Simulating Farmers' Response to Policies Based on Sustainable Agriculture (Case Study: Cropping Sub-sector of Tajan Basin)

Document Type : Research Article

Authors

• N. Heidari Zahiri
• H. Amirnejad
• S. Shirzadi Laskookalayeh

Agricultural Economics, Sari Agricultural Sciences and Natural Resources University, Sari, Iran

https://doi.org/10.22067/jead.2021.17774.0

Abstract

Introduction:Water scarcity, improper management of water resources, excessive application of chemical inputs, and lack of proper cultivation patterns are present in agriculture. Lack of attention to these cases will inflict irreparable damage on the agricultural sector. Accordingly, attention to sustainable agriculture, conservation of water resources and prevention of improper use of chemical fertilizers are essential to reduce environmental pollution. In many cases, there is agreement on the river basin scale as a suitable spatial scale for analysis of water resources management. Tajan Basin with area of about 4187 km² is one of the important parts of Caspian Sea Basin. The Current status of water resources in Tajan basin due to decrease in river runoff, has doubled the focus on the basin's water resources management.
Materials and Methods: In this study, with the help of positive mathematical planning and maximum entropy approach in GAMS, policies to reduce the use of chemical fertilizers and water in selecting the appropriate cultivation pattern for 2017 in the Tajan basin were reviewed. Within the model, the farmer maximizes the expected utility of their stochastic income, subject to resource and non-negativity constraints. To include both market and yields uncertainty, we calculated profit covariance matrices by using national averages for prices and yields for the 2018–2009 period. The resource constraints include land, water and fertilizer. Selected irrigated crops in the region include rice, wheat, rapeseed and corn. In the present study for simulating farmers' response, reduction scenarios including 5%, 10% and 15% of available water and fertilizer are considered. There are also two environmental sustainability index that are related to amount of the used fertilizer and water. The smaller the index is, the greater sustainability is provided in crop production.
Results and Discussion: Calibration of PMP pattern with maximum entropy approach showed that there is no difference between the value of target function, inputs and cultivation level in the current situation and calibration pattern. In all water reduction scenarios, the total cultivation area decreased. The results indicate that the agriculture in the basin is vulnerable due to changes in available water. The 15% decrease in water resources causes a significant decrease of 15/903% of the cultivation area. Cultivation area under fertilizer reduction scenarios has been lower in comparison with water scenarios, and so reduces the used fertilizer and increases soil conservation and water stock. In reduction scenarios of water and fertilizer, land reallocation is reduced due to less reduction in expected utility of farmers. In water scarcity conditions and lack of fertilizer, rice and wheat crops have higher economic benefits per hectare than other crops. The sustainability index for used fertilizer in all reduction scenarios of water and fertilizer is lower than the current pattern. Also the index of the used water in the PMP model is lower than the baseline in the region that decrease was 0.018%, 0.144% and 0.319% at three levels of 5%, 10% and 15%, respectively. In the scenario of 15% reduction of fertilizer, land allocation and economic benefits decreased by 13.83% and 0.034%, respectively. However used fertilizer and water index improved to 1.348% and 0.319%, respectively. Therefore, improving the water and fertilizer application index has a higher priority than reducing the expected utility in the region.
Conclusion: In the current cropping pattern, farmers do not pay attention to the environmental characteristics and sustainability of the region. While with the policies of reducing the quantity and price of chemical inputs and introducing different types of sustainability indicators, it is possible to develop a cultivation model. In addition to earning the necessary profit, it enables the optimal use of fertilizer and water inputs. Changing the behavior of farmers compared to the current pattern of input consumption requires strong motivation and reasons. Therefore, water quality tests and soil decomposition in the region, as well as providing appropriate formulas for optimal use of chemical fertilizers is needed. Extension services to increase people's awareness is a good solution for optimal use of inputs and increase the level of cultivation and farmers' profits.

Keywords

• Expected utility
• Positive mathematical programming
• Sustainability index
• Tajan basin

20.1001.1.20084722.1400.35.1.1.1

Main Subjects

• Agricultural Economics

References

1.  Abdi Rokni Kh., Hosseini Yekani S.A., Abedi S., and Kashiri Kolaei F. 2019. Effect of optimization of chemical fertilizers consumption on optimal cropping pattern in the framework of positive mathematical programming (case study of Sari Goharbaran) Journal of Agricultural Economics Research 12(2): 263-276. (In Persian with English abstract)
2. Agh M., Joolaie R., Keramatzadeh A., and SHirani Bidabadi F. 2015. Determination of cropping pattern with emphasis on reduction in chemical fertilizers and water consumption policies in Mazandaran province: case study of Behshahr. Journal of  Soil Management and Sustainable Production 5(3): 247-259. (In Persian with English abstract)
3. Aghapour Sabbaghi M., Nazari M.R., Araghinejad Sh., and Soufizadeh S. 2020. Economic impacts of climate change on water resources and agriculture in Zayandehroud river basin in Iran. Agricultural Water Management.Volume 241:1-13.
4. Agricultural Jihad Organization of Mazandaran Province. 2018. Office of Statistics and Information Technology.
5. Arfini F., Donati M., and Paris Q. 2003. A national PMP Model for Policy Evaluation in Agriculture using Micro Data and Administrative Information, paper presented at the International Conference Agricultural policy reform and the WTO: where are weheading? Capri, Italy.
6. Asaadi M.A., Khalilian S., and Moosavi S.H. 2019. Effects of deficit irrigation simultaneously with reduced usage of fertilizer and chemical pesticides on changing cropping pattern in Qazvin irrigation network. Journal of Water Research in Agricultural Economics 33(1): 121-137. (In Persian with English abstract)
7. Baniasadi M., Zare Mehrjerdi M.R., Mehrabi Boshrabadi H., Mirza'ee Khalilabad H.R., Reza'ee Estakhruoiyeh A., and Hasanvand M. 2017. Study of cropping pattern changes and groundwater resources extraction by implementing reduced water consumption policies in orzuiyeh plain of Kerman province. Journal of Agricultural Economics 11(3): 11-129. (In Persian with English abstract)
8. Brouwer R., and Hofkes M. 2008. Integrated hydro-economic modelling: approaches, key issues and future research directions. Journal of Ecological Economics 66(1): 16–22.
9. Faryadi S., Shahedi K., and Nabatpoor M. 2013.Investigation of water quality parameters in Tajan River using multivariate statistical techniques.  Journal of Watershed Management Research 3(6): 75-92.  (In Persian with English abstract) 
10. Fernández F.J., Ponce R.D., Blanco Rivera M.D., and Vásquez F. 2016. Water variability and the economic impacts on small-scale farmers. A farm risk-based integrated modelling approach. Water Resources Management 30(4): 1357–1373.
11. Field C.B., Barros V.R., Mach K., and Mastrandrea M. 2014. Climate change 2014: impacts, adaptation, and vulnerability contribution of working group ii to the fifth assessment report of the intergovernmental panel on climate change.
12. Godfray H.C.J., Beddington J.R., Crute I.R., Haddad L., Lawrence D., Muir J.F., and Toulmin C. 2010. Food Security: The Challenge of Feeding 9 Billion People. Journal of Science 327: 812-818.
13. Gohar A.A., and Cashman A. 2016. A methodology to assess the impact of climate variability and change on water resources, food security and economic welfare. Agricultural Systems 147: 51–64. 
14. Graveline N. 2016. Economic calibrated models for water allocation in agricultural production: A review. Environmental Modelling and Software 81: 12-25.
15. Gundogmus E. 2006. A comparative analysis of organic and conventional direct apricot production on small households in Turkey. Asian Journal of Plant Sciences 5(1): 98-104.
16. 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)
17. Hashemi S.R. 2018. Drought in Mazandaran. Iran Islamic Republic News Agency. Available at http://www.irna.ir/fa/News/82853748
18. Henry de Frahan B., Buysse J., Polomé P., Fernagut B., Harmignie O., Lauwers L., Van Huylenbroeck G., and Van Meensel J. 2005. Positive Mathematical Programming for Agricultural and Environmental Policy Analysis: Review and Practice. In: A. Weintraub, T. Bjorndal, R. Epstein and C. Romero (Eds.), Management of Natural Resources: A Handbook of Operations Research Models, Algorithms and Implementations.
19. Howitt R.E. 1995. Positive Mathematical Programming. American Journal of Agricultural Economics 77(2): 329-342.
20. International Food Policy Research Institute. 2013. Green revolution: curse or blessing? www.ifpri.org/sites/default/files/pubs/pubs/ib/ib11.pdf. Accessed 02.12.13.
21. Jamalimoghaddam E., Yazdani S., Salami H., and Peykani G. 2019. The impact of water supply on farming systems: A sustainability assessment. Sustainable Production and Consumption 17: 269-281.
22. Kanellopoulos A., Berentsen P., Heckelei T., Van Ittersum M., and Lansink A.O. 2010. Assessing the forecasting performance of a generic bio‐economic farm model calibrated with two different PMP variants. Journal of Agricultural Economics 61: 274–294.
23. Klátyik Sz., Darvas B., Oláh M., Mörtl M., Takács E., and Székács A. 2017. Pesticide residues in spice paprika and their effects on environmental and food safety. Journal of Food and Nutrition Research 56(3): 201–218.
24. Koochaki A., Hosseini M., and khazaei H. 1997. Sustainable Agricultural systems. ACECR Mashhad Branch Publication Center.
25. Nazari A.H., Manafi Azar R., and Abdollahi A. 2014.  Aluating the influences of pressurized irrigation system on the charging of farming structure, cropping pattern and yield. Journal of the Studies of Human Settlements Planning (Geographical Landscape) 8(25): 147 -161. (In Persian with English abstract) 
26. Petsakos A., and Rozakis S. 2015. Calibration of agricultural risk programming models. European Journal of Operational Research 242: 536–545.
27. Ponce R., Blanco M., and Giupponi C. 2014. The economic impacts of climate change on the Chilean agricultural sector: a non-linear agricultural supply model. Chilean Journal of Agricultural Research 74(4): 404–412.
28. PourzandF., and Bakhshoudeh M. 2012. Evaluating agricultural sustainability of Fars province with compromise programming approach. Journal of Agricultural Economics Research 4(1): 1-26. (In Persian with English abstract)
29. Razavi S.H., Pourtaheri M., and Roknodin Eftekhari A.R. 2017. A Proposed Model for Organic Rice Farming in Rural Areas of Guilan and Mazandaran Provinces. Journal of Rural Research 8(3): 372-387. (In Persian with English abstract)
30. Regional water Company of Mazandaran. 2018. Report of subscriber’s number and performance. 1-6. (In Persian)
31. Sadeghi S., and Jafari Talokolaei M. 2014. Estimation of relationship between TDS and EC of Tajan River in high and low water seasons. The 1rd National Conference on Drainage in Sustainable Agriculture. Tarbiat Modarres University.8 March, Tehran. (In Persian)
32. Sharifinia M., Imanpour J., and Bozorgi A. 2012. Ecological assessment of the Tajan River using feeding groups of benthic macroinvertebrates and biotic indices.  Journal of Applied Ecology 1(1): 80-95. (In Persian with English abstract)
33. Sheikhzeinoddin A., Esmaeili A., and Zibaei M. 2016. Management of water and fertilizer consumption using bio-economic approach: a case study of irrigation and drainage dorudzan 24(93): 27-47. (In Persian with English abstract)
34. Shieh J.Y., Chen J.H., Chang S.H., and Lai C.C. 2014. Environmental consciousness, economic growth, and macroeconomic instability. International Review of Economics and Finance 34: 151-160.
35. Stocker TF., Qin D., and Plattner G-K. 2013 Climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge.
36. Zamani O., Grundmann P., Libra J.A., and Nikouei A. 2019. Limiting and timing water supply for agricultural production – The case of the Zayandeh-Rud River Basin, Iran. Agricultural Water Management 222: 322–335.