CAUSAL EFFECT OF CREDIT AND TECHNOLOGY ADOPTION ON FARM OUTPUT AND INCOME: THE CASE OF CASSAVA FARMERS IN SOUTHWEST NIGERIA

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1 CAUSAL EFFECT OF CREDIT AND TECHNOLOGY ADOPTION ON FARM OUTPUT AND INCOME: THE CASE OF CASSAVA FARMERS IN SOUTHWEST NIGERIA OBISESAN Adekem A, Tawo T. AMOS & Roselne J. AKINLADE Invted paper presented at the 5th Internatonal Conference of the Afrcan Assocaton of Agrcultural Economsts, September 23-26, 2016, Adds Ababa, Ethopa Copyrght 2016 by [authors]. All rghts reserved. Readers may make verbatm copes of ths document for non-commercal purposes by any means, provded that ths copyrght notce appears on all such copes.

2 CAUSAL EFFECT OF CREDIT AND TECHNOLOGY ADOPTION ON FARM OUTPUT AND INCOME: THE CASE OF CASSAVA FARMERS IN SOUTHWEST NIGERIA BY. OBISESAN Adekem A, Tawo T. AMOS & Roselne. J. AKINLADE Federal Unversty of Technology Akure, Ngera 1

3 ABSTRACT Ths study examned credt accessblty, technology adopton and the mpact on output and ncome of cassava farmng households n Southwest Ngera. Data were collected usng structured questonnare through a mult-stage samplng procedure. Ondo and Ogun states were randomly selected from the sx States n Southwest, Ngera. The next stage nvolved the random selecton of four Local Government Areas from each State. Fnally, a total of fve hundred and forty cassava farmers were randomly selected from both States. Propensty Score Matchng, descrptve statstcs and Tobt regresson model were employed n the analyss. There were 387 respondents wth smlar characterstcs. Majorty of the farmers were males wth mean household sze of sx members. Average area of land cultvated was about 1 hectare. Credt accessblty was hgher among the adopters. Credt access had a postve and sgnfcant (p<0.01) nfluence on level of adopton. Cassava yeld and ncome (14.92 tonnes/ha and 321, respectvely) of adopters wth credt was hgher than ther counterparts (13.06 tonnes/ha and 287,110.90) wthout credt access. The mpact of technology adopton was hgher among adopters wth credt access. Technology adopton ncreased cassava yeld and ncome of adopters wth credt access by 4.68tonnes/ha and 64, respectvely compared wth 2.57 tonnes/ha and 33, for those wthout access. Ths suggests that access to credt and technology adopton have the potental to transform smallholder agrculture n Ngera. The study recommends that government should nvest more on technology advancement and dssemnaton among smallholder farmers. Polcy measures should also be orented towards the mprovement of rural credt. Key words: Cassava, Credt, Income, Technology, Southwest Ngera. 1.0 INTRODUCTION The effcency of the agrcultural sector has a multpler effect on economc development. A potent agrcultural sector s nstrumental to self-suffcency n food producton, generaton of employment, foregn exchange earnngs and provson of raw materals for agro-alled ndustres. In Afrca, agrculture accounts for over 32% of the Gross Domestc Products (GDP) and more than 70% of Afrcan populaton engages n agrculture. However, despte the contnent s huge potentals n agrcultural producton, t s alarmng that most of the Afrcan countres stll depend on food mportaton (Salam and Arawomo, 2013). In Ngera, pror to the dscovery of ol, agrcultural sector was the major source of foregn exchange contrbutng over 60% of the GDP. However, wth the advent of ol, there has been a declne n the contrbuton of agrculture to the GDP to 22.90% (NBS, 2014). Ths was as a result of the neglect of the sector and the negatve mpact of ol boom. Despte ths, agrculture stll plays sgnfcant role n the naton s economy. It employs two-thrd of total labour force and provdes lvelhood for over 90% of the rural populaton. Moreover, wth the dwndlng ol prce n the world, there s need to dversfy the natons revenue source, hence government has to shft 2

4 attenton towards the agrcultural sector whch remans a fundamental nstrument for spurrng growth and overcomng poverty. The agrcultural sector s domnated by small-holder farmers accountng for over 90% of the total output whle more than half of the farmers produce only food crops ncludng cassava (IFAD, 2010). Cassava serves as an mportant food source for an estmated 200 mllon people or averagely one-thrd of the populaton of sub-saharan Afrca (IITA et al., 2003). It plays a vtal role n the rural economy of the southern agro-ecologcal zones and ncreasngly ganng value n other parts of Ngera (FMARD, 2002). However, farmng populaton comprses predomnantly of resource-poor peasants, cultvatng an average of about two hectares of land usually on scattered holdngs wth rudmentary farmng system, low captalzaton and declnng productvty resultng to hgh food nsecurty and poverty. Consequently, ncreasng agrcultural productvty n the country s an urgent necessty and one of the fundamental ways of mprovng agrcultural productvty s through ntroducton and use of mproved agrcultural technologes (Braun et al, 2008). In ths wse, mprovng agrcultural productvty has become an urgent need. There s the desre to acheve ths mprovement n productvty whle facng the contemporary challenges of global envronmental change: global warmng, land degradaton, water polluton and scarcty, and bodversty loss (World Bank, 2007). Therefore, properly talored ncentves and polces wll be needed to ensure that future efforts to ncrease agrcultural productvty do not compromse envronmental ntegrty and publc health (Tlman et al., 2002). Introducton of, access to and the use of mproved agrcultural technologes and management practces are tools needed to mprove agrcultural productvty whch serves as the key to global food securty and fght aganst poverty. (McCalla, 2001) but t remans a challenge for agrcultural researchers to understand how these technologes are used and wth what mpacts (Braun et al, 2008).. Furthermore, agrcultural development s undermned by poor access to modern mproved technologes and low nvestment or fnance (Salam et al., 2010). In other words, agrcultural growth and development s not possble wthout yeld-enhancng technologcal optons because merely expandng the area under cultvaton (except n a few places) to meet the ncreasng food needs of growng populatons s no longer suffcent. Hence, the need to prortse nvestment n agrcultural technology n Afrca. Research and adopton of technologcal mprovement are crtcal to mprovng agrcultural productvty whch serves as a panacea to allevatng poverty and food nsecurty especally among smallholder farmers. However, credt s a major factor n technology adopton, playng a crucal role n the transformaton of smallholder agrculture nto commercal scale whch engenders agrcultural development (Abayom and Salam, 2008). Credt provson has been put forward as one of the prncpal components of rural development, whch helps to attan rapd and sustanable growth of agrculture. Rural credt s a temporary substtute for personal savngs, whch catalyses the process of agrcultural producton and productvty. To boost agrcultural producton and productvty farmers have to use mproved agrcultural technologes, however the adopton of these technologes s relatvely 3

5 expensve and small-holder farmers cannot afford to self fnance t. As a result, the use of agrcultural technologes s very low. Therefore, enhanced provson of rural credt would accelerate agrcultural producton and productvty (Odoemenem and Obnne, 2010). Accordng to Enhancng Fnancal Innovaton and Access (EFInA) (2008), 23 percent of the adult populaton n Ngera has access to formal fnancal nsttutons, 24 percent to nformal fnancal servces, whle 53 percent are fnancally excluded. Furthermore, the mportance of credt n agrcultural producton notwthstandng, farmers n rural areas fnd t dffcult to access t even when avalable (FARM, 2006). It s therefore mperatve for polcy-makers and development agences to consder the specfc needs of small-holder farmers n accessng credt and technology adopton n order to effectvely spur agrcultural development. There have been emprcal studes on credt accessblty (Khald, 2003; Lawal et al., 2009; Adegbte and Adeleye, 2011); technology adopton and agrcultural productvty n Ngera (Saka and Lawal, 2009; Olagunju and Salmonu, 2010; Awotde et al, 2012) but there s stll a dearth of studes on causal effect of credt on agrcultural producton. Furthermore, most studes on mpact assessment of adopton of hgh yeldng varety and technologes n Ngera were carred out by usng descrptve, nferental statstcs and regresson models (Udoh and Omonona, 2008; Ater et.al., 2007; Awony and Awoynka, 2007 ), these studes are relevant because they help n knowng the effect of adopton of new technologes and hgh yeldng varetes but faled to dentfy the causal effect of adopton (Heckman and Vytlacl, 2005; Lee, 2005; Rosembaum, 2002) and get the counterfactual outcomes, that s, the outcomes of the partcpant f he had not adopted the technology. Ths study used propensty score matchng (PSM) to address the evaluaton problem and employed the counterfactual outcome framework to show the mpact of the outcome defned n the modern polcy evaluaton lterature as the average effect of the treatment on the treated (ATT) whch helps to reduce based estmates. Therefore, ths study examnes the causal effect of credt accessblty and technology adopton on cassava yeld and ncome among small-holder farmers n rural Ngera. 2.0 MATERIALS AND METHODS 2.1 The Area of Study The study was carred out n Southwest, Ngera. South west s one of the sx geopoltcal zones n Ngera. It falls on lattude 6 0 to the North and lattude 4 0 to the South whle t s marked by longtude 4 0 to the West and 6 0 to the East. It s bounded n the North by Kog and Kwara States, n the East by Edo and Delta States, n the South by Atlantc Ocean and n the West by Republc of Benn. The clmate s equatoral wth dstnct wet (rany) and dry seasons wth relatvely hgh humdty. The mean annual ranfall s 1480mm wth a mean monthly temperature range of C durng the rany season and C n the dry season. Southwest Ngera covers approxmately an area of 114,271 klometer square that s approxmately 12 percent of Ngera s total land mass and the vegetaton s typcally ranforest. The total populaton s 27,581,992 as at 2006 and the people are predomnantly farmers. The clmate n the zone favours the cultvaton of crops lke maze, yam, cassava, mllet, rce, plantan, cocoa, kola nut, 4

6 coffee, palm produce, cashew etc (NPC,2006). The zone comprses of sx states namely: Ekt, Lagos, Ogun, Ondo, Osun and Oyo States. 2.1 Data Collecton and Samplng Procedure Prmary data were collected for the purpose of ths study usng structured questonnare. Some of the data nclude: soco-economc and demographc characterstcs, credt accessblty, cassava producton technology, cassava producton as well as returns to cassava producton. Multstage samplng technque was employed n ths study. The frst stage was the random selecton of Ondo and Ogun States from the sx States n Southwest, Ngera. The second stage nvolved the random selecton of four LGAs from each state. Fnally, 540 cassava farmers were randomly selected. However, a total of 482 were retreved and completely flled from the feld. 2.2 Analytcal Technques Analytcal technques employed n ths study ncludes: descrptve statstcs (tables, mean, frequency and percentages), Tobt regresson model and Propensty Score Matchng (PSM). Followng (Tamyu et al, 2009) and adaptng t to ths study, technology-use ranked score was computed for each respondents based on the dentfed elements of the technology package (mproved varetes, recommended spacng, tmely mantenance, fertlzer and applcaton) and adopton ndex was generated for ndvdual farmer. Adopton ndex of ndvdual farmer was calculated as follows: AI TS TTS...(1) n AI AAI N...(2) Where, AI = Adopton ndex of the th farmer TS = Technology-use score of the th farmer TTS= Total technology-use score obtanable AAI= Average adopton ndex 5

7 Tobt regresson model Tobt regresson model was used to analyze the effect of credt access and other socoeconomc factors on technology adopton, Followng Negash, (2007), the Tobt model for the contnuous varable adopton level, can be expressed as: Where, AL * X 0 * AL AL f 0 X AL * 0...(3) X 0 f 0 0 the latent varable and the soluton to utlty maxmzaton problem of level/ extent of adopton subjected to a set of constrants per household and condtonal on beng above certan lmt AL = Adopton level for th farmer X vector of factors affectng adopton and level of adopton vector of unknown parameters error term Selecton of explanatory varables The explanatory varables specfed as determnants of adopton level of the mproved producton technology were selected accordng to Chlot et al, (1996); Asfaw et al, (1997); Nkonya et al (1997); Mulugeta (2000); Mesfn(2005); Omonona et al,(2006) and Negash (2007) The varables are defned as follows: X 1 = Age of the household head (years) X 2 = Age square of the household head (years) X 3 = Gender of the household head (male=1, 0 otherwse ) X 4 = Martal status of the household head (marred=1,0 otherwse) X 5 = Partcpaton n off-farm actvty (yes= 1, 0 otherwse) X 6 = Level of educaton of household head 6

8 X 7 = Years of experence of household head n cassava producton (years) X 8 = Man occupaton (farmng = 1,0 otherwse) X 9 = Household sze (numbers) X 10 = Land area cultvated (ha) X 11 = Dstance of farm to nearest market (km) X 12 = Access to credt of the household head (yes=1, 0 otherwse ) X 13 = Cassava yeld (tonnes/ ha) X 14 = Contact wth extenson agents (yes=1, 0 otherwse) Table 1: A pror Expectatons of the Explanatory varables used n Adopton Analyss Model Varables Man occupaton Non-farm Actvty Market dstance Land cultvated Years of experence Yeld Access to credt Descrpton Contnuous Contnuous Dscrete Contnuous Dummy Expected Sgns Lterature Age Dscrete +/- Techane,2006; Omonona et al, 2006 Male Gender Dummy + Mesfn,2005 Martal status Dummy - Omonona et.al,2006 Level of educaton Dscrete + Chlot,1994 Household sze Dscrete +/- Omonona et.al,2006; Udoh and Omonona,2008 Dummy + Degnet et al., 2001 Dummy + Chlot et.al, Halu, 2008 Belay, 2003 Chlot et.al, 1996 Omonona et.al,2006 Mulugeta, 2000 Extenson agent contact Dummy + Omonona et.al,2006 Source: Author s complaton from past lterature 7

9 Propensty Score Matchng Propensty Score Matchng, one of the most commonly used quas-expermental methods was used to address the evaluaton problem (Mendola, 2007; Nkonya et al, 2007; Aknlade et al, 2011). The sample collected was matched usng PSM; the am of PSM s to fnd the comparson group from a sample of non-adopters that s closest to the sample of adopters so as to get the mpact of the technology on the adopters. Though, the benefcary and comparson groups may dffer n unobservable characterstcs even f they are matched n terms of observable characterstcs, however, t has been put forward that selecton on unobservable s emprcally less mportant n accountng for evaluaton bas (Baker, 2000). Also n a stuaton where the same questonnare s admnstered to both groups (so that outcomes and personal characterstcs are measured n the same way for both groups) and the partcpants and controls are placed n a common economc envronment (such as the case n ths study), matchng substantally reduce bas (Heckman et al, 1996). Man steps nvolved n the applcaton of statstcal matchng to mpact evaluaton are: estmatng the propensty score, matchng the unt usng the propensty score, assessng the qualty of the match and estmatng the mpact as well as ts standard error. Out of 482 respondents, only 387 adopters and non-adopters that had comparable propensty scores were matched. After matchng, the testng of comparablty of the selected groups was done and the result shows statstcally nsgnfcant dfference n the explanatory varables used n the probt models between the matched groups of adopters and non-adopters. Snce the match has been deemed of good qualty, ths study then used the matched sample to compute the Average Treatment Effect for the Treated (ATT) to determne mpact of the technology adopton. Ths s defned by Rosembaum and Rubn (1983) as follows: / 1 / 1 / 1 E Y Y D E Y D E Y D (4) where, E Y 1 / D 1 s the observed outcome of the treated, that s, the expected ncome earned by programme benefcares whle partcpatng n the programme and E Y 0 / D 1 s the counterfactual outcome - the expected ncome they would have receved f they had not partcpated n the project. The counterfactual outcome represents outcome of the nonbenefcares snce they have smlar characterstcs wth benefcares. Standard errors were computed usng bootstrappng method suggested by Lechner (2002) to generate robust standard errors n lght of the fact that the matchng procedure matches control households to treatment households wth replacement. 8

10 3.0 RESULTS AND DISCUSSION 3.1 Statstcal Matchng of Respondents Probt regresson model was employed n the estmaton of the propensty scores used n matchng of respondents. The adopters compared wth the non-adopters. The dependent varable n the models s a bnary varable ndcatng whether the farmer s an adopter or not. Observatons that were not n the common range of propensty scores for both groups (that s, lack common support ) were dropped from the analyss. Out of 482, only 157adopters and 230 non-adopters (387 respondents) that had comparable propensty scores were matched. After matchng, the comparablty test of the selected groups was done and the results show statstcally nsgnfcant dfference n the explanatory varables used n the probt models between the matched groups of the adopters and non-adopters, ndcatng that the propensty score matchng assured comparablty of the comparson groups (Table 2 & 3). Table 2: Probt Regresson Estmates After matchng Explanatory varables Coeffcents Standard Errors P>/z/ Gender (male=1, female=2) Age Martal status Household Sze Years of educaton Land area cultvated Constant Sample sze 387 Pseudo R Prob> ch Log lkelhood

11 Table 3: Estmates of Test of Comparablty After Matchng Varables %bas P>/t/ Treated control Gender Age Martal status Household Sze Educaton years Land cultvated Dstrbuton of Respondents by Soco-economc Characterstcs Table 4 shows the dstrbuton of the respondents by soco-economc characterstcs across the two types of respondents consdered whch are: adopters and non-adopters. The average values of ther soco-economc characterstcs are wthn the same range due to propensty score matchng (PSM) used n selectng the respondents wth smlar observable characterstcs. Majorty (74.63%) of the adopters are males whle only 25.37% are female. The average household sze was 6. The majorty of the respondents have ther household szes fallng wthn the range of 5 to 9 people, wth the average age of the respondents beng 44 and 45 for adopters and non-adopters respectvely. Implct n these fndngs s that a large proporton of the respondents were below 50 years and can therefore be regarded as actve, agle and wth more energy to dsspate and concentrate on productve effort. The average years of experence n cassava farmng was 16 years for all respondents. The average area of land cultvated was about 1 hectare for all the respondents. Accessblty to credt faclty was hgher among adopters, 82.5% of the adopters had access to credt compared to 48.26% of the non-adopters. Partcpaton n off-farm actvty was hgher among adopters compared to non-adopters. 10

12 Table 4: Dstrbuton of Respondents by Soco-economc characterstcs Characterstcs Categores/ Statstcs Adopters Percentage Nonadopters Gender Household sze Female Male Total >9 Total percentage Age Level of educaton Credt access >50 Total No formal Prmary Secondary Yes No Area of land cultvated(ha) Total Off-farm actvty Yes No

13 3.3 Credt Accessblty and Technology Adopton Level The adopton level refers to the ntensty of use of mproved technology by the farmers. The adopton ndex generated shows to what extent the farmers have adopted a technology package. The level of adopton of cassava mproved producton technology by credt accessblty revealed that adopton level was hgher among those wth access than ther counterparts wthout credt access. From Table 5, the mean adopton ndex of the adopters wth credt access was 0.86 whle that of ther counterparts wthout access was Ths mples that adopton level of farmers wth credt access was 21% sgnfcantly (p> 0.001) hgher than those wthout credt access. Table 5: The Adopton Index by Credt Accessblty Credt accessblty Percentage adopton ndex Probablty value Access No access Effect of credt accessblty and other soco-economc characterstcs on Adopton Level of cassava Improved Producton Technology The result of the determnants of adopton level of cassava mproved producton technology by farmng households n the study area s shown n Table 6. The result of the Tobt regresson model shows that the log lkelhood s and s sgnfcant at 1% level of sgnfcance. Ths ndcates that the model has a good ft to the data. The result shows that out of the 14 explanatory varables ncluded n the model, credt accessblty and seven other varables were found to sgnfcantly nfluence level of adopton. These are gender, dstance to nput market, land area cultvated, years of experence n cassava producton, cassava yeld, off-farm actvty and level of educaton. A postve sgn on a parameter ndcates that the hgher the value of the varable, the hgher the adopton level and vce-versa. Access to credt has postve and sgnfcant nfluence (p<0.01) on the adopton of mproved cassava producton technology. From the result of ths study, access to credt facltes leads to 15.82% ncrease n the adopton level. Ths s attrbuted to the fact that credt ncreases the farmers' economy to purchase mproved seed, fertlzer and other nputs. Ths s n agreement wth Mulugeta (2000) and Tesfaye et al (2001). Partcpaton n off-farm actvty has a postve and sgnfcant (p<0.05) nfluence on level of adopton. Durng slack perods many farmers can earn addtonal ncome by engagng n varous off-farm actvtes. Ths s beleved to rase ther 12

14 fnancal poston to acqure new nputs. Partcpaton n off farm actvty wll ncrease adopton level by Ths concurs wth Chlot et al (1996). The gender of the farmer s sgnfcant (p<0.01) and has a postve sgn mplyng that male household heads are more lkely to adopt the use of mproved cassava producton technology than ther female counterparts. From the result, beng a male household head wll ncrease the level of adopton by 13.83%. Ths shows that male headed households have better access to nformaton and other resources on mproved cassava producton technology and are more lkely to adopt new technology than female headed households. Ths result s n agreement wth Tesfaye et al (2001); Mesfn (2005) and Omonona et al (2006). The coeffcent of years of experence n cassava producton s postve and sgnfcant (p<0.01). A unt ncrease n years of experence n cassava producton wll ncrease the adopton level by Ths s due to the fact that farmers wth hgher experence n cassava producton appear to have full nformaton and better knowledge hence able to evaluate the advantage of the technology. The level of adopton of mproved cassava producton technology s sgnfcantly but negatvely nfluenced by dstance to the nearest nput market. Market dstance sgnfcantly (p<0.01) reduced adopton level. Ths ndcates that farmers nearer to the markets have more access to nput. The result from ths study showed that a unt decrease n market dstance wll ncrease the lkelhood of adoptng technology by Ths concurs wth Mesfn (2005); Tesfaye (2006) and Halu (2008) who reported that market dstance s negatvely and sgnfcantly assocated wth adopton of crop technologes n dfferent parts of Ethopa. The level of educaton of the household head postvely and sgnfcantly (p<0.05) nfluenced adopton level of mproved producton technology. Educatonal level wll ncrease adopton level by Educaton ncreases farmers ablty to obtan, process, and use nformaton relevant to technology adopton. The coeffcent of land cultvated s postve and sgnfcant (p<0.01). From the result of ths study, a unt ncrease n land cultvated wll ncrease adopton level of mproved producton technology by Land s perhaps the sngle most mportant resource, as t s a base for any economc actvty especally n rural and agrcultural sector. It s frequently argued that farmers cultvatng larger farm land are more lkely to adopt an mproved technology (especally modern varetes) compared wth those wth small farmland. Ths fndng s consstent wth Halu (2008) that farm sze exerts a postve nfluence on adopton of mproved teff and wheat producton technology n northern and western shewa zones of Ethopa. Cassava yeld has a postve and sgnfcant (p<0.01) nfluence on adopton level. A unt ncrease n last season s yeld wll ncrease the adopton level of mproved producton technology by Ths s n agreement wth Omonona et al (2006). 13

15 Table 6: Estmates of Tobt Regresson for the Determnants of Adopton Level Varables Margnal effect Standard error t- value Gender *** Age Martal status Level of educaton ** Man occupaton Off- farm actvty ** Dstance to market *** Land cultvated *** Year of experence *** Cassava yeld *** Credt access *** Extenson agent Household sze Age square Constant *** Sgma Prob>ch Pseudo R Log lkelhood *,**,*** are sgnfcant levels at 5% and 1% respectvely 3.5 Cassava Yeld (tonnes per ha) of Respondents and Impact by Credt Accessblty Table 7 reveals that the mean yeld of all the respondents vared by credt accessblty wth the adopters havng a hgher mean yeld than the non-adopters. The mean cassava yeld of the respondents wth access to credt was hgher than those wthout access. Ths s lkely due to the fact that credt accessblty ncreases adopton level of mproved technology. For those wth 14

16 credt access, the mean yeld was 14.92tonnes and 10.69tonnes for adopters and non-adopters respectvely whle t was 13.06tonnes and 8.02tonnes for ther respectve counterparts wthout credt access. Furthermore, Table 7 presents the mpact of the technology on the benefcares due to adopton when compared wth the non-adopters. For those wth credt access, producton technology had a sgnfcant (p<0.01) postve mpact on the yeld of the adopters. Technology adopton led to 4.68 tonnes ncrease n yeld of benefcares wth access to credt whle the mpact on the mean yeld was 2.37 tonnes on the adopters wthout credt access. Ths ndcates that credt accessblty enhances technology adopton and ts mpact on farmers yeld. Table 7: Cassava Yeld (Tonnes per ha) and Impact on Respondents by Credt Accessblty Type of Statstcs Yeld ATT respondent ADOPTERS Credt access *** (1.3893) No access NON- ADOPTERS Credt access (0.2900) No access *** s sgnfcant level at 1%. The values n parenthess are standard errors. 3.6 Level of Income of Respondents and Impact by Credt Accessblty Table 8 reveals that the mean ncome of all the respondents vared by credt accessblty wth the adopters havng a hgher mean ncome than the non-adopters. The mean ncome of the respondents wth access to credt was hgher than those wthout access. For those wth credt access, the mean ncome was 321, and 273, for adopters and non-adopters respectvely, whle t was 287, and 248, for ther respectve counterparts wthout credt access. 15

17 Furthermore, Table 8 presents the mpact of the technology on the adopters ncome. For those wth credt access, the producton technology had a sgnfcant (p<0.05) mpact on the ncome of the adopters. Technology adopton ncreased the ncome of adopters wth access to credt by 64, whle the mpact on the mean ncome was 33, for those wthout credt access. Table 8: Level of Income of Respondents and Impact by Credt Accessblty (Per Annum) Type of respondent ADOPTERS Credt access No access NON- ADOPTERS Statstcs Income ATT ** ( ) ( ) Credt access No access **sgnfcant levels at 5%. The values n parenthess are standard errors. 4.0 Concluson, Polcy Implcatons and Recommendatons Ths study centred on causal effect of credt access and technology adopton on yeld and ncome of cassava farmng households n Ngera. Emprcal evdence from ths study revealed a hgher adopton level and mpact of mproved cassava technology on those wth access to credt. Credt accessblty, partcpaton n off-farm actvty, dstance to nearest market, level of educaton, among other factors sgnfcantly nfluenced technology adopton. The cassava yeld of the adopters wth credt access was hgher than ther counterparts wthout access. Though, there was ncrease n ncome of all the adopters, mplyng that mproved producton technology has the potental to enhance ncome of small-holder farmers, however, the mpact was hgher on the ncome of those wth credt access. Hence, polcy measures should be orented towards the mprovement and support of rural credt n Ngera. Improvng credt or grant access should be consdered as a core component of 16

18 any development nterventon for small-holder farmers. Government should revew the procedures for securng loans n order to make t farmer-frendly and collaterals should be relaxed. The approprate government agences should moblze farmers to form co-operatves or thrft socetes wthn themselves. Furthermore, government should nvest more on technology advancement and there should be wde dssemnaton of technology among farmers to mprove ther productvty and welfare. Effectve extenson servces should be put n place to gve some levels of tranngs to farmers. Rural development polces should promote the creaton of enablng envronment through the provson of socal nfrastructure especally access roads to market n order to enhance technology adopton. REFERENCES Abayom, S.& Salam, O.A. (2008). Impact of Commercal Bank Lendng on Agrcultural Producton and Productvty n Ngera. Journal of Agrcultural, Food,Water and Drugs, 2 (1), pp Adegbte, D.A. & Adeleye O.A. (2011). Determnants of Farmers Access to Mcro-credt n Oyo State, Ngera. Journal of Agrcultural Research and Development, 10 (1) Aknlade, R..J, Yusuf, S.A, Omonona, B.T and Oyekale A.S. (2011). Poverty Allevaton Programme and Pro-poor Growth n Rural Ngera: Case of Fadama II Project. World Rural Observatons, 3(1), Pp Awotde, B.A, A. Dagne & T.T Awoyem (2012). Agrcultural Technology Adopton, Market Partcpaton and rural farmng households welfare n Ngera. Invted paper presented at the 4 th Internatonal Conference of the Afrcan Assocaton of Agrcultural Economsts, September 22-25, 2013, Hammamet, Tunsa. Asfaw, N., Gungal, K., Mwang, W. and Beyene S. (1997). Factors Affectng Adopton of Maze Producton Technologes n Ethopa. Ethopan Journal of Agrcultural Economcs, 2: Asfaw, A. and Admasse, A The role of educaton on the adopton of chemcal fertlzer under dfferent socoeconomc envronments n Ethopa. Agrcultural Economcs, 30: pp Ater P.I., J.C. Umeh, and W.L. Lawal Comparatve analyss of the mpact of World Bank Root and Tuber Expanson Programme on poverty allevaton of perurban and rural communtes n Benue State, Ngera. Poster paper prepared for presentaton at the Internatonal Assocaton of Agrcultural Economsts Conference, Gold Coast, Australa. 17

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