Technical Efficiency and Technological Change of Rice Farms in Mekong Delta, Vietnam

Transcription

1 Techncal Effcency and Technologcal Change of Rce Farms n Mekong Delta, Vetnam Nguyen Huu Dang, College of Economcs, Can Tho Unversty, Vetnam. E-mal: nhdang@ctu.edu.vn Abstract The am of ths study s to determne the techncal effcency and technologcal change of rce farms n Mekong Delta, Vetnam based on a panel data collected n two years (2011 and 2015) from 152 sample rce farmers n four selected provnces n Mekong Delta. The Cobb- Douglas stochastc fronter producton functon (pooled 2-perod data set) wth tme dummy as a proxy of technology, ncorporatng neffcency effects was employed to analyze the data, usng the FRONTIER 4.1. The results revealed that the average techncal effcency (TE) was 87.92%, the techncal effcent change by -1.9 percent and technologcal change by 3.1% durng perod Sgnfcant factors that were found to postvely affect rce output per farm were area, phosphate fertlzer, labor, varety, and sol ndex whle ntrogen fertlzer was negatvely related to the rce output per farm. Sgnfcant determnants of techncal effcency were postvely related to TE were educaton attanment, farm sze, tranng, membershp a farmers assocaton. Key Words: Techncal effcency, technologcal change, techncal progress, stochastc fronter producton functon, rce farms. JEL Classfcaton: C 19, G13, G 14 1

2 1. Introducton Rce producton n Vetnam s mostly concentrated n the Mekong Delta, whch s located n the Southern part of Vetnam, consstng of 13 provnces and coverng 12 percent of the total country s land area. The Mekong Delta covers more than four mllon ha of natural land area, three-fourths of whch s agrcultural land, and the rest s comprsed of rvers and other uses. The Mekong Delta plays a key role n the country s food securty and export. It contrbuted about 90 percent of the country s rce export n volume. Durng perod , the regon s rce producton growth was manly attrbuted to the ncrease n both rce croppng ntensty and yeld. Annual growth rate n croppng ntensty was 2.1 percent durng ths perod whle annual growth rate n yeld was 3.03 percent per year (ADB, 2000). Rce croppng ntensty rato ncreased from 1.3 crops per year to 1.7 crops per year n ths perod. Improvements n rrgaton and dranage n Mekong Delta allowed rce mono croppng to be converted nto double-rce and trple-rce croppng systems. Durng the perod , rce yeld growth was the only a factor that contrbuted to the regon s rce producton growth (2.4%/year) snce the average annual growth rate n rce sown area was negatve (0.16%/year). However, rce yeld n the Mekong Delta durng ths perod grew at a slow pace and was erratc. Annual growth rate n rce yeld was 2.59 percent n , whch was lower than the perod Moreover, the country experenced three years (2001, 2006, and 2009) of negatve growth n rce yeld due to unfavourable clmate and pest and dsease nfestaton. Durng the perod , the rce producton output of the regon grew at 3.57 percent/year, whch was contrbuted by the growth rate of yeld by 1.8 percent/year and sown area by 1.80 percent/year. Recently, rce producton n the Mekong Delta has been confronted wth problems such as the rapd ncrease n labor cost and other materal nput costs, whch n turn, caused the decrease n the farmers levels of nput use. A reducton n nput use may have negatve mpacts on rce yeld and the productve effcency of rce farmers as well. These lead to questons are that how are the drecton changes of techncal effcency and techncal progress (technologcal changes) of the rce farms over tme and what factors affect techncal effcency of the rce farms. Thus, ths study s am to determne the techncal effcency, technologcal change and determnant of techncal effcency of rce farms n Mekong Delta. 2. Lterature Revew Technologcal change s change n the producton functon (Mansfeld, 1968). The producton functon shows, for a gven level of technologcal change, the maxmum output rate whch can be obtaned from a gven amount of nputs (Mansfeld, 1968).Technologcal change can be nterpreted as the shft of the producton fronter over tme (Coell et al, 2005). There are three possble outcomes of technologcal change to the fronter between two perods 2

3 (Coell et al, 2005, Trumata, 2009): () change n the slope of the fronter n perod 2 leavng the ntercept unchanged; () change n the ntercept of the fronter n perod 2 leavng the slope unchanged; and () change n both the ntercept and slope of the fronter between two perods. Technologcal change s due to major factors of machnes, mechanzaton, automaton, technology, nventon, nnovaton (Benot, G., 2015). Techncal effcency, whch reflects the ablty of the frm to obtan maxmum output from a gven set of nputs (Farrell, 1957). It ndcates techncal effcency s the rato of the actual output over the maxmum output. There are plenty of studes on the determnants of techncal effcency. Kalrajan and Flnn (1983) found that the practce of transplantng rce seedlngs, ncdence of fertlzaton, years of farmng, and number of extenson contacts had sgnfcant nfluence on the varaton of the estmated rce farm techncal effcences n the Phlppnes. In addton, Najma and Atul (1996) found that techncal effcency was hgher for the hgh-yeldng varety (HYV) Boro crop as compared to the tradtonal Aman crop of rce farmers n Bangladesh. Adam et al. (2003) revealed that Farm-level specalzaton was found to have a postve effect on effcency whle land fragmentaton was detrmental to effcency n Chnese gran sector. Tjan (2006) the levels of techncal effcency largely ranged from 29.4 percent to 98.2 percent n the rce farmng n Osun State, Ngera. Surender (2007) ndcated that small-sze farms are more effcent than medum- and large-sze farms. Idong (2007) showed that farmers educatonal level, membershp n a cooperatve/farmers organzaton and access to credt sgnfcantly and postvely nfluenced the farmers effcency. Aynde et al. (2009) found that Farm sze, hred labor, fertlzer, seed, age, gender, household sze and amount of credt were the sgnfcant determnants of techncal effcency of rce farmers n Ngera. Jyot et al. (2010) the farm sze and female workers were postvely related wth techncal effcency. 3. Methodology 3.1 Research Questons There are two major research questons. One of them s that how are the drecton changes of techncal effcency and techncal progress (technologcal changes) of the rce farms over tme n study area. Another queston s that what factors affect techncal effcency of the rce farms durng examnng perod. In order to answer these questons, the stochastc fronter producton functon (pooled 2-perod data set) wth tme dummy as a proxy of technology, ncorporatng neffcency effects was employed to analyze the data. 3.2 Stochastc Fronter Producton Functon The stochastc fronter producton functon was ndependently proposed by Agner, Lovell, and Schmdt (1977) and Meeusen and van den Broeck (1977). The orgnal 3

4 specfcaton nvolved a producton functon specfed for cross-sectonal data whch had an error term wth two components, one to account for random effects and another to account for techncal neffcency. Followng Battese (1992), the stochastc fronter producton functon can be expressed n the followng form: Y f ( x ; )exp( V U ) (1) where = 1, 2,, N and Y represents the possble producton level for the th sample unt; f( x ; ) s a sutable functon (e.g., Cobb-Douglas or Translog) of the vector, x of nputs for the th unt and a vector; s a vector of parameters to be estmated; and N represents the number of the unts nvolved n a cross-sectonal survey. Ths model s such that the possble producton Y s bounded above by the stochastc quantty, f ( x );exp( v ), hence, the term stochastc fronter. Besdes, V s the symmetrc error term accountng for random varatons n output due to factors outsde the control of the farmer such as weather, dsease, bad luck, and measurement error whereas U represents the techncal neffcency relatve to the stochastc fronter, whch assumes only postve values. The dstrbuton of the symmetrc error component V s assumed to be ndependently and dentcally dstrbuted as N. 2 (0, v ) However, the dstrbuton of the one sded component u s assumed to be half normally (u > 0) dstrbuted as N and, thus, measures shortfalls n producton from ts notonal 2 (0, u ) maxmum level. If u = 0, then the farm les on the fronter obtanng maxmum output gven varable and fxed nputs; but, f u > 0, then the farm s neffcent and makes losses or the producton les below the fronter functon and the dstance of Y and Y * measures the extent of the farmers techncal neffcency (Coell et al, 2005).. Therefore, the larger the one sded error, the more neffcent the farm s. Techncal effcency. The techncal effcency of an ndvdual producng unt s defned n terms of the rato of the observed output of the correspondng fronter output, gven the avalable technology (Coell et al, 2005). Thus the techncal effcency of unt n the context of the stochastc fronter producton functon s the followng expresson. TE exp( U ) (2) TE Y Y f x V U f x V U (3) * / ( ; )exp( ) / ( ; )exp( ) exp( ) Y s an observed output and * Y s the fronter output. X, s, and V are as defned earler. In ths case, Y acheves ts maxmum value of f ( x ; ) exp( V ) f and only f TE = 1. Otherwse, TE < 1 provdes a measure of the shortfall of observed output from maxmum 4

5 feasble output n an envronment characterzed by stochastc elements that vares across producers. 3.3 The Emprcal Model Usng panel data gathered from the two surveys, ths study employed the stochastc fronter analyss followng the sngle-stage estmaton procedure developed by Battese and Coell (1995, 2005). The advantage of usng stochastc fronter model s that t can help n understandng the causes of productvty changes over tme. The stochastc fronter producton functon would be estmated by the Cobb-Douglas or the translog forms as follows: set): - The Cobb-Douglas stochastc fronter producton form: (pooled data 2-perod data 8 2 (4) lny t ln X D V U t 0 j jt l lt t t j 1 k 1 - Translog stochastc fronter producton form (pooled data 2-perod data set: lny t ln X D ln X ln X 0 j jt l lt jk jt kt j 1 l 1 2 j 1 k (5) ln X * D t ln X V U jl jt lt tj jt t t j 1 l 1 j 1 where, θ: tme dummy coeffcent; β j and β l: regresson coeffcents of the explanatory varables n the estmated stochastc producton functon, except for tme dummy, where j = k = 1, 2 8; t = 0, 1; l = 1, 2; Y : rce producton output (kg/farm). X jt are factors contrbutng to rce output per farm, consstng of: X 1t: land area (ha/farm); X 2t: amount of seed used (kg/farm); X 3t: amount of ntrogen used (kg/farm); X 4t: amount of phosphate used (kg/farm); X 5t: amount of potash used (kg/farm); X 6t: amount pestcde used (g/farm); X 7t: human labor used (man-days/farm); X 8t: sol ndex (the best sold s 100 whle the worse sol s 1 n terms of sold qualty based on the classfcaton of local government). D lt: other factors contrbutng to rce output per farm such as: D 1t: rce varety dummy (1 = mproved varety; 0 = tradtonal varety); D 2t: sowng type dummy (1 = lne sowng; 0 = broadcast sowng). t: tme dummy varable (base year = 0; year ahead = 1). V t: random varable assumed to be ndependently and dentcally dstrbuted (d) N (0, σ v2 ) and ndependent of U ; U t: nonnegatve random varable that s assumed to account for techncal neffcency n producton. The subscrpts j or l, and t refer to the j th or l th nput used of th farm n the t th tme perod. Smultaneously estmated wth the fronter model was the rce farmer level techncal neffcency (TIE) model. The TIE model for the rce farm s expressed mathematcally as follows: 5

6 TIE t U t 10 Z 0 j jt t (6) j 1 where, δ j: regresson coeffcents of the explanatory varables n the estmated techncal neffcency model where j= 1, 2 10; Z 1t: factors contrbutng to techncal neffcency such as, Z 1t: gender of farmer dummy (male = 1; female = 0); Z 2t: educaton attanment of farmer (years of schoolng); Z 3t: experence of the farmer n rce farmng (years); Z 4t: household members farmng number of famly members engaged n rce farmng (persons/household); Z 5t: farm sze dummy (area 0.6 hectare = 1; area < 0.6 hectare = 0); Z 6t: tenancy (proporton of rented land n total land area under rce) (%); Z 7t: credt access dummy (wth credt = 1; no credt = 0); Z 8t: attendance n tranng on rce producton dummy (wth tranng = 1; no tranng = 0); Z 9t: membershp n farmers organzaton dummy/cooperatve (member = 1; not member = 0); Z 10t: dstance from the rce feld to farmer s house (km); ξ t: error terms, assumed to be ndependently and dentcally dstrbuted wth mean = 0 and varance = σ ξ2 ; and the subscrpts j, and t refer to the j th characterstc of th farm n the t th tme perod. - Test for the approprate functonal form (.e., Cobb-Douglas vs. Translog): the approprate functonal form was determned usng the followng selecton crteron: () overall sgnfcance of the estmated equaton based on the generalzed Lkelhood Rato (LR) test, () the number of sgnfcant varables based on the t-test, () consstency of sgns of the MLE coeffcents wth economc theory, and (v) absence of multcollnearty. The lkelhood rato statstc ( ) used for the generalzed Lkelhood Rato (LLR) test s gven as: = -2[(L (H 0) - L (H 1)] (7) where, L (H 0): value of the log-lkelhood functon of a restrcted fronter model (or the Cobb-Douglas) as specfed by a null hypothess, H 0; L (H 1): value of the log-lkelhood functon of an unrestrcted fronter model (or translog model) as specfed by the alternatve hypothess, H 1. The LR test statstc (( ) has approxmately a ch-square ( 2 ) dstrbuton wth the number of degrees of freedom equal to the dfference between the parameters nvolved n H 0 (Cobb-Douglas) and H 1 (translog). The crtcal value was obtaned from the normal 2 table. The decson for ths test was to reject the null hypothess (H 0) f s greater than the crtcal 2 value and vce versa. - Test for the approprate fronter estmators (OLS vs. MLE): Usng the same statstcal testng procedure (generalzed LR test) as testng for approprate functonal form mentoned above. However, L (H 0) n the formula refers to the value of the log-lkelhood functon of the OLS fronter model as specfed by the null hypothess, H 0, whle L (H 1) s the value of the log-lkelhood functon under the alternatve hypothess, H 1 (.e., MLE model). Smlarly, the test statstc has approxmately a ch-square dstrbuton. The degree of freedom s equal to the number of parameters nvolved n the neffcency model plus one (k +1), where k s the 6

7 number of parameters or restrctons or explanatory varables except the ntercept. The crtcal 2 value was obtaned from the Kodde and Palm (1986). The decson rule for ths test s to reject the null hypothess (H 0) f s greater than the crtcal 2 value and vce versa. Anyway, another test would be able to employ. The value of gamma parameter may le between zero and one. A value of = 0 ndcates that techncal neffcency s absent and the OLS s a more adequate estmaton procedure to descrbe the parameters n the model. A value of close to one means that there exsts techncal neffcency n the model, or f = 1, all the devatons from the fronter are entrely due to techncal neffcency and the MLE adequately characterzes the data. LR results for the functonal and fronter estmaton method tests were automatcally derved by usng the FRONTIER 4.1 computer program. 3.4 Data A panel data collected from 152 sample rce farmers over two years (.e., 2011, and 2015) n four selected provnces n Mekong Delta, namely An Gang, Dong Thap, Tra Vnh, and Soc Trang. The survey n 2011 s a repeated survey as the same respondents wth the survey n 2008 conducted by the author for hs dssertaton. In the frst survey n 2008, stratfed random samplng was employed. The respondents were dvded nto two groups based on farm sze. The average farm sze n Mekong Delta s around 0.6 ha. About 50 percent of the 160 sample respondents were selected randomly from those who have above 0.6 ha per farm and another 50 percent from those who have below 0.6 ha per farm. For each provnce, a sample vllage was selected n consultaton wth local experts. About fourty sample farmer respondents were randomly chosen n each sample vllage. The survey was conducted n 2011, the same sample farmers were agan personally ntervewed usng pretested questonnares. However, out of the 160 sample respondents covered n the 2011 survey, fve sample respondents sold ther rce farm. Hence, the total sample respondents ncluded n the survey n 2011 was fall to 155 sample respondents. In the survey n 2015, the survey procedures were employed. However, three sample respondents sold ther rce farm. Hence, the sample sze was fall to 152 sample respondents and the total panel data set conssted of 304 observatons for the two-year perod (2011 and 2015) covered n ths study. 4. Results and Dscusson 4.1 Input Use and Yeld of the Sample Rce Farmers The average amount of seeds used by the sample rce farmers sgnfcantly decreased by 26.4 kg/ha from 2011 to 2015 (Table 1). Ths could be attrbuted to the ncrease n the number of sample farmers who swtched to the lne sowng method and the use of mproved rce varetes n The sample rce farmers who adopted the lne sowng method and the mproved varetes used, on the average, lesser amounts of seeds per hectare compared to those who used the broadcast sowng method and the conventonal rce varetes. 7

8 The sample farmer-respondents appled several types of fertlzers. The most commonly used fertlzers were urea, ammo-phos (or D-Ammonum Phosphate), complete fertlzer (contans ntrogen, phosphorous, and potassum) and murate of potash, among others. In terms of actve fertlzer ngredent form, ntrogen and phosphate use exhbted declnes of 14.1 kg/ha and 4.8 kg/ha, respectvely durng perod, whch were statstcally sgnfcant at one percent probablty level. The reducton n the afore-mentoned types of norganc fertlzers mght also be attrbuted to the ncrease n the number of farmers who shfted to the use of the mproved rce varetes and the lne sowng method, whch requre lesser amounts of most types of norganc fertlzers. Pestcde use, on the other hand, dd not sgnfcantly change from 2011 to 2015 at 10 percent probablty level. On the average, labor use sgnfcantly decreased from 34.4 man-days/ha n 2011 to 29.4 man-days/ha at one percent probablty level. There was a marked reducton n the use of labor for harvestng operatons due to the ncreased adopton of mechancal harvesters. Paddy yeld of the sample rce farmers-respondents sgnfcantly ncreased from 6,997.4 kg/ha n 2011 to 7,216.3 kg/ha n 2015 despte decreases n the use of seeds and norganc fertlzers lke ntrogen and phosphate. The ncrease n paddy yeld could be attrbuted to the ncrease n the applcaton of mproved varetes and lne sowng method. Table 1: Mean Levels of Input Use per Hectare and Paddy Yeld, 152 Sample Rce Farmer Respondents, Selected Provnces n Mekong Delta, Vetnam, 2011 and 2015 ITEM QUANTITY CHANGE PERCENT Seed (kg/ha) ,4 *** -15,61 Fertlzers by ngredents: Ntrogen (kg/ha) ,1 *** -12,08 Phosphate (kg/ha) ,8 *** 6,39 Potash (kg/ha) ,1 ns 4,00 Pestcde by actve ngredents (g/ha) 1, , ,5 ns -1,72 Labor (man-days/ha) ,7 *** -13,66 Rce yeld (kg/ha) 6, , ,9 * 3,13 Note: ***, **, and * ndcate sgnfcant at 1%, 5%, and 10% probablty level, respectvely; and ns denotes nsgnfcant at 10% probablty level. 4.2 Testng Results for Approprate Functonal Form and Estmator The result of LR test ndcated that the translog functonal form was more approprate than the Cobb Douglas snce the value of lkelhood rato statstc ( ) was , whch was greater than that of crtcal value (60.097). Therefore, the Ho was rejected. However, except the nteracton and square varables n the tranlog model, the Cobb Douglas resulted n more sgnfcant varables than the translog model based on T-test. Moreover, the sgns of 8

9 coeffcents of varables n the Cobb Douglas were more consstent than those of the translog model. In addton, based on the result of testng for multcollnearty, the translog model contaned serous multcollnearty problem. Hence, the Cobb Douglas functonal form was chosen to analyze the data. Besdes, gamma parameter was close to 1 (0.872), whch ndcated the exstng of techncal neffcency n the model. Thus, the MLE was adequately characterzes the data. 4.3 Results of the Stochastc Fronter Producton Analyss The result of the fronter producton functon revealed that the area, ntrogen, phosphate, labor, rce varety, and sol ndex sgnfcantly affected rce output per farm at one or fve percent probablty level, whle the seeds, potash fertlzer, pestcdes and sowng dummy were found to have no sgnfcant effects on rce output per farm at 10 percent probablty level. In a Cobb-Douglas fronter producton functon, the regresson coeffcents are already the output elastctes. For nstance, the regresson coeffcent of area of 0.82 ndcates that a one percent ncrease n cultvated area would result n a 0.82 percent ncrease n rce output per farm, ceters parbus. Wth regard to fertlzer usage, the regresson coeffcent of ntrogen fertlzer s , whch means that a one percent ncrease n ntrogen fertlzer would reduce rce output per farm by percent, other factors held constant. Contrary to expectatons, ntrogen fertlzer exhbted a negatve sgn. The negatve relatonshp between ntrogen fertlzer and rce output per farm mght be due to overuse of ntrogen fertlzer. Phosphate, on the other hand, postvely nfluenced rce output per farm. The regresson coeffcent of phosphate of ndcates that a one percent ncrease n phosphate fertlzer would ncrease rce output per farm by percent, other factors held constant. Lkewse, labor exhbted a postve effect on rce output per farm. Its regresson coeffcent of mples that a one percent ncrease n labor would ncrease rce output per farm by percent, ceters parbus. Smlarly, the regresson coeffcent of varety s postve (0.059), mplyng that rce farms planted to mproved varetes have a hgher rce output per farm than those planted to conventonal varetes, other factors held constant. The regresson coeffcent of sol ndex s also postve, whch means that good qualty sols (.e., sol classes wth hgh sol ndex scores) have a hgher rce output per farm than sol classes wth poor qualty (low sol ndex scores). Technologcal change: Based on the estmated stochastc producton functon, the presence of technologcal change can be determned by evaluatng the coeffcent of the tme dummy varable n the model. The results of the MLE estmates for tme dummy varable (θ) n the estmated Cobb-Douglas producton functon coverng the perod was 3.1 percent and statstcally sgnfcant at one percent probablty level. Ths mples that the 9

10 change n technology sgnfcantly altered the ntercept of the fronter. Increased adopton of mproved rce varetes was the technologcal change n the study areas. Table 3: MLE of the Cobb-Douglas Stochastc Producton and Techncal Ineffcency Functons, 152 Rce Farmer-Respondents Selected Provnces n Mekong Delta, Vetnam, 2011 and 2015 Varable symbol Varable name Parameter Coeffcent Std. Error T-rato Fronter Producton Functon Constant β *** ln A Area (kg) β *** ln S Seed (kg) β ns ln N Ntrogen (kg) β ** ln P Phosphate (kg) β *** ln K Potash (kg) β ns ln LP Pestcde (g) β ns ln L Labor (man-day) β *** DV Varety dummy β *** So Sowng dummy β ns SI Sol ndex β *** T Tme dummy θ *** Techncal Ineffcency Functon Constant ns Z 1 Gender dummy ns Z 2 Educaton attanment (years) ** Z 3 Farmng experence (years) ns Z 4 Household farm labor (persons) * Z 5 Farm sze dummy *** Z 6 Tenancy rate (%) ns Z 7 Credt access dummy ns Z 8 Tranng dummy * Z 9 Membershp dummy *** Z 10 Dstance (feld-house) (km) ns Varance Parameter σ *** *** Log-lkelhood functon LR test of the one-sded error Mean techncal effcency (%) Note: ***, **, and * ndcate statstcally sgnfcant at 1%, 5%, and 10% probablty level, respectvely; and ns denotes nsgnfcant. Determnants of techncal effcency: The average techncal effcency was percent, whch mples that wth the recent nput level, the sample farmers could be able to ncrease ther rce output by percent by mprovng techncal effcent factors. Ths s to examne the effects of soco-economc and farm-specfc factors on techncal effcency of the sample rce farmer-respondents. A negatve sgn of the regresson coeffcent of an explanatory varable n the techncal neffcency functon ndcates that the varable mproves techncal 10

11 effcency. A postve sgn means the opposte. The factors whch were found postvely affect techncal effcency of the rce farmer-respondents were educaton attanment, household farm labor, farm sze, partcpaton n rce producton tranng programs, and membershp n a farmers assocaton. The postve relatonshp between educaton attanment and techncal effcency mght also be attrbuted to that the hgher educated farmers adopted new producton technology better than the lesser educated farmers. Lkewse, the regresson coeffcent of partcpaton n tranng dummy has a negatve sgn, whch ndcates that the sample rce farmers who partcpated n tranng programs on rce producton whch were conducted by the staff of the Department of Agrculture and Rural Development and some NGOs were more techncally effcent than those who dd not attend the afore-mentoned tranng programs. The explanaton s that the sample rce farmers who attended tranng programs on rce producton learned more about new technologcal developments and therefore were able to adopt better farm management practces n rce producton. Thus, they tended to have more effcent use of resources than those who were not able to attend any tranng at all. Ths fndng confrms the results of Seyoum et al. (1998), Wlson et al., (2001), and Sedu (2008) who reported that farmers who sought techncal nformaton and had adequate extenson contact were assocated wth hgher levels of techncal effcency. Smlar fndngs were also found Kelvn, et al. (2008) n rce farmng n Bangladesh. Smlarly, the regresson coeffcent of membershp n a farmers assocaton dummy exhbted a negatve sgn and s statstcally sgnfcant at one percent probablty level. Ths suggests that the farmers who are members of farmers organzaton are more techncally effcent than non-members, whch mght also be attrbuted to that the members of assocaton have better chance to exchange producton experence among the members and more frequency n partcpate n tranng program conducted by extenson workers that help them have more effcent use of resources than those who were non-members. Ths fndng s consstent wth the fndng of Idong (2007) n hs study of small-scale rce farms n the Cross Rver State of Ngera. Lkewse, the household farm labor and farm sze were found postve effects to techncal effcency. These fndngs mght be attrbuted to that wth the larger farm, more household farm labor, the farmers tends to spend more efforts on new producton technology than those have smaller farm, lesser household farm labor. On the other hand, gender, farmng experence, tenancy, credt access dummy and the dstance of the house to the farmer s rce feld had no sgnfcant effects on techncal effcency at ten percent probablty level. Change n techncal effcency levels over tme: The average techncal effcency was 88.7 percent and 86.8 percent n 2011 and 2015, respectvely. However, there was a decrease of 1.9 percent n the average techncal effcency levels between the two study perods (Table 11

12 4). The wde techncal effcency dfferentals among the sample rce farmers over the twoyear perod are ndcaton of a substantal potental for effcency mprovement n rce producton n the study areas. The estmated mean techncal effcency ndcates that, on the average, the sample rce farmers tend to realze about 88.7 percent and 86.8 percent of ther techncal abltes n 2011 and 2015, respectvely. In other words, the sample rce farmers, on the average were producng rce up to about 88.7 percent and 86.8 percent of the potental (stochastc) fronter producton levels n 2011 and 2015, respectvely, gven the levels of ther nputs and technology beng used. The results ndcate that 11.3 percent and 13.2 percent of the techncal rce output potental were not realzed n 2011 and 2015, respectvely. In other words, rce output of the average rce farmers n 2011 could be ncreased by 11.3 percent by adoptng the technology followed by the best practce farmers. Lkewse, rce output of the sample rce farmers could be ncreased by 13.2 percent n 2015 by adoptng the technology of the best performers. Dstrbuton of techncal effcences: In 2011, the predcted techncal effcences of the sample rce farmers n selected provnces of Mekong Delta dffered substantally rangng from 64.6 percent to 97.1 percent. About 15.8 percent of the total sample farmers belonged to the most effcent category (95-100%). Only few (3.9%) of the sample farmers had techncal effcences below 70 percent. Majorty (42.1%) of the sample rce farmers belonged to the category (90 - >95%), ndcatng that most of the rce farmer-respondents were very techncally effcent (Table 4). Indvdual techncal effcency level n 2015 ranged from 66.2 percent to 98.4 percent (Table 4). Approxmately 14 percent of the total sample farmers belonged to the most effcent category (95-100%). Only few (2.6%) of the sample farmers had techncal effcences below 70 percent. Most of the sample rce farmers (42.8%) belonged to the category (90 - >95%). However, n 2015, 3 farmer-respondents who belonged to the category swtched to the less effcent category. Otherwse, another 2 farmer-respondents who belonged to the category less than 70 percent swtched to the better effcent category. Thus, the numbers of sample farmers swtched to better category are less the those swtched to the less effcent category, whch leads to the mean techncal effcency n 2015 lower than that of Table 4: Dstrbuton of Techncal Effcency of 152 Rce Farmer-Respondents, Selected Provnces n Mekong Delta, Vetnam, 2011 and 2015 TECHNICAL EFFICIENCY (TE, %) No. of Farmers CHANGE Percent No. of Farmers Percent No. of Farmers Percent < < < <

13 85-< < Total Average a * Mnmum Maxmum Std. Dev * Sgnfcant at 10 percent probablty level 5. Conclusons and Recommendatons The study s to determne the techncal effcency and technologcal change of rce farms n Mekong Delta, Vetnam based on a panel data collected n two years (2011 and 2015) from 152 sample rce farmers n four selected provnces n Mekong Delta. The Cobb-Douglas stochastc fronter producton functon (pooled 2-perod data set) wth tme dummy as a proxy of technology, ncorporatng neffcency effects was employed to analyze the data, usng the FRONTIER 4.1. The results revealed that the average techncal effcency (TE) was 87.92%, the techncal effcent change by -1.9 percent and technologcal change by 3.1% durng perod Sgnfcant factors that were found to postvely affect rce output per farm were area, phosphate fertlzer, labor, varety, and sol ndex whle ntrogen fertlzer was negatvely related to the rce output per farm. Sgnfcant determnants of techncal effcency were postvely related to TE were educaton attanment, farm sze, tranng, membershp a farmers assocaton. In order to further mprove the rce yeld and techncal effcency of rce farms, the rce producton should be mprove fertlzer management focusng on effcent use of fertlzer; Sol fertlty management; ntensfy extenson servces partcularly the conduct of tranng programs; provde contnuous support for massve propagaton and dspersal of hgh-yeldng varetes n cooperaton wth the prvate sector; strengthen/reactvate farmers assocaton; and mprove the level of educaton of farmers through short techcal tranng. References Adam, Z., E. Wallace and R. Scott, Techncal Effcency of Chnese Gran Producton: A Stochastc Producton Fronter Approach. Paper prepared for presentaton at the Amercan Agrcultural Economcs Organzaton Annual Meetng: Agner, D., C. Lovell and P. Schmdt, 1977, Formulaton and Estmaton of Stochastc Fronter Producton Functon Models. Journal of Econometrcs 6: Anuradha, N. and Y. Zala, 2010, Techncal Effcency of Rce Farms under Irrgated Condtons n Central Gujarat. Agrcultural Economcs Research Revew 23: Aynde, O., M. Adewum and V. Ojehomon, Determnants of Techncal Effcency and Varetal- Gap of Rce Producton n Ngera: A Meta-Fronter Model Approach. Paper prepared for presentaton at the Internatonal Organzaton of Agrcultural Economsts Conference, Bejng, Chna, Battese, G. and Coell, T., 1992, Fronter Producton Functons, Techncal Effcency and Panel Data: Wth Applcaton to Paddy Farmers n Inda. Journal of Productvty Analyss 3:

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