Numerical simulation of soil water dynamics in automated drip irrigated Okra field under plastic mulch

• Autorzy: Nagaraju Vidya K. , Nagarajan Karuppalaki , Kannan Balaji , Ramanathan Subbiah , Duraisamy Ramasamy

Identyfikatory

DOI

10.2478/agriceng-2023-0002

Warianty tytułu

PL

Symulacja numeryczna dynamiki wodnej gleby na polu okry nawadnianym automatycznym systemem kropelkowym pod folią

• Języki publikacji: EN

Abstrakty

EN

In India, drip irrigation with plastic mulch is a common practise for irrigation that conserves water. For the design and administration of irrigation regimes, a thorough understanding of the distribution and flow of soil water in the root zone is required. It has been demonstrated that simulation models are effective tools for this purpose. In this work, an automated drip-irrigated Okra field with seven treatments namely T1- Soil moisture-based drip irrigation to 100% FC, T2- Soil moisture-based drip irrigation to 80% FC, T3- Soil moisture-based drip irrigation to 60% FC, T4- Timer based drip irrigation to 100% CWR, T5- Timer based drip irrigation to 80% CWR, T6- Timer based drip irrigation to 60% CWR and T7- Conventional drip irrigation at 100% CWR were utilised to mimic the temporal fluctuations in soil water content using the numerical model HYDRUS2D. With the help of the observed data, the inverse solution was used to optimise the soil hydraulic parameters. The model was used to forecast soil water content for seven field treatments at optimal conditions. Root mean square error (RMSE) and coefficient of determination (R2) were used to assess the congruences between the predictions and data. With RMSE ranging from 0.036 to 0.067 cm3xcm-3, MAE ranging from 0.020 to 0.059, and R2 ranging from 0.848 to 0.959, the findings showed that the model fairly represented the differences in soil water content at all sites in seven treatments.

PL

W Indiach, nawadnianie kropelkowe pod folią jest popularną praktyką oszczędzania wody. Projekty i zarządzanie systemami nawadniania wymagają zrozumienia rozmieszczenia i przepływu wody glebowej w strefie korzeniowej. Udowodniono, że skutecznym narzędziem do tego są modele symulacyjne. W niniejszej pracy wykorzystano pole okry nawadnianej automatycznym systemem kropelkowym, które podlegało siedmiu zabiegom, mianowicie: T1 - nawadnianie kropelkowe do 100% pojemności polowej opartej na wilgotności gleby, T2 - nawadnianie kropelkowe do 80% pojemności polowej opartej na wilgotności gleby, T3 - nawadnianie kropelkowe do 60% pojemności polowej opartej na wilgotności gleby, T4 - nawadnianie kropelkowe oparte na czasomierzu do 100% CWR, T5 - nawadnianie kropelkowe oparte na czasomierzu do 80% CWR, T6- nawadnianie kropelkowe oparte na czasomierzu do 60% CWR and T7 - Tradycyjne nawadnianie kropelkowe do 100% CWR, w celu prześledzenia czasowych fluktuacji zawartości wody glebowej przy użyciu modelu numerycznego HYDRUS 2D. Przy użyciu zaobserwowanych danych zastosowano odwrotne rozwiązanie w celu optymalizacji parametrów hydraulicznych wody. Model wykorzystano do przewidzenia zawartości wody glebowej dla siedmiu zabiegów przy optymalnych warunkach. Błąd średniokwadratowy (MSE) oraz pierwiastek błędu średniokwadratowego (RMSE) oraz współczynnik determinacji (R2 ) wykorzystano do oceny zgodności pomiędzy wartościami przewidywanymi a danymi. Przy RMSE wynoszącym od 0,036 do 0,067 cm3xcm-3, MAE od 0,020 do 0,059, oraz R2 0,848 do 0,959, wyniki pokazują, że model dobrze odzwierciedlił różnice w zawartości wody glebowej we wszystkich miejscach i siedmiu zastosowanych zabiegach.

Słowa kluczowe

EN

automated drip irrigation; HYDRUS-2D; mulch scenario; rootzone moisture; soil water movement

PL

automatyczne nawadnianie kropelkowe; HYDRUS-2D; opcja z folią; zawartość wody; strefa korzeniowa; ruch wody glebowej

• Wydawca: Polskie Towarzystwo Inżynierii Rolniczej
• Czasopismo: Agricultural Engineering
• Rocznik: 2023
• Tom: Vol. 27, No. 1
• Strony: 11--32
• Opis fizyczny: Bibliogr. 30 poz., rys., tab.

Twórcy

autor

Nagaraju Vidya K.

• Department of Soil and Water Conservation Engineering, Tamil Nadu Agricultural University, Coimbatore, 641003. Tamil Nadu, India

• https://orcid.org/0000-0002-9037-078X

autor

Nagarajan Karuppalaki

• Faculty of Agricultural Engineering, Department of Soil and Water Conservation Engineering, Tamil Nadu Agricultural University, Coimbatore, 641003. Tamil Nadu, India, India

autor

Kannan Balaji

• Faculty of Agricultural Engineering, Department of Soil and Water Conservation Engineering, Tamil Nadu Agricultural University, Coimbatore, 641003. Tamil Nadu, India, India

• https://orcid.org/0000-0003-2330-0893

autor

Ramanathan Subbiah

• Faculty of Agricultural Meteorology, Department of Agro Climate Research Centre, Tamil Nadu Agricultural University, Coimbatore, 641003. Tamil Nadu, India

• https://orcid.org/0000-0002-5376-3816

autor

Duraisamy Ramasamy

• Faculty of Agricultural Statistics, Department of Physical Sciences and Information technology, Tamil Nadu Agricultural University, Coimbatore, 641003. Tamil Nadu, India, India

Bibliografia

• Allen, R. G., Pereira, L. S., Raes, D., and Smith, M. (1998). Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. Fao, Rome, 300(9), D05109.
• Autovino, D., Rallo, G., and Provenzano, G. (2018). Predicting soil and plant water status dynamic in olive orchards under different irrigation systems with Hydrus-2D: Model performance and scenario analysis. Agricultural water management, 203, 225-235.
• Azad, N., Behmanesh, J., Rezaverdinejad, V., Abbasi, F., and Navabian, M. (2018). Developing an optimization model in drip fertigation management to consider environmental issues and supply plant requirements. Agricultural water management, 208, 344-356.
• Cammalleri, C., Rallo, G., Agnese, C., Ciraolo, G., Minacapilli, M. and Provenzano, G. (2013). Combined use of eddy covariance and sap flow techniques for partition of ET fluxes and water stress assessment in an irrigated olive orchard. Agricultural Water Management, 120, 89-97. http://dx.doi.org/10.1016/j.agwat.2012.10.003.
• Dhawan, V. (2017). Water and agriculture in India: background paper for the South Asia expert panel during the Global Forum for Food and Agriculture (GFFA). Hamburg, OAV - German Asia-Pacific Business Association.
• Ebrahimian, H., Liaghat, A., Parsinejad, M., Playán, E., Abbasi, F., and Navabian, M. (2013). Simulation of 1D surface and 2D subsurface water flow and nitrate transport in alternate and conventional furrow fertigation. Irrigation Science, 31(3), 301-316.
• Enciso, J.M., Jifon, J., and Wiedenfeld, B. (2007). Subsurface drip irrigation of onions: effects of drip tape emitter spacing on yield and quality. Agricultural Water Management 92(3), 1-7.
• Feddes, R.A. (1982). Simulation of field water use and crop yield. In Simulation of plant growth and crop production. Pudoc, pp. 194-209.
• Ghazouani, H., Autovino, D., Rallo, G., Douh, B., and Provenzano, G. (2016). Using Hydrus-2D model to assess the optimal drip lateral depth for Eggplant crop in a sandy loam soil of central Tunisia. Italian Journal of Agrometeorology, 1, 47-58.
• Han, M., Zhao, C., Šimůnek, J., and Feng, G. (2015). Evaluating the impact of groundwater on cotton growth and root zone water balance using Hydrus-1D coupled with a crop growth model. Agricultural Water Management, 160, 64-75.
• Jones, H.G. (2004). Irrigation scheduling: advantages and pitfalls of plant-based methods. Journal of experimental botany, 55(407), 2427-2436.
• Kandelous, M. M., Šimůnek, J., Van Genuchten, M. T., and Malek, K. (2011). Soil water content distributions between two emitters of a subsurface drip irrigation system. Soil Science Society of America Journal, 75(2), 488-497.
• Kisekka, I., Migliaccio, K.W., Dukes, M.D., Schaffer, B., and Crane, J.H. (2010). Real-timeevapotranspiration-based irrigation scheduling and physiological response in a carambola (Averhoha carambola) orchard. Applied Engineering in Agriculture, 26(3), 373-380.
• Lozoya, C., Mendoza, C., Aguilar, A., Román, A., and Castelló, R. (2016). Sensor-based model driven control strategy for precision irrigation. Journal of Sensors, 9784071.
• Mailhol, J.C., Ruelle, P., Walser, S., Schütze, N., and Dejean, C. (2011). Analysis of AET and yield predictions under surface and buried drip irrigation systems using the Crop Model PILOTE and Hydrus-2D. Agricultural Water Management, 98, 1033-1044.
• Mei-Xian, L.I.U., Jing-Song, Y.A.N.G., Xiao-Ming, L.I., Mei, Y.U., and Jin, W.A.N.G. (2013). Numerical simulation of soil water dynamics in a drip irrigated cotton field under plastic mulch. Pedosphere, 23(5), 620-635.
• Minacapilli, M., Agnese, C., Blanda, F., Cammalleri, C., Ciraolo, G., D’Urso, G., Iovino, M., Pumo, D., Provenzano, G., and Rallo, G. (2009). Estimation of actual evapotranspiration of Mediterranean perennial crops by means of remote-sensing based surface energy balance models. Hydrology and Earth System Sciences, 13(7), 1061-1074.
• Munõz-Carpena, R., Dukes, M.D., Li, Y., and Klassen, W. (2005). Field comparison of tensiometer and granular matrix sensor automatic drip irrigation on tomato. HortTechnology, 15 (3), 584-590.
• Radcliffe, D. E., and Simunek, J. (2018). Soil physics with HYDRUS: Modeling and applications. CRC press.
• Rallo, G., Agnese, C., Blanda, F., Minacapilli, M., and Provenzano, G. (2010). Agro- Hydrological models to schedule irrigation of Mediterranean tree crops. Italian Journal of Agrometeorology, 1, 11-21.
• Rallo, G., Agnese, C., Minacapilli, M., and Provenzano, G. (2012). Comparison of SWAP and FAO agro-hydrological models to schedule irrigation of wine grape. Journal of Irrigation and Drainage Engineering, 138(1).
• Rallo, G., González-Altozano, P., Manzano-Juárez, J., and Provenzano, G. (2017). Using field measurements and FAO-56 model to assess the eco-physiological response of citrus orchards under regulated deficit irrigation. Agricultural water management, 180, 136-147.
• Ranjbar, A., Rahimikhoob, A., Ebrahimian, H., and Varavipour, M. (2019). Simulation of nitrogen uptake and distribution under furrows and ridges during the maize growth period using HYDRUS2D. Irrigation Science, 37(4), 495-509.
• Richards, L.A. (1931). Capillary conduction of liquids through porous mediums. Physics, 1, 318-333.
• Ritchie, J.T. (1972). A model for predicting evaporation from a row crop with incomplete cover. Water research, 8, 1204-1213.
• Šimůnek, J., Šejna, M., and van Genuchten, M.Th. (1999). The Hydrus-2D Software Package for Simulating Two-dimensional Movement of Water, Heat, and Multiple Solutes in Variably Saturated Media. Version 2.0, IGWMC - TPS - 53. International Ground Water Modeling Center, Colorado School of Mines Golden, Colorado 251pp.
• Šimůnek, J., Šejna, M., and van Genuchten, M.Th. (2016). Recent developments and applicationsof the Hydrus computer software packages. Vadose Zone Journal, 1-25.
• Skaggs, T.H., Trout, T.J., Šimůnek, J., and Shouse, P. J. (2004). Comparison of HYDRUS-2D simulations of drip irrigation with experimental observations. Journal of irrigation and drainage engineering, 130(4), 304-310.
• Thompson, R.B., Gallardo, M., Valdez, L.C., and Fernandez, M.D. (2007). Determination of lower limits for irrigation management using in situ assessments of apparent crop water uptake made with volumetric soil water content sensors. Agricultural Water Management 92, 13-28.
• Vrugt, J. A., Hopmans, J. W., and Šimunek, J. (2001). Calibration of a two‐dimensional root water uptake model. Soil Science Society of America Journal, 65(4), 1027-1037.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).