Providing a Roadmap for the Adaption of Agricultural Sector to Water Scarcity Conditions (Case Study: Selected Crops of Tajan Basin, Iran)

مجله اقتصاد و توسعه کشاورزی

Volume 38, Issue 3 - Serial Number 64, October 2024, Pages 350-335   

Document Type: Research Article

DOI: 10.22067/jead.2024.89018.1279

Authors

H. Fouladi¹; H. Amirnejad^{* 2}; S. Shirzadi Laskookalayeh³

¹Department of Agricultural Economics, Sari University of Agricultural Sciences and Natural Resources, Sari, Iran

²Agricultural Economics, Sari Agricultural Sciences and Natural Resources University, Sari.

³Sari Agricultural Sciences and Natural resources University-

Abstract

Introduction
In recent decades, the issue of climate change has become one of the global issues and has affected the agricultural sector. The continuation of agriculture regardless of the water shortage crisis has had an inappropriate effect on the sustainability and growth of this sector. On the other hand, the destructive effect of excessive use of chemical fertilizers and pesticides on water, soil, health of ecosystems, humans and other living beings is undeniable. For this reason, the void of using an efficient model that can provide all economic and environmental aspects at the same time was completely felt. The aim of this study was to provide an optimal cropping pattern using the integrated method of Goal and Grey planning. For this purpose, the farmers of the agronomy sub-sector of Tajan Basin were selected as the statistical population. In this regard, time series information was collected from the aggregation of the average data of 401 settlements located in this area during the years 2017-2021 from the annual reports of experts.
 
Materials and Methods
The Linear Programming (LP) Model quantifies an optimal way of integrating constraints to satisfy the objective function to optimize crop production and profits for irrigation farmers. To use LP, one must convert the problem into a mathematical model. To do this, an objective such as maximizing profit or minimizing losses is required. The model must also include decision variables that affect those objectives, and constraints that limit what user can do. Therefore, the LP Model is a single-objective method. Goal Programming (GP) is an extension of LP in which targets are specified for a set of constraints. GP is used to perform three types of analysis: Determining the required resources to achieve a desired set of objectives. Determining the degree of attainment of the goals with the available resources. Providing the best satisfying solution under a varying amount of resources and priorities of the goals. Thus, the GP model is a multi-objective method. The Grey system theory is identified as an effective methodology that can be used to solve uncertain problems with partially known information. Grey modelling approach uses accident data for estimating the model parameters. The model can reflect the dynamics, balance the conflicting the multidimensional targets of cropping patterns, and promoting the sustainable use of cultivated land. For achieving different goals in unstable economic and environmental conditions, we used a Goal-Grey model that was obtained from the integration of Goal programing and Grey Programing. The Goal-Grey model, by considering the uncertainty in the data, leads to overlap between the economic and environmental goals and provides the scope of cultivation for the selected products.
 
Results and Discussion
By estimating the Linear Programming (LP) Model, crops like wheat and canola are removed from the cropping pattern, while the cultivation areas for barley and high-yielding long-grain rice increase by 644% and 31%, respectively. In contrast, the cultivation areas for high-quality long-grain rice and maize decrease by 89% and 10%, respectively. Implementing this model boosts the gross profit of farmers in the Tajan region by 14% solely through adjusting the crop composition, without altering the current input levels. Additionally, the findings show that applying the LP Model results in fertilizer savings of 5%, 13%, and 10% for phosphate, nitrogen, and potash, respectively. The amount of herbicide and fungicide consumption in the LP Model is exactly equal to the current model of the region. However, the implementation of this model will lead to a 5% increase in the consumption of insecticides poison. The amount of irrigation water consumption in the LP Model was calculated to be 2% less than the current model of the region. In addition, the results indicate that by estimating the Goal-Grey Model, only canola is removed from the cropping pattern. Also, in order to achieve the defined goals in this study, the cultivation area of wheat and maize should be equal to 208 and 7356 hectares respectively. However, the flexibility of input usage enables adjustments to other crop cultivation areas, facilitating high-quality long-grain rice production on 970 to 18,157 hectares. Plus, the cultivation area of long-grain rice can vary from 7654 to 9995 hectares. In this model, barley can be removed from the crop composition like the linear pattern or cultivated on a maximum of 2553 hectares. The implementation of the Goal-Grey model will lead to a maximum 2% increase in the gross profit of the farmers of Tajan region compared to the current model of this region. Also, by implementing the Goal-Grey Model, on average, phosphate, nitrogen, and potash fertilizer consumption is saved by 16, 27, and 20 percent, respectively. In addition, with the implementation of the Goal-Grey Model, the consumption of agricultural pesticides will decrease from 733 to 355 thousand liters on average.
 
Conclusion
The LP Model is designed based on current regional conditions; however, as a single-objective model with fixed parameters, it lacks the flexibility to offer an adaptable program for farmers during drought or wet periods or when inputs are limited. Findings indicate that under current conditions, there is excessive use of chemical inputs and irrigation water. By accounting for data uncertainty, the Goal-Gray model addresses these limitations, balancing economic and environmental objectives and defining a cultivation range for selected crops.
 
Acknowledgement 
We are grateful to the experts of agronomy management and plant conservation management of Mazandaran Province Agricultural Jihad Organization and Sari City Agricultural Jihad Management who cooperated in data collection. This article is taken from the preliminary results of a doctoral dissertation with material and intellectual rights related to Sari Agricultural Sciences and Natural Resources University, which is gratefully acknowledged.

Keywords

Cropping pattern; Economic and environmental goals; Gray; Optimization; Uncertainty

References

1. Akbari, N., ZahediKeyvan,, & ZahediKeyvan, M. (2009). Risk determination of cultivated crops in Hamadan province with use mathematical programming with attention to risk and uncertainty conditions. Agricultural Economics and Development, 16(4), 63-82. https://doi.org/10.30490/aead.2009.58856
2. Amoozad Mahdiraji, H., Mokhtarzade, N., & Radmand, S. (2017). A Hybrid Model of Grey Fuzzy Goal Programming in Project Time, Cost, Risk and Quality Tradeoff. Journal of Industrial Management Perspective, 7(3), 47-80. https://jimp.sbu.ac.ir/article_87194_c735b2d5e74b07fe431fa334cd3490cf.pdf
3. Mazandaran Regional Water Authority. (2022). Water resources assessment report. https://www.mzrw.ir.
4. Babakhani, B., Roghanian, E., & Azarnia, S. (2013). Using linear goal programming approach in quality function deployment method in grey conditions (Case study: Checking quality of olive oil). Journal of Industrial Strategic Management, 24(8), 145-158. http://sanad.iau.ir/fa/Article/923531
5. Bagheri, A., Mohammadi, H., Dehbashi, V., & Mehdizadeh, R. M. J. (2023). Determining the optimal cultivation pattern of agricultural products in Jiroft city with the approach of reducing pollution and water consumption. Scientific & Research Journals Mnagement System, 5(1), 1-21.
6. Balovi, F., Liaghat, A., & Ebrahimian, H. (2022). Estimation of water footprint in current cropping patterns and its reduction capacity in optimal patterns under multiple goals conditions (Case study; Varamin region). Iranian Journal of Agricultural Economics and Development Research, 53(4), 987-999. https://doi.org/10.22059/ijaedr.2022.338478.669130
7. Basumatary, U.R., & Mitra, D. (2022). Application of intuitionistic fuzzy optimization techniques to study multi-objective linear programming in agricultural production planning in Baksa district, Assam, India. Advanced Apply Mathmatical Science, 21(6), 3011-3028.
8. Dadkhah, S. (2022). Determining the cropping pattern of crops in Tehran province under conditions of uncertainty. Master's thesis. Faculty of Agricultural Management, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan.
9. Iranian Islamic Council Research Center. (2023). Law on economic, social and cultural development programs of the Islamic Republic of Iran. http://www.majlis.ir
10. National Center for Weather and Climate Change. (2023). Drought crisis management report. https://nacc.doe.ir
11. Ganesan, K. (2007). On some properties of interval matrices. Journal Comp Mathematical Science, 1, 2.
12. Ghaffari-Hadigheh, A.R. (2016). Proposing two linear programming models in uncertain environment [Research]. Journal of Operational Research and Its Applications, 13(3), 27-51. http://jamlu.liau.ac.ir/article-1-1398-en.html
13. Mardani Najafabadi, M., Abdeshahi, A., & Shirzadi Laskookalayeh, S. (2020). Determining the optimal cropping pattern with emphasis on proper use of sustainable agricultural disruptive inputs: Application of robust multi-objective linear fractional p Journal of Agricultural Scince and Sustainble Production, 30(1), 241-256. https://sustainagriculture.tabrizu.ac.ir/article_10422_7159d7b2884f699491e164c8b7f43ec6.pdf
14. Ministry of Agriculture Jahad. (2022). Vice President of Information and Communication Technology Center Statistics. maj.ir
15. Ko, A.S., & Chang, N.-B. (2008). Optimal planning of co-firing alternative fuels with coal in a power plant by grey nonlinear mixed integer programming model. Journal of Environmental Management, 88(1), 11-27.
16. Kashiri Kolaei F., Hosseini Yekani, S.A., & S.M. (2021). The application of uncertainty theory in optimization of cropping pattern in Sari Goharbaran. Eqtesad-E Keshvarzi va Towse'e, 29(113), 57-92.
17. Kolahi, M., Hosseinali, F., & Karimaei Tabarestani, M. (2023). Determining the optimal cultivation pattern by considering the concept of virtual water and economic benefits (Case Study: Omrani Plain in Khorasan Razavi). Iranian Journal of Irrigation & Drainage, 16(6), 1221-1232. https://idj.iaid.ir/article_163507_8ea33ba349259d99f8ac0493901cf1ce.pdf
18. Li, M., Xu, Z., Jiang, S., Zhuo, L., Gao, X., Zhao, Y., Liu, Y., Wang, W., Jin, J., & Wu, P. (2021). Non-negligible regional differences in the driving forces of crop-related water footprint and virtual water flows: A case study for the Beijing-Tianjin-Hebei region. Journal of Cleaner Production, 279, 123670.
19. Mardani Najafabadi, M., Abdeshahi, A., & Shirzadi Laskookalayeh, S. (2020). Determining the optimal cropping pattern with emphasis on proper use of sustainable agricultural disruptive inputs: Application of Robust multi-objective linear fractional p Journal of Agricultural Science and Sustainable Producton, 30(1), 241-256. https://sustainagriculture.tabrizu.ac.ir/article_10422_7159d7b2884f699491e164c8b7f43ec6.pdf
20. Mazandaran Province Agricultural Jihad Organization. (2022). Economic statistics. https://jkmaz.ir
21. Mohseni, S., & Nabiyyan, S. (2017). Determining the cropping pattern using gray programming. Third International Conference on Environmental Engineering.
22. Razavi Hajiagha, S.H., Amoozad Mahdiraji, H., Akrami, H., & Hashemi, S.S. (2013). Topsis- based non-Iinear programming model for calculating the ldeal weights of the decision. Industrial Management Studies, 11(29), 21-39. https://jims.atu.ac.ir/article_1249_484767ac515640abc96d5393ec4dffae.pdf
23. Sabzevari, A., Rajabipour, A., Bagheri, N., & Omid, M. (2020). Determining the cropping pattern of agricultural products as a strategy to reduce food security disaster in Iran. Environmental Management Hazards, 7(1), 23-38. https://doi.org/10.22059/jhsci.2020.298174.542
24. Sengupta, A., Pal, T.K., & Chakraborty, D. (2001). Interpretation of inequality constraints involving interval coefficients and a solution to interval linear programming. Fuzzy Sets and systems, 119(1), 129-138. https://doi.org/10.1016/S0165-0114(98)00407-2
25. Soleymani Nejad, S., Dourandish, A., Sabouhi, M., & Banayan Aval, M. (2019). The effects of climate change on cropping pattern (Case study: Mashhad plain). Iranian Journal of Agricultural Economics and Development Research, 50(2), 249-263. https://doi.org/10.22059/ijaedr.2019.237998.668461
26. Solimani, M., Rahimi, D., & Yazdanpanah, H. (2021). Climate change adaptation strategy in agriculture (Rostam County). Journal of Natural Environmental Hazards, 10(29), 19-32. https://doi.org/10.22111/jneh.2020.32681.1598
27. Soltani, A., Alimagham, S., Nehbandani, A., Torabi, B., Zeinali, E., Zand, E., Vadez, V., Van Loon, M., & Van Ittersum, M. (2020). Future food self-sufficiency in Iran: A model-based analysis. Global Food Security, 24, 100351. https://doi.org/10.1016/j.gfs.2020.100351
28. Copernicus Climate Data Store. (2023). Copernicus Climate Data Store https://cds.climate.copernicus.eu/cdsapp#!/dataset/ecv-for-climate-change?tab=form
29. Yazdi, M., & Sohrabi, R. (2021). Multi-objective decision-making and goal planning along with Lingo and Excel software training. Publication of Termeh, Tehran. https://www.termehbook.com/