FANRPAN HIV & AIDS Policy Studies Lindiwe Majele Sibanda

FANRPAN Mission coordinateinfluence facilitate nationalregional globalTo coordinate, influence and facilitate policy research, analysis and dialogue at the national, regional and global levels in order to develop the food, agriculture and natural resources sector. networkingcapacity buildinginformationgenerationThe Mission is achieved through research, networking, capacity building and information generation for the benefit of the SADC region.

Impact of HIV & AIDS on Agriculture & Food Security in the SADC region: A Policy Development Framework This is part of a five-year EU funded project 2 year study Aim: To determine the impact of HIV & AIDS on food security and recommend mitigation and coping strategies for adoption by SADC Ministries of Agriculture

Implementing Countries 7 Study Countries: –Botswana, Lesotho, –Namibia, South Africa, –Swaziland, Zambia & Zimbabwe

Expected Impact Capacity building in the SADC Secretariat and FANR sector for the management and control of HIV & AIDS Development of programmes and strategies to reduce vulnerability of people in the FANR sector to HIV & AIDS and increased support to people that are living with HIV & AIDS

Overall Objective

Project Purpose Intervention LogicIndicatorsImportant Assumptions To contribute to the SADC overarching goal of decreasing the incidence of HIV & AIDS particularly in the FANR sector to promote socio- economic development HIV & AIDS built into the regional and country development policies and strategies Project implemented as planned with maximum stakeholder cooperation

Planned Results

Impact Variables Database Developed using Epi Info 2000, using Microsoft Access Database Developed from national level SPSS databases Has 167 variables and 1930 records from 7 countries. Variables have household data on demographics, health, income, expenditure and impact of HIV and AIDS. Analysis carried out at country and regional levels. Integrated framework within Epi Info allows for analysis and reporting.

Variables Tracked Key Impact AreaKey Variables / Indicators 1. Agricultural Productivity Hypothesis: H/A has led to decline in agricultural productivity. Yield (area cultivated); Overall output; Agricultural input (type and quantity); No. of productive HH members infected/ affected; Education level; Demographic variables; Type and quantity of equipment; Gender of infected/affected; Changes in HH structure; Extension and support services; Area cultivated. 2. Marketing / Livestock Asset Hypothesis: Reduces participation in the market. Sales (no. animal, no. bags); Number of strayed animals; Price per herd; Number of strayed animals; Price per head; Number of animals sold to butcheries; Size of herd; Expenditure on inputs; Availability of labour.

Variables Tracked Key Impact AreaKey Variables / Indicators 3. Mobility Hypothesis: Increase mobility of HH members. Travel expenditure; Household size / composition / structure; Changing HH structure; Number of patients at health care centres. 4. Environmental Hypothesis: Increased degradation of environment. Accumulation of disposable litter; Number of animals with measles; Educational level; Gender. 5. Food Consumption Hypothesis: Decline in household food consumption. Types of food consumed; Expenditure and income patterns; Household income levels; Size of household; Dietary composition.

Variables Tracked Key Impact AreaKey Variables / Indicators 6. Production Assets Hypothesis: Erosion of household productive asset base. Household resource allocation; Household sources of income; Household expenditure patterns. 7. Extension and Support Services Hypothesis: Erosion of extension and research services. Absenteeism due to illness; Farmer extension ratios; Number of deaths in the community; Health status of extensionists. 8. Demographic Structure Hypothesis: Increased dependency ratios. Number of children under 15 years; Number of adults above 65 years; Sex composition of HH members; Education levels of HH members; Employment status.

Example of variables collected: demographics Variable descriptionVariable nameWhether Countries collected data Country NAMIBBOTSZIMBSWAZILESOTS.AFRICAZAMBIA QUESTIONNAIRE NUMBER Questionnaire Numberyes Date no yesno yesno District or RegionDistrictyes Age Of Household HeadAge Of Head of HHyes WARD/Enumeration area/villageLocal Areanoyes noyes Sex of Household HeadSex of Head of HHyes Family nameFamily Nameno yes noyesno Position of the respondent in the familyRespondent Positionnoyes noyes Who is/are the head(s) of this family?Family Headyes noyesno How long has the family been in agriculture (Years)?Years Farmingno yes no Total household sizeTotalHouseholdSizeyes noyes Number of children/Dependents in the HouseholdDependentsyesnoyes Dependency RatioDependencyyesnoyes

LIVESTOCK IS SOLD TO FINANCE MEDICATION OF THE SICKFarmingTimeLostnoyes no yes IT TAKES FARMING TIME AS PEOPLE WILL BE LOOKING AFTER SICK PEOPLE FinancialResources Divertedno yes noyes FARMING FINANCIAL RESOURCES ARE DIVERTED TO MEDICATION for THE SICK FarmingImplements Soldno yesno yesno FARMING IMPLEMENTS ARE SOLD TO FINANCE MEDICAL EXPENSESChoresTimeLostno yesno yesno TIME TO DO HOUSEHOLD CHORES IS SACRIFICED LOOKING AFTER THE SICKSchoolTimeLostno yesno yesno IT TAKES CHILDREN'S TIME TO BE AT SCHOOL LOOKING AFTER THE SICKParentingTimeLostno yesno yesno IT TAKES AWAY PARENTS" TIME TO BE WITH THEIR CHILDREN HouseholdProperty Soldno yes noyes SICKNESS RESULTS IN THE SELLING OF HOUSEHOLD PROPERTYWarno yesno yesno Example of variables collected: impacts

Achievements to Date Lit. Review &Method. Field Data Collection Data Analysis Electronic Database Trang/ Dissem W/shops Country Moongraph Policy Brief Policy brief with Recomm. Newsletter Magazine Journal Articles Regional Book NATIONAL LEVEL BotswanaXXXXXDraftX10 Sept. NamibiaXXXXXDraftX10 Sept. LesothoXXXXXDraftX10 Sept. SwazilandXXXXXDraftX10 Sept. South AfricaXXXXXDraftX10 Sept. ZambiaXXXXXDraftX10 Sept. ZimbabweXXXXXDraftX10 Sept. REGIONAL LEVEL XXX 10/03 X 10/04 X 05/05 X 10/05 X Draft 4 Oct. Final 15 Dec.

Emerging results 1.HIV and AIDS has led to a decline in agricultural productivity: Mean household size was 6.1 About 5% of all households where headed by children under 18years (The figures were 6.4% for Botswana, 3.9% for Lesotho, 1% for Namibia, 1% for South Africa, 2.5% for Swaziland, 6% for Zambia and 3.8% for Zimbabwe) 30 % of households had 3 or more dependents. Of these, Zambian, South African and Namibian households had the largest numbers. 65% of Households reported field sizes of under 2 ha. There was no correlation between field size and amount of fertilizer used % of Households reported that HIV and AIDS illnesses and funerals deprived them of farming time. 75% of households have a dependency ratio greater than 1. ie have more dependents than economically active members.

Contributions to Policy Development Immediate Enhanced Policy Dialogue national and regional Study identified key variables in agriculture and food security. –Production and Marketing –Availability and Access Study quantified impact based on field survey and secondary data. Information database for 7 countries. Regional Database with baseline information on impact

Contributions to Policy Development Medium to Long term Develop & harmonize policies for FANR sector: (baseline)-Impact-policy development-submit for adoption-monitor implementation Develop HIV & AIDS vulnerability index for the FANR sector. This will quantify coping, acute and emergency levels at household and national levels. Help SADC develop social protection policies e.g. agricultural inputs pack, basic needs basket.

Challenges / Lessons Learnt 1. Agricultural chain is broad –Production –Processing –Marketing 2. Food Security is multi-variant –Availability –Accessibility –Utililisation

Challenges / Lessons Learnt 3. HIV & AIDS / Issue of Time Series –Sensitivity of subject 4. Data Collection –No documented records –Household mobility –Time series 5. Coordination of Multi-Country Research In country -communication/networking

Exit Intentions FANRPAN nodes need to be capacitated so they continue to collect and analyse data for longitudinal surveys Policy development takes time Develop & harmonize policies for FANR sector: (baseline)-Impact-policy development-submit for adoption-monitor implementation Formal channel for sharing information at national and regional level created/strengthened

CONCLUSIONS Study has demonstrated need for evidence based policy development Database is only as good as: –Quality of the data stored –Rigour of the analysis –Utilisation of information THERE IS NEED TO UPDATE AND SHARE INFORMATION REGULARLY

THANK YOU EU for financing the study SADC for supporting and coordination study implementation