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Vol. 16 No. 1 (2022)

Fruits section

Advances in Hass avocado irrigation scheduling under digital agriculture approach

https://doi.org/10.17584/rcch.2022v16i1.13456

Published 2022-01-01

  • Edwin Erazo-Mesa
    • ,
  • Andrés Echeverri-Sánchez
    • ,
  • Joaquin Guillermo Ramírez-Gil

Edwin Erazo-Mesa
Universidad del Valle, Facultad de Ingeniería, Escuela EIDENAR, Santiago de Cali

Andrés Echeverri-Sánchez
Universidad del Valle, Facultad de Ingeniería, Escuela EIDENAR, Santiago de Cali

Joaquin Guillermo Ramírez-Gil
Universidad Nacional de Colombia, Sede Bogotá, Facultad de Ciencias Agrarias, Departamento de Agronomía, Bogota

Some remote sensing components and their application in agriculture Photo: E. Erazo-Mesa

Abstract

Under tropical conditions, Hass avocado irrigation is a controversial issue due to insufficient scientific evidence. The rapid progression of technological advances and its incorporation in agriculture have expanded options to improve the irrigation scheduling (IS) of Hass avocado. The concept featuring those technological advances in agriculture is digital agriculture (DA). Here, we present a mixture of well-known studies in the Hass avocado irrigation focused on proximal sensing (PS) technologies and recent studies emphasizing the potential of remote sensing (RS), and application technologies to schedule the irrigation. PS takes advantage of the soil or trees' proximity to output reliable measurements with a high temporal resolution, while RS provides a broad set of spectral data in continuous and large areas that can be transformed into crop-related biophysical variables. Applications – a term grouping mobile (smartphone) apps, desktop programs, and web-based platforms – offers portability, high precision, and graphic visualization of variables obtained or estimated by sensors. Integrating RS and PS technologies through user-friendly applications can represent a suitable option to improve Hass avocado irrigation in developing countries. Our review is presented in the following sections: general introduction, DA approach definition, use of proximal sensing, use of remote sensing, and scheduling irrigation applications.

Keywords

New technologies, Agriculture 4.0, Proximal sensing, Remote sensing, Mobile and web Apps

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References

  1. Abdullah, F.A. and B.A. Samah. 2013. Factors impinging farmers’ use of agriculture technology. Asian Soc. Sci. 9(3), 120-124, Doi: 10.5539/ass.v9n3p120
  2. Abioye, E.A., M.S.Z. Abidin, M.S.A. Mahmud, S. Buyamin, M.H.I. Ishak, M.K.I.A. Rahman, A.O. Otuoze, P. Onotu, and M.S.A. Ramli. 2020. A review on monitoring and advanced control strategies for precision irrigation. Comput. Electron. Agric. 173, 105441. Doi: 10.1016/j.compag.2020.105441
  3. Ali, M.H. 2010. Crop water requirement and irrigation. Scheduling. pp. 399-452. In: Ali, M.H., Fundamentals of irrigation and on-farm water management. Vol. 1. Springer, New York, NY. Doi: 10.1007/978-1-4419-6335-2_9
  4. Asher, J.B., B.B. Yosef, and R. Volinsky. 2013. Ground-based remote sensing system for irrigation scheduling. Biosyst. Eng. 114(4), 444-453. Doi: 10.1016/j.biosystemseng.2012.09.002
  5. Awada, H., G. Ciraolo, A. Maltese, G. Provenzano, M.A. Moreno Hidalgo, and J.I. Còrcoles. 2019. Assessing the performance of a large-scale irrigation system by estimations of actual evapotranspiration obtained by Landsat satellite images resampled with cubic convolution. Int. J. Appl. Earth Obs. Geoinf. 75, 96-105. Doi: 10.1016/j.jag.2018.10.016
  6. Barker, J.B., D.M. Heeren, C.M.U. Neale, and D.R. Rudnick. 2018. Evaluation of variable rate irrigation using a remote-sensing-based model. Agric. Water Manag. 203, 63-74. Doi: 10.1016/j.agwat.2018.02.022
  7. Bazzi, H., N. Baghdadi, I. Fayad, F. Charron, M. Zribi, and H. Belhouchette. 2020. Irrigation events detection over intensively irrigated grassland plots using Sentinel-1 data. Remote Sens. 12(24), 4058. Doi: 10.3390/rs12244058
  8. Bhatti, S., D.M. Heeren, J. B. Barker, C.M.U. Neale, W.E. Woldt, M.S. Maguire, and D.R. Rudnick. 2020. Site-specific irrigation management in a sub-humid climate using a spatial evapotranspiration model with satellite and airborne imagery. Agric. Water Manage. 230, 105950. Doi: 10.1016/j.agwat.2019.105950
  9. Bousbih, S., M. Zribi, M. El Hajj, N. Baghdadi, Z. Lili-Chabaane, Q. Gao, and P. Fanise. 2018. Soil moisture and irrigation mapping in a semi-arid region, based on the synergetic use of Sentinel-1 and Sentinel-2 data. Remote Sens. 10(12), 1953. Doi: 10.3390/rs10121953
  10. Bower, J.P. 1979. Water relations of Phytophthora infected fuerte trees and their influence on management. South African Avocado Growers’ Assoc. Res. Rep. 3, 25-27.
  11. Bower, J.P. 1985. Some aspects of water relations on avocado Persea americana (Mill.) tree and fruit physiology. PhD thesis. Faculty of Agriculture, University of Natal, Pietermaritzburg, South African.
  12. Bretreger, D., I.-Y. Yeo, G. Hancock, and G. Willgoose. 2020. Monitoring irrigation using landsat observations and climate data over regional scales in the Murray-Darling Basin. J. Hydrol. 590, 125356. Doi: 10.1016/j.jhydrol.2020.125356
  13. Brocca, L., A. Tarpanelli, P. Filippucci, W. Dorigo, F. Zaussinger, A. Gruber, and D. Fernández-Prieto. 2018. How much water is used for irrigation? A new approach exploiting coarse resolution satellite soil moisture products. Int. J. Appl. Earth Obs. Geoinf. 73, 752-766. Doi: 10.1016/j.jag.2018.08.023
  14. Builes Gaitan, S. and M. Duque Ríos. 2020. Socio-economic and technological typology of avocado cv. Hass farms from Antioquia (Colombia). Cienc. Rural 50(7), e20190188. Doi: 10.1590/0103-8478cr20190188
  15. Byrne, M.P, A.G. Pendergrass, A.D. Rapp, and K.R. Wodzicki. 2018. Response of the Intertropical Convergence Zone to Climate Change: Location, width, and strength. Curr. Clim. Change Rep. 4, 355-370, Doi: 10.1007/s40641-018-0110-5
  16. Calera, A., I. Campos, A. Osann, G. D’Urso, and M. Menenti. 2017. Remote sensing for crop water management: From ET modelling to services for the end users. Sensors 17(5) 1104. Doi: 10.3390/s17051104
  17. Chauhan, Y.S., G.C. Wright, D. Holzworth, R.C.N. Rachaputi, and J.O. Payero. 2013, AQUAMAN: A web-based decision support system for irrigation scheduling in peanuts. Irrig. Sci. 31, (3), 271-283. Doi: 10.1007/s00271-011-0296-y
  18. Chevalier, R.F., G. Hoogenboom, R.W. McClendon, and J.O. Paz. 2012. A web-based fuzzy expert system for frost warnings in horticultural crops. Environ. Model. Softw. 35, 84-91. Doi: 10.1016/j.envsoft.2012.02.010
  19. Chiaraviglio, L., N. Blefari-Melazzi, W. Liu, J.A. Gutierrez, J. Van de Beek, R. Birke, L. Chen, F. Idzikowski, D. Kilper, J.P. Monti, and J. Wu. 2017. 5G in rural and low-income areas: Are we ready? 1650017. En: Proc. 2016 ITU Kaleidoscope Academic Conference: ICTs for a Sustainable World (ITU WT). Bankok, Thailand. Doi: 10.1109/ITU-WT.2016.7805720
  20. Choker, M., N. Baghdadi, M. Zribi, M. El Hajj, S. Paloscia, N.E.C. Verhoest, H. Lievens, and F. Mattia. 2017. Evaluation of the Oh, Dubois and IEM Backscatter models using a large dataset of SAR data and experimental soil measurements. Water 9(1), 38. Doi: 10.3390/w9010038
  21. Chopart, J., L. Le Mézo, and M. Mézino. 2009. PROBE-w (Water Balance PROgram): A software application for water balance modeling in a cultivated soil. Presentation and User Manual 1.0.156. CIRAD, La Réunion, France.
  22. Costa, J.O., R.D. Coelho, W. Wolff, J.V. José, M.V. Folegatti, and S.F.B. Ferraz. 2019. Spatial variability of coffee plant water consumption based on the SEBAL algorithm. Sci. Agric. 76(2), 93-101. Doi: 10.1590/1678-992x-2017-0158
  23. Crowley, D. and J. Escalera. 2013. Optimizing avocado irrigation practices through soil water monitoring. Calif. Avoc. Soc. 55-65.
  24. Ćulibrk, D., D. Vukobratovic, V. Minic, M. Alonso Fernandez, J. Alvarez Osuna, and V. Crnojevic. 2014. Sensing technologies for precision irrigation. Springer, New York, NY. Doi: 10.1007/978-1-4614-8329-8
  25. Dabach, S., U. Shani, and N. Lazarovitch. 2016. The influence of water uptake on matric head variability in a drip-irrigated root zone. Soil Tillage Res. 155, 216-224. Doi: 10.1016/j.still.2015.08.012
  26. Dari, J., P. Quintana-Seguí, M.J. Escorihuela, V. Stefan, L. Brocca, and R. Morbidelli. 2021. Detecting and mapping irrigated areas in a Mediterranean environment by using remote sensing soil moisture and a land surface model. J. Hydrol. 596, 126129. Doi: 10.1016/j.jhydrol.2021.126129
  27. Datta, S., P. Das, D. Dutta, and R.K. Giri. 2020. Estimation of surface moisture content using Sentinel-1 C-band SAR data through machine learning models. J. Indian Soc. Remote Sens. 49, 887-896. Doi: 10.1007/s12524-020-01261-x
  28. Dehnen-Schmutz, K., G.L. Foster, L. Owen, and S. Persello. 2016. Exploring the role of smartphone technology for citizen science in agriculture. Agron. Sustain. Dev. 36, 25. Doi: 10.1007/s13593-016-0359-9
  29. Domínguez-Niño, J.M., J. Oliver-Manera, J. Girona, and J. Casadesús. 2020. Differential irrigation scheduling by an automated algorithm of water balance tuned by capacitance-type soil moisture sensors. Agric. Water Manage. 228, 105880. Doi: 10.1016/j.agwat.2019.105880
  30. Dorigo, W.A., A. Grube, R.A.M. De Jeu, W., Wagner, T. Stacke, A. Loew, C. Albergel, L. Broca, D. Chung, R.M. Parinussa, R. Kidd. 2015. Evaluation of the ESA CCI soil moisture product using ground-based observations. Remote Sens. Environ. 162, 380-395. Doi: 10.1016/j.rse.2014.07.023
  31. Doupis, G., N. Kavroulakis, G. Psarras, and I. Papadakis. 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. Doi: 10.1007/s11099-016-0679-7
  32. du Plessis, S.F. 1991. Factors important for optimal irrigation scheduling of avocado orchards. South African Avocado Growers’ Association Yearbook 14, 91-93.
  33. El-Gayar, O.F., and M.Q. Ofori. 2020. Disrupting agriculture: The status and prospects for AI and Big Data in smart agriculture. pp. 174-215. In: Strydom, M. and S. Buckley (eds.). AI and Big Data’s potential for disruptive innovation. IGI Global, Hershey, PA. Doi: 10.4018/978-1-5225-9687-5.CH007
  34. Erazo-Mesa, E., J.G. Ramírez-Gil, and A.Echeverri Sánchez. 2021. Avocado cv . Hass needs water irrigation in tropical precipitation regime: Evidence from Colombia. Water 13(14), 1942. Doi: 10.3390/w13141942
  35. ESA, 2021. Sentinel-1 observation scenario. In: https://sentinel.esa.int/web/sentinel/missions/sentinel-1/observation-scenario; consulted: April, 2021.
  36. FAO. 2021. FAOSTAT – Food and agriculture data. In: http://www.fao.org/faostat/es/#home; consulted: January, 2019.
  37. FAO. 2020. The State of Food and Agriculture 2020. Overcoming water challenges in agriculture. Rome. Doi: 10.4060/cb1447en
  38. Fernández, J.E. 2017. Plant-based methods for irrigation scheduling of woody crops. Horticulturae 3(2), 35. Doi: 10.3390/horticulturae3020035
  39. Fernández, I., S. Lecina, C. Ruiz-Sánchez, J. Vera, W. Conejero, M. Conesa, A. Domínguez, J. Pardo, B. Léllis, and P. Montesinos. 2020. Trends and challenges in irrigation scheduling in the semi-arid area of Spain. Water 12(3), 785. Doi: 10.3390/w12030785
  40. Fernández, J.E., R. Romero, J.C. Montaño, A. Diaz-Espejo, J.L. Muriel, M.V. Cuevas, F. Moreno, I.F. Girón, and M.J. Palomo. 2008. Design and testing of an automatic irrigation controller for fruit tree orchards, based on sap flow measurements. Aust. J. Agric. Res. 59(7), 589-598. Doi: 10.1071/AR07312
  41. Ferreira, L.B., F.F. Cunha, R.A. Oliveira, and T.F. Rodrigues. 2020. A smartphone APP for weather-based irrigation scheduling using artificial neural networks. Pesq. Agropec. Bras. 55, e01839. Doi: 10.1590/S1678-3921.PAB2020.V55.01839
  42. Fontanet, M., D. Fernàndez-Garcia, and F. Ferrer. 2018. The value of satellite remote sensing soil moisture data and the DISPATCH algorithm in irrigation fields. Hydrol. Earth Syst. Sci. 22(11), 5889-5900, Doi: 10.5194/hess-22-5889-2018
  43. Freebairn, D., A. Ghahramani, J. Robinson, and D. McClymont. 2018. A tool for monitoring soil water using modelling, on-farm data, and mobile technology. Environ. Model. Softw. 104, 55-63. Doi: 10.1016/j.envsoft.2018.03.010
  44. Friedman, S.P., G. Communar, and A. Gamliel. 2016. DIDAS - User-friendly software package for assisting drip irrigation design and scheduling. Comput. Electron. Agric. 120, 36-52. Doi: 10.1016/j.compag.2015.11.007
  45. Garrido-Rubio, J., D. Sanz, J. González-Piqueras, and A. Calera. 2019. Application of a remote sensing-based soil water balance for the accounting of groundwater abstractions in large irrigation areas. Irrig. Sci. 37, 709-724. Doi: 10.1007%2Fs00271-019-00629-3
  46. Gil, P., L. Gurovich, B. Schaffer, J. Alcayaga, and R. Iturriaga. 2011. Electrical signal measurements in avocado trees: A potential tool for monitoring physiological responses to soil water content? Acta Hortic. 889, 371-378. Doi: 10.17660/ActaHortic.2011.889.45
  47. Goodall, G. 1986. Tensiometer: Irrigationist’s best friend. California Growers 10(7), 1-3.
  48. Google Inc., 2021, Earth engine data catalog. In: Google Developers, https://developers.google.com/earth-engine/datasets/catalog; consulted: April, 2021.
  49. Gorelick, N., M. Hancher, M. Dixon, S. Ilyushchenko, D. Thau, and R. Moore. 2017. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18-27. Doi: 10.1016/j.rse.2017.06.031
  50. Gu, Z., Z. Qi, R. Burghate, S. Yuan, X. Jiao, and J. Xu. 2020. Irrigation scheduling approaches and applications: A review: J. Irrig. Drain. Eng. 146(6), p. 1–15, Doi: 10.1061/(asce)ir.1943-4774.0001464
  51. Gustafson, C.D., A.W. Marsh, R.L. Branson, and S. Davis. 1979. Drip irrigation on avocados. California Avocado Society 1979 Yearbook 63, 95-134.
  52. Hamad, M.A.A., M.E.S. Eltahir, A.E.M. Ali, and A.M. Hamdan. 2018. Efficiency of using smart-mobile phones in accessing agricultural information by smallholder farmers in North Kordofan – Sudan. SSRN Electron. J. Doi: 10.2139/ssrn.3240758
  53. Hoeben, R., P.A. Troch, Z. Su, M. Mancini, and K.-S. Chen. 1997, Sensitivity of radar backscattering to soil surface parameters: A comparison between theoretical analysis and experimental evidence. International Geoscience and Remote Sensing Symposium (IGARSS’97), 3, 1368-1370. Doi: 10.1109/igarss.1997.606449
  54. Hoffman, J.E. and S. du Plessis. 1999. Seasonal water requirements of avocado trees grown under subtropical conditions. Rev. Chapingo Ser. Hortic. 5, 191-194.
  55. Holzapfel, E., J.A. Souza, J. Jara, and H.C. Guerra. 2017. Responses of avocado production to variation in irrigation levels. Irrig. Sci. 35(3), 205-215. Doi: 10.1007/s00271-017-0533-0
  56. Hornbuckle, J.W., E.W. Christen, and R.D. Faulkner. 2006. Development of a Pocket PC surface irrigation decision support system. pp. 433-438. In: Proc. 4th World Cong. Conf. Computers in Agriculture and Natural Resources. American Society of Agricultural and Biological Engineers, Orlando, FL. Doi: 10.13031/2013.21913
  57. Hornbuckle, J., J. Vleeshouwer, C. Ballester, J. Montgomery, R. Hoogers, and R. Bridgart. 2016. IrriSAT technical reference. Deakin University, CSIRO Land & Water, NSW DPI, Australia.
  58. Huang, Y., Z.-X. Chen, T. Yu, X.-Z Huang, and X.-F. Gu. 2018, Agricultural remote sensing big data: Management and applications. J. Integr. Agric. 17(9), 1915-1931. Doi: 10.1016/S2095-3119(17)61859-8
  59. International Trade Centre, 2021, Trade Map - Trade statistics for international business development. In: https://www.trademap.org/Index.aspx; consulted: August, 2021.
  60. Irrometer. 2021. Irrometer® reading tools. In: https://www.irrometer.com/loggers.html; consulted: April, 2021.
  61. Islam, N. and R. Want. 2014. Smartphones: Past, present, and future. IEEE Pervasive Comput. 13(4), 89-92. Doi: 10.1109/MPRV.2014.74
  62. IVFL, Institute of Surveying, Remote Sensing & Land Information. 2021, EO4water – Earth observation for water resource management. In: https://eo4water.com/; consulted: April, 2021.
  63. Jalilvand, E., M. Tajrishy, S.A.G.Z. Hashemi, and L. Brocca. 2019. Quantification of irrigation water using remote sensing of soil moisture in a semi-arid region. Remote Sens. Environ. 231, 111226. Doi: 10.1016/j.rse.2019.111226
  64. Jones, H.G., 2004, Irrigation scheduling: advantages and pitfalls of plant-based methods. J. Exp. Bot. 55(407), 2427-2436. Doi: 10.1093/jxb/erh213
  65. Jung, J., M. Maeda, A. Chang, M. Bhandari, A. Ashapure, and J. Landivar-Bowles. 2021. The potential of remote sensing and artificial intelligence as tools to improve the resilience of agriculture production systems. Curr. Opin. Biotechnol. 70, 15-22, Doi: 10.1016/j.copbio.2020.09.003
  66. Kaewmard, N. and S. Saiyod. 2014. Sensor data collection and irrigation control on vegetable crop using smart phone and wireless sensor networks for smart farm. pp. 106-112. In: ICWiSe 2014 IEEE Conference on Wireless Sensors. Subang, Malaysia. Doi: 10.1109/ICWISE.2014.7042670
  67. Kalmar, D. and E. Lahav. 1977. Water requirements of avocado in Israel. I. Tree and soil parameters. Aust. J. Agric. Res. 28(5), 859-868. Doi: 10.1071/ar9770859
  68. Kamilaris, A., A. Kartakoullis, and F. X. Prenafeta-Boldú. 2017. A review on the practice of big data analysis in agriculture. Comput. Electron. Agric. 143, 23-37. Doi: 10.1016/j.compag.2017.09.037
  69. Kamilaris, A. and F.X. Prenafeta-Boldú. 2018. Deep learning in agriculture: A survey. Comput. Electron. Agric. 147, 70-90. Doi: 10.1016/j.compag.2018.02.016
  70. Karmas, A., A. Tzotsos, and K. Karantzalos. 2016. Geospatial big data for environmental and agricultural applications. pp. 353-390. In: Yu, S. and S. Guo (eds.). Big data concepts, theories, and applications. Springer, Cham, Switzerland. Doi: 10.1007/978-3-319-27763-9_10
  71. Karthikeyan, L., M. Pan, N. Wanders, D.N. Kumar, and E.F. Wood. 2017. Four decades of microwave satellite soil moisture observations: Part 1. A review of retrieval algorithms. Adv. Water Resour. 109, 106-120. Doi: 10.1016/j.advwatres.2017.09.006
  72. Khabba, S., L. Jarlan, S. Er-Raki, M. Le Page, J. Ezzahar, G. Boulet, V. Simonneaux, M.H. Kharrou, L. Hanich, and G. Chehbouni. 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 Environ. Sci. 19, 524-533. Doi: 10.1016/j.proenv.2013.06.059
  73. Kisi, O., 2011. Modeling reference evapotranspiration using evolutionary neural networks. J. Irrig. Drain. Eng. 137(10), 636-643. Doi: 10.1061/(asce)ir.1943-4774.0000333
  74. Knipper, K., W.P. Kustas, M.C. Anderson, J.G. Alfieri, J.H. Prueger, C.R.Hain, F. Gao, Y. Yang, L.G. Mckee, H. Nieto, L. E. Hipps, M.M. Alsina, and L. Sanchez. 2018. Evapotranspiration estimates derived using thermal-based satellite remote sensing and data fusion for irrigation management in California vineyards. Irrig. Sci. 37(3), 431-449. Doi: 10.1007/s00271-018-0591-y
  75. Kramer, P. 1983. Water relations of plants. Academic Press, San Francisco, CA. pp. 187-214.
  76. Kweon, S.-K. and Y. Oh. 2015. A modified water-cloud model with leaf angle parameters for microwave backscattering from agricultural fields. IEEE Trans. Geosci. Remote Sens. 53(5), 2802-2809. Doi: 10.1109/TGRS.2014.2364914
  77. Lahav, E. and D. Kalmar. 1977. Water requirements of avocado in Israel. II. Influence on yield, fruit growth and oil content. Crop Pasture Sci. 28(5), 869-877. Doi: 10.1071/AR9770869
  78. Lahav, E. and D. Kalmar. 1983. Determination of the irrigation regimen for an avocado plantation in spring and autumn. Aust. J. Agric. Res. 34(6), 717-724. Doi: 10.1071/AR9830717
  79. Lawston, P.M., J.A. Santanello Jr, and S.V. Kumar. 2017. Irrigation signals detected from SMAP soil moisture retrievals. Geophys. Res. Lett. 44(23), 860-867. Doi: 10.1002/2017GL075733
  80. Li, W., M. Awais, W. Ru, W. Shi, M. Ajmal, S. Uddin, and C. Liu. 2020. Review of sensor network-based irrigation systems using IoT and remote sensing. Adv. Meteorol. 2020, 8396164. Doi: 10.1155/2020/8396164
  81. Li, J. and D.P. Roy. 2017. A global analysis of Sentinel-2A, Sentinel-2B and Landsat-8 data revisit intervals and implications for terrestrial monitoring. Remote Sens. 9(9), 902. Doi: 10.3390/rs9090902
  82. Linker, R. and G. Sylaios. 2016. Efficient model-based sub-optimal irrigation scheduling using imperfect weather forecasts. Comput. Electron. Agric. 130, 118-127. Doi: 10.1016/j.compag.2016.10.004
  83. Lozac’h, L., H. Bazzi, N. Baghdadi, M. El Hajj, M. Zribi, and R. Cresson. 2020. Sentinel-1/Sentinel-2-derived soil moisture product at plot scale (S2MP). pp. 168-171. In: 2020 Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS). Tunis, Tunisia. Doi: 10.1109/M2GARSS47143.2020.9105210
  84. LP Laboratories. 2019. Chloe irrigation systems. In: Apps Google Play, https://play.google.com/store/apps/details?id=com.chloeirrigation.chloe&hl=en&gl=US; consulted: May, 2021.
  85. Lynks Ingeniería, 2016, Manual LYNKBOX-Meteo. Monitoreo de variables ambientales y de suelos V 1.0. Santiago de Cali, Colombia.
  86. Ma, Y., S. Liu, L. Song, Z. Xu, Y. Liu, T. Xu, and Z. Zhu. 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 Sens. Environ. 216, 715-734. Doi: 10.1016/j.rse.2018.07.019
  87. Madry, S. 2017. Introduction and history of space remote sensing. pp. 823-832. In: Pelton, J.N., S. Madry, and S. Camacho-Lara (eds.). Handbook of satellite applications. Springer International Publishing, Cham, Switzerland. Doi: 10.1007/978-3-319-23386-4_37
  88. Mamalakis, A. and E. Foufoula-Georgiou. 2018. A multivariate probabilistic framework for tracking the intertropical convergence zone: Analysis of recent climatology and past trends. Geophys. Res. Lett. 45(23), 80-89. Doi: 10.1029/2018GL079865
  89. Mbabazi, D., K.W. Migliaccio, J.H. Crane, C. Fraisse, L. Zotarelli, K.T. Morgan, and N. Kiggundu. 2017. An irrigation schedule testing model for optimization of the Smartirrigation avocado app. Agric. Water Manag. 179, 390-400. Doi: 10.1016/j.agwat.2016.09.006
  90. McCabe, G.J. and D.M. Wolock. 2013. Temporal and spatial variability of the global water balance. Climatic Change 120(1–2), 375-387. Doi: 10.1007/s10584-013-0798-0
  91. McPhaden, M.J., S.E. Zebiak, and M.H. Glantz. 2006. ENSO as an integrating concept in earth science. Science 314(5806), 1740-1745. Doi: 10.1126/SCIENCE.1132588
  92. Mendes, W.R., F.M.U. Araújo, R. Dutta, and D.M. Heeren. 2019. Fuzzy control system for variable rate irrigation using remote sensing. Expert Syst. Appl. 124, 13-24. Doi: 10.1016/j.eswa.2019.01.043
  93. Migliaccio, K., K.T. Morgan, G. Vellidis, L. Zotarelli, C. Fraisse, B.A. Zurweller, J.H. Andreis, J.H. Crane, and D. Rowland. 2016. Smartphone apps for irrigation scheduling. Trans. ASABE 59(1), 291-301. Doi: 10.13031/trans.59.11158
  94. Miller, L., G. Vellidis, O. Mohawesh, and T. Coolong. 2018. Comparing a smartphone irrigation scheduling application with water balance and soil moisture-based irrigation methods: Part I—plasticulture-grown tomato. HortTechnol. 28(3), 354-361. Doi: 10.21273/HORTTECH04010-18
  95. Miyazaki, T. 2005. Soil and water. pp. 1-17. In: Miyazaki, T. (ed.). Water flow in soils. 2nd ed. CRC Press, Boca Raton, FL.
  96. Molina-Martínez, J.M. and A. Ruiz-Canales. 2009. Pocket PC software to evaluate drip irrigation lateral diameters with on-line emitters. Comput. Electron. Agric. 69(1), 112-115. Doi: 10.1016/j.compag.2009.06.006
  97. Moreno-Ortega, G., C. Pliego, D. Sarmiento, A. Barceló, and E. Martínez-Ferri. 2019. Yield and fruit quality of avocado trees under different regimes of water supply in the subtropical coast of Spain. Agric. Water Manag. 221, 192-201. Doi: 10.1016/j.agwat.2019.05.001
  98. Mottaleb, K. 2018. Perception and adoption of a new agricultural technology: Evidence from a developing country. Technol. Soc. 55, 126-135. Doi: 10.1016/j.techsoc.2018.07.007
  99. Nawandar, N. and V.R. Satpute. 2019. IoT based low cost and intelligent module for smart irrigation system. Comput. Electron. Agric. 162, 979-990. Doi: 10.1016/j.compag.2019.05.027
  100. Ng Cheong, L.R. and M. Teeluck. 2018. Development of an irrigation scheduling software for sugarcane. Sugar Tech 20(1), 36-39. Doi: 10.1007/s12355-017-0517-7
  101. Nhamo, L., G.Y. Ebrahim, T. Mabhaudhi, S. Mpandeli, M. Magombeyi, M. Chitakira, J. Magidi, and M. Sibanda. 2020. An assessment of groundwater use in irrigated agriculture using multi-spectral remote sensing. Phys. Chem. Earth, Part A/B/C 115, 102810. Doi: 10.1016/j.pce.2019.102810
  102. Olmedo, G.F. and D. de la Fuente-Saiz. 2018. Surface energy balance using METRIC model and water package: 2. advanced procedure. In: https://mran.microsoft.com/snapshot/2018-05-16/web/packages/water/vignettes/METRIC_advanced.html; consulted: April, 2020.
  103. Oyarce, P. and L. Gurovich. 2010. Electrical signals in avocado trees. Plant Signal. Behav. 5(1), 34-41. Doi: 10.4161/psb.5.1.10157
  104. Le Page, M., L. Jarlan, M.M. El Hajj, M. Zribi, N. Baghdadi, and A. Boone. 2020. Potential for the detection of irrigation events on maize plots using Sentinel-1 soil moisture products. Remote Sens. 12(10), 1621. Doi: 10.3390/rs12101621
  105. Parikh, H., S. Patel, and V. Patel, 2020, Classification of SAR and PolSAR images using deep learning: a review. Int. J. Image Data Fusion 11(1), 1-32. Doi: 10.1080/19479832.2019.1655489
  106. Pelton, J.N., Madry, S., Camacho-Lara, S., 2017. Satellite Applications Handbook: The Complete Guide to Satellite Communications, Remote Sensing, Navigation, and Meteorology, in: Pelton, J.N., Madry, S., Camacho-Lara, S. (Eds.), Handbook of Satellite Applications. Springer International Publishing, pp. 4–19. https://doi.org/https://doi.org/10.1007/978-3-319-23386-4
  107. Peng, J., C. Albergel, A. Balenzano, L. Brocca, O. Cartus, M.H. cosh, W.T. Crow, K. Dabrowska-Zielinska, S. Dadson, M.W.J. Davidson, P. de Rosnay, W. Dorigo, A. Gruber, S. Hagemann, M. Hirschi, Y.H. Kerr, F. Lovergine, M.D. Mahecha, F. Marzahn, F. Mattia, J.P. Musial, S. Preuschmann, R.H. Reichle, G. Satalino, M. Silgram, P.M. van Bodegom, N.E.C. Verhoest, W. Wagner, J.P. Walker, U. Wegmüller, and A. Loew. 2021. A roadmap for high-resolution satellite soil moisture applications – confronting product characteristics with user requirements. Remote Sens. Environ. 252, 112162. Doi: 10.1016/j.rse.2020.112162
  108. Piedelobo, L., D. Ortega-Terol, S. Del Pozo, D. Hernández-López, R. Ballesteros, M.A. Moreno, J.-L. Molina, and D. González-Aguilera. 2018. HidroMap: A new tool for irrigation monitoring and management using free satellite imagery. ISPRS Int. J. Geo-Inf. 7(6), 220. Doi: 10.3390/ijgi7060220
  109. Pongnumkul, S., P. Chaovalit, and N. Surasvadi. 2015. Applications of smartphone-based sensors in agriculture: A systematic review of research. J. Sens. 2015, 195308. Doi: 10.1155/2015/195308
  110. Prudente, V.H.R., V.S. Martins, D.C. Vieira, N.R.F. Silva, M. Adami, and I.D.A. Sanches. 2020. Limitations of cloud cover for optical remote sensing of agricultural areas across South America. Remote Sens. Appl.: Soc. Environ. 20, 100414. Doi: 10.1016/j.rsase.2020.100414
  111. Puértolas, J., D. Johnson, I.C. Dodd, and S.A. Rothwell. 2019. Can we water crops with our phones? Smartphone technology application to infrared thermography for use in irrigation management. Acta Hortic. 1253, 443-448. Doi: 10.17660/ActaHortic.2019.1253.58
  112. Quebrajo, L., M. Perez-Ruiz, L. Pérez-Urrestarazu, G. Martínez, and G. Egea. 2018. Linking thermal imaging and soil remote sensing to enhance irrigation management of sugar beet. Biosyst. Eng. 165, 77-87. Doi: 10.1016/j.biosystemseng.2017.08.013
  113. Raes, D., 2002, BUDGET: A soil water and salt balance model. Reference manual v 5.0. Institute for Land and Water Management, Leuven, Belgium.
  114. Ramírez-Gil, J. G., D. Castañeda-Sánchez, and J.G. Morales-Osorio. 2021. Edaphic factors associated with the development of avocado wilt complex and implementation of a GIS tool for risk visualization. Sci. Hortic. 288, 110316. Doi: 10.1016/j.scienta.2021.110316
  115. Ramírez-Gil, J.G., M.E. Cobos, D. Jiménez-García, J.G. Morales-Osorio, and A. T. Peterson. 2019. Current and potential future distributions of Hass avocados in the face of climate change across the Americas. Crop and Pasture Sci. 70(8), 694-708. Doi: 10.1071/CP19094
  116. Ramírez-Gil, J.G., J.C. Henao-Rojas, and J.G. Morales-Osorio. 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), 790. Doi: 10.3390/plants9060790
  117. Ramírez-Gil, J.G., G.O. Giraldo Martínez, and J.G. Morales Osorio. 2018b. Design of electronic devices for monitoring climatic variables and development of an early warning system for the avocado wilt complex disease. Comput. Electron. Agric. 153, 134-143. Doi: 10.1016/j.compag.2018.08.002
  118. Ramírez-Gil, J.G., J.G. Morales, and A.T. Peterson. 2018a. Potential geography and productivity of “Hass” avocado crops in Colombia estimated by ecological niche modeling. Sci. Hortic. 237, 287-295. Doi: 10.1016/j.scienta.2018.04.021
  119. Ranjan, R., A.K. Chandel, L.R. Khot, H.Y. Bahlol, J. Zhou, R.A. Boydston, and P.N. Miklas. 2019. Irrigated pinto bean crop stress and yield assessment using ground based low altitude remote sensing technology. Inf. Process. Agric. 6(4), 502-514. Doi: 10.1016/j.inpa.2019.01.005
  120. Reddy, G.P.O. 2018. Satellite remote sensing sensors: Principles and applications. pp. 21-43. In: Reddy, G.P.O. and S.K. Singh (eds.). Geospatial technologies in land resources mapping, monitoring and management. Geotechnologies and the Environment. Vol. 21. Springer International Publishing, Cham, Switzerland. Doi: 10.1007/978-3-319-78711-4_2
  121. Richards, S.J., J.E. Warneke, and F.T. Bingham. 1962. Avocado tree growth response to irrigation. California Avocado Society 46, 83-87.
  122. Richter, M. 2016, Precipitation in the tropics. pp. 363-390. In: Pancel, L. and M. Köhl (eds.). Tropical forestry handbook. Springer, Berlin. Doi: 10.1007/978-3-642-54601-3_34
  123. Rijswijk, K., L. Klerkx, and J.A. Turner. 2019. Digitalisation in the New Zealand agricultural knowledge and innovation system: Initial understandings and emerging organisational responses to digital agriculture. NJAS – Wagening. J. Life Sci. 90-91, 100313. Doi: 10.1016/j.njas.2019.100313
  124. Rodríguez, C., J.R. Francia, I.F. García, B. Gálvez, D. Franco, and V.H. Durán. 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. pp. 317-346. In: García, I.F. and V.H. Durán (eds.). Water scarcity and sustainable agriculture in semiarid environment. Elsevier, New York, NY. Doi: 10.1016/B978-0-12-813164-0.00014-4
  125. Román-Paoli, E., F.M. Román-Pérez, and J. Zamora-Echevarría. 2009. Evaluation of microirrigation levels for growth and productivity of avocado trees. J. Agric. Univ. P. R. 93(3-4), 173-186. Doi: 10.46429/jaupr.v93i3-4.5465
  126. Rose, D.C. and J. Chilvers. 2018. Agriculture 4.0: Broadening responsible innovation in an Era of smart farming. Front. Sustain. Food Syst. 2, 87. Doi: 10.3389/fsufs.2018.00087
  127. Salas, J.D., R.S. Govindaraju, M. Anderson, M. Arabi, F. Francés, W. Suarez, W.S. Lavado-Casimiro, and T.R. Green. 2014. Introduction to hydrology. pp. 1-126. In: Wang, L.K. and C.T. Yang (eds.). Handbook of Environmental Engineering. Vol 15: Modern water resources engineering. Humana Press, Totowa, NJ. Doi: 10.1007/978-1-62703-595-8_1
  128. Sales Dantas, A., M. Vasconcelos da Gama Neto, I. Dimitry Zyrianoff, and C.A. Kamienski. 2020. The SWAMP farmer App for IoT-based smart water status monitoring and irrigation control. pp. 109-113. In: 2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor). Trento, Italy. Doi: 10.1109/MetroAgriFor50201.2020.9277588
  129. Scanlon, B.R., B.J. Andraski, and J. Bilskie. 2002. 3.2.4 Miscellaneous methods for measuring matric or water potential. pp. 643-670. In: Dane, J.H. and G.C. Topp (eds.). Methods of soil analysis. Part 4: Physical methods. Soil Science Society of America, Madison, WI. Doi: 10.2136/sssabookser5.4.c23
  130. Schaffer, B., P.M. Gil, M.V. Mickelbart, and A.W. Whiley. 2013. Ecophysiology. 168-199. In: Schaffer, B., B.N. Wolstenholme, and A.W. Whiley (eds.). The avocado: Botany, production and use. CAB International, Wallingford, UK. Doi: 10.1079/9781845937010.0168
  131. Schowengerdt, R.A., 2007. Remote sensing: Models and methods for image processing. 3rd ed. Elsevier, Amsterdam. Doi: 10.1016/B978-0-12-369407-2.X5000-1
  132. Schulz, S., R. Becker, J.C. Richard‐Cerda, M. Usman, T. aus der Beek, R. Merz, and C. Schüth. 2021. Estimating water balance components in irrigated agriculture using a combined approach of soil moisture and energy balance monitoring, and numerical modelling. Hydrol. Process. 35(3), e14077. Doi: 10.1002/hyp.14077
  133. Sentek. 2019. IrriMAX software desktop, 10.1. In: https://sentektechnologies.com/download/irrimax-desktop/; consulted: April, 2020.
  134. Sharma, R., S.S. Kamble, A. Gunasekaran, V. Kumar, and A. Kumar. 2020. A systematic literature review on machine learning applications for sustainable agriculture supply chain performance. Comput. Oper. Res. 119, 104926. Doi: 10.1016/j.cor.2020.104926
  135. Sigua, G.C., K.C. Stone, P.J. Bauer, A.A. Szogi, and P.D. Shumaker. 2017. Impacts of irrigation scheduling on pore water nitrate and phosphate in coastal plain region of the United States. Agric. Water Manag. 186, 75-85, Doi: 10.1016/j.agwat.2017.02.016
  136. Silber, A., A. Naor, H. Cohen, Y. Bar-Noy, N. Yechieli, M. Levi, M. Noy, M. Peres, D. Duari, K. Narkis, and S. Assouline. 2019. Irrigation of ‘Hass’ avocado: Effects of constant vs. temporary water stress. Irrig. Sci. 37(4), 451-460. Doi: 10.1007/s00271-019-00622-w
  137. Silber, A., Y. Israeli, M. Levi, A. Keinan, O. Shapira, G. Chudi, A. Golan, M. Noy, I. Levkovitch, and S. Assouline. 2012. Response of ‘Hass’ avocado trees to irrigation management and root constraint. Agric. Water Manag. 104, 95-103. Doi: 10.1016/j.agwat.2011.12.003
  138. Silber, A., A. Naor, Y. Israeli, and S. Assouline. 2013. Combined effect of irrigation regime and fruit load on the patterns of trunk-diameter variation of ‘Hass’ avocado at different phenological periods. Agric. Water Manag. 129, 87-94. Doi: 10.1016/j.agwat.2013.07.015
  139. Silva, A.O., B.A. Silva, C.F. Souza, B.M. Azevedo, L.H. Bassoi, D.V. Vasconcelos, G.V. Bonfim, J.M. Juarez, A.F. Santos, and F.M. Carneiro. 2020. Irrigation in the age of agriculture 4.0: management, monitoring and precision. Rev. Cienc. Agron. 51(Spec. Agric. 4.0), e20207695. Doi: 10.5935/1806-6690.20200090
  140. Silva, A.M., R.M. Silva, and C.A.G. Santos. 2019. Automated surface energy balance algorithm for land (ASEBAL) based on automating endmember pixel selection for evapotranspiration calculation in MODIS orbital images. Int. J. Appl. Earth Obs. Geoinf. 79, 1-11. Doi: 10.1016/j.jag.2019.02.012
  141. Simionesei, L., T.B. Ramos, J. Palma, A.R. Oliveira, and R. Neves. 2020. IrrigaSys: A web-based irrigation decision support system based on open source data and technology. Comput. Electron. Agric. 178, 105822. Doi: 10.1016/j.compag.2020.105822
  142. Singh, G., A. Singh, and G. Kaur. 2021. Role of artificial intelligence and the internet of things in agriculture. pp. 317-330. In: Kaur, G., P. Tomar, and M. Tanque (eds.), Artificial intelligence to solve pervasive internet of things issues. Elsevier, London. Doi: 10.1016/b978-0-12-818576-6.00016-2
  143. Singhroy, V. 2017. Operational applications of radar images. pp. 911-928. In: Pelton, J.N., S. Madry, and S. Camacho-Lara (eds.). Handbook of satellite applications. Springer, Cham, Switzerland. Doi: 10.1007/978-3-319-23386-4
  144. Sinha, S., A. Santra, L. Sharma, C. Jeganathan, M.S. Nathawat, A.K. Das, and S. Mohan. 2018. Multi-polarized Radarsat-2 satellite sensor in assessing forest vigor from above ground biomass. J. For. Res. 29(4), 1139-1145. Doi: 10.1007/s11676-017-0511-7
  145. Sishodia, R.P., R.L. Ray, and S.K. Singh. 2020. Applications of remote sensing in precision agriculture: A review. Remote Sens. 12(19), 3136. Doi: 10.3390/rs12193136
  146. Smith, M. 1992, CROPWAT: A computer program for irrigation planning and management. FAO Irrigation and Drainage Paper 46. Rome.
  147. Smith, M.J. 2018. Getting value from artificial intelligence in agriculture. Anim. Prod. Sci. 60(1), 46-54. Doi: 10.1071/AN18522
  148. Taiz, L. and E. Zeiger. 2002. Plant physiology. 3rd ed. Sinauer Associates, Sunderland, UK. pp. 591-623.
  149. Tamiminia, H., B. Salehi, M. Mahdianpari, L. Quackenbush, S. Adeli, and B. Brisco. 2020. Google earth engine for geo-big data applications: A meta-analysis and systematic review. ISPRS J. Photogramm. Remote Sens. 164, 152-170. Doi: 10.1016/j.isprsjprs.2020.04.001
  150. Tempfli, K., N. Kerle, G.C. Huurneman, and L.L.F. Janssen (eds.). 2009. Principles of remote sensing: An introductory textbook. The International Institute for Geo-Information Science and Earth Observation (ITC), Enschede, The Netherlands.
  151. Turner, D., A. Neuhaus, T. Colmer, A. Blight, and B.A. Whiley. 2001. Turner et al 1 Turning water into oil- physiology and efficiency. pp. 1-12. In: Scotney, C. (ed.). Talking avocados. Australian Avocado Growers’ Federation, Bundaberg, Australia.
  152. Tzatzani, T.T., N. Kavroulakis, G. Doupis, G. Psarras, and I.E. Papadakis. 2020. Nutritional status of ‘Hass’ and ‘Fuerte’ avocado (Persea americana Mill.) plants subjected to high soil moisture. J. Plant Nutr. 43(3), 327-334. Doi: 10.1080/01904167.2019.1683192
  153. UNL, University of Nebraska-Lincoln. 2019. Crop Water App. In: https://ianr.unl.edu/crop-water-app; consulted: August, 2019.
  154. Van Pelt, R.S. and P.J. Wierenga. 2001. Temporal stability of spatially measured soil matric potential probability density function. Soil Sci. Soc. Am. J. 65(3), 668-677. Doi: 10.2136/sssaj2001.653668x
  155. Vellidis, G., V. Liakos, J.H. Andreis, C.D. Perry, W.M. Porter, E.M. Barnes, K.T. Morgan, C. Fraisse, and K.W. Migliaccio. 2016. Development and assessment of a smartphone application for irrigation scheduling in cotton. Comput. Electron. Agric. 127, 249-259. Doi: 10.1016/j.compag.2016.06.021
  156. Veysi, S., A.A. Naseri, S. Hamzeh, and H. Bartholomeus. 2017. A satellite based crop water stress index for irrigation scheduling in sugarcane fields. Agric. Water Manag. 189, 70-86- Doi: 10.1016/j.agwat.2017.04.016
  157. Vollrath, A., A. Mullissa, and J. Reiche. 2020. Angular-based radiometric slope correction for Sentinel-1 on google earth engine. Remote Sens. 12(11), 1867. Doi: 10.3390/rs12111867
  158. Vuthapanich, S., P.J. Hofman, A.W. Whiley, A. Klieber, and D.H. Simons. 1995. Effects of irrigation and foliar Cultar® on fruit yield and quality of “Hass” avocado fruit. pp. 311-315. In: Proc. Word Avocado Congress III. Israel.
  159. Weiss, M., F. Jacob, and G. Duveiller. 2020. Remote sensing for agricultural applications: A meta-review. Remote Sens. Environ. 236, 111402. Doi: 10.1016/j.rse.2019.111402
  160. Whiley, A., 1994, Ecophysiological studies and tree manipulation for maximisation of yield potential in avocado (Persea americana Mill.). PhD thesis. Department of Horticultural Science, University of Natal, Pietermaritzburg, South Africa.
  161. Winer, L. and I. Zachs. 2007. Daily trunk contraction in relation to a base line as an improved criterion for irrigation in avocado. pp. 1-7. In: Proc. VI World Avocado Congress, Viña Del Mar, Chile.
  162. Xie, Y., T.J. Lark, J.F. Brown, and H.K. Gibbs. 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 J. Photogramm. Remote Sens. 155, 136-149. Doi: 10.1016/j.isprsjprs.2019.07.005
  163. Xue, J., K.M. Bali, S. Light, T. Hessels, and I. Kisekka. 2020. Evaluation of remote sensing-based evapotranspiration models against surface renewal in almonds, tomatoes and maize. Agric. Water Manag. 238, 106228. Doi: 10.1016/j.agwat.2020.106228
  164. Yang, G., L. Liu, P. Guo, and M. Li. 2017, A flexible decision support system for irrigation scheduling in an irrigation district in China. Agric. Water Manag. 179, 378-389. Doi: 10.1016/j.agwat.2016.07.019
  165. Yohannes, D.F., C.J. Ritsema, Y. Eyasu, H. Solomon, J.C. van Dam, J. Froebrich, H.P. Ritzema, and A. Meressa. 2019. A participatory and practical irrigation scheduling in semiarid areas: the case of Gumselassa irrigation scheme in Northern Ethiopia. Agric. Water Manag. 218, 102-114. Doi: 10.1016/j.agwat.2019.03.036
  166. Zohaib, M., H. Kim, and M. Choi. 2019. Detecting global irrigated areas by using satellite and reanalysis products. Sci. Total Environ. 677, 679-691. Doi: 10.1016/j.scitotenv.2019.04.365

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