With the requirement that for any i Where: Pij = probability representing the ith respondent’s chance of falling into category j X = Predictors of response probabilities Covariate effects specific to jth response category with the first category as the reference. Appropriate normalization that removes an indeterminacy in the model is to assume that (this arise because probabilities sum to 1, so only J parameter vectors are needed to determine the J + 1 probabilities), (Greene, 2003) so that , implying that the generalized equation (4) above is equivalent to for j = 0, 2…J and…………………………………. (5) Where: y = A polytomous outcome variable with categories coded from 0… J. Note: The probability of Pi1 is derived from the constraint that the J probabilities sum to 1. That is, . Similar to binary logit model it implies that we can compute J log-odds ratios which 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 means of living. Livelihood Strategies are diverse at every level. As has been reviewed from Brown et al., (2006), several different methods of characterizing household livelihood strategies can be found in the literature. Most commonly, economists group households by shares of income earned in different sectors of the rural economy. Similarly, this study considered income shares of each livelihood activity as a means to conceptualize livelihood strategies. Income portfolio analysis was done (Table 2). From the income portfolio analysis, if we compare income share by the broad livelihood activities, the share of agriculture accounts for about 64.1%, non farm for 22.8% and off farm accounts for 13.1% in decreasing order. Further observation of the data revealed that, off-farm5 activities (agricultural wage, land rent, and environmental gathering) are survival mechanisms pursued mainly by the poor and less poor groups but not viewed as an opportunity that farmers engage in as a choice. Non farm activities, such as rural craft is also mainly choice of the poor than the counterparts. Thus, off- farming activities seem more of a coping mechanism for the rural population than a way to accumulate wealth and reduce poverty. The poor tend to concentrate on off farm activities with low entry constraints (gathering, such as charcoal making and fire wood collection and wage). This result leads to the understanding of the challenges which prevent the poor and less poor from engaging in livestock production and more remunerative non farm activities (see table 2). Table 2. Income composition of sample HHs Cash income Composition (%) Wealth categories Total Poor (N= 51) Less poor (N=42) Better off (N=27) Livestock 11.7 27.5 42.1 24.3 Crop 36.5 41.7 44.1 39.8 Agriculture sub total 48.2 69.2 86.3 64.1 Petty trade6 17.7 11.9 5.4 12.9 Remittance 0.94 2.3 6.5 2.9 Rural craft7 10.5 6.7 1.1 7.0 Non-farm sub total 29.14 20.9 13 22.8 Gathering 6.7 3.2 0.1 4.2 Wage 15.7 3.7 0.2 7.9 Hire/rent 0.4 2.4 0.2 1.0 Off-farm sub total 22.8 9.3 0.5 13.1 Mean annual income per AE 313.4 398.4 1122.5 525.2 F 14.604 p-value 0.000*** ***, significant at < 1% probability level Source Own survey, 2007 4.2. Econometric Analysis of Determinants of Livelihood Strategies Multinomial Logistic Regression Model was used to identify determinants of livelihood strategies. The model was selected based on the justification illustrated earlier. Therefore, in this section, procedures followed to select independent variables (continuous and dummy) and results of logistic regression analysis conducted to identify determinants of livelihood strategy choice by rural households is presented. Table 3. Definition of model variables Dependent variable Variables definition and unit of measurement Livelihood strategies If the choice of the HH lies in Y=0, AG Agriculture alone Y=1, AG+OFF Agriculture and off farm combination Y=2, AG+NF Agriculture and non farm combination Y=3, AG+OFF+NF Agriculture, off farm and non farm Independent variables AGE Age of Household Head in years SEX Sex of Household Head (1= Female, 0= Male) EDUCAT Education level of Household Head in years FAMILY Family Size of the household members in number AGROECO Ecology of the household (0= midland, 1= high land) LAND Land size owned by the Household in Hectares LIVESTOK Livestock hold by the household in tropical livestock unit (TLU) INPUT Farm input use by the Household (0= No, 1= Yes) EXTENS Frequency of extension contact a farmer has with extension agent in a year COOPER Participation of the household in cooperatives (0=No, 1= Yes) LEADER Leadership participation of the Household Head (0=No, 1=Yes) CREDIT Credit use by the household (0= No, 1= Yes) MKTDIS Distance of the nearest market from dwelling in kilometre REMITA Economic support to the household (0= No, 1= Yes) DEPRATIO Dependency ratio of the household Table 2. Multinomial logit regression of AG + OFF livelihood strategy choice Variables Coeff. Std.Err. t-ratio P-value Marginal effects ONE SEX AGE EDUCAT FAMILY AGROECO LAND LIVESTOK INPUT EXTENS COOPER LEADER CREDIT MKTDST REMITA 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 + NF livelihood strategy choice Variables Coeff. Std.Err. t-ratio P-value Marginal effects ONE SEX AGE EDUCAT FAMILY AGROECO LAND LIVESTOK INPUT EXTENS COOPER LEADER CREDIT MKTDST REMITA 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
-0.003 -0.005 0.143
0.061 0.171 -0.091 0.106
0.045 0.108 0.550
***, **,* Significant at <1%, 5% and 10% probability level respectively. Source: own survey, 2007 ***, **,* Significant at <1%, 5% and 10% probability level respectively. Source: own survey, 2007 Interpretation of econometric results Sex of household head (SEX): Gender affects diversification options, including the choice of income-generating activities (both farm and non-farm) due to culturally defined roles, social mobility limitations and differential ownership of/access to assets (Galab et al, 2002). In the study, as expected sex of household head is found to negatively and significantly (< 0.05) influences diversification into off farm activities by FEHHs. Thus, keeping the influence of other factors constant; the likelihood of FEHHs choice of agriculture and off farm livelihood strategy decreases by 24.8 %. The opposite is true for the male counterparts. This result is in agreement with previous studies conducted by Adugna (2005) and Berhanu (2007). Age of household head (AGE): As expected, this variable was found significant (p<0.5) to negatively influence farmers decision to diversify to non farm activities, which implies that farmers participate in non-farm activities at a decreasing rate as they age. From Table 40, it can be seen that the likelihood of a HH simultaneous choice of agriculture and non farm activities decreases by 1.4 % with increasing age. The possible reason is that farmers whose age is relatively younger, leaving other factors constant, could be pushed to engage more in non-farm activities than agriculture alone. This is because, younger farm households cannot get enough land to support their livelihood compared to the older farm households. This result is congruent with previous studies by Barrett et al, (2001); Destaw, (2003), Rao et al., (2004); Adugna, (2005); Mulat et al., (2006), Berhanu (2007), and Khan (2007). Educational level of household head (EDUCAT): Educational attainment proves one of the most important determinants of non farm earnings, especially in more remunerative salaried and skilled employment in rural Africa (Barrett et al, 2001). Education is critical since the better-paid local jobs require formal schooling, usually the completion of secondary school or beyond. Contrary to prior hypothesis, this variable has a negative and significant (p<0.01) and (p<0.05) influence on the decision of the household head participation in off and non farm activities respectively. In other words, participation in off-farm and non-farm activities and low levels of education among sample HH heads were found to be positively associated, suggesting that household heads with more years of education may have realized the low return and decided to work on agriculture. The possible explanation is that the average education achieved (which is below primary level) in by the sample households is not sufficient to be formally employed and educated farmers do not find skill demanding livelihood option in the study area. The result is in line with the findings of Galab et al, (2002), Berhanu (2007) and Khan (2007), but in contradiction with the findings of Barrett et al., (2001); Destaw (2003). Livestock holding (LIVESTOK): In line with prior expectation, livestock holding in TLU negatively influence household’s choice of AG+OFF+NF livelihood strategy at less than 10% probability level. That means the farmer with lower livestock holding would be obliged to diversify livelihoods into off and non farm in order to meet needs. In the study the likelihood of diversifying livelihoods into off and non farm activities decrease by 1.9 % for households with more livestock number in TLU. The result is in line with the findings of Tesfaye (2003) ,Berhanu (2007) and Khan (2007). Family size (FAMILY): In line with expectation, family size was found to have positive and significant relation to diversification of livelihood strategies into AG + OFF + NF at < 10% probability level. The positive correlation between family size and diversification might be due to the relation between larger family size and household labour or corresponding higher demand for food in the household which implies that while an additional member to the household increases the odds to participate in agriculture plus off farm plus non- farm activities in order to meet basic needs to the family. This means, one extra person in the household increases the likelihood of diversifying livelihoods by 3.3 %. In other words, additional family member decreases the odds to work only on farming. This finding is similar to that of Bezemer and Lerman, (2003), and Khan (2007). Agro-ecology (AGROECO): As expected, this variable has a negative and significant (P<0.10) correlation with the likelihood of choosing agriculture and off farm livelihood strategy. This means the tendency that the household diversify livelihoods into agriculture plus off farm plus non farm increases as we go from high lands to midland. Hence, the probability of diversifying into agriculture plus off farm and non farm drops by 15.7 % for highland households. The result is in line with that of Jansen et el., (2004). This might be due to differences in the quality and size of land, the amount and distribution of rainfall and population densities that influence between highlands and midlands. For instance, climatically the later is wormer than the former. Land size owned (LAND):- As hypothesized, the area of land owned by the household has a significant (P<0.05 and p<0.10) and negative correlation with the likelihood of choosing AG+ OFF and AG+OFF+NF respectively. The results of this study suggest that rural households with more land tend to follow agricultural extensification rather than diversifying from agriculture since they draw incentives of land productivity. This implies the chances of choosing agriculture in the context of having large land size decreases the probability of diversifying to off farm and non farm activities by 43.6 % and 14.0 % respectively. On the other hand the probability of diversifying livelihoods decreases by increasing land size as farmers with more land supposed to stay on farm since land stimulates farming. Increased role of off/non farm activities such as selling labour, part-time wage employment, petty trading, especially for poor and less poor households with less land holding and other necessary resources, signify how households respond to a decreasing ratio of farm size to household. This supports the view that off-farm and on-farm activities compete over the limited household resources. It also implies that those households who expect secured agricultural income stay on farm and lower off-farm intensity. Lanjouw and Lanjouw (1995) also found out that landholdings per capita are negatively correlated with participation in low productivity occupations. This result is in line with that of Berhanu (2007), Mulat et al., (2006) and Khan (2007). The implication is that farmers just switch away from off-farm activities when the farm activity is promising; and hence, this supports the necessity argument as opposed to the choice argument. Farmers consider off-farm activities as a last resort income source if crop production fails. Frequency of extension contact (EXTENS): This variable has a positive and significant (p<0.10) correlation with the likelihood of choosing agriculture and off farm livelihood strategy instead of sustaining on agriculture alone. Keeping other factors constant; the likelihood of participation in agriculture and off farm, increases by 17.1 % for those who have gained frequent extension contact than the counterparts. The objectives of extension is to change farmers outlook towards their difficulties which assists them adapt better solution to their livelihoods (Samuel, 2001).Thus, the information obtained and the knowledge and skill gained from extension organization may influence farmers’ skill and decision making on seeking diversification. The frequent extension contact received will increase the tendency of household to participate in off farm activities. This may be also explained by the factors that the message/contents that farmer gain from extension agents help them to initiate to use risk aversion strategies that seek diversification of income within and out agriculture. Credit use (CREDIT): Contrary to expectation, credit use is found to have a significant (p< 0.05) negative impact on the likelihood of choosing diversified livelihood strategy which combines agriculture, off farm and non farm. This implies that, the likelihood of participating in diversified livelihood strategy by the household drops by 9.9 % for a household using credit. This negative impact may be attributed to the fact that credit use allows farmers to follow agricultural intensification by accessing farm inputs which in turn improves productivity. This more implies that the formal and informal credit facilities that avail for rural farmers are a very important asset in rural livelihoods not only to finance agricultural inputs activities, but also to protect loss of crucial livelihood assets such as cattle due to seasonal food shortage, illness or death (Tesfaye, 2003). The result of the study, therefore, strongly suggest that farmers’ access and use of credit would play important role in promoting agricultural development rather than diversification. The result is also in agreement with that of Holden et al., (2004); Brown et al, (2006), Berhanu (2007), and Khan (2007). This implies that the incentive for accessing credit accelerates agricultural production. Dependency Ratio (DEPRATIO):- As hypothesized, dependency ratio is found to have a significant (P<0.10) positive correlation with choice decision of agriculture and non farm livelihood strategy. This indicates that with increase in dependency ratio the ability to meet subsistence needs declines and the dependency problems make it necessary in the household to diversify their income source (Khan, 2007. Households with higher dependency ratios follow less remunerative non-farm livelihood strategies (Jansen et el., 2004). This means when the dependency ratio increase, the ability of farmers to meet family needs decrease and chance of diversifying livelihood to non farm activities increases. If the dependency ratio increases by one the probability of the household’s falling into agriculture plus non-farm livelihood strategy increases by 55%. The policy implications of this pattern seem clear, a need to address rapid population growth as well as the provision of job opportunities for adult labour. This result is inconsistent with that of Warren (2002); and Rao et al., (2004). Inputs use (INPUT): Contrary to expectation, use of chemical fertilizer and HYVs was found to be positively and significantly affect the rural households’ decision to choose agriculture plus off farm plus non farm livelihood strategy at <10% level of significance. The probable reason for this is that due to improvement of productivity through farm input use the farmers might go for petty trading and other non farm activities. This suggests that those who are better-off can afford to buy fertilizer/ HYVs and those who are poor may not. As a result, those who use fertilizer /HYVs may produce more per unit area than non-users and can have access to large quantity of food and diversify income sources for accumulation. Membership to cooperatives (COOPER): This variable as hypothesized was found significant (<0.05) to positively determine choice of livelihood strategy towards agriculture plus off farm plus non farm activities by 13.2 %. That means the household who participate in cooperatives will diversify livelihoods into off and non farm since cooperatives promote access to social capital in which off/ non farm options are gained. Culturally appropriate forms of social capital also appear to have the potential to aid rural income generation and reduce vulnerability to income shocks. As group discussants revealed, cooperation in the form of credit unions, producer organizations, women credit association for milk and better, and churches have positive effects on the income generating capacity of their members and, through production linkages, on the wider local economy in the study area. The result is in line with that of Warren (2002) and Bezemer and Lerman (2002). Receiving remittance (REMITA): Rremittance refers to money sent from inside and outside the country. As expected, the multinomial logit model identified this variable as it had positive contribution to the diversification of livelihood strategies apart from agriculture to off and non farm at significance of <10 % probability level. This meant that, the likelihood of a household receiving remittance increase choice of diversification into off farm and non farm activities by 8.7 %. The result is in consistent with the findings of Bezemer and Lerman, (2002) and Brown et al, (2006). Although remittances constitute only a small part of total household income on average, they appear important for keeping rural households diversify activities. 5. SUMMARY AND POLICY RECOMMENDATIONS Based on the present study it is possible to conclude that the constraints of the rural households in choosing livelihood strategies that will lead them achieve food security goal should not be put aside since food security problem cannot be overcome by simply concentrating on the farm sector alone; intersectoral issues and farm and non-farm linkages need to be addressed as well. Moreover, the contribution made by non-agricultural sector to rural households is a significant; although for the poor these activities are survival oriented and have little to do with wealth accumulation. The result of the multinomial logistic regression model revealed that out of 15 variables included in the model, 13 explanatory variables are found to be significant up to less than 10% probability level. Accordingly, sex of household head (<0.05) education level of household head (< 0.01), land size (<0.05) were found to have negative association with agriculture plus off farm livelihood strategy. Where as, extension contact (<0.10) was found to be significant and positively influence households choice of agriculture plus off farm livelihood strategy. Meanwhile, age of household head, education level of household head negatively determine choice of agriculture plus non farm activities at < 0.05 probability level. Dependency ratios, on the other hand, positively affect the same strategy at < 0.10 probability level. In the case of diversified livelihood strategy, i. e. agriculture plus off farm plus non farm, agro-ecology (<0.10), land size (<0.10), livestock holding (<0.10), credit use (<0.10), were found significant and affect choice of this livelihood strategy negatively. Input use (<0.10), cooperatives membership (<0.05), receiving remittance (<0.10), family size (<0.10), were found to affect the choice of similar livelihood strategy positively. Recommendations Household livelihoods are highly diverse. Policy-makers need to reflect on the most suitable ways of supporting this diversity. Only with more appropriate policies that recognize the importance of diversity will it be possible for more people to make positive exits from food security risk through diversity. The key finding of the study was that diversification across income sources helps households to combat instability in income and thereby increases the probability of their maintaining livelihood security, specially the poor and the overwhelming experience of diversification is as a coping strategy for the poor. Any attempt to intervene the community need to target specific groups of societies such as female headed households, wage workers, petty traders, the food insecure, the poor, the midlanders or the highlanders. The intervention strategy should have a needs identification to address both the basic needs as well as the needs that arise from wealth category specific constraints. Mechanisms are needed to ensure that the concerns of the poor are reflected in public policies and required to bring these groups into the very center of policy making processes. The fact that the result of the study ensured more than 74.2% households to be food insecure demand development intervention strategies that enable immediate survival during emergency times as well as to promote disaster recovery and increase shock absorbing capacity of the food insecurity vulnerable households. Sticking to the findings of this study, the contribution made by income from crop and the value of own consumption was found significant and substantial in achieving food security. This implies that efforts has to be made to improve income from cash crops production (Ginger and coffee) to ensure food security through promotion of input use and marketing facilities. The poor are not merely producers but also wage labourers and consumers; extension should promote technologies not simply geared to increased production, but which are contextually sensitive to potential tradeoffs between productivity (especially labour productivity), increased employment opportunities and reduced vulnerability, doing so in ways which increase the ‘voice’ of poor people. Family size was found to be directly household livelihood diversification. The main case behind is that as family size increase there is no means of accessing more land to cultivation to meet the demand of large family size. With these scenario, having more household size aggravate the problem of meeting food leave alone education, health and other non – food demands of household that will bring future return. Thus, affirmative action based awareness creation on the impacts of population growth at the family and community level should be strongly advocated that lead to reduction in fertility and lengthen birth spacing resulted in smaller household size. The substantial effect of education on household livelihood strategy choice for each type of livelihood strategies confirms the significant role of the variable in consideration for betterment of living condition. The fact that, the average years of education achieved by sample HH heads is below primary level indicates that it has no more incentives to involve the household head in more remunerative activities since better jobs demand more than this level. Livestock sub sector plays a great role in the struggle to eliminate poverty. Its contribution to the total income is significant. Hence, necessary effort should be made to improve the production and productivity of the sector. This can be done through the provision of adequate veterinary services, improved water supply points, introduction of timely and effective artificial insemination services to up-grade the already existing breeds, launching sustainable and effective forage development program, provision of training for the livestock holders on how to improve their production and productivity, improving the marketing conditions, etc The result showed that off farm and non farm incomes make an important contribution to household cash incomes (23%), and that the proportion of cash income from off farm activities is larger for poorer wealth groups. In this regard, interventions that enhance off farm activities in sustainable manner need to be designed. Therefore the rural development strategy should not only emphasis in increasing agricultural production but concomitant attention should be given in promoting such activities in the rural areas. The agricultural sector of the district is characterized by land scarcity and increasing fragmentation of already very small farms, and shortage of draught animals. To this affect, the farming economy is not viable especially for the poor. This implies that the non-farm sector has to be developed to absorb more of the growing population. Thus, support to diversification away from precarious livelihood strategy (agriculture) towards sustainable alternatives whose returns are not correlated with land – possibly agro-industry help to shift some proportions of farmers from direct reliance on land for their livelihoods and enhancing resilience. Culturally appropriate forms of social capital (cooperatives) also appear to have the potential to aid rural income generation. Support to local NGOs, credit unions, producer organizations, organizing and wage labourer associations may have positive effects on the income generating capacity of their members and, through production linkages, on the wider local economy. The policy to promote adoption of credit to stimulate adoption of high yielding varieties and fertilizer use has not been very successful in the study area. Farmers were reporting that they failed to pose the later due to the absence of the former. Thus, enhancing and expanding rural credits to subsistence farmers in the district should be one of the primary areas of intervention and policy options 6. REFERENCES Adugna Lemi, 2005. The Dynamics of Livelihood Diversification in Ethiopia Revisited: Evidence from Panel Data, Department of Economics University of Massachusetts, Boston Barrett, C. B., Reardon, T., Webb, P., 2001. Non-farm Income Diversification and Household Livelihood Strategies in Rural Africa: Concepts, Dynamics, and Policy Implications. Food policy 26, 315-331. Berhanu Adenew, 2006. Effective Aid for Small Farmers in Sub-Saharan Africa: Southern Civil Society Perspectives; Canadian Food Security Policy Group, Addis Ababa. Berehanu Eshete, 2007. Livelihood Strategies of Smallholder Farmers and Income Poverty in draught prone areas: The case of Gena- Bosa woreda, SNNPRS. An MSc Thesis Presented to the School of Graduate Studies of Haramaya University. Bezemer, D. J. and Lerman, Z., 2002. Rural Livelihoods in Armenia: The Centre for Agricultural Economic Research, the Department of Agricultural Economics and Management Discussion Paper No. 4.03 Brown, D.R., Stephens, E., C., Okuro, M.J., Murithi, F.M., Barrette, C.B, 2006. Livelihood Strategies in the Rural Kenyan Highland. CARE, 2001. Participatory livelihoods assessment, Kosovo: CARE International UK Urban Briefing Notes. London, UK. Carswell, G., 2000. Livelihood diversification in southern Ethiopia IDS working paper 117 Chambers, R., and G. R. Conway, 1992. Sustainable rural livelihoods: practical concepts for 21st century. Institute of Development Studies Discussion Papers, 296, Cambridge CSA, 2007. Central Statistical Authority population estimates, Ethiopia, Addis Ababa Destaw Berhanu, 2003. Non-farm Employment and Farm Production of small holder Farmers: A Study in Edja District of Ethiopia. A Thesis Submitted to the School of Graduate Studies Alemaya University. DFID, 1999. Sustainable Rural Livelihoods Guidance Sheet, London, UK DFID, 2000. Sustainable Rural Livelihoods Guidance Sheet, London, UK Ellis, F., 2000. Rural Livelihoods and Diversity in Developing Countries, Oxford University Press Ellis, F., and Allison, E., 2004. Livelihood diversification and natural resource access: Overseas Development Group Working paper 9. University of East Anglia UK. Farrington, J., T. Ramasut, J. Walker, 2002. Sustainable Livelihoods Approaches in Urban Areas: General Lessons, with Illustrations from Indian Cases, ODI, London, UK. Frankenberger,T.R., Sutter, P., Amdissa,T., Alemtsehay,A., Mulugeta, T., Moges ,T., Alemayehu, S., Bernard,T., Spangler,T., Yeshewamebrat, E., 2007. Ethiopia: The Path to Self-Reliancy, Volume I: Final Report Galab, S., Fenn, B., Jones, N., Raju, D. S. R., Wilson, I., and Reddy M. G., 2002. Livelihood Diversification in Rural Andhra Pradesh: Household asset portfolios and implications for poverty reduction working paper no. 3 4 Green, H.W., 2003. Econometric Analysis: Fourth Edition. New York University Macmillan Publishing Company. Holden, S., Bekele, S., and Pender, J., 2004. Non –farm income, household welfare, and sustainable land management in a less favoured area in the Ethiopian highlands. Department of Economics and Resource Management Agricultural University of Norway. Jansen, H., Damon, P., A., John, P., Wielemaker, W., and Schipper, R., 2004. Policies for sustainable development in the hillside areas of Honduras: a quantitative livelihoods approach International Food Policy Research Institute (IFPRI), Central America Office, Washington, DC, USA Kanji, N., MacGregor, J., and Tacoli, C., 2005. Understanding market-based livelihoods in a globalising world: combining approaches and methods. International Institute for Environment and Development (IIED). Lanjouw, J.O., and Lanjouw, P., 1995. Rural non farm employment: Policy research working paper 1463 Roa, J., Niehof, A., Price, L., and Moerbeek, H., 2004. Food Security through the Livelihoods Lens: an integrative approach (the case of less favoured areas in the Philippines); /http://www:sls wau.nl/mi/response/Rao.pdf./ date accessed, December 2008. Samuel Gebre-Sellassie, 2003. Summary report on recent economic and agricultural policy. Paper prepared for the Roles of Agriculture International Conference 20-22 October, 2003Rome, Italy. Scoones, I., 1998. Sustainable livelihoods, a framework for analysis, IDS working paper number 72, Brighton. Tesfaye Lemma, 2003. Diversity in livelihoods and farmers strategies in Hararghe highlands, Eastern Ethiopia, University of Pretoria, South Africa. Warren, P., 2002. Livelihoods Diversification and Enterprise Development: An Initial Exploration of Concepts and Issues. Rome: FAO.
Is the Only Way to Secure a Mobile Device to Power it Off? Ask Codenomicon at the 2008 Wireless & Mobile Expo and Conference
Is the Only way to Secure a Mobile Device to Power it off? Ask Codenomicon at the 2008 Wireless & Mobile Expo and Conference
June 03, 2008 – Toronto, Canada – Codenomicon will answer this question during their feature presentation at the upcoming 2008 Wireless & Mobile Expo and Conference, scheduled on July 15 & 16, 2008 at the Metro Toronto Convention Centre in Toronto, Canada.
Modern smartphones and PDA’s dramatically expand the potential weaknesses hackers can use to attack and break the security of the device. One stereotypical attack is by crafting and sending malformed data packets that are intended to cause harm. Today’s mobile devices provide a rich set of targets for this. There are VoIP capabilities, web browsers, audio and video players, business cards (vCard), underlying IP infrastructure and much more. These attack vectors are made easily accessible with the integration of wireless technologies, such as Bluetooth and Wi-Fi. Indeed, the wireless technologies can be used for carrying the upper layer attack, or they can be attacked themselves, in some cases rendering the device capable of rebooting only after the re-flashing.
This presentation will explore the real-world attack vectors of mobile devices and provide case studies of how these vectors can be exploited to bring the device down. Speaker Mikko Varpiola will peer into a crystal ball to see what new challenges emerging technologies, like WiMAX or expanded SIM card usage may bring. Attendees will learn how these public and vulnerable interfaces can be hardened against these types of attacks by fighting fire with fire: subjecting device to rigorous preemptive negative testing that employs the same tactics as an attacker would.
Mikko Varpiola is security researcher and expert on negative vulnerability testing and model-based fuzzing. Before co-founding Codenomicon he was a key researcher at a globally recognized security testing research group, Oulu University Secure Programming Group (OUSPG). As a member of OUSPG he has been actively participating on development of secure programming practices and automation of software security testing.
The Wireless Mobile Expo and Conference will bring into light the latest products and solutions that brought appreciable revolution in the worlds approach to the technology in the Wireless and Mobile service industry, such as Enterprise Wireless Networking, Wi-Fi, Mobile Convergence, Bluetooth, WiMax, Wireless Broadband, Mobile Devices, Wireless Security, Mobile Computing, VoIP and much more.
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