The Effect of Climate Change and Planting Date on the Green Water Footprint of Fall Wheat 2021-2100 (Case Study: Qazvin Plain)

Document Type : Research Article

Authors

1 Department of Water Science and Engineering, Faculty of Agricultural and Natural Resources, Imam Khomeini International University, Qazvin, Iran

2 Water Science and Engineering Department, Faculty of Agricultural and Natural Resources, Imam Khomeini International University, Qazvin, Iran

Abstract

Introduction
Climate change is one of the most important issues in the world in the 21st century which affects various sectors of agriculture, forestry, water and financial markets, and has serious economic consequences (Reidsma et al., 2009). In recent years, the management of agricultural water consumption has always been considered as one of the important issues in water resources management. Koochaki and colleagues (Koochaki and Kamali, 2006) by evaluating the climatic indicators of Iran's agriculture showed that during the next 20 years, the average monthly temperature will increase in almost all regions of the country, and the increase in evaporation and transpiration is one of the most important consequences of this warming. Simulated climate parameters can be obtained through different general GCM atmospheric models. Due to the low spatial resolution of these models, its output should be downscaled using dynamic or statistical methods.
 
Materials and Methods
The LARS-WG model predicts meteorological variables for a period of time in the future by using a series of basic and fine-scale meteorological data, output of one of the GCM models. Research has shown that the LARS-WG model has the necessary accuracy for this task. Calculating the amount of evapotranspiration and yield of very complex plants are time-consuming and dependent on spending a lot of money and limited to the tests performed, the shortness of the test time and also the limitation in the number of scenarios that are checked by the test. Therefore, plant models are considered and evaluated by researchers. The AquaCrop model has demonstrated commendable accuracy in various regions of Iran and globally for forecasting plant growth, water consumption efficiency, and evapotranspiration requirements. These predictions hold significant potential for optimizing irrigation strategies across different agricultural settings. AquaCrop is one of the applied agricultural models that was obtained from the modification and revision of FAO publication No. 33 by prominent experts from all over the world. In this study, the values of green water footprint of winter wheat plant (Pishgam) were estimated in climatic conditions obtained from LARS-WG model and DKRZ database under scenarios 4.5 and 8.5 and at different planting dates (15 October, 1 November, 15 November, 30 November and 15 December), in the next 4 periods (2021-2040, 2041-2060, 2061-2080 and 2081-2100) and by Aquacrop model.
 
Results and Discussion
The results showed that if planting date is on October 15, in the climatic conditions obtained from the LARS-WG model and under scenarios 4.5 and 8.5, in all future periods, the footprint of green water will increase compared to its value in the base period, and if planting is the rest of the dates, in each of the next 4 periods, the average green water footprint will decrease compared to its value in the base period. The results obtained for the DKRZ database show that the green water footprint attained for the dates of cultivation and periods investigated in scenarios 4.5 and 8.5 does not have a particular trend. On the planting dates of October 15 and November 1 for the periods of 2061-2080 and 2081-2100, the green water footprint will decrease and on the other three dates (15 November, 30 November, and 1 November) for these periods, there will be an increasing trend. On 15 December, for the DKRZ database, in both scenarios defined for all periods, an increase in green water footprint compared to the base period is reported. However, in the period of 2081-2100 in scenario 8.5, a decrease compared to the base period will be observed. The highest amount of green water footprint in all these periods and models for the period 2041-2060 under the climatic conditions of the DKRZ database in scenario 4.5, if the planting date is 15 October, it is estimated that the amount of water consumed is equal to 4272 cubic meters per ton with a standard deviation of 5018 cubic meters per ton is predicted. The lowest footprint of green water for the period 2081-2100 under the climatic conditions obtained from the LARS-WG model in scenario 8.5, if the planting date is on 15 December, is reported to be 232 tons per hectare with a standard deviation of 52.3 tons per hectare.
 

Keywords

Main Subjects


©2024 The author(s). This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source.

  1. Ababaei, B., & Ramezani Etedali, H. (2014). Estimation of water footprint components of Iran’s wheat production: Comparison of global and national scale estimates. Environmental Processes. https://doi.org/1007/s40710-014-0017-7
  2. Ababaei, B., & Ramezani Etedali, H. (2017). Water footprint assessment of main cereals in Iran. Agricultural Water Management. https://doi.org/10.1016/j.agwat.2016.07.016
  3. Ajamzadeh, A., & Mollaeinia, M.R. (2016). Assessment of impact of climate changhe on Firoozabad river runoff with downscaling of atmospheric circulation models output by SDSM and LARS-WG softwares. Journal Iran-Water Resources Research, 109-95(1), 12. (In Persian with English abstract)
  4. Aligholinia, T., Sheibany, H., Mohamadi, O., & Hesam, M. (2019). Comparison and evaluation of blue, green and gray water footprint of wheat in different climates of Iran. Iran-Water Resources Research, 15(3), 234-245. (In Persian with English abstract). https://doi.org/1001.1.17352347.1398.15.3.18.9
  5. Bocchiola, D., Nana, E., & Soncini, A .(2013). Impact of climate change scenarios on crop yield and water footprint of maize in the Po valley of Italy. Agricultural Water Management, 116, 50-61. https://doi.org/1016/j.agwat. 2012.10.009
  6. Ene, A.S., Teodosiu, C., Robu, B., & Volf, I .(2013). Water footprint assessment in the winemaking industry: A case study of office paper. Cleaner Production, 24, 30–35. https://doi.org/1016/j.jclepro.2012.11.051
  7. Govere, S., Nyamangara, J., & Nyakatawa, E.Z. (2020). Climate change signals in the historical water footprint of wheat production in Zimbabwe. Science of the Total Environment. https://doi.org/1016/j.scitotenv.2020.140473
  8. Ghorbani, Kh., Aligholinia, T.H., Rezaie, H., & Ghorbani Nasrabad, G. (2020). Evaluation and simulation of agricultural crops water footprint in different climates of Iran considering climate change scenarios. Iran-Water Resources Research, 16(3), 80-61. (In Persian with English abstract)
  9. Golabi, M.R., Radmanesh, F., Akhoond-Ali, A.M., & Niksokhan, M.N. (2019). Choosing a suitable area for wheat production through the concept of water footprint and social decision-making methods (case study: Esfahan province). Iranian Journal ECO Haydrology. (In Persian with English abstract). https://doi.org/10.22059/ IJE.2019.284889.1156.
  10. Hajjarpour, A., Yousefi, M., & Kamkar, B. (2014). Precision test of simulators LARS-WG, weather Man and CLIMGE Ninthree different climatessimulated (Gorgan, Gonbad and Mashhad). Geography Development, 35(59), 201–16. (In Persian with English abstract)  
  11. Hashemi, M.Z., Asaad, Y.SH., & Bruce, W.M. (2011). Comparison of SDSM and LARS-WG for simulation and downscaling of extreme precipitation events in a Watershed. Journal Stochastic Environmental Research and Risk Assessment, 25(4), 475–84.
  12. Hoekstra, A.Y., & Chapagain, A.K .(2007). Water footprints of nations: Water use by people as a function of their consumption pattern. Water Resources Management, 21, 35–48. https://doi.org/1007/978-1-4020-5591-1_3
  13. Kashyap, D., & Agarwal, T. (2020). Temporal trends of climatic variables and water footprint of rice and wheat production in Punjab, India from 1986 to 2017. Journal of Water and Climate Change. https://doi.org/2166/ wcc.2020.093
  14. Kyani, A. (2017). "Using saline water for wheat irrigation. Publication of Agricultural Engineering and Technical Research Institute - Office of the National Television Network of Agriculture and Knowledge Management, first edition. (In Persian)
  15. Morillo, J.G., Díaz, J.A., Camacho, E., & Montesinos, P. (2015). Linking water footprint accounting with irrigation management in high value crops. Journal of Cleaner Production, 87, 594-602.
  16. Muratoglu, A. (2020). Assessment of wheat’s water footprint and virtual water trade: a case study for Turkey. Ecological Processes. https://doi.org/1186/s13717-020-0217-1
  17. Nana, E., Corbari, C., & Bocchiola, D. (2014). A model for crop yield and water footprint assessment: Study of maize in the Po valley. Agricultural Systems, 127, 139-149. https://doi.org/1016/j.agsy.2014.03.006
  18. Obuobie, E., Mwangi Gachanja, P., & Cristina Dörr, A. (2005). The role of green water in food trade. Bonn: Zentrum Für Entwicklungsforschung (ZEF)(Term Paper for the Interdisciplinary Course, International Doctoral Studies)
  19. Parlange, M.B., & W Katz, R. (2000). An extended version of the Richardson model for simulating daily weather variables. Journal of Applied Meteorology, 39(5), 610–22. https://doi.org/1175/1520-0450-39.5.610
  20. Racsko, P., Szeidl, L., & Semenov, M. (1991). A serial approach to local stochastic weather models. Ecological Modelling. https://doi.org/10.1016/0304-3800(91)90053-4
  21. Sadrabadi Haghighi, R., & Sakhavati, Sh. (2018). Studying the effect of foliar application of zinc, iron and manganese elements on the yield and yield components of Pishgam bread wheat. New findings of agriculture.
  22. Semenov, M.A. (2008 ). Simulation of extreme weather events by a Stochastic weather Generator. Climate Research, 35(3), 203–12. https://doi.org/3354/cr00731
  23. Semonov, M.A., & Stratonovith, P. (2010). Use of multi- model ensembles from global models for assessment of climate change impacts. Journal Climate Research, 41, 1-14.
  24. Wang, Ch., Zhao, J., & Tian, B. (2021). Assessment of water footprint for crop production: a case study in North China. IOP Conf. Series: Earth and Environmental Science. https://doi.org/10.1088/1755-1315/831/1/012047

 

CAPTCHA Image
Volume 38, Issue 1 - Serial Number 93
March and April 2024
Pages 1-21
  • Receive Date: 23 October 2023
  • Revise Date: 26 January 2024
  • Accept Date: 02 April 2024
  • First Publish Date: 02 April 2024