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Annual vegetation burns across the northern savanna region of Ghana: period of occurrence, area burns, nutrient losses and emissions
Authors:Joseph X. Kugbe  Fosu Mathias  Tamene L. Desta  Manfred Denich  Paul L. G. Vlek
Affiliation:1. Division of Ecology and Natural Resources, Center for Development Research (ZEF), University of Bonn, Walter Flex Str. 3, 53113, Bonn, Germany
2. Savanna Agriculture Research Institute, Nyankpala, P. O. Box 52, Tamale, Ghana
3. International Center for Tropical Agriculture (CIAT), Chitedze Agricultural Research Station, Lilongwe, Malawi
Abstract:This study has assessed the seasonal occurrence of annual vegetation fires and defined inter-seasonally burned area for the different vegetation cover types across Ghana and the northern region of Ghana using 10-year (2001?C2010) remote sensing data. These values were used with fire induced elemental losses to estimate greenhouse gas emissions and net plant nutrient loss due to gross bush fire nutrient transfers and annual atmospheric nutrient depositions. About 21, 68, 10 and 1?% of annual burns across the northern region of Ghana take place in the months of November, December, January and February respectively. As much as 68?±?4 thousand km2 (25?C32?%) and 37?±?2.6 thousand km2 (46?C60?%) of dry land are annually burned across Ghana and the northern region of Ghana respectively, with 53?C56?% of the total annual burns across the country taking place in the northern region. About 10,100?C28,400 Gg of C, comprising 215?C4,700 thousand Gg of CO2 equivalent (CO2, CH4) potential global warming green house gases and 48?C324 thousand Gg of local pollutants (CO, NOx) are estimated to be released annually through bush fire occurrence across Ghana. Net negative balance for P between fire-induced nutrient transfers and, annual wet and dry nutrient deposits is of concern given the high P-sorbing mineral content of the soils. The temporal loss of P suggest an input source than wet and dry atmospheric P depositions for the sustenance of the ecosystem or predict a long term threat to regional food production.
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