Factors Influencing the Implementation of Best Management Practices in the Dairy Industry

Transcription

1 Factors Influencng the Implementaton of Best Management Practces n the Dary Industry Noro C. Rahelzatovo and Jeffrey M. Gllespe Department of Agrcultural Economcs and Agrbusness Lousana State Unversty 101 Ag. Admn. Bldg. Baton Rouge, LA jmglle@lsu.edu Selected Paper prepared for the presentaton at the southern Agrcultural Economcs Assocaton Annual Meetng, Moble, Alabama, February 1-5, Copyrght 2002 by Noro C. Rahelzatovo and Jeffrey M. Gllespe. All rghts reserved. Readers may make verbatm copes of ths document for non-commercal purposes by any means, provded that ths copyrght notce appears on all such copes.

2 1 Introducton The tradtonal vew of the agrcultural communty as a good steward of the envronment has been challenged by ncreasng concern about the complex relatonshps between agrcultural producton actvtes and envronmental qualty. Many have ponted to agrculture as a nonpont source of water polluton. Pollutants such as sedment, nutrents, pestcdes, salt, and pathogens orgnate from agrculture actvtes, reach water resources through runoff, leachng, ranfall and snow melt, and mpar surface, ground and coastal waters. The Coastal Zone Management Act of 1972 and the Coastal Zone Act Reauthorzaton Amendments of 1990 requre specfc measures to handle agrcultural nonpont sources of water polluton. Voluntary mplementaton of BMPs, whch consst of specfc sets of effectve and practcal means to reduce water polluton, have been promoted. The Lousana dary ndustry has experenced the same basc trend as the naton, toward fewer, yet larger unts of producton, over the past two decades. Such structural change has been emphaszed n Rahelzatovo and Gllespe s nvestgaton of the changes n dary farm sze, entry and ext of farms n the Lousana dary ndustry n Along wth the ncreased effcency n dary producton, structural change toward larger unts of producton also results n the problem assocated wth handlng and managng larger volumes of wastewater and manure generated from large facltes (Renhard et al., 1999). Improper waste management causes dscharges of pollutants to surface waters through splls from waste storage structures and runoff from feedlots or cropland, and to groundwater through runoff seepage. Hence, dary producers n Lousana, tendng to operate larger and larger farms, face smlar requrements and pressure regardng the enhancement of envronmental qualty as producers n other major mlk producng areas. Furthermore, the concentraton of fecal colform bactera n streams and other water bodes has

3 2 rased major concern n Lousana over the past twenty years. Fndngs of research on water polluton have suggested the pathogen-contamnated water supply n the Tangpahoa Rver, wthn the dary producton regon, has been caused by woodland and dary farm pastures (Drapcho et al., 2001). Grazng cattle has been consdered to be a sgnfcant source of fecal colform contamnaton to surface waters. Best management practces (BMPs) assocated wth wastewater and runoff from dary farms have been developed and promoted to reduce the volume of polluton reachng a water body and mprove water qualty. Ths study ams to examne the current adopton of best management practces (BMPs) by Lousana dary producers. The conduct of unvarate and multvarate probt analyses allows for nvestgatng the economc and non-economc determnant factors of producers decson to adopt one, two or a set of practces. Lterature One of the earlest studes on technology adopton was Grlches exploraton n 1957 of the economcs of technologcal change, specfcally the wde dfferences n the rate of use of hybrd seed corn. Snce the publcaton of hs work, the economcs of technology adopton has captured researchers nterests, yeldng hundreds of publcatons. Researchers have nvestgated the dfferent aspects of producers adopton decson and examned the lkely determnant factors nfluencng ncreased technology adopton (Caswell and Zlberman, 1985; Shelds et al., 1993; Ghosh et al., 1994; Davs and Gllespe, 2000; Moser and Barrett, 2002). Some ponted out the need for approprate econometrc tools to account for the nterrelatonshps among adopton decsons (Feder et al., 1985; Zepeda, 1994; Dorfman, 1996; El-Osta and Morehart, 1999). Studes on the adopton of envronmentally-sound technologes explored the role of factors such as producers awareness of sol eroson, qualty of nformaton, land tenure, and economc

4 3 ncentves on the voluntary adopton of management practces (Gould et al., 1989; Barber, 1990; Govndasamy and Cochran, 1995; Westra and Olson, 1997; Cardona, 1999; Soule et al., 2000; Ipe et al., 2001; Cooper, 2001). Data and Methods A state-wde survey of the entre populaton of Lousana dary producers (428) was conducted n Summer, 2001 to collect data related to dary producton characterstcs, producers characterstcs, rsk preference and envronmental atttude, socal captal varables, farm characterstcs, and current adopton of the twenty one BMPs. A total of 131 surveys were returned wth 124 completed. Econometrc Models Ths study nvestgates the lkelhood of a producer of a specfc descrpton to adopt one or more management practces. The conduct of a probt analyss on each ndvdual BMP allows for the assessment of the probablty of a dary producer to mplement a specfc BMP based on the economc and non-economc factors hypotheszed as determnant n the dary producer s decson to adopt. The lnear random utlty assumpton s expressed n equaton (1), where (1) U U = U + e = z ' δ + w ' γ + e = U + e = z ' δ + w ' γ + e U j= average utlty perceved by ndvdual from choosng alternatve j; e j = random dsturbances assocated wth ndvdual s choce of alternatve j; z j = vectors of attrbutes assocated wth alternatve j and specfc to ndvdual ; and w = soco-economc characterstcs specfc to ndvdual. The probablty that ndvdual would choose to adopt a BMP (y =1) versus not to adopt a BMP (y = 0) s expressed as: p y y e β β * ' = = = = = * (2) ( ) ( > ) ( > x ) prob 1 x prob 0 prob ' F( X)

5 4 * where y = (the latent varable) = ( z - z ) δ + w ( γ γ ) + ( ) = [ z1 z0 ] U 1 U e1 e0 δ + + γ - γ 1 0 x β * * * ( - ), w e = x ' β e ; and F = cumulatve dstrbuton functon of e evaluated at '. The unobservable latent varable y * s lnked to the observed bnary varable y : (3) y * y > 1 f 0 = * 0 f y 0 The margnal effect assocated wth a contnuous explanatory varable x k on the probablty p, holdng the other varables constant, can be derved as n (4) where φ represents the probablty p x (4) = φ( x ' β) β k k densty functon of a standard normal random varable. Estmates of margnal effects at the mean values of all ndependent varables consttute the commonly reported summary measure n many studes. Multvarate probt analyss s conducted to determne the types of producers that adopt two or more practces. The general formulaton s as expressed n equaton (6) where the error terms e 1, e 2,, e M have a multvarate normal dstrbuton wth mean vector 0 and covarance matrx Σ wth dagonal elements equal to 1. (6) * * y β 1 1 X 1 e1 y y 1 1 * * y 2 2 X 2 e 2 y y 2 2 y y y = ' +, = 1 f > 0, and 0 otherwse = β ' +, = 1 f > 0, and 0 otherwse... = X +, = 1 f > 0, and 0 otherwse * * β M M M e M M M The probabltes that enter the lkelhood functon would become: Y Y Y x x x = prob(,,..., M /,,..., M) MVN( TZ, TRT `) (7)

6 5 where MVN stands for multvarate normal dstrbuton; T s a dagonal matrx wth element t m = 2y m 1; Z = a vector wth elements z = β ' x ; R = correlaton matrx of the errors terms; M M M and m = 1, 2,..., M. The margnal effects for the contnuous explanatory varables can be derved by takng the dervatve of the expected value of Y 1 gven that all other Y s are equal to 1, wth respect to the regressors n the model. The matrx computaton of the margnal effects assocated wth the multvarate probt model s presented n equaton (8). (8) E X 1 {(prob( Y 1, 1 Y 1,..., 2 Y 1 prob( ) 1,..., 1) M Y 2 Y M } = = = = = = X M prob( Y = 1, Y = 1,... Y = 1) 1 2 M 1 = cm * m= 1 Z m prob( Y = 1,..., 1) 2 Y = M E 1 ( Y 2 Y M ) M prob( = 1,..., = 1 1 * cm * Z m= 2 m prob( Y = 1,..., 1) 2 Y M = ( Y = 1, 1,..., 1) 1 Y = 2 Y = M Y = 1,..., 1 2 Y = M prob where E =, X = all regressors n the model, and z 1 m = X`cm= βm` xm. prob( ) Dscrete changes n the predcted probablty of adoptng all practces at the same tme, gven a change n a specfc ndependent varable, and holdng all other varables constant, expressed n equaton (9) were conducted as an alternatve to margnal effects. (9) prob( Y1,..., Y m X) x k Y1 Y m k δ Y1 Y m = prob(,..., X, x + ) prob(,..., X, x ) k where δ s the ncrement n the value of varable of nterest x k. Dscrete changes n the probabltes were estmated for varables that yelded statstcally sgnfcant coeffcents for β. The Bnary Dependent Varables Each management practce ncluded n the set of 21 BMPs for the Lousana dary ndustry was assumed to defne one equaton n each probt model and the subsequent analyss. The producer s response regardng hs current adopton of each management practce defned the

7 6 bnary dependent varable that took the value of one f the BMP was currently mplemented and zero otherwse. The management practces were grouped nto four man categores for the purpose of multvarate probt analyss, based on each practce prmary objectve. The unobservable latent varable y * assocated wth each bnary varable was assumed to be a lnear functon of the hypotheszed ndependent varables descrbed n the next secton. Factors Influencng Dary Producers Decsons to Adopt BMPs Larger szed farms have generally been assocated wth an ncreased lkelhood to adopt technology. Adopton of a new technology often nvolves hgh ntal outlay and farmers wth greater resources are better able to afford the technology. Total number of cows n the dary herd (COWS) was used as a proxy for farm sze n ths study. Larger dary farms were hypotheszed to be more nvolved n wastewater and runoff management to better handle the large amount of manure and waste produced on ther farms. Farm productvty may reflect producers openness to new technology that provdes greater productvty gans and characterze farm operator management ablty. It has usually been ncorporated n technologcal adopton studes as an endogenous varable because technology affects productvty. In ths study, cow productvty was not consdered as an endogenous varable because conservaton management practces target prmarly the enhancement of the envronment, not farm producton. Average pounds of mlk per cow (YIELD) was ncorporated as an explanatory varable to account for the dfferental ablty of the productve farm to bear the fxed adopton costs of conservaton management as hgh productvty would lkely ensure larger profts. Dversfcaton n farmng actvtes s one of the common tools for managng agrcultural rsks assocated wth yeld, prce and ncome. Rsk averson would drve farmers to engage n

8 7 alternatve enterprses. Producers engaged n dverse agrcultural enterprses were hypotheszed to lkely adopt management practces relevant to each type of actvty. Varable (OCROP) was ncluded to account for the number of other farmng actvtes n whch the dary farmer was nvolved besdes mlk producton and rasng hay. It was hypotheszed to be postvely correlated wth the probablty to adopt a BMP. The effect of land tenure has been examned n many technology adopton studes. Tenants lack of motvaton to adopt would be due to the percepton of benefts accrung to the landowner, and not to the renter. The proporton of owned land to total acres operated (LAND) was ncluded. A greater fracton of land owned was hypotheszed to ncrease the adopton of sol management practces. Both pasture-based and free-stall dary farms were expected to be nvolved n the runoff and waste management practces. However, dary farms more nvolved n grazng actvty were assumed to have nformaton about grazng management practces. Pasture-based operaton was ncluded as dummy varable (PASTU) that took the value of one f the operaton was forage based and zero otherwse, and was hypotheszed to enhance the adopton of grazng management practces. As dscussed by Feder et al. (1985), labor avalablty may affect a farmer s decson to adopt technology. Labor shortages promote the adopton of labor-savng practces, but hnder the mplementaton of technologes that requre more labor nput. The number of part-tme (PART) and full tme (FULT) employees were ncluded as explanatory varables. A greater labor force was hypotheszed to ncrease the adopton of labor demandng conservaton practces such as waste management, nutrent management and pestcde management. On the other hand, some labor savng practces mght nclude conservaton tllage.

9 8 Busness structure consttutes a decson factor that s lkely to mpact the adopton of management practces. The corporate farm structure allows producers to take greater nvestment rsk than sole propretors. Busness structured as a farm corporaton was ncluded as a dummy varable (BSTR) whch took the value of one for a corporate farm and zero otherwse. BSTR was hypotheszed to ncrease the adopton of BMPs. A dary farmer s fnancal stuaton could also mpact hs decson as to whether to ncur greater costs by mplementng management practces. Dary operatons wth greater net worth are farms wth greater resources, able to afford the costs of mplementng a BMP. Current dary operaton net worth (NWTH) was ncluded as a dummy varable that took the value of one f the farm net worth was at least $400,000, whch descrbed the level of net worth of a medum szed dary farm. The ambguous effects of debt-to-asset rato on adopton were dscussed by Fernandez-Cornejo et al. (1994). In ths study, the sgn of varable DEBT s to be explored. Operators wth land classfed as hghly erodble would have a greater need to carry out sol conservaton practces. Thus, varable HEL, whch accounts for the percentage of the farmer s land classfed as hghly erodble, was ncluded to capture ths effect. Dary farmers who have poorly draned areas may opt to mprove ther dranage system through water control structures. Varable WDL measured the percentage of the farmer s land classfed as welldraned. WDL was hypotheszed to specfcally ncrease the mplementaton of eroson and sedment control practces. Two dummy varables were ncluded to account for the exstence of a stream and/or rver on the dary farm or nearby. Varable STRM1 took the value of one f a stream and/or rver ran through the farm and was expected to ncrease the mplementaton of BMPs, especally those such as streambank and shorelne protecton. Varable STRM2 took nto consderaton the

10 9 exstence of the nearest stream or rver dstant from the farm, takng the value of one f the nearest stream or rver was more than one mle away from the dary farm and zero otherwse. STRM2 would lkely reduce the adopton of BMPs. The roles of age and educatonal attanment n farmers decsons to adopt technology have been shown n prevous studes. Varable AGE accounted for the age of the prmary operator and was hypotheszed to negatvely affect farmers adopton of BMPs because older operators wth shorter plannng horzons would be less nclned to adopt new technologes. Dummy varable EDUC took the value of one f the dary farmer held a college degree and zero otherwse. Educatonal attanment was expected to mprove the decson-makng process and enhance adopton. Consequently, varable EDUC was hypotheszed to have a postve sgn. Other factors such as holdng an off-farm job (OFFF), havng famly members who plan to take over the operaton upon the farmer s retrement (TOVR), and partcpaton n a dary cost-sharng program (CSP) such as EQIP were assessed and ncluded as dummy varables. Each varable took the value of one f the producer responded yes to the related queston n the survey, and zero otherwse. As Feder et al. (1985) suggested, off-farm ncome would permt farmers to overcome the captal constrant and carry out agrcultural practces. Hence, varable OFFF was expected to have a postve sgn. The exstence of famly plans to take over the operaton upon the farmer s retrement n effect would encourage the adopton of conservaton practces as farm operators would have an ncentve to mantan productvty of sol for future generatons. Varable TOVR was expected to ncrease the adopton of BMPs. Partcpaton n cost-sharng programs lkely ncreases producer nvolvement n governmental conservaton programs. Therefore, varable CSP was expected to have a postve sgn. The number of years the dary farmer had been operatng the farm (EXP) was ncluded to capture the ncreased effect

11 10 of experence on the adopton decson. Smlar to educaton, experence was expected to mprove farmers ablty to adopt new technologes. A farmer s decson to adopt a management practce s shaped by dfferent sources of nformaton. Tranng programs provded prmarly by the Natural Resource Conservaton Servce (NRCS) and the Lousana Cooperatve Extenson Servce (LCES) va programs such as the Master Farmer Program, would consttute dary farmers sources of nformaton regardng envronmental ssues related to agrcultural actvtes and potental solutons to such problems. More frequent meetngs wth extenson agents would ndcate the farmer s relance on the type of nformaton provded and the lkely subsequent acceptance of the recommended practces. Thus, the number of tmes the farmer met wth extenson agents n 2000 (LCES) was ncluded as an explanatory varable to capture the ncreased adopton effect. A dary farm plan developed wth NRCS would suggest the farmer s wllngness to comply wth envronmental standards and, therefore, to adopt conservaton practces. Such nformaton was ncorporated as a dummy varable (NRCSP) that took the value of one f a plan was developed or updated wth NRCS and zero otherwse. Other sources of nformaton ncluded dary cooperatves and assocatons as well as the mass meda. A farmer s awareness of other dary operators experences was lkely to be mportant n decdng whether to adopt technology. Many cooperatves promote communcaton among dary producers and provde cooperatve members nformaton through newsletters, quarterly meetngs or other actvtes. Thus, a dummy varable (COOP) to account for beng a member of a dary cooperatve was ncluded. Producers who are better record keepers were also hypotheszed to be more wllng to adopt conservaton practces snce they were lkely to be more progressve farmers. Dummy varable (DHIA) accounted for beng member of the Dary

12 11 Herd Improvement Assocaton. DHIA was hypotheszed to postvely nfluence the decson to adopt. Gatherng nformaton through semnars and meetngs that deal wth dary ndustry ssues consttutes another source of nformaton for dary farmers. Greater concern for ndustry ssues s lkely to enhance adopton of technologes. Number of semnars and/or meetngs attended n 2000 (SEM) was expected to postvely nfluence the farmer s decson to adopt. Rsk and uncertanty have been dscussed n prevous emprcal studes as mpedng factors to technology adopton. These factors urge the rsk averse farmer to selectvely adopt technology that ensures net expected margnal benefts. In ths study, producer s rsk averson was estmated based on the subjectve assessment of whether they took substantal levels of rsk, nether seek nor avod rsk, or tended to avod rsk whenever possble n ther nvestment decsons. Varable RISK was ncluded as a dummy varable that took the value of 1 f the farmer tended to avod rsk and zero otherwse. RISK was expected to ncrease the adopton of BMPs that reduce sol runoff, nsurng long-run vablty of land. Farmer s behavor toward the envronment was assessed based on the New Envronmental Paradgm (NEP) scale developed by Dunlap et al. n Varable ENV descrbed the NEP score assocated wth the dary operator s envronmental atttude and accounted for the dary producer s average score over the 15 statements. It was expected that envronmental concern would drve the farm operator to mplement conservaton practces. A dary operator s percepton of hs socal relatonshps wth neghborng farmers (SCAP1), lendng nsttutons (SCAP2), other agrcultural busnesses (SCAP3), non farmer neghbors (SCAP4) and regulatory agences (SCAP5) was hypotheszed to affect hs decson to adopt management practces. The farmer s assessment of hs relaton wth each entty as not mportant, not very mportant, somewhat mportant and very mportant was scored 0, 1,

13 12 2, or 3, respectvely. SCAP2 and SCAP5 were hypotheszed to ncrease the adopton of BMPs snce mportant relatonshps wth lendng nsttutons would ensure fnancal support for the requred nvestment and mportant relatonshps wth regulatory agency would provde better nformaton regardng the necessty to mplement specfc management practces. The remanng socal captal varables SCAP1, SCAP3 and SCAP4 were ncluded for exploratory purposes. Results Dfferent rates of adopton were found for each BMP (Table 1). Non-adopton was due manly to a need for more nformaton or the real or perceved non applcablty of the specfc practce to the farm. The group of practces targetng eroson and sedment control had the lowest rates of adopton, varyng from 28 percent (for streambank and shorelne protecton) to 48 percent (for feld borders), except for conservaton tllage, whch was adopted by 77 percent of the respondents. The low rates mght be due to producers adopton of BMPs accordng to ther prmary actvtes. The adopton of practces related to eroson and sedment reducton could be secondary n the eyes of the dary producers. Practces amng at the management of faclty wastewater recorded the hghest rates of adopton among all BMPs, varyng from 70 percent (for waste storage faclty) to 83 percent (for waste management system). The adopton of nutrent and pestcde management were 69 and 62 percent, respectvely. Survey results suggest that about 10 percent of the producers had not heard about these two BMPs, 11 percent of the respondents consdered nutrent management not applcable to ther farms and 23 percent thought the same regardng pestcde management. The three grazng management practces had hgh rates of adopton: 80 percent for fencng; 72 percent for grazng management; and 70

14 13 percent for trough or tank. The pasture-based operaton type of most respondents dares explans these rates of adopton of grazng management practces. Results from the probt models suggest that farm sze (COWS), mlk productvty (YIELD), frequency of meetngs wth LCES personnel (LCES), and rsk averson (RISK) were assocated wth sgnfcant ncreases n the adopton of 5 to 8 specfc BMPs. Nne varables were found sgnfcantly assocated wth ncreased adopton of 1 to 3 specfc BMPs. These varables nclude: havng a stream runnng through the farm land (STRM1), percentage of land classfed as hghly erodble (HEL), busness structured as a corporaton (BSTR), dary farm net worth (NWTH), the holdng of an off-farm job (OFFF), farmer s educatonal attanment (EDUC), havng a famly member plannng to take over the dary operaton upon the producer s retrement (TOVR), membershp n a mlk cooperatve (COOP), and good relatonshps wth lendng nsttutons (SCAP2). Varable AGE frequently had a negatve sgn, whch was as expected. Older producers would be expected to have shorter plannng horzons and would be less wllng to alter ther management strateges. The consstent negatve assocaton between membershp n DHIA and BMP adopton was not as expected. In ths study, better record keepers, lkely to be the more progressve farmers, were hypotheszed to be more wllng to adopt conservaton practces. The negatve correlaton could be because of DHIA targetng dary farm productvty and ensurng hgher proft through hghly montored busness management. Conservaton practces, on the other hand, prmarly target an overall mprovement of the envronment, whch may ensure long term fnancal vablty, but may not result n greater shortrun proft. Dary producers most lkely to adopt BMPs were more lkely to be operatng larger farms wth greater mlk productvty per cow. These producers were also more hghly educated and rsk averse. The sgnfcant nfluence of meetngs wth LCES personnel suggests the

15 14 mportance of nformaton dssemnaton n nducng adopton of BMPs, and the effectveness of LCES at nfluencng adopton. Selected results from the probt analyss are presented n Table 2. Fewer varables were sgnfcant as more equatons were consdered n the multvarate probt analyss. Selected results are presented n Table 3. Dary farms wth hgher mlk productvty were lkely to smultaneously mplement the fve practces targetng eroson reducton ncludng crtcal area plantng, feld borders, grassed waterways, heavy use area protecton and regulatng water n a dranage system. Mlk productvty and busness structured as a corporaton would enhance the adopton of four sedment control practces such as flter strps, sedment basn, rparan forest buffer and streambank and shorelne protecton. A hgher percentage of farmland classfed as hghly erodble and the holdng of an off-farm job lkely ncreased the adopton of waste facltes, a lagoon and proper waste utlzaton. Pasture-based operatons and dversfcaton of farmng actvtes would enhance producers mplementaton of the three grazng management practces. Conclusons Ths study showed that the adopton of BMPs by Lousana dary producers was nfluenced by factors such as farm characterstcs, operator characterstcs, nsttutons related to the dary operaton, and producers atttude. Results of the analyss emphaszed: () the postve nfluence of farm sze on the adopton of BMPs that are not partcularly captal-ntensve n nature, emphaszng the possblty of larger farms appropratng the learnng costs as fxed expenses, as suggested by Feder et al. (1985); () the effect of mlk productvty per cow on the ncreased adopton of sx BMPs, suggestng that better managers are lkely to adopt practces that ensure the long-run vablty of ther operatons; () the ncreased effect of frequent meetngs wth LCES personnel on the adopton of eght BMPs, underscorng the mportance of

16 15 nformaton dssemnaton n nducng adopton and the effectveness of LCES n provdng BMP nformaton to producers; (v) the nfluence of producer s rsk averson on the adopton of sx of the more captal ntensve BMPs lkely to ensure long-run economc vablty of the land and avodance of the rsk assocated wth decreased productvty resultng from unusually heavy ranfall events; (v) the consstent negatve effect of membershp n DHIA on the adopton of nne somewhat captal ntensve BMPs, suggestng that the adopton of BMPs mght not be consstent wth the goals of producers who place greater weght on the proft-maxmzaton goal, as opposed to other goals such as conservng and mantanng land; (v) the lower probablty that older producers had adopted BMPs that requred substantal ntal captal nvestments, as producers wth short plannng horzons would unlkely be able to beneft from the full stream of benefts, but must absorb the full costs; and (v) the greater lkelhood of more hghly educated producers to adopt BMPs. Hgher educatonal attanment allows farmers not only greater access to nformaton, but also recognton of the benefts and costs of alternatve management strateges and greater ablty to adjust to changes. The overall fndngs suggest the need to address () the lack of knowledge among dary producers about BMPs, reflected by the large number of producers unaware of legslaton and efforts to control nonpont sources of water polluton, as well as the hgh rates of respondents answerng need more nformaton and have not heard about t as reasons for not adoptng a BMP; () the low rate of producers havng a dary farm plan wth NRCS; and () the need of expanded economc ncentves to nduce the adopton of producers who fnd a BMP too expensve to adopt, or are short-run proft maxmzers.

17 16 References Barber, Edward B. The Farm-Level Economcs of Sol Conservaton: The Uplands of Java. Land Economcs. 66 (May 1990): Cardona, Hugo. Analyss of Polcy Alternatves n the Implementaton of Coastal Nonpont Polluton Control Program for Agrculture. Unpublshed PhD. Dssertaton. Lousana State Unversty. Department of Agrcultural Economcs and Agrbusness Caswell, Margret and Davd Zlberman. The Choces of Irrgaton Technologes n Calforna. Amercan Journal of Agrcultural Economcs. 67 (May 1985): Cooper, Joseph C. A Jont Framework for Analyss of Agr-Envronmental Payment Programs. Paper presented at the Annual Meetngs of the Amercan Agrcultural Economcs Assocaton, Chcago, Illnos, August 5-8, Davs, Chrstopher G. and Jeffrey M. Gllespe. Technology Adopton n U.S. Hog Producton. Paper presented at the Annual Meetngs of the Southern Agrcultural Economcs Assocaton, Fort Worth Texas, January 28-31, Dorfman, Jeffrey H. Modelng Multple Adopton Decsons n a Jont Framework. Amercan Journal of Agrcultural Economcs. 78 (August 1996): Drapcho, Caye M., James F. Beatty and Erc C. Achberger. Water Qualty and the Tangpahoa Rver. Lousana Agrculture: The Magazne of the Lousana Agrcultural Experment Staton. 44 (Sprng 2001): El-Osta, Hsham S. and Mtchell J. Morehart. Technology Adopton Decsons n Dary Producton and the Role of the Herd Expanson. Agrcultural and Resource Economcs Revew. 28 (Aprl 1999): Feder, Gershon, Rchard E. Just, and Davd Zlberman. Adopton of Agrcultural Innovatons n Developng Countres. A Survey. World Bank Staff Workng Paper No Washngton, DC. USA Ghosh, Soumen, J. T. McGuckn, and S. C. Kumbhakar. Techncal Effcency, Rsk Atttude, and Adopton of New Technology: The Case of the U.S. Dary Industry. Technologcal Forecastng and Socal Change. 46 (July 1994): Gould, Bran W., Wllam E. Saupe, and Rchard M. Klemme. Conservaton Tllage: The Role of Farm and Operator Characterstcs and the Percepton of Sol Eroson. Land Economcs. 65 (May 1989): Govndasamy, Ramu and Mark J. Cochran. The Conservaton Complance Program and Best Management Practces: An Integrated Approach for Economc Analyss. Revew of Agrcultural Economcs. 17 (1995): Ipe, Vju C., Erc A. DeVuyst, John B. Braden and Davd C. Whte. Smulaton of a Group Incentve Program for Farmer Adopton of Best Management Practces. Agrcultural and Resource Economcs Revew. 30 (October 2001): Lousana State Unversty Agrcultural Center. Dary Producton Best Management Practces (BMPs). Pub

18 17 Moser, M. Chrstne and Chrstopher B. Barrett. The Complex Dynamcs of Smallholder Technology Adopton: The Case of SRI n Madagascar. Paper presented at the AAEA- WAEA Annual Meetng, Long Beach Calforna, July 28-31, Rahelzatovo, Noro C. Adopton of Best Management Practces n the Lousana Dary Industry. PhD. Dssertaton. Lousana State Unversty. Department of Agrcultural Economcs and Agrbusness Rahelzatovo, Noro C. and Jeffrey M. Gllespe. Dary Farm Sze, Entry, and Ext n a Declnng Producton Regon. Journal of Agrcultural and Appled Economcs. 31 (August 1999): Renhard, Stjn, C. A. Knox Lovell, and Geert Thjssen. Econometrc Estmaton of Techncal and Envronmental Effcency: An Applcaton to Dutch Dary Farms. Amercan Journal of Agrcultural Economcs. 81 (February 1999): Shelds, Martn L., Ganesh P. Raunyar, and Frank M. Goode. A Longtudnal Analyss of Factors Influencng Increased Technology Adopton n Swazland, Journal of Developng Areas. 27 (July 1993): Soule, Meredth J, Abebayehu Tegene, and Keth D. Webe. Land Tenure and the Adopton of Conservaton Practces. Amercan Journal of Agrcultural Economcs. 82 (November 2000): Westra, John and Kent Olson. Farmers Decson Processes and Adopton of Conservaton Tllage. Unversty of Mnnesota. College of Agrcultural, Food, and Envronmental Scences. Department of Appled Economcs. Staff Paper P97-9. June Zepeda, Lyda. Smultanety of Technology Adopton and Productvty. Journal of Agrcultural and Resource Economcs. 19 (July 1994):

19 18 Table 1. Dary Producers Adopton Rates of BMPs. Percentage Not Adoptng Practces Percentage Adopted Need More Informaton Hgh Cost Of Implementaton Have Not Heard Of It Not Applcable to My Farm Eroson and Sedment Control Practces Conservaton Tllage Cover Crop Crtcal Area Plantng Feld Borders Flter Strps Grassed Waterways Heavy Use Area Protecton Regulatng Water Rparan Forest Buffer Sedment Basn Streambank Protecton Faclty Wastewater and Runoff Management Roof Runoff Management Waste System Waste Storage Faclty Waste Treatment Lagoon Waste Utlzaton Nutrent and Pestcde Management Nutrent Management Pestcde Management Grazng Management Fencng Prescrbed Grazng Trough or Tank

20 19 Varables Conservaton Tllage Table 2. Selected Results from the Probt Analyss of Each Indvdual BMP. Waste System Waste Storage Faclty Waste Treatment Lagoon Waste Utlzaton Fencng Prescrbed Grazng Trough or Tank B M B M B M B M B M B M B M B M ONE COWS * * * YIELD ** ** * * STRM HEL * ** BSTR LAND ** * * ** ** NWTH PASTU * * OCROP PART AGE * * ** ** OFFF ** ** ** * ** ** CSP ** ** EDUC ** * * DHIA ** * ** COOP ** ** * * LCES ** ** * ** ** ** ** ** SEM ** * * NRCSP ** ** SCAP ** ** SCAP RISK ** ** ** * * * * ENV SCAP LM McF Estrella AIC SC Predcted (a) 98 (79 %) 113 (91%) 96 (77%) 101 (81%) 98 (79%) 100 (81%) 103 (83%) 94 (76%) B: Values of the Parameters; M: Margnal effects at mean values of all varables; ** : Values sgnfcant at 5% ; * : Values sgnfcant at 10%; and (a) : Proporton of correct predcted probabltes.

21 20 Table 3. Selected Results from the Multvarate Probt Analyss. Varables Waste Storage Faclty Waste Treatment Lagoon Waste utlzaton Prescrbed Grazng Fencng -Trough B1 B2 B3 B1 B2 B3 ONE COWS YIELD STRM HEL * BSTR LAND * PASTU ** OCROP AGE OFFF * EDUC CSP DHIA COOP LCES ** SEM ** NRCSP SCAP SCAP RISK SCAP R (01, 02) ** * R (01, 03) ** ** R (02, 03) ** ** Lu (a) (c) Lr (b) (d) LR LR = 4.07 and X 2 (26) = LR = and X 2 (5) = B : Coeffcents for equaton ; : Dscrete changes n the probablty that all consdered practces are adopted wth respect to the changes n the specfc varables; ** : Values sgnfcant at 5%; * : Values sgnfcant at 10%; (a) : Full model wth 17 to 20 varables n each equaton specfed n the probt analyss; (b) : Current model constraned from the full model and ncludes varables wth at least 50% level of sgnfcance; (c) : Current model as full model wth 12 to 16 varables n each equaton specfed from the PCA ( λ = 1) and sgnfcant varables n the probt analyss; and (d) : Constraned model specfed from PCA ( λ = 1).