USDA’s Economic Research Service and Use of Weather Data Ed Allen Cross Commodity Analyst for Field Crops Market and Trade Economics Division March 31,

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Presentation transcript:

USDA’s Economic Research Service and Use of Weather Data Ed Allen Cross Commodity Analyst for Field Crops Market and Trade Economics Division March 31, 2010

ERS mission The mission of ERS is to inform and enhance public and private decision- making on economic and policy issues related to agriculture, food, natural resources, and rural development.

ERS Functions Research Market analysis and forecasting Database development Short term policy analysis

Resource & Rural Economics Agricultural Structure & Productivity Farm & Rural House- hold Well-Being Farm & Rural Business Production Economics & Technology Resources, Environmental & Science Policy Markets & Trade Economics Food & Specialty Crops Agricultural Policy and Models Field Crops International Demand & Trade Animal Products ERS research programs areas Diet, Safety & Health Food Economics Food Assistance Food Markets Food Security & Development

Commodity Market Analysis at the Economic Research Service Purpose: Timely, reliable, and objective information is essential if a market economy is to operate efficiently Analyze and explain Current market situation Short term forecast of supply, demand and prices

ERS market analysis covers a wide range of commodities, countries, and topics Wheat Rice Corn and other feed crops Oil crops Cotton and wool Fruit and tree nuts Aquaculture Sugar and sweeteners Livestock, dairy & poultry Vegetables, fruits, tree nuts & specialties Agricultural Trade Reports—Europe, China, Brazil, India, Transition economies, etc. Food Security Assessment Agricultural income and finance

What makes a commodity market reporting program effective? Information needs to be timely and available to everyone Information must be regarded as objective Analysts need to become specialists Good commodity analysts are good economists The successful analyst understands the commodity market

Quality assurance Quality assurance is an essential part of an effective outlook program. The Department speaks with one voice Interagency committees are involved in all estimates and review of all market outlook publications released by USDA. World Agricultural Outlook Board approves all forecasts Forecasts must be free from political bias so political appointees do not dictate forecasts or conclusions.

How the short-term forecasting process works... USDA Interagency Commodity Estimates Committee Process Data: - International - Domestic Information: Commodity Forecasts Appear in: WASDE Newsletters Circulars Other Forecasts: - Farm Income - Food Prices - Trade Policy Decisions: - Attaché reports - Wire service stories --Short term --Long term

The supply and use table: the basic tool for analysis The supply and use table has three main components: SUPPLY USE PRICE Describes the marketing year outcome for a single commodity Summarizes market behavior of all buyers and sellers Organizes information about a crop Provides framework for analysis

Some words about forecasting Forecasting is an essential part of our analysis But forecasts have limits The basic tool is a model A way of organizing and elaborating the relationships Based on assumptions Forecasts can be wrong Mistaken assumptions Wrong information Poor model specification

Weather Is a Key Variable Production Yield fluctuates mostly with weather (but also economic variables like fertilizer use) Area planted and abandonment are often influenced by weather (i.e. freezes or floods) Domestic use and trade Occasionally influenced by weather

Some Uses of Weather Data Past ERS used very disaggregated daily temperature and precipitation data to model crop yields for the Risk Management Agency (crop insurance) In 1989, yield models for wheat, corn, soybeans, sorghum, barley, and oats, by state, using monthly average temperature and precipitation

Some Uses of Weather Data Present Corn yield model using weekly and monthly temperature and precipitation for June, July, and August in key corn belt states Soybean yields using monthly averages for top 19 states Rainfall in West Texas and California for cotton yields U.S. drought areas overlap with hay and beef cattle pasture Argentina’s precipitation data to compare soybean yields and drought Australian drought variable to forecast cotton yield

Some Uses of Weather Data Future Climate change using regional crop yield, pest distribution and water availability weather variables

An Example of Policy Oriented Research A project studying how conservation programs function as a means of drought adaptation uses Measures of drought such as the Standardized Precipitation Index and Palmer Modified Drought Index (station level from NOAA) County-level monthly precipitation and temperature (average min, max) data derived from PRISM Climate Group data. Long-run station-level precipitation data from the U.S. Historical Climate Network to estimate regional differences in drought risk.

An Example of Policy Oriented Research ERS’s biggest obstacle is getting weather data to the county level Much data on conservation program participation, crop yields, and other variables are at the county level Access to daily station-level data and interpolating it to the county level is needed measures of heating degree days, cooling degree days measures of exposure to precipitation events of different intensities.

Historical Data Used More than Weather Forecasts USDA commodity forecasts officially assume “normal” weather. Subjective evaluation based on weekly weather briefings is used to judge satellite imagery and anecdotal reports. Weather forecasts are sometimes used to “fill in” for a variable in a model. Weather forecasts can influence how aggressive the committee is at making a change.

Potential Collaboration Between CPC, FAS, WAOB, and ERS WAOB has Major World Crop Areas and Climatic Profiles (2006) on their website Mix of atlas, crop data, calendars, and weather data A web product could showcase key production/yield analysis. For example: Drilling down from a map (i.e. Brazil) to a more detailed crucial map (such as Mato Grosso) Include more useful details about cropping patterns Show results from models relating weather and yield that have been developed but never published Analysts from different agencies could get credit for work already done Analysts from different agencies could be encouraged to collaborate

Useful websites ERS Website WAOB Weather