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Integrating participatory research and spatial analysis in land use science: scenarios of land cover changes in Tanzania or Developing scenarios of land cover change: from regional narratives to national maps. INTRODUCTION In the last decade, vast environmental changes have occurred globally, driven by fast population growth and economic development (Turner et al. 2007, xxx). During the 1980–2000 period, more than half of the new agricultural land across the tropics came at the expense of intact forests, and another 28% came from disturbed forests, raising concerns about environmental services and biotic diversity globally. (Lambin and Meyfroidt 2011, xx) Understanding LULC dynamics and their future evolution is fundamental for strategic planning of sustainable use of natural capital, and conservation of ecosystem services, and to support policies on land use, climate change and food security. LULC dynamics are driven by a combination of bio-physical, socio-economic and governance factors (XXX), where the latter often plays a major role though is difficult to model. The empirical analysis of past and present land use change has an important role in providing insights into the socio-economic and biophysical processes that shape land use transitions. Such analysis would benefit from exploring socio-economic and ecological interdependencies in the land system across spatial and temporal scales (Reenberg, 2006, 2009). Systematically linking socioeconomic and biophysical drivers and trajectories is a prerequisite for the assessment of sustainable development (Fischer-Kowalski and Haberl, 2007; Fischer-Kowalski and Rotmans, 2009). .. In developing countries, the capacity of analysing land use change dynamics is often limited at large scale by different factors such as data availability at adequate spatial or temporal resolution, and economic resources for collect or analyse the data (XXX).Furthermore, future trajectories may be different than the past as new drivers can emerge rapidly in developing economies. There are evidence that countries at different stages of economic development show different rates of deforestation (Mather, 1992, Bhattarai and Hammig, 2001, Cropper and Griffiths, 1994, Culas, 2007). In fact, technological improvement, livelihood changes and enforcement of environmental regulations may lead to a decrease in forest degradation and deforestation rates Damette and Delacote, 2009, Ewers, 2006 and Karsenty, 2008. On the other hand, decrease in deforestation rates may follow overexploitation when forest resources are no longer commercially valuable (Rudel et al., 2005XXX). Uncertainties in the detection of LULC patterns, the historical trends of change and the consistency across time make it challenging to assess possible policy impacts on future LULC dynamics and on ecosystem services. In the context of climate change mitigation, assessment of LULC changes trends and drivers is required for countries engaging in carbon PES mechanisms, particularly the Un-Redd programme for reducing deforestation and forest degradation in developing countries. Despite many countries have completed the pilot phase and are in transition towards performance based phase, only a few of them have established protocols for assessment of reference emissions level and long term monitoring at national scale (UNREDD……xxx). Local communities’ participation and integration of indigenous knowledge are also key factors for improving the interpretation of ongoing and potential future processes (xx). local system knowledge provides insights of individuals into the socio-economic, administrative, cultural, political and environmental dynamics within a particular region Walz, A., C. Lardelli, H. Behrendt, A. Grêt-Regamey, C. Lundström, S. Kytzia, and P. Bebi. 2007. Participatory scenario analysis for integrated regional modelling. Landscape and Urban Planning 81(1-2):114–131. In this context, research methods enabling multi-dimensions analysis and multi-stakeholders engagement are required. A range of different strategies exist to project future land-use patterns from regional to global scales. (Rounsevell, et al. 2012, xxx, xx). IAM, ABM ….. The focus on either top-down (multi-)sectoral approaches or bottom-up, agent-based approaches does not sufficiently capture the comlexity of human–environment interactions across different scales. The further development of models for integrated analysis of human–biophysical causal relationships would benefit from combining data with different spatial scales and from widely different sources (e.g. Crawford et al., 2005; Gaube et al., 2009; Turner et al., 2007; Walsh and Crews-Meyer, 2002)….Rounsell et al.2012 The drafting of scenarios is one of a number of possible approaches to investigating future developments (xxx, Rosenberg et al.) The scenario technique could function as a bridge concept for interdisciplinary work in research of the human-environment relationship (Santelmann et al. 2004). Scenarios can incorporate both socio-economic and environmental dimensions and are suitable for dealing with uncertainty and complexity of socio-ecological systems (Peterson et al., 2003, Nakicenovic et al., 2000; McIntyre, Herren, Wakhungu, & Watson, 2009; Parry, Rosenzweig, Iglesias, Livermore, & Fischer, 2004; Nelson et al., 2010).). On the other hand, scenario planning allows for thinking creatively on alternate futures (Reed et al. 2012) and eventually identifying the strategies to reach them. Involving stakeholders in scenario development may empower those involved, through the co-generation of knowledge with researchers and increasing participants’ capacity to use this knowledge (Kok et al. 2007 and Walz et al. 2007. There is a trend toward combined scenario methods (Walz et al. 2007), which both contain qualitative/ participatory elements, and are underpinned by quantitative models. The difficulty is the combination of these two techniques, because the results of participatory processes cannot be directly integrated into quantitative models, and mathematically by actors. Rosenberg et al. Usefulness of quantitative methods declines steadily as we look further into the future, whereas usefulness of qualitative approaches increases in this case. Therefore, both qualitative and quantitative approaches are complementary and strengthen each other when used together. Amer, M., T. U. Daim, and A. Jetter. 2013. A review of scenario planning. Futures 46:23–40. A combination of qualitative and quantitative scenarios can be the best answer to achieving the goals of a scenario analysisAlcamo, F., J. E. Environmental, F. The, E. Scenario, A. Elsevier, and J. Alcamo. 2008. The SAS Approach : Combining Qualitative and Quantitative Knowledge in Environmental Scenarios.. Combinig 3 differnet approaches: integrated numerical modelling, scenario analysis and participatory involvement, remains a major challenge in integrated, participatory and interdisciplinary research (Kok and van Delden, 2004; Pahl- Wostl, 2002). Walz, A., C. Lardelli, H. Behrendt, A. Grêt-Regamey, C. Lundström, S. Kytzia, and P. Bebi. 2007. Participatory scenario analysis for integrated regional modelling. Landscape and Urban Planning 81(1-2):114–131. However, even in most studies the components are kept distinct: is there a way for a better integration of the three, and particularly extending the participatory approach beyond the qualitative analysis to the quantitative and spatially explicit components? Multiple challenges: developing a stakeholders driven and modellers controlled process (qualitative to quantitative, mixed bottom-up top-down modelling tools), extending the participatory approach to the quantitative and spatially explicit component; integration of explorative scenarios that reflect possible land use futures with normative visions that identify desired land use futures.(Rounsevell, et al. 2012.. Need for developing innovative methods to combine not only social and natural sciences, but also qualitative and quantitative approaches (Rounsevell, et al. 2012 In this study we developed a modelling framework for building scenarios of land cover changes at country level, integrating participatory methods with quantitative and spatial analysis. We used Tanzania as case study, several developing countries share similar challenges. Tanzania is one of the country eligible for REDD scheme Tanzania and is characterised by fast growing economy and rapid social changes (World Bank…), while at the same time implementation of national/local policies and regulations is weak (XXX). GDP growth rate is not reflecting the real economic development for most of the population, due to poor elasticity of the system (XXX). This increases uncertainties on how the future socio-economic conditions, and therefore future LULC dynamics, may evolve. In Tanzania, information on LULC dynamics at national scale has been limited to date by the lack of datasets with adequate spatial and temporal resolution. The modelling framework aims at tackling the complexity of the socio-ecological systems and strengthening the link between socio-economic and environmental changes in assessing land use and cover change. This framework further develops a methodology for mapping scenarios previously applied for Eastern Tanzania (Swetnam et al. 2011), focusing on specific issues encountered in developing socio-economic-environmental scenarios: 1) incorporating local perspectives in larger scale assessments (bottom-up approach), 2) translating qualitative into quantitative information, and 3) standardising the analysis to ensure repeatability and scalability. We tested sensitivity of results to standardisation and scaling of intermediate outputs, and compared them with results obtained through model-driven approaches. METHODS Study area and workshop zones Tanzania….mainland surface about 883,600 km2, national parks 48808, game reserves 92650. Mainland population in 2012 43.6 mil inhabitants, 44.1% < 15 years old, 70.9% living in rural areas (URT, NBS, MoF 2013). Tanzania’s economy has been growing steadily for the past 10 years. In 2012, the Gross Domestic Product (GDP) at Current Market Price (CMP) was TShs. 44,718 Billion. In other words, the economy expanded by 6.9%, which is close to its more recent historical average (NBS). The growth of the communications sector, whose contribution to GDP has doubled since 2008, has transformed how Tanzanians trade and do business by facilitating a revolution in banking. Despite the country’s impressive macroeconomic performance and the attempts of the government and donors to boost agricultural production over the past decades, poverty remains prevalent and stagnant. Since 2001, the level of poverty in rural areas has remained stagnant at around 37% to 40% (World Bank). The percentage of population living with less than $” a day is 67,1%. There is an uneven spatial distribution of education and health expenditures among districts, explaining the variations in access to and quality of education and health services observed in the country (World Bank). Infant Mortality Rate (IMR) was 95 per 1000 lives birth, being the life expectancy at birth 51 years. (National Bureau of Statistics). The human development index of Tanzania is currently ranked 152nd out of 187 countries. The country is expected to reach only three out of seven MDGs by 2015; combating HIV/AIDS, reducing infant and under-?‐five mortality. However, is lagging in primary school completion, maternal health, poverty eradication, malnutrition, and environmental sustainability. (Sequeros, B. T. (2013). Context Analysis of the United Republic of Tanzania, 1–6.) The process aimed at capturing diversity of LULC dynamics at sub-national scale. Therefore, multistakeholders workshops were organised for groups of regions corresponding to seven forest management units, namely Central, Eastern, Laze, Northern, Southern Highlands, Southern and Western zones (Fig….), between February and June 2014. Stakeholders’ consultations were aimed at developing regional visions of the future under alternative scenarios and regional insight on LULC change drivers and their spatial occurrence. Scenarios were developed for each zone separately, and then harmonised in a national output for Tanzania mainland. MODELING FRAMEWORK Land use and land cover (LULC) change scenarios were developed following a mixed bottom-up and top-down modelling framework, integrating participatory methods and spatially explicit and quantitative analyses.This approach contains elements of fuzzy cognitive mapping combined with trends impact analysis (Amer et al 2013). A modellers’ team including experts n different disciplines set up a general scenario frame which was then populated with qualitative and semi-quantitative information with the help of local stakeholders. The modellers then translated this information into quantitative and spatially explicit trends and outputs. Final outputs, scenarios maps, were created after a second consultation where preliminary results were presented to stakeholders for comments and validation. The main steps of the modelling framework are detailed below/in Fig. 1) Scenarios narratives building (stakeholders): 1.1 Developing scenarios qualitative/semi-quantitative narratives on socio -economic trends and strengthening the link with LULC dynamics. 1.2 Assessing potential LULC changes and their likelihood, identifying the drivers and the spatial indicators. 2) Spatial allocation module (stakeholders and modellers): 2.1 Select most likely LULC change types for each zone. 2.2 Identify spatial indicators of LULC changes risk based on participatory activities 2.3 Identify spatial datasets related to the spatial indicators, and transform them as appropriate. 2.4 Standardise and reclassify the indicators according to risk of change categories. 2.5 Create composite indicators of LULC change risk by linear combination of standardised spatial indicators (specific to change types and zones). 3) Supply demand assessment (modellers): 3.1 Estimating supply demand for forestry, biomass energy and farming sectors based on secondary information and calibrated with information from stakeholders’ consultations. 3.2 Transforming supply demand values from production (tonnes, m3) to land use (ha). 4) Scenarios mapping (modellers): 4.1 Converting LULC classes following land demand and specific LULC change likelihood indicators. 5) Validating maps and assumptions, and iterating previous steps (stakeholders and modeller). DEVELOPING SCENARIOS NARRATIVES The first step of process consisted in the generation of the scenarios general definitions by the modellers’ team. Given time budget constraints, we adopted a minimal approach to establish the number of scenarios, and identified two critical dimensions determining future developments: economic growth and environmental impact. Since we deemed most effective to limit the amount of information people had to take in and process at once (Heugens and van Oosterhout, 2001),we initially proposed stakeholders two significantly different alternative scenarios general definitions, the business as usual (named BAU or Kama kawaida, in the national language) and the green economy (GE, Matamio mazuri). In the next steps of the process stakeholders had the opportunity to discuss the scenarios according to a four quadrant matrix based on economy and environment axes, and to envisage the quadrants actually touched by the future trajectories. Scenarios general definitions followed literature, policy review and expert discussions, building on a previous study conducted in the eastern and central side of Tanzania (Swetnam et al. 2011), We revised it according to recent socio-economic statistics (World bank 2014, NBS_URT 2012). Under the business as usual scenario the current rates of population growth, deforestation, and farmland expansion continue. Policy and regulations are not attended. Most people are employed in agriculture. Biomass (fuelwood and charcoal) remains the prevalent source for energy, not only in rural areas but particularly in big cities. Interventions to reduce forests and woodlands loss and degradation (including REDD+) are not efficient or sufficiently implemented. Under the green economy scenario there is a shift to integrated socio-economic development goals and ecosystem services conservation. Policy and regulations are enforced towards this objective. Fuel wood is produced and utilised more efficiently. Demand for farmland slows down. Programmes for reducing deforestation and forest degradation are fully implemented (including REDD+ and other PES schemes). The green economy scenario is not targeting a green economy policy, which has not been developed yet in Tanzania. This scenario is partially explorative of pathways towards sustainable economic development, and partially normative since it targets Redd+ implementation. The base year was set to 2010, according to the Naforma land cover map reference year, and the time horizon was set to 2025. This time horizon is consistent with the time frame for Redd+ implementation and with the Tanzania development strategy (National development strategy xxx). The main assumptions under BAU scenario would be realistic and plausible within this time frame, e.g. high dependence on subsistence agriculture and biomass energy, and rural/urban population distribution. In the long term, other processes could become important and significantly affect socioeconomic and environmental trajectories, e.g.: availability of natural gas in main cities for domestic use, urbanization, ITC and infrastructure development, agriculture sector improvements. The scenarios general definitions presented to stakeholders were not numeric but they expressed general trends, and were meant to leave stakeholders enough degree of freedom for developing locally oriented narratives. We did not included changes in in population growth rate, since this was not the main focus of our analysis. However, we adopted the population growth rate estimated between 2012 and 2002 censuses and equal to 2.7%/yr (National Census, URT, NBS, 2012), in agreement with specific indications from NBS (xxxx). This rate is aligned with the low variant projection for population growth rate in 2010-2025 period published by UNDESA (Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat, World Population Prospects: The 2012 Revision, http://esa.un.org/unpd/wpp/index.htm). We did not included GDP changes rate in the scenario definitions, for two reasons. First, as mentioned above we wanted stakeholders delineate their own trajectories. In fact, despite economic growth was implicitly included in both scenarios, stakeholders might envisage different future trends, and in some cases they did so. The second reason was the difficulty to model a realistic quantitative relation between economic growth and per capita consumption with effect on land use change. Paw and Thurlow, (2010) reported that economic growth have little effect on poverty in Tanzania, (Poverty headcount ratio at $2 a day (PPP): 43.5 % of population in 2012) and this may apply to a larger percentage of population as well. The authors reported the country’s poverty–growth elasticity (PGE) was at most 0.76 during 2001–2007, though they highlighted discrepancies in PGE estimate from different sources (Pauw, K., and J. Thurlow. 2010. Agricultural Growth , Poverty , and Nutrition in Tanzania. IFPRI Discussion Paper 00947). . Stakeholders were selected among delegates of local and regional governments, civil society organisations (including NGOs), business and private sectors, research institutions. They represented mainly forestry, farming, livestock, mining, water, conservation, and infrastructure sectors, but also social development (community based organisations, women and childhood rights). The 7 regional workshops were attended by 189 participants in total, 57.5% representing local and regional governments, 24.7% civil society groups, 7.5% business and private sectors, 7.5% research institutions and 1 participant belonging to a religious institution. Percentage of women ranged from a minimum of 4.5% in the Central zone to 20% in Northern zone. Women were more represented in research institutions and CSOs (38.5 and 32.5 % respectively) rather than in governmental institutions (5.2%). Delegates from NGOs involved in Redd+ pilot projects in Tanzania were present. In every workshop, stakeholders’ engagement in the scenario building process followed an introduction on the WWF conservation initiatives and the main objectives of the WWF-Redd+ pilot project. In the scenario exercises, stakeholders analysed the linkages between socio-economic conditions and environment (land use and cover) state. They started from the present situation and then interpreted the scenarios as two alternative responses, which could lead to different states and impacts. During a plenary discussion stakeholders identified the economic sectors related to land use changes, and selected the ones (5 to 6 sectors) with greatest impacts. Then, they formed parallel groups including representatives from different regions and sectors. Each group developed sectorial narratives for the zonal context, based on trade-off analysis of two dimensions, the economic growth (i.e. income, production) and the environmental impact (i.e. land use and cover changes, resources depletion). Stakeholders were requested to position each of the identified economic sectors on a 2D chart according to the economic and environmental axes, both under the current state and under the future scenarios (Suppl Mat. Fig). Stakeholders discussed direct and underlying factors determining current positions and future trajectories, and identified main environmental pressures as well as economic impacts associated with them. The output charts were then presented and discussed in plenary sessions. The results of sectorial analyses were converted from hard to soft copy afterwards, by using Geogebra 4.4.45.0 (http://www.geogebra.org/), and then translated in semi-quantitative trends. The sectorial analyses contributed to identify proximate and ultimate drivers of changes, and potential challenges and opportunities for the alternative future scenario. Sectorial trends were used to weight the allocation of land demand across the zones. On the other hand, sectorial analyses helped stakeholders in disentangling the complex relationships between socio-economic conditions and pressures on LULC. In the following activity, stakeholders focused on translating these pressures into specific and spatially defined LULC changes (impacts). For mapping exercises, we used the Naforma LULC map 2010 (Naforma 2014) as reference baseline, in each workshop limiting the analysis to the extent of the zone. The Naforma LULC map 2010 refers to Tanzania mainland and includes 18 classes, and the modellers’ team agreed which land cover classes the analyses would focused on. For example, ice and open land were set invariable, while built-up areas could only expand, but not decrease. Classes with limited distribution range, like mangrove forest and thicket, were analysed in the corresponding regions only. Land cover map was introduced to stakeholders by using explanatory pictures of the different land covers. Stakeholders were given time to assess and validate the maps, and to understand the classification system. Working in groups, stakeholders then assessed specific LULC changes which could occur under the two scenarios. For facilitating the discussion, we provided stakeholders with a table output template reporting the original land cover class (from class), the possible changes (to classes), and columns for the drivers and the spatial information (Table 1, Suppl. Mat.1) They evaluated the likelihood of each conversion type with a scale ranging from 0 (i.e. “not possible”) to 4 (i.e. “very likely) and ranked the direct drivers by their relative impact. Finally, they reported spatial information related to LULC conversions either naming specific sites (e.g. administrative units or gazetted sites, hence defined spatial locations) or using more general, bio-physical, spatial rules (hence defined spatial rules). Each stakeholder’s group was assigned a limited number of land cover classes to analyse, and in each workshop at least one key land cover class was common to every group. Group results were then presented and discussed in plenary sessions. In some cases results were corrected upon general agreement A second round workshop was then organised in October 2014 gathering national stakeholders, and involving at least 2 stakeholders from each regional workshop session. At this stage, we presented preliminary scenarios maps, and stakeholders engaged in specific focus group discussions to validate the map outputs and some of the assumptions drawn from the regional workshop results. Groups discussed on food and biomass energy production and productivity, infrastructure (roads) and industrial (mining) development, and multi-dimensions of Redd+ intervention targeting (the Authors in prep). The outputs of this workshop were used to improve the spatial allocation and supply demand modules, and then refining the scenario maps. SPATIAL ALLOCATION The spatial allocation of LULC changes was based on the interpretation of participatory activities outcomes, which led to selection of most likely LULC changes for each zone and development of specific spatial indicators for each of them. For each zone, we cross-tabulated LULC changes likelihoods in matrices reporting changes both from and to each class. Single way changes with likelihood score equal to 1, corresponding to very unlikely changes, were not considered for the BAU scenario. In some case, they included changes driven by climate, which would not actually occur by 2025, but could be possible in the long term. In other cases they referred to changes requiring land use act changes. For the GE scenario, likelihood scores were generally lower than in the BAU, since they either represented a decrease in the effects of BAU drivers, or a positive effect of specific GE drivers. Therefore, all likelihood scores were taken into account in reference to corresponding changes in BAU. . Selected LULC changes were balanced between losses and gains (Table 1, Suppl. Mat. 2). Stakeholders indicated as very likely the conversion to built-up areas in every zone. However, in the Naforma land cover map built-up area cover class represents only 0.18% of the entire surface and includes main towns only, therefore we did not apply any change to this cover class. Most villages and settlement scattered over the country are not classified as built-up areas, but actually included in cultivated areas. Therefore, we assumed that changes in cultivated land would actually account for relative changes in human settlements in rural areas. Finally, we assumed that total cultivated land would not decrease. We created composite indicators of LULC changes risk from different spatial information collected during stakeholders’ consultations. The most used information were those associated with each specific LULC change type: the spatial rules (for instance “proximity to” human settlements, roads, cultivated area, protected areas border, etc. or “at medium elevation”) and the specific spatial locations (specific protected sites, villages, wards, districts, regions). Secondly, for each LULC class, we selected the drivers with the highest impact in most regions, and we interpreted them as spatial indicators.(if they could be mapped and were not already covered by other indicators). Drivers impact was evaluated by the likelihood of the associated LULC conversion (decreasing from 2 to 4 for the selected changes) divided by the driver rank (increasing from 1 on). We selected drivers with a ratio >= 1 in most of the zones. These indicators were common to every zone and across similar LULC changes, and so they were combined to create baseline indicators, which would contribute to harmonising changes allocation across regions. the baseline indicators would ensure the possibility to allocate changes required according to land demand once the area identified by stakeholders’ specific information was entirely converted. In the case of conversion to new cultivated land, we used additional information form the national workshop. Both zonal and national consultations converged to identify important indicators, which were all included in the baseline indicator (crop suitability, irrigation sites and market accessibility). Spatial rules identified by stakeholders and baseline indicators are shown in Table 2 Suppl. Mat. 2. Finally, we also took into account the spatial information collected during the focus group discussions on economic sectors impacts, and we included them either as specific location or among the spatial rules. From the focus discussions on sectorial trends and from drivers of LULC changes, population growth was identified as the one of the main drivers of LULC changes since it trigger demand for resources. Accordingly, human settlement presence was a recurrent spatial rule for specific LULC changes. Therefore, we interpreted population density as spatial indicator of human presence, and it was included in the baseline for every zone and LULC change. Likewise, mining sector was identified among the sectors with highest impact on land use in every workshop. In fact, though mining sites have limited extent, they attract workers and locally increase new farms and settlements, and pressure on wood stock. Furthermore, they often require opening of new roads, and as a result accessibility to remote areas increases. However, the sector occurred at low frequency among drivers and even less as spatial information. Given that mining sector is deemed to further expand in the future, we considered it among the spatial indicators, but with localised effects. It was included in the set of indicators composing the baseline indicators. Finally, charcoal production was recognised one of the most important factor driving LULC changes both in the sectorial analyses and among the drivers, and Dar es Salaam was reported the most important market for charcoal, in agreement with many studies (citations>>>). However, Dar es Salaam was not mentioned as specific spatial rule, while instead the distance to roads was associated with charcoal production. We then included the spatial indicator “distance from Dar es Salaam” (see Suppl. Mat. 2). The indicators “distance to roads” was still used for those regions which are beyond the influence of Dar es Salaam market (estimated more than 250km, Kilahama…., potentially increasing in the future). Overall we identified 19 spatial indicators (Table 2), for which we identify national or global corresponding datasets (Table 3 Suppl.Mat. 2). Global datasets were adapted to data and statistics at regional or country level, and all datasets were transformed to produce consistent spatial indicators, (details in Table 4, Supp. Mat. 2). All datasets were converted to raster layers, , adopting as common standard the Coordinate Reference System (CRS) and spatial resolution (sr) of Tanzania Worldpop dataset (CRS: WGS1984, sr= 0.000833333 decimal degrees). The final spatial indicators were then projected to UTM37 South (sr = 93.319 m at the equator) and clipped to the extent of Naforma Land cover map 2010. Spatial indicators were tested for collinearity (details). The only indicators resulting slightly correlated (Rs = 0.69) were distance from roads and distance from Dar, as expected, and were not used in combination.. Spatial indicators were then standardised by reclassifying them into LULC change risk classes (Table 5, Supp. Mat. 2). . Stakeholders expressed the likelihood of change in classes from 0 to 4, and so a consistent approach was followed in the reclassification system. However, for spatial rules indicators we extended the classes from 0 to 8, so to better represent gradients over the distribution range (Tanzania extent) and allow greater variability in the resulting composite indicator. Reclassification rules followed information provided by stakeholders or reported in literature, particularly for distances indicators (Suppl. Mat. 2). The original likelihood values were maintained for the spatial locations, instead, because they do not represent continuous variables but specific sites. This way, spatial locations were given a different weight than the other indicators (where the maximum likelihood of change value would be 8). Spatial location information may be incomplete (due to limited knowledge of stakeholders), and the locations may be correlated with the other spatial indicators. However, rather than considering this information redundant, we valued it as additional “local knowledge”. In fact the location information seemed to be related to factors different than the bio-physical rules, which we could not otherwise map (e.g. local governance, private interests), then these factors could be represented in the composite indicator at least to a certain extent. In most cases the spatial locations identified areas were the other indicators have low-medium values (i.e. low risk, Fig. 1 Suppl.Mat.2). Following the workshops results, for every workshop zone and specific LULC change the spatial indicators were linearly combined to obtain composite indicators of LULC change risk. Besides, we identified indicators representing constrains to changes, which were used to mask out or reduce the change risk indicators. In particular we excluded areas with slope > 20% and above 3600 m for cultivated areas expansion. Moreover, following stakeholders’ indications, we modelled the effects of protected and reserved sites (PAs) in limiting LULC changes using two criteria: distance from PAs border and PAs designation. We grouped designations in 3 categories of high, medium and low protection, and we created a constraining parameter varying from 1 to 0.1 with distance from the borders, according to a pattern differing in the three categories (Supplementary material 2). This index was applied in the spatial allocation module to simulate the effects of different designations and reduce every LULC changes risk indicator in the BAU scenario. In the GE scenario, we assumed that legal protection inside designated areas would be enforced and Redd programme would support conservation and sustainable management of forest, and so land cover changes would not be possible at all. Likewise, the spatial indicators “Proximity to protected areas border” and “Proximity to forest reserves border” were not used in the Ge scenario. In the Ge scenario, we also considered locations where tree biomass increase was potentially envisaged, to reduce change risk indicators. We analysed the effects of the variance of the different components (baseline, spatial rules, spatial locaitons, protected areas constraints) on the total variance of the final indicators. Zonal composite indicators were harmonised to national level for each conversion type in two steps. For each conversion type, differences between zones were limited by the presence of a common baseline. However, as different specific indicators may have been identified in each zone, normalisation was required before merging the layers Therefore, we standardized the single indicators layers to a common scale (from 1 to 10) using maximum-minimum method. Secondly, we created buffer areas of 20km off each zone, and zonal indicators were extended into them. Within the buffer areas, the influence of each zone was weighted by the distance from the respective border, and the buffer indicator values were then calculated as weighted mean of the zonal indicators. Finally, countrywide indicators were created by merging zonal indicators and adopting buffer values for the buffer areas. As described in Fig. 3, to produce scenario maps we implemented a stepwise process whereby sequential changes were applied to the land cover map, starting with changes to natural forest, closed and open woodland, bushland and grassland. Finally, conversion to new farmland was over imposed. Pixels were converted based on land demand and according to the specific change risk indicators (from the highest value until demand was fulfilled). This process included a randomization of pixel selection for the lowest indicator value. We applied rules of additionality or accumulation of changes, consistently with conversion patterns reported to occur in reality, either gradual orabrupt . In fact, according to stakeholders land cover conversion would sometimes be gradual, for instance from a densely vegetated cover to a sparsely vegetated cover and finally to vegetation replacement by crops or artificial surfaces. Alternatively, intermediate states would be skipped, and densely vegetated cover would be converted to open land or farmland abruptly. In our stepwise process, additional changes are those happening in parallel on the same land cover class while cumulative changes are those happening subsequently. The accumulation effect follows spatial overlap of areas with high risk of different changes. Datasets transformation and spatial analyses were performed in ArcGIS 10.2 (Esri, 2014), QGIS and R. | SUPPLY DEMAND Following the scenarios definition and the outputs of stakeholders’ consultations, we limited to estimate possible LULC changes driven by demand of two main supply types, wood for energy and forestry sector and food crops. We did not consider demand for livestock sectors because of the uncertainties on possible alternative scenarios for this sector. In fact, in some of our workshops stakeholders could not even envisage a green economy scenario for this sector, and in most cases it was pointed out that changes in this sector trajectory would require radical changes of pastoralist culture along with technical improvement. Furthermore, part of livestock (settled farmers) is distributed in the cutivated cover classes, therefore grazing areas expansion is partially accounted for in farmland expansion. We used population growth as the major driver of supply demand increase. Population projection by 2025 were calculated applying a growth rate of 2.7/yr based on the 2012 national census estimate (National Census , NBS-URT 2012). We estimated wood demand in forestry and biomass energy sectors by taking into account wood fuel (firewood and charcoal) for households, services and rural industries (tea drying, tobacco curing), timber for domestic consume and exportation (reported and not reported). Comprehensive estimates of wood extraction from natural forest and woodland are not available for Tanzania, though various estimates of wood consumed for different uses are reported in literature (Kichonge et al 2014, Treue et al 2014, Schaafsma et al. 2014, Peter and Klas 2009, Ngaga et al. 2011). Some of the uses can be compensative. In fact, clearance of tree cover for farming is often associated with utilisation of the cleared wood for charcoal or timber (this study, xxx). Uncertainty in per capita estimates of wood fuel used by households for energy production is high, ranging from 0.26 to 1.12 m3/capita/yr. The annual growth stock is reported to be 83.7 89.2 Mil m3 (Naforma 2014) or 89.2 Mil m3 including forest plantations (Nanga 2011) for 2010, and the annual legally available cut estimate (AAC) plus recoverable deadwood is estimated about 42.7 Mil m3. In in this study, for the BAU scenario we followed the approach adopted by local experts (see Naforma 2014) and assumed total demand equal to1.06 m3/capita/yr aincluding households wood fuels (0.87 m3/capita/yr FAOSTAT 2010,) rural industries (0.09 m3/capita/yr), other uses (0.05 m3/capita/yr) illegal harvesting (0.048 m3/capita/yr) and round wood harvesting estimates (0.0036 m3/capita/yr). The adopted demand estimate could still be considered conservative, if we consider that charcoal production alone could require 1 m3/capita/yr (Peter and Klas 2009).For the GE scenario we adopted a reduction of 50 % of total demand, following the outputs of stakeholders’ consultations. For each year, total wood demand was calculated proportionally to population projections, and then compared with total annual growth and available annual cut (AAC, Naforma 2104) recalculated including the contribution of forest plantation on annual growth stock according to Nanga (2011). Nanga (2001) reported that contribute of forest plantation to total wood stock would vary by 2025. In fact, government plantation potential will be negatively affected by tree age structure, which could lead to a drop in production after 2017. Production from private plantations is mainly directed to their own use or to exportation, instead. AAC was decreased each year proportionally to forest loss, adopting a conservative value of 100,000 ha cover loss per year. Estimates of forest cover loss in Tanzania largely depend on “forest” definition (1% /yr, FAOFRA 2010, ~372,816 ha Naforma 2014, Burgess et al…). The adopted value is in line with recent findings based on remote sensing analysis, though we consider it as conservative estimate, while actual rates of loss for the entire wood stock could be higher (Mapping historical changes in land cover using Landsat data and combination of automatic classification and visual interpretation, Pekkarinen et al. 2014; CEPFM Forest cover and change for the Eastern Arc Mountains and Coastal Forests of Tanzania and Kenya circa 2000 to circa 2010, Tabor et al. 2014/15?). Wood demand exceeding the AAC was deemed to deplete the wood stock, and surplus volume was summed up over the 15 years of analysis. Surplus volume was then distributed across zones following two criteria: 1) the relative proportion of total wood stock from different pools and 2) the relative impacts of the forestry and energy sectors assessed by stakeholders. Surplus wood volume demand was then “distributed” across LULC change types within each zone, proportionally to the specific likelihood of changes (see spatial Table 1 Suppl. Mat.2) and then converted to surface by applying land cover specific Volume/surface ratio (Naforma 2014). For assessing potential demand for new farmland we followed the same approach as for the wood stock, and mainly we based our estimate on population food requirements by 2025. We selected a group of crops deemed to be produced for staple food (cereals, pulses, tubers) which covered 8.8 Mil ha in 2010, around 75% of total harvested area (FAOSTAT). Based on FAO statistics (harvested area, yield and production) we calculated the area needed to ensure the same amount of food per day to the projected population in 2025 (National Census 2012). We validated this estimate by calculating the annual food requirement by 2025 based on the Food balance sheet estimate of kcal/ capita/day (2012???) and equal to 2100 kcal from cereals + 79g of Protein + 59g of fat per capita/per day. We separately considered increase in cash crops and sugar cane plantation, applying a fixed rate (independent from population growth) (reference Cosimo study). Following stakeholders consultations, we assumed no yield increase for BAU scenario. Soil fertility and yield have been reported to decrease in the last years even by official sources (URT, MAF assessment), due to poor practices. In this scenario, estimated new farmland demand correspond to 30% increase of cultivated surface with annual crops by 2025. Under GE scenario, following sectorial trajectories analysis, we applied an increase of 10% of productivity, corresponding to farmland increase of 24% by 2025. In applying the estimated demand for modelling LULC changes we considered discrepancies between the total cultivated area reported in statistics (8,808,771 ha National Agriculture census 2007-2008), and the total surface of cultivated land cover classes in the Naforma LULC map, which reports around 23 Mil ha for“grains and other crops” and “wooded crops”, and 11.6 Mil ha for mixed wooded categories cultivated bushland and “woodland. Therefore we used the estimated farmland demand based on staple food intake as minimum, highly likely, level of expansion. Applying the same rate of change to the Naforma cultivated areas, including cultivated bushland and woodland, we then calculated a maximum level of expansion. RESULTS SCENARIOS Sectorial analysis revealed similarities and differences among the regions (Fig. 1 Suppl. Mat. 3). The core sectors resulted Agriculture (farming), Biomass energy, Livestock, and Mining, analysed in every zone. Biomass energy was included either in Energy or in Forestry, and timber industry was analysed either together or in alternative. Wildlife and beekeeping was also included in Forestry sector in Eastern, Southern and Western zones zones. Finally, Infrastructure sector was selected in every workshop, except in the Lake zone where Fishery was considered more important. Under BAU scenario, most sectors were envisaged to increase the economic performance at expenses of the environment. Agriculture and biomass energy were envisaged to have the highest most extensive impacts . In central and lake zone, stakeholders envisaged negative trend or no grow for agriculture as well, in consequence of loss of soil feritily due to unsustainable practices, which would lead to productivity drop. A recent report from the Ministry of Agriculture, food and cooperatives (MAFC 2013) confirmed this trend. In the GE scenario, stakeholders generally envisaged improvements of both economic and environmental conditions, though in some cases environmental improvement would necessary lead to decrease rate of economic growth. However, in most cases the envisaged positive trends were not sufficient to revert the current situation, and so move the sector in the first quadrant (both economy and environment positive, Fig. 1 Suppl. Mat. 3). General considerations a. socio-ecological complexity: interactions and subsidiarity between economic sectors (Figure): i. livestock keeping and charcoal production are considered complementary, “shock absorbing” activities; ii. charcoal production is often complementary to forest clearance for farmland expansion; iii. livestock keepers are settling down or reducing the range of movements, and becoming also farmers; iv. employers in offices are also farm owners, farmers own some livestock heads; farmers produce charcoal during dry season; v. evolution (negative) of fishing sector could drive shifting to farming activities with direct consequences on wood harvesting for fuel and boat construction (reduced degradation/ deforestation), but indirect consequences on woodland clearance for farmland expansion (increase deforestation); vi. evolution of farming (negative in certain areas due to soil infertility) could drive shifting to mining and infrastructures building sector (more intense impact, though more localised); vii. development of mining sector could lead to people migration and lulc changes in new, currently “remote” areas, with local high impacts. b. Cultural effects i. livestock keeping sector is highly related to pastoralist culture. Despite changes in pastoralist communities are evident, stakeholders underlined that the transition from pastoralism to modern ranching requires a cultural shift, and in some case may not be possible (no GE scenario envisaged); ii. likewise, stakeholders highlighted that in biomass energy and forestry sector, cultural mind-set changes and community awareness play a key role in successful implementation. LULC CHANGES LULC change risk indicators(Figure). LULC changes by 2 scenarios (Table) IMPORTANCE OF EACH SUB-INDICATOR IN THE COMPOSITE INDICATOR (Variability) Suppl. Mat. 3 Contribute of Spatial Indicators in final land cover changes.(Additional charts) DISCUSSION In the context of policies on climate change and food security, nations are requested to assess and monitor LULC dynamics and meanwhile to identify their underlying drivers, to plan for mitigation and adaptation interventions, while taking into account the possible impacts, e.g. trade-offs between different ecosystem services, and between environment health and human wellbeing. Moreover, there is a need for nested approaches, which should reduce the gaps between global and national policies and local implementation. We argue that such a great challenge requires multidisciplinary integrated and multiple scale approaches, , to capture the complexity of socio-ecological systems and disentangle interactions and feedbacks effects. “Unilateral” approaches may be more accurate in determining particular components of the systems, but may fail to give a consistent and coherent overall figure, which is required for policy advice and intervention planning. Despite it initially required a bigger effort in terms of time and human resources, participatory scenario analysis eventually may lead to save resources given that it provides different type of information at the same time. Moreover, it contributed to gap analysis of knowledge and information required for achieving the ultimate goals. In this study, we proposed a methodology for developing scenarios of land cover change at large scale, involving stakeholders participation and producing spatially explicit outputs. PARTICIPATORY vs MODELER DRIVEN (When asking “where are lulc changes going to happen?” during a workshop a GIS expert replied “at the forest edge”, while a local stakeholders replied “wherever people are in need for land”. This anecdote resumes the differences in participatory versus modeller driven approach). Participatory approach is highlighting important factors which may affect current and future lulc dynamics: SOCIO-ECOLOGICAL COMPLEXITY, CULTURAL EFFECTS. These factors could be hardly captured by a pure modeller driven approach. A mixed approach, therefore, may provide better hints for diversification of interventions planning: On the other hand, participatory approaches may fail to identify facts poorly known by the involved stakeholders, or deemed less important. Likewise, stakeholders can be reluctant to talk about certain topics, such as illegal activities. Furthermore, group discussion may be influenced by power dynamics related to social conditions and gender in particular at a local scale. These biases can be reduced by creating mixed groups across regions and sectors, where people usually share less interests, and where talking about illegal activities is possible without identify responsibilities. Participatory approaches may seem less accurate and more uncertain than rigorous statistical approaches (ref.). In fact, the difference is in the capacity of measuring accuracy and uncertainties. In our approach, we introduced rankings to facilitate the interpretation of qualitative data and the standardisation across units of analysis…… the participatory process could not contribute to the critical process of scenario parameterisation and quantification. The transformation of qualitative scenarios into sets of coherent input parameter values remained with the researchers. participatory involvement ensured relevance, logic, consistency and validity of the elab- orated scenarios. Walz, A., C. Lardelli, H. Behrendt, A. GrêtRegamey, C. Lundström, S. Kytzia, and P. Bebi. 2007. Participatory scenario analysis for integrated regional modelling. Landscape and Urban Planning 81(1-2):114–131. local knowledge is not always sufficiently robust or detailed enough to provide information about relationships between system components, necessary for scenario quantification (Walz et al., 2006). Engaging meaningfully with stakeholders is a time demanding process (e.g. Walz et al., 2006; Kok et al., 2007), and the choice of participating stakeholders has the potential to significantly affect the outcome of the study (Reed, M. S. et al. 2012.). SOCIAL LEARNING ………… LOCAL vs GLOBAL 1. Local diversity among zones, regions, down to the local level: besides ecological diversity, local socio-economic and political diversity can have a strong effect on lulc dynamics. Drivers can have different impacts in different areas, and response to pressure may differ based on local conditions. This cannot be captured by global IAMs, even when regionalised. On the other hand, high-resolution spatial modelling maybe more accurate in identify local patterns, but may not be reproducible at country-wide scale (lack of resources). Moreover, if not coupled with information on the ultimate drivers beside the dynamics, it could be difficult to interpolate those pattern to a larger scale or to project them to future (compare results from Green et al.). There is a need for using national scale dataset, but when this is not possible global datasets can be helpful though they should be adjusted to local conditions. 2. Global to Local interactions: at this stage of country development the effect of global market are still low on the local economy. Large scale farming are mostly inherited from colonial time rather than consequence of current “land grabbing”. Lack of security on land tenure, bureaucracy burden and the level of corruption have discouraged foreigner investors, to date. Despite stakeholders have experienced the effects of global biofuel policies or CDM, their impacts have been so far spatially limited. Therefore, stakeholders’ perception may underestimate the effects of such factors on future LULC dynamics. Global economic models may instead better account for this. However, global IAMs predictions on land use changes may vary a lot depending on the model. 3. Coupling medium resolution lulc change data with regionalised socio-economic models calibrated by stakeholders would be the best options, requiring big effort. COMPARISON WITH OTHER STUDIES Swetnam et al. 2011. Changes more likely to occur on the eastern and north side of the area. Differences in land cover map, different emphasis on the role of Dar es Salaam, changes already happened around DSM and land is less available. Lin et al 2014. Risk of changes more concentrated in the western side of the country. Underestimate of potential lulc changes along eastern coast (from Tanga to Mtwara). Possible influence of global datasets (roads). CAVEATS Subjective process. Stakeholders’ opinion can be validated only partially, given the lack of secondary information. Influenced by the context (REDD project), power dynamics, and scale of analysis. The final scenario maps represent only one of the many possible interpretation of the process outputs, and certainly one of the many possible futures which could unfold. Table 1 – Spatial indicators of LULC changes identified through stakeholders’ consultations and the associated reference datasets. ID Spatial indicators description Stakeholders input (in order of importance) SI1 Likelihood of conversion for specific LULC Spatial rules SI2 Distance from cultivated areas Spatial rules, drivers SI3 Population density Socio-economic impacts, drivers, spatial rules SI4 Legal protection constraints mask Socio-economic impacts, drivers SI5 Proximity to/inside all protected areas (Pas) borders Spatial rules SI5 Proximity to/inside forest reserves (FRs) borders Spatial rules SI6 PAs identified as specific sites of LULC changes Spatial locations SI7 Reference datasets Naforma LULC map (TFS, URT) Worldpop; National Census 2012 (NBS, URT) WDPA (UNEP-WCMC) Distance to roads Spatial rules, drivers, socio-economic impacts Global roads dataset (CIESINSEDAC); TANROADS (URT) SI8 Cost distance to Dar es Salaam, related to charcoal consumption SI9 Distance to major food markets SI10 Grazing impact based on Tropical livestock unit density Drivers, socioeconomic impacts, spatial rules Global Livestock distribution; National Census 2012 (NBS, URT) SI12 Distance to mining sites Socio-economic impacts, drivers, spatial rules Geological map of Tanzania, ACP Mining Data Bank SI13 Crop suitability related to soil condition, rainfall pattern and altitude Spatial rules, Socioeconomic impacts, drivers Crop suitability, Agricultural Research Institute Mlingano, URT SI14 Distance to irrigated sites Spatial rules, socioeconomic impacts MIRCA2000 SI15 Potential distribution of Sagcot clusters Socio-economic impacts SAGCOT clusters SI16 Elevation range (Low, medium, high) Spatial rules SI17 Elevation mask for farming Spatial rules SRTM 90m Digital Elevation Model suitability SI18 Slope mask for farming suitability Spatial rules SI19 Official boundaries of wards and districts in Tanzania Spatial locations Wards 2002, 2012 (NBS, URT) Table 2 – Land use and land cover changes under BAU and GE scenarios (minimum farmland expansion). share of total Current BAU Class cover 19.3 14.1 bushland 14.6 23.9 open woodland 7.6 7.0 grassland 0.3 0.3 built-up area montane and lowland 1.6 1.7 forest 10.3 12.1 closed woodland 6.9 6.9 cultivated woodland 19.7 19.7 grains and other crops 6.4 6.4 cultivated bushland 0.4 0.5 thickets 0.2 0.2 forest plantation 3.0 3.6 wetland 3.3 3.3 wooded crops 0.3 0.3 open land 0.1 0.1 mangrove forest 0.0 0.0 ice 6.1 0.0 new farmland * Changes compared to Grains and other crops. within class change GE 16.4 17.7 7.7 0.3 1.7 BAU 37.0 -38.9 8.7 0.0 GE 16.2 -26.2 10.6 0.0 -5.2 -1.1 10.8 6.9 19.7 6.4 0.5 0.2 3.2 3.3 0.3 -15.1 0.0 0.0 0.0 -16.4 0.0 -17.3 0.0 0.0 0.1 0.0 30.9* -10.8 0.0 0.0 0.0 0.0 0.0 -11.7 0.0 0.0 -7.7 0.0 25.3* 0.0 5.0 -9.8 Figure 1 – Study area and zones. Figure 2 – Scenario modelling framework Figure 3 – Spatial allocation process. Symbols: Fn = natural forest (mountain and lowland forest); Wc = closed woodland; Wo = open woodland; Bl = bushland; Gl = grassland; Cult = cultivated land. Figure 4 – Composite indicators of LULC change risk by scenarios. Risk of farmland expansion is shown separately from degradation risk, changes in tree cover and biomass without total replacement (e.g. from closed woodland to bushland). Just for comparison Lin et al 2014