Articles | Volume 15, issue 3
https://doi.org/10.5194/esd-15-817-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esd-15-817-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Global cropland expansion enhances cropping potential and reduces its inequality among countries
Xiaoxuan Liu
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100190, China
Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing, 100084, China
Department of Geography, The University of Hong Kong, Hong Kong SAR, 999077, China
Institute for Climate and Carbon Neutrality, The University of Hong Kong, Hong Kong SAR, 999077, China
Shu Liu
Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing, 100084, China
Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing, 100084, China
Ministry of Education Ecological Field Station for East Asia Migratory Birds, Tsinghua University, Beijing, 100084, China
Department of Earth System Science, Tsinghua University, Xi'an Institute of Surveying and Mapping Joint Research Center for Next-Generation Smart Mapping, Beijing, 100084, China
Yong Wang
Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing, 100084, China
Zhenrong Du
School of Information and Communication Engineering, Dalian University of Technology, Dalian, 116024, China
Dailiang Peng
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100190, China
Ece Aksoy
Geospatial Unit, Food and Agriculture Organization of the United Nations, Rome 00153, Italy
Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing, 100084, China
Department of Earth System Science, Tsinghua University, Xi'an Institute of Surveying and Mapping Joint Research Center for Next-Generation Smart Mapping, Beijing, 100084, China
State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing, 100084, China
Peng Gong
Institute for Climate and Carbon Neutrality, The University of Hong Kong, Hong Kong SAR, 999077, China
Department of Geography, Department of Earth Sciences and Institute for Climate and Carbon Neutrality, University of Hong Kong, Hong Kong SAR, 999077, China
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Xiyu Li, Le Yu, Zhenrong Du, and Xiaoxuan Liu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-233, https://doi.org/10.5194/essd-2024-233, 2024
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We developed a new method to update detailed maps showing where different crops are grown over time, focusing on Africa, China, and the USA. Using various data sources and machine learning, we produced accurate maps at a 10 km resolution covering up to 42 crop types from 1961 to 2022. Our work bridges statistical data and satellite imagery, helping researchers and policymakers to address global agricultural challenges in food security and environmental impacts.
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Earth Syst. Sci. Data, 16, 2297–2316, https://doi.org/10.5194/essd-16-2297-2024, https://doi.org/10.5194/essd-16-2297-2024, 2024
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We developed the first 30 m annual cropland dataset of China (CACD) for 1986–2021. The overall accuracy of CACD reached up to 0.93±0.01 and was superior to other products. Our fine-resolution cropland maps offer valuable information for diverse applications and decision-making processes in the future.
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Earth Syst. Sci. Data, 15, 5597–5615, https://doi.org/10.5194/essd-15-5597-2023, https://doi.org/10.5194/essd-15-5597-2023, 2023
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Yaozhi Jiang, Kun Yang, Youcun Qi, Xu Zhou, Jie He, Hui Lu, Xin Li, Yingying Chen, Xiaodong Li, Bingrong Zhou, Ali Mamtimin, Changkun Shao, Xiaogang Ma, Jiaxin Tian, and Jianhong Zhou
Earth Syst. Sci. Data, 15, 621–638, https://doi.org/10.5194/essd-15-621-2023, https://doi.org/10.5194/essd-15-621-2023, 2023
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Ming Yin, Yilun Han, Yong Wang, Wenqi Sun, Jianbo Deng, Daoming Wei, Ying Kong, and Bin Wang
Geosci. Model Dev., 16, 135–156, https://doi.org/10.5194/gmd-16-135-2023, https://doi.org/10.5194/gmd-16-135-2023, 2023
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EGUsphere, https://doi.org/10.5194/egusphere-2022-1110, https://doi.org/10.5194/egusphere-2022-1110, 2022
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Yaozhi Jiang, Kun Yang, Hua Yang, Hui Lu, Yingying Chen, Xu Zhou, Jing Sun, Yuan Yang, and Yan Wang
Hydrol. Earth Syst. Sci., 26, 4587–4601, https://doi.org/10.5194/hess-26-4587-2022, https://doi.org/10.5194/hess-26-4587-2022, 2022
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Kai Zhang, Wentao Zhang, Hui Wan, Philip J. Rasch, Steven J. Ghan, Richard C. Easter, Xiangjun Shi, Yong Wang, Hailong Wang, Po-Lun Ma, Shixuan Zhang, Jian Sun, Susannah M. Burrows, Manish Shrivastava, Balwinder Singh, Yun Qian, Xiaohong Liu, Jean-Christophe Golaz, Qi Tang, Xue Zheng, Shaocheng Xie, Wuyin Lin, Yan Feng, Minghuai Wang, Jin-Ho Yoon, and L. Ruby Leung
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Bowen Cao, Le Yu, Victoria Naipal, Philippe Ciais, Wei Li, Yuanyuan Zhao, Wei Wei, Die Chen, Zhuang Liu, and Peng Gong
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Yong Wang, Guang J. Zhang, Shaocheng Xie, Wuyin Lin, George C. Craig, Qi Tang, and Hsi-Yen Ma
Geosci. Model Dev., 14, 1575–1593, https://doi.org/10.5194/gmd-14-1575-2021, https://doi.org/10.5194/gmd-14-1575-2021, 2021
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Hui Lu, Donghai Zheng, Kun Yang, and Fan Yang
Hydrol. Earth Syst. Sci., 24, 5745–5758, https://doi.org/10.5194/hess-24-5745-2020, https://doi.org/10.5194/hess-24-5745-2020, 2020
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Han Liu, Peng Gong, Jie Wang, Nicholas Clinton, Yuqi Bai, and Shunlin Liang
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Ece Aksoy, Mirko Gregor, Christoph Schröder, Manuel Löhnertz, and Geertrui Louwagie
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Related subject area
Topics: Climate change | Interactions: Human/Earth system interactions | Methods: Earth system and climate modeling
Consistent increase in East Asian Summer Monsoon rainfall and its variability under climate change over China in CMIP6
Fire weather compromises forestation-reliant climate mitigation pathways
Solar radiation modification challenges decarbonization with renewable solar energy
The Indonesian Throughflow circulation under solar geoengineering
ESD Ideas: Arctic amplification's contribution to breaches of the Paris Agreement
Early warnings of the transition to a superrotating atmospheric state
Anja Katzenberger and Anders Levermann
Earth Syst. Dynam., 15, 1137–1151, https://doi.org/10.5194/esd-15-1137-2024, https://doi.org/10.5194/esd-15-1137-2024, 2024
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A fifth of the world's population lives in eastern China, whose climate is dominated by the East Asian Summer Monsoon (EASM). Therefore, it is important to know how the EASM will change under global warming. Here, we use the data of 34 climate models of the latest generation to understand how the EASM will change throughout the 21st century. The models project that the EASM will intensify and that variability between years will increase associated with an increase in extremely wet seasons.
Felix Jäger, Jonas Schwaab, Yann Quilcaille, Michael Windisch, Jonathan Doelman, Stefan Frank, Mykola Gusti, Petr Havlik, Florian Humpenöder, Andrey Lessa Derci Augustynczik, Christoph Müller, Kanishka Balu Narayan, Ryan Sebastian Padrón, Alexander Popp, Detlef van Vuuren, Michael Wögerer, and Sonia Isabelle Seneviratne
Earth Syst. Dynam., 15, 1055–1071, https://doi.org/10.5194/esd-15-1055-2024, https://doi.org/10.5194/esd-15-1055-2024, 2024
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Climate change mitigation strategies developed with socioeconomic models rely on the widespread (re)planting of trees to limit global warming below 2°. However, most of these models neglect climate-driven shifts in forest damage like fires. By assessing existing mitigation scenarios, we show the exposure of projected forestation areas to fire-promoting weather conditions. Our study highlights the problem of ignoring climate-driven shifts in forest damage and ways to address it.
Susanne Baur, Benjamin M. Sanderson, Roland Séférian, and Laurent Terray
Earth Syst. Dynam., 15, 307–322, https://doi.org/10.5194/esd-15-307-2024, https://doi.org/10.5194/esd-15-307-2024, 2024
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Most solar radiation modification (SRM) simulations assume no physical coupling between mitigation and SRM. We analyze the impact of SRM on photovoltaic (PV) and concentrated solar power (CSP) and find that almost all regions have reduced PV and CSP potential compared to a mitigated or unmitigated scenario, especially in the middle and high latitudes. This suggests that SRM could pose challenges for meeting energy demands with solar renewable resources.
Chencheng Shen, John C. Moore, Heri Kuswanto, and Liyun Zhao
Earth Syst. Dynam., 14, 1317–1332, https://doi.org/10.5194/esd-14-1317-2023, https://doi.org/10.5194/esd-14-1317-2023, 2023
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The Indonesia Throughflow is an important pathway connecting the Pacific and Indian oceans and is part of a wind-driven circulation that is expected to reduce under greenhouse gas forcing. Solar dimming and sulfate aerosol injection geoengineering may reverse this effect. But stratospheric sulfate aerosols affect winds more than simply ``shading the sun''; they cause a reduction in water transport similar to that we simulate for a scenario with unabated greenhouse gas emissions.
Alistair Duffey, Robbie Mallett, Peter J. Irvine, Michel Tsamados, and Julienne Stroeve
Earth Syst. Dynam., 14, 1165–1169, https://doi.org/10.5194/esd-14-1165-2023, https://doi.org/10.5194/esd-14-1165-2023, 2023
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The Arctic is warming several times faster than the rest of the planet. Here, we use climate model projections to quantify for the first time how this faster warming in the Arctic impacts the timing of crossing the 1.5 °C and 2 °C thresholds defined in the Paris Agreement. We show that under plausible emissions scenarios that fail to meet the Paris 1.5 °C target, a hypothetical world without faster warming in the Arctic would breach that 1.5 °C target around 5 years later.
Mark S. Williamson and Timothy M. Lenton
EGUsphere, https://doi.org/10.5194/egusphere-2023-2036, https://doi.org/10.5194/egusphere-2023-2036, 2023
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Climate models have transitioned to a superrotating atmospheric state under a broad range of warm climates. Such a transition would change global weather patterns should it occur. Here we simulate this transition using an idealized climate model and look for any early warnings of the superrotating state before it happens. We find several early warning indicators that we attribute to an oscillating pattern in the windfield fluctuations.
Cited articles
Arora, V. K. and Montenegro, A.: Small temperature benefits provided by realistic afforestation efforts, Nat. Geosci., 4, 514–518, 2011.
Bahar, N. H., Lo, M., Sanjaya, M., Van Vianen, J., Alexander, P., Ickowitz, A., and Sunderland, T.: Meeting the food security challenge for nine billion people in 2050: What impact on forests, Global Environ. Chang., 62, 102056, https://doi.org/10.1016/j.gloenvcha.2020.102056, 2020.
Bennett, E. M., Carpenter, S. R., and Caraco, N. F.: Human impact on erodable phosphorus and eutrophication: a global perspective: increasing accumulation of phosphorus in soil threatens rivers, lakes, and coastal oceans with eutrophication, BioScience, 51, 227–234, 2001.
Bonan, G. B., Pollard, D., and Thompson, S. L.: Effects of boreal forest vegetation on global climate, Nature, 359, 716–718, 1992.
Brovkin, V., Sitch, S., Von Bloh, W., Claussen, M., Bauer, E., and Cramer, W.: Role of land cover changes for atmospheric CO2 increase and climate change during the last 150 years, Glob. Change Biol., 10, 1253–1266, 2004.
Calvin, K., Bond-Lamberty, B., Clarke, L., Edmonds, J., Eom, J., Hartin, C., Kim, S., Kyle, P., Link, R., and Moss, R.: The SSP4: A world of deepening inequality, Global Environ. Chang., 42, 284–296, 2017.
Chemke, R., Kaspi, Y., and Halevy, I.: The thermodynamic effect of atmospheric mass on early Earth's temperature, Geophys. Res. Lett., 43, 11414–11422, 2016.
Chen, B.: Globally Increased Crop Growth and Cropping Intensity from the Long-Term Satellite-Based Observations, ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, 4, 45–52, https://doi.org/10.5194/isprs-annals-IV-3-45-2018, 2018.
Chen, C., Li, D., Li, Y., Piao, S., Wang, X., Huang, M., Gentine, P., Nemani, R. R., and Myneni, R. B.: Biophysical impacts of Earth greening largely controlled by aerodynamic resistance, Science Adv., 6, eabb1981, https://doi.org/10.1126/sciadv.abb1981, 2020.
Cole, M. B., Augustin, M. A., Robertson, M. J., and Manners, J. M.: The science of food security, npj Science of Food, 2, 1–8, 2018.
Defourny, P., Schouten, L., Bartalev, S., Bontemps, S., Caccetta, P., De Wit, A., Di Bella, C. M., Gérard, B., Giri, P., and Gond, V.: Accuracy assessment of a 300 m global land cover map: The GlobCover experience, s.n.33rd International Symposium on Remote Sensing of Environment (ISRSE), 4–8 May 2009, Stresa, Italy, 2009.
Defourny, P., Bontemps, S., Lamarche, C., Brockmann, C., Boettcher, M., Wevers, J., and Kirches, G.: Land Cover CCI: Product User Guide Version 2.0., ESA. Land Cover CCI Product User Guide Version 2. Tech. Rep., https://maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf (last access: 24 June 20224), 2017.
DeFries, R. S., Foley, J. A., and Asner, G. P.: Land-use choices: Balancing human needs and ecosystem function, Front. Ecol. Environ., 2, 249–257, 2004.
Delzeit, R., Zabel, F., Meyer, C., and Václavík, T.: Addressing future trade-offs between biodiversity and cropland expansion to improve food security, Reg. Environ. Change, 17, 1429–1441, 2017.
Diffenbaugh, N. S. and Burke, M.: Global warming has increased global economic inequality, P. Natl. Acad. Sci. USA, 116, 9808–9813, 2019.
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016.
Fischer, G., Shah, M., N. Tubiello, F., and Van Velhuizen, H.: Socio-economic and climate change impacts on agriculture: an integrated assessment, 1990–2080, Philos. T. Roy. Soc. B, 360, 2067–2083, 2005.
Fischer, G., Nachtergaele, F. O., van Velthuizen, H., Chiozza, F., Francheschini, G., Henry, M., Muchoney, D., and Tramberend, S.: Global Agro-ecological Zones (GAEZ v4)-Model Documentation, FAO & IIASA, Laxenburg, Austria; Rome, Italy, https://doi.org/10.4060/cb4744en, 2021.
Folberth, C., Khabarov, N., Balkovič, J., Skalský, R., Visconti, P., Ciais, P., Janssens, I. A., Peñuelas, J., and Obersteiner, M.: The global cropland-sparing potential of high-yield farming, Nat. Sustain., 3, 281–289, 2020.
Foley, J. A., DeFries, R., Asner, G. P., Barford, C., Bonan, G., Carpenter, S. R., Chapin, F. S., Coe, M. T., Daily, G. C., and Gibbs, H. K.: Global consequences of land use, Science, 309, 570–574, 2005.
Godfray, H. C. J., Beddington, J. R., Crute, I. R., Haddad, L., Lawrence, D., Muir, J. F., Pretty, J., Robinson, S., Thomas, S. M., and Toulmin, C.: Food security: the challenge of feeding 9 billion people, Science, 327, 812–818, 2010.
Gong, P., Wang, J., Yu, L., Zhao, Y., Zhao, Y., Liang, L., Niu, Z., Huang, X., Fu, H., and Liu, S.: Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data, Int. J. Remote Sens., 34, 2607–2654, 2013.
Hasan, S. S., Zhen, L., Miah, M. G., Ahamed, T., and Samie, A.: Impact of land use change on ecosystem services: A review, Environmental Development, 34, 100527, https://doi.org/10.1016/j.envdev.2020.100527, 2020.
Hu, Q., Xiang, M., Chen, D., Zhou, J., Wu, W., and Song, Q.: Global cropland intensification surpassed expansion between 2000 and 2010: A spatio-temporal analysis based on GlobeLand30, Sci. Total Environ., 746, 141035, https://doi.org/10.1016/j.scitotenv.2020.141035, 2020.
Hurrell, J. W., Holland, M. M., Gent, P. R., Ghan, S., Kay, J. E., Kushner, P. J., Lamarque, J.-F., Large, W. G., Lawrence, D., and Lindsay, K.: The community earth system model: a framework for collaborative research, B. Am. Meteor. Soc., 94, 1339–1360, https://doi.org/10.1175/BAMS-D-12-00121.1, 2013 (code availbale at: https://www.cesm.ucar.edu/models/cesm1.2/, last access: 24 June 2024).
Hurtt, G. C., Chini, L., Sahajpal, R., Frolking, S., Bodirsky, B. L., Calvin, K., Doelman, J. C., Fisk, J., Fujimori, S., Klein Goldewijk, K., Hasegawa, T., Havlik, P., Heinimann, A., Humpenöder, F., Jungclaus, J., Kaplan, J. O., Kennedy, J., Krisztin, T., Lawrence, D., Lawrence, P., Ma, L., Mertz, O., Pongratz, J., Popp, A., Poulter, B., Riahi, K., Shevliakova, E., Stehfest, E., Thornton, P., Tubiello, F. N., van Vuuren, D. P., and Zhang, X.: Harmonization of global land use change and management for the period 850–2100 (LUH2) for CMIP6, Geosci. Model Dev., 13, 5425–5464, https://doi.org/10.5194/gmd-13-5425-2020, 2020.
Iizumi, T. and Ramankutty, N.: How do weather and climate influence cropping area and intensity?, Global Food Security, 4, 46–50, 2015.
IPCC: 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, hdl:10013/epic.45156.d001, 2014.
Kates, R. W., Clark, W. C., Corell, R., Hall, J. M., Jaeger, C. C., Lowe, I., McCarthy, J. J., Schellnhuber, H. J., Bolin, B., and Dickson, N. M.: Sustainability science, Science, 292, 641–642, 2001.
Kay, J. E., Deser, C., Phillips, A., Mai, A., Hannay, C., Strand, G., Arblaster, J. M., Bates, S., Danabasoglu, G., and Edwards, J.: The Community Earth System Model (CESM) large ensemble project: A community resource for studying climate change in the presence of internal climate variability, B. Am. Meteor. Soc., 96, 1333–1349, 2015.
Klein Goldewijk, K., Beusen, A., Doelman, J., and Stehfest, E.: Anthropogenic land use estimates for the Holocene – HYDE 3.2, Earth Syst. Sci. Data, 9, 927–953, https://doi.org/10.5194/essd-9-927-2017, 2017.
Lawrence, P. J. and Chase, T. N.: Representing a new MODIS consistent land surface in the Community Land Model (CLM 3.0), J. Geophys. Res.-Biogeo., 112, 0148–0227, https://doi.org/10.1029/2006JG000168, 2007.
Lee, S., Gong, T., Johnson, N., Feldstein, S. B., and Pollard, D.: On the possible link between tropical convection and the Northern Hemisphere Arctic surface air temperature change between 1958 and 2001, J. Climate, 24, 4350–4367, 2011.
Levers, C., Butsic, V., Verburg, P. H., Mueller, D., and Kuemmerle, T.: Drivers of changes in agricultural intensity in Europe, Land Use Policy, 58, 380–393, 2016.
Li, Y., Piao, S., Chen, A., Ciais, P., and Li, L. Z.: Local and teleconnected temperature effects of afforestation and vegetation greening in China, Nat. Sci. Rev., 7, 897–912, 2020.
Liu, X.: Global cropland expansion enhances cropping potential and reduces its inequality among countries, figshare [data set], https://doi.org/10.6084/m9.figshare.26064403.v1, 2024.
Liu, S., Liu, X., Yu, L., Wang, Y., Zhang, G. J., Gong, P., Huang, W., Wang, B., Yang, M., and Cheng, Y.: Climate response to introduction of the ESA CCI land cover data to the NCAR CESM, Clim. Dynam., 56, 4109–4127, https://doi.org/10.1007/s00382-021-05690-3, 2021.
Liu, X., Liang, X., Li, X., Xu, X., Ou, J., Chen, Y., Li, S., Wang, S., and Pei, F.: A future land use simulation model (FLUS) for simulating multiple land use scenarios by coupling human and natural effects, Landscape Urban Plan., 168, 94–116, 2017.
Liu, X., Yu, L., Si, Y., Zhang, C., Lu, H., Yu, C., and Gong, P.: Identifying patterns and hotspots of global land cover transitions using the ESA CCI Land Cover dataset, Remote Sens. Lett., 9, 972–981, 2018.
Lloyd, C. T., Chamberlain, H., Kerr, D., Yetman, G., Pistolesi, L., Stevens, F. R., Gaughan, A. E., Nieves, J. J., Hornby, G., and MacManus, K.: Global spatio-temporally harmonised datasets for producing high-resolution gridded population distribution datasets, Big Earth Data, 3, 108–139, 2019.
Mauser, W., Klepper, G., Zabel, F., Delzeit, R., Hank, T., Putzenlechner, B., and Calzadilla, A.: Global biomass production potentials exceed expected future demand without the need for cropland expansion, Nat. Commun., 6, 1–11, 2015.
Mehrabi, Z., Ellis, E. C., and Ramankutty, N.: The challenge of feeding the world while conserving half the planet, Nat. Sustain., 1, 409–412, https://doi.org/10.1038/s41893-018-0119-8, 2018.
Neale, R. B., Chen, C.-C., Gettelman, A., Lauritzen, P. H., Park, S., Williamson, D. L., Conley, A. J., Garcia, R., Kinnison, D., and Lamarque, J.-F.: Description of the NCAR community atmosphere model (CAM 5.0), NCAR Tech. Note NCAR/TN-486+ STR, 1, 1–12, https://doi.org/10.5065/wgtk-4g06, 2010.
Oleson, K. W., Lawrence, D. M., Gordon, B., Flanner, M. G., Kluzek, E., Peter, J., Levis, S., Swenson, S. C., Thornton, E., and Feddema, J.: Technical description of version 4.0 of the Community Land Model (CLM), https://doi.org/10.5065/D6FB50WZ, 2010.
Ortiz-Bobea, A., Ault, T. R., Carrillo, C. M., Chambers, R. G., and Lobell, D. B.: Anthropogenic climate change has slowed global agricultural productivity growth, Nat. Clim. Change, 11, 306–312, 2021.
Parodi, A., Leip, A., De Boer, I. J. M., Slegers, P. M., Ziegler, F., Temme, E. H. M., Herrero, M., Tuomisto, H., Valin, H., Van Middelaar, C. E., Van Loon, J. J. A., and Van Zanten, H. H. E.: The potential of future foods for sustainable and healthy diets, Nat. Sustain., 1, 782–789, https://doi.org/10.1038/s41893-018-0189-7, 2018.
Potapov, P., Turubanova, S., Hansen, M. C., Tyukavina, A., Zalles, V., Khan, A., Song, X.-P., Pickens, A., Shen, Q., and Cortez, J.: Global maps of cropland extent and change show accelerated cropland expansion in the twenty-first century, Nat. Food, 3, 19–28, 2022.
Ramankutty, N., Evan, A. T., Monfreda, C., and Foley, J. A.: Farming the planet: 1. Geographic distribution of global agricultural lands in the year 2000, Global Biogeochem. Cy., 22, GB1003, https://doi.org/10.1029/2007GB002952, 2008.
Ritchie, H. and Roser, M.: Land use, Our World in Data, https://ourworldindata.org/land-use (last access: 24 June 2024), 2013.
Rockström, J., Steffen, W., Noone, K., Persson, Å., Chapin, F. S., Lambin, E. F., Lenton, T. M., Scheffer, M., Folke, C., and Schellnhuber, H. J.: A safe operating space for humanity, Nature, 461, 472–475, 2009.
Rohrschneider, T., Stevens, B., and Mauritsen, T.: On simple representations of the climate response to external radiative forcing, Clim. Dynam., 53, 3131–3145, 2019.
Sampaio, G., Nobre, C., Costa, M. H., Satyamurty, P., Soares-Filho, B. S., and Cardoso, M.: Regional climate change over eastern Amazonia caused by pasture and soybean cropland expansion, Geophys. Res. Lett., 34, L17709, https://doi.org/10.1029/2007GL030612, 2007.
Sasmito, S. D., Taillardat, P., Clendenning, J. N., Cameron, C., Friess, D. A., Murdiyarso, D., and Hutley, L. B.: Effect of land-use and land-cover change on mangrove blue carbon: A systematic review, Glob. Change Biol., 25, 4291–4302, 2019.
Searchinger, T. D., Estes, L., Thornton, P. K., Beringer, T., Notenbaert, A., Rubenstein, D., Heimlich, R., Licker, R., and Herrero, M.: High carbon and biodiversity costs from converting Africa's wet savannahs to cropland, Nat. Clim. Change, 5, 481–486, 2015.
Steinfeld, H., Gerber, P., Wassenaar, T., Castel, V., Rosales, M., Rosales, M., and de Haan, C.: Livestock's long shadow: environmental issues and options, Food & Agriculture Org., https://www.fao.org/4/a0701e/a0701e00.htm (last access: 24 June 2024), 2006.
Sterling, S. M., Ducharne, A., and Polcher, J.: The impact of global land-cover change on the terrestrial water cycle, Nat. Clim. Change, 3, 385–390, 2013.
UN: Sustainable development goals (SDG), https://unstats.un.org/sdgs/report/2018 (last access: 24 June 2024), 2018.
Wall, D. H., Nielsen, U. N., and Six, J.: Soil biodiversity and human health, Nature, 528, 69–76, 2015.
Wang, J., Vanga, S. K., Saxena, R., Orsat, V., and Raghavan, V.: Effect of climate change on the yield of cereal crops: A review, Climate, 6, 41, 2018.
Wang, Y., Zhang, G. J., and Jiang, Y.: Linking stochasticity of convection to large-scale vertical velocity to improve Indian summer monsoon simulation in the NCAR CAM5, J. Climate, 31, 6985–7002, 2018.
Wu, W., Yu, Q., You, L., Chen, K., Tang, H., and Liu, J.: Global cropping intensity gaps: Increasing food production without cropland expansion, Land Use Policy, 76, 515–525, 2018.
Wu, W.-B., Yu, Q.-Y., Peter, V. H., YOU, L.-Z., Peng, Y., and Tang, H.-J.: How could agricultural land systems contribute to raise food production under global change?, J. Int. Agr., 13, 1432–1442, 2014.
Yan, M., Liu, J., and Wang, Z.: Global climate responses to land use and land cover changes over the past two millennia, Atmosphere, 8, 64, https://doi.org/10.3390/atmos8040064, 2017.
Yang, X., Chen, F., Lin, X., Liu, Z., Zhang, H., Zhao, J., Li, K., Ye, Q., Li, Y., and Lv, S.: Potential benefits of climate change for crop productivity in China, Agr. Forest Meteorol., 208, 76–84, 2015.
Yu, L., Wang, J., Clinton, N., Xin, Q., Zhong, L., Chen, Y., and Gong, P.: FROM-GC: 30 m global cropland extent derived through multisource data integration, Int. J. Digit. Earth, 6, 521–533, 2013.
Zabel, F., Delzeit, R., Schneider, J. M., Seppelt, R., Mauser, W., and Václavík, T.: Global impacts of future cropland expansion and intensification on agricultural markets and biodiversity, Nat. Commun., 10, 1–10, 2019.
Zeng, Z., Piao, S., Li, L. Z., Zhou, L., Ciais, P., Wang, T., Li, Y., Lian, X., Wood, E. F., and Friedlingstein, P.: Climate mitigation from vegetation biophysical feedbacks during the past three decades, Nat. Clim. Change, 7, 432–436, 2017.
Zhang, X. and Ma, X.: Misplaced optimism in agricultural land usage driven by newly available climate resources: A case study of estimated and realized cropping intensity in northern and northeastern China, Climate Risk Management, 25, 100194, https://doi.org/10.1016/j.crm.2019.100194, 2019.
Zhao, Y., Feng, D., Yu, L., Cheng, Y., Zhang, M., Liu, X., Xu, Y., Fang, L., Zhu, Z., and Gong, P.: Long-term land cover dynamics (1986–2016) of northeast china derived from a multi-temporal landsat archive, Remote Sens., 11, 599, https://doi.org/10.3390/rs11050599, 2019.
Short summary
An increase of 28 % in cropland expansion since 10 000 BCE has led to a 1.2 % enhancement in the global cropping potential, with varying efficiencies across regions. The continuous expansion has altered the support for population growth and has had impacts on climate and biodiversity, highlighting the effects of climate change. It also points out the limitations of previous studies.
An increase of 28 % in cropland expansion since 10 000 BCE has led to a 1.2 % enhancement in the...
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