Economic Impact Assessment of Quantity and Quality Changes in Irrigation Water on Agriculture in Kerman Province

Document Type : Research Article

Authors

• F. Salehi Rezaabadi ¹
• M. Salarpour ²
• M. Mardani ³
• S. Ziaee ⁴

¹ University of Zabol

² Department of Agricultural Economics, Zabol University, Zabol, Iran

³ Khozestan

⁴ Shiraz university

https://doi.org/10.22067/jead2.v33i4.84119

Abstract

Introduction: Excessive extraction and depletion of groundwater aquifers and critical water status in more than 120 plains of the country have resulted in decreased water quality. In addition, the productivity of agricultural water of Iran in different years is on average lower than other countries. The results of most salinity studies show that high concentrations of salt in soil solution have significantly reduced the yield of crops and horticultural products in the country. More profitability and high market value of pistachio crops in comparison with other crops has led to many efforts by Kerman farmers to develop pistachio groves. Various studies show that over- extraction of groundwater resources in Kerman province and decreasing water quality of wells and consequently increasing salinity has reduced root growth and crop yield. In the present study, economic analysis of the effects of quantitative and qualitative changes of water in different scenarios on concepts such as yield, cropping pattern, water consumption and productivity and gross profit of farmers in major county of Kerman province has been investigated.
Materials and Methods: To achieve the goals of present study, we first obtained the salinity-water-yield function for each product. Then, by regarding this function in the positive mathematical programming model, the effect of different scenarios including changing water salinity level and changing water supply, on factors such as water productivity, crop yield, cropping pattern and gross profit of farmers especially pistachio growers are analyzed and investigated. In this regard, scenarios of 15% reduction of available water resources in different regions and increase of one salinity unit individually in combination with the above mentioned indicators are evaluated by conducting positive mathematical programming model and are identified by studied areas.
Results and Discussion: Investigation of the data reveals that discharge of aquifers is higher for recharge in all studied areas and consequently reduction of groundwater level has occurred. The result shows that the yield sensitivity of pistachio and barley crops to one unit soil salinity is lower than other crops. However, the highest yield loss as a result of increasing one unit in soil salinity as a scenario for canola and potato crops is 13 and 12 percent, respectively. Due to the decrease in quality and quantity of water resources, the total area under cultivation has decreased, with the lowest and highest reduction being in Kerman and Rafsanjan County, respectively. The results show that the scenario of 15% reduction in available water resources would just increases the area of Bardsir and leads to a decrease in one cubic meter economic productivity for other areas. On the other hand, by applying all three scenarios including decrease in quantity and quality of water resources, gross margin and water use will be reduced due to the decrease in the cultivated area of high yield and water based crops such as alfalfa and pistachio in Kerman region. Also, the results indicate that for all studied areas, increase in salinity by one unit has the most negative effect on economic productivity of one cubic meter of water consumption. In addition, the results reveal that decreasing the quality of water resources due to the increase in salinity encouraged the pistachio growers to cultivate less pistachio crop which would result in reducing their gross profit. Also, decreasing the quality of water resources caused by one unit increase in water salinity has a negative effect on the gross margin (gross profit) of farmers in different regions. This is mainly due to reduction in total area of cultivated pistachio in Rafsanjan County.
Conclusion: In general, decreasing the quantity and quality of water resources will cause irreparable damages to the agricultural economy of all studied areas except Bardsir. Therefore, it is vital to adopt appropriate policies to control the quantity and quality of water resources so improving livelihoods and water demand management in the pistachio areas of the province. The increasing salinity poses a serious challenge to the economic productivity of water use and water resources management in Rafsanjan, applying economic productivity improvement tools, such as the use of modern irrigation systems or crop pattern reform strategies and product insurance can be effective in boosting gross margin.

Keywords

• Crop-water-salinity production function
• Gross margin
• Positive mathematical programming model
• water quality
• Water Productivity

20.1001.1.20084722.1398.33.4.6.8

References

1- Abbasnejad A., and Shahidasht A. 2013. Vulnerability of Sirjan Plain Due to Aquifer over Abstraction. Geography and Territorial Spatial Arrangement 7: 85-96. (In Persian)

2- Agricultural Jihad of Kerman. 2015. Statistical Yearbook of Agriculture Department of Kerman Province. (In Persian)

3- Bakhshi A., Moghaddsi R., and Daneshvar Kakhki M. 2012. An Application of Positive Mathematical Programming Model to Analyze the Effects of Alternative Policies to Water Pricing in Mashhad Plain. Journal of Economics and Agricultural Development 25: 284-294. (In Persian)

4- Boostani F., Mohammadi H., and Moindini Z. 2014. Consequences to irrigation groundwater price and quotas policies in Fars province (positive mathematical programming approach corrected). Water Engineering 7: 65-78. (In Persian)

5- Connor J.D., Schwabe K., King D., and Knapp K. 2012. Irrigated agriculture and climate change: the influence of water supply variability and salinity on adaptation. Ecological Economics 77: 149-157.

6- Cortignani R., and Severini S. 2009. Modeling farm-level adoption deficit irrigation using positive mathematical programming. Agricultural Water Management 96(12): 1785-1791.

7- Cortignani R., and Severini S. 2009. Modeling farm-level adoption deficit irrigation using positive mathematical programming, Agricultural Water Management. Agricultural Water Management 96(12): 1785-1791.

8- Dehghani M., Tadayon Nejad M., and Feizi M. 2013. The effect of quality and irrigation interval on wheat yield and some of its components. Journal of Water Research in Agriculture 27(2): 127-135. (In Persian)

9- Delavar M., Morid S., and Moghadasi M. 2015. Optimization of Water Allocation in Irrigation Networks Considering Water Quantity and Quality Constrains, Case Study: Zayandehroud Irrigation Networks. Iran- Water Resources Research 11(2): 83-96. (In Persian)

10- Ebrahimiyan S., Nohtani M., and Sadeghi Mazidi H. 2013. Investigating the Barriers to Implementing Optimal Water Resources Management in Iran's Agricultural System for Sustainable Development. Proceedings of the First National Conference on Challenges on Water Resources and Agriculture. Iranian Association of Irrigation and Drainage, Esfahan. (In Persian)

11- Gulen H., Turhan E., and Eris A. 2006. Changes in peroxidase activities and soluble proteins in strawberry varieties under salt stress. Acta Physiol. Plant 28(2): 109-116.

12- Hasanvand V., Hasanvand M., Joulaie R., and Shirani Bidabadi F. 2015. Simulation of Farmers Behavior towards Applying Water Reduction Policies on Cropping Patterns Using Positive Mathematical Programming (PMP). Village and Development 17(4): 71-92. (In Persian)

13- Heckelei T. 2002. Calibration and estimation of programming models for agricultural supply analysis. University of Bonn.

14- Howitt R.E. 1995a. A Calibration Method for Agricultural Economic Production Models. Journal of Agricultural Economics 46(2): 147-159.

15- Howitt R.E. 1995b. Positive mathematical programming. American Journal of Agricultural Economics 77(2): 329-342.

16- Howitt R.E., Medellin-Azuara J., MacEwan D., and Lund J.R. 2012. Calibrating disaggregate economic models of agricultural production and water management. Environmental Modelling & Software 38: 244-258.

17- Jafary F., and Bradley C. 2018. Groundwater irrigation management and the existing challenges from the farmers’ perspective in central Iran. Land 7(1): 15.

18- Justice A., Mostafa Zadeh Farad B., Feizi M., and Landy A. 2013. Impact of salinity of irrigation water and soil leaching on soybean yield. Isfahan University of Technology, Isfahan, M.Sc Thesis.

19- Kan I., Schwabe K.A., and Knapp K.C. 2002. Microeconomics of irrigation with saline water. Journal of Agricultural and Resource Economics 16-39.

20- Katerji N., Van Hoorn J., Hamdy A., and Mastrorilli M. 2003. Salinity effect on crop development and yield, analysis of salt tolerance according to several classification methods. Agricultural Water Management 62(1): 37-66.

21- Khaghani, R., mahmoodi, S. H., Pazira, E. and Masihabadi, M. 2012. Investigation of Soil Salinity Changes and its Impact on Yield of Major Crops in Qazvin Plain. Crop Production under Environmental Stress 4(3): 27-37. (In Persian)

22- Kiani A.R., and Abbasi F. 2009. Assessment of the water-salinity crop production function of wheat using experimental data of the golestan province, Iran. Irrig and Drain (58): 445–455.

23- Letey J., Dinar A. and Knapp K.C. 1985. Crop-water production function model for saline irrigation waters. Soil Science Society of America Journal 49(4): 1005-1009.

24- Levidow L., Zaccaria D., Maia R., Vivas E., Todorovic M. and Scardigno, A. 2014.Improving water-efficient irrigation: prospects and difficulties of innovativepractices. Agric. Water Manage 146: 84–94.

25- Mardani M., Ziaei S., Nikouei A.R., and Ahmadpour M. 2016. Designing a Crop Pattern Decision Support System in Isfahan Province: A Multi-Purpose Crop Regional Planning Approach. University of Zabol, Zabul, PhD dissertation. (In Persian)

26- Mohseni A., and Zibaei M. 2009. Analysing Consequences of Increasing Acreage of Colza in Namdan Plain of Fars Province: An Application of Positive Mathematical Programming. JWSS. 13(47):773-784. (In Persian)

27- Nikouei A.R., Torkamani J., and Mamanpoush A.R. 2010. Water Use Management at Different Salinity Level to Release Multi-Objectives of Farmers in Zayandeh Rud Basin. Quarterly Iranian Journal of Irrigation and Drainage 4: 143-155. (In Persian)

28- Ors S., and Suarez D.L. 2017. Spinach biomass yield and physiological response to interactive salinity and water stress. Agricultural Water Management, 190: 31-41.

29- Paris Q. 2001. Symmetric positive equilibrium problem: a framework for rationalizing economic behavior with limited information. American Journal of Agricultural Economics 83(4): 1049-1061.

30- Paris Q. and Howitt R.E. 1998. An analysis of ill-posed production problems using maximum entropy. American journal of agricultural economics 80(1): 124-138.

31- Pirasteh-Anosheh H., Emam Y. and Sepaskhah A.R. 2015. Improving barley performance by proper foliar applied salicylic-acid under saline conditions. Int. J. Plant Prod 9: 467-486. (In Persian)

32- Piri, H., Ansari, H. and Parsa, M. 2016. Investigation of interactions between different irrigation and salinity levels on yield and components of forage sorghum by determining water and salinity production function in subsurface drip irrigation system. Ferdowsi University of Mashhad, Mashhad, Ph.D dissertation.

33- Pistachio Research Institute. 2013. Final Reports of Pistachio Research Institute. (In Persian)

34- Regional Water Company of Kerman. 2016. Water Resources Studies, Plain Reports of Kerman Province. (In Persian)

35- Schwabe K.A., Kan I. and Knapp K.C. 2006. Drainwater management for salinity mitigation in irrigated agriculture. American Journal of Agricultural Economics 88(1): 133-149.

36- Shirmohammadi-Ali Akbarkhani, Z., Ansari, H., Alizadeh, A. and enough, M. 2013. Evaluation of water-salinity-yield production functions in forage maize in Khorasan Razavi province. Iranian Journal of Irrigation and Drainage 4(7): 543-535. (In Persian)

37- Singh A., Panda S. N. 2013. Optimization and simulation modelling for managingthe problems of water resources. Water Resour. Manage 27(9): 3421–3431.

38- Singh, R. 2004. Simulations on direct and cyclic use of saline waters for sustaining cotton–wheat in a semi-arid area of north-west India. Agricultural Water Management, 66(2): 153-162.

39- Van Genuchten M. T. 1986. A numerical model for water and solute movement in and below the root zone. Model description and user manual. Unpubl. Res. Rep. US Salinity Lab. USDA/ARS, Riverside, Calif.

40- Van Genuchten M.T., and Hoffman G. 1984. Analysis of crop salt tolerance data. Ecological Studies: analysis and synthesis.

41- Ward, F.A. 2014. Economic impacts on irrigated agriculture of water conservation programs in drought. Journal of hydrology, 508: 114-127.

42- World Bank. 2010. World Development Indicators,. Available at: www.worldbank.org/.