OCCASIONAL PAPER
Zambia country profile Monitoring, reporting and verification for REDD+
Michael Day Davison Gumbo Kaala B. Moombe Arief Wijaya Terry Sunderland
OCCASIONAL PAPER 113
Zambia country profile Monitoring, reporting and verification for REDD+
Michael Day Center for International Forestry Research Davison Gumbo Center for International Forestry Research Kaala B. Moombe Center for International Forestry Research Arief Wijaya Center for International Forestry Research Terry Sunderland Center for International Forestry Research
Center for International Forestry Research (CIFOR) Occasional Paper 113
© 2014 Center for International Forestry Research
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ISBN 978-602-1504-42-0
Day M, Gumbo D, Moombe KB, Wijaya A and Sunderland T. 2014. Zambia country profile: Monitoring, reporting and verification for REDD+. Occasional Paper 113. Bogor, Indonesia: CIFOR.
Photo by Terry Sunderland
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Abbreviations v Executive summary vii 1 Overview of national REDD+ strategy 1 1.1 Zambia and REDD+ readiness 1 1.2 Activities under the United Nations Framework Convention on Climate Change REDD+ program 1 1.3 The Nyimba Forest Project 1 1.4 Major forest types in Zambia 2 1.5 Classification of Zambian woodlands 5 1.6 Key drivers and processes affecting forest carbon change 5 1.7 National strategy and policy development targets for REDD+ 10 1.8 Reference levels and monitoring, reporting and verification 10 1.9 Issues and challenges 11 2 Available data and current capacities 13 2.1 National datasets available for monitoring, reporting and verification of REDD+ in Zambia 13 2.2 Data sources for the Global Forest Resources Assessment 2010 report and the Initial National Communication 13 2.3 Institutional framework and capacity for forest and agricultural monitoring 13 2.4 Available data and approaches to support spatial planning and land-use management 16 2.5 Evolving technologies 17 3 State of knowledge 18 3.1 Growth rates and standing biomass 18 3.2 Allometric equations 22 3.3 Root biomass and root to shoot ratios 22 3.4 Deadwood and litter pools 23 3.5 Intergovernmental Panel on Climate Change expansion factors 23 3.6 Soil organic matter loss following deforestation 23 3.7 Wood density of tree species 24 3.8 Non-carbon dioxide emissions associated with land-use change 24 4 Future developments in forests and agriculture 25 4.1 Future trends in drivers of forest carbon change 25 4.2 Plans to expand agriculture and other land uses 25 5 Gap assessments 26 5.1 Background 26 5.2 Gap assessment for international requirements 27 5.3 Gap assessment for national requirements 27 5.4 Gap assessment for subnational requirements 28 5.5 Recommendations for gap filling 29 6 References 30 Appendix Growth rates for selected tree species in miombo woodland in Zambia and neighboring countries 37 Figures and tables
Figures 1. The ecoregions of Zambia. 3 2. The drivers of deforestation in Zambia. 7
Tables 1. Main forest or woodland types in Zambia. 6 2. National datasets available for monitoring, reporting and verification of REDD+ in Zambia. 14 3. Data sources for the Global Forest Resources Assessment 2010 report. 16 4. Growth rates for woodlands in the miombo ecoregion. 19 5. Biomass and carbon estimates in miombo woodlands in Zambia and neighboring countries. 20 Abbreviations
AGB above-ground biomass ALOS Advanced Land Observation Satellite BCEF biomass conversion expansion factor BGB below-ground biomass CEEZ Centre for Energy, Environment and Engineering Zambia
CH4 methane CIFOR Center for International Forestry Research CO carbon monoxide
CO2 carbon dioxide DBH diameter at breast height ECZ Environmental Council of Zambia FAO Food and Agriculture Organization of the United Nations FRA Forest Resources Assessment FSP Forest Support Program GDP gross domestic product GHG greenhouse gas GIS geographical information systems ILUA Integrated Land Use Assessment INC Initial National Communication IPCC Intergovernmental Panel on Climate Change LiDAR Light Detection And Ranging LULUCF land use, land-use change and forestry MAI mean annual increment MLNREP Ministry of Lands, Natural Resources and Environmental Protection MODIS moderate resolution spectroradiometer MRV monitoring, reporting and verification
N2O nitrous oxide NCCRS National Climate Change Response Strategy NFP Nyimba Forest Project NJP National Joint Programme NOx nitrogen oxides NRS National REDD Strategy
O3 ozone vi | Michael Day, Davison Gumbo, Kaala B. Moombe, Arief Wijaya and Terry Sunderland
PALSAR Phased Array type L-band Synthetic Aperture Radar REDD reducing emissions from deforestation and forest degradation REDD+ reducing emissions from deforestation and forest degredation and enhancing forest carbon stocks in developing countries SADC Southern African Development Community SAR Synthetic Aperture Radar SMA Spectral Mixture Analysis SNDP Sixth National Development Plan SPOT Systeme Pour l’Observation de la Terre SOC soil organic carbon UAV unmanned aerial vehicles UNFCC United Nations Framework Convention on Climate Change USAID United States Agency for International Development UN-REDD The United Nations Collaborative Programme on Reducing Emissions from Deforestation and Forest Degradation in Developing Countries USFS United States Forest Service ZARI Zambia Agriculture Research Institute Executive summary
National REDD+ strategy Drivers of deforestation and degradation Zambia is one of the nine pilot countries in the United Nations Collaborative Programme on Land-use change and forest loss are the main Reducing Emissions from Deforestation and contributors to Zambia’s greenhouse gas emissions. Forest Degradation in Developing Countries Deforestation rates are significant in Zambia, with (UN-REDD) and is currently at the first stage of approximately 300,000 ha of forest cover lost per REDD+1 readiness under the UN-REDD Quick year. Wood extraction, agricultural expansion, Start initiative. A National REDD Strategy (NRS) infrastructure development and fires are the main is currently being developed by the National drivers of deforestation and forest degradation. Joint Programme (NJP) to which the Center Charcoal production is considered one of the for International Forestry Research (CIFOR) is primary causes of forest degradation in Zambia and contributing technical support. A key component the main cause of carbon stock loss from forests in of the NRS is to build the capacity of relevant the country. Land clearance for agriculture is the stakeholders, such as the Ministry of Lands, primary cause of forest cover loss. Natural Resources and Environmental Protection, to undertake forest measuring and monitoring. A specific objective of the NJP is to strengthen the Issues and challenges monitoring, reporting and verification (MRV) capacity for REDD+ in Zambia, which is of critical A number of challenges need to be overcome to importance to its effectiveness. ensure an adequate MRV system for Zambia. Problems include low institutional capacity for Zambia is currently establishing a national forest monitoring, limited knowledge regarding monitoring system for REDD+, a major forest resources and carbon stocks, and technical component of which is the development of challenges such as mapping forest degradation the Integrated Land Use Assessment (ILUA), caused by charcoal production in complex dry undertaken by the Forestry Department woodland ecosystems. ILUA II and regional and supported by the Food and Agriculture projects, such as the NFP undertaken by CIFOR, Organization of the United Nations. ILUA Phase have been designed to overcome these challenges. I (ILUA I) has been completed and provides data toward meeting Intergovernmental Panel on Climate Change Tier 1 and Tier 2 requirements Major knowledge gaps for estimates of carbon stocks. Data gathering for ILUA Phase II (ILUA II) is ongoing, including data Research on woodland ecology relevant to MRV generated from the Nyimba Forest Project (NFP), of REDD+ has focused on miombo woodland in with a methodology specifically designed to enable Zambia, as the dominant and most economically MRV for REDD+ in Zambia. A national remote important forest type. Studies have examined sensing analysis is also being undertaken as part of growth rates, standing biomass, root to shoot ratios ILUA II to provide reference forest cover levels for and regeneration of miombo woodlands following the country. extraction of wood for charcoal production. However, much of this research is not current, the majority of data is over 10 years old, and there 1 Reducing emissions from deforestation and forest degradation, and are a number of key knowledge gaps and data enhancing forest carbon stocks in developing countries (REDD+) deficiencies including: viii | Michael Day, Davison Gumbo, Kaala B. Moombe, Arief Wijaya and Terry Sunderland
• verification of above-ground biomass (AGB), project is undertaking research in miombo, below-ground biomass (BGB), soil, deadwood mopane and munga forest and woodland and litter carbon stocks within all woodland formations in Nyimba District. ILUA II types and drivers of carbon stock change and the NFP will also contribute to capacity • standing biomass and growth rates within building in the country, for example through Kalahari, mopane and munga woodlands collaboration with local partners and training and evergreen forests (and other forest and of field surveyors. Additional work on MRV in woodland formations), including the causes of Zambia is being carried out by the United States variability in biomass levels within Zambian Forest Service with funding from the United forest ecosystems States Agency for International Development, • the availability of regional allometric models also in Eastern Province. BioCarbon Partners for predicting biomass and carbon stocks is also conducting work on MRV in Rufunsa specific to Zambian woodland and forest District. Opportunities for collaboration between ecosystems these various institutions should be explored as • verification of deforestation and forest they share similar objectives in improving the degradation rates; in particular carbon stock capabilities of Zambia to develop an effective losses from charcoal production and the MRV system for REDD+. ability to map changes associated with forest degradation using remote sensing As well as the major knowledge gaps and data • the impact of fire on woodland loss and deficiencies identified above, the capacity for carbon stocks, including the impact of fire on MRV of REDD+ in Zambia should be improved soil carbon. through the following: • The Government of Zambia should develop a coherent national strategy for REDD+, Gap filling including the establishment of a specific REDD+ department or center to support and ILUA II should fill a large number of data, coordinate MRV activities. This may include methodological, capacity and eligibility gaps collaboration with the inter-ministerial for MRV of REDD+ in Zambia. It should also climate change coordination unit under the provide data to enable IPCC Tier 3 estimates Ministry of Finance and National Planning. for AGB, deadwood, soil and litter carbon pools. • A data sharing protocol should be developed BGB estimates may only be possible to Tier under the Forestry Department to enable 1 or Tier 2, due to the technical challenges of the sharing of ILUA data. This should be measuring BGB in miombo woodlands. ILUA II achieved through the creation of an open will cover all the major forest types and calculate access database. specific biomass expansion factors for Zambia. • The capacity of the Forestry Department to repeat monitor ILUA field sites and use CIFOR’s NFP will complement the national remote sensing for MRV of REDD+ should level work by developing subnational scale be developed. models for MRV in Eastern Province. The 1 Overview of national REDD+ strategy
1.1 Zambia and REDD+ readiness 1.2 Activities under the United Nations Framework Convention on Zambia is one of the nine pilot countries in the Climate Change REDD+ program United Nations Collaborative Programme on Reducing Emissions from Deforestation and Zambia is a non-Annex I Party to the United Forest Degradation in Developing Countries Nations Framework Convention on Climate (UN-REDD) and is currently at the first Change (UNFCC). The Initial National phase of readiness for REDD+ under the UN- Communication (INC) was submitted in 2004 REDD Quick Start initiative. A National Joint by the Environmental Council of Zambia (ECZ) Programme (NJP), facilitated by the country’s on behalf of the then Ministry of Tourism, Forestry Department is tasked with developing Environment and Natural Resources. The Second a National REDD+ Strategy (NRS). The National Communication is currently being road to the NRS includes preparing Zambian produced with the overall goals of improving stakeholders and institutions, such as the national capacity in the following areas: inventory Ministry of Lands, Natural Resources and of GHG emissions, assessment of potential Environment Protection (MLNREP), for impacts of climate change on vulnerable sectors, coordinated implementation of REDD+. and analysis of measures with the potential to Outcome 5 of the NJP National Programme reduce GHG emissions (MTNER 2007). Document is to strengthen the monitoring, reporting, and verification (MRV) capacity Under the UNFCC REDD+ readiness process for REDD+ in Zambia. Outcome 6, closely countries can implement REDD+ in three related to this, is to undertake an assessment of phases. Phase I includes the development of reference emission levels and forest reference national action plans. Phase II requires capacity level (UN-REDD 2010a). A reliable MRV building and the development of a results-based system for assessing changes in carbon stock national monitoring system; while Phase III and greenhouse gas (GHG) emissions due requires countries to address MRV for REDD+ to deforestation and forest degradation is in line with Decision 1/CP.16 (UNFCC 2010; of critical importance to the effectiveness of Romjin et al. 2012). Having created the NJP and REDD+; MRV is therefore a key component completed the first part of its Integrated Land of REDD+ readiness in participating countries. Use Assessment (ILUA), Zambia is in Phase II of this process and is conducting data collection, As capacity in Zambia is limited, one of the monitoring and analysis in order to establish a main tasks under Phase I of UN-REDD in national monitoring system. Zambia is to build the capacity of stakeholders to conduct REDD+ projects and MRV of carbon stocks. Improving understanding of the 1.3 The Nyimba Forest Project extent, ecology and change in Zambian forests is another key component of REDD+ readiness In August 2012, the Center for International in the country. Forestry Research (CIFOR) began the Nyimba Forest Project (NFP) funded by the United States Agency for International Development (USAID). The project is currently developing models for MRV of REDD+ at the subnational 2 | Michael Day, Davison Gumbo, Kaala B. Moombe, Arief Wijaya and Terry Sunderland
level in Zambia, using data gathered from Nyimba land area (Kalinda et al. 2008). Dry woodlands District, Eastern Province. CIFOR is working with and forests form the majority of forest types in district-level partners, research institutions and Zambia, which are defined as vegetation types government departments to develop a subnational dominated by wooded plants that cover more MRV prototype in Nyimba. Developing models than 10% of the ground surface (Chidumayo and systems for MRV and REDD+ at the district and Marunda 2010). This definition encompasses level is of considerable importance to establishing a broad range of vegetation types from wooded the effectiveness of a national MRV system. grasslands and scrub, to closed forests. The District-level projects enable testing of MRV impact of fire and long history of human use, protocols and allow comparisons of national which influences the structure and distribution estimates of key variables such as carbon stocks and of vegetation, complicates the classification of deforestation rates. The prototype MRV system woodland vegetation into easily definable groups. developed by the NFP will be tested at district level In addition, many dominant tree species in and recommendations, based upon the outcomes Zambia are tolerant of a wide range of edaphic of testing, will be made to the UN-REDD conditions (Chidumayo 2012a). Despite these Coordination Unit in Lusaka. difficulties, the main dry forests and woodlands found in Zambia can be separated into miombo, In order to provide background information for Kalahari, mopane and munga or mixed the NFP, this paper summarizes the status of MRV woodlands (Makumba 2003). A significant and REDD+ readiness in Zambia, as well as the area of dry evergreen forests is also found in the current state of knowledge and studies available country (Kindt et al. 2011). A brief summary of with regards to deforestation rates, emissions from these main forest types is provided below. deforestation and degradation, available data sets for MRV in the country, and an assessment of key knowledge and data gaps. This paper will feed 1.4.1 Miombo woodland into CIFOR’s ongoing MRV project in Nyimba District and complement the national REDD+ Miombo is the most dominant woodland readiness activities undertaken by UN-REDD and formation and habitat type in southern Africa Zambian Forestry Department. (Ryan et al. 2010). Miombo woodland is also the major forest type in Zambia itself, covering approximately 45% of the total land 1.4 Major forest types in Zambia area (Kalinda et al. 2008; Stringer et al. 2012). Miombo woodlands are of considerable economic Zambia’s vegetation is dominated by miombo, importance in Zambia for the supply of firewood, which is characterized by open woodland charcoal, timber and NTFPs (Chidumayo and dominated by Caesalpinioideae tree species Kwibisa 2003). including Brachystegia, Julbernardia and Isoberlinia, often associated with a dense grass sward (Byers Dominant species are represented by the genera 2001). Caesalpinoid dry woodlands are often Brachystegia, Isoberlinia and Julbernardia, located on nutrient-poor soils and are generally and include key species such Brachystegia deciduous, shedding their leaves in dry seasons of spiciformis, B. boehmii, Julbernardia globiflora, the year. Zambian woodlands have long history J. paniulata; and Isoberlinia angolensis as well as of human use, including extraction of wood for the dipterocarp, Marquesia macroura. Miombo timber and fuel, grazing, harvesting of non-timber woodland is generally two storied, with an forest products (NTFPs) and fire. The Food and open canopy, 15–21 m high. The lower story Agricultural Organization of the United Nations comprises species such as Albizia antunesiana, (FAO) classifies all Zambian forests as secondary, Anisophyllea boehmii, Brachystegia stipulata and naturally regenerated forests with no true primary Dalbergia nitidula. The open canopy results in an forest remaining (FAO 2010). Generally, very little undergrowth of dense grass or scrub of 0.6–3.6 m undisturbed, old-growth, dry woodlands remain in high. Notable grass genera include Andropogon, southern Africa (Defrees et al. 2011). Brachiaria, Digitaria, Heteropogon, Hyparrhenia, Hyperthelia, Panicum, Pogonarthria, Tristachya Forest cover in Zambia comprises around and Urochloa (Fanshawe 1968). 50 million hectares, or over 65% of the total 25 0’E 30 0’E
Tanzania 25 0’E 30 0’E
Zaire Zambia Country Profile | 3 Tanzania Luapula 10 0’S 10 0’S
Northern Zaire
25 0’E 30 0’E LuapulaAngola 10 0’S 10 0’S
NorthernNorth-Western Copperbelt Malawi Tanzania Eastern Zambia Angola Central Zaire North-Western Copperbelt Malawi 15 0’S Eastern Lusaka 15 0’S Luapula Western 10 0’S 10 0’S Mozambique Zambia Northern Central Southern
15 0’S Lusaka 15 0’S Angola Western NamibjaMozambique Zimbabwe North-Western N Copperbelt Malawi Eastern Southern Zambia Botswana 0 50 100 200 300 400 Namibja Central Zimbabwe Km
20 0’S N 20 0’S
15 0’S Lusaka 15 0’S 25 0’E 30 0’E Western Mozambique Botswana Legend: 0 50 100 200 300 400 Lake Western Zambezian grasslands Southern Km 20 0’S Central Zambezian Miombo woodlands 20 0’S Zambezian Baikiaea woodlands Itigi-Sumbu thicket Zambezian Cryptosepalum dry forests 25 0’E 30 0’E Namibja Southern Miombo woodlands Zambezian and Mopane woodlands Legend:Zimbabwe Southern Rift montane forest-grassland mosaic Zambezian ooded grasslands N Lake Western Zambezian grasslands Central Zambezian Miombo woodlands Zambezian Baikiaea woodlands Botswana Itigi-Sumbu thicket Zambezian Cryptosepalum dry forests 0Southern50 100 Miombo200 woodlands300 400 Zambezian and Mopane woodlands Km Southern Rift montane forest-grassland mosaic Zambezian ooded grasslands 20 0’S 20 0’S
25 0’E 30 0’E FigureLegend: 1. The ecoregions of Zambia. Lake Western Zambezian grasslands Central Zambezian Miombo woodlands Zambezian Baikiaea woodlands Itigi-Sumbu thicket Zambezian Cryptosepalum dry forests Miombo woodlandsSouthern Miombo are woodlands found on geologically Zambezian and basinMopane woodlandsin Zambia’s Western and North- stable rockSouthern formations Rift montane and forest-grassland on nutrient mosaic poor soilsZambezian oodedWestern grasslands provinces (Mulombwa 1998; Sekeli (Bond et al. 2009). Tree cover generally exceeds and Phiri 2002). This woodland covers 40% with rainfall normally above 600 mm per approximately 9% of the country’s land annum. If rainfall is above 1000 mm, tree cover area (Siampale 2008). Kalahari woodland may be greater than 60%, and canopy height is a secondary vegetation type formed can exceed 15 m; these woodlands are sometimes from the disturbance of either Baikiaea or described as wet miombo (Timberlake et al. 2010; Cryptosepalum forests due to fire or agriculture Kutsch et al. 2011). Wet miombo woodlands (Kindt et al. 2011). Kalahari woodland usually have a higher diversity of tree species is similar to miombo woodland in terms than dry miombo, and are generally found above of species composition, with Brachystegia, 1000 m on the Central African Plateau; whereas Isoberlinia and Julbernardia the dominant dry miombo is found at slightly lower elevations species (Chidumayo 2012a). Other common of 500–1200 m (Byers 2001). Chidumayo (1987) species present include those of the genera separated miombo woodlands in Zambia further Guibourtia, Burkea, Diplorhynchus and into five subtypes based on dominant woody Parinari (Sekeli and Phiri 2002). Common species and rainfall (Chidumayo 2012a). grass genera and species include Andropogon, Brachiaria, Digitaria, Elyonurus, Hyparrhenia, Hyperthelia dissoluta, Panicum maximum, 1.4.2 Kalahari woodland Pogonarthria squarrosa, Setaria and Tristachya nodiglumis (Evaristo and Kitalyi 2002). Kalahari or Baikiaea–Terminalia woodland is Kalahari woodland is the main source of found on Kalahari sands of the upper-Zambezi commercial timber for Zambia. 4 | Michael Day, Davison Gumbo, Kaala B. Moombe, Arief Wijaya and Terry Sunderland
1.4.3 Mopane woodland trees up to 18 m in height. The undergrowth layer is characterized by dense, tall grass; Mopane woodlands are distributed in a band common genera include, Brachiaria, Digitaria, stretching from southern to eastern Zambia (Kindt Hyparrhenia and Setaria (Evaristo and Kitalyi et al. 2011). The woodland covers approximately 2002). Munga woodlands are found a little 3.5% of the country’s land area (Siampale 2008). beyond the drier climatic limits of miombo Mopane woodland is important economically for woodland on soils unsuitable for mopane timber and edible caterpillars, as well as charcoal woodland (Kindt et al. 2011). and fuelwood.
Mopane woodland is dominated by 1.4.5 Dry evergreen forests Colophospermum mopane, which often grows in relatively mono-dominant stands with a limited Dry evergreen forests are part of the transition shrub layer (Timberlake 1999). This formation of forest types from Guineo-Congolian is typically single storied with an open deciduous rainforest to Zambian dry woodlands. Dry canopy approximately 6–18 m high, and has a evergreen forests cover less than 3–5% of the less developed grass layer compared to miombo country’s land area and are restricted to North- woodland (Makumba 2003). C. mopane has an Western and Western provinces in Zambia extensive but relatively shallow rootstock resulting (Siampale 2008; Chidumayo 2012a). The three in a relatively high root biomass and readily subtypes are distributed on Kalahari sands regenerates from rootstock following disturbance (Cryptosepalum), lake basin (Marquesia) and on (Smit and Rethman 1998). Where the mopane the plateau (Parinari) (Kindt et al. 2011). These is mixed with other associated woodland species forest types are three storied with a canopy up it may include Adansonia digitata, Combretum to 27 m high and a dense shrub layer of 1.5– imberbe, Terminalia sericea and at times Acacia 6.0 m high. An understory of 0.3–1.3 m high is species. Grass species include Chloris virgata, also sometimes found (Fanshawe 2010). Digitaria eriantha, Hyparrhenia species and Setaria species in wetter areas; and Andropogon species, Dominant species (dependent on the forest Heteropogon contortus and Urochloa in drier areas type) include Cryptosepalum exfoliatum, (Evaristo and Kitalyi 2002). Guibourtia coleosperma, Marquesia acuminata, Marquesia macroura, Parinari excelsa, Syzygium Mopane woodland is more prevalent on heavier guineense, and Anisophyllea pomifera (Kindt et clay and nutrient rich, alkaline soils compared to al. 2011). This woodland type is confined to miombo woodland (Mulombwa 1998; Timberlake the wetter northern parts of the region with et al. 2010). Rainfall in these woodlands is 400– a mean annual rainfall of 800–1400 mm. 700 mm per year (Timberlake et al. 2010) and The majority of this forest type occurs at an the upper altitude limit is approximately 1400 m elevation of 1000–1500 m. Disturbance of (Kindt et al. 2011). these woodland types can lead to variations of miombo woodland and Chipya forests (Kindt et al. 2011). 1.4.4 Munga or undifferentiated woodlands 1.4.6 Other forest types Munga or Acacia–Combretum woodland is a more open or park-like deciduous woodland. Other forest types (both evergreen and Often viewed as secondary woodland, the munga deciduous) with limited distributions across woodlands are found over a large part of central Zambia include moist evergreen forest (divided and southern Zambia, covering almost 4% of the into montane, swamp and riparian types); land area (Mulombwa 1998; Siampale 2008). The chipya woodland (derived from dry evergreen woodland lacks the main species of miombo and forest); and closed deciduous forests, which are mopane woodlands and is dominated by Acacia, divided into Baikiaea and itigi types (Mukosha Combretum and Terminalia species (Chidumayo and Siampale 2009; Vinya et al. 2011). 2012a). It is one to two storied with emergent Zambia Country Profile | 5
Termitaria woodland A classification system based upon a combination Termitaria woodland is a bushland or scrubland of global and national requirements has been vegetation type associated with termite mounds, proposed for ILUA II (Chidumayo 2012a). The which alter the edaphic conditions and therefore main difference to the ILUA I classification, as the vegetation composition. Termite mound shown in Table 1, is that miombo woodland is flora in Zambia generally has a higher species classified as deciduous forest, rather than semi- richness compared to the surrounding vegetation evergreen forest, as the majority of its dominant (Kindt et al. 2011). All the main vegetation species are deciduous. The main topographic or types (such as forest, woodland, thicket, scrub edaphic feature of each vegetation type is also and grassland) can be found within termitaria given (Chidumayo 2012a) (Appendix 1). woodland. These woodlands are found unevenly distributed throughout the country (except on sandy soils), including certain sandy plateau 1.6 Key drivers and processes soils, montane and swamp forests, and floodplain affecting forest carbon change grassland (Fanshawe 2010). Commonly occurring species include Diospyros mespiliformis, Asparagus 1.6.1 Deforestation and forest racemosus, Boscia angustifolia, Capparis tomentosa, degradation Sterculia quinqueloba and Maerua juncea (Sekeli and Phiri 2002). Forest cover loss and forest degradation are the main processes causing forest and carbon stock Plantations change in Zambia. Deforestation rates (largely Very few forest plantations occur in Zambia. of miombo woodland) are significant in the Approximately 61,000 ha of tropical pine and country (Stringer et al. 2012). The most recent Eucalyptus plantations have been established estimate of deforestation, provided by the ILUA, across the country with around 80% in gave a range of 250,000–300,000 ha per year, or Copperbelt Province (Mukosha and Siampale an annual decline of total forest area of 0.62% 2009). The main plantation species include Pinus (Mukosha and Siampale 2009). A UN-REDD kesiya, Pinus oocarpa, Eucalyptus grandis and report based on an analysis of seven studies Eucalyptus cloeziana (Sekeli and Phiri 2002). dating from 1969–2006, provides a deforestation estimate of 298,000 ha per year (Kamelarczyk 2009). FAO estimates have ranged from 1.5 Classification of Zambian 166,000 ha to 445,000 ha of forest loss per year woodlands (FAO 2005, 2010). Chidumayo (2012b) gives an annual deforestation rate of 157,300 ha for Historically various approaches have been used 1965–1996 and 826,544 ha for 1996–2005. Due to classifying Zambian vegetation, including soil, to the variability in these estimates, verification stocking rates and species composition (Trapnell of current deforestation rates may be required 1953; Lees 1962; Chidumayo 1987). The FAO (Kamelarczyk 2009). However, based on the most classifies the woodland types found in Zambia recent estimates made by UN-REDD and the according to global definitions. These definitions ILUA, a rate of approximately 300,000 ha per are used in the ILUA for Zambia as follows: annum seems a reasonable estimate. • Miombo woodlands are classified as semi- evergreen forests. • Baikiaea and itigi forests, munga, mopane 1.6.2 Emissions from deforestation and and Kalahari woodlands are classified as forest degradation deciduous forests. • Riparian, montane, swamp, Parinari and In Zambia land-use change and forest loss is the lake basin chipya forests are classified as the main contributor to the country’s GHG evergreen forests. emissions. Deforestation and forest degradation • Termitaria are classified as shrub thickets and in Zambia is estimated to contribute 3% to are included as part of other wooded land. the total GHG emissions from deforestation • There are also classifications for other forested worldwide. The UNFCCC INC estimated that
lands, which include bamboo and raffia palms. carbon dioxide (CO2) emissions from forestry 6 | Michael Day, Davison Gumbo, Kaala B. Moombe, Arief Wijaya and Terry Sunderland
Table 1. Main forest or woodland types in Zambia.
Vegetation type Food and Agriculture Organization of the Equivalent regional subtype United Nations Closed forests Dry evergreen forests Parinari, Marquesia, Cryptosepalum, (37,564.2 km2 or almost 5% of the land area) lake basin Dry deciduous forests Baikiaea, itigi (10,189.6 km2 or 1.4% of the land area) (part of dry deciduous open forests) Moist evergreen forests Riparian, montane, swamp (944.22 km2 or 0.12% of the land area) (part of dry evergreen forests total)
Open forests Semi-evergreen forests Miombo (woodlands) (355,413.8 km2 or 47.2% of the land area)
Dry deciduous forests Kalahari, mopane, munga (167,057 km2 or 22.2% of the land area)
Bushland/ scrubland Other wooded land Termitaria (14,739.9 km2 or almost 2% of the land area)
Source: Adapted from Sekeli and Phiri 2002; MTNER 2002.
and land-use change contribute almost 70% of of a high deforestation rate and biomass estimates Zambia’s total GHG emissions (MTNER 2002). from undisturbed woodland (Kutsch et al. 2011). At the district level forest degradation has been Both of these studies demonstrate the current estimated to cause woody biomass losses of 0.3– high uncertainties associated with estimating 4.0 t per hectare annually for miombo woodland standing biomass and carbon emissions from in central Zambia (Chidumayo 2013). deforestation and forest degradation in Zambia.
Kamelarczyk (2009) provides values for emissions While the main drivers of deforestation from deforestation and forest degradation using and forest degradation in Zambia are wood biomass estimates determined by a biomass extraction, agricultural expansion, infrastructure conversion and expansion factor (BCEF) and development and fires, a number of underlying allometric equations using ILUA data. Their causes also contribute to forest loss and analysis estimated an annual decrease in carbon degradation (Chundama 2009; Vinya et al. in above-ground biomass (AGB) from 12.8 Mt to 2011). Vinya et al. (2011) summarize the drivers 20.9 Mt. Estimates of the change in forest extent of deforestation and forest degradation in Zambia and biomass density indicated a decrease of 4.7– as follows (see Figure 2). 7.5 Mt of carbon. Part of the difference in these estimates could be caused by forest degradation, which may not be captured in the lower estimate. 1.6.3 Wood extraction Kutsch et al. (2011) estimated total annual emissions for Zambia based on the most recent Wood extraction from forests includes fuelwood deforestation estimates from the FAO (2005, removal, logging and charcoal production 2010) as well as Kamelarczyk (2009) combined (Gumbo et al. 2013). Although timber is with their own biomass estimates in miombo extracted from Zambian woodlands for woodland in Zambia, giving total emissions of construction and manufacture of wood products, 13.2–102.8 Mt of carbon per year. The high level charcoal production is the biggest single driver of estimates in this study were due to a combination wood extraction and the primary cause of forest Zambia Country Profile | 7
Forest cover loss
Agricultural Infrastructure Wood Fire – expansion- development – extraction - uncontrolled Main shifting settlements, charcoal and set fires, Drivers cultivation or urban woodfuel chitemene agricultural expansion production agriculture and extensification and industry and logging natural fires
Environmental – Policy and legal Institutional – Demography – Socioeconomic climate framework – lack of funding, population - poverty, low Underlying variability, inconsistencies transport and growth, employment, Drivers topography, and weak political will, immigration, insecure land soils legislation low staffing high rural tenure and levels population economic and staff morale growth
Figure 2: The main and underlying drivers of deforestation in Zambia and the relationships between the two (adapted from Vinya et al. 2011).
degradation (Clarke and Shackleton 2007; Vinya Zambezian dry forests contain almost 70 t et al. 2011). The ILUA estimated that 5.8 million per hectare of AGB suitable for fuelwood and tonnes of wood biomass was used to produce charcoal harvesting (Malimbwi et al. 2010). The charcoal in 2008 (Kalinda et al. 2008). Fuelwood degree of forest clearing for charcoal production and charcoal make up over 70% of the national can vary considerably depending on the site energy consumption (IDLO 2011). As only (Malimbwi et al. 2010). If charcoal demand is 20% of the population has access to electricity, low in an area, harvesting is more selective in charcoal is an important source of energy for both terms of species and size classes used; whereas, rural and urban populations in Zambia (Foster in times of high demand, clear-fell harvesting and Dominguez 2010). It is estimated that 98% of woodland is more common (Clarke and of low-income families (which make up 85% Shackleton 2007). Chidumayo has reported of the urban population) depend on charcoal removal of 50–97% of the woody biomass as their main energy source (Kalinda et al. from plots in miombo woodlands in Zambia 2008). Although difficult to quantify, due to the (Chidumayo 1991, 1993). Following harvesting, unlicensed nature of the majority of the industry, tree density can recover significantly within 30 it is estimated that charcoal production employs years (Chidumayo 1993); charcoal production some 500,000 people in Zambia and contributes may therefore not lead to forest clearance (just 2.2% of the country’s gross domestic product degradation). However, charcoal production does (GDP) (Kalinda et al. 2008). frequently lead to deforestation, as thinning for 8 | Michael Day, Davison Gumbo, Kaala B. Moombe, Arief Wijaya and Terry Sunderland
charcoal or fuelwood facilitates subsequent clearance provinces. Mining contributes over 70% of for agriculture (UN-REDD 2010a). foreign exchange earnings, forming a significant part of Zambia’s GDP (IDLO 2011; GRZ 2011). It is also a growing industry, with Zambia 1.6.4 Agricultural expansion experiencing a steady increase in mining output over the last decade (GRZ 2011). Mining can Agricultural expansion is the second highest driver cause deforestation during initial clearance, as well of forest loss in Zambia (Vinya et al. 2011). A as the need for large quantities of wood for tunnel growing population has led to increased pressure supports and increasing demand for charcoal to for agricultural land in order to meet national support the energy needs of miners (Chidumayo and subsistence food requirements. Agricultural 1989; Gumbo et al. 2013). For example, at the expansion is caused both by shifting subsistence Kalumbila mining concession the development of cultivation and extensification of commercial infrastructure is estimated to have resulted in the farming. Agricultural expansion may account for loss of more than 7000 ha of forest cover (Vinya up to 90% of forest cover loss, often for small- et al. 2011). scale farming systems using shifting cultivation practices (UN-REDD 2010a; Campbell et al. 2011). Overgrazing has less impact on forest cover than 1.6.6 Fires arable land use, but has also been a problem in some provinces (such as Southern, Western and North- Caesalpinoid woodlands are strongly influenced Western provinces and parts of Lusaka) and can by both natural and anthropogenic fires. Fires inhibit woodland regeneration (Vinya et al. 2011). in miombo woodland and dambo grassland in southern Africa contributed 12.3% to total global
Linked to agricultural expansion is migration, emissions of CO2 in 2000 (Sinha et al. 2004). which has increased land pressure in some areas; Over 50% of the land area in Zambia is affected such as the movement of commercial farmers from by fire, with approximately 25% of the total land Zimbabwe to Zambia due to political and economic cover burnt annually (Archibald et al. 2010). Fire instability in Zimbabwe (UN-REDD 2010a; Vinya incidences are spread throughout the country; et al. 2011). however, high frequencies are found in the northern parts of the country and within protected forests and game management areas (Sikaundi 1.6.5 Infrastructure development and 2012). The majority (almost 90%) of fires set in industry miombo woodland are anthropogenic and linked to a number of different human activities (Ribeiro Although a less important driver than wood et al. 2012). Fires are used to control vegetation, extraction and agriculture, the growing population to enable land clearance for agriculture, to create in Zambia has resulted in the expansion of urban potash for chitemene agriculture and to facilitate settlements and infrastructure at a rate of 3.2% hunting (Vinya et al. 2011). Fires can also per annum (Gumbo et al. 2013). Infrastructure escape and spread to larger areas during charcoal development results in deforestation where production, traditional rituals, burning of sugar development projects occur on areas of woodland plantations and the creation of fire breaks around and forest cover. The importance of this driver villages, particularly at the end of the dry season. is likely to increase as Zambia needs to develop its infrastructure in a number of areas, including Although fire predominantly impacts grasslands, housing, transport (particularly rural road Archibald et al. (2010) estimated that 22% of provision), energy, water, sanitation, irrigation and uncultivated savannah woodlands (which includes communication to enable development (Foster and all of the main dry woodland types in the country) Dominguez 2010). were burnt by fires in 2001–2008. This figure rose to 33% in protected savannah woodlands Mining (predominantly of copper and cobalt) (Archibald et al. 2010). A study conducted is a significant industrial activity in Zambia, by the Zambian Environmental Management particularly in Copperbelt and North-Western Agency in 2007–2011 using MODIS (moderate Zambia Country Profile | 9
resolution spectroradiometer) imagery indicated needs, construction, fodder, wild foods and that deciduous woodland with sparse tree cover medicines (Chundama 2009). experiences the most severe fires each year (Sikaundi 2012). However, a large proportion of fires in Additional issues include inadequate governance Zambia are small scale leading to a mosaic of burnt and inadequate funding for departments tasked area patches. High-resolution imagery may therefore with managing forestry and the environment, be required in order to make accurate estimates of which has led to low staffing levels and a lack of burnt areas (Sá et al. 2007). coordination among relevant departments (IDLO 2011; Vinya et al. 2011). Government policies Dry woodlands in Zambia are tolerant of fire and have also led to deforestation and degradation. regenerate vegetatively from stumps and rootstocks Of 40 government policies relevant to REDD+ (Chidumayo 2004). However, long-term fire reviewed by Chundama (2009), 21 policies were regimes, including annual, and sometimes biannual found to promote forest loss by legitimizing the burning, can result in the transition of woodland to loss of forest resources. grassland (Bond and Keeley 2005). Smaller trees, particularly those below 5 cm DBH (diameter at An insecure land tenure system has also breast height) have high mortality (up to 12%) contributed to increased exploitation of forest in intense fires in miombo woodland (Ryan and resources, leading to a prevalence of shifting Williams 2011). Chidumayo (2013) estimated that agriculture and a lack of sustainable management fire caused 25–77% of total biomass loss at five practices (Campbell et al. 2011; Vinya et al. permanent sample plots in miombo woodland in 2011). The majority of the land in Zambia is central Zambia. Fire can also inhibit regeneration open access, where land rights are unclear or and survival of young plants and therefore unenforced, which can lead to unsustainable woodland recovery from clearance or degradation resource use for immediate gains (IDLO 2011; (Timberlake et al. 2010; Vinya et al. 2011). Less Vinya et al. 2011). Wood biomass levels in intense fire regimes may stimulate tree growth regrowth miombo woodland in Zambia have been in miombo woodlands. A long-term experiment shown to be lower on land subject to customary within miombo woodland in Zimbabwe showed tenure than land without (Chidumayo 2002). plots burned on a 3- or 4-year cycle attained greater tree height than unburned plots (Furley et al. 2008). The timing of fires has a bearing on their impact. 1.6.8 Carbon sequestration in Zambian Fires at the start of the dry season (April–June) are forests less severe than later in the season, due to weather conditions and the green state of the grass sward. Forest carbon change can be positive due to Burning at the end of the dry season (August– sequestration in growing forests. Undisturbed November) results in hotter fires, as grass is dry and woodlands in Zambia may not be carbon sinks, therefore more combustible (Robertson 2005). possibly due to the fact that growth is limited by water and nutrient availablility as opposed to carbon dioxide (Kutsch et al. 2011). However, 1.6.7 Underlying drivers the majority of woodland in Zambia is not undisturbed. Over 65% of Zambian woodlands are Linked to these main drivers of deforestation secondary, with 32% of forests either moderately and forest degradation are the underlying high or heavily disturbed, and regrowth within these poverty levels, high population growth and large woodlands may lead to carbon storage (Kalinda et rural population of the country. Zambia is ranked al. 2008; Williams et al. 2008; Kutsch et al. 2011). 163 in world poverty by the World Bank Human Woodlands in sub-Saharan Africa regenerate Development Index (HDI) (World Bank 2012). readily following wood harvesting and clearance for The population growth rate for 2012 is estimated agriculture, as many common tree species re-sprout at more than 3%, giving Zambia the 11th highest from the roots and stumps following disturbance population growth rate in the world (CIA 2012). (Luoga et al. 2004; Chidumayo 2011). The high It is estimated that almost 70% of the rural level of disturbance and regenerating ability of population depends directly on forests for energy miombo woodlands indicates a potential carbon 10 | Michael Day, Davison Gumbo, Kaala B. Moombe, Arief Wijaya and Terry Sunderland
sink in regrowth woodlands in Zambia (Kalinda et being conducted by the Forestry Department with al. 2008). In the majority of dry woodlands biomass technical support from the FAO. Phase I of the accumulation with increasing age is associated with assessment was conducted in 2005–2008. This regrowth (Timberlake et al. 2010). phase involved the collection of field data from 221 sample plots across the country (covering Fires, both natural and set for management 433.1 ha), as well as socioeconomic data from purposes, may also influence sequestration structured interviews (Kalinda et al. 2008; through lowering productivity (Murwira 2009). Mukosha and Siampale 2009). ILUA I has been Fire reduction may therefore increase carbon criticized for its highly systematic sampling design, sequestration in miombo woodlands (Frost 1996; which does not include some woodland types with Chidumayo 2013). low overall national coverage, such as evergreen forests (Chidumayo 2012a).
1.7 National strategy and policy ILUA Phase II was launched in November development targets for REDD+ 2010, with fieldwork beginning in March 2013. ILUA II has been designed to collect adequate Currently Zambia has no national action plan or information to meet local, national, regional and national strategy for REDD+; however, REDD+ international reporting requirements for MRV preparedness is taking place under the coordination of REDD+. It is also intended to strengthening of the Forestry Department (UN-REDD 2010a). the capacity of the Forestry Department to be The Government of Zambia’s involvement in able to carry out future monitoring of forest REDD+ is outlined in the National Climate resources (UN-REDD 2010a, 2012). The Change Response Strategy (NCCRS) (MTNER field component of ILUA II is scheduled to be 2010). Formed as part of the NCCRS, the Climate completed in 2014/15. ILUA II comprises over Change Facilitation Unit will coordinate climate 4000 sampling sites and will measure all major change activities, formulate policies and establish carbon pools (AGB, soil, deadwood and litter) an implementation framework between the identified by the Intergovernmental Panel on MLNREP and other ministries. A draft National Climate Change (IPCC), apart from below- Forestry Policy (2010) is also aligned with REDD+ ground biomass (BGB) which is problematic to activities, for example by stating the need for measure in Zambian woodlands (due to deep root forest carbon sequestration measurement standards systems), necessitating estimation using expansion (IDLO 2011). factors. ILUA II has been designed to assess carbon stocks by tree species in order to provide baseline reference levels for emissions (IDLO 2011). 1.8 Reference levels and monitoring, reporting and verification In addition to the ILUA field inventories, land cover change from deforestation and forest In order to develop an MRV system for Zambia degradation has been estimated using Landsat a National Forest Assessment and Monitoring data in order to develop a national reference System is currently being established. The focus emission level. The analysis has been conducted is on a distributed system, in line with the in collaboration with the Regional Centre for government’s decentralization policy, with capacity Mapping of Resources for Development in built in a number of different provinces in the Nairobi. Land cover change has been estimated country (IDLO 2011). Ten provincial forest from land cover data from 1990, 2000 and 2010 monitoring laboratories have been established, (Kasaro and Fox 2012). Results of this national equipped for forest monitoring and staffed remote sensing survey are yet to be made available. with technicians from forestry, agriculture and The national remote sensing survey should fulfill planning sectors in order to provide decentralized the requirements of the IPCC guidelines to monitoring expertise (Kasaro and Fox 2012). establish a historical reference scenario for forest cover changes, in order to assess forest cover A major component of MRV in Zambia is the change in the future. development of the ILUA. This assessment is Zambia Country Profile | 11
1.9 Issues and challenges implementation. This is potentially a significant barrier for REDD+ readiness in Zambia (UN- A number of problems need to be overcome to REDD 2010a). ensure an adequate MRV system for REDD+ in Zambia. These problems can be broadly grouped ILUA II should enable capacity building into institutional capacity, state of knowledge and during both the fieldwork and subsequent data technical challenges. analysis, through collaboration with the Forestry Department. For example, extensive training has been conducted in order for field surveyors to 1.9.1 Institutional capacity follow the ILUA survey methodology. Training and financial support has also been provided Institutional and human resource capacity to to the Kitwe Research Centre in Copperbelt monitor natural resources such as forest cover Province, to facilitate the processing and analysis and carbon stocks is low in Zambia (Campbell of soil and litter samples and identify herbarium et al. 2011). In common with a number of samples from the field teams. The ILUA should African countries, Romijn et al. (2012) assessed also improve data sharing and management the capacity gap for implementing a national for MRV in Zambia. Data collection and forest strategy in Zambia to be significant. management will be achieved using Open Foris, The draft National Forestry Policy for Zambia an open source software being developed by the acknowledges the decline in resources for forestry FAO and United States Forest Service (USFS). research in Zambia since the 1970s and the need to develop research expertise, facilities and an institutional framework to meet forestry research 1.9.2 State of knowledge needs (MTNER 2009). The capacity to monitor and report on forest cover and emissions is A major constraint to MRV in Zambia is currently limited to a small number of individuals the current state of knowledge with regards in the Survey Department (MLNREP), National to deforestation and carbon cycling within Remote Sensing Centre, Forestry Department, the key woodland ecosystems of the country. Copperbelt University and the University of It is a challenge to provide information to Zambia. In addition, access is limited to relevant enable monitoring of all five REDD+ activities technology, such as high-resolution spatial (deforestation, forest degradation, conservation, imagery. This is particularly problematic for sustainable management of forests and REDD+ as a nationwide initiative (UN-REDD enhancement of forest carbon stocks) of sufficient 2010a). Capacity building for REDD+ is one of accuracy to meet IPCC guidelines (Romijn et the key activities outlined in the NJP for Zambia al. 2012). The number of national data sets that (UN-REDD 2010a). can estimate forest cover change in Zambia is limited. Prior to ILUA I (2005–2008) the last Linked to this issue is the lack of data national forest inventory was taken in 1952–1967 coordination between departments and (Vinya et al. 2011). The impact of degradation institutions that will participate in REDD+ from charcoal production (the primary cause of in Zambia. No formal data storing or sharing forest loss and degradation in Zambia) is also protocol exists between these institutes, meaning problematic to monitor (Kutsch et al. 2011; data relevant to MRV is difficult to access (UN- Gumbo et al. 2013). REDD 2010a; Campbell et al. 2011). A report by UN-REDD identified the need for a specific In general, suitable methods for carbon stock monitoring unit for REDD+ in Zambia in order estimation are not well developed for African to tackle these issues (UN-REDD 2010a). countries (Kamelarzyck 2009). The carbon cycle in the miombo woodlands of Africa is also poorly In addition, Zambia has no specific legal and understood (Williams et al. 2008; Bond et al. policy framework for tackling climate change, 2009). Information is even more scarce regarding aside from a climate change response strategy. the carbon cycle for the other major woodland A framework for institutionalizing REDD+ types in Zambia (Kalahari, mopane and munga is needed at various levels for planning and woodlands), which form a significant proportion 12 | Michael Day, Davison Gumbo, Kaala B. Moombe, Arief Wijaya and Terry Sunderland
(approximately 20%) of woodland cover in from forest degradation (as well as fire and the country (Grace et al. 2006; Mukosha and herbivory), coupled with the variable nature of Siampale 2009). Understanding of the carbon Zambian woodlands, can result in a complex mix cycle is particularly limited in the soil, litter and of different vegetation types (Ribeiro et al. 2012). deadwood carbon pool for Zambian woodlands Mapping degradation, particularly from charcoal (Chidumayo 2011; Stringer et al. 2012). In production, is therefore much more complex miombo woodlands 50–80% of total system in Zambia than in continuous tropical forest carbon is estimated to be in the top 1.5 m below landscapes such as those found in central Africa. ground (Walker and Desanker 2004; Ryan et al. 2010). The impact of fire is another key ecological Remote sensing is also challenging in Zambia due process crucial to the understanding of carbon to high cloud coverage and the open canopy and stocks, about which current knowledge is limited seasonality of the woodlands. The open canopy (Ryan and Williams 2011). and pronounced grass layer makes distinguishing woodland from other savannah types, such as The ILUA I dataset is a starting point to address wooded grassland or scrubland, difficult. This is the inadequacies in forest inventory data for particularly true for semi-evergreen and deciduous Zambia. However, ILUA I was designed for global woodland types during the dry season when trees forest inventorying and is therefore not specific to are not in leaf. The average annual cloud cover forest ecosystems and structures found in Zambia for the country is more than 40% with high (Vinya et al. 2011). Developing a comprehensive variability; the lowest cloud cover percentages are forest database is therefore a major challenge observed in November and December (Herold (UN-REDD 2010a; Romijn et al. 2012). The 2009). However, the optimum month to capture development of ILUA II will be a key step in remote sensing imagery for forest cover changes establishing adequate MRV capacity in Zambia. in Zambia is at the start of the dry season (May– June), when trees are still in leaf but grasslands are dying back and cloud cover is relatively low. 1.9.3 Technical challenges Despite the major institutional challenges facing Linked to both current institutional capacity MRV of REDD+ in Zambia there are considerable and state of knowledge issues is the challenge of opportunities to develop an effective MRV system assessing carbon stock changes for REDD+ in for the country. The current gaps in capacity, state Zambia. Zambia is a large country (approximately of knowledge and technical challenges mean that 750,000 km2) with remote and inaccessible areas. initiatives can be specifically designed to achieve The forest cover of the country is considerable but MRV objectives. For example, ILUA II has with a highly scattered and variable distribution been designed with measuring and reporting for due to the ecology of the forests and the long REDD+ as a key objective. history and nature of human use. Disturbance 2 Available data and current capacities
2.1 National datasets available for 2.2.2 United Nations Framework monitoring, reporting and verification Convention on Climate Change National of REDD+ in Zambia Communication
A number of datasets are available for MRV The inventories for GHG emissions given in the of REDD+ in Zambia, generated from field INC were compiled by the Centre for Energy, inventories as well as remote sensing and aerial Environment and Engineering Zambia. Data for photography. They include ILUA I and II, aerial the inventories were derived from the following photographs, the USFS MRV report and satellite sources: imagery from Landsat, RapidEye, MODIS, • Energy Balance for 1994 WorldView 1 and 2, GeoEye and Systeme Pour • Energy Statistics Bulletin 1974–1996 l’Observation de la Terre (SPOT). The data type, • Agriculture Statistics Bulletin 1980–1996 dates, cost, availability, potential uses and other • Central Statistics Office characteristics are presented in Table 2. • research conducted at the University of Zambia.
The lack of reliable data on forests was identified as 2.2 Data sources for the Global a weakness in the INC. The MLNREP is currently Forest Resources Assessment in the process of preparing its Second National 2010 report and the Initial National Communication, which intends to address this Communication weakness by using remote sensing.
2.2.1 Global Forest Resources Assessment 2.3 Institutional framework and 2010 report capacity for forest and agricultural monitoring The bulk of the Global Forest Resources Assessment (FRA) 2010 report, particularly for The MLNREP has overall responsibility for current trends, uses data from ILUA I. The FAO forest resources in Zambia, with monitoring of partially funded the ILUA and provided technical forest resources being undertaken by the Forestry assistance. Specific sources for different aspects of Department. The Forestry Department has a the report that are relevant to MRV for REDD+ presence in all nine provinces and in every district are provided in Table 3 (FAO 2010). (IDLO 2011). The management of national parks in Zambia, which contain considerable forest The data sources selected for the FRA 2010 report resources, is undertaken by the Zambian Wildlife indicate the relative paucity of data relevant to Authority. The REDD+ Coordination Unit will monitoring and measuring forest resources in be based in the Forestry Department, and MRV Zambia, and the importance of ILUA I as an of REDD+ will be carried out by the Forestry initial assessment. The information regarding forest Department with technical assistance from the cover, growing stock and biomass, and therefore FAO (UN-REDD 2010a). carbon stocks, is all based on ILUA I. No data was available for forest fires and other disturbances Institutional capacity for forest monitoring for affecting forest health and vitality. Data was also REDD+ is low in Zambia. In particular the not available regarding public expenditure on Forestry Department has suffered from a decline forests (FAO 2010). in funding over the last 2 decades, which has 14 | Michael Day, Davison Gumbo, Kaala B. Moombe, Arief Wijaya and Terry Sunderland References Kalinda 2008; et al. Mukosha and Siampale 2008 2010a MTNER USFS 2012 Kalinda 2008; et al. Mukosha and Siampale 2008 Landsat: www.landsat.org GloVis: http://glovis.usgs.gov/ Potential drawbacks Potential global forest for Designed and therefore inventorying for ecosystems not specific in and structures found Zambia (Vinya 2011) et al. Accessibility: capacity for sharing data is currently government between low departments in Zambia High time-cost print, to and scan, geo-reference to mosaic images in order cover forest determine imagery resolution Lower Rapid to Eye, compared and GeoEye. WorldView make estimates May of carbon losses from or degradation forest of open deforestation problematic woodlands Potential uses Potential in major estimation stock Carbon types using DBH following forest on Panel Intergovernmental guidelines, Change (IPCC) Climate tiers 1 and 2 to equivalent 4 emissions for level Reference tiers 2/3 following carbon pools for IPPC guidelines; below-ground 1 Tier to biomass (BGB) equivalent cover of forest Historical records change cover and forest land-use for levels, Reference means it is Availability change. particularly useful on a national Sensing Remote National scale. Survey using Landsat is currently level imagery reference develop to cover forest for Cost (USD) and (USD) Cost availability Free should be data ILUA a through accessible however, database; this has not yet been established Free I data with ILUA As should be accessible Open Foris through software (under by development and the the FAO Forest States United Service) High and cost based on approval the Surveyfrom Department Free (US Geological Survey Global Viewer Visualization - (GloVis)) Dates 2005–2008 2010–ongoing (projected late completion 2014/early 2015) 1956–1996 (the last aerial photography across campaign all parts of the country) for available Data land-use maps 1990, 1995, from 2000, 2005 and 2010 Data typeData ILUA I is based on the standard I is based on the standard ILUA (NFA) Assessment Forest National the Food by developed approach of the Organization and Agriculture inventory Forest (FAO). Nations United 221 permanent is collected from data systematically sample plots distributed includes This the country. throughout height) breast at DBH (diameter within plots. of all trees measurement also collected was Socioeconomic data and livelihoods. on agriculture data provide II is to aim of ILUA The enable to estimation carbon stock for reporting and verification monitoring, II (MRV) REDD+ in Zambia. ILUA for of the ILUA an intensification features 4000 over I survey cover to design the country. sampling plots across II will include the development ILUA the carbon at estimate of methods to standards to IPCC according field level of emissions and the calculation Zambia. factors for of aerial archive extensive An the Survey is held at photographs Department within the Ministry and Resources Natural of Lands, (MLNREP). Protection Environmental 1967k, for 1:30,000-scale example For all of for available are photographs last nationwide The Province. Eastern was of aerial photography capture conducted in 1983. medium-resolution offers Landsat the It imagery. provides satellite with the longest running imagery, 7) (Landsat satellite most recent launched in 1999. Resolution is of resolution 15–60 m, with temporal be 8 is scheduled to Landsat 16 days. 2013. launched in February Dataset Field inventories Field Land Use Integrated I Phase Assessment I) (ILUA Land Use Integrated II Phase Assessment II) (ILUA sensing and aerial photography Remote photographs Aerial Landsat Table 2. National datasets available for monitoring, reporting and verification of REDD+ in Zambia. reporting and verification monitoring, for available datasets 2. National Table Zambia Country Profile | 15 References Sikaundi 2012; Murwira 2009; Rahman et 2004; al. http://modis.gsfc.nasa. gov/related/ www.rapideye.com http://www.datadesign. co.za www.geoeye.com http://www.datadesign. co.za 2009 Herold http://smsc.cnes.fr/SPOT/ www.digitalglobe.com http://www.datadesign. co.za Potential drawbacks Potential not resolution, Low assess forest suitable to change on a national cover degradation scale due to and deforestation 5 m resolution may not be may 5 m resolution all to determine sufficient degradation of forest forms accurately for coverage Limited 2010 Zambia prior to expensive, Potentially coverage and cloud-free available is not always 70% (approximately mean annual cloud-free Zambia from for coverage 2006–2008) Limited coverage for for coverage Limited 2010 Zambia prior to Used by Zambian Environmental Zambian Environmental by Used Management Agencymeasure to country. the across incidences fire with be used combined Can and Atmospheric Oceanic National Advanced (NOAA) Administration HighVery Resolution Radiometer net estimate to data (AVHRR) primary productivity as an indicator potential of carbon sequestration Potential uses Potential Assessing forest cover changes due cover forest Assessing deforestation to Assessing forest cover changes due cover forest Assessing deforestation. to regional/ for available Tasking be useful May studies. project level studies from of results validation for using Landsat changes due cover forest Assessing deforestation. to regional/ for available Tasking be useful May studies. project level studies from of results validation for using Landsat Assessing forest cover changes due cover forest Assessing deforestation. to regional/ for available Tasking be useful May studies. project level studies from of results validation for using Landsat 2 2 ; tasking = 2
2 Free. Free. for Good coverage Zambia Cost (USD) and (USD) Cost availability Astrium-geo.com around From USD 1500 per scene (60 x 60 km). Price varies considerably depending on and image satellite years Geo Data Design Geo Design Data Ltd. and tasking Archive = USD 25–35 per km Geo Data Design Geo Design Data Ltd. for Good coverage Zambia and tasking Archive = USD 1.28 per km Geo Data Design Geo Design Data Ltd. availability Limited Zambia for = USD 17.50 Archive per km USD 30 per km From 1999 From Dates 6 years of 6 years cloud-free imagery SPOT >80% covering of the country for is available 1990–2005 Worldview 2 Worldview 2010 from From 2010 From From 2010 From per day). per day). 2 per day. 2 MODIS offers low-resolution imagery low-resolution MODIS offers in 36 spectral (250–1000 m) captured Itbands. launched in 1999 and was images the earth It every is 1–2 days. measurements providing suitable for large-scalefor changes. rapid high-resolution offers RapidEye imagerysatellite (5 m) with a large data and capacity acquire to archive (4 million km areas large for Data typeData WorldView offers high-resolution high-resolution offers WorldView 1 WorldView imagerysatellite (1 m). 2 WorldView launched in 2007 and was in 2009. It collect is able to images for than 1 million kmmore satellite high-resolution offers SPOT 1 launched imagery (1.5–20 m). SPOT 7 should 6 and SPOT in 1986. SPOT imagery produce to until continue 2024. GeoEye 1 is currently the most 1 is currently GeoEye imagery satellite commercial advanced with positional 0.5 m resolution, at accuracy It CE90 and RMSE. was launched in 2008. Moderate resolution resolution Moderate spectroradiometer (MODIS) RapidEye Dataset WorldView 1 and 2 WorldView Pour Systeme de la l’Observation (SPOT) Terre GeoEye 1 GeoEye 16 | Michael Day, Davison Gumbo, Kaala B. Moombe, Arief Wijaya and Terry Sunderland
Table 3. Data sources for the Global Forest Resources Assessment 2010 report.
Variable Data source Notes Classification of land Chakanga and de Backer 1986 These forest types were merged into cover types standard forest type definitions of the Food and Agriculture Organization of the United Nations (FAO) Land cover Integrated Land Use Assessment (ILUA) Uses standard FAO forest type definitions 2005–2008 for Zambian woodlands. Reference year Kalinda et al. 2008; Mukosha and is 2007 Siampale 2009 Plantation forests cover Ministry of Tourism, Natural Resources ILUA I field assessment did not cover and expansion and Environment (MTNER) – Zambian any of the relatively small number of Forestry Action Plan (1998) and the plantation forests in the country Plantation Expansion Programme (2000) Growing stock ILUA 2005–2008 Gives estimates for both commercial Kalinda et al. 2008; Mukoshi and and overall growing stock, including all Siampale 2009 species Biomass – above- ILUA 2005–2008 Calculated using Intergovernmental ground biomass (AGB) Kalinda et al. 2008; Mukosha and Panel on Climate Change (IPCC) default and below-ground Siampale 2009 biomass conversion and expansion biomass (BGB) factors. Root to shoot ratio of 0.24 for calculating BGB from AGB
particularly impacted regional (provincial and 2.4 Available data and approaches district) capacity and the ability of the Forestry to support spatial planning and Department to carry out functions such as forest land-use management patrols and forest monitoring at a district level (IDLO 2011). Capacity is also low, in terms of ILUA I and II will contribute data to support technical expertise, for the use of remote sensing spatial planning and land-use management for forest monitoring. There is also lack of in Zambia. For example ILUA I provides technical equipment, such as computer software socioeconomic data primarily from structured and survey vehicles, as well as problems with interviews. This information can be used to low bandwidth and therefore Internet speeds design policies for sustainable management in Zambia (Herold 2009). REDD+ readiness of natural resources (Mukosha and Siampale activities as outlined in the NJP have a strong 2009). Data collected from sample households focus on capacity building for forest monitoring, includes: particularly with regard to developing the • income from and employment in forestry capabilities for completing ILUA I and II. and agriculture • population levels and degree of The Ministry of Agriculture and Livestock is encroachment into forests by communities responsible for governance of agriculture in living around forest areas Zambia. The Zambia Agriculture Research • the types of forest products and resources Institute (ZARI) is the largest agriculture used by communities; including timber research group in the country, with 10 research species and level of harvesting of important stations throughout the country. ZARI conducts timber species research and provides technical advice related to • the main agriculture crops and livestock agriculture in Zambia, particularly with regards production activities to improving productivity, such as through • land ownership and access to land. increased yields. Zambia Country Profile | 17
ILUA I had a low intensity of sampling points, from ALOS PALSAR)1 to predict AGB in four limiting its use for district-level land-use planning; African landscapes, including woodland ecosystems however, ILUA II will provide more district- and in Mozambique. However, they suggest field plots provincial-level data (MTNER 2010a). remain essential for validation and for estimating a correction factor for AGB in grasses and stems with a DBH below 10 cm (Mitchard et al. 2009). Ryan 2.5 Evolving technologies et al. (2011) used radar imagery (25 m resolution L-band radar imagery) in central Mozambique The use of remote sensing is an effective way of to produce maps capable of detecting changes in estimating base-level forest cover and deforestation carbon stocks of as little as 12 mg of carbon per rates on a national or regional scale for REDD+ hectare. Loss of carbon from degradation had a (Mitchard et al. 2009). The ILUA used Landsat greater level of uncertainty, which is significant imagery to assess land cover change, and a in a Zambian context due to the high level of national reference level for forest cover is currently degradation from charcoal production. However, being completed for Zambia using Landsat data the advantages of this method are that cloud (Mukosha and Siampale 2009). However, there are and atmospheric effects are largely irrelevant and a number of technical limitations regarding the use backscatter (or L-band normalized radar cross- of remote sensing for estimating biomass, including section) has been shown to have a reasonably direct the degree to which remote sensing technologies can relationship to woody biomass (Ryan et al. 2011). determine carbon stocks and carbon stock changes at a sufficient resolution for the needs of REDD+ Radar and LiDAR can also be used in conjunction (Le Toan et al. 2011). The open structure of the to provide complementary information regarding major woodland types and the scattered nature of forest structure (Schugart et al. 2010). A forest loss due to degradation and deforestation combination of radar and LiDAR was used by means higher resolution imagery may be required Mitchard et al. (2012) to map tropical forest for accurate estimates of forest cover change in biomass in Lopé National Park, Gabon. However, Zambia (Romijn et al. 2012). However, many high- cost and availability of LiDAR data may be resolution imagery datasets have limited coverage prohibitive for MRV of REDD+ in Zambia. within the miombo ecoregion (see Table 2) (Ribeiro Acquisition of LiDAR via aircraft is currently et al. 2012). Spectral Mixture Analysis (SMA) is a expensive and satellite LiDAR data is not available technique that has the potential to overcome the until ICESat 2 is launched in 2015 (Mitchard et spatial and temporal heterogeneity of miombo al. 2012). The ILUA intends to gather LiDAR data woodland types in order to map vegetation cover. through tasking of flights in selected test areas, in Although current SMA techniques have not been order to assess biomass and carbon for different applied to miombo, it has been used in ecosystems forest types (MTNER 2010a). with structural similarities to miombo woodland (Ribeiro et al. 2012). Another recent technology, which has potential applications for MRV in Zambia, is the use of Additional technologies with potential applications unmanned aerial vehicles (UAVs) or drones. for MRV in relation to REDD+ include radar, Small, unmanned aircraft are now available at Synthetic Aperture Radar (SAR), and LiDAR relatively cost-effective prices and are capable of (light detection and ranging) sensors (GOFC/ covering large areas of forests in a single flight. GOLD 2010). LiDAR and radar remote sensing Applications include monitoring of forest cover can measure stand structural variables (such as changes using aerial photography, particularly at height, volume and basal area) as well as biomass project-level scales. UAVs have several advantages, and changes in these variables due to disturbance such as the ability to survey on demand, ease of (Schugart et al. 2010). Radar is less sensitive to repeatable monitoring and the ability to generate weather conditions compared to optical sensors, high-resolution imagery. There is also potential making wet season measurements within Zambian for UAVs to be used in conjunction with LiDAR woodlands possible (Ribeiro et al. 2012). Mitchard devices (Wallace et al. 2012). et al. (2009) used L-band radar backscatter (derived 1 Advanced Land Observation Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) 3 State of knowledge
3.1 Growth rates and standing 1993; Frost 1996). Williams et al. (2008) biomass found that wood carbon stocks could recover within 20–30 years on abandoned farmland in 3.1.1 Growth rates Mozambique. The Forestry Department of Zambia (as well as those in Botswana and Zimbabwe) use a Growth rates both in mature and regrowth commercial cutting cycle of around 40 years (Sitoe miombo woodlands are relatively low. Net et al. 2010). For charcoal production in miombo primary production in miombo woodlands is woodland in Tanzania, Malimbwi et al. (2005) 900–1600 gm-2 per year and the annual increment estimated that miombo forests could be harvested of woody biomass is approximately 3–4% in every 8 years and maintain approximately 70% of mature stands (Frost 1996). Vinya et al. (2011) the standing biomass at this rate. cites annual growth rates in ring width of 4.4– 5.6 mm for regrowth and 2.3–4.8 mm for mature woodland in southern Africa. The average wood 3.1.2 Standing biomass annual increment for all Zambian forests is 1.3– 2.7 t per hectare per year (Siampale 2008). Table A high degree of uncertainty is associated with 4 provides a range of growth rate estimates, from biomass (and therefore carbon stocks) in savannah 0.7 t to 1.4 t of carbon per hectare annually, for landscapes (Grace et al. 2006). Standing biomass miombo woodlands in Zambia and neighboring in Zambia is highly variable and dependent on a countries. Growth rates for a number of common number of factors including stand age, soil type, species found in Zambian woodlands are given in rainfall, herbivory and fire (Frost 1996; Kutsch Appendix 1. Growth rates can vary depending on et al. 2011). Rainfall has a strong influence on the age since regrowth in disturbed plots, as well biomass in miombo woodlands; for old-growth as ecological conditions such as fire and rainfall. or lightly used miombo woodland approximately Rainfall is particularly important for productivity 55 t of dry biomass per hectare is found in dry in miombo woodlands (Ribeiro et al. 2012). miombo compared to around 90 t per hectare in wet miombo (Frost 1996; Clarke and Shackleton The average growing stock for Zambia is 2007). Table 5 provides a range of biomass estimated at 39.1 m3 per hectare over all land- estimates, mainly for miombo woodland types use classes and forest types (UN-REDD 2010a). in Zambia and neighboring countries. Biomass The national growing stock for Zambia (based estimates for evergreen forests and the deciduous on gross volume of all living trees with a DBH woodlands are limited. The ILUA and the Zambia greater than 7 cm), is estimated at 2.8 billion Country Study carried out by the Centre for cubic meters, of which 2.1 billion cubic meters Energy, Environment and Engineering Zambia is from semi-evergreen miombo dominated (CEEZ) for the INC, provide some estimates for woodlands (Kalinda et al. 2008). evergreen forests and deciduous woodland types. Evergreen woodland has a higher biomass level Carbon accumulation in Zambian miombo than miombo, whereas open deciduous woodlands woodlands is greatest in the first 50 or 60 years have a slightly lower level (CEEZ 1999; Mukosha after regrowth, with a limited increase in carbon and Siampale 2009). stocks after that (Chidumayo 2011). The most significant biomass recovery occurs within 30 years, Kamelarczyk (2009) produced AGB estimates with woody biomass production peaking within across all forest types in Zambia using ILUA data 18–20 years in regrowth woodlands (Chidumayo equivalent to tiers 2 or 3 of the IPCC guidelines. Zambia Country Profile | 19
Table 4. Growth rates for woodlands in the miombo ecoregion.
Conditions and factors of growth rate Growth rates (t C/ha/pa, or m3/ha/pa)* or Reference estimate annual ring growth in mm, or basal area increment in m2 Biomass Biomass regrowth of young miombo 0.98 t/ha/pa (2.47 fresh weight) and annual Stromgaard woodland cleared 16 years previously basal area increment of 0.5 m2/ha/pa 1985 Growth rate from yields for coppiced 2.49 t/ha (biomass) Chidumayo 1991 plots aged 6–29 years from Kabwe Rural, Lusaka Rural and Mumbwa districts in Zambia Above-ground biomass and below- 2–3 t/ha (biomass) Chidumayo 1993 ground biomass at eight old-growth miombo woodland sites in central Zambia and woody biomass regrowth at miombo woodland sites harvested for charcoal production Two sites in miombo woodlands of Mean above-ground carbon density of the Munishi et al. the Southern Highlands of Tanzania: miombo ecosystem was 19.2 t/ha-1 2010 Longisonte forest reserve in Vwawa Division and Zelezeta Village forest reserve in Igamba division of Mbozi District Assessment of the impacts of elevation, Above-ground carbon content increased with Mwakisunga tree species and management on carbon altitude, ranging from 9.2 t/ha at 2031 m to and Majule 2012 stock on the slopes of Rungwe Mountain 561.7 t/ha at 2312 m in Tanzania. Twenty 15 m-radius plots with trees of DBH >10 cm were used to Soil carbon content tended to increase down collect tree measurements as well as soil the slope, ranging from 3.8 t/ha at 2312 m to samples at depths of 10, 20 and 30 cm 4.7 t/ha at 2031 m Growth rates Wood biomass growth rate as mean 1.97 t/ha/pa (SD = 1.68). Ranged from 0.41 t/ Chidumayo 1990 annual increment of wood biomass in ha for plots aged 3–6 years to 2.91 t/ha for plots experimental coppiced woodland aged aged 7–29 years. 3–29 years and plots aged 48–49 years in 1.68 t/ha (SD = 0.39) for 48–49 year plots. Early the Copperbelt region of Zambia burnt plots had a mean annual increment (MAI) of 0.60 t/ha compared to 0.22 t/ha for late burnt plots Growth rate in dry miombo woodland in 0.9 t C/ha/pa Chidumayo 1997 Zambia over 35 years Average for dry miombo in Zambia and 0.75 t C/ha/pa Frost 1996 Zimbabwe Stand growth rate for miombo woodland 2.3 m3/ha/pa Malimbwi et al. at Kitulangalo near Morogoro, Tanzania 2005 Regrowth for 14 plots in miombo 0.7 (SD = 0.19) t C/ha/pa Williams et al. woodland in the buffer zone of Regrowth rates ranged from 0.43 t C/ha/pa to 2008 Gorongosa National Park, Central 0.87 t C/ha/pa Nhambita, Mozambique
* 1 m3 = 0.35 t 20 | Michael Day, Davison Gumbo, Kaala B. Moombe, Arief Wijaya and Terry Sunderland
Table 5. Biomass and carbon estimates in miombo woodlands in Zambia and neighboring countries.
Biomass/carbon estimate type Biomass (t/ha) Carbon Reference equivalent (t/ ha) Estimates for Zambian woodland from recent studies Four different above-ground biomass 32–52 15–24 Kamelarczyk 2009 (AGB) estimates using Integrated Land Use Assessment (ILUA) data; two biomass conversion and expansion factors (BCEFs) and two allometric equations Estimates using the Intergovernmental All forests: 83.8 (SE = 8.2) 39.39 Mukosha and Panel on Climate Change (IPCC) Evergreen: 108.2 50.85 Siampale 2009 methodological framework (2006) Semi-evergreen: 93.1 43.76 using BCEFs with growing-stock Deciduous: 61.2 28.76 levels to calculate AGB, below-ground Other natural forests: 67.2 31.59 biomass (BGB) and deadwood (AGB estimates given) AGB for disturbed and undisturbed Undisturbed: 79.37 37.30 Kalinda et al. 2008 forests using the IPCC methodological Disturbed: 37.06 17.42 framework (2006) using BCEFs with growing-stock levels to calculate AGB, BGB and deadwood (AGB estimates given) Median AGB for 3 plots in miombo Forest reserve: 150.56 70.76 Kutsch et al. 2011 woodland in the Kataba Forest Disturbed forest: 24 11.28 Reserve and one plot in disturbed forest (following logging for charcoal production) in Western Province, Zambia Recent estimates for miombo woodland in countries adjacent to Zambia Woody tree and soil carbon estimates Woody biomass: stems 45.12; 21.2 (SD = 1.4) Ryan et al. 2010 for miombo woodland in the Nhambita roots 18.09 8.5 (SD = 0.5) area, Gorongosa District, Mozambique Soil carbon: not applicable 76.2 (SD = 9.9)
AGB in miombo woodland in the Carbon: 13–30 (mean 23.4) 6.11–14.1 Shirima et al. 2011 Eastern Arc Mountains of Tanzania (mean 10.1) Mean stem and median soil carbon Stem carbon: 40.43 19.0 (SD = 8.0) Williams et al. 2008 and wood regrowth for 14 plots in Soil carbon: not applicable 57.9 miombo woodland in the buffer zone of Gorongosa National Park, Central Nhambita, Mozambique Miombo ecoregion/African dry woodland estimates Carbon stock in tonnes per hectare for 88–97 41.36–45.59 Timberlake et al. Zambezian phytoregion from a number 2010 of published sources Estimate for Zambezian warm dry 93 (SD = 4) 43.71 Chidumayo 2011 woodlands. Based on unpublished allometric equation using basal area and utilizing tree and stand measurements from a number of published studies Zambia Country Profile | 21
Table 5. Continued
Older studies Estimates given for different Closed evergreen forests: 158 74.26 CEEZ 1999 (used woodlands in the Zambian country Closed deciduous forests: 58 27.26 to calculate study used for greenhouse gas Wet miombo: 76 35.72 greenhouse inventories for Zambia’s Initial National Kalahari miombo: 43 20.21 gas emissions Communication (INC). Mopane: 46 21.62 from land use, (Kalahari miombo equates to Kalahari Munga: 46 21.62 land-use change woodland) Termitaria: 25 11.75 and forestry for Zambia’s INC) Average AGB for old-growth, mixed- Dry miombo: 55 25.85 Frost 1996 age stands in dry miombo woodland Wet miombo: 90 42.3 in Zambia and Zimbabwe, and approximate value for wet miombo old-growth stands AGB and BGB at eight old-growth AGB: 69.9 (SD = 20.8) 32.85 Chidumayo 1993 miombo woodland sites in central BGB: 38.9 (SD = 9.9) 18.29 Zambia and woody biomass regrowth in miombo woodland sites harvested for charcoal production BGB in old-growth dry miombo 32.7 15.37 Chidumayo 1993 woodland in central Zambia Mean dry biomass for miombo Mean: 81.0 (SD = 7.3) 38.07 Chidumayo 1991 woodland in 17 plots, including small Small plots: 141.8 66.65 (<0.1 ha) and large plots (>0.1 ha) from Large plots: 78.4 36.85 Kabwe Rural, Lusak Rural and Mumbwa districts in Zambia. Mean AGB for miombo woodlands 92.5 (SD = 14.1) 43.48 Chidumayo 1990 in nine old-growth plots in the Copperbelt region of Zambia Study in four plots in miombo AGB of 16-year undisturbed 22.70 Stromgaard 1985 woodland, with different levels of stand: 48.28 t/ha disturbance (ranging from complete clearance 16 years previously to harvesting 6 years previously) in Kasama, Northern Province, Zambia Note: IPCC guidelines give the conversion rate of carbon as 0.47 t of carbon per tonne of dry biomass (Eggleston et al. 2006).
Four different methods were used to estimate and soil) estimated at 2652–3323 Mt of carbon. biomass, including the IPCC BCEFs (average Due to its greater prevalence, the majority of and low values) and allometric equations from biomass was calculated to be in semi-evergreen studies by Brown (1997) and Chave et al. (2005). forests (mainly comprising miombo woodlands) These methods produced carbon estimates within with a significant proportion of biomass found AGB ranging from 15 t per hectare to 24 t per in deciduous woodlands (Kamelarczyk 2009). hectare (using the IPCC conversion rate of 0.47 Kamelarczyk (2009) produced lower biomass for biomass to carbon). BGB estimates were made estimates than some previous studies (including equivalent to Tier 1, using a below- to above- ILUA I) and concluded that verification studies ground biomass fraction of 0.28. Total above- and are needed, involving destructive sampling of trees below-ground biomass was estimated to be in in order to clarify biomass estimates. Chidumayo the range of 960–1561 Mt of carbon. With total (2012b) also produced lower estimates using carbon stock (including biomass, deadwood, litter ILUA I data with carbon in above-ground woody 22 | Michael Day, Davison Gumbo, Kaala B. Moombe, Arief Wijaya and Terry Sunderland
biomass, estimated to be approximately 14 t per Scholes 1990; Tietema 1993). No specific models hectare across all woodland types in Zambia. have been developed for the evergreen forests found in Zambia. A common equation for African The ILUA also produced its own estimates for dry forests has also been produced by Chidumayo AGB and BGB solely using the methodology (2011) in order to estimate wood biomass from provided by the IPCC best practice guidelines basal area. (Eggleston et al. 2006). Total national biomass (above and below ground) was estimated to be 5.6 Due to the limitations of the above equations, billion tonnes. This equates to approximately 2.8 the most suitable equation for national-level billion tonnes of carbon (Mukosha and Siampale inventories may be the one developed by Chave et 2009). The ILUA reports provide estimates for al. (2005). The equation uses the largest available the different forest types found in Zambia and dataset of 2410 trees harvested in 27 sites across for disturbed and undisturbed forests (Kalinda et the tropics. Generalized allometric equations al. 2008; Mukosha and Siampale 2009) (Table are often better for national-scale assessments as 5). However, these estimates using IPCC BCEFs many smaller studies are site specific having used are subject to a high degree of uncertainty and a small number of species and samples in order lead to overestimates of biomass levels in ILUA I to derive equations (Gibbs et al. 2007; Henry et (Kamelarczyk 2009; Chidumayo 2012c). al. 2010). Despite the fact that it is limited by a lack of African study sites, the Chave et al. (2005) equation has also been found to be accurate by 3.2 Allometric equations several site-specific studies within Africa, which have recommended its use for regional-scale studies The use of allometric equations is the preferred (Djomo et al. 2010; Henry et al. 2010; Vieilledent method of estimating biomass where forest et al. 2011). Two dry forest equations from inventory data exists, since it reduces uncertainty in Chave (2005) were utilized in the UN-REDD estimates. Allometric equations are also necessary study Carbon Stock Assessment and Modelling in order to achieve Tier 2 and Tier 3 level in Zambia, which was determined to be the most estimates under IPCC best practice guidelines. A useful for AGB estimates in Zambian climatic number of allometric equations are now available zones (Kamelarczyk 2009). to estimate AGB from forest data. However, there are a limited number available from Africa At a subnational level general models for each and within dry forest regions, including the open main forest type are preferable. As part of ILUA II, woodlands found in Zambia (Kamelarczyk 2009). Chidumayo (2012c) has compiled an assessment The most recent equation developed for miombo for existing models used in sub-Saharan African woodland is based on the destructive sampling of dry forests. Data from destructively sampled 119 trees, comprising 19 species, and is suitable for trees were collated for Zambia, Tanzania and estimation of biomass within miombo woodlands Botswana (including 82 species from miombo, in Zambia (Chidumayo 2013). Ryan et al. (2010) mopane and munga woodlands) to develop new developed a site-specific allometric equation for allometric equations. This study provides models both AGB and BGB in miombo woodland in for miombo, munga and mopane woodland the Nhambita area of Mozambique, based on types, as well as for all forest types and several key the destructive harvest of 29 trees, 23 of which species and species groups in Zambian woodlands were excavated for root biomass calculation. Site- (Chidumayo 2012c). specific equations have also been developed by Chidumayo (1990) from destructively harvested trees in miombo woodland in Zambia, and Abbot 3.3 Root biomass and root to shoot et al. (1997) for miombo woodlands in Malawi. ratios However, verification of these models is required to determine their applicability outside the sample A significant proportion of biomass is found below sites. A number of equations have also been ground in African dry woodlands (Chidumayo calculated for C. mopane; however, these studies 2011). Chidumayo (1993) estimated BGB as are all more than 15 years old and their current 36% of total biomass (below and above ground). applicability may need verifying (e.g. Guy 1981; Many species common to miombo woodlands Zambia Country Profile | 23
have extensive root systems with tap roots 3.5 Intergovernmental Panel on that can extend more than 5 m, which makes Climate Change expansion factors studies of root biomass difficult (Frost 1996). Chidumayo (1993) reported root biomass in There are very few country-specific BCEFs soil layers as follows: 76% in 0–50 cm, 21% in for Zambia, as a result the ILUA used default 50–100 cm and 3% below 101–150 cm. For a IPCC BCEFs in order to estimate AGB, BGB, study area in Mozambique, Ryan and Williams deadwood and litter biomass (Kalinda et al. (2010) estimated a ratio of root to stem biomass 2008). However, as the carbon stock estimates of 0.27–0.58 with a mean of 0.42. Frost (1996) derived using BCEF default values (using IPCC calculated ratios of 0.53 and 0.47. These ratios are guidelines) were significantly different from higher than the AGB/BGB biomass fraction of estimates using other methods, Kamelarczyk 0.28 for tropical dry forests from the IPPC default (2009) considered these estimates invalid. When tables (higher range) used by Kamelarczyk (2009). using a low BCEF value the estimates were Further estimates of BGB are included in Table 5. more comparable with the estimates given by allometric equations (Kamelarczyk 2009). The two latest forest inventories, the ILUA and the 3.4 Deadwood and litter pools Forest Support Program (FSP) inventory, utilized a volume function developed by the Zambian Limited information is available regarding Forestry Department based on trees sampled deadwood and litter carbon pools in the different from only one region of the country, as such it forest types in Zambia. Frost (1996) gives a range may not be representative of the whole country of standing litter within miombo woodlands of (Kamelarczyk 2009). 3.27–12.00 t of biomass per hectare. Only one of the studies cited was based within miombo woodland in Zambia, which had an annual average 3.6 Soil organic matter loss following of total litter of 5.48 t of biomass per hectare deforestation (Frost 1996). Soil carbon stock information for Zambian forests The ILUA estimated deadwood biomass using is limited. Miombo soils have low concentrations an IPCC BCEF in relation to the growing stock of organic matter with an average of 1%–2% in estimate. This gave a total deadwood estimate of the topsoil (Chidumayo 1997). Ryan et al. (2010) over 400 million tonnes within forests in Zambia found the soil carbon pool in miombo woodland (Mukosha and Siampale 2009). Kamelarczyk to be highly variable, which was correlated with (2009) also uses a BCEF to make estimates of soil texture. As with the litter carbon pool both deadwood biomass using ILUA data. However, the ILUA and Kamelarczyk (2009) made Tier 1 below-ground deadwood biomass and stump level estimates for soil carbon, which are subject deadwood biomass were excluded due to to a great deal of uncertainty. Kamelarczyk (2009) insufficient data. Estimates of carbon stock with used the value for mineral soils of 31 t per hectare deadwood were 0.37–1.79 t per hectare. All carbon (IPCC 2003). within deadwood was assumed to have the same ratio to living biomass of 0.47, which may need to In savannah ecosystems, soil carbon stocks be adjusted to a lower fraction. The ratio between often exceed woody carbon stocks. As much deadwood biomass and live biomass ranged between as 50–80% of total system carbon is estimated 0.020 and 0.057 (Kamelarczyk 2009). to be in the top 1.5 m below ground (Walker and Desanker 2004). Disturbance of soils due Both Kamelarczyk (2009) and the ILUA estimated to agriculture alters nutrient cycling (such as litter using IPCC look-up tables (equivalent to through increased microbial respiration) and can Tier 1 estimates), although Kamelarczyk (2009) cause a loss of soil carbon (Walker and Desanker used the Frost (1996) litter estimate (5.48), rather 2004; Williams et al. 2008). Soil carbon can than the IPCC default value for semi-evergreen therefore be a significant emitter of CO2 when forest. Litter was estimated to account for 2.48 t woodland is cleared. For sites in Zimbabwe, of carbon per hectare across all forest types King and Campbell (1994) calculated a carbon (Kamelarczyk 2009). 24 | Michael Day, Davison Gumbo, Kaala B. Moombe, Arief Wijaya and Terry Sunderland
loss of approximately 10% (in the top 0.5 m of to produce a new database of wood densities for soil), when miombo woodland was converted to the 350 species recorded in the ILUA I data. arable land or pine plantations. Williams et al. Chidumayo (2012b) also calculated specific wood (2008) found soil carbon stocks in abandoned density for 31 tree species found in miombo farmland had a maximum of 74 t of carbon woodland in Zambia. per hectare compared to a maximum of 140 t of carbon per hectare in miombo woodland Mean wood densities for African tree species as a soils in Mozambique. No significant increase whole have also been calculated by Brown (1997) in soil carbon stocks was found over the period 0.58 g cm-3, Henry et al. (2010) 0.59 g cm-3 of regrowth, suggesting a slow accumulation of and Duikouo et al. (2010) 0.60 g cm-3. These carbon in these soils. Wood carbon stocks on estimates are similar to the average wood density abandoned land were capable of recovery in 2–3 of 0.62 g cm-3 given by the IPCC (2003). For decades but soil stocks did not change during this miombo woodlands in Mozambique, Williams time period. Walker and Desanker (2004) found et al. (2008) calculated a mean wood density an average of 40% less soil carbon in agricultural of 0.56 g cm-3 (ranging from 0.40 g cm-3 to soils compared to miombo woodland soils in 0.71 g cm-3). A lower density was reported by Malawi and also reported slow recovery rates of Shirima et al. (2011) of 0.39 g cm-3 (ranging from soil carbon in abandoned farmland. The significant 0.22 g cm-3 to 0.56 g cm-3) for miombo woodland loss of carbon recorded in these studies may only in Tanzania. apply to woodland converted to agriculture. Kutsch et al. (2011) found no significant difference in soil carbon content between plots within 3.8 Non-carbon dioxide emissions highly disturbed (for charcoal production) and associated with land-use change undisturbed miombo woodland in Western
Province, Zambia. Major GHGs other than CO2 include methane (CH4), nitrous oxide (N2O), ozone (O3) and the Fire is another key factor influencing carbon halocarbons. In addition to CO2, deforestation and emissions from soil in Zambia. Bird et al. (2000), forest degradation and subsequent land-use change
in a study in a savannah ecosystem in Zimbabwe, cause the release of CH4 and N2O as well as carbon reported that lower fire frequencies resulted in monoxide (CO), which influences concentrations
an approximately 10% increase in soil carbon of CH4. Halocarbons are emitted as a result of stocks, whereas higher fire frequencies caused an activities such as industry, and deforestation is not approximate 10% decrease. Plots where fire was a contributor to emissions of these gases. Ozone is
excluded had a 40–50% increase in carbon stocks produced as a result of emissions from CH4, CO in the first 5 cm of soil depth. Chiduymayo and and nitrogen oxides (NOx) (Houghton 2005). Kwibisa (2003) also found fire reduced soil organic matter in Zambia. In Zambia, burning of fuelwood and charcoal, and burning associated with agriculture and natural
fires are likely to be main causes of non-CO2 3.7 Wood density of tree species emissions. The INC estimated on-site burning to
contribute 226.23 Gg of CH4, 0.311 Gg of N2O The Global Wood Density Database (Chave et and 11.24 Gg of NOx; figures were not produced al. 2009; Zanne et al. 2009) has specific wood for off-site burning. Fires in miombo woodland densities for a large number of African species and dambo grassland in southern Africa were including from within Zambia. This database estimated to contribute to global annual emissions has been used to derive wood densities for AGB from savannah fires worldwide of the following
estimates in a number of studies (Kamelarczyk GHGs: CO (12.6%) and NOx (10.3%). In 2009; Lewis et al. 2009; Djuikouo et al. 2010). Zambia, total emissions of CH4, from the use of The World Agroforestry Database is another biofuels (charcoal and fuelwood) were similar or source of species-specific wood densities (ICRAF greater than dry season emissions from woodland 2009). Kamelarzcyk (2009) used these databases and grassland fires (Sinha et al. 2004). 4 Future developments in forests and agriculture
4.1 Future trends in drivers of forest infrastructure. The government’s aim is to become carbon change a middle-income country by 2030 (GRZ 2006). In order for this to be achieved Zambia will have The deforestation rate in Zambia is likely to to utilize its natural resources including forest areas increase over the coming decade due to the (GRZ 2011). growing population and a resultant increased demand for natural resources. A trend analysis The Sixth National Development Plan (SNDP) by Vinya et al. (2011) indicated that forest cover identifies the expansion of agriculture as key for loss would increase until 2020 and then decrease economic growth, due to the size of Zambia’s during 2020–2030; however, the baseline estimate natural resources (GRZ 2011). The National and subsequent increase in deforestation rate for Agricultural Policy (2004–2015) encourages the this analysis is very high (890,400 ha in 2000– expansion of areas under cultivation (including 2010, rising to 1,358,200 ha in 2020) considering large-scale commercial farming), in order to the most recent estimates of the deforestation rate. achieve food security (IDLO 2011). The policy indicates the potential for agricultural expansion, It was not possible to gather specific information stating that of the 58% of the total land area regarding future activities that may cause forest having medium to high suitability for agricultural carbon change. For example, forest allocation production only 14% is currently used for farming maps or mining concession areas were not readily (MAC 2004). available. The lack of capacity for sharing this kind of information is one of the challenges for The Zambian government also intends to MRV of REDD+ in Zambia. Compounding this encourage the development of the mining industry, difficulty is the fact that a large proportion of the a significant contributor to the country’s GDP forest carbon change activities in Zambia, such as and economic growth, by facilitating the opening charcoal production or expansion of subsistence of new mines, promoting small-scale mining and agriculture, are illegal or unregulated and are diversifying the industry. The goal is to increase the therefore not officially recorded. contribution of mining to at least 20% of GDP (from an average of 9.1% in 2006–2009) by 2015 (GRZ 2011). The government also emphasizes 4.2 Plans to expand agriculture and the need for construction of infrastructure, such other land uses as roads, in order to enable economic growth in Zambia. The SNDP and the Zambian Vision With a growing population and high levels of document also recognize the need for construction poverty the Zambian government is duty bound of new housing to meet a national housing deficit to explore opportunities to expand agriculture and (GRZ 2006, 2011). 5 Gap assessments
5.1 Background due to charcoal production and woodland fires; and litter and deadwood carbon stocks. Soil carbon As has been outlined earlier in this report, Zambia is another carbon pool subject to a great deal of and the miombo ecoregion in general have a variation and with limited understanding regarding number of knowledge gaps in terms of scientific the causes of the variation (Williams et al. 2008). research relevant to MRV of REDD+. Bond et al. (2009) regard the limited data on deforestation, Capacity is also low in Zambia for MRV, and the high variability of miombo woodland strengthening of various institutional procedures vegetation and a poor understanding of the carbon needs to occur in order for repeat monitoring cycle as key issues for estimating carbon supply in of REDD+ to be achievable. ILUA II and other miombo region countries for REDD+. Stringer regional-level projects should go some way to filling et al. (2012) list the following as knowledge gaps many of these knowledge and capacity gaps, as regarding carbon storage and sequestration in outlined below. drylands in sub-Saharan Africa: • AGB: lack of observational data regarding present day AGB storage and lack of 5.1.1 Studies within open deciduous quantitative assessment of human and woodland and evergreen forest ecological drivers of AGB, including fire • soil organic carbon (SOC): insufficient The specific vegetation cover in Zambia is information on the amount, distribution and determined by factors such as rainfall, soil type form of SOC; lack of empirical data to test soil and altitude as well as historical human use and respiration models and unique factors effecting management. As carbon stocks vary significantly SOC in drylands due to these factors further research to differentiate • lack of understanding regarding carbon stocks, between standing biomass levels in different ecosystem services provision and the future woodland types is necessary (GOFC/GOLD drivers of change. 2011). A major knowledge gap exists in that studies that do address standing biomass growth The Global Observation for Forest Cover and rates and emissions from carbon stock losses Land Dynamics (GOFC/GOLD) Sourcebook are focused on miombo woodland, as this is the (2011) recommends incorporating the data of dominant and most economically important past scientific studies into estimation of carbon woodland type in Zambia. However, the country stocks if the data is less than 10 years old. A large has significant coverage of mopane, Kalahari and proportion of the previous studies in miombo munga woodlands and evergreen forest, which are woodland in Zambia are from the late 1980s and also subject to deforestation and degradation. It early 1990s. As such much of this research may not is therefore not known how loss of biomass from be suitable for calculation of current carbon stocks these vegetation types will affect carbon emissions. for the purposes of REDD+. ILUA I did include some sampled tracts within Kalahari (20) and mopane (12) woodland; however, In summary, the main knowledge gaps relevant tracts within munga woodland (2) and within closed to MRV for REDD+ are: accurate forest cover forest, including evergreen forest (1), were limited trend data and verification of deforestation rates; (Chidumayo 2012a). ILUA II has been designed the development of specific allometric models for to encompass all forest types, which may allow estimation of biomass in different forest types; more accurate estimates of biomass within Kalahari, estimates of emissions from forest degradation mopane and munga woodland types. Zambia Country Profile | 27
5.2 Gap assessment for international 5.2.2 Data to support an assessment of requirements land use, land-use change and forestry
5.2.1 Available data meeting the reporting Emissions from land use, land-use change requirements of the Intergovernmental and forestry (LULUCF) in Zambia come Panel on Climate Change predominantly from biomass burning on site (fires), from biomass decay and from offsite The most recent IPCC guidelines require biomass burning, mainly through charcoal. In estimation of carbon stock changes for five carbon order to compile the GHG inventory for the pools in forests: AGB, BGB, deadwood, litter INC, the emission factors and related activity and soil organic matter (Eggleston et al. 2006). data were sourced locally (MTNER 2002). The IPCC gives three tiers for reporting carbon Zambia-specific information used for the GHG stocks depending on the level of accuracy. Tier 1 inventory included estimates for standing comprises emissions factors using default global biomass within the different woodland and forest values. Tier 2 is based on country-specific activity types (see CEEZ estimates in Table 5). data and static forest biomass information. Tier 3 requires more detailed methods such as country- ILUA I provides some information regarding specific allometric models. Tier 3 also requires the degree of biomass burnt from charcoal and country-specific forest inventories and repeated fuelwood in Zambia, including energy use in measures in order to assess forest carbon change relation to forest distribution. The main gap (IPCC 2003; Eggleston et al. 2006). The data for GHG inventory from LULUCF is a lack of generated from ILUA I is suitable for meeting country-specific expansion factors for Zambia, Tier 2 specifications of the IPCC good practice including for the different woodland types. guidelines with regards to AGB and deadwood ILUA II will calculate specific expansion factors (UN-REDD 2010b). ILUA I data only enables for the woodlands in Zambia. The relative estimation of carbon stocks in BGB, litter and soil degree and contribution to land-cover change pools equivalent to Tier 1 (Kamelarczyk 2009). from fire, charcoal production and shifting ILUA II is intended to enable Zambia to move agriculture, particularly for different parts of from Tier 2 to Tier 3 levels for reporting carbon the country, are also important areas for further stocks. However, soil sampling to determine research. In addition, the variability of recent carbon content from organic matter is costly, estimates of the deforestation rate for Zambia which may limit the amount of soil sampling suggests that verification using remote sensing that can be undertaken for ILUA II (Chidumayo is required (Kamelarczyk 2009). The national 2012a). The hard mineral soils and deep tap roots remote sensing survey (currently ongoing) should of dry woodlands in Zambia also make sampling to provide a more accurate estimate of reference determine BGB problematic. level deforestation rates.
Using the IPCC look-up tables to provide default estimates per hectare for BGB (ratio to AGB), 5.3 Gap assessment for national soil and litter is subject to a high degree of requirements uncertainty. Chidumayo (2012b) suggests that the IPCC BCEFs are not suitable for REDD+ 5.3.1 Data meeting national policy reporting in Zambia as they resulted in significant requirements and principles overestimation of biomass levels in ILUA I. Kamelarczyk (2009) found that applying the Zambia currently has no national strategy with average BCEF values following IPCC guidelines regards to REDD+. National policy requirements resulted in a much higher estimate compared are therefore aligned with the international to other methods; as such, this method was reporting requirements of the IPCC. The main considered invalid. The low BCEF values resulted policy requirements related to MRV are derived in estimates more similar to those using allometric from Outcome 5 and Outcome 6 of the NJP equations. The calculation of accurate country- document for Zambia as outlined below (UN- specific BCEFs is therefore important for REDD+ REDD 2010a): monitoring and reporting in Zambia. 28 | Michael Day, Davison Gumbo, Kaala B. Moombe, Arief Wijaya and Terry Sunderland
Outcome 5: Monitoring, reporting and verification provided socioeconomic data that will contribute capacity to implement REDD+ strengthened to Output 6.2, as will the recent protected area • Output 5.1: REDD+ integrated with forestry gap analysis produced for the country (MTNER inventory system (ILUA) 2012). Data needs relevant to national priorities • Output 5.2: Operational forest monitoring are provided below. system established and institutionalized • Output 5.3: GHG emissions and removals from forest lands estimated and reported 5.4 Gap assessment for subnational requirements In terms of Outcome 5 and its outputs, ILUA II has been designed to provide specific data for The number of regional MRV projects in Zambia assessment of carbon stocks in all of the main is limited. As such CIFOR’s NFP will be an woodland types in Zambia. As such, it should important component of REDD+ readiness for enable assessment of GHG emissions from forested Zambia by developing a pilot MRV system. The land. Until ILUA II is completed and the results project will contribute to capacity building for published, an assessment of this outcome cannot MRV of REDD+ through collaboration with be completed. One concern is that the data for local partners including the Forest Department ILUA I have not been readily available. The at district level; the Zambian Wildlife Authority, data sharing capacities for ILUA II need to be through the Directorate of Game Management strengthened in order for all of the above outputs Areas; and the associated Community Resources to be realized. The results of ILUA II should be Boards, etc. The NFP will also complement made available through an open access database national work being undertaken for MRV of (Open Foris) currently being developed (MTNER REDD+; for example, field components of the 2010a). Steps have already been taken to establish project will follow the protocol for the ILUA as an operational forest monitoring system for far as possible. Zambia (Output 5.2), including the development of regional monitoring centers. The USFS is providing technical assistance for remote sensing in Zambia’s Eastern Province. An additional concern for ILUA II has been This provides an opportunity for building raised regarding the sampling design. The current capacity in remote sensing and geographical sampling design is an intensification of the information systems (GIS) of Forestry systematic design used in ILUA I. Chidumayo Department staff and other stakeholders. (2012a) suggests that ILUA II should include Another important REDD+ and MRV stratified sampling to ensure coverage of all major stakeholder operating in Zambia is the Southern land types, including the unevenly distributed African Development Community (SADC), closed forests types such as evergreen forest. which is interested in the development of integrated MRV systems for REDD+ in the Outcome 6: Assessment of reference emission level SADC region. As such they are also conducting and reference level undertaken an MRV project similar to the NFP in the • Output 6.1: Historical rates of forest area and northern part of Eastern Province. The focus carbon stock changes reviewed of the project will be on improving forest • Output 6.2: National circumstances assessed inventory data and land-use data to improve forest management at a district level. As these Output 6 will also be addressed primarily regional programs and the national program by ILUA II. The national remote sensing (ILUA) have similar objectives and aims for forest survey should provide accurate reference-level measurement and monitoring in Zambia, there deforestation rates and therefore emissions when are significant opportunities for collaboration. combined with inventory data (Output 6.1). For example, the SADC will provide funds for As with the field component of ILUA II, until intensification of ILUA field plots within their this survey is completed an assessment of this study area. outcome cannot be carried out. The ILUA has Zambia Country Profile | 29
5.5 Recommendations for gap filling 5.5.2 Eligibility and capacity gap filling
5.5.1 Data and methodological gap filling The establishment of a centralized REDD+ center or department is an important step in ensuring Research in the following areas would be the success of REDD+ in Zambia. Linked to this beneficial in filling data and methodological gaps is the need for a national REDD+ policy from the (as outline above, ILUA II and the NFP should government to ensure that a legal framework for fill many of these data gaps): REDD+ schemes is in place. The national REDD+ • Development of specific allometric equations center should have overall responsibility for MRV of for biomass estimation in Zambia; currently REDD+ in Zambia and should improve monitoring the best available regional equations are not and reporting of forest cover changes in Zambia. developed in Africa. This should include For example, the coordination center could be destructive sampling to verify standing responsible for reporting how future plans for land- biomass within different forest types use change, such as mining concessions, will affect (Kamelarczyk 2009). forest cover. • BGB, deadwood, litter and soil only have generic estimates using IPCC default values. ILUA II and work by UN-REDD, as well as regional Further research for miombo woodlands and projects by CIFOR, USAID, USFS and SADC deciduous woodlands in Zambia is needed. will contribute to building capacity for MRV of • Identification of high carbon soils and what REDD+ in Zambia. Of particular importance are causes variation in soil carbon in Zambian the following areas: woodlands (Williams et al 2008). • increasing the use of remote sensing and GIS by • The impact of fire on woodland loss and Forestry Department staff for repeat monitoring carbon stocks; including whether fire control of land cover and forest cover changes in recovering woodland can stimulate • development of a national forest monitoring accumulation of soil carbon and greater tree system that complies with international standards biomass (Williams et al. 2008). • development of (establishing and maintaining) • Deadwood estimation within all forest types. local level monitoring systems that effectively • Methods developed to assess forest and efficiently feed the national forest degradation and biomass estimates in Zambia monitoring system using remote sensing, i.e. use of different • improving the ability to estimate future carbon resolution datasets to determine the most emissions projections combining remote sensing cost-effective and accurate methods. Studies and GIS, and in situ data should also couple remote sensing with field • enhancing the ability of the Forestry Department studies for verification of measurements and other stakeholders to resurvey ILUA sites. (Ribeiro et al. 2012). 6 References
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Species and location Growth rate Reference Baikiaea plurijuga (Zambian teak) MAI 0.13–0.20 cm Crockford (unpublished) in Sitoe et al. 2010 Brachystegia floribunda – regrowth 4.4 mm mean annual ring width Syampungani et al. 2010 miombo woodland in Zambia growth Brachystegia spiciformis, Western 2.4–3.3 mm diameter per annum Grundy 2006 Province, Zambia Brachystegia spiciformis, central 0.3–2.7 mm diameter per annum Trouet et al. 2006 Zimbabwe Brachystegia spiciformis, western 3.1 mm diameter per annum Holdo 2006 Zimbabwe Burkea africana, western Zimbabwe 1.7 mm diameter per annum Holdo 2006 Erythrophleum africanum, western 1.6 mm diameter per annum Holdo 2006 Zimbabwe Isoberlinia angolensis – regrowth 5.6 mm mean annual ring width Syampungani et al. 2010 miombo woodland in Zambia growth Julbernardia paniculata – regrowth 4.4 mm mean annual ring width Syampungani et al. 2010 miombo woodland in Zambia growth Pterocarpus angolensis, South Africa MAI 4.5 mm per annum Shackleton 2002 BAI 6.4% Pterocarpus angolensis, western 0.3 mm diameter per annum Holdo 2006 Zimbabwe Pterocarpus angolensis, Tanzania 0.8–4.8 mm diameter per annum Boaler 1966; in Sitoe et al. 2010 Pterocarpus angolensis, western 3.0–4.1 mm diameter per annum Stahle et al. 1999 Zimbabwe Terminalia sericea, western Zimbabwe 2.2 mm diameter per annum Holdo 2006
BAI = basal area increase; MAI = mean annual increment. Note: Annual diameter increase = approximately mean annual ring width x 2 CIFOR’s Occasional Papers present the findings of research that are important for the tropical forests. The content is reviewed by peers inside the organisation and externally.
Zambia is one of the nine pilot countries for the UN-REDD programme and is currently at the first phase of readiness for REDD+ under the UN-REDD quick start initiative. A National Joint Programme (NJP) is tasked with developing a national REDD+ strategy. Outcome 5 of the NJP Programme Document is to strengthen the Monitoring Reporting and Verification (MRV) capacity for REDD+ in Zambia. A reliable system of Monitoring Reporting and Verification (MRV) is of critical importance to the effectiveness of REDD+.
This report provides a comprehensive overview of the national REDD+ strategy and institutional capacity for MRV of REDD+ as well as the current state of knowledge of various elements critical to MRV of REDD+ in Zambia including: Current drivers and rates of deforestation and forest degradation; a review of standing biomass, forest growth rates and carbon stock estimates; and data sets available for MRV in Zambia.
The report also identifies knowledge and data gaps that need to be filled in order to develop an effective MRV system for REDD+ in the country. Key knowledge gaps, such as carbon stocks and growth rates within different forest types, are outlined as well as recommendations as to how knowledge gaps and capacity to implement MRV of REDD+ can be addressed. The report also details how CIFOR’s research in Zambia, particularly the Nyimba forest project in the Eastern Province, will contribute to MRV of REDD+ for Zambia.
This research was carried out by CIFOR as part of the CGIAR Research Program on Forests, Trees and Agroforestry (CRP-FTA). This collaborative program aims to enhance the management and use of forests, agroforestry and tree genetic resources across the landscape from forests to farms. CIFOR leads CRP-FTA in partnership with Bioversity International, CATIE, CIRAD, the International Center for Tropical Agriculture (CIAT) and the World Agroforestry Centre.
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Center for International Forestry Research (CIFOR) CIFOR advances human well-being, environmental conservation and equity by conducting research to help shape policies and practices that affect forests in developing countries. CIFOR is a member of the CGIAR Consortium. Its headquarters are in Bogor, Indonesia, with offices in Asia, Africa and Latin America.