Predicting the Effect of Climate Change on the Suitability of Canola (Brassica napus L.) Cultivation Land using SDSM and Lars-WG Models in Mazandaran Province

Document Type : Research Article

Authors

1 Department of Agriculture, Faculty of Plant Production, Gorgan University of Agriculture and Natural Resources, Iran

2 Deptment of Agrotechnology, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran

Abstract

Introduction
In recent years, the heightened concentration of greenhouse gases has brought increased attention to the pressing issue of climate change. Therefore, monitoring climatic variables to prevent the adverse effects of climate change is more important than ever. In the pursuit of long-term climatic forecasting and the assessment of their evolving patterns, various international scientific societies have concentrated their efforts on understanding the extent of climate change and devising measures to counter its adverse effects. The development of general circulation models of the atmosphere (GCM) has been a significant stride in this direction. However, GCMs may lack precision in predicting minor changes at a local scale. To address this limitation, the utilization of downscaling models such as SDSM and Lars_WG (that were used here) becomes imperative. These models serve as essential tools for simulating the viability of cultivating agricultural species in the future, especially when considering localized impacts. Because climate change will probably change the conditions for growing canola, as one of the strategic and prominent crops in Iran, studying the effects of this worldwide event on the canola-grown fields in the future is needed.
 
Materials and Methods
In this research, using temperature and precipitation forecasting models along with GIS functions and hierarchical analysis process (AHP), canola suitability classes for 2050 were determined in Mazandaran Province. For this, 37 meteorological and synoptic stations were involved, and climatic data (including temperatures and precipitation) were generated under three RCP scenarios (2.6, 4.5, 8.5). In this study, we utilized two general circulation models (Can-ESM2 and HadGEM2-ES) that had been recommended for application in the study area.
 
Results and Discussion
A comparison of the involved models showed that the SDSM model was superior in predicting temperature, while the Lars-WG model performed better in predicting precipitation. The results for the land suitability revealed that the changes in climatic variables in the future would lead to changing the suitability of agricultural lands for growing canola. The examination of temperature change maps in the investigated region revealed that both minimum and maximum temperature variables are poised to rise under climate change scenarios, with a more pronounced increase anticipated in maximum temperatures. The findings indicate that the projected temperature increase in the future will establish more conducive conditions for canola cultivation. Additionally, precipitation patterns exhibit an increase in both RCP 2.6 and 4 scenarios, with a more substantial rise in the RCP 2.6 scenario. Conversely, the RCP 8.5 scenario predicts a decline in precipitation levels. Top of Form
Also, considering the climate change scenarios, the spatial distribution and the area of each suitability class changed slightly, so the high-suitable class will extend under RCP2.6, especially toward the center parts of the study area. Under RCP 8.5 and RCP 4.5 scenarios, not only suitable lands were not considerable, but also the less suitable land extended to the southern and western parts of the study area.
 
Conclusion
The output of the land suitability maps showed that climate change would change the suitability of the studied agricultural lands in the future. Also, with the implementation of the climate change scenarios, the area and geographical distribution of detected classes will change. In general, in the optimistic scenario, the lands will be more appropriate for canola cultivation and will cover a wider area. In other scenarios, the conditions for canola cultivation in the lands of Mazandaran will be more unsuitable compared to the present, and the decrease in the level of suitability will be more evident in the western and southern lands of the province. Therefore, solutions such as the use of more compatible cultivars and changes in agricultural management in facing new conditions in these areas should be considered. The outcomes of these studies offer practical insights for shaping regional planting strategies, aiding decisions on crop inclusion or exclusion, and informing the overall design of agricultural patterns in the area

Keywords

Main Subjects


©2023 The author(s). This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source.

  1. Aguiar, F., Bentz, J., Silva, J., Fonseca, A., Swart, R., DuarteSantos, F., & Lopes, G. (2018). Adaptation to climate change at local level in Europe: An overview. Environmental Science and Policy, (86), 38-63. https://doi.org/10.1016/j.envsci.2018.04.010
  2. Ajamzadeh, A., & Mollainia, M. (2014). Comparison of performance of SDSM and Lars-WG microscopic methods Case study: Tangab Dam Station, Fars province, First National Environmental Conference, Isfahan, Iran. (In Persian with English Summary)
  3. Bidadi, M., Kamkar, B., Abdi, O., & Kazemi, H. (2015). Land suitability analysis on rainfed wheat cropping using geospatial information systems (A case study: Qaresoo basin). Journal of Agricultural Science (University of Tabriz), (25), 131-143. (In Persian with English Summary)
  4. Darzi Naftchali, A., Karandish, F. (2016). Rice Cultivation management in Mazandaran province under climate change. Formerly Soil and Water Science, (3), 333-346. (In Persian with English Summary)
  5. Everest,, Koparan, H., Sungur, A., & Ozcan, H., (2021). An important tool against combat climate change: Land suitability assessment for canola (a case study: Çanakkale, NW Turkey). Environment, Development and Sustainability, (171), 1-14. https://doi.org/10.1007/s10668-021-01985-x
  6. Favi, G.A., Dassou, G.H., Agounde, G., Marie-Ange, J., Ouachinou, S., Djidohokpin, D., Cossi Adomou, A., Yédomonhan, H., Tossou, G., & Akoègninou, A. (2022). Current and future distribution pet tern of Cochlospermum planchonii and Cochlospermum tinctoriumin Benin (West Africa), in response to climate change. scenarioModeling Earth Systems and Environment. (8), 773–786. https://doi.org/10.1007/s40808-021-01109-4
  7. Hajarpoor, A., Yousefi, M., & Kamkar, B. (2014). Accuracy Assessment Of Weat her Assimilators Of Climgen, Lars-Wg and Weet her Man In Assimilation Of Three Different Climatic Parameters Of Three Different Climate (Gorgan, Gonbad and Mashhad). Geography and Development, (35), 201-216. (In Persian with English Summary)
  8. Hashemi, S., Kiani, F. (2018). Qualitative Land Suitability Evaluation For Canola and Sugar Beet Cultivations With Fao Different Methods (Gyan Area, Hamadan Province). Applied Soil Research, (5), 16-30. (In Persian with English Summary)
  9. Hashemi, M., Shamseldin, A., & Melville, B. (2011). Comparison of SDSM and LARSWG for simulation and downscaling of extreme precipitation events in a watershed. Stochastic Environmental Research and Risk Assessment, (25), 475-484.
  10. Kazemi, H., Tahmasebi Sarvestani, Z., Kamkar, B., Shetaee, S., & Sadeghi, S. (2012). Agroecological Zoning Of Agricultural Lands In Golestan Province For Canola Cultivation By Geographic Information System (Gis) and Analytical Hierarchy Process (Ahp). Electronic Journal Of Crop Production, (5), 123-139.
  11. Kazemi, , Akinci, H. (2018). A land use suitability model for rained farming by Multi-criteria Decision-making Analysis (MCDA) and Geographic Information System (GIS). Ecological Engineering, (166), 1-6. https://doi.org/10.1016/j.ecoleng.2018.02.021
  12. Khan, M.S., Coulibaly, P., & Dibike, Y. (2016). uncertainty Analysis of statistical Downscaling Methods, Journal of Hydrology, (319), 357-382
  13. Khorshiddoust, A., Hosseini, A., & Mohammadpour, K. (2011). Suitable site selection for canola cultivation in Kurdestan province using geographical information system (Gis). Water and Soil Science, (21), 37-48. (In Persian with English Summary)
  14. Koocheki, A., Nassiri Mahalleti, M., and Jafari, L. (2015). Evaluation of climate change effect on agricultural production of Iran: I. Predicting the future agroclimatic conditions. Iranian Journal of Field Crops Research, (13), 651-664. (in Persian with English Summary)
  15. Makhdoom, M., Darvish Sefet, A., Jafarzadeh, H., & Makhdoom, A. (2011). Environmental evaluation and planning by geographic information system. University of Tehran Press, Iran. 304 p
  16. Martin, D., Saha, S.K. (2009). Land evaluation by integrating remote sensing and GIS for cropping system analysis in a watershed. Current Science, (96), 569-575.
  17. Mozaffari, G., Mir Mousavi, H., & Khosravi, M. (2012). The assessment of geostatistic methods and linear regression in order to specify the spatial distribution of annual precipitation case study: Boushehr province. Journal of Geography and Development, (27), 93-79. (In Persian with English Summary)
  18. Shahmoradi, I. (2011). Agro-climatic zonation of canola cultivation using AHP in GIS environment in Ilam province, M.Sc. Thesis in Natural Geography, Mohaghegh Ardabili University, Ardabil, Iran. (In Persian with English Summary)
  19. Sobhani, B., Roshanali, M. (2018). Land suitability assessment of Mazandaran Province for Canola Cultivation Based On Multi-Criteria Decision Making Evaluation Methods In GIS Environment. Geography and Territorial Spatial Arrangement, (8), 129-148. (In Persian with English Summary)
  20. Whitley, E., Ball, J. (2002). Statistics review 1: Presenting and summarising data. Crit Care, (6), 66–71. https://doi.org/1186/cc1455
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Volume 15, Issue 4 - Serial Number 58
December 2024
Pages 843-862
  • Receive Date: 01 June 2022
  • Revise Date: 25 September 2022
  • Accept Date: 01 October 2022
  • First Publish Date: 01 October 2022