OPTIMIZING IRRIGATION FOR DUTCH ROSES IN BENI MELLAL, MOROCCO: PREDICTIVE MODELING BASED ON REFERENCE EVAPOTRANSPIRATION
Рубрики: RESEARCH ARTICLE
Аннотация и ключевые слова
Аннотация (русский):
Efficient water management in agriculture is crucial for sustainable crop production, particularly in regions facing water scarcity. This article introduces a comprehensive predictive model for optimizing the current irrigation of Dutch roses in the Beni Mellal region of Morocco. The model addressed the need for precise water management across four distinct plant growth stages. The integrated system proved able to estimate the daily irrigation requirements based on historical weather data and crop-specific factors. The model incorporated four main components: weather prediction for temperature, net radiation, wind speed, and dew point; calculating the reference evapotranspiration using the Penman-Monteith equation; applying the crop coefficients specific to each growth stage; as well as estimating the crop evapotranspiration and determining daily water needs. The system offered a systematic approach to predicting the daily water requirements for Dutch roses across the entire growth cycle. By leveraging historical weather patterns and growth stage-specific crop coefficients, the system provided a predictive tool for proactive irrigation management. The model proved highly adaptable as it was able to generate forecasts based on weather trends and plant growth stages, potentially leading to a more efficient water use than conventional irrigation methods. This integrated approach is expected to allow the rose farmers of Beni Mellal to optimize their irrigation practices. While field validation is needed to quantify its impact, the model’s framework already shows potential for enhancing water use efficiency in cultivating roses and other crops in arid environment.

Ключевые слова:
Precision agriculture, irrigation, evapotranspiration, crop coefficient, Dutch roses, weather prediction, rose cultivation
Список литературы

1. Shen D, Zhao X, Chai L, Guo Z, Leng C. Analysis of the agricultural economic value of a weather forecasting service based on a survey of peasant households in Chinese provinces. Humanities and Social Sciences Communications. 2024;11(1):192. https://doi.org/10.1057/s41599-024-02685-3 EDN: https://elibrary.ru/JBAWQN

2. Hussain A, Memon JA, Hanif S. Weather shocks, coping strategies and farmers’ income: A case of rural areas of district Multan, Punjab. Weather and Climate Extremes. 2020;30:100288. https://doi.org/10.1016/j.wace.2020.100288 EDN: https://elibrary.ru/INAXEA

3. Kifle T, Ayal DY, Mulugeta M. Factors influencing farmers adoption of climate smart agriculture to respond climate variability in Siyadebrina Wayu District, Central highland of Ethiopia. Climate Services. 2022;26:100290. https://doi.org/10.1016/j.cliser.2022.100290 EDN: https://elibrary.ru/PSKKQC

4. Antwi-Agyei P, Abalo EM, Dougill AJ, Baffour-Ata F. Motivations, enablers and barriers to the adoption of climate-smart agricultural practices by smallholder farmers: Evidence from the transitional and savannah agroecological zones of Ghana. Regional Sustainability. 2021;4(4):375–386. https://doi.org/10.1016/j.regsus.2022.01.005 EDN: https://elibrary.ru/NRRIAM

5. Barasa PM, Botai CM, Botai JO, Mabhaudhi T. A review of climate-smart agriculture research and applications in Africa. Agronomy. 2021;11(6):1255. https://doi.org/10.3390/agronomy11061255 EDN: https://elibrary.ru/CWAJLT

6. Mizik T. Climate-smart agriculture on small-scale farms: A systematic literature review. Agronomy. 2021;11(6):1096. https://doi.org/10.3390/agronomy11061096 EDN: https://elibrary.ru/ZOIFJG

7. Akilan T, Baalamurugan KM. Automated weather forecasting and field monitoring using GRU-CNN model along with IoT to support precision agriculture. Expert Systems with Applications. 2024;249(Part A):123468. https://doi.org/10.1016/j.eswa.2024.123468 EDN: https://elibrary.ru/UUUXRK

8. Fariz TKN, Basha SS. Enhancing solar radiation predictions through COA optimized neural networks and PCA dimensionality reduction. Energy Reports. 2024;12:341–359. https://doi.org/10.1016/j.egyr.2024.06.025 EDN: https://elibrary.ru/NCYXBH

9. Chen Z, Zhu Z, Jiang H, Sun S. Estimating daily reference evapotranspiration based on limited meteorological data using deep learning and classical machine learning methods. Journal of Hydrology. 2020;591:125286. https://doi.org/10.1016/j.jhydrol.2020.125286 EDN: https://elibrary.ru/NGZEWV

10. Shiri J, Sadraddini AA, Nazemi AH, Kisi O, Marti P, et al. Evaluation of different data management scenarios for estimating daily reference evapotranspiration. Hydrology Research. 2013;44(6):1058–1070. https://doi.org/10.2166/nh.2013.154

11. Bashir RN, Saeed M, Al-Sarem M, Marie R, Faheem M, et al. Smart reference evapotranspiration using Internet of Things and hybrid ensemble machine learning approach. Internet of Things. 2023;24:100962. https://doi.org/10.1016/j.iot.2023.100962 EDN: https://elibrary.ru/NBMJUZ

12. Raza A, Fahmeed R, Syed NR, Katipoğlu OM, Zubair M, et al. Performance evaluation of five machine learning algorithms for estimating reference evapotranspiration in an arid climate. Water.2023;15(21):3822. https://doi.org/10.3390/w15213822 EDN: https://elibrary.ru/ZIYCVE

13. Fan J, Wu L, Zhang F, Xiang Y, Zheng J. Climate change effects on reference crop evapotranspiration across different climatic zones of China during 1956–2015. Journal of Hydrology. 2016;542:923–937. https://doi.org/10.1016/j.jhydrol.2016.09.060 EDN: https://elibrary.ru/XTSLRV

14. Chartzoulakis K, Bertaki M. Sustainable water management in agriculture under climate change. Agriculture and Agricultural Science Procedia. 2015;4:88–98. https://doi.org/10.1016/j.aaspro.2015.03.011

15. Entezari A, Wang RZ, Zhao S, Mahdinia E, Wang JY, et al. Sustainable agriculture for water-stressed regions by air-water-energy management. Energy. 2019;181:1121–1128. https://doi.org/10.1016/j.energy.2019.06.045

16. Simić D, Pejić B, Bekavac G, Mačkić K, Vojnov B, et al. Effect of different ET-based irrigation scheduling on grain yield and water use efficiency of drip irrigated maize. Agriculture. 2023;13(10):1994. https://doi.org/10.3390/agriculture13101994

17. Kim YU, Webber H, Adiku SGK, Nóia Júnior RS, Deswarte JC, et al. Mechanisms and modelling approaches for excessive rainfall stress on cereals: Waterlogging, submergence, lodging, pests and diseases. Agricultural and Forest Meteorology. 2024;344:109819. https://doi.org/10.1016/j.agrformet.2023.109819

18. Daniel K, Hartman S. How plant roots respond to waterlogging. Journal of Experimental Botany. 2024;75(2):511–525. https://doi.org/10.1093/jxb/erad332

19. Soni S, Jha AB, Dubey RS, Sharma P. Nanowonders in agriculture: Unveiling the potential of nanoparticles to boost crop resilience to salinity stress. Science of The Total Environment. 2024;975:171433. https://doi.org/10.1016/j.scitotenv.2024.171433

20. Elmeknassi M, Elghali A, de Carvalho HWP, Laamrani A, Benzaazoua M. A review of organic and inorganic amendments to treat saline-sodic soils: Emphasis on waste valorization for a circular economy approach. Science of The Total Environment. 2024;921:171087. https://doi.org/10.1016/j.scitotenv.2024.171087

21. van den Burg S, Deolu-Ajayi AO, Nauta R, Cervi WR, van der Werf A, et al. Knowledge gaps on how to adapt crop production under changing saline circumstances in the Netherlands. Science of The Total Environment. 2024;915:170118. https://doi.org/10.1016/j.scitotenv.2024.170118

22. Norberg L, Aronsson H. Effects of spring and autumn tillage, catch crops, and pig manure application on long-term nutrient leaching from a loamy sand. European Journal of Agronomy. 2024;156:127156. https://doi.org/10.1016/j.eja.2024.127156

23. GP N, Dangare P, Kleinert A, Dzikiti S. Estimating crop coefficients and water use of a full-bearing mango orchard in north-eastern South Africa using the fraction of vegetation cover and a dual source evapotranspiration model. Scientia Horticulturae. 2024;336:113388. https://doi.org/10.1016/j.scienta.2024.113388

24. Lee J, Bateni SM, Jun C, Heggy E, Jamei M, et al. Hybrid machine learning system based on multivariate data decomposition and feature selection for improved multitemporal evapotranspiration forecasting. Engineering Applications of Artificial Intelligence. 2024;135:108744. https://doi.org/10.1016/j.engappai.2024.108744

25. Capurro MC, Ham JM, Kluitenberg GJ, Comas L, Andales AA. A novel sap flow system to measure maize transpiration using a heat pulse method. Agricultural Water Management. 2024;301:108963. https://doi.org/10.1016/j.agwat.2024.108963

26. Xu Q, Liu H, Li M, Gong P, Li P, et al. Optimizing water and nitrogen management for saline wasteland improvement: A case study on Suaeda salsa. Agricultural Water Management. 2024;301:108930. https://doi.org/10.1016/j.agwat.2024.108930

27. Raza A, Vishwakarma DK, Acharki S, Al-Ansari N, Alshehri F, et al. Use of gene expression programming to predict reference evapotranspiration in different climatic conditions. Applied Water Science. 2024;14:152. https://doi.org/10.1007/s13201-024-02200-8

28. Martí P, López-Urrea R, Mancha LA, González-Altozano P, Román A. Seasonal assessment of the grass reference evapotranspiration estimation from limited inputs using different calibrating time windows and lysimeter benchmarks. Agricultural Water Management. 2024;300:108903. https://doi.org/10.1016/j.agwat.2024.108903

29. Bashir RN, Mzoughi O, Shahid MA, Alturki N, Saidani O. Principal component analysis (PCA) and feature importance-based dimension reduction for Reference Evapotranspiration (ETо) predictions of Taif, Saudi Arabia. Computers and Electronics in Agriculture. 2024;222:109036. https://doi.org/10.1016/j.compag.2024.109036

30. Costa JO, Coelho RD, Guimarães EA, Quiloango-Chimarro CA, Fernandes ALT. Assessing the water use efficiency of irrigated fruit crops in semi-arid regions of Brazil using remote sensing and meteorological data. Irrigation and Drainage. 2024;73(3):974–987. https://doi.org/10.1002/ird.2919

31. DeJonge KC, Thorp KR, Brekel J, Pokoski T, Trout TJ. Customizing pyfao56 for evapotranspiration estimation and irrigation scheduling at the Limited Irrigation Research Farm (LIRF), Greeley, Colorado. Agricultural Water Management. 2024;299:108891. https://doi.org/10.1016/j.agwat.2024.108891

32. Kumar S, Kumar R, Singh MK, Yadav S, Parhi PK, et al. Crop water requirement of rice in different agroclimatic zones of Jharkhand. Journal of Agrometeorology. 2024;26(2):233–237. https://doi.org/10.54386/jam.v26i2.2358

33. Jdi H, Falih N. Comparison of time series temperature prediction with auto-regressive integrated moving average and recurrent neural network. International Journal of Electrical and Computer Engineering. 2024;14(2):1770–1778. https://doi.org/10.11591/ijece.v14i2.pp1770-1778

34. Singh VK, Tiwari K, Dt S. Estimation of crop coefficient and water requirement of Dutch roses (Rosa hybrida) under greenhouse and open field conditions. Irrigation & Drainage Systems Engineering. 2016;5(3):1000169. https://doi.org/10.4172/2168-9768.1000169


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