Farmers in semi-arid Tanzania are experiencing increased climate variability that threatens their livelihoods. A project was implemented to enhance community resilience to climate change through use of seasonal climate forecasts. Key findings included increased wind speeds, unpredictable rainfall patterns, and warmer temperatures according to community perceptions. The project helped communities assess climate risks and adopt risk reduction strategies like drought-resistant crops and rainwater harvesting. Future climate impacts were expected to worsen, emphasizing the need to strengthen community adaptation.

1. MANAGING RISKS AND ENHANCING
COMMUNITY ADAPTATION TO CLIMATE
VARIABILITY AND CHANGE USING
SUSTAINABLE AGRICULTURAL PRACTICES
AND CLIMATE FORECASTING
Presentation to the SCR Regional Workshop, AACC 23 rd – 25th June 2009
By Alphonce Katunzi
1

2. PRESENTATION OUTLINE
 Tanzania Country Context
 CC, Poverty and Livelihoods
 CCA and DRR in Tanzania
 IFTz and CCA, DRR & Development
 The Project – background & justification
 Project Objectives and Actions
 Approach, Methodology and Project Locations
 Findings, achievements and key lessons
 Issues & Project contribution to CSA to DRM
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3. COUNTRY CONTEXT
 Size = 948,350 km² (881,000km² -Mainland, 2,000 km² - Zanzibar).
 Country’s population = 40 million (80% rural)
 Poverty: 36% below Basic Needs poverty line.
 Major economic sectors: Agriculture, Mining, Services
 Agriculture:
 mainstay of the economy (35% of GNP, 80% of exports & 90% of
the employment).
 potential varies from semi-arid to fertile and highly productive land
under rain-fed agriculture.
 45-75% of area = Semi-Arid’ (SAT), with 200-800 mm rainfall
 Agriculture production in SAT mainly rain-fed = inadequate, high
variability; prolonged dry spells, even during the season.
 Main occupation in SAT: agriculture, pastoralism and agro-pastoralism.
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4. CC LINKS TO POVERTY AND LIVELIHOODS
 Livelihoods: CC undermines livelihoods, through changes in
temperature and rainfall. E.g. yields from rain-fed agriculture in
Tanzania to reduce by 50% by 2020
 Disasters: CC increases frequency and/or intensity of natural hazards -
floods and droughts.
 Rural communities: hardest hit by CC impacts –why?
 Most dependent on climate-sensitive natural resources &
ecosystems – agriculture and livestock (67% rural people)
 Live in rural areas with most exposure to climate hazards.
 Lack capacity to cope with CC effects due to limited human, financial
and institutional capacity.
 Food security: increased food shortages and famines.
 Adaptation more effective than mitigation at least in the short-term.
 Impacts of CC - risk to poverty reduction efforts (URT, 2007) &
attainment of Development Vision 2025 and MDGs.
4

5.  CC development, equity, urgent issue – threatens progress on
development goals, affects many sectors.
 Climatic elements (particularly rainfall variability & uncertainty) most
limiting factors – affects rural livelihoods and agricultural productivity.
 Drought - most critical climatic constraint to development in SAT.
 Low soil moisture retention capacity, highly variable rainfall, seasonal
availability of water, limited growing season, declining soil fertility due
to erosion. and low adaptive capacity of farmers.
 Impacts of drought:
 Makes farming more difficult especially in marginal lands
 Leads to poor or no yields, food insecurity and reduced hh income
 Food shortages, increased food prices, seasonal food crises & famines.
 Can result into loss of livestock and migration in search of pastures.
5

6. FOCUS ON CCA AND DRR
 DRR & CCA share same goal – reducing vulnerability to weather and climate related
hazards.
 Adaptation = finding and implementing ways of adjusting to CCs.
 Approaches to CCA at different levels - from community, national & intern. levels.
― UNFCCC. National Communications, Tanzania NAPA, The Hyogo Framework for
Action
― CBA and CSOs
 For dryland SAT communities - adaption options must address the deteriorating
environmental conditions that undermine their livelihoods.
 Evidence: SAT communities have various autonomous adaptation and risk
management options drawing upon their rich and extensive knowledge base to cope
with adverse environmental conditions and shocks, examples;
 Drought resistant crops,
 SWC & water harvesting innovations,
 Irrigation, cereal & seed banking,
 Diversifying income sources by engaging in other farm or non-farm enterprises
 Use of seasonal climate forecasting to reduce production risks.
 CCA efforts exploit fully such knowledge – esp. in Arid and SA regions.
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7. DRM IN TANZANIA – NATIONAL CONTEXT
Major hazards - natural and man-made, both
climate- and non-climate related
 Climate-related : Drought, floods, landslides,
epidemics, pest infestations, landslides
 Non-climate related: earth quake, fire, civil
strife.
 Examples:
 Widespread drought of 2005-06 & 2008-
09;
 Floods of Kilosa of 2008-09
 Drought and famine of Loliondo and 67
other districts of 2008-09
 Floods in Northern Tz of 2006-07;
 RVF outbreak 2007;
 Impacts: livelihoods affected, infrastructure
destroyed, food insecurity, hunger and
famine, health problems, loss of lives.
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8. DRM IN TANZANIA – NATIONAL CONTEXT CONT’D
 Aim: disaster preparedness to reduce vulnerability and improve
mitigation capacity.
 Disaster preparedness measures:
 Legislation on DRM – national disaster management policy (2004) &
enabling legislation, disaster management dept – PMO; Regional,
district, village level disaster managemet plans & committees, inter-
sector co-ordination and mainstreaming,
 National policies on environment; population growth, land
conservation…, NSGRP.
 One UN Joint Programme: strengthening national disaster
preparedness and response capacity.
 Crop & food security monitoring by MAFS, FEWS…, National SGR…
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9. IFTZ AND CCA, DRR & DEVELOPMENT
 IFTz work - fostering local development by
building local capacity to tackle poverty
using mainly local resources.
 Focus: agriculture and rural development,
livelihoods; natural resource management;
 Past - focus on environmental issues
(Projects: PFI, SFI, IK, LUM & SWC,
PROLINOVA, CCBs…)
 Present – CC from environmental to
developmental issue; impacts on
agriculture and livelihoods, worsens
poverty
 DRR – central to supporting communities
adapt to CC; Anticipatory risk management
options – common as community’s
unconscious response to CC&V.
 CC affects the poor the most - CCA reduces
their vulnerability to CC effects.
 DRR reduces poor people’s vulnerability to
hazards.
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10. THE PROJECT
 Project idea: Based on CA call for Innovative
Projects on CCA – Focus on improved
seasonal climate forecasting
 Background: Farmers & pastoralists in SAT
long relied on traditional weather forecasting
methods & other adaptation methods to warn
against impending drought, food scarcity and
other climatic stresses and design
appropriate mitigation and/or coping and
adaptation strategies.
 Project Focus: Demonstrating how the local
knowledge, skills, and creativity of the people
could be harnessed for coping with the
effects of degrading environmental, CC&V.
 Between 2008/09 to 2009/10 - supported
community-based efforts for adapting to CC,
via action-research linking scientific and local
knowledge on seasonal climate forecasting.
 Enable farmers make reliable forecasts and
increase their resilience to CC impacts.
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11. THE PROJECT – CONT’D
 Rationale:
 Increasing demands on adaptation to CC, local economic and social
development,
 Traditional strategies need to be improved and strengthened.
 Need for timely information on emerging CC to plan appropriate responses.
 Little documented evidence on use of climate forecast by smallholder farmers in
Tanzania, despite its importance for;
 Drought risk management in agriculture
 Increasing the resilience & adaptive capacity of vulnerable communities.
 Decision making on choice of crop/cropping systems, selection of crop
varieties and resource allocation.
 Improving the adaptive capacity of rural livelihoods and make them feel in
control of their lives
 Little has been focused on community level adaptation strategies and how these
can be improved using scientific techniques.
 Aim:
 Support CBA to CC and its impacts by enhancing use of potential adaptation
options.
 Draw lessons from the action-research study in order to increase the resilience of
community and up-scale the experiences to other semi-arid areas.
11

12. OBJECTIVES:
Overall Objective:
 To enhance the resilience of vulnerable communities in semi-arid
central Tanzania to cope with and adapt to CC&V and variability using
reliable information on climate forecast and prediction.
Main Actions:
 Collect, analyse and assess met. information, data & trends on climate forecast.
 Inventory and participatory assessment of local knowledge on climate and
weather forecasting.
 Participatory CRA with communities of likely CC impacts - focus on agric. sector.
 Plan and implement community-based risk management strategies for
enhancing adaptation to the impacts of CC&V.
 Strengthen capacity of communities and supporting institutions to prepare and
respond effectively to future CC risks.
 Promote engagement of communities in decision-making processes on climate-
related adaptation strategies > to influence policy change.
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13. PROJECT PARTNERS, LOCATIONS & DURATION
1. INADES Formation Tanzania
2. Hombolo Research Institute -
Dodoma
3. Tanzania Meteorological
Agency – Dodoma
4. District Government & Dept. Of
Agriculture Extension
5. Village Governments
6. Village Communities
Locations:
 4 villages (2 villages in each
district)
 2 districts in 2 regions of SAT
 Chamwino - Dodoma region.
 Manyoni - Singida region
Duration:
 2008-09 & 2009-10 (2
seasons)
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14. PROJECT APPROACH & METHODOLOGY
 Action-research process to involve
communities in the learning
process - assessing the potential
role of seasonal climate forecasts
for increasing the adaptive
capacity of rural communities.
 Participatory tools and methods -
for understanding the climate,
climate risk assessment and
forecasting (CRA tools).
 Sustainable livelihood approach -
used as a basis for exploring the
theoretical interaction of
livelihoods and climate.
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15. KEY FINDINGS - COMMUNITY PERCEPTIONS OF CC&V
 Climate variability has a large influence on the livelihoods
of communities in SAT
 Climate elements with largest influence are wind, rainfall
and temperature
 Wind - Increase of wind speed and strength – most
frequently mentioned change
 Precipitation - Rainfall pattern changed, becoming
more unreliable compared to the past, its onset and
cessation being very unpredictable, with increased
frequency of bad years compared to good years (every
2-3 years).
 Temperature - suggestion that temperatures were
getting warmer, and two villages suggested the cold
period was getting colder (cold period in June-July
getting much colder).
400
350 Total
2003/04
seasonal
Total monthly rainfall (mm) ..
300 rainfall =
665mm
250 Total
2000/01 Total
seasonal Total 2004/05
200 rainfall = 2001/02 seasonal
498mm seasonal Total rainfall =
rainfall = 469mm
2002/03
395mm
seasonal
150
rainfall =
304mm
100
50
0
Jan-00
Mar-00
May-00
Sep-00
Jan-01
Mar-01
May-01
Sep-01
Jan-02
Mar-02
May-02
Sep-02
Jan-03
Mar-03
May-03
Sep-03
Jan-04
Mar-04
May-04
Sep-04
Jan-05
Mar-05
May-05
Sep-05
Jul-00
Jul-01
Jul-02
Jul-03
Jul-04
Jul-05
Nov-00
Nov-01
Nov-02
Nov-03
Nov-04
Nov-05
15
Month and Year

16. COMMUNITY PERCEPTIONS – CONT’D
 Crop and livestock pest (including quelea quelea birds) & disease incidence
increasing, flooding and water lodging was increasing, again linking these changes
to deforestation.
 In some cases, the change is attributed to neglect of traditions and customs, e.g.
abandoning traditional rituals in today’s communities due to modernization.
 Farmers think the situation is likely to get worse in the future, e.g. in a five year
period, it is likely to have 3 bad and only two good years.
 The local perceptions of CC&V tend to link mainly to local weather patterns - no
linkage is made to global climate changes.
 Village names, linked to climate and weather events and/or patterns:
 Ikowa village – name means ‘bumper harvest’ in local ‘Gogo’ language .
 Past - common for most households to harvest record yields of sorghum and
millet.
 Nowadays - most families get zero yields due to drought and lack of rains.
Perceived strengths & weaknesses to adapt
Strengths:
 Human capital/ ability to farm – energy, strength, family labour, endurance
 Crop diversity – wide range of crops and varieties
 Ability to keep livestock – which can act as a buffer, eg exchange for food
 Crop resilience – having drought resistant seed and rain-water harvesting innovations.
 Ability to store food – food stocks and long term storage knowledge
Weaknesses:
 Natural capital – need to rent land
 Financial capital – no access to credit, lack of capital to buy and sell crops 16
 Human capital – poor understanding of weather, potential to contract AIDS

17. PERCEPTIONS - CONT’D
 Recurring pattern - Frequency of bad years
is increasing every two or three years now
and were more frequent than good years.
 Vulnerablility varies by gender, age,
lifecycle stage and socio-economic status
of the individual.
 Future: to get worse - in a five year period
three years would be bad and two would
be good.
 Perception linked mainly to local patterns,
no linkage to global changes.
17

18. FINDINGS – ADAPTATION AND RISK REDUCTION OPTIONS
Risk reduction and coping strategies adopted by
farmers include establishing demonstration plots for
testing various adaptation options for improving soil
moisture retention capacity, innovations in rain-water
harvesting and use of drought resistant crop
varieties.
 Tillage practices (Deep tillage using spring
jembe, Magoye ripper, and oxen-ridger, compared
to local practice of ‘slash and burn’)
 Drought-resistant varieties: sorghum, sunflower,
sweet potatoes, and maize.
 Use of organic manure
 Tree nurseries and tree planting.
 Installing and using simple rain gauges to
monitor rainfall – alerts farmers on rainfall
amount sufficient soil moisture for planting.
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19. FINDINGS - LOCAL FORECASTING KNOWLEDGE
Indicator % Score Rank
 A lot of available local knowledge still in use for
weather & climate forecasting. TREES
 Most of the local indicators considered to be Mwaliganza 88% 3
potential predictors of weather and future climate Mibuyu 80% 5
(score 80-100). Miondo 75% 7
 Farmers believe that the indicators give correct Msele 88% 3
forecast of the rainfall. Maembe 88% 3
 Some local predictions supported by scientific Mkungugu 85% 4
(met.) data e.g. rainfall variability.& trends ANIMALS
 Scientific & met. information inaccessible at local Ngakakuona 90% 1
level Chimuhanga 88% 3
INSECTS
Mchwa (Ants) 65% 9
BIRDS
Mguulo 89% 2
Yobwa 78% 6
STARS
Local Name 75% 7
WIND 70% 8
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20. PROJECT ACHIEVEMENTS
 Use of drought resistant varieties and deep tillage
practices gave significant yield increases compared to
other practices.
 Information on local knowledge & indicators of weather
forecasting collected, validated and shared.
 Sharing of scientific information on weather - made
communities more conscious of weather
trend/patterns and effects on production activities.
 Farmers record trends in rainfall amounts and link the
data with local forecasts. Rain gauge measurements
have been improved and are more reliable.
 Making information accessible – to community
members. Before, such information was not accessible
within the villages due to poor communication and
linkage with meteorological office.
 Improved record keeping - Use of record cards to
monitor and record patterns and progress of crop
growth stage/condition – Used to forecast yield, and
relate this to effects on crop growth and weather.
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21. PROJECT ACHIEVEMENTS – CONT’D
 Understanding by TMA on the gap that exists
between farmers’ needs and the weather forecasts
that they provide
 Recognition of the role of IK in weather forecasting
 Potential of integrating IK & scientific forecasting
 Awareness raising to stakeholders (farmers,
extension workers, district officials, TMA, NGOs)
 Facilitated formation of groups to undertake
weather forecasting based on IK
 Community-based fora where IK forecasting is
integrated with scientific forecasting from TMA
 Risk assessment & management and developing
strategies for adaptation & DRR
21

22. LESSONS LEARNT
 Improved seasonal climate forecasting, use of drought-resistant crops and moisture-
retention agronomic package are risk management options that play a key role in helping
decision making on farming operations and enhancing adaptation to CC&V.
 Farmers attach particular importance on the value of local knowledge for prediction, and
the added value of scientific knowledge on weather forecasting.
 Local predictors supported by scientific data, e.g. on rainfall pattern - the seasonal
migration pattern of birds locally known as ‘Yobwa/Koronga’ has been proved by
meteorological scientists to be perfectly correlated with the ITCZ (Inter Tropical
Convergence zone) which is the rainfall making mechanism in the East African Zone.
 Need to conserve traditional forests and other sources of local predictors in order to
sustain the local weather forecasting knowledge. However, more research is needed to
establish the potential local predictors of climate/weather forecast
 Accessibility & timely availability of meteorological information could assist farmers to
plan their agricultural activities. Particularly important for forecasting the onset of rains.
 Need to introduce simple meteorological stations in villages to monitor various climate
parameters (temperature, evaporation and rainfall) and its trend.
 Other risk management options – e,g economic activities important to help them bridge
the critical periods of poor production
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23. ISSUES & CONTRIBUTION TO CSA TO DRM
 Translating & communicating information & data
on adaptation options - to increase awareness,
understanding & responsiveness of communities &
other actors.
 Influence development of effective policies by
governments for supporting/guiding CCA & DRM
 Need in-depth shared understanding of local
people’s perceptions and adaptation strategies -
action research/learning process and appropriate
communication/learning tools.
 Integrating IK in school curricula – potential .
 Enhancing interaction btn communities and other
actors (Govt, scientists, NGOs, media..)
Contribution to CSA to DRM:
 Enhances community adaptive capacity to
cope with or manage CC risks.
 Contributes to addressing poverty by
reducing vulnerability & increasing livelihoods
options
23

24. Thank You
and
Asanteni kwa Kunisikiliza
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