National Vulnerability in Wheat's Future: GIS-based Crop Climate Suitability Analysis by CHELSA Climate dataset for Wheat (Triticum aestivum L.) in Turkey

Abstract

Wheat is critical to Turkey's national agricultural production. While the mechanisms by which climate change affects the wheat's spatial suitability, less is known about high-quality climate datasets for climate change-based estimation studies. Using the high-resolution climate dataset CHELSA and two Shared Socio-economic Pathways (SSP126 and SSP370), I sought insights into the future temperature and precipitation projections' impact on the wheat spatial suitability. I observed that the future spatial suitability of wheat is decreasing by more than 40% for all wheat areas (for rainfed/irrigated croplands and 2010-based wheat harvested areas). Moreover, more than 10% of the areas have low suitability within the areas with availability. In contrast, in low emission (SSP126) and high emission scenarios (SSP370), the most significant difference is seen in the "best suitability" class. These data demonstrate that the suitability of wheat in 2050 (2041–2070 period) will decrease throughout Turkey while suitable areas will be confined to very narrow areas. Due to growing concerns about wheat and food security, future research is urgently needed. Consequently, it is also seen that climate datasets and Crop Climate Suitability Models (CCSM) play an essential role in the projections for crop spatial suitability.

Keywords

• climate change
• wheat
• spatial suitability
• CHELSA
• climate dataset
• Shared Socio-economic Pathways
• Geographic Information Systems
• Crop Climate Suitability Modelling
• vulnerability
• Turkey

Thanks

The author prepared this study for the National Agriculture Food Union Association's (UTGB) "System Dynamics in Wheat" research.

References

1. References Asseng, S., & Pannell, D.J. (2013). Adapting dryland agriculture to climate change: Farming implications and research and development needs in Western Australia, Climatic Change, 118(2): 167–181.
2. Aydın, F. (2015). Enerji bitkisi yetiştirilebilecek alanların Coğrafi Bilgi Sistemleri, uzaktan algılama ve Analitik Hiyerarşi Prosesi desteği ile tespiti. (MSc. Dissertation), Ege University, İzmir, Turkey.
3. Aydın, F., & Sarptaş, H. (2018). İklim değişikliğinin bitki yetiştiriciliğine etkisi: model bitkiler ile Türkiye durumu. Pamukkale University Journal of Engineering Sciences, 24(3), 512-521. doi: 10.5505/pajes.2017.37880.
4. Aydin, F., Erlat, E., & Türkeş, M. (2020). Impact of climate variability on the surface of Lake Tuz (Turkey), 1985–2016. Regional Environmental Change, 20, 68. https://doi.org/10.1007/s10113-020-01656-z.
5. Brun, P., Zimmermann, N. E., Hari, C., Pellissier, L., & Karger, D. N. (2022). Global climate-related predictors at kilometer resolution for the past and future. Earth System Science Data, 14, 5573–5603, https://doi.org/10.5194/essd-14-5573-2022.
6. CDS (2022). Land cover classification gridded maps from 1992 to present derived from satellite observations. https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=doc Accessed 5 September 2022.
7. CCCS (2021). Product User Guide and Specification: ICDR Land Cover 2016-2020. ECMWF Copernıcus Report.
8. CHELSA-climate (2022). Future (CMIP6). https://chelsa-climate.org/cmip6/ Accessed 20 September 2022.

9. Dellal, İ., & McCarl, B. A. (2010). The economic impacts of drought on agriculture: The case of Turkey. Options Méditerranéennes, 95, 169-174.
10. Demir, A., & Aydin-Kandemir, F. (2022). Evaluation of Climate Change Impacts on the Geographic Distribution of Fritillaria imperialis L. (Liliaceae) (Turkey). Acta Societatis Botanicorum Poloniae, 91, 919. DOI: 10.5586/asbp.919.
11. Dhakal, K., Kakani, V., & Linde, E. (2018). Climate Change Impact on Wheat Production in the Southern Great Plains of the US Using Downscaled Climate Data. Atmospheric and Climate Sciences, 8, 143-162. doi: 10.4236/acs.2018.82011.
12. Eastman, J.R. (2015). TerrSet Manual. Clark University, Worcester, MA, USA. ecocrop: Ecocrop model. (2022). Documentation. https://www.rdocumentation.org/packages/dismo/versions/1.3-3/topics/ecocrop
13. Eitzinger, A., Carmona, S., Argote, K., Laderach, P., & Jarvis, A. (2018). Climate impacts and resilience in Caribbean agriculture: Assessing the consequences of climate change on cocoa and tomato production in Trinidad & Tobago and Jamaica (CIRCA). https://cgspace.cgiar.org/bitstream/handle/10568/569 80/circawp2presentacionjamaica.pdf?sequence=3&isAll owed=y.
14. Environmental Systems Research Institute. (2022). ArcGIS pro [Computer software]. https://www.arcgis.com/ Accessed 15 June 2021.
15. Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., & Taylor, K. E. (2016). Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development, 9(5), 1937–1958. https://doi.org/10.5194/gmd-9-1937-2016
16. Fatima, Z., Ahmed, M., Hussain, M., Abbas, G., Ul-Allah, S., Ahmad, S., Ahmed, N., Arif Ali, M., Sarwar, G., ul Haque, E., Iqbal, P., & Hussain, S. (2020). The fingerprints of climate warming on cereal crops phenology and adaptation options. Scientific Reports, 10(1), 1–21. https://doi.org/10.1038/s41598-020-74740-3.
17. FAO (2021). The State of the World's Land and Water Resources for Food and Agriculture – Systems at breaking point. Synthesis report 2021. Rome. https://doi.org/10.4060/cb7654en.
18. FAO (2022). Actual Yields and Production. https://gaez-data-portal-hqfao.hub.arcgis.com/pages/theme-details-theme-5 Accessed 2 December 2022.
19. Giorgetta, M. A., Jungclaus, J., Reick, C. H., Legutke, S., Bader, J., & Böttinger, M., et al. (2013). Climate and carbon cycle changes demo 1850 to 2100 in MPI-ESM simulations for the Coupled Model Intercomparison Project phase 5. Journal of Advances in Modeling Earth Systems, 5, 572–597. https://doi.org/10.1002/jame.20038.
20. Hausfather, Z. (2018). Explainer: How 'Shared Socioeconomic Pathways' explore future climate change. CarbonBrief, https://www.carbonbrief.org/explainer-how-shared-socioeconomic-pathways-explore-future-climate-change/ Accessed 10 October 2022.
21. Hijmans, R. J., Guarino, L., Cruz, M., & Rojas, E. (2001) Computer tools for spatial analysis of plant genetic resources data: 1. DIVA-GIS. Plant Genetic Resources Newsletter, 127, 15-19. Indexmundi. https://www.indexmundi.com/agriculture/?country=tr&commodity=wheat&graph=production Accessed 6 October 2022.
22. IPCC (2021). Climate Change 2021: The Physical Science Basis-Summary for Policymakers. Karakaş, G. (2022). Barriers to Climate Change Adaptation of Wheat Producing Farmers; the Case of Çorum Province-Türkiye. Turkish Journal of Agriculture, 10(5): 879-885, 2022 https://doi.org/10.24925/turjaf.v10i5.879-885.5104.
23. Karger, D. N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R. W., Zimmermann, N. E., Linder, H. P., & Kessler, M. (2017). Climatologies at high resolution for the Earth's land surface areas. Scientific Data, 4, 170122. DOI: 10.1038/sdata.2017.122.
24. Kourat, T., Smadhi, D., & Madani, A. (2022). Modeling the Impact of Future Climate Change Impacts on Rainfed Durum Wheat Production in Algeria. Climate, 10, 50, https://doi.org/10.3390/cli10040050. Laderach, P., & Eitzinger, A. (2013). Ecocrop suitability modeling. Data Analysis Workshop and Adaptation Strategy Development, Arusha, Tanzania, 04-06 June.
25. MacCracken, M. C. (2008). Prospects for future climate change and the reasons for early action. Journal of the Air & Waste Management Association, 58(6), 735–786. DOI:10.3155/1047-3289.58.6.735.
26. Mauritsen, T., Bader, J., Becker, T., Behrens, J., Bittner, M., & Brokopf, R., et al. (2019). Developments in the MPI-M Earth System Model version 1.2 (MPI-ESM1.2) and its response to increasing CO2. Journal of Advances in Modeling Earth Systems, 11, 998–1038. https://doi.org/10.1029/ 2018MS001400.
27. Ramirez-Villegas, J., Jarvis, A., & Laderach, P. (2013). Empirical approaches for assessing impacts of climate change on agriculture: The EcoCrop model and a case study with grain sorghum. Agricultural and Forest Meteorology, 170, 67-78.
28. Roeckner, E., Dümenil, L., Kirk, E., Lunkeit, F., Ponater, M., & Rockel, B., et al. (1989). The Hamburg version of the ECMWF model (ECHAM), Tech. Rep. 13. Geneva, Switzerland: World Meteorological Organization. Sharma, R. K., Kumar, S., Vatta, K., Bheemanahalli, R., Dhillon, J., & Reddy, K. N. (2022). Impact of recent climate change on corn, rice, and wheat in the southeastern USA. Scientific Reports, 12, 16928. https://doi.org/10.1038/s41598-022-21454-3.
29. Stefanidis, S., Alexandridis, V., Spalevic, V., & Mincato, R. L. (2022). Wildfire Effects on Soil Erosion Dynamics: The Case of 2021 Megafires in Greece. Agriculture and Forestry, 68 (2): 49-63. doi:10.17707/AgricultForest.68.2.04.
30. Tuan, N. T., Jian-jun, Q. I. U., Verdoodt, A., Hu, L. I., & Van Ranst, E. (2011). Temperature and Precipitation Suitability Evaluation for the Winter Wheat and Summer Maize Cropping System in the Huang-Huai-Hai Plain of China. Agricultural Sciences in China, 10(2): 275-288
31. Vanli, Ö., Ustundag, B. B., Ahmad, I., Hernandez-Ochoa, I. M., & Hoogenboom, G. (2019). Using crop modeling to evaluate the impacts of climate change on wheat in southeastern turkey. Environmental Science and Pollution Research, 26, 29397–29408. https://doi.org/10.1007/s11356-019-06061-6.