AUTOMATED EVAPOTRANSPIRATION: a systematic review and meta-analysis
DOI:
https://doi.org/10.20873/uft.2447-4266.2023v9n1a41ptKeywords:
Eficiência no uso da água, Automação, Evapotranspiração, Revisão sistemáticaAbstract
Given the scarcity of water resources that has been worsening over time and the high consumption of these resources by agribusiness, there is a need for studies that can manage such action in a sustainable way, providing food security for the present and future world population. Background: with the topic: What is the systemic view of automated models and techniques for determining or estimating transpiration, evaporation or evapotranspiration for plantations? Objectives: to identify in recent literature what researchers and scientists have disclosed about automation methods for supervision, with a focus on estimating evapotranspiration. identify methods, models and techniques for inferring evapotranspiration. Methods: A methodology based on exploratory theoretical testing with qualitative and quantitative characteristics through Systematic Review and Meta-Analysis of data. Results: Using specific software and methods, simulation studies with experimental data make it possible to calibrate efficient models to estimate evapotranspiration, but low-cost methods still have little adherence.
Downloads
References
Ackerley, D., & Dommenget, D. (2016). Atmosphere-only GCM (ACCESS1.0) simulations with prescribed land surface temperatures. Geoscientific Model Development, 9(6), 2077–2098. https://doi.org/10.5194/gmd-9-2077-2016
Al-Ghobari, H. M., Mohammad, F. S., El Marazky, M. S. A., & Dewidar, A. Z. (2017). Automated irrigation systems for wheat and tomato crops in arid regions. Water SA, 43(2), 354–364. https://doi.org/10.4314/wsa.v43i2.18
Allen, R. G. & FAO (Orgs.). (1998). Crop evapotranspiration: Guidelines for computing crop water requirements. Food and Agriculture Organization of the United Nations.
Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (2006). Evapotranspiración del cultivo Guías para la determinación de los requerimientos de agua de los cultivos. Food & Agriculture Org. https://www.fao.org/3/x0490s/x0490s00.htm
Amorim, R. C. F. de. (2009). Espacialização de variáveis meteorológicas combinando informações de imagens de satélite com técnicas de interpolação para o estado de Minas Gerais [Agrometeorologia; Climatologia; Micrometeorologia, Universidade Federal de Viçosa]. https://locus.ufv.br//handle/123456789/1489
Anjos, R. M. dos. (2015). Irrigação deficitária controlada por etapas na cultura do melão, utilizando o modelo MOPECO. http://www.repositorio.ufc.br/handle/riufc/18069
Attia, A., El-Hendawy, S., Al-Suhaibani, N., Alotaibi, M., Tahir, M. U., & Kamal, K. Y. (2021). Evaluating deficit irrigation scheduling strategies to improve yield and water productivity of maize in arid environment using simulation. Agricultural Water Management, 249, 106812. https://doi.org/10.1016/j.agwat.2021.106812
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, 96–105. https://doi.org/10.1016/j.jag.2018.10.016
Bai, T., Zhang, N., Wang, T., Wang, D., Yu, C., Meng, W., Fei, H., Chen, R., Li, Y., & Zhou, B. (2021). Simulating on the effects of irrigation on jujube tree growth, evapotranspiration and water use based on crop growth model. Agricultural Water Management, 243, 106517. https://doi.org/10.1016/j.agwat.2020.106517
Barbieri, J. D., Dallacort, R., Daniel, D. F., Dalchiavon, F. C., & Freitas, P. S. L. de. (2020). Cobertura do solo, evapotranspiração e produtividade do milho safrinha. Revista Cultura Agronômica, 29(1), 76–91. https://doi.org/10.32929/2446-8355.2020v29n1p76-91
Bhattacharyya, S. S., Adeyemi, M. A., Onyeneke, R. U., Bhattacharyya, S., Faborode, H. F. B., Melchor-Martínez, E. M., Iqbal, H. M. N., & Parra-Saldívar, R. (2021). Nutrient Budgeting—A Robust Indicator of Soil–Water–Air Contamination Monitoring and Prevention. Environmental Technology & Innovation, 24, 101944. https://doi.org/10.1016/j.eti.2021.101944
Bowen, I. S. (1926). The Ratio of Heat Losses by Conduction and by Evaporation from any Water Surface. Physical Review, 27(6), 779–787. https://doi.org/10.1103/PhysRev.27.779
Chen, M., Cui, Y., Wang, X., Xie, H., Liu, F., Luo, T., Zheng, S., & Luo, Y. (2021). A reinforcement learning approach to irrigation decision-making for rice using weather forecasts. Agricultural Water Management, 250, 106838. https://doi.org/10.1016/j.agwat.2021.106838
Costa Filho, S. V. S. da, Arce, J. E., Montaño, R. A. N. R., & Pelissari, A. L. (2019). Configuração de algoritmos de aprendizado de máquina na modelagem florestal: Um estudo de caso na modelagem da relação hipsométrica. Ciência Florestal, 29(4), 1501–1515. https://doi.org/10.5902/1980509828392
da Silva Júnior, J. C., Medeiros, V., Garrozi, C., Montenegro, A., & Gonçalves, G. E. (2019). Random forest techniques for spatial interpolation of evapotranspiration data from Brazilian’s Northeast. Computers and Electronics in Agriculture, 166, 105017. https://doi.org/10.1016/J.COMPAG.2019.105017
Dela Cruz, J. C., Caya, M. V. C., Ballado, A. H., Aggabao, M. C. R., Bacolor, E. I., Riego, H. A. G. G., & Vergara, M. E. M. (2020). Evapotranspiration-based Irrigation System for Mustard Green Crop Cultivation using Public Weather Forecast. 2020 11th IEEE Control and System Graduate Research Colloquium (ICSGRC), 203–208. https://doi.org/10.1109/ICSGRC49013.2020.9232454
Diepen, C. A., Wolf, J., Keulen, H., & Rappoldt, C. (1989). WOFOST: A simulation model of crop production. Soil Use and Management, 5(1), 16–24. https://doi.org/10.1111/j.1475-2743.1989.tb00755.x
Dingre, S. K., & Gorantiwar, S. D. (2020). Determination of the water requirement and crop coefficient values of sugarcane by field water balance method in semiarid region. Agricultural Water Management, 232, 106042. https://doi.org/10.1016/J.AGWAT.2020.106042
Elsevier Inc. (2021). Mendeley Data [software] (https://data.mendeley.com/; 2019.2.1) [Mendeley]. Mendeley Ltd.; 2022 Elsevier B.V.
Fabbri, S., Silva, C., Hernandes, E., Octaviano, F., Di Thommazo, A., & Belgamo, A. (2016). Improvements in the StArt tool to better support the systematic review process. Proceedings of the 20th International Conference on Evaluation and Assessment in Software Engineering, 1–5. https://doi.org/10.1145/2915970.2916013
FAO. (2022). World Food Studies Simulation Model (WOFOST) | Tierras y Aguas | Organización de las Naciones Unidas para la Alimentación y la Agricultura | Land & Water | Food and Agriculture Organization of the United Nations. FAO - Food and Agriculture Organization of the United Nations. https://www.fao.org/land-water/land/land-governance/land-resources-planning-toolbox/category/details/es/c/1236431/
Frédette, C., Grebenshchykova, Z., Comeau, Y., & Brisson, J. (2019). Evapotranspiration of a willow cultivar (Salix miyabeana SX67) grown in a full-scale treatment wetland. Ecological Engineering, 127, 254–262. https://doi.org/10.1016/j.ecoleng.2018.11.027
GT Agenda 2030. (2022).VI Relatório Luz da Sociedade Civil da Agenda 2030 de Desenvolvimento Sustentável Brasil (Relatório Luz sobre a Agenda 2030 no Brasil 2022 VI). https://gtagenda2030.org.br/relatorio-luz/relatorio-luz-2022/
Guyatt, G. H., Oxman, A. D., Kunz, R., Vist, G. E., Falck-Ytter, Y., & Schünemann, H. J. (2008). What is “quality of evidence” and why is it important to clinicians? BMJ: British Medical Journal, 336(7651), 995–998. https://doi.org/10.1136/bmj.39490.551019.BE
Guyatt, G. H., Oxman, A. D., Montori, V., Vist, G., Kunz, R., Brozek, J., Alonso-Coello, P., Djulbegovic, B., Atkins, D., Falck-Ytter, Y., Williams, J. W., Meerpohl, J., Norris, S. L., Akl, E. A., & Schünemann, H. J. (2011). GRADE guidelines: 5. Rating the quality of evidence—publication bias. Journal of Clinical Epidemiology, 64(12), 1277–1282. https://doi.org/10.1016/j.jclinepi.2011.01.011
Haghverdi, A., Singh, A., Sapkota, A., Reiter, M., & Ghodsi, S. (2021). Developing irrigation water conservation strategies for hybrid bermudagrass using an evapotranspiration-based smart irrigation controller in inland southern California. Agricultural Water Management, 245, 106586. https://doi.org/10.1016/j.agwat.2020.106586
Ihaka, R., & Gentleman, R. (2018). The R Project for Statistical Computing [software] (https://www.r-project.org/; 3.5.1) [R; R: Software Development Life Cycle]. The R Foundation; the Statistics Department of the University of Auckland.
INPE. (2022). LANDSAT [Instituto Nacional de Pesquisas Espaciais (INPE)]. Divisão de Geração de Imagens Instituto Nacional de Pesquisas Espaciais (INPE). http://www.dgi.inpe.br/documentacao/satelites/landsat
Jo, W. J., & Shin, J. H. (2021a). Development of a transpiration model for precise tomato (Solanum lycopersicum L.) irrigation control under various environmental conditions in greenhouse. Plant Physiology and Biochemistry, 162, 388–394. https://doi.org/10.1016/j.plaphy.2021.03.005
Jo, W. J., & Shin, J. H. (2021b). Development of a transpiration model for precise tomato (Solanum lycopersicum L.) irrigation control under various environmental conditions in greenhouse. Plant Physiology and Biochemistry, 162, 388–394. https://doi.org/10.1016/j.plaphy.2021.03.005
Kadlec, R. H., & Wallace, S. D. (2009). Treatment wetlands (2nd ed). CRC Press.
Kamarudin, M. H., Ismail, Z. H., & Saidi, N. B. (2021). Deep Learning Sensor Fusion in Plant Water Stress Assessment: A Comprehensive Review. Applied Sciences, 11(4), 1403. https://doi.org/10.3390/app11041403
Kang, J., Hao, X., Zhou, H., & Ding, R. (2021). An integrated strategy for improving water use efficiency by understanding physiological mechanisms of crops responding to water deficit: Present and prospect. Agricultural Water Management, 255, 107008. https://doi.org/10.1016/j.agwat.2021.107008
Kheir, A. M. S., Alrajhi, A. A., Ghoneim, A. M., Ali, E. F., Magrashi, A., Zoghdan, M. G., Abdelkhalik, S. A. M., Fahmy, A. E., & Elnashar, A. (2021). Modeling deficit irrigation-based evapotranspiration optimizes wheat yield and water productivity in arid regions. Agricultural Water Management, 256, 107122. https://doi.org/10.1016/j.agwat.2021.107122
Kisekka, I., Schlegel, A., Ma, L., Gowda, P. H., & Prasad, P. V. V. (2017). Optimizing preplant irrigation for maize under limited water in the High Plains. Agricultural Water Management, 187, 154–163. https://doi.org/10.1016/j.agwat.2017.03.023
Kitchenham, B., Madeyski, L., & Brereton, P. (2020). Meta-analysis for families of experiments in software engineering: A systematic review and reproducibility and validity assessment. Empirical Software Engineering, 25(1), 353–401. https://doi.org/10.1007/s10664-019-09747-0
Klant, L. M., & Santos, V. S. dos. (2021). O uso do software IRAMUTEQ na análise de conteúdo—Estudo comparativo entre os trabalhos de conclusão de curso do ProfEPT e os referenciais do programa. Research, Society and Development, 10(4), e8210413786. https://doi.org/10.33448/rsd-v10i4.13786
Komatsu, H. (2020). Modeling evapotranspiration changes with managing Japanese cedar and cypress plantations. Forest Ecology and Management, 475, 118395. https://doi.org/10.1016/j.foreco.2020.118395
Kunihiro, S., Vernasque, J. R. da S., Silva, C. da, Santos, M. F. dos, Cremasco, C. P., & Gabriel Filho, L. R. A. (2022). Intersectoral Actions for the Promotion and Prevention of Obesity, Diabetes and Hypertension in Brazilian Cities: A Systematic Review and Meta-Analysis. International Journal of Environmental Research and Public Health, 19(20), 13059. https://doi.org/10.3390/ijerph192013059
Lakatos, E. M., & Marconi, M. de A. (2003). Fundamentos de metodologia científica. Atlas.
LaPES. (2020). StArt (State of the Art through Systematic Review) [software] (Laboratório de Pesquisa em Engenharia de Software; 3.0.3 Beta) [Portuguese; Laboratório de Pesquisa em Engenharia de Software]. Universidade Federal de São Carlos (DC/UFSCar).
Lei, H., Gong, T., Zhang, Y., & Yang, D. (2018). Biological factors dominate the interannual variability of evapotranspiration in an irrigated cropland in the North China Plain. Agricultural and Forest Meteorology, 250–251, 262–276. https://doi.org/10.1016/j.agrformet.2018.01.007
Leite, A. R., Imada, R. N., Mantovani, J. P., Mantovani, L. P., Neves, F. P., Siqueira, J. D. M., Santos, P. S. B. D., Silva, C. da, Cremasco, C. P., & Gabriel Filho, L. R. A. (2022). Automated Evapotranspiration Research Dataset used in a Systematic Review and Meta-analysis. 1. https://doi.org/10.17632/89wr243dbt.1
Liao, R., Zhang, S., Zhang, X., Wang, M., Wu, H., & Zhangzhong, L. (2021). Development of smart irrigation systems based on real-time soil moisture data in a greenhouse: Proof of concept. Agricultural Water Management, 245, 106632. https://doi.org/10.1016/j.agwat.2020.106632
Liu, J., Cheng, F., Munger, W., Jiang, P., Whitby, T. G., Chen, S., Ji, W., & Man, X. (2020). Precipitation extremes influence patterns and partitioning of evapotranspiration and transpiration in a deciduous boreal larch forest. Agricultural and Forest Meteorology, 287, 107936. https://doi.org/10.1016/j.agrformet.2020.107936
Liu, Z., & Xu, Q. (2018). An Automatic Irrigation Control System for Soilless Culture of Lettuce. Water, 10(11), 1692. https://doi.org/10.3390/W10111692
Mancini, M. C., Cardoso, J. R., Sampaio, R. F., Costa, L. C. M., Cabral, C. M. N., & Costa, L. O. P. (2014). Tutorial for writing systematic reviews for the Brazilian Journal of Physical Therapy (BJPT). Brazilian Journal of Physical Therapy, 18(6), 471–480. https://doi.org/10.1590/bjpt-rbf.2014.0077
Martínez-Romero, A., López-Urrea, R., Montoya, F., Pardo, J. J., & Domínguez, A. (2021). Optimization of irrigation scheduling for barley crop, combining AquaCrop and MOPECO models to simulate various water-deficit regimes. Agricultural Water Management, 258, 107219. https://doi.org/10.1016/j.agwat.2021.107219
McGowan, J., Sampson, M., Salzwedel, D. M., Cogo, E., Foerster, V., & Lefebvre, C. (2016). PRESS Peer Review of Electronic Search Strategies: 2015 Guideline Statement. Journal of Clinical Epidemiology, 75, 40–46. https://doi.org/10.1016/j.jclinepi.2016.01.021
Meng, W., Sun, X., Ma, J., Guo, X., Lei, T., & Li, R. (2019). Measurement and simulation of the water storage pit irrigation trees evapotranspiration in the Loess Plateau. Agricultural Water Management, 226, 105804.https://doi.org/10.1016/j.agwat.2019.105804
Mérida García, A., Fernández García, I., Camacho Poyato, E., Montesinos Barrios, P., & Rodríguez Díaz, J. A. (2018). Coupling irrigation scheduling with solar energy production in a smart irrigation management system. Journal of Cleaner Production, 175, 670–682. https://doi.org/10.1016/j.jclepro.2017.12.093
Microsoft Corporation. (2016). Microsoft® Excel 2016 [software] (Versão 2016) [English; Microsoft Office Professional Plus 2016]. Microsoft Corporation.
Millán, S., Casadesús, J., Campillo, C., Moñino, M. J., & Prieto, M. H. (2019). Using Soil Moisture Sensors for Automated Irrigation Scheduling in a Plum Crop. Water 11(10), 2061. https://doi.org/10.3390/W11102061
Moghadam, H. R. T. (2016). Application of super absorbent polymer and ascorbic acid to mitigate deleterious effects of cadmium in wheat. Pesquisa Agropecuária Tropical, 46, 09–18. https://doi.org/10.1590/1983-40632016v4638946
Montoro, A., Mañas, F., & López-Urrea, R. (2016). Transpiration and evaporation of grapevine, two components related to irrigation strategy. Agricultural Water Management, 177, 193–200. https://doi.org/10.1016/j.agwat.2016.07.005
Montoya, M. A., & Finamore, E. B. (2020). Os recursos hídricos no agronegócio brasileiro: Uma análise insumo-produto do uso, consumo, eficiência e intensidade. Revista Brasileira de Economia, 74(4). https://doi.org/10.5935/0034-7140.20200021
Nagel, G. W., Novo, E. M. L. de M., & Kampel, M. (2020). Nanosatellites applied to optical Earth observation: A review. Rev. Ambient. Água, 15(3). SciELO Brasil. https://doi.org/10.4136/ambi-agua.2513
Narciso, K. R. (2016). “Rios Voadores” da Amazônia e o Direito Internacional. UFPR, 56. https://acervodigital.ufpr.br/handle/1884/52292
NASA. (2022). Landsat Missions | U.S. Geological Survey [Gov]. National Aeronautics and Space Administration (NASA). https://www.usgs.gov/landsat-missions
Nimah, M. N., & Hanks, R. J. (1973). Model for Estimating Soil Water, Plant, and Atmospheric Interrelations: I. Description and Sensitivity. Soil Science Society of America Journal, 37(4), 522–527. https://doi.org/10.2136/sssaj1973.03615995003700040018x
Oki, T., & Kanae, S. (2006). Global Hydrological Cycles and World Water Resources. Science, 313(5790), 1068–1072. https://doi.org/10.1126/science.1128845
Oliveira, C. T. (2018). Modelo Aquacrop: Calibração, validação e uso para soja e milho na região dos campos gerais. https://acervodigital.ufpr.br/handle/1884/56229
Olivera-Guerra, L., Merlin, O., & Er-Raki, S. (2020). Irrigation retrieval from Landsat optical/thermal data integrated into a crop water balance model: A case study over winter wheat fields in a semi-arid region. Remote Sensing of Environment, 239, 111627. https://doi.org/10.1016/j.rse.2019.111627
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. PLOS Medicine, 18(3), e1003583. https://doi.org/10.1371/journal.pmed.1003583
Penman, H. L. (1948). Natural evaporation from open water, bare soil and grass. Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences, 193(1032), 120–145. https://doi.org/10.1098/rspa.1948.0037
Pereira, V. D. C., Sediyama, G. C., De Moura, M. S. B., Da Silva, T. G. F., & De Souza, L. S. B. (2018). Eficiência do uso da água em videira ‘Syrah’ irrigada no submédio do vale São Francisco1. IRRIGA, 21(2), 269. https://doi.org/10.15809/irriga.2016v21n2p269-283
Pereira, V. da C., Sediyama, G. C., de Moura, M. S. B., da Silva, T. G. F., & de Souza, L. S. B. (2016). Water use efficiency in irrigated “Syrah” grape plantation at são francisco river valley. IRRIGA, 21(2), 269–283. https://doi.org/10.15809/irriga.2016v21n2p269-283
Perera, K. C., Western, A. W., Robertson, D. E., George, B., & Nawarathna, B. (2016). Ensemble forecasting of short-term system scale irrigation demands using real-time flow data and numerical weather predictions. Water Resources Research, 52(6), 4801–4822. https://doi.org/10.1002/2015WR018532
Priestley, C. H. B., & Taylor, R. J. (1972). On the Assessment of Surface Heat Flux and Evaporation Using Large-Scale Parameters. Monthly Weather Review, 100(2), 81–92. https://doi.org/10.1175/1520-0493(1972)100<0081:OTAOSH>2.3.CO;2
PRISMA-P Group, Moher, D., Shamseer, L., Clarke, M., Ghersi, D., Liberati, A., Petticrew, M., Shekelle, P., & Stewart, L. A. (2015). Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Systematic Reviews, 4(1), 1. https://doi.org/10.1186/2046-4053-4-1
Qin, S., Li, S., Kang, S., Du, T., Tong, L., & Ding, R. (2016). Can the drip irrigation under film mulch reduce crop evapotranspiration and save water under the sufficient irrigation condition? Agricultural Water Management, 177, 128–137. https://doi.org/10.1016/j.agwat.2016.06.022
Ratinaud, P. (2014). IRaMuTeQ (Interface de R pour les Analyses Multidimensionnelles de Textes et de Questionnaires) [software] (0.7 alpha 2) [French]. LERASS - Laboratoire d’Études et de Recherches Appliquées en Sciences Sociales.
Ribeiro, H., Jaime, P. C., & Ventura, D. (2017). Alimentação e sustentabilidade. Estudos Avançados, 31(89), 185–198. https://doi.org/10.1590/s0103-40142017.31890016
Sampaio, R. F., & Mancini, M. C. (2007). Estudos de revisão sistemática: Um guia para síntese criteriosa da evidência científica. Brazilian Journal of Physical Therapy, 11, 83–89. https://doi.org/10.1590/S1413-35552007000100013
Santos, C. M. da C., Pimenta, C. A. de M., & Nobre, M. R. C. (2007). The PICO strategy for the research question construction and evidence search. Revista Latino-Americana de Enfermagem, 15(3), 508–511. https://doi.org/10.1590/S0104-11692007000300023
Segovia-Cardozo, D. A., Rodríguez-Sinobas, L., & Zubelzu, S. (2019). Water use efficiency of corn among the irrigation districts across the Duero river basin (Spain): Estimation of local crop coefficients by satellite images. Agricultural Water Management, 212, 241–251. https://doi.org/10.1016/j.agwat.2018.08.042
Shuaishuai, L., Li, L., shiwang, C., Fanjia, M., Haihua, W., Zhanzhan, S., & Sigrimis, N. A. (2018). Prediction Model of Transpiration Rate of Strawberry in Closed Cultivation Based on DBN-LSSVM Algorithm. IFAC-PapersOnLine, 51(17), 460–465. https://doi.org/10.1016/j.ifacol.2018.08.171
Shuttleworth, W. J., & Wallace, J. S. (1985). Evaporation from sparse crops-an energy combination theory: evaporation from sparse crops. Quarterly Journal of the Royal Meteorological Society, 111(469), 839–855. https://doi.org/10.1002/qj.49711146910
Sivalakshmi, P., Shanthi, K. G., Sangeethalakshmi, K., SeshaVidhya, S., Sandhiya, G., & Rajkumar, M. (2021). Smart auction system flow model for Agro-Based sector farmers using blockchain technology. Materials Today: Proceedings, S2214785321042619. https://doi.org/10.1016/j.matpr.2021.05.634
Souza, G. S. de, Lima, J. S. de S., Xavier, A. C., & Rocha, W. S. D. da. (2010). Krigagem Ordinária e Inverso do Quadrado da Distância Aplicados na Espacialização de Atributos Químicos de um Argissolo. Scientia Agraria, 11(1), 073. https://doi.org/10.5380/rsa.v11i1.15939
Souza, M. A. R. de, Wall, M. L., Thuler, A. C. de M. C., Lowen, I. M. V., & Peres, A. M. (2018). O uso do software IRAMUTEQ na análise de dados em pesquisas qualitativas. Revista da Escola de Enfermagem da USP, 52(0). https://doi.org/10.1590/s1980-220x2017015003353
Stanghellini, C. (1987). Transpiration of greenhouse crops: An aid to climate management [Phd, IMAG]. https://library.wur.nl/WebQuery/wurpubs/2690
Sun, M., Gao, X., Zhang, Y., Song, X., & Zhao, X. (2022). A new solution of high-efficiency rainwater irrigation mode for water management in apple plantation: Design and application. Agricultural Water Management, 259, 107243. https://doi.org/10.1016/j.agwat.2021.107243
The MathWorks. (2022). Matlab R2022a [software] (R2022a 2 (9.12.0.1956245)) [Start MATLAB - The Language of Technical Computing; MATLAB and Simulink]. The MathWorks, Inc.
Thomas, S., Rane, A., Abitha V. K, Kanny, K., & Dutta, A. (Orgs.). (2019). Hydraulic rubber dam: An effective water management technology. William Andrew is an imprint of Elsevier.
Tie, Q., Hu, H., Tian, F., & Holbrook, N. M. (2018). Comparing different methods for determining forest evapotranspiration and its components at multiple temporal scales. Science of The Total Environment, 633, 12–29. https://doi.org/10.1016/j.scitotenv.2018.03.082
Vanino, S., Nino, P., De Michele, C., Falanga Bolognesi, S., D’Urso, G., Di Bene, C., Pennelli, B., Vuolo, F., Farina, R., Pulighe, G., & Napoli, R. (2018). Capability of Sentinel-2 data for estimating maximum evapotranspiration and irrigation requirements for tomato crop in Central Italy. Remote Sensing of Environment, 215, 452–470. https://doi.org/10.1016/j.rse.2018.06.035
Wang, D., & Wang, L. (2017). Dynamics of evapotranspiration partitioning for apple trees of different ages in a semiarid region of northwest China. Agricultural Water Management, 191, 1–15. https://doi.org/10.1016/j.agwat.2017.05.010
Zhang, Q., Fan, L., Wang, H., Han, H., Zhu, Z., Zhao, X., & Wang, Y. (2022). A review of physical and chemical methods to improve the performance of water for dust reduction. Process Safety and Environmental Protection, 166, 86–98. https://doi.org/10.1016/j.psep.2022.07.065
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Observatory Journal
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
[PT] Autores que publicam nesta revista concordam com os seguintes termos:
1. Autores mantém os direitos autorais e concedem à revista, sem pagamento, o direito de primeira publicação, com o trabalho simultaneamente licenciado sob a Creative Commons Attribution License (CC BY-NC 4.0), permitindo o compartilhamento do trabalho com reconhecimento da autoria do trabalho e publicação inicial nesta revista.
Leia todos os termos dos direitos autorais aqui.
