Exploring the potential and performance of maize production in Bangladesh

Transcription

1 Unversty of Plymouth PEARL Faculty of Scence and Engneerng School of Geography, Earth and Envronmental Scences Explorng the potental and performance of maze producton n Bangladesh Rahman, S /jam/ Internatonal Journal of Agrcultural Management Charlesworth Group All content n PEARL s protected by copyrght law. Author manuscrpts are made avalable n accordance wth publsher polces. Please cte only the publshed verson usng the detals provded on the tem record or document. In the absence of an open lcence (e.g. Creatve Commons), permssons for further reuse of content should be sought from the publsher or author.

2 Revsed verson ncorporatng all comments of the referees and the edtor Explorng the potental and performance of maze producton n Bangladesh Sanzdur Rahman School of Geography, Earth and Envronmental Scences, Unversty of Plymouth, Drake Crcus, Plymouth, PL4 8AA, Unted Kngdom, Phone: , Fax: , E-mal: srahman@plymouth.ac.uk Md. Sayedur Rahman On-farm Research Dvson, Bangladesh Agrcultural Research Insttute (BARI), Kushta, Bangladesh, E-mal: sayedecon@yahoo.com Address for correspondence Dr. Sanzdur Rahman Assocate Professor n Rural Development School of Geography, Earth and Envronmental Scences Unversty of Plymouth Drake Crcus Plymouth, PL4 8AA Phone: Fax: E-mal: srahman@plymouth.ac.uk July

3 Explorng the potental and performance of maze producton n Bangladesh ABSTRACT Maze s ganng mportance n recent years as a promsng crop amed at boostng agrcultural growth n Bangladesh. The present study explores the potental of maze expanson by examnng ts proftablty and economc effcency usng a survey data of 300 farmers from three regons. Maze ranks frst n terms of yeld (7.98 t/ha) and return (BCR=1.63) as compared wth rce and wheat. The economc effcency of maze producton s also estmated at a hgh 87%, although a substantal 15% [(100-87)/87)] cost reducton s stll possble whle mantanng current output level by elmnatng techncal and allocatve neffcency. Educaton postvely contrbutes towards ncreasng effcency whle large farmers are relatvely neffcent. Geography does matter. Effcency s lower n Bogra regon as compared wth Dnajpur and Kushta. Polcy mplcatons nclude nvestment n educaton, settng up approprate prce polces to stablse prces and facltaton of the nput markets for tmely delvery of requred nputs. Key words: Economc effcency, proftablty, stochastc cost fronter, maze, Bangladesh. 1. Introducton Bangladesh economy s domnated by agrculture contrbutng 14.2% to the Gross Domestc Product (GDP). Of ths, the crop sub-sector alone contrbutes 10.1% to the GDP (BBS, 2011a). Agrculture sector generates about 35.0% of the total foregn exchange earnngs (Husan, et al., 2001 and Islam, et al., 2004) and s the man source of employment absorbng 45.6% of the labour force (BBS, 2011a). Land s the most mportant and scarce means of producton resultng n ntensve croppng on all avalable cultvable land. The croppng ntensty n 2011 s estmated at a hgh 191% (BBS, 2011a). It has been ncreasngly realzed that economc development n Bangladesh can not be acheved wthout makng a real breakthrough n the agrcultural sector (Baksh, 2003). Although rce s the man staple food gran, maze s ganng mportance as a thrd crop after wheat coverng 1.2% and 2.1% of the total and net cropped area n 2011, respectvely (BBS, 2011a). The government s also keen to dversfy ts agrculture and had earmarked 8.9% of the total agrcultural allocaton (worth US$ 41.8 mllon) durng ts Ffth Fve Year Plan ( )(PC, 1998). Maze n Bangladesh 17

4 Maze s one of the oldest crops n the world and s well known for ts versatle nature wth hghest gran yeld and multple uses. In Bangladesh, maze cultvaton started n the early 19 th century (1809) n the dstrcts of Rangpur and Dnajpur (Begum and Khatun, 2006). Durng 1962, the then governor of the erstwhle East Pakstan tred to re-ntroduce maze n those areas but dd not succeed. However, the Bangladesh Agrcultural Research Insttute (BARI) has been conductng research on the varetal development of maze snce 1960 wth a thrust to develop composte varetes. So far, BARI has developed seven open pollnated and eleven hybrd varetes (Begum and Khatun, 2006; BARI, 2008). The yeld potental of the released composte varetes are t/ha and the hybrd varetes are t/ha whch are well above the world average of 3.19 t/ha (FAOSTAT, 2011). Maze producton and yeld has experenced an explosve growth n Bangladesh n recent years. The cropped area of maze has ncreased from only 2,654 ha n 1972 to 165,510 ha n 2011; producton from 2,249 t to 1,018,000 t; and yeld from 0.85 t/ha to 6.15 t/ha durng the same perod. Maze has now postoned tself as the 1 st among the cereals n terms of yeld rate (6.15 t/ha) as compared to Boro rce (3.90 t/ha) and wheat (2.60 t/ha) (BBS, 2011a). Maze possesses a wde genetc varablty enablng t to grow successfully n any envronment and n Bangladesh t s grown both n wnter and summer tme, although the former s the domnant pattern. Demand for maze s ncreasng worldwde and n Bangladesh and ts producton has crossed one mllon ton by A lmted number of soco-economc nvestgatons were made on maze cultvaton n Bangladesh whch revealed that maze s a proftable crop and stands well above from ts compettve peers, e.g., rce (Hussan et al, 1995; Fokhrul and Haque, 1995) and mustard (Haque, 1999) and has brought postve changes n dfferent aspects of lvelhood such as captal formaton, food ntake, ncome, household amentes, soco-economc condtons, etc (Islam, 2006). Gven ths backdrop, the objectve of the present study s, therefore, to assess the potental of maze producton as an alternatve crop by specfcally examnng proftablty, economc effcency and ts determnants at the farm-level n Bangladesh. Ths s because effcent use of scarce resources s an mportant ndcator n determnng potental to ncrease agrcultural producton. Although the rce-based Green Revoluton technology n Bangladesh has pad off well, there s an urgent need to dversfy agrculture n order to 18

5 sustan ts growth (Rahman, 2010). Furthermore, the focus of emprcal studes of resource use effcency n Bangladesh was on rce and wheat (e.g., Rahman, 2003; Coell et al., 2002; Asadullah and Rahman, 2009; Rahman and Hasan, 2008). The mportance of assessng economc effcency of maze arses because although maze cultvaton s hghly proftable, t requres substantal upfront costs durng the producton process. Therefore, Bangladesh farmers charactersed wth scarce land and credt constrants needs to focus on mnmzng producton cost whle keepng up the hgh yeld potental of the chosen crop n order to sustan ther farmng practces and beneft from the adopton of ths new technology. The paper s organzed as follows. Secton 2 descrbes the methodology and the data. Secton 3 presents the results. The fnal secton concludes and draws polcy mplcatons. 2. Methodology Proftablty or Cost-Beneft Analyss Proftablty or cost-beneft analyss ncludes calculaton of detaled costs of producton and return from maze on a per hectare bass. The total cost (TC) s composed of total varable costs (TVC) and total fxed costs (TFC). TVC ncludes costs of human labour (both famly suppled and hred labour, wheren the cost of famly suppled labour s estmated by mputng market wage rate), mechancal power; seed, manure, chemcal fertlzers; pestcdes; and rrgaton. TFC ncludes land rent (f owned land s used then the mputed value of market rate of land rent s appled) and nterest on operatng captal. The gross return (GR) s computed as total maze output multpled by the market prce of maze. Profts or gross margn (GM) s defned as GR TVC, whereas the Net return (NR) s defned as GR TC. Fnally, the Beneft Cost Rato (BCR) s computed as GR/TC. Analytcal framework: the stochastc cost fronter model A lmtaton of proftablty analyss presented above s that t does not tell us whether farmers are achevng the maxmum potental yeld and proft from ther producton process. However, an analyss of economc effcency allows such nformaton to be generated at the ndvdual producer level whch s mportant for farmers, polcy makers and other stakeholders alke. A cost functon, whch s a dual of the underlyng producton functon, s defned as a functon of nput prces and output level. Specfyng a cost functon avods the problem of endogenety of varables used n modellng. Ths s because nput prces are consdered 19

6 exogenous n nature and s not determned wthn the model. A conventonal cost functon assumes perfect effcency n producton whch s not a vald assumpton gven wdespread evdence of neffcency n agrcultural producton process worldwde (e.g., Bravo-Ureta et al., 2007). However, specfcaton of a stochastc cost fronter functon allows us to dentfy the level of neffcency (specfcally economc neffcency) n the producton process at the ndvdual producer level. Economc effcency, also known as cost effcency, results from both techncal effcency and allocatve effcency. Techncal effcency refers to a producer s ablty to obtan the hghest possble output from a gven quantty of nputs (Rahman, 2003). Allocatve effcency refers to a producer s ablty to maxmse proft gven techncal effcency. A producer may be techncally effcent but allocatvely neffcent (Hazarka and Alwang, 2003). Therefore, economc/cost effcency refers to a producer s ablty to produce the maxmum possble output from a gven quantty of nputs at the lowest possble cost. Consder the stochastc cost fronter functon based on the composed error model (e.g. Agner et al., 1977); lnc = α 0 + αlnq + βlnwj + ε n j= 1 (1) where C represents household s cost per ha maze producton, Q denotes the maze output per ha; W j sgnfes the household-specfc prce of varable nput, and ε s a dsturbance term consstng of two ndependent elements as follows: ε =u + v (2) v, assumed to be ndependently and dentcally dstrbuted as N(0, σ ), represents random varaton n cost per acre due to extraneous factors such as the weather, crop dseases, and statstcal nose. The term u s taken to represent cost neffcency relatve to the stochastc 2 v cost fronter, n = α + αlnq + j=1 lnc 0 βlnwj + v. It s, therefore, one-sded as opposed to beng symmetrcally dstrbuted about the orgn. In other words, u = 0 f costs are, ceters parbus, as low as can be, and u > 0 f cost effcency s mperfect. u s assumed to be dentcally and ndependently dstrbuted as truncatons at zero of the normal dstrbuton N(μ, σ ), The stochastc cost functon (1), may be estmated by maxmum-lkelhood. Gven 2 u the above dstrbutonal assumptons, 20

7 * ( µ ) * ( µ ) ( ) = σλ φ * E u ε 2 µ (1+ λ ) 1 Φ where φ and Φ denote, respectvely, the standard normal p.d.f. and the standard normal (3) c.d.f., λ = σ u + σ, v σ σ u + σ v 2 2 * =, and µ = ( ελ/ σ) + ( µ / σλ) (Hazarka and Alwang, 2003). Replacng ε n the above expresson by the regresson resdual and the other parameters by ther ML estmates yelds an estmate, u, of farm-specfc cost neffcency (Jondrow et al., 1982). Next, n determnng the predctors of cost neffcency, we use the sngle stage approach proposed by Battese and Coell (1995) wheren the cost neffcency parameter (u ) s specfed as a lnear functon of farm-specfc manageral and household characterstcs subject to statstcal error, such that: u m = δ Z k + ζ 0, (4) where, Z are the farm-specfc manageral and household characterstcs and the error ζ s 2 dstrbuted as ζ ~ N(0, σ ζ ). Snceu 0, ζ δz, so that the dstrbuton of ζ s truncated from below at the varable truncaton pont, δz (Rahman and Hasan, 2008). Study areas and the sample farmers Maze s cultvated almost all over the country, though the ntensty of planted area and land sutablty are not equal n all regons. Therefore, we computed a maze area ndex for each greater dstrct 1. The maze area ndex for the jth dstrct s expressed as: MAI j = ( Areaj/ GCAj)*100, (5) where MAI s the maze area ndex, Area s the maze area and GCA s the gross cropped area. Based on ths ndex, maze growng regons were classfed nto three levels of ntensty: hgh ntensty (MAI 1.00), medum ntensty (1.00<MAI 0.50), and low ntensty areas (MAI<0.50). A multstage samplng procedure was adopted to select the sample farmers. Frst, three areas were selected accordng to the rank of MAI as well as percent of total wnter maze area. The selected regons are Kushta, Bogra and Dnajpur whch covered 59% of 1 Although there are 64 dstrcts n Bangladesh, most secondary data are stll reported at the level of these 21 former greater dstrcts. 21

8 total maze area of the country. In the second stage, one new dstrct was chosen from each aforesad selected greater dstrct accordng to hgher percent of maze area and ease of communcaton. Then, one upazla (sub dstrct) from each new dstrct and one unon from each upazla were selected purposvely. Fnally, three vllages (one from each unon) were selected randomly for collecton of prmary data. In the thrd stage, a number of steps were followed to select the households to ensure a hgh level of representaton. At frst, a lst of all maze growng farmers was collected from the Department of Agrcultural Extenson (DAE). Then, these farm holdngs were stratfed nto three standard farm-sze categores commonly adopted n Bangladesh (e.g., Rahman and Hasan, 2008). Then, a total of 300 maze producng households were selected followng a standard stratfed random samplng procedure. Structured questonnare was admnstered for data collecton whch was pretested pror to fnalzaton. Data on producton technologes of maze, nputs, outputs and prces were recorded seasonally by three vsts coverng the crop season. Frst vst was done just after sowng of seeds, second vst followng completon of all ntercultural operatons and the last one after harvestng and threshng of the crop. Data also ncludes socoeconomc profle of the sampled farmers. The survey covered wnter maze growng perod from November 2006 to Aprl The emprcal model An extended general form of the Cobb-Douglas stochastc cost fronter functon s used 2. Ths was done n order to nclude varables representng envronmental producton condtons wthn whch the farmers operate (e.g., Sherlund et al., 2002; Rahman and Hasan, 2008). Hence, the model s wrtten as: = 0 j l j= 2 l= 1 d * * lnc α + αlnq + βlnw + ωe + τd + u + v d (5) and u 10 = δ 0 + δz k + ζ (6) k= 1 2 We dd not use the translog model because of the lmted sample sze and the large number of explanatory ndcators (22 n the cost fronter model). Moreover, Kopp and Smth (1980) suggest that the choce of functonal form has a lmted effect on effcency. Consequently, the Cobb-Douglas specfcaton s wdely used n producton or cost fronter studes (e.g., Hazarka and Alwang, 2003; Rahman and Hasan, 2008; Asadullah and Rahman, 2009; Alene, 2007). 22

9 where C* s the total cost of maze cultvaton normalzed by one of the nput prces 3 (Murate of Potash prce), W* j s jth normalzed prce of the jth nput for the th farmer; D d s the dth dummy varable used to account for zero values of nput use and have the value of 1 f the jth nput used s postve and zero otherwse 4 ; E l s the lth dummy varable representng envronmental producton condtons, v s the two sded random error, u s the one sded half-normal error, ln natural logarthm, Z k s the kth varable representng manageral and soco-economc characterstcs of the farm to explan cost neffcency, ζ s the truncated random varable; α 0, α, β, ω, τ, δ 0, and δ are the parameters to be estmated. One unque feature of maze cultvaton n Bangladesh s the use of a wde range of norganc fertlzers, organc fertlzer and other modern nputs. As a result, a total of 14 nput prces (W), two envronmental producton condton varables (E), and fve dummy varables (D) to account for zero use of nputs are used n the cost fronter model, and 10 varables representng manageral and soco-economc characterstcs of the farmer along wth two regonal dummy varables (Z) are ncluded n the neffcency effects model as predctors of cost neffcency. Table 1 presents the defntons, unts of measurement, and summary statstcs for all the varables. [Insert Table 1 here] Lmtaton of the parametrc approach used One lmtaton of adoptng a stochastc cost fronter approach s that t requres assumptons regardng specfcaton of the producton technology and behavour of the market and the producer. We have specfed an extended Cobb-Douglas cost functon to represent the true underlyng technology whch does not allow any nteracton amongst nput varables and assumes market to be perfectly compettve and mpose cost mnmzng behavour on the part of the producer. Snce maze s produced manly for sale, these assumptons seem qute logcal. In fact, market for agrcultural products (e.g., maze) closely approxmate perfectly compettve market snce buyers and sellers cannot dctate prce and the products are 3 The Murate of Potash prce (Taka/kg) was used for normalzaton of total cost and all other nput prces. The homogenety condton s mposed by ths normalzaton. 4 In ths study, nputs that contan zero values for some observatons are specfed as ln {max (X j, 1 D j )} followng Battese and Coell (1995). 23

10 homogenous n nature. Therefore, we are qute confdent that our approach portrays real stuaton qute closely and s a vald approach. 3. Results Proftablty of maze Proftablty of maze cultvaton by regons s presented n Table 2. The hghest cost component s human labour followed by chemcal fertlzers and mechancal power servces. Land rent, whch s a fxed cost element, s also very hgh and represents a real burden partcularly for tenants and landless farmers. It s clear from Table 2 that although there are sgnfcant regonal varatons n all elements of costs and returns, the Beneft-Cost Rato (BCR) s very hgh estmated at The comparable estmates of BCR for wheat s 1.40 (Hasan, 2006) and Boro rce (dry wnter season) s 1.14 (Baksh, 2003) thereby, establshng that maze stands hgh n terms of returns amongst major cereals n Bangladesh. Also, maze ranks frst n terms of yeld estmated at 7.97 t/ha (Table 1) as compared to wheat at 2.40 t/ha (Hasan, 2006) and Boro rce at 5.05 t/ha (Baksh, 2003). [Insert Table 2 here] Determnants of maze producton cost Parameter estmates of the stochastc cost fronter along wth neffcency effect model are reported n Table 3 usng the Maxmum Lkelhood Estmaton (MLE) procedure n STATA Verson 8 (STATA Corp, 2003). Frst we checked the sgn of the thrd moment and the skewness of the Ordnary Least Squares (OLS) resduals of the data n order to justfy the use of the stochastc fronter framework (and hence the MLE procedure) 5. The computed value of Coell s (1995) standard normal skewness statstc (M3T) based on the thrd moment of the OLS resduals s 1.77 (p<0.10) H 0 : M3T = 0. In other words, the null hypothess of no neffcency component s rejected and, therefore, the use of the stochastc fronter framework s justfed. The sgnfcant value of the coeffcent on γ reported n Table 3 also strongly suggests presence of cost neffcency. Cost per ha of maze producton sgnfcantly ncreases wth maze output as expected (p<0.01). Most of the sgns on the coeffcents of nput prces are postve consstent wth theory. The two negatve sgns on the coeffcents of gypsum and land rent 5 In the stochastc fronter framework, the thrd moment s also the thrd sample moment of the u. Therefore, f t s negatve, t mples that the OLS resduals are negatvely skewed and techncal neffcency s present. 24

11 varables are not sgnfcantly dfferent from zero and may not be the true relatonshp. Snce Cobb-Douglas model s used, the coeffcents on the varables can be drectly read as cost elastctes. The coeffcent on the output varable s 0.41, ndcatng that a one percent ncrease n output level wll ncrease cost by 0.41%. Cost per ha of maze producton sgnfcantly ncreases wth the use of labour, mechancal power, seed, rrgaton, pestcdes, Trple Super Phosphate (TSP), Znc sulphate, and manure. The elastcty values of mechancal power and labour are the hghest estmated at 0.17 and 0.16 ndcatng that a one percent rse n the prces of these nputs wll ncrease the cost of producng maze by 0.17% and 0.16%, respectvely. Smlarly, a one percent rse n the cost of TSP and znc sulphate fertlzers wll ncrease maze producton cost by 0.12% and 0.09%, respectvely. Movement n other fertlzer prces (e.g., urea, borax, mxed fertlzers and gypsum) do not seem to have a statstcally sgnfcant nfluence on the producton cost of maze. It s surprsng to see lack of the nfluence of envronmental varables. One reason may be that 99% and 65% of the farmers are cultvatng maze on the most sutable land (n terms of elevaton) and sol type, respectvely (Table 1). Controllng for the non-use of some nputs are justfed as ndcated by the sgnfcant coeffcents on the dummy varables (p<0.01 to p<0.10). Also the formal jont test of hypothess of no effect of controllng dummes were strongly rejected at 1 percent level (χ 2 (5, 0.99) = , p<0.01). [Insert Table 3 here] Economc neffcency n maze producton and ts determnants The economc/cost effcency of maze cultvaton s estmated at 87% mplyng that 15% [(100-87)/87] of cost reducton s stll possble whle mantanng current level of output by removng techncal and allocatve effcency (Table 4). Our estmate s at the hgher end of the range seen n the lterature (e.g., Alene, 2007; Hazarka and Alwang, 2003; Rahman and Hasan, 2008; Coell et al., 2002; Bravo-Ureta et al., 2007) mplyng that maze also performs relatvely better than rce and wheat, partcularly n Bangladesh (e.g., Rahman and Hasan, 2008; Coell et al., 2002). The cost effcency ranges between 67% to 99% percent and threequarter of the farmers were operatng at an effcency range above 80% whch s very encouragng. [Insert Table 4 here] 25

12 The predctors of economc neffcency are presented at the lower panel of Table 3. The jont test of hypothess of no neffcency effects was strongly rejected at 1 percent level (χ 2 (10, 0.99) = 35.93, p<0.01). Educaton of the farmers sgnfcantly mproves effcency whle large farmers are relatvely cost neffcent whch are consstent wth the exstng lterature (e.g., Alene, 2007; Asadullah and Rahman, 2009). Use of optmal varety (.e., 900M) or sowng durng optmum date has no sgnfcant nfluence on cost neffcency. However, geography does matter. Farmers n Bogra regon are relatvely neffcent as compared to ther Dnajpur and Kushta peers. The reason may be due to dfferences n mcro-clmate, sol type, other regonal factors as well as producton practces of the farmers. For example, farmers from Bogra used lowest doses of chemcal fertlzers (except urea) as compared wth farmers from Dnajpur and Kushta. Smlarly, the use rate of organc manure by farmers n Bogra s about a quarter of the amount appled by farmers n Dnajpur and Kushta. 4. Conclusons and polcy mplcatons The present study assessed the potental for maze expanson by examnng proftablty and economc effcency of maze producers n Bangladesh usng an extended Cobb-Douglas stochastc cost fronter model. Our results demonstrate that yeld and proftablty of maze s hgher than rce and wheat. The cost of maze producton ncreases sgnfcantly wth ncrease n nput prces and output level. The level of economc effcency s also relatvely hgh at 87% although scope stll exsts to reduce cost by 15% by elmnatng techncal and allocatve neffcency whle mantanng current producton level. Educaton has a sgnfcant nfluence on reducng neffcency whle large operaton sze ncreases ths. The polcy mplcatons are clear. Facltaton of the nput markets by settng approprate prce polces would sgnfcantly reduce cost of producton and rase proftablty of the farmers. Hgh prce of good qualty seed and TSP fertlzers and low prce of maze were ranked as the 1 st, 4 th and 6 th major constrants by these maze growers. Wde varaton n nput prces presented n Table 1 further proves that farmers ndeed face hghly varable farm-specfc nput prces. The reasons may be due to market mperfectons and/or lack of nfrastructure for tmely delvery of nputs resultng n hghly varable nput prces. The Drectorate of Marketng (DAM) and Bangladesh Agrcultural Development Corporaton (BADC) of the Mnstry of Agrculture have an mportant role to play n ths regard. DAM can 26

13 play a role n stablsng prces whle BADC can expand/mprove on ts tradtonal role of supplyng nputs to farmers at the rght tme and n rght quanttes, whch n turn wll support prce stablty. Investment n educaton targeted at farmers wll sgnfcantly mprove economc effcency. Lteracy rate n Bangladesh s on the rse, estmated at 57.7% n 2010 (defned as populaton aged 7 years and over who can read and wrte) (BBS, 2011b) whch s partly due to government sponsored adult lteracy program snce the early 1980s, strengthenng of state run unversal prmary educaton as well as several thousand fxed term prmary schools run by BRAC (a leadng NGO) and other NGOs. The average level of educaton of farmers n our sample s just above the prmary level qualfcaton (Table 1). Asadullah and Rahman (2009) noted that the mpact of educaton on effcency kcks n when farmers educaton level les between prmary and secondary level educaton. Therefore, the Mnstry of Educaton has an mportant role to play n creatng opportuntes for secondary level educaton whch wll enable farmers to gan more out of ther producton processes. Also wth easy access of cell phone technology throughout Bangladesh, the adult lteracy program can be further strengthened and dssemnated to farmers effectvely. For example, the exstng tenant farmer scheme of BRAC provdes an nsttutonal set up whch can make ths feasble along wth NGO run learnng centres n rural communtes. The geographcal varaton n producton performance of farmers may be due to a number of factors such as mcro-clmate, sol types, hgh nput costs and/or dfferences n producton practces whch needs further nvestgaton. Nevertheless, maze has strong potental and should be promoted. A boost n maze producton could sgnfcantly curb dependence on rce as the man staple n Bangladesh det, whch s a goal worth pursung. 27

14 References Agner, D.J., Lovell, C.A.K., Schmdt, P Formulaton and estmaton of stochastc fronter producton functon models. Journal of Econometrcs, 6: do: / (77) Alene, A.D Unexploted food producton potentals of new varetes: evdence from hybrd maze producton n western Ethopa. Outlook on Agrculture, 36: Asadullah, N., Rahman, S Farm productvty and effcency n rural Bangladesh: the role of educaton revsted. Appled Economcs, 41: do: / Baksh, M. E Economc effcency and sustanablty of wheat producton n wheatbased croppng systems n north-west Bangladesh. Ph.D. dssertaton. Department of Agrcultural Economcs, Bangladesh Agrcultural Unversty, Mymensngh-2202, Bangladesh. BARI Udvabta Krshprojukt (In Bangla). Bangladesh Agrcultural Research Insttute (BARI), Joydebpur, Gazpur, Battese, G.E., Coell, T.J A model for techncal neffcency effects n a stochastc fronter producton functon for panel data. Emprcal Economcs, 20, Coell-1995.pdf BB, Economc Trends (monthly publcatons) Aprl Bangladesh Bank, Dhaka. BBS, 2011a. Yearbook of Agrcultural Statstcs of Bangladesh Bangladesh Bureau of Statstcs, Dhaka, Bangladesh. MenuKey=314 BBS, 2011b. Report on the Bangladesh Lteracy Survey, Industry and Labour Wng, Bangladesh Bureau of Statstcs, Dhaka, Bangladesh. WebTestApplcaton/userfles/Image/Survey%20reports/Bangladesh%20Lteracy%20S urver%202010f.pdf Begum, M., Khatun, F Present status and future prospect of hybrd maze n Bangladesh. Tranng on hybrd maze seed producton technology September 20-21, 28

15 2006, Tranng Manual, Development of Hybrd Maze Research Project (GOB), Plant Breedng Dvson, BARI, Joydebpur, Gazpur Bravo-Ureta, B.E., Sols, D., Lopez, V.H.M., Marpan, J.F., Tham, A., Rvas, T Techncal effcency n farmng: a meta regresson analyss. Journal of Productvty Analyss, 27: do: /s Coell, T., Rahman, S., Thrtle, C Techncal, allocatve, cost and scale effcences n Bangladesh rce cultvaton: a non-parametrc approach. Journal of Agrcultural Economcs, 53: do: /j tb00040.x Coell, T.J Estmators and hypothess tests for a stochastc fronter functon: a Monte- Carlo analyss. Journal of Productvty Analyss, 6, do: /BF FAO, FAOSTAT Food and Agrcultural Organzaton of the Unted Natons. Rome, Italy. Fokhrul, M., Haque, M.F Integratng maze nto exstng croppng system. Opportuntes and Constrants n Exstng Agro-economc Nches. On Farm Research Dvson, Bangladesh Agrcultural Research Insttute, Joydebpur, Gazpur. Haque, N An economc study of maze and ts compettve crops: a study n Sherpur thana of Bogra dstrct. M.S. thess. Department of Agrcultural Economcs, Bangladesh Agrcultural Unversty, Mymensngh. Hasan, M. K Yeld gap n wheat producton: a perspectve of farm specfc effcency n Bangladesh. Ph.D. dssertaton, Department of Agrcultural Economcs, Bangladesh Agrcultural Unversty, Mymensngh-2202, Bangladesh. Hazarka, G., Alwang, J Access to credt, plot sze, and cost neffcency among smallholder tobacco cultvators n Malaw. Agrcultural Economcs, 29: do: /S (03) Hussan. M.S., Islam, M.N., Anwar, M.M Comparatve study on hybrd and composte varety of maze n selected areas of Bangladesh. Annual Report, Agrl. Economcs Dvson, BARI, Joydebpur, Gazpur. pp Husan, A.M.M., Hossan, M., Janaah, A Hybrd Rce Adopton n Bangladesh: Socoeconomc Assessment of Farmers Experences. BRAC Research Monograph Seres No. 18. Dhaka: BRAC, Bangladesh. 29

16 Islam, M. M Impact of maze producton on ncome and lvelhood of farmers: a study n a selected area of Lalmonrhat dstrct. M.Sc. thess. Department Agrcultural Economcs, Bangladesh Agrcultural Unversty, Mymensngh. Islam, M. R., Hossan, M., Jam, W.M.H Techncal effcency of farm producng transplanted Aman rce n Bangladesh: a comparatve study of aromatc, fne and coarse varety. Bangladesh Journal of Agrcultural Economcs, 27: Jondrow, J., Lovell, C.A.K., Materov, I.S., Schmdt, P On-the estmaton of techncal neffcency n the stochastc fronter producton functon model. Journal of Econometrcs, 19: do: / (82) , Kopp, R.J. and Smth, V.K. 1980, Fronter producton functon estmates for steam electrc generaton: a compettve analyss. Southern Economc Journal, 47, &ud=2&ud=70&ud=3&ud=32419&ud= &ud=67&ud=62&sd= PC, The Ffth Fve Year Plan ( ). Mnstry of Plannng, Government of Bangladesh, Dhaka. Rahman, S Proft effcency among Bangladesh rce farmers. Food Polcy, 28: do: /j.foodpol Rahman, S Sx decades of agrcultural land use change n Bangladesh: effects on crop dversty, productvty, food avalablty and the envronment, Sngapore Journal of Tropcal Geography, 31: do: /j x Rahman, S., Hasan, M.K Impact of envronmental producton condtons on productvty and effcency: the case of wheat producers n Bangladesh. Journal of Envronmental Management, 88: do: /j/jenvman Sherlund, S.M., Barrett, C.B., Adesna, A.A Smallholder techncal effcency controllng for envronmental producton condtons. Journal of Development Economcs, 69: do: /S (02) STATA Corp, STATA Verson 8. Stata Press Publcatons, College Staton, Texas, USA. 30

17 Table 1: Defnton, measurement and summary statstcs of varables Varables Measure Mean Standard devaton Dependent varable Cost of maze producton Taka per ha , Output Maze output Kg per ha Input prces Murate of Potash prce a Taka per kg Urea prce Taka per kg Znc sulphate prce Taka per kg Gypsum prce Taka per kg Borax prce Taka per kg Trple Super Phosphate prce Taka per kg Mxed fertlzer prce Taka per kg Manure prce Taka per kg Pestcde prce Taka per ha Labour wage Taka per person-day Mechancal power prce Taka per ha Seed prce Taka per kg Irrgaton prce Taka per ha Land rent Taka per ha , Cow dung users Dummy (1 = Yes, 0 = No) Pestcde users Dummy (1 = Yes, 0 = No) Gypsum users Dummy (1 = Yes, 0 = No) Borax users Dummy (1 = Yes, 0 = No) Mxed fertlzer users Dummy (1 = Yes, 0 = No) Envronmental factors Land sutablty Dummy (1 = Medum hgh land or Hgh land sutable, 0 otherwse) Sol type Dummy (1 = loamy, sandy loam or

18 Varables Measure Mean Standard devaton clay loam, 0 otherwse) Regonal dummes Dnajpur regon Dummy (1 = Yes, 0 = No) Bogra regon Dummy (1 = Yes, 0 = No) Manageral varables Area under maze ha Age of the farmer Years Educaton of the farmer Completed years of schoolng Experence n growng maze Years Famly sze Persons per household Sowng date Dummy (1 = f sown durng optmum tme, 0 otherwse) Varety Dummy (1 = f 900M varety s used, 0 otherwse) Lnk wth extenson servces Dummy (1 = f had extenson contact or receved tranng on maze producton, 0 otherwse) Total number of observatons 300 Note: Murate of Potash prce s used to normalze total cost and all other nput prces for the regresson analyss. Exchange rate of USD 1.00 = Taka n (BB, 2010) Source: Feld survey

19 Table 2: Cost, return and proftablty of maze producton Items Taka per hectare F-test for Bogra Kushta Dnajpur All regons regonal dfferences a Human Labour *** Mechancal power *** Seed *** Manure *** Chemcal fertlzers *** Pestcdes *** Irrgaton *** Interest on *** operatng captal Land rent *** Total varable cost *** (TVC) Total cost (TC) *** Gross Return (GR) *** Gross Margn (GM = *** GR-TVC) Net return (NR = GR *** TC) Beneft-Cost Rato (BCR = GR/TC) *** a Note: = One-way ANOVA usng the Generalsed Lnear Model (GLM). *** sgnfcant at 1 percent level (p<0.01). Source: Feld survey

20 Table 3: Jont parameter estmates of the stochastc cost fronter wth neffcency effects model Varables Parameter Coeffcent t-rato Stochastc cost fronter model Constant α *** Maze output level α *** 7.51 Normalzed nput prces Urea prce β Gypsum prce β Borax prce β Trple Super Phosphate prce β *** 3.87 Znc sulphate prce β *** 3.63 Mxed fertlzer prce β Manure prce β *** 3.01 Pestcde prce β *** 9.75 Labour wage β *** 3.99 Mechancal power prce β *** 5.60 Seed prce β *** 4.55 Irrgaton prce β *** Land rent β Cow dung users τ *** 6.79 Pestcde users τ *** 6.64 Gypsum users τ *** 3.73 Borax users τ Mxed fertlzer users τ * Envronmental factors Land sutablty ω Sol type ω Varance Parameters σ 2 = σ u 2 + σ v 2 σ ***

21 Varables Parameter Coeffcent t-rato γ = σ 2 u /(σ 2 u + σ 2 v ) γ 0.99*** Log lkelhood Wald χ 2 (21 df) χ *** Ineffcency effects functon Constant δ *** 4.40 Maze area δ * 1.81 Age of the farmer δ Educaton of the farmer δ * Experence n growng maze δ Famly sze δ Sowng date δ Varety δ Lnk wth extenson servces δ Dnajpur regon δ Bogra regon δ *** 4.72 Total number of observatons 300 Note: *** sgnfcant at 1 percent level (p<0.01) ** sgnfcant at 5 percent level (p<0.05) * sgnfcant at 10 percent level (p<0.10) 35

22 Table 4: Cost effcency dstrbuton Items Percentage of farmers Effcency levels up to 60% % % % % and above Mean effcency by farm sze Large farms 0.85 Medum farms 0.87 Small farms 0.87 Mean effcency by regon Kushta 0.91 Dnajpur 0.90 Bogra 0.79 Overall Mean effcency score 0.87 Standard devaton 0.07 Mnmum 0.67 Maxmum