Impact of climate change on water requirement, water productivity, and rice yield using risk analysis

Document Type : Research Paper

Authors

1 PhD student in irrigation and Drainage, Water Engineering Department, Sari Agricultural Sciences and Natural Resources University, Sari, Iran.

2 Faculty of Agriculture, Professor, Water Engineering Department, Sari Agricultural Sciences and Natural Resources University, Sari, Iran

3 Faculty of Agriculture, Associate Professor, Water Engineering Department, Sari Agricultural Sciences and Natural Resources University, Sari, Iran

4 Department of Agricultural and Biological Engineering, University of Florida, Gainesville, Florida 32611–0570, US.

Abstract

Climate change means any specific change in the long-term average weather state that occurs for a given location or for the entire globe (Goudarzi and Koupaei,­ 2020). Climate change is one of the most critical factors threatening food security, and it is expected to make food and nutrition security more challenging in the future (Carpena et al., 2019). It will affect the agricultural sector by changing the irrigation water requirements, crop yield, and water productivity (Boonwichai et al., 2018; Liu et al., 2019). Rice is the third most important crop in the world, following wheat and maize (Kapela et al., 2020). The occurrence of water shortages and droughts have raised concerns about the sustainability of rice production, including the main rice cultivation production region of Mazandaran in Iran (Yosefian 2018). Most studies have reported that rice production will decrease in the future due to a projected increase in temperature and a projected decrease in precipitation) Basak et al. 2010; Boonwichai et al. 2018; Nasir et al. 2020; Nicolas et al. 2020). Although many farmers, particularly in Iran, feel that permanent flooding conditions for rice farming are inevitable, climate change forces the use of water-saving technologies to ensure the long-term viability of irrigated rice production in paddy fields (Yosefian 2018; Mirfenderski 2022). Accordingly, it is necessary to find new methods for rice cultivation that reduce water use and make optimal use of the available water for irrigation while maintaining yield under climate change. The goal of this study was to examine the water requirement, water productivity, and risk of rice yield for different irrigation levels under various climate change scenarios using a crop simulation model.   

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Main Subjects

References

  • Acharjee T. K. Ludwig F. van Halsema G. Hellegers P and Supit I,2017. Future changes in water requirements of Boro rice in the face of climate change in North-West Bangladesh Agricultural water Management, 194, 172-183. Doi:10.1016/j.agwat.2017.09.008.

 

  • Alizadeh, A., 1999. The relationship between water, soil and plant. University of Emam Reza. (In Persian).

  

  • Allen RG, Pereira LS, Raes D and Smith M.,1998. Crop evapotranspiration: guidelines for computing crop water requirements. FAO irrigation and drainage paper 56. FAO, Rome

 

  • Araya A, Kisekka I, Gowda PH, Prasad PVV.,2017. Evaluation of water-limited cropping systems in a semi-arid climate using DSSAT-CSM. Syst 150:86-90. Doi: 10.1016/j.agsy.2016.10.007.

 

  • Arshad A, Zhang Z, Zhang W and Gujree I., 2019. Long-term perspective changes in crop irrigation requirement caused by climate and agriculture land use changes in rechna doab, Water, 11(8), 1567. Doi: 10.3390/w11081567.

 

  • Asadi, M., 2005. Introducing a new rice irrigation method in China. Dryness and drought promotional Scientific Quarterly in Agriculture.­ (21), 13-17. (In Persian)

 

  • Ashraf, B., Alizadeh, A., Musavi, M. and Banayan M., 2014. Evaluation of temperature and precipitation data simulated by individual and group execution of five AOGCM models for the northeastern region of Iran. Journal of Water and Soil. 28 (2), 253-266. (In Persian)

 

  • Boonwichai, S., Shrestha, S., Babel, M.S., Weesakul, S. and Datta, A., 2018. Climate change impacts on irrigation water requirement, crop water productivity and rice yield in the Songkhram River Basin, Thailand. Journal of Cleaner Production, 198, pp.1157-1164.doi: 10.1016/j.jclepro.2018.07.146.

 

  • Buddhaboon, C., Jintrawet, A. and Hoogenboom, G., 2018. Methodology to estimate rice genetic coefficients for the CSM-CERES-Rice model using GENCALC and GLUE genetic coefficient estimators. The Journal of Agricultural Science, 156(4), pp.482-492.156 (4):1-11. DOI:1017/S0021859618000527.

 

  • Charles A, Bertrand T, Elodie F and Harry H.,2013. Analog downscaling of seasonal rainfall forecasts in the Murray Darling basin. Mon. Weather Rev. 141: 1099– doi: 10.1175/MWR-D-12-00098.1.

 

  • Chylek, P., Li, J., Dubey, M.K., Wang, M. and Lesins, G.J.A.C., 2011. Observed and model simulated 20th century Arctic temperature variability: Canadian earth system model CanESM2. Atmospheric Chemistry and Physics Discussions, 11(8), pp.22893-22907. Doi: 10.5194/acpd-11-22893-2011.

 

  • Darand, M. and Hamidi, S., 2021. Projected changes in Iran temperature based on different scenarios RCP. Journal of Natural Environmental Hazards, 10(28), pp.85-106. (In Persian).

 

  • Darikandeh, D., Shahnazari, A., Khoshravesh, M., Hoogenboom, G., 2022­.Evaluating rice yield and adaptation strategies under climate change based on the CSM-CERES-Rice model: a case study for northern Iran. Theor Appl Climatol . doi: 10.1007/s00704-022-04188-0.

 

  • Devkota, K.P., Manschadi, A.M., Devkota, M., Lamers, J.P.A., Ruzibaev, E., Egamberdiev, O., Amiri, E. and Vlek, P.L.G., 2013. Simulating the impact of climate change on rice phenology and grain yield in irrigated drylands of Central Asia. Journal of Applied Meteorology and Climatology, 52(9), pp.2033-2050. Doi: 10.1175/JAMC-D-12-0182.1.

 

  • Doorenbos J and KassamA. H., 1979. Yield response to water. Irrigation and drainage paper, 33, 257. Easterling W. E, 2007. Climate change and the adequacy of food and timber in the 21st century. Proceedings of National Academy of Sciences 104: 19679.

 

  • Gao L, Jin Z, Huang Y and Zhang L.,1992. Rice clock model: a computer model to simulate rice development, Agricultural and Forest Meteorology. 60.1–2: 1–16. Doi:10.1016/0168-1923(92)90071-B.

 

  • Gholizadeh, H., Ebadzadeh , H., Hatami , F., and Hosseinpour, R., 2014. Statistics of crops. Ministry of Agriculture. Economic planning deputy. Information and Communication Technology Center. (In Persian).

 

  • Goudarzi, M., and Koupaei, J., 2020. Effects of climate change on water requirement and irrigation water requirement in irrigated areas, case study: Najaf Abad Plain. Promotional Journal of Water Conservation and Productivity. 1(1), 23-30. (In Persian).

   

  • Guo Y, Wu W, Du M, Liu X, Wang J and Bryant C R.,2019. Modeling climate change impacts on rice growth and yield under global warming of 1.5°C and 2.0°C in the Pearl River delta, China. Atmosphere 10 (10): 567. doi: 10.3390/atmos10100567.

 

  • Hoogenboom G, CH Porter, V Shelia, KJ Boote, U Singh, JW White, LA Hunt, R Ogoshi, JI Lizaso, J Koo, S Asseng, A Singels, LP Moreno and JW Jones.­,2019. Decision Support System for Agrotechnology Transfer (DSSAT) Version 4.7.5 (https://DSSAT.net). DSSAT Foundation, Gainesville, Florida, USA.

 

  • Hunt, L.A., Pararajasingham, S., Jones, J.W., Hoogenboom, G., Imamura, D.T. and Ogoshi, R.M., 1993. GENCALC: Software to facilitate the use of crop models for analyzing field experiments. Agronomy Journal, 85(5), pp.1090-1094. Doi: 10.2134/agronj1993.00021962008500050025x.

 

  • Islam A. T. Shen S. H. and Yang S. B.,2018. Predicting design water requirement of winter paddy under climate change condition using frequency analysis in Bangladesh. Agricultural Water Management, 195, 58-70. Doi: 10.1016/j.agwat.2017.10.003.

 

  • Jones, J.W., Hoogenboom, G., Porter, C.H., Boote, K.J., Batchelor, W.D., Hunt, L.A., Wilkens, P.W., Singh, U., Gijsman, A.J. and Ritchie, J.T., 2003. The DSSAT cropping system model. European Journal of Agronomy, 18(3-4), pp.235-265. Doi: 10.1016/S1161-0301(02)00107-7.

 

  • Khan M. S, Coulibaly P and Dibike Y,2006. Uncertainty analysis of statistical downscaling methods. Journal of Hydrology, 319 (1), 357-382. Doi: 10.1016/j.jhydrol.2005.06.035.

 

  • Liu J, Yao W, Peng ZH, Fu Y, Fan X and Wang H., 2019. Risk assessment of possible impacts of climate change and irrigation on wheat yield and quality. Agricultural Sciences & Agronomy. doi: 10.20944/preprints201912. 0352.v1.

 

  • Lotfirad, M., Adib, A., Salehpoor, J., Ashrafzadeh, A. and Kisi, O., 2021. Simulation of the impact of climate change on runoff and drought in an arid and semiarid basin (the Hablehroud, Iran). Applied Water Science, 11, pp.1-24.doi:10.1007/s13201-021-01494-2.

 

  • Mahajan, G., Bharaj, T.S. and Timsina, J., 2009. Yield and water productivity of rice as affected by time of transplanting in Punjab, India. Agricultural Water Management, 96(3), pp.525-532.Doi: 10.1016/j.agwat.2008.09.027

 

  • Mohammadi, H., Rabbani, F., and Mazaheri, D., 2015. Simulating the effect of climate change on rice plant phenology under Different irrigation managements in the Caspian region: Rasht station. Journal of Geographical Sciences Applied Research. 15 (38), 187-205. (In Persian).

 

  • Moriasi, D.N., Arnold, J.G., Van Liew, M.W., Bingner, R.L., Harmel, R.D. and Veith, T.L., 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, 50(3), pp.885-900.

 

  • Motha, R.P. and Baier, W., 2005. Impacts of present and future climate change and climate variability on agriculture in the temperate regions: North America. Climatic Change, 70(1-2), pp.137-164.

 

  • Nagarajan S, Jagadish S.V.K, Hariprasad A.S, Thomar A.K., Anand A., Pal M and Aggarwal P.K., 2010. Local climate affects growth, yield and grain qual/ity of aromatic and nonaromatic rice in northwestern. Indian Agriculture Ecosystems & Environment, 138: 274–281. Doi: 10.1016/j.agee.2010.05.012.

 

  • Nicolas F, Migliaccio K W, Hoogenboom G, Rathinasabapathi B R and Eisenstadt W R.,2020. Assessing the potential impact of climate change on rice yield in the Artibonite valley of Haiti using the CSM-CERES-RICE model. American Society of Agricultural and Biological Engineers (ASABE) 63 (5): 1385-1400. DOI:13031/trans.13868.

 

  • Peng S.B., J.L. Huang J.E. Sheehy R.C. Laza R.M. Visperas X.H. Zhong G.S. Centeno G.S. Khush and K.G. Cassman., 2004. Rice yields decline with higher night temperature from global warming. Proceedings of the National Academy of Sciences,S.A. 101, 9971–9975.doi: 10.1073/pnas.0403720101.

 

  • Prasad S, Singh M. P, and Yadav R. K., 2012. Physio-chemical changes in rice varieties under drought stress condition. Plant Archives, 12:63-66.

 

  • Prescott J.,1940. Evaporation from a water surface in relation to solar radiation. Transactions of The Royal Society of South Australia. 64 (1): 114–118.

 

  • Priestley, C.H.B. and Taylor, R.J., 1972. On the assessment of surface heat flux and evaporation using large-scale parameters. Monthly Weather Review, 100(2), pp.81-92. doi:10.1175/1520-0493(1972)100<0081: OTAOSH>2.3.CO;2

 

  • Rao B.B., Santhibhushan Chowdary P., Sandeep V.M., Rao V.U.M and Venkateswarlu B., 2014. Rising minimum temperature trends over India in recent decades: Implications for agricultural production. Global and Planetary Change 117: 1–8. Doi: 10.1016/j.gloplacha.2014.03.001.

 

  • Rezaei, M., Shahnazari, A., Raeini Sarjaz, M. and Vazifedoust, M., 2015. Large-scale Simulation of Rice yield and Water Productivity Using CERES-Rice Model. Iranian Journal of Irrigation & Drainage, 9(2), pp.283-291.. (In Persian).

 

  • Schoups G, Maurer E.P and Hopmans G.W.­,2008. Climate change impacts on water demand and salinity in California’s irrigated agriculture. American Geophysical Union, Fall Meeting.

 

  • Shrestha, S., Deb, P. and Bui, T.T.T., 2016. Adaptation strategies for rice cultivation under climate change in Central Vietnam. Mitigation and Adaptation Strategies for Global Change, 21, pp.15-37. DOI 10.1007/s11027-014-9567-2.

 

  • Soler, C.M.T., Sentelhas, P.C. and Hoogenboom, G., 2007. Application of the CSM-CERES-Maize model for planting date evaluation and yield forecasting for maize grown off-season in a subtropical environment. European Journal of Agronomy, 27(2-4), pp.165-177.doi: 10.1016/j.eja.2007.03.002.

 

  • Thiemig V, Rojas R, Zambrano-Bigiarini M and De Roo A.,2013. Hydrological evaluation of satellite-based rainfall estimates over the Volta and Baro-Akobo Basin. Hydrol 499: 324–338. doi: 10.1016/j.jhydrol.2013.07.012

 

  • Wailes J and Chavez E.C., 2012. World Rice Outlook. International Rice Baseline with Deterministic and Stochastic Projections, 2012-2021. University of Arkansas Department of Agricultural Economics and Agribusiness, Division of Agriculture Staff. 81 pp.

 

  • Wilby R. L, Tomlinson O. J and Dawson C. W.,2003. Multi-site simulation of precipitation by conditional resampling. Climate Research, 23, pp 183–194. doi:10.3354/cr023183.

 

  • Yan, D., Zhu, Y., Wang, S. and Cao, W., 2006. A quantitative knowledge-based model for designing suitable growth dynamics in rice. Plant Production Science, 9(2), pp.93-105.. doi: 10.1626/pps.9.93.

 

  • Yosefian, M., 2018. Study the effects of partial root drying (PRD) and regulated deficit irrigation (RDI) on water productivity, methane gas emissions and some physiological characteristics of Hashemi cultivar. D. thesis of irrigation and drainage engineering. Sari Agricultural Sciences and Natural Resources University, Iran.100p. (In Persian).    
Irrigation Sciences and Engineering
Volume 46, Issue 3
December 2023
Pages 35-55

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History

  • Receive Date: 06 August 2022
  • Revise Date: 12 October 2022
  • Accept Date: 16 October 2022
  • Publish Date: 22 November 2023

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