Sustainable agriculture and climate forecasting   inades- regional consultation
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Sustainable agriculture and climate forecasting inades- regional consultation Presentation Transcript

  • 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 2
  • 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. 3
  • 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. 6
  • 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. 7
  • 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… 8
  • 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. 9
  • 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. 10
  • 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. 12
  • 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) 13
  • 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. 14
  • 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. 18
  • 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 19
  • 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. 20
  • 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 22
  • 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 24