Future Water Requirements and Crop Productivity at Al-Najaf Governorate Under Different Climate Change Scenarios (2020–2080)

Sabah, Noor; Al-Mukhtar, Mustafa; Shemal, Khalid

doi:10.30684/etj.2023.137102.1335

	Future Water Requirements and Crop Productivity at Al-Najaf Governorate Under Different Climate Change Scenarios (2020–2080)
Engineering and Technology Journal
Article 8, Volume 41, Issue 5, May 2023, Pages 687-697 PDF (682.63 K)
Document Type: Research Paper
DOI: 10.30684/etj.2023.137102.1335
Authors
Noor Sabah^* ; Mustafa Al-Mukhtar; Khalid Shemal
Civil Engineering Dept., University of Technology-Iraq, Alsina’a street, 10066 Baghdad, Iraq.
Abstract
This study aims to predict the effect of climate change on net irrigation water requirements (NIWR) and agricultural productivity from five common crops (wheat, barley, summer maize, and sorghum) in the Al-Najaf Governorate in Iraq. GFDL-ESM2M mode was used to predict the lower and upper temp and precipitation for two time periods (2020-2080) with 30 years for two periods (P1 and P2) under representative concentrations paths (RCP 2.5, RCP6, and RCP8.5). The CROPWAT model is used to determine NIWR, and the extreme learning model was used to estimate agricultural yields using previous crop yield production and weather data, supported vectors machine (SVM) is executed as a Machines Learns algorithm. Results showed NIWR increment to consider cropping owing to climate change. Barley is the crop most affected by climate change under the (RCP2.5, RCP6, and RCP8.5) scenarios, with increasing crop water requirements (NIWR) of (22%, 23%, and 24% ) for P1 and (23%, 24%, and 29%) for P2, respectively. Summer maize is the crop least affected by climate change under all climate change scenarios, with increasing crop water requirements of (1%, 2%, and 4%) for P1 and (1%, 2%, and 5% for P2. Climate change negatively affects the crop yield of all crops under the different climate change scenarios. The findings of this study could be used as a guide to developing adaptation strategies for dealing with potential changes in water availability and agricultural water productivity due to climate change.
Highlights
The effect of climate change on net irrigation water requirements and crop productivity was investigated. Barley has the most influence on climate change. An increase in the quantity of water required for irrigation for the common crops under climate change. Climate change has negative effects on all crops' yield under different climate change scenarios.
Keywords
AL-Najaf Governorate; Climate change; Crop productivity; Crop water requirements; Machine learning

References
[1] S. H. Ewaid , S. A. Abed, Water quality index for Al-Gharraf river, southern Iraq, Egypt. J. Aquat. Res., 43(2017) 117–122. https://doi.org/10.1016/j.ejar.2017.03.001 [2] N. Al-Ansari, A. A. Ali, S. Knutsson, Iraq Water Resources Planning: Perspectives and Prognoses, Int.Conf. Civil . Cons. Eng. J., 13 (2015)2097–2108. [3] S. H. Ewaid, S. A. Abed, S. A. Kadhum, Predicting the Tigris River water quality within Baghdad, Iraq by using water quality index and regression analysis, Environ. Technol. Innov., 11(2018)390–398. https://doi.org/10.1016/j.eti.2018.06.013 [4] N. A. Al-Ansari, Management of Water Resources in Iraq: Perspectives and Prognoses, Engi., 5 (2013) 667–684. https://doi.org/10.4236/eng.2013.58080 [5] J. M. Trondalen, Climate changes, water security and possible remedies for the Middle East, Sci. Pap. from Potential Confl. to Co-operation Potential (UNESCO PCCP, 2009. http//www. unwater. org/downloads/181886E. pdf, 2009. [6] A. Abdulloev, Water, Energy, and Food Nexus in the Amu-Darya River Basin: Analysis of Water Demand and Supply Management Infrastructure Development at Transboundary Leve. 2020. [7] J. Verdin , R. Klaver, Grid‐cell‐based crop water accounting for the famine early warning system, Hydrol. Process., 16 (2002) 1617–1630. http://dx.doi.org/10.1002/hyp.1025 [8] M. Frere , G. F. Popov, Agrometeorological crop monitoring and forecasting. FAO, 1979. [9] D. Clarke, M. Smith, K. El-Askari, CropWat for Windows, (Version 4.2), Southampt. Univ. Southampt., 43(1998) .Available: http://eprints.soton.ac.uk/73992/. [10] S. Gheorghe, A. Marica, L. Toulios, Assimilation of earth observation data in the cropwat model, Geophys. Res. Abstr., 6 (2004) 4791. [11] M. S. Babel, A. Agarwal, D. K. Swain, S. Herath, Evaluation of climate change impacts and adaptation measures for rice cultivation in Northeast Thailand, Clim. Res., 46 (2011) 137–146. http://dx.doi.org/10.3354/cr00978 [12] S. Shrestha, R. Chapagain, M. S. Babel, Quantifying the impact of climate change on crop yield and water footprint of rice in the Nam Oon Irrigation Project, Thailand, Sci. Total. Environ., 599 (2017) 689–699. [13] E. Blanc, The impact of climate change on crop yields in Sub-Saharan Africa, 2012. [14] X. Yang , Potential benefits of climate change for crop productivity in China, Agric. For. Meteorol., 208 (2015) 76–84. [15] F. H. Saeed, M. S. Al-Khafaji, F. A. Mahmood Al-Faraj, Sensitivity of irrigation water requirement to climate change in arid and semi-arid regions towards sustainable management of water resources, Sustain., 13(2021). http://dx.doi.org/10.3390/su132413608 [16] S. K. Al-Mamoori, L. A. Jasem. Al-Maliki, A. H. Al-Sulttani, K. El-Tawil, H. M. Hussain, N. Al-Ansari, Horizontal and vertical geotechnical variations of soils according to USCS classification for the city of An-Najaf, Iraq using GIS, Geotech. Geol. Eng., 38(2020)1919–1938. https://doi.org/10.1007/s10706-019-01139-x [17] K. N. Kadhim , G. A. J. Al-Baaj, The geotechnical maps for gypsum by using GIS for Najaf city (Najaf - Iraq), Int. J. Civ. Eng. Technol., 7 (2016) 329–338. [18] B. F. Yousif, The use of remote sensing techniques in the classification of Al-Najaf Soil, M. Sc. Thesis, Building and Construction Engineering Department, University , 2004. [19] S. M. Mail, U. Somorowska, A. H. Al-Sulttani, Seasonal and inter-annual variation of precipitation in Iraq over the period 1992-2010, Pr. i Stud. Geogr., 61( 2016) 71-84. [20] S. Maingi, J. Ndiiri, B. Mati, Estimation of Crop Water Requirements for Garden Pea, Sweet Pepper and Tomato using the CropWAT Model in Maragua Watershed, Murang’a County, Kenya, Int. J. Agric. Sci., 5(2020). [21] M. Hossain, S. Yesmin, M. Maniruzzaman, J. Biswas, Irrigation Scheduling of Rice (Oryza sativa L.) Using CROPWAT Model in the Western Region of Bangladesh, Agric., 15 (2017)19–27. http://dx.doi.org/10.3329/agric.v15i1.33425 [22] A. Kumari , Estimation of Actual Evapotranspiration and Crop Coefficient of Transplanted Puddled Rice Using a Modified Non-Weighing Paddy Lysimeter, Agron., 12 (2022) 2850. [23] P. Thimme Gowda, S. B. Manjunaththa, T. C. Yogesh, S. A. Satyareddi, Study on water requirement of Maize (Zea mays L.) using CROPWAT model in northern transitional zone of Karnataka, J. Environ. Sci. Comput. Sci. Eng. Technol., 2(2013)105–113. [24] X. Sun, J. Xu, C. Jiang, J. Feng, S.S. Chen, F. He, Extreme learning machine for multi-label classification, ENTR., 18 (2016) 225. https://doi.org/10.3390/e18060225 [25] G.B. Huang, Q.-Y. Zhu, C.-K. Siew, Extreme learning machine: a new learning scheme of feedforward neural networks, IEEE. int. j. conf. neur. Netw., 2(2004)985–990. [26] H. Dehghanisanij, S. Emami, V. Rezaverdinejad, A. Amini, Potential of the hazelnut tree search–ELM hybrid approach in estimating yield and water productivity, Appl. Water. Sci., 13 (2023) 61. https://doi.org/10.1007/s13201-022-01865-3 [27] Input data set: Future land-use patterns. https://www.isimip.org/gettingstarted/input-data-bias-adjustment/details/57/ (accessed Dec. 16, 2022). [28] H. Ritchie , M. Roser, Crop Yields , Our World in Data, 2013. https://ourworldindata.org/crop-yields (accessed Dec. 15 (2022). [29] S. H. Ewaid, S. A. Abed, N. Al-Ansari, Crop water requirements and irrigation schedules for some major crops in Southern Iraq, Water, 11 (2019) 756. https://doi.org/10.3390/w11040756 [30] P. V .de Azevedo, C. B. de Souza, B. B. da Silva, V. P. R. da Silva, Water requirements of pineapple crop grown in a tropical environment, Brazil, Agric. Water. Manag., 88 (2007)201–208. https://doi.org/10.1016/j.agwat.2006.10.021 [31] J. Chenoweth , Impact of climate change on the water resources of the eastern Mediterranean and Middle East region: Modeled 21st century changes and implications, Water. Resour. Res., 47( 2011). https://doi.org/10.1029/2010WR010269 [32] K. Waha , Climate change impacts in the Middle East and Northern Africa region and their implications for vulnerable population groups, Reg. Environ. Chang., 17(2017)1623–1638. https://dx.doi.org/10.1007/s10113-017-1144-2 [33] Z. A. Mimi , S. A. Jamous, Climate change and agricultural water demand: Impacts and adaptations, Afr. j. environ. sci. technol., 4 (2010)183–191. [34] B. Lalic, M. Francia, G. Jacimovic, Assessment of climate change impact on crop water requirements in Serbia in 2030 using CROPWAT model, 1(2013) 9–11. [35] A. Crane-Droesch, Machine learning methods for crop yield prediction and climate change impact assessment in agriculture, Environ. Res. Lett., 13( 2018). https://dx.doi.org/10.1088/1748-9326/aae159 [36] B. Mirgol, M. Nazari, M. Eteghadipour, Modelling climate change impact on irrigation water requirement and yield of winter wheat (Triticum aestivum L.), barley (Hordeum vulgare L.), and fodder maize (Zea mays L.) in the semi-arid Qazvin Plateau, Iran, Agriculture, 10 (2020) 60. https://doi.org/10.3390/agriculture10030060
Statistics Article View: 33 PDF Download: 32