Food Security Update Report Malawi

Transcription

1 Food Security Update Report Malawi November 5 Areas at Risk: October 5 March 6 Based on the extrapolation of price trends recorded up to October 5 VAC Chitipa Karonga Rumphi Nkhata Bay MALAWI Vulnerability Assessment Committee Malawi Vulnerability Assessment Committee Mzimba Representation for Poorest Households L A K E M A L A W I In collaboration with Legend Country Districts Lakes Parks Cities Nkhotakota Kasungu Ntchisi Dowa Mchinji Salima Lilongwe Dedza Mangochi LAKE MALOMBE Ntcheu Machinga Balaka LAKE CHIUTA The SADC-FANR Regional Vulnerability Assessment Committee Missing Food Entitlements (% Min Energy Requirements, Per Capita) No Significant Risk 1% - 9% % - 19% % - 29% > 3% LAKE CHILWA Zomba Mwanza Blantyre Phalombe Chiradzulu Mulanje Thyolo Chikwawa Nsanje VAC SADC FANR Vulnerability Assessment Committee 5 Kilometers Updated: 3 November 5 World Food Programme Government of the Republic of Malawi

2 Acknowledgements The Malawi Vulnerability Assessment Committee (MVAC) would like to thank the following participants and their organisations, who contributed either directly or indirectly to researching, analysing, writing up and presenting the information in this report: Moses Kachale (Ministry of Economic Planning and Development) Simon Mulungu (Ministry of Economic Planning and Development) Charles Rethman (Ministry of Economic Planning and Development/Save the Children US) Fiskani Nkana (Ministry of Agriculture) Idrissa Mwale (Ministry of Agriculture) Kautuka (Department of Poverty and Disaster Management Affairs) Sam Chimwaza (FEWS-NET) Evance Chapasuka (FEWS-NET) Caesar Kachale (Food and Agriculture Organization) Masozi Kachale (World Food Programme) Sarah Kaphamtengo (World Food Programme) Neil Fisher (Action Against Hunger) Maria Bernardez (Action Against Hunger) James Bwirani (Oxfam) William (Save the Children US) In addition, the following agencies assisted the Malawi Vulnerability Assessment Committee by providing the data that much of this analysis is based upon: The National Statistics Office The Ministry of Agriculture and Food Security The Department of Poverty and Disaster Management Affairs The Ministry of Health Action Against Hunger World Food Programme Food and Agriculture Organization (Crop and Food Supply Assessment Mission) FEWS-NET SADC Regional Remote Sensing Unit National Meteorological Centre The Malawi Vulnerability Assessment Committee thanks the British Department for International Development (DFID) and FEWS-NET, who have generously funded this report. This report is published by: The Malawi Vulnerability Assessment Committee (MVAC) Secretariat Private Bag B56, Lilongwe 3, Malawi Tel.: ext. 166 Fax: The MVAC Secretariat is under the Ministry of Economic Planning and Development and is supported by Save the Children US with funding from the British Department for International Development (DFID). This document contains the views and findings of the MVAC but does not necessarily reflect the views of the Government of Malawi, any single member of the MVAC or any of the donors or funding agencies. Inside Cover

3 Summary The MVAC assessment carried out at harvest time in May 5 identified certain parts of the country as having significant populations that are at risk of acute vulnerability to hunger and food insecurity for the coming agricultural consumption year (March 5 to April 6). The risk of hunger is expressed as a missing food entitlement (MFE). An entitlement is what a household must have in terms of its food stocks, own production, assets or income that it can use to meet its members minimum basic needs, including food. What are termed missing food entitlements in this document are that which are missing from the entitlements a household has in order to achieve its members recommended minimum daily food intake. For simplicity of calculations, the MVAC concentrates on a single dietary nutrient within food intake, energy. 1 Missing food entitlements can be converted into any commodity, or a basket of commodities. For the sake of making comparisons, the MVAC expresses its missing food entitlements in three ways: As a percentage of the minimum food energy requirements ( kcal). As an equivalent in a common food commodity that can be weighed and compared with elsewhere. Maize has been chosen but this in no way implies that maize is the only commodity that should be provided in response to the missing entitlement. The resultant maize equivalent can be totalled up over the population identified as being at risk and presented as a single value. As a cash equivalent. Since food can be bought or sold (unless markets are constrained by some massive catastrophic event or by the deliberate interventions of an authority or belligerent party), missing food entitlements can also be expressed in cash form. This cash form can also be totalled up over the population identified as being at risk and presented as a single value. In May, the MVAC presented a forecast of vulnerability and this was dependent on many factors, some of which were not known at the time. For the variables that were known, data from the most rigorous sources were used but for those that were not, scenarios had to be constructed based on important assumptions. As far as possible, these assumptions were derived from the evidence and likelihood of various outcomes occurring during the year. This report therefore presents an update on all these variables, both those that were known and those for which assumptions were made. As such, it should be read in conjunction with the MVAC Monitoring Report for June 5 to make good sense. The June report also contains many details and descriptions on methodology as well as details on each livelihood zone. This report contains a minimum of discussion and presents updated tables and maps for planners. The June 5 MVAC report presented two scenarios for analysis, based on two possible outcomes for the purchase price of the main staple. Scenario 1 assumed that prices on the open market in Malawi would remain roughly within the levels of the previous three years year-on-year inflation. This means that prices would rise during the hunger period this consumption year (December 5 to March 6) by roughly 11%, when compared with the hunger period of the 4-5 consumption year. Scenario 2 was based on the assumption that much of the food being sold in the markets during the main purchasing period would be come from further a field than neighbouring areas of Mozambique, Tanzania and Zambia but from within the region and would be landed in Blantyre at $2 a tonne. For this scenario, the rise in price, compared with last year, would be around 7%. As the year has now unfolded, information has become available that either challenges or confirms the data and assumptions made in May and June at harvest time. This forces the resulting analysis to be modified. The modifications are described in the next section, Changes to the problem specification but broadly speaking, the notable changes that have taken place since the analysis of May and June 5 are: The Third Round Crop Estimates are available. In many areas these show a worse production in maize than the second round but the data on other food crops seems to point to a small increase. There is also an increase in the quantities of cash crops (but not necessarily in quality). Some areas that were previously considered normal are now known to be quite badly affected, for example, parts of Mzimba District Average prices for cash crops were very bad this year; undermining the benefits of increased production The maize price on the open market has increased dramatically, especially during the month of October 5, while the amounts being sold by ADMARC (at a far lower, subsidised rate) are too small to greatly affect households access. 1 This in no way implies that the MVAC does not recognise the importance of other nutrients. MVAC information contains much that can be fed 3

4 There have been deliveries of food aid and cash-for-work projects have been targeted at areas with vulnerable populations. Table I provides a summary of the resulting analysis, comparing it with Scenario 1 in the June 5 MVAC report. Looking at the whole consumption year, with humanitarian interventions excluded, there has been an increase in the number of vulnerable areas and consequently in the population at risk (from million people to 5.44 million people) and in the total missing food entitlements (269,4 MT maize equivalent to 336,5 MT). However, the aim of this information is to inform the planning of interventions and to do this, we need to include all food and cash households have received through interventions that have already taken place. Interventions that are set up well in advance and are unlikely to change (such as large-scale existing public works programmes) must have their planned quantities included as well. When this is done, there is somewhat of a decrease in missing food entitlements and the populations at risk, to 278,568 MT of maize equivalent and 4,854 million people, respectively. This must not be interpreted as being a reduction in the overall yearly humanitarian need; rather, it shows the impact of food and cash interventions that have taken place so far. The fact that vulnerability has increased from that forecast by the old scenario 1 is underlined by looking at missing food entitlements for the period October 5 to March 6, or the second half of the consumption year. These are shown in the last line of Table I. Clearly, when one looks at these two figures, both the population at risk and the missing food entitlements have increased substantially (an additional 629,4 people at risk and an extra 51,5 MT maize equivalent spread across all populations at risk) from the old Scenario 1. Table I - Comparison of Populations at Risk and Missing Food Entitlements for the earlier May 5 Scenario 1 and the present updated analysis Analysis: May 5, Scenario 1 Analysis: November 5 The Whole Year, excluding all interventions The Whole Year, including food aid up until 3 September 5 and all other existing interventions The period October 5 to March 6, inclusive of interventions Pop At Risk Missing Food Entitlement Maize eq. (MT) Cash equivalent (MWK) Pop At Risk Missing Food Entitlement Maize eq. (MT) Cash equivalent (MWK) 4,224,4 269,4 6.4 billion 5,71, 335, billion 4,916, 28, billion 4,224,4 1,8 4,916, 255, 8.82 billion Changes to the Problem Specification The MVAC defines the nature and extent of the problem this year in terms of a problem specification. This is presented for a given food, income or expenditure source for a given area as a percentage, derived by dividing this years figure by the baseline. Crop Production For crop production, the starting point is to compare the latest crop estimates from the Ministry of Agriculture for this season with those of previous seasons 2. Hence, the problem specification actually represents a change in production from normal, not the absolute value. Only crops that are grown to a significant extent in a particular livelihood zone are included. The percentages obtained this way are used critically by the MVAC, as sometimes they do not actually represent the true production for the different wealth groups that are affected. This may come about because a large amount of production stems from a few commercial farms or from a project that only benefits a few farmers, for example, that do not really represent poor smallholder figures. The results of this analysis are presented in Table VI. 2 The baseline for crop problem specifications is a composite figure based on the five-year average of 1998 to 2. This reason for this choice is that these five years incorporate good, bad and average years and represent a reasonable figure for normal production. 4

5 Commodity Prices The prices households receive for commodities that they sell are key in providing both direct income and income for the poor through reditributive mechanisms, such as ganyu provision and local kinship or community support devices. The MVAC converts price changes into problem specifications by dividing this year s average price during the buying or selling period by the average price during the same period in the baseline year. This gives a figure in percentage terms that represents the change in prices; for example a commodity that sold for K in the baseline and which sells today at K, has a problem specification of %. Given that Malawi has fairly high rates of inflation, normal prices are not figures that are same as those in the baseline (i.e., %) but rather figures that have increased by approximately 35% (a problem specification of 135%). Consequently, commodities with price problem specifications of % are actually cheaper today than in the baseline, in real terms. The consumption year 5-6 has been poor in terms of prices for agricultural commodities. Tobacco prices were particularly low this year, a reflection of the poor internetional market for the product and the poor quality crop that was brought to market (a consequence of the rains and lack of fertiliser in 4). Based on figures from the Tobacco Association of Malawi (TAMA) and others, tobacco prices are 85%-9% of the baseline prices, in Kwacha terms. To be the same in real terms they would need to be 135%. Hence this year s prices were well below normal. From 2 until last year, prices for cotton improved quite drastically from what they were before that. For most farmers losses in maize or other food crop production were offset by high returns on cotton. This took a turn for the worse this season and, compared with last year, price fell considerably. However, when compared with baseline, prices are almost the same and a problem specification of % has been used. Nevertheless, it must be remembered that for the cotton price to be the same in real terms, the cotton price problem specification would need to be 135%, making this year s price actually well below normal. Other food commodity selling prices varied considerably, depending on the commodity and the market. With some notable exceptions, the overall contribution of these sales to household incomes is not as significant as tobacco or cotton. Staple Purchase Prices Since exchange options play an important role in people s access to food, the price at which food is retailed is an important variable in determining their missing entitlements. Given this years poor harvest, many poor households started running out of own-produced food much earlier than is normally the case and therefore depended on the markets for food. Consequently, market demand for maize has been steadily increasing. Informal cross-border trade plays an important role in reducing the country s food deficit, now estimated at about 39, MT in the consumption period 1st April 5 to 31st March 6. The crops used to derive this figure include maize, rice, sorghum, millet, wheat and cassava. According to the Cross-border Trade Monitoring System, these six crops registered total net imports of about 83, MT in maize equivalent terms from April to October 5. On its own, this reduces the food shortfall to about 37, MT. This deficit represents a shortage of supply 3 (it does not represent a shortage in household access). This imbalance of supply and demand has resulted in high open market maize prices and the only remaining affordable source for poor households to purchase food is the ADMARC markets, where a fixed and highly subsidised price has been offered. However, due to low stocks, ADMARC has been rationing maize sales to 25kg per person day, on occasions when they do have stocks to sell. Most of the local markets are recording prices well above MK25./kg (some as high as K45/kg) compared to the ADMARC fixed price of MK17./kg. 4 Although the majority of the markets with very high maize prices are in the southern region, there are some markets in the central region that are experiencing very high prices, which could trigger serious food insecurity situations in these areas. Examples of these are Mtakataka (MK4./kg) in Dedza District, Sharpevale (MK45.45/kg) and Ntonda 3 Some of which may be covered by increased imported food aid. 4 There are two possible routes to making maize (and other food) prices more affordable: lowering the price through consumer subsidies or raising incomes, which for the rural poor in Malawi are appallingly low. Subsidies can be applied by an agent such as ADMARC, through the private sector or by a humanitarian organisation (food aid can be thought of as imported food that has been subsidised to a price of zero). Incomes can be raised in the short term through cash grants or cash for work programmes. Both routes have their advantages and disadvantages. Subsidies are expensive and discourage investment in trade and farming, therefore increasing the unpredictability of supply, while cash grants may have inflationary effects (although this should eventually be countered by increases in supply through a stimulated economy). Subsidies can be made cheaper and more effective through careful targeting, which can be difficult (though not impossible) to implement properly. 5

6 Figure 1 Maize Price Developments in the Northern Livelihood Zones Comparison of 5 prices to VAC scenarios 5 45 scenario 1 scenario april may jun jul aug sep oct nov dec jan feb mar Chitipa Maize & Millet Livelihood Zone: Chitipa District april Comparison of 5 prices to VAC scenarios may jun jul aug sep Scenario 1 Scenario 2 5 oct nov dec jan feb mar Central Karonga Livelihood Zone: Karonga District Comparison of 5 prices to VAC scenarios Comparison of 5 prices to VAC scenarios Scenario 1 Scenario Scenario 1 Scenario april m ay jun jul aug sep oct nov dec jan feb m ar Western Rumphi Livelihood Zone: Rumphi District Com parison of 5 prices to VAC scenarios april may jun jul aug sep o ct n o v d ec jan feb mar Scenario 1 Scenario 2 5 Mzimba Self Sufficient Livelihood Zone: Mzimba District april may jun jul aug sep oct nov dec jan feb mar Northern Lakeshore Livelihood Zone: Parts of Nkhotakota and Nkhatabay Districts (MK45./kg) in Ntcheu District and Kasiya (MK45./kg) in Lilongwe District. These markets are all in the Rift Valley Escarpment and Kasungu-Lilongwe Plain livelihood zones. Maize price trends for each livelihood zone are presented in Figure 1, Figure 2, and Figure 3. Increases in maize prices negatively affect the food access all other things being equal, as the prices increase, the number of vulnerable people and their vulnerability increases too. Other Local Options Available to Households For the poor, casual labour or ganyu plays a very important part as a source of income and of food. During more abundant times, receiving households prefer cash, as it helps them to pay their debts and to purchase non-food essentials, such as clothing or household assets. However, during food scarcity, recipients do better when paid in food, which is usually maize. The converse usually applies to the employers, who prefer to pay in cash during scarcity and food during more abundant times. As it is usually the employer s market, it is they who normally determine the mode of payment, although hiring ganyu is also a way that better-off households can provide some social responsibility. Hence, while people may actively seek more ganyu (a normal coping response) they may only find less than what they seek. For the most part, the problem specifications used for ganyu-for-food ranged from 5% to %, depending on local harvests or other key supplies (such as cheaper imported food). With ganyu-for-cash there is the extra variable of wage rate, which may go down significantly when supply (more people seeking work) exceeds demand (limited amounts of work on offer). The MVAC chose a more generous range from 7-% on the availability of ganyu, but pegged wage rates at a level that is lower than originally forecast in May 5. 6

7 Figure 2 Maize Price Developments in the Central Livelihood Zones Comparison of 5 prices to VAC scenarios Comparison of 5 prices to VAC scenarios Scenario 1 Scenario Scenario 1 Scenario april may jun jul aug sep oct nov dec jan feb mar High Productive Border Livelihood Zone: Parts of Neno, Ntcheu and Dedza Districts april may jun jul aug sep oct nov dec jan feb mar Kasungu-Lilongwe Plain Livelihood Zone: Kasungu, Dowa, Ntchisi, and Mchinji Districts Comparison of 5 prices to VAC scenarios april may jun jul scenario 1 scenario 2 5 aug sep oct nov dec jan feb mar Southern Lakeshore Livelihood Zone: Mangochi and Salima Districts Since 3, terms of trade between casual labour and food have been steadily improving, effectively making up for the lost ground incurred when Kwacha-based food prices increased in 1-3. As a rule, ganyu wage rates seem to be one year behind step with inflation; this makes sense when one considers that employers use the proceeds from last year s sales to pay this year s wages. Had wage rates this year followed this rule, then the problem specifications would have been in the order of 123%; however, because many more households ran out of their own food far earlier this year than in previous years, many more households have been seeking more ganyu than previously. This has put downward pressure on the market and wages are now similar to what they were three years ago a problem specification of % which, in real terms, is far worse than normal. In addition to ganyu, many households engage in activities that generate incomes for their members. Chief among these are the collection, manufacture and sale activities, such as firewood, charcoal and home-products like the manufacture of thatched products and implements. These activities are all grouped as self-employment. The legality of practising some of these activities places a constraint on the extent to which they may be practised, as well as households ability to access the raw materials. For example, as more people collect and sell firewood, the numbers of trees diminish, people have to collect from further away. The result is that it takes longer to produce the same quantity and this caps the rate of production. Also, with a fairly constant demand for these products, prices tend to fall when supply increases reducing net income per household. The maximum access to income from self-employment is based on the experience in 2. In most areas, the MVAC agreed that the situation this year would be similar 2, both in price and availability terms. The Food Security and Nutrition Surveillance project managed by Action Against hunger systematically collects data on these two important food and income sources. Presently, data collection has been going on for just over one year. This will become an important secondary data source in coming months and years as comparisons can be made from one year to another. Fishing is important for the lakeshore areas. Although there is some fluctuation in the size of catches each year and there is evidence of a gradual decline in production, this does not present a hazard in for this year. Fish price have also been reasonably stable. Humanitarian Interventions The Ministry of Agriculture and Food Security and the Department of Poverty and Disaster Management Affairs collect information from as many stakeholders as possible. This is then combined onto analysis spreadsheets that compare actual deliveries with needs. 7

8 For comparison purposes with the previous analysis in April-May, the MVAC did do a complete analysis that excludes all interventions, predictably the total missing food entitlements and the populations at risk are higher for this analysis than for the one where interventions are included. Food aid in Malawi this year has largely been managed by the World Food Programme (WFP) and the Government of Malawi (with support from DFID). The country has been divided between these two with the North, Centre and some of the South (Mangochi and Balaka districts) going to the Government while the rest of the South goes to WFP. In caseload terms, WFP handles roughly two-thirds of beneficiaries. Figure 3 Maize Price Developments in the Southern Livelihood Zones Comparison of 5 prices to VAC scenarios Scenario 1 Scenario 2 5 Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Middle Shire Livelihood Zone: Machinga, Mwanza and Balaka Comparison of 5 prices to VAC scenarios 5 scenario 1 45 scenario april may jun jul aug sep oct nov dec jan feb mar Lake Chilwa/Phalombe Plain Livelihood Zone: Phalombe and parts of Machinga and Zomba Comparison of 5 prices to VAC scenarios Comparison of 5 prices to VAC scenarios scenario 1 scenario Scenario 1 Scenario april may jun jul aug sep oct nov dec jan feb mar Shire Highlands Livelihood Zone: Parts of Mangochi, Zomba, Chiradzulu and Blantyre ditricts 5 april may jun jul aug sep oct nov dec jan feb mar Thyolo/Mulanje Tea Livelihood Zone: Thyolo and Mulanje Districts Comparison of 5 prices to VAC scenarios 5 Scenario 1 45 Scenario april may jun jul aug sep oct nov dec jan feb mar Lower Shire Livelihood Zone: Nsanje and Chikwawa District Since this analysis aims to inform emergency food aid, the analysis includes only food aid transfers that have actually taken place, that is, all deliveries up to the 1 st October 5. The food aid response is based on a fixed ration, which, if all commodities are included, provides about 1,6 kcal per person per day. The total amount of food going to a beneficiary group is adjusted by varying the total period over which the ration is delivered. The distribution period always covers the last part of the agricultural consumption year. Hence, as the year progresses, more and more beneficiaries are targeted, necessitating revisions to the registration lists. It also means that earlier on in the year, a very small percentage of beneficiaries are actually targeted, in most cases far fewer than the actual people at risk. The MVAC faced a dilemma on how to reflect this small target percentage in its analysis. Strictly speaking, an analysis should have been done for each of the beneficiary households and for non-beneficiaries. However, when including the cash components this would have made for a complex profile which would have been doubtful in use for planning 8

9 future programming. The MVAC therefore decided to effectively dilute the impact of the ration in proportion to the population targeted versus that at risk. This dilution was reflected in the problem specification applied to the food aid as a source of food. Previous post-distribution studies also indicated that households do not usually consume all that they receive; food is often lost to pay for transportation, to repay debts and in sharing with others. The amount varies according to circumstance but is in the order of 25-4%. The MVAC therefore included a further problem specification of 7% to reflect these losses. Significant cash transfers take place in Malawi, mostly through the World Bank funded Malawi Social Action Fund and the European Union (EU) funded Public Works programme. These cash programmes are pre-determined, that is, they are not expected to change after the publication of this report. Hence all programmes reported on, both delivered and planned, are included in this analysis. The exceptions to this are the new SPRINT Programme (funded by the EU) and new direct unconditional cash transfers, such as those being undertaken by Oxfam and Concern Worldwide. The cash programmes have an even smaller coverage (in terms of households targeted) than the food aid, so the dilution is considerable. Nevertheless, cash transfers covered the equivalent of at least 19,8 MT (at recent food prices). There is some confusion over the role ADMARC, the parastatal charged with purchasing and distributing commercial government maize. Since ADMARC retail prices are well below those on the open market, (ADMARC prices are set by a Cabinet Committee), food passed through this parastatal has a significant humanitarian impact. However, because ADMARC does not carry out any formal targeting of its food 5, it is difficult to say who the beneficiaries really are and what the impact is on food access. ADMARC does carry out rationing, i.e., food sales are limited to a fixed quantity per buyer. Outcome This update of the analysis done in May tends to confirm that the situation at national level has deteriorated both in terms of the population at risk and the missing food entitlements. This development illustrates the seriousness of the food security situation in the country this season, especially in the southern region. The missing food entitlements, as a percentage of people s energy needs, are shown for each EPA in each district in Table II. The table also shows the relationship between the administrative entities of EPAs and districts with the livelihood zones that were used in the analysis. There are three groups of data columns in this table: the first group contains the average missing entitlements that a single person from either the poor or middle wealth group will face in the whole agricultural consumption year, while the second group contains the average missing entitlements that a single person faces in the second half of the consumption year. Table II - Individual Missing Food Entitlements (Percentage of Energy Needs) and Populations at Risk for Districts, EPAs and Livelihood Zones Affected Area MFEs (Percentage of kcal) Population At Risk Oct 5 to Mar Whole Year 6 District EPAs Livelihood Zone Poor Middle Poor Middle Poor Middle Balaka Blantyre Chikwawa Chiradzulu Chitipa Mpilisi, Phalula, Utale, Rivirivi, Bazale Middle Shire Valley 4-5%15-% >75% 3-4% 117,758 73,321 Ulongwe Middle Shire Valley 45-55%-3% >75% 4-55% 34,745 21,634 Chipande, Lirangwe Middle Shire Valley 45-55%-3% >75% 4-55% 68,93 42,398 Chipande Shire Highlands 25-4% 55-7% 14,133 Ntonda, Kunthembwe Shire Highlands 4-5% -5% >75% 1-% 61,9 9,65 Dolo, Mbewe, Mitole Lower Shire Valley 45-6%35-45% >75% 65-75% 159,4 188,8 Kalambo, Livunzu, Mikalango Lower Shire Valley 4-5%15-25% 65-75% 25-4% Mbulumbudzi, Mombezi, Thumbwe Shire highlands 4-5% -5% >75% 1-% 66,44 96,64 Thumbwe Lake Chilwa & Phalombe Plain 45-6% -5% >75% 1-% 23,279 38,799 Lufita, Mwamkumbwa, Kameme Chitipa Maize & Millet 15-% 25-4% 27,721 Chisenga Chitipa Maize & Millet -3% 4-55% 5,929 Kavukuku Chitipa Maize & Millet -15% -3% 9,322 5 There is some targeting of ADMARC food insomuch that only those households that can afford the (subsidised) price of the ration will purchase it. Many households cannot even afford this. 9

10 Affected Area MFEs (Percentage of kcal) P opulation At Risk Whole Year Oct 5 to Mar 6 District EPAs Livelihood Zone Poor Middle Poor Middle Poor Middle Golomoti, Mtakataka Rift Valley Escarpment 3-4%25-4% 55-65% 55-65% 15,7 19, Golomoti, Mtakataka Southern Lakeshore -3% 45-6%, Dedza Dowa Karonga Linthipe Kasungu Lilongwe Plain -% 25-4% 16,281 Chafumbwa Kasungu Lilongwe Plain -% -35% 12,638 Mayani Kasungu Lilongwe Plain 15-3% 35-5% 16,979 Kanyama, Linthipe Border Productive Highlands -% 1-% 25-35% 5-15% 37,15 45,46 Bowe Kasungu Lilongwe Plain 15-3% 35-5% 14,738 Mndolera Kasungu Lilongwe Plain -% 25-4% 19,429 Mponela, Mvera, Nachisaka Kasungu Lilongwe Plain 25-35% 45-6% 43,74 Lupembe Central Karonga -15% -3% 3,5 Vinthukutu Nkhata Bay Cassava -5% 1-5% 13, Chamama, Nkhamenya Kasungu Lilongwe Plain -35% 4-55% 63, Kasungu Chulu, Santhe Kasungu Lilongwe Plain -15% -3% 35,479 Chipala Kasungu Lilongwe Plain -% -35% 28,452 Lisasadzi Kasungu Lilongwe Plain 15-3% 35-5% 16,394 Chilaza, Chileka, Ming ongo Kasungu Lilongwe Plain 25-35% 45-6% 4,812 Lilongwe Demela, Chitekwele, Kasungu Lilongwe Plain 15-3% 35-5% 33,73 Mlomba, Mitundu, Mpenu, Chigonthi, Chitsime, Chiwamba Kasungu Lilongwe Plain -% -35% 9,615 Mng wangwa, Malingunde Kasungu Lilongwe Plain -15% -3% 31,765 Chikweo Lake Chilwa & Phalombe Plain 45-6% 1-5% >75% 5-% 13,449 22,415 Nampeya, Nanyumbu, Lake Chilwa & Phalombe Plain 35-45% 7-8% 27,969 Machinga Mangochi Mchinji Mulanje Mwanza Nsanama Lake Chilwa & Phalombe Plain 25-35% 45-6%,95 Ntubwi, Domasi Lake Chilwa & Phalombe Plain 45-55%15-% >75% 3-4% 13,965 23,275 Mbonechera, Nyambi, Ntubwi, Shire Highlands 4-5% -5% >75% 1-% 45,421 66,67 Malosa/Domasi Upland Shire Highlands 5-65%15-25% >75% 35-45% 9,359 13,614 Ntubwi Middle Shire 45-55%-3% >75% 4-55% 9,9 6,177 Chilipa, Mbwadzulu, Mthiramanja, Namkumba, Nasenga Phirilongwe Hills -% 25-4% 55,283 Masuku, Ntiya Shire Highlands 25-4% 3-4% 43,595 Katuli, Lungwena Shire Highlands 15-3% 35-5% 28,623 Mbwadzulu, Namkumba, Nasenga Southern Lakeshore -% 35-45% 66,4 Lungwena, Maiwa, Mpilipili Southern Lakeshore 15-% 3-45% 81,7 Chioshya, Mikundi Kasungu Lilongwe Plain 15-3% 35-5% 33,142 Mkanda Kasungu Lilongwe Plain 25-35% 45-6% 22,376 Msitu Kasungu Lilongwe {lain -% -35% 18,86 Kamwendo, Msikawanjala, Thuchila Lake Chilwa & Phalombe Plain 45-55%-25% >75% 3-4% 7,4 178,9 Milonde, Mulanje Boma Thyolo Mulanje Tea Estates 4-5%3-4% >75% 6-75% 59,8 52,4 Mwanza Rift Valley Escarpment 15-25%-% 3-45% 25-35% 14, 8,8 Thambani Middle Shire Valley 4-5%15-% >75% 3-4%,7 25, Emfeni, Luwerezi Kasungu Lilongwe Plain 15-3% 35-5% 17,1 Mzimba Neno Bulala, Eswazini, Njuyu, Mbawa, Vibangalala, Mjinge, Bwengu Mzimba Self-Sufficient -15% -3% 138,689 Euthini, Mbalachanda Western Rumphi & Mzimba 15-% 25-4% 21,86 Neno Rift Valley Escarpment 15-25%-% 3-45% 25-35%,473 24,673 Lisungwi Middle Shire Valley 4-5%-25% >75% 3-4% 13,6 8,5

11 Affected Area MFEs (Percentage of kcal) P opulation At Risk Whole Year Oct 5 to Mar 6 District EPAs Livelihood Zone Poor Middle Poor Middle Poor Middle Linga, Nkhunga, Northern Lakeshore 5-15% 15-% 39,237 Nkhotakota Nsanje Zidyana Northern Lakeshore -% 25-35%,535 Mwansambo Rift Valley Escarpment 3-4%15-% 35-45% 3-4%,353 24,528 Zidyana Rift Valley Escarpment 15-%-% 3-4% -35% 12,45 15,4 Nkhunga Nkhata Bay Cassava -5% 1-5% 8, Makhanga, Mogoti, Muona Lower Shire Valley 4-5%15-25% 65-75% 25-4% 32,99 39,67 Mpatsa Lower Shire Valley 45-6%35-45% >75% 65-75% 8,777 Nyachilenda, Zunde Lower Shire Valley 4-5%-% 7-8% 25-35% 43,146 51,94 Njolomole Border Productive Highlands -% 1-% 25-35% 5-15% 19,718 24, Ntcheu Ntchisi Phalombe Bilira, Nsipe, Rift Valley Escarpment 15-%-% 25-35% 25-35% 62,686 75,544 Kandeu, Manjawira Rift Valley Escarpment 15-%-% 3-4% -35% 42,292 5,968 Sharpevale Rift Valley Escarpment 3-4%25-4% 55-65% 55-65% 27,741 33,432 Chipuka, Kalira, Malomo Kasungu Lilongwe Plain -% -35% 38,83 Kalira Rift Valley Escarpment 3-4%15-% 35-45% 3-4% 17,86 21,458 Kosongo, Mpinda, Naminjiwa Lake Chilwa & Phalombe Plain 45-55%-25% >75% 3-4% 35,353 58,921 Tamani, Nkhulambe Waruma Lake Chilwa & Phalombe Plain 35-45% 7-8% 5,693 Rumphi Bolero, Katowo, part of Muhuju Western Rumphi & Mzimba 15-% 25-4% 21,6 Chinguluwe, Chipoka Rift Valley Escarpment 3-4%-25% 35-45% 3-4% 3,59 36,225 Salima Thyolo Thembwe Rift Valley Escarpment 15-25%-% 3-45% 25-35% 9,422 11,355 Kamuona (Khombedza) Rift Valley Escarpment 15-%-% 3-4% -35% 24,391 29,394 Chipoka Southern Lakeshore 25-35% 55-65% 18,39 Kamuona (Khombedza), Thembwe Southern Lakeshore 15-% 3-45% 43,349 Dwale, Khonjeni, Thyolo Central Thyolo Mulanje Tea Estates 3-4%15-25% 6-75% 35-45% 2,55 89,299 Masambanjati, Thekerani Thyolo Mulanje Tea Estates 25-4%15-25% 6-7% 35-45% 6,99 92,836 Matapwata Shire highlands 25-4% 55-7% 6,3 Chingale Middle Shire 3-4%5-15% 6-7% 15-3% 25, 15,6 Zomba Dzaone, Chingale, Shire Highlands 4-5% -5% >75% 1-% 63,519 92,392 Thondwe Shire Highlands 5-65%15-25% >75% 35-45% 37,938 55,183 Mpokwe, Likangala, Msondole Lake Chilwa & Phalombe Plain 25-35% 45-6% 61,429 Ngwerero Lake Chilwa & Phalombe Plain 35-45% 7-8% 4,59 The third group of columns provides information on the approximate numbers of people in each of the affected wealth groups that are at risk of having missing food entitlements. Of the 4.91 million people at risk, 2.85 million are children under 18 years of age and 88 are children under five 6. Figure 4 shows the breakdown of the missing food entitlements per capita for each three-month season of the consumption year. It will be noted that in some areas there are still high (>%) missing food entitlements during the period July to September. People can survive on substantially less than kcal/person/day, if only for a short period. We would also expect that some of these households would have experienced unacceptable levels of deprivation, due to their adopting coping strategies that are destructive to their livelihoods, are irreversible or are destructive to their local environment. This analysis is pegged at a level that allows for some preservation of livelihoods, which in many cases starts from a very low point. 6 According to the MDHS (Table 2.1), household population by age, sex and residence, on average 53% of household members are aged -18. (In the last years age group the number was divided by 5 and multiplied by 3 to get the number for 15-17) In an average household of 5.5 members there will be therefore be an average of 2.9 children under 18 years of age. The number of children under five, according to the same table, is 17.9% or, for an average household size of 5.5 members there will be an average of.98 children under five. 11

12 Figure 4 - Seasonal development of Missing Food Entitlements Per Capita (for the poorest households) by threemonth season April to June 5 July to September 5 Representation for Poorest Households October to Decenber 5 Key:- Districts Cities Parks Lakes January to March 6 Missing Food Entitlements No significant risk 1% - 9% % - 19% % - 29% > 3% Kilometers Updated: 3 November 5 12

13 Figure 5 maps out the total missing food entitlements in maize equivalent (MT) for the second half of the consumption year. The MVAC defines the nature and extent of the problem this year in its problem specification, a derived number that is expressed in percentage terms for each food, income and expenditure source and is obtained by simply dividing this years figure by that of the baseline. Figure 5 - Map of Total Missing Food Entitlements for the Period October 5 to March 6 Chitipa Karonga Rumphi Mzimba Nkhata Bay L A K E M A L A W I Legend Country Districts Lakes Parks Cities Nkhotakota Kasungu Ntchisi Dowa Mchinji Salima Lilongwe Dedza Mangochi LAKE MALOMBE Ntcheu Machinga Balaka LAKE CHIUTA Missing Food Entitlements Maize Equivalent No Significant Risk Up To 6 MT 61-1 MT MT Greater Than 231 MT LAKE CHILWA Zomba Mwanza Blantyre Phalombe Chiradzulu Mulanje Thyolo Chikwawa Nsanje 5 Kilometers Updated: 3 November 5 13

14 Food Security Monitoring Report November 5 Appendix Common Problem Specifications (Hazard Definitions) Item Problem Specification May November Kwacha Prices of Most Commodities (Inflation over two years at current rates) 135% 135% Kwacha Labour (for Cash) Pay Rates 1% % Kwacha Cotton Prices 18% % Kwacha Tobacco Prices Northern 125% 91% Kwacha Tobacco Prices Central 125% 88% Kwacha Tobacco Prices Southern 125% 85% Fishing Availability % % Self-Employment Opportunities % % Self-Employment prices 135% % Table III Maize Selling and Purchase Problem Specifications (comparing the figures in the May Analysis with those of the November Analysis) May Analysis (Scenario 1, Livelihood Zone Scenario 2) November Analysis Sales Purchase Sales Purchase Lower Shire 117% 135%, 2% 135% 2% Shire Highlands % 135%, 2% 115% 173% Thyolo-Mulanje Tea Estates 117% 135%, 2% 121% 23% Lake Chilwa-Phalombe Plain % 135%, 2% 123% 17% Middle Shire 86% 135%, 2% 128% 2% Southern Lakeshore, Phirilongwe Hills 53% 135%, 2% 135% 2% Border Productive, Rift Valley Escarpment 73% 135%, 2% 173% 2% Kasungu-Lilongwe Plain 82% 135%, 2% 1% 2% Mzimba Self-Sufficient 4% 135%, 2% 4% 2% Western Rumphi and Mzimba % 135%, 2% 159% 2% Northern Lakeshore, Nkhata Bay Cassava % 135%, 2% 131% 173% Central Karonga % 135%, 2% 194% 226% Chitipa Maize and Millet % 135%, 2% 135% 151% 14

15 Missing Food Entitlements and Cash Requirements by District Table IV Maize or Cash Requirements to Alleviate Missing Food Entitlements District Data Total Balaka Blantyre Blantyre City (not under assessment) Chikwawa Chiradzulu Chitipa Dedza Dowa Karonga Pop: Total 34,969 Pop At Risk - Poor 152,53 Pop At Risk - Middle 94,954 Pop At Risk 247,457 % Pop At Risk 81% MFE (Maize - MT) 19,814 MFE (Cash - K Mil) Pop: Total 358,94 Pop At Risk - Poor 144,146 Pop At Risk - Middle 132,463 Pop At Risk 276,69 % Pop At Risk 77% MFE (Maize - MT) 17,289 MFE (Cash - K Mil) Pop: Total 711,233 Pop: Total 437,678 Pop At Risk - Poor 159,4 Pop At Risk - Middle 188,776 Pop At Risk 348,186 % Pop At Risk 8% MFE (Maize - MT) 3,356 MFE (Cash - K Mil) 1, Pop: Total 282,158 Pop At Risk - Poor 89,7 Pop At Risk - Middle 135,439 Pop At Risk 225,159 % Pop At Risk 8% MFE (Maize - MT) 9,85 MFE (Cash - K Mil) Pop: Total 157,872 Pop At Risk - Poor 42,972 Pop At Risk - Middle Pop At Risk 42,972 % Pop At Risk 27% MFE (Maize - MT) 1,77 MFE (Cash - K Mil) Pop: Total 62,696 Pop At Risk - Poor 118,933 Pop At Risk - Middle 64,356 Pop At Risk 183,289 % Pop At Risk 3% MFE (Maize - MT) 7,234 MFE (Cash - K Mil) Pop: Total 483,1 Pop At Risk - Poor 77,97 Pop At Risk - Middle Pop At Risk 77,97 % Pop At Risk 16% MFE (Maize - MT) 4,262 MFE (Cash - K Mil) Pop: Total 236,748 Pop At Risk - Poor 16,634 Pop At Risk - Middle Pop At Risk 16,634 % Pop At Risk 7% MFE (Maize - MT) 129 MFE (Cash - K Mil) 4.18 Pop: Total 68,917 Pop At Risk - Poor 143,317 Pop At Risk - Middle Kasungu Pop At Risk 143,317 % Pop At Risk 24% MFE (Maize - MT) 6,526 MFE (Cash - K Mil) Likoma Pop: Total,251 Lilongwe City (not under assessment) Pop: Total 669,114 Pop: Total 1,125,998 Pop At Risk - Poor 196,923 Pop At Risk - Middle Lilongwe Rural Pop At Risk 196,923 % Pop At Risk 17% MFE (Maize - MT) 8,89 MFE (Cash - K Mil) Pop: Total 425,69 Pop At Risk - Poor 131,34 Pop At Risk - Middle 131,548 Machinga Pop At Risk 262,582 % Pop At Risk 62% MFE (Maize - MT) 15,354 MFE (Cash - K Mil) Pop: Total 732,653 Pop At Risk - Poor 259,622 Pop At Risk - Middle Mangochi Pop At Risk 259,622 % Pop At Risk 35% MFE (Maize - MT) 11,6 MFE (Cash - K Mil) Pop: Total 49,59 Pop At Risk - Poor 74,378 Pop At Risk - Middle Mchinji Pop At Risk 74,378 % Pop At Risk 18% MFE (Maize - MT) 3,722 MFE (Cash - K Mil) 3.9 Pop: Total 522,893 Pop At Risk - Poor 167,213 Pop At Risk - Middle 231,39 Mulanje Pop At Risk 398,522 % Pop At Risk 76% MFE (Maize - MT) 28,98 MFE (Cash - K Mil) Pop: Total 89,817 Pop At Risk - Poor 34,86 Pop At Risk - Middle 33,744 Mwanza Pop At Risk 68,55 % Pop At Risk 76% MFE (Maize - MT) 3,478 MFE (Cash - K Mil) Pop: Total 587,12 Pop At Risk - Poor 177,696 Pop At Risk - Middle Mzimba Pop At Risk 177,696 % Pop At Risk 3% MFE (Maize - MT) 5,78 MFE (Cash - K Mil) Mzuzu City (not under assessment) Pop: Total 134,399 15

16 Neno Pop: Total 78,139 Pop At Risk - Poor 34,64 Pop At Risk - Middle 33,135 Pop At Risk 67, % Pop At Risk 86% MFE (Maize - MT) 3,395 MFE (Cash - K Mil) Nkhata Bay Pop: Total 191,87 Pop: Total 283,761 Pop At Risk - Poor 9,791 Pop At Risk - Middle 39,532 Nkhotakota Nsanje Ntcheu Ntchisi Phalombe Pop At Risk 13,324 % Pop At Risk 46% MFE (Maize - MT) 5,48 MFE (Cash - K Mil) Pop: Total 228,656 Pop At Risk - Poor 84,913 Pop At Risk - Middle,555 Pop At Risk 185,468 % Pop At Risk 81% MFE (Maize - MT) 13,135 MFE (Cash - K Mil) Pop: Total 459,331 Pop At Risk - Poor 152,438 Pop At Risk - Middle 184,44 Pop At Risk 336,482 % Pop At Risk 73% MFE (Maize - MT) 13,315 MFE (Cash - K Mil) Pop: Total 215,51 Pop At Risk - Poor 53,69 Pop At Risk - Middle 21,458 Pop At Risk 75,67 % Pop At Risk 35% MFE (Maize - MT) 4,16 MFE (Cash - K Mil) Pop: Total 29,42 Pop At Risk - Poor 86,46 Pop At Risk - Middle 58,921 Pop At Risk 144,967 % Pop At Risk 5% MFE (Maize - MT),849 MFE (Cash - K Mil) Pop: Total 149,486 Pop At Risk - Poor 21,629 Pop At Risk - Middle Rumphi Pop At Risk 21,629 % Pop At Risk 14% MFE (Maize - MT) 817 MFE (Cash - K Mil) 26.3 Pop: Total 319,947 Pop At Risk - Poor 126,151 Pop At Risk - Middle 76,974 Salima Pop At Risk 3,125 % Pop At Risk 63% MFE (Maize - MT),548 MFE (Cash - K Mil) Pop: Total 556,7 Pop At Risk - Poor 214,472 Pop At Risk - Middle 182,135 Thyolo Pop At Risk 396,67 % Pop At Risk 71% MFE (Maize - MT) 24,299 MFE (Cash - K Mil) Pop: Total 574,7 Pop At Risk - Poor 192,497 Pop At Risk - Middle 163,154 Zomba Pop At Risk 355,651 % Pop At Risk 62% MFE (Maize - MT),543 MFE (Cash - K Mil) Zomba Municipality (Not under Pop: Total 1,423 assessment) Total Pop: Total 12,341,17 Total Pop At Risk - Poor 3,43,826 Total Pop At Risk - Middle 1,872,497 Total Total Pop At Risk 4,916,323 Total % Pop At Risk 4% Total MFE (Maize - MT) 28,4 Total MFE (Cash - K Mil) 9,683.6 Table V - Seasonal Breakdown of Populations at Risk and Total Missing Food Entitlements (Maize Equivalent) District Data Grand Total Balaka Blantyre Chikwawa 152,53 Pop At Risk: Q2 174,136 Pop At Risk: Q3 247,457 Pop At Risk: Q4 247,457 MFE Mz Eq: Q1 575 MFE Mz Eq: Q2 1,454 MFE Mz Eq: Q3 7,19 MFE Mz Eq: Q4,658 13,13 Pop At Risk: Q2 186,544 Pop At Risk: Q3 186,544 Pop At Risk: Q4 276,69 MFE Mz Eq: Q1 612 MFE Mz Eq: Q2 1,477 MFE Mz Eq: Q3 6,645 MFE Mz Eq: Q4 8, ,186 Pop At Risk: Q2 348,186 Pop At Risk: Q3 348,186 Pop At Risk: Q4 348,186 Chiradzulu Chitipa Dedza MFE Mz Eq: Q1 1,872 MFE Mz Eq: Q2 2,873 MFE Mz Eq: Q3 9,594 MFE Mz Eq: Q4 15,27 89,7 Pop At Risk: Q2 89,7 Pop At Risk: Q3 89,7 Pop At Risk: Q4 225,159 MFE Mz Eq: Q1 43 MFE Mz Eq: Q2 952 MFE Mz Eq: Q3 3,745 MFE Mz Eq: Q4 4,84 Pop At Risk: Q2 42,972 Pop At Risk: Q3 42,972 Pop At Risk: Q4 42,972 MFE Mz Eq: Q1 MFE Mz Eq: Q2 131 MFE Mz Eq: Q3 642 MFE Mz Eq: Q ,725 Pop At Risk: Q2 51,654 Pop At Risk: Q3 137,884 16

17 Dowa Karonga Kasungu Lilongwe Rural Machinga Mangochi Mchinji Mulanje Mwanza Pop At Risk: Q4 183,289 MFE Mz Eq: Q1 77 MFE Mz Eq: Q2 3 MFE Mz Eq: Q3 1,954 MFE Mz Eq: Q4 5,6 Pop At Risk: Q2 58,478 Pop At Risk: Q3 77,97 Pop At Risk: Q4 77,97 MFE Mz Eq: Q1 MFE Mz Eq: Q2 392 MFE Mz Eq: Q3 1,419 MFE Mz Eq: Q4 2,486 Pop At Risk: Q2 Pop At Risk: Q3 3,455 Pop At Risk: Q4 16,634 MFE Mz Eq: Q1 MFE Mz Eq: Q2 MFE Mz Eq: Q3 35 MFE Mz Eq: Q4 9 Pop At Risk: Q2 79,386 Pop At Risk: Q3 143,317 Pop At Risk: Q4 143,317 MFE Mz Eq: Q1 MFE Mz Eq: Q2 561 MFE Mz Eq: Q3 2,7 MFE Mz Eq: Q4 3,848 Pop At Risk: Q2 74,542 Pop At Risk: Q3 196,923 Pop At Risk: Q4 196,923 MFE Mz Eq: Q1 MFE Mz Eq: Q2 4 MFE Mz Eq: Q3 2,97 MFE Mz Eq: Q4 5,554 92,115 Pop At Risk: Q2 137,211 Pop At Risk: Q3 174, Pop At Risk: Q4 262,582 MFE Mz Eq: Q1 473 MFE Mz Eq: Q2 1,375 MFE Mz Eq: Q3 5,444 MFE Mz Eq: Q4 8,154 Pop At Risk: Q2 43,595 Pop At Risk: Q3 259,622 Pop At Risk: Q4 259,622 MFE Mz Eq: Q1 MFE Mz Eq: Q2 218 MFE Mz Eq: Q3 3,857 MFE Mz Eq: Q4 7,655 Pop At Risk: Q2 55,518 Pop At Risk: Q3 74,378 Pop At Risk: Q4 74,378 MFE Mz Eq: Q1 MFE Mz Eq: Q2 27 MFE Mz Eq: Q3 1,117 MFE Mz Eq: Q4 2, ,213 Pop At Risk: Q2 219,581 Pop At Risk: Q3 398,522 Pop At Risk: Q4 398,522 MFE Mz Eq: Q1 88 MFE Mz Eq: Q2 2,32 MFE Mz Eq: Q3,268 MFE Mz Eq: Q4 15,8 14,82 Pop At Risk: Q2 14,82 Pop At Risk: Q3 68,55 Pop At Risk: Q4 68,55 MFE Mz Eq: Q1 49 MFE Mz Eq: Q2 122 Mzimba Neno Nkhotakota Nsanje Ntcheu Ntchisi Phalombe Rumphi Salima Thyolo MFE Mz Eq: Q3 1,45 MFE Mz Eq: Q4 2,271 Pop At Risk: Q2 17,1 Pop At Risk: Q3 177,696 Pop At Risk: Q4 177,696 MFE Mz Eq: Q1 MFE Mz Eq: Q2 46 MFE Mz Eq: Q3 1,688 MFE Mz Eq: Q4 3,876 13,591 Pop At Risk: Q2 13,591 Pop At Risk: Q3 67, Pop At Risk: Q4 67, MFE Mz Eq: Q1 48 MFE Mz Eq: Q2 117 MFE Mz Eq: Q3 1,17 MFE Mz Eq: Q4 2,222 Pop At Risk: Q2 44,881 Pop At Risk: Q3 82,871 Pop At Risk: Q4 13,324 MFE Mz Eq: Q1 MFE Mz Eq: Q2 154 MFE Mz Eq: Q3 1,491 MFE Mz Eq: Q4 3, ,374 Pop At Risk: Q2 185,468 Pop At Risk: Q3 185,468 Pop At Risk: Q4 185,468 MFE Mz Eq: Q1 68 MFE Mz Eq: Q2 1,4 MFE Mz Eq: Q3 3,793 MFE Mz Eq: Q4 7,77 27,741 Pop At Risk: Q2 123,859 Pop At Risk: Q3 312,382 Pop At Risk: Q4 336,482 MFE Mz Eq: Q1 136 MFE Mz Eq: Q2 45 MFE Mz Eq: Q3 2,957 MFE Mz Eq: Q4 9,84 Pop At Risk: Q2 39,264 Pop At Risk: Q3 75,67 Pop At Risk: Q4 75,67 MFE Mz Eq: Q1 MFE Mz Eq: Q2 135 MFE Mz Eq: Q3 1,23 MFE Mz Eq: Q4 2,627 35,353 Pop At Risk: Q2 86,46 Pop At Risk: Q3 144,967 Pop At Risk: Q4 144,967 MFE Mz Eq: Q1 169 MFE Mz Eq: Q2 917 MFE Mz Eq: Q3 3,685 MFE Mz Eq: Q4 6,112 Pop At Risk: Q2 Pop At Risk: Q3 21,629 Pop At Risk: Q4 21,629 MFE Mz Eq: Q1 MFE Mz Eq: Q2 MFE Mz Eq: Q3 228 MFE Mz Eq: Q4 569 Pop At Risk: Q2 85,215 Pop At Risk: Q3 3,125 Pop At Risk: Q4 3,125 MFE Mz Eq: Q1 MFE Mz Eq: Q2 295 MFE Mz Eq: Q3 3,249 MFE Mz Eq: Q4 7,27 17

18 Zomba Pop At Risk: Q2 214,472 Pop At Risk: Q3 396,67 Pop At Risk: Q4 396,67 MFE Mz Eq: Q1 MFE Mz Eq: Q2 773 MFE Mz Eq: Q3 7,852 MFE Mz Eq: Q4 15,651 1,458 Pop At Risk: Q2 192,497 Pop At Risk: Q3 263,259 Pop At Risk: Q4 355,651 MFE Mz Eq: Q1 621 MFE Mz Eq: Q2 1,68 MFE Mz Eq: Q3 6,616 MFE Mz Eq: Q4 11,746 Total 1,322,73 Total Pop At Risk: Q2 2,578, Total Pop At Risk: Q3 4,379,87 Total Pop At Risk: Q4 4,916,323 Total MFE Mz Eq: Q1 6,551 Total MFE Mz Eq: Q2 18,448 Total MFE Mz Eq: Q3 9,966 Total MFE Mz Eq: Q4 164,69 18

19 Crops Problem Specification Table VI - Crop production problem specifications (comparing the figures in the May Analysis, derived from the Second Round Crop Estimates with those in the November Analysis, derived from the Third Round Crop Estimates). Sources: Ministry of Agriculture, MVAC team members fieldwork) Livelihood Zone EPA Sweet Maize Cassava Rice Sorghum Millet Groundnuts Pulses Tobacco Cotton Grouping Potatoes Number May Nov May Nov May Nov May Nov May Nov May Nov May Nov May Nov May Nov May Nov BOR 124% 58% 6% 34% 42% % % 1 66% 5% 31% 49% 67% % % CHI 2 111% 85% 5% 17% 75% 77% 98% 2 89% 9% 7% 65% 19% 17% 19% 19% % 14% % 195% 84% 18% 2 89% 117% 7% 189% 13% 17% 19% 29% % 9% % 44% 84% 3% CKA 3 44% 91% 4% % 25% 8% 43% % % % KAS 8% 78% % 127% 1% 4 64% 62% % 4% 116% 8% 55% 129% 75% 74% 5 64% 77% % 56% 116% 75% 55% 151% 75% 8% 6 42% 36% % 79% 47% % 112% 199% 75% 75% 4 64% 57% % 52% 116% % 55% 1% 75% 1% 5 42% 75% % 123% 47% 77% 112% 15% 75% 47% 4 42% 6% % 38% 47% 75% 112% 87% 75% 47% LSH 7 48% 32% % 23% % 3% 4% % % 25% % % % 26% 4% 75% 8 48% 48% % 23% % 3% 4% % % 18% % 5% % 35% 4% % % 19% % % 5% 3% 4% % % 13% % % % % % 5% MSH 11 33% 41% 42% 46% 6% % 36% 5% 9% % 34% 39% 115% 12 33% 32% 42% 5% % % 12% 5% 44% % 46% 39% 149% 13 33% 71% 42% 35% 14% % 38% 5% 42% % 73% 39% % MSK 144% 1% 56% 193% 76% 269% MZS 6% 119% 147% 192% 167% 134% 14 72% 1% 81% 86% 116% 134% NKA 127% 53% % 1% 33% 78% NKH % 85% 97% 12% 133% 8% 41% 15 6% 6% 85% 85% 25% 8% 41% 6% 25% 8% % 85% NLA 7% % 95% % 16 51% 85% 75% 75% 13% 45% % % 17 51% 57% 75% 75% 13% 17% % 29% PHA 18 65% 65% 77% 61% 3% 15% 5% 48% 5% % 1% % 4% 14% 217% 19 65% 65% 77% 61% 3% % 5% 17% 5% 4% 1% 25% 4% 44% 66% 65% 65% 77% 21% 3% 13% 5% 57% 5% 4% 1% 25% 4% 44% 19

20 21 65% 65% 77% 21% 3% 13% 5% 57% 5% 4% 1% 25% 4% 93% PIR 22 5% 5% % 68% % 47% % 5% % % % 73% 67% 83% 5% % 23 5% 5% % 66% % 47% % 53% % % % 76% 67% 66% 5% % RFT 6% 6% % 34% 6% % 17% 6% 23% 64% % 26% % 16% 5% 75% 24 26% 47% % 85% 6% % 17% 56% 23% 53% % 57% % 66% 5% 75% 25 6% 6% % 34% 6% % 17% 6% 23% 64% % 26% % 43% 5% 75% 26 26% 19% % 29% 6% 17% % 23% 62% % 25% % 4% 5% 78% % 26% % % 6% 17% 17% 23% 23% % 54% % 42% 5% 7% SHI 6% 98% 57% 56% 47% 88% 55% 1% 55% 1% % 51% 27 6% 64% 57% 76% 47% 41% 55% 81% 55% 88% % 33% 28 4% 46% 55% 54% 23% 53% 3% 55% 4% 53% % % 29 4% 31% 55% 16% 23% % 3% 22% 4% 42% % 15% SLA 3 4% 6% 5% 49% 12% 67% 13% % % 38% 31 4% 54% 5% 113% 5% 34% 12% 75% 7% % 94% % % 234% 32 4% 23% 5% 33% 12% 16% 6% % 79% 13 4% 26% % % 12% 1% 23% % 54% TMT 4% 98% 6% 44% % % % 8% % 5% 57% 33 4% 69% 6% 78% % % % 5% % 5% 113% 34 4% 31% 6% 51% % 23% % 25% 43% 5% 3% WRM 95% % % % % 35 4% 68% % 73% 116% % % 96% 75% 8% Key to livelihood zones (see map in Figure 6 for geographical location of the areas the areas): BOR Border Productive Highlands LZ CHI Chitipa Maize and Millet LZ CKA Central Karonga LZ KAS Kasungu-Lilongwe Plain LZ LSH Lower Shire Valley LZ MSH Middle Shire Valley LZ MSK Misuku Hills LZ MZS Mzimba Self-Sufficient LZ NKA Northern Karonga LZ NKH Nkhata Bay Cassava LZ NLA Northern Lakeshore LZ PIR Phirilongwe Hills LZ RFT Rift Valley Escarpment LZ SHI Shire Highlands LZ SLA Southern Lakeshore LZ TMT Thyolo-Mulanje Tea Estates LZ WRM Western Rumphi and Mzimba LZ Key to District (RDP) and EPA Grouping Numbers: 1 Dedza: Kanyama & Linthipe EPAs, Ntcheu: Njolomole EPAs 2 Chitipa: Mwamkumbwa, Kameme & Lufita EPAs 2 Chitipa: Chisenga EPA 2 Chitipa: Kavukuku EPA 3 Karonga: Lupembe EPA 4 Dowa: Bowe EPA, Kasungu: Lisasadzi EPA, Mchinji: Chioshya & Mikundi EPAs, Dedza: Mayani EPA,, Lilongwe: Chitekwele & Demela EPAs, Mzimba: Emfeni & Luwerezi EPAs 4 Kasungu: Chulu & Santhe EPAs, Lilongwe: Mng wangwa & Malingunde EPAs 4 Dowa: Mponela, Mvera & Nachisaka EPAs, Mchinji: Mkanda EPA, Lilongwe: Chilaza, Chileka & Ming'ongo EPAs 5 Kasungu: Chipala EPA, Mchinji: Msitu EPA, Ntchisi: Chipuka, Malomo & Kalira EPAs, Dedza: Chafumbwa EPA, Lilongwe: Chigonthi, Chitsime, Chiwamba, Mpenu, Mlomba & Mitundu EPAs 5 Dowa: Mndolera EPA, Dedza: Linthipe EPA 6 Kasungu: Chamama & Kaluluma EPAs 7 Nsanje: Kalambo, Mogoti & Muona EPAs, Chikwawa: Livunzu, Nkhate, Mikalango & Makhanga EPAs 8 Nsanje: Nyachilenda & Zunde EPAs Chikwawa: Dolo, Mbewe & Mitole EPAs, Nsanje: Mpatsa EPA 11 Mwanza: Thambani EPA, Neno: Lisungwi EPA, Balaka: Bazale, Mpilisi, Phalula, Rivirivi & Utale EPAs 12 Balaka: Ulongwe EPA, Machinga: Ntubwi EPA, Blantyre: Lirangwe & Chipande EPAs

21 13 Zomba: Chingale EPA 14 Mzimba: Bulala, Bwengu, Eswazini, Mbawa & Mjinge EPAs 15 Karonga: Vinthukutu & Nyungwe EPAs, Rumphi: Muhuju, Chiweta & Ntchenachena EPAs, Nkhotakota: Nkhunga EPA, Nkhata Bay: Chikwina, Mpamba & Mzenga EPAs 16 Nkhotakota: Linga & Nkhunga EPAs 17 Nkhotakota: Zidyana EPA 18 Mulanje: Msikawanjala, Kamwendo & Thuchila EPAs, Phalombe: Kasongo, Mpinda & Naminjiwa EPAs, Machinga: Domasi & Ntubwi EPAs 19 Phalombe: Nkhulambe, Tamani & Waruma EPAs, Machinga: Nampeya & Nanyumbu EPAs, Zomba: Ngwerero EPA Machinga: Chikweo EPA, Chiradzulu: Thumbwe EPA 21 Machinga: Nsanama EPA, Zomba: Mpokwa, Likangala & Msondole EPAs 22 Mangochi: Chilipa, Mbwadzulu, Nasenga & Namkumba EPAs 23 Mangochi: Mthiramanja EPA 24 Neno: Neno EPA, Salima: Tembwe EPA, Mwanza: Mwanza EPA 124 Dedza: Golomoti & Mtakataka EPAs, Ntcheu: Sharpevale EPA 25 Ntcheu: Kandeu & Manjawira EPAs, Nkhotakota: Zidyana EPA, Salima: Kamuona (Khombedza) EPA 26 Nkhotakota: Mwansambo EPA, Ntchisi: Kalira EPA, Salima: Chinguluwe & Chipoka EPAs 27 Mangochi: Masuku & Ntiya EPAs, Thyolo: Matapwata EPAs 28 Mangochi: Ntubwi, Nyambi & Mbonechera EPAs, Blantyre: Ntonda & Kunthembwe EPAs, Chiradzulu: Mombezi, Mbulumbuzi, & Thumbwe EPAs, Zomba: Chingale & Dzaone EPAs 29 Zomba: Thondwe EPA, Machinga: Malosa/Domasi Upland EPA 3 Mangochi: Mbwadzulu, Nankumba & Nasenga EPAs 13 Dedza: Golomoti & Mtakataka EPAs 31 Mangochi: Maiwa, Lungwena & Mpilipili EPAs, Salima: Kamuona (Khombedza) & Tembwe EPAs 32 Salima: Chipoka EPA 33 Thyolo: Masambanjati & Thekerani EPAs 34 Mulanje: Mulanje Boma & Milonde EPAs 35 Rumphi: Katowo & Bolero EPAs, Mzimba: Euthini & Mbalachanda EPAs All remaining EPAs in each of the livelihood zones 21

22 Table Detailing the Criteria for Defining the Wealth Groups in Each Livelihood Zone Table XI - 'Poor' and Middle Wealth Groups Household Characteristics Livelihood Zone Chitipa maize and millet Land holding (acres) Land cultivated (acres) Livestock 7 Approx. Hh yearly cash income 8 Poor Middle Poor Middle Poor Middle Poor Middle Central Karonga Western Rumphi Nkhata Bay Cassava Northern Lakeshore Kasungu- Lilongwe plain Rift Valley escarpment Southern Lakeshore Shire Highlands Phirilongwe Hills Middle Shire Lake Chilwa and Phalombe Plain Thyolo Mulanje tea estates Lower Shire G: -2 C: 2-4 G: C: 1- P: -3 G: C: 7- P: -3 G: C: 2-5 G: -5 C: 5-15 G: -3 C: 4-6 P: -3 G: -2 C: 5 G: -5 C: -3 G: -2 C: 4 G: -2 C: 4-6 G: C: 4-5 T: G: -4 C: 4-7 G: 2-5 C: 4- T: 2-5 C: - P: 2-5 G: 5 C: -15 P: -5 G: -5 C: 5-7 T: -3 G: -6 C: - G: 4-7 C:8- P: 3-5 G: 5 C: 15 G: 3-5 C: 8- G: 6 C: G: 1-4 C: 6-8 G: C: 6- T: 3-4 G: 5-8 C: 4-8 B:3 N:77 B: 855 N: 889 B:183 N:1715 B: 96 N: 57 B: 1753 N: 2 B: 914 N: 884 B: 1987 N: 19 B: 257 N: 2283 B: 1238 N: 1639 B: 2643 N: 6 B: 147 N: 94 B: 128 N: 69 B: 2193 N: 153 B: 1143 N: 6 B:46 N:4575 B:11 N:83 B: 4123 N: 3694 B: 14 N: 139 B: 3538 N: 4475 B: 3125 N: 28 B: 2678 N: 187 B: 755 N: 737 B: 264 N: 287 B: 534 N: 3739 B: 76 N: 1599 Other The poor normally depletes own production by July. -The poor lack inputs - Poor normally consumes own production up to December. -Livestock sales expands income -The poor normally consumes own production up to October. -The poor normally consumes own production up to December. -The poor normally consumes own production up to November. -The poor normally finish own production by July. The poor lack farm inputs. -The poor normally depletes own production by August. -The poor do not use inputs. -The poor cultivate marginal land. -Labour shortages for the poor. -The poor normally consumes own production up to September. B: 41 -The poor normally consumes own N: 1815 production up to July. B: 236 -The poor normally consumes own N: 163 production up to September. -Lack of farm inputs for the poor. B: The poor normally consumes own N: 1196 production up to August. 7 C Chickens, G Goats, P Pigs, T Cattle 8 B Baseline, N this year; Income denominated in Malawi Kwacha, for the whole household 22

23 CHIKWINA Map of the Livelihood Zones in Malawi Figure 6 - Livelihood Zones, EPAs and Districts in Malawi 33 '"E 34 '"E 35 '"E 36 '"E MWAMKUMBWA '"S MISUKUKAPORO NORTH Chitipa LUFITA KAPORO SOUTH CHISENGA KARONGA CENTRAL Karonga '"S Livelihood Zones Border Productive Veg Central Karonga KAVUKUKU KARONGA SOUTH Chitipa Millet & Maize Kasungu Lilongwe Plain 11 '"S BOLERO MPHEREMBE Rumphi BWENGU ZOMBWE EUTHINI NTCHENACHENA MUHUJU MPHOMPHA 11 '"S Lake Chilwa - Phalombe Plain Lower Shire Middle Shire Valley Misuku Hills Mzimba Self Sufficient Nkhata Bay Cassava Northern Karonga 12 '"S BULALA ESWAZINI Nkhata Bay Mzimba MJINGE MANYAMULA CHITHEKA CHINTHECHE MPAMBA NKHATABAY BOMA 12 '"S Northern Lakeshore Phirilongwe Hills Rift Valley Escarpment Shire Highlands MBAWA CHAMPHIRA KHOSOLO NKHUNGA Southern Lakeshore Thyolo Mulunje Tea Estates Western Rumphi & Mzimba KALULUMA EMFENI EPAs CHULU Nkhotakota 13 '"S Kasungu KASUNGU CHIPALA CHAMAMA LINGA 13 '"S 14 '"S 15 '"S MKANDA MIKUNDI KALULU Mchinji MLONYENI CHIOSHYA MSITU SANTHE LISASADZI CHILAZA BOWE CHISEPO UKWE MADISI Ntchisi Dowa Lilongwe SINYALA MALOMO NTCHISI BOMA CHIPUKA MPONELA MNDOLERA MLOMBA KALIRA MWANSAMBO MVERA CHITEKWELE LOBI ZIDYANA KHOMBEDZA NTHONDO NACHISAKA CHINGULUWE CHIVALA M'NGWANGWA DEMELA CHIGONTHI TEMBWE CHIWAMBA MING'ONGO MPINGU MPENU KAPHUKA CHITSIMENYANJA NAKACHOKA LINTHIPE KABWAZI KAMBANIZITHE CHAFUMBA CHAFUMBA MAYANI Dedza L A K E M A L A W I Salima KANYAMA CHIPOKA BEMBEKE MTAKATAKA NJOLOMOLE KANDEU CHILIPA Ntcheu TSANGANO SHARPEVALE NSIPE MANJAWIRA MPILIPILI NAMKUMBA GOLOMOTI MBWADZULU BILIRA BAZALE NASENGA LUNGWENA Balaka Mangochi MAIWA MASUKU MTHIRAMANJA LAKE MALOMBE ULONGWE MPILISI KATULI Machinga NTUBWI NTIYA MAIWA MBONECHERA NYAMBI CHIKWEO NANYUMBU NSANAMA LAKE CHIUTA NAMPEYA 14 '"S 15 '"S 16 '"S KALAMBO NENO LISUNGWI ZombaMPOKWE THONDWE Mwanza KASONGO DZAONE MAYAKA NGWERERO TAMANI MPINDA BlantyreMOMBEZI Phalombe NAMINJIWA WARUMA NTONDAChiradzulu MWANZA LIRANGWE MATAPWATA Mulanje Thyolo THYOLO BOMA Chikwawa MITOLE PHALULA CHINGALE MALOSA THUMBWE MULANJE WEST MBEWE MASAMBANJATI MSONDOLELAKE CHILWA NKHULAMBE MULANJE SOUTH MSONDOLE 16 '"S DOLO LIVUNZU MIKALANGO MAKHANGA MAGOTI 17 '"S Nsanje MPATSA NSANJE NYACHILENDA 17 '"S Kilometers 33 '"E 34 '"E 35 '"E 36 '"E NB: This map shows the pre-2 EPA boundaries. These old EPA boundaries are still used by the MVAC for longitudinal comparison reasons. This map in no way implies endorsement by the MVAC or any of its members of any boundary or geographical entity. 23