A scenario-based climate change environmental impacts on the food security modelling

Abstract

Climate change has turned as on eof the most critical global environmental challenges. Food security is a key sector for intervention which is highly vulnerable to climate change. In recent years, frequent climate change has threatened the food security in East Azerbaijan Province, Iran. The current reserach aims to evaluate the impact of climate changes indicators and in particularly drought on the food security in East Azerbaijan province (EAP)of Iran from 2000 to 2024. For this goal, relevant criteria of food security assessment were considered as base of the GIS based spatial analysis approach for producing a Food Security Risk Map (FSRM). In the next step, the spatial correlation between results of GIS based FSM and Climate Change Factors(CCF) were computed by applying integrated spatiotemporal and geostatistical approaches. Based on the results, the FSRM map in 2000 indicated that 18% of the province areas were in high and very high secure zones which has increased to the reik of 26% in the year of 2024. Results of this investigation indicated that the food security in EPA still may not reached up the critical level of climate change impacts but it is expacted the tanginable impacts become more obviouse in next decadeds when the Urmia lake drought being taken into account. The result of spatial correlation within the FSRM and CCF illustrated that, precipitation has significant role in the food security of the study area and any increase in precipitation may decress the food security issue in EAP. While, the inverse relationship figured out between the secure zones and temperature. The results of the present research are of great importance to decision makers and stakholdres and, in particular, to government departments such as the Ministry of Agriculture, the Ministry of Water Resource Management, and the Ministry of Natural Resources for East Azerbaijan for the task of sustanble dvelopente, crop modification and crices mangment in the EAP. Since the climate change is also a global issues, from the methological perspective the outcome of this study can be optimized and appalide for any other study area as stat of art and progressive research in domain of GISceince.

Keywords

• Climate change
• Environmental assessment
• Food security
• Spatial analysis

References

1. IPCC. (2014). Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press. https://www.ipcc.ch/report/ar5/wg2/.
2. Mohammed Abdulwahid, W., Feizizadeh, B., Blaschke, T., Karimzadeh, S.(2025). A Geoinformation Approach for Spatiotemporal Mapping of Climate Change Environmental Impacts on Sustainable Food Production in Iraq, International Journal of Environmental Research, 9, 175 (2025). https://doi.org/10.1007/s41742-025-00821-8.
3. Feizizadeh, B., Mohmadzadeh Alajujeh, K., Makki, M. (2023). A scenario-based food security analysis and halophyte crop suitability assessment in dying lake environments impacted by climate change, International Journal of Applied Earth Observation and Geoinformation, https://doi.org/10.1016/j.jag.2023.103425.
4. Feizizadeh, B., Lakes, T., Omarzadeh, D., Sharifi, A., Blaschke, T., Karmizadeh, S., M.(2022). Scenario-based analysis of the impacts of lake drying on sustainable food prodection, Nature of Scientific Report, https://doi.org/10.1038/s41598-022-10159-2.
5. FAO. (2002). The state of food insecurity in the world 2002. Food and Agriculture Organization. http://www.fao.org/3/y1500e/y1500e00.htm
6. Mirzabaev, A., et al. (2023). Climate Change and Food Security: A GIS Perspective. Climatic Change, 176(2), 22. https://doi.org/10.1007/s10584-023-03590-5
7. Yildirim, N., & Acar, A. (2020). Determination of geotechnical properties of some natural rocks used in construction. International Journal of Engineering and Geosciences, 5(3), 145-152.
8. Liu, B., et al. (2025). Climate Change and Staple Crop Yields in Sub-Saharan Africa. Agricultural Systems, 201, 103-118. https://doi.org/10.1016/j.agsy.2025.103118

9. Jarvis, A., Reuter, H. I., Nelson, A., & Guevara, E. (2008). Hole-filled SRTM for Global version 4. International Centre for Tropical Agriculture (CIAT). http://srtm.csi.cgiar.org
10. Haldar, A. and Pearlin, K. (2023). Climate-smart agriculture for food security. Sustainability, 15(4), 4321. https://doi.org/10.3390/su15044321.
11. United Nations, Department of Economic and Social Affairs, Population Division.(2022). World Population Prospects 2022, Online Edition. https://population.un.org/wpp.
12. Alam, M., Rahman, M., & Hasan, M. (2017). Impact of climate change on agriculture and food security: a review. Environmental Science and Policy, 76, 55–65. https://doi.org/10.1016/j.envsci.2017.06.003
13. Ashofteh, P., Nejati, M., Mahdavi, M. (2014). Food security under climate change: A review. Journal of Agricultural Science and Technology, 16(3), 345–360. http://jast.modares.ac.ir/article-23-345-en.html
14. Kabubo-Mariara, J., & Kabara, P. (2014). Climate change has affected African agriculture. Climate and Development, 6(3), 263–274. https://doi.org/10.1080/17565529.2013.820833
15. Brown, M. E., et al. (2009). The impact of climate variability on food security. Global Environmental Change, 19(2), 176–186. https://doi.org/10.1016/j.gloenvcha.2009.01.002
16. Choudhary, M., Singh, R., & Kumar, P. (2012). The impact of climate change on crop productivity in Asia. Environmental Research Letters, 7(4), 045301. https://doi.org/10.1088/1748-9326/7/4/045301
17. Anwar, M. R., Liu, D. L., Farquharson, R., & Wang, L. (2007). Climate change has affected food security and agriculture. International Journal of Climate Change Strategies and Management, 1(1), 25–40. https://doi.org/10.1108/17568690710730020
18. Challinor et al. (2005). Climate change and food security. Philosophical Transactions of the Royal Society B, 360(1463), 2139–2154. https://doi.org/10.1098/rstb.2005.1745
19. Torriani, D., Conti, P., & Sacchi, R. (2007). Global warming and agriculture: modeling crop productivity under a changing climate. Agricultural Systems, 94(2), 202–214. https://doi.org/10.1016/j.agsy.2006.09.006
20. Hertel, T. W., Burke, M., & Lobell, D. B. (2010). Poverty implications of climate-induced crop yield changes by 2030. Global Environmental Change, 20(4), 577–585. https://doi.org/10.1016/j.gloenvcha.2010.07.001
21. Burke, M., Lobell, D. B., & Guarino, L. (2010). Food security and climate change in Africa. Food Policy, 35(4), 389–401. https://doi.org/10.1016/j.foodpol.2010.04.00.
22. Kılıç, M., & Özer, Z. (2021). Seismic hazard analysis for a specific region using historical data. International Journal of Engineering and Geosciences, 6(2), 89-98.
23. Lobell, D. B. & Field, C. B. (2007). Global food security under climate change. Science, 319(5863), 607–610. https://doi.org/10.1126/science.1152741
24. Lobell, D. B., Bänziger, M., et al. (2011). Impact of climate on maize yields. Nature Climate Change, 1, 22–26. https://doi.org/10.1038/nclimate1056
25. Lobell, D. B., Schlenker, W., & Costa-Roberts, J. (2011). Climate trends and global crop production. Environmental Research Letters, 6, 014010. https://doi.org/10.1088/1748-9326/6/1/014010
26. Ahmed, M., Lobell, D., Bathke, G. (2011). Impact of climate on crop yields in developing countries. Agricultural and Forest Meteorology, 151(12), 1749–1763. https://doi.org/10.1016/j.agrformet.2011.06.001
27. Georgescu, M., Lobell, D., Field, C. (2011). Urbanization and climate change have affected agriculture. Environmental Research Letters, 6(1), 014009. https://doi.org/10.1088/1748-9326/6/1/014009
28. Chambwera, M. & Stage, J. (2010). Climate change adaptation in developing countries. World Development, 38(3), 361–372. https://doi.org/10.1016/j.worlddev.2009.06.007
29. Bonfils, C., et al. (2008). Observational Evidence of Climate Change. Geophysical Research Letters, 35(L16709), 1–6. https://doi.org/10.1029/2008GL034864
30. Cahill et al. (2007). Climate Change and Agriculture: Evidence and Implications. Climatic Change, 84, 107–124. https://doi.org/10.1007/s10584-007-9250-0
31. Parry, M. L. (2007). Climate change and food security: A global perspective
32. Demir, I., & Yılmaz, H. (2019). Landslide susceptibility mapping using GIS-based multi-criteria decision analysis. International Journal of Engineering and Geosciences, 4(1), 22-30.
33. Zarghami, M., Karimi, V., Shokri, A. (2011). Agricultural land use planning in East Azerbaijan Province using GIS-based multicriteria decision analysis. Journal of Environmental Planning and Management, 54(8), 1047–1065. https://doi.org/10.1080/09640568.2011.583034
34. Begna, D. (2025). GIS-based assessment of climate impacts on food security. Environmental Monitoring and Assessment, 197(4), 432. https://doi.org/10.1007/s10661-025-9172-1.
35. Al-Hanbali, A., Shibuta, K., Alsaaideh, B., & Tawara, Y.(2022) . Analysis of land suitability for paddy fields in Tanzania using a GIS-based analytical hierarchy process. Geo-spatial Information Science, 25(2), 212-228.
36. Mohammadzadeh, M. (2010). Food security assessment: Methods and applications. Journal of Agricultural Research, 4(2), 112–128. https://www.sid.ir/en/journal/ViewPaper.aspx?id=214926
37. Kane, S. (2014). GIS applications in food security and agricultural development. Journal of Geographical Systems, 16(2), 111–129. https://doi.org/10.1007/s10109-013-0180-9
38. Çelik, S., & Koç, Y. (2022). The effect of climate change on groundwater resources: A case study in Anatolia. International Journal of Engineering and Geosciences, 7(4), 112-120.
39. Sapkota, T., White, R., & Chhetri, N. (2019). GIS-based assessment of climate change impacts on food security. Applied Geography, 105, 101–113. https://doi.org/10.1016/j.apgeog.2019.03.012
40. Feizizadeh,B., Omrazadeh,D., Ghasemi,M., Bageri,S., Lakes,T., Kitzmann,R., Ghanbari, A., Blaschke. T.(2023). Urban restaurants and online food delivery during the COVID-19 pandemic: a spatial and socio-demographic analysis, International Journal of Digital Earth, 16:1, 1725-1751, DOI: 10.1080/17538947.2023.2210313.
41. Yıldırım, Y., & Arslan, M. (2021). Remote sensing techniques for land use/land cover change detection. Geomatik, 9(2), 150-162.
42. Dey, P. K., & Ramcharan, E. K. (2008). Multi-criteria decision making for food security planning. Omega, 36(5), 679–690. https://doi.org/10.1016/j.omega.2006.08.002
43. Vahidnia, M. H., Alesheikh, A. A., & Alimohammadi, A. (2009). Fuzzy analytic network process-based spatial multi-criteria evaluation for sustainable land-use planning. Environmental Modelling & Software, 24(11), 1342–1350. https://doi.org/10.1016/j.envsoft.2009.04.007
44. Chang, D. Y., et al. (2000). Multi-criteria decision making: Methods and applications. European Journal of Operational Research, 123(2), 345–360. https://doi.org/10.1016/S0377-2217(99)00364-7
45. Chang, D. Y., et al. (2007). Fuzzy ANP methods for decision-making under uncertainty. Applied Mathematics and Computation, 187(2), 353–368. https://doi.org/10.1016/j.amc.2006.05.012
46. Chang, D. Y. (1996). Applications of the Extent Analysis Method to Fuzzy ANP. European Journal of Operational Research, 95(3), 649–655. https://doi.org/10.1016/0377-2217(95)00300-2
47. Toprak, A., & Akça, S. (2020). Integration of UAV photogrammetry and GIS for urban planning. Geomatik, 8(3), 201-215.
48. Bottero, M., Feizizadeh, B., Blaschke, T. (2011). Application of Fuzzy ANP for Land Suitability Analysis: A Case Study. Environmental Earth Sciences, 64(8), 2247–2257. https://doi.org/10.1007/s12665-011-1045-0
49. Levary, R., & Wan, D. (1998). Fuzzy ANP in decision making. Computers & Industrial Engineering, 34(2), 419–422. https://doi.org/10.1016/S0360-8352(98)00137-0
50. Van der Wel, F., Borkowski, J., & Mengel, J. (2004). Interpolation of climate data for regional applications. International Journal of Climatology, 24(4), 459–476. https://doi.org/10.1002/joc.1005
51. Li, J., & Heap, A. D. (2014). Spatial interpolation methods in GIS. Geographical Research, 52(4), 417–439. https://doi.org/10.1111/1745-5871.12019
52. Li, J., Heap, A.D., Potter, A., Daniell, J. (2017). Application of spatial interpolation methods in climate change studies: a review. Environmental Modelling & Software, 96, 125–145. https://doi.org/10.1016/j.envsoft.2017.06.001
53. Hengl, T. (2009). Practical guide for geostatistical mapping of environmental variables. EUR 22904 EN. Luxembourg: Office for Official Publications of the European Communities. https://doi.org/10.2788/18099
54. Hengl, T. (2007). Practical guide for geostatistical mapping of environmental variables. European Commission, Joint Research Centre. https://doi.org/10.2788/22528
55. Lioubimtseva, E. and Henebry, G. (2009). Climate and food security in Central Asia. Environmental Research Letters, 4(1), 014011. https://doi.org/10.1088/1748-9326/4/1/014011
56. Thornton, P. K., Ericksen, P. J., Herrero, M., & Challinor, A. J. (2014). Climate variability and vulnerability to climate change: a review. Global Change Biology, 20(11), 3313–3328. https://doi.org/10.1111/gcb.12581
57. Korkmaz, S., & Demir, L. (2022). 3D modeling of historical buildings using terrestrial laser scanning. Geomatik, 10(1), 78-89.
58. Feizizadeh, B., & Blaschke, T. (2012a). GIS-based land use suitability assessment for agriculture in the East Azerbaijan Province, Iran. Applied Geography, 35(1–2), 219–231. https://doi.org/10.1016/j.apgeog.2012.06.006
59. Feizizadeh, B. & Blaschke, T. (2011). Land suitability evaluation using GIS and ANP: the impact of natural hazards in East Azerbaijan, Iran. Environmental Earth Sciences, 64(7), 2021–2034. https://doi.org/10.1007/s12665-011-1004-7
60. Feizizadeh, B. & Blaschke, T. (2012). GIS-multi-criteria decision analysis for land suitability mapping: Integrating OWA and fuzzy ANP. International Journal of Applied Earth Observation and Geoinformation, 18, 285–294. https://doi.org/10.1016/j.jag.2012.03.001
61. Feizizadeh, B., & Blaschke, T. (2012b). Spatial multi-criteria evaluation for land use suitability analysis: Integrating natural hazard considerations in Iran. Natural Hazards, 61(1), 237–265. https://doi.org/10.1007/s11069-011-9978-2
62. Saaty, T. L. (1980). The analytic hierarchy process: planning, priority setting, and resource allocation. McGraw-Hill. ISBN: 9780070543713
63. Feizizadeh, B., Blaschke, T., Darvishsefat, A. (2014). Spatial analysis of food security in Iran using GIS-based MCDM. Applied Geography, 55, 118–133. https://doi.org/10.1016/j.apgeog.2014.09.006
64. Malczewski, J. (2006). GIS-based multi-criteria decision analysis: A survey of the literature. International Journal of Geographical Information Science, 20(7), 703–726. https://doi.org/10.1080/13658810600661508
65. Perfecto, I., & Vandermeer, J. (2010). Agroecology and the design of sustainable food systems. Journal of Sustainable Agriculture, 34(8), 807–831. https://doi.org/10.1080/10440046.2010.515583
66. Feizizadeh, B. & Blaschke, T. (2013). GIS-based Fuzzy Multi-Criteria Decision Analysis approach for flood risk mapping. Journal of Environmental Management, 129, 83–95. https://doi.org/10.1016/j.jenvman.2013.05.016
67. Gorsevski, P., Zorn, M., & Hollenhorst, S. (2012). Multi-criteria Evaluation of Spatial Decision Support. Environmental Modelling & Software, 37, 286–295. https://doi.org/10.1016/j.envsoft.2012.04.011
68. Vaidya, O. S. & Kumar, S. (2006). Analytic hierarchy process: An overview of applications. European Journal of Operational Research, 169(1), 1–29. https://doi.org/10.1016/j.ejor.2004.04.028
69. Deng, H. (1999). Multicriteria analysis with fuzzy pairwise comparison. International Journal of Approximate Reasoning, 21(3), 215–231. https://doi.org/10.1016/S0888-613X(99)00015-1
70. Kahraman, C., Cebeci, U., Ruan, D. (2003). Multi-criteria decision-making in engineering and management: Fuzzy-analytic network process approach. Expert Systems with Applications, 25(3), 301–315. https://doi.org/10.1016/S0957-4174(03)00014-1
71. Lee, S., Kang, J., & Kim, H. (2013). Fuzzy ANP for prioritizing renewable energy alternatives: An empirical study. Renewable and Sustainable Energy Reviews, 24, 58–68. https://doi.org/10.1016/j.rser.2013.03.019
72. Yager, R. R. (1988). Ordered weighted averaging aggregation operators in multi-criteria decision-making. IEEE Transactions on Systems, Man, and Cybernetics, 18(1), 183–190. https://doi.org/10.1109/21.87068
73. Yager, R. R. (1996). Quantifier-guided aggregation using OWA operators. International Journal of Intelligent Systems, 11(2), 49–73. https://doi.org/10.1002/(SICI)1098-111X(199602)11:2<49::AID-INT2>3.0.CO;2-6
74. Boroushaki, S., & Malczewski, J. (2010). Implementing an ordered weighted averaging aggregation operator in GIS-based multicriteria decision analysis. Computers, Environment and Urban Systems, 34(6), 496–507. https://doi.org/10.1016/j.compenvurbsys.2010.06.002
75. Jiang, H. & Eastman, J. R. (2000). Application of fuzzy measures in the multi-criteria evaluation of GIS. International Journal of Geographical Information Science, 14(2), 173–184. https://doi.org/10.1080/136588100240897
76. Collins, D., and Bolstad, P. (1996). Comparison of spatial interpolation techniques in a GIS: Empirical examples from environmental studies. Photogrammetric Engineering & Remote Sensing, 62(5), 591–597. https://www.asprs.org/wp-content/uploads/pers/1996journal/may/1996_may_591-597.pdf
77. Hartkamp, A., White, J., Hoogenboom, G., Jones, J. (1999). Interpolation techniques for climate variables. Applications of GIS in Agriculture and Forestry. CRC Press. https://www.crcpress.com/Applications-of-GIS-in-Agriculture-and-Forestry/Hartkamp-White-Hoogenboom-Jones/p/book/9780849380338
78. Burrough, P. A., & McDonnell, R. A. (1998). Principles of geographical information systems. Oxford University Press. https://global.oup.com/academic/product/principles-of-geographical-information-systems-9780198233655
79. Laslett, G. M., McBratney, A. B., and Zala, B. (1987). Variograms and spatial prediction in soil science. Geoderma, 39(1–2), 1–18. https://doi.org/10.1016/0016-7061(87)90004-7
80. Isaaks, E. H. & Srivastava, R. M. (1989). An introduction to applied geostatistics (vol. 21). Oxford University Press. https://global.oup.com/academic/product/an-introduction-to-applied-geostatistics-9780195050134
81. De Smith, M. J., Goodchild, M. F., & Longley, P. A. (2007). Geospatial analysis: A comprehensive guide to principles, techniques, and software tools. Troubador Publishing. https://www.taylorfrancis.com/books/mono/10.4324/9780203920699
82. Journel, A. G., & Huijbregts, C. J. (1978). Mining geostatistics. Academic Press. https://doi.org/10.1016/C2013-0-04156-6
83. Cressie, N. (1993). Statistics for spatial data. Wiley. https://doi.org/10.1002/9781119115151
84. Webster, R., & Oliver, M. A. (2007). Geostatistics for Environmental Scientists (2nd ed.). Wiley. https://doi.org/10.1002/9780470517270
85. Oliver, M. A., & Webster, R. (2015). Basic steps in geostatistics: variogram and kriging. Springer Handbook of Geographic Information, 7–36. https://doi.org/10.1007/978-3-319-11456-7_2
86. Goovaerts, P. (1997). Geostatistics for Natural Resource Evaluation. Oxford University Press. https://global.oup.com/academic/product/geostatistics-for-natural-resources-evaluation-9780195100156
87. Amiri, M. and Eslamian, S. (2010). Climate Change Impacts on Agriculture in Iran: A Regional Assessment. Water Resources Management, 24, 1357–1372. https://doi.org/10.1007/s11269-009-9546-3
88. Perry, M., & Hollis, D. (2005). Generation of monthly gridded datasets for a range of climatic variables in the United Kingdom. International Journal of Climatology, 25(8), 1041–1054. https://doi.org/10.1002/joc.1161
89. Buishand, T. A. (2008). Spatial interpolation of precipitation totals using the kriging method with external drift. Hydrology and Earth System Sciences, 12, 69–78. https://doi.org/10.5194/hess-12-69-2008
90. Hastaoğlu, K. Ö., Göğsu, S., & Gül, Y. (2022). Determining the relationship between the slope and directional distribution of the UAV point cloud and the accuracy of various IDW interpolations. International Journal of Engineering and Geosciences (IJEG), 7(2), 161 173. https://doi.org/10.26833/ijeg.940997
91. Sluiter, R. (2009). Interpolation of precipitation in mountainous regions. Hydrology and Earth System Sciences Discussions, 6, 1025–1053. https://doi.org/10.5194/hessd-6-1025-2009
92. Sagaydak, A., & Sagaydak, A. (2022). Agricultural land consolidation versus land fragmentation in Russia. International Journal of Engineering and Geosciences (IJEG). https://dergipark.org.tr/en/pub/ijeg/issue/65508/919939
93. Boutallaka, M., Benhaddou, D., & Oukid, S. (2025). Assessment of current and future land sensitivity to degradation under climate change in the upstream Ouergha watershed (Morocco) using GIS and AHP methods. International Journal of Engineering and Geosciences (IJEG). https://doi.org/10.26833/ijeg.1521350
94. Madani, K. (2014). Water management challenges in Iran. Water Resources Management, 28(10), 3935–3953. https://doi.org/10.1007/s11269-014-0662-2
95. Çolak, E., Memişoğlu Baykal, T., & Genç, N. (2024). Production of fuel-based carbon footprint distribution map using spatial interpolation methods based on GIS. International Journal of Engineering and Geosciences (IJEG), 9(1), 99 114. https://doi.org/10.26833/ijeg.1330784