Browsing by Author "Kilawe, Edward"
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Item Getting ready for REDD+ in Tanzania: a case study of progress and challenges(Fauna & Flora International, 2010) Dalsgaard, SØren; Funder, Mikkel; Hagelberg, Niklas; Harrison, Paul; Haule, Christognus; Kabalimu, Kekilia; Kilahama, Felician; Kilawe, Edward; Lewis, Simon L.; Lovett, Jon C.; Lyatuu, Gertrude; Marshall, Andrew R.; Meshack, Charles; Miles, Lera; Milledge, Simon A.H.; Munishi, Pantaleo K.T.; Nashanda, Evarist; Shirima, Deo; Swetnam, Ruth D.; Willcock, Simon; Williams, Andrew; Zahabu, Eliakim; Burgess, Neil D.; Bahane, Bruno; Clairs, Tim; Danielsen, FinnThe proposed mechanism for Reducing Emis- sions from Deforestation and Degradation (REDD+) offers significant potential for conserving forests to reduce negative impacts of climate change. Tanzania is one of nine pilot countries for the United Nations REDD Pro- gramme, receives significant funding from the Norwegian, Finnish and German governments and is a participant in the World Bank’s Forest Carbon Partnership Facility. In combination, these interventions aim to mitigate green-house gas emissions, provide an income to rural commu- nities and conserve biodiversity. The establishment of the UN-REDD Programme in Tanzania illustrates real-world challenges in a developing country. These include currently inadequate baseline forestry data sets (needed to calculate reference emission levels), inadequate government capacity and insufficient experience of implementing REDD+-type measures at operational levels. Additionally, for REDD+ to succeed, current users of forest resources must adopt new practices, including the equitable sharing of benefits that accrue from REDD+ implementation. These challenges are being addressed by combined donor support to im- plement a national forest inventory, remote sensing of forest cover, enhanced capacity for measuring, reporting and verification, and pilot projects to test REDD+ imple- mentation linked to the existing Participatory Forest Man- agement Programme. Our conclusion is that even in a country with considerable donor support, progressive forest policies, laws and regulations, an extensive network of managed forests and increasingly developed locally-based forest management approaches, implementing REDD+ pre- sents many challenges. These are being met by coordinated, genuine partnerships between government, non-government and community-based agencies.Item NAFORMA: National forest resources monitoring and assessment of Tanzania Mainland(FAO, 2022) Rajala, Tuomas; Heikkinen, Juha; Gogo, Sophia; Ahimbisibwe, Joyce; Bakanga, Geofrey; Chamuya, Nurdin; Perez, Javier Garcia; Kilawe, Edward; Kiluvia, Shani; Morales, David; Nzunda, Emmanuel; Otieno, Jared; Sawaya, Jonathan; Vesa, Lauri; Zahabu, Eliakimu; Henry, Matieuhree options for the sampling design of the field plot clusters of NAFORMA II biophysical survey are compared in this report. Option 1 consists of re-measuring all NAFORMA I field sample plots (3 205 clusters) and Option 2 of re-measuring only those that were established as permanent (848 clusters). The recommended Option 3 is a compromise between these two “extreme” options: Re-measure a subset (1 405 clusters) of NAFORMA I field sample plots including (almost) all permanent clusters and a carefully selected set of other NAFORMA I field plot clusters to obtain a uniform sample within each TFS zone. Design Option 3 has the following features: • • • • Sampling intensity is uniform within each TFS zone. This makes it simple to use the data. For example, mean volumes can be estimated by averages over the plots. The selected clusters are well-spread over the target population. The anticipated precision of land-class area and mean wood volume relative to sample size is nearly as good as that of NAFORMA I. All proposed clusters were measured in NAFORMA I, which enables precise estimation of change based on repeated measurements. The costs and precision were anticipated by utilizing NAFORMA I field data, information about subsequent improvements in the road network, and changes in land-use using satellite imaging derived land-class maps.