Rural Farm Households Income Diversification: The Case of Wolaita Zone, Southern Ethiopia

Transcription

1 Socal Scences 2017; 6(2): do: /j.ss ISSN: (Prnt); ISSN: X (Onlne) Rural Farm Households Income Dversfcaton: The Case of Wolata Zone, Southern Ethopa Yshak Gecho Rural Development and Agrcultural Extenson Department, Wolata Sodo Unversty, Ethopa Emal address: To cte ths artcle: Yshak Gecho. Rural Farm Households Income Dversfcaton: The Case of Wolata Zone, Southern Ethopa. Socal Scences. Vol. 6, No. 2, 2017, pp do: /j.ss Receved: Aprl 6, 2016; Accepted: June 3, 2016; Publshed: Aprl 26, 2017 Abstract: Despte the economc domnance of agrculture n the study area, farm households wdely practce dverse ncome generatng actvtes as lvelhood strateges to overcome dverse challenges and rsks. The exstng capacty of agrculture to attan food and lvelhood securty s tremendously declnng from tme to tme. The man am of ths study was to dentfy the determnants of farmers' partcpaton n ncome dversfcaton n the study area. The study nvolved prmary data whch were collected from randomly selected 300 households n four dstrcts of the zone. For selecton of study unts probablty proportonal to the sze was appled and respondents were selected through systematc samplng technque. In addton, key nformant ntervew and focus group dscusson were used to supplement the survey wth qualtatve nformaton. Secondary data were also collected from varous relevant sources. Descrptve statstcs were appled to characterze the sample households socal, economc, demographc and nsttutonal factors. The fndngs of the study ndcates that rural households n the study area practce dversfed ncome sources, n that about 57.7% of the households combne agrculture wth other actvtes (non/off-farm). Some farmers were pursung non-farm and off-farm actvtes as the prmary ncome sources rather than agrculture. Consderng the wealth status, the poor households derve almost half (50%) of ther ncome from nonagrcultural actvtes whereas the latter accounts for only 6.4% of the ncome of the better-off households. Bnary logt model was appled to nvestgate factors nfluencng the households partcpaton n ncome dversfcaton. In ths regard, out of total explanatory varables ncluded n the model, 8 were sgnfcant. The results confrm that factors such as sex, farm sze, lvestock ownershp, oxen ownershp, educaton, leadershp, annual cash ncome and market dstance were key determnants of farmers partcpaton n ncome dversfcaton. Further, the study dentfes ncome dversfcaton as a cumulatve effects of several factors, and therefore urges polcy makers to gve due attenton to them wth a vew to overcomng the challengng bottlenecks. Keywords: Income Dversfcaton, Rural Households, Bnary Logt Model, Wolata, Ethopa 1. Introducton Agrculture s the bass of Ethopan economy. It contrbutes over 50 percent of the GDP and 90 percent of raw materal requrements of the country's small and medumszed ndustres. It s also estmated that agrculture provdes employment for about 85% of labour force [1, 2]. However, Ethopan agrculture s characterzed by low productvty. Over the last two decades, t has not been able to produce suffcent food to feed the country's rapdly growng populaton. A close look at the country's declnng agrcultural outputs and at the same tme, ever ncreasng populaton growth begs for a search of alternatves [3]. Recent studes n Sub-Saharan Afrca (SSA) ndcate that rural households are ncreasngly dversfyng ther ncome sources by combnng farm and non-farm actvtes to sustan ther lvelhoods [4, 5, 6, 7, 8]. That asset, actvty and ncome dversfcaton characterze the lvelhood strateges of rural households n rural Afrca [9]. Incomes from nonfarm sources have grown n mportance and account for between 35 50% of rural household ncomes n SSA [10, 11], wth relance on non-farm ncome sources hgher n some areas (e.g. as hgh as 80 90% n southern Afrca [12]. Other experts aver that dversfcaton can be represented as

2 46 Yshak Gecho: Rural Farm Households Income Dversfcaton: The Case of Wolata Zone, Southern Ethopa a falure of agrculture as means of provdng lvelhood for a substantal proporton of rural nhabtants n SSA. They express dversfcaton n Afrca as an actve process of "deagraranzaton" whereby farmng becomes a part-tme, resdual, or fall-back actvty and lvelhoods become ncreasngly orented to non-farm and non-rural actvtes [13]. Dversfcaton has been analyzed as a ratonal response by households to lack of opportuntes for specalzaton, and was ntally consdered not the most desrable opton. However, recent studes ndcate that rather than promotng specalzaton wthn exstng portfolos, upgradng them to augmentng ncome could be more realstc and relevant for poverty reducton [14]. There are numerous factors that determne rural households ablty to dversfy ther lvelhood strateges away from crop and lvestock producton nto off- and nonfarm economc actvtes. Several studes have reported a substantal and ncreasng share of non/off-farm ncome n total household ncome among rural households n most developng countres [15]. Reasons for ths observed ncome dversfcaton nclude declnng farm ncome and the desre to nsure aganst agrcultural producton and market rsks [16, 17, 15]. That s, when farmng becomes less proftable and more rsky as a result of populaton growth and crop and market falures, households are pushed nto non/off-farm actvtes, leadng to dstress-push dversfcaton. In other case, however, households are rather pulled nto the off-farm sector, especally when returns to off-farm employment are hgher or less rsky than n agrculture, resultng n demandpull dversfcaton [18]. The ncreasng populaton growth n rural Ethopa oblged households to cultvate and make ther lvng on extremely small sze of land. For nstance, 29% of gran farmers n 2006/7 had cultvated a land less than 0.5 ha per household [19]. Accordng to recent Federal Democratc Republc of Ethopa [20] evdence, nearly 55 percent of all smallholder farmers operate on one hectare or less. Moreover, CIDA [21] ndcates the declnng average per capta farmland holdngs - 36 per cent of households cultvate land below half a hectare, 59.8 per cent below one hectare, and 83.8 per cent below two hectares. Due to the smaller farm sze and low return from farmng actvtes, majorty of rural households are engaged n dversfed ncome sources. It s ncreasngly becomng clear that the agrcultural sector alone cannot be reled upon as the core actvty for rural households as a means of mprovng lvelhood and reducng poverty. One phenomenon that s ganng promnence n the rural development lterature s the promoton and support for nonfarm dversfcaton opportuntes [22]. Non/off-farm economc actvtes nclude seasonal mgraton, off-farm to engage n wage employment, handcraft producton, tradng and processng of agrcultural produce, provson of agrcultural servces, etc. Such nonfarm actvtes provde a way of off-settng the dverse forms of rsks and uncertantes (relatng to clmate, fnance, markets, etc) assocated wth agrculture and create a way of smoothng ncome over years and seasons [11, 8]. Lke other Sub-Saharan Afrca countres, Ethopa s characterzed by a complex, dverse and rsk-prone agrcultural producton envronment [23, 24]. Natural dsaster (drought) forced people nto alternatve lvelhood such as the collecton and sale of frewood and grasses [25]. The agrcultural producton has been deteroratng over tme, and has forced people to look for alternatve employment opton other than agrculture. That means, households engage n dverse lvelhood strateges away from purely crop and lvestock producton towards farm, non-farm and off-farm actvtes that are undertaken to broaden and generate addtonal ncome for survval and lvelhood mprovement. Regardng the rural economy n Ethopa, polcy makers gve almost full attenton to agrcultural sector. Nevertheless, there s a growng evdence that rural sector s much more than just farmng [26]. Ths mples that, multple employments are also potental lvelhood strateges on part of the rural people when the farm does not provde an adequate amount of ncome to the peasant famles [27]. For nstance, about 25% of the households n rural Ethopa own one or more nonfarm actvtes. It profts on average account for approxmately 38% of total household ncome for those households who run t [28]. Smlarly, outsde agrculture the rural households n Wolata zone generate ncome from non/off-farm wage, tradng and remttance from mgrants. Dstant mgraton as a way to maxmze ncome across seasons and cope wth food shortage has been a long hstory. However, the majorty (more than 50%) of the populaton lves on subsstence margn wth lttle or no land and lvestock and dependent on margnal non-farm ncome sources (.e., casual labor, petty trade). The very poor are often wthout workng labor, wth no assets (.e., land, lvestock) and dependent on ncome transfers [29]. The most recent evdence ndcates that about 57 percent of households n the study area are possessng less than 0.25 hectare of land whch could not help to attan adequately hand to mouth subsstence farmng [30]. If there are no alternatve means of lvelhoods substtutng ths stuaton the newly born generaton wll face serous challenges than exstng. Even f there s economc centralty of agrculture n the study area, many households engage and pursue dverse off/non-farm lvelhood actvtes to mantan and mprove ther lvelhood/wellbeng. Therefore, comprehendng the drvng factors of each lvelhood strategy s crucal to mprove the response mechansms related to poverty, food securty and lvelhoods mprovement n the study area. However, research work on household ncome dversfcaton under a condton of resource scarcty n study area s lmted. The factors that determne farmers partcpaton n dversfed ncome actvtes are not well dentfed. The dverse ncome sources pursued by rural people n the study area are not assessed n detal. Ths study therefore ams to assess the exstng ncome sources adopted by the dfferent socoeconomc groups; and dentfy the determnants that nfluence farmers partcpaton n ncome dversfcaton.

3 Socal Scences 2017; 6(2): Research Methodology 2.1. Descrpton of the Study Area Ths study was conducted n Wolata Zone of southern Ethopa. It s located at 390 km to southwest from the captal cty of the country, Adds Ababa. The Zone s roughly located between N and E, lattude and longtude respectvely. It covers a total area of 4,511km 2 and s composed of 12 admnstratve woredas (dstrcts) and 3 regstered towns Samplng Technques Mult-stage samplng procedure was employed to select sample households. In the frst stage, out of the 12 woredas n the zone, four woredas (dstrcts) (Humbo, Damot Woyde, Sodo Zura and Damot Gale) were selected purposvely to capture dfferent agro-ecologcal zones exstng n the area. In the second stage, the kebeles (peasant assocatons) n each woreda were lsted based on ther agro-ecologcal characterstcs and grouped/stratfed nto three ecologcal zones hghland, mdland and low land. Based on ths, 16 sample kebeles were selected by usng smple random samplng technques. In the thrd stage, samplng frame (complete vllage household lsts) was obtaned from each kebele s admnstratve offce. Then, wealth rankng exercse was conducted wth the help of partcpatory rural apprasal (PRA) tools. In the fourth stage, the probablty proportonal to sample sze method was appled to draw the sample household from each wealth category accordng to the number of households n dfferent category. Fnally, a total sample of 300 households were selected by usng systematc random samplng technques, of whch 145, 105 and 50 were poor, medum (less poor) and better-off households respectvely. Ths study appled a smplfed formula provded Yamane [31] to determne the requred sample sze at 95% confdence level, degree of varablty = 0.5 and level of precson = 5.7% (0.057) Types and Methods of Data Collecton Both prmary and secondary data were used whch were qualtatve and quanttatve n nature. Prmary data were collected from sample households usng structured ntervew schedules. Secondary data were obtaned from dfferent sources. In addton to ths, focus group dscusson, key nformant ntervew and wealth rankng were conducted to supplement the research fndngs wth qualtatve nformaton Methods of Data Analyss Two types of data analyss, namely descrptve statstcs and econometrc models were used to analyze the data collected from sample households. Accordng to descrptve statstcal methods, quanttatve categorcal types of data were analyzed usng percentage, frequency and ch-square test. Whle quanttatve contnuous types of varables were analyzed usng t-test, mnmum, maxmum, mean and standard devaton. After computng the descrptve statstcs, bnary logstc regresson [32] was used to dentfy factors affectng households partcpaton n ncome dversfcaton where the dependent varable was found to be dchotomous (for example, 1 f household partcpate n ncome dversfcaton and 0 otherwse. STATA 11 and SPSS 16 for WINDOWS were used for the econometrc analyss. Both probt and logt analyss are well-establshed approaches n the lterature to estmate dummy dependent varables [33]. The cumulatve probablty functons of the probt and logt models are qute smlar, so they usually generate predcted probabltes that are almost dentcal. Logt, however, has the advantage that these predcted probabltes can be arrved at by hand calculator. Further, when there are many observatons at the extremes of the dstrbuton then logt s preferred over probt [34]. Also, Sharma [35] reported that the logt model s computatonally easer to use than the other type. The logt model was appled n ths study to assst n estmatng the probablty of farmers' partcpaton n ncome dversfcaton actvtes that can take one of the two values, partcpated or not partcpated. Accordng to Gujarat [36], the functonal form of the logt model s presented as follows: P = E ( Y X ) = P Y = E = X 1 ( 0 1X ) 1+ e β + β 1 1+e Z Where P s a probablty that a th household partcpated n ncome dversfcaton and ranges from 0 to 1; Z s a functonal form of m explanatory varables (X) whch s expressed as: m (6) Z = β 0 + β X, =1, 2, m (7) = 1 Where; β0 s the ntercept and β are the slope parameters n the model. The slope tells how the log-odds n favor of a gven household partcpatng n ncome dversfcaton change as ndependent varables change. If P s the probablty of a household beng n ncome dversfcaton, then 1- P ndcates the probablty that a gven household dd not partcpate n ncome dversfcaton, whch can be gven as: 1-P = 1 Z 1+e Dvdng equaton (6) by equaton (8) and smplfyng gves Z e = 1 P = 1 Z + e Z P 1+ e Equaton (9) ndcates the odds rato n favor/n terms of a gven household partcpatng n ncome dversfcaton. It s (8) (9)

4 48 Yshak Gecho: Rural Farm Households Income Dversfcaton: The Case of Wolata Zone, Southern Ethopa the rato of the probablty that a household wll partcpate n ncome dversfcaton to the probablty he wll not partcpate. Lastly, the logt model s obtaned by takng the natural logarthm of equaton (9) as follows: L = ln P 1 P β + β X (10) = 0 1 Where; P =the probablty that Y=1 (that a gven household s partcpatng n ncome dversfcaton); 1-P =the probablty that Y=0 (that a gven household does not partcpate n ncome dversfcaton); L=the natural log of the odds rato or logt; β =the slope, measures the change n L (logt) for a unt change n explanatory varables (X); P β 0 =the ntercept. It s the value of the log odd rato,, 1 +P when X or explanatory varable s zero. Thus, f the stochastc dsturbance term (U ) s taken nto consderaton the logt model becomes L = β0 + β1x +U Descrpton of Varables Used n bnary Logt Model and ther Hypotheses Dependent varable: The dependent varable n ths study was partcpaton of households n ncome dversfcaton. Household ncome dversfcaton s a dchotomous varable representng the status of household ncome dversfcaton takng value of 1 f a household dversfed and 0 otherwse. Income dversfcaton stuaton of a household s dentfed by assessng the man ncome sources undertaken by respondents. Households who generated ther ncome from only agrculture were consdered as non dversfed, whle farmers' who derved addtonal ncome from non-farm or off-farm actvtes were consdered as partcpatng n ncome dversfcaton. Revew of lteratures and author s knowledge of the ncome dversfcaton stuaton of the study area were used to dentfy the potental determnants of household ncome dversfcaton. Therefore, assgnng the household partcpaton n ncome dversfcaton as the dependent varable, the followng varables were selected to analyze whether they explan household s partcpaton n ncome dversfcaton or not. Defntons and measurement of the ndependent varables and ther workng hypothess are descrbed n Table 1. Table 1. Defnton and unts of measurement of explanatory varables used n bnary logt model. Varables Descrpton and measurement Expected sgn SEX s a dummy varable takng value 1 f the household head s male, 0 otherwse - AGE Age of household head (years). + EXPERIENCE Farm experence of household head (years) EDUCTN Formal educaton of household head (grades or number of years n school). + FAMLSZ Famly sze of household n number + FARMSZ Total farm sze of household (hectare). - LABOUR Actve labour force (number) + SOILFERT Sol fertlty status takes value 1 f the sol s fertle and 0 otherwse - TRLU Total lvestock owned by the farm household (TLU). - OXEN The number of oxen owned (number). + FERTLZR It s a dummy varable that takes value 1 f a household use fertlzer and 0 otherwse. - IMSEED It s a dummy varable takes 1 f a household use mproved seed, 0 otherwse. - EXTCNT Number of tme extenson agent vsted/advsed farmer (number). - TRAIN Farmer attended formal agrcultural tranng, dummy varable (= 1, f yes; =0, otherwse). + CREDIT Farmers access to formal credt, dummy varable (=1, f yes; =0, otherwse) + MKTDISTN Dstance of the respondents' house from nput and output market (km). - COOP Households membershp n cooperatve organzaton, dummy varable (=1, f yes; =0, otherwse + LEADER Households partcpaton n local socal organzaton leadershp, dummy varable takes value of 1 f a household partcpated, 0 otherwse + PSNP It takes value 1, f a household s benefcary of safety net program, 0 f not nvolvng n t. + INCOM Total annual cash ncome of households (Brr) + 3. Results and dscusson 3.1. Descrptve Statstcal Results The results show that 56.7% of sampled households have partcpated n non/off-farm actvtes (.e. engaged n dversfcaton) whle 42.3% dd not engage n any form of non/off-farm employment and ther sole employment was only farmng (Table 4). Of the total sampled households, about 81.9% were male and 18.1% were female headed households (Table 3). Accordng to descrptve analyss, some varatons/dfferences were observed between those farmers who are partcpatng n ncome dversfcaton and nonpartcpants n terms of demographc, soco-economc and nsttutonal factors (Tables 2 and 3). The two groups dffer to some extent n ther farm experence, age, farm sze, lvestock possesson, famly sze, actve labour, ownershp of oxen, extenson contact, use of fertlzer and mproved seed, partcpaton n agrcultural tranng, membershp of cooperatve, communty leadershp role, beneftng from safety net ad and farm ncome. The study revealed that the mean age of farmers who engaged n ncome dversfcaton was less than non-dversfed households. In terms of farmng experence, average farmng experence of dversfed household was about years whle non-dversfed

5 Socal Scences 2017; 6(2): households (engaged on farm actvty only) had a mean farmng experence of 25 years. Average farm sze of dversfed households was less than non-dversfed households. Lvestock ownershp was another mportant household's characterstc. Average lvestock owned by the total sampled households was four TLU. Proportonally, Table 2. Mean value of contnuous varables. farmers who not engaged n ncome dversfcaton owned almost twce greater lvestock than those households who partcpate n ncome dversfcaton. Varatons were also observed n other soco-economc and nsttutonal factors (see Table 2). Household Category Varables HH that dd not partcpate n HH that partcpated n ncome ncome dversfcaton (127) dversfcaton (173) Total (300) T-test Mean STD Mean STD Mean STD Age *** Educaton Farmng experence *** famly sze *** Actve labour *** Dependency rato ** Farm sze *** Tropcal Lvestock unt *** Oxen *** Extenson contact *** Market dstance ** Total ncome *** Source: Feld Survey, 2014/2015. ** and *** represent sgnfcance at 5% and 1% levels respectvely. T- tests and Ch-Square tests (Tables 2 and 3) were used to examne presence or absence of dfference between the two groups of farmers. The mean values of the contnuous varables n both categores were compared usng t-test. Accordng to the t-values, out of 12 contnuous varables, the two categores were found to dffer sgnfcantly n 11 of them. The computed t- values ndcate the mean dfferences for eleven varables, namely famly sze, farm sze, number of tropcal lvestock unts, number of oxen owned, total annual ncome, extenson vst, market dstance, age, farmng experence, dependency rato and actve labor. Smlarly, the mean dfferences for farmng Table 3. Categorzaton of Households on some hypotheszed dummy varables. experence and age of household head were found to be sgnfcant at 1% probablty level (Table 2). On the other hand, the Ch-Square test was used to examne the exstence of statstcally sgnfcant dfferences between the dscrete varables of the two categores. Accordngly, dscrete varables were consdered and the two categores were found to be sgnfcantly dfferent n terms of sx of the nne dscrete varables. More specfcally, the ch-square test reveals that use of fertlzer, mproved seed use, tranng, partcpaton n cooperatve, local leadershp and safety net ad were statstcally sgnfcant at 1% probablty level (Table 3). Respondents category Varables Category No dversfcaton Farm only (127) 42.3% Dversfy (173)57.7% Total Ch-square N % N % n % Sex Male Female Sol fertlty Fertle Otherwse Fertlzer use No *** Yes Improved seed No *** Yes Tranng No *** Yes Credt use No Yes Cooperatve Yes *** No Leadershp Yes *** No Safety net ad Yes * No Source: Own survey, 2014/2015. * and ***, represent sgnfcant at 10% and 1% probablty sgnfcance level, respectvely.

6 50 Yshak Gecho: Rural Farm Households Income Dversfcaton: The Case of Wolata Zone, Southern Ethopa 3.2. Household Income Dversfcaton As observed from the survey result about 42.3% of the total sampled households depend solely on agrculture (crop and anmal producton) for ther ncome. The remanng respondents (57.7%) combne agrculture wth non-farm and off-farm actvtes. The non-farm and off-farm actvtes help farmers to fll ncome and food gap that agrculture s unable to provde. About 37% of the total sampled households derved ther ncome from farmng plus non-farmng actvtes. The varous types of non-farm actvtes pursued by the respondents nclude petty trade, handcrafts, weavng/spnnng, sale of local drnk, rent of pack anmal lke donkey for transportaton and remttance. About 13.3% of the sampled respondents derved ther ncome by combnng farmng wth off-farm actvtes, whle about 7.3% of the sampled households dversfed lvelhood actvtes by engagng n farmng plus non-farmng as well as off-farm actvtes. Fndngs also revealed that very few households n the study area combned the three lvelhood strateges (Table 4). The ch-square test shows the exstence of sgnfcant dfference between the three wealth groups regardng ncome dversfcaton at 1% level. Ths dfference n ncome dversfcaton mples the exstence of sgnfcant entry barrers for the most remuneratve lvelhood strateges especally engagng n non-farm actvtes whch requre hgh nvestments. Eghty four percent of better-off households engaged solely on farmng, whle only 23.4% and about 48.6% of poor and medum households respectvely pursue the same actvtes. Non-farm actvtes play a crucal role n ncome of the poor and medum households, wth 39% of the poor and 43.8% of the medum households dervng ncome from nonfarm actvtes. Only 16% of better-off households derved ther ncome from farmng and non-farmng actvtes. As the survey data ndcates, combnng farmng wth off-farm actvtes s a man ncome source for 24.8% of the poor households and 3.8% of medum households. None of the better-off households dversfed ther ncome nto a combnaton of farm and non-farm actvtes. From the three wealth groups, very low percentage (7.3%) of respondents generate ncome from a combnaton of farmng, non-farm and off-farm sources (Table 4). The fndng of ths study depcts that large number of poor households generate ncome from non-farm and off-farm actvtes n addton to agrculture. Smlarly, many scholars [37, 38, 18] found out that poor households tend to have a more dversfed portfolo of lvelhood actvtes than the better-off households. Accordng to them, t s a last resort rather than an attractve alternatve lvelhood. Table 4. Dstrbuton of sampled household by lvelhood strateges and wealth category. Wealth category of Household Lvelhood strateges Poor Medum Better-off Total n % N % n % N % Farmng Farmng and non-farmng Farmng and off-farmng Farmng, non-farmng and off-farmng χ P-value.000 Source: survey result, 2014/2015 The survey result also shows that out of the total sample households (300), about 246 households (82%) pursued agrculture as a prmary ncome source. About 51 respondents (17.3%) reported that agrculture was ther second alternatve gvng frst prorty to ether non-farm or off-farm actvtes whle only three respondents (1%) put agrculture n the thrd place (Table 5). On the other hand, 37 respondents (12.3%) reported that non-farm actvty was ther prmary ncome source. Wthn the non-farm category about 90 respondents (30%) clamed non-farm s the 2 nd ncome source, next to farm. Out of the total sample respondents, about 134 sample households (44.6%) pursue non-farm actvtes besde agrculture. Off-farmng s a source of ncome for poor households on whch they manly depend for ther lvelhood due to low resource endowment, especally farm land. Out of total sampled households, about 17, 33, and 12 respondents ranked off-farm actvty as frst, second and thrd n that order (Table 5). Table 5. Rankng respondents choce of varous lvelhood strateges. Ranks Category 1 st 2 nd 3 rd Total n % N % n % n % Poor (n=145) On-farm Non-farm Off-farm Medum (n=105) On-farm Non-farm Off-farm Better-off (n=50) On-farm Non-farm Off-farm Total (n=300) On- farm Non-farm Off-farm Source: survey result, 2014/15

7 Socal Scences 2017; 6(2): Income Share and Actvtes Consderng the ncome share of three man ncome sources, the hgh share s taken by agrculture whch accounts for about 78.1%, whle non-farm and off-farm actvtes only account for about 16.5% and 5.4% respectvely. The combned share of non-agrcultural actvtes (non/off-farm) n the ncome of sampled household was about 22%. The result approxmates that of the natonal level, n whch the share of non-agrcultural sector accounts for 25% [28]. Ths fndng suggests that off-farm actvtes are survval mechansms pursued manly by the poor and medum ncome groups but not vewed as an opportunty that fnancally strong farmers engaged n as confrmed n Table 6. Thus, offfarmng actvtes seem more of a copng mechansm for the rural populaton than a way to accumulate wealth and reduce poverty. The poor tend to concentrate on off-farm actvtes wth low entry constrants. Ths result calls for the understandng of the challenges whch prevent the poor and less poor from engagng wdely n agrcultural actvtes as better-off households. On average, the sampled households n the research area earned a total ncome of around 1.6 mllon Ethopan Brr dervng from a wde varety of actvtes wth agrculture beng the most mportant source (Table 6). Agrcultural actvtes contrbuted 78% to total household ncome wth the remanng 22% comng from non-farm actvtes.e. non/off farm actvtes. The most mportant ncome source s crop producton, whch accounts for about 39.3% of the ncome, followed by ncome from lvestock and ther products (38.8%), and non-farm actvtes (lke petty trade, handcrafts, weavng/spnnng, remttance, local drnk sale, and rent pack anmal) helped to generate about 16.5%. Non-farm ncome accounts for almost one-thrd of the total household ncome for poor households. Among non and off farm actvtes, petty trade and handcrafts are the most mportant actvtes for the poor households, whch share 24% of ther total ncome. Table 6 shows the contrbuton of each lvelhood actvtes to ncome of the three wealth groups. Accordngly, the ncome composton of the poor n ncreasng order s lvestock ncome (30.2%), crop ncome (21.7), wage ncome from local, urban and other regons (17.9%), petty trade (13.8%), handcrafts (10.3%), weavng (2.4%) and remttance (1.2%). In other words, the poor households generate nearly 50% of ther cash ncome from of-farm and non-farm actvtes. Contrary to ths, the ncome of the better off households s composed of crop (50.1%), lvestock (43.6%), remttance (4.2%) and petty trade (2.1%). Ths means that the better off households ncome s manly from crop and lvestock. Lke the better-off households, on-farm (crop and anmal producton) actvtes share hgh percentage of medum households ncome source. The possble justfcaton for the medum and better off households hgh share of on-farm ncome s that ther endowment of better productve land and other resources unlke ther poor counterpart. Berry [39] and Mujb et al. [38] also found that poor farmers who are ncreasngly engaged n off-farm actvtes may rather be dong so as a long-term adaptaton to stress, shocks and poverty these farmers are tryng smply to survve n a poorer, rsker world, rather than to mprove lvelhoods and nvest n producton. Table 6. Proporton of total annual cash ncome composton generated from dfferent lvelhood actvtes. Cash ncome composton Wealth category of HH (%) Poor Medum Better-off Total Non-farm Petty trade Handcrafts Weavng/spnnng Remttance Local drnk sale Rent pack anmal Off-farm Daly labor n local area Daly labor near unban Wage labor n other area Fre wood/grass sale On-farm Crop Lvestock and ther product Total sum of cash ncome Mean Source: survey result, 2014/ Bnary Logt Model Result In ths secton, selected explanatory varables were used to estmate the logstc regresson model to analyze the determnants of households' ncome dversfcaton behavor. A logt model was ftted to estmate the effects of the hypotheszed explanatory varables on the probabltes of households' partcpaton n ncome dversfcaton. Fnally, a set of 20 explanatory varables (11 contnuous and 9 dscrete) were ncluded n the logstc analyss. These varables were selected on the bass of theoretcal explanatons, personal observatons and the results of the survey studes. To determne the best subset of explanatory varables that are good predctors of the dependent varable, the logstc regresson was estmated usng the method of maxmum lkelhood estmaton, whch s avalable n statstcal software program (SPSS verson 16). All the above-mentoned varables were entered n a sngle step. The defnton and unt of measurement of the varables used n the model are presented n Table 1.

8 52 Yshak Gecho: Rural Farm Households Income Dversfcaton: The Case of Wolata Zone, Southern Ethopa Table 7. Logt model estmates for factors affectng farmers partcpaton n ncome dversfcaton. Varables Coeffcent S.E. Wald Sg Exp(B) Sex Agee Educton Famly Sz Labor Actve AE FRMSZ Fertlty Other TLU Oxen Numbe Fertlzer ImproveSeed ExtesConFrequcy Tranng Credtmodel Marketdsta Coop Leader SafetAd Income FarExp Constant Log lkelhood Rato Ch-square (χ2) *** Correctly predcted overall sample 82.3 Correctly predcted adopters (%) 75.6 Correctly predcted non-adopters (%) 87.3 Sample sze 300 Source: computed from survey data, 2014/15. Note: *=sgnfcant at p<0.1; **=sgnfcant at p<0.05; ***=sgnfcant at p<0.001 The logt model results used to study factors nfluencng farmers' partcpaton n ncome dversfcaton are shown n Table 7. Among the 20 varables used n the model, eght were sgnfcant wth respect to ncome dversfcaton wth less than 10% of the probablty level. These varables nclude sex (SEX), educaton (EDUC), oxen ownershp (OXEN), tropcal lvestock (TLUs), farm sze (FARMSZ), dstance to market (DISTNCE), partcpaton n local leadershp and annual farm ncome, whereas the rest 12 explanatory varables were found to have no sgnfcant nfluence on farmers' partcpaton n ncome dversfcaton. The effect of the sgnfcant explanatory varables on ncome dversfcaton n study area s dscussed below: Sex (SEX): It was found that sex has a negatve and sgnfcant effect on the probablty of ncome dversfcaton at less than 10% probablty level. Ths result mples that the households headed by female are less lkely to partcpate n off-farm actvtes. The possble reason s households headed by female have more responsbltes n home management. Opposte to ths, male household heads have more tendency of engagng n dfferent actvtes and then ths mproves ther ncome. As observed n the tradton of the study area, gender dsparty reduces the ablty of female-headed households to partcpate n off-farm ncome generaton actvtes. For nstance, women moblty and travel to urban area n search of off-farm actvtes s not culturally perceved as postve. Other thngs kept constant, the lkelhood of a household dversfyng nto off-farm actvtes decrease by 4% when a household s headed by a female. Ths result agrees wth the pror fndngs by Amare and Belaneh [40] and Ells [41]. Farm sze (FRMSZ): It was found that farm sze had negatvely and sgnfcantly nfluenced the probablty of farmers partcpaton n ncome dversfcaton nto non-farm and off-farm actvtes at less than 1% sgnfcant level. Ths result mples that farmers wth large farm sze are less lkely to dversfy ther lvelhood nto non-farm and/or off-farm than those farmers who have small land sze. Large farm sze helps farmers to cultvate and produce more, whch n turn ncreases farm ncome and mproves lvelhood of a household. On the other hand, declnng land szes under populaton pressure may encourage rural households to dversfy ther sources of ncome. That means, farmers havng more land sze rely on crop producton than to go for non farm and off-farm n order to satsfy basc needs. The odds rato of for farm sze ndcates that, other thngs beng constant, the odds rato n favor of farmers' partcpaton n ncome dversfcaton ncreases by a factor of as the farm sze ncreases by one hectare. The result of ths study confrms the earler fndngs of Dlruba Khatun and Roy [42] Reardon, Delgado and Matlon [43]. Educaton (EDUCT): As the model result ndcates, the varable educaton had postvely and sgnfcantly nfluenced the households' partcpaton n ncome dversfcaton at 1% level of sgnfcance. Ths fndng ndcates that those farmers wth hgh educatonal level are more lkely to dversfy lvelhood strateges nto non-farmng and/or off-farmng actvtes than those who less educated. Ths s due to most probably educated person gan better skll, experence, knowledge and ths agan help them to engage n dversfed lvelhood strateges. Lterate ndvduals are very ambtous

9 Socal Scences 2017; 6(2): to get nformaton and use t. And t also determnes the capablty of fndng a job. The odd rato reveals that, holdng other varables constant, a change n household head educaton level by one unt wll ncrease a probablty of partcpatng n ncome dversfcaton by a factor of Thus, educaton s a fundamental nstrument n equppng farmers wth necessary sklls whch enables them to dversfy ncome sources than uneducated ones. Ths fndng s n lne wth the fndng of varous authors [42, 18, 40]. Valdva and Quroz [44] n ther research found educaton as essental n ncreasng off/non-farm earnngs and tme allocaton of rural famles and to dversfy the rural economy away from agrculture. Tropcal lvestock unt (TLUs): The model result ndcates that number of tropcal lvestock unt affected negatvely and sgnfcantly the probablty of dversfyng household ncome nto non/off farm actvtes at P<0.1. Ths result shows that those farmers wth large number of tropcal lvestock unts are less lkely to partcpate n ncome dversfcaton than those who own small number of TLUs. The negatve assocaton between lvelhood dversfcaton and number of TLU ndcates that herd sze creates better opportunty to earn more ncome from lvestock producton. On the other hand, households wth less number of lvestock try to dversfy ther ncome portfolo by partcpatng n nonfarm and off-farm actvtes and ths accelerates the rate of dversfcaton. Other thngs held constant, the odds rato for number of TLU shows that, as the number lvestock unts ncreases by one TLU, the odds rato n favor of adoptng mproved maze technology ncreases by a factor of Dfferent studes support ths dea (for nstance, Dercon and Krshnan [45] and BARRETT et al. [9]. The number of oxen owned (OXEN): Oxen ownershp negatvely and sgnfcantly nfluenced the probablty of dversfyng ncome nto non/off farm actvtes at less than 10% sgnfcance level. Ths result suggests that, those farmers who owned more oxen were less lkely to engage n ncome dversfcaton than those who owned small number of oxen. Other thngs beng held the same, the odds rato of for the number of oxen owned ndcates that, the odds rato n favor of partcpatng n ncome dversfcaton decreases by a factor of as the number of oxen ncreases by one unt. Smlar result was found by Kaja Darlson [46] and Idowu et al. [47] Market dstance (DISTNCE): Market dstance to nput and output center negatvely and sgnfcantly assocated wth the probablty of household's partcpaton n ncome dversfcaton actvtes at less than 5% sgnfcance level. The negatve assocaton suggests that the lkelhood of partcpatng n dversfed ncome actvtes declnes as the dstance from market center ncreases. The mplcaton of ths negatve relatonshp s that f the dstance between farmers' lvng home and the market area s longer, the farmers wll be dscouraged from engagng n non/off farm ncome generatng actvtes. The odds rato of for market dstance reveals that, other thngs beng constant, the odds rato n favor of partcpatng n dversfed ncome sources decreases by a factor of as the market dstance ncreases by one klometer. Ths result s n consstence wth the fndng of LANJOUW et al. [48] and SMITH et al. [49]. Studes by LANJOUW et al. [48] n Tanzana and SMITH et al. [49] n Uganda show that a better physcal access to markets ncreases non-farm earnngs. Leadershp (LEADER): Ths varable was found to be postve and of sgnfcant nfluence on ncome dversfcaton nto non/off-farm actvtes at less than 10% probablty level. Ths mples that those farmers who partcpate n socal leadershp n local area have more lkelhood to dversfy ncome nto non/off-farm actvtes than those farmers who have no leadershp role n ther communty. The possble reason may be farmers partcpaton n local leadershp can help to have more access for nformaton, share more experence wth others n socal envronment, creatng more socal network wth outsde socetes, get more access to formal as well as nformal credts. Wth regard to credt access, local leaders are more trusted and accepted by formal and nformal fnancal nsttutons. Access to credt can help to adopt producton enhancng technology, and ths n turn help to generate more ncome. Sometmes, credt helps them to nvest drectly n non-farmng actvtes. Further, the postve relatonshp shows better leaders educaton whch equps them wth necessary knowledge to engage n non-farmng actvtes. The result ndcates that, other factor kept constant, odd rato showed that the probablty of partcpatng n dversfed ncome actvtes s ncreased by 2.324% as a household gets access to nvolve n local leadershp. The study by Dlruba and Roy [42] and Awony et al [50] found smlar result. Total annual cash ncome (INCOM): ths varable was found to have postve and sgnfcant nfluence of ncome dversfcaton nto non/off farm actvtes at l% probablty level. Ths result mples that households havng large cash ncome are more lkely to dversfy the ncome generatng actvtes nto non/off farm actvtes. Ths result shows that those farmers wth low ncome are less lkely to partcpate n ncome dversfyng ncome actvtes than those who have hgh ncome. The possble reason s that those farmers who have adequate ncome sources can overcome fnancal constrants to engage n alternatve ncome-generatng actvtes. Hence, hgher ncome can encourage them to nvest n other ncome-generatng (especally non-farm) actvtes. From the model result, other thngs beng constant, odd rato reveals that the probablty of a household dversfyng nto non-farm and off-farm actvtes ncreased by 0.379% for those farmers wth hgher level of ncome. Stefan Schwarze and Manfred Zeller [51] and Isaac [52] show that ncome s one of the most mportant determnants of lvelhood dversfcaton nto non-farm and/or off-farm actvtes. 4. Concluson and Recommendaton Agrcultural producton has been declnng from tme to tme due to frequent land fragmentaton, uncontrolled populaton growth and recurrent drought, and ths has forced

10 54 Yshak Gecho: Rural Farm Households Income Dversfcaton: The Case of Wolata Zone, Southern Ethopa people to look for alternatve ncome optons other than agrculture. A sgnfcant number of rural households engage n dverse ncome generatng actvtes away from purely crop and lvestock producton. Accordng to the study, t s ncreasngly becomng clear that the agrcultural sector alone cannot be reled upon as the man actvty for rural households as a means of mprovng lvelhood, achevng food securty and reducng poverty n the study area. Income dversfcaton s ganng promnent role n rural households ncome and food securty. Even though, regardng the rural economy n Ethopa, polcy makers gve more attenton to agrcultural sector. Nevertheless, there s growng evdence that the rural sector s much more than just farmng. The Bnary logstc regresson model was used to answer the questons why rural households pursue dverse ncome generatng actvtes. The model results suggest that farmers partcpaton n ncome dversfcaton s nfluenced by varous factors. The result ndcated that out of the 20 hypotheszed varables eght were found to sgnfcantly nfluence household s partcpaton n dversfed ncome actvtes at dfferent probablty levels. These varables nclude educaton sex, farm sze, lvestock ownershp, oxen ownershp, local leadershp, annual cash ncome and market dstance. The model result ndcated that the educaton of household head, farm ncome and partcpaton n local leadershp nfluenced postvely and sgnfcantly farmers partcpaton n ncome dversfcaton actvtes, whle the ownershp of lvestock n TLU, sex, total farm sze oxen ownershp and market dstance negatvely and sgnfcantly affected the dversfcaton of ncome nto non-farm, off-farm and combnng non-farm and off-farm actvtes. The fndng of the study came up wth possble polcy recommendatons n the area of ncome dversfcatons. Sex had sgnfcant and negatve nfluence on household ncome dversfcaton, and ths consders government and other responsble bodes to desgn necessary strateges so as to create awareness among the communty to partcpate women equally wth man n all development actvtes. The negatve and sgnfcant nfluence of farm sze on ncome dversfcaton suggests concerned bodes to develop approprate strateges and polces especally for land resource-poor farmers. The presence of very small sze of land also calls for gvng emphass n agrcultural ntensfcaton to enhance the productvty of the land so that generate adequate ncome and food. The postve and sgnfcant nfluence of educaton on ncome dversfcaton suggests to gve due attenton n promotng farmers educaton through strengthenng and establshng both formal and nformal type of educaton and expandng lteracy. The negatve and sgnfcant mpact of lvestock and oxen ownershp n ncome dversfcaton suggests to desgn development strategy for lvestock sector through mprovng lvestock breeds, veternary servces, forage development, marketng, access to credt and overall management of lvestock producton. The strong sgnfcant assocaton of total annual cash ncome on dversfcaton calls for polcy measures n order to pave the way to solve fnancal problems through developng and strengthenng fnancal nsttuton, creatng credt access and promotng better ncome generatng optons. The sgnfcant and postve effect of households local leadershp partcpaton on the dversfcaton ponts the drecton to create access to nformaton and other necessary servces lke credt for people n the same communty. Ths also consders government and other responsble bodes n buldng capacty through educaton and tranng so as to partcpate actvely n socal actvtes and leadershp. References [1] MEDaC (Mnstry of Economc Development and Plannng), Survey of the Ethopan Economy. Revew of post reform development. MEDAc, Adds Ababa. [2] World Bank Dversfyng the rural economy: An assessment of the nvestment clmate for small and nformal enterprses n Ethopa, October 6, [3] Jrström, M., A. Andersson and G. Djurfeldt. (2011). Smallholders caught n poverty flckerng sgns of agrcultural dynamsm. In Afrcan Smallholders: food crops, markets and polcy. (eds Djurfeldt et al.) London: CABI. Chapter 4, pp [4] Losch, B., S. Fregungresh and E. Whte. (2011). Rural Transformaton and Late Developng Countres n a Globalzng World: A Comparatve Analyss of Rural Change. Fnal Report of the RuralStruc Program, Revsed Verson. Washngton, DC: World Bank. [5] Wnters, P., T. Essam, A. Zezza, B. Davs and C. Carletto (2010). Patterns of Rural development: A Cross-Country Comparson Usng Mcroeconomc Data. Journal of Agrcultural Economcs, 61(3): [6] Ells, F. (2005). 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