نوع مقاله : مقاله پژوهشی
نویسندگان
1 دانشجوی دکتری فیزیک و حفاظت خاک، گروه خاک‌شناسی، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران
2 استاد پژوهشکده حفاظت خاک و آبخیزداری، سازمان تحقیقات آموزش و ترویج کشاورزی، تهران، ایران
3 استاد گروه خاک‌شناسی، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران
چکیده
پدیده سرمازدگی منجر به خسارات جبران‌ناپذیری به شالیزارهای استان گیلان می‌شود. در کشورهای درحال‌توسعه مانند ایران، ایستگاه‌های هواشناسی معمولاً پراکنده هستند به همین دلیل از سنجنده و حسگرهایی مانند MODIS که به‌صورت رایگان در دسترس هستند استفاده می‌کنند که کمک شایانی به کمبود توزیع ایستگاه‌های زمینی می‌کند. همچنین با استفاده از مدل‌های درون‌یابی، می‌توان مشاهدات پراکنده را به نقشه‌های پیش‌بینی پیوسته در کل منطقه موردمطالعه تبدیل کرد. هدف از انجام این پژوهش بررسی خطر سرمازدگی در مراحل مختلف رشد گیاه برنج با بهره‌گیری از فناوری سنجش‌ازدور و تصاویر ماهواره MODIS در استان گیلان می‌باشد. در این پژوهش از بین محصولات سنجنده مودیس(MODIS) از داده‌های دمای روزانه سطح زمین (LST) با کد MOD11A1 استفاده شد و مختصات ایستگاه‌های هواشناسی استان گیلان و داده‌های موردنیاز از این ایستگاه‌ها طی دوره آماری 2000 تا 2017 از اداره هواشناسی اخذ گردید. تعیین دوره رشد گیاه برنج بر اساس اطلاعات و آمار استان انجام شد و با استفاده از تصاویر سنجده MODIS و با توجه به حد بحرانی دما که کمتر از آن گیاه دچار تنش سرما می‌شود، نقشه‌های پهنه‌بندی خطر سرمازدگی در نرم‌افزار ArcGIS، باتوجه‌به فراوانی و تعداد روزهایی که خطر سرمازدگی در هر مرحله رشد گیاه در منطقه رخ‌داده بود با روش درون‌یابی IDW تهیه گردید. پنج نوع کلاس متفاوت برای خطر سرمازدگی برنج تحت عنوان بسیار کم، کم، متوسط، زیاد و بسیار زیاد در نظر گرفته شد. بر اساس نقشه‌های پهنه‌بندی خطر، رخ داد سرمازدگی طی 17 سال مورد بررسی قرار گرفت؛ و نشان داد که بیشترین خطر سرمازدگی در سطح کل استان مربوط به ارتفاعات 1000 تا 2000 متر بوده و در مرحله جوانه‌زنی و زایشی میزان این خطر زیاد و خیلی زیاد است و هر چه که به دریا نزدیک‌تر می‌شویم میزان خطر در همه مراحل رشد حتی در مراحل جوانه‌زنی و زایشی کم و خیلی کم است.
کلیدواژه‌ها
Ahmed, K., Shahid, S., Harun, S.B., 2014. Spatial interpolation of climatic variables in a predominantly arid region with complex topography. Environment Systems and Decisions, 34, 555–563. https://doi.org/10.1007/s10669-014-9519-0
Ahuja, I., de Vos, R.C., Bones, A.M., Hall, R.D., 2010. Plant molecular stress responses face climate change. Trends in Plant Science, 15, 664–674.
Andaya, V.C., Mackill, D.J., 2003. Mapping of QTLs associated with cold tolerance during the vegetative stage in rice. Journal of Experimental Botany, 54, 2579–2585. https://doi.org/10.1093/jxb/erg243
Antal, A., Guerreiro, P.M.P., Cheval, S., 2021. Comparison of spatial interpolation methods for estimating the precipitation distribution in Portugal. Theoretical and Applied Climatology, 145, 1193–1206. https://doi.org/10.21203/rs.3.rs-329689/v1
Arnous, M.O., Omar, A.E., 2018. Hydrometeorological hazards assessment of some basins in Southwestern Sinai area, Egypt. Journal of Coastal Conservation, 22, 721–743. https://doi.org/10.1007/s11852-018-0604-2
Azizian, A., Shokoohi, A., 2015. Investigation of the Effects of DEM Creation Methods on the Performance of a Semidistributed Model: TOPMODEL. Journal of Hydrologic Engineering, 20, 05015005. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001204
Becker, C.C., Streck, N.A., Schwab, N.T., Uhlmann, L.O., Tomiozzo, R., Ferraz, S.E.T., 2021. Climate risk zoning for gladiolus production under three climate change scenarios. Brazilian Journal of Agricultural and Environmental Engineering., 25, 297–304. https://doi.org/10.1590/1807-1929/agriambi.v25n5p297-304
Belal, A.-A., El-Ramady, H.R., Mohamed, E.S., Saleh, A.M., 2014. Drought risk assessment using remote sensing and GIS techniques. Arabian Journal of Geosciences, 7, 35–53. https://doi.org/10.1007/s12517-012-0707-2
Bhunia, G.S., Shit, P.K., Maiti, R., 2018. Comparison of GIS-based interpolation methods for spatial distribution of soil organic carbon (SOC). Journal of the Saudi Society of Agricultural Sciences, 17, 114–126. https://doi.org/10.1016/j.jssas.2016.02.001
Caldana, N.F.da S., Nitsche, P.R., Ferreira, L.G.B., MartelÃ3cio, A.C., Caramori, P.H., Zaccheo, P.V.C., Martins, J.A., 2020. Agroclimatic risk zoning of mango (Mangifera indica) in the hydrographic basin of Paran River III, Brazil. African Journal of Agricultural Research, 16, 983–991. https://doi.org/10.5897/AJAR2020.14737
Carvalho, K.S., Wang, S., 2019. Characterizing the Indian Ocean sea level changes and potential coastal flooding impacts under global warming. Journal of Hydrology, 569, 373–386. https://doi.org/10.1016/j.jhydrol.2018.11.072
Chauhan, Y.S., Allard, S., Krosch, S., Ryan, M., Rachaputi, R.C.N., 2022. Relationships of frequencies of extreme low temperatures with grain yield of some Australian commercial chickpea cultivars. International Journal of Biometeorology, 66, 2105–2115. https://doi.org/10.1007/s00484-022-02344-9
Cheng, Y., Huang, J., Han, Z., Guo, J., Zhao, Y., Wang, X., Guo, R. 2013. Cold damage risk assessment of double cropping rice in Hunan, China. Journal of Integrative Agriculture, 12, 352–363.
Crimp, S., Bakar, K.S., Kokic, P., Jin, H., Nicholls, N., Howden, M., 2015. Bayesian space-time model to analyse frost risk for agriculture in Southeast Australia: SPACE-TIME MODEL TO ANALYSE FROST RISK. International Journal of Climatology, 35, 2092–2108. https://doi.org/10.1002/joc.4109
Cruz, R.P. da, Sperotto, R.A., Cargnelutti, D., Adamski, J. M., de FreitasTerra, T., Fett, J.P., 2013. Avoiding damage and achieving cold tolerance in rice plants. Food and Energy Security, 2, 96–119. https://doi.org/10.1002/fes3.25
Davatgar, N., Shahdi Koomleh, A., Amiri Larijani, B., Tarang, A.R., Padasht, F., Majidi, F., Mohammadian, M., Fallah, A., Farzaneh, D., Azadpeyma, V.A., Karbalaei, M.T., Guilani, A.A., Babazadeh, S., Yaghoubi, B., Nasiri, M., Allahgholipour, M., Dorosti, H., Sodaei, S., Mousanejad, S., 2012. Guidelines for assessing damage by separation of management and natural factors in different stages of rice growth. Research project number 40091 dated 19/02/2012 Agriculture information and science document center in Areeo (Agriculture research education extention organizetion) [In Persian].
Elumalai, V., Brindha, K., Sithole, B., Lakshmanan, E., 2017. Spatial interpolation methods and geostatistics for mapping groundwater contamination in a coastal area. Environmental Science and Pollution Research, 24, 11601–11617. https://doi.org/10.1007/s11356-017-8681-6
Emamifar, S., Rahimikhoob, A., Noroozi, A.A., 2013. Daily mean air temperature estimation from MODIS land surface temperature products based on M5 model tree: DAILY MEAN AIR TEMPERATURE ESTIMATION FROM MODIS. International Journal of Climatology, 33, 3174–3181. https://doi.org/10.1002/joc.3655
Fallah, A., Miarostami, P., 2015. Effect of temperature treatments on growth stages and yield of rice varieties in greenhouse. Applied Field Crops Research, 28, 94–103. [In Persian with English summary]. https://doi.org/10.22092/aj.2015.105728
Garnero, G., Godone, D., 2014. Comparisons Between Different Interpolation Techniques. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-5-W3, 139–144. https://doi.org/10.5194/isprsarchives-XL-5-W3-139-2013
Gharachorloo, M., Zulfiqar, A., Bayat, M. H., Bahrami, F., 2019. Arsenic Tracking in Iranian Rice: Analysis of Agricultural Soil and Water, Unpolished Rice and White Rice. Journal of Food Biosciences and Technology, 9, 19–34.
Ghorbani, A., Zarinkamar, F., Fallah, A., 2011. Effect of cold stress on the anatomy and morphology of the tolerant and sensitive cultivars of rice during germination. Cell and Tissue Journal, 2, 235–244. [In Persian with English summary]. https://doi.org/10.52547/JCT.2.3.235
Gobbett, D. L., Nidumolu, U., Crimp, S., 2020. Modelling frost generates insights for managing risk of minimum temperature extremes. Weather and Climate Extremes, 27, 100176. https://doi.org/10.1016/j.wace.2018.06.003
Gross, B.L., Zhao, Z., 2014. Archaeological and genetic insights into the origins of domesticated rice. Proceedings of the National Academy of Sciences, 111, 6190–6197. https://doi.org/10.1073/pnas.1308942110
Huang, F., Liu, D., Tan, X., Wang, J., Chen, Y., He, B., 2011. Explorations of the implementation of a parallel IDW interpolation algorithm in a Linux cluster-based parallel GIS. Computers and Geosciences,.
Jalili Pirani, F., Modarres, R., 2020. Geostatistical and deterministic methods for rainfall interpolation in the Zayandeh Rud basin, Iran. Hydrological Sciences Journal. 65, 2678–2692. https://doi.org/10.1080/02626667.2020.1833014
Ji, R., Yu, W., Feng, R., Wu, J., Zhang, Y., 2021. Identification and characteristics of combined agrometeorological disasters caused by low temperature in a rice growing region in Liaoning Province, China. Scientific Reports, 11, 9968. https://doi.org/10.1038/s41598-021-89227-y
Kayess, M.O., Hassan, M.M., Nurhasan, M., Ahmed, K., 2020. Effect of Low Temperature on Chlorophyll and Carotenoid Content on the Seedlings of Some Selected Boro Rice Varieties. American Journal of Plant Sciences, 11, Article 2. https://doi.org/10.4236/ajps.2020.112010
Keshtkar, A.R., Moazami, N., Afzali, A., 2021. Assessment of spatial interpolation techniques for drought severity analysis in Iran’s Salt Lake Basin. Desert, 26. https://doi.org/10.22059/jdesert.2021.305618.1006786
Kimura, K., Kudo, K., Maruyama, A., 2021. Spatiotemporal distribution of the potential risk of frost damage in tea fields from 1981-2020: A modeling approach considering phenology and meteorology. Journal of Agricultural Meteorology, 77, 224–234. https://doi.org/10.2480/agrmet.D-21-00011
Lagrini, K., Ghafiri, A., Ouali, A., Elrhaz, K., Feddoul, R., Elmoutaki, S., 2020. Application of geographical information system (GIS) for the development of climatological air temperature vulnerability maps: An example from Morocco. Meteorological Applications, 27, e1871. https://doi.org/10.1002/met.1871
Lakra, N., Soni, A., Munjal, R., 2020. Biotechnological approaches to develop rice tolerance to low and high temperature stress. Rice Research for Quality Improvement: Genomics and Genetic Engineering: 1, 549–578. https://doi.org/10.1007/978-981-15-4120-9_23
Li, S., Wang, Z., Huang, J. 2018. Evaluation of Tea Frost Risk in Zhejiang Province Based on GIS. 7th International Conference on Agro-Geoinformatics (Agro-Geoinformatics) IEEE, 2018. 1–4. https://doi.org/10.1109/Agro-Geoinformatics.2018.8476062
Liang, C., Qiao-jun, L.O.U., Zong-xiu, S.U.N., Yong-zhong, X., Xin-qiao, Y.U., Li-jun, L.U. O., 2006. QTL mapping of low temperature on germination rate of rice. Rice Science, 13, 93.
Liu, X., Zhang, Z., Shuai, J., Wang, P., Shi, W., Tao, F., Chen, Y., 2013. Impact of chilling injury and global warming on rice yield in Heilongjiang Province. Journal of Geographical Sciences, 23, 85–97. https://doi.org/10.1007/s11442-013-0995-9
Long, J., Liu, Y., Xing, S., Zhang, L., Qu, M., Qiu, L., Huang, Q., Zhou, B., Shen, J., 2020. Optimal interpolation methods for farmland soil organic matter in various landforms of a complex topography. Ecological Indicators, 110, 105926. https://doi.org/10.1016/j.ecolind.2019.105926
Lou, W., Zhao, Y., Huang, X., Zhu, T., Yang, M., Deng, S., Zhou, Z., Zhang, Y., Sun, Q., Chen, S., 2023. Frost risk assessment based on the frost-induced injury rate of tea buds: A case study of the Yuezhou Longjing tea production area, China. European Journal of Agronomy, 147, 126839. https://doi.org/10.1016/ j.eja.2023.126839
Masoodian, S.A., Keikhosravi Kiany, M.S., 2020. Trend analysis of snow accumulation season start in Iran using remote sensing data. Geography and Environmental Planning, 31, 1–14. [In Persian with English summary]. https://doi.org/10.22108/gep.2020.120775.1249
Mosleh, M., Hassan, Q., Chowdhury, E., 2015. Application of remote sensors in mapping rice area and forecasting its production: A review. Sensors, 15, 769–791. https://doi.org/10.3390/s150100769
Navabi, M., Allahgholipoor, M., 2014. Rice phenological stages. Ministry of Agriculture-Jahad Agricultural Research, Education and Extension Organization Rice Research Institue of Iran. Research project number 88/329 dated 2014 Agriculture information and science document center in Areeo (Agriculture research education extension organization). [In Persian].
Najeeb, S., Mahender, A., Anandan, A., Hussain, W., Li, Z., Ali, J., 2021. Genetics and Breeding of Low-Temperature Stress Tolerance in Rice. Rice Improvement: Physiological, Molecular Breeding and Genetic Perspectives. 221–280. https://doi.org/10.1007/978-3-030-66530-2_8
Nosrati, M., Barghi, H., Ghanbari, Y. 2022. Changing the cultivation pattern and its effect on the structure of stable economy (Case study: Rural areas of Gilan province). Geography and Environmental Sustainability. 12, 109-125. [In Persian with English summary]. https://doi.org/10.22126/GES.2022.7432.2498
Pachecoy, M.I., Ramirez, I.A., Marín, A., Pontaroli, A.C., 2014. Assessment of cold tolerance at early developmental stages and allelic variation at candidate genes in South American rice germplasm. Euphytica, 197, 423–434. https://doi.org/10.1007/s10681-014-1078-4
Pandi, H., Asadi Kapourchal, S., Vazifedoust, M., Rezaei, M., 2020. Simulation of rice yield and its components using SWAP model and remote sensing technology for optimal use of water and soil. Environment and Water Engineering, 6, 374–387. https://doi.org/10.22034/jewe.2020.242119.1398
Piri, I., Khanamani, A., Shojaei, S., Fathizad, H., 2017. Determination of the best geostatistical method for climatic zoning in Iran. Applied Ecology and Environmental Research, 15, 93–103.
Ranawake, A. L., Manangkil, O. E., Yoshida, S., Ishii, T., Mori, N., Nakamura, C., 2014. Mapping QTLs for cold tolerance at germination and the early seedling stage in rice (Oryza sativa L.). Biotechnology and Biotechnological Equipment. 28, 989–998. https://doi.org/10.1080/13102818.2014.978539
Robinson, T.P., Metternicht, G., 2006. Testing the performance of spatial interpolation techniques for mapping soil properties. Computers and Electronics in Agriculture, 50, 97–108. https://doi.org/10.1016/j.compag.2005.07.003
Saadat, M., Hasanlou, M., Homayouni, S., 2019. Rice crop mapping using sentinel-1 time series images (Case study: Mazandaran, Iran). The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-4-W18, 897–904. https://doi.org/10.5194/isprs-archives-XLII-4-W18-897-2019
Sarangi, S.K., Maji, B., Singh, S., Sharma, D.K., Burman, D., Mandal, S., Singh, U.S., Ismail, A. M., Haefele, S.M., 2016. Using improved variety and management enhances rice productivity in stagnant flood -affected tropical coastal zones. Field Crops Research. 190, 70–81. https://doi.org/10.1016/j.fcr.2015.10.024
Selvaraj, M. G., Ishizaki, T., Valencia, M., Ogawa, S., Dedicova, B., Ogata, T., Yoshiwara, K., Maruyama, K., Kusano, M., Saito, K., Takahashi, F., Shinozaki, K., Nakashima, K., Ishitani, M., 2017. Overexpression of an Arabidopsis thaliana galactinol synthase gene improves drought tolerance in transgenic rice and increased grain yield in the field. Plant Biotechnol J., 15, 1465–1477. https://doi.org/10.1111/pbi.12731
Sheng, J., Yu, P., Zhang, H., Wang, Z., 2021. Spatial variability of soil Cd content based on IDW and RBF in Fujiang River, Mianyang, China. Journal of Soils and Sediments, 21, 419–429. https://doi.org/10.1007/s11368-020-02758-1
Singha, M., Sarmah, S., 2019. Incorporating crop phenological trajectory and texture for paddy rice detection with time series MODIS, HJ-1A and ALOS PALSAR imagery. European Journal of Remote Sensing. 52, 73–87.
Suzuki, K., Nagasuga, K., Okada, M., 2008. The chilling injury induced by high root temperature in the leaves of rice seedlings. Plant & Cell Physiology, 49, 433–442. https://doi.org/10.1093/pcp/pcn020
Torabi Golsefidi, H., Givi, J., Karimian Eghbal, M. 2005. Land evaluation of paddy soils by FCC and parametric methods and their comparisons, in eastern Gilan Province. Pajouhesh-va-Sazandegi. 18, 28–31. [In Persian with English summary].
Van Mierlo, C., Faes, M.G.R., Moens, D., 2021. Inhomogeneous interval fields based on scaled inverse distance weighting interpolation. Computer Methods in Applied Mechanics and Engineering, 373, 113542. https://doi.org/10.1016/j.cma.2020.113542
Wan, Z., 2006. MODIS land surface temperature products users’ guide. ICESS, University of California Santa Barbara, CA, USA. 805, 7
Wang, S., Huang, G.H., Lin, Q.G., Li, Z., Zhang, H., Fan, Y.R., 2014. Comparison of interpolation methods for estimating spatial distribution of precipitation in Ontario, Canada. International Journal of Climatology, 34, 3745–3751. https://doi.org/10.1002/joc.3941
Wang, Z., Wang, J., Wang, F., 2009. Genetic control of germination ability under cold stress in rice. Rice Science, 16(3), 173–180. https://doi.org/10.1016/S1672-308(08)60076-1.
Xiong, Q., Deng, Y., Zhong, L., He, H., Chen, X. 2018. Effects of drought-flood abrupt alternation on yield and physiological characteristics of rice. International Journal of Agriculture and Biology, 20, 1107–1116. https://doi.org/10.17957/IJAB/15.0609
Xu, C., Qu, J. J., Hao, X., Zhu, Z., Gutenberg, L., 2020. Surface soil temperature seasonal variation estimation in a forested area using combined satellite observations and in-situ measurements. International Journal of Applied Earth Observation and Geoinformation, 91, 102156. https://doi.org/10.1016/j.jag.2020.102156
Xu, J., Guga, S., Rong, G., Riao, D., Liu, X., Li, K., Zhang, J., 2021a. Estimation of Frost Hazard for Tea Tree in Zhejiang Province Based on Machine Learning. Agriculture. 11, 607. https://doi.org/10.3390/agriculture11070607
Xu, J., Guga, S., Rong, G., Riao, D., Liu, X., Li, K., Zhang, J., 2021b. Estimation of Frost Hazard for Tea Tree in Zhejiang Province Based on Machine Learning. Agriculture. 11, 607. https://doi.org/10.3390/agriculture11070607
Xu, S., Yang, X., Sun, R., Fu, S., Liang, H., Chen, L., 2018. Cold wave climate characteristics and risk zoning in Jilin Province. Journal of Geoscience and Environment Protection, 6, 38–51. https://doi.org/10.4236/gep.2018.68004
Ye, T., Zong, S., Kleidon, A., Yuan, W., Wang, Y., Shi, P., 2019. Impacts of climate warming, cultivar shifts, and phenological dates on rice growth period length in China after correction for seasonal shift effects. Climatic Change, 155, 127–143.
Yoshida, S., 1981. Fundamentals of Rice Crop Science. International Rice Research Institute.1- 278.
Yue, Y., Zhou, Y., Wang, J., Ye, X., 2016. Assessing Wheat Frost Risk with the Support of GIS: An Approach Coupling a Growing Season Meteorological Index and a Hybrid Fuzzy Neural Network Model. Sustainability. 8, 1308. https://doi.org/10.3390/su8121308
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