Studying the changes in rice yield and water balance in Guilan Province affected by climate change

Document Type : Original Article


1 Department of Agrotechnology, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran

2 Department of Agriculture, Faculty of Plant Production, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran

3 Department of Agriculture, Faculty of Agriculture, Shahrekord University, Shahrekord, Iran

4 Department of Water Engineering, Faculty of Engineering, Islamic Azad University, Lahijan Branch, Iran


Introduction: Increasing greenhouse gases will have different effects on crop yields, so that the interaction of these effects may increase or decrease yields. Crop simulation models have been used to investigate different levels of crop and environmental managements. The aim of this study was to investigate the AquaCrop model based on past, present and future climate in Rasht city located in Guilan Province to achieve maximum water productivity and rice grain yield.
Material and methods: In order to study the changes in rice yield, water balance and productivity in Rasht city located in Gilan province under the past, present and future climate, the AquaCrop model was used. For this purpose, long-term data (over 30 years) were used to evaluate the yield and water balance in rice cultivation in the past and present climate. Also, using LARS-WG6 software, meteorological data for the next 83 years were generated based on the available daily meteorological data. The AquaCrop model was evaluated in the past, present and future climates based on daily data of minimum and maximum temperatures, precipitation and sun hours. The studied treatments included four levels of irrigation including 55, 70, 85 and 100% of water requirement and the planting dates were April 21th, May 11th and May 31th. By examining the effect of different treatment levels based on RCP 4.5 and RCP 8.5 climate change scenarios, the rate of changes in grain yield, evapotranspiration and water productivity based on evapotranspiration in the past, present and future climates were investigated. Also, the best irrigation treatment and planting date were introduced to increase rice yield and reduce water consumption.
Results and discussion: The evaluation results showed that the LARS-WG6 model is able to simulate the climatic components including  temperature, precipitation and radiation with high accuracy. The results showed that the minimum and maximum temperatures increased during the climate change scenarios and the amount of radiation and precipitation decreased. The result of rice biomass and grain yield under RCP 4.5 and RCP 8.5 showed that the highest grain and biomass yield was obtained in irrigation of 100% of water requirement and planting date on April 21th. The study of water productivity showed that irrigation treatment of 100% of water requirement and planting date of May 31th had an effective role in increasing soil water storage and reducing evapotranspiration from the soil surface. The highest water productivity in grain production based on evapotranspiration was obtained in irrigation of 100% of water requirement and planting date was May 31th.
Conclusion: According to the obtained results, considering the water consumption productivity and yield and problems that will exist in the future including water shortage, it seems that late cultivation of rice in conditions of water shortage is a good solution, but under conditions where there is no water shortage, early cultivation of rice, such as April 21th, can increase the production. The study of irrigation levels showed that grain production is the most effective factor in increasing water use productivity and the use of low irrigation levels will not play an effective role in increasing water productivity.


Aalaee Bazkiaee, P., Kamkar, B., Amiri, E., Kazemi, H., Rezaei, M. and Akbarzadeh, S., 2020. Simulation of growth and yield and evaluation of rice production productivity under irrigation management and planting date using Aquacrop model. Journal of Water and Soil Resources Conservation. 9, 17-34. (In Persian with English abstract).
Agricultural Statistics., 2020. Volume I: Crop products., 2018-19. Office of Statistics and Information Technology, Deputy Director of Planning and Economic Affairs. Ministry of Agricultural Jihad Report. Iran.
Challinor, A., Wheeler, T.R., Craufurd, P.Q., Ferro, C.A.T. and Stephenson, D.B., 2007. Adaptation of crops to climate change through genotypic responses to mean and extreme temperatures. Agriculture, Ecosystems and Environment. 119, 190-204.
Chung, S.O., 2010. Simulating evapotranspiration and yield responses of rice to climate change using FAO-AquaCrop. Journal of the Korean Society of Agricultural Engineers. 52, 57-64.
Droogers, P. and Aerts, J., 2005. Adaptation strategies to climate change and climate variability: a comparative study between seven contrasting river basins. Physics and Chemistry of the Earth. 30, 339-346.
Eyni Nargeseh, H., Deihimfard, R., Soufizadeh, S., Haghighat, M. and Nouri, O., 2016. Predicting the impacts of climate change on irrigated wheat yield in Fars province using APSIM model. Electronic Journal of Crop Production. 8, 203-224. (In Persian with English abstract).
FAO, 2019. Agriculture organization of the United Nations. Available Online at: http://faostat. fao. org/site/573/default. aspx# ancor.
IPPC, CC., 2007. A report of Working Group I of the Intergovernmental Panel on Climate Change, Summary for Policymakers. International Plant Protection Convention report. Cambridge, United Kingdom, and Cambridge University Press.
IPPC, I., 2014. Climate Change 2014 Synthesis Report. Fifth Assessment Report.  International Plant Protection Convention report. Cambridge, United Kingdom, and Cambridge University Press.
Jin, X., Li, Z., Nie, C., Xu, X., Feng, H., Guo, W. and Wang, J., 2018. Parameter sensitivity analysis of the AquaCrop model based on extended fourier amplitude sensitivity under different agro-meteorological conditions and application. Field Crops Research. 226, 1-15.
Kang, Y., Khan, S. and Ma, X., 2009. Climate change impacts on crop yield, crop water productivity and food security–A review. Progress in Natural Science. 19, 1665-1674.
Krishnan, P., Swain, D.K., Bhaskar, B.C., Nayak, S.K. and Dash, R.N., 2007. Impact of elevated CO2 and temperature on rice yield and methods of adaptation as evaluated by crop simulation studies. Agriculture, Ecosystems and Environment. 122, 233-242.
Luo, Y., Jiang, Y., Peng, S., Cui, Y., Khan, S., Li, Y. and Wang, W., 2015. Hindcasting the effects of climate change on rice yields, irrigation requirements, and water productivity. Paddy and Water Environment. 13, 81-89.
Mereu, V., Cesaraccio, C., Dubrovsky, M., Spano, D., Carboni, G. and Duce, P., 2010. Climate change impacts on durum wheat in Sardinia. In Proceedings 29th Conference on Agricultural and Forest Meteorology, 1-6 August, Crestone Peak, U.S. p. 587.
Mo, X., Liu, S., Lin, Z. and Guo, R., 2009. Regional crop yield, water consumption and water use efficiency and their responses to climate change in the North China Plain. Agriculture, Ecosystems and Environment. 134, 67-78.
Nyakudya, I.W. and Stroosnijder, L., 2014. Effect of rooting depth, plant density and planting date on maize (Zea mays L.) yield and water use efficiency in semi-arid Zimbabwe: Modelling with AquaCrop. Agricultural Water Management. 146, 280-296.
Pazoki, A.R., Karimi Nejad, M. and Foladi Toroghi, A.R., 2010.  Effect of planting dates on yield of ecotypes of saffron (Crocus sativus L.) in Natanz region. Crop Physiology. 2, 3-12. (In Persian with English abstract).
Raes, D., Steduto, P., Hsiao, T.C. and Fereres, E., 2009. AquaCrop-The FAO crop model for predicting yield response to water: II. Main algorithms and software description. Agronomy. 101, 438-447.
Raes, D., Steduto, P., Hsiao, T.C. and Fereres, E., 2012. Reference manual AquaCrop, FAO, Land and Water Division, Rome, Italy.
Raoufi, R.S. and Soufizadeh, S., 2020. Simulation of the impacts of climate change on phenology, growth, and yield of various rice genotypes in humid sub-tropical environments using AquaCrop-Rice. International Journal of Biometeorology. 64, 1657-1673.
Singh, R., Van Dam, J.C. and Feddes, R.A., 2006. Water productivity analysis of irrigated crops in Sirsa district, India. Agricultural Water Management. 82, 253-278.
Soltani, A., Rahimzadeh Khoei, F., Ghassemi-Golezani, K. and Moghaddam, M., 1999. Cicer: A computerized model for simulating chickpea growth and yield. Agricultural Science. 9, 89-106. (In Persian with English abstract).
Steduto, P., Hsiao, T.C., Raes, D., and Fereres, E., 2009. AquaCrop-The FAO crop model to simulate yield response to water: I. Concepts and underlying principles. Agronomy. 101, 426-437.
Tan, S., Wang, Q., Zhang, J., Chen, Y., Shan, Y., and Xu, D., 2018. Performance of AquaCrop model for cotton growth simulation under film-mulched drip irrigation in southern Xinjiang, China. Agricultural Water Management. 196, 99-113.
Todorovic, M., Albrizio, R., Zivotic, L., Abi Saab, M., Stockle, C. and Steduto, P., 2009. Assessment of AquaCrop, CropSyst, and WOFOST models in the simulation of sunflower growth under different water regimes. Agronomy Journal. 101, 509-521.
Zolfagari, H., Farhadi, B. and Rahimi, H., 2016. Climatic potentials in Iran for soybean cultivation. Journal of Geography and Planning. 20, 89-105. (In Persian with English abstract).