Abstract
Sugarcane is one of the most important crops in the world and has a major influence on environmental concerns. This study aims to examine the association between sugarcane crop yield, climate change factors, and technical advancement using time series data for the period of 1989 to 2015 in Pakistan. An autoregressive distributed lag (ARDL) model and descriptive statistics analysis were employed in this study. The outcomes of the bound F-test for co-integration confirmed that there is a long-run and short-run equilibrium among sugarcane crop yield, temperature, rainfall, fertilizer use, and agricultural machinery. The results of long-run estimate that the coefficient of area, rainfall, and fertilizer use have significantly positive impacts on sugarcane crop yield. The coefficient of temperature had positive and non-significant while agricultural machinery had negative and statistically significant relationship with sugarcane crop yield. In the short-run estimates, the coefficient of area, rainfall, and fertilizer use have statistically positive impact, temperature had non-significant impact, and agricultural machinery had significantly negative impact on the yield of sugarcane crop. In addition, both CUSUM and CUSUMsq test results confirmed the goodness of fit of this model. The outcomes of our study suggest that climate change has negative impact on the yield of sugarcane. Based on the study findings, the Government requires to take effective measures for constructive policy-making and identification of environmental threats in Pakistan. Large-scale mechanical activities and rapid growing may be useful initiatives for raising the yield of sugarcane. Furthermore, technical advancement needs to be improved because it plays a vital role in increasing the yield of sugarcane and other major crops.
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References
Abid M, Schneider UA, Scheffran J (2016) Adaptation to climate change and its impacts on food productivity and crop income: perspectives of farmers in rural Pakistan. J Rural Stud. https://doi.org/10.1016/j.jrurstud.2016.08.005
Adams MR, Rosenzweig C, Peart MR (1990) Global climate change and agriculture. Nature 345:219–224. https://doi.org/10.1201/9781420077544
Afzal M, Farooq M, Ahmad H (2010) Relationship between school education and economic growth in Pakistan: ARDL bounds testing approach to cointegration. Pak Econ Soc Rev 48(1):39–60. https://doi.org/10.2307/41762413
Ahmed A, Iftikhar H, Chaudhry GM (2007) Water resources and conservation strategy of Pakistan. Pak Dev Rev 46(4):997–1009
Akaike H (1974) A new look at the statistical model identification. IEEE Trans Autom Control 19(6):716–723. https://doi.org/10.1109/TAC.1974.1100705
Ali S, Gucheng L, Ying L, Ishaq M, Shah T (2019a) The relationship between carbon dioxide emissions, economic growth and agricultural production in Pakistan: an autoregressive distributed lag analysis. Energies 12(24). https://doi.org/10.3390/en12244644
Ali S, Liu Y, Abdullah Nazir A, Ishaq M, Shah T, Ye X, Ilyas A, Ahmad Khan A, Din Izhar-Ud D, Tariq A (2019b) The effect of climate change on economic growth: evidence from Pakistan. Pacific Int J 2(3):44–53. https://doi.org/10.1007/s10018-015-0116-3
Ali S, Liu Y, Nazir A, Ishaq M, Khan SB, Abdullah, Shah T (2020) Does technical progress mitigate climate effect on crops yield in Pakistan? J Ani Plant Sci 30(3):663–676. https://doi.org/10.36899/JAPS.2020.3.0079
Ali S, Ying L, Shah T, Tariq A, Chandio AA, Ali I (2019c) Analysis of the Nexus of CO2 Emissions, economic growth, land under cereal crops and agriculture value-added in Pakistan using an ARDL approach. Energies 12(23):1–19
Asumadu-Sarkodie S, Owusu PA (2016) The relationship between carbon dioxide and agriculture in Ghana: a comparison of VECM and ARDL model. Environ Sci Pollut Res 23(11):10968–10982. https://doi.org/10.1007/s11356-016-6252-x
Atif SB, Saqib Z, Ali A, Zaman MH (2018) The impacts of socio-economic factors on the perception of residents about urban vegetation: a comparative study of planned versus semi-planned cities of Islamabad and Rawalpindi, Pakistan. Appl Ecol Environ Res 16(4):4265–4287. https://doi.org/10.15666/aeer/1604_42654287
Attavanich W, McCarl BA (2013) How is CO2 affecting yields and technological progress? A statistical analysis. Clim Chang 124(4):747–762. https://doi.org/10.1007/s10584-014-1128-x
Ben Jebli M, Ben Youssef S (2017) Renewable energy consumption and agriculture: evidence for cointegration and Granger causality for Tunisian economy. Int J Sust Dev World 24(2):149–158. https://doi.org/10.1080/13504509.2016.1196467
Bokhari SA, Saqib Z, Ali A (2018) Perception of residents about urban vegetation: a comparative study of planned versus semi-planned cities of Islamabad and Rawalpindi, Pakistan. J Ecosyst Ecogr 8(1):1–8. https://doi.org/10.4172/2157-7625.251
Boko, M., Niang, I., Nyong, A., Vogel, C., Githeko, A., Medany, M., Osman-Elasha, B., Tabo, R., & Yanda, P. (2007). Climate change 2007: impacts, adaptation and vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. In Cambridge University Press, Cambridge, UK, 2007. 976 pages. https://doi.org/10.2134/jeq2008.0015br
Brauman KA, Viart N (2016) Development of a regionally sensitive water-productivity indicator to identify sustainable practices for sugarcane growers. Integr Environ Assess Manag 12(4):811–820. https://doi.org/10.1002/ieam.1744
Brown ME, Carr ER, Grace KL, Wiebe K, Funk CC, Attavanich W, Backlund P, Buja L (2017) Do markets and trade help or hurt the global food system adapt to climate change? Food Policy 68:154–159. https://doi.org/10.1016/j.foodpol.2017.02.004
Brown RL, Durbin J, J E (1975) Techniques for testing the constancy of regression relationships over time. J R Stat Soc Ser B Methodol:149–192
Bruinsma, J. (2009). The resources outlook: by how much do land, water and crop yields need to increase by 2050. In Looking ahead in world food and agriculture: Perspectives to 2050 (p. 558). I2280E/1/06.11
Cammarano D, Ceccarelli S, Grando S, Romagosa I, Benbelkacem A, Akar T, Al-Yassin A, Pecchioni N, Francia E, Ronga D (2019) The impact of climate change on barley yield in the Mediterranean basin. Eur J Agron 106(November 2018):1–11. https://doi.org/10.1016/j.eja.2019.03.002
Cardozo NP, Sentelhas PC, Panosso AR, Ferraudo AS (2014) Multivariate analysis of the temporal variability of sugarcane ripening in south-eastern Brazil. Crop Pasture Sci 65(3):300–310. https://doi.org/10.1071/CP13160
Cardozo NP, Sentelhas PC (2013) Climatic effects on sugarcane ripening under the infl uence of cultivars and crop age. Sci Agric 70(6):449–456. https://doi.org/10.1590/S0103-90162013000600011
Carr MKV, Knox J (2011) The water relations and irrigation requirements of coconut (Cocos nucifera): a review. Exp Agric 47(1):1–25. https://doi.org/10.1017/S0014479710000645
Conradt T, Gornott C, Wechsung F (2016) Extending and improving regionalized winter wheat and silage maize yield regression models for Germany: enhancing the predictive skill by panel definition through cluster analysis. Agric For Meteorol 216:68–81. https://doi.org/10.1016/j.agrformet.2015.10.003
Danish, Zhang B, Wang Z, Wang B (2018) Energy production, economic growth and CO2 emission: evidence from Pakistan. Nat Hazards 90(1):27–50. https://doi.org/10.1007/s11069-017-3031-z
David BL, Wolfram S, Justin C-R (2011) Climate trends and global crop production since 1980. SCIENCE 333(July):616–621. https://doi.org/10.5040/9780755621101.0007
Dickey DA, Fuller WA (1979) Distribution of the estimators for autoregressive time series with a unit root. J Am Stat Assoc 74(366a):427–431. https://doi.org/10.1080/01621459.1979.10482531
Donaldson R, Redshaw K, Rhodes R, Antwerpen VAN (2008) Season effects on productivity of some commercial South African sugarcane cultivars, I : biomass and radiation use efficiency. Proc S Afr Sug Technol Ass 81:517–527
Duan, R. (2011). The study on total factor productivity of wheat production in China.
El Chami D, Daccache A (2015) Assessing sustainability of winter wheat production under climate change scenarios in a humid climate - an integrated modelling framework. Agric Syst 140:19–25. https://doi.org/10.1016/j.agsy.2015.08.008
Engle RF, Granger CWJ (1987) Co-integration and error correction: representation, estimation, and testing. Econometrica:251–276
FAO (2013) The state of food insecurity in the world the multiple dimensions of food security. Food and Agriculture Organization of the United Nations. Food and Agriculture Organizaiton., Rome
FAO (2016) Food and Agriculture Organization of the United Nations. FAO
FAO (2019) Food and Agriculture Organization of the United Nations
Farhani S, Ozturk I (2015) Causal relationship between CO2 emissions, real GDP, energy consumption, financial development, trade openness, and urbanization in Tunisia. Environ Sci Pollut Res 22(20):15663–15676. https://doi.org/10.1007/s11356-015-4767-1
Gömann H (2015) How much did extreme weather events impact wheat yields in Germany? - a regionally differentiated analysis on the farm level. Procedia Environ Sci 29(Agri):119–120. https://doi.org/10.1016/j.proenv.2015.07.197
Gornott C, Wechsung F (2016) Statistical regression models for assessing climate impacts on crop yields: a validation study for winter wheat and silage maize in Germany. Agric For Meteorol 217:89–100. https://doi.org/10.1016/j.agrformet.2015.10.005
Granger CWJ, Jji E (1988) Concept of causality. J Econ 39:199–211
Guo J (2015) Advances in impacts of climate change on agricultural production in China. J Appl Meteorol Sci 1:1–11
Guo Z, Shi Y, Yu Z, Zhang Y (2015) Supplemental irrigation affected flag leaves senescence post-anthesis and grain yield of winter wheat in the Huang-Huai-Hai Plain of China. Field Crop Res 180:100–109. https://doi.org/10.1016/j.fcr.2015.05.015
Guo Z, Yu Z, Wang D, Shi Y, Zhang Y (2014) Photosynthesis and winter wheat yield responses to supplemental irrigation based on measurement of water content in various soil layers. Field Crop Res 166:102–111. https://doi.org/10.1016/j.fcr.2014.06.004
Huang Y, Li H, Campbell KA, Han Z (2011). Defending false data injection attack on smart grid network using adaptive CUSUM test. 2011 45th Annual Conference on Information Sciences and Systems, CISS 2011. https://doi.org/10.1109/CISS.2011.5766111
IPCC (2007) Summary for policy-makers. In: Climate Change 2007: Synthesis Report. [M]//Change). Contribution of Working Groups I, II and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change; Cambridge University Press: London, England.
IPCC (2014). Climate change 2014: synthesis report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. In Core Writing Team, R.K. Pachauri and L.A. Meyer. https://doi.org/10.1017/CBO9781107415324.004
IPCC (2018). Summary for policymakers of IPCC Special Report on Global Warming of 1.5oC approved by governments. In IPCC (Issue October). https://www.ipcc.ch/site/assets/uploads/2018/11/pr_181008_P48_spm_en.pdf. Accessed 18 Jun 2020
Jaiswal D, De Souza AP, Larsen S, Lebauer DS, Miguez FE, Sparovek G, Bollero G, Buckeridge MS, Long SP (2017) Brazilian sugarcane ethanol as an expandable green alternative to crude oil use. Nat Clim Chang 7(11):788–792. https://doi.org/10.1038/nclimate3410
Jalota SK, Vashisht BB, Kaur H, Kaur S, Kaur P (2014) Location specific climate change scenario and its impact on rice and wheat in Central Indian Punjab. Agric Syst 131:77–86. https://doi.org/10.1016/j.agsy.2014.07.009
Khim V, Liew S (2004) Which lag length selection criteria should we employ ? Econ Bull 3(33):1–9
Lee S, Ha J, Na O, Na S (2003) The cusum test for parameter change in time series models. Scand J Stat 30(4):781–796. https://doi.org/10.1111/1467-9469.00364
Li Z, Jin X, Zhao C, Wang J, Xu X, Yang G, Li C, Shen J (2015) Estimating wheat yield and quality by coupling the DSSAT-CERES model and proximal remote sensing. Eur J Agron 71:53–62. https://doi.org/10.1016/j.eja.2015.08.006
Lobell DB, Burke MB (2010) On the use of statistical models to predict crop yield responses to climate change. Agric For Meteorol 150(11):1443–1452. https://doi.org/10.1016/j.agrformet.2010.07.008
Martin EC, Stephens W, Wiedenfeld R, Bittenbender HC, Beasley Jr JP, Moore JM, Neibling H, Gallian JJ (2007) Section IV : irrigation of crops Chapter 9: sugar, oil, and fiber. Agronomy Monographs.
Mendelsohn R, Nordhaus WD, Shaw D (1994) The impact of global warming on agriculture: a ricardian analysis. Am Econ Rev 84(4):753–771
Miao R, Khanna M, Huang H (2015) Responsiveness of crop yield and acreage to prices and climate. Am J Agric Econ 98(1):191–211. https://doi.org/10.1093/ajae/aav025
Muñoz I, Flury K, Jungbluth N, Rigarlsford G, Canals LM, King H (2014) Life cycle assessment of bio-based ethanol produced from different agricultural feedstocks. Int J Life Cycle Assess 19(1):109–119. https://doi.org/10.1007/s11367-013-0613-1
Naseem S, Guang Ji T, Kashif U (2020) Asymmetrical ARDL correlation between fossil fuel energy, food security, and carbon emission: providing fresh information from Pakistan. Environ Sci Pollut Res 27(25):31369–31382. https://doi.org/10.1007/s11356-020-09346-3
National Fertilizer Development Center. (2014) Agriculture.
Nkoro E, Uko AK (2016) Autoregressive distributed lag (ARDL) cointegration technique: application and interpretation. J Stat Econ Methods 5:63–91
Özdoǧan M (2011) Modeling the impacts of climate change on wheat yields in Northwestern Turkey. Agric Ecosyst Environ 141(1–2):1–12. https://doi.org/10.1016/j.agee.2011.02.001
Parry ML, Rosenzweig C, Iglesias A, Livermore M, Fischer G (2004) Effects of climate change on global food production under SRES emissions and socio-economic scenarios. Glob Environ Chang 14(1):53–67. https://doi.org/10.1016/j.gloenvcha.2003.10.008
Payne JE (2002) Inflationary dynamics of a transition economy: the Croatian experience. J Policy Model 24:219–230
Pesaran MH, Shin Y, Smith RJ (2001) Bounds testing approaches to the analysis of level relationships. J Appl Econ 16(3):289–326. https://doi.org/10.1002/jae.616
Pesaran MH, Pesaran B (1997) Working With Microfit 4.0: Interactive Econometric Analysis. Oxford University Press, Oxford
Pesaran MH, Shin Y (1998) An autoregressive distributed-lag modelling approach to cointegration analysis. Econ Soc Monogr 31:371–413
Phillips PCB, Perron P (1988) Biometrika trust testing for a unit root in time series regression testing for a unit root in time series regression. Biometrika 75(2):335–381
Ploberger W, Kramer W (1992) The Cusum Test with Ols Residuals. Econometrica 60(2):271–285
Potgieter A, Meinke H, Doherty A, Sadras VO, Hammer G, Crimp S, Rodriguez D (2013) Spatial impact of projected changes in rainfall and temperature on wheat yields in Australia. Clim Chang 117(1–2):163–179. https://doi.org/10.1007/s10584-012-0543-0
Rahman MM, Kashem MA (2017) Carbon emissions, energy consumption and industrial growth in Bangladesh: empirical evidence from ARDL cointegration and Granger causality analysis. Energy Policy 110(March):600–608. https://doi.org/10.1016/j.enpol.2017.09.006
Rauf A, Zhang J, Li J, Amin W (2018) Structural changes, energy consumption and carbon emissions in China: empirical evidence from ARDL bound testing model. Struct Chang Econ Dyn 47:194–206. https://doi.org/10.1016/j.strueco.2018.08.010
Raymundo R, Asseng S, Robertson R, Petsakos A, Hoogenboom G, Quiroz R, Hareau G, Wolf J (2018) Climate change impact on global potato production. Eur J Agron 100(December 2017):87–98. https://doi.org/10.1016/j.eja.2017.11.008
Rehman A, Rauf A, Ahmad M, Chandio AA, Deyuan Z (2019) The effect of carbon dioxide emission and the consumption of electrical energy, fossil fuel energy, and renewable energy, on economic performance: evidence from Pakistan. Environ Sci Pollut Res 26(21):21760–21773. https://doi.org/10.1007/s11356-019-05550-y
Rosenzweig C, Iglesius A, Yang XB, Epstein PR, Chivian E (2001) Climate change and extreme weather events - implications for food production, plant diseases, and pests. Glob Change Human Health 2(2):90–104 http://digitalcommons.unl.edu/nasapub%0A , http://digitalcommons.unl.edu/nasapub/24
Scarpare FV, Hernandes TAD, Ruiz-Corrêa ST, Kolln OT, Gava GJDC, Dos Santos LNS, Victoria RL (2016) Sugarcane water footprint under different management practices in Brazil: Tietê/Jacaré watershed assessment. J Clean Prod 112:4576–4584. https://doi.org/10.1016/j.jclepro.2015.05.107
Schlenker W, Robert JM (2009) Nonlinear temperature effects indicate severe damages to U.S. crop yields under climate change. Proc Natl Acad Sci U S A 106(43):15594–15598. https://doi.org/10.1073/pnas.0910618106
Schwarz G (1978) Estimating the dimension of a model. Ann Stat 6(2):461–464 http://projecteuclid.org/euclid.aop/1176996548
Seker F, Ertugrul HM, Cetin M (2015) The impact of foreign direct investment on environmental quality: a bounds testing and causality analysis for Turkey. Renew Sust Energ Rev 52:347–356. https://doi.org/10.1016/j.rser.2015.07.118
Shahbaz M, Hye QMA, Tiwari AK, Leitão NC (2013) Economic growth, energy consumption, financial development, international trade and CO2 emissions in Indonesia. Renew Sust Energ Rev 25:109–121. https://doi.org/10.1016/j.rser.2013.04.009
Siddiqui R, Samad G, Nasir M, Jalil H (2012) The impact of climate change on major agricultural crops: evidence from Punjab, Pakistan. Pak Dev Rev 68:13–19
Singels A, Smit MA, Redshaw KA, Donaldson RA (2005) The effect of crop start date, crop class and cultivar on sugarcane canopy development and radiation interception. Field Crop Res 92(2-3 SPEC. ISS):249–260. https://doi.org/10.1016/j.fcr.2005.01.028
Singh I, Verma RR, Srivastava TK (2018) Growth, yield, irrigation water use efficiency, juice quality and economics of sugarcane in pusa hydrogel application under different irrigation scheduling. Sugar Tech 20(1):29–35. https://doi.org/10.1007/s12355-017-0515-9
Smadja J, Aubriot O, Puschiasis O, Duplan T, Hugonnet M (2015) Climate change and water resources in the Himalayas: field study in four geographic units of the Koshi. J Alpine Res 103(2):2–22. https://doi.org/10.4000/rga.2910
Tao F, Yokozawa M, Liu J, Zhang Z (2008) Climate-crop yield relationships at provincial scales in China and the impacts of recent climate trends. Clim Res 38(1):83–94. https://doi.org/10.3354/cr00771
Tao F, Yokozawa M, Xu Y, Hayashi Y, Zhang Z (2006) Climate changes and trends in phenology and yields of field crops in China, 1981-2000. Agric For Meteorol 138(1–4):82–92. https://doi.org/10.1159/000092636
Tao F, Zhang S, Zhang Z (2012) Spatiotemporal changes of wheat phenology in China under the effects of temperature, day length and cultivar thermal characteristics. Eur J Agron 43:201–212. https://doi.org/10.1016/j.eja.2012.07.005
Tao F, Zhang Z (2013) Climate change, wheat productivity and water use in the North China Plain: a new super-ensemble-based probabilistic projection. Agric For Meteorol 170:146–165. https://doi.org/10.1016/j.agrformet.2011.10.003
Tao F, Zhang Z, Xiao D, Zhang S, Rötter RP, Shi W, Liu Y, Wang M, Liu F, Zhang H (2014) Responses of wheat growth and yield to climate change in different climate zones of China, 1981-2009. Agric For Meteorol 189–190:91–104. https://doi.org/10.1016/j.agrformet.2014.01.013
Tian W, Wan GH (2000) Technical efficiency and its determinants in China’s grain production. J Prod Anal 13(2):159–174. https://doi.org/10.1023/A:1007805015716
Turner AG, Annamalai H (2012) Climate change and the South Asian summer monsoon. Nat Clim Chang 2(8):587–595. https://doi.org/10.1038/nclimate1495
USGCRP. (2009). States, Global climate change impacts in the United.
Webb LB, Whetton PH, Barlow EWR (2008a) Climate change and winegrape quality in Australia. Clim Res 36(2):99–111. https://doi.org/10.3354/cr00740
Webb LB, PH W, EWR B (2008b) Climate change and winegrape quality in Australia. Clim Res 36:99–111
Westerlund J (2005) A panel CUSUM test of the null of cointegration. Oxf Bull Econ Stat 67(2)
Wilcox J, Makowski D (2014) A meta-analysis of the predicted effects of climate change on wheat yields using simulation studies. Field Crop Res 156:180–190. https://doi.org/10.1016/j.fcr.2013.11.008
Woods BA, Nielsen HØ, Pedersen AB, Kristofersson D (2017) Farmers’ perceptions of climate change and their likely responses in Danish agriculture. Land Use Policy 65(March):109–120. https://doi.org/10.1016/j.landusepol.2017.04.007
Xiangxiang W, Quanjiu W, Jun F, Qiuping F (2013) Evaluation of the AquaCrop model for simulating the impact of water deficits and different irrigation regimes on the biomass and yield of winter wheat grown on China’s Loess Plateau. Agric Water Manag 129:95–104. https://doi.org/10.1016/j.agwat.2013.07.010
Xiao Z, Phillips PCB (2002) A CUSUM test for cointegration using regression residuals. J Econ 108(1):43–61. https://doi.org/10.1016/S0304-4076(01)00103-8
Xu B, Lin B (2017) What cause a surge in China’s CO2 emissions? A dynamic vector autoregression analysis. J Clean Prod 143:17–26. https://doi.org/10.1016/j.jclepro.2016.12.159
You L, Rosegrant MW, Wood S, Sun D (2009) Impact of growing season temperature on wheat productivity in China. Agric For Meteorol 149(6–7):1009–1014. https://doi.org/10.1016/j.agrformet.2008.12.004
Zhang T, Huang Y (2013) Estimating the impacts of warming trends on wheat and maize in China from 1980 to 2008 based on county level data. Int J Climatol 33(3):699–708. https://doi.org/10.1002/joc.3463
Zhao C, Liu B, Piao S, Wang X, Lobell DB, Huang Y, Huang M, Yao Y, Bassu S, Ciais P, Durand JL, Elliott J, Ewert F, Janssens IA, Li T, Lin E, Liu Q, Martre P, Müller C et al (2017) Temperature increase reduces global yields of major crops in four independent estimates. Proc Natl Acad Sci U S A 114(35):9326–9331. https://doi.org/10.1073/pnas.1701762114
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The authors are thankful to the Chinese Scholarship Council (CSC) for providing financial assistance to carry out this research as part of his PhD studies in China. In addition, the authors would also like to extend gratitude to anonymous reviewers for providing helpful suggestions on an earlier draft of this paper.
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Conceptualization by Sajjad Ali; data curation by Sajjad Ali and Muhammad Zubair; formal analysis by Sajjad Ali; methodology by Sajjad Ali and Sadeed Hussain; writing—original draft by Sajjad Ali; writing—review and editing by Muhammad Zubair.
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Ali, S., Zubair, M. & Hussain, S. The combined effect of climatic factors and technical advancement on yield of sugarcane by using ARDL approach: evidence from Pakistan. Environ Sci Pollut Res 28, 39787–39804 (2021). https://doi.org/10.1007/s11356-021-13313-x
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DOI: https://doi.org/10.1007/s11356-021-13313-x