Download Climate Change Adaptation in Tanzania Agricultural Systems

Impacts of and Adaptation to Climate Change and Variability in Tanzania Agricultural Systems:
A Review
B.P. Mbillinyia*, H.F. Mahooa, S.D. Tumboa, E. Mpetab, F.B. Rwehumbizaa, K. Mutabazia and
F.C. Kahimbaa
a
Sokoine University of Agriculture, P. O. Box 3003 Morogoro, Tanzania.
Tanzania Meteorological Agency, P. O. Box 3056, Dar es Salaam, Tanzania
b
*Corresponding autgor email:
[email protected]
ABSTRACT
The effects of climate change and variability are undeniably clear with impacts already affecting
the agricultural sector, ecosystems, biodiversity and people. Recent scientific evidence suggests
that the frequency and severity of climatic extremes is increasing, making adaptation an absolute
necessity. This paper reviews the impacts of climate change and variability in agricultural
systems of Tanzania and existing adaptation strategies, including their deficit. The review
indicated that agriculture remains the most important livelihood strategy to the majority of the
population in Tanzania. The sector needs to be strengthened to facilitate survival in a changing
climate. The sector is quite vulnerable to projected climate change, making adaptation an extreme
necessity. It was also noted that, while some adaptation to current climate variability is taking
place, this is insufficient to cope with future changes in climate. In addition, deficiencies still
exist on the current adaptation strategies. The paper urges that risk management strategies should
aim at addressing the existing adaptation deficit and be adjusted to face additional climate risks
associated with climate change. The implication for adaptation may not be to only focus on
minimizing risks, but also to capitalize on opportunities associated with the changing climate.
Keywords:
agricultural systems, climate change, climate variability, adaptation to climate
change, Tanzania
1.
Introduction
The agricultural sector is the leading sector of the economy of Tanzania and accounts for over
half of the Gross Domestic Product (GDP). It provides 85% of export earnings, and employs over
80% of the work force (URT, 2001). Unfortunately, as in other African countries, the sector is
highly vulnerable to climate change and variability because of its over-dependence in rainfall.
According to World Bank (2002), only 3.3% of the cropland was irrigated as of 1999. Any
attempt to improve agriculture must therefore tackle the problems associated with rainfall
variability in amount and timing.
In response to climate variability and change, farmers have developed different farming systems
finely tuned to many aspects of their environment. Adaptation measures include adjustments to
planting dates, rainwater harvesting and selection of animal species. However, while local coping
strategies may be able to deal with shocks in the short-term, they are unlikely to be able to cope
with more frequent and severe climate events (Orindi et al. 2005) and expected future climate
change.
In order to reduce such impacts and vulnerability of climate change and variability on agricultural
systems, adaptation to climate variability and change should include measures aimed at
addressing the existing “adaptation deficit”. Adaptation deficit is lack of adaptive capacity to deal
with climate variability and climate change. A useful starting point in addressing adaptation can
be to tackle the adaptation deficit before embarking on new adaptation activities. Without action
to reduce exposure and improve the capacity to cope, the gradual and sudden changes associated
with climate change will increase vulnerability in many areas (IDS, 2006). Sound government
policies, backed up by technologies to aid drought prediction, monitoring, and management, are
expected to lead to more self-reliant management at the farm level, and the development of
agricultural systems that are physically, biologically, and financially sustainable.
Effective and sustainable adaptation to climate change and variability depends on, among other
things, our ability to assess and understand the impacts and potential opportunities in relation to
and help develop functional strategies to address them. However, according to a number of
reports, for example UNFCC (2006), DFID (2004), and IIED (2006), in Africa, including
Tanzania, there is still a general deficiency of knowledge, expertise and data on the current
climate variability and the impacts of climate change. This is also a constraint to better
understanding of current and future climate variability (DFID, 2004).
This paper reviews the current vulnerability to climate variability, the projected impacts of
climate change and the various strategies and policies that are being deployed to address climate
issues, including their deficit, focusing mainly on the agricultural sector of Tanzania.
2.
Climate variability and trends
The climate of Tanzania is mainly influenced by its location close to the equator, the impact of
the Indian Ocean and the physiography in general. As a consequence, Tanzania experiences a
variety of climatic conditions ranging from humid coastal to alpine deserts. The coastal area and
all of the islands in the Indian Ocean experience a tropical climate, and most of the country is
sub-tropical except for the areas at higher altitudes. Average temperatures range between 17°C
and 27°C, depending on location. Temperature variations have significant impact on the agroecological zones and the adaptation strategies in the agriculture sector. Rainfall in about 75% of
the country is erratic and only 21% of the country can expect an annual rainfall of more than 750
mm with a 90% probability. As a result, crop and livestock production under such conditions
remains vulnerable to the adequacy, reliability and timeliness of rainfall. The mean annual
rainfall varies considerably, ranging from less than 400 mm to over 2,500 mm per annum.
Analysis of monthly minimum and maximum temperatures over years for meteorological stations
located in regions of Arusha, Bukoba, Dodoma, Iringa, Kilimanjaro, Mbeya, Morogoro, Mwanza,
Songea, Tanga, Zanzibar and Shinyanga showed an upward trend (Figure 1). The increasing trend
was mostly associated with the months of January, July and December. The retreat of the glaciers
of Mt. Kilimanjaro., the submersion of Maziwe Island in the Indian Ocean near the coast of
Tanga, and decrease in water levels of Lake Victoria and increasing malaria endemicity in
highland areas of the country could be linked to the observed temperature trend (NAPA, 2005).
Estimates made through analyzing pictures taken in 1912 and then in 2001 have claimed that
Mount Kilimanjaro has lost 82 percent of its ice cover, with the warning that the snow cap would
disappear between 2015 and 2020 (Thompson et. al., 2002). However, a field study, done by
researchers from Portsmouth University of UK, on the mountain top, determined that the rate at
which the snow cap is melting does not suggest that it will disappear in the near future
(http://www.thaindian.com/newsportal/world-news/mt-kilimanjaros-snow-cap-may-notdisappear-by-2020-or-2030_10084058.html).
(Insert Figure 1)
Analysis of annual rainfall time series for most of the stations indicated a decreasing trend and a
greater variability in cycles (TMA, 2007). Figure 2 shows the trend of rainfall observed from
1952 to 2006 at Songea, Bukoba, Arusha and Zanzibar meteorological stations.
(Insert Figure 2)
Rainfall and temperature are critical determinants of crop performance and eventual agricultural
production. Increase in temperature could, for instance, result in increase in evapo-transpiration
which could, in turn, reduce the soil moisture, and ultimately, the rate of plant growth,
development and yields. However, analysis of only annual rainfall and temperature to explain the
effect of climate variability on crop production is limited and might not give a full picture. The
analysis lacks important information on growing season characteristics such as occurrence of dry
spells. As shown in Figure 3, while the analysis of rainfall amount for Same station, Kilimanjaro
Region showed no significant changes (p=0.318)., the analysis showed a significant increase in
occurrence of long dry-spells (21 days or longer) during Masika (p=0.024).
(Insert Figure 3)
Tanzania is also prone to droughts and flooding. According to Hatibu and Mahoo (2000),
historically, floods have caused about 38% of all declared disasters, while droughts caused 33%.
Over the last four decades, the country has been hit by a string of severe droughts and flooding,
the most recent droughts occurring in 1971, 1975-76, 1983, 1985, 1987, 1992, 1996-97, 19992000. On the other hand, some of the most severe flooding episodes in recent years occurred in
1993, 1997/98 (El Niño) and 2000/01 (Kandji et. al. (2006)).
3.
Climate change projections
Projections in temperature and rainfall changes over Tanzania are well documented in URT
(2003) and OECD (2003). While OECD (2003), based on outputs from over a dozen Global
Circulation Models (GCMs) processed using MAGICC/SCENGEN), predicts an average annual
increase of 2.2°C in temperature by 2100, URT (2003) predicts that the mean annual
temperatures will rise by 3-5°C under doubling of carbon dioxide by 2075. Both studies agree,
however, that the rise in temperature will be greater during cooler months (June to August) than
warmer ones (December to February) and consistent patterns of seasonal temperature increase.
Rainfall predictions are less certain. Indeed, major discrepancies remain between climate models.
However, the most commonly used projection for Tanzania foresees an overall annual increase of
rainfall by 10 % by 2100 (OECD, 2003). Significant regional variations will also occur.
According to URT (2003), areas with two rainfall seasons including the north eastern part, the
north western, the Lake Victoria basin and the northern part of the coastal belt may experience an
increase in rainfall for both the short and long rainy seasons ranging from 5 to 45% while areas
experiencing unimodal rainfalls like the southern, south-western, western, central and eastern
parts of the country will potentially experience reduced rainfall by 5 to 15%. The results should,
however, be interpreted with caution as they do not include an uncertainty analysis and rely on
one or two older climate models (OECD, 2003). It is also projected that extreme events, in the
form of droughts, flooding, tropical storms and cyclones to become more frequent, intense and
unpredictable (IPCC, 2003).
In order to capture the climate issues relevant for livelihoods, location based assessments are
needed to complement the broad impact assessments such as those described in URT (2003),
OECD (2003) and (Feenstra et al. 1997). However, downscaling the global reference scenarios to
local conditions remains a significant methodological challenge in Africa and Tanzania in
particular and few institutions have the capacity to do it. This is also a constraint to better
understanding of current and future climate variability (DFID, 2004). To address this gap,
coordinated effort of capacity building, training, research and development should be
emphasized. There is also a need to explore the capacity of these institutions in enriching the
development of reliable climate change scenarios, which can then be transformed into useful
products for wide spectrum stakeholders.
4.
Impacts and vulnerability of climate change and variability on agricultural systems
The effects of climate change and variability are undeniably clear with impacts already affecting
ecosystems, biodiversity and people. Large-scale events, such as the 1997/98 El Niño, illustrate
ways in which many communities are already suffering from less predictable and more extreme
weather patterns. The 1997/98 El Niño, for example, resulted in cereal deficit at 916,000 metric
tons in Tanzania. As a consequence, the President of the United Republic of Tanzania declared a
national food crisis and appealed for additional food aid (Karen O'Brien et. el., 2000). The
livestock sector also underwent severe losses due to increased disease infection (especially Rift
Valley Fever), drowning, damaged water facilities (dams, boreholes, water troughs), and
disruptions in market infrastructure and road systems (Kandji et. al., 2006). Despite these massive
losses, the abundant rainfall was beneficial in some areas. In some agriculturally marginal areas,
production of crops was above average and there were improved fodder and water stocks, leading
to an improvement in livestock performance in those areas. The La-Niña event of 1996/97 was
responsible for the severe drought that occurred in most parts of Tanzania leading to insufficient
rainfall for hydroelectric power generation and urban water supplies. Crop failure was widespread
and rangelands could not support livestock resulting in massive production shortfalls. Recently,
farmers in Dodoma region have reported an 80% fall in expected yields due to late and poor rains.
Food Assessment Reports conducted by Food Situation Investigation Team in 2005 showed that
poor rains during the short rainy season (locally called “vuli”) resulted in food relief distribution
in over half of the districts in the north-east and coastal regions (Karen O'Brien et. el., 2000).In
2005 the agricultural sector grew by only 5.2% compared to 5.8% growth in 2004 and the GDP
was targeted to grow by 6.9% but it grew by 6.8%. This was attributed to severe drought that
affected most parts of the country, triggering food shortage and power crisis (NAPA, 2005).
Systematic documentation and publication of these impacts is required to build up a knowledge
base of how much damage is occurring and where. As information on impacts of climate change
becomes more available and understood, it is possible to study, discuss, and implement adaptation
measures (Downing, 1996). Current national annual statistical summaries need to incorporate the
information which will contribute to the monitoring of climate change and its impacts,
information that will be very useful in national communications to the Framework Convention on
Climate Change (Desanker and Justice, 2001) and National Adaptation Programme of Action
(NAPA).
Predicted changes in climate will have significant impacts on Tanzania’s rain-fed agriculture and
food production, and possibly undermine economic development, increasing poverty and
delaying or preventing the realization of the Millennium Development Goals (MDGs). The
predictions indicate that Tanzania may suffer a loss of over 10% of its grain production by the
year 2080 (Parry et al., 1999). The cultivation of maize is going to be particularly hard hit.
Estimates from Crop Environment Resource Synthesis Model (CERES-Maize) (Jones and Kiniry,
1986) show that the average yield decrease over the entire country would be 33% and as high as
84% in the central regions of Dodoma and Tabora. Yields in the north-eastern highlands showed
decreases of 22% while in the Lake Victoria region decreases of 17% were indicated. The
southern highland areas of Mbeya and Songea were estimated to have decreases of 10-15%.
Consequently, the continued reliance on maize as a staple crop over wide areas of the country
could be at risk. Change of staple crops to millet and cassava may be necessary in the hinterland.
There is considerable uncertainty regarding the effects of climate change on the yields of most
important cash crops such as coffee and cotton. However, according to URT (2003), the two cash
crops are projected to experience increases in yield. For example, coffee yields are expected to
increase by 18% and 16% for areas located within bimodal and unimodal rainfall respectively.
The agriculture sector thus may have both negative and positive impacts. Climate change is
expected to further shrink the rangelands which are important for livestock keeping communities
and change the prevalence of vector-borne diseases. Projected climate change could result in a
shift of the distribution of tsetse fly north-eastwards, which would further reduce the area of land
suitable for grazing and human settlement (IPCC, 2001). Shrinkage of rangelands is likely to
exacerbate conflicts between farmers and pastoralists in many areas (NAPA, 2005).
In addition to its direct effect on the agricultural sector, climate change could affect the sector in a
number of different ways. For instance, the projected 10% and 6% decrease of annual basin
runoff in the Wami-Ruvu basin and the Rufiji River, respectively, (URT, 2003), will reduce the
potential area for irrigation, leading to decline in rice production in these areas.
5.
Adaptation to climate change and variability: Options and challenges
5.1
Use of indigenous and improved soil-water management technologies
In response to climate change and variability, farmers and livestock keepers have developed
different farming systems finely tuned to many aspects of their environment. Adaptation
measures include adjustments to planting dates, rainwater harvesting (RWH), selection of animal
species, transhumance (strategic movement of livestock to manage pasture and water resources),
distributing stock among relatives and friends in various places to minimize the risk of losing all
animals if a drought strikes one particular area, and the opportunistic cultivation of food and cash
crops to meet some of their needs. In Tanzania, and elsewhere in the world, studies have shown
that indigenous technologies are good in reducing the risk in bad years/seasons. For example, in
Same District of Tanzania, macro-catchment RWH was found to give yield benefits by alleviating
the impacts of intra-seasonal dry spells, especially when direct rainfall and run-on occur nonsynchronously (Hatibu et. al., 2003). The same was observed in Botswana where sorghum yield
doubled compared with a control years with below average or poorly distributed rainfall (Carter
and Miller, 1991). Imbira (1986) obtained even greater increases (more than 500%) with sorghum
in Baringo district of Kenya. However, as shown in Machakos, Kenya, the indigenous
technologies are not so effective in capitalizing the opportunities from good season. It was
revealed that farmers could harvest only 600–700 kg/ha in seasons with good rainfall as opposed
to more than 2 t/ha with carefully planned investments in low-risk technologies (ICRISAT,
2007). In addition, a rapid change of the vulnerability context due to more frequent and severe
climate events does not always allow for traditional coping mechanisms to take place and often
results in an overall loss or severe ineffectiveness in the adaptive capacity of the communities. In
this regard, exogenous and improved technologies may play a crucial role through their
integration, where appropriate, into local strategies. As observed by Thiombiano (2004), the
efficiency of traditional practices can be increased, not by introducing completely different ones,
but by identifying those elements, which could be improved in the local context.
Some of the potential exogenous and improved technologies for adaptation to climate change and
variability include; small scale irrigation, drought tolerant seed varieties, strengthen early warning
system, cross breeding for resistant breeds, strengthened livestock extension services, improve
livestock marketing infrastructure and zero grazing (NAPA, 2005). Although there have been
some success stories with these technologies, adoption is still very low. For example, despite all
the efforts to promote the use of improved maize varieties (drought resistant seed), by 1997,
about 90 percent of farmers in Tanzania were still relying on their farm-saved seed (ICRISAT,
2005). Some of the reasons for the observed low use of improved varieties are; lack of
information and incentives or means to purchase them. Efforts to enhance adoption of these
technologies and hence the adaptive capacity of the communities to climate change, should
consider among other things levels of risk, local priorities and experiences, net benefits in terms
of
economic
viability,
environmental
sustainability,
and
public
acceptability,
and
implementability, as central evaluative criteria.
Since climate is changing and climate variability is expected to increase in frequency and
intensity (IPCC, 2001), it will be expected that current coping strategies may not be considered as
sufficient adaptation strategies in the future. Far more work is needed if adaptation itself has to be
seen as an essentially dynamic, continuous and non-linear process (ILRI, 2006). The projected
trends in temperature, precipitation, and extremes will push future climate variations and
extremes beyond the bounds of what people and places have been exposed to and had to cope
with in the past. The implication is that current practices, processes, systems and infrastructure
that are more or less adapted to the present climate will become increasingly inappropriate and
maladapted as the climate changes. Fine tuning current strategies to reduce risks from historically
observed climate hazards will not be sufficient in this dynamically changing environment. More
fundamental adjustments will be needed. This will require recognizing what changes are
happening, predicting the range of likely future changes, understanding the vulnerabilities and
potential impacts, identifying appropriate adjustments, and mobilizing the resources and will to
implement them.
5.2
The Role of Policy and Institutions
Tanzania is a party to various international environmental agreements, including the UNFCCC,
UNCCD, and UNCBD. In 1997 Tanzania developed her first National Action Plan on Climate
Change, and in July 2003 submitted its Initial National Communication to the UN FCCC and
recently submitted its National Adaptation Programme of Action (NAPA). NAPA undertakes
climate change vulnerabilities assessment across key sectors, including agriculture, develops and
prioritizes key adaptation options and strategies that would best address those vulnerabilities.
NAPA is a valuable means of identifying impacts and vulnerabilities and building multistakeholder participation through joint problem solving exercises. However, according to CARE
(2006), there has been little evidence of stakeholder consultation or civil society involvement.
The actors involved have largely been government-based. In addition, according to Orindi et. al.,
(2006), the current NAPA methodology of assessing climate change vulnerabilities takes
scenarios of climate change rather than existing adjustments and sources of vulnerability to
climatic changes as a starting point for analysis. This leads to a lack of emphasis on local coping
strategies and adjustments. As a consequence, many of the options suggested in NAPA are
technical and expensive, making them inaccessible to vulnerable groups including the poor, who
are supposed to benefit from using them.
In addition to country commitment to international environmental agreements, Tanzania has
developed a number of national and district level sectoral policies and plans that are intended to
increase its ability to cope with climate variability. Such policies include increasing agriculture's
drought resistance by, for example, promoting early maturing seeds and drought resistant crops.
Drought-resistant crops and technologies are perceived in policies as a principal means of
addressing problems related to climate variability and drought in particular. Promotion of such
crop varieties is integrated into national, multi-sectoral and sectoral policies, and District
Agricultural Development Programmes (DADPs). However, efforts to promote drought resistant
crops face several constraints. Farmers are reluctant to adopt certain drought-resistant crop
varieties, in part due to low market and consumption values. It is likely that successful increasing
cultivation of drought-resistant species requires numerous measures addressing socio-economic
and technical constraints. Warehouse Receipt Systems (WRS), whereby small farmers are assured
of the market, input, and credits, could provide answers to some of the problems. A further
advantage of WHRS, is that it offers stable prices, linking the small farm sector to sources of
extension advice, mechanization, seeds, fertilizer and credit, and to guaranteed and profitable
markets for produce. However, the system is currently limited to a small area and applied mainly
on coffee and cotton production systems (Mukwenda 2005).
The National Strategy for Growth and Reduction of Poverty (NSGRP) explicitly recognizes the
fact that poor people rely heavily on natural resources and are most vulnerable to external shocks
and environmental risks, including drought and floods. As a result, the strategy stresses a need to
have approaches to mitigate effects of natural disasters, halt desertification and promote water
conservation practices. However, some of these goals have been articulated in previous plans, but
have not been successfully implemented. Therefore, despite the obvious synergies between such
policies and climate change adaptation, a key obstacle facing successful “mainstreaming” is
successful “implementation” (OECD, 2003).
Early warning systems have been realized as one of the adaptation measures to climate change.
Currently, the government is implementing Famine Early Warning System and Livestock Early
Warning System (LEWS). Case studies carried out in Laikipia, Kenya indicate that LEWS is
capable of alerting pastoral communities to emerging drought conditions earlier than the use of
traditional methods (Ryan, 2004). This should allow pastoralists to respond early and utilize
coping mechanisms more effectively. However, according to Ryan (2004), further research is
necessary to ensure the accuracy of the extrapolation and projection techniques. This will require,
among other things, improved weather data.
5.3
The Role of Seasonal Climate Forecasting
As climatic extremes such as droughts increase in frequency and intensity, predicting the interannual and inter-seasonal variations in rainfall will be paramount for short-term decision making.
Use of seasonal climate forecasts is obvious and efficient contributor that can facilitate adaptation
to climate change and variability. For example, yield simulations using the APSIM model suggest
that average maize production could increase by 61 percent compared to farmers’ current
practices if farmers accessed climate outlook information and adjusted their farming techniques
accordingly (Rao and Okwach, 2005).
In Tanzania, the Tanzania Meteorological Agency (TMA) is responsible for provision of agrometeorological services, including daily weather forecast, 10-days forecast, monthly forecasts,
seasonal forecasts and agronomical advisory (such as when to prepare fields, and what to plant).
The information is also used in early warning and drought risk monitoring. However, lack of
widespread use of this information and knowledge to improve farm management suggests that
access to the information has been a problem and farmers have difficulty translating the
information into tangible economic farm outputs. Difficulties in interpreting and applying the
forecasts in their current form include, mismatch between the variables forecast and the
operational needs of farmers, and lack of trust or comprehension of the forecasts (Eakin, 2000;
Phillips et al., 2001; Roncoli et al., 2002). The above deficiencies in weather forecasts provided
by TMA were also reported during a workshop conducted in Same, Tanzania. The workshop was
designed to explore, among other things, on the climate information needs of various stakeholders
[SWMRP, 2007]. The workshop revealed that some of the information not provided by the
current forecasts but farmers would prefer to get included: the amount and distribution of the
expected rains, changes likely to occur during the season, such as possibility of dry spells and
periods of excessive rainfall. Such information was important to them since it would enable them
to determine and prepare for the risks ahead if the rains are poor. They also wanted to know the
type of crop and seed varieties they should grow in view of the predicted/forecast.
The eventual benefits from climate forecasts to prepare for expected adverse conditions or take
advantage of expected favourable conditions, will rely not only on the accuracy of the forecasts,
but also on other conditions which must be in place. These include, among others, viable decision
options that benefit from forecast information, identification of components of climate variability
that are relevant to decisions, and adequate communication and understanding of relevant
information by the right audience at the right time. All these require capacity building not only of
the researchers but the whole spectrum of stakeholders who are or will be affected by climate
change and variability.
6.
The IDRC research project
To address some of the observed challenges and deficiencies in the existing adaptation strategies,
a research project titled “Managing risk, reducing vulnerability and enhancing agricultural
productivity under a changing climate” is being implemented in four GHA countries, namely
Ethiopia, Kenya, Sudan and Tanzania with financial support from IDRC/DFID.
The project has adopted the conceptual framework developed by Klein et al. (1999) of the process
of planned adaptations, aimed at changing existing management practices in coastal zones. In this
model, adaptation is considered as a continuous and iterative cycle, involving several steps:
information collection and awareness raising, design and evaluation of strategies (incorporating
policy criteria and development objectives), continuous assessment of evolving needs and
opportunities, and strengthen institutions and capacities to assess, monitor, communicate and
implement appropriate strategies (Figure 4). An understanding of how the climate system works,
why climate matters and how social systems respond to climate change and variability requires
knowledge and information on how they are affected by those conditions today and how they
might respond in the future if those conditions change. This provides an opportunity for
businesses, governments, community leaders, resource managers and scientists to explore ways in
which GHA communities could reduce their vulnerability, either by reducing exposure or
sensitivity, or enhancing resilience, or both. The project has started to generate the required
information, knowledge and strategies while building the capacity of the research and
developmental community to continuously monitor, communicate and implement appropriate
strategies.
(Insert Figure 4)
The project is guided by the following assumptions/hypotheses;
i)
the existing knowledgebase and capacity in the GHA region are inadequate to deal with
current and future impacts of climate change and variability
ii) the current coping strategies will not be adequate to address the future climate change and
variability
iii) current tools that support decision making for selecting appropriate responses are not
robust enough to address climate change and variability; and
iv) improved communication of existing climate information can lead to reduced
vulnerability and improved adaptation to climate change and variability.
The research design involves a critical interpretive analysis for qualitative and quantitative
assessment of impacts of and vulnerability to climate variability, and participatory action research
to develop innovative approaches and identify best practices for reducing risks and take
advantage of opportunities created by variable climate. The action research is being in the four
countries and conducted at limited but carefully selected representative sites based on
characteristics such as impacts of current and projected climate, vulnerability of small-scale
farmers, and regional significance in terms of agro-ecological and socioeconomic conditions.
Special emphasis is given to assess and take into consideration the impacts of climate change on
vulnerable sections of the society especially women. An understanding of how the roles, status,
and economic power of men and women affect, and are affected differently by, climate change
will improve actions taken to reduce vulnerability and combat climate change.
The project is planned to be completed in four years but though partnerships with different
stakeholders (e.g. NGos, CBOs, inputs suppliers) the activities developed through the project are
expected to be sustained beyond the project life.
7.
Conclusions
The following conclusions can be drawn from this paper:
1).
Agriculture, which includes crop growing and livestock production, remains the most
important livelihood strategy to the majority of the population in Tanzania. The sector
needs to be strengthened to facilitate survival in a changing climate.
2).
The effects of climate change and variability are undeniably clear with impacts already
affecting agricultural systems. Given the low level of human development, extreme
poverty, and high dependence on rainfed agriculture, Tanzania is quite vulnerable to
projected climate change, making adaptation an extreme necessity.
3).
While some adaptation to current climate variability is taking place, this is insufficient to
cope with future changes in climate. In addition, a number of deficiencies do still exist on
the current adaptation strategies. Risk management strategies should therefore aim at
addressing the existing adaptation deficit and be adjusted to face additional climate risks
associated with climate change. One effective option could be a combination of local
strategies and “recommended” adaptive measures. Ongoing monitoring, documentation and
dissemination of good agricultural practices, indigenous and newly developed ones, is
therefore an essential part.
4).
The agricultural sector is expected to experience both negative and positive impacts under
climate change. For example, while production of maize is projected to decline, the
production of coffee and cotton is expected to increase. The implication for adaptation
therefore may be not to only focus on minimizing risk, but also to capitalize on
opportunities associated with the changing climate.
Acknowledgements
This publication was supported by the Climate Change Adaptation in Africa (CCAA) program, a
joint initiative of Canada’s International Development Research Centre (IDRC) and the United
Kingdom’s Department for International Development (DFID). The views expressed are those of
the authors and do not necessarily represent those of DFID or IDRC.
References
Carter and Miller, 1991. Three year experience with an on-farm macro-catchment water
harvesting scheme in Botswana. Agricultural water management 19:191-203
Department for International Development (DFID), UK. 2004. Key sheet 10 Climate change in
Africa/ Global and Local Environment Team, Policy Division, DFID.
Enfors, E. I. and L. J. Gordon. (2007). Analysing resilience in dryland agro-ecosystems: A case
study of the Makanya catchment in Tanzania over the past 50 years. Land Degradation and
Development. 18: 680-696.
Feenstra, J.F., Burton, I., Smith, J.B., and Tol, R.S.J. (Eds.). 1998. Handbook on Methods for
Climate Change Impact Assessment and Adaptation Strategies. Nairobi and Amsterdam:
UNEP and Institute for Environmental Studies/Vrije Universiteit
Hatibu N., M. D. B.Young, J. W. Gowing, H. F.Mahoo and O. B. Mzirai (2003). Developing
Improved Dryland Cropping Systems for Maize in Semi-Arid Tanzania. Part I: Experimental
Evidence for the Benefits of Rainwater Harvesting. Journal of Experimental Agriculture,
Volume 39, pp. 279-292.
Hatibu, N., Mahoo, H.F. (Eds.), 2000. Rainwater Harvesting for Natural Resources Management:
A Planning Guide for Tanzania. Technical Handbook No., 22. Sida Regional Land
Management Unit (RELMA), Nairobi, Kenya.
ICRISAT (International Crops Research Institute for the Semi-Arid Tropics) Eastern and
Southern Africa Region. (2007). 2006 Highlights. PO Box 39063, Nairobi, Kenya: ICRISAT.
56 pp.
Imbira , J. (1986). Runoff harvesting for crop production in semi-arid areas of Baringo. In Soil
and Water Conservation in Kenya. Proceedings of Third National Workshop, Kabete, Nairobi,
Kenya, 1986.
Intergovernmental Panel on Climate Change (IPCC). (2001). Climate Change 2001: Impacts,
Adaptation, and Vulnerability. Cambridge: Cambridge University Press.
Intergovernmental Panel on Climate Change (IPCC). (2003). IPCC Press information on AR4 and
emissions scenarios. Media release, 8 Dec, 2003. http://www.ipcc.ch/press/pr08122003.htm
International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), 2005. Raising
income of resource-poor farmers: enhancing pigeon pea productivity and profitability in
eastern and southern Africa. Annual Report 2004/05 submitted to DANIDA.
International Institute for Environment and Development (IIED). 2006. Climate Change in
Africa: Linking Science and Policy for Adaptation Workshop Report The Royal Society,
London, March 30th 2006.
J.W. Gowing, M.D.B. Young, N. Hatibu, H.F. Mahoo, F. Rwehumbiza and O.B. Mzirai (2003).
Developing Improved Dryland Cropping Systems for Maize in Semi-Arid Tanzania. Part II:
Experimental Evidence for the Benefits of Rainwater Harvesting. Journal of Experimental
Agriculture, Volume 39, pp. 293-306.
Karen O'Brien, Linda Sygna, Lars Otto Næss, Robert Kingamkono and Ben Hochobeb (2000).Is
Information Enough? User Responses to Seasonal Climate Forecasts in Southern Africa:
Report to the World Bank, AFTE1-ENVGC. Adaptation to Climate Change and Variability in
Sub-Saharan Africa, Phase II. Report 2000:3
National Adaptation Programme of Action (NAPA) for Tanzania. 2005. Division of
Environment, Vice President Office, Dar es Salaam, Tanzania
Organisation for Economic Co-operation and Development (OECD), 2003. Development and
climate change in Tanzania: focus on Mount Kilimanjaro
Orindi, V.A. and L.A. Murray (2005), Adapting to Climate Change in East Africa: A Strategic
Approach, Gatekeeper Series 117. International Institute for Environment and Development:
London, UK.
Parry, M., Rosenzweig, C., Iglesias, A., Fischer, G. and Livermore, M. 1999. Climate Change and
World Food Security: A New Assessment. Global Environmental Change. 9: S51-S67.
Paul V. Desanker and Christopher O. Justice (2001). Africa and global climate change: critical
issues and suggestions for further research and integrated assessment modeling. CLIMATE
RESEARCH, Vol. 17: 93–103.
Rao, K.P.C. and Okwach, G.E., 2005. Enhancing productivity of water under variable climate.
Conference Proceedings “The East African Integrated River Basin Management Conference”
Morogoro, Tanzania, 7 – 9, March 2005.
Serigne Tacko Kandji and Louis V. Verchot. Impacts of and Adaptation to Climate Variability
and Climate Change in the East African Community: A Focus on the Agricultural Sector
Serigne Tacko Kandji, Louis Verchot and Jens Mackensen (2006). Climate Change Climate and
Variability in Southern Africa: Impacts and Adaptation in the Agricultural Sector. UNEP &
ICRAF.
Shayo C.M. (2006). Adaptation Planning And Implementation: Agriculture And Food Security.
UNFCCC African Regional Workshop on Adaptation, 21-23 September 2006, Accra, Ghana
Tarhule, A., Lamb, P.J. (2003), Climate research and seasonal forecasting for West Africans.
Perception, dissemination, and use? Bull. Am. Meteorol. Soc., Boston, 8(12), 1741-1759.
Soil Water Management Research Programme (SWMRP) (2007). Proceedings on Stakeholders’
Inception Workshop on IDRC RESEARCH PROJECT “Managing risks, reducing
vulnerability and enhancing agricultural productivity under a changing climate” held at R.C
Conference Hall, Same Kilimanjaro between 17th – 19th September, 2007.
Thompson, L. G., E. Mosley-Thompson, M. E. Davis, K. A. Henderson, H. H. Brecher,V.S.
Zagorodnov, T. A. Mashiotta, P-N. Lin, V. N. Mikhalenko, D. R. Hardy and J. Beer (2002.).
Kilimanjaro Ice Core Records: Evidence of Holocene Climate Change in Tropical Africa
Science, Volume 298, 5593, 18 October 2002.
United Nations Framework Convention for Climate Change (UNFCCC). 2006. Documentation
for the African regional workshop on climate change adaptation held in Accra, Ghana, in
September 2006. Also available at http://unfccc.int
United Republic of Tanzania (URT). (2001). Agricultural Sector Development Strategy (ASDS).
Ministry of Agriculture, Dar-es Salaam.
United Republic of Tanzania (URT). 2003. Initial Communication under the United Nations
Framework Convention on Climate Change (UNFCCC). Vice President's Office, Dar es
Salaam, Tanzania, http://unfccc.int/resource/docs/natc/tannc1.pdf.
World Bank. 2002. World Bank Development Indicators. On CD Rom. World Bank, Washington,
DC.
Zola Ryan (2004).Establishment and Evaluation of a Livestock Early Warning System for
Laikipia, Kenya. MSc. Thesis, Graduate Studies of Texas A&M University
Figure
y = 0.0207x + 26.277
y = 0.0185x + 25.281
2
R = 0.221
2
R = 0.3975
2005
2002
1999
1996
1993
1990
1987
1984
1981
1978
1975
1972
1969
1966
1963
1960
2006
2003
2000
1997
1994
1991
1988
1985
1982
23
1979
1958
24
1976
25
1973
26
1970
27
1967
28
1964
29
1961
Temperature(°C)
Temperature(°C)
30
27
26.5
26
25.5
25
24.5
24
23.5
23
YEAR
YEAR
b) Bukoba station
a) Songea station
y = 0.0156x + 25.272
y = 0.0066x + 30.397
2
R2 = 0.0817
Temperature(°C)
32
31.5
31
30.5
30
29.5
2006
2003
2000
1997
1994
1991
1988
1985
1982
1979
1976
1973
1970
1967
1964
1961
1958
2005
2002
1999
1996
1993
1990
1987
1984
1981
1978
1975
1972
1969
1966
1963
29
1960
Temperature(°C)
R = 0.1154
27.5
27
26.5
26
25.5
25
24.5
24
23.5
23
22.5
22
YEAR
YEAR
d) Zanzibar station
c) Arusha station
Figure 1: Mean annual maximum temperature time series for Songea, Bukoba, Arusha
and Zanzibar stations (1958 – 2005) (Source: TMA, 2007).
1200
yjfm = -2.6146x + 782.8
1000
yond = -3.1783x + 324.81
1200.0
1000.0
Linear
(JFM)
Linear
(OND)
Linear
(JFM)
Linear
(JFM)
Linear
(OND)
400
200
yjfm = -2.8658x + 623.79
800.0
600.0
400.0
200.0
YEAR
a) Songea station
b) Bukoba station
JFM
Linear (JFM)
2005
2001
1997
1993
1989
1985
1981
1977
1973
0.0
1969
YEARS
1965
19
61
19
63
19
65
19
67
19
69
19
71
19
73
19
75
19
77
19
79
19
81
19
83
19
85
19
87
19
89
19
91
19
93
19
95
19
97
19
99
20
01
20
03
20
05
0
1961
Rainfall (mm)
600
Rainfall (mm)
OND
800
yond = -2.5321x + 614.57
JFM
OND
Linear (OND)
yjfm = 1.8196x + 300.24
y = -2.1387x + 470.28
yond = 0.6557x + 140.76
MAM
600
y = -1.7962x + 268.86
SOND
2005
2001
1997
1993
1989
1985
1981
1977
1973
1969
1965
YEAR
Rainfall (mm)
500
Linear
(MAM)
Linear
(SOND)
1961
Rainfall (mm)
700
1000.0
900.0
800.0
700.0
600.0
500.0
400.0
300.0
200.0
100.0
0.0
OND
JFM
Linear (JFM)
Linear (OND)
Linear (OND)
400
300
200
100
20
05
20
03
20
01
19
99
19
97
19
95
19
93
19
91
19
89
19
87
19
85
19
83
19
81
19
79
19
77
19
75
19
73
19
71
19
69
19
67
19
65
19
63
19
61
0
YEAR
c) Arusha station
d) Dodoma station
Figure 2: Year to year rainfall fluctuations for Songea, Bukoba, Arusha and Dodoma
stations (Source: TMA, 2007).
Figure 3: Rainfall variations and dryspells distribution for Same station, Kilimanjaro
Region (Enfors, E. I. and L. J. Gordon. (2007).
Climate
variability
Climate
Change
Impacts
Information/
awareness
Design and
evaluation
of
strategies
Assess future
needs and
opportunities
Communicat
ion/outreach/
education
Management
practices
External
influences
Figure 4: Conceptual framework showing, in shaded area, iterative steps involved in
planned adaptation to climate variability and change based on Klein et al., 1999