Global Rangeland Primary Production and Its Consumption by Livestock in 2000–2010
Abstract
:1. Introduction
2. Methods
2.1. General
2.2. Livestock Populations and Total Intake Requirements
2.3. Annual Fodder Stocks per Nation, Estimation of Fodder Losses and Waste, and Fodder Intake
2.4. Rangeland Extent, NPP, and ANPP
2.5. Sub-National Allocation of Fodder and Forage Intake
2.6. Sub-National Allocation of Fodder and Forage Intake
3. Results
3.1. Global GI and GP
3.2. Regional Patterns
3.2.1. Africa
3.2.2. The Americas
3.2.3. East, South East, and South Asia
3.2.4. Central to West Asia and Europe
3.2.5. Oceania
4. Discussion
4.1. Global GI
4.2. High or Impossible Regional GIs and Grazing Deficits
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Bar-On, Y.M.; Phillips, R.; Milo, R. The Biomass Distribution on Earth. Proc. Natl. Acad. Sci. USA 2018, 115, 6506–6511. [Google Scholar] [CrossRef] [Green Version]
- Wolf, J.; Asrar, G.R.; West, T.O. Revised Methane Emissions Factors and Spatially Distributed Annual Carbon Fluxes for Global Livestock. Carbon Balance Manag. 2017, 12, 16. [Google Scholar] [CrossRef] [Green Version]
- Klein Goldewijk, K.; Beusen, A.; Van Drecht, G.; De Vos, M. The HYDE 3.1 Spatially Explicit Database of Human-Induced Global Land-Use Change over the Past 12,000 Years: HYDE 3.1 Holocene Land Use. Glob. Ecol. Biogeogr. 2011, 20, 73–86. [Google Scholar] [CrossRef]
- Wolf, J.; West, T.O.; Le Page, Y.L.; Kyle, G.P.; Zhang, X.; Collatz, G.J.; Imhoff, M.L. Biogenic Carbon Fluxes from Global Agricultural Production and Consumption. Glob. Biogeochem. Cycles 2015, 29, 1617–1639. [Google Scholar] [CrossRef] [Green Version]
- Wu, D.; Piao, S.; Zhu, D.; Wang, X.; Ciais, P.; Bastos, A.; Xu, X.; Xu, W. Accelerated Terrestrial Ecosystem Carbon Turnover and Its Drivers. Glob. Chang. Biol. 2020, 26, 5052–5062. [Google Scholar] [CrossRef] [PubMed]
- Houghton, R.A. The Worldwide Extent of Land-Use Change: In the Last Few Centuries, and Particularly in the Last Several Decades, Effects of Land-Use Change Have Become Global. BioScience 1994, 44, 305–313. [Google Scholar] [CrossRef]
- Lambin, E.F.; Meyfroidt, P. Global Land Use Change, Economic Globalization, and the Looming Land Scarcity. Proc. Natl. Acad. Sci. USA 2011, 108, 3465–3472. [Google Scholar] [CrossRef] [Green Version]
- Pongratz, J.; Dolman, H.; Don, A.; Erb, K.-H.; Fuchs, R.; Herold, M.; Jones, C.; Kuemmerle, T.; Luyssaert, S.; Meyfroidt, P.; et al. Models Meet Data: Challenges and Opportunities in Implementing Land Management in Earth System Models. Glob. Chang. Biol. 2018, 24, 1470–1487. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Wolf, J. Quantifying the role of livestock in climate change. In Burleigh Dodds Series in Agricultural Science; Deryng, D., Ed.; Burleigh Dodds Science Publishing: Cambridge, UK, 2020; ISBN 978-1-78676-320-4. [Google Scholar]
- FAO Food and Agriculture Organization of the United Nations Statistics Division (FAOSTAT). Available online: http://faostat.fao.org/ (accessed on 1 December 2015).
- Georges, M.; Charlier, C.; Hayes, B. Harnessing Genomic Information for Livestock Improvement. Nat. Rev. Genet. 2019, 20, 135–156. [Google Scholar] [CrossRef]
- Livestock Environmental Assessment and Performance Partnership. Environmental Performance of Feed Additives in Livestock Supply Chains—Guidelines for Assessment; FAO: Rome, Italy, 2019. [Google Scholar]
- Thornton, P.K. Livestock Production: Recent Trends, Future Prospects. Philos. Trans. R. Soc. Lond. B Biol. Sci. 2010, 365, 2853–2867. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Klemm, T.; Briske, D.D. Retrospective Assessment of Beef Cow Numbers to Climate Variability Throughout the U.S. Great Plains. Rangel. Ecol. Manag. 2019. [Google Scholar] [CrossRef]
- Beef Export Growth for South America | Meat & Livestock Australia. Available online: https://www.mla.com.au/prices-markets/market-news/2019/beef-exports-exceptional-for-south-america/ (accessed on 29 July 2021).
- MacDonald, J.; McBride, W. The Transformation of U.S. Livestock Agriculture: Scale, Efficiency, and Risks. In USDA Economic Research Service—EIB43. Available online: http://www.ers.usda.gov/publications/eib-economic-information-bulletin/eib43.aspx (accessed on 29 January 2014).
- Koneswaran, G.; Nierenberg, D. Global Farm Animal Production and Global Warming: Impacting and Mitigating Climate Change. Environ. Health Perspect. 2008, 116, 578–582. [Google Scholar] [CrossRef]
- Bouwman, A.F.; Van der Hoek, K.W.; Eickhout, B.; Soenario, I. Exploring Changes in World Ruminant Production Systems. Agric. Syst. 2005, 84, 121–153. [Google Scholar] [CrossRef]
- Krausmann, F.; Erb, K.-H.; Gingrich, S.; Lauk, C.; Haberl, H. Global Patterns of Socioeconomic Biomass Flows in the Year 2000: A Comprehensive Assessment of Supply, Consumption and Constraints. Ecol. Econ. 2008, 65, 471–487. [Google Scholar] [CrossRef]
- Asner, G.P.; Elmore, A.J.; Olander, L.P.; Martin, R.E.; Harris, A.T. Grazing Systems, Ecosystem Responses, and Global Change. Annu. Rev. Environ. Resour. 2004, 29, 261–299. [Google Scholar] [CrossRef]
- Gerber, P.J.; Hristov, A.N.; Henderson, B.; Makkar, H.; Oh, J.; Lee, C.; Meinen, R.; Montes, F.; Ott, T.; Firkins, J.; et al. Technical Options for the Mitigation of Direct Methane and Nitrous Oxide Emissions from Livestock: A Review. Animal 2013, 7, 220–234. [Google Scholar] [CrossRef] [Green Version]
- Smart, A.J.; Derner, J.D.; Hendrickson, J.R.; Gillen, R.L.; Dunn, B.H.; Mousel, E.M.; Johnson, P.S.; Gates, R.N.; Sedivec, K.K.; Harmoney, K.R.; et al. Effects of Grazing Pressure on Efficiency of Grazing on North American Great Plains Rangelands. Rangel. Ecol. Manag. 2010, 63, 397–406. [Google Scholar] [CrossRef]
- Raynor, E.J.; Derner, J.D.; Hoover, D.L.; Parton, W.J.; Augustine, D.J. Large-Scale and Local Climatic Controls on Large Herbivore Productivity: Implications for Adaptive Rangeland Management. Ecol. Appl. 2020, 30, e02053. [Google Scholar] [CrossRef] [PubMed]
- Izaurralde, R.C.; Thomson, A.M.; Morgan, J.A.; Fay, P.A.; Polley, H.W.; Hatfield, J.L. Climate Impacts on Agriculture: Implications for Forage and Rangeland Production. Agron. J. 2011, 103, 371–381. [Google Scholar] [CrossRef] [Green Version]
- Hegarty, R.S. Livestock Nutrition—A Perspective on Future Needs in a Resource-Challenged Planet. Anim. Prod. Sci. 2012, 52, 406–415. [Google Scholar] [CrossRef]
- RFA Ethanol Co-Products. Available online: http://ethanolrfa.org/resources/industry/co-products/#1456865649440-ae77f947-734a (accessed on 8 November 2016).
- USDA Economic Research Service, U.S. Bioenergy Statistics. Available online: https://www.ers.usda.gov/data-products/us-bioenergy-statistics/us-bioenergy-statistics/#Coproducts (accessed on 11 October 2018).
- Herrero, M.; Havlík, P.; Valin, H.; Notenbaert, A.; Rufino, M.C.; Thornton, P.K.; Bluemmel, M.; Weiss, F.; Grace, D.; Obersteiner, M. Biomass Use, Production, Feed Efficiencies, and Greenhouse Gas Emissions from Global Livestock Systems. Proc. Natl. Acad. Sci. USA 2013, 110, 20888–20893. [Google Scholar] [CrossRef] [Green Version]
- Chang, J.; Ciais, P.; Herrero, M.; Havlik, P.; Campioli, M.; Zhang, X.; Bai, Y.; Viovy, N.; Joiner, J.; Wang, X.; et al. Combining Livestock Production Information in a Process-Based Vegetation Model to Reconstruct the History of Grassland Management. Biogeosciences 2016, 13, 3757–3776. [Google Scholar] [CrossRef] [Green Version]
- Fetzel, T.; Havlik, P.; Herrero, M.; Kaplan, J.O.; Kastner, T.; Kroisleitner, C.; Rolinski, S.; Searchinger, T.; Van Bodegom, P.M.; Wirsenius, S.; et al. Quantification of Uncertainties in Global Grazing Systems Assessment. Glob. Biogeochem. Cycles 2017, 31, 1089–1102. [Google Scholar] [CrossRef] [Green Version]
- Fetzel, T.; Havlik, P.; Herrero, M.; Erb, K.-H. Seasonality Constraints to Livestock Grazing Intensity. Glob. Chang. Biol. 2017, 23, 1636–1647. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Irisarri, J.G.N.; Aguiar, S.; Oesterheld, M.; Derner, J.D.; Golluscio, R.A. A Narrower Gap of Grazing Intensity. Reply to Fetzel et al., 2017. Seasonality Constrains to Livestock Grazing Intensity. Glob. Chang. Biol. 2017, 23, 3965–3966. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Chen, M.; Parton, W.J.; Del Grosso, S.J.; Hartman, M.D.; Day, K.A.; Tucker, C.J.; Derner, J.D.; Knapp, A.K.; Smith, W.K.; Ojima, D.S.; et al. The Signature of Sea Surface Temperature Anomalies on the Dynamics of Semiarid Grassland Productivity. Ecosphere 2017, 8, e02069. [Google Scholar] [CrossRef] [Green Version]
- IPCC. 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Volume 4: Agriculture, Forestry and Other Land Use; Institute for Global Environmental Strategies: Kanagawa, Japan, 2006. [Google Scholar]
- Ray, D.K.; Foley, J.A. Increasing Global Crop Harvest Frequency: Recent Trends and Future Directions. Environ. Res. Lett. 2013, 8, 044041. [Google Scholar] [CrossRef]
- FAO Global Livestock Production and Health Atlas (GLiPHA). Available online: http://kids.fao.org/glipha/ (accessed on 1 January 2014).
- USDA Quickstats 2.0. Available online: http://quickstats.nass.usda.gov/ (accessed on 1 August 2019).
- Osborn, T.W. Elemental Composition of Soybean Meal and Interlaboratory Performance. Available online: https://pubs.acs.org/doi/pdf/10.1021/jf60210a028 (accessed on 25 June 2020).
- Buckmaster, D. Cooperative Extension Fact Sheet Listing I-107: Forage Losses Equal Economic Losses, so Minimize Them; Penn State College of Agricultural Sciences, Agricultural and Biological Engineering: University Park, PA, USA, 1990. [Google Scholar]
- Russelle, M. The Alfalfa Yield Gap: A Review of the Evidence. Forage Grazinglands 2013. [Google Scholar] [CrossRef]
- Rees, D.V.H. A Discussion of Sources of Dry Matter Loss during the Process of Haymaking. J. Agric. Eng. Res. 1982, 27, 469–479. [Google Scholar] [CrossRef]
- Idowu, J.; Grover, K.; Marsalis, M.; Lauriault, L. Circular 668: Reducing Harvest and Post-Harvest Losses of Alfalfa and Other Hay; New Mexico State University: Las Cruces, NM, USA, 2013. [Google Scholar]
- Pepin, R. Reduce Feed Waste/Feed Shrink: Manure Management and Environmental Quality: University of Minnesota Extension. Available online: https://apps.extension.umn.edu/agriculture/manure-management-and-air-quality/manure-application/reduce-feed-waste/index.html (accessed on 24 June 2020).
- Ishmael, W. Reduce Hay Waste | Beef Magazine. Available online: https://www.beefmagazine.com/feeding-systems/reduce-hay-waste (accessed on 24 June 2020).
- Kallenbach, R. G4570 Reducing Losses When Feeding Hay to Beef Cattle | University of Missouri Extension; Forages; University of Missouri Extension: Columbia, MO, USA, 2000. [Google Scholar]
- Carr, J. Management Practices To Reduce Expensive Feed Wastage—The Pig Site. Available online: https://web.archive.org/web/20170425122901/http://www.thepigsite.com/pigjournal/articles/2169/management-practices-to-reduce-expensive-feed-wastage/ (accessed on 24 June 2020).
- Stockdale, C.R. Wastage of Conserved Fodder When Feeding Livestock. Anim. Prod. Sci. 2010, 50, 400–404. [Google Scholar] [CrossRef]
- Chen, M.; Vernon, C.R.; Huang, M.; Calvin, K.V.; Kraucunas, I.P. Calibration and Analysis of the Uncertainty in Downscaling Global Land Use and Land Cover Projections from GCAM Using Demeter (v1.0.0). Geosci. Model Dev. 2019, 12, 1753–1764. [Google Scholar] [CrossRef] [Green Version]
- Vernon, C.R.; Le Page, Y.; Chen, M.; Huang, M.; Calvin, K.V.; Kraucunas, I.P.; Braun, C.J. Demeter—A Land Use and Land Cover Change Disaggregation Model. J. Open Res. Softw. 2018, 6, 15. [Google Scholar] [CrossRef] [Green Version]
- Friedl, M.A.; Sulla-Menashe, D.; Tan, B.; Schneider, A.; Ramankutty, N.; Sibley, A.; Huang, X. MODIS Collection 5 Global Land Cover: Algorithm Refinements and Characterization of New Datasets. Remote Sens. Environ. 2010, 114, 168–182. [Google Scholar] [CrossRef]
- Chen, M.; Rafique, R.; Asrar, G.R.; Bond-Lamberty, B.; Ciais, P.; Zhao, F.; Reyer, C.P.O.; Ostberg, S.; Chang, J.; Ito, A.; et al. Regional Contribution to Variability and Trends of Global Gross Primary Productivity. Environ. Res. Lett. 2017, 12, 105005. [Google Scholar] [CrossRef]
- Milchunas, D.G.; Lauenroth, W.K. Quantitative Effects of Grazing on Vegetation and Soils Over a Global Range of Environments. Ecol. Monogr. 1993, 63, 327–366. [Google Scholar] [CrossRef]
- Petz, K.; Alkemade, R.; Bakkenes, M.; Schulp, C.J.E.; van der Velde, M.; Leemans, R. Mapping and Modelling Trade-Offs and Synergies between Grazing Intensity and Ecosystem Services in Rangelands Using Global-Scale Datasets and Models. Glob. Environ. Chang. 2014, 29, 223–234. [Google Scholar] [CrossRef]
- Food and Agriculture Organization of the United Nations (Ed.) Transhumant Grazing Systems in Temperate Asia; Plant Production and Protection Series; Food and Agricultural Organization of the United Nations: Rome, Italy, 2003; ISBN 978-92-5-104977-8. [Google Scholar]
- Roy, A.K.; Singh, J.P. Grasslands in India: Problems and Perspectives for Sustaining Livestock and Rural Livelihoods. Trop. Grassl.-Forrajes Trop. 2013, 1, 240. [Google Scholar] [CrossRef] [Green Version]
- USDA ERS—Major Land Uses Grassland Pasture and Range, 1945–2012, by State. Available online: https://www.ers.usda.gov/data-products/major-land-uses/major-land-uses/#Grassland%20pasture%20and%20range (accessed on 30 June 2020).
- Bohn, T.J.; Vivoni, E.R.; Mascaro, G.; White, D.D. Land and Water Use Changes in the US–Mexico Border Region, 1992–2011. Environ. Res. Lett. 2018, 13, 114005. [Google Scholar] [CrossRef] [Green Version]
- Feed Grains Custom Query. Available online: https://data.ers.usda.gov/FEED-GRAINS-custom-query.aspx (accessed on 18 August 2019).
- Irisarri, J.G.N.; Oesterheld, M. Temporal Variation of Stocking Rate and Primary Production in the Face of Drought and Land Use Change. Agric. Syst. 2020, 178, 102750. [Google Scholar] [CrossRef]
- Spinoni, J.; Barbosa, P.; De Jager, A.; McCormick, N.; Naumann, G.; Vogt, J.V.; Magni, D.; Masante, D.; Mazzeschi, M. A New Global Database of Meteorological Drought Events from 1951 to 2016. J. Hydrol. Reg. Stud. 2019, 22, 100593. [Google Scholar] [CrossRef]
- USDA Foreign Agricultural Service. Annual Report: Livestock and Products Annual Argentina 2010; USDA Foreign Agricultural Service Global Agricultural Information Network: Washington DC, USA, 2010. [Google Scholar]
- Bai, Z.; Ma, W.; Ma, L.; Velthof, G.L.; Wei, Z.; Havlík, P.; Oenema, O.; Lee, M.R.F.; Zhang, F. China’s Livestock Transition: Driving Forces, Impacts, and Consequences. Sci. Adv. 2018, 4, eaar8534. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Hölzel, N.; Haub, C.; Ingelfinger, M.P.; Otte, A.; Pilipenko, V.N. The Return of the Steppe Large-Scale Restoration of Degraded Land in Southern Russia during the Post-Soviet Era. J. Nat. Conserv. 2002, 10, 75–85. [Google Scholar] [CrossRef]
- Van den Pol-van Dasselaar, A.; Hennessy, D.; Isselstein, J. Grazing of Dairy Cows in Europe—An In-Depth Analysis Based on the Perception of Grassland Experts. Sustainability 2020, 12, 1098. [Google Scholar] [CrossRef] [Green Version]
- Van Dijk, A.I.J.M.; Beck, H.E.; Crosbie, R.S.; de Jeu, R.A.M.; Liu, Y.Y.; Podger, G.M.; Timbal, B.; Viney, N.R. The Millennium Drought in Southeast Australia (2001–2009): Natural and Human Causes and Implications for Water Resources, Ecosystems, Economy, and Society: Causes and Impacts of Australia’s Record Drought. Water Resour. Res. 2013, 49, 1040–1057. [Google Scholar] [CrossRef]
- Wint, G.; Robinson, T. Gridded Livestock of the World 2007; Food and Agriculture Organization: Rome, Italy, 2007; p. 131. [Google Scholar]
- Scurlock, J.M.O.; Olson, R.J. NPP Multi-Biome: Grassland, Boreal Forest, and Tropical Forest Sites, 1939–1996, R1; ORNL DAAC: Oak Ridge, TN, USA, 2013. [Google Scholar] [CrossRef]
- Boone, R.B.; Conant, R.T.; Sircely, J.; Thornton, P.K.; Herrero, M. Climate Change Impacts on Selected Global Rangeland Ecosystem Services. Glob. Chang. Biol. 2018, 24, 1382–1393. [Google Scholar] [CrossRef]
- Booker, K.; Huntsinger, L.; Bartolome, J.W.; Sayre, N.F.; Stewart, W. What Can Ecological Science Tell Us about Opportunities for Carbon Sequestration on Arid Rangelands in the United States? Glob. Environ. Chang. 2013, 23, 240–251. [Google Scholar] [CrossRef] [Green Version]
- Hudson, T.D.; Reeves, M.C.; Hall, S.A.; Yorgey, G.G.; Neibergs, J.S. Big Landscapes Meet Big Data: Informing Grazing Management in a Variable and Changing World. Rangelands 2021, 43, 17–28. [Google Scholar] [CrossRef]
- Jackson, H.; Prince, S.D. Degradation of Net Primary Production in a Semiarid Rangeland. Biogeosciences 2016, 13, 4721–4734. [Google Scholar] [CrossRef] [Green Version]
- Klemm, T.; Briske, D.D.; Reeves, M.C. Vulnerability of Rangeland Beef Cattle Production to Climate-Induced NPP Fluctuations in the US Great Plains. Glob. Chang. Biol. 2020, 26, 4841–4853. [Google Scholar] [CrossRef]
- Agri-Environmental Indicator—Livestock Patterns. Available online: https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Agri-environmental_indicator_-_livestock_patterns (accessed on 23 May 2021).
- Lee, T.; Hansen, J.; Ash, M. Major Factors Affecting Global Soybean and Products Trade Projections; USDA Economic Research Service: Washington, DC, USA, 2016. [Google Scholar]
Nation | Cropland Harvest Frequency a |
---|---|
Argentina | 1.02 |
Bangladesh | 1.67 |
Belgium | 1.20 |
Brunei | 2.30 |
Burkina Faso | 1.04 |
China | 1.29 |
Colombia | 1.04 |
North Korea | 1.09 |
Denmark | 1.08 |
Egypt | 1.75 |
Gambia | 1.03 |
Germany | 1.66 |
Hungary | 1.03 |
India | 1.14 |
Laos | 1.01 |
Malawi | 1.03 |
Myanmar | 1.45 |
Nepal | 1.91 |
Netherlands | 1.21 |
Nigeria | 1.16 |
Papua New Guinea | 1.08 |
Paraguay | 1.75 |
Philippines | 1.32 |
South Korea | 1.03 |
Rwanda | 1.28 |
Sri Lanka | 1.04 |
Tajikistan | 1.09 |
United Arab Emirates | 1.35 |
Vietnam | 1.39 |
Continent/Major Region 1 | Sub-Region 1 | Nations * |
---|---|---|
Africa | North Africa | Algeria, Egypt, Libya, Morocco, Sudan (former), Tunisia, Western Sahara |
East Africa | Burundi, Comoros, Djibouti, Eritrea, Ethiopia, Kenya, Madagascar, Malawi, Mauritius, Mayotte, Mozambique, Reunion, Rwanda, Seychelles, Somalia, Uganda, United Republic of Tanzania, Zambia, Zimbabwe | |
Middle Africa | Angola, Cameroon, Central African Republic, Chad, Congo, Democratic Republic of the Congo, Equatorial Guinea, Gabon, Sao Tome and Principe | |
Southern Africa | Botswana, Lesotho, Namibia, South Africa, Swaziland/Eswatini | |
West Africa | Benin, Burkina Faso, Cabo Verde, Code D’Ivoire, the Gambia, Ghana, Guinea, Guinea Bissau, Liberia, Mali, Mauritania, Niger, Nigeria, Senegal, Sierra Leone, Togo | |
Asia | West Asia | Armenia, Azerbaijan, Bahrain, Cyprus, Georgia, Iraq, Israel, Jordan, Kuwait, Lebanon, State of Palestine, Oman, Qatar, Saudi Arabia, Syrian Arab Republic, Turkey, United Arab Emirates, Yemen |
South Asia | India * | |
Afghanistan, Bangladesh, Bhutan, India, Iran, Maldives, Nepal, Pakistan, Sri Lanka | ||
South East Asia | Brunei Darussalam, Cambodia, Indonesia, Lao Peoples Democratic Republic, Malaysia, Myanmar, Philippines, Singapore, Thailand, Timor Leste, Vietnam | |
East Asia | China * | |
Democratic People’s Republic of Korea, Japan, Mongolia, Republic of Korea | ||
Central Asia | Kazakhstan * | |
Kyrgyzstan, Tajikistan, Turkmenistan, Uzbekistan | ||
Europe | East Europe | Russian Federation * |
Belarus, Bulgaria, Czech Republic, Hungary, Poland, Republic of Moldova, Romania, Slovakia, Ukraine | ||
North Europe | Denmark, Estonia, Faroe Islands, Finland, Iceland, Ireland, Latvia, Lithuania, Norway, Sweden, United Kingdom | |
South Europe | Albania, Bosnia and Herzegovina, Croatia, Greece, Italy, Malta, Montenegro, Portugal, Serbia, Slovenia, Spain, Yugoslavia | |
West Europe | Austria, Belgium, France, Germany, Luxembourg, Netherlands, Switzerland | |
Oceania | Oceania | American Samoa, Australia, Cook Islands, Fiji, French Polynesia, Guam, Kiribati, Micronesia, Nauru, New Caledonia, New Zealand, Niue, Pacific Islands Trust, Papua New Guinea, Samoa, Solomon Islands, Tokelau, Tonga, Tuvalu, Vanuatu |
Americas | North America | United States * |
Canada * | ||
Central America | Mexico * | |
Antigua and Barbuda, Bahamas, Barbados, Belize, Bermuda, British Virgin Islands, Cayman Islands, Costa Rica, Cuba, Dominica, Dominican Republic, El Salvador, Grenada, Guadeloupe, Guatemala, Haiti, Honduras, Jamaica, Martinique, Montserrat, Nicaragua, Panama, Trinidad and Tobago | ||
South America | Argentina * | |
Brazil * | ||
Chile * | ||
Bolivia, Colombia, Ecuador, French Guiana, Guyana, Paraguay, Peru, Suriname, Uruguay, Venezuela |
Global Quantities 1: | 2000 | 2001 | 2002 | 2003 | 2004 | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 |
---|---|---|---|---|---|---|---|---|---|---|---|
Grassland ANPP 2 (Pg C) | 5.14 | 5.01 | 4.77 | 4.83 | 5.07 | 4.92 | 5.07 | 5.05 | 5.05 | 5.06 | 5.16 |
Shrubland ANPP 2 (Pg C) | 8.81 | 8.64 | 8.23 | 8.30 | 8.42 | 8.35 | 8.68 | 8.63 | 8.69 | 8.61 | 8.65 |
Total rangeland ANPP 2 (Pg C) | 13.96 | 13.65 | 13.00 | 13.12 | 13.49 | 13.27 | 13.75 | 13.68 | 13.73 | 13.67 | 13.81 |
Fodder consumed 3 | 0.86 | 0.88 | 0.87 | 0.90 | 0.93 | 0.94 | 0.94 | 0.98 | 1.00 | 0.98 | 0.99 |
Grazing 4 intake required (Pg C) | 1.54 | 1.54 | 1.59 | 1.61 | 1.64 | 1.68 | 1.72 | 1.74 | 1.75 | 1.80 | 1.82 |
Grazing intake supplied (Pg C) | 1.50 | 1.50 | 1.54 | 1.57 | 1.59 | 1.63 | 1.67 | 1.69 | 1.69 | 1.73 | 1.74 |
Unmet grazing requirement 5 (Pg C) | 0.04 | 0.04 | 0.05 | 0.04 | 0.05 | 0.05 | 0.06 | 0.05 | 0.06 | 0.07 | 0.08 |
GI 6 (%) | 10.74 | 10.99 | 11.87 | 11.96 | 11.79 | 12.27 | 12.11 | 12.34 | 12.31 | 12.64 | 12.61 |
GP 7 (%) | 63.59 | 63.03 | 63.82 | 63.64 | 63.18 | 63.45 | 63.88 | 63.26 | 62.87 | 63.71 | 63.68 |
Region | Subregion or Nation: | Annual Grazing Intake Requirement (Tg C/Year) 1 | Annual Grazing Deficits (Tg C/Year) 2 | ||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2000 | 2001 | 2002 | 2003 | 2004 | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2000 | 2001 | 2002 | 2003 | 2004 | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | ||
Africa | East Africa | 93.8 | 95.6 | 100.9 | 103.2 | 105.8 | 109.5 | 112.0 | 127.8 | 134.7 | 137.8 | 141.0 | 0.27 | 0.27 | 0.31 | 0.28 | 0.32 | 0.15 | 0.19 | 0.19 | 0.16 | 0.33 | 0.19 |
Middle Africa | 17.8 | 17.9 | 17.8 | 18.2 | 18.3 | 18.7 | 19.1 | 20.0 | 19.7 | 19.8 | 19.6 | ||||||||||||
North Africa | 66.7 | 67.7 | 68.7 | 70.2 | 72.2 | 74.6 | 75.2 | 77.5 | 78.9 | 77.8 | 80.4 | 5.13 | 9.42 | 5.96 | 6.88 | 13.66 | 9.17 | 3.34 | 6.98 | 13.68 | 19.85 | ||
Southern Africa | 20.7 | 21.1 | 20.1 | 20.3 | 19.8 | 19.8 | 19.5 | 20.1 | 20.2 | 20.5 | 20.6 | ||||||||||||
West Africa | 54.2 | 56.8 | 57.4 | 59.8 | 62.0 | 62.7 | 63.8 | 68.4 | 68.7 | 74.7 | 75.3 | 0.16 | 1.72 | 1.34 | 1.77 | 1.82 | 2.49 | 3.43 | 3.61 | 5.27 | 6.30 | ||
Americas | Argentina | 31.3 | 31.9 | 35.1 | 38.0 | 39.0 | 37.3 | 38.2 | 35.9 | 36.1 | 36.9 | 24.5 | |||||||||||
Brazil | 185.6 | 190.5 | 200.3 | 206.5 | 215.2 | 219.7 | 216.7 | 208.7 | 205.8 | 213.6 | 215.2 | ||||||||||||
Canada | 14.5 | 15.9 | 16.5 | 14.8 | 17.2 | 17.6 | 18.0 | 15.9 | 15.7 | 16.1 | 16.9 | ||||||||||||
Central America excl. Mexico | 25.0 | 25.6 | 27.5 | 27.8 | 28.3 | 29.2 | 29.5 | 30.0 | 30.3 | 30.7 | 31.4 | 0.57 | 0.52 | 0.51 | 0.48 | 0.42 | 0.42 | 0.45 | 0.43 | 0.44 | 0.46 | 0.46 | |
Mexico | 36.3 | 35.0 | 36.1 | 34.8 | 35.1 | 36.4 | 35.1 | 35.3 | 34.5 | 37.3 | 35.9 | ||||||||||||
South America excl. Arg. & Brazil | 112.0 | 112.5 | 112.0 | 112.7 | 113.2 | 115.7 | 115.9 | 115.3 | 116.9 | 119.4 | 118.1 | 0.15 | |||||||||||
United States | 76.4 | 76.7 | 81.3 | 75.5 | 69.2 | 74.1 | 83.9 | 83.1 | 90.6 | 89.7 | 114.9 | ||||||||||||
E., S.E., and S. Asia | China | 244.9 | 254.5 | 264.1 | 273.9 | 289.4 | 303.8 | 313.4 | 319.5 | 325.3 | 331.4 | 337.5 | |||||||||||
East Asia excl. China | 15.8 | 13.9 | 11.7 | 12.1 | 11.9 | 13.3 | 15.1 | 16.6 | 16.5 | 18.0 | 15.4 | ||||||||||||
India | 148.2 | 149.3 | 166.4 | 156.1 | 166.0 | 167.7 | 174.0 | 171.3 | 173.2 | 180.9 | 172.3 | ||||||||||||
South Asia excl. India | 86.4 | 89.1 | 87.8 | 88.7 | 93.9 | 94.3 | 101.0 | 100.5 | 107.2 | 108.9 | 111.8 | 29.64 | 31.35 | 33.87 | 3.55 | 31.75 | 3.33 | 38.17 | 35.34 | 4.36 | 44.43 | 44.17 | |
Southeast Asia | 51.0 | 52.7 | 56.9 | 57.0 | 56.2 | 55.4 | 56.4 | 59.6 | 58.7 | 62.1 | 61.7 | 0.16 | 0.12 | 0.53 | 0.56 | 0.55 | 0.59 | 0.64 | 0.70 | 0.76 | 0.74 | 0.83 | |
W. and Ctrl. Asia | Central Asia excl. Kazakhstan | 13.1 | 13.6 | 13.3 | 14.6 | 15.7 | 16.1 | 17.3 | 17.5 | 19.8 | 20.2 | 22.0 | 0.24 | 0.17 | 0.35 | 0.17 | 3.63 | 0.84 | 1.99 | ||||
Kazakhstan | 3.5 | 1.9 | 2.2 | 3.1 | 4.8 | 5.1 | 4.6 | 3.8 | 6.3 | 4.1 | 8.3 | ||||||||||||
Russian Federation | 4.1 | 0.0 | 0.0 | 1.4 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 2.1 | ||||||||||||
West Asia/Arabian Peninsula | 33.7 | 33.6 | 31.5 | 32.0 | 32.9 | 33.8 | 35.8 | 38.8 | 37.7 | 36.6 | 35.2 | 4.84 | 3.73 | 3.40 | 3.94 | 6.12 | 5.83 | 6.13 | 7.00 | 8.31 | 5.68 | 4.76 | |
Europe | East Europe excl. Russian Federation | 31.0 | 25.2 | 23.8 | 28.6 | 19.7 | 21.6 | 23.0 | 22.2 | 15.6 | 17.8 | 15.8 | |||||||||||
North Europe | 38.2 | 36.2 | 35.6 | 35.3 | 35.4 | 34.7 | 34.6 | 32.9 | 31.6 | 30.8 | 31.4 | ||||||||||||
South Europe | 24.3 | 19.0 | 19.7 | 21.7 | 17.5 | 18.9 | 19.8 | 19.4 | 18.7 | 21.7 | 21.6 | 0.12 | 0.25 | 0.23 | 0.22 | 0.23 | 0.23 | 0.29 | 0.18 | 0.36 | 0.28 | 0.33 | |
West Europe | 31.6 | 30.6 | 29.3 | 33.7 | 24.1 | 26.1 | 26.8 | 25.6 | 22.6 | 23.7 | 25.4 | 1.35 | 0.42 | 0.14 | 0.16 | 0.29 | |||||||
Oceania | Australia | 53.2 | 52.3 | 52.4 | 48.8 | 50.3 | 50.3 | 49.5 | 47.9 | 45.2 | 45.1 | 42.1 | |||||||||||
New Zealand | 21.0 | 21.0 | 21.6 | 22.0 | 22.1 | 22.2 | 22.5 | 22.2 | 21.4 | 21.8 | 21.6 | ||||||||||||
Oceania excl. Australia and N.Z. | 1.5 | 1.6 | 1.6 | 1.6 | 1.6 | 1.6 | 1.6 | 1.7 | 1.7 | 1.7 | 1.8 | 0.17 | 0.17 | 0.18 | 0.29 | 0.28 | 0.22 | 0.23 | 0.24 | 0.25 | 0.26 | 0.26 |
Area: | GI 1 Based on ANPP = 43% of NPP 2 | GI Based on ANPP = 60% of NPP 3 |
---|---|---|
Globe | 15.4–18.4% | 11–13.2% |
Middle Africa | 2.3–2.5% | 1.7–1.8% |
North Africa | 106.5–129.6% | 76.4–92.9% |
Southern Africa | 6.6–12.2% | 4.8–8.7% |
East Africa | 10.2–16% | 7.3–11.5% |
West Africa | 12.4–18.1% | 8.9–12.9% |
West Asia and Arabian Peninsula | 36–50.6% | 25.8–36.3% |
China | 35.3–46.7% | 25.3–33.4% |
East Asia excl. China | 16.4–25.5% | 11.8–18.3% |
South East Asia | 12.9–17.7% | 9.2–12.7% |
India | 78.2–101.3% | 56.1–72.6% |
South Asia excl. India | 99.1–121.9% | 71.1–87.4% |
East Europe excl. Russian Fed. | 15.7–32% | 11.2–23% |
North Europe | 18.6–24.3% | 13.3–17.4% |
West Europe | 21.4–34.2% | 15.3–24.5% |
South Europe | 12.1–19.4% | 8.7–13.9% |
Russian Federation | 0–0.4% | 0–0.3% |
Kazakhstan | 1.8–9.1% | 1.3–6.5% |
Central Asia excl. Kazakhstan | 48.9–99.9% | 35–71.6% |
U.S. | 8.7–14.8% | 6.3–10.6% |
Canada | 2.9–3.7% | 2–2.6% |
Mexico | 15.8–19.9% | 11.3–14.3% |
Central America excl. Mexico | 20.2–24.4% | 14.5–17.5% |
Argentina | 9.3–16.3% | 6.7–11.6% |
Brazil | 15.8–21.2% | 11.3–15.2% |
South America excl. Argent., Brazil | 17.7–19.5% | 12.7–14% |
Australia | 6.3–12.2% | 4.5–8.7% |
New Zealand | 55.5–62.2% | 39.8–44.6% |
Oceania excl. Australia, N.Z. | 6.1–7.2% | 4.4–5.2% |
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Wolf, J.; Chen, M.; Asrar, G.R. Global Rangeland Primary Production and Its Consumption by Livestock in 2000–2010. Remote Sens. 2021, 13, 3430. https://doi.org/10.3390/rs13173430
Wolf J, Chen M, Asrar GR. Global Rangeland Primary Production and Its Consumption by Livestock in 2000–2010. Remote Sensing. 2021; 13(17):3430. https://doi.org/10.3390/rs13173430
Chicago/Turabian StyleWolf, Julie, Min Chen, and Ghassem R. Asrar. 2021. "Global Rangeland Primary Production and Its Consumption by Livestock in 2000–2010" Remote Sensing 13, no. 17: 3430. https://doi.org/10.3390/rs13173430
APA StyleWolf, J., Chen, M., & Asrar, G. R. (2021). Global Rangeland Primary Production and Its Consumption by Livestock in 2000–2010. Remote Sensing, 13(17), 3430. https://doi.org/10.3390/rs13173430