Environmental Impact of Reducing Chemical Fertilizer Subsidies (Agricultural Crops in Zarandieh, Markazi Province)

مجله اقتصاد و توسعه کشاورزی

Article 2, Volume 39, Issue 1 - Serial Number 66, April 2025, Pages 32-19   

Document Type: Research Article

DOI: 10.22067/jead.2024.88266.1273

Authors

S.M.J. Esfahani^{* 1}; E. Barikani²

¹natural resource and environmental economics, Agricultural Planning, Economics and Rural Development Research Institute (APERDRI)

²Agricultural Planning, Economics and Rural Development Research Institute (APERDRI), Tehran, Iran

Abstract

Introduction
Many governments provide subsidies to members of the agricultural supply chain to ensure food security, maintain economic stability, and uphold the social benefits associated with the agriculture sector. The conflicting goals of food security and environmental protection have become a major problem, especially in developing countries. On the one hand, the government aims to boost food production by offering agricultural subsidies. On the other hand, the excessive use of chemical inputs due to these subsidies has raised concerns about environmental pollution. Therefore, one of the most significant global challenges is to balance agricultural production to meet the increasing demand of the growing population while maintaining the quality of the environment. Any changes in government support policies for the agricultural sector can lead to fluctuations in input and product prices, directly impacting farmers' profits. As a result, these changes can influence cultivation patterns and the use of agricultural inputs, ultimately affecting the environment. Therefore, before implementing any policy changes, it is crucial to assess both the economic and environmental impacts and make informed decisions based on these considerations.
Materials and Methods
This study uses positive mathematical programming (PMP) on the environmental impact of chemical fertilizers’ subsidies change and transfer subsidies to crops in Zarandieh city of Markazi province. The necessary information was collected through the statistical sources of the Ministry of Agricultural Jihad for the crop year 2023 for the three crops including irrigated wheat, irrigated barley, and silage corn, which occupies more than 85 percent of the cultivated area of this region. At the first stage, the amount of greenhouse gas (GHG) emissions by each product was calculated, and then the environmental impact of different subsidy policies was investigated. To calculate the greenhouse gas emissions, the emission coefficient of each of the inputs that have been cited in various studies was used. To model and analyze the data, positive mathematical programming with the cost function approach was used. Excel and GAMS software has been used to run the models.
Results and Discussion
The results of the study showed that the highest amount of greenhouse gas emissions is related to corn silage, and electricity, diesel, and chemical fertilizers have the largest share of the greenhouse gas emissions. The simulation results for the region’s cultivation patterns, considering scenarios where only chemical fertilizers—N-fertilizer, P-fertilizer, and K-fertilizer—were used separately and together with increases of 25%, 50%, 75%, and 100%, indicate that as input prices rise, both the cultivated area and farmers' income decrease. Additionally, increasing the price of P-fertilizer has a greater potential to reduce environmental impact compared to raising the price of other chemical fertilizers.To assess the environmental impact of reallocating subsidies from chemical inputs to agricultural products, a scenario was simulated in which the price of chemical inputs increased by 100%, while product prices rose by 5% and 10%, respectively. The model results revealed that the lowest environmental impact per hectare of crop production occurs when chemical fertilizer prices increase by 100% and product prices rise by 5%.Based on these findings, reallocating subsidies to agricultural products rather than production inputs appears to yield more favorable environmental outcomes. In other words, when the subsidy is allocated to the product instead of chemical inputs, the environmental impact of crop production in this area would be reduced and the amount of emissions per hectare of farm or million Tomans of gross profit would be less compared to other situations.
Conclusion
It is necessary to support the agricultural sector to boost food production but these supports should be done with the least environmental impact. According to the findings of this study, if subsidies are given to agricultural products instead of inputs, greenhouse gas emissions will be reduced while maintaining the area of crops and the amount of gross profit of farmers. The policy of setting a guaranteed price for basic agricultural products in Iran can be a suitable tool to realize this. In other words, transferring the credits allocated for purchasing chemical fertilizers to the guaranteed purchase of agricultural products will be an effective step in reducing the emission of greenhouse gases and their impact, as well as maintaining the country's food security.

Keywords

Crop pattern; Green house gas emission; Markazi province; Positive mathematical programming; Zarandieh city

References

1. Abbas, A., Zhao, C., Waseem, M., Ahmed khan, K., & Ahmad, R. (2022). Analysis of energy input–output of farms and assessment of greenhouse gas emissions: A case study of cotton growers [Community case study]. Frontiers in Environmental Science, 9. https://doi.org/10.3389/fenvs.2021.826838
2. Abdi Rokni, K., Abedi, S., & 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). Agricultural Economics Research, 11(42), 263-276.
3. Agriculture-Jahad, M.O. (2023). Statistics of crop production. Dputy of Planning and economy.
4. Alijani, F., & Azadegan, E. (2018). Analysis of pricing policy agricultural products. Agricultural Economics Research, 10(40), 85-104.
5. Arfini, F., Donati, M., & Paris, Q. (2003). A national PMP model for policy evaluation in agriculture using micro data and administrative information. International Conference on Agricultural Policy Reform and the WTO: Where are we heading. Capri, Italy.
6. Bakhshi, M.R., Peykani, G., Hosseini, S.S., & Saleh, I. (2010). Evaluating effects of removing fertilizer subsidy and direct payment polices on cropping pattern and inputs use (Case study: Agronomy subsector of Sabzevar Township). Agricultural Economics, 4(2), 185-207.
7. Baldi, L., Arfini, , Calzolai, S., & Donati, M. (2023). CAP reform and GHG emissions: policy assessment using a PMP agent-based model Research in Agricultural & Applied Economics. https://doi.org/10.36253/bae-14592
8. Balezentis, T., Chen, X., Galnaityte, A., & Namiotko, V. (2020). Optimizing crop mix with respect to economic and environmental constraints: An integrated MCDM approach. Science of The Total Environment, 705, 135896. https://doi.org/10.1016/j.scitotenv.2019.135896
9. Bigdeli, s., Ebrahimi, K., Davudirad, A., & Hoorfar, A. (2024). Evaluation of the Zarandieh Saveh aquifer sustainability, Iran. Iranian Journal of Irrigation & Drainage, 17(5), 817-830. https://idj.iaid.ir/article_182245_7576878b3cf06f087488c79bcf26cf2f.pdf
10. Buysse, J., Van Huylenbroeck, G., & Lauwers, L. (2007). Normative, positive and econometric mathematical programming as tools for incorporation of multifunctionality in agricultural policy modelling. Agriculture, Ecosystems & Environment, 120(1), 70-81. https://doi.org/10.1016/j.agee.2006.03.035
11. Chen, Y.-H., Chen, M.-X., & Mishra, A.K. (2020). Subsidies under uncertainty: Modeling of input- and output-oriented policies. Economic Modelling, 85, 39-56. https://doi.org/10.1016/j.econmod.2019.05.005
12. Cortignani, R., & Dono, G. (2015). Simulation of the impact of greening measures in an agricultural area of the southern Italy. Land Use Policy, 48, 525-533. https://doi.org/10.1016/j.landusepol.2015.06.028
13. Cortignani, R., & Dono, G. (2019). CAP's environmental policy and land use in arable farms: An impacts assessment of greening practices changes in Italy. Science of The Total Environment, 647, 516-524. https://doi.org/10.1016/j.scitotenv.2018.07.443
14. Daneshgar, H., Bagheri, M., Mardani Najafabadi, M., Alijani, F., & Yavari, G. (2021). Effects of climate change on hydrological and economic conditions of Bushkan plain farmers. Agricultural Economics Research, 13(2), 259-280.
15. Deng, L., & Zhao, J. (2024). Assessing economic and environmental impacts of subsidies for organic fertilizer in vegetable production: insights from a combined PMP-LCA-LCC approach. Environment, Development and Sustainability. https://doi.org/10.1007/s10668-024-04825-w
16. Dijak, W.D., Hanberry, B.B., Fraser, J.S., He, H.S., Wang, W.J., & Thompson, F.R. (2017). Revision and application of the LINKAGES model to simulate forest growth in central hardwood landscapes in response to climate change. Landscape Ecology, 32, 1365-1384. https://doi.org/10.1007/s10980-016-0473-8
17. Esfahani, S.M.J. (2022). Management of energy consumption and greenhouse gas emissions using the optimal farm scale: Evidence from wheat production in South Khorasan Province. Iran Agricultural Research, 40(2), 71-83.
18. Esfahani, S.M.J., & Rafati, M. (2022). The share of farm-scale on optimizing energy consumption and greenhouse gas emissions in irrigated wheat farms in eastern Iran. Sustainable Energy Technologies and Assessments, 53, 102465. https://doi.org/10.1016/j.seta.2022.102465
19. Hasanvand, M., Tahmasebi, J., & Keramatzadeh, A. (2016). Survey of farmerâs reaction to agricultural water policies in sub sector of farm in Khorramabad County using positive mathematical programming approach (PMP). Agricultural Economics and Development, 24(1), 167-192. https://doi.org/10.30490/aead.2016.59026
20. Ilahi, S., Wu, Y., Raza, M.A.A., Wei, W., Imran, M., & Bayasgalankhuu, L. (2019). Optimization approach for improving energy efficiency and evaluation of greenhouse gas emission of wheat crop using data envelopment analysis. Sustainability, 11(12), 3409. https://doi.org/10.3390/su11123409
21. Jamali Jaghdani, T., & Kvartiuk, V. (2021). The energy-water nexus in Iran: The political economy of energy subsidies for groundwater pumping. In S. Hülsmann & M. Jampani (Eds.), A Nexus Approach for Sustainable Development : Integrated Resources Management in Resilient Cities and Multifunctional Land-use Systems (pp. 107-128). Springer International Publishing. https://doi.org/10.1007/978-3-030-57530-4_8
22. Jamali, M., Soufizadeh, S., Yeganeh, B., & Emam, Y. (2021). A comparative study of irrigation techniques for energy flow and greenhouse gas (GHG) emissions in wheat agroecosystems under contrasting environments in south of Iran. Renewable and Sustainable Energy Reviews, 139, 110704. https://doi.org/10.1016/j.rser.2021.110704
23. Kazemi, H., Bourkheili, S.H., Kamkar, B., Soltani, A., Gharanjic, K., & Nazari, N.M. (2016). Estimation of greenhouse gas (GHG) emission and energy use efficiency (EUE) analysis in rainfed canola production (case study: Golestan province, Iran). Energy, 116, 694-700. https://doi.org/10.1016/j.energy.2016.10.010
24. Laborde, D., Mamun, A., Martin, W., Piñeiro, V., & Vos, R. (2021). Agricultural subsidies and global greenhouse gas emissions. Nature Communications, 12(1), 2601. https://doi.org/10.1038/s41467-021-22703-1
25. Moulogianni, C., & Bournaris, T. (2021). Assessing the impacts of rural development plan measures on the sustainability of agricultural holdings using a PMP model. Land, 10(5). https://doi.org/10.3390/land10050446
26. Mousavi, S. N., Farajzadeh, Z., & Taheri, F. (2015). Study of economic and environmental consequences of eliminating chemical and pesticides subsidy using general equilibrium analysis. Agricultural Economics and Development, 2(4), 171-205. https://doi.org/10.30490/aead.2015.58949
27. Mu, L., Wang, Y., & Xue, B. (2023). Does the dynamic adjustment of agricultural water prices drive variation of the agricultural production?., 02 February 2023, PREPRINT (Version 1) available at Research Square https://doi.org/10.21203/rs.3.rs-2470829/v1
28. Paris, Q., & Howitt, R.E. (1998). An analysis of Ill-Posed production problems using maximum entropy. American Journal of Agricultural Economics, 80(1), 124-138. https://doi.org/10.2307/3180275
29. Šarauskis, E., Masilionytė, L., Juknevičius, D., Buragienė, S., & Kriaučiūnienė, Z. (2019). Energy use efficiency, GHG emissions, and cost-effectiveness of organic and sustainable fertilisation. Energy, 172, 1151-1160. https://doi.org/10.1016/j.energy.2019.02.067
30. Shabanzadeh-Khoshrody, M., Azadi, H., Ahangarkolaee, S.S., Värnik, R., Viira, A.-H., & Kurban, A. (2022). Reviewing the effects of guaranteed and purchasing price policies on cultivation pattern of agronomic crops in Qazvin Plain, Iran. Journal of International Food & Agribusiness Marketing. https://doi.org/10.1080/08974438.2022.2085223
31. Sharafi, S., Kazemi, A., & Amiri, Z. (2023). Estimating energy consumption and GHG emissions in crop production: A machine learning approach. Journal of Cleaner Production, 408, 137242. https://doi.org/10.1016/j.jclepro.2023.137242
32. Smith, V.H., Glauber, J.W., Goodwin, B.K., & Sumner, D.A. (2017). Agricultural policy in disarray: Reforming the farm bill—An overview. American Enterprise Institute.
33. Viaggi, D., Raggi, M., & Paloma, S.G. (2010). An integer programming dynamic farm-household model to evaluate the impact of agricultural policy reforms on farm investment behaviour. European Journal of Operational Research, 207(2), 1130-1139. https://doi.org/10.1016/j.ejor.2010.05.012
34. Wang, S., Yang, P., Tan, Q., & Yao, L. (2024). Identification of optimal organic fertilizer subsidy policies under dual uncertainty via a self-calibrated fuzzy-boundary interval programming method. Journal of Cleaner Production, 438, 140762. https://doi.org/10.1016/j.jclepro.2024.140762
35. Zhai, Z., Martínez, J.F., Beltran, V., & Martínez, N.L. (2020). Decision support systems for agriculture 4.0: Survey and challenges. Computers and Electronics in Agriculture, 170, 105256. https://doi.org/10.1016/j.compag.2020.105256
36. Zhang, R., Ma, W., & Liu, J. (2021). Impact of government subsidy on agricultural production and pollution: A game-theoretic approach. Journal of Cleaner Production, 285, 124806. https://doi.org/10.1016/j.jclepro.2020.124806
37. Zhao, Z., Wang, G., Chen, J., Wang, J., & Zhang, Y. (2019). Assessment of climate change adaptation measures on the income of herders in a pastoral region. Journal of Cleaner Production, 208, 728-735. https://doi.org/10.1016/j.jclepro.2018.10.088
38. Ziliaskopoulos, K., & Papalamprou, K. (2022). A bilevel linear programming model for developing a subsidy policy to minimize the environmental impact of the agricultural sector. Sustainability, 14(13). https://doi.org/10.3390/su14137651