Several agricultural and related industry groups in the Pacific Coast states have expressed concern about the competitive position of these states in the production of feed grains and livestock products. This study was directed toward the investigation of these concerns. In order to permit the real world situation, with its accompanying multivariable reality, to be reduced to workable size, a linear programming model was designed. The 48 contiguous states were divided into five regions with smaller regions in the western United States to permit a more detailed analysis of the West. The quantities of feed grains produced in each state were determined and summed for the states in a region. The quantities of fed beef, pork, broilers, turkeys, eggs, and milk (the products of the major grain consuming classes of livestock) demanded in each state were computed. A matrix of transportation costs between regions was developed for feed grains and for the livestock products of the model. Regional weighted average prices received by farmers for each feed grain and for each livestock product were determined. The model was then utilized to indicate production of all the livestock products required for consumption by region at the least cost of producing the products. Optimal solutions were obtained using 1968 and 1969 relative prices and these solutions were analyzed. The analysis indicates that generally the states which are deficit in beef, pork, broiler, and egg production have a slight economic advantage in producing these products for local consumption until the locally produced feed supply is utilized. Each region in the model produced the milk consumed in that region. Region I (Oregon and Washington) has traditionally been self-sufficient in turkey production, and Region III (California) has been a turkey exporting state. According to the model, both of these regions should import the turkey consumed in the region to derive optimum economic benefits. These conclusions are based on the relative prices and transportation costs that existed in 1968 and 1969. After the solutions were obtained, the price of wheat in Region I was varied using a parametric procedure available with the linear programming package. Results of this analysis using 1968 and 1969 relative prices were described. The parametric analysis indicated that at the 1968 price of wheat in Region I more than twice the quantity of wheat allocated to livestock feeding in the basic model could have been economically utilized and would have reduced costs of producing the livestock products consumed in Region I. The 1969 wheat price in Region I was sufficiently low that the parametric analysis indicated an allocation of over four times the quantity used in the basic model for livestock feeding. The basic model utilized 1,043,000 tons of wheat for livestock feeding. It can be concluded from the analysis that Region I could have utilized much larger quantities of wheat for livestock feeding than was allocated for feeding in the basic model. Based on the relative feed ingredient costs that existed in 1968, Region I producers of pork, broilers, eggs, and milk are competitive with other regions in supplying the quantities of these products demanded for regional consumption. The 1969 relative prices made Region I even more competitive in producing pork, broilers, eggs, and milk, and added beef production as an economically advantageous alternative. These conclusions are based only on feed ingredient and transportation costs. If non-feed costs and relative feeder cattle costs for beef production are included, Region I producers appear to have a slight margin for producing beef,for local consumption until locally produced feed supplies are exhausted. / Graduation date: 1972

Call number: LD2668 .T4 1956 T38 / Master of Science

Corn-based ethanol production has increased dramatically in the past ten years, causing an increase in demand for corn by ethanol producers and an increase in production of ethanol by-products such as distillers' grains. The increase in ethanol production can be attributed to ethanol policy at the state and federal levels. Because of the increase in production of corn-based ethanol, cattle producers face greater competition for a major feed source, corn, and greater supply of an emerging feed source, distillers' grains. The objective of this study is to analyze and quantify the effects of ethanol policy on cattle production. A theoretical model and an econometric model are used to fulfill the objectives of this study. The theoretical model contains an ethanol model and a general livestock model. Results of the theoretical model present the possibilities of ethanol policy affecting cattle production. The econometric model identifies the indirect and direct effects of ethanol policy on cattle production. The results of the econometric model indicate that there is a relationship between ethanol policy, specifically the Renewable Fuel Standard, and cattle production.

Call number: LD2668 .T4 1984 N44 / Master of Science

Call number: LD2668 .T4 1986 B36 / Master of Science

The objective of this study was to examine the use of technical indicators, in an econometric context, as guides in making pricing decisions relative to feeder cattle production. Technical indicators were developed for both the short and long term. Short term indicators were designed to capture divergence/convergence between the Relative Strength Index and the futures price stream to form the Price Linked to Divergence index. A long term indicator using an average difference in price over a longer period was developed to form the Price Linked to Trend index. These indicators were used in the econometric models which in conjunction with cash production costs formed a hold/price/sell decision framework. Analysis was conducted on fall-to-spring, spring-to-fall, and fall-to-fall production programs. Results for the three production programs in both the in and out-of-sample environments showed that on average a futures price in the top half of the futures price range was captured. This resulted in average improvement in returns through hedging for every program. / Master of Science

M.S.

The farmer that fed silage made a profit of $606.20 for the twenty-six years or had received an average profit of $23.31 each year per steer. The farmer that fed hay and straw made a profit of $298.76 for the twenty-six years or an average profit of $11.49 each year per steer. The farmer that fed stover and corn meal made a profit of $140.56 for the twenty-six years or an average profit each year of $5.40 per steer. From a survey of seventy farms and 4,380 head of cattle, we find that the average carload shipment is about three care per cattlemen in Southwest Virginia allowing twenty head to the car, this would mean sixty per year. At this rate the farmer that fed silage would have made $36,372.00. The farmer that fed hay and straw would have made $17,925.00 and the farmer that fed stover and corn meal would have made $8,433.60. The cost of silage for the twenty-six years was $412.04, for hay and straw $521.54, and for stover and corn meal $439.70. / M.S.

Call number: LD2668 .T4 1984 W365 / Master of Science

Thesis (PhD (Agric))--University of Stellenbosch, 2005. / ENGLISH ABSTRACT: It is generally accepted that feed intake and growth (gain) are the most important economic components when calculating profitability in a growth test or feedlot. Feeding cost of animals is a major determinant of profitability in livestock production enterprises. Genetic selection to improve feed efficiency aims to reduce the cost of feeding in beef cattle production and thereby improve profitability. The objective of this study was to define a clear selection objective to enable South African beef breeders and especially the feedlot industry to select for post-weaning growth or feedlot performance and to identify factors influencing profitability in a feedlot environment. Because of the recording of individual feed intake and weight gain values in the South African Agricultural Research Councils' centralized growth tests, it was also possible to calculate a phenotypic value for feedlot profitability (R-value) for each bull tested in a centralized growth test. (Co)variances, using multitrait as well as random regression models, for and between feedlot profitability, weaning weight and other production, reproduction and efficiency traits were estimated. Residual feed intake (RFI) and feed conversion ratio (FCR) as efficiency traits were also compared to growth (average daily gain (ADG), weaning weight (WW) and shoulder height (SHD)), reproductive (scrotum circumference (SCR)) and profitability (feedlot profitability) traits measured in growth tests of young Bonsmara bulls. Consequently, a single post-weaning growth selection index value based on the economic and breeding values of different selection criteria related to feedlot profitability was composed. (Co)variance components, heritabilities and genetic correlations for and between initial weight (lW), final weight (FW), total feed intake (FI) and shoulder height (SHD) were estimated through the use of multitrait restricted maximum likelihood (REML) procedures. These breeding values (EBV s) were then used in a selection index to calculate a single economical value for each animal. This economical value is an indication of the gross profitability value or gross test value (GTV) of the animal in a post-weaning growth test. The heritability estimate of 0.36 for R-value, obtained from the multitrait analysis, shows that this trait is genetically inherited and that it can be selected for. The heritability for R-value obtained from the single trait random regression model varied between 0.57 and 0.62. The genetic correlations between the R-value and the other traits, obtained from the multitrait analysis, varied from negligible to high. The heritability estimated for FCR was 0.34 and for RFl 0.31 with a genetic correlation estimate of 0.75 between the traits. The estimated genetic correlation between profitability (R-value) and FCR and RFl were -0.92 and -0.59, respectively. The genetic correlation estimate of -0.92 between FCR and R-value is largely due to the part-whole relationship between these two traits. This is also shown in their genetic trends. The genetic correlations and expected correlated responses between RFl and FCR with R-value suggest that indirect selection for R-value through the direct selection for FCR and/or RFl will result in slower genetic progress in Rvalue than direct selection for R-value. However, where the R-value cannot be calculated and/or where direct selection for R-value is not possible, it would be better to select indirectly for R-value through the use of FCR rather than RF!. Consequently, a regression equation was developed (with an R2 of 0.82) to estimate a feed intake value for all performance-tested Bonsmara bulls which were group fed and whose feed intakes were unknown. These predicted feed intake values made it possible to calculate a feedlot or post-weaning growth profitability value (R-value) for all tested bulls even where individual feed intakes were unknown. Subsequently, an R-value for each bull was calculated in a favourable economic environment (FEE), an average economic environment (AEE) and in an unfavourable economic environment (VEE). The high Pearson and Spearman correlations between the EBV s based on AEE and the other two environments suggested that the average economic environment could be used to calculate EBVs for R-value or feedlot growth profitability. It is therefore not necessary to change the carcass, weaner or feed price on a regular basis to account for possible re-rankings based on R-value EBVs. Heritabilities for lW, FW, Fl and SHD were 0.41, 0.40, 0.33 and 0.51, respectively. The highest genetic correlations between these traits were the 0.78 (between lW and FW) and 0.70 (between Fl and FW). GTV values varied between -R192.l7 and R231.38, with an average of R9.31. The Pearson correlations between EBVs (for production and efficiency traits) and GTV range from -0.51 to 0.68. The lowest correlation (closest to zero) was 0.26 between the Kleiber ratio (KLB) and GTV. Correlations of 0.68 and -0.51 were estimated between average daily gain (ADG) and GTV and feed conversion ratio (FCR) and GTV, respectively. The heritabilities of the different traits included in the selection index suggest that it is possible to select for a GTV. The selection index can benefit feedlotting In selecting offspring of bulls with high GTV values to maximize profitability. The Pearson and Spearman correlations between the R-value EBVs and the index values (GTV) were very high (0.97). This high correlation of 97% indicates that it is not important which method is used to calculate a genetic post-weaning growth of feedlot profitability value. The selection index value is, however, more simplified than the feedlot profitability with less assumption. Therefore, it is recommended that the post-weaning selection index value be used as a selection objective in breeding programmes to improve post-weaning growth profitability rather than the more complex feedlot profitability value. / AFRIKAANSE OPSOMMING: 'N GENETIESE ANALIESE VAN DIE BIOLOGIESE EN EKONOMIESE DOELTREFFENTHEID VAN NASPEENSE GROEI IN VLEISBEESTE IN DIE VOERKRAAL: Dit word algemeen aanvaar dat voerinname en groei die twee ekonomies mees belangrike komponente in die berekening van 'n naspeense groei- of voerkraalwinsgewindheidswaarde is. Voerkostes is 'n bepalende faktor van winsgewindheid in enige lewendehawe boerderypraktyk. Seleksie om voerdoeltreffendheid te verbeter, verminder dus die voerkostes in vleisbeesproduksie en gevolglik 'n verhoging in die winsgewindheid. Die doelwit van die studie was om faktore te identifiseer wat 'n invloed op winsgewindheid in die voerkraaiomgewing het asook om 'n duidelike seleksiedoelwit te formuleer wat die Suid- Afrikaanse vleisbeesteiers en veral die voerkraalbedryf instaat sal kan stelom vir naspeense groeidoeltreffendheid ofvoerkraalwinsgewindheid te selekteer. As gevolg van die aantekening en rekordhouding van weeklikse individuele voennnames en gewigstoenames van alle prestasiegetoetsde bulle, in 'n gesentraliseerde groeitoets deur die Suid- Afrikaanse Landbou Navorsingsraad getoets, was dit moontlik om vir elk van hierdie bulle 'n fenotipiese voerkraalwinsgewindheidswaarde (R-waarde) te kon bereken. (Ko)variansies is vir en tussen voerkraalwinsgewindheid, speengewig en ander produksie-, reproduksie- en doeltreffendheidseienskappe bereken deur van meereienskap en ewekansige regressie modelle gebruik te maak. Twee doeltreffendheidseienskappe naamlik residuele voerinname (RFI) en voeromsetverhouding (FCR) is ook met groei (gemiddelde daaglikse toename (ADG), speengewig (WW) en skouerhoogte (SHD)), reproduksie (skrotumomvang (SCR)) en winsgewindheidseienskappe (voerkraalwinsgewindheid (R-waarde)) vergelyk, om sodoende te bepaal watter een die mees geskikte eienskap is om indirek vir voerkraalwinsgewindheid of groei, gebaseer op teeltwaardes en ekonomiese waardes vir die verskillende eienskappe, wat 'n invloed op naspeense groei winsgewindheid het, gestruktueer. (Ko)variansiekomponente, oorerflikhede en genetiese korrelasies vir en tussen begingewig (lW), eindgewig (FW), voerinname (FI) en skouerhoogte (SHD) is bereken deur van 'n meereienskap (REML) ontleding gebruik te maak. Hierdie teelwaardes (EBVs) is vervolgens in 'n seleksie-indeks gebruik om 'n enkele ekonomies of voerkraal-winsgewindheids seleksie-indekswaarde (GTV) vir elke dier te bereken. Hierdie ekonomiese waarde is 'n aanduiding van die bruto winsgewindheidswaarde ofbruto toetswaarde (GTV) van die dier in 'n naspeentoets. Die oorerflikheid, vanuit die meereienskapontleding vir R-waarde beraam, was 0.36. Hierdie oorerflikheid dui daarop dat die eienskap oorerflik is en dat dit wel moontlik is om daarvoor te selekteer. Die ooreflikhede van R-waarde, voorspel vanuit die enkeleienskap ewekansige regressieontleding varieer tussen 0.57 en 0.62. Die genetiese korrelasie tussen R-waarde en ander eienskappe, vanuit die meereienskap ontleding beraam, varieer tussen weglaatbaar klein tot hoog. Die oorerflikheid van FeR was 0.34 en van RFI 0.31 met 'n genetiese korrelasie van 0.75 tussen die twee eienskappe. Die genetiese korrelasie tussen R-waarde en FeR, en R-waarde en RFI was onderskeidelik -0.92 en -0.59. Die rede vir die hoë negatiewe genetiese korrelasie tussen R-waarde en FeR van -0.92 is omdat dieselfde komponente in die berekening van die twee eienskappe gebruik is. Dit word ook in die genetiese tendense weerspeël. Die genetiese korrelasies en verwagte gekorreleerde responsies tussen R-waarde en FeR, en tussen R-waarde en RFI dui daarop dat stadiger genetiese vordering verkry sal word in R-waarde deur direkte seleksie vir beide FeR en RFI as wat verkry sal word deur die direkte seleksie vir R-waarde. Wanneer 'n R-waarde egter nie bereken kan word nie of waar dit nie moontlik is om direk vir R-waarde te selekteer nie, sal vinniger genetiese vordering in R-waarde gemaak word deur die direkte seleksie vir FeR as vir RF!. 'n Regressievergelyking is geformuleer (met 'n R2 van 0.82) om vir alle prestasiegetoetsde bulle, waar bulle in 'n groep gevoer is en individuele voerinnames onbekend is, 'n voerinnamewaarde te voorspel. Hierdie voorspelde voerinnames maak dit moontlik om vir elke prestasiegetoetsde bul ,'n naspeengroei- of voerkraalwinswaarde (R-waarde) te bereken, al is hulle individuele voerinnames onbekend. Vervolgens is drie verskillende R-waarde vir vleisproduksie vir elke bul bereken naamlik, in 'n gunstige ekonomiese omgewing (FEE), 'n gemiddelde ekonomiese omgewing (AEE) en 'n ongunstige ekonomiese omgewing (VEE). Die hoë Pearson en Spearman korrelasies tussen die EBVs vir R-waarde, bereken in die AEE en die EBVs in die ander twee ekonomiese omgewings, dui daarop dat die AEE gebruik kan word om EBVs vir naspeense groeiof voerkraalwins te bereken. Dit is dus nie nodig om op 'n gereelde grondslag die karkasprys, lewendige speenkalfprys of die voerprys te verander nie. Oorerflikhede, vanuit die meereienskap ontledings VIr lW, FW, FI en SHD verkry, was onderskeidelik 0.41, 0.40, 0.33 en 0.51. Die hoogste genetiese korrelasies tussen die eienskappe was 0.78 tussen lW en FW en 0.70 tussen FI en FW. GTV indekswaardes varieer tussen -Rl92.17 en R231.38 met 'n gemiddelde waarde van R9.31. Die Pearson korrelasies tussen die EBVs van produksie- en doeltreffenheidseienskappe en GTV het tussen -0.51 en 0.68 gevarieer. Die korrelasie naaste aan zero, van 0.26, was die korrelasie tussen GTV en die Kleiber-verhouding. Die korrelasies tussen GTV en ADG, en GTV en FeR was onderskeidelik 0.68 en -0.51. Die oorerflikhede van die verskillende eienskappe wat in die seleksie-indeks ingesluit is, dui daarop dat die indekswaarde weloorerflik is en dat seleksie hiervoor wel moontlik is. Hierdie indekswaarde kan deur die voerkraaiindustrie gebruik word om nageslag van diere met hoë GTV waardes te selekteer om sodoende maksimum wins uit die voerkraai te genereer. Die Pearson en Spearman korrelasies tussen R-waarde EBVs en die indekswaardes (GTV) was besonder hoog (0.97). Hierdie hoë korrelasie dui daarop dat dit geen verskil sal maak watter een van die twee metodes gebruik word in die berekeninge van 'n naspeense groei- of voerkraalwinswaarde nie. Die seleksie-indeks metode is egter minder gekompliseerd met minder aannames as in die geval van die rekeningkundige fenotipiese benadering (R-waarde). As gevolg hiervan, word die naspeense seleksie-indeks waardes (GTV) aanbeveel om te gebruik as 'n teeldoelwit in telingsprogramme om naspeense groei- of voerkraaiwins geneties te verbeter, eerder as die meer gekompliseerde fenotipiese voerkraaiwins (R-waardes) metode.