ROBUST PLANNING OF IRRIGATION CONSIDERING WATER CONSUMPTION AND REVENUE

Year 2024, , 105 - 134, 06.11.2024

Özgü Turgut

https://doi.org/10.56850/jnse.1478848

Abstract

Water scarcity is a problem for many regions which requires immediate action, and solutions cannot be postponed for a long time. It is known that farming consumes a significant portion of usable water. In this study, a decision-support model of biobjective stochastic linear formulation is proposed. The model is generating annual planting plans together with water consumption projections for each farmer in the region while taking revenue of the overall harvest into account. The structure of the proposed model maintains robustness against the volatilities in precipitation, yield, and market price. The inherent trade-off between water consumption and revenue lends itself to multi-objective planning. This is a perspective especially useful for regional administrations to plan next year's crop pattern together with agricultural incomes and irrigation expenses. Furthermore, it is also shown how the model can be used to investigate the potential of rainwater harvesting or switching to water-efficient irrigation methodologies. The decision support model is especially unique in the sense that it can generate a set of Pareto optimum solutions as opposed to a single objective counterpart. This property is helpful in terms of not only providing a broader perspective to evaluate and project the possibilities but also increasing the applicability of the results by providing a flexible design framework.

Keywords

decision support, robust optimization, irrigation, farming planning, rain harvesting

Ethical Statement

The study does NOT contain any conflicting interest with another party. All the data used in the demonstrations are gathered from open and referenced resources for the sake of replicability. All the effort belongs to the author.

SU TÜKETİMİ VE KAZANÇ ODAKLI, DİRENÇLİ (GÜRBÜZ) SULAMA PLANLAMASI

Abstract

Su kıtlığı birçok bölgede ertelemeye göz yumulamayacak ölçüde acil çözümler bekleyen bir problem haline gelmiştir. Tarımın kullanılabilir su kaynaklarının ise önemli bir kısmını tükettiği bilinen bir gerçektir. Bu çalışmada bu gözlemlerden yola çıkılarak, çift amaçlı, doğrusal ve stokastik bir karar destek modeli sunulmuştur. Söz konusu model seçilen bölgede yıllık ekim planı ile birlikte yıl boyu gerçekleşecek su tüketim tahminlerini, yılsonu hasatına ait muhtemel kazancı göz önünde bulundurarak çıktı olarak vermektedir. Modelin dirençli yapısı tüm bu çıktıyı üretirken yağış, verim ve ürünlerin ortalama birim fiyatlarındaki belirsizliği hesaba katmasından kaynaklanmaktadır. Çift amaçlı yapı ise su tüketimi ve kazanç arasında varolan çelişkinin doğal bir sonucudur. Sunulan modelin bakış açısı genel olarak yıllık ekim planlaması ve su dağıtımı gibi hizmetleri yürütmeden, ayrıca yıllık tarım gelirlerinin öngörülmesinden sorumlu idari birimlerle örtüşmektedir. İlaveten, çalışmada yağmur hasatı ve yağmur sulama sistemleri gibi iki önemli teknolojinin potansiyel katkısı da örnek alan çalışması üzerinde gösterilmiştir. Bu noktada, modelin çift amaçlı yapısından kaynaklanan avantajlarının bir kez daha altı çizilmiştir. Zira, tek amaçlı benzerlerine kıyasla bu yapıdaki modeller, birden fazla optimum plan çıkarabildiği için uygulayıcıya karar alma noktasında daha geniş bir perspektif ve esneklik sunmaktadır.

Keywords

Karar destek ünitesi, eniyileme, sulama, yağmur hasatı, tarım planlama

References

• Ağlamış, N. and Ali Tokgöz, M., 1997. “Ankara Murted Sulamasında Su Kullanım ve Dağıtım Etkinliğinin Belirlenmesi. ” Journal of Agricultural Sciences, 3(02), pp.83-86.
• Beyribey, M., Sönmez, F. K., Çakmak, B., & Mehmet, Oğuz. (1997). “Devlet Sulama Şebeketerinde Aylık Su Temini Oranının Belirlenmesi” Journal of Agricultural Sciences, 3(02), 33-37.
• Bwambale, E., Abagale, F. K., & Anornu, G. K. (2022). “Smart irrigation monitoring and control strategies for improving water use efficiency in precision agriculture: A review.” Agricultural Water Management, 260, 107324.
• Cultice, A., Bosch, D. J., Pease, J. W., Boyle, K. J., & Xu, W. (2016). “Horticultural Growers’willingness to Adopt Recycling of Irrigation Water.” Journal of Agricultural and Applied Economics, 48(1), 99-118.
• Daskalakis, S.N.; Goussetis, G.; Assimonis, S.D.; Tenzeris, M.M.; Georgiadis, A. (2018) “W backscatter-morse leaf sensor for low-power agricultural wireless sensor networks. ” IEEE Sens. J., 18, 7889–7898.
• Demenge, J., Alba, R., Welle, K., Manjur, K., Addisu, A., Mehta, L., Woldearegay, K. (2015). “Multifunctional Roads the Potential Effects of Combined Roads and Water Harvesting Infrastructure on Livelihoods and Poverty in Ethiopia.” Journal of Infrastructure Development, 7(2), 165–180
• Dong, C., Huang, G. H., & Tan, Q. (2015). “A robust optimization modelling approach for managing water and farmland use between anthropogenic modification and ecosystems protection under uncertainties.” Ecological Engineering, 76, 95-109.
• FAO. (2020). The State of Food and Agriculture 2020. Overcoming water challenges in agriculture. Rome: Food and Agriculture Organisation of the United Nations
• FAO. AQUASTAT: Water Uses. 2016. Available online: http://www.fao.org/nr/water/aquastat/water_use (accessed on 5 January 2019).
• García, L., Parra, L., Jimenez, J. M., Lloret, J., & Lorenz, P. (2020). “IoT-based smart irrigation systems: An overview on the recent trends on sensors and IoT systems for irrigation in precision agriculture.” Sensors, 20(4), 1042.
• García, L., Parra, L., Jimenez, J.M., Lloret, J. and Lorenz, P., 2020. “IoT-based smart irrigation systems: An overview on the recent trends on sensors and IoT systems for irrigation in precision agriculture.” Sensors, 20(4), p.1042.
• Guo, S., Yang, Y., & Guo, P. (2023). “An integrated distributed robust optimization framework for agricultural water-food-energy management integrating ecological impact and dynamic water cycle processes.” Journal of Hydrology, 624, 129859.
• Guruprasadh, J.P.; Harshananda, A.; Keerthana, I.K.; Krishnan, K.Y.; Rangarajan, M.; Sathyadevan, S. (13–16 September 2017) “Intelligent Soil Quiality Monitoring System for Judicious Irrigation.” In Proceedings of the 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Udupi, India,.
• Hari, D., Reddy, K. R., Vikas, K., Srinivas, N., & Vikas, G. (March 2018). “Assessment of rainwater harvesting potential using GIS.” In IOP Conference Series: Materials Science and Engineering (Vol. 330, No. 1, p. 012119). IOP Publishing.
• Jin, Y., Lee, S., Kang, T., Park, J., & Kim, Y. (2023). “Capacity Optimization of Rainwater Harvesting Systems Based on a Cost–Benefit Analysis: A Financial Support Program Review and Parametric Sensitivity Analysis.” Water, 15(1), 186.
• Kalkınma Bakanlığı 2018 Tarimda Toprak Ve Suyun Sürdürülebilir Kullanimi Özel İhtisas Komisyonu Raporu, On Birinci Kalkinma Plani (2019-2023)
• Kartal, S., Değirmenci, H., Arslan, F., & Gizlenci, İ. (2023). “Evaluation of some Water, Energy and Financial Indicators: A Case Study of Esenli Water User Association in Yozgat, Türkiye.” Journal of Agricultural Sciences, 29(2), 643-654.
• Kodal, S., Selenay, M.F., Sönmez, F.K. and Yıldırım, Y.E., (1997). “Sulama Suyu ihtiyacı Açısı ndan Su Tüketimi ve Yağış Analizi.” Journal of Agricultural Sciences, 3(01), pp.59-68.
• Kovacs, K., & Durand-Morat, A. (2017). “The influence of on-and off-farm surface water investment on groundwater extraction from an agricultural landscape.” Journal of Agricultural and Applied Economics, 49(3), 323-346.
• Laureti, T., Benedetti, I., & Branca, G. (2021). “Water use efficiency and public goods conservation: A spatial stochastic frontier model applied to irrigation in Southern Italy”. Socio-Economic Planning Sciences, 73, 100856.
• Li, J., Qiao, Y., Lei, X., Kang, A., Wang, M., Liao, W., & Ma, Y. (2019). “A two-stage water allocation strategy for developing regional economic-environment sustainability.” Journal of environmental management, 244, 189-198.
• Mahdi, M., Xueqian, S., Gai, Q., Basirialmahjough, M., & Yuan, H. (2023). “Improving robustness of water supply system using a multi-objective robust optimization framework. ” Environmental Research, 232, 116270.
• Maqsood, I., Huang, G., Huang, Y., & Chen, B. (2005). “ITOM: an interval-parameter two-stage optimization model for stochastic planning of water resources systems.” Stochastic Environmental Research and Risk Assessment, 19, 125-133.
• McOmber, C., Zhuang, Y., Raudales, R. E., Vadas, T. M., & Kirchhoff, C. J. (2021). “What is recycled water, anyway? Investigating greenhouse grower definitions, perceptions, and willingness to use recycled water. ” Renewable Agriculture and Food Systems, 36(5), 491-500.
• Melek, Işık. (2023). “Determining alternative crops with multi criteria decision making methods within the framework of land risk criteria.” Journal of Agricultural Sciences, 29(1), 161-170.
• Meteoroloji Genel Müdürlüğü Resmi web sitesi: https://mgm.gov.tr/veridegerlendirme/il-ve-ilceler-istatistik.aspx?k=H&m=KAYSERI
• Molle, F., López-Gunn, E., & Van Steenbergen, F. (2018). “The local and national politics of groundwater overexploitation. ” Water Alternatives, 11(3).
• Nissen-Petersen E (2006) Water from Roads: A handbook for technicians and farmers on harvesting rain water from roads. ASAL Consultants Ltd. Nairobi, Kenya
• Othman, M. M. (2023). “Modeling of daily groundwater level using deep learning neural networks.” Turkish Journal of Engineering, 7(4), 331-337.
• Parra, L.; Ortuño, V.; Sendra, S.; Lloret, J. (6–8 August 2013). “Low-Cost Conductivity Sensor based on Two Coils.” In Proceedings of the First International Conference on Computational Science and Engineering, Valencia, Spain.
• Romero, R., Muriel, J.L., García, I. and de la Peña, D.M., (2012). “Research on automatic irrigation control: State of the art and recent results.” Agricultural water management, 114, pp.59-66.
• Quintana-Ashwell, N. E., & Gholson, D. M. (2022). “Optimal Management of Irrigation Water from Aquifer and Surface sources.” Journal of Agricultural and Applied Economics, 54(3), 496-514.
• Sendra, S.; Parra, L.; Ortuño, V.; Lloret, L. (25–31 August 2013). “A Low Cost Turbidity Sensor Development.” In Proceedings of the Seventh International Conference on Sensor Technologies andApplications, Barcelona, Spain,
• Şenyiğit, U., & Arslan, M. (2018). “Effects of irrigation programs formed by different approaches on the yield and water consumption of black cumin (Nigella sativa L.) under transition zone in the West Anatolia conditions.” Journal of Agricultural Sciences, 24(1), 22-32.
• TAGEM and DSI (2017) (Guideline): Türkiye’de Sulanan Bitkilerin Bitki Su Tüketimleri Ankara
• TÜİK, Tarımsal Yapı (Üretim, Fiyat, Değer) Yayını
• UN (2022) United Nations 2023 Summary Report for Water Conference Global Online Stakeholder Consultation for the Proposed Themes of the Interactive Dialogues https://www.unwater.org/news/historic-un-2023-water-conference-generates-transformative-commitments)
• Van Steenbergen, F., Woldearegay, K., Agujetas Perez, M., Manjur, K., & Al-Abyadh, M. A. (2018). “Roads: instruments for rainwater harvesting, food security and climate resilience in arid and semi-arid areas. Rainwater-Smart Agriculture in Arid and Semi-Arid Areas: Fostering the Use of Rainwater for Food Security, Poverty Alleviation, ” Landscape Restoration and Climate Resilience, 121-144.
• Yasari, E., & Pishvaie, M. R. (2015). “Pareto-based robust optimization of water-flooding using multiple realizations.” Journal of Petroleum Science and Engineering, 132, 18-27.