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Farming the planet: 1. Geographic distribution of global agricultural lands in the year 2000
Open accessAbstract
Agricultural activities have dramatically altered our planet's land surface. To understand the extent and spatial distribution of these changes, we have developed a new global data set of croplands and pastures circa 2000 by combining agricultural inventory data and satellite‐derived land cover data. The agricultural inventory data, with much greater spatial detail than previously available, is used to train a land cover classification data set obtained by merging two different satellite‐derived products (Boston University's MODIS‐derived land cover product and the GLC2000 data set). Our data are presented at 5 min (∼10 km) spatial resolution in longitude by longitude, have greater accuracy than previously available, and for the first time include statistical confidence intervals on the estimates. According to the data, there were 15.0 (90% confidence range of 12.2–17.1) million km 2 of cropland (12% of the Earth's ice‐free land surface) and 28.0 (90% confidence range of 23.6–30.0) million km 2 of pasture (22%) in the year 2000.
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“The extent of permanent pastures and meadows, the key category for which global statistical data are available (http://faostat.fao.org;any grazing land used for more than five consecutive years), spans 28 (33) to 34 Mkm², including extensively used natural grasslands (Section 2.2), intensively managed pastures and meadows established in natural grassland areas, as well as grassland that was estab”
From Restoring Degraded Lands (2021) · cites this paper as [33]
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“The grassland management option was further narrowed down and considered to only be applicable in the grazing lands as defined by Ramankutty et al. (2008).”
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“…growing area shares GA (Figure 1e), which we calculated by summing maximum rainfed and irrigated growing areas for each crop (Portmann, Siebert, & D€oll, 2010) and dividing this sum by the total cropland proportions of agricultural lands multiplied by the pixel area (Ramankutty et al., 2008).”
MethodInfluentialBarriers and Drivers of Renewable Energy Penetration in Rural Areas
Štreimikienė, D., Baležentis, T., Volkov, A., Morkūnas, M., Žičkienė, A., & Štreimikis, J. (2021) · Energies · doi:10.3390/en14206452 · Cited by 119
Assessing human and environmental pressures of global land-use change 2000–2010
Creutzig, F., Bren d'Amour, C., Weddige, U., Fuss, S., Beringer, T., Gläser, A., Kalkuhl, M., Steckel, J. C., Radebach, A., & Edenhofer, O. (2019) · Global Sustainability · doi:10.1017/sus.2018.15 · Cited by 96
Ecosystem services and the resilience of agricultural landscapes
Bennett, E. M., Baird, J., Baulch, H. M., Chaplin‐Kramer, R., Fraser, E., Loring, P., Morrison, P. H., Parrott, L., Sherren, K., Winkler, K. J., et al. (2021) · Advances in ecological research/Advances in Ecological Research · doi:10.1016/bs.aecr.2021.01.001 · Cited by 111
Rockström, J., & Karlberg, L. (2010) · AMBIO · doi:10.1007/s13280-010-0033-4 · Cited by 107
Altchenko, Y., & Villholth, K. G. (2015) · Hydrology and earth system sciences · doi:10.5194/hess-19-1055-2015 · Cited by 96
Predicting invasion in grassland ecosystems: is exotic dominance the real embarrassment of richness?
Seabloom, E. W., Borer, E. T., Buckley, Y. M., Cleland, E. E., Davies, K. F., Firn, J. L., Harpole, W. S., Hautier, Y., Lind, E. M., MacDougall, A. S., et al. (2013) · Global Change Biology · doi:10.1111/gcb.12370 · Cited by 96
Kumar, R., Swapnil, P., Meena, M., Selpair, S., & Yadav, B. G. (2022) · Sustainability · doi:10.3390/su142315514 · Cited by 101
“Ramankutty et al. [32] reported that Cold stress affects the physiological, metabolic and genetic processes in plants. approximately 12% of the earth’s surface can be used for the agricultural practices due to Under cold stress, plants produce protective compounds like inositol, sorbitol, rebitol,…”
BackgroundGavier-Pizarro, G. I., Calamari, N. C., Thompson, J. J., Canavelli, S. B., Solari, L. M., Decarre, J., Goijman, A. P., Suárez, R. P., Bernardos, J., & Zaccagnini, M. E. (2011) · Agriculture Ecosystems & Environment · doi:10.1016/j.agee.2011.08.013 · Cited by 94
Seeing the forest for the trees: Putting multilayer networks to work for community ecology
Hutchinson, M. C., Mora, B. B., Pilosof, S., Barner, A. K., Kéfi, S., Thébault, É., Jordano, P., & Stouffer, D. B. (2018) · Functional Ecology · doi:10.1111/1365-2435.13237 · Cited by 98
Zamorano, J., Bartomeus, Í., Grez, A. A., & Garibaldi, L. A. (2020) · Insect Conservation and Diversity · doi:10.1111/icad.12454 · Cited by 93
“A large part of the area originally occupied by many temperate and tropical natural terrestrial ecosystems has become agricultural land (Ramankutty et al., 2008) and the expansion and intensification of agriculture have greatly reduced local biodiversity (Foley et al., 2005).”
BackgroundUncertainty in simulating gross primary production of cropland ecosystem from satellite-based models
Yuan, W., Cai, W., Nguy-Robertson, A. L., Fang, H., Suyker, A. E., Chen, Y., Dong, W., Liu, S., & Zhang, H. (2015) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2015.03.016 · Cited by 94
Maxwell, S. K., & Sylvester, K. M. (2012) · Remote Sensing of Environment · doi:10.1016/j.rse.2012.01.022 · Cited by 94
Potential of Using Remote Sensing Techniques for Global Assessment of Water Footprint of Crops
Romaguera, M., Hoekstra, A. Y., Su, Z., Krol, M. S., & Salama, M. S. (2010) · Remote Sensing · doi:10.3390/rs2041177 · Cited by 94
Bagley, J. E., Desai, A. R., Dirmeyer, P. A., & Foley, J. A. (2012) · Environmental Research Letters · doi:10.1088/1748-9326/7/1/014009 · Cited by 92
Self-citationBowles, N. I., Alexander, S., & Hadjikakou, M. (2019) · Ecological Economics · doi:10.1016/j.ecolecon.2019.01.033 · Cited by 93
Zhong, L., Hu, L., Zhou, H., & Tao, X. (2019) · Remote Sensing of Environment · doi:10.1016/j.rse.2019.111411 · Cited by 86
Guilpart, N., Iizumi, T., & Makowski, D. (2022) · Nature Food · doi:10.1038/s43016-022-00481-3 · Cited by 73
Konzmann, M., Gerten, D., & Heinke, J. (2013) · Hydrological Sciences Journal · doi:10.1080/02626667.2013.746495 · Cited by 98
Kueppers, L. M., & Snyder, M. A. (2011) · Climate Dynamics · doi:10.1007/s00382-011-1123-0 · Cited by 95
Multidimensional characterization of global food supply from 1961 to 2013
Bentham, J., Singh, G. M., Danaei, G., Green, R., Lin, J. K., Stevens, G. A., Farzadfar, F., Bennett, J. E., Di Cesare, M., Dangour, A. D., et al. (2020) · Nature Food · doi:10.1038/s43016-019-0012-2 · Cited by 91
Climatic influence on anthrax suitability in warming northern latitudes
Walsh, M., de Smalen, A. W., & Mor, S. M. (2018) · Scientific Reports · doi:10.1038/s41598-018-27604-w · Cited by 97
The impacts of biofuel crops on local biodiversity: a global synthesis
Tudge, S. J., Purvis, A., & Palma, A. D. (2021) · Biodiversity and Conservation · doi:10.1007/s10531-021-02232-5 · Cited by 269
The current bioenergy production potential of semi-arid and arid regions in sub-Saharan Africa
Wicke, B., Smeets, E., Watson, H., & Faaij, A. (2011) · Biomass and Bioenergy · doi:10.1016/j.biombioe.2011.03.010 · Cited by 91
Metadata
- Authors
- Ramankutty, N., Evan, A. T., Monfreda, C., & Foley, J. A.
- Affiliations
- McGill University (CA)
- University of Wisconsin–Madison (US)
- Venue
- Global Biogeochemical Cycles
- Publisher
- American Geophysical Union (AGU)
- Published
- 2008-01-17
- Volume
- 22
- Issue
- 1
- ISSN
- 0886-6236 (ISSN-L)
- 1944-9224
- OpenAlex
- W1838764073
- Cited by
- 1,942
- References
- 68
- Citations by year
- 2026: 55 · 2025: 79 · 2024: 142 · 2023: 123 · 2022: 119 · 2021: 173 · 2020: 149 · 2019: 141 · 2018: 142 · 2017: 119 · 2016: 145 · 2015: 86 · 2014: 104 · 2013: 87 · 2012: 88
- FWCI
- 55.51
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Global Biogeochemical Cycles (publishedVersion)
- Funders
- National Aeronautics and Space Administration
- Wageningen University and Research
- Topics
- Physical Sciences › Environmental Science › Global and Planetary Change › Land Use and Ecosystem Services
- Physical Sciences › Environmental Science › Ecology › Remote Sensing in Agriculture
- Social Sciences › Development › Agriculture, Land Use, Rural Development
- Keywords
- cropland extent, land cover classification, MODIS land cover, GLC2000, land surface change, confidence intervals
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