Integrated Context Analysis Malawi. Photo credit: WFP/Mikael_Bjerrum

Transcription

1 Integrated Context Analysis Malawi Photo credit: WFP/Mikael_Bjerrum

2 TABLE OF CONTENTS BACKGROUND ON THE ICA... 4 SECTION 1: MAIN FINDINGS... 6 SUMMARY... 6 HISTORICAL TRENDS POPULATION AFFECTED... 8 SECTION 2A - CORE DIMENSIONS: ANALYSIS AND METHODOLOGIES FOOD SECURITY SHOCK TRENDS LAND DEGRADATION SECTION 2B - CORE LENSES: ANALYSIS AND METHODOLOGIES LIVELIHOODS & SEASONALITY NUTRITION SECTION 3: SUMMARY OF CATEGORIES CATEGORY 1 - BUILDING RESILIENCE TO SHOCKS CATEGORY 2 - REDUCING SEASONAL FOOD INSECURITY CATEGORY 3 - LONGER-TERM PROGRAMMES CATEGORY 4 - REDUCING RISKS TO SHOCKS SECTION 4: APPLICATIONS & USES ANNEXES DATA SOURCES CONSTRAINTS & OPPORTUNITIES OF THE ANALYSIS

3 LIST OF MAPS Map 1: ICA Categories of Food Insecurity Trends, Risk to Shocks, and Degradation... 7 Map 2: ICA Focus Areas of Food Insecurity Trends, Risk to Shocks, and Degradation... 7 Map 3: Percentage of Food Insecure Population (WFP, average) Map 4: Estimated Number of Food Insecure Population (MVAC 2013/2014 average) Map 5: Recurrence of food insecure populations >30% ( WFP/IHS) Map 6: Number of Drought Events Between 2004 and Map 7: Number of poor rainy seasons in the last 5 years Map 8 Number of Poor Rainy Seasons in the Last 19 Years Map 9 Number of Flood Events between 2000 and 2013 by District Map 10 Combined Drought and Flood Risk by District Map 11 Land cover in Map 12 Land cover in Map 13 Land cover Change 1990 TO Map 14 Percentage of Vegetation Loss by District ( ) Map 15 Square Kilometers of Vegetation Loss by District ( Map 16 Vegetation Loss Reclassified (Land Degradation Proxy) Map 17 Livelihood Zones, Settlements and Vegetation Loss Map 18 Reclassified Land Cover Classes Affected by Poor Growing Seasons ( ) by Livelihood Zone Map 19 Inter-annual Vegetation variability Map 20 Prevalence of Global Acute Malnutrition (DHS 2010) Map 21 Prevalence of STUNTING (DHS 2010) Map 22 Stunting and recurrence of food insecurity by ICA Focus Areas Map 23: Category 1 Districts Map 24: Category 2 Districts Map 25: Category 3 Districts Map 26: Category 4 Districts LIST OF TABLES Table 1: Number of Food Insecure people from 2009 to 2013 as per MVAC Annual Reports... 8 Table 2: Planning estimates... 8 Table 3: ICA Categories, Focus Areas and Key ICA Indicators... 9 Table 4: Classification of recurring Food Insecurity LIST OF FIGURES Figure 1: World Food Programme s Three Pronged Approach (3PA)... 4 Figure 2: Conceptual Framework for ICA

4 Background on the ICA The Integrated Context Analysis (ICA) is an analytical process that contributes to the identification of broad national programmatic strategies, including resilience building, disaster risk reduction, and social protection for the most vulnerable and food insecure populations. The ICA can be used to identify more specific programme responses at sub-national levels, and identifies areas where further in-depth studies or food security monitoring and assessment Figure 1: World Food Programme s Three Pronged Approach (3PA) systems are needed. They guide the identification of priority areas in which to conduct Seasonal Livelihood Programming (SLP) consultations to identify areaspecific complementary and multi-sectorial programmes with governments and partners, which in turn set the foundations for targeted joint efforts with communities and partners to plan and implement programmes through Community-Based Participatory Planning (CBPP). The ICA is a series of analytical steps that undertakes a historical trend analysis of three core sectors; food security, shocks, and aggravating factors to identify areas of convergence. This trend analyses provides an understanding of what has happened in the past and what may (or may not) be changing to act as a proxy as to what may occur in the future. This is intended to identify short, medium, and longer term programming efforts and broad strategies, highlighting geographic differences in strategies. This core analysis then layers additional core lenses of nutrition, seasonality, and livelihoods to provide further insight to the strategies of the final ICA product and providing additional overlays to better refine the strategies, such as recurrence of food insecurity, livelihood appropriate strategies, and any convergence of nutritional outcome. With the core analysis completed additional data, from relevant stakeholders, on broad underlying issues provide additional overlays. The combinations of which can help identify broad programmatic strategies that may be required to address underlying issues in a more holistic manner, drawing on the comparative advantages and technical expertise of governments, partners, communities, and of affected populations themselves. A generalised conceptual framework for this analysis is laid out in Figure 2. This ICA has been carried out in consultation with WFP s Emergency Preparedness (OMEP) and Programme (VAM and Programme Design) Divisions. It acts as a contribution to discussions on programming strategies and on which to build further in-depth studies together with government and other stakeholders in Malawi. The core components of the ICA covers three broad analytical domains trends of food insecurity, main natural and man-made shocks (droughts and floods), and loss of vegetative cover as a proxy to land degradation as a factor that increases the risk and heightens the impact of natural shocks. Further to this, we add three additional lenses of nutrition, livelihoods, and seasonality that are chosen for their broad applicability to multiple agencies. 4

5 Figure 2: Conceptual Framework for ICA The report is structured in 3 sections as follows: SECTION 1: MAIN FINDINGS This section provides an overview of the different areas where convergences of recurring food insecurity and exposure to natural shocks were found, and what this may imply in programmatic terms. SECTION 2a CORE DIMENSIONS: Analytical Approach And Methodology In this section the various sub-sets of the analyses that were used in the ICA are discussed and presented. SECTION 2b CORE LENSES: Analytical Approach And Methodology In this section the lenses of livelihoods, seasonality, and nutrition are applied to the ICA, discussed and presented SECTION 3: DESCRIPTIONS OF CATEGORIES This section gives an overall description of the five Category areas identified, providing broad entry points to contribute to designing more strategic approaches to overall programming in these areas. Section 4: Applications and Uses of the ICA This section is intended to show the broad areas in which the ICA has application and to what purpose. The idea is to start the discussion for further analysis and use of the ICA in common decision-making processes. Lastly, a set of Annexes is provided on data sources, and limitations and opportunities of the ICA. 5

6 SECTION 1: MAIN FINDINGS SUMMARY The ICA found five distinct patterns based on the convergence of recurring food insecurity and exposure to shocks (floods and droughts). Districts where these patterns were identified have been grouped into specific geographical areas, classified as Categories 1 to 5. Broad programmatic strategies were identified for each of these categories, to be used as a basis for discussion with partners: Risk HIGH MEDIUM LOW EXPOSURE TO NATURAL SHOCKS CATEGORY 1 Recurrence of FOOD INSECURITY HIGH MEDIUM LOW Longer-term programming to improve food security, reduce risk, and build resilience to natural shocks and other stressors. CATEGORY 3 CATEGORY 2 Seasonal/Recovery programmes to restore and improve food security, reduce risk, and build resilience to natural shocks and other stressors Longer-term programmes to improve food security and mitigate impact of natural shocks and other stressors. CATEGORY 4 Programming that strengthens preparedness, reduce risk and build resilience to natural shocks and other stressors. CATEGORY 5 Programming to strengthen preparedness and mitigate impact of natural shocks and other stressors. Map 1 presents the geographic spread of the categories identified by District. Each category was further sub-divided into two Focus Areas (Map 2), based on the level of recurring food insecurity and risk of shocks, as shown in the table below. Additional information on programme implications of Categories and Focus areas are provided in Section C. DROUGHT & FLOOD RISK LOW LEVEL (very low low) MEDIUM LEVEL (moderate) HIGH LEVEL (high very high) FOOD INSECURITY TRENDS (WFP) LOW (1) MEDIUM (2) HIGH (3) CATEGORY 5 CATEGORY 3 CATEGORY 3 Focus area 5 Focus area 3b Focus area 3a CATEGORY 4 CATEGORY 2 CATEGORY 1 Focus area 4b Focus area 2b Focus area 1b CATEGORY 4 CATEGORY 2 CATEGORY 1 Focus area 4a Focus area 2a Focus area 1a No districts were found in Category 5, although in time districts would be expected to shift from Category 3 to Category 5, as food insecurity is reduced. Overall, food security programmes should aim to shift counties from High to Medium, and then to Low food insecurity categories (i.e. from right to left on the table above). If these programmes are delivered with an added objective to reduce risks and building resilience to natural shocks, then shifts from High to Medium and Low (i.e. from the bottom to the top of the table) should be achieved. 6

7 Map 1: ICA Categories of Food Insecurity Trends, Risk to Shocks, and Degradation Map 2: ICA Focus Areas of Food Insecurity Trends, Risk to Shocks, and Degradation 7

8 HISTORICAL TRENDS POPULATION AFFECTED Food insecure populations in Malawi are predominately based on the MVAC / HEA (Malawi Vulnerability Assessment Committee / Household Economy Analysis) assessment conducted each year. This indicates the most vulnerable population in particular parts of districts most affected by food deficits. The MVAC / HEA data is not used to determine the percentage of the district population that is food insecure but as they are the most commonly used numbers they are a useful guide for the analysis of historic trends to better anticipate planning figures. By using the second round (update) of the MVAC / HEA for the years 2009 to 2013 we can show the average population most vulnerable to food deficits, shown in Map 4. In Table 1 the total number of the population identified each year (2009 to 2013) is presented. For longer-term programme planning, an indication of the number of people that are likely to require assistance is needed (recognizing that plans are then adjusted throughout the programming cycle through assessments that reflect the current situation i.e. MVAC). To do this, a trend analysis using the total number of food insecure people identified by the MVAC in the last 5 years was conducted (table below): Table 1: Number of Food Insecure people from 2009 to 2013 as per MVAC Annual Reports ,168 1,642, ,854 1,977,992 1,894,683 The overall average of the number of people of in the last 5 years would reflect people that are either most vulnerable, or have experienced high vulnerability to food deficits at some point and could be recovering from an event that caused them to be regarded as most vulnerable. This would represent an overall longer-term planning estimation. The estimated number of additional people that could fall into crisis in the event of a shock is the difference between the overall average of the number of most vulnerable people (previous step) and the average of the two highest peaks (highlighted in red in the table above) of food insecure people. Note however that this is just a planning estimate, and actual numbers should be derived from emergency assessments in the event of a crisis. Similarly, the average number from the two lowest points (highlighted in yellow in the table above) over the recall period provides an estimate of a core group of people that were most vulnerable to food deficit, irrespective of whether there were good harvests in the last five years reflecting an estimate of the core group of most vulnerable people for planning purposes. In summary, planning estimates (rounded up) would be as follows: Table 2: Planning estimates Average number of food insecure people in the last three years: Long-term planning 1,198,535 Estimated core number of food insecure people (average of two lowest figures): Additional number of people in the event of a shock (difference between 3-year % and two highest peaks): Most vulnerable 238,511 Preparedness planning 737,802 The WFP/IHS data analysis considers not just the numbers affected by food insecurity. The analysis of this data provides an indication of a wider dimensions on food insecurity, measuring food consumption, coping capacity, coping strategies, and expenditure. The WFP/IHS data provides a district wide perspective on the population and illustrates the degree of food insecurity experienced in the population at the time of the survey. The ICA provides an additional layer of information on what the underlying degree of food insecurity is throughout the district and the mean value indicates the long-term average of food insecure by district; see Map 3. This provides a comparative analysis of where the greatest depth of food insecurity occurs, permitting planners to see where the highest levels of food insecurity are being experienced over time. 1 It is to be noted that in 2009 and 2011 only a few districts were included in the annual assessments therefore the figures are lower compared to the other years. 8

9 Table 3 ICA Categories, Focus Areas and Key ICA Indicators ICA Category & Focus Areas Category 1 Category 2 Category 3 Southern Region Nsanje 274,797 Poor High 64, % High High High High Low Lower Shire Southern Region Balaka 383,887 Poor Very High 78, % High High Med. High Med. Middle Shire Valley Southern Region Chikwawa 518,287 Acceptable Very High 132, % High High Med. High Med. Lower Shire 1a Southern Region Phalombe 364,282 Acceptable Very High 54, % High High Med. High Low Lake Chilwa, Phalombe Plain TOTAL: 1,541,253 TOTAL: 329,963 Central Region Dedza 718,747 Acceptable Very High 63, % High Med. Med. Med. Low Kasungu Lilongwe Plain Southern Region Mulanje 564,976 Poor Very High 91, % High Med. Low Med. Med. Lake Chilwa, Phalombe Plain 1b Central Region Salima 407,148 Acceptable Very High 75, % High Med. Med. Med. Low Rift Valley Escarpment TOTAL: 1,690,871 TOTAL: 231,220 Southern Region Zomba 648,882 Acceptable Very High 58, % Med. High High High Low Shire Highlands Southern Region Blantyre 389,906 Acceptable Very High 73, % Med. High Med. High Med. Middle Shire Valley Southern Region Machinga 589,709 Poor Very High 51, % Med. Med. High High Low Lake Chilwa, Phalombe Plain 2a Southern Region Mangochi 982,058 Poor Very High 66, % Med. Med. High High Med. Shire Highlands TOTAL: 2,610,555 TOTAL: 250,828 Southern Region Chiradzulu 314,059 Acceptable Very High 35, % Med. High Low Med. Low Shire Highlands Central Region Ntchisi 276,481 Acceptable Very High 23, % Med. Med. Low Med. Low Kasungu Lilongwe Plain Northern Region Rumphi 211,170 Acceptable High 29, % Med. Low Med. Med. Med. Protected Area Central Region Nkhotakota 367,776 Acceptable Very High 38, % Med. Low Med. Med. Low Protected Area Central Region Ntcheu 557,433 Serious Very High 55, % Med. Med. Med. Med. High Rift Valley Escarpment Southern Region Mwanza 102,571 Acceptable Very High 21, % Med. Med. Low Med. Med. Middle Shire Valley 2b Southern Region Thyolo 633,019 Acceptable Very High 112, % Med. Med. Low Med. Med. Thyolo Mulunje Tea Estate TOTAL: 2,462,509 TOTAL: 317,597 Central Region Dowa 732,343 Acceptable Very High 31, % High Low Low Low Low Kasungu Lilongwe Plain Central Region Mchinji 569,085 Acceptable Very High 58, % High Low Low Low Low Kasungu Lilongwe Plain Central Region Lilongwe 1,421,454 Poor Very High % High Low Low Low Low Kasungu Lilongwe Plain Western Rumphi, Mzimba self 3a Northern Region Mzimba 211,170 Acceptable Very High 211, % High Low Low Low High sufficient TOTAL: 2,934,052 TOTAL: 302,061 Nkhata Bay Cassava- Northern Region Nkhata Bay 260,583 Acceptable Very High % Med. Low Low Low Med. Southern Karonga Chitipa, Northern Karonga, Northern Region Chitipa 211,170 Acceptable Very High % Med. Low Low Low High Central Koronga, Misuku Hills 3b Central Region Kasungu 794,991 Acceptable Very High 113, % Med. Low Low Low Med. Kasungu Lilongwe Plain TOTAL: 1,266,744 TOTAL: 113,813 Chitipa, Northern Karonga, 4a Northern Region Karonga 327,084 Acceptable High 56, % Low Med. High High High Central Koronga, Misuku Hills TOTAL: 327,084 TOTAL: 56,005 Uncategorised Province District Pop Wasting Class (2010/ 2011) Stunting Class (2010/ 2011) Avg. Pop. Est. as Most Vulnerable ( ) % of Food Insecure Pop. ( WFP/IHS) # Yrs Prev. Poor/ Border FCS > 30% (2009/11/13) Drought Freq Flood Freq Risk of Droughts & Floods Veg. Dec Most Prevalent Livelihood Type Northern Region Likoma 10,441 No Data No Data % No data High Low Med. Low Northern Lakeshore, Southern Lakeshore Southern Region Neno 143,824 Acceptable Very High 45, % No data Med. Low Med. Med. Middle Shire Valley TOTAL: 154,265 TOTAL: 45,958 9

10 Map 3: Percentage of Food Insecure Population (WFP, average) Map 4: Estimated Number of Food Insecure Population (MVAC 2013/2014 average) 10

11 Section 2a - CORE DIMENSIONS: Analysis and Methodologies FOOD SECURITY The food security analysis uses data from the CFSVA, IHS, and EFSA reports. This information provides a population based deeper understanding of the food security situation than only that of the population affected. WFP and IHS data: (CFSVA 2009, IHS , EFSA 2012 urban and 2013 rural). The main indicator used for the analysis was the Food Consumption score. The food consumption score (FCS) is based on dietary diversity, food frequency, and relative nutritional importance of the various food that groups consumed. The higher the FCS, the higher is the dietary diversity and frequency. High food consumption increases the possibility that a household achieves nutrient adequacy. Households are divided into one of three groups based on their food consumption score: poor, borderline or acceptable food consumption. Data from WFP/VAM and IHS assessments were available from three years: CFSVA 2009, IHS 2010/2011 and EFSA For the data from the WFP assessments, how many times out of three a district had prevalence of food insecurity above 30% was identified, and when not all districts had been included the numbers were adjusted to represent all three times. The district had to be included at least 2 out of the three times to be included in the analysis. Three classes were then identified to better interpret whether the recurrence of food insecure populations above 30% could either be seasonal or a result of a shock/crisis. It was assumed that if a district had a food insecurity prevalence over 30% consistently over the three years, its situation was to be considered as of high food insecurity (=stable) while if out of three years only in one year the prevalence was over 30% it could be a case of a shock and therefore the classification was low; see Table 4 and Map 5. Table 4: Classification of recurring Food Insecurity Classification Number of years out of 3 district food insecure population >30% 1 (low) 0 or 1 year out of 3 2 (medium) 2 years out of 3 3 (high) 3 years out of 3 11

12 Map 5: Recurrence of food insecure populations >30% ( WFP/IHS) 12

13 SHOCK TRENDS Overview: reclassification methods for shocks & aggravating factors The two main natural shocks identified in Malawi were droughts and floods. Reclassifications have been made through an integration of quantitative and qualitative approaches i.e. the quantitative approach is the standard mathematical classification schemes behind the classifications, whilst the qualitative approach is the perception/assumption of what it is/is not according to its context. The classification process that generates the creation of differences is a crucial step for the interpretation of the information, and decisions taken at this stage have a significant impact on the representation of the final maps. When considering classifications for shocks and aggravating factors, thresholds that define each district as having a low, medium and high surface percentage for each type of natural hazard were identified. To make these decisions, it is important to have in mind the heterogeneity of the data between the different types of natural hazards and the distribution of the percentages within each type of natural hazard (histogram). Therefore, it has not been possible to establish common classification thresholds between the different types of natural hazards and aggravating factors. According to the nature of the information and to the cartographic needs, different classification methods can be chosen: The Equal Interval method states that the classes are divided into regular intervals (i.e. 1--3, 4--6, 7--, etc.). The Quintile method contains the same number of elements by class, and the intervals/cut-offs are set in this way depending on the sample type. Other methods, such as Geometric Standard or Deviation Interval are suitable for the representation of data with high dispersion and internal heterogeneity. For the shocks and aggravating factors in this ICA, preference to using natural breaks (Jenks) was given. This method is designed to obtain the best possible categorization within the values of the different classes. The technique is based on natural groupings inherent to the data: the class breaks are identified to best group similar values and to maximize the differences between classes. This method reduces the variance within the classes and maximizes the variance between different classes. After classifying each layer using this method, a consultative qualitative approach was then applied to slightly adjust the classes. Whilst recognizing that there is no perfect method of classification, the ICA analysts believed this was the most appropriate approach given the context and type of data. Droughts Ministry of Finance and Economic Planning and Development national level data on drought occurrences by district from 2004 to 2013 was used for the analysis (see table below and Map 6). To better understand the recent and long-term exposure to droughts, this data was triangulated with a 19-years and 5-years rainfall trend analysis. The analysis identified the number rainy seasons significantly below average in the past 5 and 19 years, respectively (Map 7 and Map 8). DROUGHT EVENTS BETWEEN 2004 AND 2013 Drought Recurrence 1 3 TIMES 4 6 TIMES > 7 TIMES Reclassification LOW (1) MODERATE (2) HIGH (3) The 5-years rainfall trend analysis determined the number of times in the last 5 years that rainfall (<80%) was significantly below the 15 year average this would be used as 13

14 a proxy to determine which districts would have more recently experienced significant below-average rainfall (i.e. possible drought conditions). Similarly, a second analysis was conducted to identify which areas in the last 19 years experienced more frequent below-average rainfall seasons and therefore possible belowaverage vegetation growth. This is used a proxy to identify those areas experiencing more drought-like conditions. Floods The analysis is based on historical flood data from the Department of Disaster Management Affairs (DoDMA), providing information on the number of flood events by district between 2000 and The analysis identified those districts regularly affected by floods and provided additional information on long -term trends. These were computed and results placed in 3 classes (see table below) and displayed in Map 9. Flood Frequency < 3 TIMES 4 7 TIMES > 8 TIMES Reclassification LOW (1) MODERATE (2) HIGH (3) Combining droughts and floods into a single layer of risk Once the recurrence values of drought and flood risks were computed, a new variable was created to estimate the risk of these shocks to each district. For each of these two shocks, each district was assigned a risk value of 1 (low-level), 2 (medium-level), and 3 (high level). Values from each shock risk were added and reclassified into a risk score (tables below and Map 10). FLOOD RISK DROUGHT RISK RISK 1 (LOW) 2 (MEDIUM) 3 (HIGH) SCORE CLASSIFICATION 1 (LOW) VERY LOW 2 (MEDIUM) LOW 3 (HIGH) MODERATE 5 HIGH 6 VERY HIGH 14

15 Map 6: Number of Drought Events Between 2004 and 2013 Map 7: Number of poor rainy seasons in the last 5 years 15

16 Map 8 Number of Poor Rainy Seasons in the Last 19 Years Map 9 Number of Flood Events between 2000 and 2013 by District 16

17 Map 10 Combined Drought and Flood Risk by District 17

18 LAND DEGRADATION The status of the natural environment can magnify the impact of shocks. Heavily degraded land is no longer protected due to soils being laid bare as vegetation cover is lost, and becomes unable to withstand the natural elements it is exposed to such as rain, wind, and temperatures. These elements on degraded land further increase land degradation and erosion, leading to a cyclical and destructive effect that makes land extremely fragile and unable to withstand even normal climatic patterns. Given that people draw on the surrounding natural environments for their livelihoods and to cope during times of crisis, poor land practices and unsustainable use of environmental resources will further aggravate land degradation and the risk of shocks. This becomes part of the pattern, with human pressure on land contributing to the risk of increasing degradation, and further stripping of vegetation and soils in an effort to cope with the resulting increase in shocks. No land degradation data was available for the analysis, thus a deforestation analysis was performed using remotely sensed land cover data for 1990 and 2010 from the European Space Agency (ESA) as a proxy for land degradation 2. First, the land cover data has been depicted as shown on Map 11 and Map 12. Secondly, a land cover change analysis from 1990 to 2010 was conducted in order to understand change patterns in land cover and deforestation trends over time (Map 13). Thirdly, land cover change classes that implied loss of vegetation cover were selected and merged in order to better understand the overall vegetation loss from 1990 to 2010 (see Map 14). Then, for each district the surface and percentages of vegetation loss was calculated. Using natural breaks (Jenks), these two variables (percentage and surface) were reclassified with values from 1 to 3 (see tables below and Map 15 and Map 16). Percentage of Vegetation Cover Loss Surface of Vegetation Cover Loss 1 (LOW) 0-15% 1 ( LOW) 0 1,000 Km2 2 (MEDIUM) 15-28% 2 (MEDIUM) 1,001 2,000 Km2 3 (HIGH) 28-39% 3 (HIGH) > 2,001 Km2 Fourthly, these two variables were then combined in order to understand the overall severity of deforestation between 1990 and For each of these two variables, each district was assigned a value of 1 (low-level), 2 (medium-level), and 3 (high level). Values from each variable were added and reclassified into a vegetation loss score (tables below). % OF SURFACE LOSS SQUARE KM OF SURFACE LOSS VEGETATION 1 (LOW) 2 (MEDIUM) 3 (HIGH) LOSS SCORE CLASSIFICATION 1 (LOW) VERY LOW 2 (MEDIUM) LOW 3 (HIGH) MODERATE 5 HIGH 6 VERY HIGH Lastly, these scores were finally reclassified into the following 3 categories and displayed in Map 16: Levels of land degradation Very Low - Low Moderate High Very High Reclassification Low Medium High 2 The Land Cover maps have been developed from Landsat Imagery (30m x 30m) resolution using supervised classification. Image interpretation was done per scene. Images used for classification were selected based on seasonality, dry season images preferred. Classification scheme used is based on Intergovernmental Panel on Climate Change (IPCC). 18

19 Map 11 Land cover in 1990 Map 12 Land cover in

20 Map 13 Land cover Change 1990 TO 2010 Map 14 Percentage of Vegetation Loss by District ( ) 20

21 Map 15 Square Kilometers of Vegetation Loss by District ( Map 16 Vegetation Loss Reclassified (Land Degradation Proxy) 21

22 Section 2b - CORE LENSES: Analysis and Methodologies LIVELIHOODS & SEASONALITY An understanding of livelihoods and seasonality informs how shocks may impact households, the times of the year that are most critical for people, and how to select programming interventions. Livelihoods Fourteen main livelihood zones were identified in Malawi (MVAC, 2009) as illustrated in Map 17, where settlement points were overlaid on livelihood zones in order to allow for a more refined geographical presentation of the spread of different livelihoods based on where people are most likely to be found. Driving factors for production are linked to the rainy seasons that govern vegetation growth at different times of the year be it for agricultural production or pasturelands and these will vary throughout the year depending on geographical location. An analysis was conducted to identify which livelihoods are mostly affected by poor growing seasons, used as a proxy for drought, in the last 5 years Map 18. Regional vegetation dynamics from year to year (inter-annual) were analyzed to help understand the temporal variation of vegetation in Malawi, which could have an impact on livelihoods that rely on agriculture, and is important in order to inform programming and monitoring efforts. This inter-annual variation of NDVI was analyzed by using the standard deviation of monthly (only the vegetation growth months) composite NDVI (Map 19). Seasonality of Food Insecurity Given that livelihoods are closely linked to seasonal events which will determine periods of production and scarcity, a broad review of seasonal factors is important in order to determine whether there are any key differences throughout the year to better inform programming decisions and design. This part of the analysis requires data on the seasonal changes in food insecurity. The principal source of estimates of changes in food insecurity within the year are the MVAC/HEA initial report followed by the updates closer to the beginning of the lean period. This analysis will provide an indication as to the seasonal change in vulnerability and will be carried out once these data are complied and processed. 22

23 Map 17 Livelihood Zones, Settlements and Vegetation Loss Map 18 Reclassified Land Cover Classes Affected by Poor Growing Seasons ( ) by Livelihood Zone 23

24 Map 19 Inter-annual Vegetation variability 24

25 NUTRITION Insufficient national-level by district nutrition data was available to conduct a trend analysis 3. Thus, the most recent nutritional survey (DHS 2010) was used to compare against the 5 year combined trend analysis of MVAC and WFP recurrence food insecurity above 30% of the district total and FEWSNET food security classifications. Global Acute Malnutrition and stunting were mapped according to the WHO cut-off values for public health significance (Reference: WHO; 1995): Global Acute Malnutrition (low weight for height) Stunting (low height for age) < 5%: Acceptable < 20%: Low prevalence 5 9%: Poor 20 29%: Medium prevalence 10 14%: Serious 30 39%: High prevalence 15%: Critical 40%: Very high prevalence* *For the purposes of the ICA analysis an additional category was created ( 50%: very very high prevalence ) in order to better indicate the geographic differences in the degree of stunting between districts in Malawi. Global Acute Malnutrition (GAM): the to DHS 2010 showed that in all districts - except seven - wasting was found to be within acceptable limits (Map 20). As wasting can quickly change over time, this indicator was not used for the trend analysis as only one point in time was available. Stunting: the rates of stunting however show a more serious picture. In all but 4 districts stunting prevalence falls into the very high prevalence category. For this analysis an additional category was created to identify where the situation was much worse ( 50%) and within which 7 districts fell. WHO s Interpretation Guide for Nutrition states that the percentage of stunted children reflects the cumulative effects of long-term nutritional deprivation and infections since and even before birth, and that stunting can be interpreted as an indication of poor environmental conditions or long-term restriction of a child's growth potential as a result of poor diets or recurrent infections. Critically, stunting often results in delayed mental development, poor school performance and reduced intellectual capacity that, in turn, affects economic productivity at national level. Children born to smaller women are at greater risk of having a low birth weight, which also contributes to the intergenerational cycle of malnutrition as infants of low birth weight or retarded intrauterine growth also tend be smaller as adults 4. Map 21 indicates the prevalence of stunting from DHS 2010 data. Overlaying latest nutrition information on food security trends This stunting data was overlaid onto the recurrence of food insecure populations above 30% of the district total to identify areas of convergence. This overlay is presented in Map 22. Results show that there are a few districts where high level of food insecurity are also combined with serious or critical level of stunting prevalence, therefore indicating areas of the country where persistent and chronic food insecurity problems are likely to occur. These areas should be closely monitored considering that stunting prevalence do not change quickly over time and that these are most likely chronically food insecure areas where long-term food security interventions should be planned. 3 Previous DHS data 4 WHO: Nutrition Landscape Information System (NLIS) country profile indicators: interpretation guide; 2010 (ISBN ) 25

26 Map 20 Prevalence of Global Acute Malnutrition (DHS 2010) Map 21 Prevalence of STUNTING (DHS 2010) 26

27 Map 22 Stunting and recurrence of food insecurity by ICA Focus Areas 27

28 SECTION 3: SUMMARY OF CATEGORIES Map 23: Category 1 Districts Category 1 Building Resilience to shocks Category 1 is characterized by districts where 30% or more of the population have been consistently identified as food insecure through the MVAC and WFP data, and with a high to medium exposure to droughts and floods subdivided as Focus Areas 1a and 1b respectively. This evidence indicates that in these districts in the last 5 years, one (or more) in three households: Never met their food needs i.e. 4 or 5 times out of 5 assessments May have met their food needs in one year, but not for the other four i.e. the 1 to 3 times (out of 5 MVAC/WFP assessments). Inability to meet food needs for the rest of the time could be the result of a shock year(s) and a subsequent recovery period Category 1 districts show a convergence between large proportions of consistently food insecure populations that are vulnerable/experienced shocks. Higher exposure to shocks will result in reduced recovery time between crises, which in turn erodes coping capacities and the natural resource base around them and further increases risk to natural shocks. In such scenarios, development gains face constant setbacks and people s own ability to move Recurrence of Food Insecurity above 30% Low (1) Medium (2) High (3) Low level Focus area 5 Focus area 3b Focus area 3a Medium level Focus area 4b Focus area 2b Focus area 1b High level Focus Area 4a Focus area 2a Focus area 1a out of food insecurity is a major challenge. RISK SCORE Populations in these districts require longer-term efforts to reduce food insecurity and build their resilience to frequently occurring and/or high risk to natural shocks. Given the predictability of high food insecurity and likelihood of shocks, social and productive safety nets that assist people to meet basic needs, reduce food insecurity and poverty by strengthening livelihoods, and simultaneously reducing the risk and impact of shocks should be considered as a key foundation for building resilience to recurring crisis that compromises development. This would include stabilizing landscapes and reducing land degradation that aggravates the likelihood of risk, and the strengthening of early warning systems and preparedness to better enable people to manage these shocks. Such distinctions should be used as part of discussions on prioritization of specific areas and programmes when resources are constrained, and how the balances between humanitarian and development actions in the same areas should be leveraged for complementarities and greater partnerships. Additional information to assist decision making The information presented by the ICA can contribute to strategy development, planning, and the selection/prioritization of specific areas for programming. Data relevant specifically to Category 1 is summarised here below: 28

29 Food Security and Nutrition ICA Focus Area 1a 1b District Pop Wasting Class (2010/2011) Stunting Class (2010/2011) Avg. Pop. Est. as Most Vulnerable ( ) % of Food Insecure Population ( WFP/IHS) Nsanje 274,797 Poor High 64, % High Balaka 383,887 Poor Very High 78, % High Chikwawa 518,287 Acceptable Very High 132, % High Phalombe 364,282 Acceptable Very High 54, % High TOTAL: 1,541,253 TOTAL: 329,963 Dedza 718,747 Acceptable Very High 63, % High Mulanje 564,976 Poor Very High 91, % High Salima 407,148 Acceptable Very High 75, % High TOTAL: 1,690,871 TOTAL: 231,220 Natural shocks and livelihoods ICA Focus Area District Pop Drought Freq a 1b Flood Freq Risk of Droughts & Floods Veg. dec # Yrs prev. Poor/ Border. FCS > 30% (2009, 2011, 2013) Most Prevalent Livelihood Type Nsanje 274,797 High High High Low Lower Shire Balaka 383,887 High Medium High Medium Middle Shire Valley Chikwawa 518,287 High Medium High Medium Lower Shire Phalombe 364,282 High Medium High Low Lake Chilwa, Phalombe Plain TOTAL: 1,541,253 Dedza 718,747 Medium Medium Medium Low Kasungu Lilongwe Plain Mulanje 564,976 Medium Low Medium Medium Lake Chilwa, Phalombe Plain Salima 407,148 Medium Medium Medium Low Rift Valley Escarpment TOTAL: 1,690,871 This information can be used by Government to support the design of overall strategy design, and through discussions and agreements provide partners with direction as where their efforts can be targeted and coordinated to ensure that their programming is in support of and complementary to on-going government efforts, and that duplication and gaps are avoided. It is noted that this information is not exhaustive and far more is required such as information on health, education, markets, infrastructure, resourcing etc. however, it does provide a foundation on which to expand and add additional information. Given the magnitude of food insecurity and stunting and through discussion with Government (and each other) this information can be used by partners to target specific populations and geographical areas based on the broad programmes and areas of expertise they can deliver. Note however that this does not mean one partner per area, but rather, multi-partners identifying complementary multi-sectorial activities and delivering support as a package of interventions. For example, partners can consider: Selecting specific districts and within those, targeting all or a proportion of the estimated food insecure population number identified (e.g. 75% or 50% etc.) although this would need to be coordinated with others to ensure that there are no gaps for food insecure population Deciding to provide support in districts where food insecurity levels are above a certain percentage (e.g. 50%) or number of people (e.g. where there are more than 100,000 food insecure people) etc. Identifying those districts where they have the expertise or comparative advantage in programming geared towards reducing the risk of the specific shock in the area Selecting adjoining districts (within and between the different districts and Categories) to where they may already be operating in order to ensure geographical continuity and maximize resources 29

30 Where resource/programme gaps exist and not all of food insecure populations are being reached Any combination of the above Note that these considerations are not exhaustive, and are also relevant to the remaining categories when discussing the design of programme strategies and programme planning (and hence will be repeated in other sections. 30

31 Category 2 Reducing Seasonal Food Insecurity Map 24: Category 2 Districts Category 2 is characterized by districts where 30% or more of the population have been identified as food insecure half of the time through the MVAC and WFP data, and with a high to medium exposure to droughts and floods subdivided as Focus Areas 2a and 2b respectively. These districts indicate that in the last 5 years, one (or more) in three households: May have experienced a shock year(s) - i.e. 2 or 3 times (out of 5 MVAC/WFP assessments) could be related to shocks, followed by a recovery period. Category 2 districts show a convergence between large proportions of recurrently food insecure populations that are vulnerable/experienced seasonal shocks. This suggests that people either face periods where they may not be meeting their full food needs i.e. seasonal hunger - or have experienced an event/shock in the last five years that resulted in their inability to meet their full food requirements during that period. This is confirmed by the fact that during the five years they have experienced recovery periods. Seasonal hunger compromises and slows down vulnerable people s own abilities to invest and move out of food insecurity, as every year they need to draw down on assets and savings they accumulated during better times to cope with difficult times during the year e.g. savings made during harvest periods will be depleted during the lean season, etc. Where populations have experienced a shock, the most vulnerable people will need time to recover and restore livelihood (and natural and environmental) assets that would have been lost in trying to cope with the crisis. High exposure and risks to shocks aggravates and heightens vulnerability for such populations. RISK SCORE Recurrence of Food Insecurity above 30% Low (1) Medium (2) High (3) Low level Focus area 5 Focus area 3b Focus area 3a Medium level Focus area 4b Focus area 2b Focus area 1b High level Focus Area 4a Focus area 2a Focus area 1a Vulnerable people living in these districts would benefit from longer-term efforts to strengthen livelihoods and build their resilience to the high risk of recurrent shocks. Productive seasonal safety nets geared towards strengthening livelihoods and stabilizing landscapes/reversing degradation to reduce the risk of shocks would simultaneously ensure food and other basic needs are met without depleting assets in order to cope. Combined with early warning systems and preparedness, this would lay the foundations for building resilience to recurrent crises, and assists in safeguarding people s own investments and any development gains that have been made. 31

32 Food security and Nutrition ICA Focus Area 2a 2b District Pop MALAWI ICA (2014) Wasting Class (2010/2011) Stunting Class (2010/2011) Avg. Pop. Est. as Most Vulnerable ( ) % of Food Insecure Population ( WFP/IHS) # Yrs prev. Poor/ Border. FCS > 30% (2009, 2011, 2013) Zomba 648,882 Acceptable Very High 58, % MEDIUM Blantyre 389,906 Acceptable Very High 73, % MEDIUM Machinga 589,709 Poor Very High 51, % MEDIUM Mangochi 982,058 Poor Very High 66, % MEDIUM TOTAL: 2,610,555 TOTAL: 250,828 Chiradzulu 314,059 Acceptable Very High 35, % MEDIUM Ntchisi 276,481 Acceptable Very High 23, % MEDIUM Rumphi 211,170 Acceptable High 29, % MEDIUM Nkhotakota 367,776 Acceptable Very High 38, % MEDIUM Ntcheu 557,433 Serious Very High 55, % MEDIUM Mwanza 102,571 Acceptable Very High 21, % MEDIUM Thyolo 633,019 Acceptable Very High 112, % MEDIUM TOTAL: 2,462,509 TOTAL: 317,597 Natural Shocks ICA Focus Area District Pop a 2b Drought Freq Flood Freq Risk of Droughts & Floods Veg. dec Most Prevalent Livelihood Type Zomba 648,882 HIGH HIGH HIGH LOW Shire Highlands Blantyre 389,906 HIGH MEDIUM HIGH MEDIUM Middle Shire Valley Machinga 589,709 MEDIUM HIGH HIGH LOW Lake Chilwa, Phalombe Plain Mangochi 982,058 MEDIUM HIGH HIGH MEDIUM Shire Highlands TOTAL: 2,610,555 Chiradzulu 314,059 HIGH LOW MEDIUM LOW Shire Highlands Ntchisi 276,481 MEDIUM LOW MEDIUM LOW Kasungu Lilongwe Plain Rumphi 211,170 LOW MEDIUM MEDIUM MEDIUM Protected Area Nkhotakota 367,776 LOW MEDIUM MEDIUM LOW Protected Area Ntcheu 557,433 MEDIUM MEDIUM MEDIUM HIGH Rift Valley Escarpment Mwanza 102,571 MEDIUM LOW MEDIUM MEDIUM Middle Shire Valley Thyolo 633,019 MEDIUM LOW MEDIUM MEDIUM Thyolo Mulunje Tea Estate TOTAL: 2,462,509 32

33 Category 3 Longer term programmes Map 25: Category 3 Districts MALAWI ICA (2014) These districts are characterized by 30% or more of the population having been consistently identified as food insecure through the MVAC and WFP data, yet with a low exposure to either droughts or floods. Focus Area 3a and 3b indicate that in the last five years, one (or more) in three households: Never met their food needs all year every year i.e. 4 or 5 times (out of 5 MVAC/WFP assessments) May have experienced a shock year(s) i.e. once out of 5 times (MVAC/WFP assessments) could be related to shocks, followed by a recovery period These districts have a population that recurrently or consistently do not meet their food needs, yet where the risk of flood and droughts converging is lower and possibly absent. This suggests food insecurity is pervasive, can be seasonal, and not necessarily related to one of these two shocks. Although the ICA does not look into an in depth causal analysis, it is likely that food security in these areas is linked to chronic poverty and deteriorated livelihoods. Populations living in these districts would benefit from longer-term food programmes to reduce food insecurity, as well as development. Such programmes can be coupled with actions to stabilize natural environments and reverse land degradation that will lead to a heightened risk of an increase in natural shocks that can shift them upwards into Categories 1 and 2. Recurrence of Food Insecurity above 30% Low (1) Medium (2) High (3) Low level Focus area 5 Focus area 3b Focus area 3a Medium level Focus area 4b Focus area 2b Focus area 1b High level Focus Area 4a Focus area 2a Focus area 1a RISK SCORE Given the prevalence of food insecurity, populations living in this area would benefit from predictable social protection and productive safety nets geared towards strengthening and improving livelihoods, and safeguarding development efforts. Where relevant, land stabilization to prevent an increased risk to natural shocks should be considered, together with strengthened early warning systems and preparedness. Food Security and Nutrition ICA Focus Area District Pop Wasting Class (2010/2011) Stunting Class (2010/2011) Avg. Pop. Est. as Most Vulnerable ( ) % of Food Insecure Population ( WFP/IHS) # Yrs prev. Poor/ Border. FCS > 30% (2009, 2011, 2013) Dowa 732,343 Acceptable Very High 31, % HIGH Mchinji 569,085 Acceptable Very High 58, % HIGH 3a Lilongwe 1,421,454 Poor Very High 43.06% HIGH Mzimba 211,170 Acceptable Very High 211, % HIGH TOTAL: 2,934,052 TOTAL: 302,061 Nkhata Bay 260,583 Acceptable Very High 20.85% MEDIUM 3b Chitipa 211,170 Acceptable Very High 32.34% MEDIUM Kasungu 794,991 Acceptable Very High 113, % MEDIUM TOTAL: 1,266,744 TOTAL: 113,813 33

34 Natural Shocks ICA Focus Area District Pop Drought Freq MALAWI ICA (2014) Flood Freq Risk of Droughts & Floods Veg. dec Most Prevalent Livelihood Type Dowa 732,343 LOW LOW LOW LOW Kasungu Lilongwe Plain Mchinji 569,085 LOW LOW LOW LOW Kasungu Lilongwe Plain 3a Lilongwe 1,421,454 LOW LOW LOW LOW Kasungu Lilongwe Plain Mzimba 211,170 LOW LOW LOW HIGH Western Rumphi, Mzimba self sufficient TOTAL: 2,934,052 Nkhata Bay 260,583 LOW LOW LOW MEDIUM Nkhata Bay Cassava Southern Karonga 3b Chitipa 211,170 LOW LOW LOW HIGH Chitipa, Northern Karonga, Central Koronga, Misuku Hills Kasungu 794,991 LOW LOW LOW MEDIUM Kasungu Lilongwe Plain TOTAL: 1,266,744 34

35 Category 4 Reducing Risks to Shocks Map 26: Category 4 Districts MALAWI ICA (2014) Only one district was found in Category 4, and occurred in Focus Area 4a. Category 4 is characterized by a district where the recurrence of 30% or more of the population having been identified as food insecure was low (i.e. only one year or less) through the MVAC and WFP data, yet with a high exposure to droughts and floods. Although the category has been subdivided as Focus Areas 4a and 4b, no district was identified in Focus area 4b. The only district identified indicates that in the last 5 years, one (or more) in three households: May have experienced a shock year i.e. 2 or 3 times (out of the 5 MVAC/WFP assessments) could be related to a shock Are consistently below 30% of prevalence of food insecurity Although the recurrence of food insecure populations above 30% was found to be low or minimal, this does not mean that there will be no pockets of food insecurity. The high risk exposure to droughts and floods however could result in these populations falling into food insecurity when these events occur. If mitigating measures are not put in place and such risk reduced, in time food insecurity amongst these populations may increase. RISK SCORE Recurrence of Food Insecurity above 30% Low (1) Medium (2) High (3) Low level Focus area 5 Focus area 3b Focus area 3a Medium level Focus area 4b Focus area 2b Focus area 1b High level Focus Area 4a Focus area 2a Focus area 1a A focus on stabilizing landscapes and reversing degradation to reduce the risk / mitigate the impacts of these events should be considered, accompanied with continuing development programmes which should be safeguarded with early warning systems and preparedness efforts. Food Security and Nutrition ICA Focus Area District Pop Wasting Class (2010/2011) Stunting Class (2010/2011) Avg. Pop. Est. as Most Vulnerable ( ) % of Food Insecure Population ( WFP/IHS) # Yrs prev. Poor/ Border. FCS > 30% (2009, 2011, 2013) 4a Karonga 327,084 Acceptable High 56, % LOW TOTAL: 327,084 TOTAL: 56,005 Natural Shocks ICA Focus Area District Pop Drought Freq Flood Freq Risk of Droughts & Floods Veg. dec a Karonga 327,084 MEDIUM HIGH HIGH HIGH TOTAL: 327,084 Most Prevalent Livelihood Type Chitipa, Northern Karonga, Central Koronga, Misuku Hills 35