Potential effects of climate change on Brazil’s land use policy for renewable energy from sugarcane

Resources, Conservation and Recycling - Tập 144 - Trang 158-168 - 2019
Gabriel Granco1, Marcellus Caldas2, Paulo De Marco3
1Kansas State University, Dept. of Geography, 1002 Seaton Hall, Manhattan, KS, 66506-1111, USA
2Kansas State University, Dept. of Geography, 1001 Seaton Hall, Manhattan, KS, 66506-1111, USA
3Federal University of Goias, Dept. of Ecology, Instituto de Ciências Biológicas (Bloco ICB IV), Universidade Federal de Goiás, Campus II/UFG, Avenida Esperança, s/n, Câmpus Samambaia, CEP. 74.690-900, Goiânia, Goiás, Brazil

Tài liệu tham khảo

Akhter, 2017, Habitat distribution modelling to identify areas of high conservation value under climate change for Mangifera sylvatica Roxb. of Bangladesh, Land Use Policy, 60, 223, 10.1016/j.landusepol.2016.10.027 Alkimim, 2015, Converting Brazil’s pastures to cropland: an alternative way to meet sugarcane demand and to spare forestlands, Appl. Geogr., 62, 75, 10.1016/j.apgeog.2015.04.008 Allouche, 2006, Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS), J. Appl. Ecol., 43, 1223, 10.1111/j.1365-2664.2006.01214.x Anderson, 2003, Evaluating predictive models of species’ distributions: criteria for selecting optimal models, Ecol. Modell., 162, 211, 10.1016/S0304-3800(02)00349-6 Araújo, 2012, Uses and misuses of bioclimatic envelope modeling, Ecology, 93, 1527, 10.1890/11-1930.1 Arima, 2014, Public policies can reduce tropical deforestation: lessons and challenges from Brazil, Land Use Policy, 41, 465, 10.1016/j.landusepol.2014.06.026 Barney, 2011, Global climate niche estimates for bioenergy crops and invasive species of agronomic origin: potential problems and opportunities, PLoS One, 6, 10.1371/journal.pone.0017222 Batista, 2016 Bergtold, 2017, Indirect land use change from ethanol production: the case of sugarcane expansion at the farm level on the Brazilian Cerrado, J. Land Use Sci., 1 Berry, 2006, Assessing the vulnerability of agricultural land use and species to climate change and the role of policy in facilitating adaptation, Environ. Sci. Policy, 9, 189, 10.1016/j.envsci.2005.11.004 Bini, 2009, Coefficient shifts in geographical ecology: an empirical evaluation of spatial and non-spatial regression, Ecography, 32, 193, 10.1111/j.1600-0587.2009.05717.x Blanchard, 2015, Anticipating potential biodiversity conflicts for future biofuel crops in South Africa: incorporating spatial filters with species distribution models, Gcb Bioenergy, 7, 273, 10.1111/gcbb.12129 Broennimann, 2012, Measuring ecological niche overlap from occurrence and spatial environmental data, Glob. Ecol. Biogeogr., 21, 481, 10.1111/j.1466-8238.2011.00698.x Bunn, 2015, Multiclass classification of agro-ecological zones for arabica coffee: an improved understanding of the impacts of climate change, PLoS One, 10, 10.1371/journal.pone.0140490 Campbell, 2016, Reducing risks to food security from climate change, Glob. Food Sec., 11, 34, 10.1016/j.gfs.2016.06.002 Center for Advanced Studies on Applied Economics (CEPEA), 2018 Centro de Cana, 2018 Chandiposha, 2013, Potential impact of climate change in sugarcane and mitigation strategies in Zimbabwe, African J. Agric. Res., 8, 2814 CONAB, 2016 Cunningham, 2016, Abiotic and biotic constraints across reptile and amphibian ranges, Ecography, 39, 1, 10.1111/ecog.01369 de Lucena, 2009, The vulnerability of renewable energy to climate change in Brazil, Energy Policy, 37, 879, 10.1016/j.enpol.2008.10.029 de Souza, 2014, The use of species distribution models to predict the spatial distribution of deforestation in the western Brazilian Amazon, Ecol. Modell., 291, 250, 10.1016/j.ecolmodel.2014.07.007 Drake, 2006, Modelling ecological niches with support vector machines, J. Appl. Ecol., 43, 424, 10.1111/j.1365-2664.2006.01141.x Duan, 2014, The predictive performance and stability of six species distribution models, PLoS One, 9, 10.1371/journal.pone.0112764 Elith, 2009, Species distribution models: ecological explanation and prediction across space and time, Annu. Rev. Ecol. Evol. Syst., 40, 677, 10.1146/annurev.ecolsys.110308.120159 EPE, 2015 EPE, 2016 Estes, 2013, Comparing mechanistic and empirical model projections of crop suitability and productivity: implications for ecological forecasting, Glob. Ecol. Biogeogr., 22, 1007, 10.1111/geb.12034 Ettema, 2002, Spatial soil ecology, Trends Ecol. Evol. (Amst.), 17, 177, 10.1016/S0169-5347(02)02496-5 Evans, 2010, Using species distribution models to identify suitable areas for biofuel feedstock production, Gcb Bioenergy, 2, 63, 10.1111/j.1757-1707.2010.01040.x Faleiro, 2013, Defining spatial conservation priorities in the face of land-use and climate change, Biol. Conserv., 158, 248, 10.1016/j.biocon.2012.09.020 Faleiro, 2015, Ring out the bells, we are being invaded! Niche conservatism in exotic populations of the Yellow Bells, Tecoma stans (Bignoniaceae), Nat. Conserv., 13, 24, 10.1016/j.ncon.2015.04.004 Fourcade, 2014, Mapping species distributions with MAXENT using a geographically biased sample of presence data: a performance assessment of methods for correcting sampling Bias, PLoS One, 9, 10.1371/journal.pone.0097122 Furtado, 2011, The Brazilian sugarcane innovation system, Energy Policy, 39, 156, 10.1016/j.enpol.2010.09.023 Garcia, 2013, Predicting geographic distribution and habitat suitability due to climate change of selected threatened forest tree species in the Philippines, Appl. Geogr., 44, 12, 10.1016/j.apgeog.2013.07.005 Gilio, 2016, Sugarcane industry’s socioeconomic impact in São Paulo, Brazil: A spatial dynamic panel approach, Energy Econ., 58, 27, 10.1016/j.eneco.2016.06.005 Globo Rural, 2013 Goldemberg, 2007, Ethanol for a sustainable energy future, Science, 315, 808, 10.1126/science.1137013 Goldemberg, 2003, Ethanol learning curve - the Brazilian experience, Biomass Bioenergy, 26, 301, 10.1016/S0961-9534(03)00125-9 Granco, 2017, Exploring the policy and social factors fueling the expansion and shift of sugarcane production in the Brazilian Cerrado, GeoJournal, 82, 63, 10.1007/s10708-015-9666-y Granco, 2018, Factors influencing ethanol mill location in a new sugarcane producing region in Brazil, Biomass Bioenergy, 111, 125, 10.1016/j.biombioe.2018.02.001 Guillera-Arroita, 2015, Is my species distribution model fit for purpose? Matching data and models to applications, Glob. Ecol. Biogeogr., 24, 276, 10.1111/geb.12268 Harahap, 2017, Land allocation to meet sectoral goals in Indonesia—an analysis of policy coherence, Land Use Policy, 61, 451, 10.1016/j.landusepol.2016.11.033 Heumann, 2013, Land suitability modeling using a geographic socio-environmental niche-based approach: a case study from Northeastern Thailand, Ann. Assoc. Am. Geogr., 103, 764, 10.1080/00045608.2012.702479 Hijmans, 2005, Very high resolution interpolated climate surfaces for global land areas, Int. J. Climatol., 25, 1965, 10.1002/joc.1276 Hijmans, 2017 Hirzel, 2008, Habitat suitability modelling and niche theory, J. Appl. Ecol., 10.1111/j.1365-2664.2008.01524.x Howard, 2014, Improving species distribution models: the value of data on abundance, Methods Ecol. Evol., 5, 506, 10.1111/2041-210X.12184 Jaiswal, 2017, Brazilian sugarcane ethanol as an expandable green alternative to crude oil use, Nat. Clim. Chang., 7, 788, 10.1038/nclimate3410 Jiménez-Valverde, 2008, Not as good as they seem: the importance of concepts in species distribution modelling, Divers. Distrib., 14, 885, 10.1111/j.1472-4642.2008.00496.x Karatzoglou, 2004, Kernlab -- an S4 package for kernel methods in r, J. Stat. Softw., 11, 1, 10.18637/jss.v011.i09 Kline, 2016, Reconciling food security and bioenergy: priorities for action, Gcb Bioenergy Leal, 2013, Land demand for ethanol production, Spec. Issue Adv. Sustain. biofuel Prod. use - XIX Int. Symp. Alcohol Fuels - ISAF, 102, 266 Lewis, 2017, Biotic and abiotic factors predicting the global distribution and population density of an invasive large mammal, Sci. Rep., 7, 44152, 10.1038/srep44152 Liaw, 2002, Classification and regression by randomForest, R News, 2, 18 Loarie, 2011, Direct impacts on local climate of sugar-cane expansion in Brazil, Nat. Clim. Chang., 1, 105, 10.1038/nclimate1067 Lobell, 2008, Prioritizing climate change adaptation needs for food security in 2030, Science, 319, 607, 10.1126/science.1152339 Lozier, 2009, Predicting the distribution of Sasquatch in western North America: anything goes with ecological niche modelling, J. Biogeogr., 36, 1623, 10.1111/j.1365-2699.2009.02152.x Lucon, 2010, São Paulo—the “Other” brazil: different pathways on climate change for state and federal governments, J. Egypt. Acadmic Soc. Environ. Dev., 19, 335 Machovina, 2013, Climate change driven shifts in the extent and location of areas suitable for export banana production, Ecol. Econ., 95, 83, 10.1016/j.ecolecon.2013.08.004 Manzatto, 2009 Marin, 2013, Climate change impacts on sugarcane attainable yield in southern Brazil, Clim. Change, 117, 227, 10.1007/s10584-012-0561-y Marin, 2016, Prospects for increasing sugarcane and bioethanol production on existing crop area in Brazil, Bioscience, 66, 307, 10.1093/biosci/biw009 Martins, 2015, Species conservation under future climate change: the case of Bombus bellicosus, a potentially threatened South American bumblebee species, J. Insect Conserv., 19, 33, 10.1007/s10841-014-9740-7 Monteiro de Carvalho, 2015, Deforested and degraded land available for the expansion of palm oil for biodiesel in the state of Pará in the Brazilian Amazon, Renew. Sustain. Energy Rev., 44, 867, 10.1016/j.rser.2015.01.026 Moraes, 2011, Perspective: lessons from Brazil, Nature, 474, 10.1038/474S025a Moraes, 2016, Accelerated growth of the sugarcane, sugar, and ethanol sectors in Brazil (2000–2008): effects on municipal gross domestic product per capita in the south-central region, Biomass Bioenergy, 91, 116, 10.1016/j.biombioe.2016.05.004 Myers, 2000, Biodiversity hotspots for conservation priorities, Nature, 403, 853, 10.1038/35002501 Nelson, 2014, Climate change effects on agriculture: economic responses to biophysical shocks, Proc. Natl. Acad. Sci. U. S. A., 111, 3274, 10.1073/pnas.1222465110 Niles, 2013, Perceptions and responses to climate policy risks among California farmers, Glob. Environ. Chang., 23, 1752, 10.1016/j.gloenvcha.2013.08.005 Olesen, 2002, Consequences of climate change for European agricultural productivity, land use and policy, Eur. J. Agron., 16, 239, 10.1016/S1161-0301(02)00004-7 Pearson, 2003, Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful? Glob, Ecol. Biogeogr., 12, 361, 10.1046/j.1466-822X.2003.00042.x Pearson, 2006, Model-based uncertainty in species range prediction, J. Biogeogr., 33, 1704, 10.1111/j.1365-2699.2006.01460.x Peterson, 2003, Predicting the geography of species’ invasions via ecological niche modeling. Q. Rev. Biol. 419 q, Rev. Biol., 78, 419, 10.1086/378926 Peterson, 2007, Transferability and model evaluation in ecological niche modeling: a comparison of GARP and Maxent, Ecography, 30, 550, 10.1111/j.0906-7590.2007.05102.x Petitpierre, 2016, Will climate change increase the risk of plant invasions into mountains? Ecol, Appl., 26, 530 Pettersson, 2013, Adaptive capacity of legal and policy frameworks for biodiversity protection considering climate change, Land Use Policy, 34, 213, 10.1016/j.landusepol.2013.03.007 Phalan, 2016, How can higher-yield farming help to spare nature?, Science, 351, 450, 10.1126/science.aad0055 Phillips, 2006, Maximum entropy modeling of species geographic distributions, Ecol. Modell., 190, 231, 10.1016/j.ecolmodel.2005.03.026 Phillips, 2008, Transferability, sample selection bias and background data in presence-only modelling: a response to Peterson et al. (2007), Ecography, 31, 272, 10.1111/j.0906-7590.2008.5378.x Pugh, 2016, Climate analogues suggest limited potential for intensification of production on current croplands under climate change, Nat. Commun., 7, 12608, 10.1038/ncomms12608 Ranjitkar, 2016, Suitability analysis and projected climate change impact on banana and coffee production zones in Nepal, PLoS One, 11, 10.1371/journal.pone.0163916 Rosenzweig, 2014, Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison, Proc. Natl. Acad. Sci. U. S. A., 111, 3268, 10.1073/pnas.1222463110 Rudorff, 2010, Studies on the rapid expansion of sugarcane for ethanol production in São Paulo State (Brazil) using landsat data, Remote Sens. (Basel), 2, 1057, 10.3390/rs2041057 Sano, 2008, Mapeamento semidetalhado do uso da terra do Bioma Cerrado, Pesqui. Agropecuária Bras., 43, 153, 10.1590/S0100-204X2008000100020 Sant’Anna, 2016, Ethanol and sugarcane expansion in Brazil: what is fueling the ethanol industry? Int, Food Agribus. Manage. Rev., 19, 163 Santos, 2016, A agroindústria canavieira e a produção de etanol no Brasil: características, potenciais e perfil da crise atual, 17 Schlenker, 2010, Robust negative impacts of climate change on African agriculture, Environ. Res. Lett., 5, 10.1088/1748-9326/5/1/014010 Segurado, 2004, An evaluation of methods for modelling species distributions, J. Biogeogr., 31, 1555, 10.1111/j.1365-2699.2004.01076.x Shikida, 2013, Expansão canavieira no Centro-Oeste: limites e potencialidades, Rev. Política Agrícola, 22, 122 Silva, 2011, Avanço do setor sucroalcooleiro e expansão da fronteira agrícola em Goiás. Pesqui. Agropecuária Trop, Agric. Res. Trop., 41 Silva, 2012, A expansão do setor sucroenergético em Goiás: a contribuição das políticas públicas, CAMPO-TERRITÓRIO Rev. Geogr. agrária, 7, 97, 10.14393/RCT71313766 Silva, 2014, Seeking the flowers for the bees: integrating biotic interactions into niche models to assess the distribution of the exotic bee species Lithurgus huberi in South America, Ecol. Modell., 273, 200, 10.1016/j.ecolmodel.2013.11.016 Silva, 2014, Using ecological niche models and niche analyses to understand speciation patterns: the case of sister neotropical orchid bees, PLoS One, 9, 10.1371/journal.pone.0113246 Silva, 2015, Distributional modeling of Mantophasmatodea (Insecta: notoptera): a preliminary application and the need for future sampling, Org. Divers. Evol. Soccol, 2005, Brazilian biofuel program: an overview, J. Sci. Ind. Res. (1942), 64, 897 Spera, 2017, The drivers of sugarcane expansion in Goiás, Brazil, Land use policy, 66, 111, 10.1016/j.landusepol.2017.03.037 Stockwell, 2002, Effects of sample size on accuracy of species distribution models, Ecol. Modell., 148, 1, 10.1016/S0304-3800(01)00388-X Svetnik, 2003, Random forest: a classification and regression tool for compound classification and QSAR modeling, J. Chem. Inf. Comput. Sci., 43, 1947, 10.1021/ci034160g Thomson, 2010, Climate mitigation and the future of tropical landscapes, Proc. Natl. Acad. Sci., 107, 19633, 10.1073/pnas.0910467107 Thomson, 2011, RCP4.5: a pathway for stabilization of radiative forcing by 2100, Clim. Change, 109, 77, 10.1007/s10584-011-0151-4 Trabucco, 2010, Global mapping of Jatropha curcas yield based on response of fitness to present and future climate, GCB Bioenergy, 2 UNFCCC, 2015. URL http://www4.unfccc.int/submissions/indc/SubmissionPages/submissions.aspx. Unica, 2014 Walter, 2014, Brazilian sugarcane ethanol: developments so far and challenges for the future, Wiley Interdiscip. Rev. Energy Environ., 3, 70, 10.1002/wene.87 Wisz, 2008, Effects of sample size on the performance of species distribution models, Divers. Distrib., 14, 763, 10.1111/j.1472-4642.2008.00482.x Zhang, 2012, Using species distribution modeling to improve conservation and land use planning of Yunnan, China, Biol. Conserv., 153, 257, 10.1016/j.biocon.2012.04.023 Zhao, 2015, Climate change and sugarcane production: potential impact and mitigation strategies, Int. J. Agron., 2015, 1, 10.1155/2015/547386 Zullo, 2018, Sugar-energy sector vulnerability under CMIP5 projections in the Brazilian central-southern macro-region, Clim. Change, 149, 489, 10.1007/s10584-018-2249-4