Seasonal Food Security Assessment Afghanistan

Transcription

1 Seasonal Food Security Assessment Afghanistan July-September 2013 Integrated Food Security Phase Classification Team Food Security and Agriculture Cluster United Nations Food and Agriculture Organization, Afghanistan 1

2 Contents ACRONYMS... 4 Executive Summary Introduction Objective of the assessment Methodology Stratum identification Selection of districts and communities Selection of HHs Sample size Data collections tools Training Data collection Data entry and analysis Limitations Household characteristics Household size Gender of household head Age of household head Disability of household head Marital status of household head Shelter Type of households Time of Interview-Season Major crop harvesting (wheat) Shocks Shocks in general Types of shock Severity of shocks Expenditure Expenditure on food and non-food items Livelihoods Seasonal livelihoods Cash income sources Agriculture and livestock Land ownership Crops Grown Land holding Food crops grown Seed availability Livestock Pasture condition Food Access and Consumption Food Stocks Food consumption Meals frequency Food Consumption Score Sources of food

3 12. Coping Strategies Coping Strategy Index (CSI) Household Hunger Scale (HHS) General Coping Strategies Food Security How many and where are the food insecure? Who are the food insecure? Food insecurity and level of significance of various groups Conclusions Recommendations ANNEXE 1: Food Security Analysis Methodology ANNEXE 2: More charts and tables

4 ACRONYMS ACF Action Contre la Faim ACTED Agency for Technical Cooperation and Development AGC Afghan Green Leaf Consultant AMRAN Afghan Mobile Reconstruction Association AREA Agency for Rehabilitation & Energy Conservation in Afghanistan ATWG Assessment Technical Working Group CARE International Cooperative Relief Every Where - International CFSVA Comprehensive Food Security and Vulnerability Assessment CFW Cash For Work CHA Coordination of Humanitarian Assistance CHAP Common Humanitarian Action Plan CRS Catholic Relief Services CSI Coping Strategy Index CSO Central Statistics Organization DAIL Department of Agriculture, Irrigation and Livestock EFSLS Emergency Food Security & Livelihood Survey FAO Food and Agriculture Organization of the United Nations FCS Food Consumption Score FEWS NET Famine Early Warning System Network FFA Food for Assets FFT Food for Training FFW Food For Work FSAC Food Security and Agriculture Cluster GAM Global Acute Malnutrition GoA Government of Afghanistan HHS Household Hunger Scale HHs Households IDP internal Displaced Person immap Information Management and Mine action Programme INGO International Non Governmental Organization IR Afghanistan Islamic Relief Afghanistan IRC International Rescue Committee LR Logistic Regression LSU Livestock Unit LZ Livelihood Zone MADERA Mission d Aide au Développement des Economies Rurales en Afghanistan NFI Non Food Items NGO Non Governmental Organization NPO/RRAA Norwegian Project Office/ Rural Rehabilitation for Afghanistan NRVA National Risks and Vulnerability Assessment OSDR Organization of Social Development and Rehabilitation PPS Proportion to Population Size PSU Primary Sampling Unit RCSI Reduced Coping Strategy Index SI Solidarités International 4

5 SMART SFSA SOFAR UN USU WFP WIQ WVI ZOA Standard Monitoring Approach for Relief and Transition Seasonal Food Security Assessment Slam Organization for Afghanistan Rehabilitation United Nations Ultimate Sampling Unit World Food Programme of the United Nation Wealth Index Quintile World Vision International - Afghanistan Relief Hope Recovery (Zuid osst Azie) 5

6 Executive Summary The Seasonal Food Security Assessment (SFSA) aimed at identifying the causes and intensity of food insecurity by various geographical locations, namely provinces. During , many provinces were hit by natural disasters like flood and drought, while others by high security incidents and risks. These multidimensional shocks adversely affected the livelihoods and made many people highly food insecure and vulnerable to various shocks. The SFSA was carried out in all 34 provinces, covering 8,500 households, 850 communities and 136 traders throughout the country. The assessment was conducted during July-September A total of 24 NGOs (16 international and 8 national) participated in the fieldwork. According to the assessment, the female-headed households are 4.8 percent, while 50.6 percent of the household heads are at age years. Disability rate is quite high: 8.7 percent percent disabled not able to work and 6 percent disabled able to work. Majority of the household heads are married. However, 5.7 percent of them are widows/ers. A significant percentage of households (5.4 percent) are living in temporary shelters/tents, while 2.1 percent live in open space. Internally Displaced Persons (IDPs) are 6.3 percent and returnees are 6 percent. More than 40 percent of the households have suffered one or more shocks during the past 3 months. Livestock disease was a major shock in Nooristan. More than 52 percent of households that suffered shocks reported these as more severe than usual. Households spent 22 percent on food and 78 percent on non-food items during the month before the assessment. Among the non-food spending, major expenses were on construction, followed by education and medicines. Main livelihood for 66.8 percent of households was non-farm activities; for 23.9 percent was agriculture and for 9.3 percent livestock. Agriculture, as livelihood, declined from 38.6 percent in spring to 7.9 percent in winter. The first income source, on average, contributed 52.4 percent of the total income. For the highest percentage of households, the first income source is non-farm labours, followed by crop sales. Income from production of handicraft is the one that declined most (20.6 percent less as compared with last year s), followed by traders/shops income (18.3 percent less). However, usage of credit increased compared to last year. Majority of households purchased food from markets either on cash or on credit. However, a great percentage of households used their own production. Given the dependency on livestock, 35.5 percent of the households used their own dairy products. Similarly, 32.7 percent of households used their own cereals for consumption. Overall, 6.8 percent of the surveyed households are food insecure, 31.7 percent are at borderline and 61.5 percent are food secure. The highest share of food insecure households is found in Ghor (28.4 percent), followed by Badghis (18.9 percent). Food insecurity was higher in groups like femaleheaded households, households with elderly head, widow/er-headed households, households with disabled head, households living in tents and open space, nomadic and IDPs households, households 6

7 holding no or few livestock and households with no income or relying on agricultural labour and/or credit. 7

8 1. Introduction Keeping in view frequent disasters during the past few years, the Food Security and Agriculture Cluster decided to undertake a rapid assessments in order to know the impact of such disasters and plan for resources accordingly. Since NRVA results were not released, it became more important to carry out an independent assessment in order to identify the most vulnerable households. During 2012, FSAC conducted the Emergency Food Security and Livelihood assessment in 22 highly vulnerable provinces of the country. In these provinces, 58 districts, 536 communities and 4,288 households were covered. The results of the assessment were fed into IPC 2012 and also used by implementers. Although the assessment was quite useful, by providing latest data on acute food insecurity, since it did not cover all the provinces it was difficult to compare the results. Therefore, FSAC decided to undertake the Seasonal Food Security Assessment (SFSA), during 2013, covering all provinces. Being much more representative, the latter should provide comparable results for CHAP, IPC and implementing partners, which are very important for resource planning and distribution. Till mid-2013, the NRVA results were not officially released and there were no data available that could portray the food security situation during the year. Moreover, for the acute food insecurity, an up-to-date dataset is required. The assessment also aimed at providing data and information for the development of the 2014 Common Humanitarian Action Plan (CHAP) of the FSAC. The SFSA was supposed to start in April-May (pre-harvest period) but it was delayed due to the late arrival of consultants responsible for the whole process, including data analysis and report writing. The process was started in July with trainings in various regions. The Assessment covered all 34 provinces and interviewed 8,500 households, 850 communities and 136 traders across the country. 2. Objective of the assessment The SFSA primarily objectives are to: Know the level of food insecurity in the provinces as well as at national level; Identify vulnerable groups, at provincial and at national level; Learn about shocks causing food insecurity; Know about food utilization (intra-households) along with food accessibility. 3. Methodology 3.1. Stratum identification Each province is considered as a stratum. All 34 provinces are included in the survey amounting to 34 strata. The possibility was carefully considered to use the livelihood zone (LZ) in each province to randomly select some districts. However, that option is not suitable, for the following reasons: i) 29 LZs are not 8

9 officially recognized by the GoA; ii) lack of official population data for LZs and iii) lack of clear boundaries of different (more than one) LZs in certain communities/districts/provinces, which would not allow us to establish an accurate sampling frame to select districts and communities Selection of districts and communities In each province, 25 clusters (communities) are selected. In total, 850 communities are included in 34 provinces. The following steps are applied to select districts and identify the number of communities for each selected district: Group all urban districts in one Urban group. From this Urban group, select 1 urban district for the survey by applying simple random sampling technique. If there are no urban districts, simply skip. Group all rural districts in another Rural group. Within this Rural group, divide all districts into 3 sub-rural groups based on their population (as per CSO 2013 population data) with High, Medium and Low population. From each sub-group, select 1 rural district for the survey by applying simple random technique. The population thresholds used to rank districts into 3 sub-groups can vary by province depending on actual population in each province. Certainly, we have to clearly document the defined thresholds. Sum up population from all selected districts. In the above example, population of 4 selected districts (1 urban, 3 rural) should be summed up. Calculate the weight of population of each district in the total population of all selected districts. E.g. population of district A accounts for 25 percent of total population, the one of district B for 42 percent of total population, etc. Apply the population weight to divide 25 communities for each selected district. E.g. a district accounting for 25 percent of total population has 6 communities to be selected for the survey (25 communities x 25 percent = 6.25 communities). In each selected district, communities are selected proportionally to population size (PPS) by using systematic random sampling technique based on a CSO s list of communities and their 2013 population. By this way, for the SFSA 2013, we will significantly reduce the number of districts to 3-4 districts per province Selection of HHs In each cluster (community), 10 HHs are selected using systematic random sampling technique, based on a list of HHs Sample size Based on the above mentioned criteria, a total of 8,500 households were interviewed. Moreover, 850 communities were selected for community level interviews. 9

10 3.5. Data collections tools Three data collection tools Household questionnaire, Community questionnaire and Traders questionnaire were developed by the Assessment Technical Working Group (ATWG), tested during and after training and used in the SFSA 2013 for interviews of households and community key informants, respectively Training To meet the requirement for data collection, a large number of enumerators were required for the SFSA Twenty-four international and national organizations (8 national NGOs and 16 international NGOs) took part in the survey. These are ZOA, WHH, SI, SCI, PU-AMI, MTDO, MADERA, MAAO, KRA, JDAI, IRC, IR, INTERSOS, FOCUS, EVO, CVG, CoAR, CHA, ANCC, AMRAN, AKF, Afghan AID, ACTED and ACF. In order to improve the quality of data, 3-day trainings were arranged in 5 regions, i.e. East, South, North/North-East, Centre (including South-East and Central Highland) and West. The trainings were started on 15th July at Jalalabad and ended on 2nd August 2013 at Herat. A total of 220 enumerators and supervisors were trained to conduct the assessment Data collection The data collection in the field took place from 17 th August through late September The data collection was delayed due fasting (Ramadan) month and Eid Data entry and analysis The database was designed by immap, in MS Access. A national NGO was hired to enter the data at Kabul. All NGOs involved in fieldwork were asked to send filled questionnaires to Kabul for data entry. The data were cleaned and analyzed by the IPC team (IPC Manager) using SPSS software Limitations a. The SFSA was started in August and continued till the end of September. Therefore, it covered the period of post-harvest in certain provinces, pre-harvest in a few of them and harvest in other provinces. b. Furthermore, since the SFSA is a cross-sectional survey covering only 2 months (August- September 2013), comparison of the SFSA results with that of other food security surveys, implemented throughout the year (like NRVA 2007/08 and NRVA 2011/12) or during different periods in time, should be done with caution. 4. Household characteristics 4.1. Household size Among the surveyed households, the average household size is found at 9.6 (4.9 males, 4.7 females). By age group, 2.2 are children under Average number of households members

11 years, 3.3 are at age 5-17 years, 3.7 are at age years and 0.4 are at age 65 years and above. The provinces with highest number of children under 5 is Urozgan (4.5), followed by Nangarhar (4.1), Khost (4.1), Paktya (4.0) and Helmand (3.8). The highest family size was recorded in Khost (16.5), followed by Urozgan (15.7) and Nangarhar (15) Gender of household head Overall, 95.2 percent of the surveyed households are male-headed while 4.8 percent are female-headed. The highest number of female-headed households was reported in Faryab (14.2 percent), followed by Bamyan (13.4 percent) and Balkh (13.2 percent). The lowest percentage of female-headed households is reported in Khost (0.9 percent), followed by Ghazni (1.6 percent) and Ghor (1.6 percent). Age of the household head 41% 8% 4.3. Age of household head The majority of the household heads are in the range of 45 to 64 years (50.6 percent), while a great percentage (40.8) of them are in the range of years. Elderly heads of households (65 years and above) are 8.6 percent. The proportion of younger heads of households ( % yr) is highest in Kapisa (77.5 percent), followed by yr yr 65 yr & above Kunduz (74.2 percent), Laghman (71.5 percent) and Samangan (67.2 percent). On the other hand, the highest percentage of household heads in the range of years was found in Zabul (59.1), followed by Wardak (58.7), Parwan (57.1) and Paktya (56). The elderly head of households ratio was highest in Zabul (19.1 percent), followed by Kandahar (15.4 percent), Paktya (14.9 percent) and Nimroz (14.8 percent) Disability of household head Disability of the head of household is an issue in a country like Afghanistan, where livelihood opportunities are limited and/or unsustainable. The disability of the head makes the household more vulnerable and dependent on members of the family for essential supports and services. 11

12 On average, 8.7 percent of the household heads are disabled. Among these, 2.7 percent are not able to work while 6 percent are able to do some work. Disability of household head The highest percentage of disabled heads of household not able to work is reported in Zabul (8.7), followed by Parwan (6.9), Nimroz (6.8), Daykundi (6.7) and Faryab (6.2). The highest percentage that are able to do some work is recorded in Parwan (18.5), followed by Bamyan (18), Ghor (13.7), Daykundi (12.1) and Samangan (11.5). By gender of the head, 11.3 percent of the femaleheaded households are disabled compared to 8.6 No disability percent of male. Among female-headed households, 5.5 percent are disabled and unable to work as compared to 2.6 percent in male-headed households. 91% permanently disabled-unable to work permanently disabled-able to work 3% 6% Marital status of the household head 5.7% 5.5% 88.6% 0.1% Married Widow/er Single/engaged Separared/divorsed 4.5. Marital status of household head The majority of the household heads are married (88.6 percent), while 5.7 percent are widows/ers, 5.5 percent are single or engaged, and only 0.1 percent is separated/divorced. Many more widows/ers are found in Faryab (18.3 percent), Takhar (17.2 percent), Balkh (15.7 percent), Laghman and Zabul (11.8 percent each) Shelter Shelter is one of the important indicators of household s vulnerability. In the surveyed areas, 90 percent reside in regular houses with hard walls, while 2.1 percent has no house and live in open space; 5.4 percent living in tents. The later groups are at a higher risk of exposure to climate and weather changes. The highest number of households living in temporary shelter/tents is found in Nimroz province (26.2 percent), followed by Kunduz (16.2 percent), Balkh (12.4 percent), Logar and Badghis (12.1 percent each) and Parwan (10.3 percent). The number of households living in open space is highest in Nimroz (29.6 percent), followed by Daykundi (14 percent), Panjsher (12.9 percent) and Kandahar (12.1 percent). 12

13 4.7. Type of households A significant number of households, 6.3 percent, are IDPs, while 6.0 percent are returnees and only 1.1 percent are Kuchis. The province with highest percentage of IDPs is Kabul (17.6 percent), followed by Kandahar (11.8 percent), Nangarhar (11.6 percent) and Khost (11.3 percent). Returnees are found mostly in Kandahar (20 percent) and Kabul (13.9 percent). The shelter condition is poorer among IDPs, returnees and Kuchis as compared to permanent residents. Among the IDP households, 12.9 percent live in temporary shelters/tents and 5.3 percent in open space. Within Kuchis, 74 percent live in temporary shelters/tents and 5.3 percent in open space. Returnees are highly vulnerable as 13.5 percent of them live in temporary shelters/tents and 2.9 percent in open space, while 8 percent live in public buildings. 5. Time of Interview-Season 5.1. Major crop harvesting (wheat) The Seasonal Food Security Assessment (SFSA) was started at a time when harvesting of wheat was almost completed in plane areas, while it continued in other areas. The fieldwork was spread over a period of 1.5 months, from mid-august to the end of September Majority of the provinces were covered after harvest of wheat crop, as reported by 80.6 percent of the interviewed households. A significant number of households (14.4 percent) were interviewed at the time of harvest, while 5 percent at pre-harvest time. Provinces interviewed at post-harvest time are Farah, Kunar 1, Nangarhar, Balkh, Khost, Kapisa, Paktya, Paktika, Kabul, Zabul, Helmand, Logar, Baghlan, Urozgan and Kunduz. Provinces where majority of the households were interviewed at harvesting time are Badakhshan, Wardak, Sar-i-Pul and Daykundi. Provinces interviewed mostly at pre-harvest time were Nooristan and Bamyan. However, please note, in some of the provinces, different households were involved in different phases of crop harvest, at the same time. 1 In some cases, this province is mentioned as Kunarha. 13

14 BADAKH HERAT KANDAH JAWZJAN KABUL TAKHAR PARWAN BAGHLAN BALKH SAR-I- PUL FARAH LOGAR GHAZNI NANGAR GHOR KUNARHA NOORIST ZABUL PANJSHER KUNDUZ UROZGAN BAMYAN PAKTYA HELMAND WARDAK FARYAB SAMANG DAYKUNDI LAGHMAN BADGHIS NIMROZ PAKTIKA KAPISA KHOST Total 13% 17% 21% 23% 26% 29% 33% 36% 38% 42% 43% 44% 44% 44% 45% 41% 5 52% 53% 54% 55% 56% 56% 58% 59% 6 65% 66% 67% 68% 68% 78% 79% 89% 95% 6. Shocks 6.1. Shocks in general Shocks are common in Afghanistan, especially in rural areas, where no risk-reduction measures are in place. SFSA's results shows that 40.8 percent of the households experienced one or more shocks during the past 3 months. The highest percentage of households that suffered shocks was reported in Khost (94.7 percent), followed by Kapisa (88.9 percent), Paktika (79.3 percent), Nimroz (78.3 percent) and Badghis (68.2 percent). Minimum shocks are reported in Badakhshan, Herat and Kandahar. However, this is quite surprising, as Badakhshan and Kandahar both are highly vulnerable, the first to natural disasters and the latter to security incidents. Households that suffered shocks Types of shock Major types of shock reported are severe sickness/death of breadwinner, livestock disease outbreak, huge increase in food prices, huge influx of IDPs, natural disasters, crop pest outbreak, very high insecurity and extreme weather condition. Shocks reported by high percentages of households are the following: severe sickness/death of breadwinner: Takhar (68.9 percent), Sar-i-Pul (55.7 percent) and Baghlan (51.5 percent); livestock disease outbreak: Nooristan (50.4 percent); huge increase in food prices: Paktika (83 percent) and Kunduz (69 percent); natural disasters: Wardak (55.8 percent) very high insecurity: Parwan (58.2 percent). The list concerns provinces where more than 50 percent of the respondents reported for one of the shocks. However, all provinces have experienced many shocks with different degree of impact. For details see Annexe 2. 14

15 6.3. Severity of shocks Majority of the respondents reported the shocks as more severe than usual, 29.2 percent reported them as same as usual while 18.5 percent reported them as less severe than usual. The province where the percentage of households reporting shocks as more severe than usual was highest is Helmand, followed by Paktika, Daykundi, Kunduz, Ghor, Wardak, Bamyan, Khost, Ghazni, Zabul, Samangan and Paktya. 7. Expenditure 7.1. Expenditure on food and non-food items The share of food expenditure out of total expenditure is a proxy indicator of household food access. The higher the share of food expenditure, the greater the likelihood that a household has poor food access. The commonly used thresholds for the share of food expenditure are used to classify households into poor, average and good food expenditure groups: Poor: food expenditure is more than 60 percent of total household expenditure; Average: food expenditure is at percent of total household expenditure; Good: food expenditure is less than 40 percent of total household expenditure. It should be noted that the consumption of food from sources other than purchases (e.g. from own production, gift, food aid, etc.) are also included in this food expenditure. On average, the surveyed households spent 22 percent of the total expenditure on food. The second major spending is for construction/repair of house (16 percent), debt payment (13 percent) and expenses on celebration/social events (11 percent). The assessment reports that 28 percent of the households are likely to have poor food access, 31 percent to have average food access and 41 percent good food access. The food share is associated with the total household income. Households with higher income tend to spend a lower share of it on food. When the level of income declines, the percentage of food expenditure increases. Among provinces, the highest 15

16 percentage of households in the poor-food-access group is found in Paktika (61.8 percent), followed by Ghor (53.4 percent), Herat (47 percent), Kabul (45.1 percent), Nooristan (42.6 percent), Parwan (41.7 percent) and Helmand (41.2 percent). 8. Livelihoods 8.1. Seasonal livelihoods All the respondents were asked to mention a main livelihood activity by season, i.e. in spring, summer, fall and winter. A composite estimation of all the seasonal livelihoods is used to calculate the share of each livelihood activity in a year. The result shows that 23.9 percent of the households have agriculture as the main livelihood, 9.3 percent have livestock while 66.8 percent have a nonfarm activity. In terms of seasonality, on average agriculture was the main livelihood of 38.6 percent of the households in spring, which reduced to 30.1 percent in summer, to 19 percent in fall and only to 7.9 percent in winter. Similarly, livestock as main livelihood was reported by 4.6 percent in spring, 9.4 percent in summer, 10.3 percent in fall and 13 percent in winter. The non-farm livelihood was at peak in winter (79.2 percent), declined to 70.8 percent in fall, to 60.5 percent in summer and to 56.8 percent in spring. This means that many people gradually shift to non-farm livelihoods when winter season approaches. These seasonal livelihoods are mostly in the form of casual labour, which are highly vulnerable and result in food insecurity of the respective households. The sharp and significant changes in the livelihoods by season determine the level of unsustainability of livelihoods and thus result in high degree of vulnerability and risk to be food insecure at most of the time. 9. Cash income sources The surveyed households were asked to mention the three main sources of cash income during the current season (last 3 months) and rank them in order of importance. All the three cash sources are analyzed. First one is the major source, which contributes on average 52.4 percent of the total income; second source contributes 27.8 percent and third source only 20 percent. Hence, the first source is very important for the households in term of food security. 16

17 The first source of income reported by highest number of households was non-farm labour (24.4 percent), followed by crop sale (23.3 percent). Trade/shops was reported by 13.4 percent and other by 11.9 percent. Remittances were the major source of income for 5 percent of households, while agricultural labour remained the major source of 8.7 percent of households. Livestock/products sale was reported as major income source by 5.2 percent of the households. The second source of income reported by the highest number of households was again non-farm labour (21 percent), followed by agricultural labour (14.7 percent). It means that a greater percentage of households (35.7 percent) depend on labour work as a second major source of income. Livestock products are also a major source of income by many households (12.2 percent). In the rural economy of Afghanistan, livestock plays a significant role in households livelihoods. There is a great potential in enhancing income from livestock sources with better breed, management and feed. As a third source of income, non-farm income is again reported by the highest percentage of households (19.8 percent), followed by other (15.8 percent) and trade/shops (15.1 percent). Sustainability of income and the source of cash are very important for the households food security. On average, the households income declined by 4.4 percent this season compared to last year. The first source of income contributing more than 50 percent of the total household income reported a setback. The income from handicrafts declined by 20.6 percent, trade/shops by 18.3 percent, agricultural labour by 16.9 percent and so on. The only increase was reported by livelihood groups of charcoal/firewood (31.3 percent) and credit (21.1 percent). The first group is small in number, however, the usage of credit by later group is a sign of depleting resources and that households had to repay from their earnings from productive sources. It will increase pressure on the households and will result in higher vulnerability to food insecurity. 10. Agriculture and livestock Land ownership According to the SFSA 2013 s results, 48 percent of the households are directly associated with agricultural land, 17

18 while 52 percent have no access to land. Among these, 34.8 percent of the households own and cultivate land, 4.4 percent only own land but do not cultivate it, 2.8 percent are share croppers/tenants and 2.5 percent have rented land. However, those who work on agriculture land as labours are not counted as part of the households associated with land. The highest percentage of households that own and cultivate land were found in Urozgan (77.4 percent), followed by Ghor (74.6 percent), Nooristan (73.9 percent), Nimroz (68.3 percent), Bamyan (64 percent), Sar-i-Pul (62.1 percent), Paktika (62 percent) and Khost (60.9 percent) Crops Grown During this season, the percentage of households cultivating crops, compared to year 2012, has reduced from 79.6 percent to 71.3 percent. A major decline was observed in Ghor, where cultivation of crops this season has reduced from 85.2 percent to 9 percent only. This decline was due to drought condition in the area. The second major decline was observed in Daykundi with reduction from 89.7 percent to 42.6 percent of households, followed by Badghis (94 to 43.3 percent), Herat (85.1 to 60.2 percent) and Bamyan (84.7 to 65.6 percent). lack of interest First Reason for not sowing this season other lack of labour 8% 5% lack of seeds 4 35% 3 25% 2 15% 1 5% 2% 3% 31% lack of power tilling 2% 9% lack of tools lack of irrigation 4 lack of fertilizer Second Reason for not sowing this season lack of interest other lack of labour 3% 3 25% 2 15% 1 5% 4% 3% lack of seeds 35% 24% 8% lack of power tilling 11% 15% lack of tools 32% lack of irrigation lack of fertilizer lack of interest Third Reason for not sowing this season other lack of labour 1 8% 8% lack of seeds 3 25% 2 15% 1 5% 12% lack of power tilling 12% 13% 12% lack of tools lack of fertilizer 25% lack of irrigation There are many reasons for not growing crops during the current season. The first reason mentioned by the highest percentage of households (39.9 percent) was lack of irrigation facilities, followed by lack of seed (31.2 percent) and lack of tools (9.4 percent). The second reason was reported as lack of tools (32.1 percent), followed by lack of seed (24.3 percent), lack of irrigation (14.6 percent) and lack of fertilizer (10.9 percent). Similarly, for the 3 rd reason, the highest percentage reported was lack of fertilizer (25.4 percent), followed by lack of power (12.1 percent) Land holding Cultivated land is categorized in 3 types, i.e. irrigated, rainfed and orchard/vegetable. The average cultivated landholding of all these three types is 7.9 jerib, where 3.7 jerib are irrigated, 2.6 are rainfed and 1.6 are orchard/vegetable. The highest average surface of irrigated land per household was reported in Jawzjan (16 jerib) followed by Logar (11.1 jerib), Balkh (10.2 jerib), Helmand (9.6 jerib), Farah (8 jerib), Herat (7.1 jerib), Urozgan (7.1 jerib), Paktika (6.3 jerib) and Kunduz (6.2 jerib) Food crops grown Major food crop in Afghanistan is wheat and almost all farmers try to give it first priority in cultivation. The SFSA 2013 reveals that wheat is cultivated by 72.4 percent of farmers; maize by

19 percent; vegetables by 35.8 percent; barley by 23.8 percent and fruits by 21.4 percent. The provinces with higher percentage of households cultivating wheat this season (above 90 percent) were Paktika, Jawzjan, Urozgan, Herat, Badghis, Samangan, Khost, Wardak, Nimroz and Parwan. Provinces Food crops grown this season with the higher percentage of farmers (above % percent) cultivating barley were Jawzjan. 7 6 Badghis, Ghor, Samangan and Sar-i-Pul. Majority 5 of farmers cultivating rice crop were found in 4 36% Kunduz, Laghman and Takhar. Maize is another important cereal crop and staple food in Afghanistan. Provinces where majority of farmers cultivate maize are Helmand, Nooristan, Kapisa, Nangarhar, Zabul, Paktya, Nimroz, Urozgan, Paktika and Parwan % 9% 21% 19% 21% 36% Seed availability Seed availability is a serious issue in many parts of the country. Improved seed is inadequate and farmers have limited or no access to it. According to SFSA, 47.3 percent of farmers had no access to improved seed for irrigated wheat cultivation, while 21.2 percent had limited access during this season. Moreover, the local seed for irrigated land was also not accessible to 20.9 percent of farmers, while partly accessible to 35.1 percent. Similarly, for rainfed cultivation 32.8 percent had no access to seed whereas 27.7 percent had limited access. Seed Availability Access to wheat seed this season-rainfed 12% 28% 33% 28% Access to wheat seed this season-irrigated-local 3 35% 21% 14% Access to wheat seed this season-irrigated-improved 18% 21% 47% 14% yes, sufficient yes, partially not at all NA Livestock Livestock is an essential part of agriculture livelihoods and significantly contributes to food security of rural households. In pastoral communities, livestock is almost the only source of income. In Afghanistan, livestock is the backbone of rural economy and it is used as a coping strategy at the time of stress by rural households in many provinces. On average, 55.8 percent of the households keep livestock of different types. At the provincial level, the highest percentage of households (more than 80 percent) keeping livestock is found in Nooristan, followed by Khost, Urozgan, Badghis, Bamyan, Sar-i-Pul, Ghor, Zabul, Daykundi, Takhar, Kunar, Nimroz, Logar and Wardak. Results of other provinces are given in the Annexe 2. 19

20 Major livestock are cattle, buffaloes, goat, sheep and poultry. The average holding of cattle per household is 1.8, which reduced from 2 compared to last year. No change was observed in the holding of buffaloes (0.1 per HH), horse/mule/donkey and camels (0.5 and 0.1 respectively). The holding of goats/sheep declined from 12.7 per households last year to 9.2 this year. A slight decline in poultry holding was observed this year (10.7 to 10.2). 14 Livestock holding 12,7% ,2% 10,2% 10,7% ,8% 2, 0,1% 0,2% 0,5% 0,5% 0,1% 0,1% During the assessment the households were asked to give reasons for the decline in livestock, when this happened. In the case of cattle the major reason was stated to be the selling of animals. Similar was the case of goats and sheep. These animals were sold during stress period but there were no resources to replenish the herd/flock afterwards. Selling of livestock above normal is one of the negative coping strategies and it is adopted when level of income declines and/or expenditure increases. Selling of such productive assets makes the Reasons for livestock decrease 4% households more vulnerable 36% Poultry 1% and in most cases they 34% 25% become food insecure. On 2% 18% the other hand, livestock Goat/Sheep 2% 12% 66% products, like dairy 2% products, are the major 5% Cattle 2% source of food in rural 13% 78% economy. Hence, depriving the households from such vital source has a serious other self consumption exchanged dead sold impact on their food security Pasture condition Pasture condition determines the capacity of livestock rearing. Grazing of livestock and especially of small animals like goats and sheep is quite common in Afghanistan. Provinces with mountainous topography have more reliance on livestock due to vast pasture areas. However, in years with low 20

21 precipitation, pastures are not in good condition and animals have inadequate feed for survival. This results in lowering productivity and sometimes selling of animals. On average, 22.7 percent of households reported bad pasture condition during the current season, while 30.6 percent reported it as average. However in certain provinces, like Nooristan, Ghazni, Baghlan, Kunar and Takhar, due to good rains, the situation was reported to be better compared to last year. On the other hand, depleting pasture condition was reported in Ghor, Bamyan, Daykundi, Helmand and Badghis. Total JAWZJAN GHOR HELMAND SAR-I- PUL KUNDUZ KABUL DAYKUNDI BADGHIS PANJSHER KAPISA FARYAB SAMANGAN ZABUL KANDAHAR PARWAN BAMYAN NANGARHAR LOGAR PAKTYA FARAH PAKTIKA LAGHMAN UROZGAN NIMROZ BADAKHSHAN KHOST WARDAK BALKH HERAT TAKHAR KUNARHA BAGHLAN GHAZNI NOORISTAN 6% 8% 9% 9% 11% 14% 17% 17% 18% 18% 18% 26% 15% 13% 27% 34% 4% 33% 6% 19% 25% 28% 28% 3 32% 32% 34% 38% 4 41% 45% 19% Pasture condition 55% 52% 25% 17% 47% 49% 56% % 69% 31% 39% 49% 3% 29% 6% 52% 93% 35% 2 18% 94% 27% 67% 51% 5 45% 7% 78% 12% 76% 62% 59% 7 43% 4 33% 9% 23% 54% 15% 8% 61% 45% 4 42% 36% 51% 38% 27% 25% 2 32% 26%22% 5% 6% 14% 5% 8% 15% 7% 17% 14% 11% 11% 6% 4% 6% 24% 9% 3% 23% 42% 13% 6% 2 21% 21% 32% 5% 4% 1% 1 21% 13% 5% 15% 22% 9% 6% good average bad dont know Bad pasture situation was reported mostly in Ghor province (92.8 percent of the households), followed by Bamyan (75.8 percent) and Daykundi (66.8 percent). In the provinces of Laghman, Paktika, Urozgan, Helmand, Paktya and Logar, average pasture condition was reported. 11. Food Access and Consumption Food Stocks Households in rural areas keep food stock, especially cereals, for their own consumption. The holding of stocks varies depending on the household level of production. SFSA 2013 s results show that, on average, households kept stock for 6.2 months during The cereal stocks at household level improved since 2011 by 5 percent. The cereal stocks declined in certain provinces during 2012 compared to For example, in Balkh it declined by 14.5 percent, in Daykundi 5.1 percent, in Faryab 2.6 percent, in Khost 7.7 percent, in Zabul 4 percent, in Wardak 3.8 percent and in Urozgan 2.3 percent. During the survey, respondents were asked to predict the stock availability during current year compared to year 2012 or Majority of the respondents (above 58 percent of the households) were not able to make such a prediction, due to high level of uncertainty in production. However, 21

22 16.4 percent of the households predicted that it would be like 2012, while 12 percent replied that it would be like Food consumption Meals frequency In order to know the frequency of meals eaten by different groups of people at home, the households were asked to record the meals eaten by children and adults yesterday, separately. According to the results, adult male, on average, eat 2.9 times per day, while children under 5 eat 4.2 times. There is no difference between young male and female regarding frequency of meals per day and both have 3.2 times, a bit higher than adults Food Consumption Score The FCS is considered as a proxy indicator of current food security. FCS is a composite score based on dietary frequency, food frequency and relative nutrition importance of different food groups. Dietary diversity is the number of individual foods or food groups consumed over the past seven days. Food frequency is the number of days (in the past 7 days) that a specific food item has been consumed by a household. Household food consumption is the consumption pattern (frequency * diversity) of households over the past seven days. The most diversified and best consumption, with maximal FCS at 112, means that all food groups are eaten 7 days a week. On average, poor food consumption (FCS = 1-28) is reported among 5.1 percent of the households; 18.9 percent of households have borderline consumption (FCS = ) and 76 percent have acceptable food consumption (FCS > 42). Badghis, Bamyan, Daykundi, Ghor, Panjsher, Takhar, Sar-i- Pul and Balkh host many more households with poor food consumption than other provinces. A reverse picture is seen in Kabul, Nooristan, Paktika, Urozgan and Kandahar, where the majority of surveyed households have acceptable food consumption. 22

23 26% 24% 18% 16% 12% 12% 11% 1 1 7% 6% 5% 4% 3% 3% 3% 2% 2% 1% 5% 1% 4% 1% 1% 4% 1% 3% 5% 7% 1 14% 9% 15% 6% 9% 12% 11% 8% 18% 22% 23% 37% 11% 25% 21% 26% 28% 28% 41% 35% 19% 36% 46% 3 31% 47% 61% 66% 39% 89% 87% 83% 89% 94% 94% 84% 92% 95% 99% 91% 97% 87% 89% 92% 73% 62% 67% 63% 67% 68% 75% 82% 77% 76% 45% 45% 53% 51% 58% 64% 35% 23% 24% 31% 10 Food Consumption Groups Poor (1-28) Borderline ( ) Acceptable (>42) Sources of food Sources of food are important in rural economy in the case of shock or vulnerability. Respondents, during the assessment, were asked to report the sources of food eaten last week. Majority of the households purchased food from markets, either on cash or on credit. Purchase on credit is also common in rural areas of the country. However, a great percentage of households used their own production. Given the dependency on livestock, 35.5 percent of the households used their own dairy products. Similarly, 32.7 percent of households used their own cereals for consumption. Purchase on credit, higher than other commodities, was witnessed in sugar and oil. Food aid as a whole contributed 1.4 percent in oil and 1.3 percent in sugar. 23

24 Food Sources Sugar Oil Meat Pulses Tubers Fruit Vegetable Cereals Dairy own production purchase on cash purchase on credit bartering gift/charity collection wild foods food aid (Govt, NGO/WFP) 12. Coping Strategies Coping Strategy Index (CSI) The CSI (more accurately, it is RCSI: Reduced Coping Strategy Index) is used to quantify the severity of food-based coping strategies. A 7 days recall period is used. It is based on 5 robust negative coping strategies and applies a standard weight. It is very useful for comparing across regions and countries, or across income/livelihood groups, because it focuses on a set of behaviours. The maximal CSI is 56 during the past 7 days prior to the SFSA (i.e. all 5 strategies are applied every day). There are no universal thresholds for RCSI. But the higher the RCSI, the more severely the coping is applied by a household. The CSI was divided in 5 groups, i.e. minimal (0-8), moderate (8.1-15), severe ( ), very severe ( ) and extremely severe (above 30). Provinces of Kunar, Helmand and Wardak have minimal CSI, while other provinces have a significant percentage of households with moderate to extremely severe CSI. Provinces with highest CSI is Khost, followed by Jawzjan, Takhar, Daykundi, Paktika, Herat, Badghis, Baghlan, Kunduz, Logar and Badakhshan. 24

25 12.2. Household Hunger Scale (HHS) HHS is a measure of household food access. The HHS is built around 3 questions about perceptions of a household on varying degrees of hunger by the number of times it has experienced hunger within the past 30 days prior to the survey. The higher the HHS, the more severe is the hunger. It was widely used for measuring the current food security by a number of agencies during the nineties, when other food security quantitative measures were not available. With the introduction of quantitative measures like food consumption score and the share of household expenditure on food, the usage of HHS has decreased. However, the HHS is a tool for measuring the acute food insecurity combining with other vital indicators, like food diversity, food groups, etc. In the SFSA, there are stronger proxies for measuring food insecurity and, hence, their use is recommended. Anyhow, HHS is also included and its usage depends on the stakeholders interest. The HHS is the sum of 3 responses that range from 0 to maximum 6. The HHS is the basis for categorizing households with respect to household hunger: Score Level of hunger 0 none 1 little 2-3 moderate 4-5 severe 6 extreme 25

26 Provinces of Jawzjan, Kunar, Sar-i-Pul and Herat did not report significant hunger during the SFSA interviews. However, provinces of Kapisa, Paktika, Panjsher, Logar, Faryab, Kunduz, Farah, Khost, Nimroz and Nangarhar showed significant impact of hunger. Severe hunger was reported in Farah, Nimroz, Kunduz and Badakhshan. Please note that, in many parts of Afghanistan, traditionally, households are not comfortable to answer such questions. There is a need to find better ways to ask such questions and tailor them not to offend the respondents General Coping Strategies General coping strategy covers a period of 3-6 months and comprised of a series of questions in order to know the food security situation in the short-run (acute food insecurity). In SFSA the coping strategy covers a period of 6 months. On average, 53.4 percent of the households have adopted one or more general coping strategies during the last 6 months. The highest percentage of households that used coping strategies was found in Khost (98.2 percent), followed by Badghis (90.6 percent), Urozgan (88.3 percent), Laghman (85.4 percent), Logar (82.6 percent), Kapisa (82.3 percent) and Daykundi (80.3 percent). The graph shows the most frequently adopted short-term strategies. They include increased daily labour (59.3 percent), working to feed himself (adopted by 58.8 percent of households), migrated to look for work (53 percent), spent saving or investment (34.5 percent), decreased expenditure on health, education etc. (33.6 percent) and begging/rely on kinship (33.1 percent). 26

27 The longer-term (distress) strategies include selling productive assets like productive livestock (30.7 percent), abnormal sale of animals (27.7 percent) and income generating equipment (7.8 percent), begging (10 percent). It should also be mentioned that one fourth of households sent children to work as labourers outside the house and 18.9 percent pulled children out from schools to have more labour force, which would have long term impact on human capital development in the future. The coping strategies vary by livelihood group. The above mentioned short-term strategies are found more often among households relying on non-farm labour, handicrafts, trade/shop and remittances. In addition to these groups, households relying on agricultural labours and trading also applied some of the above noted distress strategies. 13. Food Security Food security, in the absence of data on quantitative calories and nutrients intake, is estimated by combining two proxies, which are food consumption score and share of food expenditure, to classify households in three food security groups: poor (food insecure), borderline and food secure (see Annex 1 for details) How many and where are the food insecure? Overall, 6.8 percent of the surveyed households are food insecure, 31.7 percent are at borderline and 61.5 percent are food secure. Food Security Groups 6,8 31,7 61,5 Food security by province reflects a different picture. The highest share of food insecure households is found in Ghor (28.4 percent), followed by Badghis (18.9 percent), Nimroz (18.5 Food Insecure Borderline Food Secure 27

28 GHOR BADGHIS NIMROZ DAYKUNDI LOGAR KUNARHA KAPISA BAMYAN HELMAND JAWZJAN FARYAB SAMANGAN SAR-I- PUL TAKHAR BALKH LAGHMAN PAKTIKA PARWAN PANJSHER FARAH BADAKHSHAN KABUL KANDAHAR HERAT WARDAK ZABUL NOORISTAN PAKTYA BAGHLAN NANGARHAR GHAZNI KHOST KUNDUZ UROZGAN percent), Daykundi (17.3 percent), Logar (15.9 percent) and Kunar (15.1 percent) % 90.2% % % % % 44.5% 72.3% 51.6% % 54.7% 36.7% % 70.3% 63.5% 68.3% 64.9% 51.8% 29.4% 54.3% 49.2% 61.1% 50.5% 57.3% 46.8% 49.2% % 18.9% 28.4% 31.4% 36.1% 23.8% 33.6% 25.4% 34.8% 31.9% 18.5% 15.9% 17.3% Food Security Groups by Province 57.1% 8.2% % 15.5% 24.6% 52.2% 24.1% 44.3% 20.6% % 39.9% 57.8% 35.6% % 26.1% 21.1% 24.1% 36.9% 10.4% 10.6% % 13.4% 14.3% 14.7% 15.1% 2.4% 2.9% 3.2% 3.6% 4.1% 4.4% 4.9% 5.3% 5.4% 5.6% 7.4% 7.9% 9.6% 6.5% 9.8% 26.5% 15.5% 0 0.4%.8%.8% 1.6% % Food Insecure Borderline Food Secure The proportion of borderline households is much higher in Paktika (57.8 percent), Helmand (57.1 percent), Herat (52.2 percent), Panjsher (46.3 percent) and Kabul (44.3 percent), signalizing a higher probability of these households to slip into the food insecure group should they be exposed to shocks. The highest percentage of food secure households was found in Urozgan (93.5 percent), followed by Kunduz (90.2 percent), Ghazni (83.7 percent), Baghlan (82.4 percent) and Paktika (79 percent). Besides these provinces, in general, female-headed households have a higher percentage of food insecure as compared to male-headed households. The highest percentage of food insecure male-headed households is found in Ghor (28 percent), followed by Badghis (18.7 percent), Nimroz (18.6 percent), Daykundi (16.4 percent) and Logar (14 percent) Who are the food insecure? Female-headed households Food security is significantly related to gender of the head of households. The percentage of food insecure households is higher among female headed households (17.9 percent) as compared to male headed ones (6.3 percent). Highest percentage of food insecurity among female-headed households was reported in Kunar (57.1%), followed by Ghor (5), Logar (44.4%), Parwan (4) and Sar-i-Pul (37.5%). Besides these provinces, generally, female Food insecurity by gender of household head 62% 51% 31% 32% 6% 18% Male Female Food Insecure Borderline Food Secure 28

29 headed households have a higher percentage of food insecure than male-headed households. The highest percentage of food insecure male-headed households are found in Ghor (28 percent), followed by Badghis (18.7 percent), Nimroz (18.6 percent), Daykundi (16.4 percent) and Logar (14 percent) Food insecurity by household head's age 58% 63% 59% 35% 3 33% 7% 7% 8% yr yr 65 yr & above Food Insecure Borderline Food Secure Households with elderly head Results show that the age of household head plays a significant role in household food security. Household heads aged 65 years and above has the highest percentage of the food insecure as compared to other age groups. The second highest food insecure group is of years. The age group of years is comparatively better in food security. Households heads with age years are quite active and have more physical capacity to enhance income. Widows/ers Food security varies by marital status of the household heads. Widows/ers are likely to imply more food insecurity than other marital conditions. The second highest food insecure group is households whose head is divorced/separated. Households whose head is single are less food insecure, likely due to no or lower number of dependents Food insecurity by household head's marital status 56% 81% 62% 57% 28% 31% 4 8% 17% 1 6% 3% Food Insecure Borderline Food Secure Households with disabled heads Disability of the head of household results in higher food insecurity as compared to non-disabled. Disabled heads of households unable to work imply a much higher rate of food insecurity than those disabled but still able to work. Households with disabled head are highly vulnerable especially in cases where their physical activity is hampered and cannot make use of livelihood support 29

30 interventions. The majority of this group needs support in livelihood activities, which should be designed to suit their limited physical capabilities. Households living in tents and open space Household shelter normally reflects its level of poverty. Members of households with better economic status usually live in a better shelter condition in order to protect themselves from environmental hazards and harsh weather conditions. Conversely, poor households have little means to cope with such situation and their shelters are prone to various environmental hazards. The SFSA 2013 found significantly higher food insecurity (both in the food insecure and the borderline classes) among households living in open space and tent Food insecurity by household's residence type 33% 56% 7 22% 62% 54% 32% 4 11% 8% 7% 6% Nomadic (Kuchis) IDP Permanent resident Returnee Food Insecure Borderline Food Secure Nomadic and IDPs Food security varies across the vulnerable groups of the respective provinces. In general the higher rate of food insecurity is found among nomadic households (11.1 percent) followed by IDP households (8.2 percent). The lower food insecurity among returnees may be explained by the fact that a number of them might have had more assets and cash brought back from abroad (Pakistan and Iran). Historically, nomads have lower food diversity and poor eating habits thus they remain highly food insecure. The IDPs have no choice of food selection as they mostly depend on food aid. Households with no or lower livestock holding In the rural economy of Afghanistan, livestock plays a vital role in household food security. The majority of families keep livestock for domestic consumption of milk and milk products. They raise poultry to get eggs and cook food for their children on a regular basis. Those who have no livestock hardly buy milk for tea and are not even able to get enough milk for young children. 30

31 Food insecurity is associated with livestock holding. Households with no livestock have higher food insecurity compared to those that keep livestock Food insecurity by irrigated land cultivation (jerib) 63% 55% 72% 68% 72% 71% Households with none, small or very large irrigated land Productive landholding ensures better household food security and leads to sustainable livelihoods and better resilience to shocks. The irrigated land is more productive and sustainable. The highest percentage of food insecure households are found within the group with less than 2 jerib of irrigated land, followed by the group with no irrigated land. Households holding larger area of irrigated land have lower levels of food insecurity. However, the percentage of families classified as borderline in terms of food security varies in the different groups and their risk to become food insecure depends on the nature of shock they may suffer, which can affect each group with a different degree of severity. 82% 37% 3 22% 27% 24% 26% 16% 7% 8% 6% 5% 4% 3% 2% no below 2 above 2 above 3 above 5 above 10 above 20 irrigated to 3 to 5 to 10 to 20 to 50 land Food Insecure Borderline Food Secure % 34% 23% Food insecurity by household's main source of income 42% 62% 64% 56% 63% 61% 71% 61% 71% 61% 26% 25% 33% 53% 28% 32% 23% 34% 24% 36% 12% 11% 1 8% 7% 5% 5% 5% 5% 3% Food Insecure Borderline Food Secure Households with no income or relying on agriculture labour, credit Food security varies among livelihood groups. The households with no income have the highest percentage of food insecure, followed by households relying on agriculture labour and those depending on credit. Traders/shopkeepers have the lowest percentage of food insecure households compared to all other groups. The second lowest food insecure group is the crop-sale group, followed by "other" and livestock holders. 31

32 Food insecurity varies by seasonality Food insecurity by household's livelihoods in spring 68% 25% 57% 36% 66% 29% 7% 7% 5% Agriculture Non farm activity Liverstock Food insecurity by household's livelihoods in summer 66% 58% 7 26% 36% 25% 8% 6% 5% Agriculture Non farm activity Liverstock Food Insecure Borderline Food Secure Food Insecure Borderline Food Secure Food security varies by season although the differences are not so high. Food insecurity of households with agriculture as a main livelihood is high in spring and summer. It is followed by the non-farm-activities group during both these seasons. During fall and winter, the livestock group has higher food insecurity compared to other two main livelihoods. Food insecurity of non-farm-activities group is higher in spring and declines gradually in summer, fall and winter. Thus, the non-farm group has lower food insecurity during winter. The percentage of households at borderline is highest for non-farm-activities group in all the four seasons. Reduction in number of food groups increases food insecurity Consumption of various food groups affects the food security level of households. Households using more food groups in a week have better food security. The result shows that households eating only 2 food groups (cereal + one of vegetable or pulses or sugar or oil) are highly food insecure. By adding one additional food group, the food insecurity decreases. Households consuming all 8 groups in a week time are mostly food secure Food insecurity by number of food groups 26% 34% 5 46% 54% 7 68% 67% 36% 4 34% 33% % 27% 31% 26% 2 13% 5% 2% Food Insecure Borderline Food Secure 14. Food insecurity and level of significance of various groups To determine the general vulnerability factors of households and understand why they are food insecure, the correlation between Food Consumption Score (FCS) and key food security related factors presented as continuous variables in the entire surveyed sample is analyzed by using Bivariate correlation (based on Pearson s Correlation Coefficient). Significant Pearson s correlation coefficients (P<0.05) are presented in the Table below. Negative correlations are shaded in red color. 32

33 The results show that food consumption or current food security is NEGATIVELY correlated with female-male ratio and number of buffaloes kept by households, dependency ratio, Coping Strategy Index and Household Hunger Scale. This means that the higher these numbers, the lower FCS and the poorer food security. Conversely, FCS is POSITIVELY correlated with the total cash income in this season, households head age, number of people at different age groups, number of months the food stock lasted during 2011 and The higher these numbers, the higher the FCS and the better food security. Pearson Correlation Groups Food consumption score SN 1 Food consumption score 1 2 Female-male ratio ** 3 Dependency ratio Land cultivated irrigated equivalent Total income this year.223 ** 6 HH head age.033 ** 7 Under 5 male.142 ** 8 Under 5 female.129 ** male.131 ** female.111 ** male.166 ** female.118 ** and above male.029 ** and above female.072 ** 15 Agricultural land ** 16 Rainfed land (jerib) Orchard/vegetable land (jerib) Type of food crop grown this season.057 ** 19 Number of months food stock lasted in ** 20 Number of months food stock lasted in ** 21 General coping strategy.195 ** 22 CSI reduced ** 23 HHS ** 24 Dependency ration category Land Irrigated.083 ** 26 Food groups.688 ** 27 Total Income last year.218 ** **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed). 33

34 15. Conclusions 1. The assessment took place in different seasons and in different provinces. Majority of the households (80.6 percent) were interviewed at post-harvest time, 14.4 percent at harvest time and 5 percent at pre-harvest time. 2. Based on the combined Food Consumption Score and Food Expenditure, overall, 6.8 percent of surveyed households are food insecure, 31.7 percent are in borderline to food insecurity and 61.5 percent are food secure. 3. Although SFSA looked for acute food insecurity, overall food insecurity is a mixture of transitory and chronic food insecurity, which is attributed to low resilience and structural factors existing before the shocks. 4. Food insecurity is higher among households headed by females, widows/ers, permanently disabled and elderly; households living in open space and tents, nomadic and IDPs; households with no livestock; with no or with less than 2 jerib of irrigated land; with no cash income; relying on agriculture labour, credits or handicrafts. 5. Seasonality has significant impact on livelihoods regarding food security. Agriculture implies higher level of food insecurity in spring and summer, while livestock does that in fall and winter. 6. Food groups have positive impact on households food security. Increasing food groups in weekly consumption results in better food security. 7. Food consumption is NEGATIVELY correlated with female-male ratio and number of buffaloes kept by households, dependency ratio, Coping Strategy Index and Household Hunger Scale. This means that the higher these numbers, the lower FCS and poorer food security. 8. Food consumption is POSITIVELY correlated with total cash income this season, householdhead age, number of people at different age groups, number of months the food stock lasted during 2011 and The higher these numbers, the higher the FCS and better food security. 9. In general, the household s coping strategies are reported as short-term and non-distress. However, 24.7 percent of surveyed households sent children to work as labourers outside the house and 18.9 percent pulled children out from schools to have more labour force, which would have a long term impact on human capital development in the future. 10. Around 41 percent of households experienced one or more shocks during the past 3 months. Among these, more than 53 percent considered it more severe than usual. 11. Results show that 23.9 percent of households have agriculture as main livelihood, 9.3 percent have livestock while 66.8 percent have non-farm activities. 12. In terms of seasonality, on average, agriculture was the main livelihood of 38.6 percent in spring, which reduced to 30.1 percent in summer, to 19 percent in fall and only to 7.9 percent in winter. Similarly, livestock as main livelihood was adopted by 4.6 percent in spring, 9.4 percent in summer, 10.3 percent in fall and 13 percent in winter. The non-farm livelihood was at peak in winter (79.2 percent), declined to 70.8 percent in fall, to 60.5 percent in summer and to 56.8 percent in spring. 34

35 13. The average cultivated landholding of all these three types is 7.9 jerib, where 3.7 jerib are irrigated, 2.6 jerib are rainfed and 1.6 jerib are orchard/vegetable. 14. Coping Strategy Index (CSI): the province with highest CSI is Khost, followed by Jawzjan, Takhar, Daykundi, Paktika, Herat, Badghis, Baghlan, Kunduz, Logar and Badakhshan. According to Household Hunger Scale (HHS), severe hunger was reported in Farah, Nimroz, Kunduz and Badakhshan. 16. Recommendations 1. Food insecure households should be prioritized to meet the food and cash gap. The highly food insecure groups of population, like female-headed households, households with disabled head, IDPs and households that lost all assets, should be provided relief through food or cash/voucher, depending on markets and food availability. Use cash and voucher where markets are functional and the transfer modality is feasible. In the most remote areas, where markets are not functioning properly and food prices are high, transfer mechanisms through food for work would be more relevant than cash. 2. Geographical targeting should first focus on provinces with higher level of food insecurity and then districts with higher food insecurity prevalence. 3. Programming of food security interventions to respond to and increase the household resilience to general shocks should focus on households with higher vulnerability and food insecurity. 4. Programming of specific emergency interventions to respond to natural disasters or increased market prices should consider specific vulnerability characteristics related to these shocks, which are also highlighted in the Vulnerability Analysis section of this report. 5. Some households faced economic and weather related shocks from which they have not recovered. Thus, they should be provided with employment opportunities that will mitigate risks and reduce high food insecurity. 6. For weather shocks, an effort is needed to educate the farmers (through food for training where appropriate) on how best they can mitigate the weather-related shocks to ensure sustainability of livelihoods. 7. FFW and FFA should focus on improving water sources, irrigation, health facilities and roads to enhance community s resilience to shocks. Next SFSA 8. In order to avoid the seasonal impact on food security, the next SFSA should be conducted at pre-harvest time, preferably in April Some of the questions like CSI and HHS will need special attention. These modules will need to be simplified and made more understandable to the enumerators. 35

36 10. In order to keep the quality intact, in the future, the fieldwork should be conducted by one specialized NGO/company. 11. Enumerators need thorough training in data collection prior to SFSA. FSAC should develop a roster and provide intensive training in data collection. 12. The quality of data has improved considerably compared to last year. However, independent monitoring/supervision is required to improve data quality, since some of the results might be affected by poor data quality. 36

37 ANNEXE 1: Food Security Analysis Methodology Calculation of household food security in the Seasonal Food Security Assessment 2013 in Afghanistan (July-September 2013) Household food security in this SFSA Report is analyzed using a methodology guided in the second edition of Emergency Food Security Assessment Handbook (EFSA, WFP, 2009) and in the first edition of Comprehensive Food Security and Vulnerability Assessment (CFSVA, WFP, 2009). The analysis is based on the Food and Nutrition Security Conceptual Framework which considers food availability, food access and utilization as core pillars of food security and links these to households livelihood strategies and assets. The analysis used two measures to assess household food security which are the Share of Food Expenditure out of total expenditure and Food Consumption Score (FCS). To assess the severity of household coping strategies, the Reduced Coping Strategy Index (RCSI) is also applied. 1. Share of Food Expenditure The Share of Food Expenditure out of total expenditure is a proxy indicator of household food security. The higher the share of food expenditure, the greater is the likelihood that a household has poor food access. The commonly used thresholds for the share of food expenditure are used to classify households into poor, average and good food expenditure groups: Poor: food expenditure is more than 60 percent of total household expenditure; Average: food expenditure is at percent of total household expenditure; Good: food expenditure is less than 40 percent of total household expenditure. 2. Food Consumption Score (FSC) The FCS is considered as a proxy indicator of current food security. FCS is a composite score based on dietary frequency, food frequency and relative nutrition importance of different food groups. Dietary diversity is the number of individual foods or food groups consumed over the past seven days. Food frequency is the number of days (in the past 7 days) that a specific food item has been consumed by a household. Household food consumption is the consumption pattern (frequency * diversity) of households over the past seven days. Calculation of FCS and household food consumption groups 1. Using standard 7-day food frequency data, group all the food items into nine specific food groups. 2. Sum all the consumption frequencies of food items of the same group, and recode the value of each group above 7 as Multiply the values obtained for each food group by its weight and create new weighted food group scores. 4. Sum the weighed food group scores, thus, creating the food consumption score (FCS). The most diversified and best consumption with maximal FCS at 112 means that all food groups are eaten 7 days a week. 5. Using the appropriate thresholds, recode the variable food consumption score, from a continuous variable to a categorical variable, to calculate the percentage of households of poor, borderline and acceptable food consumption. 37

38 Food Items, Food Group and Weight of the EFSLS (Afghanistan, July August 2012) No Food groups Weight 1 Cereals and tuber 2 2 Pulses 3 3 Vegetables 1 4 Fruit 1 5 Meat, fish and eggs 4 6 Dairy 4 7 Sugar Oil/fat Condiments (salt) 0 Food Consumption Score thresholds The following thresholds of FSC are used to categorize households into three food consumption groups Poor, Borderline and Acceptable: Food consumption groups Food Consumption Score 38 Description Poor 1-28 An expected consumption of staple 7 days, vegetables 5-6 days, sugar 3-4 days, oil/fat 1 day a week, while animal proteins are totally absent Borderline An expected consumption of staple 7 days, vegetables 6-7 days, sugar 3-4 days, oil/fat 3 days, meat/fish/egg/pulses 1-2 days a week, while dairy products are totally absent Acceptable > 42 As defined for the borderline group with more number of days a week eating meat, fish, egg, oil, and complemented by other foods such as pulses, fruits, milk 3. Reduced Coping Strategy Index (RCSI) When livelihoods are negatively affected by a shock/crisis, households may adopt various mechanisms (strategies) which are not adopted in a normal day-to-day life, to cope with reduced or declining access to food. Coping Strategy Index (CSI) is often used as a proxy indicator of household food insecurity. CSI is based on a list of behaviors (coping strategies). CSI combines: (i) the frequency of each strategy (how many times each strategy was adopted?); and (ii) their severity (how serious is each strategy?) for households reporting food consumption problems. Higher CSI indicates a worse food security situation and vice versa. CSI is a particularly powerful tool for monitoring the same households or population over time. There are two types: Full CSI and Reduced CSI. In this EFSLS, RCSI is used. RCSI is based on the same short list of 5 coping strategies, and the same severity weights. It is very useful for comparing across regions and countries, or across income/livelihood groups, because it focuses on the same set of behaviors. The maximal RSCI is 56 during the past 7 days prior to the EFSLS (i.e. all 5 strategies are applied every day). There are no

39 universal thresholds for RCSI. But the higher the RCSI, the more severe the coping is applied by a household. Table below is an example of RCSI of this analysis, with RCSI at 27. Coping Strategies Raw score Universal Severity Weight Weighted Score = Frequency x Weight 1. Rely on less preferred and less expensive foods Borrow food or rely on help from friends or relatives Limit portion size at mealtime Restrict consumption by adults in order for small children to eat Reduce number of meals eaten in a day Total Reduced CSI Sum down the total for each individual strategy Estimation of proportion of food insecure households based on combined food consumption and food expenditure The level of household food security is calculated through a cross-tabulations of the above two indicators. Food expenditure groups and food consumption groups derived from the above calculations (in Point 1 and 2) are cross-tabulated to identify three food security groups (food insecure, borderline and food secure). The table below indicates that percentage of households falling in red blocks are considered poor, those falling in yellow blocks are at borderline and the ones falling in green blocks are in acceptable groups of food security. Food Security Groups Poor >6 Expenditure on food Borderline 40-6 Acceptable <4 Poor (1-28) Poor Poor Borderline Food consumption groups Borderline ( ) Poor Borderline Acceptable Acceptable (>42) Borderline Acceptable Acceptable Note: Red = Food insecure, Yellow =Borderline, Green = Food secure 39

40 5. Household Hunger Scale (HHS) HHS is a measure of food access. The HHS is built around 3 questions about perceptions of a household on varying degrees of hunger by the number of times a household has experienced hunger within past 30 days prior to the survey. The three questions are: 1. In the past 30 days, was there ever no food of any kind to eat in your house because of lack of resources to get food? 2. In the past 30 days, did you or any household member go to sleep at night hungry because there was not enough food? 3. In the past 30 days did you or any household member go a whole day and night without eating anything at all because there was not enough food? The three scoring options for scoring the response to each question are: Never (0 times) =0 score Rarely/Sometimes (1-10 times) = 1 score Often (more than 10 times) =2 scores HHS = response 1 + response 2 + response 3. The HHS ranges from 0 to maximum 6. The HHS is the basis for categorizing households with respect to household hunger: Score Level of hunger 0 none 1 little 2-3 moderate 4-5 severe 6 extreme 40

41 ANNEXE 2: More charts and tables Type of shocks Total HELMAND WARDAK PAKTIKA KUNDUZ JAWZJAN KHOST KUNARHA BAMYAN GHOR GHAZNI BADGHIS BADAKHSHAN NOORISTAN PARWAN ZABUL DAYKUNDI PANJSHER UROZGAN PAKTYA LOGAR KAPISA KANDAHAR NANGARHAR FARAH KABUL LAGHMAN BALKH SAMANGAN HERAT FARYAB NIMROZ BAGHLAN SAR-I- PUL TAKHAR 2% 1% 1% 4% 1% 5% 6% 6% 8% 2% 6% 2% 26% 7% 25% 1% 1 9% 1 9% 4% 3 49% 11% 4% 11% 2% 56% 1% 23% 2% 83% 1% 2% 1% 9% 1% 69% 1% 4% 11% 7% 23% 11% 3 8% 17% 25% 24% 25% 2% 14% 2% 4% 1% 42% 3% 26% 2% 7% 6% 3% 1% 6% 38% 29% 3% 4% 18% 4% 11% 8% 34% 7% 8% 31% 1% 24% 4% 9% 7% 8% 3% 2% 22% 22% 27% 3 3% 3% 47% 5 9% 16% 4% 2% 2% 3% 1 6% 58% 4% 5% 33% 1% 8% 18% 15% 2% 2% 2 1% 12% 1% 38% 5% 24% 14% 1% 12% 6% 18% 5% 22% 19% 4% 3% 47% 3% 1% 1% 23% 4% 18% 1% 36% 1% 1 4% 4% 23% 2% 5% 1% 41% 4% 14% 8% 2% 27% 5% 26% 25% 11% 1% 1% 4% 27% 9% 36% 4% 4% 2% 16% 2% 28% 8% 17% 4% 11% 2% 15% 9% 7% 29% 13% 15% 1% 6% 34% 3% 3 9% 45% 2% 8% 6% 32% 26% 3 1% 7% 3% 1% 33% 6% 13% 2 15% 1% 6% 7% 37% 3% 11% 1% 11% 1% 31% 5% 4 8% 3 2% 2% 2% 4% 9% 4% 42% 4% 27% 1% 9% 7% 7% 2% 42% 8% 28% 14% 1% 3% 2% 3% 52% 6% 17% 6% 1 3% 3% 3% 56% 6% 11% 14% 4% 4% 5% 69% 7% 7% 1% 5% 4% 1% 5% 13% 14% 14% 15% 16% 17% 17% 19% 19% 2 21% Severe sickness/death of bread winner Livestock disease outbreak Huge increase in food prices Huge influx of IDPs Natural disaster Crop pest outbreak Very high insecurity Extreme weather Other 41

42 42