Determinants of Profit Efficiency among Rice Farmers in Kien Giang Province, Vietnam

Transcription

1 Determnants of Proft Effcency among Rce Farmers n Ken Gang Provnce, 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 dentfy determnants of proft effcency among rce farmers n Ken Gang provnce, Vetnam based on the data collected from 302 sample rce farmers n three dstrcts of Ken Gang provnce. The Cobb-Douglas stochastc fronter proft functon ncorporatng proft neffcency effects was employed to analyze the data, usng the FRONTIER 4.1. The results revealed that the proft effcency was range between to percent, averages of percent. Sgnfcant factors that were found nagatve affect the rce farm proft were prces of fertlzer, pestcde whle postve effects were prce of seed, wage rate and land area (fxed factor. Sgnfcant determnants that were found postve affect proft effcency of rce farmers were educaton attanment, household farm labor, farm sze dummy, tranng dummy, farmer s assocaton membershp dummy whle negatve effects were farmng experence and dstance from the man feld to the key nput maket. Key Words: proft effcency, determnant of proft effcency, stochastc fronter proft 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. Rce cultvaton s the most mportant subsector of n Ken Gang snce t plays a crucal role n employment creaton, ncome generaton especally from rce exports, poverty reducton, and food securty for the regon and for the country as a whole. However, t s dffcult to expand rce producton by ncreasng rce land area or crop ntensfcaton snce almost all the agrcultural land n Ken Gang have been utlzed. There are also lmtatons related to crop ntensfcaton such as sol eroson, pest nfestaton, and other ssues concernng sustanable development n agrculture. Especally, rce prce always fluctuates over tme that the government does not effectvely control. Therefore, promotng polces amed at sustanable growth n rce yeld and prce wll be the bass for sustanable development n the rce subsector n Ken Gang n the future. Recently, rce producton n Ken Gang 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 that how s the proft and proft effcency of rce and what factors affect farm s proft and techncal effcency. Thus, ths study s am to dentfy proft effcency and determnant of proft effcency among the rce farmers n Ken Gang provnce. 2. Lterature Revew Proft effcency s defned as the ablty of a farm to acheve hghest possble proft gven the prces and levels of fxed factors of that farm and proft neffcency n ths context s defned as loss of proft from not operatng on the fronter (Al and Flnn, 1989; Rahman, S., 2003). Measurement of effcency began wth the study of Farrell (1957) who gave defnton for both techncal and allocaton effcences, startng from the determnstc fronters concept (Tamyu,.S.A. et al, 2010). Based on the Farrell s fronter concept, the proft effcency ndex s the rato of maxmum proft over actual proft of a farm, gven nput prces and fxed factors. The predcted effcency ndexes were regressed aganst a number of household characterstcs, n an attempt to explan the observed dfferences n effcency among farms (Rahman, S., 2003). 2

3 There are a number of studes on determnants of proft effcency. Al and Flnn (1989) dound that socoeconomc factors related to proft loss were the farm household's educaton, nonagrcultural employment, and credt constrant n the Basmat rce producers. Bhattacharyya and Glover (1993) found that small farms were less proft neffcent than ther larger counterparts. Abdula and Huffman (1998) showed that farmers human captal represented by the level of schoolng contrbuted postvely to proft effcency of the rce farmers n the Northern Regon of Ghana. Rahman (2003) ndcated the effcency dfferences were explaned largely by nfrastructure, sol fertlty, experence, extenson servces, tenancy and share of nonagrcultural ncome among Bangladesh rce farmers. Ogundar (2006) revealed that proft effcency was postvely nfluenced by age, educatonal level, farmng experence, and household sze among small- scale paddy rce farmers n Ngera. Kolawole (2006) revealed that age, educatonal attanment, farmng experence, and household sze postvely affected proft effcency. Adeleke et al. (2008) ndcated that labor was an mportant factor explanng changes n proft among female smallholder farmers n Atba, Oyo State. Karaflls and Papanagotou (2009) found that more nnovatve farmers performed better than less nnovatve ones regardng n terms of proft effcency among organc olve farmers n Greece. Wadud (2011) Soco economc varables such as agrcultural nformaton and famly dependency ratos showed postve effect on proft neffcences among rce farmers n selected dstrcts of Bangladesh. 3. Methodology 3.1 Research Questons There are two major research questons. One of them s that what factors affect the rce farmng proft n study area. Another queston s that how s the proft effcency level and what factors affect proft effcency of the rce farmers. In order to answer these questons, the stochastc fronter proft functon ncorporatng neffcency effects was employed to analyze the data. 3.2 Stochastc Fronter Proft Functon The stochastc fronter producton functon was ndependently proposed by Agner, Lovell, and Schmdt (1977) and Meeusen and van den Broeck (1977). The orgnal 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. 3

4 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 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. Model for estmatng the proft effcency. Ths study adopted the models developed by Battesse and Coell (1995) and Abdula and Huffman (1998) by postulatng a proft functon, whch s assumed to behave n a manner consstent wth the stochastc fronter framework. Consder a frm that maxmzes profts subject to perfectly compettve nput and output markets and a sngle output technology that s quas-concave n the (n x 1) vector of varable nputs, X, and the (m x 1) vector of fxed factors, Z. The actual normalzed proft functon can be expressed as: π (p, Z) = Y(X * *, F) - Σp X (2) where Y (X*, F) s producton functon; the astersk denotes optmzed values. p s the normalzed prce of nput, p = W/P, where P and W are the output and nput prces, respectvely. The stochastc normalzed proft functon can then be expressed as: π = f(p j, F j) exp(v U ) (3) where 4

5 π s normalzed proft of the th farm, computed as gross revenue less varable cost, dvded by farm specfc output prce P y; p j s the normalzed prce of nput j for the th farm, calculated as nput prce dvded by farm specfc output prce P y; F j s the level of the j th fxed factor for the th farm V s the symmetrc error term and U s a one-sded error term. v s normally 2 ndependently and dentcally dstrbuted as N (0, ) two-sded error term representng varous random shocks and effects of measurement error of varables. The U s the non negatve or one-sded resdual representng farm-specfc proft neffcency. Hence f U = 0, the farm s proft neffcency s nonexstent,.e., the farm makes maxmum possble proft (beng on the fronter) gven ts nput prces and fxed factors. Conversely, U > 0 ndcates that the farm forgoes proft due to neffcency (Al and Flnn, 1989). The proft effcency (PE) n relaton to the stochastc proft fronter s gven by PE U f ( p j, Fj) exp( V U ) exp( U ) f ( p, F ) exp( V ) * j j s an observed proft and π * s the fronter proft. The p j, F j, U and u (4) V have been defned earler. In ths case, π acheves ts maxmum value of f(p j, F j) exp(v ) f and only f PE = 1. Otherwse, PE < 1 provdes a measure of the shortfall of observed proft from maxmum feasble proft. The farm-specfc proft neffcency ndex (PIE) derved usng the results from equaton (4) s gven as: PIE = 1 exp (-U ) (5) The stochastc proft fronter model as shown n (3) above could be estmated usng maxmum lkelhood method, whch s asymptotcally more effcent than the other alternatve, Corrected Ordnary Least Squares (COLS) method (Battese and Coell, 1995). Mean proft effcency (ndustry proft effcency) could be easly predcted usng the mathematcal expectaton of proft effcency. A natural predctor of mean proft effcency would be the arthmetc mean of the farm specfc effcences n the sample. 3.3 The Emprcal Model Several studes (e.g. Battesse and Safraz, 1998; Kolawole, 2006) used the Cobb-Douglas functonal form to estmate the proft functon for dfferent commodtes. For ths study, the Cobb-Douglas functonal form was also used to estmate two stochastc proft functon models. The dfference n the two Cobb-Douglas proft functons/models s the measurement of the land as a fxed varable. The specfc farm proft functon s expressed as follows: 5

6 5 2 0 j ln Pj k ln j 1 k 1 ln F V U (6) k Where β 0 β j, β jk π = Intercept; = regresson coeffcents of the explanatory varables n the estmated stochastc proft functon, except for the tme dummy, where j = 1, 2 5; k = 1, 2; = Restrcted normalzed proft computed for jth farm defned as gross revenue less varable costs per farm dvded by farm specfc rce prce (p y). P j = Prces of varable nputs contrbutng to proft effcency where (for = 1, 2 5), consstng of: P 1 P 2 P 3 P 4 P 5 = Prce of seed (VND/kg) normalzed by prce of output (P y); = Weghted prce of fertlzer (VND/kg) normalzed by prce of output (P y); = Weghted prce of chemcal pestcde (VND/lter) normalzed by prce of output (P y); = Prce of labor (VND/man-day) normalzed by prce of output (P y); and = Prce of tractor servce (VND/hectare) normalzed by prce of output (P y). F k = Value of fxed nputs contrbutng to proft effcency where l = 1, 2, consstng of: F 1 F 2 V t U Ln = Land area (ha/farm); = Fxed captal (value of farm producton equpment and machnery) (VND/farm); = Random varable assumed to be ndependently and dentcally dstrbuted (d) N (0, σ v2 ) and ndependent of U ; = Non-negatve random varable that s assumed to account for proft neffcency n rce producton; = Natural logarthm; = 1 N, N = number of observatons; and The subscrpts j or k, refer to the j th or k th nput used of th farm. The rce farm level proft neffcency (PIE) model was smultaneously estmated wth the stochastc fronter proft functon model. The PIE model for the rce farm s expressed mathematcally as follows: PIE U 10 Z 0 j j (7) j 1 where δ 0 = Intercept; 6

7 δ j Z 1 = regresson coeffcents of the explanatory varables n the estmated proft neffcency model where j= 1, 2 10; = Factors contrbutng to proft neffcency such as: Z 1 = Gender of farmer dummy (male = 1; female = 0); Z 2 Z 3 Z 4 = Educatonal attanment of the farmer (years of schoolng); = Experence of the farmer n rce farmng (years); = Household members n farmng number of famly members engaged n rce farmng (number of persons/household); Z 5 = Farm sze dummy (area 0.6 hectare = 1; area < 0.6 hectare = 0); Z 6 = Credt access dummy (wth credt access= 1; no credt access= 0); Z 7 Z 8 Z 9 ξ = Attendance n tranng on rce producton dummy (wth tranng = 1; no tranng = 0); = Membershp n a farmers organzaton/cooperatve dummy (member = 1; not member = 0); = Dstance from the rce feld to the farmer s house (km); = Error terms, assumed to be ndependently and dentcally dstrbuted wth mean = 0 and varance = σ ξ2 ; and The subscrpts j, refer to the j th characterstc of the th farm. 3.4 Data The data ths study s cross sectonal data collected by drect ntervew 302 rce farmers n three dstrcts of Ken Gang provnce, namely Chau Thanh, Gong Reng and Tan Hep. About 100 rce farmers per each dstrct were selected by random samplng. The data collecton ncludes quantty of nput use, paddy yeld, prces of nput use and paddy n the frst crop of 2015 and other data related to the farm s specfc characterstcs. 4. Results and Dscusson 4.1 Farm s Specfc Characterstcs On average, the rce farmer-respondents have 8.74 years of schoolng, years of rce farmng experence, 0.87 ha of rce farmng, 3.14 household labors. There are percent of rce farmer-respondents who rent extra land for rce farmng; the average dstance from the man rce feld to the key nput market s 3.28 km. 87 percent of rce farmerrespondents are male, 61% of that accesses the formal credt, 71 percent of that partcpate n rce producton tranng, 61 percent of that s member of local farmer s assocaton (Table 1). 7

8 Table 1: Specfc Characterstcs of 302 Sample Rce Farmer-Respondents n the Frst Crop of 2015 n Ken Gang Provnce Std. Farm s chracterstcs Unt Average Dev. Gender dummy 1: male; 0: female Educatonal attanment Year Rce farmng experence Year Household farm labor Person Farm sze dummy Ha Tenancy rate % Credt access dummy 1: borrowed; 0: not Tranng dummy 1: Partcpated; 0: not Farmer s assocaton membershp dummy 1: member; 0: not Dstance from the man feld to the key nput market Source: Author s survey 4.2 Costs and Return of Rce Producton Km 3, On average, paddy yeld and prce are 6,907.3 kg/ha and 5,238.4 VND/kg, respectvely whch results n a gross ncome of 36,183.2 VND (~ USD). Wth the total costs of producton of 23,618.4 VND/ha, the net ncome s 12,564.8 VND/ha (~ USD). The net ncome on gross ncome and total cost ratos are 34.7 percent and 53.2 percent, respectvely (Table 1). These proftable ratos are too hgh n comparson wth other ndustres (.e. manufacturng, servce) Among cost tems, cost of labor occupes the largest share of total costs, around 28 percent of total costs, n whch cost of hred and famly labor are account for about 17 percent and 11 percent of total costs, respectvely. The followng s the cost of fertlzer, accountng for 22.4 percent of total costs. Thus, costs of labor and fertlzer are account for about 50 percent of total costs whle other 8 cost tems are account for another 50%. Gross ncome Table 2: Costs and Returns per Hectare n Rce Producton of 302 Sample Rce Farmer- Respondents n the Frst Crop of 2015 n Ken Gang Provnce Cost structure Item Unt Value Std. Dev. (%) Kg/ha Paddy yeld 6, Average sellng prce VND/kg 5, ,017.4 Total gross ncome (GI) 000 VND 36, ,653.7 Costs Cash costs Land preparaton ( 000 vnd) 000 VND 1,

9 Seeds ( 000 vnd) 000 VND 1, Fertlzers ( 000 vnd) 000 VND 5, ,687.1 Pestcdes ( 000 vnd) 000 VND 2, ,911.0 Hred labor ( 000 vnd) 000 VND 4, ,780.9 Irrgaton ( 000 vnd) 000 VND 1, Harvest and transport ( 000 vnd) 000 VND 3, ,082.7 Interest payment ( 000 vnd) 000 VND 1, Sub-total 20, ,668.5 Non-cash costs Famly labor ( 000 vnd) 000 VND 2, Deprecaton ( 000 vnd) 000 VND Sub-total ( 000 vnd) 000 VND 3, ,380.7 Total cost (TC) ( 000 vnd) 000 VND 23, ,049.3 Net ncome (GI-TC) ( 000 vnd/ha) 000 VND 12, ,604.7 Net ncome/gross ncome (%) % Net ncome/total costs (%) % Note: USD 1 VND 23,000 Source: Author survey n Results of the Stochastc Fronter Proft Analyss In the stochastc Cobb-Douglas fronter proft model, the estmated coeffcents of the ndependent varables are proft elastctes. The proft elastcty shows the percent change n farm proft wth respect to a percent change n gven varable nput prce or fxed factor, ceters parbus. Among the seven parameters of nput prces and fxed factors ncluded n stochastc fronter proft model, only servce track rate and equpment had no sgnfcant effect on farm proft n rce producton n the study areas (Table 3). The regresson coeffcents of prces of fertlzer, pestcde exhbt negatve sgns and are statstcally sgnfcant, whch mply that the ncreases n prces of these nputs would lead to decrease of the farm proft level and vce versa. Ths fndng conforms to the results of the studes on rce farmng conducted by Rahman (2003) n Bangladesh, Ogundar (2006) n Ngera, and Wadud (2011) n selected dstrcts of Bangladesh. Otherwse, the coeffcent of seed s found postve sgn and s statstcally sgnfcant at fve percent level, whch means that the hgher prce of seed leads to the hgher proftable and vce versa. Ths would attrbute to that the farmers who use the hgher qualty seed (mply the mproved seeds) gan more proftable than those who use the lower qualty one, gven assumpton prce of seed s consstent wth ts qualty. Lkewse the seed prce, wage rate or the prce per man day of labor s statstcally sgnfcant at fve percent probablty level. However, contrary to expectatons, the sgn of the regresson coeffcent of labor s postve. Ths may be due to the fact that rce producton s 9

10 labor ntensve consderng that most farm operatons are done manually whch resulted to an ncrease n the cost of labor snce the servces of hred laborers are frequently used by the farmers especally n plantng and nursng actvtes. The same fndng was found by Ogundar (2006), Adeleke et al. (2008) n Atba, Oyo State, and Wadud (2011). Smlarly, the coeffcent of land area s also found postve sgn and s statstcally sgnfcant at one percent level, whch means that the larger farm gan greater proftable than the smaller one and vce versa. Ths would attrbute to that the larger farms gan more economc scale n terms of costs of producton, whch lead to more proftable than smaller one. Table 3: MLE of the Cobb-Douglas Stochastc Proft Functon and Proft n Effcency Model, 302 Sample Rce Farmer-Respondents n Ken Gang Provnce n 2015 Varable Symbol Varable name Parameters Coeffcent Std. Error t-rato Fronter proft functon Constant β ** ln Ps Prce of seed (vnd/kg) β *** Ln Pf Prce of fertlzer (vnd/kg) β *** ln Pp Prce of pestcde (vnd/ltter) β * ln W Wage rate (vnd/day) β ** ln St Servce tractor rate (vnd/ha) β ns ln L Land area (hectare) β *** ln E Equpment (vnd) β ns Proft Ineffcency Functon Constant ns Z 1 Gender dummy ns Z 2 Educatonal attanment (years) * Z 3 Rce farmng experence (years) ** 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 Assocaton membershp dummy ** Z 10 Dstance from the man feld to the key nput market (km) * Varance Parameter σ ** *** Log-lkelhood functon

11 Varable Symbol Varable name Parameters Coeffcent Std. Error t-rato LR test of the one-sded error Mean proft effcency (%) N 302 Note: ***, **, and * ndcate statstcally sgnfcant at 1%, 5%, and 10% probablty level, respectvely; and ns denotes nsgnfcant at 10% probablty level. Determnants of proft effcency: the average proft effcency was percent, whch mples that wth the recent prce of nputs and fxed factors, the sample farmers - respondents could be able to ncrease ther rce farmng proft by percent by mprovng techncal effcency factors. Ths s to examne the effects of soco-economc and farm-specfc factors on proft effcency of the sample rce farmer-respondents. A negatve sgn of the regresson coeffcent of an explanatory varable n the proft neffcency functon ndcates that the varable mproves proft effcency. A postve sgn means the opposte. The factors whch were found postvely affect proft effcency of the rce farmer-respondents were educaton attanment, rce farmng experence, household farm labor, farm sze, partcpaton n rce producton tranng programs, and membershp n a farmers assocaton. Educatonal attanment. As expected, educaton exhbts a sgnfcant effect on proft effcency. The regresson coeffcent of the educatonal attanment of the farmer-respondents s negatve and statstcally sgnfcant at ten percent probablty level. The negatve coeffcent means that as the farmer s educatonal level ncreases, the proft neffcency of the farmer decreases. In other words, ths mples that that the more educated farmerrespondents have hgher proft effcency than those wth lower educatonal attanment. Ths could be explaned by the fact that the more educated farmers have better access to nformaton on nput and output prces as well as other economc and techncal nformaton, whch help them n makng better farm management decsons as compared to less educated farmers. Ths fndng s n conformty wth the works of Abdula and Huffman (1998) n the Northern Regon of Ghana, Rahman (2003) n Bangladesh, and Ogundar (2006) n Ngera. Farmng experence. Contrary to pror expectaton, the regresson coeffcent of the farmer s length of experence n rce farmng s postve and statstcally sgnfcant at fve percent probablty level (Table 3). The postve sgn of the regresson coeffcent of ths varable means that as farmng experence ncreases, the proft neffcency of the farmer ncreases. In other words, the more rce farmng experence the farmer has, the hgher the level of proft neffcency. The postve relatonshp between farmng experence and proft effcency found n ths study corroborates the fndngs of Rahman (2003), Ogundar (2006), and Kolawole (2006). 11

12 Farm sze. Farm sze has a negatve regresson coeffcent and s statstcally sgnfcant at fve percent probablty level. Ths ndcates that farmers wth larger farms earn sgnfcantly hgher proft and operate at sgnfcantly hgher level of proft effcency than those operatng smaller farms. Ths s logcal snce those operatng large farms frequently purchase materal nputs n bulk or n larger volume to get prce dscounts and n turn lower ther nput procurement cost. Sellng a larger volume of ther produce also enables them to bargan for a hgher prce for ther product and mnmze marketng/transportaton cost as well. Farmers partcpaton n tranng programs on rce producton. As expected, the partcpaton n tranng dummy has a negatve sgn and s statstcally sgnfcant at fve percent probablty level. Ths suggests that the farmers who partcpated n tranng programs on mproved rce farmng technologes and practces have hgher levels of proft effcency than those who dd not partcpate n such tranng programs. Membershp n farmers organzatons/cooperatves. The regresson coeffcent of the dummy varable for membershp n a farmers assocaton has a negatve sgn and s statstcally sgnfcant at fve percent probablty level. Ths ndcates that the farmers who are members of a farmers assocaton receve hgher profts and have hgher levels of proft effcency than non-members. Ths could be attrbuted to the fact that the members of a farmers assocaton have better access to support servces lke extenson and tranng. Members of a farmers assocaton may also beneft from sellng ther produce through ther assocaton than sellng ndvdually snce ther assocaton can bargan for a hgher prce. Ths result corroborates wth the fndngs of Al and Flnn (1989) who reported that farmers n remote vllages were less proft effcent, even when other factors were taken nto account. The common practce n Vetnam nowadays s for the nput supplers delverng the materal nputs such as fertlzers purchased by the farmers to the latter s house or farm. Thus, the farther the dstance of the farmers rce felds from the nput markets, the hgher the procurement cost of materal nputs, and vce-versa. Moreover, the condton of the road nfrastructure partcularly n remote areas affects the tmelness n the delvery of farm nputs to the farmers. A badly developed road nfrastructure has negatve effects on both techncal and allocatve effcency. Techncal effcency would be adversely affected by the late delvery of nputs at the tme when they are needed most and allocatve and proft effcency would be negatvely affected as well. Dstrbuton of techncal effcences: The predcted proft effcences of the sample rce farmer - respondents n Ken Gang provnce dffered substantally rangng from 29.8 percent to 96.7 percent. About 5.3 percent of the total sample farmers - respondents belonged to the most effcent category (95-100%). Around 7.3% of the sample farmer respondents had proft effcences below 50 percent. Majorty (42.1%) of the sample rce farmer - 12

13 respondents belonged to the category (80 - >90%), ndcatng that most of the rce farmerrespondents were very proft effcent (Table 4). Table 4: Dstrbuton of Proft Effcences of 302 Rce Farmer-Respondents n Ken Gang Provnce Proft effcency (PE, %) No. of farmers Percent < < < < < < Total Average Mnmum 29.8 Maxmum 96.7 Std. Dev a The dfference n mean proft effcency between 2008 and 2011 was sgnfcantly dfferent at 5% probablty level. 5. Conclusons and Recommendatons The am of ths study s to dentfy determnants of proft effcency among rce farmers n Ken Gang provnce, Vetnam based on the data collected from 302 sample rce farmers n three dstrcts of Ken Gang provnce. The Cobb-Douglas stochastc fronter proft functon ncorporatng proft neffcency effects was employed to analyze the data, usng the FRONTIER 4.1. The results revealed that the proft effcency was range between to percent, averages of percent. Sgnfcant factors that were found negatve affect the rce farm proft were prces of fertlzer, pestcde whle postve effects were prce of seed, wage rate and land area (fxed factor. Sgnfcant determnants that were found postve affect proft effcency of rce farmers were educaton attanment, household farm labor, farm sze dummy, tranng dummy, farmer s assocaton membershp dummy whle negatve effects were farmng experence and dstance from the man feld to the key nput market. In order to further mprove proft and proft effcency of rce producton, the local government and the rce farmers should be mprove fertlzer management focusng on effcent use of fertlzer; ntensfy extenson servces partcularly the conduct of tranng programs; promote hgh-yeldng varetes n cooperaton wth the prvate sector; strengthen/reactvate farmers assocaton; and mprove the level of educaton of farmers through short techncal tranng; and promote rce land market. 13

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