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Forecasting the rice crop calendar in the northern regions of Iran with emphasis on climate change models

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Abstract

The impact of climate change and fluctuations in the production of agricultural products can affect food security. Rice, as a critical grain product in the north of Iran and especially in Mazandaran province, is also affected by these factors. This study was done to investigate the impact of climate change on the rice crop calendar. In this study, changes in climate variables were extracted based on CMIP6 models under the SSP scenario from 2021 to 2050 and compared to the base period (1985–2014) in different phonological stages. The results of the evaluation of observational and simulated data by linear scale bias correction (LSBC) show that the model accuracy differs in different stations. So that the highest and lowest accuracy of precipitation is between 4.3 and 12 mm, relative humidity between 1 and 3%, wind speed 0.1–0.2 m/s, maximum temperature between 0.1 and 0.9 °C, average temperature between 0.1 and 0. 7 °C, and the minimum temperature is between 0.1 and 0.5 °C, which indicates the high accuracy of this model. The prediction of climatic variables showed that the maximum, minimum, and average temperature, precipitation, and relative humidity in different stages of rice phenology will have significant changes in the future climate under the SSP scenario. The forecasting results of climatic variables show different behavior in phonological stages, so that, in SSP1-2.6 and SSP3-7.0 scenarios, mainly decreasing changes and SSP5-8.5 scenarios mainly increasing precipitation will occur. Meanwhile, changes in wind speed in all phonological stages and the entire growth period in future scenarios will not have significant changes compared to the base period; however, the significant increase of temperature variables will be evident in all phonological stages and scenarios compared to the base period, especially in the SSP5-8.5 scenario. Also, changing the planting date will change the length of the growth period and the amount of precipitation.

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Availability of data and material

The data used in this paper have been prepared by referring to Earth System Grid Federation (ESGF) from this link: https://esgf-node.llnl.gov/search/cmip6/.

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References

  • Anser MK, Hina T, Hameed S, Nasir MH, Ahmad I, Asad M (2020) Modeling adaptation strategies against climate change impacts in integrated rice-wheat agricultural production system of Pakistan. Int J Environ Res Publ Health 17(2):1–18

    Google Scholar 

  • Asaadi Oskoui A, Shokohi M, Mohammadpour Panchah M, Akbarzadeh Kashani A (2020) Development of meteorological recommendations system for rice farming in the northern regions of the country. Country Rice Research Institute. Rice Promotion Magaz 4:27–18

    Google Scholar 

  • Asaadi Oskoui A, Kozegran S, Yazdani M, Rahmani A (2021) The effect of different probability levels in estimating the net water requirement of rice in the northern provinces of Iran. Water Soil 79:671–659

    Google Scholar 

  • Bakhsandeh A, Fathi N, Pirdashti H, Nasiri M (2016) Effect of temperature and radiation intensity on yield and yield components of rice in Mazandaran climatic conditions. Beh Zra’i 1:163–176

    Google Scholar 

  • Bazgir S, Kamali G (2008) Prediction of dry farming of wheat yield using agricultural meteorological indices in some western regions of the country. J Agric Sci Natural Resour 64:113–121

    Google Scholar 

  • Behar O, Khellaf A, Mohammedi K (2015) Comparison of solar radiation models and their validation under Algerian climate–the case of direct irradiance. Energy Convers Manag 98:236–251

    Article  Google Scholar 

  • Boonwichai S, Shrestha S, Babel M, Weesakul S, Datta A (2019) Evaluation of climate change impacts and adaptation strategies on rainfed rice production in Songkhram River Basin, Thailand. Sci Environ 652(3):189–201

    Google Scholar 

  • Chauhan BS, Jabran K, Mahajan G (2017) Rice production worldwide. Springer, Switzerland

    Book  Google Scholar 

  • Chhogyel N, Kumar L, Bajgai Y, Sadeeka Jayasinghe L (2020) Prediction of Bhutan’s ecological distribution of rice (Oryza sativa L.) under the impact of climate change through maximum entropy modelling. J Agric Sci 158:25–37

    Article  Google Scholar 

  • Corbeels M, Berre D, Rusinamhodzi L, Lopez-Ridaura S (2018) Can we use crop modelling for identifying climate change adaptation options? Agric For Meteorol 256:46–52

    Article  Google Scholar 

  • Darzi Naftchali A, Karandish F (2015) Management of rice cultivation in Mazandaran province under climate change conditions. Water Res Agric 30(3):346–333

    Google Scholar 

  • de Sousa K, Casanoves F, Sellara J, Ospina A (2018) How climate awareness influences farmers adaptation decisions in Central America. J Rural Stud 64:11–19

    Article  Google Scholar 

  • Döll P (2002) Impact of climate change and variability on irrigation requirements: a global perspective. Clim Change 54(3):269–293

    Article  Google Scholar 

  • Eyshi Rezaei E, Gaiser T, Siebert S, Ewert F (2015) Adaptation of crop production to climate change by crop substitution. Mitig Adapt Strat Glob Change 20(7):1155–1174

    Article  Google Scholar 

  • Ferrer AJG, Kiet NT, Chuong PH, Trang VT, Hopanda JC, Carmelita BM, Gummadi S, Bernardo EB (2022) The impact of an adjusted cropping calendar on the welfare of rice farming households in the Mekong River Delta, Vietnam. Econ Anal Policy 73:639–652

    Article  Google Scholar 

  • Giorgi F (2010) Uncertainties in climate change projections, from the global to the regional scale. In EPJ Web Conf 9:115–129

    Article  Google Scholar 

  • Goudarzi M, Ahmadi H, Hosseini SA (2017) Examination of relationship between teleconnection indexes on temperature and precipitation components (Case Study: Karaj Synoptic Stations). Iran J Ecohydrol 4(3):1–25

    Google Scholar 

  • GRiSP (2013) Rice Almanac: Source Book for One of the Most Important Activities on Earth. Los Banos, Philippines: Global Rice Science Partnership (GRiSP). International Rice Research Institute (IRRI).

  • Grose MR, Narsey S, Delage FP, Dowdy AJ, Bador M, Boschat G, Chung C, Kajtar JB, Rauniyar S, Freund MB, Lyu K, Rashid H, Zhang X, Wales S, Trenham C, Holbrook NJ, Cowan T, Alexander L, Arblaster JM, Power S (2019) Insights from CMIP6 for Australia’s future climate. Earth’s Future 8(5):1–24

    Google Scholar 

  • Guan K, Sultan B, Biasutti M, Baron C, Lobell DB (2017) Assessing climate adaptation options and uncertainties for cereal systems in West Africa. Agric For Meteorol 232:291–305

    Article  Google Scholar 

  • Hejazizadeh Z, Hosseini SA, Karbalaee A, Poorkarim Barabadi R, Mousavi SM (2022) Spatiotemporal variations in precipitation extremes based on CMIP6 models and Shared Socioeconomic Pathway (SSP) scenarios over MENA. Arab J Geosci 15:160

    Article  Google Scholar 

  • Helali J, Momenzadeh H, Oskouei EA, Lotfi M, Hosseini SA (2021) Trend and ENSO-based analysis of last spring frost and chilling in Iran. Meteorol Atmos Phys 133(4):1203–1221

    Article  Google Scholar 

  • Helali J, Asaadi S, Jafarie T, Habibi M, Salimi S, Momenpour SE, Saeidi V (2022a) Drought monitoring and its effects on vegetation and water extent changes using remote sensing data in Urmia Lake watershed, Iran. J Water Clim Change 13(5):2107–2128

    Article  Google Scholar 

  • Helali J, Oskouei EA, Hosseini SA, Modirian R (2022b) Projection of changes in late spring frost based on CMIP6 models and SSP scenarios over cold regions of Iran. Theor Appl Climatol 149:1405–1418

    Article  Google Scholar 

  • Hoseini Tabesh S, Aghashriyatmadari Z (2019) The effect of climate change on rice irrigation needs under radiation forcing scenarios (case study: Anzali). Iran Water and Soil Res 51(10):2607–2621

    Google Scholar 

  • Houshyar M, Sobhani B, Hosseini SA (2018) Future projection of maximum temperature in Urmia through downscaling output of CanESM2 model. Geogr Plan 22(63):305–325

    Google Scholar 

  • IPCC (2013) Climate change 2013. The physical science basis. Contribution of working group I to the fifth assessment report of the Intergovernmental Panel on Climate Change (IPCC). Cambridge University, Cambridge.

  • Jones JW, Hoogenboom G, Porter CH, Boote KJ, Batchelor WD, Hunt LA, Ritchie JT (2003) The DSSAT cropping system model. Eur J Agron 18(3–4):235–265

    Article  Google Scholar 

  • Khorsandi (2007) Assessing the vulnerability and adaptability of the agriculture, animal husbandry and fisheries sectors to climate change in Iran.

  • Kochaki A, Nasiri Mahalati M, Mansouri H, Moradi R (2016) the effect of weather and management factors on the potential and gap of wheat yield in Iran using the WOFOST model. Iran Agric Res 2:256–244

    Google Scholar 

  • Liu Y, Wang S, Chen J, Chen B, Wang X, Hao D, Sun L (2022) Rice yield prediction and model interpretation based on satellite and climatic indicators using a transformer method. Remote Sensing 14(19):5045

    Article  Google Scholar 

  • Ma C, Iqbal M (1984) Statistical comparison of solar radiation correlations monthly average global and diffuse radiation on horizontal surfaces. Sol Energy 33:143–148

    Article  Google Scholar 

  • Majdi F, Hosseini SA, Karbalaee A, Kaseri M, Marjanian S (2022) Future projection of precipitation and temperature changes in the Middle East and North Africa (MENA) region based on CMIP6. Theoret Appl Climatol 147:1249–1262

    Article  Google Scholar 

  • McMaster GS, Wilhelm WW (1997) Growing degree-days: one equation, two interpretations. Agric For Meteorol 87(4):291–300

    Article  Google Scholar 

  • Mesgari E, Hosseini SA, Hemmesy MS, Houshyar M, Golzari PL (2022) Assessment of CMIP6 models’ performances and projection of precipitation based on SSP scenarios over the MENAP region. J Water Clim Change 13(10):3607–3619

    Article  Google Scholar 

  • Miller P, Lanier W, Brandt S (2001) Using growing degree days to predict plant stages. Ag/extension Communications Coordinator, Communications Services, Montana State University-Bozeman, Bozeman, MO 59717(406):994–2721

  • Mishra B, Busetto L, Boschetti M, Laborte A, Nelson A (2021) A rice crop calendar for Asia based on MODIS multiyear data. Int J Appl Earth Obs Geoinf 103:1024710

    Google Scholar 

  • Mohanty S, Wassmann R, Nelson A, Moya P, Jagadish SK (2013) Rice and climate change: significance for food security and vulnerability. International Rice Research Institute (IRRI), Los Banos, Philippines, p 14.

  • Mousavi Baygi M, Asadi Eskoui A, Yazdani M, Alizadeh A (2016) The comparison of temperature elements measured in station and in paddy filed. Water Soil Conserv Res 24(5):129–145

    Google Scholar 

  • Muthayya S, Sugimoto JD, Montgomery S, Maberly GF (2014) An overview of global rice production, supply, trade, and consumption. Ann N Y Acad Sci 1324:7–14

    Article  PubMed  Google Scholar 

  • Nagabhushanam U, Bhatt PS (2020) Effect of sowing dates and different crop establishment methods on yield and economics of rice (Oryza sativa L.). J Pharmacognosy Phytochem 9(2):1075–1079.

    Article  Google Scholar 

  • Nouri M, Homaee M, Bannayan M, Hoogenboom G (2017) Towards shifting planting date as an adaptation practice for rainfed wheat response to climate change. Agric Water Manag 186:108–119

    Article  Google Scholar 

  • Nyadzi E, Werners SE, Biesbroek R, Ludwig F (2022) Towards weather and climate services that integrate indigenous and scientific forecasts to improve forecast reliability and acceptability in Ghana. Environ Dev 42:100698

    Article  Google Scholar 

  • O’Neill BC et al (2016) The scenario model intercomparison project (Scenario MIP) for CMIP6. Geosci Model Dev 9:3461–3482

    Article  Google Scholar 

  • Osborne T, Rose G, Wheeler T (2013) Variation in the global-scale impacts of climate change on crop productivity due to climate model uncertainty and adaptation. Agric For Meteorol 170:183–194

    Article  Google Scholar 

  • Patel AR, Patel ML, Patel RK, Mote BM (2019) Effect of different sowing date on phenology, growth and yield of rice—a review. Plant Arch 19(1):12–16

    Google Scholar 

  • Petzold J, Andrews N, Ford JD, Hedemann C, Postigo JC (2020) Indigenous knowledge on climate change adaptation: a global evidence map of academic literature. Environ Res Lett 15(11):113007

    Article  Google Scholar 

  • Portalanza D, Horgan FG, Pohlmann V, Vianna Cuadra S, Torres-Ulloa M, Alava E, Ferraz S, Durigon A (2022) Potential impact of future climates on rice production in Ecuador determined using kobayashi’s ‘very simple model.’ Agriculture 12(11):18–28

    Article  Google Scholar 

  • Ray DK, West PC, Clark M, Gerber JS, Prishchepov AV, Chatterjee S (2019) Climate change has likely already affected global food production. PLoS ONE 14:e0217148

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Sabziparvar AA, Torkman M, Marianaji Z (2012) Investigating the effect of agricultural meteorological indicators and variables on the best performance of wheat (Case Study: Hamedan Province). Water Soil J (Agricultural Sciences and Technology) 26:1554–1567

    Google Scholar 

  • Sadidi Shall SMT, Asadi Eskoui A, Zohad Qodsi MJ, Amin Deldar Z (2021) Comparison of the degree of growth day of different phenological stages of Hashemi variety rice in Gilan province. Climatol Res 45:143–152

    Google Scholar 

  • Salahi B, Goudarzi M, Hosseini SA (2016) Predicting the temperature and precipitation changes during the 2050s in Urmia Lake Basin. Watershed Eng Manage 8(4):425–438

    Google Scholar 

  • Seif-zadeh Momen-saraei AR, Sabzi-Parvar AAK (2021) Optimization of rice (Oryza sativa L.) and second soybean (Glycine max) cultivation calendar under climate change conditions using Dynamic models of atmospheric general circulation and DSSAT plant model. J Agric Sci Iran 23(4):357–372

  • Shidayan M, Zia-Tabar Ahmadi M, Fazl Oli R (2015) The effect of climate change on the net irrigation requirement and rice yield (case study: Tajen Plain). Water Soil 28(6):1284–1297

    Google Scholar 

  • Shimoda S, Kanno H, Hirota T (2018) Time series analysis of temperature and precipitation-based weather aggregation reveals significant correlations between climate turning points and potato (Solanum tuberosum L) yield trends in Japan. Agric For Meteorol 263:147–155

    Article  Google Scholar 

  • Shrestha M, Acharya SC, Shrestha PK (2017) Bias correction of climate models for hydrological modelling–are simple methods still useful? Meteorol Appl 24(3):531–539

    Article  Google Scholar 

  • Sperna Weiland FC, Tisseuil C, Dürr HH, Vrac M, Van Beek LPH (2012) Selecting the optimal method to calculate daily global reference potential evaporation from CFSR reanalysis data for application in a hydrological model study. Hydrol Earth Syst Sci 16(3):983–1000

    Article  Google Scholar 

  • Teutschbein C, Seibert J (2012) Bias correction of regional climate model simulations for hydrological climate-change impact studies: review and evaluation of different methods. J Hydrol 456:12–29

    Article  Google Scholar 

  • Trisurat Y, Aekakkararungroj A, Ma HO, Johnston JM (2018) Basin-wide impacts of climate change on ecosystem services in the lower Mekong basin. Ecol Res 33:73–86

    Article  PubMed  PubMed Central  Google Scholar 

  • Waongo M, Laux P, Traoré SB, Sanon M, Kunstmann H (2014) A crop model and fuzzy rule based approach for optimizing maize planting dates in Burkina Faso, West Africa. J Appl Meteorol Climatol 53(3):598–613

    Article  Google Scholar 

  • Wilby RL, Dawson CW (2013) The statistical downscaling model: insights from one decade of application. Int J Climatol 33(7):1707–1719

    Article  Google Scholar 

  • Willmott CJ, Matsuura K (2006) On the use of dimensioned measures of error to evaluate the performance of spatial interpolators. Int J Geogr Inf Sci 20:89–102

    Article  Google Scholar 

  • Willmott CJ, Matsuura K, Robeson SM (2009) Ambiguities inherent in sums-of-squares-based error statistics. Atmos Environ 43:749–752

    Article  CAS  Google Scholar 

  • Wu W, Duncan RW, Ma BL (2017) Quantification of canola root morphological traits under heat and drought stresses with electrical measurements. Plant Soil 415(1):229–244

    Article  CAS  Google Scholar 

  • Yoshida S (1981) Fundamentals of Rice Crop Science. Los Banos, Philippines: International Rice Research Institute.

  • Zamani P (2019) Statistical designs in animal science, with SAS software training, Bu-Ali Sina University Press

  • Zhang Z, Zhang H, Xu E (2022) Enhancing the digital mapping accuracy of farmland soil organic carbon in arid area using agricultural land use history. J Clean Prod 334:13–23

    Article  Google Scholar 

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Acknowledgements

The authors of the present paper are grateful to Earth System Grid Federation (ESGF) and Islamic Republic of Iran Meteorological Organization (IRIMO) for providing the data needed to conduct this research.

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The authors received no financial support for the research, authorship, and/or publication of this article.

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Authors and Affiliations

  1. Department of Earth Sciences, College of Science and Converging Technologies, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Anahita Khairkhah, Gholamali Kamali, Amir Hossein Meshkatei & Hossein Babazadeh

  2. Atmospheric Science and Meteorology Research Center (ASMERC), Tehran, Iran

    Ebrahim Asadi Oskouei

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  1. Anahita Khairkhah
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  2. Gholamali Kamali
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  3. Amir Hossein Meshkatei
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  4. Hossein Babazadeh
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Contributions

AK wrote the main manuscript text. GK and AHM conceived of the presented idea and developed the theory and performed the computations. HB and EAO verified the analytical methods and supervised the findings of this work. All authors discussed the results and contributed to the final manuscript.

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Correspondence to Gholamali Kamali.

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Khairkhah, A., Kamali, G., Meshkatei, A.H. et al. Forecasting the rice crop calendar in the northern regions of Iran with emphasis on climate change models. Paddy Water Environ 22, 41–60 (2024). https://doi.org/10.1007/s10333-023-00951-9

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