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Estimation of national cost share structures in beef finishing
Project
Project code: TI-BW-08-PID1535
Contract period: 01.10.2010
- 30.04.2011
Purpose of research: Inventory & Assessment
Is there an opportunity to estimate national cost share structures in beef finishing? Production costs for beef finishing are determined by several components. Their influence can vary a lot between the different countries. However it is difficult to find consistent data between different countries to be able to compare the costs for beef finishing. Nevertheless these data are the basis for example Organisation for Economic Co-operation and Development (OECD) market models. Information about cost structures is helpful for the calibration and operation of aggregated trade models. Further, it allows to assess to which extent different countries and production systems are affected by changes in price relations. For this purpose, cost structures should represent the majority of the farms in the countries considered. The objective of the project is the estimation of national cost share structures of beef finishing farms in selected countries of the agri benchmark Beef and Sheep Network. To examine the feasibility of national cost share structures from agri benchmark data an exploratory approach of generalising the typical farms results for key selected countries (Australia, Brazil, Germany and the US) is used. However, the intent is not to arrive at representative costs in a statistical sense. In principle, agri benchmark data provides a reliable source for estimating national cost share structures based on product specific production system data. All key inputs - be it direct inputs, machinery, labour and buildings - are measured in physical and monetary terms. However, existing agri benchmark data for typical farms is meant to represent the majority of production (systems) for a particularly region of a country. Countries consist of a large number of different production regions and as such, the number of typical farms does not necessarily reflect this diversity. Therefore, it is suggested that agri benchmark data, without any refinement, can usually not be used to develop national averages. The key issue for developing national averages is to put existing agri benchmark data into perspective with regard to the entire farm population and other regions, which are not covered by typical agri benchmark farms. Data used are from the following sources: 1. All countries: agri benchmark typical farm and production system data. 2. Australia: Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) and Department of Agriculture, Fisheries and Forestry (DAFF) data. 3. Brazil: Instituto Brasileiro de Geografia e Estatística (IBGE) and Centro de Estudios avancados em Economia aplicada (CEPEA - ESALQ/USP) data. 4. Germany: Statistisches Bundesamt (DESTATIS) data. 5. USA: National Agricultural Statistics Service (NASS)/USDA and Iowa, Nebraska and Kansas State University feedlot budget data. The following general approach was taken: a) All farms are classified into one of the following four production systems: pasture; silage; feedlot; and cut & carry. Criteria for classification are dry matter feed content, extent of purchase feed and housing system. b) The origin and the age of the feeder cattle (dairy calves, weaners, backgrounders) entering the finishing process have an important impact on the proportion of animal purchases in total cost. c) The various combinations of production system and animal origin were examined. Each of these combinations can be characterised by distinct cost compositions. d) A key result is that throughout all farms, animal purchase costs and feed related costs are the two most important cost components.
Section overview
Subjects
- Animal Husbandry
- Business administration
- Computer science
