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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