Wudpecker Journal of Agricultural Research Vol. 2(1), pp. 008 - 014, January 2013

ISSN 2315-7259 2013 Wudpecker Journals

Levels of household food insecurity in rural areas of Guraghe zone, Southern Ethiopia Zerihun Nigussie* and Getachew Alemayehu Bahir Dar University, College of Agriculture and Environmental Sciences, P.O.Box 1094, Bahir Dar, Ethiopia. *Corresponding author E-mail: [email protected]. Accepted 28 October 2012 Food insecurity has been an issue for a long time, but it began to make a serious impact and became a prominent issue on the development debate recently. Emblematic to this problem is Ethiopia, in which about half of the population is categorized as food insecure. In light of understanding the incidence and severity of food insecurity at household level, this study was undertaken in the Mareko woreda, Guraghe zone. The data came mainly from 150 rural households’ in five different kebeles which were under the Productive Safety Net Program. A structured questionnaire was employed to collect the information. The survey result showed that the calorie intake approach of the absolute head count index, the normalized calorie deficiency gap index and the square calorie deficiency gap were about 60%, 5.8% and 1.8%, respectively. The Gini coefficient for the households was also estimated to be 0.212. These figures called for proper identifying tools to reach to those in acute food insecurity condition; and the inequality measurement (Gini coefficient) indicated relative homogeneity of calorie consumption. Key words: Calorie, food insecurity, gini coefficient, kebeles, mareko woreda, Ethiopia.

INTRODUCTION Food insecurity has been in the public eye for a long time, since the biblical story of Joseph at the pharaoh’s court predicting seven years of plenty food followed by seven years of famine and stored crop harvests that saved lives at famine years, is an early example of food security planning in practice. However, food security began to make a serious impact and became a prominent issue on the development debate in recent history since 1970. Since then it has rarely been out of scene (Devereux and Maxwell, 2001). Even though the problem of food insecurity has been the concern of developing countries for long time, now-adays it is a world-wide issue. Estimates indicate that about 925 million people worldwide are chronically malnourished of which 906 million are in developing countries, in which two-thirds of these live in just seven countries (Bangladesh, China, Democratic Republic of Congo, Ethiopia, India, Indonesia and Pakistan) and the rest 19 million in the developed countries. Moreover, the proportion of undernourished people remains highest in sub-Saharan Africa, at 30 percent (that is, 239 million) in 2010 (FAO, 2010). Ethiopia with an estimated population of over 80 million is the second populous nation in Africa (Nigussie et al.,

2012). Out of the total population of the country 83.9 percent is found in rural areas (ECSA, 2011). The country is predominantly agrarian, and agriculture plays an important role in the national economy (Di Falco et al., 2011; Adugna and Wagayehu, 2012). It accounts about 45 percent of the total GDP, employing and supporting about 84 percent of the total population and accounts for about 90 percent of the exports (Workneh, 2004; Tesfay, 2006; FDRE, 2008; CIA, 2011), but its productivity and performance in terms of feeding the country’s population which is growing at 2.6 percent per annum is dismal (Habtom et al., 2005; FDRE, 2008). The per capita gross national income is among the lowest worldwide and estimated to be USD 220 in 2007 (World Bank, 2008). Nearly half its population is food insecure or live below the poverty line (Devereux, 2000; Ramakrishna and Demeke, 2002; Jema, 2008) and it has a long history of famines and food shortages that can be traced back to 250 BC (Webb et al., 1992; Ramakrishna and Demeke, 2002). Since 1965 there have been 15 major droughts in the country, with four consecutive years of drought beginning in 1999 (Hiensch, 2009). The average dietary energy supplies of an individual in the country is approximated to be 1,770 Kcal per capita

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per day, which is 16 percent below the minimum level of the one set by the government in 1994 i.e., 2,100 Kcal per capita per day (Tesfaye, 2003). This minimum is also less than the international minimum standard for an adequate diet of 2,400 Kcal per adult equivalent per day (Belayneh, 2005). The problem of food insecurity in Ethiopia could also be manifested through the level of nutritional deprivation, stunting and wasting of children less than five years of age. According to the 2005 Ethiopian Demographic and Health Survey 37%, 47% and 11% of children under age of five were underweight, stunted and wasted, respectively (ECSA and Macro, 2006). The above facts show the extent and depth of the food insecurity problem at the national level. However, the current study was carried out with the aim of generating area specific information to answer the question ‘how much severe is the household’s food insecurity problem among the households in the woreda1?’ METHODOLOGY Location of the study area The present study was conducted from May to June 2012 in Mareko woreda, found in the Guraghe zone of the Southern Nations, Nationalities and Peoples Regional States. The woreda is located on the geographical coordinates of 80 00' 30'' N Latitude and 380 31' 30'' E Longitude, and it comprises of 25 rural kebeles2 and the woreda capital town, Koshe. The elevation is estimated to be in the range of 1,700 to 1,850 meters above sea level and agro-ecologically, it is composed of two agro-climatic zone i.e., 45 percent ‘woyena-dega’3 and 55 percent ‘kolla’4 with a minimum annual temperature of 17.60C and a maximum of 200C. The mean annual rainfall of the area ranges from about 801 to around 1,200 mm with a unimodal pattern. The soil type which is dominant in the area is silt type with some black clay soil. According to ECSA (2011), population projection report the woreda is home to 75,098 people of which 38,138 are male and 36,960 are female. About 89.19 percent of the population resides in the rural part of the woreda undertaking their livelihood on subsistence agriculture. Mixed farming, including cereal, cash crop and livestock production are of paramount importance. According to the Woreda Office of Agriculture, the woreda has a total land mass of 22,329 ha, out of which about 81 percent is cultivable and till this 1

Local administrative ward which is equivalent to a district and it is composed of a number of kebeles 2 The smallest political administrative unit 3 Commonly used Ethiopian term for areas of altitude between 1,800 and 2,400 meters 4 Commonly used Ethiopian term for areas of altitude around 1,800 meters and below

time about 95 percent of the cultivable land is cultivated. Data types, collection and sampling technique The data examined in the present study came from both primary and secondary sources. For the collection of the primary data from the sampled households, structured questionnaire has been employed as an instrument. The survey questionnaire covered issues such as socioeconomic and institutional characteristics and identification of the quantities of the different types of crops, livestock meat and other by-products that come to and go out of the given household’s possession over the period of study through own production, purchase, sale, transfer, kept in reserve and post-harvest loss to calculate dietary food energy intake. Secondary data were sourced from published and unpublished literature to describe the area under study, population size, nutritional equivalent of unit food item consumed by households and other recommended food bench-marks and major economic activities in the woreda. A total of 150 households were selected through twostage probability sampling procedure. Five kebeles were selected randomly from the 25 rural kebeles in the woreda. Thereafter, the required sample size was shared among those selected kebeles using probability proportional to size method. Then, the sample households in each kebele were selected using systematic random sampling technique from the complete list obtained from the respective kebele administrations. Measurements of variables Food insecurity severity measurement One possible food insecurity incidence, depth and severity measurement tool, according to Hoddinott (2002), is the Foster-Greer-Thorbecke (FGT) index which is widely used for poverty measurement studies. Adopting this, the present study makes use of the Foster et al. (1984) Pα class of poverty measures to measure the level of household food insecurity. FGT index measures the mean of the household food insecurity gaps raised to the aversion parameter α, where it represents the weight attached to the severity of the food insecurity. The mathematical expression of the FGT model is specified as follows:

 Z Y i  1  P  n   Z 



q



i 1

where, if Yi>Z then Z-Yi = 0; Z is the cut-off level of calorie used to classify a household as food secure or

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Figure 1. Map of Mareko woreda.

not, here it is 2100 Kcal/AE/day; Yi is the per capita calorie intake of household i; q is the number of food insecure households and n is the total number of sample households. Household food calorie consumption level According to Getachew and O’Connor (2006), the subsistence potential ratio (SPR) could be one way of measuring the potential of households to sustain their consumption throughout the year. This is computed for this study as the ratio of total amount of food energy that was available for household (total household food balance) to the amount of food energy that a household needs in a year.

cumulative percentage of households when the households are arranged in their ascending order of per capita calorie intake. The value of Gini coefficient lies between 0 and 1. If the per capita calorie intake is the same among the households, the Lorenz curve overlap on the diagonal and the Gini coefficient would be zero. If the per capita calorie intake among the households shows perfect inequality, the Lorenz curve would lie on the right-angled sides opposite to the diagonal and the Gini coefficient would equal one. Mathematically, the Gini coefficient could be specified as:

G

n

2

n

2





  r i 1

i



n 1 y 2  i

Calorie intake inequality measurement

where G is the Gini coefficient; yi is the per capita calorie intake of household i; is the mean per capita calorie intake; ri is the rank of household i in the y and n is the total number of sample households.

This study relies on the Gini coefficient measures of inequality to capture the inequality of calorie intake among the households included in the study. Gini coefficient is based on the concept of Lorenz curve that relates the cumulative percentage of calorie intake to the

Method of data analysis The data collected were subjected to both descriptive and

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inferential statistics such as mean, percentage, t-test, Foster-Greer-Thorbecke index and Gini coefficient. To analyze the data, Statistical Package for Social Scientists for windows version 17 was employed.

while that of the food insecure Birr 900 in the study period. Extent and situation of household food insecurity

RESULTS AND DISCUSSION Socio-economic and institutional characteristics As shown in Table 1 below the food secure household heads seem to be relatively younger than the food insecure household heads. Of the total sampled household heads female comprises 14 percent, and out of the female and male heads about 67 and 59 percent are food insecure, respectively. The average adult equivalent household size of the food insecure and food secure households was 4.22 and 4.16 adult persons, respectively; while the unadjusted household size of the food insecure and food secure households was somewhat larger, with an average of 5.40 and 5.28 persons, respectively. Thus on average, the food requirements of the food insecure households would be, all other things being equal, greater than those of the food secure households. Moreover, t-test on the mean education level of household heads shows that food secure households have attained, relatively, higher education level than the food insecure. The mean land holding of the food insecure and secure households was about 1.04 ha and 1.77 ha, respectively which shows that an average food secure household has at its disposal about 0.73 ha more land as compared to the average food insecure household. The mean livestock holding in Tropical Livestock Unit (TLU) for the sample households is about 3.62. The t-test for equality of the means in livestock holding between the food secure and insecure households show a statistical significant difference, in which on the average the food secure household own 5.49 TLU and their counterpart own 2.38 TLU. Furthermore, the average number of oxen appeared to be greater for food secure than the insecure households, this difference was statistically significant. Food insecure households that engaged in off-farm and non-farm activities in the study period generated on average about Birr 1,067 in the study period, while their counterpart generated on average about Birr 1,204 in the same period. The mean amount of credit received by those food insecure households that took credit is found to be Birr 177.11, while to that of their counterpart it is Birr 296.83. However, these mean amounts of credit received by the two groups of households did not show statistical significant difference. The table further revealed that the mean number of extension contacts was only five; and regarding the annual income generated from the safety net program, food secure households obtained Birr 1,018

Although definitions of food security and insecurity revolve mostly around “food”, the main player behind is calorie and not protein, micro-nutrients, etc. This is due to the fact that analysis operates on the principle that other needs are usually met when calorie intake is satisfactory (Maxwell and Smith, 1992). With this much importance placed on food calorie consumption by different literatures, it is useful to look to the extent and situation of food insecurity measured as food calorie intake among the households included in the study. Extent of household food insecurity To determine and describe the extent of food insecurity (i.e., measured using calorie intake) among the interviewed households,Foster et al. (1984) (FGT) measures of poverty are employed. The FGT model allowed the estimation of head count index of calorie deficiency (P0), the calorie deficiency gap index (P1), and the square calorie deficiency gap (P2). The survey result shows that the calorie intake approach of the absolute head count index is about 60 percent indicating that only 60 percent of the respondents were actually in the state of food insecurity, that is, unable to get the minimum required calorie recommended for subsistence. However, this index did not show the depth of food insecurity below the minimum recommended calorie level for it takes no account of the magnitude of shortfall. Applying this rate to the Mareko woreda rural population of 56,578 (FDRE, 2008) and assuming that the threshold calorie level is stable or inflexible downward, it can be said that about 33,947 people are receiving below the minimum calorie requirement. This agrees with the findings of Gebre (2012) and Nigatu (2011) that about 58 and 54 percent, respectively, of the households consume less than their dietary requirements. For the present study the normalized calorie deficiency gap index, the percentage of total consumption needed to bring the entire population to the minimum calorie requirement, is calculated to be 5.8 percent. This means that the sample households need to be supplied with 5.8 percent of the daily minimum calorie requirement to get out of the food insecurity problem. The extent of the calorie deficiency gap for the sampled households is, therefore, 121.8 Kcal/AE/day; which means, on average 121.8 Kcal/AE/day of additional food energy would be needed to lift the households out of food insecurity, then at least in theory, food insecurity could be eliminated. This is in consonance with the findings of Bogale et al.

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Table 1. Socio-economic and institutional characteristics of the households.

Characteristics Age (Years) Sex

Male Female Household size (Adult equivalent=AE) Household size (Unadjusted number) Education level Land holding (ha) Livestock holding (TLU) Oxen ownership Off-farm and non-farm income (Birr) Credit received (Birr) Extension contact (Frequency) Safety net receipt (Birr) Access to market (km) **

p=0.05;

Food secure 41.4 53 7 4.16 5.28 2.32 1.77 5.49 1.47 1,204 296.83

Food insecure 45.2 76 14 4.22 5.40 1.24 1.04 2.38 0.57 1,067 177.11

Total 43.67

t-value 1.696

1.67

-2.33

3.62 0.93

5.33 *** 4.79

**

-1.33 5

1,018

900 10

***

p=0.01

(2005) and Messay (2009). Finally, α = 2 means that a distinction is made between the food insecure and most food insecure households. The square calorie deficiency gap is 0.018, which conveys that the severity of the food insecurity among the sampled households is 1.8 percent. This severity level is much higher than the one reported by Bogale et al. (2005) and lesser than the one reported by Shimeles et al. (2011). Situation of food insecurity Another crude measure of food security situation of households is subsistence potential ratio (SPR). It is measured as the ratio of total amount of calorie (i.e., food energy) that was readily available to the households in the study to the minimal amount of food energy that the households need in a year. The ratio was computed to be 1.24 implying that the households in this study were food secure and had about 24 percent food energy over their minimum calorie requirement, on average. Extent of calorie intake inequality The average calorie intake of the households covered in this study is about 2,631.24 Kcal/AE/day, which is 25.3 percent more than the national minimum recommended level of calorie intake. However, a look at the distribution of the per capita calorie intake between the dichotomized food secure and insecure households reveals that 60 percent of the households are under the 2,100 Kcal/AE/day criterion with a mean per capita calorie consumption of 1,894.45 Kcal/AE/day, which is 9.8 percent below the recommended daily allowance; while average food secure households acquire 3,706.22 Kcal/AE/day. Therefore, average food secure households consumed nearly 96 percent more calories than the

average food insecure households. These mean values and percentages mask the existing inequality in per capita calorie intake among the households covered in the study. Therefore, this study chooses to use Gini coefficient5 as a measure of inequality. The Gini coefficient for the households is estimated to be 0.212 which indicate a more equitable distribution of per capita calorie consumption. On the other hand, the Gini coefficient calculated from the total household calorie consumption (G=0.287) is relatively higher than the one calculated from the per capita calorie consumption. Conclusion and recommendations The measurement of the per capita calorie intake, using the household food balance sheet model, has transpired that sixth-tenths of the sample households lived with food insecurity problem with calorie deficiency gap of 5.8%. In addition, the square calorie deficiency gap which measures the severity of food insecurity among the sample households is 1.8%. These figures, although crude, revealed a need for proper identifying tools to reach to those in acute food insecurity condition and to ensure that the Productive Safety Net Program continues to benefit the poorest section of the woreda society. The study also employed Gini Coefficient to measure the inequality of food calorie intake among the households covered in the study. In which case, the measure is found to be 0.212 which implies egalitarian type of distribution. The possible explanation to this could be the relative homogeneity of calorie consumption among the rural households in the study area. 9

It is measured as the ratio of the area between the diagonal and the

Lorenz curve to the total area of the triangle under the diagonal.

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Acknowledgement The authors sincerely acknowledge APPEARTRANSACT project for its financial support in publishing this manuscript. REFERENCES Adugna E, B Wagayehu (2012). Determinants of livelihood strategies in Wolaita, Southern Ethiopia. Agr. Res. Rev., 1(1): 153-161. Belayneh B (2005). Analysis of food insecurity causes: The case of rural farm households in Metta woreda, Eastern Ethiopia. M.Sc. Thesis, Department of Agricultural Economics, Haramaya University, Ethiopia, pp: 130. Bogale A, Hagerdon K, B Korf (2005). Determinants of poverty in rural Ethiopia. Quart. J. Int. Agric., 44(2): 101-120. CIA (2011). The World Fact Book. https://www.cia.gov/library/publications/the-worldfactbook/geos/et.html (Accessed in 12/08/2012). Devereux S (2000). Food insecurity in Ethiopia: A discussion paper for DFID. IDS, Sussex. Devereux S, S Maxwell (2001). Food Security in SubSaharan Africa. ITDG, South Africa, ISBN: 9781853395239, pp: 350 Di Falco, S Veronesi M, M Yesuf (2011). Does Adaptation to Climate Change Provide Food Security? A Micro-Perspective from Ethiopia. Amer. J. Agr. Econ., 1–18. doi: 10.1093/ajae/aar006. ECSA (2011). Central Statistical Agency Population Estimates. Ethiopia, Addis Ababa. ECSA, ORC Macro (2006). Ethiopian Demographic and Health Survey 2005. Ethiopia and Calverton, Maryland, USA. FAO (2010). The state of food insecurity in the world: Addressing food insecurity in protracted crises. Rome, Italy. FDRE (2008). Summary and statistical report of the 2007 population and housing census: Population size by age and sex. Addis Ababa, Ethiopia. Foster, J, J Greer, E Thorbecke (1984). A Class of Decomposable Poverty Measures. Econometrica, 52(3): 761-766. Gebre GG (2012). Determinants of Food Insecurity among Households in Addis Ababa city, Ethiopia. Interdis. Desc. of Comp. Syst., 10(2): 159-173. Getachew L, DO’Connor (2006). A study of household food security situation of farm households: A case from Hetosa District, South Eastern Ethiopia. Proceedings of the 9th Annual Conference of the Agricultural Economics Society of Ethiopia, April 14-15, 2006, Agricultural Economics Society of Ethiopia, Addis Ababa, pp: 9-34. Habtom K, Z Gudeta, G Kundhlande (2005). Causes of

household food insecurity in koredegaga peasant association oromiya zone, Ethiopia. J. Agric., 44: 543560. Hiensch A (2009). Surviving Shocks in Ethiopia: The Role of Social Protection for Food Security. In: Case Studies in Food Policy for Developing Countries, Vol. I. Pinstrup-Andersen, P and F Cheng, (Eds.). Cornell University Press, Ithaca, New York, pp. 163-172. Hoddinott J (2002). Food Security in Practice: Methods for Rural Development Projects. International Food Policy Research Institute. Washington, D.C. http://siteresources.worldbank.org/datastatistics/resource/ GNIPC.pdf. http://www.ids.ac.uk/files/FoodSecEthiopia4.pdf Jema H (2008). Economic efficiency and marketing performance of vegetable production in the eastern and central parts of Ethiopia. Ph.D. Thesis, Submitted to Swedish University of Agricultural Sciences, Uppsala. Maxwell, S and M Smith (1992). Household Food Security: A Conceptual Review. University of Sussex, Institute of Development Studies. Mimeo, Brighton, UK. Messay, MT (2009). Causes of Rural Household Food Insecurity: A Case from Kuyu District, Central Ethiopia. J. Sust. Dev. in Afr., 11(4): 286-304. Nigatu R (2011). Small holder farmers coping strategies to household food insecurity and hunger in Southern Ethiopia. Ethiopian J. Env. Stud. and Manage., 4(1): 39-48. doi: 10.4314/ejesm.v4i1.5. Nigussie Z, G Alemayehu, S Yehuala, G Siraw, Y Worku (2012). Analysis of internal and external factors in establishing inter-and-trans-disciplinary training and research framework in Amhara Region, Ethiopia. Educ. Res., 3: 612-619. Ramakrishna G, A Demeke (2002). An empirical analysis of food insecurity in Ethiopia: The case of North Wello. J. Afr. Dev., 27: 127-143. Shimeles A, Janekarnkij P, Vute Wangwacharakul (2011). Dimensions of Food Insecurity and Adoption of Soil Conservation Technology in Rural Areas of Gursum District, Eastern Ethiopia. Kasetsart J. (Soc. Sci), 32: 308-318. Tesfay G (2006). Agriculture, resource management and institutions: A socioeconomic analysis of households in Tigray, Ethiopia. Ph.D. Thesis, Wageningen University, Netherlands. Tesfaye L (2003). Livelihood strategies in the context of population pressure: A Case study in the hararghe highlands, eastern Ethiopia. PhD Thesis, Presented to the Department of Agricultural Economics, Extension and Rural Development. Faculty of Natural and Agricultural Sciences, University of Pretoria, South Africa, pp: 226. Webb P, JV Braun, Y Yohannus (1992). Famine in Ethiopia: Policy Implications of Coping Failure at National and Household Levels. Washington, DC: International Food Policy Research Institute, pp: 167. Workneh N (2004). Reasons for food insecurity of farm

Nigussie and Alemayehu

households in south wollo, Ethiopia: Explanations at the grassroots. Institute of Development Research, Addis Ababa University in collaboration with BASIS CRSP. http://crsps.net/resource/reasons-for-food-

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insecurity-of-farm-households-in-south-wollo-ethiopiaexplanations-at-grassroots/ World Bank (2008). Gross national income per capita 2007: Atlas method and PPP.