Skip to main content

Advertisement

SpringerLink
Log in

A model-based assessment of the environmental impact of land-use change across scales in Southern Amazonia

  • Original Article
  • Published:
Regional Environmental Change Aims and scope Submit manuscript

Abstract

This article describes the design of a new model-based assessment framework to identify and analyse possible future trajectories of agricultural development and their environmental consequences within the states of Mato Grosso and Pará in Southern Amazonia, Brazil. The objective is to provide a tool for improving the information basis for scientists and policy makers regarding the effects of global change and national environmental policies on land-use change and the resulting impacts on the loss of natural vegetation, greenhouse gas emissions, hydrological processes, and soil erosion within the region. For this purpose, the framework combines the regional land-use models, LandSHIFT and alucR, the farm-level model, MPMAS, and the MONICA crop model, with a set of environmental impact models that are operating at the regional and watershed levels. As a first application of the framework, four scenarios with the time horizon 2030 were specified and analysed. Future land-use change will strongly depend on the interplay between the production of agricultural commodities, the agricultural intensification in terms of increasing crop yields and pasture biomass productivity, and the enforcement of environmental laws and policies. On the regional level, the scenarios with the highest increase in agricultural production in combination with weak law enforcement (Trend and Illegal Intensification) generated the highest losses in natural vegetation due to the expansion of agricultural area as well as the highest greenhouse gas emissions. Also, at the watershed level, these scenarios are characterised by the highest changes in river discharge and soil erosion that might lead to a further decline in soil fertility in the long term. Moreover, the analysis of the Sustainable Development scenario indicates that a shift in agricultural production patterns from livestock to crop cultivation, together with effective law enforcement, can effectively reduce land-use change and its negative effects on the environment. With the scenario analysis, we could illustrate that our assessment framework is capable to provide a large variety of valuable information to support the development of future land-use strategies in the study region.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Aguiar APD, Vieira ICG, Assis TO, Dalla-Nora EL, Toledo PM, Oliveira Santos-Junior RA, Batistella M, Coelho AS. Savaget EK, Aragão LEOC, Nobre CA, Ometto JPH. (2016) Land use change emission scenarios: anticipating a forest transition process in the Brazilian Amazon. Glob Chang Biol 22: 1821–1840. https://doi.org/10.1111/gcb.13134

  • Alcamo J (2008) Chapter six the SAS approach: combining qualitative and quantitative knowledge in environmental scenarios. Dev Integr Environ Assess 2:123–150. https://doi.org/10.1016/S1574-101X(08)00406-7

    Article  Google Scholar 

  • Amaral S, Câmara G, Monteiro AMV, Quintanilha JA, Elvidge CD (2005) Estimating population and energy consumption in Brazilian Amazonia using DMSP night-time satellite data. Comput Environ Urban Syst 29(2):179–195. https://doi.org/10.1016/j.compenvurbsys.2003.09.004

    Article  Google Scholar 

  • An l ZA, Liu JG, Axinn W (2014) Agent-based modeling in coupled human and natural systems (CHANS): lessons from a comparative analysis. Ann Assoc Am Geogr 104(4):723–745. https://doi.org/10.1080/00045608.2014.910085

    Article  Google Scholar 

  • Arnold JG, Moriasi DN, Gassman PW, Abbaspour KC, White MJ, Srinivasan R, Santhi C, Harmel RD, van Griensven A, Van Liew MW, Kannan N (2012) SWAT: model use, calibration, and validation. Trans ASABE 55(4):1491–1508. https://doi.org/10.13031/2013.42256

  • Arvor D, Dubreuil V, Simões M, Bégué A (2013) Mapping and spatial analysis of the soybean agricultural frontier in Mato Grosso; Brazil, using remote sensing data. GeoJournal 78:833–850. https://doi.org/10.1007/s10708-012-9469-3

    Article  Google Scholar 

  • Barni PE, Fearnside PM, de Alencastro Graça PML (2015) Simulating deforestation and carbon loss in Amazonia: impacts in Brazil’s Roraima State from reconstructing Highway BR-319 (Manaus-Porto Velho). Environ Manag 55(2):259–278. https://doi.org/10.1007/s00267-014-0408-6

    Article  Google Scholar 

  • Böhner J, Dietrich H, Fraedrich K, Kawohl T, Kilian M, Lucarini V, Lunkeit F (2013) Development and implementation of a hierarchical model chain for modelling regional climate variability and climate change over southern Amazonia. In: Interdisciplinary analysis and modeling of carbon-optimized land management strategies for Southern Amazonia. Universitätsdrucke Göttingen, pp 119–128

  • Bukovsky MS, Karoly DJ (2009) Precipitation simulations using WRF as a nested regional climate model. J Appl Meteorol Climatol 48(10):2152–2159. https://doi.org/10.1175/2009JAMC2186.1

    Article  Google Scholar 

  • Carauta M, Latynskiy E, Mössinger J, Gil J, Libera A, Hampf A, Moteiro L, Siebold A, Berger T (2017) Can preferential credit programs speed up the adoption of low-carbon agricultural systems in Mato Grosso, Brazil? Results from bioeconomic microsimulation. Reg Environ Chang:1–12. https://doi.org/10.1007/s10113-017-1104-x

  • CESB (2015) Desafio Nacional de Máxima Produtividade da Soja. http://www.cesbrasil.org.br/Sobre.aspx. Accessed Jue 2015

  • Chaplin-Kramer R, Sharp RP, Mandle L, Sim S, Johnson J, Butnar I, Milà i, Canals L, Eichelberger BA, Ramler I, Mueller C, McLachlan N, Yousefi A, King H, Kareiva PM (2015) Spatial patterns of agricultural expansion determine impacts on biodiversity and carbon storage. Proc Natl Acad Sci 112(24):7402–7407. https://doi.org/10.1073/pnas.1406485112

    Article  CAS  Google Scholar 

  • Coy M, Klingler M (2014) Frentes pioneiras em transformação: o eixo da BR-163 e os desafios socio ambientais. Territórios e Fronteiras 7:1–26. https://doi.org/10.22228/rt-f.v7i0.282

    Article  Google Scholar 

  • Dalla-Nora EL, de Aguiar APD, Lapola DM, Woltjer G (2014) Why have land use change models for the Amazon failed to capture the amount of deforestation over the last decade? Land Use Policy 39:403–411. https://doi.org/10.1016/j.landusepol.2014.02.004

    Article  Google Scholar 

  • de Oliveira Silva R, Barioni LG, Hall JAJ, Folegatti Matsuura M, Zanett Albertini T, Fernandes FA, Moran D (2016) Increasing beef production could lower greenhouse gas emissions in Brazil if decoupled from deforestation. Nat Clim Chang 6:493–497. https://doi.org/10.1038/nclimate2916

    Article  Google Scholar 

  • Embrapa (2014) Ensaio Nacional de Cultivares de Milho. http://www.cnpms.embrapa.br/ensaio/. Accessed 15 Jul 2015

  • FAO (2015) Food outlook—biannual report on global food markets. Food and Agricultural Organization of the United Nations (FAO), Rome

    Google Scholar 

  • Fearnside PM (2015) Environment: deforestation soars in the Amazon. Nature 521(7553):423–423. https://doi.org/10.1038/521423b

    Article  CAS  Google Scholar 

  • Fearnside PM (2016) Brazil’s Amazonian forest carbon: the key to Southern Amazonia’s significance for global climate. Reg Environ Chang:1–15. https://doi.org/10.1007/s10113-016-1007-2

  • Fearnside PM, Righi CA, de Alencastro Graça PML, Keizer EW, Cerri CC, Nogueira EM, Barbosa RI (2009) Biomass and greenhouse-gas emissions from land use change in Brazil’s Amazonian “arc of deforestation”: the states of Mato Grosso and Rondônia. For Ecol Manag 258(9):1968–1978. https://doi.org/10.1016/j.foreco.2009.07.042

  • Forster P, Ramaswamy V, Artaxo P, Berntsen T, Betts R, Fahey DW, Haywoos J, Lean J, Lowe DC, Myhre G, Nganga J, Prinn R, Raga G, Schulz M, Can Dorland R (2007) Changes in atmospheric constituents and in radiative forcing. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (ed) Climate change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge

  • Galford GL, Melillo JM, Mustard JF, Cerri CEP, Cerri CC (2010) The Amazon frontier of land-use change: croplands and consequences for greenhouse gas emissions. Earth Interact 14:1–24. https://doi.org/10.1175/2010EI327.1

    Article  Google Scholar 

  • Gibbs HK, Rausch L, Munger J, Schelly I, Morton DC, Noojipady P, Soares-Filho B, Barreto P, Micol L, Walker NF (2015) Brazil’s soy moratorium. Science 347(6220):377–378. https://doi.org/10.1126/science.aaa0181

    Article  CAS  Google Scholar 

  • Gil J, Siebold M, Berger T (2015) Adoption and development of integrated crop–livestock–forestry systems in Mato Grosso, Brazil. Agric Ecosyst Environ 199:394–406. https://doi.org/10.1016/j.agee.2014.10.008

    Article  Google Scholar 

  • Gollnow F, Göpel J, Schaldach R, Lakes T (2017) Scenarios of land-use change in a deforestation corridor in the Brazilian Amazon: combining two scales of analysis. Reg Environ Chang:1–17. https://doi.org/10.1007/s10113-017-1129-1

  • Göpel J, Schüngel J, Schaldach R, Meurer KHE, Jungkunst HF, Franko U, Boy J, Strey R, Strey S, Guggenberger G, Hampf AC, Parker PS (2017) Future scenarios of land-use change in southern Amazonia and its impacts on greenhouse gas emissions from agricultural soils. Reg Environ Chang: 1-14. https://doi.org/10.1007/s10113-017-1235-0

  • Green WH, Ampt GA (1911) Studies on soil physics. J Agric Sci 4(01):1–24. https://doi.org/10.1017/S0021859600001441

    Article  Google Scholar 

  • Hamilton SH, El Sawah S, Guillaume JHA, Jakeman AJ, Pierce SA (2015) Integrated assessment and modelling: overview and synthesis of salient dimensions. Environ Model Softw 64:215–229. https://doi.org/10.1016/j.envsoft.2014.12.005

    Article  Google Scholar 

  • Hertel TW (2015) The challenges of sustainably feeding a growing planet. Food Sec 7(2):185–198. https://doi.org/10.1007/s12571-015-0440-2

    Article  Google Scholar 

  • IMEA - Instituto Mato-grossense de Economia Agropecuária (2013) Production Cost Survey from the Mato Grosso Institute of Agricultural Economics—MEA. (private survey—unpublished raw data). Cuiabá, Brazil http://www.imea.com.br/sinc/web2/login.php. Accessed 01 Dec 2016

  • INPE (2015) Projecto TerraClass. http://www.inpe.br/cra/projetos_pesquisas/terraclass2010.php. Accessed 23 Apr 2015

  • Lamparter G, Nobrega RLB, Kovacs K, Amorim S, Gerold G (2017) Modelling hydrological impacts of agricultural expansion in two macro-catchments in Southern Amazonia, Brazil. Reg Environ Chang:1–13. https://doi.org/10.1007/s10113-016-1015-2

  • Lapola DM, Schaldach R, Alcamo J, Bondeau A, Koch J, Koelking C, Priess JA (2010) Indirect land-use changes can overcome carbon savings from biofuels in Brazil. Proc Natl Acad Sci 107(8):3388–3393. https://doi.org/10.1073/pnas.0907318107

    Article  CAS  Google Scholar 

  • Lapola DM, Schaldach R, Alcamo J (2011) Impacts of climate change and the end of deforestation on land use in the Brazilian Legal Amazon. Earth Interact 1:2–29. https://doi.org/10.1175/2010EI333.1

    Google Scholar 

  • Latynskiy E, Berger T, Troost C (2014) Assessment of policies forlow-carbon agriculture by means of multi-agent simulation. In: Ames DP, Quinn NWT, Rizzoli AE (eds) Proceedings of the 7th international congress on environmental modelling and software, June 15–19, San Diego, California

  • Lima AJN, Suwa R, de Mello Ribeiro GHP, Kajimoto T, dos Santos J, da Silva RP, De Souza CAS, de Barros PC, Noguchi H, Ishizuka M, Higuchi N (2012) Allometric models for estimating aboveand belowground biomass in Amazonian forests at Sao Gabriel da Cachoeira in the upper Rio Negro, Brazil. For Ecol Manag 277:163–172. https://doi.org/10.1016/jforeco201204028

  • Macedo MN, DeFries RS, Morton DC, Stickler CM, Galford GL, Shimabukuro YE (2012) Decoupling of deforestation and soy production in the southern Amazon during the late 2000s. Proc Natl Acad Sci 109:1341–1346. https://doi.org/10.1073/pnas.1111374109

    Article  CAS  Google Scholar 

  • Malhi Y, Aragão LE, Galbraith D, Huntingford C, Fisher R, Zelazowski P, Sitch S, McSweeney C, Meir P (2009) Exploring the likelihood and mechanism of a climate-change-induced dieback of the Amazon rainforest. Proc Natl Acad Sci 106(49):20610–20615. https://doi.org/10.1073/pnas.0804619106

    Article  CAS  Google Scholar 

  • Martinelli LA, Naylor R, Vitousek PM, Moutinho P (2010) Agriculture in Brazil: impacts, costs, and opportunities for a sustainable future. Curr Opin Environ Sustain 2(5):431–438. https://doi.org/10.1016/j.cosust.2010.09.008

    Article  Google Scholar 

  • Meinshausen M, Smith SJ, Calvin K, Daniel JS, Kainuma MLT, Lamarque JF, Matsumoto K, Montzka A, Rape CB, Riahi K, Thomson A, Velders GJM, van Vuuren.DPP (2011) The RCP greenhouse gas concentrations and their extensions from 1765 to 2300. Clim Chang 109(1–2): 213. https://doi.org/10.1007/s10584-011-0156-z

  • Melillo JM, Steudler PA, Feigl BJ, Neill C, Garcia D, Piccolo MC, Tian H (2001) Nitrous oxide emissions from forests and pastures of various ages in the Brazilian Amazon. J Geophys Res Atmos 106(D24):34179–34188. https://doi.org/10.1029/2000JD000036

    Article  CAS  Google Scholar 

  • Meurer KHE, Franko U, Stange CF, Dalla Rosa J, Madari BE, Jungkunst HF (2016) Direct nitrous oxide (N2O) fluxes from soils under different land use in Brazil—a critical review. Environ Res Lett 11:02001. https://doi.org/10.1088/1748-9326/11/2/023001

    Article  Google Scholar 

  • Meyfroidt P, Lambin EF, Erb KH, Hertel TW (2013) Globalization of land use: distant drivers of land change and geographic displacement of land use. Curr Opin Environ Sustain 5(5):438–444. https://doi.org/10.1016/j.cosust.2013.04.003

    Article  Google Scholar 

  • Miranda SC, Bustamante M, Palace M, Hagen S, Keller M, Ferreira LG (2014) Regional variations in biomass distribution in Brazilian savanna woodland. Biotropica 46(2):125–138. https://doi.org/10.1111/btp.12095

    Article  Google Scholar 

  • Moreira E, Costa S, Aguiar AP, Câmara G, Carneiro T (2009) Dynamical coupling of multiscale land change models. Landsc Ecol 24(9):1183. https://doi.org/10.1007/s10980-009-9397-x

    Article  Google Scholar 

  • NAS, National Academy of Sciences (2014) Advancing land change modeling: opportunities and research requirements. The National Academic Press, Washington DC

    Google Scholar 

  • Neill C, Steudler PA, Garcia-Montiel DC, Melillo JM, Feigl BJ, Piccolo MC, Cerri CC (2005) Rates and controls of nitrous oxide and nitric oxide emissions following conversion of forest to pasture in Rondonia. Nutr Cycl Agroecosyst 71:1–15. https://doi.org/10.1007/s10705-004-0378-9

    Article  CAS  Google Scholar 

  • Nendel C, Berg M, Kersebaum KC, Mirschel W, Specka X, Wegehenkel M, Wenkel KO, Wieland R (2011) The MONICA model: testing predictability for crop growth, soil moisture and nitrogen dynamics. Ecol Model 222(9):1614–1625. https://doi.org/10.1016/j.ecolmodel.2011.02.018

    Article  CAS  Google Scholar 

  • Nepstad, D.; Soares-Filho, B. S.; Merry, F.; Lima, A.; Moutinho, P.; Carter, J. and Stella, O. (2009): The end of deforestation in the Brazilian Amazon. In: Science 326 (5958), 1350–1351. https://doi.org/10.1126/science.1182108

  • O’Neill BC, Kriegler E, Ebi KL, Kemp-Benedict E, Riahi K, Rothman DS, van Ruiven BJ, van Vuure DP, Birkmann J, Kok K, Levy M, Solecki W (2017) The roads ahead: narratives for shared socioeconomic pathways describing world futures in the 21st century. Glob Environ Chang 42:169–180. https://doi.org/10.1016/j.gloenvcha.2015.01.004

    Article  Google Scholar 

  • Orlowsky B, Bothe O, Fraedrich K, Gerstengarbe FW, Zhu X (2010) Future climates from bias-bootstrapped weather analogs: an application to the Yangtze River Basin. J Clim 23(13):3509–3524. https://doi.org/10.1175/2010JCLI3271.1

    Article  Google Scholar 

  • Richards PD, Walker RT, Arima EY (2014) Spatially complex land change: the indirect effect of Brazil’s agricultural sector on land use in Amazonia. Glob Environ Chang 29:1–9. https://doi.org/10.1016/j.gloenvcha.2014.06.011

    Article  Google Scholar 

  • Roeckner E, Bäuml G, Bonaventura L, Brokopf R, Esch M, Giorgetta M, Hagemann S, Kirchner I, Kornblueh L, Manzini E, Rhodin A, Schlese U, Schulzweida U, Tompkins A (2003) The atmospheric general circulation model ECHAM 5. PART I: model description. Report, MPI für Meteorologie, 349

  • Rudorff BFT, Adami M, Aguiar DA, Moreira MA, Mello MP, Fabiani L, Amaral DF, Pires BM (2011) The soy moratorium in the Amazon biome monitored by remote sensing images. Remote Sens 3(12):185–202. https://doi.org/10.3390/rs3010185

    Article  Google Scholar 

  • Schaldach R, Alcamo J, Koch J, Kölking C, Lapola DM, Schüngel J, Priess JA (2011) An integrated approach to modelling land-use change on continental and global scales. Environ Model Softw 26(8):1041–1051. https://doi.org/10.1016/j.envsoft.2011.02.013

    Article  Google Scholar 

  • Schmidt J (1991) A mathematical model to simulate rainfall erosion. In: Bork HR, de Ploey J, Schick AP (eds) Erosion, transport and deposition processes—theories and models, vol 19. Catena Suppl 101–109

  • Schmidt J (1992) Modelling long-term soil loss and landform change. In: Abrahams AJ, Parsons AD (eds) Overland flow—hydraulics and erosion mechanics. University College London Press, London

    Google Scholar 

  • Schmidt MWI, Torn MS, Abiven S, Dittmar T, Guggenberger G, Janssens IA, Kleber M, Kögel-Knabner I, Lehmann J, Manning DAC, Nannupieri P, Rasse DP, Weiner S, Trumbore SE (2011) Persistence of soil organic matter as an ecosystem property. Nature 478:49–56. https://doi.org/10.1038/nature10386

    Article  CAS  Google Scholar 

  • Schönenberg R, Schaldach R, Lakes T, Göpel J, Gollnow F (2017) Inter-and transdisciplinary scenario construction to explore future land-use options in southern Amazonia. Ecol Soc 22(3):13. https://doi.org/10.5751/ES-09032-220313

    Article  Google Scholar 

  • Schreinemachers P, Berger T (2011) An agent-based simulation model of human environment interactions in agricultural systems. Environ Model Softw 26:845–859. https://doi.org/10.1016/j.envsoft.2011.02.004

    Article  Google Scholar 

  • Soares-Filho BS, Nepstad DC, Curran LM, Cerqueira GC, Garcia RA, Ramos CA, Voll E, McDonald A, Lefebvre P, Schlesinger P (2006) Modelling conservation in the Amazon basin. Nature 440:520–523. https://doi.org/10.1038/nature04389

    Article  CAS  Google Scholar 

  • Soares-Filho B, Moutinho P, Nepstad D, Anderson A, Rodrigues H, Garcia R, Silvestrini R (2010) Role of Brazilian Amazon protected areas in climate change mitigation. Proc Natl Acad Sci 107(24):10821–10826. https://doi.org/10.1073/pnas.0913048107

    Article  CAS  Google Scholar 

  • Specht JE, Hume DJ, Kumudini SV (1999) Soybean yield potential—a genetic and physiological perspective. Crop Sci 39(6):1560–1570. https://doi.org/10.2135/cropsci1999.3961560x

    Article  Google Scholar 

  • Starkloff T, Stolte J (2014) Applied comparison of the erosion risk models EROSION 3D and LISEM for a small catchment in Norway. Catena 118:154–167. https://doi.org/10.1016/j.catena.2014.02.004

    Article  Google Scholar 

  • Stehfest E, Bouwman L, van Vuuren DP, den Elzen MG, Eickhout B, Kabat P (2009) Climate benefits of changing diet. Clim Chang 95(1–2):83–102. https://doi.org/10.1007/s10584-008-9534-6

    Article  CAS  Google Scholar 

  • Strey S, Boy J, Strey R, Weber O, Guggenberger G (2016) Response of soil organic carbon to land-use change in central Brazil: a large-scale comparison of Ferralsols and Acrisols. Plant Soil 408(1–2):327–342. https://doi.org/10.1007/s11104-016-2901-6

    Article  CAS  Google Scholar 

  • Tölle MH, Gutjahr O, Busch G, Thiele JC (2014) Increasing bioenergy production on arable land: does the regional and local climate respond? Germany as a case study. J Geophys Res Atmosp 119(6):2711–2724. https://doi.org/10.1002/2013JD020877

    Article  Google Scholar 

  • Turner BL, Lambin EF, Reenberg A (2007) The emergence of land change science for global environmental change and sustainability. Proc Natl Acad Sci 104(52):20666–20671. https://doi.org/10.1073/pnas.0704119104

    Article  CAS  Google Scholar 

  • Turner BL, Geoghegan J, Lawrence D, Radel C, Schmook B, Vance C, Vester H (2016) Land system science and the social–environmental system: the case of Southern Yucatán Peninsular Region (SYPR) project. Curr Opin Environ Sustain 19:18–29. https://doi.org/10.1016/j.cosust.2015.08.014

    Article  Google Scholar 

  • Van Vuuren DP, Carter TR (2014) Climate and socio-economic scenarios for climate change research and assessment: reconciling the new with the old. Clim Chang 122(3):415–429. https://doi.org/10.1007/s10584-013-0974-2

    Article  Google Scholar 

  • Vuuren DP, Stehfest E, Elzen MG, Kram T, Vliet J, Deetman S, Isaac M, Klein Goldewijk K, Hof A, Mendoza Beltran A, Oostenrijk R, van Ruijven B (2011) RCP2. 6: exploring the possibility to keep global mean temperature increase below 2 °C. Clim Chang 109(1–2):95–116. https://doi.org/10.1007/s10584-011-0152-3

    Article  CAS  Google Scholar 

  • Wenkel KO, Berg M, Mirschel W, Wieland R, Nendel C, Köstner B (2013) LandCaRe DSS—an interactive decision support system for climate change impact assessment and the analysis of potential agricultural land use adaptation strategies. J Environ Manag 127:168–183. https://doi.org/10.1016/j.jenvman.2013.02.051

    Article  Google Scholar 

Download references

Acknowledgements

This study was conducted in the framework of the integrated project CarBioCial funded by the German Ministry of Education and Research (BMBF) under the grant number 01LL0902K. We thank all involved stakeholders, farmers, and our Brazilian scientific colleagues for their support and CNPq, Embrapa, and FAPEMAT for co-funding of Brazilian counterpart projects.

Author information

Authors and Affiliations

  1. Center for Environmental Systems Research (CESR), University of Kassel, Wilhemshöher Allee 47, 34109, Kassel, Germany

    Rüdiger Schaldach & Jan Göpel

  2. Helmholtz Centre for Environmental Research – UFZ, Halle (Saale), Leipzig, Germany

    Katharina H. E. Meurer

  3. Georg August University of Göttingen, Göttingen, Germany

    Katharina H. E. Meurer & Gerhard Gerold

  4. University of Koblenz-Landau, Mainz, Germany

    Hermann F. Jungkunst

  5. Institute of Landscape Systems Analysis, Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany

    Claas Nendel, Anna Hampf & Phillip S. Parker

  6. Humboldt University Berlin, Berlin, Germany

    Tobia Lakes & Florian Gollnow

  7. Institute of Soil Science, Leibniz Universität Hannover, Hannover, Germany

    Jens Boy, Georg Guggenberger, Robert Strey & Simone Strey

  8. University of Hohenheim, Stuttgart, Germany

    Thomas Berger & Evgeny Latynskiy

  9. Free University of Berlin, Berlin, Germany

    Regine Schönenberg

  10. University of Hamburg, Hamburg, Germany

    Jürgen Böhner

  11. University of Freiberg, Freiberg, Germany

    Marcus Schindewolf

  12. Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, Brazil

    Paulo César Sentelhas

Authors
  1. Rüdiger Schaldach
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Katharina H. E. Meurer
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hermann F. Jungkunst
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Claas Nendel
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Tobia Lakes
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Florian Gollnow
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jan Göpel
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Jens Boy
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Georg Guggenberger
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Robert Strey
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Simone Strey
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Thomas Berger
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Gerhard Gerold
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Regine Schönenberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Jürgen Böhner
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Marcus Schindewolf
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Evgeny Latynskiy
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Anna Hampf
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Phillip S. Parker
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Paulo César Sentelhas
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rüdiger Schaldach.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Schaldach, R., Meurer, K.H.E., Jungkunst, H.F. et al. A model-based assessment of the environmental impact of land-use change across scales in Southern Amazonia. Reg Environ Change 18, 161–173 (2018). https://doi.org/10.1007/s10113-017-1244-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10113-017-1244-z

Keywords

Search

Navigation