usda’s economic research service and use of weather data ed allen cross commodity analyst for...
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USDA’s Economic Research Service and Use of
Weather Data
Ed AllenCross Commodity Analyst for Field Crops
Market and Trade Economics DivisionMarch 31, 2010
ERS mission
The mission of ERS is to inform and enhance The mission of ERS is to inform and enhance public and private decision-making on public and private decision-making on economic and policy issues related to economic and policy issues related to agriculture, food, natural resources, and rural agriculture, food, natural resources, and rural development. 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