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Journal Article

Quantification of uncertainties in global grazing systems assessment

Authors

Fetzel,  T.
External Organizations;

Havlik,  P.
External Organizations;

Herrero,  M.
External Organizations;

Kaplan,  J. O.
External Organizations;

Kastner,  T.
External Organizations;

Kroisleitner,  C.
External Organizations;

/persons/resource/Rolinski

Rolinski,  Susanne
Potsdam Institute for Climate Impact Research;

Searchinger,  T.
External Organizations;

Van Bodegom,  P. M.
External Organizations;

Wirsenius,  S.
External Organizations;

Erb,  K.-H.
External Organizations;

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Fulltext (public)

7963oa.pdf
(Publisher version), 976KB

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Citation

Fetzel, T., Havlik, P., Herrero, M., Kaplan, J. O., Kastner, T., Kroisleitner, C., Rolinski, S., Searchinger, T., Van Bodegom, P. M., Wirsenius, S., Erb, K.-H. (2017): Quantification of uncertainties in global grazing systems assessment. - Global Biogeochemical Cycles, 31, 7, 1089-1102.
https://doi.org/10.1002/2016GB005601


Cite as: https://publications.pik-potsdam.de/pubman/item/item_22242
Abstract
Livestock systems play a key role in global sustainability challenges like food security and climate change, yet many unknowns and large uncertainties prevail. We present a systematic, spatially explicit assessment of uncertainties related to grazing intensity (GI), a key metric for assessing ecological impacts of grazing, by combining existing data sets on (a) grazing feed intake, (b) the spatial distribution of livestock, (c) the extent of grazing land, and (d) its net primary productivity (NPP). An analysis of the resulting 96 maps implies that on average 15% of the grazing land NPP is consumed by livestock. GI is low in most of the world's grazing lands, but hotspots of very high GI prevail in 1% of the total grazing area. The agreement between GI maps is good on one fifth of the world's grazing area, while on the remainder, it is low to very low. Largest uncertainties are found in global drylands and where grazing land bears trees (e.g., the Amazon basin or the Taiga belt). In some regions like India or Western Europe, massive uncertainties even result in GI > 100% estimates. Our sensitivity analysis indicates that the input data for NPP, animal distribution, and grazing area contribute about equally to the total variability in GI maps, while grazing feed intake is a less critical variable. We argue that a general improvement in quality of the available global level data sets is a precondition for improving the understanding of the role of livestock systems in the context of global environmental change or food security.