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Creating a Solar-Powered Drip Irrigation Optimal Performance model (SDrOP) to lower the cost of drip irrigation systems for smallholder farmers

Author

Listed:
  • Grant, Fiona
  • Sheline, Carolyn
  • Sokol, Julia
  • Amrose, Susan
  • Brownell, Elizabeth
  • Nangia, Vinay
  • Winter, Amos G.

Abstract

Smallholder farmers, who hold 84% of the approximately 570 million farms worldwide, are vital stakeholders in the process of sustainable agricultural intensification, but often lack the capital to invest in sustainable farming practices. Solar-powered drip irrigation has the potential to increase crop productivity for minimal water use, but these systems are prohibitively expensive for smallholders. Reducing the life cycle cost (LCC) of solar-powered drip irrigation systems could make this technology more accessible, enabling smallholders to increase their household incomes and contribute to greater global food security. This paper presents the Solar-Powered Drip Irrigation Optimal Performance model (SDrOP), which optimizes solar-powered drip irrigation system designs. Unlike existing commercial software, SDrOP models the behavior of the entire system and simulates seasonal performance to reduce LCC while maintaining operational reliability. SDrOP improves on previous design optimization frameworks by taking in all location-dependent parameters as inputs, which makes the model independent of case specifics and, therefore, broadly applicable. To demonstrate the model theory, the sensitivity of the optimal design to field area, the system reliability constraint, and varying weather conditions are explored for a Moroccan olive orchard case study. The results demonstrate opportunities for system cost reduction, including operational changes to reduce the system power requirement, irrigation pump opportunities for the smallholder market, and reductions in system reliability when it is shown to have minimal impact on crop yield. When benchmarked against a commercially available software, SDrOP was able to reduce system LCC by up to 56%. The simulated performance of an SDrOP optimal design was benchmarked against operational data from an existing field site, and was shown to be capable of operating 92% of the recorded irrigation events. These results indicate that SDrOP offers an advantage over existing software as it produces significantly reduced cost designs that can operate in real-world conditions.

Suggested Citation

  • Grant, Fiona & Sheline, Carolyn & Sokol, Julia & Amrose, Susan & Brownell, Elizabeth & Nangia, Vinay & Winter, Amos G., 2022. "Creating a Solar-Powered Drip Irrigation Optimal Performance model (SDrOP) to lower the cost of drip irrigation systems for smallholder farmers," Applied Energy, Elsevier, vol. 323(C).
    • Handle: RePEc:eee:appene:v:323:y:2022:i:c:s0306261922008741
    DOI: 10.1016/j.apenergy.2022.119563
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    References listed on IDEAS

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    1. Kelley, Leah C. & Gilbertson, Eric & Sheikh, Anwar & Eppinger, Steven D. & Dubowsky, Steven, 2010. "On the feasibility of solar-powered irrigation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(9), pages 2669-2682, December.
    2. Peter Rosset, 2000. "The Multiple Functions and Benefits of Small Farm Agriculture in the Context of Global Trade Negotiations," Development, Palgrave Macmillan;Society for International Deveopment, vol. 43(2), pages 77-82, June.
    3. Donald F. Larson & Keijiro Otsuka & Tomoya Matsumoto & Talip Kilic, 2014. "Should African rural development strategies depend on smallholder farms? An exploration of the inverse-productivity hypothesis," Agricultural Economics, International Association of Agricultural Economists, vol. 45(3), pages 355-367, May.
    4. Friedlander, Lonia & Tal, Alon & Lazarovitch, Naftali, 2013. "Technical considerations affecting adoption of drip irrigation in sub-Saharan Africa," Agricultural Water Management, Elsevier, vol. 126(C), pages 125-132.
    5. Lowder, Sarah K. & Skoet, Jakob & Raney, Terri, 2016. "The Number, Size, and Distribution of Farms, Smallholder Farms, and Family Farms Worldwide," World Development, Elsevier, vol. 87(C), pages 16-29.
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    1. Zha, Junjie & Ge, Maosheng & Tang, Zhengwen & Lei, Junyao & Zhao, Haoyu & Zhang, Yongqiang, 2024. "The incorporation of solar energy and compressed air into the energy supply system enhances the environmentally friendly and efficient operation of drip irrigation systems," Agricultural Water Management, Elsevier, vol. 302(C).
    2. Alam, Marzia & Imran, Muhammad & Sultan, Muhammad & Manzoor, Umar & Khan, Zafar A. & Rezk, Ahmed & Alaswad, Abed, 2024. "System-level optimisation of hybrid energy powered irrigation system," Renewable Energy, Elsevier, vol. 234(C).
    3. Sheline, Carolyn & Grant, Fiona & Gelmini, Simone & Pratt, Shane & Winter V., Amos G., 2025. "Designing a predictive optimal water and energy irrigation (POWEIr) controller for solar-powered drip irrigation systems in resource-constrained contexts," Applied Energy, Elsevier, vol. 377(PA).
    4. Zhang, Qianwen & Ge, Maosheng & Wu, Pute & Wei, Fuqiang & Xue, Shaopeng & Wang, Bo & Ge, Xinbo, 2023. "Solar photovoltaic coupled with compressed air energy storage: A novel method for energy saving and high quality sprinkler irrigation," Agricultural Water Management, Elsevier, vol. 288(C).
    5. Isaacs, Stewart & Kalashnikova, Olga & Garay, Michael J. & van Donkelaar, Aaron & Hammer, Melanie S. & Lee, Huikyo & Wood, Danielle, 2023. "Dust soiling effects on decentralized solar in West Africa," Applied Energy, Elsevier, vol. 340(C).

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