DEVELOPMENT EXPERIENCE AND WAYS OF IMPROVEMENT OF IRRIGATION MANAGEMENT SYSTEMS

Keywords: irrigation management systems, moisture transfer’s modelling, automated measurements, recommendations for irrigation timing, software

Abstract

The paper provides an overview of models and software used in decision support systems in irrigation. The models of biomass accumulation or evapotranspiration are the base of decision support systems in irrigation. The overview of the most famous systems is given, as well as an innovative irrigation control system "Irrigation online" is presented.

The objective of the work is to share the experience of development and implementation of irrigation management systems and outline the ways of their improvement.

The "Irrigation online" system consists of hardware and software components. The part of the system's hardware is located in the field consisting of iMetos or Davis weather stations, as well as of own-developed equipment. The software part, intended for storing, processing and providing recommendations, is hosted and run on a server. It sends the recommendations about start watering and necessary irrigation rates to a user’s computer or mobile device.

The system is based on modelling of moisture transfer, automated measurements of soil moisture and meteorological indicators in the field and weather data from automated forecast web-sites. Water retention curve of  soil and the dependence of the moisture transfer coefficient on the head, which are the input parameters of the model, are given  for every layer according to the van Genuchten-Mualem Model.

The application of the system took place in 2019 in SE EF“Askaniiske” Kherson region and LLC “APC “Mais” in Cherkasy region. The system "Irrigation Online" provided the recommendations on watering winter rape, wheat, corn, soybeans, alfalfa and potatoes.

The system provided the recommendations on watering winter rape, wheat, corn, soybeans, alfalfa and potatoes. It was specified that the use of the system "Irrigation Online" enables to schedule irrigation regimes, the implementation of which requires watering with less (by 15-25%) in comparison with the current irrigation rates, due to which  more favourable conditions for the maximum realization of crop varieties and hybrids potential  are created. It is accompanied by enhancing the environmental safety of irrigation as a result of minimization of irrigation water losses for infiltration.

Irrigation control system "Irrigation Online" uses a range of soil moisture suction pressure rather than a soil moisture range as an optimum moisture supply range for plants. For setting up irrigation terms and rates, the value of suction pressure, which corresponds to the part of water field capacity when it is determined by water retention curve of soil, is taken. The pre-irrigation threshold of suction pressure is the value, which at non-irrigation for some short period will not cause water stress for plants

Monitoring of meteorological parameters and soil moisture level in the "Irrigation Online" system allows daily adjusting irrigation terms and rates for next 5 day period and significantly improves the accuracy of their forecasting.

Author Biographies

M. I. Romashchenko, Institute of Water Problems and Land Reclamation NAAS, Kyiv

Doctor of technical sciences, academician of NAAS

T. V. Matіash, Institute of Water Problems and Land Reclamation NAAS, Kyiv

Ph. D. in technical sciences

V. O. Bohaienko, V.M.G lushkov Institute of Cybernetics of the NAS, Kyiv

Ph. D. in technical sciences

V. P. Kovalchuk, Institute of Water Problems and Land Reclamation NAAS, Kyiv

Doctor of technical sciences

O. P. Voitovich, Institute of Water Problems and Land Reclamation NAAS, Kyiv

Ph. D. student

A. V. Krucheniuk, Institute of Water Problems and Land Reclamation NAAS, Kyiv

researcher

V. V. Knysh, Institute of Water Problems and Land Reclamation NAAS, Kyiv

researcher

V. V. Shlikhta, Institute of Water Problems and Land Reclamation NAAS, Kyiv

researcher

References

1. Stratehiia zroshennia ta drenazhu v Ukraini na period do 2030 roku [Irrigation and drainage strategy in Ukraine until 2030.]: Skhvaleno rozporiadzhenniam Kabinetu Ministriv Ukrainy № 688-r. (2019, August 14). Uriadovyi kurier, 170. Retrieved from: https://zakon.rada.gov.ua/laws/show/688-2019-%D1%80. [in Ukrainian]
2. Mainaa, M.M., Amina, M.S.M., & Yazidb, M.A. (2014). Web geographic information system decision support system for irrigation water management: a review. Acta Agriculturae Scandinavica (Section B. Soil & Plant Science), Vol. 64, No. 4, 283–293. http://dx.doi.org/10.1080/09064710.2014.896935
3. Rinaldi, M., & He, Z. (2014). Decision Support Systems to Manage Irrigation in Agriculture. In: Advances in Agronomy, Vol. 123, 229-279
4. Bouman, B.A.M., van Keulen, H., van Laar, H.H., & Rabbinge, R. (1996). The “school of de Wit” crop growth simulation models: a pedigree and historical overview. Agric. Syst. 52, 171–198.
5. Van Ittersum, M.K., Ewert, F., Heckelei, T., Wery, J., Alkan Olsson, J., Andersen, E., et al. (2008). Integrated assessment of agricultural systems—a component-based framework for the European Union (SEAMLESS). Agric. Syst.,96 (1–3), 150–165.
6. Monteith, J.L. (1977). Climate and the efficiency of crop production in Britain. Philos. Trans. R. Soc. Lond. B Biol. Sci., 281, 277–294.
7. Sinclair, T.R., & Muchow, R.C. (1999). Radiation use efficiency. Adv. Agron., 65, 215–265.
8. de Wit, C.T. (1958). Transpiration and crop yields. Versl. Landbouwk. Onderz. 64.6 Institute of Biological Chemistry Researchon FieldCrops and Herbage, Wageningen, The Netherlands.
9. Steduto, P., Hsiao, T.C., & Fereres, E. (2007). On the conservative behaviour of biomass water productivity. Irrig. Sci., 25, 189–207.
10. Steduto, P., Hsiao, T.C., Raes, D., & Fereres, E. (2009). AquaCrop—the FAO crop model to simulate yield response to water: I. Concepts and underlying principles. Agron. J., 101, 426–437.
11. Steduto, P., Raes, D., Hsiao, T.C., & Fereres, E. (2012). AquaCrop: concepts, rationale and operation. In: Steduto, P., Hsiao, T.C., Fereres, E., Raes, D. (Eds.), Crop Yield Response to Water. FAO irrigation and drainage paper no. 66, 17–49.
12. Allen, R.G., Pereira, L.S., Smith, M., Raes, D., & Wright, J.L. (2005). FAO-56 dual crop coefficient method for estimating evaporation from soil and application extensions. J. Irrig. Drain. Eng. ASCE, 131 (1), 2–13.
13. Alpatev, A.M. (1974). O metodakh rascheta potrebnostei v vode kulturnykh fytotsenozov v sviazy s razvytyem oroshenya v SSSR [On methods for calculating the water requirements of cultivated phytocenoses in connection with the development of irrigation in the USSR]. M.: Nauka. Byolohycheskye osnovy oroshaemoho zemledelia, 85-89. [in Russian]
14. Shtoiko, D.A., Pysarenko, V.A., Bychko, O.S., & Yelazhenko, L.I. (1977). Rozrakhunkovi metody vyznachennia sumarnoho vyparovuvannia i strokiv polyvu s.-h. kultur [Estimated methods for determining total evaporation and irrigation time of crops]. Zroshuvalne zemlerobstvo, 3-8. [in Ukrainian]
15. Budyko, M.I. (1974) Climate and Life; New York: Academic Press, NY, USA.
16. Ivanov, N.N. (1954). Ob opredelenyy velychyn yspariaemosti. [On the determination of evaporation values] Moskow: Yzv. HHO, 189 – 196. [in Russian]
17. Blaney, H.F. & Criddle, W.D. (1950). Determining Requirements Water in Irrigated Areas from Climatological and Irrigation Data. Washington Soil Conservation Service, 48.
18. Dugas, W.A., Fritschen, L.J., Gay, L.W., Held, A.A., & Mathias, A.D. (1991). Bowen ratio, eddy correlation, and portable chamber measurements of sensible and latent heat flux over irrigated spring wheat. Agric. Forest Meteorol., 56 (1/2), 1–20.
19. Bastiaanssen, W., Noordman, E., Pelgrum, H., Davids, G., Thoreson, B., & Allen, R. (2005). SEBAL model with remotely sensed data to improve water-resources management under actual field conditions. J. Irrig. Drain. Eng., 131 (1), 85–93.
20. Bastiaanssen, W.G.M., Menenti, M., Feddes, R.A., & Holtslag, A.A.M. (1998). A remote sensing surface energy balance algorithm for land (SEBAL). 1. Formulation. J. Hydrol., 212–213, 198–212.
21. Kite, G.W., & Droogers, P. (2000). Comparing evapotranspiration estimates from satellites, hydrological models and field data. J. Hydrol., 229 (1–2), 3–18.
22. Allen, R.G., Tasumi, M., & Trezza, R. (2007a). Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)—model. J. Irrig. Drain. Eng., 133 (4), 380–394.
23. Droogers, P., & Bastiaanssen, W., (2002). Irrigation performance using hydrological and remote sensing modeling. J. Irrig. Drain. Eng., 128 (1), 11–18.
24. Kite, G.W. (2000). Using a basin-scale hydrological model to estimate crop transpiration and soil evaporation. J. Hydrol., 229 (1–2), 59–69.
25. Santos, C., Lorite, I.J., Tasumi, M., Allen, R.G., & Fereres, E., (2008). Integrating satellite-based evapotranspiration with simulation models for irrigation management at the scheme level. Irrig. Sci., 26 (3), 277–288.
26. Williams, J.R., & Izaurralde, R.C. (2005). The APEX model. BRC Report 2005-02. Blackland Research and Extension Center, Blackland
27. Borah, D.K., Yagow, G., Saleh, A., Barnes, P.L., Rosenthal, W., Krug, E.C., & Haucket, L.M. (2006). Sediment and nutrient modeling for TMDL development and implementation. Trans. ASABE, 49 (4), 967–986.
28. Panagopoulos, Y., Makropoulos, C., & Mimikou, M. (2012). Decision support for diffuse pollution management. Environ. Model Softw., 30, 57–70.
29. Styczen, M., Poulsen, R.N., Falk, A.K., & Jørgensen, G.H. (2010). Management model for decision support when applying low quality water in irrigation. Agric. Water Manage., 98, 472–781.
30. Smith, M., (1992). CROPWAT, A computer program for irrigation planning and management. FAO Irrigation and Drainage Paper No. 46.
31. Doorenbos, J., & Kassam, A.H. (1979). Yield Response to Water: FAO Irrigation and Drainage Papers No. 33. FAO, Rome.
32. Jensen, M.E. (1968). Water consumption by agricultural plants. In: In: Kozlowski, T.T. (Ed.), Water Deficits in Plant Growth, vol. 1. Academic Press, 1–22.
33. Car, N.J., Christen, E.W., Hornbuckle, J.W., & Moore, G.A. (2012). Using a mobile phone short messaging service (SMS) for irrigation scheduling in Australia—farmers’ participation and utility evaluation. Comput. Electron. Agric., 84, 132–143.
34. Abrahamsen, P., & Hansen, S. (2000). Daisy: an open soil-crop-atmosphere model. Environ. Model Softw., 15, 313–330.
35. Zhang, Y., & Feng, L. (2010). CropIrri: a decision support system for crop irrigation. IFIP Advances in Information and Communication Technology, vol. 317, 90–97.
36. Keating, B.A., Carberry, P.S., Hammer, G.L., Probert, M.E., Robertson, M.J., & Holzworth, D., et al. (2003). An overview of APSIM, a model designed for farming system simu- lation. Eur. J. Agron., 18 (3), 267–288.
37. Stockle, C.O., Donatelli, M., & Nelson, R. (2003). CropSyst, a cropping systems simulation model. Eur. J. Agron., 18, 289–307.
38. Marsal, J., & Stockle, C.O. (2010). Use of CropSyst as a decision support system for scheduling regulated deficit irrigation in a pear orchard. Irrig. Sci., 30, 139–147.
39. Mateos, L., Lopez-Cortijo, I., Sagardoy, J.A. (2002). SIMIS, the FAO decision support system for irrigation scheme management. Agric. Water Manage., 56, 193–206.
40. Cheng-cai, Z, Mao, Z, & Xi-mei, S. (2012). Henan Zhaokou irrigation management system design based on flex viewer. Procedia Eng., 28, 723–728.
41. Zhovtonoh, O.I., Filipenko, L.A., Demenkova, T.F., Babych, V.A., & Polishchuk, V.V. (2014). Komp’iuterna prohrama «Informatsiina systema operatyvnoho planuvannia zroshennia IS «HIS Polyv («IS «HIS Polyv»). [Computer Program "Information System for Operational Irrigation Planning of IS" GIS Poliv ("IS" GIS Poliv ")]. Svidotstvo pro reiestratsiiu avtorskykh prav na tvir № 5450 vid 07.05.2014.
42. Zhovtonog, O., Hoffmann, M., Polishchuk, V. & Dubel A. (2011). New planning technique to master the future of water on local and regional level in Ukraine. Journal of Water and Climate Change , 2 (2-3), 189-200.
43. Gadzalo, Ya., Romashchenko, M., Kovalchuk, V., Matiash, T., & Voitovich O. (2019). Using smart technologies in irrigation management. International Commission on Irrigation and Drainage: 3rd World Irrigation Forum (WIF3). Bali, Indonesia: WIF3, 178. Retrieved from: https://www.icid.org/wif3_bali_2019/wif3_abst_vol.pdf
44. IRROMETER Company Inc. irrometer.com. Retrieved from: https://www.irrometer.com/sensors.html
45. Kovalchuk, V.P, Voitovich, O.P, & Demchuk, D.O. (2019). Automatic tensiometer with data transmission over the Internet and refueling with water manually. Patent of Ukraine №132271. [in Ukrainian].
46. Kovalchuk, V., Demchuk, O., Demchuk, D., & Voitovich, O. (2018). Data mining for a model of irrigation control using weather web-services. Advances in Computer Science for Engineering and Education. International Conference on Computer Science, Engineering and Education Applications: Springer International Publishing, 133-143. https://doi.org/10.1007/978-3-319-91008-6_14
47. Romashchenko, M.I., Bohaienko, V.O., Matiash, T.V., Kovalchuk, V.P., & Danylenko, Iu.Iu. (2019). Influence of evapotranspiration assessment on the accuracy of moisture transport modeling under the conditions of sprinkling irrigation in the south of Ukraine Archives of Agronomy and Soil Science. https://doi.org/10.1080/036503402019.1674445
48. Van Genuchten, M. T. (1980). A closed-form equation for predicting the hydraulic conductivity of unsaturated soils 1. Soil science society of America journal, 44(5), 892-898.
49. Rosetta Version 1.0 (Free downloaded program). U.S.Salinity Laboratory ARSUSDA; Retrieved from: htpp://www.ussl.ars.usda.gov. Accessed 10 Sep 2019.
50. Kolomiiets S.S., & Yatsyk M.V. (2009). Method of determining the pore space structures of soils of dispersed media to determine the lowest soil moisture (HB). Patent of Ukraine № 45287. [in Ukrainian].
Published
2019-12-12
How to Cite
Romashchenko, M., MatіashT., Bohaienko, V., Kovalchuk, V., Voitovich, O., Krucheniuk, A., Knysh, V., & Shlikhta, V. (2019). DEVELOPMENT EXPERIENCE AND WAYS OF IMPROVEMENT OF IRRIGATION MANAGEMENT SYSTEMS. Land Reclamation and Water Management, (2), 17 - 30. https://doi.org/10.31073/mivg201902-207

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