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Agriculture, including rangelands, covers >30% of land globally, with trees being an integral part of most smallholdings. Trees on Farms (TonF) remain largely invisible in countries’ approaches to meet their UNCBD or UNCCC targets. The implementing partners have been working on TonF for more than 30 years. In particular the sentinel landscape initiative (SL) led by ICRAF provides integrated data sets to improve understanding of TonF roles for sustainable development for all of the implementing countries. The SL provides a unique opportunity to transform national capacities for harnessing TonF, development of tools and interventions and greatly increasing the contribution of agricultural land to Aichi Targets (ATs). Building on the SL initiative, the project aims to improve countries’ knowledge of TonF importance for biodiversity and for human wellbeing. It will develop and disseminate practical, context-specific strategies to increase TonF based on local knowledge and local needs. Furthermore, the project will strengthen partnerships between governmental agencies in different sectors (Agriculture, Forestry, Conservation) and with potential financial investors to garner non-public finances for TonF. In addition, the project will build countries’ technical expertise to monitor, assess and report on TonF through innovative, practical approaches and training of NGOs and governmental staff.
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Unknown - 13.7 KB - MD5: accf76051153ce7179a0188815a72e79
Clean dataData
MS Excel Spreadsheet - 9.6 KB - MD5: 315111915d8bd141e792b5b40a96ff66
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R Data - 698 B - MD5: b5ac8a74d573f6d9223534b041dde1a6
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Unknown - 2.5 KB - MD5: ec86b6d4256d5fa4df3d0eeaa8bd5125
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MS Excel Spreadsheet - 5.8 KB - MD5: 501251fd4735b33e6961ccd48b8b3916
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Nov 22, 2020 - Work Package II: Trees on Farms’ Biodiversity Assessment Tool
Kihumuro, Phillip; Harrison, Sam; Okia, Clement; Harrison, Rhett, 2020, "Farm inventory surveys in Uganda: Plots data", https://doi.org/10.34725/DVN/QAJ8W8, World Agroforestry - Research Data Repository, V3, UNF:6:35MNRNssbLfv7WF3fZlbUg== [fileUNF]
Plots data from Uganda
R Data - 7.2 KB - MD5: 0f9cfb2598f04a6b437eba9b0cb42b9b
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Unknown - 36.0 KB - MD5: 44372506a06f151ac7d797525945d4ec
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MS Excel Spreadsheet - 22.9 KB - MD5: 854a6c55177bb93943b5cf7b5e94c390
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R Data - 1.4 KB - MD5: 0679d453ebf376051709e4f66e1ca951
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