat/en/#homeFeddes, 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)90182-5Fernández, 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/w12030785Fernández, J. (2017). Plant-based methods for irrigation scheduling of woody crops. Horticulturae, 3(2), 1–37. https://doi.org/10.3390/horticulturae3020035Fernández, 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/AR07312Ferreira, 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.V55.01839Fessehazion, 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/10.2989/10220119.2014.906504Fick, 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.5086Fischer, 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.v34n2.56799Fontanet, 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-22-5889-2018Freebairn, 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.2018.03.010Fresh 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/Friedman, 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.2015.11.007Fritsch, 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.pdfGao, 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/10.1117/12.2068966Galushkin, A. (2007). Neural Networks Theory. Springer. https://doi.org/10.1007/978-3-540-48125-6García, 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/s20041042Garrido‑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%2Fs00271-019-00629-3Gil, 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.2011.889.45González, 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.2016.07.017González-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-zGonzález-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.2019.106015Goodall, G. (1986). Tensiometer : Irrigationist's Best Friend. California Growers, X(7), 1–3. http://www.avocadosource.com/papers/research_articles/goodallgeorge1986.pdfGoogle Inc. (2021). Earth Engine Data Catalog \| Google Developers. https://developers.google.com/earth-engine/datasets/catalogGorelick, 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.2017.06.031Grajales, 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.pdfGu, 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.1943-4774.0001464Guimberteau, 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/s00382-011-1252-5Guo, 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.pdfGustafson, 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.Gustafson, 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.pdfGustafson, 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.pdfGuzmán, 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/Hallikainen, 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.1985.289497Hamad, 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.3240758Han, 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.2020.105833Han, 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-zHandwert, 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/Hernández, 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/2016016Hill, 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)Hillel, D. (2014). Water Flow in Unsaturated Soil. In Introduction to Environmental Soil Physics (pp. 149–166). Academic Press.Hoeben, 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.1997.606449Hoffman, 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.pdfHolzapfel, 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/s00271-017-0533-0Hornbuckle, J., Vleeshouwer, J., Ballester, C., Montgomery, J., Hoogers, R., & Bridgart, R. (2016). IrriSAT Technical Reference. https://irrisat-cloud.appspot.com/doc/IrriSAT_Technical_Reference.pdfHornbuckle, 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/10.13031/2013.21913Hou, 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/10.1080/02626667.2016.1170130Huang, 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)61859-8Huang, 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.2021.146973IDEAM. (2019). Consulta y Descarga de Datos Hidrometeorológicos. http://dhime.ideam.gov.co/atencionciudadano/IDEAM. (2021). Boletín Hidroclimatológico Mensual. http://www.ideam.gov.co/web/tiempo-y-clima/climatologico-mensualImbert, 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/Irrometer. (2021). Irrometer Reading Tools. https://www.irrometer.com/loggers.htmlIslam, N., & Want, R. (2014). Smartphones: Past, Present, and Future. IEEE Pervasive Computing, 13(4), 89–92. https://doi.org/10.1109/MPRV.2014.74IVFL. (2021). eo4water – Earth observation for water resource management. https://eo4water.com/Jalilvand, 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.2019.111226Jobbágy, 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/2016/2/35Jones, 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/erh213Julich, 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_152Jung, 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.2020.09.003Kadbhane, 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.2021.137109Kaewmard, 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.2014.7042670Kalmar, 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/AR9770859Kang, 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/s41612-017-0004-6Karthikeyan, 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.2017.09.006Khabba, 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.2013.06.059Khanna, 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.2018.12.039Kiggundu, 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/s00271-011-0280-6Kisi, 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.1943-4774.0000333Knipper, 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-yKramer, P. (1983a). Water: Its Functions and Properties. In Water Relations of Plants (pp. 1–22). Academic Press. https://doi.org/10.1016/b978-0-12-425040-6.50004-7Kramer, P. (1983b). Water Movement in the Soil-Plant-Atmosphere Continuum. In Water Relations of Plants (pp. 187–214). Academic Press.Kumar, 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/10.1080/02626667.2012.678583Kweon, 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.2014.2364914Lahav, 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/AR9770869Lahav, 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/AR9830717Lahav, 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/10.1079/9780851993577.0259Lawston, 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/10.1002/2017GL075733Le 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/rs12101621Lee, 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/V5222RQPLi, 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.2019.106645Li, 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/rs9090902Li, 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/10.1155/2020/8396164Li, 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.2021.103673Linker, 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.2016.10.004Linker, Raphael. (2021). Model‑based optimal delineation of drip irrigation management zones. Precision Agriculture, 22, 287–305. https://doi.org/10.1007/s11119-020-09743-1Lorite, 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/2012101-243-11Lozac, 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/M2GARSS47143.2020.9105210LP Laboratories. (2019). Chloe Irrigation Systems - Apps on Google Play. https://play.google.com/store/apps/details?id=com.chloeirrigation.chloe&hl=en&gl=USLynks 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.pdfMa, 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.2018.07.019MADR. (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.pdfMADR. (2019). Estrategia de Ordenamiento de la Producción Agropecuaria Pesquera y Acuícola. https://sioc.minagricultura.gov.co/Documentos/Estrategia Ordenamiento de la producción.PDFMADR, 2020. Cadena productiva Aguacate. Primer trimestre de 2020. Bogotá D.C., Colombia.Madry, 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/10.1007/978-3-319-23386-4Mainuddin, 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/s00271-014-0451-3Mamalakis, 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/10.1029/2018GL079865Marsh, 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.pdfMbabazi, 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.2016.09.006Mendes, 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.2019.01.043Mekonnen, 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-15-1577-2011Mesa-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.899Meza, 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.Migliaccio, 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.59.11158Miller, 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/HORTTECH04010-18Miyazaki, T. (2005). Soil and Water. In Water Flow in Soils (2nd ed., pp. 1–17). Taylor & Francis. https://doi.org/10.1016/B978-0-444-88080-2.50009-0Molina-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.2009.06.006Montesinos, 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_10Montgomery, 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.Mora, 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.pdfMoran, 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-XMoreno-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.2019.05.001Mottaleb, 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.2018.07.007Mualem, 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/WR012i003p00513Mulderij, R. (2018). Overview Global Avocado Market. https://www.freshplaza.com/article/2196118/overview-global-avocado-market/Mullissa, 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/rs13101954Muthoni, 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/s00704-018-2712-1Nawandar, 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.2019.05.027Ng 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/s12355-017-0517-7Ngongondo, 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/s00704-011-0413-0Nhamo, 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.2019.102810Nikolaou, 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/agronomy10081120NOAA. (2020). NOAA Interpolated Outgoing Longwave Radiation (OLR). https://psl.noaa.gov/data/gridded/data.interp_OLR.htmlNOAA. (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.phpNovoa, 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.2019.03.127NRC. (1997). Precision Agriculture in the 21st Century. National Academy Press. http://www.nap.edu/catalog.php?record_id=5491NRCS. (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.wbaOliver, M.A., Webster, R., 2015. Basic Steps in Geostatistics:The Variogram and Kriging. Springer, Cham. https://doi.org/10.1007/978-3-319-15865-5Olmedo, 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.htmlOuaadi, 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.2020.112050Oyarce, 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.5.1.10157Parikh, 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/10.1080/19479832.2019.1655489Paudel, 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/tpw013Paull, R., & Duarte, O. (2012). Tropical Fruits. In Crop Production Science in Horticulture Series (2nd ed., Vol. 1). CAB International.Pelton, 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/10.1007/978-3-319-23386-4Peng, 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.2020.112162Perry, 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.2009.05.005Picoli, 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/s40808-019-00619-6Piedelobo, 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/ijgi7060220Pierce, F. J. (2010). Precision Irrigation. Landbauforschung Völkenrode, 340, 45–56. https://literatur.thuenen.de/digbib_extern/dn046667.pdfPleguezuelo, 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/s10795-011-9112-3Pongnumkul, 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/10.1155/2015/195308Prévot, 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-ZPROCOLOMBIA. (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-mundoPrudente, 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.2020.100414Puértolas, 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.2019.1253.58QGIS Development Team. (2020). QGIS Geographic Information System (3.14). Open Source Geospatial Foundation. http://qgis.osgeo.orgQu, 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/10.1007/978-3-540-37294-3Quebrajo, 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.2017.08.013R 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/Raes, 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.zipRamírez-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.v28i3.23688Ramírez-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.v71n2.66465Ramírez-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.2018.04.021Ramírez-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/CP19094Ramírez-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/plants9060790Ramírez-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/agronomy10010008Ramírez-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.2018.04.021Ranjan, 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.2019.01.005Reddy, 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_2Richards, S. J., Warneke, J. E., & Bingham, F. . T. (1962). Avocado Tree Growth Response to Irrigation. California Avocado Society, 46, 83–87.Richter, M. (2016). Precipitation in the Tropics. In L. Pancel & M. Köhl (Eds.), Tropical Forestry Handbook (2nd ed., pp. 363–390). Springer. https://doi.org/10.1007/978-3-642-54601-3Ritsema, 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-JRobson, 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/rs9121223Rodríguez, 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/B978-0-12-813164-0.00014-4Roets, 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.pdfRomán-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.v93i3-4.5465Ruiz-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=8490508/54350354Salas, 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_1Salazar-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.pdfSales, 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/MetroAgriFor50201.2020.9277588Salgado, 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.2008.02.005Samek, 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/10.1007/978-3-030-28954-6Sapna, 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/aict50176.2020.9368742Satizábal, 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/10.1007/978-3-540-74690-4Scanlon, 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.c23Schaffer, B., Wolstenholme, N., & Whiley, W. (Eds.). (2013). The Avocado: Botany, Production and Uses (2nd ed.). CAB International.Schowengerdt, R. A. (2007). Remote Sensing (3rd ed.). Elsevier Inc. https://doi.org/10.1016/B978-0-12-369407-2.X5000-1Schulz, 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.14077Sentek. (2019). IrriMAX Software Desktop (10.1; p. 2). Sentek. https://sentektechnologies.com/download/irrimax-desktop/Serrano, 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/10.1177/2514848619838195Shock, 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/228762944Sigua, 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.2017.02.016Silber, 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.2012.10.006Silber, 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.2011.12.003Silber, 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-wSilber, 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.2018.06.094Silber, 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.2013.07.015Silva, 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.2019.02.012Simionesei, 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.2020.105822Šimůnek, J., Van Genuchten, M. T., & Šejna, M. (2012). Hydrus: Model use, calibration, and validation. Transactions of the ASABE, 55(4), 1261–1274.Šimůnek, 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/vzj2007.0077Singh, 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/b978-0-12-818576-6.00016-2Singh, 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).Singhroy, 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/10.1007/978-3-319-23386-4Sinha, 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/s11676-017-0511-7Sishodia, 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/rs12193136Smith, 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/n1Sommaruga, R., Eldridge, H.M., 2020. Avocado Production: Water Footprint and Socio- economic Implications. EuroChoices 0, 1–6. https://doi.org/10.1111/1746-692X.12289Stafford, 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.2000.0577Steele, 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/s00271-014-0445-1Stephens, 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/10.1029/2018JD029394Taiz, L., & Zeiger, E. (2002a). Photosynthesis: The Light Reactions. In Plant Physiology (3rd ed., p. 690). Sinauer Associates. https://doi.org/10.1093/aob/mcg079Taiz, L., & Zeiger, E. (2002b). Stress Physiology. In Plant Physiology (3rd ed., pp. 591–623). Sinauer Associates. https://doi.org/10.1093/aob/mcg079Takahashi, 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/JCLI4198.1Tamiminia, 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.2020.04.001Tolomio, 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/rs12233945Torres, 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)00043-2Trabucco, A., & Zomer, R. (2019). Global High-Resolution Soil-Water Balance. https://doi.org/10.6084/m9.figshare.7707605.v3Tsou, C.-S., 2013. Elitist Non-dominated Sorting Genetic Algorithm based on R.Tu, 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.2020.106426Turner, 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=pdfTzatzani, 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/10.1080/01904167.2019.1683192UN. (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/849767Unal, & CIAT. (2018). Incremento de la competitividad sostenible en la agricultura de ladera en todo el departamento del Valle del Cauca , Occidente.UNL. (2019). Crop Water App. https://ianr.unl.edu/crop-water-appUPRA. (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-a085-75fabb866d86van 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/sssaj1980.03615995004400050002xVan 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/sssaj2001.653668xVellidis, 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.2016.06.021Veysi, 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.2017.04.016Vollrath, 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/rs121118677Von 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.pdfVuthapanich, 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.comWaller, P., & Yitayew, M. (2016). Irrigation and drainage engineering. In Irrigation and Drainage Engineering. Springer. https://doi.org/10.1007/978-3-319-05699-9Wani, S. P., Rockström, J., & Oweis, T. (2009). Rainfed Agriculture: Unlocking the Potential. In The Indian Economic Journal. CAB International. https://doi.org/10.1177/0019466220090204Weiss, 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.2019.111402Whiley, 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.htmWiner, 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.pdfWorldClim. (2020). Historical climate data. https://www.worldclim.org/data/worldclim21.htmlZhuang, 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.2020.108203Wu, 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.2020.102091Xie, 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.2019.07.005Xu, 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.2020.112248Xue, 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.2020.106228Yandún, 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.2016.10.012Yang, 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.2016.07.019Yang, 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.2019.01.036Yohannes, 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.2019.03.036Zhou, 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.2021.106019Zinkernagel, 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.2020.106404Zohaib, 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.2019.04.365Universidad del ValleEstudiantesInvestigadoresORIGINAL94043757.2022.pdf94043757.2022.pdfDocumento de tesis de doctoradoapplication/pdf360569https://repositorio.unal.edu.co/bitstream/unal/81854/1/94043757.2022.pdf4569ab538cb901d056012d33b5f7e111MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-84074https://repositorio.unal.edu.co/bitstream/unal/81854/2/license.txt8153f7789df02f0a4c9e079953658ab2MD52THUMBNAIL94043757.2022.pdf.jpg94043757.2022.pdf.jpgGenerated Thumbnailimage/jpeg4928https://repositorio.unal.edu.co/bitstream/unal/81854/3/94043757.2022.pdf.jpgba5aa2e507ff63ab880f1f9f9dd5636eMD53unal/81854oai:repositorio.unal.edu.co:unal/818542023-08-06 23:03:41.901Repositorio Institucional Universidad Nacional de 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EVESURBIFBPUiBMQSBTRUNSRVRBUsONQSBHRU5FUkFMLiAqTEEgVEVTSVMgQSBQVUJMSUNBUiBERUJFIFNFUiBMQSBWRVJTScOTTiBGSU5BTCBBUFJPQkFEQS4gCgpBbCBoYWNlciBjbGljIGVuIGVsIHNpZ3VpZW50ZSBib3TDs24sIHVzdGVkIGluZGljYSBxdWUgZXN0w6EgZGUgYWN1ZXJkbyBjb24gZXN0b3MgdMOpcm1pbm9zLiBTaSB0aWVuZSBhbGd1bmEgZHVkYSBzb2JyZSBsYSBsaWNlbmNpYSwgcG9yIGZhdm9yLCBjb250YWN0ZSBjb24gZWwgYWRtaW5pc3RyYWRvciBkZWwgc2lzdGVtYS4KClVOSVZFUlNJREFEIE5BQ0lPTkFMIERFIENPTE9NQklBIC0gw5psdGltYSBtb2RpZmljYWNpw7NuIDE5LzEwLzIwMjEK

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

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