Pattern, Trend and Determinants of Crop Diversification: Empirical Evidence from Smallholders in Eastern Ethiopia

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1 Pattern, Trend and Determnants of Crop Dversfcaton: Emprcal Evdence from Smallholders n Eastern Ethopa Wondmagegn Mesfn School of Agrcultural Economcs and Agrbusness, Haramaya Unversty Moble: P.O.Box 103, Dre Dawa, Ethopa E-mal: wonde22@gmal.com Bekabl Fufa School of Agrcultural Economcs and Agrbusness, Haramaya Unversty P.O.Box 138, Dre Dawa, Ethopa Moble: E-mal: akassa2000@yahoo.com Jema Haj School of Agrcultural Economcs and Agrbusness, Haramaya Unversty P.O.Box 138, Dre Dawa, Ethopa Moble: E-mal: jemmahaj@yahoo.com Receved: October 29, 2011 Accepted: October 30, 2011 Publshed: November 4, 2011 The study was sponsored by Mnstry of Educaton (Haramaya Unversty) Abstract Crop dversfcaton s the most mportant rsk management strateges. The study nvestgated the pattern, trend and covarates of crop dversfcaton n eastern Ethopa based on data collected from 167 households randomly and proportonately selected. In order to manage rsks of drought, pests and dseases, sol fertlty declne and nput prces varatons, farmers n the study areas employ crop dversfcaton as a self-nsurng strategy. The farmers are becomng rsk-averse whch has mplcatons on technology adopton. Tobt model result ndcated that farmers wth more extenson contacts and larger lvestock sze are lkely to specalze whereas those who have access to market nformaton and rrgaton, those who own machnery and more number of farm plots are more lkely to dversfy. In order to promote crop dversfcaton, provdng farm machnery through easy loans and mprovng access to market nformaton and rrgaton should be gven attenton. The extenson system should nclude rsk-mnmzaton as a strategy. Keywords: crop dversfcaton, rsk, rsk management strateges, rsk-averse, Ethopa. 1. Introducton Ethopa s a country whose economy s sgnfcantly dependent on agrculture sector. The sector s domnated by smallholder peasant agrculture whch accounts for more than 95% of the total food producton. The sector supports employment for 85% of the populaton, 90% of exports and 50% of GDP. 78 P a g e

2 Out of the total agrcultural land, about 97% s cultvated by smallholders who produce more than 97% of the agrcultural output. Despte ts mportance, the agrcultural sector s tradtonal and subsstence-orented (Jema 2008) and t characterzed by poor and declnng performance. Ths performance s attrbuted to many nterrelated factors whch nclude recurrent drought, land degradaton, paucty of land, crop and lvestock pests and dseases, lack of mproved and sutable technologes, poor marketng and servce nfrastructure (Grma 2002; Belaneh 2003). In Ethopa, farmng s partcularly weather-dependent and farmers face prce, yeld and resource rsks that arse from the bophyscal and soco-economc envronment n whch they operate. Rural households n the country are exposed to a varety of rsks that nclude harvest falure as a result of drought, frost, floods and other clmatc events and death of lvestock (Dercon 2002). These rsks nfluence the producton and resource allocaton decsons of smallholder farmers. Lack of nsttutonal nnovatons such as credt and nsurance schemes n most developng countres makes ndvdual rsk management a sgnfcant ssue to cash-constraned smallholder farmers. In an effort to adapt to the rsky envronment, smallholders make certan producton decsons and employ varous rsk mtgatng strateges. Crop dversfcaton s one method of reducng ncome varablty (Brglauer 2000). Accordng to the comparatve advantage theory, dversfcaton can reduce rsks but at the expense of ncome. Despte the sgnfcant role crop dversfcaton plays n agrculture, there are only few emprcal studes on the factors that determne dversfcaton. Ths study has examned the effect of varous household characterstcs and other factors on crop dversfcaton. In Eastern Ethopa, the economy s characterzed by subsstence mxed farmng systems, low agrcultural productvty and poor access to major markets (Bezabh & Hadera 2006; Jema 2008). Agrcultural producton n the area s generally characterzed by a hgh degree of nstablty. Ths s manly attrbuted to the nature of the agrcultural producton whch s assocated wth hgh degree of uncertanty whch arses from dependency of the agrcultural producton on uncontrollable weather condtons (erratc and varable ranfall) whch caused great fluctuatons on crops yeld on the one hand, and large fluctuatons n nput and output prces. It s argued that dversfyng by growng more enterprses may lead to farm ncome stablty (Tefera et al. 2003). The smallholder farmers n the area practce crop dversfcaton of dfferent types. However, the pattern and trend of crop dversfcaton and ts mplcatons are not studed. The study therefore, attempted to analyze the pattern and trend of crop dversfcaton n eastern Ethopa. 2. Research Methodology 2.1. The Study Area The study s conducted n Eastern Hararghe zone whch s among the seventeen zones of Oroma Natonal Regonal state of Ethopa. It s located between N N lattude and E E longtude. The Zone has an average alttude of 1750 meters above sea level. Clmatcally, the zone s subdvded nto Kola (note 1), Wonadega and Dega. The zone has a total area of about 24,900 km 2 and dvded nto 17 dstrcts. From the zone, Kombolcha and Haramaya dstrcts are selected for ths study. Haramaya dstrct s found n the North Central part of the Zone. The dstrct comprses of 33 peasant assocatons (PAs) and t s characterzed by plateau, mountans, hlls and plans. Alttudnally stretchng between 1400 and 2340 meters above sea level, Haramaya falls under mddle hgh land agro-clmatc zone and the mean annual ran fall s 790 mm. The dstrct has a total populaton of about of whch about 90% lve n rural areas. Kombolcha dstrct s found n the northern part of East Hararghe zone. Physographcally, t s characterzed by dssected plateaus, mountans, hlls, plans and valleys. Of the 19 peasant assocatons n the Dstrct, seven (37%) are located n the lowlands and the remanng 12 (63%) are located n the Wonadega. Alttudnally, Kombolcha dstrct stretches between 1200 and 2460 meters above sea level and t falls under Wonadega (74%) and Kola (26%) agro-clmatc zones. The dstrct has a total populaton of about and more than 90% of the populaton resdes n rural areas. The annual ranfall ranges from 600 mm to 900 mm. Mxed farmng system wth the predomnance of crop producton s practced n the two dstrcts. Farmers produce dfferent crops n order to secure ther famly food supply and cover varous household 79 P a g e

3 expendtures. Permanent crops nclude sorghum, maze, harcot bean and groundnut. Vegetables manly potato, onon, cabbage, lettuce, tomato, carrot and beet root are commonly produced. In addton, chat (note 2) s the domnant cash crop wdely produced n the areas. Keepng anmals n ther homestead to provde feed by the cut and carry system s commonly practced n the hghland areas. The producton system n the areas s both ran-fed and rrgated systems the ran-fed producton system beng most domnant. Unrelable ranfall, low adopton of modern technologes, lack of credt facltes, shortage of potable water supply, backward nfrastructural facltes, deteroraton of forests and sol fertlty are the major problems. On the other hand, vared and favorable clmatc condtons are the major potentals of the dstrct Types and Sources of Data The study was based on both prmary and secondary data. Prmary data related to the socoeconomc characterstcs of the farmers such as land sze, famly sze, land, labor, crop yeld, and lvestock owned, machnery owned, rsk sources and mtgaton strateges were gathered from the sample farmers. Secondary data lke prces of commodtes and other related nformaton were collected from Agrculture and Rural Development and marketng offces of the dstrcts. Dfferent publshed and unpublshed materals were also revewed. In addton, webstes were consulted for secondary data and supportng lterature Samplng Desgn and Methods of Data collecton For the study, both probablty and non-probablty samplng technques were employed. Haramaya and Kombolcha dstrcts (note 3) were selected purposvely snce they have vegetable domnated-mxed farmng systems and smallholder farmers n these dstrcts are exposed to varous types of rsks. A two stage random samplng procedure was then employed to select 167 respondents from the dstrcts. In the frst stage, 10 peasant assocatons (4 from Kombolcha and 6 from Haramaya dstrcts) were selected proportonately. In the second stage, 167 sample households were selected from each peasant assocaton usng probablty proportonal to sze method. The data for the study were collected wth the ad of a questonnare. In addton, nformaton was obtaned from dscussons wth Dstrct Agrcultural Offcers and extenson workers and other farmers outsde the formal sample to supplement the data. Before conductng the formal survey, the questonnare was pre-tested by ntervewng seven farmers. Ths greatly helped to mprove the questonnare to generate relevant data and to have clear understandng about the farmng system n the areas. The researcher recruted twelve enumerators, who have good knowledge and experence on farmng system and they were gven tranng on the objectve of the data collecton, how they approach and clarfy questons to farmers and record the data properly Methods of Data Analyss In order to address the objectves of the study, descrptve analyss and econometrc model were employed n ths study Descrptve statstcs Descrptve statstcs was employed to analyze and present the socoeconomc characterstcs of sample households and to rank farmers responses wth regard to certan questons. In addton, prortzng factors nfluencng farm household decsons were made usng smple descrptve statstcal tools. Descrptve analyss nvolved calculatng of frequency, percentages, means, and standard devaton Econometrc model Tobt regresson was used to analyze covarates of crop dversfcaton and ts ntensty. The varable used to measure crop dversfcaton s a modfcaton of the Entropy ndex that les strctly between zero and one. The dversty ndex s censored because some of ts values cluster at the lmt (.e., 0 for complete specalzaton and 1 for perfect dversfcaton). Standard ordnary least squares (OLS) or seemngly 80 P a g e

4 unrelated regresson (SUR) of the dversty ndex wll yeld based and nconsstent estmates n ths stuaton. For the same reasons as above, t s not approprate to use a classcal regresson model for ths purpose. Hence, optng for other econometrc models s n v v evtable for handlng the matter. One of such models s censored regresson models. Censored regresson models refer to a model n whch we observe the dependent varable only f t s above or below some cut off level. Tobt model s a specal case of censored regresson models that arse when the dependent varable s lmted (or censored) from above and/or below. It s a non-lnear model whch employed maxmum lkelhood estmaton technque whch estmates the lkelhood of crop dversfcaton and ts ntensty. The Tobt approach has been appled n prevous studes of crop dversfcaton (e.g. Allson 2010). Ths model s approprate snce the dependent varable s an ndex whch takes values between 0 and 1 nclusve. The dependent varable of the model can be ether left-censored, rght-censored, or both left-censored and rght-censored, where the lower and/or upper lmt of the dependent varable can be any number. The two-lmt Tobt model can be specfed as: y ' * x * where y s a latent varable (unobserved for values smaller than 0 and greater than 1) representng specalzaton or dversfcaton ndex; x s a vector of explanatory varables; ' s a vector of unknown parameters; and s a dsturbance term. Denotng y (crop dversfcaton ndex) as the observed dependent varable the two lmt Tobt model can be specfed as: 0 0 * y y * f 0 y 1 (2) 1 f f y y * * 1 In prncple, a maxmum lkelhood approach may be employed to address the censorng (e.g., Tobt model) and account for correlatons n error terms across equatons by specfyng a multvarate densty functon for the error terms (Benn et al. 2004). Censored regresson models (ncludng the standard Tobt model) are usually estmated by the Maxmum Lkelhood (ML) method. The log lkelhood functon s specfed wth an assumpton that the dsturbance term follows a normal dstrbuton wth mean 0 and 2 varance. Unlke tradtonal regresson coeffcents, the Tobt coeffcents cannot be nterpreted drectly as estmates of the magntude of the margnal effects of changes n the explanatory varables on the expected value of the dependent varable. In a Tobt equaton, margnal effect ncludes both the nfluence of the explanatory varable on the probablty of crop dversfcaton as well as ts ntensty. More explctly, the margnal effect takes nto consderaton that a change n an explanatory varable wll affect smultaneously the number of sample farmers dversfyng ther producton and the extent of dversfcaton. Defnton of Varables and Hypothess Dependent Varable: The dependent varable used s crop dversfcaton measured by the modfed entropy ndex. The modfed entropy ndex accounts for both the mx of crops and the relatve mportance of each crop to the farm busness. The ndex spans a contnuous range from 0 to 1; the value of the ndex for a completely specalzed farm producng one crop s 0 where as a completely dversfed farm wth equal shares of each crop has an ndex of 1. The modfed entropy ndex whch represents the dependent varable n ths study s obtaned usng the formula: (1) 81 P a g e

5 D ndex 1 ln( ) p N p ) 1 ln( N) ( (3) Where, D ndex s crop dversfcaton ndex, p s proporton of area under th crop, refers to each of the N possble enterprses. At maxmum dversfcaton, ths ndex takes a value of 1 and at maxmum specalzaton t attans a value of 0. The modfed entropy ndex provdes a unform and fxed scale and hence t s used as a norm to compare and rank the extent of dversfcaton spatally. Other emprcal studes on determnants of farm dversfcaton have used the same ndex (e.g. Mshra & El-Osta 2002). Explanatory varables: The followng were expected to be explanatory varables determnng the level of farm dversfcaton. These varables are dentfed based on revew of the emprcal works and economc theory. Farm sze: Farm sze s a contnuous varable referrng to the total land holdng by the farmer measured n hectares. It was expected to postvely related to crop dversfcaton. Prevous fndngs ndcate that dversfcaton actvtes are concentrated on large farms (Wess and Brglauer 2000; Mshra and El-Osta 2002; Benn et al. 2004; Feten et al. 2009). It was, therefore, hypotheszed that larger farms may be more dversfed. Age of the household head: Age s a contnuous varable and a negatve relatonshp was expected between age of the farm operator and dversfcaton. Prevous studes suggest that older farm operators are less lkely to dversfy (Mshra and EL-Osta 2002). One possble explanaton s that older farm operators are less rsk-averse and less dversfed whereas young and begnnng farm operators mght be more rskaverse. Household sze: The sze of the farm household was expected to be postvely related wth dversfcaton. Prevous studes also support ths hypothess (Wess & Brglauer 2000; Mshra & El-Osta 2002; Benn et al. 2004). Dstance to markets: Prevous works on farm dversfcaton hghlghted the mportance of proxmty to man roads and markets for development of other farm enterprses (Benn et al. 2004). A negatve correlaton was expected between crop dversfcaton and proxmty to markets. It s a contnuous varable and measured n klometers. Number of extenson contacts: The larger the number of contacts a farmer has wth an extenson agent, the more he s lkely to engage n producton of large number of enterprses. It s a contnuous varable and measured as the number of contacts the famer has wth the agent per year. The fndng of Feten et al. (2009) also supported ths hypothess. Farm Machnery (tractor, water pump): Whether the farmer has farm machnery or not was expected to be postve determnant of crop dversfcaton. One study n Inda has ndcated that there s a postve relaton between possesson of machnery (tractor) and farm dversfcaton (Mshra & El-Osta 2002). Off/non-farm ncome: Ths varable s defned for ths partcular study as all ncomes except on-farm ncome earned by the sample household. Off-farm/non-farm ncome dversfes a farm operator s ncome portfolo and reduces the degree of farm dversfcaton and possbly off-farm work s not compatble wth the labor demands of farm dversfcaton. Off-farm ncome from wages and salares s a contnuous varable that was expected to be negatvely correlated wth crop dversfcaton. Mshra & El-Osta (2002), Wess & Brglauer (2000) have also reported nverse relatons. Number of farm plots: Ths varable s a contnuous and expected to postvely nfluence crop dversfcaton. The more the number of farm plots, the more s the dversfcaton (Benn et al. 2004). Access to market nformaton: It s a dummy varable takng value 0 f a household have no access to market nformaton and 1 otherwse. Ths varable was hypotheszed to have a postve relaton wth crop 82 P a g e

6 dversfcaton. Irrgaton ntensty: It s a contnuous varable measured as the total rrgated area under each crop dvded by the total cropped area by a farmer. The hypothess s based on Lonne et al. (1989). Sex of the household: Ths s a dummy varable assumng 0 f female and 1 f male household head. The varable was expected to have a postve effect on dversfcaton due to the fact that there s skll or requrements for frequent and early ploughng. Hence, male headed households are more lkely to dversfy (Feten et al. 2009). 3. Results and Dscussons The major fndngs of the study are dscussed n the followng sectons. The frst secton deals wth the soco-economc characterstcs of the sample farmers n the two dstrcts whle the second secton descrbes the patterns and trend of crop dversfcaton n the study area. The fnal secton presents and dscusses the covarates of crop dversfcaton usng the Tobt regresson Socoeconomc Characterstcs of the Sample Households The socoeconomc characterstcs of the sample households are presented n the Tables 1 and 2. The descrptve statstcs results ndcate that the majortes (more than 95%) of the households are male-headed and marred. About 70% of the sample respondents n Haramaya dstrct are llterate whereas about 75% of the respondents n Kombolcha dstrct are at least able to read and wrte. Aganst ths background, t s nterestng to note that the number of household heads wth no educaton s clearly hgher n Haramaya dstrct than n Kombolcha. Ths suggests lower nvestment n human captal among the households of Haramaya dstrct. About 6% of the sample households n the study area are engaged n off-farm or non-farm ncome generatng actvtes. Ths ndcates that consderable proporton of the farmers does not have access to offfarm and non-farm ncome sources. Ths stuaton s supposed to constran the off-farm/non-farm ncome to serve as a hedge aganst crop falure rsk. The soco-economc profle of households for the contnuous varables s summarzed n table 2. The average age of the household heads n the study area s above 40 years and farmers n Haramaya dstrct have slghtly longer years of experence n farmng than household heads n Kombolcha dstrct. On average, the n relatvely large household sze s mantaned n the study areas (around 5 adults per household). Larger household sze allows the household flexblty to pool resources and share rsks by takng advantage of household return to scale and labor supply they need durng peaked demand season. The average land holdng n the study areas s less than the natonal average whch s 1.53 hectares. The average land holdng n Haramaya dstrct (0.74 ha) s hgher than that of Kombolcha (0.41 ha) on average. In Haramaya dstrct, there s greater varablty n land ownershp as ndcated by the hgher standard devaton (0.598). The average lvestock holdng measured n tropcal lvestock unts s slghtly hgher n Haramaya (3.05) than n Kombolcha (2.72) dstrct. Ths mght be related to the fact that households n Haramaya dstrct have enough land to rase lvestock besdes growng crops. Farmers n Haramaya are expected to walk long dstance to reach the nearest market as ndcated by the average dstance to market. Ths attrbuted to the fact that most of the peasant assocatons of the dstrct are located far from the dstrct captal, Haramaya town. So, transport costs are therefore, potental constrants partcularly for the farmers n Haramaya dstrct. The average annual farm ncome s relatvely hgher and more varable n Haramaya dstrct than Kombolcha. The result of the descrptve statstcs ndcated that the average non/off-farm ncome was hgher n Kombolcha dstrct. However, there s greater varablty among households n Kombolcha dstrct than n Haramaya Crop choces and trend n Crop dversfcaton 83 P a g e

7 Land allocaton s an ndcator of mportance of the crop under consderaton. Crop portfolo composton of the study areas seems to contan sgnfcantly hgher proporton of low- rsk low- return crops. To some extent, on the other hand, they contan low concentraton of hgh value cash crops. Farms n the hghlands are commonly producng vegetables and staple crops and producton of chat s common n the mdhghlands. The areas allocated to major crops n the study areas for sx producton years ( ) are presented n Table 3. The data s obtaned from the Dstrct Agrcultural Offces. The area allocated for all crops remans nearly the same ndcatng no sgnfcant ncrease n area allocated to both cereal crops and vegetables for the sx years. However, the ncrease n area allocated for cereals seems hgher than that of vegetables ndcatng farmers are tendng to be less commercalzed through tme. Hence, there s a need to revert ths tendency. There appears an ncreasng trend n dversfcaton n the study areas. As already ndcated, analyzng the trend n dversfcaton may help to know rsk-averson behavor of smallholder farmers. As long as crop dversfcaton domnates the lterature as a domnant rsk management strategy, rsk-averse smallholder farmers are characterzed by dversfed farms. Hence, ncrease n dversfcaton ndex wth tme ndcates ncrease n rsk averson. Crop dversfcaton as measured by land under each crop has shown an ncreasng trend for the 2004 to 2009 producton perod. There have been changes n the pattern of agrcultural dversfcaton at the dstrct level. Farms keep on dversfyng ther farms and the usual noton of crop dversfcaton as a rsk management practce s also approved n the present study. The study also found that farmers n eastern Hararghe hghlands of Ethopa are tendng to be rsk-averse over tme. The major contrbutor to ncreased farm dversfcaton s also beleved to be the ncrease n land fragmentaton n the study area. Ths has mportant mplcaton to technology adopton behavor of smallholder farmers. Wth ncrease n rsk averson through tme, farmers mght become more reluctant to adopt technologes Pattern of crop dversfcaton The dfferent forms of crop dversfcaton are crop rotaton and ntercroppng. Intercroppng s the most common form of enterprse dversfcaton n eastern Hararghe. The result of the survey ndcates that farmers ntercrop for three major reasons: to ncrease sol fertlty, better use of resource (land n ths case snce t s scarce) and for mnmzng rsk due to loss from another enterprse(s). Accordng to Bezabh and Hadera (2007), the advantage of ntercroppng may also ental supplementary relatonshp whch calls for physcal support of one crop to the other crop and eroson control through provdng contnuous leaf cover over the ground surface. Crop rotaton s beleved to reduce dsease ncdence and ncrease sol fertlty. Intercroppng mght allow effcent use of land by growng more crops together. Farm dversfcaton s growng dfferent crops on dfferent plots hence reduce rsk of losng crops from all felds. The survey result also ndcated that ntercroppng as a form of dversfcaton plays sgnfcant roles. About 78% of the respondents ndcate that ther reason for ntercroppng s to reduce rsk of losng yeld from crop due to specalzaton. Other reasons for ntercroppng were shortage of land and to ncrease sol fertlty by ntercroppng wth vegetables and legumnous crops. Ths result ndcates that farmers n the study area are much concerned about land scarcty, sol fertlty and rsk management. The proporton of farmers practcng crop rotaton and crop rotaton, average number of farm plots operated and number of crops grown n a sngle producton perod are ndcated n Table 4. The result ndcated that sgnfcant proporton of the sample farmers practce crop rotaton and ntercroppng. The major crop rotaton practces are sorghum between two vegetable crops and vegetables between maze years. The result related to the average number of farm plots operated by a household ndcates that a household on average operates about 4 farm plots. Ths ndcates that farm land fragmentaton s persstent n the area. A typcal farmer grows 4 crops n a sngle producton perod. If number of enterprses grown by a farmer s taken as a measure of crop dversfcaton, t tells that the farmers are dversfyng ther producton. 84 P a g e

8 3.4. Determnants of Farm Dversfcaton The results of a Tobt model whch analyzed covarates of crop dversfcaton were estmated usng STATA Verson 9.10 statstcal package. The results are summarzed and presented n Table 5. Multcollnearty was checked usng varance nflaton factor (VIF) for contnuous varables and Contngency Coeffcents for dummy varables. The calculated VIF values are all less than 10 (the cut-off pont) and contngency coeffcents were less than 0.75 (the cut-off pont) whch ndcated that multcollnearty s not a serous problem. Snce the Tobt model has a probt component and ts results are senstve to the assumpton of homoscedastcty, a robust standard error Tobt regresson was run. The coeffcent of lvestock ownershp as s negatve and sgnfcant at 5% ndcatng an nverse relatonshp between lvestock ownershp and crop dversfcaton. The explanaton for the result s, lvestock as measure of wealth may act as nsurance aganst crop producton rsk, bearng a negatve relatonshp wth crop dversfcaton. So, households wth large number of lvestock are less lkely to grow more crops. The result s consstent wth the fndngs of Benn et al. (2004), but n contrast to that of Feten et al. (2009). The postve coeffcent for the number of farm plots operated by a household ndcates that households wth more number of farm plots are more lkely to dversfy by growng dfferent crops on each plot of land. The varable s sgnfcant at 1% probablty level. More fragmented farms wth larger numbers of farm plots have more crops that are lkely to be grown more evenly snce the farm plots are approxmately of equal sze. Lkewse, by dversfyng plot locatons across the sol toposequence, farmers are able to reduce plot-yeld co-varaton and thus reduce aggregate producton varablty. The result of the study s n lne wth the fndng of Berhanu & Mot (2010) who found land fragmentaton to be most mportant determnant of crop dversfcaton. An ncrease n farm plots wll ncrease the ntensty of dversfcaton by 1.40% on average. Access to market nformaton sgnfcantly affects crop dversfcaton at 10% probablty level. Households havng access to market nformaton are more lkely to dversfy ther producton snce they have the nformaton related to supply, demand and prces of most crops. It s evdent from the result that households who own farm machnery (water pump) are more lkely to dversfy because they can properly perform dfferent farmng operatons on tme and can market ther produce easly. The varable s sgnfcant at 5% probablty level and the result s consstent wth the fndng of Muhammed et al. (2008). Ownng machnery (water pump) s related to an ncrease n probablty of crop dversfcaton by 0.29% and the ntensty by 3.85% on average. There appears a negatve and sgnfcant relatonshp between number of extenson contacts per year and crop dversfcaton and the coeffcent s sgnfcant at 5% probablty level. Ths mght be assocated wth the extenson system whch s concentrated on enhancng farmers productvty and proftablty whch favors specalzaton at mcro-level and overlooks the role of crop dversfcaton n rsk mnmzaton. The result s consstent wth the fndng of Feten et al. (2009) and n lne wth the explanaton of Rchard (1998) who stated that extenson contact has dscouraged ntercroppng for a number of years and has promoted the growng of pure crops targeted for commercal purposes. The result s also supported by Aynde et al. (2008) where postve relatonshp was found between rsk coeffcents of the household and ther access to extenson servces. Irrgaton ntensty measured as the proporton of area rrgated was found to sgnfcantly and postvely affect crop dversfcaton at 10% probablty level. Farmers who have access to rrgaton have opportuntes to grow more crops. Ths s observed n the study area where farmers havng access to rrgaton grow vegetables on ther farms. The result of ths study s n lne wth the explanaton of Lonne et al. (1989) who found postve relatonshp between rrgaton and enterprse dversfcaton. Fnally, geographcal locaton of farms determnes croppng pattern, ranfall, sol productvty and so on. To ths end, n addton to the dversty wth respect to Peasant assocatons wthn the sample dstrcts, a dstrct dummy was defned and ncluded n the model. The result ndcates that compared to farmers n Kombolcha dstrct (northern part), farmers n Haramaya dstrct (North central part) are more lkely to 85 P a g e

9 dversfy ther farms. However, ths dfference may be attrbuted to varables whch are not mportant for classfyng the two dstrcts as havng dfferent farmng systems or the dfferentatng factor mght be smply locaton of farms n the study area. 4. Concluson and Polcy Issues 4.1. Concluson Consderng the multdmensonal mportance of crop dversfcaton as a rsk management strategy and an engne for ensurng food securty, the present study was conceved to assess the patterns, trend and determnants of crop dversfcaton at farm level. Large number of farmers practces crop rotaton and ntercroppng for dfferent mportant reasons whch nclude reducng dsease and pest nfestatons, manage wth land scarcty, mprovng sol fertlty and reducng rsks. There appears an ncreasng trend n crop dversfcaton whch attrbutes to ncrease n area allocated to cereal crops. Ths ndcated that farmers are tendng to be more rsk-averse and ths wll have an mplcaton n technology adopton and agrcultural development n the area. A modfed entropy ndex was consdered as an approach to measure crop dversfcaton based on area n hectare under each crop. The Tobt estmates ndcates that access to market nformaton, rrgaton ntensty, machnery ownershp, lvestock sze, extenson contact, number of farm plots and locaton of farms are found to be the most mportant factors that sgnfcantly nfluence crop dversfcaton Polcy Implcatons and Further Research Based on the emprcal evdences, the followng recommendatons are suggested to promote crop dversfcaton whch s a mechansm for farmers for avodng rsks and to ensure a sustanable level of farm returns. Polcy makers need to devse a way to revert the farmers tendency to become more rsk-averse through tme. Ths mght mprove farmers technology adopton decsons. Attenton should be gven to desgn strateges so that farmers mght dversfy ther producton to hgh value crops leavng small land for staple cereal crops. Farm machnery especally water pumps should be provded through easy loan schemes and nvestment n rrgaton projects would also save the farmers from drought and erratc ranfall. Access to market nformaton need to be gven attenton. Infrastructures lke farm to market roads and access to markets can play postve role n enhancng dversfcaton among farmers. The extenson system should not stck only to ncreasng productvty or proftablty; rsk reducton should be part and parcel of an extenson system. Negatve relaton between dversfcaton and extenson contact urges that producton under rsk should be made part of farmers tranng and extenson programs brought n to mprove the effcency of ndvdual farms. Dversfcaton of farmng s very useful nvestment to mtgate rsk. Incluson of lvestock enterprses mght mnmze the rsks n the farmng. In addton, t mght contrbute to effcent use of labor. Hence, future research n the areas should ntegrate lvestock n the model. Even though crop dversfcaton s thought to be domnant rsk management strategy, further study regardng farmers percepton about t, ts costs and benefts need to be explctly studed. 5. References Alson, K. B. (2010), Crop Dversfcaton and Technology Adopton: The Role of Market Isolaton n Ethopa, M Sc Thess, Montana State Unversty, Bozeman, Montana. Aynde, O. E., Omotesho, O. A., & Adewum M.O. (2008), Rsk atttudes and management strateges of small-scale crop producers n Kwara State, Ngera: A rankng approach, Afrcan journal of busness Management 2(12): P a g e

10 Belaneh, L. (2003), Rsk Management Strateges of Smallholder Farmers n the Eastern Hghlands of Ethopa, Doctoral Thess, Swedsh Unversty of Agrcultural Scences, Department of Rural Development Studes, Uppsala. Benn, S. M., Smale, Gebremedhn, B., Pender, J. & Ehu, S. (2004), The determnants of cereal crop dversty on farms n the Ethopan Hghlands, Contrbuted paper for the 25 th Internatonal Conference of Agrcultural Economsts, Durban, South Afrca. Berhanu G. & Mot J. (2010), Market Orentaton, Dversfcaton and Market Partcpaton of Smallholders: Evdence from Ethopa, World Food System - A Contrbuton from Europe, Tropentag, Zurch. Internatonal Lvestock Research Insttute (ILRI), Improvng Productvty and Market Success (IPMS) Project, Ethopa. Bezabh E. & Hadera G. (2007), Constrants and Opportuntes of Hortculture Producton and Marketng n Eastern Ethopa. Drylands Coordnaton Group Report No. 46 Brglauer, W. (2000), Motves for Frm Dversfcaton: A survey on Theory and Emprcal Evdence, WIFO, Workng Papers, Vol. 26, pp Dercon, (2002), Income rsk, copng strateges and safety nets, The World Bank Research Observer, 17(2) Feten A., Asmund B. & Melnda S. (2009), Measurng on Farm Dversty and Determnants of Barely Dversty n Tgray: Northern Ethopa. Grma, T. (2002), Management of Mosture Stress Rsk n Crop Producton n North Eastern Ethopa: The Case of Kalu Dstrct, M Sc Thess, School of Graduate Studes, Alemaya Unversty, Ethopa. Greene, W. (2000), Econometrc Analyss, 4 th edton, NJ. Prentce-Hall Inc. Upper Saddle Rver, NJ. Jema, H. (2008), Economc Effcency and Marketng Performance of Vegetable Producton n the Eastern and Central Parts of Ethopa, Doctoral Thess, Swedsh Unversty of Agrcultural Scences, Faculty of Natural Resources and Agrcultural Scences, Department of Economcs, Uppsala. 42p. Lonne, R., Vandeveer, Kenneth W. Paxton, & Davd, R. L. (1989), Irrgaton and potental dversfcaton benefts n humd clmates, Southern Journal of Agrcultural Economcs, Mshra, A. & El-Osta H. (2002), Rsk Management through Enterprse Dversfcaton. A farm level Analyss, Paper presented at AAEA meetngs n Long Beach, CA, U.S.A. Muhammed, A., Hassan, S., Nasser M. Z., Irfan A. B. & Asma, J. (2008), Factors Affectng Farm Dversfcaton n Rce-Wheat, Department of Agrcultural Economcs, Unversty of Agrculture, Fasalabad. Rchard, F. (1998), Rsk Dversfcaton Opportuntes through Legumes n smallholder farmng systems n the sem ard areas of Zmbabwe. Tefera, T. L., Krsten J. F. & Perret, S. (2003), Market Incentves, Farmers Response and a Polcy Dlemma: A Case Study of Chat Producton n the Eastern Ethopan hghlands, Agrekon, 42: 3 Wess, C. R. & Brglauer, W. (2000), Determnants and Dynamcs of Farm Dversfcaton, Workng paper EWP Department of Food Economcs and Consumpton Studes, Unversty of Kel. Notes Note 1. Kola (lowland) agro-clmatc zone ( m.a.s.l.) s characterzed by annual mean ranfall of mm and temperature of C. Wonadega (md-hghland) agro-ecologcal zone ( m.a.s.l.) s characterzed by an average annual ran fall of mm and mean temperature of C ( Hurn 1995 cted n Belaneh 2003). Note 2. Chat or khat (Catha eduls) s cultvated as cash crop for ts young leaves and tender stems chewed as a mld natural stmulant, whch lke coffee or alcoholc beverages, plays an mportant role n the socal lfe of people n Ethopa n general and n Hararghe and Somal regon n partcular ( Klngele 1998). 87 P a g e

11 Note 3. Ethopa s a federal state of regons. Every regon s admnstratvely structured nto zones and zones are agan dvded nto weredas whch are called dstrcts. Every wereda s dvded nto Peasants Assocaton, whch s the lowest admnstratve unt. The peasant assocatons are called Kebeles n urban areas. Table 1: Summary statstcs of the sample households (categorcal varables) (%) Varables Haramaya Kombolcha Total Sex of household head Male Female Martal Status of household head Sngle Marred Educatonal level of household head Illterate Elementary/Prmary Hgh School and above Table 2: Summary statstcs of the sample households (contnuous varables) Varables Haramaya Kombolcha Total Mean SD Mean SD Mean SD Age of head (Years) Farmng Experence (Years) Famly Sze (Persons) Famly Sze (Adult Equvalent) Farm Sze (hectares) Dstance to market (Km) Dstance to farm (Km) Lvestock Owned (TLU) Farm Income (Brr) , , ,076.5 Non/Off farm Income (Brr) Table 3: Proporton of area allocaton and dversfcaton ndex for the producton perod Crops Maze Sorghum Potato Onon Beet root Cabbage Carrot Dversfcaton Index P a g e

12 Table 4: Pattern of dversfcaton n the study area Partculars Mean Stand. Dev. Maxmum Mnmum Number of farm plots Number of enterprses Proporton of Farmers (%) Intercroppng Crop rotaton Yes No Table 5: Tobt regresson results, determnants of farm dversfcaton Varables Coeffcents Std. Error ME(Probablty) ME(Intensty) Dstrct * Access to Market Informaton * Farm sze (hectares) Irrgaton Intensty ** Machnery Ownershp ** Sex of household head Market dstance to Dstrct (Km) Age of household head (Years) Number of Extenson Contacts ** Dstance from farm to home (Km) Number of Farm plots *** Lvestock holdng (TLU) ** Educaton level of head (Year) Household sze (Adult Equvalent) Farmng Experence (Years) Constant *** LR (16) = Log lkelhood = 72.20*** Number of observatons = 167 * Statstcally sgnfcant at 10% level; ** statstcally sgnfcant at 5% level; *** statstcally sgnfcant at 1% level respectvely. ME = margnal effects 89 P a g e

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