Factors Affecting Adoption of Modern Beehive in Saese Tsaeda District of Tigray Ethiopia

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1 Journal of Energy Technologes and Polcy ISSN (Paper) ISSN (Onlne) Factors Affectng Adopton of Modern Beehve n Saese Tsaeda Dstrct of Tgray Ethopa Tadele adsu halesselass Tgray agrcultural research nsttute (mekele agrcultural mechanzaton and rural energy research centre department of soco-economy and extenson) Abstract Ths paper evaluates factors that determne adopton of modern hve technology usng prmary data of 250 households collected n 2015 from four kebelas n Saese e tsa eda emba woreda of Tgray, Ethopa. Both descrptve and econometrc methods employed to analyze the demographc, socoeconomc and nsttutonal factors affectng beekeepng households decsons. Determnant factors of adopton of modern hve were analyzed usng logt model. Accordng to the result of descrptve econometrc analyss, the dfference between adopters and non-adopters n terms of educatonal level of household head, labor avalablty n the household, access to extenson servce, land tenure and access to loan servce were statstcally sgnfcant. Keywords: Logt, Descrptve analyss, modern hve, adopton, Background of the Study In Ethopa, tradtonal beekeepng s the oldest and the rchest practce, whch has been carred out by the people for thousands of years. Several mllon bee colones are managed wth the same old tradtonal beekeepng methods n almost all parts of the country (Fchtl and Admasu, 1994). Tradtonal beekeepng s of two types: forest beekeepng and backyard beekeepng. The tradtonal types of hves and the way of keepng bees vary from area to area. Based on locally avalable materals used for constructon of hves, envronmental condtons and postons used to keep bees, the followng varants of basc desgn are found throughout the country: hollowed logs, bark hve, bamboo or reed grass hve, mud (clay) hve, anmal dung (mxed wth ash) hve, woven straw hve, gourd hve, earthen pot hve and so on. The beekeepers that are experenced and sklful n usng these hves could do many operatons wth less faclty. Gezahegne (2001) reported that under Ethopan farmers management condton, the average amount of crude honey produced from tradtonal hve s estmated to be 5 kg / hve / year. Ths low productvty of honey per hve was due to the type of hve beekeepng farmers use. To enhance the low yeld of honey per hve dfferent packages was mplemented and among them was the ntroducton of modern hve. Modern beekeepng methods am to obtan the maxmum honey crop, season after season, wthout harmng bees (Ncola, 2002). Modern movable- frame hve conssts of precsely made rectangular box hves (hve bodes) supermposed one above the other n a ter. The number of boxes s vared seasonally accordng to the populaton sze of bees. In Ethopa, about 5 types of movable frame hves were ntroduced snce 1970 (HBRC, 1997) and the most commonly used are: Zander and Langstroth style hves. Based on the natonal estmate, the average yeld of pure honey from modern hve s kg/year, and the amount of beeswax produced s 1-2% of the honey yeld (Gezahegne, 2001). However, n potental areas, up to kg harvest has been reported (HBRC, 1997). The amount of honey produced from one beehve per year vares from places to places; n most cases, t determned by the exstences of pollen and nectar source plants, level of management & nput. Movable frame hves allow colony management and use of a hgher level of technology, wth larger colones, and can gve hgher yeld and qualty honey but are lkely requre hgh nvestment cost and traned man power. Even f the productvty capacty of modern hve s hgh and effcent the adopton rate of ths technology s found at low level n Ethopa and Tgray regonal state, but n Saese e tsa eda emba dstrc,t the adopton rate was hgh and encouragng. Accordng the dstrct annual report (2015), t was reported above 50% adopton rate n ths dstrct and why ths paper was done to see what determnant factors are there and what best experence of adopton works are worked. Data Source and Samplng System Based upon ther beekeepng potental and number of modern hve ntroduced, nearest geographcal locaton and accessblty four Kebele were selected purposely from 26 kebels of ths dstrct. Based on the crtera, Gumuse,maymegelta, snkat,sendada kebeles were selected. Beekeepers were stratfed nto farmers havng modern hve (adopters) and farmers havng tradtonal hve (non adopters). Accordng to Storck et al. (1991), the sze of the sample depends on the avalable fund, tme and other reasons and not necessarly on the total populaton. A total 250 sample szes were randomly drown from the selected four kebeles and each kebele had a proportonal sze on the sample. Sample sze of adopters was 100 and sample sze of non adopters 150. Model Specfcaton In ths secton, models that we used n order to address the general objectve and the specfc objectves are 29

2 Journal of Energy Technologes and Polcy ISSN (Paper) ISSN (Onlne) dentfed wth ther approprate specfcaton. In ths paper, both descrptve and econometrc analyss approaches are used to nvestgate the research questons. Logt model was used to analyze factors nfluencng modern hve adopton and propensty score matchng was employed to evaluate the mpact of modern hve on households ncome gan. Model Specfcaton for Adopton Decson Logt Model Independent Lnear Probablty Model (LPM), probt or logt models, have been wdely used to analyze factors that nfluence dscrete behavor such as the adopton decsons (Greene, 1993; Gujarat, 2004). The lnear probablty model (LPM) whch s expressed as a lnear functon of the explanatory varables s computatonally smple. However, despte ts computatonal smplcty, as ndorsed by Pndyck and Rubnfeld (1981), Amemya and Gujarat (1988), t has a serous defect n that the estmated probablty values can le outsde the normal (0-1) range. Hence logt model s advantageous over LPM n that the probabltes are bound between 0 and 1. The logt model assumes cumulatve logstc probablty functon whereas the probt model s assocated wth the cumulatve normal dstrbuton (Gugarat, 2004). Although logt and probt models yeld smlar parameter estmates, a cumulatve logstc regresson model s preferred because of easer to compute and nterpret than the Probt and Tobt models (Pndyck and Rubnfeld, 1991). The logt model has less restrctve assumptons and a smpler functonal form than the probt model (Gujarat & Sangetha, 2009). The character of adopters and non adopters was essentally a unvarate approach where dfference between the means of selected characterstcs of adopters and non adopters were compared usng par wse statstcal test. A bnary choce model, usng the logt specfcaton, was also used to examne the adopton decson n a multvarate framework. Logt model used to dentfy factors affectng farmers decson whether to adopt modern beehve or not. Accordng to the logt model, the probablty of an ndvdual farmer adoptng a modern beehve gven a well defned set of soco-economc and physcal characterstcs (X), s represented accordngly. Followng Pndyck and Rubnfeld (1981), the cumulatve logstc probablty functon s specfed as: 1 P Y 1 + e 1 PY = 1) = ( Z ) 1+ e ( = ) = ( β ) X ( Where: =1 s the probablty that a farmer adoptng modern beehve Is the functon of a vector of n explanatory varables, represents the base of natural logarthms and equaton (2) s the cumulatve dstrbuton functon. If =1 s the probablty of farmers adoptng modern beehve n that area, then 1 =1 represents the probablty of farmers not adoptng modern beehve n the research area and s expressed as: e e 1 P( Y = 1) = 1 ( ) = ( ) Z Z Interpretaton of coeffcents wll be easer f the logstc model can be wrtten n terms of the odds and log of odds (Gujarat, 2004). The odd rato, the rato of the probablty that a farmer adopt modern beehve to the probablty of the non-adopter s expressed as: P( Y = 1 1+ e = 1 P( Y = 1) 1+ e Z Z = e Takng the natural log of equaton (4), we obtan = P( Y = 1) L = 1 P( Y = 1) Z Z ln Where s the log of the odds rato whch s not only lnear n the explanatory varables but n the parameters also.thus, ntroducng the stochastc error term,, the logt model can be wrtten as: Z β + + β X + µ = + β1x1 + β2x2 0 n n Where = are explanatory varables, s the constant term and ` are coeffcents to be estmated. 30

3 Journal of Energy Technologes and Polcy ISSN (Paper) ISSN (Onlne) Table 3.1. Descrpton of varables used n the logstc model Varable name type Varable Descrpton Measurement Expected effect Dependent Varables Adopton Dummy Adopton of modern hve 1 f yes, 0 otherwse Honey yeld Contnuous Kg of honey harvest Klo gram Honey sale Contnuous Brr gan from honey sale Ethopan Brr Total hh ncome Contnuous Brr gan from all actvty Ethopan Brr Explanatory varables (ndependent varables) fasze Contnuous famly sze No. of HH members + basceduca Dummy Educaton status of household head Lterate =1, 0 otherwse + Age Contnuous Age of household head years completed +/- Martalsta Dummy Martal status of household head 1 f marred, 0 otherwse +/- Sex Dummy sex of household head 1 f male, 0 otherwse + Laborav Contnuous Labor avalablty n the HH adult equvalents + totalandhold Contnuous Total land owned by the household hectare + own arado Dummy Ownng a moble phone 1 f yes, 0 otherwse + Own a rado Dummy Ownng a rado 1 f yes, 0 otherwse + lvestockhol Contnuous Lvestock holdng tropcal lvestock unts + landtenure Dummy certfed & own Land 1 f yes, 0 otherwse +/- access toprcenfor, Dummy access to prce nformaton 1 f yes, 0 otherwse + accesstoloan Dummy access credt servce 1 f yes, 0 otherwse + acctoexteserv, Dummy access extenson servces 1 f yes, 0 otherwse + dsttovecroad Contnuous Home dstance to vehcular road klo meters _ dstancetonput market Contnuous Dstance to nput market klo meters +/- dstopromarket Contnuous Dstance to product market klo meters +/- Result and Dscusson Descrptve Statstcs In ths part, descrptve statstcs and econometrc model results are presented and dscussed. Under descrptve statstcs mportant determnant characterstcs of households and outcome varables are dsplayed wth approprate statstcal tools lke mean, standard devaton and percentages. Based on descrptve results household characters and soco-economc factors are presented as fellow. Household Demographc Character Age of household head: As shown n many emprcal lteratures, the role of age n explanng adopton decson of new technology s somewhat controversal. In most adopton studes older people have more farmng experence that helps them to adopt new technologes. Accordng to Mgnouna et al, 2011; Karyasa and Dew 2011, older farmers are assumed to have ganed knowledge and experence over tme and are better able to evaluate technology nformaton than younger farmers. On the other hand, a study by Abatana(2005) and Rahmeto(2007) shows that age and adopton decson are nversely assocated. As farmers age ncreases, the lkelhood of new technology adopton tends to declne. Because of rsk avertng nature aged farmers s hgh; they need to mnmze rsk takng acton of newly ntroduced technology and they become more conservatve (not ready to accept the new one) than the youngest one to adopt new technology. The survey result depcts that the average age of household head for adopters and non-adopters s and years, respectvely. From t-test statstcs result (table 4.1), the average age dfference between adopters and non-adopters s not statstcally sgnfcant. Famly sze: n ths study famly sze s consdered as the number of ndvdual who resdes n the respondent s household. Large famly sze assumed to be an ndcator of better labor avalablty n the household. Belyu, Tewodros and Edward 2010 works, ndcates that as a household sze ncreases, adopton also expected to ncreases and correlate postvely. The average famly sze of adopters and non-adopters s 5.29 and 4.83 respectvely. Even f there s no statstcally sgnfcant dfference between adopters and non-adopters wth respect to ther average famly sze, stll adopters have relatvely hgh number of famly sze and they are also n better poston of adopton status. Educaton of the household head: household head farmers who can read and wrte are more advantageous n understandng new technology and apculture practces when compared wth those who cannot read and wrte. Lterate farmers can manage and nterpret producton nstructons themselves any tme wth what they had wrtten and prnted materals. Moreover, household heads that have better educaton level are more lkely to adopt modern hve than those who are llterate. Lterate beekeepers are more ready to understand new dea and concepts provded by extenson workers and other nformants. The avalablty educaton Level of famly member s household head was categorzed nto two levels; lterate (can read and wrte) and llterate (can t read and wrte). The statstcal test ndcates that there s statstcally sgnfcant dfference between adopters and non-adopters. 31

4 Journal of Energy Technologes and Polcy ISSN (Paper) ISSN (Onlne) Household Economc factors Total land holdng Land s the sngle most mportant endowment, as t s a base for any economc actvty especally n rural and agrcultural sector. Farm sze nfluences household s decson to adopt or not to adopt new technologes. It s expected that more land holdng and adopton decson are postvely correlated, Nzomo et al.(2007), Belyu, Tewodros and Edward(2010) and Kaguongo(2010). The survey result showed that, the average land holdng of sample households was found to be hectare wth a standard devaton of Ths fgure s lower than the natonal fgure, whch s 1.5 hectare mplyng n the study area land holdng s low. The average land holdng of adopter and non-adopter was and 0.68 hectare respectvely. The t-test ndcates that, the mean dfference of farm sze between adopters and non-adopters s not statstcally sgnfcant. But t mportant to see the advantage of havng the most constrants to agrcultural technology adopton s, the avalablty of cultvable land (de Janvry et al, 2011; Carletto et al, 2007; Pngal et al, 1987). Lvestock Sze In rural context, lvestock holdng s an mportant ndcator of household wealth. In addton, lvestock s consdered to be a source of ncome, food and draftng power for crop cultvatons. The number of lvestock owned by farmers was hypotheszed to be postvely assocated wth adopton decson n most adopton lterature. The average lvestock holdng of adopter sample households was TLU wth standard devaton of It ranges from 0 to 14 TLU wthn groups. On the other hand non adopters hold 3.89 TLU wth standard devaton of The range was from 0 to 18.5TLU wthn the group. Wthn adopton categores the result of ths study shows that, there was hgh varaton n lvestock holdng. Even f the t-test shows that the mean dfference n lvestock holdng among adopters and non-adopters s not statstcally sgnfcant t was postve relaton wth adopton and t s consstent wth some study. Havng more unts of lvestock hypotheszed s to be postvely related to the adopton of agrcultural technologes because t serve as proxy for wealth status B.Kafle and P.Shah(2012) Own farm Land Land ownershp status s the legal rght to have and own the natural land entty to use for producton and to beneft from ts outputs wthout any dffculty whch s stated by low. Farm households have relatvely dfferent Land ownershp status because of many reasons. The statstcal test ndcates that, adopter households was found statstcally sgnfcant dffer from non adopters wth respect to ther land ownng status. From (table 4.1) we can see that adopters have had 82 percent certfed and own land but non adopters had have certfed and own land only 71.3percent. Dfferent Access & Insttutonal Factors Access to loan Feder et al. (1985) observed that credt programs enable farmers to purchase nputs or acqure physcal captal needed for technology adopton. Credt may be essental to acqure farm technologes lke modern beekeepng whch the farmers perceve to be a costly actvty to engage n (Workneh, 2007). Adopters and non adopters of beekeepng farmers on the research area have an access to loan and 36 percent of adopters used the advantage of credt but non adopters benefted only percent of the credt advantage. The t-test ndcates that, the mean dfference of access to loan between adopters and non-adopters s statstcally sgnfcant. Access to extenson servce: Extenson s as major sources of agrcultural nformaton for adopton process s seen as the man mportant servce to farmers. The adopton of agrcultural technologes prmarly depends on access to nformaton and on the wllngness and ablty of farmers to use nformaton provded by extenson agents. Informaton helps decsonmakng process s to reduce rsk and uncertanty and enable farm households to made rght choces from avalable technologes. Out of the total sample households 46.8 percent of them had got extenson servce; whereas the remanng 53.2 percent had not got extenson servce. As ndcated (Table 4.1), 56 and 37.3 percent of adopter and non-adopter had access to extenson servce respectvely. Ths mples that majorty of the adopters had access to extenson servce whch enable them to have more nformaton about new technologes. The t test result shows that, there s statstcally sgnfcant dfference between adopters and non-adopters wth respect to access to extenson servces. 32

5 Journal of Energy Technologes and Polcy ISSN (Paper) ISSN (Onlne) Table 4.1 Descrptve Characterstc Household character and soco economc factors of adopton for adopters and non adopters n research kebleas based on sample survey on Characterstc Household character demographc Adopters (N=100) (adopton =1,0 otherwse ) Standard Mean devaton Non adopters (N=150) Mean Standard devaton t-test Age Sex fasze basceduca *** martalsta Household economc factors Laborav *** totalandhold own mo phone(yes) own arado (yes) lvestockhol (tlu) landtenure * Market Access & Insttutonal factors access toprcenfor accesstoloan *** dsttovecroad acctoexteserv ** Dstance to nput market Dstance to product market Source: model output based on prmary collected data,2015. HH=household,. N=number of sample populaton. ***,**, * sgnfcant levels at 1%, 5% &10% respectvely Econometrc Analyss Determnants of Modern Hve Adopton Wth descrptve statstcs of sample households we test of the exstence of relatonshp between the dependant and ndependent varables to dentfy factors affectng adopton of modern beehve technology. Identfcatons of these factors alone are however not enough unless the relatve nfluence of each factor s statstcally determned. In ths secton, logt model was used to see the relatve nfluence of demographc, soco-economc and nsttutonal varables on adopton of modern hve. Out of the total hypotheszed varables, 10 of them were found to be statstcally sgnfcant n affectng modern hve adopton. Thus, age, sex, educaton status of household head, Labor avalablty, Land tenure, access to loan, access to extenson servce and dstance to vehcle road, nput market, product market. Others determnant factors lke total land hold ownng phone and rado, lves stock, famly sze, martal status and access to prce nformaton whch was not sgnfcant to determne between adopters and non-adopters were excluded from further explanatons. Age of household Dependng on the nature of the technology, age of farmer s lkely to play dfferent roles n technology adopton. Age had a negatve and sgnfcance nfluence on adopton of modern hve technology at 1percent level of sgnfcance. As farmers get old they are lkely to be rsk (probable loss of producton or other beneft due to new technology) avert and they become none adopters of beekeepng technologes. Other thngs beng constant beekeepers are reluctant to new technology as they get older. The margnal effect ndcates that probablty of adopton of mproved modern hve technologes decreases by 0.9 percent as the house hold ncrease ts age by one year. (Table, 4.2). The result n lne wth Yohanns (1992) and; Shferaw and Holden (1998) who also ndcated 33

6 Journal of Energy Technologes and Polcy ISSN (Paper) ISSN (Onlne) that age of the household head negatvely nfluenced adopton of farm technology. Access to loan As a lqudty factor, the more farmers have access to source of fnance, the more lkely to adopt agrcultural technologes that could possbly ncrease honey yeld. Access to loan was postve and sgnfcant nfluence on adopton of modern hve at 1percent sgnfcance level. Farmers, who had access to loan, keepng other thngs constant, had 35.8% hgher probablty of adoptng modern beehve unlke non-adopter farmers. Ths fndng s consstent wth Kasse et al. (2012). Access to extenson servce Access to extenson servce has postve nfluence on the probablty of modern hve adopton at 1 percent sgnfcance level. From ths result t s possble to state that those household who have access to extenson servce lke tranng and demonstraton are more lkely to adopt modern hve than those who have not. The margnal effect result also shows that the estmated ncrease n the probablty of adopton mproved of modern hve technologes due to access to extenson servce was 28.2 percent. In addton to offerng nformaton and creatng awareness, extenson servce also ncludes advces, tranng, demonstratons and tmely dstrbuton of nputs. Farmers who are frequently vsted by extenson agents tend to be more progressve and more lkely to experment wth modern hve technology. The result s consstent wth Shferaw et al.(2008) for mproved pgeon pea varetes n Tanzana, Krstjanson et al. (2005) for cowpea varetes, Kalba et al.(2000) for maze varetes and Gebreselasse and Sanders (2008) for sorghum n Ethopa. Smlarly, ths fndng s also match wth the fndng of Rahmeto (2007), Belyu, Tewodros and Edward(2010). Household head educaton Educatonal level of the household head s mportant to note as determnant of adopton to farm technologes. The possble reasons for more adopton of modern hves by beekeepers wth hgher educatonal backgrounds, could be that educaton may ncreases access to nformaton and ther knowledge to understand the technology. Beekeepers, who can read and wrte, can have smple and dversfed communcaton ways to extenson servces. As the logt estmaton result ndcates (table 4.2), educaton status of house hold head s postve and sgnfcantly correlated wth adopton at 1percent level of sgnfcance. Farmers, those who can read and wrte, keepng other thngs constant, have 22.2% hgher probablty of adoptng, modern beehve unlke llterate farmers. The result s also supported by earler studes (Workneh et. al., 2008);Workneh, 2011). Labor avalablty Agrculture needs labor as an nput n order to perform actvtes. Havng large workng labor force n a famly ncreases the chance of dong any practce by themselves and they may not need to hre more addtonal labor from the market. On the other hand the money saved due to use of own labor force can be used to buy modern nputs and facltate adopton of modern hve technology. From our logt result (table 4.2), Labor avalablty has postve and sgnfcant nfluence on adopton of modern bee hve at 1percent level of sgnfcance. Avalablty of labor n a household s assocated wth an ncrease n the probablty of adoptng modern hve technology; by 8.3percent, ceters parbus. Our result s consstent wth fndngs of Bekele et.al, (2000) and Mllon (2004).It s also observed that the avalablty of adult famly members wthn households may facltate technology adopton because most farmng households suffer from offerng hred labor due to lqudty constrants (Carletto et al, 2007). Own farm land Ownershp of large tracts of land can facltate expermentaton wth new agrcultural technologes, and also determne the pace of adopton as large land owners are more lkely to be the early adopters (de Janvry et al, 2011). Farmers those who are certfed and have secured land ownershp status (havng land usage certfcate) accept new technologes. Such legal rghts of land use, assures and motvate contnuous nvestment of beekeepng socety on ther own land by usng new nputs and technology practces. Beng a ratonal decson makers, whle ncurrng a cost for technologes(bee forage development), farmers want totally to employ technologes wthn ther own land where the fnal yeld could not be shared and sub-dvded by others, whch s too common n sharecroppng system.(honey share producton s common n Ttgray). Land ownershp status of farm households was found to be statstcally sgnfcant n determnng adopton decson of modern hve at 5 percent level of sgnfcance. Keepng other thngs constant, adopters had 21.1percent hgher probablty of adoptng modern beehve, unlke ther counterparts. 34

7 Journal of Energy Technologes and Polcy ISSN (Paper) ISSN (Onlne) Dstance to vehcle road Beekeepers lvng n the research area were nfluenced postvely and sgnfcantly by dstance from vehcle road nfrastructure at one percent level of sgnfcance. The model margnal effect result ndcates that (table 4.2), as 1 KM move far away to vehcle road 7.7 percent hgher adoptng probablty of modern hve was resulted. Dstance to nput market Modern beekeepng use newly ntroduced technology nputs. Among these nputs modern bee hve, protecton materals, honey extractor, swarm bee and others are avalable mportant nputs whch are found at dfferent market locatons. As expected, the farthest nput market has a negatve and sgnfcance nfluence on the adopton decson of modern hve at one percent sgnfcance level (p=0.000). From ths result t can be stated that those households who are near to nput market are more lkely to adopt modern hve than those who are lve far away from nput market. The margnal effect n the model wth regard to dstance to nput market mples that, other thng held constant, the probablty of adopton modern hve was decrease by 13.9 percent as one beekeepng farmer move far away one KM from the nput market. Results ndcate that farm households that are located n remote areas are less lkely to adopt modern hve technology. Ths s not surprsng because n such areas, access to extenson servces, feld vsts by agrcultural staff and nteractons wth farmers (human captal nputs) s usually lmted due to poor road nfrastructure. Dstance to product market Dstance from farmers house to product market was postvely related to the adopton of modern hve technology. The probablty adopton of technology was sgnfcantly affected by market dstance at 1percent sgnfcance level (Table 4.2). Product market result ndcated that as market dstance ncrease, the probablty of adopton of modern hve technology ncrease by 8.6 percent. The market gan of honey sale s postvely ncreased as farmers were sale ther product at reasonable market prce f they are travel far away from ther local market. Beekeepers can sale ther honey bee product at home to locale traders at low prce whch s nconvenence to motvate them for farther honey producton and farmers always travel to search the rght prce and place even f t has travel cost. These all honey producers are most lkely motvated by bg ctes honey prce to adopt new bee technology. But the fndng s nconsstent wth fndng was dentfed by (Halu, 2008), as market dstance ncreases adopton and ntensty of adopton decreased. Concluson The man conclusons can be drawn from the results of ths study on factors of adopton was that; the group of farm households that dd adopt modern hve had dfferent characterstcs than the group of farm households that dd not adopt. These dfferences represent sources of varaton between the two groups that the estmaton of a logt model ncludng of all varable for adopton can take n to consderaton. Recommendaton Based on the result every extenson worker should be consder physcal and soco economc factor that facltate any acceptance of new technology and there s a need of prortzed and rankng of factors accordng ther nfluence on adopton. The presence of loan access, strong extenson servces and beng lterate( can wrte and read) are among the strong determnate factors for adopton of modern hve technology and any provson of extenson servce should be analyze the nfluence of these factors as an mportant focus. 35

8 Journal of Energy Technologes and Polcy ISSN (Paper) ISSN (Onlne) Table: 4.2 Logt estmaton result for determnants of modern hve adopton Explanatory varables Coeffcents Odds rato Margnal effect Robust Std. Err Z P> z age *** sex ** fasze basceduca *** martalsta Laborav *** totalandhold own mo phone own arado lvestockhol landtenure ** access toprcenfor accesstoloan *** dsttovecroad *** acctoexteserv *** dstancetonput market *** dstopromarket *** _ cons Log pseudo Lkelhood Number Of observaton = Wald ch2(17) = Prob > ch2 = Pseudo R2 = = Source: model output based on own survey 2015 *** Sgnfcant at 1 percent ** sgnfcant at 5 percent * sgnfcant at 10 percent ( ) Dummy varable; margnal effect (dy/dx) s for dscrete change of dummy varable from 0 to 1 Reference Ayalew Kassaye Promoton of beekeepng n rural sector of Ethopa: Proceedngs of the thrd Natonal Annual Conference of Ethopan Beekeepers Assocaton. Fchtl,R and admasu,a,1994.honey bee flora of Ethopa.the natonal herbarum,adds Absba unversty &Deurscher Entwcklungsdeeent(DED),mergaf verlage,germany. Gezahegn T(2001).Beekeepng n amharc.mega prntng enterprse,adda abebea,ethopa BoANR (Bureau of Agrculture and Natural Resources) dstrct annual report Tgray, Ethopa. Tgray Bureau of Agrculture and Rural Development(2010) annual report Gezahegn T(2001).Beekeepng n amharc.mega prntng enterprse,adda abebea,ethopa greene,w. Econometrc analyss, 2nd ed. Englewood clffs, nj: prentce hall, 1993Guajrat, Domodar, 2004 Storck, H., Bezabh Emana, Berhanu Adenew, Boroweck, A., Shmels W/Hawarat, Holeta bee research center Progressve report for 1996/97, adds ababa, ethopa Feder L, Just RE and Zlberman O Adopton of agrcultural nnovaton n developng countres: A survey. Economc Development and Cultural Change 32(2): Grma Deffar, Non-Wood Forest Products n Ethopa. EC-FAO Partnershp Programme ( ). Adds Ababa. pp Gezahegn T(2001).Beekeepng n amharc.mega prntng enterprse,adda abebea,ethopa 36