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Adaptability of global olive cultivars to water availability under future Mediterranean climate

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Adaptation to climate change is a major challenge facing the agricultural sector worldwide. Olive (Olea europaea L.) is a global, high value crop currently cultivated in 28 countries worldwide. Global data to assess the vulnerability of the crop to climate variability are scarce, and in some notable cases, such the United Nations Food and Agricutlure Organization database (FAO, 2006), qualitative assessments rather than quantitative indicators are provided. The aim of this study is to demonstrate a new approach to help overcome these constraints toward a globally applicable method to assess the adaptability of olive cultivars. The adaptability of 11 cultivars, widely used in 11 countries worldwide, was studied using a new generic approach based on the evaluation of soil hydrological regime against cultivar-specific hydrological requirements. The approach requires local data, notably on soil hydrological properties, but it is easily transferable to other countries and regions. We applied an agrohydrological model in 60 soil units to determine hydrological indicators both in a reference (1961–1990) and a future (2021–2050) climate case. We compared indicators with cultivar-specific requirements to achieve the target yield; requirements were established using experimental yield response curves. We estimated the probability of adaptation, i.e., the probability that a given cultivar attains the target yield, and we used it to evaluate the cultivar potential distribution in the study area. At the locations where soil hydrological conditions were favorable, the probabilities of adaptation of the cultivars were high in both climate cases. The results show that the area with suitable conditions for the target yield (area of adaptability) decreased under future climate for all the cultivars, with higher reduction for Frantoio and Maiatica and smaller reduction for Itrana, Nocellara, Ascolana, and Kalamata. These cultivars are currently grown in Argentina, United States (US), Australia, France, Greece, and Italy. Our results indicate also that these cultivars require higher available soil water to attain the target yield, i.e., we may expect similar vulnerability in other parts of the world. Based on these findings, we provide some specific recommendations for enrichment of global databases and for further developments of our approach, to increase its potential for global application.

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  1. The A1B scenario depicts a future world characterized by very rapid economic growth, increase in global population, and rapid introduction of new and more efficient technologies. The energy system relies on a balanced combination of energy sources.


  • Abdel-Razik M (1989) A model of the productivity of olive trees under optional water and nutrient supply in desert conditions. Ecol Model 45:179–204

    Google Scholar 

  • Alfieri SM, De Lorenzi F, Menenti M (2013) Mapping air temperature using time series analysis of LST: the SINTESI approach. Nonlinear Process Geophys 20:513–527., 2013

  • Aspinwall MJ, Loik ME, Resco de Dios V, Tjoelker MG, Payton PR, Tissue DT (2015) Utilizing intraspecific variation in phenotypic plasticity to bolster agricultural and forest productivity under climate change. Plant, Cell Environ 38:1752–1764

    Google Scholar 

  • Asseng S, Ewert F, Martre P, Rötter RP, Lobell DB, Cammarano D, Kimball BA, Ottman MJ, Wall GW, White JW, Reynolds MP, Alderman PD, Prasad PVV, Aggarwal PK, Anothai J, Basso B, Biernath C, Challinor AJ, de Sanctis G, Doltra J, Fereres E, Garcia-Vila M, Gayler S, Hoogenboom G, Hunt LA, Izaurralde RC, Jabloun M, Jones CD, Kersebaum KC, Koehler AK, Müller C, Naresh Kumar S, Nendel C, O’Leary G, Olesen JE, Palosuo T, Priesack E, Eyshi Rezaei E, Ruane AC, Semenov MA, Shcherbak I, Stöckle C, Stratonovitch P, Streck T, Supit I, Tao F, Thorburn PJ, Waha K, Wang E, Wallach D, Wolf J, Zhao Z, Zhu Y (2015) Rising temperatures reduce global wheat production. Nat Clim Chang 5:143–147

    Google Scholar 

  • Avolio E, Orlandi F, Bellecci C, Fornaciari M, Federico S (2012) Assessment of the impact of climate change on the olive flowering in Calabria (southern Italy). Theor Appl Climatol 107:531–540

    Google Scholar 

  • Bacelar EA, Correia CM, Mountinho-Pereira JM, Goncalves BC, Lopes JI, Torres-Pereira JMG (2004) Sclerophylly and leaf anatomical traits of five field-grown olive cultivars growing under drought conditions. Tree Physiol 24:233–239

    Google Scholar 

  • Basile A, Coppola A, De Mascellis R, Randazzo L (2006) Scaling approach to deduce field unsaturated hydraulic properties and behavior from laboratory measurements on small cores. Vadose Zone J 5:1005–1016

    Google Scholar 

  • Bastiaanssen WGM, Allen RG, Droogers P, D’Urso G, Steduto P (2007) Twenty-five years modeling irrigated and drained soils: state of the art. Agric Water Manag 92:111–125

    Google Scholar 

  • Ben-Asher J, van Dam J, Feddes RA, Jhorar RK (2006) Irrigation of grapevines with saline water. II. Mathematical simulation of vine growth and yield. Agric Water Manag 83:22–29

    Google Scholar 

  • Berni JAJ, Zarco-Tejada PJ, Sepulcre-Cantó G, Fereres E, Villalobos F (2009) Mapping canopy conductance and CWSI in olive orchards using high resolution thermal remote sensing imagery. Remote Sens Environ 113:2380–2388

    Google Scholar 

  • Bonfante A, Basile A, Langella G, Manna P, Terribile F (2011) A physically oriented approach to analysis and mapping of terroirs. Geoderma 167-168:103–117

    Google Scholar 

  • Bonfante A, Monaco ASM, De Lorenzi F, Manna P, Basile A, Bouma J (2015) Climate change effects on the suitability of an agricultural area to maize cultivation: application of a new hybrid land evaluation system. Adv Agron 133:33–69

    Google Scholar 

  • Bonfante A, Alfieri SM, Albrizio R, Basile A, De Mascellis R, Gambuti A, Giorio P, Langella G, Manna P, Monaco E, Moio L, Terribile F (2017) Evaluation of the effects of future climate change on grape quality through a physically based model application: a case study for the Aglianico grapevine in Campania region, Italy. Agric Syst 152:100–109

    Google Scholar 

  • Bongi G, Palliotti A (1994) Olive. In: Schaffer B, Andresen P (eds) Handbook of environmental physiology of fruit crops, vol 1. CRC, Boca Raton, pp 165–187

    Google Scholar 

  • Bongi G, Soldatini GF, Hubick KT (1987) Mechanism of photosynthesis in olive tree (Olea europaea L.). Photosynthetica 21:572–578

    Google Scholar 

  • Bosabalidis AM, Kofidis G (2002) Comparative effects of drought stress on leaf anatomy of two olive cultivars. Plant Sci 163:375–379

    Google Scholar 

  • Cammalleri C, Ciraolo G, Minacapilli M, Rallo G (2013) Evapotranspiration from an olive orchard using remote sensing-based dual crop coefficient approach. Water Resour Manag 27:4877–4895

    Google Scholar 

  • Challinor AJ, Watson J, Lobell DB, Howden SM, Smith DR, Chhetri N (2014) A meta-analysis of crop yield under climate change and adaptation. Nat Clim Chang 4:287–291

    Google Scholar 

  • Chartzoulakis K, Patakas A, Bosabalidis A (1999) Changes in water relations, photosynthesis and leaf anatomy induced by intermittent drought in two olive cultivars. Environ Exp Bot 42:113–120

    Google Scholar 

  • Connor DJ, Fereres E (2005) The physiology of adaptation and yield expression in olive. Hortic Rev 31:155–229

    Google Scholar 

  • Correa-Tedesco G, Rousseaux MC, Searles PS (2010) Plant growth and yield responses in olive (Olea europaea) to different irrigation levels in an arid region of Argentina. Agric Water Manag 97:1829–1837

    Google Scholar 

  • Craufurd PQ, Vadez V, Krishna Jagadish SV, Vara Prasad PV, Zaman-Allah M (2013) Crop science experiments designed to inform crop modeling. Agric For Meteorol 170:8–18

    Google Scholar 

  • Crescimanno G, Morga F, Ventrella D (2012) Application of the SWAP model to predict impact of climate change on soil water balance in a Sicilian vineyard. Ital J Agron 7(e17):116–123

    Google Scholar 

  • d’Andria R, Lavini A, Alvino A, Tognetti R (2008) Effects of deficit irrigation on water relations of olive trees (Olea europaea L. cultivars Frantoio and Leccino). Acta Hortic (792):217–223

  • De Lorenzi F, Alfieri SM, Monaco E, Bonfante A, Basile A, Patanè C, Menenti M (2017) Adaptability to future climate of irrigated crops: the interplay of water management and cultivars responses. A case study on tomato. Biosyst Eng 157:45–62

    Google Scholar 

  • De Melo-Abreu JP, Barranco D, Cordeiro A, Tous J, Rogado BM, Villalobos FJ (2004) Modelling olive flowering date using chilling for dormancy release and thermal time. Agric For Meteorol 125:117–127

    Google Scholar 

  • Doorenbos J, Plusje JMGA, Kassam AH, Branscheid V, Bentvelsen CLM (1979) Yield response to water. FAO Irrigation and Drainage Paper 33. Rome: FAO

  • EEA (2010) The European environment—state and outlook 2010: synthesis. European Environment Agency, Copenhagen

    Google Scholar 

  • Ennajeh M, Vadel AM, Cochard H, Khemira H (2010) Comparative impacts of water stress on the leaf anatomy of a drought-resistant and a drought-sensitive olive cultivar. J Hortic Sci Biotechnol 85:289–294

    Google Scholar 

  • Esposito S (2010) Prime caratterizzazioni agro-climatiche delle aree di studio di AGROSCENARI mediante i dati dei nodi di griglia. AGROSCENARI (Ed.). CREA -CMA, Rome, Italy

  • Estes LD, Beukes H, Bradley BA, Debats SR, Oppenheimer M, Ruanek AC, Schulze R, Tadross M (2013) Projected climate impacts to South African maize and wheat production in 2055: a comparison of empirical and mechanistic modeling approaches. Glob Chang Biol 19:3762–3774

    Google Scholar 

  • FAO (2006) Olive Germplasm Database. Accessed 1 March 2018

  • FAO (2018) Food and agriculture data. http://wwwfaoorg/faostat/ Accessed 2 May 2018

  • Faraloni C, Cutino I, Petruccelli R, Leva AR, Lazzeri S, Torzillo G (2011) Chlorophyll fluorescence technique as a rapid tool for in vitro screening of olive cultivars (Olea europaea L.) tolerant to drought stress. Environ Exp Bot 73:49–56

    Google Scholar 

  • Feddes RA, Kowalik PJ, Zaradny H (1978) Simulation of field water use and crop yield. Pudoc, Wageningen

    Google Scholar 

  • Fernandes-Silva AA, Ferreira TC, Correia CM, Malheiro AC, Villalobos FJ (2010) Influence of different irrigation regimes on crop yield and water use efficiency of olive. Plant Soil 333:35–47

    Google Scholar 

  • Fernández JE (2014) Understanding olive adaptation to abiotic stresses as a tool to increase crop performance. Environ Exp Bot 103:158–179

    Google Scholar 

  • Fernández JE, Moreno F (1999) Water use by the olive tree. J Crop Prod 2:101–162

    Google Scholar 

  • Fernández JE, Torres-Ruiz JM, Diaz-Espejo A, Montero A, Álvarez R, Jiménez MD, Cuerva J, Cuevas MV (2011) Use of maximum trunk diameter measurements to detect water stress in mature ‘Arbequina’ olive trees under deficit irrigation. Agric Water Manag 98:1813–1821

    Google Scholar 

  • Field CB, Barros VR, Mach KJ, et al (2014) Technical summary. In: Field CB, Barros VR, Dokken DJ et al (eds) Climate change 2014: impacts, adaptation, and vulnerability. Part A: global and sectoral aspects. Contribution of working group II to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, pp. 35–94

  • Fiorino P (2003) Olea. Trattato di olivicoltura. Edagricole, New Business Media, Milano, IT. ISBN 978-88-506-4938-9

  • Fujihara Y, Tanaka K, Watanabe T (2008) Assessing the impacts of climate change on the water resources of the Seyhan River Basin in Turkey: use of dynamically downscaled data for hydrologic simulations. J Hydrol 353:33–48

    Google Scholar 

  • Giorio P, Sorrentino G, d’Andria R (1999) Stomatal behaviour, leaf water status and photosynthetic response in field-grown olive trees under water deficit. Environ Exp Bot 42:95–104

    Google Scholar 

  • Guerfel M, Boujnah D, Baccouri B, Zarrouk M (2007) Evaluation of morphological and physiological traits for drought tolerance in 12 Tunisian olive varieties (Olea europaea L.). J Agron 6:356–361

    Google Scholar 

  • Gutierrez AP, Ponti L, Cossu Q (2009) Effects of climate warming on olive and olive fly (Bactrocera oleae (Gmelin)) in California and Italy. Clim Chang 95:195–217

    Google Scholar 

  • Hargreaves GH, Samani ZA (1985) Reference crop evapotranspiration from temperature. Appl Eng Agric 1:96–99

    Google Scholar 

  • Hengl T, Heuvelink GB, Stein A (2003) Comparison of kriging with external drift and regression-kriging. Technical note, ITC, NL

  • Iglesias A, Quiroga S, Schlickenrieder J (2010) Climate change and agricultural adaptation: assessing management uncertainty for four crop types in Spain. Clim Res 44:83–94

    Google Scholar 

  • Iniesta F, Testi L, Orgaz F, Villalobos FJ (2009) The effects of regulated and continuous deficit irrigation on the water use, growth and yield of olive trees. Eur J Agron 30:258–265

    Google Scholar 

  • Kroes JG, Van Dam JC, Groenendijk P, Hendriks RFA, Jacobs CMJ (2009) SWAP version 3.2. Theory description and user manual. Alterra report 1649(02). Alterra, Wageningen

    Google Scholar 

  • Kroes J, Supit I, Van Dam J, Van Walsum P, Mulder M (2017) Impact of capillary rise and recirculation on crop yields. Hydrol Earth Syst Sci Discuss:1–31.

  • Lo Bianco R, Scalisi A (2017) Water relations and carbohydrate partitioning of four greenhouse-grown olive genotypes under long-term drought. Trees 31:717–727

    Google Scholar 

  • Lobell DB, Field CB, Cahill KN, Bonfils C (2006) Impacts of future climate change on California perennial crop yields: model projections with climate and crop uncertainties. Agric For Meteorol 141:208–218

    Google Scholar 

  • Mäkinen H, Kaseva J, Virkajärvi P, Kahiluoto H (2017) Shifts in soil–climate combination deserve attention. Agric For Meteorol 234–235:236–246

    Google Scholar 

  • Maraseni TN, Mushtaq S, Reardon-Smith K (2012) Climate change, water security and the need for integrated policy development: the case of on-farm infrastructure investment in the Australian irrigation sector. Environ Res Lett 7:034006

    Google Scholar 

  • Martinez-Ferri E, Muriel-Fernandez J, Diaz J (2013) Soil water balance modelling using SWAP: an application for irrigation water management and climate change adaptation in citrus. Outlook Agr 42:93–102

  • Menenti M, De Lorenzi F, Bonfante A, Cavallaro V, Lavini A, Raccuia A, d’Andria R, Leone A, De Mascellis R (2008) Biodiversity of most important Mediterranean crops: a resource for the adaptation of agriculture to a changing climate. Ital J Agrometeorol 2:22–37

  • Menenti M, Alfieri SM, Bonfante A, Riccardi M, Basile A, Monaco E, De Michele C, De Lorenzi F (2015) Adaptation of irrigated and rainfed agriculture to climate change: the vulnerability of production systems and the potential of intraspecific biodiversity. Case studies in Italy. In: Leal Filho W (ed) Handbook of climate change adaptation. Springer, Berlin, pp 1381–1421

    Google Scholar 

  • Minacapilli M, Agnese C, Blanda F, Cammalleri C, Ciraolo G, D’Urso G, Iovino M, Pumo D, Provenzano G, Rallo G (2009) Estimation of actual evapotranspiration of Mediterranean perennial crops by means of remote-sensing based surface energy balance models. Hydrol Earth Syst Sci 13:1061–1074

    Google Scholar 

  • Ministero dell'Agricoltura e delle Foreste (1990) Analisi climatologica e progettazione della rete agrometereologica nazionale. Nord Italia, Puglia e Sicilia. Ministero dell'Agricoltura e delle Foreste (MAF). Ufficio Centrale di Ecologia Agraria, Roma, pp 1–97

    Google Scholar 

  • Monaco E, Bonfante A, Alfieri SM, Basile A, Menenti M, De Lorenzi F (2014) Climate change, effective water use for irrigation and adaptability of maize: a case study in southern Italy. Biosyst Eng 128:82–99

    Google Scholar 

  • Monteith JL (1965) Evaporation and environment. Symp Soc Exp Biol 19:205–234

    Google Scholar 

  • Moriana A, Orgaz F, Pastor M, Fereres E (2003) Yield responses of a mature olive orchard to water deficits. J Am Soc Hortic Sci 128:425–431

    Google Scholar 

  • Moriondo M, Stefanini F, Bindi M (2008) Reproduction of olive tree habitat suitability for global change impact assessment. Ecol Model 218:95–109

    Google Scholar 

  • Moriondo M, Trombi G, Ferrise R, Brandani G, Dibari C, Ammann CM, Lippi MM, Bindi M (2013) Olive trees as bio-indicators of climate evolution in the Mediterranean Basin. Glob Ecol Biogeogr 22:818–833

    Google Scholar 

  • Moriondo M, Ferrise R, Troimbi G, Brilli L, Dibari C, Bindi M (2015) Modelling olive trees and grapevines in a changing climate. Environ Model Softw 72:387–401

    Google Scholar 

  • Mualem Y (1976) A new model for predicting the hydraulic conductivity of unsaturated porous media. Water Resour Res 12(3):513–522

    Google Scholar 

  • OLEA Databases (2008) Olive germplasm (Olea europaea L.). doi: Accessed 10 March 2018

  • Olesen JE, Trnka M, Kersebaum KC, Skjelvåg AO, Seguin B, Peltonen-Sainio P, Rossi F, Kozyra J, Micale F (2011) Impacts and adaptation of European crop production systems to climate change. Eur J Agron 34:96–112

    Google Scholar 

  • Orlandi F, Garcia-Mozo H, Dhiab AB, Galán C, Msallem M, Romano B, Abichou M, Dominguez-Vilches E, Fornaciari M (2013) Climatic indices in the interpretation of the phenological phases of the olive in Mediterranean areas during its biological cycle. Clim Chang 116:263–284

    Google Scholar 

  • Osborne C, Chuine I, Viner D, Woodward F (2000) Olive phenology as a sensitive indicator of future climatic warming in the Mediterranean. Plant Cell Environ 23:701–710

    Google Scholar 

  • Priestley C, Taylor R (1972) On the assessment of surface heat flux and evaporation using large-scale parameters. Mon Weather Rev 100(2):81–92

    Google Scholar 

  • Rallo G, Agnese C, Blanda F, Minacapilli M, Provenzano G (2010) Agro-hydrological models to schedule irrigation of Mediterranean tree crops. Ital J Agrometeorol 1:11–21

  • Rallo G, Agnese C, Minacapilli M, Provenzano G (2012) Comparison of SWAP and FAO agro-hydrological models to schedule irrigation of wine grape. J Irrig Drain Eng 138:1–11

    Google Scholar 

  • Reyer CPO, Leuzinger S, Rammig A, Wolf A, Bartholomeus RP, Bonfante A, de Lorenzi F, Dury M, Gloning P, Abou Jaoudé R, Klein T, Kuster TM, Martins M, Niedrist G, Riccardi M, Wohlfahrt G, de Angelis P, de Dato G, François L, Menzel A, Pereira M (2013) A plant’s perspective of extremes: terrestrial plant responses to changing climatic variability. Glob Chang Biol 19:75–89

    Google Scholar 

  • Reynolds WD, Elrick DE (2002) Pressure infiltrometer. In: Dane JH, Topp GC (eds) Methods of soil analysis. Soil Science Society of America, Madison, pp 826–836

    Google Scholar 

  • Ritchie JT (1972) Model for predicting evaporation from a row crop with incomplete cover. Water Resour Res 8:1204–1213

    Google Scholar 

  • Rötter RP, Höhn J, Trnka M, Fronzek S, Carter TR, Kahiluoto H (2013) Modelling shifts in agroclimate and crop cultivar response under climate change. Ecol Evol 3:4197–4214

  • Sofo A (2011) Drought stress tolerance and photoprotection in two varieties of olive tree. Acta Agric Scand Sect B Soil Plant Sci 61:711–720

    Google Scholar 

  • Sofo A, Manfreda S, Fiorentino M, Dichio B, Xiloyannis C (2008) The olive tree: a paradigm for drought tolerance in Mediterranean climates. Hydrol Earth Syst Sci 12:293–301

    Google Scholar 

  • Tanasijevic L, Todorovic M, Pereira LS, Lionello P (2014) Impacts of climate change on olive crop evapotranspiration and irrigation requirements in the Mediterranean region. Agric Water Manag 144:54–68

    Google Scholar 

  • Terribile F, Di Gennaro A, De Mascellis R (1996) Carta dei suoli della Valle Telesina. Progetto U.O.T. Relazione finale convenzione CNR-ISPAIM-Regione Campania Assessorato alla Agricoltura

  • Terribile F, Agrillo A, Bonfante A, Buscemi G, Colandrea M, D’Antonio A, de Mascellis R, de Michele C, Langella G, Manna P, Marotta L, Mileti FA, Minieri L, Orefice N, Valentini S, Vingiani S, Basile A (2015) A web-based spatial decision supporting system for land management and soil conservation. Solid Earth 6:903–928

    Google Scholar 

  • Tognetti R, Sebastiani L, Vitagliano C, Raschi A, Minnocci A (2001) Responses of two olive tree (Olea europaea L.) cultivars to elevated CO2 concentration in the field. Photosynthetica 39:403–410

    Google Scholar 

  • Tognetti R, d’Andria R, Morelli G, Calandrelli D, Fragnito F (2004) Irrigation effects on daily and seasonal variations of trunk sap flow and leaf water relations in olive trees. Plant Soil 263:249–264

    Google Scholar 

  • Tognetti R, d’Andria R, Lavini A, Morelli G (2006) The effect of deficit irrigation on crop yield and vegetative development of Olea europaea L. (cvs. Frantoio and Leccino). Eur J Agron 25:356–364

    Google Scholar 

  • Tognetti R, Giovannelli A, Lavini A, Morelli G, Fragnito F, d’Andria R (2009) Assessing environmental controls over conductances through the soil–plant–atmosphere continuum in an experimental olive tree plantation of southern Italy. Agric For Meteorol 149:1229–1243

    Google Scholar 

  • Tomozeiu R, Cacciamani C, Pavan V, Morgillo A, Busuioc A (2007) Climate change scenarios for surface temperature in Emilia-Romagna (Italy) obtained using statistical downscaling models. Theor Appl Climatol 90(1–2):25–47

    Google Scholar 

  • Tomozeiu R, Agrillo G, Cacciamani C, Pavan V (2013) Statistically downscaled climate change projections of surface temperature over northern Italy for the periods 2021–2050 and 2070–2099. Nat Hazards:1–26

  • Tugendhaft Y, Eppel A, Kerem Z, Barazani O, Ben-Gal A, Kadereit JW, Dag A (2016) Drought tolerance of three olive cultivars alternatively selected for rain fed or intensive cultivation. Sci Hortic 199:158–162

    Google Scholar 

  • Van der Linden P, Mitchell J F B, editors (2009) ENSEMBLES: climate change and its impacts: summary of research and results from the ENSEMBLES project. Met Office Hadley Centre, FitzRoy Road, Exeter EX1 3PB, UK 160

  • Van Genuchten MT (1980) A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Sci Soc Am J 44(5):892–898

    Google Scholar 

  • Ventrella D, Charfeddine M, Moriondo M, Rinaldi M, Bindi M (2012) Agronomic adaptation strategies under climate change for winter durum wheat and tomato in southern Italy: irrigation and nitrogen fertilization. Reg Environ Chang 12:407–419

    Google Scholar 

  • Villalobos FJ, Testi L, Hidalgo J, Pastor M, Orgaz F (2006) Modelling potential growth and yield of olive (Olea europaea L.) canopies. Eur J Agron 24:296–303

    Google Scholar 

  • Villani G, Tomei F, Tomozeiu R, Marletto V (2011) Climatic scenarios and their impacts on irrigated agriculture in Emilia-Romagna, Italy. Ital J Agrometeorol 16(1):5–16

  • Viola F, Noto LV, Cannarozzo M, La Loggia G, Porporato A (2012) Olive yield as a function of soil moisture dynamics. Ecohydrology 5:99–107

    Google Scholar 

  • Viola F, Caracciolo D, Pumo D, Noto LV, La Loggia G (2014) Future climate forcings and olive yield in a Mediterranean orchard. Water 6:1562–1580

    Google Scholar 

  • Wackernagel H (2003) Multivariate geostatistics. Springer, Berlin, p 388

    Google Scholar 

  • Way DA, Oren R, Kroner Y (2015) The space-time continuum: the effects of elevated CO2 and temperature on trees and the importance of scaling. Plant Cell Environ 38:991–1007

    Google Scholar 

  • White JW, Hoogenboom G, Kimball BA, Wall GW (2011) Methodologies for simulating impacts of climate change on crop production. Field Crop Res 124:357–368

    Google Scholar 

  • Wind GP (1966) Capillary conductivity data estimated by a simple method. In: Water in the unsaturated zone. IASH, Proceedings of the Wageningen Symposium, Wageningen, NL, pp 181–191

  • Wösten JHM, Lilly A, Nemes A, Le Bas C (1998) Using existing soil data to derive hydraulic parameters for simulation models in environmental studies and in land use planning. Report 156. DLO-Staring Centre, Wageningen, NL

  • Xiloyannis C, Palese AM (2002) Efficienza dell’uso dell’acqua nella coltivazione dell’olivo. International course “Gestione dell’acqua e del territorio per un’olivicoltura sostenibile”. Napoli (IT), 24–28 September 2001, pp. 121–137

  • Zupanc V, Pintar M, Kajfez-Bogataj L, Bergant K (2007) Impact estimation of climate change on the irrigation demand for fruit growing in western Slovenia. Die Bodenkultur 83:1–4

    Google Scholar 

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The climatic datasets were produced by the Agenzia Regionale Prevenzione e Ambiente (Arpae – Emilia Romagna) and by the “Research Unit for Climatology and Meteorology applied to Agriculture” (CREA-CMA) within the project AGROSCENARI. The authors are grateful to Dott. Alberto Ziello of the Campania Region SeSIRCA for supplying information on olive groves management in the Valle Telesina; Dott. Riccardo d’Andria, Dr. Antonella Lavini, Dott. Giovanni Morelli, and Dott. Fulvio Fragnito for supplying some cultivar datasets. Thanks are also extended to Mrs. Nadia Orefice for performing soil hydraulic property measurements.


The work was carried out within the Italian national project AGROSCENARI funded by the Ministry for Agricultural, Food and Forest Policies (MIPAAF, D.M. 8608/7303/2008).

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1.1 Appendix 1: Description of the procedure for producing the climate datasets

1.1.1 Reference climate case

The reference climate dataset, produced by the Research Unit for Climatology and Meteorology applied to Agriculture (CREA-CMA), was derived by applying the kriging with external drift method (Hengl et al. 2003; Wackernagel 2003) to the meteorological data of the national agrometeorological database (Ministero dell’Agricoltura e delle Foreste 1990). Three auxiliary variables were applied: distance, exposure, and difference of altitude between points. Daily meteorological data were gridded at 35 × 35-km resolution for the period 1950–onwards (Esposito 2010).

1.1.2 Future climate case

Daily values of maximum and minimum temperatures as well as precipitation in the future climate case were produced by the Agenzia Regionale Prevenzione e Ambiente (Arpae – Emilia Romagna) in two phases. Firstly, seasonal mean and standard deviation of the meteorological variables were calculated by a statistical downscaling model (Tomozeiu et al. 2007) using predictions by means of atmosphere-ocean-coupled general circulation models (AOGCM) under emission scenario A1BFootnote 1 (ENSEMBLES, Van der Linden and Mitchell 2009). The results were then applied to a weather generator to produce 50 realizations of the daily values of the variables for a representative year of the period between 2021 and 2050. Further details about the procedure were provided by Villani et al. (2011) and Tomozeiu et al. (2013).

1.2 Appendix 2: Description of the soil-water-atmosphere-plant (SWAP) model

The water balance analysis was performed using the SWAP model (Kroes et al. 2009). Assuming one-dimensional vertical flow processes, it calculates the soil water flow through the Richards’ equation that can be written as:

$$ C(h)\cdot \frac{\partial h}{\partial t}=\partial \left[k(h)\cdot \left(\frac{\partial h}{\partial z}+1\right)\right]/\partial z-S(h) $$

where C(h) = ∂θ/∂h is the soil water capacity, θ (cm3 cm−3) is the volumetric soil water content, h (cm) is the soil water pressure head, t (days) is the time, z (cm) is the vertical coordinate taken positively upward, k (cm day−1) is the hydraulic conductivity, and S (cm3 cm−3 day−1) is the water extraction rate by plant roots. The Richards’ equation is solved for the unsaturated-saturated zone using an implicit finite difference scheme for numerical integration. Soil water retention is described by the unimodal θ(h) relationship proposed by Van Genuchten (1980) and expressed here in terms of the effective saturation, Se, as follows:

$$ {\displaystyle \begin{array}{l}\mathrm{Se}={\left[\frac{1}{1+{\left(\alpha \left|h\right|\right)}^n}\right]}^m\\ {}\end{array}} $$

where Se = (θ − θr)/(θ0 − θr), θr and θ0 are the residual water content and the water content at h = 0, respectively, and α (cm−1), n (–), and m (–) are curve-fitting parameters. Mualem’s expression (Mualem 1976) was applied to calculate relative hydraulic conductivity (kr). Assuming m = 1 − 1/n, Van Genuchten (1980) obtained a closed-form analytical solution to predict kr at specified volumetric water content:

$$ {k}_{\mathrm{r}}\left(\mathrm{Se}\right)=\frac{k\left(\mathrm{Se}\right)}{k_0}={\mathrm{Se}}^{\tau }{\left[1-{\left(1-{\mathrm{Se}}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$m$}\right.}\right)}^m\right]}^2 $$

where k0 is the hydraulic conductivity at θ0 and τ is a parameter which accounts for the dependence of the tortuosity and partial correlation between adjacent pores.

In SWAP, the condition at the bottom boundary can be set in several ways (e.g., pressure head, water table, fluxes, impermeable layer, unit gradient). The upper boundary condition is generally described by the potential evapotranspiration (ETp), daily precipitation, and irrigation (if it is applied). ETp can be calculated by the Penman–Monteith equation (Monteith 1965). At a large scale, due to the lack of detailed information required for its application (i.e., air temperature and humidity, net radiation, wind speed), simplified approaches can be used, i.e., Priestley and Taylor (1972) or Hargreaves and Samani (1985). Specifically, SWAP calculates potential evapotranspiration rate of a canopy (ETp) from reference evapotranspiration (ETo) and a crop factor. Then, the potential evapotranspiration is partitioned in soil potential evaporation (Ep) and crop potential transpiration (Tp) according to the LAI evolution, following the approach of Ritchie (1972).

SWAP simulates water uptake and actual transpiration according to the model proposed by Feddes et al. (1978) where actual root water uptake (S) is described as a function of the soil water pressure head (h):

$$ S(h)=\alpha (h)\cdot {S}_{\mathrm{max}}=\alpha (h)\cdot \frac{T_{\mathrm{p}}}{\left|{z}_r\right|} $$

where Smax is the maximum root water extraction rate; α(h) is a semi-empirical function of pressure head h, varying between 0 and 1; and zr (cm) is the thickness of the root layer. The function α(h) defines four critical values of h, which are related to crop type and to potential transpiration rates. The actual transpiration rate (Ta) is computed by the integration of S(h) over the root layer. The root depth is specified by the user as a function of crop development stage.

1.3 Appendix 3: A generic yield response function

Fig. 10
figure 10

Relative yield of a cultivar as a function of water availability indicators: observed relative soil water deficit (RSWDobs) or observed relative evapotranspiration deficit (RETDobs). The relative yield desirable for adaptation (Yrtarget) and the corresponding values of cultivar-specific hydrological requirements (RSWDreq or RETDreq) are shown

1.4 Appendix 4: Description of the hydrological behavior of two soil typological units (STU 52 and 91): physical properties and simulated data

Figure 11a, b shows the time series of daily values of calculated relative transpiration (i.e., Tcalc/TMcalc) and soil water pressure head (expressed as log10|h|) between − 0.4 and − 0.6 m (representing the interval of maximum root extraction in the soil layer)in STU 52 (Fig. 11a) and in STU 91 (Fig. 11b). Daily values were averaged within the reference climate. Close to day of year (DoY) 160, the increase of log10|h| and the decrease of relative transpiration indicated the start of water stress. Close to DoY 240, the maximum level of water stress was reached; the variability of log10|h| values in the maximum root extraction layer was negligible and log10|h| values reached approximately the wilting point. This indicated that water-stressed olive tree was able to extract all the available water in the maximum root extraction layer. On DoY 240, relative transpiration was higher in STU 52 (Fig. 11a) than in STU 91 (Fig. 11b) suggesting a lower plant water deficit. Moreover, in STU 52, the water stress period (Tcalc/TMcalc ≤ 0.8) was shorter than in STU 91. Figure 12a, b shows respectively soil hydraulic conductivity and water retention curves, based on the Mualem-van Genuchten model (Van Genuchten 1980), both in STU 52 and 91. In each soil, the variability among the horizons was low both in hydraulic conductivity and water retention, but differences between the two STUs were larger. The soil water retention curves showed a similar pattern close to saturation but they differentiated as |h| increased, with large differences (more than 0.10 cm3 cm−3) at very dry conditions (i.e., |h> 10,000 cm). It should be noted that STU 52 had a higher available water capacity (AWC = 205 mm) than STU 91 (AWC = 172 mm). The vertical profile of log10|h| on DoY 240 shows the different patterns of soil water pressure head in STU 52 (Fig. 11c) and in STU 91 (Fig. 11d), caused by different soil physical properties and profile layering. In STU 91, the average of log10|h| was higher than 3.6 in the entire root layer, whereas lower log10|h| values occurred in the upper and lower soil layers of STU 52. Therefore, a larger amount of water was available for root uptake in the upper and lower soil layers of STU 52.

Fig. 11
figure 11

a, b Mean (lines) and standard deviation (bars) of daily values, averaged over the 1961–1990 period, of calculated relative transpiration (Tcalc/TMcalc) (black line) and soil water pressure head (expressed as log10|h|, gray line) in the soil layer of maximum root extraction. Variables were simulated in soil typological unit (STU) 52 (a) and in STU 91 (b). c, d Mean (lines) and standard deviation (bars) of water pressure head (log10|h|) along the soil profile, on DoY 240 in the 1961–1990 period. Variables were simulated in STU 52 (c) and STU 91 (d)

Fig. 12
figure 12

Soil hydraulic conductivity (a) and water retention curves (b) of soil horizons in soil typological units 52 and 91

1.5 Appendix 5: Table of changes in fractional area of Valle Telesina from reference to future climate within the ranges of the distributions of hydrological indicators

Table 6 Transition matrix of the changes within the ranges of the distributions of calculated hydrological indicators relative evapotranspiration deficit (RETDcalc) and relative soil water deficit (RSWDcalc), from reference to future climate case. Data were expressed as percentage of the surface of Valle Telesina. The distribution of each hydrological indicator was set up by pooling indicator values in each soil and year in reference and future climate case; the ranges of the distributions were defined by the quartiles (Q)

1.6 Appendix 6: Symbols and abbreviations


Available water capacity


Bottom boundary condition


Carbon dioxide




Crop water stress index


Day of year

E p :

Soil potential evaporation


Calculated actual crop evapotranspiration (mm)


Reference evapotranspiration (mm)


Observed actual crop evapotranspiration (mm)


Crop potential evapotranspiration (mm)


Calculated maximum crop evapotranspiration (mm)


Observed maximum crop evapotranspiration (mm)


Free-air CO2 enrichment


Food and Agriculture Organization of the United Nations

h :

Soil water pressure head (cm)

k :

Soil hydraulic conductivity (cm day−1)


Leaf area index (m2 m−2)

M :

Median of probability of adaptation (–)

p :

Soil water depletion fraction

P :

Probability of adaptation (–)

Q :


R :

Yearly rainfall (mm)

R 2 :

Coefficient of determination of regressions


Calculated relative evapotranspiration deficit ( ̶)


Calculated relative evapotranspiration deficit averaged over time ( ̶)


Calculated relative evapotranspiration deficit averaged over 1 year (–)


Observed relative evapotranspiration deficit (–)


Cultivar-specific hydrological requirement calculated on relative evapotranspiration deficit (–)


Threshold value of relative evapotranspiration deficit (–)


Calculated relative soil water deficit (–)


Calculated relative soil water deficit averaged over time (–)


Calculated relative soil water deficit averaged over 1 year (–)


Observed relative soil water deficit (–)


Cultivar-specific hydrological requirement calculated on relative soil water deficit (–)


Threshold value of relative soil water deficit (–)


Calculated relative transpiration deficit (–)


Standard error


Sensitivity index (mm−1)


Soil mapping unit


Soil typological unit


Soil-water-atmosphere-plant model

T calc :

Calculated actual crop transpiration (mm)

T p :

Crop potential transpiration (mm)


Calculated maximum crop transpiration (mm)

Y obs :

Observed actual yield (t)


Relative yield (–)


Target relative yield for adaptation (–)


Observed maximum yield (t)

θ :

Volumetric soil water content (cm3 cm−3)

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Alfieri, S.M., Riccardi, M., Menenti, M. et al. Adaptability of global olive cultivars to water availability under future Mediterranean climate. Mitig Adapt Strateg Glob Change 24, 435–466 (2019).

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