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Livelihood strategies and their determinants in Southern Ethiopia
Livelihood strategies and their determinants in Southern Ethiopia
The case of pain Bowling wolaita area
* Eneyew Adugna (MSc)
** Wagayehu Bekele (PhD)
* Author of the article, a professor at the University Arba Minch, Ethiopia, adugna_e@yahoo.com
** Co-author of the article, the president of the University Diredawa, Ethiopia, wagayehu_bekele@yahoo.com )
December 2008 ACKNOWLEDGEMENTS
Above all I would like to thank God Almighty for his unselfishness. In addition, First, I thank my major advisor Dr. Wagayehu Bekele which is duly obliged to express my gratitude. I am also deeply indebted to small Wolayta College ATVET class for providing the necessary support for the completion of this study
1. INTRODUCTION
1.1 Background to the Study
Ethiopia, with an estimated population of 76.5 million is the third most populated country in Africa. Over 85% are rural and urban population remaining (CSA, 2006). Ethiopia is a country-based agricultural economy in the agricultural sector plays a important in the national economy, livelihoods and socio-cultural system of the country. The industry supports the employment of more than 80% of the population, accounts for 45-50% of the product gross domestic product (GDP) (Berhanu, 2006).
Rural people on your side to participate in a series of strategies, including agricultural intensification and diversification livelihood, enabling them to achieve the goal of food security, however, still unable to escape food insecurity. The rural poor struggle to ensure the rule food security through participation in diversification activities. However, the contribution to be made by livelihood diversification rural livelihoods has often been ignored by policy makers who have chosen to focus its activities in agriculture (Carswell, 2000). The problem is getting worse, despite the enormous resources invested each year in humanitarian aid and food security programs (Frankenberger et al., 2007).
Therefore, a deep understanding of alternative livelihoods strategies of rural households and communities is indispensable in all efforts for improvement. This is important not to make a limited resource available for rural development based on untested assumptions about the rural poor and their survival strategies (Tesfaye, 2003).
This study, therefore, try to see the determinants of livelihood strategy choice of the rural population in their struggle to achieve the goal of food security.
1.2. Purpose of the Study
The general objective of this study was to examine the livelihood strategies pursued by rural households and analyze the factors influencing the choice of subsistence strategies in the context of achieving food security in the area of study. The specific objectives of the study are:
1. to evaluate strategies of life pursued by the various categories of rural households in the study area,
2. to identify the determinants of rural households' choice subsistence strategies, and
2. Framework for Strategy Analysis subsistence
The framework provides livelihoods a large and complex approach to the understanding of how people earn their living. Can be used as a loose guide to a number of issues that are important to livelihoods or it may be rigorously investigated in all its aspects (Kanji et al, 2005). Livelihood approaches (LA) emphasizes understanding the context in which people live, the assets available to them, the livelihood strategies that follow in the face of policies and institutions, and the results of livelihoods are intended to achieve (DFID, 2000).
The key question to be addressed in any analysis of Livelihood given a particular context (of policy setting, politics, history, agroecology and socio-economic conditions), the combination subsistence resource (Different types of "capital") result in the ability to follow a combination of strategies subsistence (Agricultural intensification and extensification, livelihood diversification and migration) with what results? (Scoones, 1998).
Livelihoods
The concept of livelihoods is widely used in contemporary writing on poverty and rural development, but its meaning can often seem difficult to achieve either due to the vagueness or the different definitions in different sources (Ellis, 2000)
A popular definition is that provided by Chambers and Conway (1992) in which a livelihood comprises the capabilities, assets (including both material and social assets) and activities required for a livelihood. In short, life could be described as a combination of resources used and activities undertaken for a living (DFID, 2000).
Vulnerability Context
vulnerability context refers to the seasonality, trends and crises affecting the livelihoods of people. The key feature of these factors is that they are susceptible to control by local people themselves, at least in the short and medium term (DFID, 2000).
Right Livelihood
With the approach of livelihood, resources are known as "active" or capital "(Ellis and Allison, 2004) and the definition of each is given as:
livelihoods: they are the resources on which people draw in order to carry out their livelihood strategies (Farrington et al., 2002). Family members to combine their strengths, skills and knowledge with different resources at your disposal to create activities that allow them achieving better livelihoods for themselves. All that is going to create livelihoods that can be thought of as a livelihood asset (Messer and Townsley, 2003). The most important assets livelihoods are human capital such as age, education, gender, health status, household size, dependency ratio and potential leadership, etc. (Bezemer and Lerman, 2003, Farrington et al., 2002; Kollmar and Gamper, 2002), physical capital has the infrastructure Basic and producer goods needed to support livelihoods (DFID, 1999), social capital refers to networks and connectivity, The financial capital such as savings, credit, and remittances from relatives working outside the home (CARE, 2001, Bezemer and Lerman, 2003) and Natural capital is the stock of natural resources.
Policies and institutions affecting rural households access to livelihood assets are also important to the living (DFID, 2000). Institutions are the social glue that link those involved with access to capital of different types to the means of exercising power and define the catwalks that cross the route a] livelihood [positive or negative adjustment (Scoones, 1998).
Livelihood strategies
According to DFID (1999) Strategies of survival period is defined as the range and mix of activities and choices that people make in order to achieve their livelihood objectives, including productive activities, investment strategies, reproductive choices, the strategies of subsistence, are composed of activities generating livelihoods from home and planned activities that men and women are committed to building their livelihoods (Ellis, 2000).
Support results
livelihood outcomes are the achievements of livelihood strategies, such as higher income (Cash, for example), increased welfare (for example, no material goods, self-esteem, health status, access to services, the sense of inclusion) and reducing vulnerability (for example, improved by increasing resilience in the state of assets), improving food security (eg capital increase Financial for the purchase of food) and a more sustainable use of natural resources (eg for property rights) (Scoones, 1998)
3. METHODOLOGY
3.1. Description of Study Area
Bowling pain is located about 420 km south of Addis Ababa, title = "Nations, Nationalities and Peoples "> Nations, Nationalities and" Peoples Region (SNNPR) in Wolayta Zone (Figure 2). The total population Bowling pain for years 2007 is 96 341 196 614 of which 100 273 are men and women, with a population density of 637 per square kilometer (next Damot Gale District 750); Out of the population total 92% live in rural areas (BoFED, 2005, CSA, 2007).
3.2. Sampling distribution
Table 1. Sample size distribution of sample AP
AP
Household size
Sample size (no)
Sample
elaborated
Poor (1)
Less poor (2)
Best (3)
Midland AP
Yukara
1046
9
8
4
21
Dangara Madalcho
968
2
10
7
19
Ashura
1331
9
9
9
27
Highland PA
Afam Mino
2664
32
15
6
53
Total
6009
51
42
27
120
Source: own survey, 2007
3.3. Collection method data
Primary data on household socioeconomic characteristics were collected from sample households using structured interview schedule. In the case of qualitative data to capture better the socio-economic context and type of homes in the area focus group discussions (men, women and youth groups), key interviews and exercises informant3 wealth ranking in each PA were carried out. The Secondary data was gathered from various sources such as pain Bowling office of agriculture and rural areas
3.4. Data Analysis Techniques
Descriptive analysis
Descriptive statistics data analysis methods used for quantitative data were a Way ANOVA, average, percentage, t-test, chi square, and the diversity indices. Descriptive analysis was performed using Statistical Package for Social Sciences (SPSS) version 13.
Econometric Model
To identify the factors behind the decision of rural households to participate in different livelihood strategies is assumed that in a given period available to the endowment assets, a householder rational to choose between four alternatives each exclusive life strategy that provides maximum utility. Following Greene (2003), pose to the ith sued j options, specifies the choice j utility as:
Uij = Zij? +? Ij ……………………………… … … … … … … … … … … … …. (1)
If the defendant makes choice j in particular, then it is assumed that Uij is the highest among the utilities j. So the statistical model gives the probability that choice j do, that is:
Prob (Uij> uik) for all other K? j … … … … … … … … … … … … … … …. (2)
Where is Uij the utility to the ith respondent form of subsistence strategy j uik the utility to the defendant from k th livelihood strategy
If the household maximizes defined utility revenues on projects, then choice of the family unit is only one optimal allocation of the endowment assets to choose the means subsistence that maximizes their utility (Brown et al., 2006). Therefore, the decision of the ith household can therefore be modeled as maximizing utility j expected by choosing the strategy of life among subsistence strategies J discrete, ie … … … … … … … … … … … … … … … (3)
In general, for an outcome variable with J categories, let the life strategy that the j th family chooses to maximize its utility may have value 1 if the ith household livelihood strategy choice j 0 otherwise. The probability that a household with characteristics chosen livelihood strategy xj, Pij is modeled as:
- J = 0 … 3 …………………………………………. ……….. (4)
With the requirement that for any i
Where: Pi j = probability that represents the ith respondent likely to fall into the category j
X predictors of the probability of response =
j covariate category specific effects in response to the first category reference.
appropriate normalization removes an uncertainty in the model assumes that (that arise from the sum of probabilities to 1, so that only J parameter vectors are needed to determine the odds J + 1), (Greene, 2003) for, which implies that the generalized equation (4) above is equivalent to
for j = 0, 2 and J … … … … … … … … … … … … … …. (5)
Where: y = A polytomous outcome variable with categories coded 0 … J.
Note: The probability of PI1 is derived from the restriction that J probabilities sum to 1. Ie. Similar to the binary logit model implies that we can compute J log-odds ratio that are specified as: … … … … … … … … … … … … … (6)
4. RESULTS AND DISCUSSION
4.1. Livelihood strategies
Livelihood strategies are defined as those activities undertaken by households to provide a livelihood. Livelihood strategies are diverse at all levels. As discussed by Brown et al., (2006), different methods of characterization household livelihood strategies can be found in the literature. In general, economists group homes for the shares of income earned in the various sectors of the economy rural. Similarly, this study considered income shares of each subsistence activity as a means to conceptualize livelihood strategies. analysis income portfolio was made (Table 2).
From the analysis of portfolio income, when comparing the income share of broad livelihood activities, the share of agriculture accounts for about 64.1%, 22.8% non-farm and farm accounts 13.1% in descending order. observation post of the data revealed that, out of farm5 activities (agricultural employees, rent of land, and the collection of the environment) are survival mechanisms out mainly by the poor and less poor, but not seen as an opportunity for farmers as an option. Non-farm activities such as rural crafts is also mainly the poor choice of partners. Therefore, non-agricultural activities appear to be more of a survival mechanism for the population rural one way to accumulate wealth and reduce poverty. The poor tend to focus on off-farm activities with low entry restrictions (Collection, as manufacturing charcoal and firewood collection and wages). This result leads to the understanding of the challenges that prevent the poor and less poor to participate in the production livestock and agricultural activities more profitable (see Table 2).
Table 2. Income composition of the sample HH
Income in cash
Composition (%)
Wealth category
Total
Poor
(N = 51)
Less poor
(N = 42)
Best
(N = 27)
Cattle
11.7
27.5
42.1
24.3
Culture
36.5
41.7
44.1
39.8
Agriculture sub total
48.2
69.2
86.3
64.1
Small trade6
17.7
11.9
5.4
12.9
Consignment
0.94
2.3
6.5
2.9
Rural craft7
10.5
6.7
1.1
7.0
Sub-total non-farm
29.14
20.9
13
22.8
Meeting
6.7
3.2
0.1
4.2
Wage
15.7
3.7
0.2
7.9
Hire and rental
0.4
2.4
0.2
1.0
Off-farm sub total
22.8
9.3
0.5
13.1
Average income
by AE
313.4
398.4
1122.5
525.2
F
14.604
p-value
0.000 ***
*** Significant at <1% probability level
Source own 2007 survey
4.2. Econometric analysis of determinants of life strategies
Multinomial logistic regression model was used to identify the determinants of livelihood strategies. The model was selected on the basis of justification is illustrated above. Therefore, in this section, the procedures followed to select the independent variables (continuous and fictitious) and the results of logistic regression analysis to identify the determinants of choice of livelihood strategy of rural households is presented.
Table 3. Definition of model variables
Dependent variable Variables definition and unit of measure
Support If the election strategies of the HH is in
Y = 0, AG Agriculture alone
Y = 1, AG + of agriculture and nonagricultural combination
Y = 2 + NF AG Agriculture and combination of exploitation
Y = 3, AG + OFF + NF Agriculture, off-farm and nonfarm
Independent variables
AGE Age of head of household in years
SEX Sex of household head (1 = female, 0 = male)
Educating Education level of household head in years
FAMILY family size household members in number
Ecology Agroecosystems home (0 = Midland, 1 = upland)
LAND Land size owned by the family in Hectares
Livestock LIVESTOK maintained by the home of tropical livestock unit (TLU)
Agricultural input use of inputs by the household (0 = No, 1 = Yes)
Extens contact frequency extension of a farmer extension agent in a year
COOPER participation household in the cooperative (0 = no, 1 = Yes)
LEADER Leadership participation of the household head (0 = no, 1 = Yes)
Credit CREDIT used by the household (0 = no, 1 = Yes)
MKTDIS Distance from nearest market housing in km
REFER Financial Support home (0 = no, 1 = Yes)
DEPRATIO household dependency ratio
Table 2. Multinomial logit regression of AG + choice of livelihood strategies OFF
Variables
Coef.
Std.Err.
t-ratio
P-value
The marginal effects
UNO
SEX
AGE
Educate
FAMILY
Agroecosystems
LAND
LIVESTOK
INPUT
Extens
COOPER
LEADER
CREDIT
MKTDST
REFER
DEPRATIO
5.409
-1.901
-0.061
-1.002
0.063
-0.489
-4.099
-0.280
1.017
1.553
1.180
0.227
-1.311
-0.018
0.864
0.180
2.318
1.008
0.045
0.384
0.207
1.048
1.853
0.212
1.057
0.912
1.329
1.055
1.139
0.193
1.143
1.606
2.333
-1.884
-1.338
-2.603
0.304
-0.466
-2.212
-1.319
0.962
1.702
0.888
0.215
-1.150
-0.093
0.756
0.112
0.019
0.059 *
0.180
0.009 ***
0.761
0.641
0.026 **
0.186
0.335
0.088 *
0.374
0.829
0.249
0.925
0.449
0.910
0.551
- -0.248
-0.003
-0.079
0.014
-0.073
- -0.436
-0.025
0.048
- 0.171
0.046
0.086
- -0.156
-0.013
0.042
-0.089
Table 3. Multinomial logit regression of AG + choice of livelihood strategies NF
Variables
Coef.
Std.Err.
t-ratio
P-value
The marginal effects
UNO
SEX
AGE
Educate
FAMILY
Agroecosystems
LAND
LIVESTOK
INPUT
Extens
COOPER
LEADER
CREDIT
MKTDST
REFER
DEPRATIO
2.449
-0.016
-0.081
-0.831
-0.158
0.495
-1.511
-0.143
1.107
0.694
1.353
-0.526
-0.108
0.177
0.901
2.151
1.842
0.697
0.038
0.336
0.168
0.911
1.091
0.160
0.905
0.747
0.985
0.896
0.885
0.153
0.905
1.280
1.329
-0.023
-2.137
-2.470
-0.939
0.543
-1.383
-0.897
1.223
0.928
1.373
-0.587
-0.122
1.157
0.995
1.680
0.183
0.981
0.032 **
0.013 **
0.347
0.586
0.166
0.369
0.221
0.353
0.169
0.556
0.902
0.247
0.319
0.092 *
- 0.121
- 0.156
-0.014
- -0.114
-0.054
- 0.209
-0.003
-0.005
- 0.143
0.061
- 0.171
-0.091
- 0.106
0.045
- 0.108
- 0.550
***, **, * Significant at <1%, 5% and the 10% level of probability, respectively.
Source: own survey, 2007
***, **, * Significant at <1% 5% and 10% level of probability, respectively.
Source: own survey, 2007
Interpretation of econometric results
Sex of household head (sex): Gender influences the diversification options, including the choice of generating activities income (both agricultural and nonagricultural), due to culturally defined roles, social mobility limitations and the differential ownership of / access to assets (Galab et al, 2002). In the study, as expected sex of household head is on the negative and significantly (p <0.05) influences on diversification non-agricultural activities by FEHHs. Therefore, maintaining the influence of other factors constant, the probability of choosing FEHHs agriculture and off farm strategy of life decreases by 24.8%. The opposite is true for men. This result agrees with previous studies by Adugna (2005) and Berhanu (2007).
Age of household head (AGE): As expected, this variable was found significant (P <0.5) to negatively influence the decision to farmers to diversify farm activities, which implies that farmers participate in activities non-farm at a decreasing rate as they age. From Table 40, we can see that the probability of a simultaneous choice of farming HH and decreases the activities nonfarm 1.4% with age. The possible reason is that farmers whose age is relatively young, leaving the other factors constant, could be pushed to participate more in agricultural non-agricultural activities. This is because younger farm families can not get enough land to sustain their livelihood compared to more rural families. This result is consistent with previous studies by Barrett et al, (2001); Destaw, (2003), Rao et al. (2004); Adugna, (2005); Mulat et L., (2006), Berhanu (2007) and Khan (2007).
Educational level of head of household (education): The level of studies show one of the most important determinants of farm income, especially in the most cost-effective and qualified employees employment in rural Africa (Barrett et al, 2001). Education is critical because local jobs better paid, formal education, usually the completion of high school or beyond. Contrary to previous hypotheses, this variable has a (negative significant and p <0.01) and (p <0.05) influence on the decision of the participation of heads of households and off-farm activities, respectively. In other words, participation in non-agricultural and non-agricultural activities and low levels of education among heads HH sample showed a positive association suggesting that household heads with more years of education have realized the low profitability and decided to work in agriculture. The possible explanation is that secondary education achieved (which is below primary level) in the homes of the sample is not enough to be formally employed and educated farmers are not demanding ability livelihood option in the study area. The result is in line with the results of Galab et al, (2002), Berhanu (2007) and Khan (2007), but contradict the findings of Barrett et al., (2001); Destaw (2003).
exploitation livestock (LIVESTOK): In line with previous expectations, TLU livestock farm households negatively influence the choice of the strategy of AG + OFF + NF support to less than 10% probability level. That means that farmers with less animal husbandry would be forced to diversify livelihoods is disabled and non-farm to meet the needs. In the study of the possibility of diversifying livelihoods is disabled and non-agricultural activities decrease by 1.9% for households with more livestock in TLU number. The result is consistent with the findings of Tesfaye (2003), Berhanu (2007) and Khan (2007).
Size family (family): In line with expectations, family size was found to have positive and significant diversification subsistence strategies in AG + OFF + NF <10% probability level. The positive correlation between family size and diversification may be due the relationship between the size of the largest family and family labor, or increased demand for food at home which means that while another the household increases the chance to participate more in agriculture off the farm, plus farm activities to meet the basic needs of the family. This means an extra person in the household increases the likelihood of diversification of livelihoods by 3.3%. In other words, a member of the family decreases possibilities of working exclusively in agriculture. This finding is similar to that of Bezemer and Lerman (2003) and Khan (2007).
Agro-ecology (agro): As expected, this variable has a (negative and significant P <0.10) correlation with the probability of choosing the strategy of agriculture and livelihood off the farm. This means that the tendency for the diversification of household livelihood in agriculture, more off-farm plus nonfarm increases as we move into the highlands to Midland. Therefore, the likelihood of diversification in agriculture, more non-agricultural use drops to 15.7% of households in the highlands. The result is consistent with that of Jansen et., (2004). This could be due to differences in the quality and size of the land, the amount and distribution of rainfall and population density influence between land upper and middle lands. For example, the climate is the first Wormer.
Land size owned (land): – As a hypothesis, the area of land owned by the family has a significant effect (P <0.05 and p <0.10) and negatively correlated with the probability of choosing AG and AG + + + NF OFF OFF respectively. The results of this study suggest that rural households with more land tend to follow agricultural extensification and diversification agriculture and to develop incentives for land productivity. This implies the choice of agriculture in the context of having large field decreases the probability that diversification off-farm and non-operating activities of 43.6% and 14.0% respectively. Moreover, the probability that the diversification of media life decreases with increasing size of the land as farmers with more land should remain on the farm since the earth encouraged agriculture. Increased role off / Non-agricultural activities as the sale of labor, part-time employment wages, small businesses, especially for poor and less poor households with less tenure land and other resources, means how families respond to a decreasing proportion of farm size of households. This supports the idea that off-farm and farm activities compete for limited household resources. It also means that households expect to remain secured to the on-farm agricultural income and a lower intensity of off-farm. Lanjouw and Lanjouw (1995) also found land ownership per capita are negatively correlated with participation in low productivity. This result is consistent with that of Berhanu (2007), Mulat et al., (2006) and Khan (2007). The implication is that farmers simply switch to non-agricultural activities as agricultural activity is promising, and therefore it supports the argument for the argument against the election. Farmers believe that non-farm activities for income Ultimately, if no crop production.
Frequency of extension contact (large): This variable has one and significant (P <0.10) correlation with the probability of choosing agriculture and farm life strategy of holding out of agriculture alone. Holding other factors constant, the probability of participation in agriculture and nonagricultural increased by 17.1% for those who have won the extent of frequent contact counterparts. The objectives of the extension is to change the mindset of farmers on their difficulties to help them adapt best solution for their livelihoods (Samuel, 2001). Thus, the information obtained and the knowledge and expertise gained in the extension organization can influence the ability of farmers and decision making in the pursuit of diversification. Frequent contact extension received increase the tendency of families to participate in activities outside the farm. This can also be explained by factors that the message / content that farmers gain extension agents helps to start to use risk aversion strategies seeking diversification of income within and outside agriculture.
Credit use (credit): Contrary to expectations, Use credit is having a significant effect (P <0.05) negative impact on the probability of choosing diversified livelihood strategy that combines agriculture, off-farm and nonfarm. This implies that the probability to participate in the strategy of diversified livelihoods for the home fell by 9.9% for households with credit. This negative impact can be attributed to the fact that the use of credit allows farmers to continue agricultural intensification through access to agricultural inputs which in turn improves productivity. This implies that the more formal and informal credit benefiting rural farmers are an important asset in rural livelihoods not only to finance the activities of agricultural inputs but also to protect the loss of their livelihoods such as livestock crucial due to seasonal food shortages, disease or death (Tesfaye, 2003). The result of the study, therefore, strongly suggest that farmers using credit access and play important role in promoting agricultural development and not diversification. The result also agrees with that of Holden et al., (2004), Brown et al, (2006), Berhanu (2007); Khan (2007). This implies that the incentive to accelerate access to credit, agricultural production.
Dependence Ratio (DEPRATIO): – As a hypothesis, dependency ratio is found to have a significant effect (P <0.10) positive correlation with the decision of choice agricultural and nonagricultural livelihood strategy. This indicates that with the increase in the rate of dependence on the ability to meet subsistence needs and the decrease dependency problems that require at home to diversify their sources of income (Khan, 2007. Households with higher dependency ratios adopt livelihoods less remunerative agricultural strategies (Jansen et., 2004). This means that when the increase in the dependency ratio, the ability of farmers to meet family needs and reduce opportunity for diversification of means of subsistence increases in farm activities. If you increase the dependency ratio the probability for a family unit fall in agriculture, most non-farm livelihood strategy increases by 55%. The policy implications of this model are clear, the need to address rapid population growth and providing employment opportunities for adult labor. This result is consistent with that of Warren (2002) and Rao et al., (2004).
Check usage (INPUT): Contrary to expectations, the use of chemical fertilizers and high-yielding varieties were found significantly and positively affect rural households decision to select agriculture as well off the farm, more strategy of non-farm livelihoods in <10% level of significance. The probable reason for this is that due to improved productivity through the use of agricultural inputs farmers could go for small shops and other farm activities. This suggests that they are better off can afford to buy fertilizers or varieties high performance and can not be poor. As a result, the use of fertilizers / High yielding varieties may produce more per unit area than non-users and can access a large amount of food and diversify income sources for accumulation.
Membership of cooperatives (Cooper): This variable was found significantly predicted (P <0.05) for positively determining the choice of livelihood strategy towards agriculture most agricultural and off-farm activities by 13.2%. That means that the household participating in the cooperative to diversify livelihoods is disabled and non-agricultural and cooperatives to promote access to social capital in what was / nonfarm earning options. Culturally appropriate forms of social capital also seems to have potential to help generate rural incomes and reduce vulnerability to income shocks. As speakers group revealed, cooperation in the form of credit unions producer organizations, women's associations credit for better quality milk and churches have positive effects on income generation capacity of members and, through production linkages in the wider local economy in the study area. The result is consistent with that of Warren (2002) and Bezemer and Lerman (2002).
receiving remittances (REFER): Rremittance refers to money sent from within and outside the country. As expected, multinomial logit model identified this variable as it had positive contribution to the diversification of livelihoods other than agriculture and off-farm in importance of the probability level of less than 10%. This means that the probability of a choice of households receiving remittances increased diversification into non-agricultural and nonagricultural 8.7%. The result is consistent with the findings of Bezemer and Lerman (2002) and Brown et al, (2006). While remittances constitute only a small part of total household income, on average, seem important to maintain rural families diversify activities.
5. SUMMARY AND POLICY RECOMMENDATIONS
On the basis of this study it was concluded that the limitations of rural households in election livelihood strategies that lead to the goal of food security should not be put aside and food security problems can not be overcome just by concentrating in the agricultural sector alone cutting issues and the farm and nonfarm linkages should also be addressed. Moreover, the contribution made by industry nonfarm rural households is an important, but for the poor of these activities are geared to survival and have little to do with the accumulation of wealth.
The results of multinomial logistic regression model revealed that of 15 variables included in the model of 13 variables found to be significant explanatory to less than 10% probability level. Consequently, sex of head of household (<0.05) level of education of household head (<0.01), the size of land (<0.05) were found to have negative association with agriculture, plus the media strategy of life outside the farm. Where as, the extension contact (<0.10) decreased significantly and positively affect household choice of agriculture, plus the media strategy of life outside the farm. Meanwhile, age of household head, education level of household head negatively determine the choice of agriculture and nonagricultural activities <0.05 probability level. Dependency ratios, however, positively affect the same strategy at <0.10 probability level. In the case of diversified livelihood strategy, agriculture is more off-farm, more non-agricultural, agro-ecology (<0.10), the size of the earth (<0.10), animal husbandry (<0.10), use of credit (<0.10) were significant and affect the choice of this strategy of negative life. input use (<0.10), the membership cooperatives (<0.05) that receive remittances (<0.10), family size (<0.10) had an effect, the choice of strategy similar life positively.
Recommendations
livelihood of the household are very diverse. Policy makers should reflect on the best ways to support this diversity. Only with more appropriate policies that recognize the importance of diversity be possible for more people to make positive solutions for food safety risk through diversity. The main conclusion was that diversification through sources of income to families to help combat the instability of income and therefore increases the likelihood of maintaining their livelihood security, especially the poor and the overwhelming experience of diversification is as a survival strategy for the poor.
Any attempt to intervene community should target specific groups of societies, such as households headed by women, salaried workers, small traders, food insecurity, poor, the inhabitants of the mountains or the midlanders. The intervention strategy must have an identification of needs to address the basic needs and the needs arising from the limitations category of species richness. Mechanisms are needed to ensure that the interests of the poor are reflected in public policies and to bring these groups in the center of decision-making processes. The fact that the outcome of the study said 74.2% poorest households to food insecurity demand intervention strategies for development that enable immediate survival in times of emergency, and to promote disaster recovery and increase capacity impact absorption of food insecurity of vulnerable households.
Comply with the findings of this study, the contribution of income from the cultures and the value of own consumption is found significant and substantial in achieving food security. This implies that efforts must be made to improve the income of cash crop production (ginger and coffee) to ensure food security by promoting the use of inputs and marketing.
The poor are not only producers but also employees and consumers should promote technologies spread not only aimed to increase production, but are context-sensitive potential tradeoffs between productivity (labor productivity in particular), increased opportunities employment and reduction of vulnerability, so it increases the "voice" of the poor.
Family size was to be considered directly to the diversification of family subsistence. The main point is that behind the increase in size of the family there is no way to access more land to meet growing demand for large families. With these scenarios, with more people per household is aggravate the problem of meeting the food left in peace education, health and other non – food demand of the households that will bring the future return. Therefore, the creation of affirmative action based on awareness of impacts of growth population in the household and community level should be strongly argued that the birth of lead to reduced fertility and spacing led to enlarge the size smaller home.
The important effect of education on the choice of the survival strategy for each type of livelihood strategies confirms the important role of the variable in consideration for the improvement of living conditions. The fact that the average years of education attained by the sample HH heads below the primary level I do not have incentives to involve the heads of households in more profitable activities as a greater demand for more jobs this level.
Livestock sub-sector plays a major role in the fight to eliminate poverty. Its contribution to total revenue is significant. Therefore, effort should be made to improve production and productivity of the sector. This can be done through the provision of adequate veterinary services, improvement of water points, introduction of artificial insemination services in a timely and effective modernization of existing breeds, implementation of the forage program sustainable and effective development, provision of training for livestock owners on how to improve production and productivity, improved marketing conditions, etc.
The results showed that off-farm and off-farm income to make a significant contribution to household cash income (23%) and the proportion of cash income from farm activities is higher for the poorest wealth. In this sense, interventions that improve operating activities in a sustainable manner should be designed. Therefore rural development strategy should not only focus on increasing production agriculture, but attention must be given concomitantly in promoting such activities in rural areas.
The agricultural sector in the region is characterized by land scarcity and the increasing fragmentation of farms and very small, and the shortage of draft animals. To this affect, the growing economy is not viable, especially for the poor. This implies that the non-agricultural sector must be developed to absorb more of the growing population. Thus, support for diversification outside the precarious subsistence strategy (agriculture) towards sustainable alternatives whose returns are uncorrelated with the earth – possibly to help the agro-industry for a change the proportions of farmers in direct dependence on land for their livelihoods and enhancing resilience.
Culturally appropriate ways social capital (cooperatives) also appear to have the potential to help rural income generation. Support to local NGOs, credit unions, producer organizations, organization and associations of workers' wages can have positive effects on income-generating capacity of its members and, through production linkages in the local economy in general.
The policy to promote the adoption of credit to encourage the adoption of varieties high yield and fertilizer use has not been very successful in the study area. Farmers reported that they could not raise the afternoon due to the absence of the former. Therefore, improving and expanding rural credit for subsistence farmers in the district must be one of the main areas of intervention and policy options
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About the Author
The author holds MSc. degree in rural development from Haramaya University and have been teaching in Arba Minch university, Ethiopia