The Economic Impacts of Climate Change and Water Resources Management Scenarios on Agronomy Subsector: Using Risk-Based Hydro-Economic Model

مجله اقتصاد و توسعه کشاورزی

Article 7, Volume 34, Issue 4 - Serial Number 50, January 2021, Pages 483-501   

Document Type: Research Article

DOI: 10.22067/jead.2021.17813.0

Authors

F. Sani¹; Gh. Dashti^{* 1}; A. Majnooni²; J. Hosseinzad¹

¹Department of Agricultural Economics, University of Tabriz, Tabriz, Iran

²Department of of Water Engineering, University of Tabriz, Tabriz, Iran

Abstract

Introduction: Ajichay basin is one of the largest agricultural areas and water consumption in Urmia Lake basin. During the recent years, the impact of climate change on one hand, and human factors on the other hand, have changed Ajichay basin to a center of crisis as it has lost its efficiency in supplying water for Urmia Lake. Having the main branches of Ajichay, Sarab county has a great role in crop production and therefore agricultural water consumption compared to other counties around the basin. Therefore, managing water consumption in Sarab County is suggested to resolve the decreased quality and quantity of water in Ajichay basin. Therefore, the aim of the current study is to investigate the impact of climate and water management scenarios on water resources, cropping pattern, yield, and profits of farmers in Sarab County.
Material and Methods: To achieve the study aims, the hydro-economic model was used. In the economic section, quadratic risk programming model and in the hydrological section, the WEAP-MABIA model was used. The purpose in quadratic risk programming model is to maximize the expected farmers’ utility to some technical and structural restrictions. Maximum expected utility of farmers which is calculated by subtracting the risk element from the net income for each crop. MABIA uses a two-part crop coefficient. In the dual crop coefficient approach, the effects of crop transpiration and soil evaporation are determined separately. Two coefficients are used: the basal crop coefficient (Kcb) to describe plant transpiration, and the soil water evaporation coefficient (Ke) to describe evaporation from the soil surface. The current study applied HadCM3, as the general circulation model. LARS-WG was used for downscaling climatic generator and producing rainfall, radiation, and minimum and maximum temperature in a station under A2, B1, and A1B emission scenarios. The period 1987-2018 was used as the base and the future considered period was 2018-2050. All required variables such as information about input values, production quantities, and economic information were collected from 210 questionnaires filled by farmers during 2018 which were selected through stratified random sampling.
Results and Discussion: The results showed that the average rainfall decreased in the range of 21-38% under the emission scenarios of A2, B1, and AIB during 2018-2050 period. In the next period of 2018-2050, the average annual temperature will also increase by 2.5 °C compared to the baseline period under A2 scenario. The results of simulations revealed that the crop yields would undergo a decrease after climate change scenarios. The most considerable yield reduction belongs to A2 scenario in which potato will have the highest yield reduction of 17%. The crop yields of barley and wheat shows a slight reduction. Thus, these two products have larger cropping area in the climatic scenarios. The results of climate change indicate a diminishing trend in available water and water supply reliability for agricultural purposes. The available water for irrigation areas had 21.92% decrease after applying climate change scenario. The mean for water supply reliability in the sub-basin decrease from 84.93% to 62.35% if the future years continue to have a decrease in rainfall and increase in temperature. By applying the scenario of agricultural water reduction along with climate change scenario, the profit in each region will decrease compared to the reference scenario. The highest reduction rate belongs to Asbforushan 2 area with 28% compared to the reference scenario. The profit in all the sub-bases had a rise after increasing irrigation efficiency scenario. Thus, applying increasing efficiency scenario, in addition to more useful and efficient use of allocated water, will also increase farmers' profits which offer a better situation than the scenario of reducing the share of agricultural water. Among the studied crops, bean had the highest reduction in cultivation, which stemmed from its high-water requirement. However, potato also had a high water requirement compared to bean but maintained a high cropping area due to higher gross profits. Findings of the current study revealed that wheat and barley had more resistance against the effects of climate change and shifting the patterns of cropping was an adaptive strategy for coping with the effects of climate change. Climate change reduces the labor employment. As Implementing A2 scenario results in a 14.48% decrease in the average of agricultural employment in the area. The agricultural water reduction scenario alone results in 5.9% decrease in labor, whereas the increasing irrigation efficiency scenario has an 8.9% decrease. Applying the agricultural water reduction scenario along with climate change reduces the employment by 17.2% in the region by reducing the area under cultivation of crops that require a lot of labor. The increasing irrigation efficiency along with climate change scenario also results in a 20.9% reduction in labor employment.
Conclusion: Overall, the findings of the current study revealed that without changing the management strategies there would be a considerable reduction in crop yield in near future. Optimizing management methods, selection of right time for crop cultivation, optimized harvest, studying the feasibility of cultivating crops with shorter growth period and using cultivars with higher yield are the effective ways to confront the effects of climate change. The analysis of scenarios revealed that policies alone cannot compensate for water problems and there is a need for plenty of scenario for optimum results.

Keywords

Ajichay basin; Hydro-economic model; Quadratic risk programming; WEAP-MABIA

References

1. Agriculture– Jihad Organization of East Azerbaijan. 2018. Available Online at: https://www.eaj.ir/.
2. Ahmadifar R., Mousavi S.M., and Rahimzadegan M. 2020. Investigating the effects of climate change on groundwater (case study: Sarab Plain). Water and Soil Science 30(1): 153-166. (In Persian with English abstract)
3. Alefi, Dashti G., and  Ghahremanzadeh M. 2018. The impact of climatic factors on land use change between annually crops groups in Iran: application of spatial fractional multinomial logit model. Agricultural Economics 11(4): 21-40. (In Persian with English abstract)
4. Allen R.G., Pereira L.S., Raes D., and Smith M. 1998. Crop evapotranspiration-guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. FAO, Rome. 300(9): D05109.
5. Amin A., Iqbal J., Asghar A., and Ribbe L. 2018. Analysis of current and future water demands in the upper Indus basin under IPCC climate and socio-economic scenarios using a hydro-economic WEAP model. Water 10(5): 1-20.
6. Binswanger H.P. 1980. Attitudes toward risk: experimental measurement in rural India. American Journal of Agricultural Economics 62(3): 395-407.
7. Blanco- Gutiérrez I., Varela-Ortega C., and Purkey D. 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.
8. Buysse J., Van Huylenbroeck G., and Lauwers L. 2007. Normative, positive and econometric mathematical programming as tools for incorporation of multifunctionality in agricultural policy modeling. Agriculture, Ecosystems & Environment 120: 70-81.
9. Cattivelli L., Rizza F., Badeck F.W., Mazzucotelli E., Mastrangelo A.M., Francia E., Mare C., Tondelli A., and Michele Stanca A. 2008. Drought tolerance improvement in crop plants: an integrated view from breeding to genomics. Field Crops Research 105: 1-14.
10. Chavas J.P. 2004. Risk analysis in theory and practice. Elsevier Academic Press.
11. Esteve P., Varela-Ortega C., Blanco-Gutiérrez I., and Downing T.E. 2015. A hydro-economic model for the assessment of climate change impacts and adaptation in irrigated agriculture. Ecological Economics 120: 49-58.
12. FAO, WFP., and IFAD. 2012. The state of food insecurity in the world: economic growth is necessary but not sufficient to accelerate reduction of hunger and malnutrition, food and agricultural organization of the United Nations (FAO), the international fund for agricultural development (IFAD), and the world food programming (WFP), FAO, Rome, Italy.
13. Foster T., Brozović N., and Butler A.P. 2014. Modeling irrigation behavior in groundwater systems. Water Resource Research 50: 6370–6389.
14. Friedman M., and Savage L.J. 1948. The utility analysis of choices involving risk. The Journal of Political Economy 56: 279-304.
15. Groves D.G., Yates D., and Tebaldi C. 2008. Developing and applying uncertain global climate change projections for regional water management planning. Water Resources Research 44: 1-16.
16. GWP (Global Water Partnership). 2000. Integrated water resources management. Global water partnership TAC background Paper 4. Stockholm, Sweeden.
17. Hazell P.B., and Norton R.D. 1986. Mathematical programming for economic analysis in agriculture. Macmillan, New York.
18. Kahil M.T., Dinar A., and Albic J. 2015. Modeling water scarcity and droughts for policy adaptation to climate change in arid and semiarid regions. Journal of Hydrology 522: 95-102.
19. Kazemzadeh M., Malekian A., Moghaddam Nia A., and Khalighi Sigaroudi Sh. 2019. Evaluation of climate change impacts on hydrological characteristics of watershed (case study: Aji- Chai Watershed). Iran Watershed Management Science & Engineering 13(45): 1-12. (In Persian with English abstract)
20. Lee H., Sumner D. and Howtt R. 2001. Potential economic impacts of irrigation-water reductions estimated for Sacramento Valley. California Agriculture 55(2): 33-40.
21. Mahmoodi, and Parhizkari A. 2015. Economic analysis of the climate change impacts on products yield, cropping pattern and farmer's gross margin (case study: Qazvin plain). Journal of Economic Growth and Development Research 5(3): 25-40. (In Persian with English abstract)
22. Mainuddin M., Kirby M., and Qureshi M.E. 2007. Integrated hydrologic-economic modelling for analyzing water acquisition strategies in the Murray River Basin. Agricultural Water Management 93: 123-135.
23. Maneta M.P., Torres M.O., Wallender W.W., Vosti S., Howitt R., Rodrigues L., Bassoi L.H., and Panday S. 2009. A spatially distributed hydro economic model to assess the effects of drought on land use, farm profits, and agricultural employment. Water Resources Research 45: 1-19.
24. Medellan-Azuara J., Harou J.J., and Howitt R.E. 2010. Estimating economic value of agricultural water under changing conditions and the effects of spatial aggregation. Science of Total Environment 408(3): 5639–5648.
25. Mirmusavi SH. 2005. The study of oscillations of temperature and precipitation in the North-West of Iran in order to assessment of the changes of climate elements trend. PhD thesis, Physical Geography, University of Tabriz. (In Persian with English abstract)
26. Morid S., and Massah Bavani A.R.M. 2010. Exploration of potential adaptation strategies to climate change in the Zayandeh Rud irrigation system, Iran. Irrigation and Drainage 59(2): 226-238.
27. Parhizkari A. 2018. Evaluate the effects of climate change impertinence on agricultural production and farmer's income situation in down lands of Taleghan dam. Journal of agricultural Economics Research 9(36): 125-152. (In Persian with English abstract)
28. Purkey D.R., Joyce B., Vicuna S., Hanemann M.W., Dale L.L., Yates D and Dracup J.A.J. Robust analysis of future climate change impacts on water for agriculture and other sectors: a case study in the Sacramento Valley. Climatic Change 87: 109-122.
29. Qureshi M.E., Ahmad M.U.D., Whitten S.M. and Kirby M. 2014. A multi-period positive mathematical programming approach for assessing economic impact of drought in the Murray-Darling Basin, Australia. Economic Modelling 39: 293-304.
30. Rafiei Darani H., Kohansal M.R., Ghorbani M., and Saboohi M. 2017. An integrated hydro-economic modeling to evaluate marketing reform policies of agricultural products. Bulgarian Journal of Agricultural Science 2(2): 189–197.
31. Rosenzweig M.R., and Binswanger H.P. 1992. Wealth, weather risk, and the composition and profitability of agricultural investments. The Economic Journal 103(416): 56-78.
32. Salahi B., Valizadeh kamran K.H., and Ghavidel Rahimi Y. 2008. The simulation of Tabriz temperature and precipitation in atmospheric carbon dioxide doubling condition using Goddard institute of space studies general circulation model (GIS GCM). Geographical Research Quarterly 39(62): 55-66. (In Persian with English abstract)
33. Salami H., Shahnooshi N., and Thomson K.J. 2009. The economic impacts of drought on the economy of Iran: An integration of linear programming and macro econometric modeling approaches. Ecological Economics 68(4): 1032-1039.
34. Sanikhani H., Dinpajoh Y., Pour Yusef S., Ghavidel S.Z., and Solati B. 2014. The impacts of climate change on runoff in watersheds (case study: Ajichay watershed in East Azerbaijan province, Iran). Journal of Water and Soil 27(6): 1225-1234. (In Persian with English abstract)
35. Sarie Sarraf B., Jalali Ansaroodi T., and Sarafrouzeh F. 2015. The effects of global warming on the climate of cities located in the Urmia Lake Basin. Biannual Journal of Urban Ecology Researches 6(2): 33-48. (In Persian with English abstract)
36. Sieber J., and Purkey D. 2011. User guide for WEAP21 (water evaluation and planning system). Stockholm Environment Institute. Available online from: www. weap21.org.
37. 2018. Urmia Lake Restoration Program. www. http://www.ulrp.ir.
38. Varela-Ortega C., Blanco-Gutie´rrez I., Esteve P., Bharwani S., Fronzek S., and Downing T.E. 2016. How can irrigated agriculture adapt to climate change? Insights from the Guadiana basin in Spain. Regional Environmental Change 16(1): 59–70.
39. Varela-Ortega C., Blanco-Gutiérrez I., Swartz C., and Downing T.E. 2011. Balancing groundwater conservation and rural livelihoods under water and climate uncertainties: an integrated hydro-economic modeling framework. Global Environmental Change 21(2): 604-619.
40. Veijalainen N., Dubrovin T., Marttunen M., and Vehvileinen B. 2010. Climate change impacts on water resources and lake regulation in the Vuoksi watershed in Finland. Water Resource Management 24: 3437-3459.
41. Von Neuman J., and Morgenstern O. 1944. Theory of games and economic behavior. Princeton University Press, Princeton, NJ.
42. Zahedi M., Sari Saraf B., and Jameei J. 2007. The analysis of spatio-temporal variations of temperature in the North-West of Iran. Geography and Development 5(10): 183-197. (In Persian with English abstract)
43. Zhang P., Zhang J., and Chen M. 2017. Economic impacts of climate change on agriculture: the importance of additional climatic variables other than temperature and precipitation. Journal of Environmental Economics and Management 83: 8-31.