Skip to main navigation menu Skip to main content Skip to site footer
Plant Science Today (PST)

Research Articles

Early Access

A linear programming-based approach for optimizing drip irrigation systems and crop productivity

DOI
https://doi.org/10.14719/pst.8496
Submitted
25 March 2025
Published
11-09-2025
Versions

Abstract

Sustainable agriculture requires the integration of advanced technologies to enhance productivity and resource efficiency. This study aimed to optimize an automated drip irrigation system. Using linear programming, optimal values for key crop attributes were determined based on different irrigation systems and water quality treatments. Attributes such as plant height, number of leaves, number of branches, number of fruits and yield in respective of irrigation system, water quality and days after transplanting. The results revealed that all the independent attributes affected the plant height, number of leaves, number of fruits, number of branches and yield. The optimum values of plant height, number of leaves, number of branches, number of fruits and optimum yield were higher for the wireless-based drip irrigation system (IS2) with treated fruit processing wastewater (WQ2) than for the wire-based irrigation system. Based on the substantially increased net return in the wireless irrigation system model, it is the better-suited model under the given conditions. Hence, the combination of treated fruit processing wastewater with a wireless-based system is recommended.

References

  1. 1. Alperovitz E, Shamir U. Design of optimal water distribution systems. Water Resour Res. 1977;13:885-900. https://doi.org/10.1029/WR013i006p00885
  2. 2. Indian agriculture and farmers’ capacity [Internet]. Economic Times; 2013 Aug 24 [cited 2025 Jun 11]. Available from: http://articles.economictimes.indiatimes.com/2013-08-24/news/41444004/indian-agriculture-one-crore-farmers-farmers-capacity
  3. 3. Accelerating growth of Indian agriculture [Internet]. Hindustan Times; 2016 [cited 2025 Jun 11]. Available from: http://articles.economictimes.indiatimes.com/2013-08-24/news/414440041/indian-agriculture-one-crore-farmers-farmers-capacity
  4. 4. How many farmers does India really have? Hindustan Times [Internet]. 2014 [cited 2025 Jun 11]. Available from: http://www.hindustantimes.com/india/how-many-farmers-does-india-really-have/story-431phtct5O9xZSjEr6HODJ.html
  5. 5. ICT post: Building bridge opportunity in the Indian economy [Internet]. 2017 [cited 2025 Jun 11]. Available from: http://ictpost.com/india-needs-to-address-the-challenges-of-agriculture-through-ict-2/
  6. 6. Provenzano G, Rallo G, Sdao F, Simoni S. Assessing water resource management under limited water availability. J Irrig Drain Eng. 2013;139(7):555–63. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000574
  7. 7. Douh B, Boujelben A. Improving water use efficiency for sustainable productivity of agricultural systems using subsurface drip irrigation. J Nat Prod Plant Resour. 2011;1(3):8–14 https://www.scholarsresearchlibrary.com/archive/jnppr-volume-1-issue-3-year-2011.html
  8. 8. Al-Ghobari HM. The assessment of automatic irrigation scheduling techniques on tomato yield and water productivity under a subsurface drip irrigation system in a hyper-arid region. WIT Trans Ecol Environ. 2014;185:65–76. https://doi.org/10.2495/SI140061
  9. 9. Bhave PR. Selecting pipe sizes in network optimization using linear programming. J Hydraul Div. 1979;105:1019-25. https://doi.org/10.1061/JYCEAJ.0005248
  10. 10. Dandy GC, Simpson AR, Murphy LJ, editors. A review of pipe network optimization techniques. In: Water Comp 93: Proceedings of the 2nd Australian Conference on the Use of Computers in the Water Industry; 1993; Australia. Reston (VA): ASCE; 1996. p. 265-75. https://doi.org/10.1061/(ASCE)0733-9437(1996)122:5(265)
  11. 11. Mancosu N, Snyder RL, Kyriakakis G, Spano D. Water scarcity and future challenges for food production. Water. 2015;7:975–92. https://doi.org/10.3390/w7030975
  12. 12. Siebert S, Burke J, Faures JM, Frenken K, Hoogeveen J, Doll P, et al. Groundwater use for irrigation: A global inventory. Hydrol Earth Syst Sci. 2010;14:1863-80. https://doi.org/10.5194/hess-14-1863-2010
  13. 13. World Bank, editor. Deep wells and prudence: Towards pragmatic action for addressing groundwater overexploitation in India. Washington (DC): World Bank; 2010. https://siteresources.worldbank.org/INDIAEXTN/.../DeepWellsGroundWaterMarch2010
  14. 14. Perez R, Martinez F, Vela A. Improved design of branched networks using pressure-reducing valves. J Hydraul Eng. 1991;119:169-79. https://doi.org/10.1061/(ASCE)0733-9429(1993)119:2(164)
  15. 15. Sharma H. Evaluation of groundwater depletion scenario and its impacts in India using geodetic techniques and modeling approaches [thesis]. Visakhapatnam: Andhra University; 2014.
  16. 16. Oron G, Karmeli D. Procedure for the economical evaluation of water network parameters. Water Resour Bull. 1979;15:1050–59. https://doi.org/10.1111/j.1752-1688.1979.tb01083.x
  17. 17. Oron G, Walker W. Optimal design and operation of permanent irrigation systems. Water Resour Res. 1981;17:11-7. https://doi.org/10.1029/WR017i001p00011
  18. 18. Siddapur AD, Polisgowdar BS, Nemichandrappa M, Ayyanagowder MS, Satishkumar U, Hugar A. Water use efficiency for marigold flower (Tagetes erecta L.) under furrow and drip irrigation systems. In: Micro irrigation scheduling and practices. Boca Raton (FL): CRC Press/Taylor & Francis; 2017. p. 143–56. https://doi.org/10.1201/9781315207384-11
  19. 19. Grant Thornton. Strategy paper - Future prospects of micro-irrigation in India [Internet]. 2016 [cited 2025 Apr 21]. Available from: http://www.grantthornton.in/globalassets/1.-member-firms/india/assets/pdfs/micro-irrigation_report.pdf
  20. 20. Mishra PK, Singh M, Gill JS, Mandal B, Patel B. Economic analysis and feasibility of tractor-operated cotton harvesters. Indian J Econ Dev. 2017;13(4):761–66. https://doi.org/10.5958/2322-0430.2017.00242.6
  21. 21. Topak R, Suheri S, Acar B. Effect of different drip irrigation regimes on sugar beet (Beta vulgaris L.) yield, quality and water use efficiency in Middle Anatolian, Turkey. Irrig Sci. 2011;29:79–89. https://doi.org/10.1007/s00271-010-0219-3
  22. 22. United Nations World Water Assessment Programme (WWAP). The United Nations World Water Development Report 2018: Nature-Based Solutions for Water [Internet]. Paris: UNESCO; 2018 [cited 2025 Apr 21]. Available from:https://unesdoc.unesco.org/ark:/48223/pf0000261424
  23. 23. Food and Agriculture Organization of the United Nations (FAO). The State of Food and Agriculture 2020 [Internet]. Rome: FAO; 2020 [cited 2025 Apr 21]. Available from: https://openknowledge.fao.org/server/api/core/bitstreams/6e2d2772-5976-4671-9e2a-0b2ad87cb646/content
  24. 24. World Bank. India's Water Resources: A Critical Perspective [Internet]. Washington, DC: World Bank; 2022 [cited 2025 Apr 21]. Available from: 1. https://documents1.worldbank.org/curated/en/099102211102224772/pdf/IDU0a8831b08028b604d070aa0104893aa4ceda2.pdf
  25. 25. Ministry of Jal Shakti. Annual Water Report 2021 [Internet]. New Delhi: Government of India; 2021 [cited 2025 Apr 21]. Available from: https://master-jalshakti-ddws.digifootprint.gov.in/static/uploads/2024/02/annual-report-2021-22-eng.pdf
  26. 26. Indian Council of Agricultural Research (ICAR). Sustainable Water Use in Indian Agriculture [Internet]. New Delhi: ICAR; 2021 [cited 2025 Apr 21]. Available from: https://www.ceew.in/sites/default/files/CEEW-FOLU-Sustainable-Agriculture-in-India-2021-20Apr21.pdf
  27. 27. International Water Management Institute (IWMI). Water Scarcity and Food Security [Internet]. Colombo: IWMI; 2019 [cited 2025 Apr 21]. Available from: https://doi.org/10.3910/2009.019
  28. 28. Central Ground Water Board (CGWB). India’s Groundwater Crisis [Internet]. New Delhi: Ministry of Jal Shakti, Government of India; 2023 [cited 2025 Apr 21]. Available from: https://www.cgwb.gov.in/old_website/index/SoP %20Source %20Sustainability %204.4.23.pdf
  29. 29. Galande SG, Agrawal GH. Embedded controlled drip irrigation system. Int J Emerg Trends Technol Comput Sci. 2013;2:37–41. https://www.scribd.com/document/184790966/IJETTCS-2013-08-25-030
  30. 30. Almarshadi MH, Ismail SM. Effects of precision irrigation on productivity and water use efficiency of alfalfa under different irrigation methods in arid climates. J Appl Sci Res. 2011;7(3):299–308 .https://api.semanticscholar.org/CorpusID:110707218
  31. 31. Faye RM, Mora-Camino F, Sawadogo S, Niang A. An intelligent decision support system for irrigation management. IEEE Trans. 1998;33:107–16. https://doi.org/10.1109/ICSMC.1998.726698
  32. 32. Zhang Y, Wang X, Jin L, Ni J, Zhu Y, Cao W, et al. Research and development of an IoT smart irrigation system for farmland based on LoRa and edge computing. Agron. 2025;15(2):366. https://doi.org/10.3390/agronomy15020366
  33. 33. Hanson B, May D. Effect of subsurface drip irrigation on processing tomato yield, water table depth, soil salinity and profitability. Agric Water Manag. 2004;68:1–17. https://doi.org/10.1016/j.agwat.2004.03.003
  34. 34. Karam F, Geagea L, Saadeh M. Improving water use efficiency for sustainable agricultural productivity in Lebanon. In: Proceeding of the WEMED Workshop: How to Advance Knowledge on Water Use Efficiency in the Mediterranean Region; 2007 Apr 10-12; Bari, Italy. Bari: Ciheam; 2007. p. 107–15.
  35. 35. Karmeli D, Gadish Y, Meyers S. Design of optimal water distribution networks. J Pipeline Div. 1968;94:1-10. https://doi.org/10.1061/JPLEAZ.0000116
  36. 36. Gupta I. Linear programming analysis of a water supply system. Trans. 1969;1:56-61. https://doi.org/10.1080/05695556908974414

Downloads

Download data is not yet available.