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Climate change impact and adaptation on wheat yield, water use and water use efficiency at North Nile Delta

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Abstract

Investigation of climate change impacts on food security has become a global hot spot. Even so, efforts to mitigate these issues in arid regions have been insufficient. Thus, in this paper, further research is discussed based on data obtained from various crop and climate models. Two DSSATcrop models (CMs) (CERESWheat and N-Wheat) were calibrated with two wheat cultivars (Gemiza9 and Misr1). A baseline simulation (1981-2010) was compared with different scenarios of simulations using three Global Climate Models (GCMs) for the 2030s, 2050s and 2080s. Probable impacts of climate change were assessed using the GCMs and CMs under the high emission Representative Concentration Pathway (RCP8.5). Results predicted decreased wheat grain yields by a mean of 8.7%, 11.4% and 13.2% in the 2030s, 2050s and 2080s, respectively, relative to the baseline yield. Negative impacts of climatic change are probable, despite some uncertainties within the GCMs (i. e., 2.1%, 5.0% and 8.0%) and CMs (i.e., 2.2%, 6.0% and 9.2%). Changing the planting date with a scenario of plus or minus 5 or 10 days from the common practice was assessed as a potentially effective adaptation option, which may partially offset the negative impacts of climate change. Delaying the sowing date by 10 days (from 20 November to 30 November) proved the optimum scenario and decreased further reduction in wheat yields resulting from climate change to 5.2%, 6.8% and 8.5% in the 2030s, 2050s and 2080s, respectively, compared with the 20 November scenario. The planting 5-days earlier scenario showed a decreased impact on climate change adaptation. However, the 10-days early planting scenario increased yield reduction under projected climate change. The cultivar Misr1 was more resistant to rising temperature than Gemiza9. Despite the negative impacts of projected climate change on wheat production, water use efficiency would slightly increase. The ensemble of multi-model estimated impacts and adaptation uncertainties of climate change can assist decision-makers in planning climate adaptation strategies.

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References

  • Ahmed M A, Magda A F, Ebtesam A E (2012). Growth and yield attributes of some newly wheat cultivars in relation to missing an irrigation at different stages of growth. J Appl Sci Res, 8: 5075–5080

    Google Scholar 

  • Aldieri L, Vinci C P (2017). The role of technology spillovers in the process of water pollution abatement for large international firms. Sustainability, 9(5): 868–875

    Google Scholar 

  • Aldieri L, Vinci C P (2018). Innovation effects on employment in hightech and low-tech industries: evidence from large international firms within the triad. Eurasian Business Review, 8(2): 229–243

    Google Scholar 

  • Alexandrov V A, Hoogenboom G (2000). The impact of climate variability and change on crop yield in Bulgaria. Agric Meteorol, 104(4): 315–327

    Google Scholar 

  • Andarzian B, Hoogenboom G, Bannayan M, Shirali M, Andarzian B (2015). Determining optimum sowing date of wheat using CSMCERES-Wheat model. J Saudi Soc Agric Sci, 14(2): 189–199

    Google Scholar 

  • Archontoulis S V, Miguez F E, Moore K J (2014). A methodology and an optimization tool to calibrate phenology of short-day species included in the APSIM PLANT model: application to soybean. Environ Model Softw, 62: 465–477

    Google Scholar 

  • Arora V K, Singh H, Singh B (2007). Analyzing wheat productivity responses to climatic, irrigation and fertilizer-nitrogen regimes in a semi-arid sub-tropical environment using the CERES-Wheat model. Agric Water Manage, 94(1–3): 22–30

    Google Scholar 

  • Asseng S, Ewert F, Martre P, Rotter R P, Lobell D B, Cammarano D, Kimball B A, Ottman M J, Wall G W, White J W, Reynolds M P, Alderman P D, Prasad P V V, Aggarwal P K, Anothai J, Basso B, Biernath C, Challinor A G, De Sanctis G, Doltra J, Fereres E, Garcia-Vila M, Gayler S, Hoogenboom G, Hunt L A, Izaurralde R C, Jabloun M, Jones C D, Kersebaum K C, Koehler A K, Müller C, Naresh Kumar S, Nendel C, O’Leary G, Olesen J E, Palosuo T, Priesack E, Eyshi Rezaei E, Ruane A C, Semenov M A, Shcherbak I, Stöckle C, Stratonovitch P, Streck T, Supit I, Tao F, Thorburn P J, Waha K, Wang E, Wallach D, Wolf J, Zhao Z, Zhu Y (2015). Rising temperatures reduce global wheat production. Nat Clim Chang, 5(2): 143–147

    Google Scholar 

  • Asseng S, Ewert F, Rosenzweig C, Jones J W, Hatfield J L, Ruane A C, Boote K J, Thorburn P J, Rötter R P, Cammarano D, Brisson N, Basso B, Martre P, Aggarwal P K, Angulo C, Bertuzzi P, Biernath C, Challinor A J, Doltra J, Gayler S, Goldberg R, Grant R, Heng L, Hooker J, Hunt L A, Ingwersen J, Izaurralde R C, Kersebaum K C, Müller C, Naresh Kumar S, Nendel C, O’Leary G, Olesen J E, Osborne T M, Palosuo T, Priesack E, Ripoche D, Semenov M A, Shcherbak I, Steduto P, Stöckle C, Stratonovitch P, Streck T, Supit I, Tao F, Travasso M, Waha K, Wallach D, White J W, Williams J R, Wolf J (2013). Uncertainty in simulating wheat yields under climate change. Nat Clim Chang, 3(9): 827–832

    Google Scholar 

  • Asseng S, Keating B A, Fillery I R P, Gregory P J, Bowden J W, Turner N C, Palta J A, Abrecht D G (1998). Performance of the APSIMwheat model in western Australia? Field Crops Res, 57(2): 163–179

    Google Scholar 

  • Asseng S, Kheir A M S, Kassie B T, Hoogenboom G, Abdelaal A I N, Haman D Z, Ruane A (2018). Can Egypt become self-sufficient in wheat? Environ Res Lett, 13: 094012

    Google Scholar 

  • Asseng S, Martre P, Maiorano A, Rötter R P, O’Leary G J, Fitzgerald G J, Girousse C, Motzo R, Giunta F, Babar M A, Reynolds M P, Kheir AMS, Thorburn P J, Waha K, Ruane A C, Aggarwal P K, Ahmed M, Balkovič J, Basso B, Biernath C, Bindi M, Cammarano D, Challinor A J, De Sanctis G, Dumont B, Eyshi Rezaei E, Fereres E, Ferrise R, Garcia-Vila M, Gayler S, Gao Y, Horan H, Hoogenboom G, Izaurralde R C, Jabloun M, Jones C D, Kassie B T, Kersebaum K C, Klein C, Koehler A K, Liu B, Minoli S, Montesino San Martin M, Müller C, Naresh Kumar S, Nendel C, Olesen J E, Palosuo T, Porter J R, Priesack E, Ripoche D, SemenovMA, Stöckle C, Stratonovitch P, Streck T, Supit I, Tao F, Van der Velde M, Wallach D, Wang E, Webber H, Wolf J, Xiao L, Zhang Z, Zhao Z, Zhu Y, Ewert F (2019). Climate change impact and adaptation for wheat protein. Glob Change Biol, 25(1): 155–173

    Google Scholar 

  • Cairns J E, Hellin J, Sonder K, Araus J L, Macrober J F, Thierfelder C, Prasanna B M (2013). Adapting maize production to climate change in sub-Saharan Africa. Food Secur, 5(3): 345–360

    Google Scholar 

  • Ceglar A, Črepinšek Z, Kajfež-Bogataj L, Pogačar T (2011). The simulation of phenological development in dynamic crop model: the Bayesian comparison of different methods. Agric Meteorol, 151(1): 101–115

    Google Scholar 

  • Challinor A J, Watson J, Lobell D B, Howden S M, Smith D R, Chhetri N (2014). A meta-analysis of crop yield under climate change and adaptation. Nat Clim Chang, 4(4): 287–291

    Google Scholar 

  • Challinor A, Wheeler T, Garforth C, Craufurd P, Kassam A (2007). Assessing the vulnerability of food crop systems in Africa to climate change. Clim Change, 83(3): 381–399

    Google Scholar 

  • Cooper P J M, Coe R (2011). Assessing and addressing climate-induced risk in sub-Saharan rainfed agriculture. Exp Agric, 47(2): 179–184

    Google Scholar 

  • Deryng D, Elliott J, Folberth C, Müller C, Pugh T A M, Boote K J, Conway D, Ruane A C, Gerten D, Jones J W, Khabarov N, Olin S, Schaphoff S, Schmid E, Yang H, Rosenzweig C (2016). Regional disparities in the beneficial effects of rising CO2 concentrations on crop water productivity. Nat Clim Chang, 6(8): 786–790

    Google Scholar 

  • Dettori M, Cesaraccio C, Motroni A, Spano D, Duce P (2011). Using CERES-Wheat to simulate durum wheat production and phenology in Southern Sardinia. Field Crops Res, 120(1): 179–188

    Google Scholar 

  • Feng Z, Yao Y, Huilong C, Feng Y (2014). Reusable component model development approach for parallel and distributed simulation. Sci World J, 2014:1–12

    Google Scholar 

  • Godfray H C J, Beddington J R, Crute I R, Haddad L, Lawrence D, Muir J F, Pretty J, Robinson S, Thomas S M, Toulmin C (2010). Food security: the challenge of feeding 9 billion people. Science, 327 (5967). 812–818

    Google Scholar 

  • Godwin D C, Singh U (1998). Nitrogen balance and crop response to nitrogen in upland and lowland cropping systems. In: Tsuji G, Hoogenboom G, Thornton P. eds. Understanding Options for Agricultural Production. Netherlands: Springer, 55–77.

    Google Scholar 

  • Hawkins E, Sutton R (2011). The potential to narrow uncertainty in projections of regional precipitation change. Clim Dyn, 37(1–2): 407–418

    Google Scholar 

  • Hájek P, Stejskal J (2018). R&D cooperation and knowledge apillover effects for sustainable business innovation in the chemical industry. Sustainability, 10(4): 1064–1083

    Google Scholar 

  • Heng L K, Asseng S, Mejahed K, Rusan M (2007). Optimizing wheat productivity in two rain-fed environments of the West Asia-North Africa region using a simulation model. Eur J Agron, 26(2): 121–129

    Google Scholar 

  • Hertel T W (2011). The global supply and demand for agricultural land in 2050: a perfect storm in the making? Am J Agric Econ, 93(2): 259–275

    Google Scholar 

  • Holzworth D P, Huth N I, Devoil P G, Zurcher E J, Herrmann N I, Mclean G, Chenu K, van Oosterom E J, Snow V, Murphy C, Moore A D, Brown H, Whish J P M, Verrall S, Fainges J, Bell LW, Peake A S, Poulton P L, Hochman Z, Thorburn P J, Gaydon D S, Dalgliesh N P, Rodriguez D, Cox H, Chapman S, Doherty A, Teixeira E, Sharp J, Cichota R, Vogeler I, Li F Y, Wang E, Hammer G L, Robertson M J, Dimes J P, Whitbread A M, Hunt J, van Rees H, McClelland T, Carberry P S, Hargreaves J N G, MacLeod N, McDonald C, Harsdorf J, Wedgwood S, Keating B A (2014). APSIM-evolution towards a new generation of agricultural systems simulation. Environ Model Softw, 62: 327–350

    Google Scholar 

  • Hoogenboom G, Jones J W, Wilkens P W, Porter C H, Boote K J, Hunt L A, Singh U, Lizaso J I, White J W, Uryasev O, Ogoshi R, Koo J, Shelia V, Tsuji G Y (2015). Decision Support System for Agrotechnology Transfer (DSSAT) Version 4.6. DSSAT Foundation, Prosser, Washington

    Google Scholar 

  • Hunt L A, Boote K J (1998). Data for model operation, calibration, and evaluation. In: Tsuji G, Hoogenboom G, Thornton P, eds. Understanding Options for Agricultural Production. Netherlands: Springer, 9–39

    Google Scholar 

  • Jacovides C P, Kontoyiannis H (1995). Statistical procedures for the evaluation of evapotranspiration computing models. Agric Water Manage, 27(3–4): 365–371

    Google Scholar 

  • Jeunesse I, Cirelli C, Aubin D, Larrue C, Sellami H, Afifi S, Bellin A, Benabdallah S, Bird D N, Deidda R, Dettori M, Engin G, Herrmann F, Ludwig R, Mabrouk B, Majone B, Paniconi C, Soddu A (2016). Is climate change a threat for water uses in the Mediterranean region? Results from a survey at local scale. Sci Total Environ, 543(Pt B): 981–996

    Google Scholar 

  • Johnen T, Boettcher U, Kage H (2012). A variable thermal time of the double ridge to flag leaf emergence phase improves the predictive quality of a CERES-Wheat type phenology model. Comput Electron Agric, 89: 62–69

    Google Scholar 

  • Jones JW, Hoogenboom G, Porter C H, Boote K J, BatchelorWD, Hunt L A, Wilkens P W, Singh U, Gijsman A J, Ritchie J T (2003). The DSSAT cropping system model? Eur J Agron, 18(3–4): 235–265

    Google Scholar 

  • Kassie B T, Asseng S, Porter C H, Royce F S (2016). Performance of DSSAT N-Wheat across a wide range of current and future growing conditions. Eur J Agron, 81: 27–36

    Google Scholar 

  • Keating B A, Carberry P S, Hammer G L, Probert M E, Robertson M J, Holzworth D, Huth N I, Hargreaves J N G, Meinke H, Hochman Z, Mclean G, Verburg K, Snow V, Dimes J P, Silburn M, Wang E, Brown S, Bristow K L, Asseng S, Chapman S, Mccown R L, Freebairn D M, Smith C J (2003). An overview of APSIM: a model designed for farming systems simulation. Eur J Agron, 18(3–4): 267–288

    Google Scholar 

  • Kheir A M S, El Baroudy A, Aiad M A, Zoghdan M G, Abd El-Aziz M A, Ali M G M, Fullen M A (2019). Impacts of rising temperature, carbon dioxide concentration and sea level on wheat production in North Nile delta. Sci Total Environ, 651(Pt 2): 3161–3173

    Google Scholar 

  • Lin Y, Wu W, Ge Q (2015). CERES-Maize model-based simulation of climate change impacts on maize yields and potential adaptive measures in Heilongjiang Province, China. J Sci Food Agric, 95(14): 2838–2849

    Google Scholar 

  • Ma L, Ahuja L R, Saseendran S A, Malone R W, Green T R, Nolan B T, Bartling P N S, Flerchinger G N, Boote K J, Hoogenboom G (2011). A protocol for parameterization and calibration of RZWQM2 in field research. In: Ahuja L R, Ma L, eds. Methods of Introducing System Models into Agricultural Research. Am Society Agron, Crop Science Society of America, Soil Science Society of America, 1–64

    Google Scholar 

  • Makkonen T, Inkinen T (2018). Sectoral and technological systems of environmental innovation: the case of marine scrubber systems. J Clean Prod, 200: 110–121

    Google Scholar 

  • Martre P, Wallach D, Asseng S, Ewert F, Jones J W, Rötter R P, Boote K J, Ruane A C, Thorburn P J, Cammarano D, Hatfield J L, Rosenzweig C, Aggarwal P K, Angulo C, Basso B, Bertuzzi P, Biernath C, Brisson N, Challinor A J, Doltra J, Gayler S, Goldberg R, Grant R F, Heng L, Hooker J, Hunt L A, Ingwersen J, Izaurralde R C, Kersebaum K C, Müller C, Kumar S N, Nendel C, O’leary G, Olesen J E, Osborne TM, Palosuo T, Priesack E, Ripoche D, Semenov MA, Shcherbak I, Steduto P, Stöckle C O, Stratonovitch P, Streck T, Supit I, Tao F, Travasso M, Waha K, White JW, Wolf J (2015). Multimodel ensembles of wheat growth: many models are better than one. Glob Change Biol, 21(2): 911–925

    Google Scholar 

  • Moriasi D N, Arnold J G, Liew M W V, Bingner R L, Harmel R D, Veith T L, Moriasi D N, Arnold J G, Van Liew M W, Bingner R L, Harmel R DVeith T L (2007). Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans ASABE, 50(3): 885–900

    Google Scholar 

  • Moss R H, Edmonds J A, Hibbard K A, Manning M R, Rose S K, van Vuuren D P, Carter T R, Emori S, Kainuma M, Kram T, Meehl G A, Mitchell J F, Nakicenovic N, Riahi K, Smith S J, Stouffer R J, Thomson A M, Weyant J P, Wilbanks T J (2010). The next generation of scenarios for climate change research and assessment. Nature, 463(7282). 747–756

    Google Scholar 

  • Mostegl N M, Pröbstl-haider U, Jandl R, Haider V (2019). Targeting climate change adaptation strategies to small-scale private forest owners. For Policy Econ, 99: 83–99

    Google Scholar 

  • Müller C, Cramer W, Hare W L, Lotze-Campen H (2011). Climate change risks for African agriculture. Proc Natl Acad Sci USA, 108(11): 4313–4315

    Google Scholar 

  • Osborne T, Rose G, Wheeler T (2013). Variation in the global-scale impacts of climate change on crop productivity due to climate model uncertainty and adaptation. Agric Meteorol, 170: 183–194

    Google Scholar 

  • Paymard P, Bannayan M, Haghighi R S (2018). Analysis of the climate change effect on wheat production systems and investigate the potential of management strategies. Nat Hazards, 91(3): 1237–1255

    Google Scholar 

  • Piras M, Mascaro G, Deidda R, Vivoni E R (2016). Impacts of climate change on precipitation and discharge extremes through the use of statistical downscaling approaches in a Mediterranean basin. Sci Total Environ, 543(Pt B): 952–964

    Google Scholar 

  • Pretty J, Sutherland W J, Ashby J, Auburn J, Baulcombe D, Bell M, Bentley J, Bickersteth S, Brown K, Burke J, Campbell H, Chen K, Crowley E, Crute I, Dobbelaere D, Edwards-Jones G, Funes-Monzote F, Godfray H C J, Griffon M, Gypmantisiri P, Haddad L, Halavatau S, Herren H, Holderness M, Izac A M, Jones M, Koohafkan P, Lal R, Lang T, McNeely J, Mueller A, Nisbett N, Noble A, Pingali P, Pinto Y, Rabbinge R, Ravindranath N H, Rola A, Roling N, Sage C, Settle W, Sha J M, Shiming L, Simons T, Smith P, Strzepeck K, Swaine H, Terry E, Tomich T P, Toulmin C, Trigo E, Twomlow S, Vis J K, Wilson J, Pilgrim S (2010). The top 100 questions of importance to the future of global agriculture. Int J Agric Sustain, 8(4): 219–236

    Google Scholar 

  • Ritchie J T, Singh U, Godwin D, Bowen W T (1998). Cereal growth, development, and yield. In: Tsuji G Y, Hoogenboom G, Thornton P K, eds., Understanding Options for Agricultural Production. Dordrecht: Kluwer Academic, 79–98

    Google Scholar 

  • Robertson M J, Carberry P S, Huth N I, Turpin J E, Probert M E, Poulton P L, Bell M, Wright G C, Yeates S J, Brinsmead R B (2002). Simulation of growth and development of diverse legume species in APSIM. Aust J Agric Res, 53(4): 429–446

    Google Scholar 

  • Rosenzweig C, Elliott J, Deryng D, Ruane A C, Müller C, Arneth A, Boote K J, Folberth C, Glotter M, Khabarov N, Neumann K, Piontek F, Pugh T A M, Schmid E, Stehfest E, Yang H, Jones J W (2014). Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. Proc Natl Acad Sci USA, 111(9): 3268–3273

    Google Scholar 

  • Rosenzweig C E A, Jones J W, Hatfield J L, Ruane A C, Boote K J, Thorburn P, Antle J M, Nelson G C, Porter C, Janssen S, Asseng S, Basso B, Ewert F, Wallach D, Baigorria G, Winter J M (2013). The agricultural model intercomparison and improvement project (AgMIP): protocols and pilot studies. Agric Meteorol, 170: 166–182

    Google Scholar 

  • Roser M, Ortiz-Ospina E (2018). World Population Growth vol 2017: Our World In Data.org

    Google Scholar 

  • Rötter R P, Carter T R, Olesen J E, Porter J R (2011). Crop-climate models need an overhaul? Nat Clim Chang, 1(4): 175–177

    Google Scholar 

  • Rymbai D, Sheikh F M (2018). The insight of agricultural adaptation to climate change: a case of rice growers in Eastern Himalaya, India. Int J Biometeorol, 62(10): 1833–1845

    Google Scholar 

  • Said R (1993). The River Nile Geology and Hydrology and Utilization. Oxford: Pergamon

    Google Scholar 

  • Sayre K D, Rajaram S, Fischer R A (1997). Yield potential progress in short bread wheats in Northwest Mexico. Crop Sci, 37(1): 36–42

    Google Scholar 

  • Swart R, Biesbroek R, Lourenço T C (2014). Science of adaptation to climate change and science for adaptation. Front Environ Sci, (2)

    Google Scholar 

  • Tao F, Rötter R P, Palosuo T, Gregorio Hernández Díaz-Ambrona C, Mínguez M I, Semenov M A, Kersebaum K C, Nendel C, Specka X, Hoffmann H, Ewert F, Dambreville A, Martre P, Rodríguez L, Ruiz-Ramos M, Gaiser T, Höhn J G, Salo T, Ferrise R, Bindi M, Cammarano D, Schulman A H (2018). Contribution of crop model structure, parameters and climate projections to uncertainty in climate change impact assessments. Glob Change Biol, 24(3): 1291–1307

    Google Scholar 

  • Tebaldi C, Knutti R (2007). The use of the multi-model ensemble in probabilistic climate projections. Philos Trans A Math Phys Eng Sci, 365(1857). 2053–2075

    Google Scholar 

  • Thornton P K, Jones P G, Alagarswamy G, Andresen J, Herrero M (2010). Adapting to climate change: agricultural system and household impacts in East Africa. Agric Syst, 103(2): 73–82

    Google Scholar 

  • Tubiello F N, Ewert F (2002). Simulating the effects of elevated CO2 on crops: approaches and applications for climate change. Eur J Agron, 18(1–2): 57–74

    Google Scholar 

  • UNEP (2010). Africa Water Atlas. Division of Early Warning and Assessment United Nations Environment Programme, Nairobi, Kenya

    Google Scholar 

  • USDA (2010). Keys to Soil Taxonomy. 3rd ed. United State Department of Agriculture. Natural Resources Conservation Services (NRCS)

    Google Scholar 

  • van Vuuren D P, Edmonds J, Kainuma M, Riahi K, Thomson A, Hibbard K, Hurtt G C, Kram T, Krey V, Lamarque J F, Masui T, Meinshausen M, Nakicenovic N, Smith S J, Rose S K (2011). The representative concentration pathways: an overview. Clim Change, 109(1–2): 5–31

    Google Scholar 

  • Wallach D, Martre P, Liu B, Asseng S, Ewert F, Thorburn P J, van Ittersum M, Aggarwal P K, Ahmed M, Basso B, Biernath C, Cammarano D, Challinor A J, De Sanctis G, Dumont B, Eyshi Rezaei E, Fereres E, Fitzgerald G J, Gao Y, Garcia-Vila M, Gayler S, Girousse C, Hoogenboom G, Horan H, Izaurralde R C, Jones C D, Kassie B T, Kersebaum K C, Klein C, Koehler A K, Maiorano A, Minoli S, Müller C, Naresh Kumar S, Nendel C, O’Leary G J, Palosuo T, Priesack E, Ripoche D, Rötter R P, Semenov M A, Stöckle C, Stratonovitch P, Streck T, Supit I, Tao F, Wolf J, Zhang Z (2018). Multimodel ensembles improve predictions of crop-environment-management interactions. Glob Chang Biol, 24(11): 5072–5083

    Google Scholar 

  • Wheeler T, von Braun J (2013). Climate change impacts on global food security. Science, 341(6145). 508–513

    Google Scholar 

  • White J W, Hoogenboom G, Kimball B A, Wall G W (2011). Methodologies for simulating impacts of climate change on crop production. Field Crops Res, 124(3): 357–368

    Google Scholar 

  • Whitfield S (2013). Uncertainty, ignorance and ambiguity in crop modelling for African agricultural adaptation. Clim Chang, 1–16

    Google Scholar 

  • Wilby R, Charles S P, Zorita E, Timbal B, Whetton P, Mearns L O (2004). Guidelines for use of climate scenarios developed from statistical downscaling methods. IPCC task group on data and scenario support for impacts and climate analysis

    Google Scholar 

  • Willmott C J (1984). On the evaluation of model performance in physical geography. In: Gaile G L, Willmott C J, eds. Spatial Statistics and Models, Boston: D. Reidel, 443–460

    Google Scholar 

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Acknowledgments

We thank the Agricultural Research Center; Soils, Water and Environment Research Institute (SWERI) for financial support. We are grateful to Dr Alex. C. Ruane (NASA Goddard Institute for Space Studies, New York, USA) for providing us with GCMs of the study area. Authors declare that there is no conflict of interest.

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Ali, M.G.M., Ibrahim, M.M., El Baroudy, A. et al. Climate change impact and adaptation on wheat yield, water use and water use efficiency at North Nile Delta. Front. Earth Sci. 14, 522–536 (2020). https://doi.org/10.1007/s11707-019-0806-4

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