Abstract
ERA5-Land reanalysis (ELR) climate time series has proven useful in (hydro)meteorological studies, however, its adoption for local studies is limited due to accuracies constraints. Meanwhile, local agricultural use of ELR could help data-scarce countries by addressing gaps in (hydro)meteorological variables. This study aimed to evaluate the first applicability of the ELR climate time series for modeling maize and potato irrigation water demand (IWD) at field scale and examined the performance of ELR precipitation with bias correction (DBC) and without bias correction (WBC). Yield, actual evapotranspiration (ETa), irrigation, water balance, and crop water productivity (CWP) were evaluated using the deficit irrigation toolbox. The study found that maize (13.98–14.49 ton/ha) and potato (6.84–8.20 tons/ha) had similar mean seasonal yield under different irrigation management strategies (IMS). The Global Evolutionary Technique for OPTimal Irrigation Scheduling (GET-OPTIS_WS) IMS had the highest mean seasonal yields under DBC and WBC, while rainfall and constant IMS had the most crop failures. DBC had a higher mean seasonal ETa than WBC, except for the potato FIT and rainfall IMS. Global Evolutionary Technique for OPTimal Irrigation Scheduling: one common schedule per crop season (GET-OPTIS_OS) and GET-OPTIS_WS IMS outperformed conventional IMS in IWD by 44%. Overall, GET-OPTIS_OS and GET-OPTIS_WS performed best for maize and potato CWP in terms of IWD, scheduling, and timing. Therefore, adoption of ELR climate time series and advanced irrigation optimization strategies such as GET-OPTIS_OS and GET-OPTIS_WS can be beneficial for effective and efficient management of limited water resources, where agricultural water allocation/resource is limited.
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References
Adeboye OB, Schultz B, Adeboye AP, Adekalu KO, Osunbitan JA (2021) Application of the AquaCrop model in decision support for optimization of nitrogen fertilizer and water productivity of soybeans. Inform Process Agric 8(3):419–436. https://doi.org/10.1016/j.inpa.2020.10.002
Al-Mukhtar M, Dunger V, Merkel B (2014) Assessing the impacts of climate change on hydrology of the upper reach of the spree river: Germany. Water Resour Manage 28:2731–2749. https://doi.org/10.1007/s11269-014-0675-2
Andales AA, Chávez JL, Bauder TA (2011) Irrigation scheduling: the water balance approach (Doctoral dissertation, Colorado State University. Libraries)
Arnold JG, Kiniry JR, Srinivasan R, Williams JR, Haney EB, Neitsch SL (2013) SWAT 2012 input/output documentation. Texas Water Resources Institute
Aryal JP, Sapkota TB, Khurana R, Khatri-Chhetri A, Rahut DB, Jat ML (2020) Climate change and agriculture in South Asia: adaptation options in smallholder production systems. Environ Dev Sustain 22(6):5045–5075. https://doi.org/10.1007/s10668-019-00414-4
Awogbemi O, Kallon DV, Von, Owoputi AO (2022) Biofuel generation from potato peel waste: current state and prospects. Recycling 7(2):23. https://doi.org/10.3390/recycling7020023
Badr MA, El-Tohamy WA, Salman SR, Gruda N (2022) Yield and water use relationships of potato under different timing and severity of water stress. Agric Water Manag. https://doi.org/10.1016/j.agwat.2022.107793
Bell B, Hersbach H, Simmons A, Berrisford P, Dahlgren P, Horányi A, Muñoz-Sabater J, Nicolas J, Radu R, Schepers D, Soci C, Villaume S, Bidlot JR, Haimberger L, Woollen J, Buontempo C, Thépaut JN (2021) The ERA5 global reanalysis: preliminary extension to 1950. Q J R Meteorol Soc 147(741):4186–4227. https://doi.org/10.1002/qj.4174
Chai Q, Gan Y, Zhao C, Wang HL, Waskom RM, Niu Y, Siddique KHM (2016) Regulated deficit irrigation for crop production under drought stress: a review. Agron Sustain Dev 36(1):1–21. https://doi.org/10.1007/s13593-015-0338-6
de Andrade Santana R, Bezerra S, de Santos TM, Coutinho SM, Coelho AP, Pessoa RVS (2019) Assessing alternatives for meeting water demand: a case study of water resource management in the Brazilian semiarid region. Util Policy 61:100974. https://doi.org/10.1016/j.jup.2019.100974
de la Casa A, Ovando G, Bressanini L, Martínez J (2013) Aquacrop model calibration in potato and its use to estimate yield variability under field conditions. Atmos Clim Sci 03(03):397–407. https://doi.org/10.4236/acs.2013.33041
Doorenbos J, Kassam AH (1979) Yield response to water. Irrig Drain Pap 33:257
Drastig K, Prochnow A, Libra J, Koch H, Rolinski S (2016) Irrigation water demand of selected agricultural crops in Germany between 1902 and 2010. Sci Total Environ 569:1299–1314. https://doi.org/10.1016/j.scitotenv.2016.06.206
Egerer S, Puente AF, Peichl M, Rakovec O, Samaniego L, Schneider UA (2023) Limited potential of irrigation to prevent potato yield losses in Germany under climate change. Agric Syst. https://doi.org/10.1016/j.agsy.2023.103633
Ezekiel O, Evonameh Igbadun H, Oyebode MA, Igbadun HE, Mudiare OJ, Oyebode MA (2017) Development of deficit irrigation for maize crop under drip irrigation in Samaru-Nigeria. CIGR J 19(1):94–107
Felten D, Fröba N, Fries J, Emmerling C (2013) Energy balances and greenhouse gas-mitigation potentials of bioenergy cropping systems (Miscanthus, rapeseed, and maize) based on farming conditions in Western Germany. Renewable Energy 55:160–174. https://doi.org/10.1016/j.renene.2012.12.004
Fereres E, Orgaz F, Gonzalez-Dugo V (2011) Reflections on food security under water scarcity. J Exp Bot 62(12):4079–4086. https://doi.org/10.1093/jxb/err165
Foster T, Brozović N, Butler AP, Neale CMU, Raes D, Steduto P, Fereres E, Hsiao TC (2017) AquaCrop-OS: an open source version of FAO’s crop water productivity model. Agric Water Manage 181:18–22. https://doi.org/10.1016/j.agwat.2016.11.015
Gadédjisso-Tossou A, Avellán T, Schütze N (2018) Potential of deficit and supplemental irrigation under climate variability in northern Togo, West Africa. Water (Switzerland). https://doi.org/10.3390/w10121803
Geerts S, Raes D, Garcia M (2010) Using AquaCrop to derive deficit irrigation schedules. Agric Water Manag 98:213–216. https://doi.org/10.1016/j.agwat.2010.07.003
Gerwin W, Raab T, Birkhofer K, Hinz C, Letmathe P, Leuchner M, Roß-Nickoll M, Rüde T, Trachte K, Wätzold F, Lehmkuhl F (2023) Perspectives of lignite post-mining landscapes under changing environmental conditions: what can we learn from a comparison between the Rhenish and Lusatian region in Germany? Environ Sci Europe. https://doi.org/10.1186/s12302-023-00738-z
Gomis-Cebolla J, Rattayova V, Salazar-Galán S, Francés F (2023) Evaluation of ERA5 and ERA5-Land reanalysis precipitation datasets over Spain (1951–2020). Atmos Res. https://doi.org/10.1016/j.atmosres.2023.106606
Grossmann M, Dietrich O (2012) Integrated economic-hydrologic assessment of water management options for regulated wetlands under conditions of climate change: a case study from the Spreewald (Germany). Water Resour Manage 26(7):2081–2108. https://doi.org/10.1007/s11269-012-0005-5
Hassler B, Lauer A (2021) Comparison of reanalysis and observational precipitation datasets including era5 and wfde5. Atmosphere. https://doi.org/10.3390/atmos12111462
Herrmann A (2013) Biogas production from maize: current state, challenges and prospects. 2. Agronomic and environmental aspects. Bioenergy Res 6:372–387. https://doi.org/10.1007/s12155-012-9227-x
Herrmann A, Rath J (2012) Biogas production from maize: current state, challenges, and prospects. 1. Methane yield potential. Bioenergy Res 5:1027–1042. https://doi.org/10.1007/s12155-012-9202-6
Hersbach H, Bell B, Berrisford P, Hirahara S, Horányi A, Muñoz-Sabater J, Nicolas J, Peubey C, Radu R, Schepers D, Simmons A, Soci C, Abdalla S, Abellan X, Balsamo G, Bechtold P, Biavati G, Bidlot J, Bonavita M, Thépaut JN (2020) The ERA5 global reanalysis. Q J R Meteorol Soc 146(730):1999–2049. https://doi.org/10.1002/qj.3803
Hristov J, Barreiro-Hurle J, Salputra G, Blanco M, Witzke P (2021) Reuse of treated water in European agriculture: potential to address water scarcity under climate change. Agric Water Manag. https://doi.org/10.1016/j.agwat.2021.106872
Hsiao TC, Heng L, Steduto P, Rojas-Lara B, Raes D, Fereres E (2009) Aquacrop-the FAO crop model to simulate yield response to water: III. Parameterization and testing for maize. Agron J 101(3):448–459. https://doi.org/10.2134/agronj2008.0218s
Huang S, Krysanova V, Österle H, Hattermann FF (2010) Simulation of spatiotemporal dynamics of water fluxes in Germany under climate change. Hydrol Process 24(23):3289–3306. https://doi.org/10.1002/hyp.7753
Huynh HT, Hufnagel J, Wurbs A, Bellingrath-Kimura SD (2019) Influences of soil tillage, irrigation and crop rotation on maize biomass yield in a 9-year field study in Müncheberg, Germany. Field Crops Res. https://doi.org/10.1016/j.fcr.2019.107565
Igbadun H, Salim B (2014) Simulation study of yield and soil water balance responses of a maize crop to farmers: irrigation scheduling practices in Tanzania. Irrig Drain Syst Eng. https://doi.org/10.4172/2168-9768.1000119
Igbadun HE, Salim BA, Tarimo AKPR, Mahoo HF (2008) Effects of deficit irrigation scheduling on yields and soil water balance of irrigated maize. Irrig Sci 27(1):11–23. https://doi.org/10.1007/s00271-008-0117-0
Jiang Q, Li W, Fan Z, He X, Sun W, Chen S, Wen J, Gao J, Wang J (2021) Evaluation of the ERA5 reanalysis precipitation dataset over Chinese Mainland. J Hydrol. https://doi.org/10.1016/j.jhydrol.2020.125660
Jiao D, Xu N, Yang F, Xu K (2021) Evaluation of spatial-temporal variation performance of ERA5 precipitation data in China. Sci Rep. https://doi.org/10.1038/s41598-021-97432-y
Jones JW, Hoogenboom G, Porter CH, Boote KJ, Batchelor WD, Hunt LA, Wilkens PW, Singh U, Gijsman AJ, Ritchie JT (2003) The DSSAT cropping system model. Eur J Agron 18(3–4):235–265. https://doi.org/10.1016/S1161-0301(02)00107-7
Keating BA, Carberry PS, Hammer GL, Probert ME, Robertson MJ, Holzworth D, Huth NI, Hargreaves JNG, Meinke H, Hochman Z (2003) An overview of APSIM, a model designed for farming systems simulation. Eur J Agron 18(3–4):267–288. https://doi.org/10.1016/S1161-0301(02)00108-9
Kelly TD, Foster T (2021) AquaCrop-OSPy: bridging the gap between research and practice in crop-water modeling. Agric Water Manag 254:106976. https://doi.org/10.1016/j.agwat.2021.106976
Krümmelbein J, Horn R, Raab T, Bens O, Hüttl RF (2010) Soil physical parameters of a recently established agricultural recultivation site after brown coal mining in Eastern Germany. Soil Tillage Res 111(1):19–25. https://doi.org/10.1016/j.still.2010.08.006
Li M, Xu Y, Fu Q, Singh VP, Liu D, Li T (2020) Efficient irrigation water allocation and its impact on agricultural sustainability and water scarcity under uncertainty. J Hydrol. https://doi.org/10.1016/j.jhydrol.2020.124888
Liang S, McDonald AG (2014) Chemical and thermal characterization of potato peel waste and its fermentation residue as potential resources for biofuel and bioproducts production. J Agric Food Chem 62(33):8421–8429. https://doi.org/10.1021/jf5019406
Ligate F, Ijumulana J, Ahmad A, Kimambo V, Irunde R, Mtamba JO, Mtalo F, Bhattacharya P (2021) Groundwater resources in the East African Rift Valley: understanding the geogenic contamination and water quality challenges in Tanzania. Sci Afr. https://doi.org/10.1016/j.sciaf.2021.e00831
Liu X, Shi L, Engel BA, Sun S, Zhao X, Wu P, Wang Y (2020) New challenges of food security in Northwest China: water footprint and virtual water perspective. J Clean Prod. https://doi.org/10.1016/j.jclepro.2019.118939
Liu Y, Wang P, Gojenko B, Yu J, Wei L, Luo D, Xiao T (2021) A review of water pollution arising from agriculture and mining activities in Central Asia: facts, causes and effects. Environ Pollut. https://doi.org/10.1016/j.envpol.2021.118209
Longo-Minnolo G, Vanella D, Consoli S, Pappalardo S, Ramírez-Cuesta JM (2022) Assessing the use of ERA5-Land reanalysis and spatial interpolation methods for retrieving precipitation estimates at basin scale. Atmos Res. https://doi.org/10.1016/j.atmosres.2022.106131
Maja MM, Ayano SF (2021) The impact of population growth on natural resources and farmers’ capacity to adapt to climate change in low-income countries. Earth Syst Environ 5(2):271–283. https://doi.org/10.1007/s41748-021-00209-6
Masia S, Trabucco A, Spano D, Snyder RL, Sušnik J, Marras S (2021) A modelling platform for climate change impact on local and regional crop water requirements. Agric Water Manag 255:107005. https://doi.org/10.1016/j.agwat.2021.107005
Mirschel W, Terleev VV, Wenkel K-O (2019) Innovations in landscape research. Landscape modelling and decision support. Springer, Cham. https://doi.org/10.1007/978-3-030-37421-1
Muñoz-Sabater J (2022) ERA5-Land hourly data from 1950 to present. https://doi.org/10.24381/cds.e2161bac
Muñoz-Sabater J, Dutra E, Agustí-Panareda A, Albergel C, Arduini G, Balsamo G, Boussetta S, Choulga M, Harrigan S, Hersbach H, Martens B, Miralles DG, Piles M, Rodríguez-Fernández NJ, Zsoter E, Buontempo C, Thépaut JN (2021) ERA5-Land: a state-of-the-art global reanalysis dataset for land applications. Earth Syst Sci Data 13(9):4349–4383. https://doi.org/10.5194/essd-13-4349-2021
Nasir MW, Toth Z (2022) Effect of drought stress on potato production: a review. Agronomy. https://doi.org/10.3390/agronomy12030635
Ngoma H, Lupiya P, Kabisa M, Hartley F (2021) Impacts of climate change on agriculture and household welfare in Zambia: an economy-wide analysis. Clim Chang. https://doi.org/10.1007/s10584-021-03168-z
Pe’er G, Bonn A, Bruelheide H, Dieker P, Eisenhauer N, Feindt PH, Hagedorn G, Hansjürgens B, Herzon I, Lomba Â, Marquard E, Moreira F, Nitsch H, Oppermann R, Perino A, Röder N, Schleyer C, Schindler S, Wolf C, Lakner S (2020) Action needed for the EU common agricultural policy to address sustainability challenges. People Nat 2(2):305–316. https://doi.org/10.1002/pan3.10080
Peichl M, Thober S, Meyer V, Samaniego L (2018) The effect of soil moisture anomalies on maize yield in Germany. Nat Hazards Earth Syst Sci 18(3):889–906. https://doi.org/10.5194/nhess-18-889-2018
Pfeiffer D, Thrän D (2018) One century of bioenergy in Germany: wildcard and advanced technology. Chem Ing Tech 90(11):1676–1698. https://doi.org/10.1002/cite.201800154
Puy A, Borgonovo E, Lo Piano S, Levin SA, Saltelli A (2021) Irrigated areas drive irrigation water withdrawals. Nat Commun. https://doi.org/10.1038/s41467-021-24508-8
Raes D, Steduto P, Hsiao TC, Fereres E (2009) AquaCrop—the FAO crop model to simulate yield response to water: II. Main algorithms and software description. Agron J 101(3):438–447. https://doi.org/10.2134/agronj2008.0140s
Razzaghi F, Zhou Z, Andersen MN, Plauborg F (2017) Simulation of potato yield in temperate condition by the AquaCrop model. Agric Water Manag 191:113–123. https://doi.org/10.1016/j.agwat.2017.06.008
Saxton KE, Rawls WJ (2006) Soil water characteristic estimates by texture and Organic Matter for Hydrologic solutions. Soil Sci Soc Am J 70(5):1569–1578. https://doi.org/10.2136/sssaj2005.0117
Schleich J, Hillenbrand T (2009) Determinants of residential water demand in Germany. Ecol Econ 68(6):1756–1769. https://doi.org/10.1016/j.ecolecon.2008.11.012
Schmitz GH, Schütze N, Wöhling T (2007) Irrigation control: towards a new solution of an old problem. IHP/HWRP-Sekretariat
Schuetze N, Mialyk O (2019) Deficit irrigation toolbox: a new tool to improve crop water productivity and food security under limited water resources. Geophys Res Abstr 21:1
Schütze N, Schmitz G (2010) Occasion: a new planning tool for optimal climate change adaption strategies in irrigation. J Irrig Drain Eng-ASCE 136:836–846. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000266
Schütze N, Wagner M (2016) Integrated management of water resources demand and supply in irrigated agriculture from plot to regional scale. IAHS-AISH Proc Rep 373:51–55. https://doi.org/10.5194/piahs-373-51-2016
Schütze N, De Paly M, Shamir U (2012) Novel simulation-based algorithms for optimal open-loop and closed-loop scheduling of deficit irrigation systems. J Hydroinform 14(1):136–151. https://doi.org/10.2166/hydro.2011.073
Shahzad A, Ullah S, Afzal, Dar A, Fahad Sardar M, Mehmood T, Tufail MA, Shakoor A, Haris M (2021) Nexus on climate change: agriculture and possible solution to cope future climate change stresses. Environ Sci Pollut Res 28:14211–14232. https://doi.org/10.1007/s11356-021-12649-8/Published
Singer MB, Asfaw DT, Rosolem R, Cuthbert MO, Miralles DG, MacLeod D, Quichimbo EA, Michaelides K (2021) Hourly potential evapotranspiration at 0.1° resolution for the global land surface from 1981-present. Sci Data. https://doi.org/10.1038/s41597-021-01003-9
Singh A (2014a) Conjunctive use of water resources for sustainable irrigated agriculture. J Hydrol 519:1688–1697. https://doi.org/10.1016/j.jhydrol.2014.09.049
Singh A (2014b) Simulation and optimization modeling for the management of groundwater resources. II: combined applications. J Irrig Drain Eng 140(4):04014002. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000689
Singh A (2022) Judicious and optimal use of water and land resources for long-term agricultural sustainability. Resour Conserv Recycl Advhttps://doi.org/10.1016/j.rcradv.2022.200067
Singh J, Garai S, Das S, Thakur JK, Tripathy BC (2022) Role of C4 photosynthetic enzyme isoforms in C3 plants and their potential applications in improving agronomic traits in crops. Photosynth Res 154:233–258. https://doi.org/10.1007/s11120-022-00978-9
Soriano E, Mediero L, Garijo C (2019) Selection of bias correction methods to assess the impact of climate change on flood frequency curves. Water (Switzerland). https://doi.org/10.3390/w11112266
Spänhoff B, Dimmer R, Friese H, Harnapp S, Herbst F, Jenemann K, Mickel A, Rohde S, Schönherr M, Ziegler K (2012) Ecological status of rivers and streams in Saxony (Germany) according to the water framework directive and prospects of improvement. Water 4(4):887–904. https://doi.org/10.3390/w4040887
Steduto P, Hsiao TC, Raes D, Fereres E (2009) AquaCrop—The FAO crop model to simulate yield response to water: I. concepts and underlying principles. Agron J 101(3):426–437. https://doi.org/10.2134/agronj2008.0139s
Tian Z, Wang JW, Li J, Han B (2021) Designing future crops: challenges and strategies for sustainable agriculture. Plant J 105(5):1165–1178. https://doi.org/10.1111/tpj.15107
Tokatli C (2021) Health risk assessment of toxic metals in surface and groundwater resources of a significant agriculture and industry zone in Turkey. Environ Earth Sci. https://doi.org/10.1007/s12665-021-09467-z
Torney F, Moeller L, Scarpa A, Wang K (2007) Genetic engineering approaches to improve bioethanol production from maize. Curr Opin Biotechnol 18(3):193–199. https://doi.org/10.1016/j.copbio.2007.03.006
van Ittersum MK, Leffelaar PA, Van Keulen H, Kropff MJ, Bastiaans L, Goudriaan J (2003) On approaches and applications of the Wageningen crop models. Eur J Agron 18(3–4):201–234. https://doi.org/10.1016/S1161-0301(02)00106-5
Vanuytrecht E, Raes D, Steduto P, Hsiao TC, Fereres E, Heng LK, Vila MG, Moreno PM (2014) AquaCrop: FAO’s crop water productivity and yield response model. Environ Model Softw 62:351–360. https://doi.org/10.1016/j.envsoft.2014.08.005
Vorobevskii I, Kronenberg R, Bernhofer C (2020) Global BROOK90 R package: an automatic framework to simulate the water balance at any location. Water 12(7):2037. https://doi.org/10.3390/w12072037
Wang Y, Liu S, Shi H (2023) Comparison of climate change impacts on the growth of C3 and C4 crops in China. Ecol Inf. https://doi.org/10.1016/j.ecoinf.2022.101968
Williams JR (1990) The erosion-productivity impact calculator (EPIC) model: a case history. Philos Trans Royal Soc Lond Ser B: Biol Sci 329(1255):421–428. https://doi.org/10.1098/rstb.1990.0184
Xu Y, Xu MY (2020) Package ‘hyfo’. Hydrology and climate forecasting R package for data analysis and visualization
Yaqoob N, Ali SA, Kannaiah D, Khan N, Shabbir MS, Bilal K, Tabash MI (2023) The effects of agriculture productivity, land intensification, on sustainable economic growth: a panel analysis from Bangladesh, India, and Pakistan Economies. Environ Sci Pollut Res 30(55):116440–116448. https://doi.org/10.1007/s11356-021-18471-6
Zamani O, Grundmann P, Libra J, Nikouei A (2019) Limiting and timing water supply for agricultural production: the case of the Zayandeh-Rud River Basin. Agric Water Manag. https://doi.org/10.1016/j.agwat.2019.05.047
Zwart SJ, Bastiaanssen WGM (2004) Review of measured crop water productivity values for irrigated wheat, rice, cotton and maize. Agric Water Manag 69(2):115–133. https://doi.org/10.1016/j.agwat.2004.04.007
Acknowledgements
Olawale Q. Ogunsola expresses gratitude to the European Union for its financial support in facilitating his MSc research on Groundwater and Global Change - Impact and Adaptation (GroundwatCh). He also expresses gratitude to Prof. Dr. Niels Schütze and Ms. Dietz, Alexandra for their comments during the MSc research. The authors express our gratitude to Sakinat Ahmad for her helpful suggestions and engaged conversation.
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O. Q. O. wrote the main manuscript text, prepared the figures and tables. O. Q. O., A. O. B., L. A. S., J. O. A., and A. A. M. reviewed and provided feedback on the methods, analysis, and interpretation of results.
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Ogunsola, O.Q., Bankole, A.O., Soboyejo, L.A. et al. Modeling deficit irrigation water demand of maize and potato in Eastern Germany using ERA5-Land reanalysis climate time series. Irrig Sci (2024). https://doi.org/10.1007/s00271-024-00939-1
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DOI: https://doi.org/10.1007/s00271-024-00939-1