Forage Outsourcing in the Dairy Sector: The Extent of Use and Impact on Farm Profitability

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1 Forage Outsourcng n the Dary Sector: The Extent of Use and Impact on Farm Proftablty Jeffrey Gllespe, Rchard Nehrng, Carmen Sandretto, and Charles Hallahan The extent of forage purchasng behavor n mlk producton and ts mpact on proftablty are analyzed usng data from the 2000 and 2005 dary versons of the Agrcultural Resource Management Survey. Forage outsourcng s more common wth hay than wth slage and haylage, and s more prevalent n the western Unted States. Though slage and haylage outsourcng s found to mpact proftablty, the major proftablty drvers appear to be farm sze and effcency. Evdence of sgnfcant forage contractng s found n the western Unted States. Key Words: forage, nput purchasng, outsourcng, contractng, mlk producton As U.S. mlk producton has shfted locaton to non-tradtonal producton areas such as the Amercan West, larger dary farms have emerged n those areas farms that rely less on homegrown and more on purchased forages. These farms are not only less lkely to produce ther own forage, but also less lkely to utlze grazng as a prmary source of nutrton for dary anmals, allowng more anmals to be carred on fewer acres. Ths represents a move away from the tradtonal producton of the feed nput and the fnal product, mlk, on the same farm (vertcal ntegraton) and a move toward specalzaton n mlk producton and outsourcng the forage nput. The goals of ths study are to determne the types of dary farmers optng to outsource rather than produce ther own forages, and to determne the mpact of these decsons on farm proftablty. A secondary goal s to provde nsght nto factors nfluencng the types of busness arrangements that specalzed forage producers are usng to sell ther forage. A pooled dataset ncludng years Jeffrey Gllespe s Martn D. Woodn Endowed Professor n the Department of Agrcultural Economcs and Agrbusness at Lousana State Unversty Agrcultural Center n Baton Rouge, Lousana. Rchard Nehrng, Carmen Sandretto, and Charles Hallahan are economsts at the Economc Research Servce, U.S. Department of Agrculture, n Washngton, D.C. The vews expressed here are the authors and may not be attrbuted to the Economc Research Servce or the U.S. Department of Agrculture and 2005 of the Agrcultural Resource Management Survey (ARMS) s used to examne structural change n ths ndustry. Rumnants such as dary cows requre substantal forage. The daly estmated forage requrement of a 1,300-pound Holsten mlkng cow s about 26 pounds of dry matter or pounds of hay-equvalent (Amaral-Phllps and McAllster 2007). Ths can be met usng one of a contnuous set of combnatons of hay, slage, and/or pasture. Farmers choose a forage raton dependng upon a number of factors, three of whch are proftablty, management preferences, and forage avalablty. Regardless of the chosen feedng system and whether forages are outsourced or produced on-farm, provson of forages and other feedstuffs consttutes about 50 to 60 percent of the cost of producng mlk (Amaral-Phllps and McAllster 2007). Forage Outsourcng versus Vertcal Integraton wth the Forage Segment n Mlk Producton A wealth of lterature has addressed the make or buy, produce or purchase, or vertcally ntegrate versus outsource frm decson (Coase 1937, Wllamson 1975, 1979, 1985, Grossman and Hart 1986, Grossman and Helpman 2002). Vertcal coordnaton n agrculture has also re- Agrcultural and Resource Economcs Revew 39/3 (October 2010) Copyrght 2010 Northeastern Agrcultural and Resource Economcs Assocaton

2 400 October 2010 Agrcultural and Resource Economcs Revew ceved sgnfcant attenton (Barry, Sonka, and Lajl 1992, Hobbs 1997, Davs and Gllespe 2007). Less work, however, has been devoted to understandng vertcal coordnaton n dary producton than n other lvestock sectors, wth exceptons such as Sumner and Wolf (2002), who found sgnfcant relatonshps between dary farm sze, vertcal ntegraton, specalzaton, dversfcaton, and regon usng 1993 USDA Farm Costs and Returns Survey data. The present study focuses on the forage nput segment, where we fnd sgnfcant relatonshps between farm sze, regon, forage purchasng behavor, and farm proftablty. Consderable effort has been devoted to the development of economc theory to dentfy those parameters most relevant for decdng whether to produce or outsource nputs. Wllamson (1975, 1985) and Grossman and Hart (1986) emphasze the roles of asset specfcty and transacton costs n determnng whether a frm should vertcally ntegrate or outsource nputs. The presence of costly assets hghly specfc to a partcular functon and sgnfcant transacton costs tends to encourage well-defned, complete contracts or vertcal ntegraton. Grossman and Helpman (2002) expand the economc theory of the frm s produce or outsource decson, ctng the roles of transacton costs, competton, and the holdup problem. They conclude that n hghly compettve markets, outsourcng must lead to a sgnfcant cost advantage to offset the transacton costs assocated wth searchng for a relable nput source and the costs assocated wth holdup. Wth mlk producton, the large number of frms producng the commodty s ndcatve of a compettve market, suggestng that the magntude of transacton and holdup costs s partcularly mportant n the decson. Grossman and Helpman (2002) further suggest that n cases where producton costs are hghly senstve to specfc characterstcs of the nput (such as nput qualty), the vablty of outsourcng wll be reduced. What characterstcs specfc to U.S. mlk and forage producton would nfluence the forage outsourcng versus vertcal ntegraton decson? In areas wth well-developed forage markets, the long-run cost assocated wth purchasng forage mght be expected to be compettve wth the cost of growng t, ncludng a charge for the operator s labor. The corn slage market prce would be determned by the market prce for corn gran, bushels of corn per ton of slage, harvest costs, and adjustments for qualty. Whether to produce or outsource forage would depend prmarly upon manageral factors such as the benefts assocated wth specalzaton and debt concerns. Specalzaton n producng ether mlk or forage allows the operator to develop expertse by concentratng effort on one enterprse. Net of transacton costs, the mproved management assocated wth a specalzed mlk-producng farm and a second specalzed forage-producng farm potentally allows each to produce mlk or forage at lower cost per unt than would one farm producng both products. Furthermore, resource constrants would allow the farm to produce more of ether mlk or forage through specalzaton than could be produced f both were beng produced, leadng to greater scale economes n ether of the enterprses and, hence, lower cost per unt produced. The relatonshp between larger-scale dares and lower levels of vertcal ntegraton nto feed and replacement hefer producton has been prevously shown by Sumner and Wolf (2002). Another ncentve for mlk producers to purchase forage s the lower ntal nvestment assocated wth the operaton relatve to growng forages. Equpment purchases assocated wth forage plantng, harvestng, and other feld operatons such as plowng and fertlzng requre substantal start-up costs, perhaps requrng credt. Barry, Sonka, and Lajl (1992) cte fnancal constrants as a reason for farmers to enter contracts wth upstream or downstream frms supplyng nputs or output marketng servces versus vertcally ntegratng. Ths may be partcularly mportant for new farmers who are credt-constraned and/or desre to lmt debt. Boucher and Gllespe (2007) estmate the resource use and costs of feld operatons assocated wth forage producton. For corn slage producton, the total estmated cost of purchasng new mplements and tractors s $105,830 and $138,288, respectvely. Establshment and producton of alfalfa hay nvolves a total estmated cost of purchasng new mplements and tractors of $81,782 and $48,371, respectvely. Land cost assocated wth growng the forage must be added to the fxed costs for both of these enterprses. Of these fxed expenses, mplements such as hay balers and slage choppers are lkely to represent the assets that are most specfc to forage producton.

3 Gllespe et al. Forage Outsourcng n the Dary Sector: The Extent of Use and Impact on Farm Proftablty 401 Labor requred for feld operatons to establsh alfalfa s estmated at 1 hour, 4 mnutes per acre, and for harvestng, 6 hours, 54 mnutes per acre. The labor requrement for corn slage feld operatons s 4 hours, 19 mnutes per acre. For the dary farmer decdng whether to produce or purchase forage, the sgnfcant captal, labor, management, and asset specfcty assocated wth growng and harvestng the forage must be consdered. The arguments made for producng forage and mlk on separate farms do not consder the benefts of vertcal ntegraton, as dscussed by Wllamson (1979) thus, the condtonal phrase mentoned earler, net of transacton costs. 1 If transacton costs assocated wth acqurng forage are sgnfcant, they could alter the relatve proftablty assocated wth producng or outsourcng forage. In addton, rsk s expected to change wth specalzaton, as the mlk producer must be assured of a steady supply of qualty forage from a separate frm; thus, relatonshps between buyer and seller, such as strategc allances and contracts, would be expected to arse. The shft of mlk producers away from producng forage s akn to, but less extensve than, shfts n the hog and broler ndustres. Hog producers feed less home-produced feedgran than n years past. Gven dary forage requrements and expected qualty varablty, some forage contractng would be expected, as has evolved wth feed n the hog and broler ndustres. Though lmted data show the dstrbuton of forage transactons under contract versus spot markets, t s apparent that forage contractng s becomng more common wth respect to corn slage (Tranel et al. 2003). Extenson publcatons assst corn slage producers n determnng a far prce for corn slage (e.g., Tranel et al. 2003, Stellato 2008, Rankn 2008), wth some focus on determnng a far contract prce. Evdence gleaned from extenson specalsts, farmers, and popular press suggests forage contractng to be a common procurement strategy of dary farmers. 1 What s seen n the case of larger, often more ndustralzed mlk producton frms s a movement away from vertcal ntegraton and toward outsourcng wth regard to feed. Ths suggests that, as opposed to popular usage of the term vertcal ntegraton to mply ndustralzaton, a movement toward ndustralzaton n lvestock s not necessarly assocated wth a movement toward vertcal ntegraton of all upstream and downstream producton stages. Modelng the Forage Purchase Decson and Its Impact on Farm Proftablty The produce versus outsource forage choce could nfluence farm proftablty n a number of both postve and negatve ways, wth potental nfluental factors ncludng management specalzaton, economes of sze, economes of scope, transacton costs, holdup costs, and others. Though our dataset does not allow for full assessment of the ndvdual nfluences of each of these factors due to the ndvsblty of nputs, we can determne whch farms among those that produce versus outsource forages are the most proftable, provdng an assessment of the nfluence of outsourcng on overall frm performance. A model for estmatng the mpact of outsourcng forage on farm proftablty for frm, π, assumes that proft s a lnear functon of the extent of forage purchasng, F, and a vector of other explanatory varables, X. The equaton can be wrtten as (1) π = X ' β+ω F + e, where e s a random error term. Vector X represents farmer and farm characterstcs hypotheszed to nfluence farm proftablty other than forage purchasng, such as regon, farm sze, dversfcaton, and demographcs. Forage purchasng decsons are also lkely to depend on farm and farmer characterstcs, so the outcomes are not expected to be random, but rather based on the farmer s self selecton. As such, n the sprt of Heckman (1990) and as used by dary economcs studes such as McBrde, Short, and El-Osta (2004) and Foltz and Chang (2002), nstrumental varables for F are approprate for estmatng ther mpact on proft. The equaton used for estmaton of an nstrumental varable F * for forage purchasng ntensty s (2) F = Z ' γ+ ψ, * where F * represents an unobservable dfference n utlty assocated wth purchased versus homegrown forage, measured as the percentage of forage that s purchased. The term Z γ represents estmates of utlty usng farm characterstcs Z, and ψ s the error term. Because the range of proportons of forage produced on the farm s

4 402 October 2010 Agrcultural and Resource Economcs Revew bounded at zero and 100 percent, wth substantal numbers ether producng or outsourcng 100 percent of ther forage, the dstrbuton for equaton (2) s truncated at the zero and 100 percent levels, suggestng an estmator that approprately models ths truncaton: the two-lmt tobt. For ths model, f F s the observed dependent varable, then, accordng to Maddala (1983, p. 161): (3) F = L 1 f F L 1 F = F* f L 1 < F* < L 2 F = L 2 f F* L 2. In ths model, the upper and lower lmts are represented by L 1 and L 2, respectvely. As such, the tobt model can be estmated as (4) EF F= F = EF < F <. * * * [ ] [ 0 100] Gven the estmaton of the nstrumental varables, the mpact on proftablty from equaton (1) can be expressed n smlar manner as that n Foltz and Chang (2002): (5) E X E F F F F F + EF [ F = 0]*Pr( F = 0) + EF [ F= 1]*Pr( F= 1)}. * * [ π ] = ' β+ω { [ = ]*Pr( = ) In sum, ths model can be estmated usng () the tobt model to estmate the percentage of forages outsourced, and () ordnary least squares (OLS) to estmate the mpact of outsourcng and other factors on proftablty. Specfc varables used n each of the equatons, as well as the data used, are dscussed n the followng secton. Data and Explanatory Varables Data used for ths analyss were collected va USDA s Agrcultural Resource Management Survey (ARMS), an annual comprehensve survey of U.S. farms. In selected years, versons of the Phase III survey are conducted wth addtonal questons to collect detaled data on specfc enterprses. In 2000 and 2005, dary farms were targeted, resultng n a sample of 870 usable dary observatons for 2000 and 1,814 for 2005, for a combned total of 2,684. Weghts ncluded n the dataset allow the sample to be expanded to the populaton of U.S. dary farms. Percentage of Purchased Forage Tobt models [equaton (2)] are estmated to determne types of farmers more lkely to outsource versus produce hay and straw, and slage and haylage. The frst model estmates percentage of hay and straw purchased, ncludng alfalfa and all other hay. The second estmates percentage of slage and haylage purchased, ncludng corn and sorghum slage and haylage. In the ARMS dary survey, there are categores for alfalfa hay, other hay, and straw, so these were grouped together as dry forage. Lkewse, there are categores for corn slage, sorghum/mlo slage, and other slage and haylage, all of whch were grouped together. Separate equatons are estmated for dry and ensled forages due to ther nherent dfferences n producton and bulkness, as well as the fact that dfferences were noted n the percentages of each that were purchased, as shown n Table 1. Regon has been shown to be a sgnfcant determnant of whether frms vertcally ntegrate or outsource (e.g., Chntz 1961). Regonal factors expected to nfluence the percentages of hay and straw, and slage and haylage, purchased are represented by regonal varables: Southeast, Lake States, Appalacha, Southern Plans, West, Corn Belt, and Pacfc, wth the base regon beng the Northeast. 2 These are the major U.S. farm producton regons, as desgnated by the USDA s Economc Research Servce, wth substantal dary producton. It s expected that greater percentages of forage would be outsourced n the Southern Plans, West, and Pacfc states. Remund, Moore, and Martn (1977) show that changes n the nature of transactons between upstream and downstream frms are often accompaned by changes n producton locaton. States n the western regons such as Idaho and New Mexco are relatve 2 Regons and states ncluded n the ARMS Phase III dary survey nclude the Northeast (Mane, New York, Pennsylvana, and Vermont), Lake States (Mchgan, Mnnesota, and Wsconsn), Corn Belt (Illnos, Indana, Iowa, Mssour, and Oho), Appalacha (Kentucky, Tennessee, and Vrgna), Southeast (Georga and Florda), Southern Plans (Texas), West (Arzona, Idaho, and New Mexco), and Pacfc (Calforna, Oregon, and Washngton). The ARMS Phase III survey (non-enterprse specfc) ncludes all states surveyed n the ARMS.

5 Gllespe et al. Forage Outsourcng n the Dary Sector: The Extent of Use and Impact on Farm Proftablty 403 Table 1. Means of Independent and Dependent Varables Varable Unts N Weghted Mean Porton purchased hay & straw % / Porton purchased slage & haylage % / Net farm ncome $ Net farm ncome per cow $ Net farm ncome per cwt mlk $ Southeast Lake Appalacha Southern Plans Corn Belt West Pacfc Acres No Cows No College Off-farm hours No./Yr Operator age Yrs Porton of farm ncome from mlk % / Graze Year Mlk prce $ / cwt Mlk per cow Cwt/Cow/Yr newcomers n large-scale mlk producton. Sumner and Wolf (2002) show regonal dfferences n vertcal ntegraton wthn the U.S. dary sector. Farm sze varables nclude numbers of cows and acres. As dary enterprse sze ncreases, constraned resources ncludng but not lmted to management are expected to move from forage to mlk producton, leadng to greater forage outsourcng. A squared term on Cows allows for nonlneartes assocated wth dary sze and forage outsourcng. Greater acreage, on the other hand, would ndcate ncreased resources potentally avalable for forage producton. The nfluence of off-farm work s explored va Off-farm hours, the number of hours per year the operator works off the farm. Off-farm work s expected to ncrease forage outsourcng, gven addtonal constrants placed on the operator. Offfarm employment may sgnal sgnfcant opportunty cost assocated wth operator labor beng allocated to forage producton. The utlzaton of grazng as a forage source for dary cattle s ncluded as a dummy varable, Graze. Forage grazng would substtute for hay, haylage, and/or slage. Demographc varables nclude operator age and whether a 4-year college degree s held. A dummy varable, Year 2005, s ncluded to determne changes n forage outsourcng between 2000 and Year 2005 s expected to have a postve sgn. The approprateness of poolng 2000 and 2005 data was tested usng the lkelhood rato test, where the unrestrcted model ncluded all ndependent varables plus each of the ndependent varables nteracted wth Year The restrcted model dd not nclude the nteracton terms. Results ndcated the napproprateness of poolng 2000 and 2005 data wthout nteracton terms, whch would result n based estmates. Interacton terms for the pooled model are desgnated as Year*[...], where * s followed by the n-

6 404 October 2010 Agrcultural and Resource Economcs Revew dependent varable of nterest. Incluson of nteracton terms allows for more extensve analyss of structural change over the perod of study. Sgnfcant estmates suggest nonstatonarty of estmates, ndcatng that the nfluence of a partcular ndependent varable on outsourcng behavor has changed over tme, an expected result n the presence of changng technology and shftng consumer demand. Second-Stage Proft Estmaton The second-stage estmaton of equaton (1) determnes the nfluence of forage outsourcng on two whole-farm proft measures: () net return per hundredweght of mlk produced to operator and land, and () net return per hundredweght of mlk produced, ncludng opportunty costs of operator labor and land. Net farm ncome, a whole-farm concept, s constructed as (gross cash farm ncome adjusted by changes n nventory, estmated value of home-consumed products, and rental value of dwellngs on the farm) less total operatng expenses, ncludng nterest payments and deprecaton on captal stock. In addton, proft measure () ncludes the opportunty cost for operator labor, determned as the total hours of operator labor multpled by the wage rate for hred agrcultural labor n the state where the farm s located, and a land opportunty cost. The land opportunty cost s determned usng the qualtyadjusted land measure developed by Nehrng, Ball, and Breneman (2002). Land value s based upon ts agrcultural productvty and dstance to market usng a hedonc prcng model. Value s adjusted usng 2002 values by state. Qualty-adjusted land values for 2000 and 2005 are adjusted dependng upon the relatve land values reported by the USDA s Natonal Agrcultural Statstcs Servce (USDA ). The two proftablty measures are used, n the case of (), to account for the stuaton where operator labor and land are costless, and n the case of (), to consder the sgnfcant operator labor and land requred for producng homegrown forage. Wholefarm proftablty measures are preferred to enterprse measures for ths study, gven the ndvsblty of nputs used for both forage and other crop and lvestock producton. Independent varables nclude regonal varables, sze of operaton, specalzaton, demographc varables, technology varables, forage purchase varables, and data year. The regonal varables ncluded n the frst-stage equatons are used n ths stage. Regonal and year varables allow for consderaton of prce dfferences, as well as producton relatonshps over tme and space. Regonal dfferences n dary farm proftablty have been found by prevous researchers (e.g., McBrde, Short, and El-Osta 2004). Sze varables nclude Cows and Acres. Wth scale economes, average cost would decrease wth sze, as shown by Tauer and Mshra (2006a) wth dary farms. Ths would yeld hgher net farm ncome per cow or hundredweght of mlk produced. An enterprse specalzaton varable s Percent of farm ncome from mlk. Farm ncome from sources other than the dary would be expected to ncrease net farm ncome per unt of output. Demographc varables nclude College and Age. It s expected that more hghly educated farmers would realze greater net farm ncome due to superor manageral ablty. Foltz and Chang (2002) found hgher proftablty among more hghly educated Connectcut dary farmers. The mpact of age s explored. Tauer and Mshra (2006b) found age to be assocated wth hgher cost dary operatons, whch would mply lower net farm ncome. On the other hand, older farmers mght realze hgher net farm ncome due to experence. A proxy for technology use s Mlk per cow, measured as the hundredweght of mlk produced per cow per year. The use of advanced technologes s expected to yeld greater net farm ncome f used on suffcently large operatons to spread the fxed costs over more cows. Mlk per cow s a proxy for technologes and management strateges that would nfluence cow productvty such as breed type, use of recombnant bovne somatotropn, record-keepng, and others. Mlk prce, the average prce of mlk receved per hundredweght, s ncluded. Decsons nvolvng whether to outsource or produce forages are ncluded n the models as two nstrumental varables estmated usng equaton (2) Pr-percentage purchased hay and straw and Pr-percentage purchased slage and haylage whch represent the predcted percentages of hay or straw and slage or haylage purchased, derved from the frst-stage estmates. In the sprt of

7 Gllespe et al. Forage Outsourcng n the Dary Sector: The Extent of Use and Impact on Farm Proftablty 405 Heckman (1990), these are consdered vald nstruments f the ndependent varables used n the tobt models to predct them provde reasonable predctors of forage purchasng behavor. Smlar to the tobt analyses, nteracton terms are ncluded as Year* to test the null hypothess that effects of ndependent varables on measures of proftablty are statonary. How Are Forage Producers Marketng Ther Forage? Lmted data lnk mlk producton wth specfc forage procurement strateges. The ARMS does, however, collect forage producton data, ncludng quanttes of alfalfa hay, other hay, corn slage, and sorghum slage produced, used on the farm, and sold va contract or spot markets. These data are avalable va the Phase III ARMS. If contract forage sales were more common relatve to spot market sales n regons of greater forage outsourcng by dary farmers, ths would suggest ncreased forage procurement va contract n these regons. Such results would allow us to extend our knowledge of dary farmers outsourcng decsons (demand) to understandng the nature of the outsourcng on the supply sde va contract or spot market. For consstency wth the dary analyss, the 2000 and 2005 ARMS Phase III data were used for the forage analyss, but the data were not lmted to dary farms. These surveys nclude 10,309 and 22,843 observatons, respectvely, to determne the types of producers who produce hay or slage () for only ther own anmals consumpton, () for sale va spot markets, and () for sale va producton or marketng contract. A fourth opton s that the farmer does not produce hay or slage. The prmary objectve s to determne where most of the hay and slage sold under contract s produced and the types of farmers producng t. In smlar manner to Davs and Gllespe s (2007) analyss of busness arrangement selecton n U.S. hog producton, the multnomal logt model, expressed n equaton (6), whch follows Greene (2000, p. 859), s used: (6) Prob(Y = j) = e β j ' x 3 βk ' x e k = 0, j = 0,...,3. Wth four possble choces, the desgnaton j = 0,,3 holds. Two separate models are estmated, one for slage and the other for hay. Independent varables n the multnomal logt model nclude regonal dummy varables Pacfc and West, wth the base beng all other regons. For the slage analyss, Southern Plans s also ncluded. We requred the regon to have at least 15 observatons n each category to be ncluded as a separate regonal dummy varable. Other varables n both analyses were Dary (value of dary producton), Cattle (value of cattle producton), Acres, Percent value forage (percentage of farm producton value n forage), and Year As n the dary analyss, weghts extend the analyss to the U.S. farm populaton. Smlar to the other analyses n ths paper, nteracton terms by year allow poolng of the data and testng of the null hypothess of estmate nonstatonarty. Weghted regresson procedures were used to estmate all models. The mult-phase samplng underlyng ARMS data provdes challenges n estmatng varances usng classcal methods, thus the delete-a-group jackknfe estmator s used, as dscussed by the Panel to Revew USDA s Agrcultural Resource Management Survey (2008). 3 A convenent property of the delete-a-group jackknfe procedure s that t s robust to unspecfed heteroscedastcty. Results Table 1 shows weghted means of ndependent and dependent varables. Table 2 shows comparsons of percentages of hay and straw purchases relatve to homegrown of 50 percent and < 50 percent, and of slage and haylage purchases relatve to homegrown of 50 percent and < 50 percent usng weghted means tests for selected varables. Operators wth 50 percent of hay and 3 The emprcal regresson results reported n the tables n the results secton are derved usng farm-level annual data. The data come from a complex survey desgn (both an area and lst frame), not a model-based random sample commonly used n econometrc analyss. Hence, a jackknfng procedure s used wth 15 replcates to estmate sample varances (to get t-statstcs on the coeffcents from the base run regressons) n order to make nferences about the populaton. For a further explanaton as to why nonclasscal econometrcs must be employed to acheve sensble nferences about the populaton of the sample, see Understandng Amercan Agrculture: Challenges for the Agrcultural Resource Management Survey (Natonal Academes Press, Washngton, D.C., 2008). In partcular, see Chapter 4 on survey desgn and Chapter 7 on methods for analyss of complex surveys.

8 406 October 2010 Agrcultural and Resource Economcs Revew Table 2. Dfferences n Means of Selected Varables Hay and Straw Purchase Slage and Haylage Purchase Varable 50% < 50% 50% < 50% Age A B A B Acres A B A B Debt-asset rato 0.20 A 0.16 B 0.23 A 0.17 B Mlk per cow (cwt/cow/year) A B College A 0.12 B Off-farm hours Cows A B A B Percent of farm ncome from mlk 87.4 A 82.9 B 91.5 A 83.7 B Net farm ncome ($) 569,313 A 316,911 B 853,015 A 356,157 B Net farm ncome / cow ($) 524 A 631 B Net farm ncome / cwt ($) 2.82 A 3.68 B Regon Southeast Northeast Appalacha 0.04 A 0.06 B 0.03 A 0.06 B Southern Plans 0.03 A 0.01 B 0.03 A 0.01 B Corn Belt A 0.19 B Lake States 0.30 A 0.43 B 0.25 A 0.40 B West 0.08 A 0.02 B 0.12 A 0.03 B Pacfc 0.12 A 0.01 B 0.15 A 0.04 B Note: Letters A and B ndcate sgnfcant column dfference tests based on parwse two-taled delete-a-group jackknfe t-statstcs at a 90 percent confdence level or hgher wth 15 replcates and 28 degrees of freedom. Tests pertan only wthn each of the two groupngs on the frst row of the table. For regon varables, numbers n each column sum to 1. straw, and slage and haylage, beng outsourced were younger, farmed fewer acres, held greater debt, mlked more cows, receved a hgher percentage of farm ncome from mlk, and realzed hgher total net farm ncome. In addton, purchasers of slage and haylage were more lkely to hold college degrees, and purchasers of hay and straw were more lkely to realze lower net farm ncome per cow and per hundredweght of mlk produced. Regonal dfferences n the weghted means are strkng, wth Appalacha, Corn Belt, and Lake States dary farmers relyng less heavly, and West, Pacfc, and Southern Plans dary farmers relyng more heavly on outsourced forage. Dfferences n weghted means are valuable n that they show whether outsourcng behavor vares wth one partcular varable, provdng nsght as to whether there s a smple correlaton between purchasng behavor and the varable, phenomena whch are often observed n the ndustry. However, these statstcs provde lmted capablty to analyze the relatonshp between the varables; thus, a multvarate analyss s requred for fuller understandng of the factors nfluencng outsourcng. Tobt results show that a number of factors nfluence the decson of whether to outsource or produce forages for the dary operaton (Tables 3 and 4). As expected, regon was mportant. Southern Plans & West and Pacfc dary producers were more lkely and Lake States dary producers less lkely to purchase hay and straw than were those n the Northeast. Southern Plans & West dary producers were more lkely and Lake States and Corn Belt dary producers less lkely to purchase slage and haylage than were those n the Northeast. These results, coupled

9 Gllespe et al. Forage Outsourcng n the Dary Sector: The Extent of Use and Impact on Farm Proftablty 407 Table 3. Tobt Results for Porton of Purchased Hay and Straw Varable Estmate β Std. Error Constant Southeast Year*Southeast Lake *** Year*Lake *** Appalacha Year*Appalacha Southplans&west *** Year*Southplans&west Corn Belt Year*Corn Belt Pacfc *** Year*Pacfc Acres * Year*Acres Cows * Year*Cows Cows squared -3.68E E-7 Year*Cows squared 1.20E E-7 College Year*College Off-farm hours * Year*Off-farm hours * Operator age * Year*Operator age Graze Year*Graze Year Sgma *** N 2368 Log lkelhood Note: ***, **, and * ndcate sgnfcance at the 1 percent, 5 percent, and 10 percent levels, respectvely, usng the delete-agroup jackknfe varance estmator wth 15 replcates. wth Remund, Moore, and Martn s (1977) thess that changes n the relatonshp between upstream and downstream frms (toward contractng) generally occur n new producton regons. It s also consstent wth Sumner and Wolf s (2002) results showng regonal dfferences n vertcal ntegraton on U.S. dary farms. Chntz (1961) was among the early economsts to pont out that organzatonal structure of ndustres and frm sze often dffer among regons, ctng potental reasons and callng for greater work n the area. Older producers those expected to use tradtonal technologes and busness arrangements were less lkely to outsource hay and straw. Producers workng more off-farm hours were more lkely to outsource slage and haylage. Results of the off-farm hours varable were mxed for purchases of hay and straw, as the sgn on Off-farm hours was postve but the sgn for Year*Off-farm hours was negatve, suggestng that the effect of off-farm employment depended on year. Holdng a college degree led to ncreased outsourcng of slage and haylage. The postve coeffcent on the Cows varable for percentage of purchased hay and straw suggests that larger dares are more lkely to outsource hay and straw. 4 As the dary enterprse becomes larger, the frm specalzes by concentratng management n mlk producton. Increased acreage, however, was assocated wth reduced forage outsourcng. Outsourcng of hay and straw, and slage and haylage, s not shown to have ncreased from 2000 to Sgnfcant nteracton terms, however, show that the coeffcents were non-statonary over the perod. Specfcally, the Year*Lake States and Year*Corn Belt varables are postve and sgnfcant n the hay and straw, and slage and haylage, runs, respectvely, suggestng relatvely greater movement toward forage purchasng n those regons relatve to the Northeast. Factors Influencng Net Farm Income wth the dfferences n weghted means tests, suggest that the transton from vertcally ntegrated forage and mlk producton to producton by separate frms has been more pronounced n the Southern Plans & West and Pacfc regons, and less so n the tradtonal Corn Belt, Lake States, and Northeast regons. Ths s consstent As expected, regon and mlk prce nfluenced both measures of net farm ncome (Table 5). The 4 The negatve and sgnfcant estmate for Cows squared suggests a decreasng effect on forage purchasng wth sze. The estmate would suggest the percentage purchased s maxmzed at 3,200 cows, though one cannot place much confdence n ths snce there would be relatvely few observatons from whch to draw n the very large range.

10 408 October 2010 Agrcultural and Resource Economcs Revew Table 4. Weghted Tobt Results for Porton of Purchased Slage and Haylage Varable Estmate β Std. Error Constant Southeast Year*Southeast Lake *** Year*Lake Appalacha Year*Appalacha Southplans&west *** Year*Southplans&west Corn Belt *** Year*Corn Belt ** Pacfc Year*Pacfc Acres *** Year*Acres Cows Year*Cows Cows squared -7.55E E-7 Year*Cows squared 6.51E E-7 College *** Year*College Off-farm hours *** Year*Off-farm hours Operator age Year*Operator age Graze Year*Graze Year Sgma *** N 2040 Log lkelhood Note: ***, **, and * ndcate sgnfcance at the 1 percent, 5 percent, and 10 percent levels, respectvely, usng the delete-agroup jackknfe varance estmator wth 15 replcates. most proftable farms had larger dary herds when opportunty costs for land and operator labor were ncluded. The farm sze varable, Acres, had mxed effects on farm proftablty, wth nteracton terms Year*Acres beng sgnfcant n both cases and opposte n sgn of the Acres varable, ndcatng dfferng effects by year. Dfferences by year were expected, lkely the result of dfferent nput and output prces other than mlk between the two years. More proftable farms generally derved greater addtonal ncome from other farm enterprses. Age had mxed effects on proftablty, dependng upon year. As expected, farms realzng greater mlk producton per cow were more proftable when opportunty costs were consdered, but mxed effects by year were found when opportunty costs were not consdered. Controllng for other factors that could nfluence farm proftablty, the only forage outsourcng varable that was sgnfcant was Pr-percent purchased slage & haylage, ndcatng that farms outsourcng a hgher percentage of slage and haylage were less proftable than those outsourcng less. These numbers, however, must be vewed wth cauton, as the Year*Pr-percent purchased slage & haylage coeffcents are larger and opposte n sgn of the Pr-percent purchased slage & haylage coeffcents for one of the regressons, and close for the other, but non-sgnfcant. Ths lkely explans the lack of sgnfcance for any of the percent purchased varables n the restrcted (no Year* nteracton terms) models that are not reported here. Results, however, suggest that the benefts to outsourcng rather than growng one s own slage and haylage do not mprove the representatve farm s total proftablty or ts proftablty per unt of nput or output, whch helps to explan why only 7.4 percent of slage and haylage was purchased over the perod of study. It s of nterest that the Pr-percent purchased slage & haylage coeffcents were sgnfcant, whle those for the Pr-percent purchased hay and straw coeffcents were not. The coeffcents are larger and the standard errors smaller for the former than the latter, suggestng a consstently greater mpact of outsourcng of slage and haylage than hay and straw on proftablty. Ths may be partally the result of transportaton and subsequent storage costs for slage beng greater than those costs for hay, suggestng on-farm producton beng lower-cost. At the least, these results suggest that, for the representatve farm, proftablty assocated wth producng one s own forage s not exceeded by the proftablty assocated wth purchasng t.

11 Gllespe et al. Forage Outsourcng n the Dary Sector: The Extent of Use and Impact on Farm Proftablty 409 Table 5. Ordnary Least Squares Regressons for Net Farm Income (NFI) NFI / cwt NFI Less Opportunty Costs / cwt Varable β Std. Error β Std. Error Constant 11.37*** *** 0.13 Southeast -1.73*** *** 0.47 Year*Southeast Lake 1.05*** *** 0.01 Year*Lake 0.89*** *** 0.05 Appalacha 1.29*** *** 0.01 Year*Appalacha -2.74*** *** 0.01 Southern Plans *** 1.45 Year*Southern Plans -1.82* *** 1.34 Corn Belt 0.96*** *** 0.02 Year*Corn Belt -0.15* *** 0.05 West -1.82** *** 1.47 Year*West * 1.34 Pacfc * 2.67 Year*Pacfc Cows *** 0.00 Year*Cows *** 0.00 Porton of farm ncome from mlk -9.09*** *** 0.06 Year*Percent of farm ncome from mlk 3.62*** *** 0.12 Operator age 0.01*** *** 0.00 Year*Operator age -0.02*** * 0.00 Acres 0.00*** Year*Acres 0.00*** *** 0.00 Pr-percent purchased hay & straw Year*Pr-percent purchased hay & straw Pr-percent purchased slage & haylage -1.81** *** 1.02 Year*Pr-percent purchased slage & haylage Mlk prce 0.12*** *** 0.01 Year*Mlk prce 0.05*** *** 0.01 Mlk per cow -0.01*** *** 0.00 Year*Mlk per cow 0.01*** *** 0.00 Year *** *** 0.34 N Note: ***, **, and * ndcate sgnfcance at the 1 percent, 5 percent, and 10 percent levels, respectvely, usng the delete-a-group jackknfe varance estmator wth 15 replcates. Where Is Forage Contractng Occurrng? Results of the forage contractng multnomal logt model are shown n Tables 6 and 7 and ndcate that slage contractng s more prevalent n the West and Pacfc regons relatve to the rest of the Unted States. Relatve to other regons, Pacfc and West farmers were more lkely to have marketng contracts for ther hay than to produce for on-farm use or to produce no hay. Relatve to other regons, Pacfc and West farmers were more lkely to have marketng contracts for ther

12 410 October 2010 Agrcultural and Resource Economcs Revew 410 October 2010 Agrcultural and Resource Economcs Revew Table 6. Multnomal Logt Results for Hay Producton n the Unted States, n = 31,313 On-Farm Use vs. No Hay Hay Sale vs. No Hay Hay Contract vs. No Hay Hay Sale vs. On-Farm Use Hay Contract vs. On-Farm Use Hay Contract vs. Hay Sale Constant *** *** *** *** *** (0.0486) (0.1050) (0.7852) (0.1104) (0.7872) (7.8308) Pacfc *** *** ** * *** (0.2803) (0.3920) (0.0486) (0.5704) (1.5107) (6.2860) Year*Pacfc (0.2901) (1.3939) (1.8763) (1.4611) (1.9091) (7.2313) West ** *** ** *** (0.3211) (0.2977) (0.8546) (0.3242) (0.8861) (6.2092) Year*West (0.4399) (0.3693) (1.7612) (0.4280) (1.6895) (7.2313) Dary 1.45E-6*** 8.69E-7*** E-7** E-5 (3.14E-7) (1.84E-7) (0.0055) (2.46E-7) (0.0036) (0.0003) Year*Dary -1.08E-6*** -5.19E-7** E-7** E-5 (3.41E-7) (2.44E-7) (0.0055) (2.42E-7) (0.0036) (0.0003) Cattle 2.45E E E E-8* 3.88E E-8 (4.63E-8) (4.69E-8) (6.90E-8) (6.09E-9) (3.02E-7) (7.72E-6) Year*Cattle 3.69E E E E E E-8 (4.97E-8) (5.08E-8) (2.14E-6) (1.06E-8) (2.16E-6) (8.95E-6) Acres 3.07E E E E E-6* 4.18E-6 (1.29E-5) (1.13E-5) (1.13E-5) (4.09E-6) (4.17E-6) (0.0002) Year*Acres 1.39E E E E E E-6 (2.63E-5) (2.77E-5) (2.75E-5) (4.95E-6) (8.06E-6) (0.0003) Percent value forage ** ** ** ** (0.5539) ( ) ( ) ( ) ( ) (2.8780) Year*Percent value forage ** ** * ( ) ( ) ( ) ( ) ( ) (3.5927) Year *** ** *** ** (0.0761) (0.2794) (0.9450) (0.2849) (0.9464) (9.1352) Note: ***, **, and * ndcate sgnfcance at the 1 percent, 5 percent, and 10 percent levels, respectvely, usng the delete-a-group jackknfe varance estmator wth 15 replcates.

13 Gllespe et al. Forage Outsourcng n the Dary Sector: The Extent of Use and Impact on Farm Proftablty 411 Gllespe et al. Forage Outsourcng n the Dary Sector: The Extent of Use and Impact on Farm Proftablty 411 Table 7. Multnomal Logt Results for Slage Producton n the Unted States, n = 31,313 Varable On-Farm Use vs. No Slage Slage Sale vs. No Slage Slage Contract vs. No Slage Slage Sale vs. On-Farm Use Slage Contract vs. On-Farm Use Slage Contract vs. Slage Sale Constant *** *** *** *** *** *** (0.0471) (0.1234) (0.4840) (0.14) (0.5012) (0.4430) Pacfc *** * ** *** ** (0.6127) (0.8943) (1.0043) (0.9137) (1.3186) (1.5344) Year*Pacfc * * (0.8941) (1.2294) (1.3317) (1.3579) (1.5539) (2.0644) West * ** * *** (1.1926) (0.3736) (1.3640) (1.2383) (2.2901) (1.4159) Year*West *** (0.8017) (0.8120) (1.7766) (1.0007) (2.2320) (1.9602) Southern Plans (3.1036) (2.4647) (1.9070) (3.7482) (3.6581) (3.0446) Year*Southern Plans (3.1384) (2.6339) (2.1803) (3.89) (4.2188) (2.6566) Dary 1.05E-5*** 9.53E-6*** 7.16E E-7** -3.35E E-6 (1.45E-6) (1.30E-6) (0.0015) (4.81E-7) (0.0015) (0.0007) Year*Dary -3.77E-6** -4.29E-6*** -2.58E E E E-6 (1.91E-6) (1.60E-6) (0.0015) (7.44E-7) (0.0015) (0.0007) Cattle 6.44E-8*** 5.55E-8** -3.93E E E E-7 (2.63E-8) (2.76E-8) (5.08E-5) (1.09E-8) (5.08E-5) (5.80E-5) Year*Cattle 1.88E-7** 1.61E-7** 5.30E E E E-7 (7.66E-8) (7.86E-8) (5.08E-5) (2.22E-8) (5.08E-5) (5.08E-5) Acres 1.07E E E E E E-6 (8.24E-6) (9.63E-6) (6.23E-5) (3.95E-6) (5.92E-5) (6.22E-5) Year*Acres 2.29E E E E E E-6 (2.14E-5) (1.90E-5) (6.70E-5) (4.69E-6) (5.89E-5) (6.14E-5) Percent value forage ** *** *** ** (1.2056) (0.2617) (1.0062) (1.1466) (1.6243) (1.0982) Year*Percent value forage ** (1.2410) (0.4187) (1.0113) (1.2008) (1.6227) (1.0871) Year *** *** (0.0603) (0.1462) (1.0933) (0.1732) (1.0831) (1.0940) Note: ***, **, and * ndcate sgnfcance at the 1 percent, 5 percent, and 10 percent levels, respectvely, usng the delete-a-group jackknfe varance estmator wth 15 replcates.

14 412 October 2010 Agrcultural and Resource Economcs Revew slage than to sell t va spot markets, produce t for on-farm use, or n the case of the West, to produce no slage. These results are consstent wth other results reported n ths paper showng these regons to be the most lkely for producers to outsource slage or haylage and hay or straw. Expected results were also found for the Acres, Dary, and Percent value forage varables. Though several of the Year 2005 coeffcents are sgnfcant, further research should be conducted to flush out whether the results represent a trend or smply are a functon of the condtons of these two specfc years. Lkewse, a number of Year* nteracton terms are sgnfcant, suggestng that the mpact of some of the varables on ndustry structure was non-constant over tme. These results provde evdence that, n the regons where forage outsourcng has been shown to be most prevalent among dary farmers, the transactons are more lkely to be under contract, provdng evdence of vertcal coordnaton among dary farmers and forage producers. Outsourcng va contract would serve to reduce the transacton costs assocated wth forage producers searchng for buyers and dary farmers searchng for supplers of qualty forage, whch would be partcularly mportant gven the sgnfcant captal nvestments n assets specfc to the producton of forage or mlk. Conclusons The way that dary producers procure forages has evolved sgnfcantly over the past two decades, wth forage outsourcng becomng more common as dary farms have become larger and more specalzed n mlk producton. The trend for dary farms has been to move away from vertcally ntegrated forage and mlk producton. Through dscusson wth dary extenson specalsts, farmers, extenson fact sheets, popular publcatons, and our analyss of forage contractng, ndcatons are that a substantal porton of purchased forages are procured va contract; however, a better understandng of the varaton n specfc arrangements of these transactons would be a frutful area for future research. Ths paper represents an early attempt to dentfy those farms most lkely to outsource forages, and to develop a better understandng of whether the decson to purchase can be attrbuted to greater farm proftablty or better allocaton of management va specalzaton wth a larger dary. Sgnfcant dfferences were found n forage purchasng behavor by regon a concluson that s consstent wth Sumner and Wolf s (2002), and Remund, Moore, and Martn s (1977) orgnal and Gllespe, Karantnns, and Storey s (1997) revsed thess that fundamental changes n the vertcal structure of an ndustry are lkely to occur n nontradtonal producton regons. These authors suggest that as technology s developed and farm sze ncreases, shfts n the locaton of dares to new producton areas occurs where busness arrangements evolve to deal wth the ncreased assocated rsk and transacton costs. The westward movement of dary producton and the establshment of larger dares have concded wth greater forage purchasng. Thus, ths pattern of structural change appears to be followng a trend that would be expected from prevous observaton of the evoluton of agrcultural ndustres. Furthermore, ncreased contractng of hay and slage s occurrng n the relatvely new western dary producton regons, where there s greater forage outsourcng by dary farmers. We cannot lnk ths forage contractng specfcally to dary farms, but evdence suggests sgnfcant contractng n dary, consderng the extensve demand for outsourced slage and hay n those regons. Gven the extensve specfc assets assocated wth both dary and forage producton and sgnfcant transacton costs n the sale and procurement of forage, the evoluton of contractng s not surprsng. Further nvestgaton of the specfc types of contracts beng utlzed s of nterest. Along wth sze and locaton nfluences on the forage outsourcng decson, a number of complementary factors also lend nsght nto the movement toward outsourcng. Specfcally, younger, ostensbly newer producers are more lkely to purchase, along wth those workng more hours off-farm and holdng college degrees. Though forage outsourcng was not shown to have ncreased from 2000 to 2005, t s evdent that the factors nfluencng forage procurement behavor were not constant over tme, suggestng structural change. Together, ths nformaton suggests that newer, younger producers are establshng larger dares n non-tradtonal regons of producton,

15 Gllespe et al. Forage Outsourcng n the Dary Sector: The Extent of Use and Impact on Farm Proftablty 413 and these producers exhbt a tendency to outsource rather than produce ther forage. Ths study fnds that the forage procurement strategy has a sgnfcant nfluence on dary farmers whole-farm net farm ncome, though t s somewhat unclear whether ths could hold over multple years, as the sgns on the year nteracton terms were opposte those of the man effect, and the magntude larger, though non-sgnfcant. Thus, cauton s urged aganst readng too much nto the man effects. What s clear from the analyss s that forage outsourcng dd not result n greater farm proftablty. What must be realzed, however, s that one of the consstent drvers toward greater proftablty was farm sze, measured by number of cows, whch s postvely assocated wth greater forage purchases. When consdered n a multvarate framework, the major drvers of greater net farm ncome appear to be producton locaton, farm sze, farm dversfcaton, technology, farmer demographcs, and mlk prce. Forage purchasng releases management tme, so the farm can expand to a larger sze, dversfy nto other enterprses, and lmt debt, hence allowng the farm to realze greater proft. References Amaral-Phllps, D.M., and J. McAllster Plannng the Yearly Forage and Commodty Needs for a Dary Herd. Report No. ASC-160, Cooperatve Extenson Servce, Unversty of Kentucky, Lexngton, KY. Barry, P.J., S.T. Sonka, and K. Lajl Vertcal Coordnaton, Fnancal Structure, and the Changng Theory of the Frm. Amercan Journal of Agrcultural Economcs 74(5): Boucher, R., and J. Gllespe Projected Commodty Costs and Returns, Beef Cattle, Dary, and Forage Crop Producton n Lousana. AEA Informaton Seres No. 241, Department of Agrcultural Economcs and Agrbusness, Lousana State Unversty Agrcultural Center, Baton Rouge, LA. Chntz, B Contrasts n Agglomeraton: New York and Pttsburgh. Amercan Economc Revew 51(2): Coase, R The Nature of the Frm. Economca 4(16): Davs, C.G., and J.M. Gllespe Factors Affectng the Selecton of Busness Arrangements by U.S. Hog Farmers. Revew of Agrcultural Economcs 29(2): Foltz, J.D., and H. Chang The Adopton and Proftablty of rbst on Connectcut Dary Farms. Amercan Journal of Agrcultural Economcs 84(4): Gllespe, J., K. Karantnns, and G. Storey The Expanson and Evoluton of Vertcal Coordnaton n the Quebec Hog Industry. Revew of Agrcultural Economcs 19(2): Greene, W.H Econometrc Analyss (4th edton). Upper Saddle Rver, NJ: Prentce-Hall. Grossman, S.J., and O. Hart The Costs and Benefts of Ownershp: A Theory of Vertcal and Lateral Integraton. Journal of Poltcal Economy 117(1): Grossman, G.M., and E. Helpman Integraton Versus Outsourcng an Industry Equlbrum. Quarterly Journal of Economcs 94(4): Heckman, J Varetes of Selecton Bas. Amercan Economc Revew 80(2): Hobbs, J.E Measurng the Importance of Transacton Costs n Cattle Marketng. Amercan Journal of Agrcultural Economcs 79(4): Maddala, G.S Lmted-Dependent and Qualtatve Varables n Econometrcs. Cambrdge: Cambrdge Unversty Press. McBrde, W.D., S. Short, and H. El-Osta The Adopton and Impact of Bovne Somatotropn on U.S. Dary Farms. Revew of Agrcultural Economcs 26(4): Nehrng, R., V.E. Ball, and V. Breneman Land Qualty n an Internatonal Comparson: Its Importance n Measurng Productvty. Paper presented at the annual meetngs of the European Assocaton of Agrcultural Economsts, Zaragosa, Span (August). Paper s avalable from authors. Panel to Revew USDA s Agrcultural Resource Management Survey Understandng Amercan Agrculture: Challenges for the Agrcultural Resource Management Survey (Natonal Research Councl). Washngton, D.C.: Natonal Academes Press. Rankn, M Contractng Corn Slage Acres. Mmeo, Unversty of Wsconsn Extenson Forage Resources. Avalable at (accessed March 7, 2008). Remund, D.A., C.V. Moore, and J.R. Martn Factors Affectng Structural Change n Agrcultural Subsectors: Implcatons for Research. Southern Journal of Agrcultural Economcs 9(1): Stellato, J Contract Feed Producton Arrangements. Mmeo, Unversty of Wsconsn Extenson Forage Resources. Avalable at Slage.htm (accessed March 7, 2008). Sumner, D.A., and C.A. Wolf Dversfcaton, Vertcal Integraton, and the Regonal Pattern of Dary Farm Sze. Revew of Agrcultural Economcs 24(2): Tauer, L., and A. Mshra. 2006a. Can the Small Dary Farm Reman Compettve n U.S. Agrculture? Food Polcy 31(5): b. Dary Farm Cost Effcency. Journal of Dary Scence 89(12): Tranel, L.F., D.B. Fsher, R.C. Tgner, and D. Thoreson Contractng Corn Slage for Your Dary. Fact Sheet No. LT-130, Iowa State Unversty, Ames, IA.