Farm-Specific Factors Affecting the Choice Between Conventional and Organic Dairy Farming

Transcription

1 Farm-Specfc Factors Affectng the Choce Between Conventonal and Organc Dar Farmng Cornels Gardebroek E-mal: Paper prepared for presentaton at the X th EAAE Congress Explorng Dvers n the European Agr-Food Sstem, Zaragoza (Span), August 2002 Coprght 2002 b Cornels Gardebroek. All rghts reserved. Readers ma make verbatm copes of ths document for non-commercal purposes b an means, provded that ths coprght notce appears on all such copes.

2 Prelmnar verson, please do not ce Farm-specfc factors affectng the choce between conventonal and organc dar farmng. Cornels Gardebroek Department of Socal Scences Agrcultural Economcs and Rural Polc group Wagenngen Agrcultural Unvers Hollandseweg 1, 6706 KN Wagenngen, The Netherlands tel fax E-mal: Abstract Organc dar farmng n the Netherlands s a growng sector. Ths paper nvestgates the mpact of a number of economc and farm-specfc varables on the choce between conventonal and organc farmng. Based on expected utl maxmsaton, a theoretcal framework s developed that explcl accounts for the mpact of non-economcs varables lke age and educaton level and unobserved characterstcs. The model s estmated wh an unbalanced panel of Dutch dar farms usng a random effects prob specfcaton. Kewords: organc dar farmng, expected utl maxmsaton, technolog choce, farmspecfc varables, random effects prob model.

3 Farm-specfc factors affectng the choce between conventonal and organc dar farmng. Abstract Organc dar farmng n the Netherlands s a growng sector. Ths paper nvestgates the mpact of a number of economc and farm-specfc varables on the choce between conventonal and organc farmng. Based on expected utl maxmsaton, a theoretcal framework s developed that explcl accounts for the mpact of non-economcs varables lke age and educaton level and unobserved characterstcs. The model s estmated wh an unbalanced panel of Dutch dar farms usng a random effects prob specfcaton. Kewords: organc dar farmng, expected utl maxmsaton, technolog choce, farmspecfc varables, random effects prob model. 1. Introducton In the Netherlands, the number of organc dar farmers has ncreased rapdl n recent ears. The number of specalsed organc dar farmers has ncreased from 158 (0.5% of total number of specalsed dar farmers) n 1995 to 434 (1.6% of total) n 2000 (Statstcs Netherlands, 2001). A potental explanaton for the ncreased nterest of farmers n organc farmng s the sequence of crses n agrculture (BSE, foot-and-mouth dsease). After these crses some farmers ma have come to the concluson that the conventonal wa of farmng s not sustanable. Other explanatons are the hgh premum prce of organc mlk, the explc government support b nvestment subsdes, tax benefs and ncome support durng the transon perod or the ncreased envronmental legslaton that reduced the dfference between conventonal and organc farmng sstems, makng easer for farmers to swch. The Dutch mnstr of agrculture has even set a polc target for the number of organc farmers n B that tme 10% of the Dutch farmers should farm organcall (LNV, 2000). However, although some farmers apparentl have an nterest n organc farmng and the Dutch government s stmulatng swchng from conventonal to organc farmng, stll ltle s known about the motvaton of farmers to swch and the barrers for swchng to organc farmng. Knowledge about ths motvaton and potental barrers ma help polc makers n formulatng effectve polces for stmulatng organc farmng. Ths paper nvestgates the role of a number of farm-specfc varables n the choce between conventonal and organc dar farmng. A farmer s decson on whether to farm conventonall or organcall can be consdered as a choce between two avalable technologes. Gven the mportance of technolog n agrculture, economsts have pad consderable attenton to the analss of technolog adopton. Sundng and Zlberman (2001) provde an extensve revew of the lerature on technolog adopton. A popular approach to analsng technolog adopton s b usng bnar choce models (see e.g. D Souza et al., 1993; Burton et al., 1999). Usuall, a set of economc and other varables (e.g. age, educaton) s used to explan the dfference between the groups of adopters and non-adopters. What s often lackng n these studes s a theoretcal motvaton for ncludng a set of varables n the model. For example, the role of age and educaton n adopton decsons s often not made clear. Ths paper presents a theoretcal framework that explcl accounts for the role of personal characterstcs n adopton decsons. 1

4 The paper s bult up n the followng wa. In secton 2 a theoretcal model for the choce between conventonal and organc farmng s presented. Secton 3 dscusses a number of estmaton ssues. In secton 4 nformaton on the data set that s used n ths stud s gven followed b a presentaton of the estmaton results n secton 5. Fnall, n secton 6 some concludng comments are gven. 2. A theoretcal framework for the choce of organc farmng In ths secton a theoretcal model explanng the choce between conventonal and organc farmng s presented. Although the nterest n ths paper s n dar farmng, the theoretcal model presented n ths secton s more general applcable. Each tme perod a farmer decdes whether to farm conventonall or organcall. Snce farmers n the Netherlands can swch and swch back at an tme ths s a reasonable assumpton. Note that when a farmer decdes to swch to organc farmng he faces a transon perod of about two or three ears n whch he uses organc producton technques but where the produce cannot be sold as organc. Ths mples that organc premum prces cannot be obtaned et, whereas farmers usuall face a decrease n producton due to nexperence wh organc farmng and ncreased producton rsk. In order to overcome ths decrease n ncome the Dutch government (partall) remburses the loss of ncome durng ths transon perod. Moreover, varable costs are usuall lower for organc farmng than for conventonal farmng (Padel and Lampkn, 1994: 304). Therefore, the dfference between farmers n transon and certfed farmers s not explcl taken nto account. Assumng that farmers maxmse utl, the decson whether to farm conventonall ( =0) or organcall ( =1) s based on a comparson of expected utles of both producton practces. Usng the dfference n expected utles gves the followng decson rule: 1 = 0 f f O C [ U Ω, t 1, z, λ ] O C [ U Ω, z, λ ] E U E U, t 1 > 0 0 where E s the expectaton operator whch s condonal on the nformaton set, 1 of farmer (the nformaton a farmer has from perod t-1 to form hs expectatons for perod t), a vector z contanng varables that have an effect on the wa expectatons are formed (e.g. age) and a farm-specfc parameter λ reflectng dfferences among farmers n formng O expectatons that are not accounted for b the varables n z. U denotes utl of organc C farmng and U s utl of conventonal farmng. The utl level of farm practce j depends upon profs attaned wh that practce (π j ) and a vector of attrbutes of the practce (a j ): j j (, a ) j = O C j U = f π, (2) Note that the profs var per farm and per ear. The attrbutes of the farm practce ma drectl be related to the producton technque (e.g. the use or non-use of fertlser and herbcdes or the wa lvestock s kept) or ma be of a more general nature (e.g. vew of socet on the producton practce). The superscrpt j onl denotes that utl, ncome and attrbutes dffer for both farm practces. Utl levels as gven n equaton (2) are usuall not observed. However, assumng a j functonal form for f(.) makes possble to substute for both U s n equaton (1). For convenence a lnear relaton s assumed: Ω t (1) 2

5 N j 1π + α,1 + n n= 1 j U = α a (3) j 1+ n Organc farmng and conventonal farmng ma dffer n N attrbutes. Note however, that the weghts attached to these attrbutes dffer b farmer. Farmer A ma have a hgher preference for usng fertlser than farmer B (for reasons of applcaton and effects on plant growth), whereas farmer A s utl level s much less affected b the vews of socet on farm practces than farmer C s utl level. Furthermore, the parameter for ncome (α 1 ) s assumed to be equal for all farmers, mplng that ncome has the same effect on utl for all farmers. Snce the N farm practce attrbutes dffer for conventonal and organc farmng, the can be represented b a set of dummes takng the value 1 f organc and 0 f conventonal. Ths mples that utl of organc farmng s gven b O U = α N O 1π + α,1 + n n= 1, whereas for C C conventonal farmng s gven b U = α 1 π. For reasons of convenence 1 the sum of ndvdual attrbute parameters n the utl of organc farmng s aggregated to a sngle ndvdual constant δ reflectng the ndvdual preference for organc farmng, so O U = α 1 π + δ. If these expressons are used n equaton (1) follows that the expected O utl dfference s a functon of the dfference n profs and a farm-specfc parameter. Taken together the condonal expectatons n equaton (1) can be wrten as: O C [ U U Ω,, 1 z,λ ] = g( x, ) E µ (4) t where x denotes varables that explan dfferences n formng expectatons and varables n the nformaton set that are used to determne ncome. Furthermore, µ = δ + λ s a compose farm-specfc effect reflectng dfferences n utl and expectatons formaton. 3. Emprcal model and estmaton In ths secton the emprcal specfcaton s gven for the model developed n the prevous secton. Frst, the choce of the explanator varables that are used n the model s motvated. Next, an approach for estmatng the emprcal model s dscussed. Ths s closel related to the thrd ssue of ths secton,.e. how to deal wh the farm-specfc effects. As specfed n equaton (4), x represents varables that have an effect on how expectatons are formed and varables n the nformaton set that are used to determne ncome. In the analss the followng varables are ncluded: - Age. It s often stated that organc farmers are ounger on average than conventonal farmers (Padel and Lampkn, 1994: 296; Burton et al., 1999). The hpothess for ths observed dfference n age s that organc farms practces are often mplemented wh a change of farm ownershp (e.g. farmer's chld takng over farm control from parents). An addonal hpothess s that older farmers are more conservatve than ounger farmers are and therefore more resstant to organc farmng. - Educaton. Another often stated dfference between organc and conventonal farmers s the educaton level (Padel and Lampkn, 1994: 296). Explanatons that are gven are that 1 Note that n estmaton we also cannot estmate the N ndvdual parameters due to sngular (the dumm varable s the same for each parameter). 3

6 part of the organc farmers are new entrants to farmers that usuall hgh-educated and dealstc. However, could also be that hgher educated farmers expect to cope wh dffcultes n organc farmer better than conventonal farmers. - Father and chld. If a farm s owned and operated b a father and (one of hs) chldren, there ma be a potental conflct about the future strateg of the farm (Freer et al., 1994: ). Older farmers (parents) ma be more conservatve and reluctant towards organc farmng compared to ther chldren. A farm wh a sngle (oung) operator ma not be hndered b such conflcts to go organc. - Rent. If the major part of the farm s rented, decdng to farm organcall ma rase objectons from the landlord. Ths conflct ma also have an mpact on the decson process. - Sze of mlk quota. Dar farmers that want to expand ther producton need to bu addonal mlk quota. Instead of expandng and bung expensve addonal quota n order to mantan farm ncome at an acceptable level, farmers ma decde to choose organc farmng as an alternatve strateg. The extensve nature of organc farmng mples less producton capac. So, a small mlk quota ma nduce conventonal farmers to start organc farmng. Moreover, organc farms ma expect conventonal farmng to be not profable enough gven ther small mlk quota. - Sze of farm n hectares. The relaton between organc farmng and farm sze n hectares dffers b countr (Padel and Lampkn,1994: 296). However, the hpothess s that there exsts a posve relaton between organc farmng and number of hectares. Organc farms are more extensve than conventonal farms requrng more land for pasture. Moreover, organc farms use more roughage than concentrated feed and ths roughage ma be produced on the farm, requrng more land. - Anmal feed produced on-farm. Above was explaned that organc farmers ma produce more roughage than conventonal farms. For conventonal farms that alread produce a large amount of roughage, ma be easer to start farmng organcall. - On-farm sellng of mlk (products). Organc dar farms ma be more nvolved n off-farm sellng of mlk and mlk products produced on the farm. Although the on-farm processng mples addonal costs, organc farmers ma expect to generate addonal ncome n ths wa. The on-farm sellng of mlk and mlk products also refers to the 'natural' wa of farmng of organc farmng. - Profs n the prevous perod. The profs obtaned n the prevous perod ma be used as to determne the expected prof for ths ear. If a farmer had low profs n the recent past usng a partcular producton technolog, he ma be nclned to swch to the alternatve producton technolog. - Premum mlk prce. The premum organc mlk prce s hpothessed to be mportant n determnng the expected dfference n ncome under conventonal and organc farmng. If the prce dfference s large enough ths ma keep exstng organc farmers to ther practce and nduce conventonal farmers to swch to organc farmng. Besdes these varables the farm-specfc effect accounts for remanng dfferences n attudes, farmer's phlosoph of lfe and expectaton formaton. For the estmaton of the specfed dscrete choce model there are three often used methods avalable: the lnear probabl model, the log and the prob model (see e.g. Verbeek, 2000: ). However, the lnear probabl model suffers from a number of drawbacks. The dstrbuton of error terms s hghl non-normal and errors are heteroskedastc. However, the bggest problem s that predcted probables are not guaranteed to le between zero and one. The log and prob models overcome ths problem b transformng the underlng latent process, as gven n equaton (1), b a logstc or normal 4

7 dstrbuton functon. Therefore, a log or prob model s usuall preferred n emprcal analss. However, the presence of farm-specfc effects n the model also has mplcatons for the bnar choce model to be used. Farm-specfc effects can be specfed as fxed parameters (fxed effects) or as random error components, ndependentl and dentcall dstrbuted over ndvduals (random effects). The latter assumpton s more approprate for a large sample wh man ndvdual havng few observatons over tme (Verbeek, 2000: 319). A drawback of the random effects assumpton s that the random error components have to be ndependent from the explanator varables (whch of course also holds for the regular error terms). A standard lnear fxed effects model s usuall estmated b dfferencng the fxed effects out, eher b usng devatons from ndvdual means (whn estmator) or b takng frst dfferences. However, n non-lnear models lke the log or prob ths s not possble. Ths s referred to as the ncdental parameters problem (Lancaster, 2000). For a random effects prob model a somewhat related problem arses, makng maxmum lkelhood estmaton nfeasble (for detals see Maddala, 1987). Therefore, alternatve approaches are necessar to estmate bnar choce models wh panel data. It appears that estmaton of a fxed effects log model and a random effects prob model, both under some specal condons, s possble (Maddala, 1987; Verbeek, 2000: ). Major drawbacks of the fxed effects log model are that onl observatons that have a change n the value of can be used and that explanator varables that are constant for ndvduals (dummes) cannot be used. Gven the choce of varables for the model made above (educaton, rentng, father and son), the fxed effects log model s not feasble here 2 and therefore a random effects prob model s used. The restrcton for the random effects prob model s that the correlaton of the combned resduals over tme (va the random effects) s the same for all ndvduals. Usng a latent varable * for the dfference n expected utl, the model s wrten n the standard bnar choce formulaton: * = x β + ν ' = 1 = 0 f f * * > 0 0 (5) where ν = µ + ε. Assumng that the jont dstrbuton of ν 1,...,ν T s normal wh zero 2 mean, varance equal to 1 and cov{ ν, ν s} = σ µ, s t. Ths mples that µ s normall 2 dstrbuted wh zero mean and varance σ µ, whereas ε s normall dstrbuted wh zero 2 mean and varance 1 σ µ. The specfcaton of the error dstrbutons makes estmaton of the random effects model b maxmum lkelhood feasble (for detals see Verbeek, 2000: ). Estmaton was performed usng Stata Note that a fxed effects lnear probabl model s also no soluton. Although the fxed effects can be removed b devatons from the means (whn estmator) or frst-dfferences, ths mples that a major part of the varance n the data s not taken nto account. The whn estmator onl uses varance whn the observatons for a gven ndvdual and gnores varance between ndvduals. Snce a number of dumm varables are ncluded n the model (denotng dfferences between ndvduals but not for an ndvdual over tme) ths wll result n ver poor estmates. Frst-dfferences transforms all dumm varables, ncludng the dumm dependent varable nto columns of zero s, whch makes estmaton mpossble. See Verbeek (2000: ) for a dscusson on whn and between varance. 5

8 4. Data Data on specalsed dar farms coverng the perod are obtaned from a stratfed sample of Dutch farms keepng accounts on behalf of the farm accountng sstem of the Dutch Agrcultural Economcs Research Instute (LEI). Specalsed dar farms are defned as farms havng a share of dar output n total output exceedng 50%. In the sample there are 41 organc farms of whch 5 have swched to organc farmng n the sample perod. In total there are 121 observatons on organc farmng. The major of the farms n the sample are conventonal. In total there are 795 conventonal farms, havng 2841 observatons. The sample s representatve for the (specalsed) dar sector n the Netherlands. Sample averages standard devatons for the varables used n the emprcal analss are gven n table 1 for organc farms and conventonal farms separatel. Table 1. Sample means for model varables of conventonal and organc dar farms (standard devatons n parentheses) Varable Un Conventonal ( = 0) Organc ( = 1) age ears (11.09) (9.109) educ dumm (0.499) (0.373) faso dumm (0.450) (0.412) rent dumm (0.383) (0.451) szequo kg (2.652) (1.379) szeha hectares (22.18) (18.09) feedrt rato (0.053) (0.102) locmlk rato (0.021) (0.112) prof t Dutch gulders (2.651) (2.568) premum t-1 gulders (0.005) (0.005) N Age s the age of the (man) farm operator and s gven n ears. Educaton s represented b a dumm varable denotng whether the farm operator has had hgher educaton (educ=1) or not (educ=0). The dumm varable faso s 1 for farms that are operated b father and a chld and rent s 1 for farms that are rented for the major part. The sze of the mlk quota (szequo) s gven n kg. mlk, whereas the farm sze n hectares s gven b szeha. Furthermore, feedrt s defned as the rato of anmal feed produced on the farm to the amount of feed purchased and locmlk s the rato of revenues from on-farm sellng of mlk(products) to total revenues. Prof t-1 and premum t-1 gve one perod lagged profs and organc mlk premum prce respectvel. Table 1 shows that organc farmers are on average ounger and hgher educated. Furthermore, organc farms have a slghtl lower percentage of farms run b father and son together, are more often rented, have more sellng of mlk (products) on the farm and use more feed produced on the farm. Wh respect to sze, should be noted that organc farms have on average more land but less quota than conventonal farms, reflectng the more extensve wa of organc farmng. Surprsngl, average profs are lower for organc farms. The premum s the same for each farm n a gven ear. 5. Results Usng the varables descrbed n the prevous secton, the model gven b equaton (5) was estmated usng a random effects prob specfcaton. Estmaton results are gven n table 2. Table 2. Estmaton results for random effects prob model 6

9 Parameter Estmate Standard error P-value constant age educ faso rent szequo szeha feedrt locmlk prof t premum t N 2131 Wald χ 2 (10) McFadden's R P-value Wald test ρ (st.error ρ) (0.003) LR-test ρ= From the 11 estmated parameters, 7 are sgnfcantl dfferent from zero at the 5% crcal level. Educaton, the sze of the farm n hectares and the rato of revenues of on-farm sellng of mlk (products) to total revenues (locmlk) have a posve mpact on the choce between conventonal and organc farmng. Stated n other words, farms wh these characterstcs have a hgher probabl of beng or transformng to organc. These results are n accordance wh what was expected. Hgher educated farmers ma choose more conscousl for organc farmng or expect more often to be able to solve potental problems. The large land base accords wh the extensve nature of organc farmng. On-farm sellng of mlk refers to the desre of organc farmers to show ther natural wa of farmng to the publc. Conventonal farms wh on-farm sellng have a hgher probabl of swchng n order to obtan premum prces. The evdence of these posve effects obtaned b comparng the varable means for both groups as gven n table 1, s confrmed b the sgnfcance of these varables. Farms operated b a father and a chld, farms wh a large mlk quota and wh hgh profs n the prevous perod have a lower chance of beng or becomng organc. These fndngs are also n accordance wh the hpotheses stated n secton 3. Farms operated b a father and a chld ma have conflcts about swchng to organc farmng, provdng an extra barrer for swchng. However, could also be that organc arms are new entrants n agrculture wh a sngle operator onl. What can be concluded s that a potental posve effect of havng a father wh a chld runnng the farm, vz. the addonal avalabl of labour that s often requred for organc farmng, s not present. Farms wh a large mlk quota consder enlargement of producton scale as a better strateg for the development of ther farm, than swchng to organc farmng. The effect of profs n the prevous perod s also not surprsng. Table 1 alread showed that conventonal farms had hgher profs than organc farms, whch s n accordance wh ths result. So, profs n the prevous perod can be used to dscern between both groups. However, low profs n the prevous perod ma also nduce conventonal farms to swch to organc farmng. It appears that age, whether a farm s rented or not, the amount of anmal feed that s produced on the farm and the sze of the premum do not have a sgnfcant effect on choce between conventonal and organc farmng. Especall the fndng for age s surprsng. Although s often mentoned that organc farmers are oung than conventonal farmers, age does not seem to have an effect on the choce of producton technolog. Or n terms of the theor, age does not have an effect on how expectatons are formed about the utl of a partcular producton technolog. It also appears that the prce dfferental between conventonal mlk and organc mlk does not have an mpact on the choce of producton technolog. 7

10 A Wald test was performed to test whether the parameters are all equal to zero. At the 10% crcal level, ths hpothess was rejected. A lkelhood rato (LR) test was performed to test for absence of correlaton of resduals over tme. Absence of such correlaton mples that there s no persstence of ndvdual (random) effects over tme. However, ths hpothess s frml rejected wh a LR statstc of exceedng an crcal value. In other words, random effects, accountng for unobserved dfferences among farmers n expectaton formaton and utl percepton, are not rejected b ths test. The estmated model was used to predct the probabl of beng organc for the sample used n estmaton. Note that these predctons are not completel relable, snce the random effects could not be calculated for each farms. Therefore, predctons were made assumng the random effects to be zero for all farms. Snce the number of conventonal farms s much larger than the number of organc farms and the average probabl s therefore close to zero, a weghted cut-off pont of was used to classf the predctons (Har et al., 1990:86). From the 2131 observatons used n estmaton 3, 2032 were correctl predcted as conventonal, 19 correctl as organc and 80 predctons were wrong. From the 80 wrong predctons, 9 observatons on conventonal farmng were predcted to be organc, whereas 71 'organc' observatons were classfed as conventonal. Therefore, the predctve power of organc farmng of the model s onl 19/90=0.21. However, ths low percentage of correct predctons ma be due to settng all the random effects to zero. Takng random effects nto account should ncrease the total number of correct predctons. A fnal remark s on the dfference n sample szes for conventonal and organc farmng. In estmaton 90 out of 2131 (4.2%) observatons were organc. Does ths small sze of organc farms have an mpact on organc farmng? It appeared to be not. From the total sample of conventonal farms a number of small random samples were drawn and used n estmaton together wh the sample of organc farms so that the dstrbuton of observatons was one-thrd organc and two-thrds conventonal. It appeared that the sgns and effects of the varables remaned largel the same, whereas the standard errors of the parameter estmates ncreased. The outcomes were of course dependent on the sample drawn. In order to have as much precson n estmaton (small standard errors) and because the total sample s representatve for the Dutch dar sector, estmaton s based on the total sample avalable. 6. Conclusons and Dscusson Ths paper nvestgates the choce between conventonal and organc producton technologes for ndvdual farmers. A theoretcal framework s developed that explcl accounts for the effects of farm-specfc varables lke age and educaton on the expectatons farmers have on the utl of both producton technologes. Furthermore, farmers ma also dffer n ther utl levels for both producton technologes. Unobserved farm-specfc effects account for dfferences not represented b the varables ncluded n the model. The model was estmated on a panel data set of Dutch dar farmers for the perod usng a random effects prob specfcaton. It appears that educaton, the sze of the farm n hectares and on-farm sellng of mlk (products) have a posve mpact on the choce between conventonal and organc farmng, whereas jont operaton of the farm b father and chld, the sze of the mlk quota and profs n the prevous perod have a negatve effect on ths choce. Age, rentng a farm, the amount of anmal feed produced on the farm and the sze of the premum do not have a sgnfcant effect on choce between conventonal and organc farmng. 3 Note that onl 2131 out of the total 2962 observatons were used n estmaton because of takng lagged values for profs and the premum on the mlk prce. Onl 90 from 121 observatons on organc farmng were used. 8

11 So, can be concluded that a number of farm-specfc varables (e.g. educaton) have an mpact on the decson to farm organc or conventonal. The effect of these varables s va the expectatons farmers have on the utl of these producton technologes. Furthermore, unobserved farm-specfc effects also mpact on these decsons. Polc mplcatons of these fndngs are that besdes economc varables, farm-specfc characterstcs are also mportant n decsons on swchng to organc farmng. In developng polc measures that stmulate swchng to organc farmng, these characterstcs should be taken nto account. In other words, polc measures should be amed at the group of farmers havng the characterstcs found to be mportant n ths stud. REFERENCES Burton, M., Rgb, D. and Young, T. (1999). Analss of the Determnants of Adopton of Organc Hortcultural Technques n the UK. Journal of Agrcultural Economcs 50: D Souza, G., Cphers, D. and Phpps,T. (1993). Factors Affectng the Adopton of Sustanable Agrcultural Practces. Agrcultural and Resource Economcs Revew, 22: Freer, B., Rantzau, R. and Vogtmann, H. (1994). Case Studes of Farms Convertng to Organc Agrculture n German. In: Lampkn, N.H. and Padel, S. (eds), The Economcs of Organc Farmng. An nternatonal perspectve. Wallngford, CAB Internatonal. Har, J.F., Anderson, R.E. and Tatham, R.L. (1990). Multvarate data analss. Wh readngs. 2 nd edon. New York, MacMllan Publshng Compan. Lancaster, T. (2000). The ncdental parameter problem snce Journal of Econometrcs 95(2): LNV. (2000). Een bologsche markt te wnnen. Beledsnota Bologsche Landbouw Den Haag. Maddala, G.S. (1987). Lmed Dependent Varable Models Usng Panel Data. The Journal of Human Resources 22: Padel, S. and Lampkn, N.H. (1994). Converson to Organc Farmng: An Overvew. In: Lampkn, N.H. and Padel, S. (eds), The Economcs of Organc Farmng. An nternatonal perspectve. Wallngford, CAB Internatonal. Statstcs Netherlands. (2001). Bologsche Landbouw Voorburg, Statstcs Netherlands. Sundng, D. and Zlberman, D. (2001). The Agrcultural Innovaton Process: Research and Technolog Adopton n a Changng Agrcultural Sector. In: Gardner, B. and Rausser, G. (eds). Handbook of Agrcultural Economcs. Elsever/North Holland. Verbeek, M. (2000). A gude to Modern Econometrcs. Chchester, John Wle & Sons, Ltd. 9