بهینه‌سازی الگوی کشت در چارچوب اهداف کشاورزی اقلیم-هوشمند: مطالعه موردی شبکه آبیاری درودزن- ایران

نوع مقاله : مقالات پژوهشی به زبان انگلیسی

نویسندگان

1 دانشجوی دکتری، بخش اقتصاد کشاورزی، دانشکده کشاورزی، دانشگاه شیراز، شیراز، ایران

2 استاد، بخش اقتصاد کشاورزی، دانشکده کشاورزی، دانشگاه شیراز، شیراز، ایران

چکیده

سیستم­های نوین آبیاری به عنوان یک راهبرد انطباقی برای مدیریت اثرات تغییر اقلیم و بهبود امنیت آب در نظر گرفته می­شود. استفاده از چنین سیستم­هایی علاوه بر صرفه­جویی در مصرف آب، چالش­هایی را در زمینه افزایش مصرف انرژی و انتشار گازهای گلخانه­ای ایجاد کرده است. اگرچه برخی از مطالعات اخیر تحلیل‌های ارزنده­ای از رابطه بین آب و انرژی در سیستم‌های آبیاری کشاورزی ارائه کرده‌اند، اما توجه همزمان به بهره‌وری، سازگاری و کاهش اثرات مخرب محیط زیستی در بهینه‌سازی الگوی کشت یک سیستم کشاورزی به عنوان یک ضرورت اساسی  کمتر مورد توجه قرار گرفته است. کشاورزی اقلیم-هوشمند به عنوان یک مفهوم برنامه­ای قوی که به این سه هدف می‌پردازد، پتانسیل یک راه‌حل برد سه­جانبه را ایجاد کرده است. این مطالعه با توسعه یک مدل یکپارچه اقتصادی-هیدرولوژیکی-محیط­زیستی به نام WECSAM در سطح حوضه، متشکل از یک مدل هیدرولوژیکی به نامWEAP و یک مدل بهینه‌سازی چند-هدفه و ترکیب آن با مفاهیم ردپای آب، ردپای انرژی و انتشار گازهای گلخانه­ای در چارچوب کشاورزی اقلیم-هوشمند، در جهت پر کردن این خلأ است.  این مدل برای منطقه شمالی حوضه آبریز بختگان به نام شبکه آبیاری درودزن اجرا شد. نتایج مدل WECSAM نشان داد که با بهینه‌سازی همزمان اهداف متناقض حداکثرسازی سود اقتصادی و حداقل­سازی ردپای آب، ردپای انرژی و انتشار دی­اکسید کربن، در مقایسه با مدل تک-‌هدفه حداکثرسازی سود، باعث کاهش 2/8 درصد ردپای آب، کاهش 2/21 درصد ردپای انرژی، کاهش 9/6 درصد انتشار انتشار دی اکسید کربن و کاهش 4/7 درصد سود اقتصادی می­شود. سهم سیستم قطره‌ای در آبیاری الگوی کشت آب-هوشمند، انرژی-هوشمند و اقلیم-هوشمند 5/54 درصد و برای سیستم بارانی نیمه متحرک 2/26 درصد است، در حالی که سیستم بارانی کلاسیک ثابت کمتر از یک درصد از آبیاری الگوی کشت بهینه را به خود اختصاص می­دهد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Cropping Pattern Optimization in the Context of Climate-Smart Agriculture: A Case Study for Doroodzan Irrigation Network- Iran

نویسندگان [English]

  • D. Jahangirpour 1
  • M. Zibaei 2
1 Ph.D. Student, Department of Agricultural Economics, College of Agriculture, Shiraz University, Shiraz, Iran
2 Professor, Department of Agricultural Economics, College of Agriculture, Shiraz University, Shiraz, Iran
چکیده [English]

Modern irrigation systems are considered as a way to both respond to the effects of climate changes and improve the water security. Applying such systems, save the water used in farming activities and consequently made some environmental challenges in terms of increasing energy consumption and greenhouse gas emissions. Although some recent studies analyzed the relationship between water and energy in the agricultural irrigation systems, considering the objectives on productivity, adaptation, and mitigation in a cropping pattern optimization problem is necessary. Climate-Smart agriculture as a strong programming concept, addresses these three objectives and has created the potential for a "triple-win" solution. This study is an effort to fill the study gap on triple-win solution in modern irrigation by developing an integrated economic-hydrological-environmental model called WECSAM at the basin level using a hydrological model called WEAP. For this purpose, a multi-objective optimization model has been developed with the concepts of water footprint, energy footprint, and the greenhouse gas emissions in the context of CSA. We applied the model to the northern region of Bakhtegan basin called Doroodzan irrigation network located in Iran. The result of the WECSAM model indicated that by simultaneously optimizing the conflicting objectives of maximizing profit and minimizing water footprint, energy footprint, and CO2 emissions, as compared to the single-objective model of maximizing economic profit, the water footprint decreases by 8.2%, Energy footprint decreases by 21.2%, CO2 emissions decreases by 6.9% and profit decreases by 7.4%. The share of each system in irrigating the water-smart, energy-smart, and climate-smart cropping pattern is as follow: 54% for drip system, 26% for semi-permanent sprinkler system, 11% for surface systems, 8% for center-pivot, and <1% for classic permanent sprinkler system.

کلیدواژه‌ها [English]

  • Cropping Pattern
  • Climate-Smart Agriculture
  • CO2 Emission
  • Irrigation Systems
  • Multi-objective Optimization
  • Water Footprint

مراجع

  1. Ababaei, B., and H.R. Etedali. 2014. Estimation of water footprint components of Iran’s wheat production: Comparison of global and national scale estimates. Environmental Processes, 1(3): 193-205.
  2. Abbasi, F., F. Sohrab, and N. Abbasi. 2015. Irrigation efficiencies: Temporal and spatial changes in Iran. Agricultural Technical and Engineering Research Institute, Iran. (In Persian)
  3. Ashktorab, N. and M. Zibaei. 2019. Virtual Water Transfer through Iranian Interprovincial Cereals Trade. Journal of Agricultural Economics and Development, 33(1): 55-74. (‎In Persian with English abstract‎)
  4. Blanco-Gutiérrez, I., C. Varela-Ortega, and D.R. Purkey. 2013. Integrated assessment of policy interventions for promoting sustainable irrigation in semi-arid environments: A hydro-economic modeling approach. Journal of Environmental Management, 128: 144-160.
  5. Bodenhofer, U. 2003. Genetic algorithms: theory and applications. Lecture notes, Fuzzy Logic Laboratorium Linz-Hagenberg, Winter, 2004.
  6. Bogdanski, A. 2012. Integrated food–energy systems for climate-smart agriculture. Agriculture and Food Security, 1(1): 1-10.
  7. Carrillo Cobo, M., E.C. Poyato, P. Montesinos, and J.R. Díaz. 2014. New model for sustainable management of pressurized irrigation networks. Application to Bembézar MD irrigation district (Spain). Science of the Total Environment: 473: 1-8.
  8. Collette, Y., and P. Siarry. 2004. Multi-objective optimization: principles and case studies. Springer Science and Business Media.
  9. Daccache, A., J.S. Ciurana, J.R. Diaz, and J.W. Knox. 2014. Water and energy footprint of irrigated agriculture in the Mediterranean region. Environmental Research Letters, 9(12): 124014.
  10. Dai, C., X.S. Qin, and W. T. Lu. 2020. A fuzzy fractional programming model for optimizing water footprint of crop planting and trading in the Hai River Basin, China. Journal of Cleaner Production, 278: 123196.
  11. Dehghanipour, A.H., G. Schoups, B. Zahabiyoun, and H. Babazadeh. 2020. Meeting agricultural and environmental water demand in endorheic irrigated river basins: A simulation-optimization approach applied to the Urmia Lake basin in Iran. Agricultural Water Management, 241: 106353.
  12. Díaz, J.R., P. Montesinos, and E.C. Poyato. 2012. Detecting critical points in on-demand irrigation pressurized networks–a new methodology. Water Resources Management, 26(6): 1693-1713.
  13. Elsoragaby, S., A. Yahya, M.R. Mahadi, N.M. Nawi, M. Mairghany, S.M.M. Elhassan, and A. Kheiralla. F. 2020. Applying multi-objective genetic algorithm (MOGA) to optimize the energy inputs and greenhouse gas emissions (GHG) in wetland rice production. Energy Reports, 6: 2988-2998.
  14. Escriva-Bou, A., J.R. Lund, M. Pulido-Velazquez, R. Hui, and J. Medellín-Azuara. 2018. Developing a water-energy-GHG emissions modeling framework: Insights from an application to California's water system. Environmental Modelling and Software, 109: 54-65.
  15. Espinosa-Tasón, J., J. Berbel, and C. Gutiérrez-Martín. 2020. Energized water: Evolution of water-energy nexus in the Spanish irrigated agriculture, 1950–2017. Agricultural Water Management, 233: 106073.
  16. Esteve, P., C. Varela-Ortega, I. Blanco-Gutiérrez, and T.E. Downing. 2015. A hydro-economic model for the assessment of climate change impacts and adaptation in irrigated agriculture. Ecological Economics, 120: 49-58.
  17. Fouial, A., R. Khadra, A. Daccache, and N. Lamaddalena. 2016. Modelling the impact of climate change on pressurised irrigation distribution systems: Use of a new tool for adaptation strategy implementation. Biosystems Engineering, 150: 182-190.
  18. Galan-Martin, A., P. Vaskan, A. Anton, L.J. Esteller, and G. Guillen-Gosalbez. 2017. Multi-objective optimization of rainfed and irrigated agricultural areas considering production and environmental criteria: a case study of wheat production in Spain. Journal of Cleaner Production. 140: 816-830.
  19. García, I.F., J.R. Díaz, E.C. Poyato, P. Montesinos, and J. Berbel. 2014. Effects of modernization and medium-term perspectives on water and energy use in irrigation districts. Agricultural Systems, 131: 56-63.
  20. Giupponi, C. 2007. Decision support systems for implementing the European water framework directive: the MULINO approach. Environmental Modelling and Software, 22: 248–258.
  21. Hanjra, M.A., and M.E. Qureshi. 2010. Global water crisis and future food security in an era of climate change. Food Policy, 35(5): 365-377.
  22. Hardy, L., A. Garrido, and L. Juana. 2012. Evaluation of Spain's water-energy nexus. International Journal of Water Resources Development, 28(1): 151-170.
  23. Hoekstra, A.Y., and A.K. Chapagain. 2011. Globalization of water: Sharing the planet's freshwater resources. John Wiley & Sons.
  24. Hoekstra, A.Y., A. Chapagain, M. Martinez-Aldaya, and M. Mekonnen. 2009. Water footprint manual: State of the art 2009.
  25. Imran, M.A., A. Ali, M. Ashfaq, S. Hassan, R. Culas, and C. Ma. 2019. Impact of climate smart agriculture (CSA) through sustainable irrigation management on Resource use efficiency: A sustainable production alternative for cotton. Land Use Policy, 88: 104113.
  26. Jabloun, M., and A. Sahli. 2012. WEAP-MABIA tutorial: a collection of stand-alone chapters to aid in learning the WEAP-MABIA module. Federal Institute for Geosciences and Natural Resources, Hannover, Germany.
  27. Jacobs, S. 2006. Comparison of life cycle energy consumption of alternative irrigation systems.
  28. Khoshnevisan, B., E. Bolandnazar, S. Shamshirband, H.M. Shariati, N.B. Anuar, and M.L.M. Kiah. 2015. Decreasing environmental impacts of cropping systems using life cycle assessment (LCA) and multi-objective genetic algorithm. Journal of Cleaner Production, 86: 67-77.
  29. Levidow, L., D. Zaccaria, Maia, R., Vivas, E., Todorovic, M., and Scardigno, A. 2014. Improving water-efficient irrigation: Prospects and difficulties of innovative practices. Agricultural Water Management 146: 84-94.
  30. Levidow, L., D. Zaccaria, R. Maia, E. Vivas, M. Todorovic, and A. Scardigno. 2014. Improving water-efficient irrigation: Prospects and difficulties of innovative practices. Agricultural Water Management, 146: 84-94.
  31. Li, M., Q. Fu, V.P. Singh, Y. Ji, D. Liu, C. Zhang, and T. Li. 2019. An optimal modelling approach for managing agricultural water-energy-food nexus under uncertainty. Science of the Total Environment, 651: 1416-1434.
  32. Liaqat, A.M., B. Nazarai, H.A. Alizadeh, M. Ghadirianfar, and Z. Sarayshad. 2012. Analysis of energy consumption in pressurized irrigation systems and presentation of methodology for studying energy consumption in the process of design and development of systems. Ministry of Agriculture Jihad, Iran. (In Persian)
  33. Long, T.B., V. Blok, and I. Coninx. 2016. Barriers to the adoption and diffusion of technological innovations for climate-smart agriculture in Europe: evidence from the Netherlands, France, Switzerland and Italy. Journal of Cleaner Production, 112: 9-21.
  34. Mango, N., C. Makate, L. Tamene, P. Mponela, and G. Ndengu. 2018. Adoption of small-scale irrigation farming as a climate-smart agriculture practice and its influence on household income in the Chinyanja Triangle, Southern Africa. Land, 7(2): 49.
  35. Mardani Najafabadi, M., S. Ziaee, A. Nikouei, and M.A. Borazjani. 2019. Mathematical programming model (MMP) for optimization of regional cropping patterns decisions: A case study. Agricultural Systems, 173: 218-232.
  36. Mateos, L., A.C. dos Santos Almeida, J.A. Frizzone, and S.C.R.V. Lima. 2018. Performance assessment of smallholder irrigation based on an energy-water-yield nexus approach. Agricultural Water Management, 206: 176-186.
  37. Mirzaei, A., and M. Zibaei. 2020. Water Conflict Management between Agriculture and Wetland under Climate Change: Application of Economic-Hydrological-Behavioral Modelling. Water Resources Management, 35(1): 1-21.
  38. Molden, D. (Ed.). 2013. Water for food water for life: A comprehensive assessment of water management in agriculture. Routledge.
  39. Mushtaq, S., T.N. Maraseni, and K. Reardon-Smith. 2013. Climate change and water security: estimating the greenhouse gas costs of achieving water security through investments in modern irrigation technology. Agricultural Systems, 117: 78-89.
  40. Neufeldt, H., M. Jahn, B.M. Campbell, J.R. Beddington, F. DeClerck, A. De Pinto, and D. LeZaks. 2013. Beyond climate-smart agriculture: toward safe operating spaces for global food systems. Agriculture and Food Security, 2(1): 1-6.
  41. Nouri, H., B. Stokvis, A. Galindo, M. Blatchford, and A.Y. Hoekstra. 2019. Water scarcity alleviation through water footprint reduction in agriculture: the effect of soil mulching and drip irrigation. Science of the Total Environment, 653: 241-252.
  42. Olayide, O.E., I.K. Tetteh, and L. Popoola. 2016. Differential impacts of rainfall and irrigation on agricultural production in Nigeria: Any lessons for climate-smart agriculture?. Agricultural Water Management, 178: 30-36.
  43. Palombi, L., and R. Sessa. 2013. Climate-smart agriculture: sourcebook. Climate-smart agriculture: sourcebook.
  44. Playán, E., and L. Mateos. 2006. Modernization and optimization of irrigation systems to increase water productivity. Agricultural Water Management, 80(1-3): 100-116.
  45. Pourmohamad, Y., A. Alizadeh, M.M. Baygi, M. Gebremichael, A.N. Ziaei, and M. Bannayan. 2019. Optimizing cropping area by proposing a combined water-energy productivity function for Neyshabur Basin, Iran. Agricultural Water Management, 217: 131-140.
  46. Rodríguez Díaz, J.A., E. Camacho Poyato, and M. Blanco Pérez. 2011. Evaluation of water and energy use in pressurized irrigation networks in Southern Spain. Journal of Irrigation and Drainage Engineering, 137(10): 644-650.
  47. Rodríguez-Díaz, J.A. 2012. More ‘crop per drop’–the energy trade-off in Spanish irrigated agriculture.
  48. Schwabe, K., K. Knapp, and I. Luviano. 2017. The Water–Energy Nexus and Irrigated Agriculture in the United States: Trends and Analyses. In Competition for Water Resources (pp 80-104). Elsevier.
  49. Singh, H., D. Mishra, and N.M. Nahar. 2002. Energy use pattern in production agriculture of a typical village in arid zone, India––part I. Energy Conversion and Management, 43(16): 2275-2286.
  50. Streimikis, J., Z. Miao, and T. Balezentis. 2020. Creation of climate‐smart and energy‐efficient agriculture in the European Union: Pathways based on the frontier analysis. Business Strategy and the Environment, 30(1): 576-589.
  51. Tarjuelo, J.M., J.A. Rodriguez-Diaz, R. Abadía, E. Camacho, C. Rocamora, and M.A. Moreno. 2015. Efficient water and energy use in irrigation modernization: Lessons from Spanish case studies. Agricultural Water Management, 162: 67-77.
  52. Wang, Y.B., D. Liu, X.C. Cao, Z.Y. Yang, J.F. Song, D.Y. Chen, and S.K. Sun. 2017. Agricultural water rights trading and virtual water export compensation coupling model: a case study of an irrigation district in China. Agricultural Water Management, 180: 99-106.
  53. Yates, D., J. Sieber, D. Purkey, and A. Huber-Lee. 2005. WEAP21—A demand-, priority-, and preference-driven water planning model: part 1: model characteristics. Water International, 30(4): 487-500.
  54. Zhao, Y., Q. Wang, S. Jiang, J. Zhai, J. Wang, H. Guohua, and Y. Zhu. 2020. Irrigation water and energy saving in well irrigation district from a water-energy nexus perspective. Journal of Cleaner Production, 122058.
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  • تاریخ دریافت: 12 آبان 1400
  • تاریخ بازنگری: 13 آذر 1400
  • تاریخ پذیرش: 22 آذر 1400

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