Assessment of the Impacts of Climate Change on Soybean Yield and Water Requirement Using Crop Models

Document Type : Original Research

Authors
1 Department of Water Engineering, Faculty of Agriculture, Razi University, Kermanshah, Islamic Republic of Iran.
2 Department of Plant Production and Genetics, Faculty of Agriculture, Razi University, Kermanshah, Islamic Republic of Iran.
Abstract
Climate change can have significant impacts on crop growth, yield, water requirement and, consequently, crop water productivity. In this study, the effect of climate change under RCP2.6, RCP4.5, and RCP8.5 projection scenarios of the CanESM2 model on soybean yield and water requirement was investigated in Kermanshah, west of Iran. Crop growth was simulated using crop growth simulation models (DSSAT and AquaCrop) based on historical (1985-2015) and projected (2025-2064) weather data. Using the AquaCrop model in RCP2.6, RCP4.5, and RCP8.5 scenarios, the average increase in seasonal crop evapotranspiration (ETc) was estimated to be 9.4, 11, and 14.9%, respectively. The results of the DSSAT model showed 4.1, 8.5, and 12.1% increase in seasonal ETc under the RCP2.6, RCP4.5, and RCP8.5 scenarios, respectively. Based on the AquaCrop and DSSAT models, soybean yield decreases by 5.3, 3.7, and 2% and by 5.7, 4.8, and 1.6% for the RCP8.5, RCP4.5, and RCP2.6 scenarios, respectively. The results also show a decrease in crop water productivity under climate change scenarios as a result of increased ETc and reduced grain yield. According to AquaCrop and DSSAT models, the maximum daily ETc that should be used for the design of irrigation systems will increase by 11.5 and 10.2%, respectively.

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Abd-Elmabod, S.K., Muñoz-Rojas, M., Jordán, A., Anaya-Romero, M., Phillips, J.D., Jones, L., Zhang, Z., Pereira, P., Fleskens, L., van Der Ploeg, M., 2020. Climate change impacts on agricultural suitability and yield reduction in a Mediterranean region. Geoderma 374, 114453. https://doi.org/10.1016/j.geoderma.2020.114453
Ahmadi, H., Azizzadeh, J., 2020. The impacts of climate change based on regional and global climate models (RCMs and GCMs) projections (case study: Ilam province). Model. Earth Syst. Environ. 6 (2), 685-696. 10.1007/s40808-020-00721-0
Ahmadpour, A., Farhadi Bansouleh, B., Ghobadi, M., 2017. Effects of deficit irrigation on growth trend, quantity and quality characteristics of maize in Kermanshah. Journal of Water and Soil Resources Conservation 6 (3), 99-112. (in Persian).
Allen, R., Pereira, L., Raes, D., Smith, M., 1998. FAO irrigation and drainage paper no. 56. , Rome, Italy

Baghanam, A.H., Eslahi, M., Sheikhbabaei, A., Seifi, A.J., 2020. Assessing the impact of climate change over the northwest of Iran: an overview of statistical downscaling methods. Theor. Appl. Climatol. 141, 1135-1150. https://doi.org/10.1007/s00704-020-03271-8
Boote, K.J., Jones, J.W., Hoogenboom, G., 2018. Simulation of crop growth: CROPGRO model, in: Peart, R.M., Curry, R.B. (Eds.), Agricultural Systems modeling and Simulation. CRC Press, pp. 651-692.
Doorenbos, J., Kassam, A., 1979. Yield response to water, FAO Irrigation and drainage paper NO. 33, Rome, Italy, p. 257
Emami, F., Koch, M., 2018. Evaluation of statistical-downscaling/bias-correction methods to predict hydrologic responses to climate change in the Zarrine river basin, Iran. Climate 6 (2), 30.
Esmaeili, M., 2014. Estimation the effects of water deficit irrigation on soybean crop yield in Kermanshah under climate change scenarios using AquaCrop model. Ms.C. Thesis, Razi University, Kermanhah, Iran (in Persian).
Esmaeili, M., Farhadi Bansouleh, B., Ghobadi, M., 2015. Effects of deficit irrigation on quantity and quality of soybean crop yield in Kermanshah region. Water and Soil 29 (3), 551-559. https://doi.org/10.22067/jsw.v0i0.31375 (in Persian).
Farhadi Bansouleh, B., Asadi, A., Hafezparast Mavadat, M., 2017. Changes in potential evapotranspiration of maize and barley under climate change situation in Kermanshah Province. Journal of Water and Soil Conservation 24 (3), 185-202. 10.22069/jwfst.2017.12019.2656 (in Persian).
Figueiredo Moura da Silva, E.H., Silva Antolin, L.A., Zanon, A.J., Soares Andrade, A., Antunes de Souza, H., dos Santos Carvalho, K., Aparecido Vieira, N., Marin, F.R., 2021. Impact assessment of soybean yield and water productivity in Brazil due to climate change. Eur J Agron 129, 126329. https://doi.org/10.1016/j.eja.2021.126329
Fiseha, B., Melesse, A., Romano, E., Volpi, E., Fiori, A., 2012. Statistical downscaling of precipitation and temperature for the Upper Tiber Basin in Central Italy. Int. J. Water Sci. 1.
Ghorbani, K., Soltani, A., 2014. The effect of climate change on soybean yield in Gorgan. Journal of Plant Production Research 21 (2), 67-85. (in Persian).
Heng, L.K., Hsiao, T., Evett, S., Howell, T., Steduto, P., 2009. Validating the FAO AquaCrop model for irrigated and water deficient field maize. Agron J . 101 (3), 488-498. https://doi.org/10.2134/agronj2008.0029xs
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., Ritchie, J.T., 2003. The DSSAT cropping system model. Eur J Agron 18 (3), 235-265. https://doi.org/10.1016/S1161-0301(02)00107-7
Karimi, V., Karami, E., Keshavarz, M., 2018. Climate change and agriculture: Impacts and adaptive responses in Iran. J. Integr. Agric 17 (1), 1-15. https://doi.org/10.1016/S2095-3119(17)61794-5
Koocheki, A., Nasiri mahalati, M., Kamali, G., 2007. Climate indices of Iran under climate change. Iranian Journal of Field Crops Research 5 (1), 133-142. 10.22067/gsc.v5i1.904 (in Persian).
Kothari, K., Battisti, R., Boote, K.J., Archontoulis, S.V., Confalone, A., Constantin, J., Cuadra, S.V., Debaeke, P., Faye, B., Grant, B., Hoogenboom, G., Jing, Q., van der Laan, M., Macena da Silva, F.A., Marin, F.R., Nehbandani, A., Nendel, C., Purcell, L.C., Qian, B., Ruane, A.C., Schoving, C., Silva, E.H.F.M., Smith, W., Soltani, A., Srivastava, A., Vieira, N.A., Slone, S., Salmerón, M., 2022. Are soybean models ready for climate change food impact assessments? Eur J Agron 135, 126482. https://doi.org/10.1016/j.eja.2022.126482
Laflamme, E.M., Linder, E., Pan, Y., 2016. Statistical downscaling of regional climate model output to achieve projections of precipitation extremes. Weather Clim Extrem. 12, 15-23. https://doi.org/10.1016/j.wace.2015.12.001
Muluye, G.Y., 2012. Comparison of statistical methods for downscaling daily precipitation. J. Hydroinformatics 14 (4), 1006-1023. https://doi.org/10.2166/hydro.2012.197
Phuong, D.N.D., Duong, T.Q., Liem, N.D., Tram, V.N.Q., Cuong, D.K., Loi, N.K., 2020. Projections of future climate change in the Vu Gia Thu Bon River Basin, Vietnam by using statistical downscaling model (SDSM). Water 12 (3), 755. https://doi.org/10.3390/w12030755
Raes, D., Steduto, P., Hsiao, T.C., Fereres, E., 2009. AquaCrop—the FAO crop model to simulate yield response to water: II. Main algorithms and software description. Agron J . 101 (3), 438-447. https://doi.org/10.2134/agronj2008.0140s
Rodríguez Díaz, J.A., Weatherhead, E.K., Knox, J.W., Camacho, E., 2007. Climate change impacts on irrigation water requirements in the Guadalquivir river basin in Spain. Reg. Environ. Change 7 (3), 149-159. https://doi.org/10.1007/s10113-007-0035-3
Rostami Ajirloo, A.A., Asgharipour, M.R., Ganbari, A., Joudi, M., Khoramivafa, M., 2021. Soybean yield in future climate scenarios under low irrigation conditions: case study: Pars Aabad of Moghan Plain, Iran. Agrotech Ind Crops 1 (1), 36-51. https://doi.org/10.22126/etic.2021.6376.1006
Saymohammadi, S., Zarafshani, K., Tavakoli, M., Mahdizadeh, H., Amiri, F., 2017. Prediction of climate change induced temperature & precipitation: The case of Iran. Sustainability 9 (1), 146. https://doi.org/10.3390/su9010146
Shahriar, S.A., Siddique, M.A.M., Rahman, S.M.A., 2021. Climate change projection using statistical downscaling model over Chittagong Division, Bangladesh. Meteorol. Atmos. Phys. 133 (4), 1409-1427. https://doi.org/10.1007/s00703-021-00817-x
Sharafati, A., Moradi Tayyebi, M., Pezeshki, E., Shahid, S., 2022. Uncertainty of climate change impact on crop characteristics: a case study of Moghan plain in Iran. Theor. Appl. Climatol. 149 (1), 603-620. https://doi.org/10.1007/s00704-022-04074-9
Soddu, A., Deidda, R., Marrocu, M., Meloni, R., Paniconi, C., Ludwig, R., Sodde, M., Mascaro, G., Perra, E., 2013. Climate variability and durum wheat adaptation using the AquaCrop model in Southern Sardinia. Procedia Environ. Sci. 19, 830-835. https://doi.org/10.1016/j.proenv.2013.06.092
Souvignet, M., Gaese, H., Ribbe, L., Kretschmer, N., Oyarzún, R., 2010. Statistical downscaling of precipitation and temperature in north‐central Chile: an assessment of possible climate change impacts in an arid Andean watershed. Hydrol Sci J . 55 (1), 41-57. https://doi.org/10.1080/02626660903526045
Steduto, P., Hsiao, T.C., Raes, D., Fereres, E., 2009. AquaCrop—The FAO crop model to simulate yield response to water: I. Concepts and underlying principles. Agron J . 101 (3), 426-437. https://doi.org/10.2134/agronj2008.0139s
Stennett‐Brown, R.K., Jones, J.J., Stephenson, T.S., Taylor, M.A., 2017. Future Caribbean temperature and rainfall extremes from statistical downscaling. Int. J. Climatol. 37 (14), 4828-4845. https://doi.org/10.1002/joc.5126
Tabari, H., Paz, S.M., Buekenhout, D., Willems, P., 2021. Comparison of statistical downscaling methods for climate change impact analysis on precipitation-driven drought. Hydrol Earth Syst Sci. 25 (6), 3493-3517. https://doi.org/10.5194/hess-25-3493-2021
Trzaska, S., Schnarr, E., 2014. A review of downscaling methods for climate change projections. United States Agency for International Development by Tetra Tech ARD, p. 42
Voloudakis, D., Karamanos, A., Economou, G., Kalivas, D., Vahamidis, P., Kotoulas, V., Kapsomenakis, J., Zerefos, C., 2015. Prediction of climate change impacts on cotton yields in Greece under eight climatic models using the AquaCrop crop simulation model and discriminant function analysis. Agric. Water Manag. 147, 116-128. https://doi.org/10.1016/j.agwat.2014.07.028
Wilby, R.L., Dawson, C.W., Barrow, E.M., 2002. SDSM — a decision support tool for the assessment of regional climate change impacts. Environ. Model. Softw. 17 (2), 145-157. https://doi.org/10.1016/S1364-8152(01)00060-3
Woznicki, S.A., Nejadhashemi, A.P., Parsinejad, M., 2015. Climate change and irrigation demand: Uncertainty and adaptation. J. Hydrol.: Reg. Stud. 3, 247-264. https://doi.org/10.1016/j.ejrh.2014.12.003
Yang, C., Fraga, H., Ieperen, W.V., Santos, J.A., 2017. Assessment of irrigated maize yield response to climate change scenarios in Portugal. Agric. Water Manag. 184, 178-190. https://doi.org/10.1016/j.agwat.2017.02.004