Skip to main navigation menu Skip to main content Skip to site footer

Research Articles

Vol. 13 No. sp5 (2026): Recent Advances in Agriculture

Comparative evaluation of economic returns and water-saving efficiency of IoT-based smart drip irrigation and conventional irrigation methods in maize (Zea mays L.)

DOI
https://doi.org/10.14719/pst.14808
Submitted
1 April 2026
Published
01-08-2026

Abstract

Precision irrigation is critical for improving water productivity in maize under conditions of increasing water scarcity and climate variability. This study evaluated the impact of internet of things (IoT)-based smart drip irrigation and irrigation scheduling strategies on yield, water-use efficiency and economic returns of hybrid maize. Eight irrigation treatments, including IoT-based sensor-controlled drip irrigation, pan evaporation (PE)-based scheduling, irrigation water/cumulative pan evaporation ratio (IW/CPE) methods, conventional drip irrigation, surface irrigation and flood irrigation, were evaluated under field conditions. In the IoT-based treatments, soil moisture sensors continuously monitored root-zone moisture and automatically initiated irrigation when soil water depletion reached either 60 % or 80 % of the available soil water (ASW), representing moderate and high allowable depletion levels respectively. In contrast, conventional drip irrigation was operated using fixed irrigation schedules without real-time soil moisture sensing or automated feedback control. Results indicated significant differences (p ≤ 0.05) among treatments. The IoT-based drip irrigation at 60 % ASW depletion recorded the highest grain and straw yields, with grain yield increasing by 39 % compared with flood irrigation. The same treatment achieved the highest water-use efficiency, producing 2.62 times more grain per unit of water applied and reducing irrigation water use by 47 %. Economic analysis showed that IoT-based irrigation generated the highest profitability, with net returns increasing by 88 % over flood irrigation. The results demonstrate that sensor-based automated drip irrigation scheduled at 60 % allowable soil water depletion is an efficient and economically viable strategy for improving maize productivity, water-use efficiency and water conservation under water-limited conditions.

References

  1. 1. FAO. The state of the world's land and water resources for food and agriculture: Systems at breaking point. Rome: Food and Agriculture Organization of the United Nations; 2021.
  2. 2. Foley JA, Ramankutty N, Brauman KA, Cassidy ES, Gerber JS, Johnston M, et al. Solutions for a cultivated planet. Nature. 2011;478:337–42. https://doi.org/10.1038/nature10452
  3. 3. United Nations Department of Economic and Social Affairs. World population prospects 2022: Summary of results. New York: United Nations; 2022.
  4. 4. Pereira LS, Cordery I, Iacovides I. Improved indicators of water use performance and productivity for sustainable water conservation and saving. Agric Water Manag. 2012;108:39–51. https://doi.org/10.1016/j.agwat.2011.08.022
  5. 5. Fereres E, Soriano MA. Deficit irrigation for reducing agricultural water use. J Exp Bot. 2007;58(2):147–59. https://doi.org/10.1093/jxb/erl165
  6. 6. Howell TA. Enhancing water use efficiency in irrigated agriculture. Agron J. 2001;93(2):281–9. https://doi.org/10.2134/agronj2001.932281x
  7. 7. Keller J, Bliesner RD. Sprinkle and trickle irrigation. New York: Van Nostrand Reinhold; 1990.
  8. 8. Narayanamoorthy A. Drip irrigation in India: Can it solve water scarcity? Water Policy. 2004;6(2):117–30. https://doi.org/10.2166/wp.2004.0008
  9. 9. Enciso J, Jifon J, Ribera L, Zapata SD, Ganjegunte G. Yield, water use efficiency and economic analysis of drip irrigated corn in South Texas. Int J Agron. 2015;2015:628034. https://doi.org/10.1155/2015/628034
  10. 10. Farré I, Faci JM. Deficit irrigation in maize for reducing agricultural water use in a Mediterranean environment. Agric Water Manag. 2009;96(3):383–94. https://doi.org/10.1016/j.agwat.2008.07.002
  11. 11. Kim Y, Evans RG, Iversen WM. Remote sensing and control of an irrigation system using a distributed wireless sensor network. IEEE Trans Instrum Meas. 2008;57(7):1379–87. https://doi.org/10.1109/TIM.2008.917198
  12. 12. Ayaz M, Ammad-Uddin M, Sharif Z, Mansour A, Aggoune EHM. Internet-of-Things (IoT)-based smart agriculture: Toward making the fields talk. IEEE Access. 2019;7:129551–83. https://doi.org/10.1109/ACCESS.2019.2932609
  13. 13. Vellidis G, Tucker M, Perry C, Kvien C, Bednarz C. A real-time wireless smart sensor array for scheduling irrigation. Comput Electron Agric. 2008;61(1):44–50. https://doi.org/10.1016/j.compag.2007.05.009
  14. 14. Boursianis AD, Papadopoulou MS, Diamantoulakis P, Liopa-Tsakalidi A, Barouchas P, Salahas G, et al. Internet of Things (IoT) and agricultural unmanned aerial vehicles (UAVs) in smart farming: A comprehensive review. Internet Things. 2021;18:100187. https://doi.org/10.1016/j.iot.2020.100187
  15. 15. Khanna A, Kaur S. Evolution of Internet of Things (IoT) and its significant impact in the field of precision agriculture. Comput Electron Agric. 2019;157:218–31. https://doi.org/10.1016/j.compag.2018.12.039
  16. 16. Elijah O, Rahman TA, Orikumhi I, Leow CY, Hindia MN. An overview of Internet of Things (IoT) and data analytics in agriculture: Benefits and challenges. IEEE Internet Things J. 2018;5(5):3758–73. https://doi.org/10.1109/JIOT.2018.2844296
  17. 17. FAOSTAT. Crops and livestock products database. Rome: Food and Agriculture Organization of the United Nations; 2023.
  18. 18. Çakir R. Effect of water stress at different development stages on vegetative and reproductive growth of corn. Field Crops Res. 2004;89(1):1–16. https://doi.org/10.1016/j.fcr.2004.01.005
  19. 19. Pandey RK, Maranville JW, Admou A. Deficit irrigation and nitrogen effects on maize in a Sahelian environment. Agric Water Manag. 2000;46(1):1–13. https://doi.org/10.1016/S0378-3774(00)00073-1
  20. 20. Navarro-Hellín H, Martínez-del-Rincon J, Domingo-Miguel R, Soto-Valles F, Torres-Sánchez R. A decision support system for managing irrigation in agriculture. Comput Electron Agric. 2016;124:121–31. https://doi.org/10.1016/j.compag.2016.04.003
  21. 21. Sharifnasab H, Mahrokh A, Dehghanisanij H, Łazuka E, Łagód G, Karami H. Evaluating the use of intelligent irrigation systems based on the IoT in grain corn irrigation. Water. 2023;15(7):1394. https://doi.org/10.3390/w15071394
  22. 22. Tamil Nadu Agricultural University. Crop production guide. Coimbatore: Tamil Nadu Agricultural University and Department of Agriculture, Government of Tamil Nadu; 2020.
  23. 23. Allen RG, Pereira LS, Raes D, Smith M. Crop evapotranspiration: Guidelines for computing crop water requirements. FAO Irrigation and Drainage Paper No. 56. Rome: Food and Agriculture Organization; 1998.
  24. 24. Michael AM. Irrigation: Theory and practice. 2nd ed. New Delhi: Vikas Publishing House Pvt Ltd; 2010.
  25. 25. Nayyar A, Puri V. Smart farming: IoT based smart sensors agriculture stick for live temperature and moisture monitoring using Arduino, cloud computing and solar technology. In: Proceedings of the International Conference on Communication and Computing Systems; 2016. p. 717–21. https://doi.org/10.1201/9781315364094-121
  26. 26. Doorenbos J, Pruitt WO. Guidelines for predicting crop water requirements. FAO Irrigation and Drainage Paper No. 24. 2nd ed. Rome: Food and Agriculture Organization; 1977.
  27. 27. Watson DJ. The physiological basis of variation in yield. Adv Agron. 1952;4:101–45. https://doi.org/10.1016/S0065-2113(08)60307-7
  28. 28. Gomez KA, Gomez AA. Statistical procedures for agricultural research. 2nd ed. New York: John Wiley & Sons; 1984.
  29. 29. Palaniswamy KM, Gomez KA. Length-width method for estimating leaf area of rice. Agron J. 1974;66:430–3. https://doi.org/10.2134/agronj1974.00021962006600030027x
  30. 30. Paliwal RL. Introduction to maize and its importance. In: Paliwal RL, Granados G, Lafitte HR, Violic AD, editors. Tropical maize: Improvement and production. FAO Plant Production and Protection Series No. 28. Rome: Food and Agriculture Organization; 2000. p. 1–3.
  31. 31. Singh V, Kumar R, Singh AK. Maize production technology and management. New Delhi: Indian Agricultural Research Institute; 2018.
  32. 32. Prihar SS, Sandhu BS. Irrigation of field crops: Principles and practices. New Delhi: Indian Council of Agricultural Research; 1987.
  33. 33. Majumdar DK. Irrigation water management: Principles and practice. 3rd ed. New Delhi: PHI Learning Pvt Ltd; 2014.
  34. 34. Viets FG Jr. Fertilizers and the efficient use of water. Adv Agron. 1962;14:223–64. https://doi.org/10.1016/S0065-2113(08)60439-3
  35. 35. Singh H, Mishra D, Nahar NM, Reddy KS. Energy use pattern in production agriculture of a typical village in arid zone, India. Energy Convers Manag. 2002;43(16):2275–86. https://doi.org/10.1016/S0196-8904(01)00161-3
  36. 36. Stout BA. Handbook of energy for world agriculture. Amsterdam: Elsevier; 1990. https://doi.org/10.1007/978-94-009-0745-4
  37. 37. Reddy SS, Ram PR. Agricultural economics. Hyderabad: Oxford and IBH Publishing Co Pvt Ltd; 2018.
  38. 38. Payero JO, Tarkalson DD, Irmak S, Davison D, Petersen JL. Effect of irrigation amounts applied with subsurface drip irrigation on maize yield. Agric Water Manag. 2008;90:170–81.
  39. 39. Abioye EA, Abidin MSZ, Mahmud MSA, Buyamin S, Abd Rahman MKI, Otuoze AO, et al. IoT-based monitoring and data-driven irrigation control system for drip irrigation. Comput Electron Agric. 2020;175:105601.

Downloads

Download data is not yet available.