August 11, 2015

How Crop Production Reports are Collected

USDA’s National Agricultural Statistics Service (NASS) will release the first survey-based yield and production forecasts for the 2015 corn and soybean crops this month. Even though a description of the NASS crop production forecast methodology is widely available, there always seems to be some misconceptions about how NASS makes corn and soybean yield forecasts. University of Illinois agricultural economists Darrel Good and Scott Irwin put together a brief overview of that methodology and posted it to the FarmDocDaily website.

While they say their summary does not do full justice to the very comprehensive forecasting methodology, it is useful to place the upcoming yield forecasts in the proper perspective.

NASS corn and soybean yield forecasts are made in August, September, October and November. The final yield estimate is released in January based on the comprehensive December Agricultural Survey of producers. Two types of surveys are used each month to collect the forecast data.

The Monthly Agricultural Yield Survey (AYS) of producers is conducted in 32 states for corn and 29 states for soybeans with a total of about 25,000 producers surveyed for all crops in August. The Objective Yield Survey (OYS) is conducted in 10 states for corn and 11 states for soybeans. The surveys are generally conducted in a two week period ending about a week before the release of the forecasts.

For the Agricultural Yield Survey, a sample of farm operations to be surveyed is drawn from those who responded to the June acreage survey. While the sample of operations to be surveyed changes from year-to-year, for any particular year the same operations are interviewed each month from August through November. Survey respondents are asked to identify the number of acres of corn and soybeans to be harvested and to provide a forecast of the final yield of each crop. Based on these responses, average yields are forecast for each survey state and for the nation.

The goal of the Objective Yield Survey program is to generate yield forecasts based on actual plant counts and measurements .The sample of fields (1,920 for corn and 1,835 for soybeans) is selected from farms that reported corn (soybeans) planted or to be planted in the June acreage survey. A random sample of fields is drawn with the probability of selection of any particular field being proportional to the size of the tract. Two plots are then randomly selected in each field.

Data collected from each corn plot during the forecast cycle are used to measure the size of the unit and to measure or forecast the number of ears and grain weight. These data include (as available based on maturity) row width, number of stalks per row, number of stalks with ears or ear shoots per row, number of ears with kernels, kernel row length, ear diameter, ear weight in dent stage, weight of shelled grain, moisture content, total ear weight of harvested unit, lab weight of sample ears, weight of grain from sample ears, and moisture content of shelled grain from sample of mature ears. Corn yield is forecast based on the forecast (or measurement if maturity allows) of the number of ears, the weight per ear, and harvest loss.

Data collected from soybean plots (as available based on maturity) include row width; number of plants in each row; number of main stem nodes, lateral branches, dried flowers and pods, and pods with beans; weight and moisture content of beans harvested by enumerator; and weight and moisture content of harvest loss. The data collected are used to forecast yield based on a forecast (or measurement if maturity allows) of the number of plants per acre, the number of pods with beans per plant, the average bean weight, and harvest loss.

For both corn and soybeans, the state average yield forecast based on the Objective Yield Survey is the simple average of the yields for all the sample fields. In addition, a state yield forecast is also made by first averaging the forecast or actual yield factors (such as stalk counts, ear counts, and ear weight) and then forecasting the state average yield directly from these averages. This forecast is based on a regression analysis of the historical relationship (15 years) between the yield factors and the state average yield. State average yields are combined to forecast the U.S. average yield.

The NASS corn and soybean survey and forecasting procedures produce a number of indictors of the average yield. In August these indicators include: average field level yields from the Objective Yield Survey, average state level counts from the Objective Yield Survey, and the average yield reported by farmers in the Agricultural Yield Survey. Each of the indicators provides input into the determination of the official yield forecasts by the USDA’s Agricultural Statistics Board.

The accuracy of the USDA yield forecasts, write Darrel Good and Scott Irwin, relative to the final yield estimates varies from year to year, but as would be expected, improves each month through the forecast cycle as the crops become more mature.



Understanding USDA Crop Forecast

Marketing & Outlook Brief

July 23, 2015

Projected 2015 Net Incomes on Grain Farms

by Gary Schnitkey, Extension Agricultural Economist - University of Illinois

Average 2015 net income for grain farms in Illinois is projected at around $15,000 per farm, down considerably from the 2014 average of slightly above $100,000 per farm (see Figure 1). Furthermore, the 2015 net income will be below incomes in 2010 through 2012 which were above $200,000 per farm. This decline in incomes raises questions.


What do incomes in Figure 1 represent?

Historical values in Figure 1 are average net farm incomes of grain farms enrolled in Illinois Farm Business Farm Management (FBFM). These farms are located throughout Illinois and represent a variety of farm sizes, tenure relationships, and debt positions. Farms have increased in size over time. In 2014, average farm size was close to 1,500 acres, but the sample included many smaller farms and may larger farms. There were a relatively large number of farms of over 5,000 acres.

How was the 2015 net farm income projected?

Commodity prices, yields, input costs, and cash rents were projected for 2015. More detail on these projections are contained in the 2015 budgets which are summarized in the July 7th FarmDoc daily article. Key items impacting projections are:

  • Commodity prices are $4.20 per bushel for corn and $10.00 per bushel for soybeans.
  • Yields are presumed to be near trend line levels.
  • Non-land costs are projected to declines slightly from 2014 levels.
  • Cash rents are projected to decrease slightly from 2014 levels.

Can 2015 net incomes vary from projections?

Of course. Differences in prices and yields from those used in projections will change incomes. For example, corn price could easily be $.50 per bushel different from the $4.20 price used in projections. With higher prices or higher yields, 2015 net incomes could be above $50,000. However, it is difficult to build a case where 2015 incomes are not considerably lower than 2014 incomes.

Why are 2015 net incomes projected so much lower than 2014 net incomes?

The projected 2015 commodity prices ($4.20 for corn and $10.05 for soybean) are above commodity prices received for 2014 crop (likely $3.70 for corn and $9.75 for soybeans). Given higher prices, why then are projected 2015 incomes lower than 2014 net incomes? Two reasons:

  • Trend line yields are used in 2015 projections. Much of Illinois had above average yields in 2014, contributing to higher net incomes.
  • Marketing gains contributed a large amount to 2014 incomes. Grain produced in 2013 was valued at a lower price on the end-of-year 2013 income than it was sold in 2014. More detail is provided in the May 27th FarmDoc daily article.

Why is projected 2015 net income lower than averages between 2000 through 2005?

From 2000 to 2005, net incomes on Illinois grain farms averaged $57,500, higher than incomes projected in 2015. When making 2015 projections, a $4.20 corn price and $10.00 soybean price are used. These 2015 projected prices are significantly above prices from 2000 to 2005 when prices received by Illinois farmers averaged $2.18 for corn and $5.69 for soybeans. Given higher prices, revenue is projected higher in 2015 than from 2000–2015. However, costs are projected much higher as well. For example, non-land costs for corn have increased 224% from $256 per acre average from 2000–05 to $578 per acre in 2015. Cash rents have increased 205% from $139 per acre to a projected $286 per acre. These cost increases are the primary factor offsetting higher commodity prices, leading to lower projected incomes in 2015.

Will lower incomes signal financial stress?

These lower incomes suggest the need for continuing financial adjustments. More on the financial strength and need for adjustments will be covered in the July 28th FarmDocDaily article.

July 21, 2015

Something USDA NASS Cooked Up

Scroll over each state to reveal how its crop condition has changed over the season. Ohio is particularly interesting.

July 17, 2015

Wheat Consumption Tracks Our Eating Habits

The following chart and commentary are posted to a USDA ERS website. Essentially it tracks how many pounds of wheat flour the average U.S. citizen has consumed per year since 1964. The ERS commentary on the reasons for the increase in consumption through the mid–1990’s and sudden drop near the turn of the century reflect the eating habits of a couple generations of Americans.

Wheat consumption stable among U.S. consumers in recent years

Per capita wheat flour consumption has been relatively stable in recent years, and is estimated in 2014 at 135 pounds per person, unchanged from 2013 but down 3 pounds from the recent peak in 2007. The 2014 estimate is down 11 pounds from the 2000 level when flour use started dropping sharply, partially due to increased consumer interest in low-carbohydrate diets. From the turn of the 20th century until about 1970, U.S. per capita wheat use generally declined, as strenuous physical labor became less common and diets became more diversified. However, from the early 1970s until the late 1990s, wheat consumption trended upward, reflecting growth in the foodservice industry and away-from-home eating, greater use and availability of prepared foods for home consumption, and promotion by industry organizations of the benefits of wheat flour and pasta product consumption. During this time, the domestic wheat market expanded on both rising per capita food use and a growing U.S. population.  Relatively stable per capita flour use in more recent years means that expansion of the domestic market for U.S. wheat is largely limited to the growth of the U.S. population. This chart is based on the April 2015 Wheat Outlook report.

July 10, 2015

The Consequences of a Foot of Rain in June

The rainfall in May and June has put the corn crop in a difficult position this growing season. Late in June the corn crop in eastern Illinois, north of Interstate 74, was under water. It looked bad, really bad. Oh, there was some of it that looked pretty good, but not much. Things across the border in Indiana aren’t much better, and neither, apparently, is a large part of Missouri and southern Illinois. The crop has just gotten way to much water says University of Illinois Extension Agronomist Emerson Nafziger.

July 02, 2015

Crop Conditions in Illinois

Emerson Nafziger, Extension Agronomist - Univeristy of Illinois Rainfall in Illinois has caused serious problems in the state's cash crops. University of Illinois Extension Agronomist Emerson Nafziger discusses the condition of each.

Wheat Corn Soybean

June 20, 2015

Farmers Overwhelmingly Choose ARC County

Original Article

The U.S. Department of Agriculture, Farm Service Agency (USDA, FSA) recently released enrollment data on commodity program choices made under the 2014 Farm Bill. This article summarizes how farmers split program acres between Agricultural Risk Coverage - County Option (ARC-CO), ARC - Individual Option (ARC-IC), and Price Loss Coverage (PLC). Overall, ARC-CO was the overwhelming choice. ARC-CO was elected on 76% of program acres. PLC was next with 23% of acres, followed by ARC-IC with less than 1% of acres. There were differences in program choices across crops, as discussed below.

Program Choices

Farmers choose ARC-CO for 97% of soybean base acres and 94% for corn base acres (see Figure 1). Analysis indicated that expected payment from ARC-CO were larger than from PLC for both corn and soybeans (see farmdoc daily January 27, 2015 for more detail), suggesting high use of ARC-CO. However, the fact that ARC-CO accounted for over 90% of program acres for both corn and soybeans is astonishing. The large share suggests:

  • Farmers did not split decisions between ARC-CO and PLC. One strategy was to choose ARC-CO on some farms and PLC on other farms, splitting protection between a revenue program whose guarantee will change over time and a target price program with a fixed reference price. Most farmers did not follow the strategy of splitting choices.

  • Farmers raising corn and soybeans placed little value on having the option to purchase Supplemental Coverage Option (SCO). SCO is a county-level crop insurance program that rides on top of individual plans. SCO is only available if PLC was chosen.

  • When making decisions, the default was PLC. Farmers had to make an active decision to sign up for ARC-CO. Most farmers raising corn and soybeans made an active decision to choose ARC-CO.

  • The large percentages suggest that farmers raising corn and soybeans were comfortable with revenue-based programs. Some questioned this because ACRE - a revenue program available in the 2008 Farm Bill that preceded ARC-CO - was chosen by few farmers. The decision to use ARC-CO also mirrors crop insurance decisions made by corn and soybean farmers, where farmers overwhelmingly choose to use revenue insurances.

On corn, farmers used ACRE on 8.1% of base acres in 2013. Hence, revenue program use on corn increased from 8.1% in 2013 up to 94% after 2014 program choices. There are a number of reasons that could have caused this change:

  • To enroll in ACRE, an individual had to give up 20% of direct payments and loan rates were reduced by 30%. Since direct payments were eliminate and loan rates were the same no matter the choice in the 2014 Farm Bill, this tradeoff did not exist for ARC-CO.

  • ACRE was more complicated than ARC-CO, especially as ACRE required two triggers to be met before a farmer could receive payments.Farmers had to provide yields to FSA when enrolling in ACRE. This was not the case for ARC-CO.

  • Given the elimination of direct payments and the choices posed in the 2014 Farm Bill, farmers likely gave the choices more consideration in 2014.

  • Price expectations were different in 2014 than when ACRE decisions were made. There also are expectations for larger up front ARC-CO payments.

At the other end of the spectrum, near 100% of peanut and long grain rice base acres were enrolled in PLC (see Figure 1). These large percentages are not a surprise as studies suggested that PLC would make larger payments than ARC-CO for these crops (see farmdoc daily January 27, 2015 for more detail). Reference prices for these crops are well above market-level prices, leading peanuts and rice farmers to overwhelmingly choose PLC.

Perhaps the surprise in rice is the fact that ARC-CO was elected for a relatively high percentage of acres for Japonica rice. ARC-CO was selected on 34% of acres, ARC-IC was selected on 4%, and PLC for 62%. Note that yield and price dynamics are different for japonica rice than for long grain prices and Japonica's reference price was set at 115% of the long and medium grain reference price. Also, all Japonica rice base acres are located in California, and the drought situation may be playing a role in program choice.

Wheat choices were split relatively evenly between ARC and PLC (see Figure 1). ARC-CO was used on 56% of base cases, ARC-IC on 2%, and PLC on 42%. Studies of expected payments suggested that ARC-CO and PLC were near one another, potentially leading to the relatively even split.

ARC-IC was used on the fewest program acres. Crops having the most use of ARC-IC include large chickpeas (11% of base acres), small chickpeas (9%), lentils (7%), dry peas (6%), mustard (6%), temperate japonica rice (4%), barley (4%), and safflower (3%). There is a geographical dimension to where these crops are raised, with most of the states being located in the northwest. Oregon had the highest share of base acres enrolled in ARC-IC, with 12% of base acres enrolled in ARC-IC. Oregon was followed by Montana (9%), Washington (4%), Idaho (4%), Wyoming (2%), Minnesota (2%), South Dakota (2%), North Dakota (1%), and Colorado (1%).

Geographic Distribution

Overall there was a geographical pattern to program choice, as would be indicated by signup by crop. Figure 2 shows states giving percentages of base acres enrolled in PLC. In general, PLC was used more in states in the south and west. Highest PLC use occurred in Arizona (95% of program acres), New Mexico (87%), Texas (84%), and Utah (82%). PLC use in Corn-Belt states were small. For example, PLC was used on 2% of program acres in Iowa, 3% in Illinois, and 2% in Indiana. 


To a large extent, program choices followed predictions made prior to sign-up. Two facts, however, stand out. First, ARC-CO was the overwhelming program choice across program crops, particularly on corn and soybean acres. This suggests that farmers will use revenue-based programs, particularly those of relatively straight-forward design. The second was the relatively small use of ARC-IC. While ARC-IC has the desirable feature that it protects farm yields, ARC-IC also is a more complicated program relative to ARC-CO and PLC, combining all crops when determining payments and requiring farmers to report yields to be FSA. These complications may result in its unpopularity.

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