 The rationale for this project reflects the concern that future climate change will further exacerbate current climatic risks. Conceptually, this is an “overlay” (to use an analogy from geographic information systems) of future climate change onto the present vulnerability. The project follows an approach to adaptation planning, which is based on scenarios of possible futures and concepts of robust decision-making and social learning. It will work from the perspective of a nested multifunctional landscape to take the complexity of agro landscapes and their inhabitants into account. The generic framework of analysis will be the “Driver (Climate Change) Pressure (Land use change) State (bio geophysical and socio-economic conditions) Impact (environmental, social and economic impacts) Response (decision making, awareness building)” framework.
A combination of data-driven analysis of land use change in relation to climate change with a stakeholder led bottom-up approach of valuating environmental, social and economic goods and services generated from land use will be used. These analytical activities will be instrumental to identify the leverage points for system change towards better adaptive capacity of farming systems and enhanced resilience of the agro-landscape.
The research will be conducted in selected sites of the Morogoro Region (Eastern Arc) of Eastern Tanzania where significant climate change is expected to happen (e.g. Thornton et al. 2006) and where different altitudes will allow for the comparison of contrasting sites. Final selection of localities for field activities and sub-catchments for hydrological investigations will be done in consultation with national partners, in particular the lead agency for the National Adaptation Plan of Action (NAPA).
For the downscaling of global climate scenarios to regional level the non-hydrostatic regional climate model CLM (Böhm et al. 2006a,b) will be used to generate spatial (7 km grid) and temporal (daily to hourly) high resolution climate change scenarios as drivers for the hydrological and agro-ecosystem models. This physically based model considers the complex land surface-atmosphere interactions, is suitable especially in regions with structured relief and allows generating climate data sets also for regions without historical climate time series. Perennial woody plants constitute a rich archive of climate from their own lifespan. Analysis of tree rings together with stable C isotopes can help to unravel those and make them available as data source for regions where such data is very scarce and can also help in validating model data. Hydrological catchment models determine the impact of climate and land use change on the hydrological cycle in landscapes, primarily in river basins. Results from the more detailed SVAT (Soil-Vegetation-ATmosphere) models will be used for calibration and validation of the vegetation-soil water part of the hydrological model and results from the hydrological model are important boundary conditions for the SVAT models. Process-oriented SVAT models are required, which are able to simulate the bio-geophysical interactions between climate, soil and vegetation. These models are sensitive to changes concerning soil hydrology, nutrient cycling, and crop response to assess combined climate change and management effects on crop production, water resources and soil fertility. Furthermore, these models evaluate the best management practices for future climatic conditions. The decision, which concrete hydrological and SVAT models will be used in the project, depends on the selection of the concrete study location and will be taken in the first phase of the project. Experimental data on field and catchments scale will be obtained to test and calibrate the hydrological and agro-ecosystem models.
The added values of including trees into the farming systems will be investigated using the Water, Nutrient and Light Capture in Agroforestry Systems (WaNuLCAS) model (vanNoordwijk & Lusiana 1999), which has been developed at ICRAF and which is also to be employed within the ALUCCSA Project in Burkina Faso. This can serve as an additional bridge of integrating results within the BMZ funded Climate Change Adaptation research projects. For data collection, respective agroforestry trees with dbh > 5 cm will be equipped with sap-flow sensors and their active xylem area will be established subsequently through a tree core. Measurements for leaf area index (LAI) will be conducted together with crop-soil modelling. An albedometer will render radiation data. Through participatory farm/landscape appraisal methods with stakeholders from different decision making levels (farm level to landscape level), adaptation scenarios and criteria for good practices will be identified. Based on presented climate scenarios, stakeholders develop options of potential future agro-landscapes in their regions. All three sustainability dimensions (social, environmental and economic) will be taken into account by different groups of decisions makers (farmers, local politicians, local experts, etc..
Subsequently, joint results from model and stakeholder work will be evaluated in stakeholder workshops together with experts and analysed together through impact assessment methods. Different scenarios will be compared and evaluated by experts (thresholds) and stakeholders (targets) concerning sustainable development in the region. Scenarios of viable, climate change adapted agro-landscapes are the output of these exercise. CCGP will be mainly identified in the area of agroforestry (suitable tree species for the region). Research on measures to adapt farming systems to global warming is an urgent need. To explore plant responses to recent climate changes, dated climate proxy data are essential. Tree rings constitute an annually resolved archive that contains climate and other environmental information. Hence tree ring width and stable isotopes ratios of carbon, oxygen and nitrogen laid down in stem wood of agroforestry trees will be used to reconstruct past changes in the growth conditions caused by human influences, with special focus on increasing atmospheric CO2. The study will derive recommendations to select suitable tree species adapted to future environmental conditions to ensure a sustainable management of agroforestry systems.
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 The IPCC (2007) predicts that by 2020, between 75 and 250 million people will be exposed to increased levels of water stress due to climate change. Coupled with increased demand for food and other land based goods and services, this will adversely affect livelihoods and exacerbate water-related problems. Agricultural production and the access to sufficient food are projected to be severely compromised by more ronounced inter annual variability climate and long term climate change. Countries in East Africa are already among the most food insecure countries in the world. Climate change will only aggravate falling yield insecurities. In Tanzania, famine resulting from either floods or drought has become increasingly common since the mid-1990s. For the Easten Arc and central regions of Tanzania, many of the GCMs predict significant reductions in the length of growing periods (LGP). For large parts of the country, the percentage of “failing seasons” is likely to be in the range of 20-50% by 2050 (Thornton et al. 2006). Further anticipated impacts of climate change in Eastern Africa are decreased rainfall, increased temperature and evaporation rates in dry areas, more frequent drought spells, changes in planting dates of annual crops, increased fungal outbreaks and insect infestations due to changes in temperature and humidity, decrease in forest and cultivated area shares, decline in crop yields, reduction in ecosystem integrity and resilience, and decline in biodiversity.
Households and communities have developed a number of coping strategies dealing with increasing amplitudes and frequencies of extreme weather events related to climate change. They react to increasing livelihood risks and vulnerabilities. There is a need to strengthen these coping strategies, to enable households to live with current climate variability as well help them to adapt to long term climate changes. This is only possible if we first understand local people’s vulnerabilities, capacities and risks.
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