Determinant of Improved Modern Agricultural Inputs Adoption in Case of Woliso Woreda Abstract Keywords: 1. Introduction

Transcription

1 Determnant of Improved Modern Agrcultural Inputs Adopton n Case of Wolso Woreda Amsalu Dachto Habtamu Lema Aby Muluken Gutema Arole Mureta Abrahm Elsabeth Alemu Ambo Unversty, Wolso Campus, Department of Economcs, Wolso, Ethopa Abstract Ths study analyzes the determnant of mproved modern agrcultural technology adopton by farmers n Wolso woreda. The data used for the study was obtaned from 100 randomly selected sample households n the study area. Probt model s employed n analyzng the determnants of farmers decsons to adopt modern technologes on ther farm land as measured by the use of modern fertlzer. The varable such as land ownershp, level of educaton, number of oxen and famly sze, postvely and sgnfcantly affected the farm households fertlzer adopton decson; whle age affected ther decson negatvely and sgnfcantly. On the other hand, varables such as dstance from market, credt accessblty, educaton, famly sze and access to extensons servce have a postve and sgnfcant mpact on adoptons of Hgh Yeld Varety whle age has a negatve and sgnfcant mpact on ther decson. Keywords: Technology Adopton, Improved seed, Agrcultural Sector, Fertlzer and Hgh yeld Varety probt model 1. Introducton Agrculture s the man economc actvtes n the developng countres. The role of agrculture n economc development has been vewed as largely passve, supportve and secondary. Nevertheless, t provdes us wth almost all our basc needs: clothng, shelter, and foods. Besdes, agrculture provdes materal used n makng ndustral products such as pants and medcne. About 50.8% of world populatons are engaged n agrcultural sector. Based on the hstorcal experence of western naton s economes, economc development was seen to requre a rapd structural transformaton, from agrculture led economy to modern ndustral, and servce sectors. As a result agrculture prmary role was to provde suffcently low prce food and man power to expand ndustral outputs n the process of economc development (Ngate and Akllu, 2014 E.C) Accordng to Unted Naton Development Program (UNDP, 2014 G.C) two thrd of the people n developng countres are lvng n rural areas. Agrculture s the man source of subsstence and ncome for the majorty of the rural people; many of them are small scale farmers. Farmers n developng countres are dependng on the farm ncome and experencng a hand to mouth way of lvng. Ths s because of technologcal backwardness, rapd populaton growth and low productvty of lvestock (FAO, 2014G.C). Approxmately 1.4 bllon people n the world today lves n extreme poverty level that-they survve on less than the unted states dollar of 1.25 per day,(world bank data base 2014). 842 mllon people or one out of eght people n the world do not have enough food to eat. 98% of the worlds undernourshed people lves n developng countres. Among those 223 mllon people lves n sub-saharan Afrca.75%of world poorest people lves n rural areas and depends on agrculture and related actvtes,(fao,2014). Ethopa, the thrd largest user of norganc fertlzer, has been strvng for agrcultural productvty. It was durng the mperal perod that the the government started to adopt dfferent technologcal nput such as fertlzer, pestcde and herbcdes, amng to enhance agrcultural productvty and to ensure food self-suffcency. (Befekadu, 2014). Fertlzer s one of the major productvty enhancng nputs. Hence ncreased and effcent use of fertlzer can be consdered as a more plausble alternatve n Ethopa to brdge the made gap of food shortage at least n the mmedate future. There s no domestc producton of norganc fertlzer n Ethopa. Chemcal fertlty s mported from abroad n the form of D-ammonum phosphate (DAP) and UREA. Fertlzer mport s manly fnanced by funds obtaned from donors and credtors. More than 90% of all fertlzer are used by small- holder farmers and the remanng 10% s used by prvate commercal farmers and the research centers. Of the total quantty of n organc fertlzer used n the country 94% was used for the producton of major cereals (CSA, 2013). Agrculture sector has always been an mportant component of the Ethopan economy that accounts for about 42% of gross domestc product (GDP), employs 80% of the Ethopan labor force and supply 90% by value of all exports, (MOA, 2014). A predomnant porton of Agrcultural producton takes place at subsstence level. In all 99% of coffee producton and 94% of other agrcultural producton comes from peasant farms, (Samuel G, 2014). Thus the farmer has to ncrease agrcultural productvty based on the adopton of modern farm nput (nnovaton) more successfully. It s mportant that the effect of agro-clmatc and soco-economc factors on ther adopton of modern agrcultural nputs shall be assessed. As t has been clearly explaned above, 59

2 there are numerous studes, reports, and fndngs of dfferent aspects of agrcultural and adopton process of nnovaton technologes. However the factors responsble for adopton varaton s not well addressed. Therefore the am of the study s to assess (nvestgate) the factors responsble for the adopton of modern agrcultural nnovatons n Wolso Woreda. 2. Statement of the problem Ethopan economy s domnated by agrculture. The agrculture accounts for about 40% of GDP. Ths sector provdes employment opportunty for around 85% of the total populaton. As agrculture plays a domnant role n the economy, the real and sustanable development s unthnkable wthout prortzng the progress of agrcultural output. It s for ths reason that every government has been gven prorty to the sector by ntroducng dfferent strateges. (Fact about Ethopa, 2014 page.71). Although populaton s growng fast n developng countres n general, populaton of Ethopa s ncreasng at the rate of above 2.5% annually (EEA, 2014) for a long perod of tme. The exstng conceptual and emprcal lterature on the topc also suggest that the adopton behavor of modern agrcultural technologes s affected by the nter-relaton of agro-clmatc and soco-economc factors such as age, famly sze, educaton, rsk averson, access to nformaton, access to credt, farm labor supply, supply of hgh yeldng varetes, prce of farm output, chemcals, extenson contacts, and etc (Ibd). The ultmate goal of any rural or agrcultural strategy or program s to mprove the welfare of rural households. Ths goal s to be acheved among other thngs by ncreasng productvty at farm level and by rasng farmer s ncome and by mprovng ther welfare. Ths s possble f and only f mprovements n agrcultural technologes are properly transferred and dstrbuted to farmers, so as to expand and ntensfy ther producton (Assefa and Gezahegne, 2014). Agrcultural development project would be successful f evdences on factors responsble for adopton and rsk takng behavor are known n advance (EEA, 2014). Accordngly approprate measure can be taken to mtgate the effect of rsk and provde complementary nput and output. It would not be possble to ncrease the productvty of agrcultural sector through development programs that nvolves new producton technologes unless farmers are convnced wth the benefts expected from the partcpaton (EEA, 2014). Although the total consumpton of modern agrcultural nputs has shown an ncreasng trend, farmers n Ethopa are stll usng very lttle, due to varous nsttutonal, economc and physcal factors. Several modern technology packages have been ntroduced n Ethopa over the past four decades. Whle some studes n the past have attempted to access the factors behnd the adopton behavor of farmers, the adopton and dffuson of these technologes has not been satsfactorly and comprehensvely assessed even at natonal and regonal level. So we beleve that there are lttle or not enough emprcal and conceptual studes regardng the adopton of mproved agrcultural modern nputs at Wolso Woreda. Therefore ths study s ntated to analyze determnants of modern agrcultural nputs adopton by small holder farmers amng to assess (nvestgate) the effect of those factors on the adopton of modern agrcultural nnovatons n Wolso Woreda so as to fll the spatal gap n ths topc. 3 Objectve of the study 3.1. General objectves The general objectve of ths study s to assess the determnant of mproved modern agrcultural nputs adopton n Wolso Woreda. 3.2 Specfc objectves The specfc objectves of ths study: To dentfy the determnant of fertlzer adopton n the study area. To dentfy the determnant of HYV adopton n the study area. To come up wth some possble recommendaton for polcy n the study area. 3.3 SCOPE AND LIMITATION OF THE STUDY Ths study s conducted n Wolso Woreda tryng to assess the determnant of mproved agrcultural nputs adopton. One of the man constrants for ths study s lack of enough full nformaton because of unwllngness of the respondents n gvng relable nformaton. In addton to the lack of full nformaton, shortages of tme and budget have been constraned ths study. 4. METHDOLGY OF THE STUDY 4.1. Descrpton of the study area The study s based on survey result from household n wolso Dstrct. wolso Dstrct s found n southwest showa zone whch s located about 114 km away from the center Adds Ababa. And t s located n tropcal 60

3 clmate zone. The clmate condton of the Dstrct s medum that the alttude of the land s waynadega. The Dstrct s one of the south west showa zone n Oromya regonal state. The relatve locaton or vsonal poston of the Dstrct has physcally contacts wth four Dstrcts namely Bachow Dstrct, goro Dstrct, wanch Dstrct, and sadden sodo Dstrct and one regon namely SNNP. In terms of these Dstrcts locaton, Wolso Dstrct s boarded n the North by bacho Dstrct, n the west by wanch Dstrct, n the south west by SNNP, n the south by gooro Dstrct and n the east by sadden sodo Dstrct. The total area of the Dstrct s approxmately around km 2 (WDARDO). Accordng to natonal populaton housng census of 2007, the total populaton of the Dstrct was around 165,280. Based on the result of ths census the number of female was greater than that of males. The numbers of females out of total populaton of the Dstrct are around 82,788 and the number of male 82, 492, (Ibd) Types and Source of data Ths study uses both sources of data, prmary and secondary sources. Prmary data s obtaned through questonnare whereas secondary data s collected from dfferent government offces Method of data collecton For ths study the ntervew and questonnare were chosen to be used as an approprate tools to measure the varable under consderaton. The data was collected more by questonnares dstrbuted to randomly selected farmers 4.4. Samplng technques and sze Ths study used the sample survey from the total number of households wth n the dstrcts; we preferred to apply a smple random samplng method were each farm household s exposed to equal chance. The reason for selectng the three kebeles s that t s very dffcult to consder all kebeles the researcher faced tme and fnance lmtatons. Selecton of the three kebeles s reasonable because the total dstrbutons of the farm households of the woreda are socoeconomcally, culturally and nsttutonally smlar. The admnstraton, technology dffuson procedures and plans of development by the woreda leaders are almost the same for all the 35 kebeles and so any household from any Kebele of the woreda can be representatve of the woreda. Mult stage samplng technque was adopted n ths study by adoptng a smple random technque at each stage. Out of 35 kebele 3 kebeles were randomly selected, out of whch 100 respondents were taken as a sample based on the procedures descrbed below SAMPLING SIZE DETERMINATION. Accordng to Wolso dstrct agrcultural and rural development offce, the total household n the three kebeles are And the precson level s 10 percent. The sample sze determnaton technque adopted was as of Yaman T. (1967) Sample sze (n) = Where n= sample sze, N=total households of target populaton n the case study, e= level of precson n= The stratum also calculated as N= where N= the total number of observaton n the kebele. N= total numbers of house hold heads n the study area. ns= total number of sample sze. So by usng the above samplng formula the proportonal number of respondent n each kebele s calculated as follows. 1. From D/dullatt kebele ( ) 100=37 2. From Ob-koj Kebele ( 3. From Adam gootuu ( ) 100 =44 ) 100 = Method of data analyss For data analyss the researcher used both descrptve statstcs and econometrcs model. The descrptve analyss ncludes orderly arrangng the data n tabulaton frequency percentage and table forms. Econometrc model (probt model) s adopted n ths study to analyze factors that determne the probablty of technology adopton by farm households n the study area. Probt model s preferred due to the fact that our dependent varable (technology adopton) s dscrete valued by ts nature havng bnary outcomes. 61

4 4.7. Theoretcal Model and Emprcal Specfcaton In ths paper, regardless of the ntensty and quantty of technologes beng used, a farmer was taken as an adopter f he or she sows any mproved seed and uses chemcal fertlzer; ether ndependently or together wth ther ndgenous seeds and manure. The dependent varable, technology adopton, has a bnary nature takng the value of 1 for adopters (chemcal fertlzer and HYV ndependently) and 0 for non-adopters. In ths regard an econometrc model s employed to examnng the probablty of farm households agrcultural technology adopton decson usng the probt model. Often, probt model s mperatve when an ndvdual s to choose one from two alternatve choces, n ths case, ether to adopt or not to adopt chemcal fertlzer and HYV. Hence, an ndvdual makes a decson to adopt chemcal fertlzer and HYV f the utlty assocated wth (the net beneft) from adopton choce (V1) s hgher than the utlty assocated wth decson not to adopt (V0). Hence, n ths model there s a latent or unobservable varable that takes on the values rangng from (-, + ). Accordng to Koop (2010) these two dfferent alternatves and respectve utltes can be quantfed as: Y* = V1 - V0 (we assumed that farmers decde not to adopt f the expected net beneft from adopton s zero) and the econometrc specfcaton of the model s gven n ts latent as: Y* = X ' β + U 1.. fy* f 0 Y = 0.. fy* 0 Where Y takes the value of One (1) for adopters and Zero (0) for non-adopters Where u x s a normally dstrbuted wth mean zero and constant varance. From ths unobserved or latent model specfcaton, therefore, the utlty functon depends on household specfc attrbutes X and a dsturbance term (u) havng a zero mean: As utlty s random, the th household wll adopt f and only f U1>U0. Thus, for the household, the probablty of adopton s gven by: PY ( = 1 X) PY ( * f 0 X) = P( X ' β + U > 0 X) = PU ( X > X ' β) Φ( X ' β) Where: P() s the probablty of adoptng chemcal fertlzer and HYV, Φ () s the cumulatve dstrbuton functon of the standard normal dstrbuton. βs the parameters that are estmated by maxmum lkelhood x s a vector of exogenous varables that explans adopton of chemcal fertlzer and HYV(e.g. age of household head, sex of the household head, educaton, membershp to an agrcultural assocaton, access to credt, etc). Therefore, on the bass of the two dependent varables ndcated: chemcal fertlzer and HYV, probt model was appled ndependently for each bnary dependent varable; gven below. P( CHEMF) =Φ ( α + α SEX + α AGE+ α EDUC+ α FAMILYSZ + α MSTATUS α6noxen + α7extension + α8landown) P( HYVAD) =Φ ( β + β SEX + β AGE+ β EDUC+ β FAMILYSZ + β MSTATUS β NOXEN + β EXTENSION + β LANDOWN + β CREDIT + β ASSOC β11orhtodox + β12landsz + β13dismkt) Where: CHEMF s a dependent varable ndcatng for probablty of chemcal fertlzer adopton; and HYVAD s a dependent varable representng the probablty of Hgh Yeldng Varety adopton P ( ) probablty of adopton decson,φ ( ) s a cumulatve standard normal dstrbuton functon (CDF) Gven the above two dependent varables (chemcal fertlzer and HYV adopton), to estmate the magntude of parameters or varables bascally to put clearly the percentage probablty of adopton, margnal effect of varables was calculated ( for margnal effect results). Margnal effect of a varable s the effect of unt change of that varable on the probablty of adopton decson Gven that all other varables constant the margnal effect s expressed as: PY ( = 1 X) = βφ ( X ' β) X Where φ() s a probablty densty functon (PDF) and all other varables are evaluated at ther mean values 62

5 4.8. Hypothess and expected sgns The varables used n the analyss and ther theoretcal expectatons about the sgn and magntude of these varables on the adopton decson of agrcultural technologes more partcularly chemcal fertlzer and HYV as well as ts mpact on farm ncome are dscussed below. These varables were chosen based on the avalable lterature revewed. Sex of household head (GEN): It s a dummy varable 1 f gender of the household head s male and 0 otherwse. Male-headed households would have better opportunty to adopt both chemcal fertlzer and HYV snce they are exposed to new nformaton and tend to be rsk takers (Adeby & Okunlola, 2014). In such nstances, negatve sgn was hypotheszed whle adoptng chemcal fertlzer due to ther reluctant behavor and hgher probablty of adoptng manure as a proxy for chemcal fertlzer; whereas postve coeffcent was expected for HYV adopton. Age of household head (AGE): It s a contnuous varable measured n numbers; as age ncreases households probablty of adoptng chemcal fertlzer and HYV were expected to decrease; where younger farmers were expected to adopt unlke elder farmers. The hypotheszed coeffcent n the fnal result for both chemcal fertlzer and HYV was negatve. Educaton (EDUC): It s a contnuous varable measured n number of years of schoolng; where the educated farmers are beleved to acqure, analyze and evaluate nformaton on dfferent agrcultural nputs and market opportuntes. Postve was the coeffcent expected from the fnal result both for chemcal fertlzer and HYV adopton. Famly sze (FAMILYZE); Ths s a contnuous varable measured n number; as famly sze of household ncrease the probablty of adoptng chemcal fertlzer and HYV were expected to ncrease.. Martal status (MRT) t was expected postvely nfluencng the adopton of HYV. Especal marred farmers hghly adopton and use of HYV Hence, as compared to unmarred HHs, beng marred ctrus parbus, would ncrease, the probablty of HYV adopton decson of farm households Number of oxen (NOOFOXEN); ths s a contnuous varable measured n number; as number of oxen of household ncrease the probablty of adoptng chemcal fertlzer and HYV were expected to ncrease.. Land Sze (LANDSZ): Ths s a contnuous varable measured n hectare. Those wth large land sze could use chemcal fertlzer and HYV manly to ncrease productvty. On the other hand, those wth large land sze could not be n a poston to adopt chemcal fertlzer snce they could use fallowng system. Besdes, large land sze holders may not use HYV so long they could use ther own ndgenous seed. On the other sde of the con, small land sze holders may use chemcal fertlzer and HYV so as to heghten producton and productvty and thereby satsfy ther annual household consumpton needs. Hence, the coeffcent was not determned or hypotheszed n pror. Plot Dstance (PLTDIST): It s a contnuous varable measured n mnutes walkng; as plot s far away from the homestead, the less wll be on tme plot preparaton, weed, harvest and nput utlzaton and then less wll be farm ncome (Mnaleet al., 2012). Hence, farmers wll be less probable to adopt chemcal fertlzer and HYV. As a result, negatve coeffcent was hypotheszed from the fnal probt estmaton result. Land Ownershp (LANDOWN): It s a dummy varable 1 f farm households have land ownershp rght and certfed for that 0 otherwse. If farm households do have ownershp rght and certfcate, they tend to purchase and adopt both chemcal fertlzer and HYV; on the other hand, f they do not have ownershp rght, they become reluctant to adopt and ncur a cost for chemcal fertlzer and HYV. Hence, for these two dfferent ndependent varables, postve coeffcent was expected from the fnal probt estmaton result. Access to Credt (CREDIT): It s a categorcal varable; representng 1 f household has had credt access and 0 otherwse. Credt access reduces lqudty problems that household could face whle ntendng to purchase agrcultural nputs; and hence paves the way for tmely applcaton of nputs thereby ncrease the overall productvty and farm ncome (Mpawenmana, 2005). Hence, from the fnal estmaton result, access credt was expected to have a postve sgn both for chemcal fertlzer and HYV adopton decson. Extenson Agents Contact (EXTENS): It s a categorcal varable representng 1 f households were vsted by extenson agents and 0 otherwse. Farmers vsted by extenson agents are beleved to be exposed for dfferent, new, updated nformaton used to adopt chemcal fertlzer and HYV thereby ncrease and double agrcultural producton that fnally could ncrease farm ncome (Wondmagegnet al., 2011). Hence, both for chemcal fertlzer and HYV adopton decson, extenson agents contact was expected to have a postve sgn or coeffcent from the fnal probt estmaton result. Membershp to an Assocaton (ASSOCI): It s a categorcal varable; 1 represents f a household was a member of a certan farmers assocaton or cooperatves and 0 otherwse. Membershp to an assocaton let farmers to access nputs easly wth an affordable prce that s pertnent to ncrease agrcultural producton and thereby farm ncome (Uwagboeet al., 2014 and Tewodajet al., 2009). Hence, farmers can easly adopt chemcal fertlzer and HYV on tme through an affordable prce as well as through credt that wll be returned back soon after harvestng. Due to ths, whle determnng chemcal fertlzer and HYV, membershp to an assocaton was 63

6 expected to have a postve coeffcent. Dstance to the nearest market (DISMRKT): Ths s a contnuous varable measured n klo meters; and the longer the dstance of farmers resdence to the nearest market, the mprobable wll be ther adopton decson for chemcal fertlzer and HYV. Hence, negatve sgn was expected from the fnal probt estmaton result.b.kassa,b.kassaand,k.aregaw 5. DATA ANALYSIS AND RESULTS Ths secton dscusses the estmated results of the probt model. 5.1 Determnants of Agrcultural Technology adopton Decson of Farm Households As we have already ndcated n model specfcaton part, there are two dependent varables (adopton of Chemcal Fertlzer and adopton of HYV) where dfferent ndependent varables have been dentfed and used. Before rushng to econometrc estmaton1 and result dsplay, dfferent econometrc assumptons were tested. In cross sectonal data set, expectng and facng multcollnearty s very much common. To check and address multcollnearty problem, par-wse correlaton matrx was made that could let to drop some of the varables that really show a serous multcollnearty problem. Robust standard error calculaton of probt model was made. Estmate of the probt model for the two dependent varables and the estmaton of Margnal effects of each explanatory varable on the dependent varables are depcted n table below. 5.2.Determnant of chemcal fertlzer Adopton Results from Probt Models The dchotomous regresson model was explanng only the probablty of adopton versus non adoptons. In ths study we used 8 ndependents varables whch are expected to have sgnfcant effect on farmer s adopton of chemcal fertlzer based on the emprcal studes. Based on the Probt regresson and the estmated coeffcents, the frst equaton become; P( CHEMF) =Φ( SEX 0.196AGE EDUC FAMILYSZ MSTATUS NOXEN EXTENSION LANDOWN) Where P ( ) s probablty of adopton of the th farmer andφ ( ) s a cumulatve standard normal dstrbuton functon (CDF) TABLE 5.1 DETERMINANT OF FERTILIZER ADOPTION FROM PROBIT REGRESSION Fertlzer Coef. Std. Err. Z z P>z Margnal effect Sex Age *** Famlysze ** Mstatus Edu Noofoxen ** Extenson Lownersp ** _con LR ch2(8) = Prob>ch = Log lkehood = Pseudo R2 = Source: Own Estmaton Result, 2017 astersks*, **and*** sgnfcant at 10, 5 and 1 % respectvely. SE; Standard Error The estmated parameters of the varables taken as the determnants of new technology adopton (fertlzer use) are dsplayed under table 5.1. Total of 8 explanatory varables were consdered n the econometrc model 3 of whch were found to be nsgnfcant n nfluencng the adopton probablty of fertlzer among farm households. From table 5.1.the R-squared of the model s whch mpled out of the total varaton n the dependent varable, percent s explaned by the varaton n the ndependent varable ncluded n the model whereas the remanng s explaned by other varables not ncluded n the model. Thus these varables collectvely have good explanatory power for the varaton of the dependent varable (new technology adopton) by farm households n Oroma regon. The overall sgnfcance of the model s nsured based on the lkelhood raton (LR) test above as the value of the ch-squares s suffcently low.(prob > ch2 =0.0000) ndcatng (estmated coeffcents are collectvely sgnfcant) that strong statstcal sgnfcance, whch enhanced the relablty and valdty of the model. The regresson result show that educaton, famly sze, land ownershp, and number of oxen, have postve 64

7 nfluence onthe chemcal fertlzer adopton decson; whle age s carred a negatve sgn ndcatng ts negatve correlaton wth chemcal fertlzer adopton decsons. Age was found to be a determnant factor of chemcal fertlzer adopton, postvely; t was found to be sgnfcant at 1% level and negatvely related wth fertlzer adopton decson. Hence, as age ncreases by one year, ctrus parbus, the probablty of fertlzer adopton decson of farm households would decrease by 1.8%. The possble explanaton here s, as age ncreases, farm households would become too reluctant and conservatve n adoptng chemcal fertlzer and do prefer ther anmal dung. Alternatvely, the old age farmers are relatvely conservatve n keepng ther old age technologes and relatvely reluctant n replacng by the modern nputs. Land ownershp status of farm households was found to be statstcally sgnfcant n determnng adopton decson of chemcal fertlzer at 5% sgnfcance level. The magntude of the estmated margnal effect shows that, those who are certfed (whose land ownershp status s secured), keepng other thngs constant, have 55 % hgher probablty of adoptng chemcal fertlzer unlke ther counterparts. Possbly, ownng an arable land could best be taken as a prerequste to adopt and employ agrcultural technologes snce farmers could ncur a cost. Beng a ratonal decson makers, whle ncurrng a cost for technologes, farmers want totally to employ technologes wthn ther own land where the fnal crop yeld could not be shared and sub-dvded whch s too common n sharecroppng system Famly sze s statstcally sgnfcant at 5% sgnfcance level. Hence, as famly sze of household ncreases by one person, ctrus parbus, the probablty of fertlzer adopton decson of farm households would ncrease by 13.13%. Household wth large famly sze has hgh productvty, and hgh lkelhood of adopton of modern agrcultural nputs. Ths may be because of the fact that agrcultural sector s hghly labor ntensve and farmers wth large famly sze take ths advantage. Number of oxen s statstcally sgnfcant at 5% n determnng adopton decson. Hence, as number of oxen ncreases by one unt, ctrus parbus, the probablty of chemcal fertlzer adopton decson of farm households would ncrease by 24.4%. The farmer wth two or more oxen has a hgh fertlzer adopton behavor Determnant of HYV Adopton Results from Probt Model In ths study, we used 14 ndependents varables as the explanatory varables for farmer s adopton of Hgh Yeld Varety (HYV) seeds. The estmates of the parameters of the varable as well as the margnal effect of a unt change of each varable on the probablty of the dependent varable are dsplayed n table 5.2. Total of 14 explanatory varables were consdered n the econometrc model out of whch 6 varables were found to be sgnfcantly nfluencng the adopton probablty among farm households. From table 5.2. The goodness of ft (R-squared) of the model s ndcatng the total varaton n the dependent varable that can be explaned by the ndependent varable s Equvalently, out of the total varaton n the dependent varable, 56 per cent s explaned by the data and the remanng 44 per cent s explaned by the factors other than the explanatory varables. Thus these varables collectvely have good explanatory power for a HYV adopton n the study area, south west shoa zone Wolso dstrct. Gven the estmated coeffcents of the probt model, the equaton for HYV become; P( HYVAD) =Φ( SEX 0.040AGE EDUC FAMILYSZ MSTATUS NOXEN EXTENSION 0.317LANDOWN CREDIT ASSOC ORHTODOX 0.165LANDSZ DISMKT) Where P ( ) s probablty of adopton of the th farmer and Φ ( ) s a cumulatve standard normal dstrbuton functon (CDF). 65

8 Table4.13. Determnant of HYV adopton from probt regresson LR ch2(14) = Prob > ch2 = Log lkelhood = Pseudo R2 = HYV Coef. Std. Err. Z P>z Margnal effect Sex Age * Famlysze ** Mstatus Edu *** Orthodox Lsze Noofoxen Credt * Extenson * Dstance * Lownershp farmassoc~n Dsfromhome _cons ** Source: Own Estmaton Result, 2017 astersks*, **and*** sgnfcant at 10, 5 and 1 % respectvely. SE; Standard Error The regresson result show that famly sze, dstance from market, educaton, access to credt, and contact wth extenson agents have postve mpact on HYV adopton decson whle age of a farmer contrbutes negatvely on the HYV adopton decson. Age was found to be sgnfcant at 10% level and negatvely affects HYV adopton decsons. Hence, as age ncreases by one year, ctrus parbus, the probablty of HYV adopton decson of farm households would decrease by 1.5%. The possble nterpretaton here s, as age ncreases, farm households would become too reluctant and conservatve n adoptng new seed varetes and do prefer ther ndgenous seeds. Educaton was postvely nfluencng the adopton and use of HYV and t s sgnfcant at 1% level. Hence, as educaton level of household ncreases by one year, ctrus parbus, the probablty of HYV adopton decson of farm households would ncrease by 8.5%. Therefore educated farmers are more lkely to adopt HYV as compared to the llterates. Famly sze s statstcally sgnfcant at 5% sgnfcance level. Hence, as famly sze of household ncreases by one ndvdual, ctrus parbus, and the probablty of HYV adopton decson of farm households would ncrease by 20%. Household has several famly sze hgh productvty,and hgh adopton modern technology of agrcultural nputs because of hgh labor force partcpate n producton.. In lne wth ths, farm households who have credt access; keepng other thngs constant, have 30% hgher probablty of adoptng HYV unlke the credt ratoned farmers respectvely. As a lqudty factor, the more farmers have access to source of fnance, the more lkely to adopt agrcultural technologes that could possbly ncrease crop yeld. Dstance to the nearest market was postvely related wth adopton of HYV and statstcally sgnfcant at 10% level of sgnfcance. Actually, there mght be HYV supplers wthn a dstrct or vllage, Hence, there s on tme procurement and dstrbuton., as dstance from the nearest market ncreases by one meter, keepng other thngs constant, the probablty of adoptng HYV would ncrease by 3.86%. ths fndng s aganst the expectaton as access to market would have been contrbuted postvely. The regresson result reveals that contact wth extenson workers postvely affects adopton of HYV and statstcally sgnfcant at 10% level of sgnfcance. The magntude of postve sgn show that, farmers who are vsted by extenson agents, keepng other thngs constant, have 33.8% hgher probablty of adoptng, HYV unlke non-vsted or non-contacted farmer The fndng corroborates wth the fndngs of Ransom et al. (2003); Kandj et al. (2006) and Paudel and Matsuoka (2008). It s worth to note that, access to credt s one best opton whereby smallholders could be nstgated n dversfyng ther economc base and adopt all mperatve yeld ncreasng technologes. As a lqudty factor, the more farmers have access and source of fnance, the more lkely to adopt agrcultural technologes that could possbly ncrease crop yeld. The fndng s n lne wth the fndngs of Uaene et al. (2009) n Ngera. Possbly, ownng an arable land could best be taken as a prerequste to adopt and employ agrcultural technologes snce farmers ncur a cost. Beng ratonal decson makers, whle ncurrng a cost for technologes, totally, farmers want to employ technologes wthn ther own land where the fnal crop yeld could not be shared and sub-dvded whch s too common n sharecroppng system. The fndng s consstent wth the fndngs of Lugandu (2013) 66

9 5. Concluson Ethopa s one of the developng countres strvng for economc development and food self-suffcency through adoptng dfferent polces and strateges. Snce, 1992 the development of Ethopa revolves around productvty enhancement of small scale farmer and ndustralzaton based on utlzaton of domestc raw materal. Agrcultural productvty enhancement requred applcaton of technologcal productve nputs, such as norganc fertlzers. In ths study factors nfluencng fertlzer adopton among small holder farm households were analyzed n the selected Wolso dstrct. The study was based on the data obtaned from randomly selected households through questonnare. Analyss of the extent of fertlzer adopton by the sample households had shown that 75% of the sample households were adopters of chemcal fertlzer and 25% non-adopters. To come up wth the factors responsble of the varaton n the adopton among the HHs, ths research has employed both descrptve and econometrcs analyss. The probt model was chosen as the dependent varables under consderaton are bnary choce n ther nature. Ths research paper examned the underlyng determnants of chemcal fertlzer and HYV adopton by the rural households n Wolso dstrcts of Oroma regon, Ethopa. The probt regresson result show that famly sze, number of oxen land ownershp rght securty were found to be postve n determnng chemcal fertlzer adopton decson. Besdes, ages was statstcally sgnfcant whle nfluencng chemcal fertlzer adopton decson negatvely. Whle adoptng HYV, farm households decson were postvely nfluenced by famly sze, educaton, credt, extenson contact and dstance from market, whereas age carred a negatve coeffcent. Recommendaton Based on the fndngs, the researchers draw the followng polcy mplcatons that could be appled for the enhancement of adopton of mproved modern agrcultural nputs n partcular and the development of agrculture to the rural poor n general. Government should work towards strengthenng the relatonshps between farmers and extenson agents and wden the outreach scheme by gvng dfferent tranng for both of the farm-households and extenson agents. Government should expand and strengthen the extent of educaton and tranng of the farmers to brng necessary mprovements of awareness and change n atttudes. And ths educaton should also be accessble by old age farmers and farmers located n the remote areas Government should work to ensure farmers sense of ownershp by facltatng the provson of land ownershp rght for farmers owng to the fact that tmely provson of landownershp rght (land certfcate) s one way of encouragng farm housholds to use mproved modern agrculture nputs to ther farm land. Credt and savng assocaton and Agrcultural credt nsttutons should wden ther lendng scheme by reachng the low ncome farmers who are unable to purchase agrcultural nputs n order to ncrease productvty and government should work n collaboraton wth these nsttutons References Adeby, S., & Okunlola, J.O. (2013). Factors Affectng Adopton of Cocoa Farm Rehabltaton Technques n Oyo State of Ngera. World Journal of Agrcultural Scences 9 (3): , 2013 ISSN Adesna, A., & Znnah, M. (1993). Technology characterstcs, farmers perceptons and adopton decsons: a Tobt model analyss n Serra Leone. Agrcultural Economcs, 9 Akens, M.T., Havens, A.E., Flnn, W.L. (1975). The adopton of nnovatons: the neglected role of nsttutonal constrants. Mmeograph. Department of Rural Socology. The Oho State Unversty. Columbus, Oho Aknola, A. A. and T. Young (1985) An Applcaton of the Tobt Model n the Analyss of Agrcultural Innovaton Adopton Process: a Study of the Use of Cocoa Sprayng Chemcals among Ngeran Cocoa Farmers, Oxford Agraran Studes, 37: Asfaw, A., and Assefa Admasse. (2004). The role of educaton on the adopton of chemcal fertlzer under dfferent soco-economc envronments n Ethopa. Agrcultural Economcs 30(3): Befekadu D, Berhanu N. And Getahun T,( 2002) EEA annual report on Ethopan economy Adds Ababa, Ethopa Bellanosa and Pava. (1987),Rubensten, J.M. (2003). Defnton of Agrculture Bera, A.K. and T.G. Kelley (1990) Adopton of Hgh Yeldng Rce Varetes n Bangladesh: An Econometrc Analyss, Journal of Development Economcs,33: Bhalla, S. (1979) Farm Sze, Productvty and Techncal Change n Indan Agrculture n R. A. Berry and W. R. Clne (1979) (eds.). Bsrat Akllu (1980) The Dffuson of Fertlzer n Ethopa: Pattern, Determnants and Implcaton, The 67

10 Journal of Developng Areas, 14: Burmester, L. L.(1987) The South Korean Green Revoluton: Induced or Drected Chambers, R.D., A. Pacey and Thrupp L.A. (1989) Farmer Frst- Farmer Innovaton Colman, D. and T. Young (1989) Prncples of Agrcultural Economcs: Market Prces n Less Developed Countres. Cambrdge, Cambrdge Unversty Press CSA (2001), (2003), Central Statstcs Authorty Statstcal Abstract, Adds Ababa, Ethopa EEA (2004) Annual Report on the Ethopan Economy, Vol III, 2003/2004, The Ethopan Economc Assocaton (Adds Ababa, Ethopa) ELls, F. and Freeman, H. Ade, H. (2004). Rural Lvelhoods and Poverty Reducton Strateges n Four Afrcan Countres. Journal of Development Studes 40(4):1-30 FAO (2002). Food and Agrcultural Organzaton, Annual Report, Adds Ababa, Ethopa FAO (World Bank, 2011), Agrcultural knowledge and nformaton system for rural development Feder, G., R.E. Just and D. Zlberman (1985) Adopton of Agrcultural Innovatons n Developng Countres: A Survey, Economc Development and Cultural Change, 33: Gemeda, A., Aboma, G. Verkujl, H., &Mwang,W. (2001). Farmers Maze Seed Systems. Western Oroma, Ethopa. Mexco, D.F.: Internatonal Maze and Wheat Improvement Center (CIMMYT) and Ethopan Agrcultural Research Organsaton (EARO). p 32. Green, G. A. D. and D. H. Ng ong ola (1993) Factors Affectng Fertlzer Adopton n Less Developed Countres: An Applcaton of Multvarate Logstc Analyss n Malaw, Journal of Agrcultural Economcs, 44: Grlches, Z. (1957). Hybrd Corn: An Exploraton n the Economcs of Technologcal Change. Econometrca, 25: Gujarat, D. N. (1992). Essentals of Econometrcs, Fourth Edton, New York: McGraw-hll Inc. Gutknd, E. and D. Zlberman (1985) An Economc Approach to the Dffuson Process, Indan Journal of Economcs, 65: Hayam, Y. and V. W. Rut tan (1984) The Green Revoluton: Inducement and Dstrbuton, The Pakstan Development Revew4 Hesay, et al (1993) Varetals Change n Post-Green Revoluton Agrculture: Emprcal Evdence for Wheat n Pakstan, Journal of Agrcultural Economcs, 44: Innovaton, Economc Development and Cultural Change, 35: Jansen, H. G. P., T. S. Walker and R. Barker (1990) Adopton Celngs and Modern Coarse Cereal Cultvars n Inda, Amercan Journal of Agrcultural Economcs,72: Kandj, S.T., Verchot, L., & Mackensen, J. (2006). Impacts and Adaptaton Strateges n the Agrcultural Sector Impacts and Adaptaton Strateges n the Agrcultural Sector. World Agroforestry Centre (ICRAF) and Unted Natons Envronment Programme (UNEP) Legesse Dad (1998) Adopton and Dffuson of Agrcultural Technologes: A Case of East and West Shewa Zones, Ethopa, PhD Thess, unversty of Manchester School of Economcs Studes, UK. LegesseDad (1992). Analyss of Factors Influencng Adopton and the Impact of Wheat and Maze Technologes. ArsNegele Area of Ethopa, M.Sc. Thess, Haramaya Unversty of Agrculture, Haramaya, Ethopa. Lelssa Chalchssa (1998) The Determnants of Adopton, Intensty and Proftablty offertlzer Use: The Case of Ejere Dstrct, West Shewa Zone, M.Sc. Thess, Adds Ababa Unversty. Ln, J. Y. (1991) Educaton and Innovaton Adopton n Agrculture: Evdence from Hybrd Rce n Chna, Amercan Journal of Agrcultural Economcs, 73: Lugandu, S. (2013). Factors Influencng the Adopton of Conservaton Agrculture by Smallholder Farmers n Karatu and Kongwa Dstrcts of Tanzana Presented at REPOA s18thannual research workshop held at the Kunduch Beach Hotel, Dar es Salaam, Tanzana Mahabub, H. (1988) Nature and Impact of the Green Revoluton n Bangladesh, Internatonal Food Polcy Research Insttute n Collaboraton wth The Bangladesh Insttute of Development Studes, Research Report 67,Washngton D. Mansfeld, E. (1961) Techncal Change and the Rate of Imtaton, Econometrca, 29: Morrs, M., Doss, C. (1999) and schumters. (2001). How does gender affect the adopton of agrcultural nnovatons? The case of mproved maze technology n Ghana : Paper Presented at the Annual Meetng, Amercan AgrculturalEconomcs Assocaton (AAEA), Nashvlle, Tennessee, August 8-11 Mosher, T.A. (1979) An Introducton to Agrcultural Extenson. Sngapore Unversty Press for the Agrcultural Development Councl. Mulat and Bekele (2003), the determnant of yeld of major cereals n Ethopa, namely Amhara, Tgray, Oromya and Southern Natons and Natonaltes Peoples (SNNP). Mulat Demeke and Bekele Hunde (2003). The Determnants of Yeld of Major Cereals: The Contrbuton 68

11 of New Technologes n Selected Vllages of Ethopa, Mulat et al (2003) Technologcal Progress n Ethopan Agrculture : Proceedngs of the Natonal Workshop or Technologcal Progress n Ethopan Agrculture, Adds Aba. Ngusse Tefera (2001) The Productvty and Proftablty of Wheat and Teff Technologes n Selected Vllages of Ethopa M. Sc. Thess, Adds Ababa Unversty. Perrn, R. and D. Wnkelmann (1976) Impedment to Techncal Progress on Smaleversus Large Farms, Amercan Journal of Agrcultural Economcs, 58: Ptt, M. M. and G. Sumodnngrat (1991) Rsk, Schoolng and the Choce of Seed Technology n Developng Countres: A Meta Proft Functon Approach, Internatonal Economc Revew, 32: Rahm, M. R. and W. E. Huffman (1984) The Adopton of Reduced Tllage: The Role of Human Captal and Other Varables, Amercan Journal of Agrcultural Economcs, 66: Rogers, E.M. (1983) Dffuson of Innovatons, New York: The Free Press. Ruttan, V. W. (1977) The Green Revoluton: Seven Generalzatons, Internatonal Development Revew, 19: Ryan, J.G. and K.V. Subrahamanyam (1975) Package of Practce Approach n Adopton of Hgh Yeldng Varetes: An Apprasal, Economc and Poltcal Weekly. Samuel G. (2010). Technques of ncreasng agrcultural productvty n developng countres. Gezahegn Ayele (2004) Adoptons of Improved Technologes n Ethopa, Ethopan Development Research Insttute, Research Report 3. Sngn, (1986). Xasong, (1998). The dffuson Model Theory.Department of agrculture n USA. Strauss, J., M. Barbosa, S. Texera, D. Thomas and R. G. Junor (1991) Role of Educaton and Extenson n the Adopton of Technology: A Study of Upland Rce and Soyabean Farmers n Central West Brazl, Agrcultural Economcs, 5: Tesfa Tekle (1975) Applcaton of Multvarate Probt Analyss to Adopton Model of New Agrcultural Practces, Ethopan Journal of Development Research, 2: Tewodaj, M., et al (2009). Agrcultural Extenson n Ethopa through a Gender and Governance Lens.Ethopa Strategy Support Program 2 (ESSP2) Dscusson Paper No. ESSP2 007 Yaman T. (1967) The sample sze determnaton Yohannes Kebede, K. Gunjal and G. Coffn (1991) Adopton of New Technologes n Ethopan Agrculture: the Case of Tegulet-Bulga Dstrct, Shewa Provnce, Agrcultural economcs, 4: Zegeye Halu (1989) Adopton of Modern Farm Practces: The Role of Locaton, Market Structures and Household Characterstcs n North Ghana. 69