Assessing factors influencing farmers adoption of improved soybean varieties in Malawi Eliya Gideon Kapalasa

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1 Scholarly Journal of Agrcultural Scence Vol. 4(6), pp June, 2014 Avalable onlne at ISSN Scholarly-Journals Full Length Research Paper Assessng factors nfluencng farmers adopton of mproved soybean varetes n Malaw Elya Gdeon Kapalasa Department of Economcs Chancellor College Unversty of Malaw, Malaw. Emal:elyakapalasa@yahoo.co.uk. Accepted 14 June, 2014 Cultvaton of mproved varetes s one way of ncreasng productvty of many crops especally n developng countres where there s pressure of land due to hgh populaton growth. Adopton studes have proved to be helpful n gvng the pcture of the performance of technologes amongst users lke farmers and n lne wth ths the study was carred out to assess the factors that nfluence a farmer to adopt mproved soybean varetes usng cross-sectonal data that was collected from 300 households by the Department of Agrcultural Research Servces (DARS) n The study used a Hurdle Posson model n order to effectvely assess the soco-economc and demographc characterstcs that nfluence farmers to adopt mproved soybean varetes. The results of the study show that amongst the household soco-economc characterstcs that were ncluded n the model, age of the household head and farm sze were sgnfcant at 5 percent level of sgnfcance whlst access to nformaton through extenson agents and dstance to the market were the nsttutonal factors that sgnfcantly nfluence a farmer. The results further show that the varety characterstcs that were sgnfcant n nfluencng adopton of mproved soybean varetes ncluded hgh yeld, early maturty and taste. Results of the decson on how many soybean varetes ndvdual farmer decdes to grow shows that age of the household head, access to nformaton and varetes that are hgh yeldng had a sgnfcant nfluence. The study recommends that there s a need to conduct adopton studes regularly n order to have a clear pcture of the performance of many varetes developed by research nsttutons as they provde feedback to a number of players n the breedng program. Another polcy recommendaton s that there s a need to strengthen the extenson servces as they have proved to be the relable source of nformaton n the rural areas. Key words: Cultvaton, populaton growth, soybean varetes. INTRODUCTION The potental of soybean stems from the fact that crop s manly grown by smallholder farmers who accounts for over 80 percent of the natonal producton of food crops n Malaw. It s wdely grown by smallholder farmers n Malaw because of ts multple uses that t has on the farm enterprse for nstance, t s a source of cheap proten at household level at the same tme t s used as feed for lvestock, and at farm level the crop has the ablty to fx ntrogen n the sols (Lungu, 1998). The other reason why soybean s currently beng promoted by government s because of the ever ncreasng demand for the crop both on the domestc and world markets that has resulted n the rsng of the world market prces. In response to all these benefts, government s puttng more resources towards promotng such crops that have the ablty to rase ncomes of smallholder farmers by reorentng farmers to be cultvatng crops wth hgher gross margns and more so move away from subsstence to commercal agrculture. Varous studes on the producton of soybean n Malaw reveals that productvty of the crop has been very low compared to that of other crops. For nstance Lungu (1998) reported that most farmers n

2 Scholarly J. Agrc. Sc. 340 Malaw only manage to get 25 percent of the potental yeld of the crop and that the average yeld has stagnated at 600 kg/ha aganst the world average of above 2000 kg/ha over a perod of 5 years. Lack of hgh yeldng cultvars, poor farmng practces, and use of poor qualty seeds were some of the major reasons reported. Smlar fndngs were also reported by Estrada n 2004 where n addton to these also mentoned that low productvty was as result of the tendency of many farmers recyclng ther seed from prevous years that keeps on reducng the yeldng potental of the crop, a reason attrbuted to unavalablty of soybean seeds n many markets where mproved seeds for other crops are sold. In spte of the number of modern soybean varetes avalable n the country the use of these varetes by smallholder farmers s very low a reason Estrada (2004) ndcated to have contrbuted to the low productvty of the crop. In order to understand the reason that contrbutes to low usage of modern varetes by farmers, Doss (2006) emphaszed on the mportance of conductng adopton studes so that results obtaned should be channelled to the respectve stakeholders n the seed systems. Several adopton studes have been conducted n Malaw and other countres on dfferent crops to establsh the reasons that nfluence a farmer to adopt modern farmng technologes. For nstance Chrwa (2005) studed adopton of fertlser and maze n Malaw, Kormawa, Ezednma and Sngh (2004) studed adopton of mproved cowpea varetes n Ngera, Paudel and Matsuoka (2008) studed adopton of mproved maze varetes n Nepal and Namwata, Lwelamra and Mzra (2010) studed adopton of rsh potatoes n Tanzana found out that farmers are more lkely to adopt and use technologes that are user frendly and more so that are compatble wth ther exstng envronment but above all technologes that meet expectatons and characterstcs of the farmers. Ths paper therefore seeks to under the underlyng factors that nfluence smallholder farmers to adopt mproved soybean varetes n Malaw. Theoretcal framework Adopton model has been studed by makng use of varous theores coned by a number of scholars. For nstance Rodgers (1983) came up wth the popular nnovaton decson model that shows the process through whch an ndvdual (or other decson makng unt) passes from frst knowledge of an nnovaton to formng an atttude towards the nnovaton, to a decson to adopt or reject, to mplement of the new dea, and to confrmaton of ths decson. Dffuson s the process by whch an nnovaton s communcated through certan channels overtme among the members of a socal system (Rogers, 1983). When new deas are nvented, they are dffused and adopted or rejected. We use the concept of dffuson n our study n terms of understandng how many farmers know and use the technology. The second model s the economc constrant outlned by Adesna and Znnah (1993) that contends that economc constrants reflected n asymmetrcal dstrbuton patterns of resource endowments are the major determnants of observed adopton behavor. Lack of access to captal or land could sgnfcantly constran adopton decsons. Whle attempts have been made to assert the superorty of the economc constrant model over the nnovaton-dffuson model such conclusons have been challenged (Adesna and Znnah, 1993). The thrd one s the adopter percepton model by Kvln and Flegel (1966) that suggests that the perceved attrbutes of nnovatons condton adopton behavor. The lmted quanttatve studes that have consdered farmers perceptons n the context of adopton decsons have ncluded a percepton varable - measurng farmers percepton of a problem (e.g. sol eroson) - n ther models. However, by beng concerned prmarly wth only the farmers perceptons regardng the severty of the problem to be solved, the studes mplctly take the techncal nnovatons (desgned to solve the problem) as approprate for farmers (Adesna and Znnah, 1993). In the context of soybean some of the perceved attrbutes nclude; taste of the crop, yeld, cookng tme that the varety takes, gran sze and colour. Ths study adopted the approach of combnng adopter percepton model and the economc constrant to look at how these nfluences adopton of mproved soybean varetes n Malaw. The adopton decson can be modeled as a dchotomous choce of whether to adopt a new technology or not to adopt. Snce ths varable can take on only two values: 1 and 0 (adopt or not adopt), a bnary choce model s used to analyze ths adopton decson. Assumptons underlyng bnary choce models are that: (1) the economc agent s faced wth a choce between two alternatves e.g. to adopt or not adopt; and (2) the choce the agent makes wll depend on hs/her attrbutes or characterstcs (Pattanayak et al., 2 003). The conceptual framework s then to buld a model that wll predct the adopton decson of an economc agent wth gven attrbutes. The utlty maxmzaton framework can be used to motvate ths bnary choce model. A household s adopton choce s based on whether the expected net utlty derved from adoptng the new technology s greater than from not adoptng. For a new crop speces, a household chooses between whether or not to plant the new crop n order to maxmze ther utlzaton of the land. Adopton s treated as an nvestment choce, where the farm household s seekng to maxmze agrcultural proft n relaton to a chosen set of nputs and outputs. The decson whether to adopt or not s based on whether the new technology wll brng more utlty to the farm household than the current

3 Kapalasa 341 technology. METHODOLOGY Defnton of adopton and conceptualzaton Agrcultural research focuses on developng new technologes to mprove agrcultural productvty and farmers well-beng. The rapd adopton of new agrcultural technologes n developed and some developng countres has ncreased agrcultural productvty, contrbuted to overall economc growth, and reduced food nsecurty and poverty (Bandera and Rasul, 2005; Cornejo and McBrdgje, 2002). The defnton and conceptualzaton of agrcultural technology adopton vares among experts. In ther study of adopton of agrcultural technology n developng countres, Feder, Just and Zlberman (1985) conceptualze adopton of agrcultural technologes at two dfferent levels: aggregate and ndvdual (farm -level) adopton. They defne aggregate technology adopton and dffuson as the process of the spread of a new technology wthn a regon. Aggregate adopton s measured at the populaton level, rather than at the ndvdual level. In contrast, the authors defne ndvdual adopton as the degree of use of a new technology n long-run equlbrum, when the farmer has full nformaton about the new technology and t s potental. Several studes (Adesna and Znnah, 1993; Adesna and Forson, 1995; Chrwa, 2005; Doss, 2006) carred on adopton usually start by defnng adopter and proceed by outlnng some of the lkely factors that affect adopton. However t s worth notng that the defnton of adopter vares across studes. For nstance Doss (2006) reported that the defnton of adopter vares wdely even across the 22 studes that the Internatonal Centre for Wheat and Maze Improvement (CIMMYT) conducted n East Afrca examnng the adopton of mproved varetes of wheat and maze and fertlzer. In defnng adopton the frst thng s to consder whether adopton s a dscrete state wth bnary varables or whether adopton s a contnuous measure. Many studes use a smple dchotomous varable approach. There s a dstncton that s made between dscrete and contnuous technology adopters among typcal farmers who use ether unmproved or mproved nputs. A farmer s classfed as an adopter f he/she s found to be cultvatng mproved varetes or usng modern technologes. Wth respect to the adopton of mproved varetes, dscrete adopton refers to a farmer who stops usng a local (tradtonal) varety and adopts an mproved varety. In contrast, contnuous adopton refers to stuatons where farmers ncreasngly plantng more land to mproved varetes, whle contnung to grow some local varetes. Thus a farmer may be classfed as an adopter and stll grow some local varetes (Doss, 2003). Furthermore, Doss (2006) emphaszes that defnng agrcultural technologcal adopton s complex. Studes carred out by CIMMYT have used several dfferent adopton defntons to dstngush between, for example, varetes that were orgnally ntroduced as mproved hybrds, but have been repeatedly recycled (e.g., farmers plant seed from a prevous harvest) versus plantng new certfed seeds. The author also argues that t s necessary to dstngush between farmers who contnue to use a newly adopted technology from those who dscontnue usng t. The rate of adopton s defned as the percentage of farmers who have adopted a gven technology. On the other hand, the ntensty of adopton s defned as the level of adopton of a gven technologcal package. Put t n a dfferent way, the number of hectares planted wth mproved seed also tested as (the percentage of each farm planted to mproved seed) or the amount of nput appled per hectare represent the ntensty of adopton of the respectve technologes (Nkonya, Schroeder a nd Norman, 1997). Ths study adopted the approach of estmatng adopton as a dchotomous varable where a farmer s classfed as adopter or non-adopter. Because of several varetes of soybean that are currently grown by smallholder farmers n Malaw, there are a number of varetes that have been categorzed as mproved and as such farmers are classfed as beng adopters f they ndcate to have grown any of the mproved varetes and otherwse classfed as non-adopters. Specfcally f a farmer reported to have grown any of the followng (Nasoko, Makwacha, Ocepara-4 and Magoye) soybean varetes s consdered to be an adopter and non-adopter otherwse. Emprcal model specfcaton Farmers decson to adopt or not to adopt a technology s assumed to be the outcome of a complex set of factors related to the farmers objectves and constrants. In other words, there are certan factors ncludng market forces, socal, nsttutonal, and management factors that affect the lkelhood that farmers adopt a technology. Thus f each farmer and each technology can be classfed based on a core set of varables, then t s possble that the probablty of a farmer adoptng that technology could be estmated. As earler ndcated n the theoretcal model, the study consder bnary dependent varable, Y to model adopton where t takes the value of 1 f the farmer was found to be growng any of the mproved varetes of soybean and 0 f otherwse. It further assumes that the probablty to adopt mproved soybean varety s nfluenced by a set of demographc characterstcs, economc and nsttutonal factors. For the second hurdle (truncated model), mproved varety adopton becomes contnuous and the dependent varable s the number of mproved soybean varetes grown by a household.

4 Scholarly J. Agrc. Sc. 342 The household characterstcs deemed to nfluence mproved soybean adopton n ths study nclude household heads characterstcs (age, gender and educaton) and household sze. The conventonal approach to adopton study consders age to be negatvely related to adopton based on the assumpton that wth age farmers become more conservatve and less amenable to change. On the other hand, t s also argued that wth age farmers gan more experence and acquantance wth new technologes and hence are expected to have hgher ablty to use new technologes more effcently. Educaton normally s expected to have postve relatonshp wth adopton as t beleved to enhance the allocatve ablty of decson makers (farmers) by enablng them to thnk crtcally and use nformaton sources effectvely. However, just as Doss et al. (2003) reported, educaton n ths study s not expected to have strong effects on adopton because soybean s not a new technology. Insttutonal and economc factors consdered mportant n ths study nclude access to extenson that has been proxed by number of vsts by extenson agents reported by a household durng the study perod, membershp to any farmer based clubs or assocatons and farm sze owned by the household. The sze of landholdng s expected to be postvely correlated wth adopton of soybean, as farmers wth bgger farms are assumed to have the ablty to purchase mproved soybean seeds and the capacty to bear rsk f the technology fals and have adequate land for the dfferent varetes (Feder et al., 1985). Exposure to nformaton reduces subjectve uncertanty and, therefore, ncreases lkelhood of adopton of new technologes (Doss, 2003). Extenson and club membershp are thus expected to have a postve correlaton wth adopton. Dstance to the market s expected to have a negatve correlaton wth adopton as longer dstances reduces the lkelhood of adopton because of the transportaton costs that farmers wll have to ncur n gong to purchase nputs. To capture the nfluence of agro ecologcal factors n the two dstrcts on adopton, we nclude a dummy for dstrct. Llongwe s used as a base due to the fact that between the two dstrcts, Llongwe s relatvely deal for most of the mproved varetes manly because most of the trals are conducted at chtedze where most of the research actvtes are conducted. Agro ecology varables pck up varaton n ranfall, sol qualty and producton potental. These varables may also pck up varaton unrelated to agrcultural potental, such as nfrastructure and avalablty of markets for nputs (Feder et al., 1985). Technology specfc attrbutes were captured usng dummes because of the lmtaton n the way data was collected. Farmers were asked to ndcate whether the character was an mportant attrbute or not compared to the tradtonal varetes. The study managed to pck and use three top characterstcs ndcated by household whch are maturty, taste and yeld. The Double Hurdle Model Ths study extends the conventonal adopton decson modelng of lookng at the factors nfluencng adopton decson by lookng further at the factors that nfluence the number of varetes that a farmer grows. Ths modelng requres the use of count models of whch the most commonly used are the Posson, Negatve Bnomal and Hurdle Posson. As Cameron and Trved (1998) noted Posson model s the smplest and perhaps the most common method for count varables and t s the model that s derved from the Posson dstrbuton by parameterzng the relaton between the mean parameter and covarates ( repressors). One of the assumptons of Posson models s the equdsperson whch mples equalty of mean and varance and once ths assumpton s volated t results n over dsperson or under dsperson whch s usually common wth zero-nflated data (Cameron and Trved, 1998). The authors further hghlghted that ths assumpton s smlar to homoscedastcty under the ordnary least square and as such statstcal test for over dsperson s hghly requred after runnng a Posson model. Presence of over dsperson results n mpacts such as nvald conclusons, naccurate t-statstcs and naccurate standard errors (Cameron and Trved, 1998). One frequent manfestaton of over dsperson s that the ncdence of zero counts s greater than expected for the Posson dstrbuton and ths s of nterest because zero counts frequently have specal status. An alternatve model that can address the problems assocated wth standard Posson models s the Negatve Bnomal model (NB2). However, although ths model takes care of the problem of over dsperson, t has another weakness n that t does so wthout knowledge of the possble reason for over dsperson and also t s not deal for data that has a larger number of zeros (Cameron and Trved, 1998). To address the shortfalls of the two models, Hurdle models are usually used. Hurdle models are based on the assumpton that zero counts are generated from a dfferent process than are the postve counts n a gven data stuaton. Ths study used Hurdle nstead of the standard Posson or the Negatve Bnomal because the count data has a lot of zero (about 27 percent). In order to captu re the sequental bnary choce decson, a hurdle model or two part models s appled. A hurdle model has the nterpretaton that t reflects a two-stage decson-makng process. Orgnally formulated by Cragg (1971), the double-hurdle model assumes that households make two sequental decsons wth regard to adoptng and ntensty of use of a technology. Each hurdle s condtoned by the household s soco-economc characterstcs. In the double-hurdle model, a dfferent latent varable s used to model each decson process. The decsons to adopt mproved soybean varety and subsequently plant a number of mproved varetes over tme are examned usng a hurdle count model (Cameron

5 Kapalasa 343 and Trved, 2005). Hurdle models are typcally appled to attend to problems arsng from sample selecton bas and the dscrete, non-negatve nature of the outcome (.e., the number of mproved soybean varetes a farmer reported to have planted). In the study, a producer must have grown soybean as a crop to answer the queston: how many mproved varetes dd the farmer planted durng the prevous growng season? Thus, the frst stage of the model (the hurdle ) explans the decson to adopt mproved soybean varety usng a logt regresson that models the adopton decson (1 = yes, 0 = no) to use mproved soybean varetes. Gven the decson to adopt mproved soybean varety (a bnary outcome), the number of varetes grown (a postve, dscrete varable; k = 1, 2,,K) s subsequently modeled usng a Posson regresson. The hurdle model s wdely used, and the hurdle negatve bnomal model s qute flexble. Farmers usually make ratonal decsons when t comes to adopton of any partcular technology. Snce the objectve of the farmer s to maxmze expected (dscounted) profts over tme horzon subject to nput and commodty prces and technology constrant, farmers wll usually wegh the benefts assocated wth a partcular technology before they decde to adopt. Ratonally a farmer wll adopt a technology f the expected (dscounted) utlty of profts of usng that technology s greater than utlty from the old technology (Adesna and Forson, 1995). The Posson model s the smplest and perhaps the most common method for modelng counts varables (Cameron and Trved 1999). The Posson probablty dstrbuton s gven as y exp() Pr Y y, y 0,1,2..., (1) y! where Y s the number of soybean varetes grown by the household and μ s mean parameter. The Posson regresson model s derved from the Posson dstrbuton by parameterzng the relaton between the mean parameter μ and covarates (regressors) x. The standard specfcaton for the mean parameter s exp(x), where a vector of the unknown parameters s. In applcatons, however, the Posson model s usually restrctve. In partcular, t mposes the restrcton that the mean and varance are equal, but for most observed count data, the varance usually exceeds the mean, a feature called over dsperson. Ths makes the Posson model defcent. A common alternatve to the Posson model n case of over dsperson s the negatve bnomal model whch s gven as f ( y,) () y ()() ( y 1)(), 0 y 1,2,3... y, (2) where the functon (.) s the s the gamma functon. However, both the Posson and negatve bnomal models are not sutable for data wth excess of zero (Cameron and Trved, 1998). Ths s the case wth data used n ths study. The 300 households used n ths study were soybean farmers who were growng soybean and they nclude those growng tradtonal varetes and those growng mproved. In ths case, the zeros are comng from households that grew soybean but dd not grow mproved varetes due to other factors lke preference of tradtonal varetes or lack of access to seed of mproved varetes. From the study about twenty seven percent of the sample households dd not grow mproved soybean varetes durng the study perod. To handle data sets that contan excess zero, two part models have been used, wth the hurdle Posson and Zero Inflated Posson (ZIP) models beng the common ones. Each of these two models conssts of an equaton for partcpaton and a model for the event count that s condtoned on the outcome of the frst decson (Cameron and Trved, 2005). The hurdle Posson model combnes a bnary model (partcpaton part) to predct zeros and a zero-truncated Posson model (count part) to predct non -zero counts (Cameron and Trved, 1998). In ths way, the hurdle Posson relaxes the mplct assumpton n the Posson and the negatve bnomal models that the zeros and the postves come from the same data generatng process. The advantages of usng a hurdle Posson are two-fold; frstly, the hurdle Posson model s sutable for takng nto account the over dsperson or under dsperson of the data (Cameron and Trved, 1998). Secondly, hurdle Posson model controls for data selecton. The startng pont of the hurdle Posson model s a bnary process, whch determnes whether the varable takes on the value zero or a postve value (Cragg, 1971). The probablty mass functon s gven as; Pr() Y y, y0 1 y1,2,3,... The zero-truncated Posson process has probablty mass functon; y, y1,2,3,... ( e 1) y! (4) Pr( Y y Y 0) 0, 0therwse Ths gves the followng uncondtonal mass functon for Y;, y0 (5) Pr() Y y y (1), y1,2,3,... ( e 1) y! (3)

6 Scholarly J. Agrc. Sc. 344 The log lkelhood for the t th observaton, assumng the observatons are ndependently and dentcally dstrbuted, s ln L(,, y) ln(), y0 y ln{(1) }, y1,2,3,... ( e 1) y! Therefore, the log lkelhood functon of the hurdle Posson can be vewed as the sum of the log lkelhoods from two separate models: a bnomal probablty model and a truncated-at-zero Posson model. As such, the hurdle model log lkelhood can always be maxmzed, wthout loss of nformaton, by maxmzng the two components separately. Ths feature allows estmaton of the hurdle Posson model n two separate steps. In the frst step, bnomal probablty model s estmated followed by truncated Posson model (McDowell, 2003). Zero-nflated Posson model provdes another way to model excess zeros. In ZIP regresson, the counts equal 0 wth probablty (6) Y and follow a Posson dstrbuton wth mean (x,) probablty1 zero-nflated Posson s gven as Pr() Y y wth. The probablty mass functon for the (1)exp(), y0 y exp() (1) y 1,2,3,... y! The probablty s parameterzed as a logstc functon of the observable vector of covarates z, thereby ensurng nonnegatvty of, that s exp() z 1 exp() z where z s a vector of covarates whle s a vector of coeffcents. Let 1( y 0) y (7) denote an ndcator varable, and zero otherwse. The log- that takes value 1 f 0 lkelhood functon for the double hurdle model s: Emprcal results by both Cragg (1971) and Moffat (2003) reveal that the double-hurdle model gves superor results to those obtaned from Tobt model. Thus n ths study we estmated the decson to adopt mproved soybean varety and the ntensty of adopton (number of mproved varetes grown) usng a double-hurdle model. After adopton, the producer decdes how many mproved soybean varetes to grow n a partcular season. Because the choce set s observed as number of varetes (a dscrete, countable decson), the decson s approprately modeled usng a count regresson model such as the Posson or negatve bnomal models (Cameron and Trved, 2005). The model was estmated usng full nformaton maxmum lkelhood estmaton (FIML). Maxmum lkelhood estmaton of the hurdle model nvolves separate maxmzaton of the two terms n the lkelhood, one correspondng to the zeros and the other to the postves. Ths s straghtforward (Cameron and Trved, 2005).The statstcal software program STATA 11 was used to run the model wth the help of the STATA user wrtten program hplogt. A heteroskedastc robust covarance matrx was estmated usng the survey weghts (Wooldr dge, 2004), whch was subsequently used to make nferences about the covarates explanng adopton of mproved soybean varetes. RESULTS AND DISCUSSION Table 1 presents coeffcents and standard errors (n parenthess) results from the Hurdle Posson model of mproved soybean adopton. The results of the model are presented n two ways, frstly the partcpaton part where the farmer decdes to adopt mproved soybean varety or not and then the second part of the model where a decson s made on how many of the mproved varetes to grow. The results of the model show that amongst the household soco-economc characterstcs that were ncluded n the model, age of the household head and farm sze were sgnfcant at 5 percent level of sgnfcance showng that they have an nfluence on the farmers decson to adopt mproved soybean varetes whlst educaton level and sex, although postvely related wth adopton do not sgnfcantly nfluence the decson to adopt. Further to ths t s also clear from the results that amongst the nsttutonal factors that were hypothessed to nfluence adopton, extenson, market dstance and the dstrct where the farmer resdes are sgnfcant factors that nfluence a farmer to adopt soybean varetes just lke the technology specfc varables such as taste, yeld and earlness to maturty. However, the second decson on how many varetes to adopt s nfluenced by age of the household head, access to nformaton that was proxed by the number of extenson vsts and also by the yeldng potental of the varety. Table 2 presents the odds ratos and standard errors (n parenthess) and Incdence rate ratos (IRR) and standard errors (n parenthess) results of the hurdle Posson model. In case of land, the results show that a unt ncrease n land sze owned by a farmer ncreases the odds of adoptng mproved soybean varety by 1.8

7 Kapalasa 345 Table 1: Hurdle Posson regresson results of the factors nfluencng adopton of mproved soybean varetes. Varable Logt (Decdng to Adopt or not) Posson (Number of varetes grown Coeffcent (Robust std errors) Coeffcent (Robust std errors) Age ** (0.090) (0.089) Age ** (< 0.001) (0.001) Educaton (0.056) (0.058) Sex (0.545) (0.497) Household sze (0.104) (0.085) Wealth (< 0.001) (0.010) Land 0.603** (0.211) (0.125) Extenson 0.006** 0.004** (0.003) (0.002) Club (0.407) (0.427) Market ** (0.070) (0.056) Dstrct 1.178** (0.433) (0.379) Yeld 1.029** 1.137** (0.392) (0.510) Taste 1.889** (0.711) (0.488) Maturty 1.370** (0.456) (0.371) Constant Source: Computed from study data Note: ** (p-value < 0.05) (2.107) (2.532) whlst a unt ncrease n the number of vsts by extenson agents ncreases the odds of farmers adoptng mproved soybean varety by 1. As expected, dstance to market center has also a negatve and sgnfcant relatonshp (at 5 percent level of sgnfcance) wth probablty of adopton of mproved soybean varetes. The odds-rato of 0.9 for market dstance mples that other thngs beng kept constant, the odds-rato n favor of adoptng mproved soybean varetes decreases by a factor of 0.9 as the market dstance ncrease by one klometer. The results further show that agro-ecologcal dfferences have an nfluence on the decson to adopt and also on the number of varetes to grow. For nstance farmers who are located n Llongwe ncreases the odds of adoptng by

8 Scholarly J. Agrc. Sc. 346 Table 2: Odds rato and Incdent Rate Rato of the factors nfluencng adopton of mproved soybean varetes. Varable Logt (Decdng to Adopt or not) Posson (Number of varetes grown Odds rato (Robust std errors) IRR (Robust std errors) Age ** (0.092) (0.089) age ** (0.001) (0.001) Educaton (0.059) (0.058) Sex (0.831) (0.497) Household sze (0.087) (0.085) Wealth (0.001) (< 0.001) Land 1.827** (0.385) (0.125) Extenson 1.006** 0.004** (0.003) (0.002) Club (0.418) (0.427) Market ** (0.060) (0.056) Dstrct 3.248** (1.405) (0.379) Yeld 2.797** 1.137** (1.097) (0.510) Taste 6.616** (4.701) (0.488) Maturty 3.935** (1.793) (0.371) Source: Computed from study data Note: ** (p-value < 0.05) a probablty of about 3. In terms of varety specfc attrbutes, the results show that varetes that are hgher yeldng mature earler and also have a pleasant taste have hgher odds (3, 7 and 4 respectvely) of beng adopted. Detaled results and nterpretaton of the double hurdle Posson model for the 8 sgnfcant varables on adopton are presented n the followng paragraph. The postve sgn on the coeffcent of age show that there s a postve correlaton between age and adopton. However, age appears to have a sgnfcant mpact on the second decson level especally when a farmer s decdng how many of the mproved varetes to grow,.e. showng that age does not matter at the extensve margn but rather at the ntensve margn. In other words ths means that age mnmal nfluence on the decson of whether to grow mproved soybean varetes but rather have more nfluence on the number of mproved to be grown. The mplcaton of ths s that as farmers are agng they tend to gather experence wth the crop and as such realzes the benefts assocated wth t hence they are able to grow a number of varetes. Smlar results were reported by Cornejo and McBrdge (2002) where t was argued that experence wth technology s one of the crtcal factors that determnes the number of technology that a farmer wll adopt and ths s usually captured by age of the farmer n stuatons where nformaton on the perod snce the farmer started cultvatng the crop s not known. The odds-rato n favor of adoptng mproved soybean varetes, other factors kept constant ncreases by a factor of 1.8 as land ncreases by one unt. Ths mples that a farmer who has more land wll be more lkely to adopt mproved soybean varetes. The mplcaton for ths s that farmers wth more land should not be gnored when promotng cultvaton of mproved varetes as they are the ones that are lkely to adopt. Several studes have

9 Kapalasa 347 reported smlar fndngs for nstance Adesna and Znna (1993) argued that farmers wth larger farm sze are more lkely to adopt agrcultural technologes because they are able to bear the rsk assocated wth tryng new thngs because of land area where they can dversfy by growng a number of crops, Doss et al. (2003) reported that households wth larger farm sze adopts mproved varetes because they usually have better access to credt and nformaton that have been wdely documented to nfluence adopton. The postve coeffcent on extenson ndcates that there s postve and sgnfcant (at 5 percent) relatonshp between adopton of mproved soybean varetes and access to nformaton that has been proxed by the number of contacts wth extenson agents. Ths mples that soybean farmers wth access to nformaton through contacts wth extenson workers are the ones who are more lkely to adopt mproved varetes. Access to nformaton s sgnfcant both at the frst (decdng to adopt mproved soybean varetes or not) and second (decdng how many varetes to grow) decson level ndcatng that nformaton plays a crucal role n decson makng by farmers just as reported by Feder et al. (1985) where t was argued that farmers who have access to nformaton about a partcular technology are more lke to adopt. The mplcaton of ths fndng s that extenson should really be ntensfed to promote the adopton of mproved soybean varetes. Smlar fndngs were also reported by a number of scholars lke Chrwa (2005) argued that farmers wth access to nformaton through extenson servces adopt modern technologes faster because they are well nformed about the advantages assocated wth modern technologes as such they make nformed decsons based on the nformaton gven. Kalba et al. (2000) reported that extenson vsts have a postve nfluence on adopton because farmers are exposed to new technologes and n the process get convnced to adopt them. The results have shown that there s a negatve but sgnfcant relatonshp between adopton and the dstance to the nearest market. Ths mples that as the dstance to the nearest market ncreases/reduces the probablty of farmers adoptng mproved varetes s reduced/ncreased hence showng that farmers who are close to the markets are more lkely to adopt soybean varetes. The mplcaton for ths s that there s a need to open up more markets n the rural areas so as to acheve hgher results of adopton. The results concur wth what was found by Kabul (2005); Namwata et al. (2010); Paudel and Matsuoka (2008). Kabul (2005) argued that farmers closer to markets adopt mproved technologes because they do not have to travel long dstance wth ther produce to sell hence ncur no costs on transport unlke those that are far and have bulky and a lot of harvest. In the case of soybean, beng partly a cash crop entals that farmers have to thnk of the markets where they wll sell ther crop once t matures and as such there s hgher lkelhood that farmers closer to the market wll adopt mproved varetes because they have access to market. Namwata et al. (2010) argued that farmers closer to markets have hgher probablty of adoptng mproved varetes because they have access to nformaton about the avalablty of market and also prces prevalng on the market and ths nform the farmers when decdng what to grow for the next growng season. Paudel and Matsuoka (2008) reported that dstance to the market s an mportant determnant of adopton for farmers producng bulky commodtes because of transportaton and nfrastructure challenges. Poor nfrastructure lke roads rases transportaton costs and as such farmers closer to markets are more lkely to adopt mproved technologes of bulky crops lke soybeans. Locaton where a farmer lves was used to capture the agro-ecologcal dfferences exstng between the two dstrcts where the study was conducted. Results have shown that farmers located n Llongwe are more lkely to adopt mproved soybean varetes than those n Dowa. The nterpretaton of ths s that most of the exstng varetes of soybean are short duraton varetes and as such they are very conducve for Llongwe that receves relatvely less ranfall compared to Dowa that enjoys longer ran duratons. Another explanaton for the result s that all the varetes were developed and released at Chtedze Research Staton that s located n Llongwe as such they are more sutable for Llongwe condtons than Dowa. The results then demonstrate the need for havng more work done on research so that other varates are developed that wll be deal under dfferent agroecologcal zones. Smlar result was reported by Kabul (2005) where t was argued that farmers are more lkely to adopt technologes that are matchng the exstng agroecologcal condtons. Yeldng potental had postve and sgnfcant nfluence on adopton of mproved soybean varetes at 5 percent level of sgnfcance. Smlar results were also obtaned for the other varety specfc characterstcs lke earlness to maturty and the taste of the varety. The results mples that farmers are lkely to adopt soybean varetes that are hgh yeldng, early maturng and havng a pleasant taste when cooked. The results are n agreement wth what Adesna and Znnah (1993) found where they argued that technologes that meet the characterstcs that farmers look for when selectng a partcular crop varety have hgher probablty of beng adopted. The report further reveals that some of the characterstcs are usually assocated wth ndvdual s perceptons for nstance attrbute lke taste s very subjectve as what s pleasng to a sngle farmer may not please everyone. Adesna and Forson (1995) argued that technologes that rase agrcultural producton lke mproved varetes and fertlzer have hgher probablty of beng adopted because of the problem of land szes. The report ague that most developng countres have land problem as a challenge due to hgher populaton and as

10 Scholarly J. Agrc. Sc. 348 such the only way farmers ncreases output s to adopt technologes that are hgher yeldng, and resstant to drought (earl y maturng) whch s another serous problem n developng countres. CONCLUSION AND POLICY RECOMMENDATIONS In general, the study concludes that soco-economc factors such as age of the household head sgnfcantly nfluence farmers decson to adopt mproved soybean varetes n the study areas. Insttutonal factors such as access to extenson servces, dstance to the nearest market and locaton where a farmer lves are very mportant determnants of adopton of mproved soybean varetes. The study also fnds that farm level characterstcs such as farm sze play a crucal role n nfluencng the farmers decson to adopt mproved soybean varetes. The study further fnds that varety specfc characterstcs lke earlness to maturty, hgher yeldng and pleasant taste are sgnfcant attrbutes that nfluence a farmer to adopt mproved soybean varety. The study also ndcates that the decson to adopt a number of varetes s nfluenced by age of the household head, access to nformaton (acce ss to extenson servces) and the yeldng potental of the varety. However, the study fnds that certan farm level characterstcs and soco-economc factors such as club membershp, gender, total household ncome, educaton, and household sze do not nfluence the adopton decson at household level. The study has shown that adopton rate of soybean varetes has ncreased from around 5 percent n 1999 to 60 percent durng the 2008 growng season. However, ths result may not represent the realtes on the ground because most of the varetes consdered durng the study were released longtme ago wth the most recent one beng n It s therefore worthy notng that there are some varetes that have been released after 2004 but were not consdered n the study because of other reasons beyond the scope of ths study. The study recommends that government should contnue ncreasng resources to the Department of extenson as t has been shown n the study that as the source of nformaton to farmers, extenson vsts to farmers have a bearng on the adopton decsons taken by farmers because by vstng farmers, extenson agents demonstrate and nform farmers of the advantages assocated wth modern varetes. Furthermore there s a need for more rural markets to be opened up closer to farmers f adopton of modern technologes s to be successful because farmers wll have a clear pcture on what crops are beng demanded on the market and also whch varetes are fetchng better prces on the market. Fnally there s a need to focus more research and breedng to come up wth more varetes that are sutable under dfferent agro-ecologcal zones. REFERENCE Adesna, A.A and Znnah, M.M. (1993). Technology characterstcs, farmers perceptons and adopton decsons: A Tobt model applcaton n Serra Leone. Agrc. Econ. 9: Adesna, A.A., and Forson, J.B. (1995). Farmers' Perceptons and Adopton of New Agrcultural Technology: Evdence from Analyss n Burkna Faso and Gunea, West Afrca. Agrc. Econ.13: 1-9. Bandera, O., and Rasul, I. (2005). Socal Networks and Technology Adopton n Northern Mozambque. The Economc Journal 116(514): Calcutta.pp Cameron, A. C., & Trved, P. K. (1998). Regresson analyss of count data. New York: Cambrdge Unversty Press. Cameron, A.C. and Trved, P.K. (2005). Mcroeconometrcs: Methods and Applcatons. New York, NY: Cambrdge Unversty Press. Chrwa, E.W. (2005). Adopton of fertlser and hybrd seeds by smallholder maze farmers n southern Malaw. Develop. Southern Afr. 22(1). CIMMYT Economcs Program, (1993). The adopton of agrcultural technology. Agude for survey desgn. Mexco, D.F.: CIMMYT. Cornejo, J. F., and McBrdge, W. D. (2002) Adopton of Boengneered Crops. Agrc. Econ. Report No Washngton, DC Cragg, J. (1971). Some Statstcal Models for Lmted Dependent Varables wth Applcaton to the Demand for Durable Goods. Econometrca 39: Department of Agrcultural Research Servces (2005). Product on gudes of dfferent crops n Malaw. Agrcultural research actvtes and publcatons, Chtedze Research Staton, Llongwe, Malaw. Department of Agrcultural Research Servces (2008). Producton gudes of dfferent crops n Malaw. Agrcultural research actvtes and publcatons, Chtedze Research Staton, Llongwe, Malaw. Doss, C. R., Wang, M., Verkujl, H. and De Groote, H. (2003). Adopton of Maze and Wheat Technologes n Eastern Afrca: A Synthess of the Fndngs of 22 Case Studes. CIMMYT Economcs Workng Paper Mexco, D.F.: CIMMYT. Doss, C.R. (2003). Understandng farm level technology adopton: Lessons learned from CIMMYT s mcro surveys n Eastern Afrca. CIMMYT economcs workng paper Mexco, D.F.: CIMMYT. Doss, C.R. (200 6). Analysng technology adopton usng mcrostudes. Lmtatons, challenges, and opportuntes for mprovement. Agrc. Econ. 34: Estrada, J.M. (2004). Regonal overvew of the soybean markets: Challenges and opportuntes for smallholder farmers n Southern Afrca Study commssoned by the Internatonal Insttute for Tropcal Agrculture (IITA). Feder, G., R. Just, and Zlberman, D. (1985). Adopton of Agrcultural Innovatons n Developng Countres: A Survey. Economc Development and Cultural Change 33: The Unversty of Chcago Press. Kabul, A. (2005). Economc assessment of the benefts of soybean ncorporaton wthn smallholder maze based croppng systems and factors affectng adopton n Malaw. Msc. Thess, Bunda College. Kalba, A.R.M., Verkujl, H. and Mwang, W. (2000) Factors Affectng Adopton of Improved Maze Seeds and Use of Inorganc Fertlzer for Maze Producton n the Intermedate and Lowland Zones of Tanzana. Journal of Agrcultural and Appled Economcs, 32:35 47 Kvln, J.E. and Flegel, F.C. (1966). Attrbutes of nnovatons as factors n dffuson. Amercan J. Soc. 72: Kormawa, P.M., Ezednma, C.I. and Sngh B.B. (2004). Factors Influencng Farmer-to-Farmer Transfer of an Improved Cowpea varety n Kano State, Ngera. J. Agrc. and Rural Develop. n the Tropcs and Subtropcs. 105(1): 1 13 Lungu, V. (1998). Gran legume prce survey n Malaw. A Malaw bean mprovement project publcaton. Department of Agrcultural Research and Techncal Servces Malaw. McDowell A. (2003). From the Help Desk: Hurdle Models. The Stata J. 3(2), Moffatt, P. G. (2003). Hurdle models of loan default. School of Economc and Socal Studes Unversty of East Angla. Namwata B.M.L., Lwelamra, J. and Mzra, O.B. (2010). Adopton of mproved agrcultural technologes for Irsh potatoes (Solanum tuberosum) among farmers n Mbeya Rural dstrct, Tanzana: A

11 Kapalasa 349 case of Ilungu ward. J. Anmal and Plant Sc. 8(1): Nkonya, E., Schroeder, T. and Norman, D. (1997). Factors affectng adopton of mproved maze seed and fertlzer n North Tanzana. Indan J. Agrc. Econ. 48(1):1-12. Pattanayak, S. K., Mercer. D. E., Slls, E. and Yang, J. C. (2003). Takng Stock of Agroforestry Adopton Studes. Agroforestry Systems 57: Kluwer Academc Publshers, Netherlands. Paudel, P. and Matsuoka, A. (2008). Factors Influencng Adopton of Improved Maze Varetes n Nepal: A Case Study of Chtwan Dstrct. Australan J. Basc and Appl. Sc. 2(4): Wooldrdge, J. (2004). Introductory Econometrcs. Oho: Thomson South-Western. York, NY: Cambrdge Unversty Press. The error term has a constant varance