EFFICIENCY ANALYSIS ON CATTLE FATTENING IN TURKEY

Transcription

1 EFFICIENCY ANALYSIS ON CATTLE FATTENING IN TURKEY Altug Ozden, Goksel Armagan Department of Agrcultural Economcs, Faculty of Agrculture, Adnan Menderes Unversty PK , Aydın, Turkey Phone:+90 (256) ; Fax:+09 (256) ; E-mal: aozden@adu.edu.tr, garmagan@adu.edu.tr Abstract. In Turkey, t s a common knowledge that there are many structural and sectoral problems n cattle fattenng whch s closely related to the beef producton. Nowadays, due to these emergng problems, beef prce has greatly ncreased and beef mport began to cope wth hgher prces. Man purpose of ths research s to determne the techncal effcency and workng condtons of cattle fattenng farms n Aydn provnce of Turkey, where cattle farmng has progressed for the last decade. Effcency scores are calculated wth Data Envelopment Analyss and Stochastc Fronter Analyss technques. Prmary data used for ths stuaton are obtaned by survey technque from cattle farms whch are determned by samplng method n Aydn provnce. The average techncal effcency calculated by both methods s found as and When analyzng the calculated techncal effcency of the farms, t s found that techncal effcency s ncreased by the scale of the farm. Accordng to results of analyses, there s a postve relaton between effcency scores of cattle fattenng farms, and certan factors such as attendng tranng and extenson events, general farmng experence, and cattle fattenng experence. It s found that through a few crtcal mprovements cattle fattenng farmng can be mproved and as a consequence of ths stuaton beef prces may settle down to the average after ths process. Keywords: cattle fattenng, DEA, effcency, stochastc, Turkey. Introducton Meetng the demands for beef, whch s vtally mportant for human nutrton, cattle breedng has become the man agrcultural polcy of almost all countres. In lvestock sector n Turkey, especally n cattle fattenng, whch s drectly related to beef producton, t s a wellknown fact that there are many nsttutonal and sectoral problems. It has been assumed that agrcultural polces proposals should be formed by determnng the weakest lnks n the red meat supply chan and takng steps strengthenng these lnks. Due to some unjustfed speculatve market expectaton and the fact that meat supply does not meet the demand meat prce farly ncreased n Turkey. Thus, n the mdst of 200 the Mnstry of Food, Agrculture and Lvestock chose to mport lve cattle. However, many of these problems n ths sector contnue. Analyss of the numbers of butchered anmals showed that sheep are n frst place followed by cattle and goat. Yet beef meat has the frst place n the overall meat producton and sheep meat come second. The Turksh Aydn provnce keeps approxmately 3% of all lvestock of Turkey (Turkstat, 202). There are three man targets formng the bass of agrcultural polcy n Turkey. These are macroeconomc stablty, matchng standards for EU and matchng the standards for nternatonal competton. Producton effcency s prerequste to match these targets. To create and apply sustanable polces about effcency, the need for the studes, whch would analyze relatons between the current effcency scores of agrcultural enterprse and factors affectng these scores, ncreases (Gunduz, 20). Studes n Turkey and nternatonal feld related to agrcultural producton effcency are generally about crop producton. Studes related to cattle farmng are focused on dary farms. Studes addressng cattle fattenng are very rare. Stochastc fronter analyss (SFA) method s used n the research related to dary cattle farmng n England and found no postve relaton between such varables as educaton, experence and effcency (Bravo- Ureta and Reger, 99). Renhard et al. (999) calculated envronmental and techncal effcency by usng SFA method n Dutch dary farms and found that these farms work wth an average of 0.89 techncal effcency and 0.44 envronmental effcency. By usng Data Envelopment Analyss (DEA), the effcency scores of cattle fattenng farms n USA-Lousana were calculated and t was found that there s a postve relaton between age, educaton and effcency scores (Rakpova and Gllespe, 2000). In the research, whch DEA and SFA methods were used to calculate techncal effcency scores of dary farms n Sweden, t was found that these farms work wth an average of 0.77 effcency and farm scale has a postve effect on effcency (Johansson, 2005). By usng SFA method, Trestn (2006) reported that farms work wth 0.79 effcency, related to cattle fattenng n Italy. Producton effcency of dary farms n Turkey- Burdur was found 0.50 by usng SFA and Cobb-Douglas producton functon, and t s accepted that, such varables as age and educaton has no sgnfcant effect on effcency. However feedng system and herd scale have postve effects on effcency (Bnc et al., 2006). Accordng to the research n Malaysa, whch used SFA, Cobb-Douglas and Translog Producton functons, the average effcency s 0.68 and farm scale and type has no sgnfcant effect, however farmng experence should be ncreased (Sern et al., 2008). Accordng to other research n Turkey-Cukurova on small-scale dary farms, whch used the SFA method, the techncal effcency was on average 0.78 and t s concluded that the effect of herd scale s statstcally nsgnfcant on effcency (Alemdar et al., 200). Ceyhan and Haznec (200) used DEA method on the cattle-fattenng farms n Turkey-Amasya 88

2 and found that fattenng perod and economc effcency have postve relaton. In USA-Wsconsn, a research, whch used SFA and Cobb-Douglas producton functon, revealed that the average effcency of dary farms s 0.88 (Cabrera et al., 200). By usng DEA method n Turkey- Eastern Medterranean regon, Dagstan et al. (2009) noted that farmers should adapt the new technology to ncrease effcency. That dary farms n Turkey-Izmr work wth the average of 0.59 effcency and farms scale has a postve effect on effcency were found by (Koyubenbe and Ozden, 20). In a Lthuanan research on crop farms, lvestock farms and mxed farms, wth DEA method employed, the techncal effcency was found wth an average of 0.48 for crop farms, 0.72 for lvestock farms and 0.56 for mxed farms (Balezents and Krscukatene, 202). In researches of lvestock farms such varables as farm or herd scale, farmer's age, educaton, and experence are ncluded nto the research because they are consdered as effectve factors on effcency. Based on the mentoned lterary sources, the chosen am of ths study was to dentfy the parameters, whch are effcent on techncal effcency scores of cattle fattenng farms n Turkey-Aydn, and to make some proposals to help fnd the solutons of problems n beef and cattle fattenng n Turkey. Materals and methods The bulk materal of ths research conssts of the data, whch were gathered by face-to-face ntervew technque from cattle farms n Aydn. The macro data requred for ths research were taken from such nsttutons as Turksh Statstcal Insttute (202) and Aydn Provncal Drectorate of Agrculture (20). To determne the effcency of cattle fattenng n Turkey-Aydn, cattle farms were consdered as a populaton. The number of sample was calculated by usng maxmum sample sze n lmted populaton (Newbold, 995). As these farms were not separated from each other n terms of whether they are cattle fattenng farms or dary farms, the number of cattle farms n all dstrcts of provnce was calculated accordng to total cattle count. For ths purpose 95% confdence nterval and 0% error level were used. It was planned to make a survey of 95 cattle fattenng farms. Cattle Count Unt (CCU) was used to homogenze the overall cattle number of related farms (Aras, 988). In the calculated effcency, the number of fattenng cattle (X ), fattenng perod (X 2 ), overall cost of compound feed (X 3 ), overall cost of roughage (X 4 ), overall cost of gran (X 5 ), cost of vet-medcne X 6 ), general expenses (X 7 ), labor used n cattle fattenng (X 8 ) were used as nput and gross producton value (GPV) was used as output (Y). Male labour unt (MLU) was consdered whle calculatng labour n cattle fattenng calculatons (Erkus and Demrc, 985). The number of fattenng cattle (NFC), daly weght gans (DWG), fattenng perod and marketng prces were used for calculatng GPV (Koknaroglu et al., 2006). The factors leadng to neffcency, such as educaton level (Z ), farmer's age (Z 2 ), general farmng experence (Z 3 ), cattle fattenng experence (Z 4 ), attendng tranng and extenson events (Z 5 ), barn type (Z 6 ) (closed, sem closed, open), use of addtves to fasten fattenng (Z 7 ), actng accordng to the raton formulaton (Z 8 ), plannng feld of forage crops (Z 9 ), total farm land (Z 0 ) and farm scale (Z ) (groups) were consdered. Effcency analyss was carred out n analyzng the cattle-fattenng farms. For calculaton of effcency score, the dstance of decson-makng unt (DMU) to an effcent fronter should ether be calculated or predcted. Hypotactc fronter can be calculated or predcted wth dfferent methods under varous hypotheses. These methods can be classfed nto two groups; parametrc and non-parametrc methods. In ths study, SFA method s used as an example of parametrc and DEA method s used as an example of non-parametrc method. Man effcency crteron n DEA s the dvson of overall output to the overall nput. In other words, techncal effcency crteron (for j th decson pont) for any decson pont can be expressed wth the formula below. u y + u y u y 2 2 n n TE = () v x + v x v x 2 2 In ths formula, there "n" peces output and m" peces nput for j th decson pont. Here u n represents the weght of n th output, y n represents the amount of n th output, v m represents weght of m th nput and x m represents the amount of m th nput. SFA, also known as econometrcal approach, was developed by varous researchers n 977 (Agner et al., 977; Battese and Corra, 977; Meeusen and Van Den Broeck, 977). Producton functon s formulated as below (Agner et al., 977; Meeusen and Van Den Broeck, 977); Y = X β + ν u ( :,2,..., n) (2) = ν ε u (3) Here x represents the nput vector of th DMU, β represents coeffcent, v represents the random varable ndependent from u and the one whch has normal dstrbuton and that can not be controlled by DMU, u represents ndependent random varable that reflects neffcency and ε represents the error term. In explanaton of u the model below s developed (Battese and Coell, 995). u = z (4) δ In ths formula, z represents specfc features that reflect the techncal neffcency of DMU, and δ represents coeffcent. Ths nstance s defned as the rate of observed techncal effcency output to the calculated producton functon (Coell et al., 2005). TE = e x β + ν u β + ν m m x u / e = e (5) In effcency analyss done by usng SFA method n developed and developng countres, mostly Cobb- Douglas type producton functon method s preferred (Bnc et al., 2006). In ths research, effcency s estmated by Cobb-Douglas producton functon, whch has dscrete normal dstrbuton and maxmum lkelhood method (Battese and Coell, 995). Estmated Cobb- Douglas type producton functon for ths research 89

3 conssts of: one output, eght and eleven nputvarables whch, are consdered to have effect on neffcency as below; ln Y u 8 = β + β + ν u (6) 0 j ln X j j= = δ 0 + δ m Z m (7) m= DEA and SFA analyss were made by usng Deap 2. and Fronter 4. software, whch were developed by Tm Coell. Non-parametrc Spearman rank correlaton method was used to show how DEA and SFA scores cover each other. Results As large scaled farms are outnumbered, n analyss, approprate dstrbuton between groups s not provded. To fulfll ths am, n Turkey-Aegean regon farms were classfed under four dfferent groups whch conssted of 4.99 or less, , and or more CCU (Armagan et al., 2004). In ths study, all data were analyzed n four groups. Descrptve statstcs are gven n Table. Table. Summary Statstcs of Varables Used n Effcency Analyss Output Y:(TL) Group (N:5) Group2 (N:5) Group3 (N:8) Group4 (N:2) Total (N:95) (3748.9)* (338.2) (3273.7) ( ) ( ) Inputs X:(head) 3.78 (2.34) 9.67 (6.59) 9.3 (5.73) (83.73) (55.04) X2:(month) 6.25 (2.98) 9.8 (3.78) 7.3 (2.47) 7.24 (4.50) 7. (3.63) X3:(TL**) ( ) (370.50) (536.) (948.05) ( ) X4:(TL) (969.45) (2747.4) ( ) (972.) (2870.7) X5:(TL) (686.79) (935.93) ( ) ( ) (442.47) X6:(TL) (98.4) (44.0) (395.35) ( ) ( ) X7:(TL) 79.8 (326.75) ( ) (9378.7) ( ) (366.0) X8:(MLU) (Hour) 4.59 (2.55) 5.27 (.9) 7.00 (3.38) 0.05 (5.32) 6.3 (3.96) Ineffcency Varables Z:(-5).92 (.02) 2.4 (.7) 2.00 (.20) 2.38 (.24) 2.05 (.0) Z2:(year) (2.64) (.46) (9.7) (.64) (2.) Z3:(year) (5.83) (.67) 3.88 (3.08) (4.6) (4.6) Z4:(year) (5.80) (3.5) 23.3 (8.84) (6.9) (4.9) Z5:(Yes,No;,0) 0.35 (0.48) 0.53 (0.52) 0.38 (0.52) 0.43 (0.5) 0.4 (0.49) Z6:(,2,3) 2.47 (0.8) 2.33 (0.90) 2.75 (0.46) 2.48 (0.75) 2.47 (0.78) Z7:(Yes,No;,0) 0.06 (0.24) 0.3 (0.35) 0.38 (0.52) 0.9 (0.40) 0.3 (0.33) Z8:(Yes,No;,0) 0.27 (0.45) 0.20 (0.4) 0.50 (0.53) 0.52 (0.5) 0.34 (0.48) Z9:(da) 4.25 (4.69) (20.38) (43.33) (386.76) (87.77) Z0:(da) 56.8 (58.98) (34.6) 6.63 (06.62) (24.97) 8.9 (20.9) Z:(,2,3,4) (.23) DWG (kg) 0.78 (0.2) 0.75 (0.5) 0.96 (0.30) 0.96 (0.23) 0.83 (0.23) Market Prces (TL) 5.39 (.05) 5.80 (.57) 5.9 (0.99) 6.07 (0.98) 5.59 (.5) * Values n parentheses s the standard devaton; ** Local Currency ( US Dollar=.869 TL) Average effcency scores calculated for cattle fattenng farms were 80% for DEA and 7% for SFA. Result of Spearman rank correlaton to reveal how the concluson of these two analyses agree wth each other was (p<0.0). Therefore, t s possble to say that n farms whose effcency scores are calculated wth DEA method have also hgher result whch are calculated wth SFA method. When effcency scores of farm groups are consdered, t s seen that scale of farm postvely affects effcency scores. It s a prescrbed stuaton that DEA values are hgher than SFA scores because DEA s a nonparametrc method (Table 2). These values are close to results of some studes (Trestn, 2006), hgher for other studes (Sern et al., 2008). Wth Cobb-Douglas type producton functon, the results of SFA calculated by usng maxmum lkelhood 90

4 method are gven n Table 3. It shows that varance parameters are statstcally sgnfcant at the 0.0 level. These values show that any tradtonal producton functon s not enough to explan GPV and techncal effcency (neffcency) has an mportant role on GPV. Hgh Gamma values (73%) reveal that farms have an neffcency problem. The LR test revealed that neffcency varables have an effect on GPV and these varables reflect a stochastc process. As Cobb-Douglas type producton functon s represented completely n a logarthmc way, the coeffcents belongng to the model gve rate of return to scale and gve elastcty at the same tme (Kumbhakar and Lovell, 2000). The sum of the explanatory varables regresson coeffcent s Table 2. Summary Statstcs of Farm Effcences Group Number Group (N:5) Group2 (N:5) Group3 (N:8) Group4 (N:2) Total (N:95) Analyse DEA SFA DEA SFA DEA SFA DEA SFA DEA SFA Mn Max Mean standard devaton Table 3. Maxmum Lkelhood Estmates of Stochastc Cobb-Douglas Fronter Analyss Coeffcents Std. Err. t-rato Stochacstc Effcency Fronter Functon Constant *** X :(head) *** X 2 :(month) *** X 3 :(TL ** ) * X 4 :(TL) * X 5 :(TL) * X 6 :(TL) * X 7 :(TL) ** X 8: (MLU) (Hour) *** Statstcal Results of The Model σ *** γ *** Number of Observatons 95 Log- lkelhood Functon LR statstc (X 2 ) *** H 0 Rejected Ineffcency Model Constant *** Z :(-5) Z 2 :(year) Z 3 :(year) ** Z 4 :(year) *** Z 5 :(Yes,No;,0) *** Z 6 :(,2,3) Z 7 :(Yes,No;,0) Z 8 :(Yes,No;,0) ** Z 9 :(da) Z 0 :(da) Z :(,2,3,4) *** * P<0.0 ** P<0.05, *** P<0.0 Roughage expenses, vet-medcne expenses and ncrease n labor usage decreases the GPV whle ncrease of NFC, fattenng perod, compound feed expenses, gran expenses, and general costs ncrease the GPV. All of these results are statstcally sgnfcant. In the study n USA-Wsconsn t s concluded that all nputs have a postve effect on outputs (Cabrera et al., 200) whle n the study carred out n Turkey-Cukurova t s concluded that labor has negatve effects (Alemdar et al., 200). It s seen that NFC, fattenng perod and roughage expenses have great nfluence on GPV. In the study of Turkey- Amasya t s found that effcency and fattenng perod have postve relaton (Ceyhan and Haznec, 200). It s not wrong to say accordng to research results that farms 9

5 msuse roughage. The farms producng ther own roughage do not consder producton cost and overuse roughage whereas those who purchase roughage from other producers often are unable to adjust roughage, compound feed, and gran mxture densty. Vet-medcne expenses and labor usage generally effects the GPV negatvely as they are most expensve low scale famly farms. As NFC ncreases, t s proved n the research that cost of fattenng per cattle decreases and results n ncrease of GPV. The results related to varables, whch show the reasons of neffcency of farms, are gven n Table 3. Among these varables, the general farmng experence, cattle fattenng experence, attendng tranng and extenson courses, actng accordng to the raton formulaton and farm scale are found negatvely statstcally sgnfcant. As general farmng experence and cattle fattenng experence of farmers and farm scale ncrease, the neffcency decreases. These results are n agreement wth Johansson (2005), Bnc et al. (2006), Trestn (2006), Koyubenbe and Ozden (20) whle they contradct wth Bravo-Ureta and Reger (99), Alemdar et al. (200). Furthermore t s seen that attendng tranng and extenson courses and actng accordng to the raton formulaton ncreases the probablty of workng effcently. Smlarly, the study carred out n Malaysa shows that ncreased experence has a postve effect on effcency (Sern et al., 2008). Farms are determned to work wth 7% effcency accordng to SFA method. If these farms happen to decrease ther nput by 29% as long as they keep ther output amounts, they wll reach ther full effcency potental. Dscusson and concluson Accordng to the results of ths study, farms work wth 7% effcency and among the varables that provde ths effcency percentage, roughage expenses, vet-medcne expenses, and labor usage affect GPV negatvely whle NFC, fattenng perod, gran expenses, compound feed expenses and general expenses affect GPV postvely. The results of the model showed that neffcency varables have vtal mportance on GPV. Based on these results, there are some advces below to ncrease producton effcency; Increasng the scale of farm wll decrease the cost for per cattle and wll ncrease effcency n producton. However, the ntervews wth farmers about the necessary sources n order to ncrease cattle numbers have showed that they have fnte opportuntes because only large scale farms have suffcent credt ratng. For ths reason, when supportng producers wth credts thscrcumstance should be taken nto consderaton. Producers must care about roughage amounts used and to act accordng to the raton formulaton to overcome problems about roughage and to ncrease effcency of fattenng. Problem of nactve labor was ponted out n most farms. Ths problem s a result of both falng to separate dary and fattenng farms what s characterstc of small scale famly farms. Makng these farms more professonal both solves nactve labor problem and decreases unnecessary expenses. It s an accepted fact that experence has postve effect on effcency. Cattle fattenng should be made conscous as n all other lvestock farms. Therefore captal and experence should be unfed and to make ths possble, ether contractual producton or organzaton should be selected. It s obvous that there s a need for profesonal tranng and extenson courses, experence and mprovement above ponted ssues. Agrculture consultancy system s a good step n ths drecton. Producers should be supported by ncreasng the number of free tranng and extenson courses close to ther resdences. Worlds Meat consumpton wll grow by 73 percent over the current levels by The surge n lvestock producton that took place over the last 40 years resulted largely from an ncrease n the overall number of anmals beng rased but t s not possble to ncrease the number of anmals endlessly (FAO, 20). As t stands, there are no techncally or economcally vable alternatves to ntensve producton for provdng the bulk of the lvestock food supply for growng ctes. Effcency gans the only way to meet the demand. So each regon has to develop ther own polces to ncrease effcency. Acknowledgements Ths study was supported by Adnan Menderes Unversty board of scentfc research projects. References. Agner D., Lovell C.A.K., Schmdt P. Formulaton And Estmaton Of Stochastc Fronter Producton Functon Models. Journal of Econometrcs P Alemdar T., Bahadr B., Oren M.N. Cost and Return Analyss and Techncal Effcency of Small Scale Mlk Producton: A Case Study For Cukurova Regon, Turkey. Journal of Anmal and Veternary Advances (4). P Aras A. Agrcultural Accountng. İzmr, Ege Unversty Press, p. 4. Armagan G., Koc A., Kzlkaya K. Determnng the Szes of Dary Farms by Integratng Some Data From Research Done n the Aegean Regon, Journal of Adnan Menderes Unversty Agrcultural Faculty (2). P Aydn Provncal Drectorate of Agrculture Balezents T., Krscukatene I. Applcaton of the Bootstrapped DEA for the Analyss of Lthuanan Famly Farm Effcency. Management Theory and Studes for Rural Busness and Infrastructure Development (5). P Battese G.E., Corra G.S. Estmaton of A Producton Fronter Model Wth Applcaton to the Pastoral Zone of Eastern Australa. Australan Journal of 92

6 Agrcultural Economcs P Battese G.E., Coell T.J. A Model For Techncal İneffcency Effects In a Stochastc Fronter Producton For Panel Data. Emprcal Economcs P Bnc T., Demrcan V., Zulauf C.R. Assessng Producton Effcency of Dary Farms n Burdur Provnce, Turkey. Journal of Agrculture and Rural Development n the Tropcs and Subtropcs (). P Bravo-Ureta B.E., Reger L. Dary Farm Effcency Measurement Usng Stochastc Fronters And Neoclasscal Dualty. Amercan Journal of Agrcultural Economcs P Cabrera V.E., Sols D., Del Corral J. Determnants of Techncal Effcency Among Dary Farms In Wsconsn. Journal of Dary Scence (). P Ceyhan V., Haznec K. Economc Effcency of Cattle-Fattenng Farms In Amasya Provnce, Turkey. Journal of Anmal and Veternary Advances () P Coell T.J., Prasada Rao D.S., O Donnell C.J., Battese G.E. An Introducton to Effcency and Productvty Analyss. Kluwer Academc Publshers, Second Edton, Boston p. 4. Dagstan E., Koc B., Gul M., Parlakay O., Akpnar M.G. Identfyng Techncal Effcency of Dary Cattle Management In Rural Areas Through A Non-Parametrc Method: A Case Study For The East Medterranean In Turkey. Journal of Anmal and Veternary Advances (5). P Erkus A., Demrc R. Farm Management and Plannng. Ankara Unversty Press, Ankara, Turkey p. 6. FAO. World Lvestock 20 - Lvestock n Food Securty. Rome, FAO p. 7. Gunduz O. Techncal Effcency n Dary Farms: Stochastc Fronter Effcency Approach. Journal of Harran Unversty Agrcultural Faculty (). P Johansson H. Techncal, Allocatve and Economc Effcency n Swedsh Dary Farms: The Data Envelopment Analyss Versus the Stochastc Fronter Approach. XI th Internatonal Congress of the European Assocaton of Agrcultural Economsts (EAAE), Copenhagen, Denmark P Koknaroglu H., Ylmaz H., Demrcan V. Expedtonary Effect of Feed Rate on Fattenng Performance and Proftablty n Beef Cattle Farms n Afyon provnce. Journal of Suleyman Demrel Unversty Agrcultural Faculty (). P Koyubenbe N., Ozden A. Parametrc Effcency Measurement n Dary Farms: Turkey-Izmr. Journal of Adnan Menderes Unversty Agrcultural Faculty (2) P Kumbhakar, S., Lovell K. Stochastc Fronter Analyss. Cambrdge Unversty Press, Cambrdge, UK p. 22. Meeusen W., Van den Broeck J. Effcency Estmaton From Cobb-Douglas Producton Functons Wth Composed Error. Internatonal Economc Revew P Newbold P. Statstcs for Busness and Economcs. Prentce Hall, New Jersey p. Rakpova A., Gllespe J. Techncal Effcency of Beef Cattle Producers Lousana. Lousana Rural Economst (). P Renhard S., Lovell C.A.K., Thjssen G. Econometrc Estmaton of Techncal and Envronmental Effcency: An applcaton to Dutch dary farms. Amercan Journal of Agrcultural Economcs P Sern T., Radam A., Shamsudn M.N., Mohamed Z. The Effcency of Beef Cattle Producton: A Case Study n the Target Area of Concentraton n Johor, Malaysa. Economc and Technology Management Revew P Trestn S. Techncal Effcency of Italan Beef Cattle Producton Under a Heteroscedastc Non-Neutral Producton Fronter Approach. 0 th Jont Conference on Food, Agrculture and the Envroment Dluth, Mnnesota Turkstat. Tme Seres Data for The Producton of Lvestock Receved 5 February 203 Accepted 6 May