WO2002063424A2 - Procede de prevision de prix et d'autres attributs de produits agricoles - Google Patents

Procede de prevision de prix et d'autres attributs de produits agricoles Download PDF

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WO2002063424A2
WO2002063424A2 PCT/US2002/002739 US0202739W WO02063424A2 WO 2002063424 A2 WO2002063424 A2 WO 2002063424A2 US 0202739 W US0202739 W US 0202739W WO 02063424 A2 WO02063424 A2 WO 02063424A2
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commodities
amount
processed
regions
primary
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PCT/US2002/002739
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WO2002063424A3 (fr
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Thomas L. Cox
Jean-Paul Chavas
Zhu Yong
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Wisconsin Alumni Research Foundation
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Priority to AU2002237992A priority Critical patent/AU2002237992A1/en
Publication of WO2002063424A2 publication Critical patent/WO2002063424A2/fr
Publication of WO2002063424A3 publication Critical patent/WO2002063424A3/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising

Definitions

  • This invention relates to a method and system for forecasting prices and amounts of production, consumption and inter-regional trade of agricultural commodities across types of commodities (primary, intermediate, processed), over time and under varying trade and domestic policy scenarios.
  • the market in such agricultural commodities has become component- based. That is, trade in the constituent intermediate component commodities, in addition to the primary and processed commodities themselves, is fueling the economies of many industries.
  • the method and system of the present invention is particularly useful for (but not limited to) the dairy industry in which advances in reconstitution technologies have enabled the increased use of and trade in intermediate commodities.
  • the component based marketing and procurement strategy implications of dairy product development and markets are driven by the constituent components of milk [milk fat (and fat fractionations), protein (casein, whey protein, and other protein fractionations), and lactose (and lactose fractionations)], rather than by milk itself, the primary commodity.
  • milk fat (and fat fractionations), protein (casein, whey protein, and other protein fractionations), and lactose (and lactose fractionations) rather than by milk itself, the primary commodity.
  • milk fat and fat fractionations
  • protein casein, whey protein, and other protein fractionations
  • lactose and lactose fractionations
  • dairy based component ingredients currently in demand include the intermediate commodities of whey proteins, lactose and milk protein concentrate fractionations of milk.
  • the present invention addresses this need by providing a method and system for forecasting a price, an amount of consumption, an amount of production and an amount of trade flow of a plurality of dairy primary, intermediate and processed commodities inter-regionally, so as to enable an optimal allocation of industrial or technological resources employed in the production, consumption or trading of the dairy commodities based on the forecasted values for the commodity prices and amounts of consumption, production and trade flow.
  • the present invention thus solves the technical problem of accounting for the effects of intermediate commodities and reconstitution technologies on inter-regional trade. It furthermore enables said solution under variable policy scenarios.
  • an industry dealing at any level of commodity production, use or trade is thereby able to gain the information required to manage for minimal costs and maximal profits.
  • Industries may also manage private stocks of commodities to minimize their economic risks in futures markets depending on forecasted price and availability trends as well as potential changes to trade and domestic policies. They may furthermore use forecasted trends data to increase the functionality of their commodity products and/or open new markets for their final or intermediate commodities.
  • the present invention is directed to a method and system for forecasting the price and amounts of consumption, production and trade flow of primary, intermediate and processed agricultural commodities, so as to enable their use to optimally allocate industrial or technological resources employed in the production, consumption or trading of the commodities based on the forecasted values for the commodity prices and amounts of consumption, production and trade flow.
  • the method for forecasting a price, an amount of consumption, an amount of production and an amount of trade flow of a plurality of primary, intermediate and processed agricultural commodities comprises (a) creating an inputs database comprising inputting to the inputs database a definition of a plurality of regions, a definition of an at least one forecast scenario in the regions comprising a set of trade policies on the amount of the primary and processed commodities exported and imported between the regions, and a set of domestic policies, one of the forecast scenarios being a base forecast scenario in which the set of trade policies and the set of domestic policies are set to recent values, and inputting a plurality of data from an at least one source database, the data comprising the actual price and amounts of consumption, production and trade flow in the regions over a plurality of recent years, said trade flow comprising an amount of imports and an amount of exports of the primary, intermediate and processed commodities; (b) refining a multi-component spatial equilibrium function approximating an inter-regional market in the primary and processed commodities; (c) forecasting the price
  • creating the database further comprises values for a cost of transporting and marketing the primary, intermediate and processed commodities, and a cost of processing the primary and intermediate commodities into the processed commodities.
  • the method utilizing the function 220 further comprises solving for an optimal amount of intermediate commodities consumed in the making of the final processed commodities by region under an assumption of optimal use, given the forecasted amount of final processed commodities produced and an amount of intermediate commodities available for consumption in each of the regions.
  • the agricultural commodities are dairy commodities.
  • an apparatus is provided having means for performing one or more of the processes described above.
  • a program storage device readable by a machine, tangibly embodying a program of instructions executable by the machine is provided to perform one or more of the processes described above.
  • an article having a computer-usable medium has computer-readable program code embodied in the medium for performing one or more of the processes described above.
  • a computer program product is provided to perform one or more of the processes described above.
  • a management and procurement tool is provided to enable managers in industries that utilize agricultural commodities to generate accurate price forecasts for commodities with constituent components, and to furthermore utilize information regarding optimal component mixtures to further refine those forecasts, so as to enable the minimizing of an industry's market risk and the maximizing of profits by informing their decisions under variable forecast scenarios, in regard to commodity procurement strategies, investments in the regional markets for said commodities, the management of stocks of commodities, futures contracting, and the like.
  • This summary is not meant to be exhaustive. Further features, aspects, and advantages of the present invention will become better understood with reference to the following description, accompanying drawings and appended claims.
  • the invention is described in its application to dairy commodities, it may also be applied to other types of agricultural commodities where the commodities are likewise comprised of constituent components.
  • Fig. 1 shows a flow chart depicting a version of the methodology for forecasting attributes of agricultural commodities
  • Fig. 2 shows the flow chart of Fig. 1 in part with the creating a database and refining the function steps expanded;
  • Fig. 3a shows a flow chart detailing the forecasting step with inclusion of an optimization step;
  • Fig. 3b shows Fig. 3a with representative data inputs and outputs included
  • Fig. 4a shows a version of the equilibrium function ("basic function" 210);
  • Fig. 4b shows a version of the equilibrium function including intermediate commodities ("intermediates function" 220);
  • Fig. 5a shows a general processing flow chart of dairy primary and processed commodities
  • Fig. 5b shows a general processing flow chart of dairy primary, intermediate and processed commodities, accounting for reconstitution technologies
  • Fig. 6a shows a flow diagram of the allocation process of primary and processed commodities among regions
  • Fig. 6b shows a flow diagram of the allocation process of primary, intermediate and processed commodities among regions
  • Fig. 7 shows a sample of an annualized forecast of whole milk powder price over a period of 5 years, including validations, when a version of the methodology of the present invention is applied to a world dairy market;
  • Fig. 8 shows a flow chart of decisions representative of those typically made by a cheese maker to optimize a casein to fat ratio in a cheese vat mixture
  • Fig. 9a and 9b show a sample cheese optimization subroutine detailing its function and constraints
  • Fig. 10 shows results from an application of a version of the methodology of the present invention to a U.S. dairy market, including commodity prices and their variation under a variety of different forecast scenarios;
  • Fig. 11 shows results from an application of a version of the methodology of the present invention to a U.S. dairy market, including regional variations in consumer and producer surplus amounts under different forecast scenarios;
  • Fig. 12 shows the general system of the present invention.
  • the method and system of the present invention may be applied to analyze dairy markets at a variety of levels including global, regional and national levels, as described below.
  • the method may, however, also be applied to several different agricultural sectors of commodities made from constituent components in addition to dairy, such as grains, meats, oil seeds and the like.
  • the solving for optimal utilization of intermediate commodities step may employ optimization subroutines developed for a variety of processing areas in addition to the cheese making process as described below, such as the process of making soft and frozen dairy products, breads from grain components, sausages from meat components or the like.
  • the present invention comprises a method and system for accurately forecasting a price and other attributes such as production, consumption and trade flow levels of agricultural commodities comprised of constituent components over geographic regions, under various trade forecast scenarios and on at least an annual basis.
  • the method generally involves the steps of creating a database of agricultural sector data 100, approximating the market by a function that simulates the multi-component spatial equilibrium state of the agricultural market 200, refining the function with trade parameters set to current or recent trade conditions (base forecast scenario) 250, and forecasting the agricultural commodity prices and other attributes by running the function under a specified forecast scenario 300 (see Fig. 2 for an expansion of some of these steps).
  • the forecasting step 300 may be accomplished using a basic version of the function 210 or an intermediates version 220.
  • the forecasting step 300 may optionally also comprise an optimization step 350 during which knowledge of how the intermediate components are optimally processed to make final reprocessed commodities may be used to further refine the forecasts (see Fig. 3a and 3b).
  • the function comprises a multi-component (a.k.a. hedonic) spatial equilibrium model of the market of the dairy market with vertical linlcages among production stages.
  • the function provides a representation of a competitive market equilibrium both across commodities and over space (regions). It extends the Samuelson-Takayama- Judge (STJ) approach to spatial market equilibrium [see Samuelson, P.A., Spatial Price Equilibrium and Linear Programming, Amer. Econ. Rev 42 (June 1952): 283-303; Takayama, Y. and G.G. Judge, Spatial and Temporal Price and Allocation Models. Amsterdam: North Holland, 1971 at pp.
  • STJ Samuelson-Takayama- Judge
  • the equilibrium function in its basic version (“basic function" 210, Fig. 4a) accounts for trade in primary and processed dairy commodities across all regions and allows for the definition of forecast scenarios comprising trade and domestic policies (see sec. 1-a, Figs. 5a and 6a). Building on the basic version of the equilibrium function, a further sophistication was added to specifically account for intermediate commodities as well (see sec. 1-b, Figs. 5b and 6b).
  • the resulting intermediates version of the function (“intermediates function" 220, Fig. 4b) thus enables greater accuracy in the forecasts because it accounts for the effects of reconstitution technologies on the dairy market.
  • the basic function 210 models competitive spatial resource allocation among I regions and considers vertical markets.
  • Resources consist of primary commodities and processed commodities which can all be traded in markets assumed to be competitive.
  • primary commodities farm milk from cow, buffalo, camel, sheep, goat
  • processed commodities cheeses, butters, whole milk powders, skim milk powders, dry wheys, caseins, evaporated milks, condensed milks, and other dairy commodities
  • the linkages between primary and processed commodities are the milk components (milk fats, caseins, whey proteins, other non-fat solids and further fractionations thereof) that are "rearranged" by dairy processing plants.
  • the primary commodities are not consumer goods, they are exclusively used as inputs in the production of the processed commodities that are consumer goods.
  • Each region may be: 1) a producer of the primary commodities; 2) a producer of the processed commodities; 3) a consumer of the processed commodities; or some combination of the three possibilities (see Fig. 6a).
  • Each region can trade both primary and processed commodities with any other region.
  • the present equilibrium function is used to analyze the corresponding competitive spatial market equilibrium.
  • the function represents a multi-component spatial market equilibrium model of resource allocation and trade over the I regions. Development of the basic function 210. The following sections are included to explain how the basic function 210 was developed and to explain how it works to model the multi- component spatial equilibrium state of the dairy market.
  • the primary commodities can be transformed into multiple processed dairy products (cheeses, butters, whole milk powders, skim milk powders, dry wheys, caseins, evaporated/condensed milks, and other dairy products)(see Fig. 5a).
  • processed dairy products cheeses, butters, whole milk powders, skim milk powders, dry wheys, caseins, evaporated/condensed milks, and other dairy products
  • the crucial linkages between primary and processed products are the milk components (milk fats, caseins, whey proteins, other non-fat solids and further fractionations thereof) that are "rearranged" by dairy processing plants.
  • the total amount of components found in processed products must come from the primary products. To the extent that each product has fixed composition, this means that the processing technology can be represented by a Leontief technology with respect to milk components.
  • the optimization problem (5) In the absence of government intervention (i.e., no tax/subsidy and no quota distortions), the optimization problem (5) generates a Pareto efficient resource allocation. It also generates a competitive market equilibrium where the Lagrange multipliers associated with constraints (4) are interpreted as market prices.
  • the tariff-rate quota policy is modeled by introducing two-tiered tariff restrictions.
  • the basic idea is to divide imports of a commodity into two parts: one is imported at the in-quota (lower) tariff rate; and the other is imported at the over-quota (higher) tariff rate. The sum of these two parts is then available either as consumption or as inputs for further processing.
  • Import quotas are always filled first at the lower in-quota rate before importing the commodity at higher over-quota tariff rates.
  • OQ denote over-quota import restrictions, and superscript s denote subsidized exports.
  • import quotas for each region are pooling quotas (i.e., not bilateral quotas)
  • the distorted market equilibrium can be expressed as the basic function 210 given above.
  • Domestic policies include governmental price support programs, production quotas and classified pricing.
  • Price supports can be modeled by introducing a government sector (funded by tax-payers) with a perfectly elastic demand at the price support level.
  • Milk production quotas are handily modeled by adding appropriate constraints to farm milk production and adjusting farm level milk prices (the marginal cost of production) as market milk prices minus milk quota rents. If over-quota taxes are not too prohibitive, then a two-tier pricing scheme is needed for modeling domestic production (/. e., using a within- and over-quota pricing scheme in a way similar to the two-tier pricing discussed above).
  • Classified pricing is modeled by introducing appropriate "price wedges" for the relevant products (e.g., fluid milk)[for disclosure of incorporation of price wedges into the function 210, see Thomas L. Cox and Jean Paul Chavas, An Interregional Analysis of Price Discrimination and Domestic Policy Reform in the U.S. Dairy Sector, 2001, 83(l)(Feb. 2001): 89-106, the disclosure of which is incorporated herein by reference].
  • a value, ⁇ i can be interpreted as the market price of y- in region i.
  • the price of commodity y, k in region i ( ⁇ j k ) equals its marginal cost (dG- dy- ), plus the price wedge Ri -
  • the price wedge R generates a departure from marginal cost pricing for yi k in region i.
  • a positive (negative) price wedge Rj contributes to an increase (decrease) in the price of commodity yi k in region i.
  • the equilibrium function 210 generates a distorted equilibrium under the price wedges Q and R.
  • these price wedges are quite flexible and can be associated with a variety of price distortions both among commodities and across regions and between different levels in the marketing chain.
  • the existence of positive (negative) R price wedges could reflect processor (or retailer) market power in selling (buying).
  • the Q price wedges could reflect the degree of market power of dairy farmers through cooperatives and/or the federal/state Milk Marketing Orders (MMOs).
  • MMOs federal/state Milk Marketing Orders
  • the absence or diminution of farm price wedges Q relative to the revenues generated by the R price wedges could then be interpreted as a relativelack of market power compared to up-market participants (processors and/or retailers) who keep the gains from price discrimination for themselves.
  • the basic function 210 thus may represent dairy markets under domestic and/or trade policies.
  • the first line in function 210 is similar to (5), but is then expanded in the subsequent lines to include classical trade distortions (within and over quota tariffs, export subsidies, and production and import quotas) which reflect the price distortions and quantity restrictions generated by government policies, and to include price wedges to reflect domestic policies (see Fig. 4a).
  • the parameters of the basic function 210 can be set to reflect only trade policies (as when the basic function 210 is used to model regions at in the world dairy sector), only domestic policies (as when the function 210 is used to model regions in a national dairy sector, such as that of the U.S.
  • Solving the basic function comprises maximizing the consumer and producer surplus net of all transaction costs.
  • the equilibrium function modeling the multi-component spatial equilibrium state of the market specifying primary, intermediate and processed commodities, trade and domestic policies (intermediate commodities version, "intermediates function " 220).
  • the intermediates function 220 is a version of the basic function 210 in which intermediate commodities have been incorporated.
  • the intermediates function 220 is given in Fig. 4b with modified and new notation defined (see sec. above on basic function 210 for definition of terms common to both).
  • Milk, or other commodity, reconstitution technology may be reflected in the basic function 210 with the inclusion of intermediate commodities as per the intermediates function 220 in Fig. 4b (see also Figs. 5b and 6b).
  • the intermediates function 220 enables the forecasting of the prices and other attributes of those intermediate commodities and, thereby, more accurate forecasting of the prices and other attributes of the dairy commodities.
  • Several categories of commodities can be used as intermediate dairy processing commodities (e.g., butters/butter oils, skim milk powders, whole milk powders, condensed and evaporated milks, caseins, dry wheys, milk protein concentrates and other commodities embodying fractionated milk components) that may be used in the production of other dairy commodities.
  • dairy processing commodities e.g., butters/butter oils, skim milk powders, whole milk powders, condensed and evaporated milks, caseins, dry wheys, milk protein concentrates and other commodities embodying fractionated milk components
  • cream may be considered an intermediate commodity as it can be further processed into butter, butter oil, ice cream, buttermilk and many other dairy commodities.
  • milk powders, milk fat commodities, and other dairy commodities are converted back into fluid milk for consumption or are used for making other dairy commodities.
  • Reconstitution technology is reflected in the intermediates function 220, by assuming there are two stages in the processing sector. First, the primary commodities are converted into intermediate commodities. At the second stage, some of the intermediate commodities are further processed into final reprocessed commodities. The remaining intermediate commodities and the reprocessed commodities compose the final consumption commodities (see Fig. 5b). Trade is possible following the first stage of processing.
  • Gi (XJ, Uj) be the cost (i.e., costs of other inputs except for dairy material inputs) of transforming the x; of primary commodities into Uj of intermediate commodities.
  • Gj(xi, Ui) can be written as g,(uj) plus component balance restrictions.
  • Hi (VVJ, yi ) be the transformation costs converting Wj of intermediate commodities into yj of final commodities, which can be written as hi(y ⁇ ) plus component balance restrictions.
  • ⁇ y be the shipment of intermediate commodities from the i th region to the j n region.
  • E be the matrix representing the nutrient composition of reconstituted commodities
  • Fj be the matrix representing the nutrient composition of intermediate commodities.
  • the basic function 210 with an intermediate commodity reprocessing stage is characterized by the intermediates function 220 (Fig. 4b) assuming that reprocessed commodities share the same trade policies as other commodities.
  • the intermediates function 220 extends the optimization problem of the basic function 210 by incorporating: 1) the cost of processing intermediate commodities into final commodities (hi(y ); 2) the shipments of intermediate commodities ( ⁇ y) under in-quota ( ⁇ y IQ ) and over quota ( ⁇ i j 0Q ) tariffs and export subsidies ( ⁇ y); 3) an expanded component balance incorporating the conversion of intermediate commodities into final commodities (Ej' yi ⁇ ⁇ Wj, noting that Bj' u-_ ⁇ Aj' x;, is equivalent to (2)); and 4) expanding the trade balance (vi - Wj + yi > ⁇ j ty, ⁇ j t j i > Zi), import quota ( ⁇ i ⁇ j (t y IQ + ⁇ ij IQ )
  • the multi-component spatial equilibrium state of the dairy sector may be approximated by either the basic function 210 or the intermediate function 220 depending on the needs of the user.
  • Employing the intermediates function 220 may give more accurate predictions because of its reflection of reconstitution technologies by incorporation of intermediate commodities.
  • 2. Creating an inputs database of dairy sector data 100. Generally, a tremendous amount of data is required to operationahze the multi-component spatial equilibrium function 220. As a result, a step in the methodology is compiling and conditioning a preliminary set of data (for a number of recent years) for use as the input to the equilibrium function 220.
  • 2-a Compiling and updating a preliminary database of dairy sector data 110.
  • Much of the information on the dairy commodity attributes of production, consumption and trade that is needed to perform the method of the present invention is available in raw form from public and/or private source databases 90.
  • These definitions are input by industry experts or analysts 95. These external data are then pre-processed and manipulated to produce a database of dairy sector data 190.
  • Industry experts, analysts or other users may define the regions over which trade in the commodities is to be analyzed. They may also define the base and other forecast scenario parameters for analysis.
  • a user of the methodology of the present invention may define what those regions are to be, given their particular interests. Data in regard to the dairy commodities can then be organized by those regions when input to the equilibrium function 220.
  • Example - world dairy sector the regions may comprise several countries each and span the globe in order to simulate and analyze the regional market equilibrium impacts of trade policies in the world dairy sector.
  • the regions i were determined to include the U.S., Canada, Mexico, China, India, Japan, Australia, New Zealand, western Europe, eastern Europe and the former Soviet Union (FSU).
  • the U.S. dairy sector may be analyzed (see Thomas L. Cox and Jean Paul Chavas, An Interregional Analysis of Price Discrimination and Domestic Policy Reform in the U.S. Dairy Sector, 2001, 83(l)(Feb. 2001): 89-106, at p. 96, the disclosure of which is incorporated herein by reference).
  • the regions i were determined to include 12 regions: Northeast, Appalachia, Florida, Southeast, Mideast, Upper-Midwest, Central, Southwest, Western, Northwest, Arizona and California.
  • the regions may be defined by other countries (e.g. Canada, Japan, etc.) or regions (e.g. European Union (E.U.); U.S. and Canada; U.S., Canada and E.U., etc.) according to the needs of the user.
  • Defining an at least one forecast scenario If changes to current trade policies are under consideration, one or more alternative forecast scenarios may be defined. In defining a forecast scenario, the trade parameters of the equilibrium function 220 may be changed accordingly. As was described under the approximating the equilibrium function step 200 above, various trade and domestic policy parameters are inco ⁇ orated into the function. Trade policy parameters include tariff (in-quota, over-quota) rates on imports of commodities and subsidies on exports. Domestic policy parameters include price wedges to reflect price discrimination or other domestic policies.
  • sources of dairy sector data for the world dairy sector might include publicly available sources like the Food and Agriculture Organization of the United Nations (F AO), the International Monetary Fund (IMF), the Organization for Economic Cooperation and Development (OECD) or private sources.
  • F AO Food and Agriculture Organization of the United Nations
  • IMF International Monetary Fund
  • OECD Organization for Economic Cooperation and Development
  • Data on the U.S. dairy sector may be obtained from the U.S. Department of Agriculture (USD A) and/or other sources including private sources.
  • Raw data tables are tables that include one or more fields that can be mathematically manipulated.
  • Raw data tables are used to store disaggregate raw data, e.g., by region (as defined above) and commodity.
  • Raw data tables may include those for production (milk and commodity), composition (milk and component), import quantity, import value, export quantity, export value, price, stock, exchange rate (in the case of multi-currency regions), GDP growth and the like.
  • grouping tables store information to define aggregation and sorting criteria for a specific field.
  • Grouping tables may include region, commodity category, region order, category order and the like.
  • Commodity categories may likewise vary with the particular application.
  • categories may include milk (that may include milk of cows, buffalos, goats, sheep, and camels); cheese (that may include all types of cheese and curd including fresh cheeses, such as cottage cheese); butter (that may include all milk fat commodities, consisting of butter, ghee, and butter oil); whole milk, skim milk and buttermilk powders; dry wheys, caseins and caseinates; condensed and evaporated milks; and dairy not included in the previous categories such as fluid milk, soft, and frozen commodities (see. Figs. 5a and 5b generally for additional categories).
  • the preliminary database (updated with new data as and when it becomes available) contains demand and supply data for dairy commodities organized by regions.
  • the database may contain annual production data (generally recorded in metric tons) for the selected commodities.
  • Trade data in the database generally include those for all commodities and for consistency are generally given in metric tons for quantities and 1,000 US dollars for costs.
  • Price data in the database may be included for certain of the commodities and recorded in local currency units per metric ton (if regions span different currencies).
  • the database 190 also may include official exchange rate data that are used to convert price data from local currencies into U.S. dollars.
  • the preliminary database also may contain stock data that are generally available in aggregated form. For example, rather than data for different types of cheese, only ending stock data for cheese as a whole may be available. There are generally five commodity categories having stock data: cheese, butter, whole milk powder, skim milk powder, and casein. If only annual stock " change data rather than ending stocks are available for a region, it is converted into ending stock by arbitrarily adding starting stock data for the first year. Since in the majority of studies only stock changes are of interest, this "conversion" should not affect data accuracy. To estimate the trends in demand and supply changes the database also may include real GDP growth rate data. Real GDP growth includes both the population growth and GDP per capita change, and is adjusted for inflation.
  • Trade policy and milk component data are generally not stored in Access because they are in rather aggregated forms and involve many calculations. These data may be stored in a variety of Excel (or other spreadsheet) files instead.
  • Example 2 in which the equilibrium function 210 is applied to the world dairy sector, country level data are required and need tremendous manipulation and processing to obtain regional level computer input data (regions being defined as groups of countries).
  • the compiled database tables are queried to retrieve information of whatever sort is needed by the equilibrium function 220, and/or further calculations are made to derive new information from the data.
  • regional level data and other calculated data are prepared for input to the equilibrium function 220 run under current or recent trade conditions (the base forecast scenario).
  • Queries may be constructed to retrieve information for regional milk production, milk price and milk composition, for example. Standardization and/or reconstitution parameters may also be derived. For example, the degree of intermediate dairy commodities (skim and whole milk powder, evaporated/condensed milks, dry whey protein concentrates, butter/anhydrous milk fat) and usage to make the final demand dairy commodities (cheese and residual category commodities such as fluid milk, frozen and soft commodities) may be calculated by region. Any number of additional queries are possible limited only by the imagination and requirements of the user. The results of the queries may also be exported in spreadsheet format, if desired.
  • Standardization and/or reconstitution parameters may also be derived. For example, the degree of intermediate dairy commodities (skim and whole milk powder, evaporated/condensed milks, dry whey protein concentrates, butter/anhydrous milk fat) and usage to make the final demand dairy commodities (cheese and residual category commodities such as fluid milk, frozen and soft commodities) may be calculated by region. Any number of additional queries are possible limited only by the imagination and requirements of
  • Various calculations are also performed to determine other values stored in the database for use by the equilibrium function 220.
  • Another calculation is performed to increase the accuracy of FAO data on production and prices, if FAO data is used.
  • a three-year average may be calculated for any given year's data (e.g. 1999-2001 data averaged to give year 2000 value). In this way the more recent year data of the older database are updated using current year data.
  • commercial sources can be used to obtain more detailed and country-to-country specific transportation costs.
  • Distance data may be derived from Defense Mapping Agency data or other sources. 2-c. Forecasting updated supply/demand trends and exchange rates 130.
  • Na ⁇ ve supply and demand trends are updated by choosing compound growth rates (by commodity and by region) to minimize forecast error over the 5 years prior to and including the current base forecast scenario year data ("base data").
  • Annual quantity forecasts are generated from base data using compound growth rates for each commodity and region.
  • Prices are adjusted to quantity forecasts by subtracting price changes/demand (supply) elasticity from the forecast demand (supply) changes.
  • the GDP and population projections are used with income elasticities to forecast demand for commodity /region.
  • the equilibrium function 220 is run under the base forecast scenario to generate linear regional supply and demand curves using regional supply and demand elasticities (e.g., that may be derived from USDA SWOPSIM data; see, Roningen, V., J. Sullivan, and P. Dixit, 1991, Documentation of the Static World Policy Simulation (SWOPSIM) Modeling Framework, Staff Report No. AGES 9151, Washington, D.C: USDA/ERS) and base level prices and quantities.
  • Regional income elasticity data may be derived from USDA SWOPSIM or other sources data for major countries when the world dairy sector is being considered, and may be computed for other countries assuming that countries having similar development status have similar demand characteristics.
  • the result of these steps in creating a database 100 is to transform the files of dairy sector information stored in the inputs database 190 to accurately reflect the current economic trade conditions and to be usable by the equilibrium function 220.
  • the equilibrium function 220 run under the base forecast scenario, is specified to provide an accurate representation of the dairy market(s) and reflects recent trade conditions in the regions as specified.
  • the resulting inputs database of dairy (or other agricultural) sector data 190 includes data for use in the refining the function 250 and forecasting 300 steps of the method.
  • the inputs database 190 provides data for use in solving the equilibrium function 220 in the forecasting step 300 that generally comprise: a) farm milk prices, production, use, consumption data; b) aggregate commodity supply/demand trend and price data; c) commodity production by region; d) commodity consumption by region; e) domestic and trade policy assumptions; f) transportation distances and costs; and, g) supply and demand elasticities by region.
  • the equilibrium function 220 is run under the base forecast scenario to generate a preliminary set of dairy (or other agricultural) sector forecasts.
  • This price calibration procedure is to search for the values for those unknowns that are consistent with the equilibrium function 220 specifications, equilibrium conditions and the parameters based on data that are available. This involves solving the equilibrium function 220 a number of times with the calibrated data updated in each run.
  • the procedure can be divided into the following steps. Step one: "guess" the values of the unknown manufacturing and other cost parameters as the starting values and solve the model. Step two: compare the equilibrium function 220 solutions with the data, which include the original "guessed” data. Adjust those "guessed” data/parameters in the direction that will potentially reduce the deviation of equilibrium function 220 solutions from the data, and solve the function again. Step three: repeat step two until no further significant changes are needed to alter the function solution.
  • the goal of calibration via updating manufacturing costs is to replicate the data for regional milk price and production data by choosing region-specific adjustments on processing costs. Using the procedure described above we obtain region-specific price calibration wedges that make the regional milk prices in the equilibrium function 220 solution the same as (or sufficiently close to) the observed price data.
  • calibrating the milk price in this manner is equivalent to calibrating regional milk production because the calibration procedure is to move the equilibrium points along the fixed supply curves.
  • the position of the associated regional demand curves is adjusted to the points that are relatively consistent with milk supply curves and other demand curves, on which good information is available.
  • the regional demand curves are then reset with the updated prices by re-computing prices intercepts and slopes under standard formulas using assumed demand elasticities, base forecast scenario quantity and calibrated price data. After sufficient iteration of the calibration process, base data is replaced with the current equilibrium function 220 solutions for prices.
  • the base forecast scenario is run to forecast annually to 2000 using only information available in 1995.
  • Naive supply/demand shifters based on 1989-1994 data and annual exchange rate forecasts are employed.
  • the resulting annual forecasts are then compared with actual annual data from 1996, 1997, 1998, and 1999 for farm prices, milk and commodity production, trade, etc.
  • the accuracy of the equilibrium function 220 can then be assessed and the function's assumptions (e.g., supply/demand trends) refined accordingly.
  • the focus is on near-term assumptions as these will affect the accuracy of the shorter-term forecasts.
  • Some of the equilibrium function 220 parameters refined by this process include (a) domestic (regional) policy parameters (e.g. intervention prices, production/ consumption subsidies, quota rents, fluid/manufacturing milk price wedges), (b) trade policy parameters [e.g.
  • GATT commitments for world dairy sector version import quotas, two-tiered import tariffs (within and over quota), export subsidies (quantity and expenditure)], and (c) standardization/reconstitution parameters [e.g., the degree of intermediate dairy commodities usage (skim and whole milk powder, evaporated/ condensed milks, dry whey protein concentrates, butter/anhydrous milk fat) to make final demand dairy commodities (e.g., cheese and residual category (fluid milk, frozen and soft commodities) by region].
  • the equilibrium function 220 is run again (as in step 256) with the refined parameters and the validation step 258 repeated until the function's solutions conform acceptably to the actual data.
  • the equilibrium function 220 is deemed to be refined sufficiently for its forecasts to be accurate. 3-f. Updating base equilibrium function 262. Once the equilibrium function 220 is validated, its parameters are updated to reflect those values that resulted in valid forecasting by the function. The refined equilibrium function 220 is now ready for use in forecasting.
  • the equilibrium function 220 may produce variable number of years worth of annual (or semi-annual) forecasts that can be updated periodically as new data are acquired. These forecast data are stored in a results database 390 (see Fig. 3b for list of representative types of forecast results). Under base forecast scenario. Running the refined equilibrium.function 220 under the base forecast scenario yields forecasted optimal regional values for dairy commodity prices, production and consumption levels, trade flows, and implicit component prices (e.g., fat, casein, whey protein, lactose and other components).
  • implicit component prices e.g., fat, casein, whey protein, lactose and other components.
  • the base set of annualized forecasts may include farm level prices and production; commodity prices, production and consumption by commodity and region; imports and exports by commodity and by region; commodity trade flows by commodity and by region; and producer and consumer su ⁇ lus (welfare), net costs to treasury (tariff revenues minus export subsidy and intervention price expenditures), and effects of price wedges on market prices or the like.
  • the forecast scenario parameters of equilibrium function 220 may be changed to reflect another forecast scenario (as defined above), the function re-run under the new forecast scenario and results compared to those for the base forecast scenario to determine how the price and other attributes of dairy commodities and their constituent components will vary under the alternative trade scenario.
  • Knowledge of how the dairy sector will react to changes in trade policies enables dairy industry management to inform decisions regarding how to plan for those changes and minimize risk to their industry.
  • the equilibrium function 220 may be run under any number of forecast scenarios according to the needs of the user.
  • the resulting database 390 of forecasted values under specified forecast scenarios may then be used in industry management to inform decisions regarding dairy commodity procurement strategies, investments in the regional markets, the management of stocks of commodities, futures contracting, and the like.
  • the results database 390 may be queried by a user to provide specified presentations of the results and/or further analyses 370. For example, results may be used in management and procurement decisions. They may be queried to create versions of the results data customized to the needs of the user.
  • the methodology of the present invention may optionally further include a step in which an optimal amount of intermediate commodities consumed to make one or more of the processed commodities is calculated 350 (see Figs. 3a and 3b).
  • the optimal mixture of intermediate commodities consumed in the making of a processed commodity is one that minimizes the cost of making a processed commodity given the local availability and pricing of intermediate commodities for use in this process.
  • Step 350 employs these already existing optimization subroutines in a new way by inco ⁇ orating them into the spatial equilibrium state approximated by the intermediates version of the function 220. Doing so enables the further refinement of forecasts of price and other attributes of dairy commodities by working in concert with the intermediates function 220. Essentially, this step solves for the mixture of intermediate commodities that would have been used to make a forecasted amount of a processed commodity made in a given region i (an amount forecasted in the prior step of forecasting by solving the intermediates function 220), if the processed commodities were made in an optimal cost-minimizing way.
  • Several optimization subroutines are currently available that enable this calculation for certain kinds of processed commodities (e.g., cheeses, soft and frozen commodities). Referring to Figs.
  • the amounts of intermediate commodities and components consumed in the "optimal" making of the forecasted amount of processed commodities are calculated by running the optimization subroutine 350.
  • These new optimal amounts of intermediate commodities and components consumed in regions i can then be used to modify the data input to the intermediates function 220 and the function re-run.
  • Several iterations of • solving the function 220 and optimization subroutine 350 may occur until the difference in forecasts between iterations approaches 0.
  • Example - optimizing the making of cheese Although the example given applies to the production of cheese, other optimization subroutines may be employed that are applicable to various other types of processed commodities and may also be employed in the present methodology.
  • optimization subroutines may obtain ordinary input of standardizing agent prices, using them in conjunction with the multi-component spatial equilibrium intermediates function 220 may expand the potential of the present methodology to even more accurately forecast attributes of dairy commodities.
  • the optional optimization step 350 allows an optimization subroutine to utilize the component price forecasts from the solving of function 220, and generate values for the optimal amounts of intermediates and components consumed in the making of the amount of cheese produced (see Fig. 3b for examples of data inputs and outputs using a cheese optimization subroutine).
  • a cheese making optimization subroutine is included here by way of example (see Figs. 9a and 9b).
  • the optimization subroutine shown below includes the cheese-specific variable cost associated with processing a pound of cheese milk, unlike the cheese maker's ordinary objective function. This enables a cheese maker to capture the gains from a higher yield.
  • the objective function of the optimization subroutine is:
  • v is inte ⁇ reted as a cheese-specific variable cost associated with processing a pound of cheese milk. Without this cost, the cheese maker simply makes his cheese from the cheapest components available, without considering the yield implications. This cost was incorporated into the objective function in order to capture the gains from a higher yield to the cheese maker.
  • the cheese making process takes place in a vat (or vats) of fixed capacity, and increasing the yield means the same amount of cheese can be produced from less cheese milk, requiring less use of the vat or producing more cheese from the same vat constraints. Since the output level q, is fixed, reducing the amount in cost of inputs used to make that output increases the yield and/or decreases the cost.
  • the reader may refer to Fig. 9b for constraint equations and parameter definitions.
  • Cheese-milk constraints include a constraint on the lactose level in the vat, the amount of solids in the vat, constraints on the fat and casein levels in the vat, and a vat capacity constraint. The amount of solids is the second crucial constraint in understanding the cheese making process (see mass balance constraints.
  • the amount of fat and casein levels in the vat are the only constraints in this problem because the cheese production is fixed. Ordinarily, these levels are implicitly chosen by the cheese maker when he decides how much cheese to make. Since the amount of cheese to make is already determined in the model, the fat and casein levels become equality constraints. It is in satisfying these two constraints that the cheese maker produces the desired amounts of cheese.
  • the production constraints force the optimization model to conform to observed industry-wide utilization levels of intermediate commodities or standardizing agents for the years analyzed. Production levels enter the model as maximums for milk, standardizing agents, and cream.
  • the representative profit- maximizing cheese maker cannot use more milk than is produced for the dairy industry in a given year, for instance, but he is not forced to use all the milk if he can do it with less or by substituting standardizing agents within legal and technical processing constraints.
  • Whey-stream production levels are the minimum amounts of these commodities that the cheese maker must produce. Since these only come from the cheese making process, the cheese maker must have at least enough components in the whey to satisfy the production of these commodities.
  • the mass balance constraints ensure that there are enough components in the system to produce all of the outputs. Furthermore, they are the only restrictions on whey-stream utilization. Whey commodities are "produced" by subtracting them from the components in the whey stream.
  • the cheese optimization subroutine uses these inputs 340 to generate outputs 360 of optimal intermediate commodity and component utilization (consumption) data by region. These outputs 360 are then used to update same in the inputs to the forecasting step 300, and the forecasting 300 and optimizing 350 steps iterated until the differences between consecutive forecasts approach 0. Similarly, other optimization subroutines may be employed at step 350 as a further means of refining the forecasted results. 6. Using the forecast results. Once the forecasts are stored in the results database 390, they may be used in deciding how to optimally allocate industrial or technological resources employed in the production, consumption or trading of the commodities.
  • the results database 390 may be queried by an end user 392 who can request specific information from the system through the query 394 and thereby produce customized output 370 (Figs. 1 and 12).
  • a user may create queries to customize output 370 according to the user's particular needs. For example, output may be directed to only certain types of commodities in certain regions. Or the results may be presented in a variety of formats useful to the user such as graphs, spreadsheets, maps, HTML documents, or other formats. Because of the regional geographic nature of the output, it may be suited to a geographic presentation using mapping software. Any number of queries 394 may be formulated to fulfill a user's needs for forecasts of a certain type or in a certain form for using to make management and/or procurement decisions.
  • the output 370 is furthermore storable in other databases or deliverable through a variety of channels, including facsimile, e-mail, local area networks (LANs), wide area networks (WANs) and the worldwide web. It can also, of course, be provided in hard copy.
  • LANs local area networks
  • WANs wide area networks
  • the output 370 is furthermore storable in other databases or deliverable through a variety of channels, including facsimile, e-mail, local area networks (LANs), wide area networks (WANs) and the worldwide web. It can also, of course, be provided in hard copy.
  • Example 1 Method for Accurately Forecasting Prices of Dairy Commodities
  • the U.S. dairy sector has experienced several reforms in both government price support and classified pricing under state and federal MMOs.
  • Solving the equilibrium function 210 provided quantitative measures of the aggregate and regional impacts of alternative domestic deregulation forecast scenarios on dairy commodity prices, production, consumption and interregional trade flows in the U.S.
  • the equilibrium function 210 was adapted to the U.S. dairy sector and used to simulate the allocation of farm milk used in the production of nine dairy commodities in a way consistent with milk component balances for milk fat, protein, and carbohydrate both within and across regions (see determining the regions section above for listing of U.S. regions used).
  • the resulting function reflected the effect of a price discrimination forecast scenario on the market.
  • step one consider a classified pricing scheme represented by the price wedges k that increase the prices of commodity k in region i.
  • step two obtain some preliminary guess about the associated price wedges Qj n .
  • step three solve the function 210 given R k and Q n .
  • the above refined equilibrium function 210 was applied to the U.S. dairy industry, with farm milk as the (only) primary commodity, and nine processed commodities: (1) fluid milk,
  • the marginal effect of this restriction measures the shadow value of each component in each region. This has two attractive features. First, this generates empirical estimates of regional shadow prices for each component (milk fat, protein and carbohydrate). Second, for each commodity in each region, the equilibrium function 210 gives market prices that are consistent with component pricing.
  • the equilibrium function 210 solves for regional farm level prices and regional milk production as well as regional wholesale level price, supply, demand and trade flows for the nine dairy commodities.
  • Most production and price data were obtained from USDA sources.
  • regional projections of wholesale dairy commodity demand were obtained using aggregate U.S. wholesale demand functions and regional population data.
  • Component yields • i.e., the amount of milk fat, protein and carbohydrates per unit of milk and wholesale dairy commodity
  • 1995 exports and net government stocks/removals the equilibrium function 210 starts from a farm, wholesale and component supply/demand balance that characterized the U.S. dairy sector in 1995.
  • Results suggest current policies induce substantive aggregate and regional distortions in dairy commodity prices (and production, consumption) relative to an unregulated policy context.
  • commodity prices and their variation under the different forecast scenarios is presented in Fig. 10.
  • Regional variations in consumer and producer su ⁇ lus amounts under the different forecast scenarios are presented in Fig. 11.
  • Knowledge of how these trade scenarios do or will affect commodity prices can be used to enable dairy industry managers to minimize their industry's risk and to maximize profits by informing their decisions in regard to the management of their stocks, investments in the regional markets and the like.
  • Example 2 Method for Accurately Forecasting prices of Dairy Commodities and Components Thereof in the World Dairy Market.
  • An example of applying the method and system of the present invention to the world dairy market was disclosed in U.S. patent application serial number 09/775,946 filed 2001 February 2, entitled “Method for Forecasting the Effects of Trade Policies and Supply and Demand Conditions on the World Dairy Sector,” by inventors Thomas L. Cox, Jean-Paul Chavas and Yong Zhu, the disclosure of which is inco ⁇ orated herein by reference.
  • the equilibrium function 210 was applied to the world dairy sector divided into several multi-country regions throughout the world.
  • Five types of farm milk were considered (cow, buffalo, camel, sheep and goat) embodying several milk hedonic characteristics (fats, casein proteins, whey proteins, other nonfat solids (lactose, salts, other minerals and ash) and further fractionations thereof) that can be processed into eight types of dairy products (cheeses, butters, whole milk powders, skim milk powders, dry wheys, caseins, evaporated/condensed milks, and other dairy products). Regional designations were given above in the determining the regions step. Some of the results are presented in Fig. 7. The reader is referred to the referenced application for further details regarding the methodology, forecast scenarios investigated and results.
  • a general-pu ⁇ ose computer, its component devices, and software, provide means for implementing the method steps described above (Fig. 12).
  • the inputs database 190 is created by inputting, preprocessing, and further manipulating (according to steps 100-140), the external database 90 and industry expert or analyst 95 inputs to the system.
  • the inputs database software 510 resides on a program storage device 512 having a computer usable medium 414 for storing the program code.
  • the program storage device 512 may be of a conventional variety, such as a conventional disk or memory device.
  • the inputs database software 510 may be created using general-pmpose application development tools such as programming languages, graphical design tools, and commercially available reusable software components.
  • a general database engine may be used to manage inputs data storage and retrieval.
  • the processor 520 is part of a general-purpose computer system.
  • any general-pu ⁇ ose computer may be used, provided that the processing power is sufficient to achieve the desired speed of computation for the amount of inputs data being processed by the system.
  • the equilibrium function software 410 resides on a program storage device 412 having a computer usable medium 414 for storing the program code.
  • the program storage device 412 may be of a conventional variety, such as a conventional disk or memory device.
  • the equilibrium function software 410 may be created using general-purpose application development tools such as programming languages, graphical design tools, and commercially available reusable software components.
  • a general database engine may be used to manage data storage and retrieval.
  • the processor 420 is part of a general-pu ⁇ ose computer system. Any general-pu ⁇ ose computer may be used, provided that the processing power is sufficient to achieve the desired speed of computation for the amount of data being processed by the system.
  • the inputs database module 500 and equilibrium function module 400 may be provided separately as described above, they, and their component parts, may alternatively be combined. That is, the modules (400 and 500) may be provided as combined into a single module in which the respective software (410 and 510) is fully integrated and shares a single program storage device and data processor.
  • the results database 390 may be queried by an end user 392 who can request specific information from the system through a query 394 and thereby produce customized output 370.
  • the system accommodates postprocessing of the output data 370, allowing delivery in various formats and through various electronic media.
  • the system can generate output 370 in the form of further analyses and presentation as graphs, spreadsheets, maps, HTML documents, or other formats. Because of the regional geographic nature of the output, it may be suited to a geographic presentation using mapping software.
  • Queries 394 may be formulated to a user's specifications in order to create customized output to use in making management and/or procurement decisions.
  • the output 370 can be delivered electronically through a variety of channels, including facsimile, e- mail, local area networks (LANs), wide area networks (WANs) and the worldwide web. It can also, of course, be provided in hard copy.
  • the results database 390 itself, or customized output data 370 may be incorporated into an industry's information management system for intra-net online access (via a LAN or WAN) to enable industry-wide access to results such as annual forecast data, assumptions of the function 220, current forecast results (e.g. for production, consumption, stocks, imports, exports, and prices of commodities) by type of commodity and by region or country.
  • results such as annual forecast data, assumptions of the function 220, current forecast results (e.g. for production, consumption, stocks, imports, exports, and prices of commodities) by type of commodity and by region or country.
  • An industry's buyers may also supply inputs to the system 95 (e.g., regional GDP and/or commodity demand and milk supply growth rates by year and by region or country) via the intra-net information system.
  • the system of the present invention may be fully inco ⁇ orated into an industry's information system to provide a seamless interface to their current management and procurement decision-making structure.
  • a management tool is provided to enable agricultural industry managers, including dairy industry managers, to generate accurate price forecasts for commodities and constituent components, and to specify optimal component mixtures for use in the production of processed commodities, so as to minimize their industry's market risk and to maximize profits by informing their decisions under variable trade scenarios, in regard to commodity and constituent component procurement strategies, investments in the regional markets for said commodities, the management of stocks of commodities and components, futures contracting, and the like.
  • the present invention can be used as a method and system for forecasting a price, an amount of consumption, an amount of production and an amount of trade flow of a plurality of primary and processed agricultural commodities, so as to enable their use to optimally allocate industrial or technological resources employed in the production, consumption or trading of said agricultural commodities based on said forecasted values for the commodity prices and amounts of consumption, production and trade flow.

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Abstract

L'invention concerne un procédé et un système permettant la prévision de prix de produits agricoles ainsi que les quantités de ces produits consommées, produites et commercialisées à travers ces régions, pour toute une variété de scénarios d'offre et de demande, de politique commerciale et de politique domestique et sur au moins une période d'un an. Ce procédé fait appel à une fonction (220) spatiale à plusieurs composantes, permettant l'approximation d'un marché interrégional concernant des produits agricoles tels que des produits laitiers, et permet le réglage d'instruments de politique commerciale et de politique intérieure pour la prévision selon toute une variété de scénarios de prévision. Ladite fonction (220) permet également l'incorporation de produits intermédiaires, en plus de produits primaires et manufacturés, pour la prise en compte des effets de technologies de reconstitution sur les valeurs prévues. Le procédé comprend généralement les opérations suivantes: création d'une base de données d'entrées (190) comprenant une définition des régions et des scénarios de prévision, ainsi qu'une pluralité de données du secteur laitier couvrant un nombre d'années récentes, y compris des prix de produits et des quantités consommées, produites et commercialisées dans les régions; ajustement de la fonction (250); résolution de la fonction ajustée par maximisation du surplus des consommateurs et du surplus des producteurs déduction faite des coûts de transaction (300), pour générer les prévisions, et émission des prévisions vers une base de données de résultats (390). Ce procédé permet également de déterminer une quantité optimale de produits intermédiaires consommés dans la réalisation des produits finaux manufacturés par région, cette détermination étant fondée sur l'hypothèse d'une utilisation optimale pour encore ajuster les prévisions (350). Les données (370) émises par le système sous la forme de graphiques, de feuilles de calcul, de cartes, ou sous d'autres formes peuvent être délivrées électroniquement par l'intermédiaire de divers médias.
PCT/US2002/002739 2001-02-02 2002-02-01 Procede de prevision de prix et d'autres attributs de produits agricoles WO2002063424A2 (fr)

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US10/058,002 US6865542B2 (en) 2001-02-02 2002-01-29 Method and system for accurately forecasting prices and other attributes of agricultural commodities
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