Showing posts with the label NASS

Anticipating June 1 Corn Stocks

Next week (June 30th) USDA will release the quarterly grain stocks report for corn. These numbers have not been updated since March. It will reflect consumption patterns during the coronavirus pandemic.

The third-quarter grain stocks number is important because it gives the trade an actual tally of how much corn is left from the total available supply in the United States. Early this month USDA projected about 5.7 billion bushels of corn would be used this marketing year in the feed and residual category. This is the one that has the most scrunch room in it. University of Illinois Extension Agricultural Economist Todd Hubbs says if the June stocks report shows 4.89 billion bushels left in the bin, then things are on track, “It will be on track and you make actually see feed and residual move up a little bit if it is in that range. We typically see a fourth quarter feed and residual higher than what that would imply for the third quarter or the first three quarters’ feed and residual use. So, it is on track with the possibility of USDA raising feed and residual numbers.”

The feed and residual number, of course, isn’t the only consumption category for corn. Ethanol took a big hit during the first two months of the pandemic shut down as people stayed home and cars sat idle. The ethanol grind was down 26.7 percent in March and April. It was off in May, too, says Hubbs, “I assume that we will see the kind of convergence rate we’ve seen under the last couple of months of the lockdown. I have the (month of) May number at around 308 million bushels which puts total use for the quarter at around 969 million bushels. Which is way down from what we would normally do in the third quarter of the marketing year.”

The third primary consumption category is the export of corn. Hubbs expects it to be about 1.2 billion in total for the first 9 months of the marketing year. When you total it all up, the exports and the domestic usage, third quarter consumption looks to be right at three-billion-bushels. Hubbs says that number would put June 1 stocks at 4.89 and that figure is less than what was on hand last year at this time, “We would have slightly lower (stocks of corn), about 300 million bushels lower. We must remember we had much smaller crop in the previous year than we did in 2019. So, we will have fewer bushels in the bin, but we won’t be using as many bushels as we did in the last marketing year.”

USDA will update the grain stocks report next Tuesday, June 30th at 11am central time.

USDA NASS Soybean Objective Yield Pod Weight

Tuesday’s USDA Crop Production report included a very heavy soybean pod weight. Todd Gleason talks with USDA NASS Chief of the Crops Branch about the weight, how it is calculated, and how it might change over time.

How USDA Collects Yield Data

How USDA NASS Gathers Crop Production Report Data

USDA NASS will release the first corn and soybean Crop Production Report of the season Thursday August 10th, 2017 at 11am central. Todd Gleason talks with USDA NASS State Statistician Mark Schleusener (shloy-seh-ner) about how the information is collected and calculated.

USDA Crop Production Reports | a primer with Scott Irwin

A year ago University of Illinois agricultural economist Scott Irwin and Darrel Good wrote an article about how USDA predicts corn yields for the farmdocDaily website. Todd reviews this article with Scott Irwin as a primer to the August Crop Production report.

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Opening Up the Black Box

We have written extensively about U.S. corn yield forecasts issued by the National Agricultural Statistics Service (NASS) of the USDA (Irwin and Good, 2006; Irwin and Good, 2011; Irwin, Sanders and Good, 2014; farmdoc daily, August 28, 2013; August 29, 2014; August 18, 2016). As indicated in these previous publications, NASS uses two surveys to provide indicators of corn yield prior to harvest. These include the Agricultural Yield Survey (or farmer-reported survey) and the Objective Yield Survey (or field-measurement survey) in major corn producing states. A formal Agricultural Statistics Board (ASB) of NASS statisticians is convened in each month of the forecasting cycle to review yield indicators and determine an official yield forecast. In our previous publications we focused on the survey procedures used by NASS and had relatively little to say about the methodology used by the ASB to combine the various yield indicators and determine an official yield forecast. This omission reflects the fact that NASS has historically provided very little information about this part of the forecasting methodology. Fortunately, several publications have appeared in the scientific literature during the last several years that provide important insights into the nature of the different survey yield indications and the other non-survey information used by the ASB to determine official published yield forecasts. The purpose of this article is to review the information in these scientific articles and provide a more informed understanding of the role of the ASB in generating these important market-moving forecasts.

NASS Corn Yield Survey Indicators

We begin with a brief review of the two surveys that form the backbone of the NASS corn yield forecasting methodology–the Agricultural Yield Survey (or the farmer-reported survey) and the Objective Yield Survey (or the field measurement survey). For the August 2016 forecast, the Agricultural Yield Survey (AYS) included 22,144 operations for all crops and was conducted in 32 states for corn. The sample of farm operations surveyed was drawn from those who responded to the survey of planted acreage in June. The same operations will be interviewed each month from September through November. In the August survey, respondents were asked to identify the number of acres of corn to be harvested and to provide a forecast of the final yield.

The Objective Yield Survey (OYS) is designed to generate yield forecasts based on actual plant counts and measurements. The sample of fields for the OYS survey is selected from farms that reported corn planted or to be planted in the June survey of acreage. Samples are selected in the 10 principal corn producing states. A random sample of fields is drawn with the probability of selection of any particular field being proportional to the size of the tract. For the August 2016 OYS, a total of 4,544 plots were sampled for corn, soybeans, cotton, and winter wheat.

Two counting areas, or plots, are randomly selected in each field. Objective measurements (such as counts of plants and ears) are made for each plot each month during the survey cycle. When mature, the plots are harvested and yield is calculated based on actual production minus an allowance for harvest loss. During the August survey, the operator is asked to verify, field-by-field, the acreage reported in June. For corn, each of the two independently located sample plots in a field consists of two parallel 15 foot sections of row. Each plot is selected by using a random number of rows along the edge of the field and a random number of paces into the field. Enumerators count all fruit and fruiting positions in corn and, if ears have formed, a sample of ears is measured for length and circumference. Just before the field is harvested, both plots are hand harvested and weighed by the enumerator.

At each visit, the enumerator establishes a corn maturity category for the plot, ranging from 1 (no ear shoots) to 7 (mature). Prior to the blister stage the number of ears is forecast based on the number of stalks, ear shoots, or ears and both the weight per ear and harvest loss are forecast based on the 5-year average. From the blister through the dough stage, the weight per ear is forecast based on kernel row length and harvest loss is forecast based on the past 5-year average. Ear weight is measured in the dent and/or mature stage. Harvest loss is measured following harvest.

Prior to maturity and harvest, the OYS corn yield is forecast based on the forecast of the number of ears, the forecast of the weight per ear, and the forecast of harvest loss. Forecasts are based on conditions as of the survey date and projected assuming normal weather conditions for the remainder of the growing season. The OYS forecast of gross corn yield then is based on the following formula:

Gross Yield= [number of ears X weight per ear at 15.5% moisture] ÷ 56

Number of ears and ear weight are either forecast or actual and 56 is a conversion from weight to bushels.


The survey and forecasting procedures described above produce a number of indictors of the net yield of corn from the AYS and OYS. It is the job of the Agricultural Statistics Board (ASB) to combine the information from these indicators and determine the official published estimates. How does the ASB actually do this? The latest version of the official NASS publication on yield forecasting programs describes the ASB’s role this way:

“The sample surveys are designed to produce State level estimates of acreage, expected yields, final yields, and total production. The surveys are conducted by each State, and the first level of analysis is done by each State. Each Field Office does its independent appraisal of the relationships between the survey estimates and the final official statistics and forwards this information to Headquarters. While each Field Office is analyzing its survey data, statisticians in Headquarters are doing a parallel analysis of all survey data at the State, U.S., and regional levels. For the major field crops discussed in this paper, a formal Agricultural Statistics Board is convened to review regional indications and determine the official forecast or estimate. This Board is made up of 7 to 10 statisticians representing different divisions of NASS. Each Board member evaluates the regional survey indications and supporting data and determines their forecast or estimate. Each member brings their individual perspective to the review which can result in different conclusions being drawn. Through review and discussion, the Board must collectively reach a consensus and establish the National number. The Board process ensures all perspectives are examined and the national or regional forecast or estimate is the result of a thorough analysis. The summation of the individual State estimates as prepared by each State is compared to the Board number. The Headquarters statisticians will re-examine all national and State data relationships and either adjust State estimates so they sum to the U.S. or change the previously determined U.S. number.”

“The formal meeting of the ASB to establish the final numbers and prepare the report is conducted under ”lock up“ conditions. Lock up begins with a complete isolation of all facilities required by the Board. All doors are locked, windows and elevators are covered and sealed, phones are disconnected, and the computer network inside ”lock up“ is isolated from the full network. Transmitters are not permitted and the area is monitored for electronic signals. Highly speculative data are decrypted only after the area is secure. Only after all security is in place does the Board begin final deliberations. The area remains locked up until a prescribed release time (8:30 a. m. for Crop Production) at which time the report is disseminated in electronic and paper forms.” (pp. 96–97)

While this provides a helpful overview of the ASB in general times, it provides almost no detail on the nature of yield indications, the non-survey information, if any, used to supplement the survey indications, or how the ASB reaches a collective consensus.

In terms of statistical properties, NASS does acknowledge in its official yield forecasting publication that the AYS yield indications are biased:

“Note that AYS data are the respondents’ expected yield. The crop may not be mature and ready for harvest for another several weeks after the respondent has been contacted. Experience has shown these responses tend to be conservative (biased down). Under drought conditions, this bias gets much larger as respondents perceptions of a crop are influenced by current weather conditions. Therefore, the interpretation phase of the review must recognize this tendency and factor it into the final deliberations.” (p. 19)

Likewise, NASS acknowledges that OYS yield indications may also be biased:

“Forecasts of state or regional yields are inherently subject to differ from the final, administratively determined, yield. The difference is due to weather and crop conditions yet to be encountered at the time the forecast is made, the difference between weather and crop conditions in the current year and the historic weather and crop conditions utilized to predict current yields, and systematic non-sampling errors which contribute to forecast error. NASS makes every attempt to minimize the impact of these three sources of error by means of administratively determine final values. These final, sometimes referred to as official, values are used as dependent variables to estimate an ordinary least squares equation with the averages calculated from objective yield samples as the independent variable. For example, each state will have an administratively determined, official corn yield for all previous years. To forecast the official corn yield for the current year, NASS regresses the objectively determined State level average corn yields from previous years to the corresponding official corn yields. This process provides a historical context for weather, biases, and non-sampling errors.” (p. 38)

The fact that both yield indications are biased is interesting in and of itself, but the really important question is by how much and in what direction. We know the direction of bias for the AYS (downward) but neither the direction nor magnitude of bias for the OYS. If the biases are relatively small then this is largely a technical issue that has little practical import, but if the biases are large it becomes an important part of how the ASB generates published yield estimates.

A recent paper by Wang et al. (2011) is the first we know of to present data on actual AYS and OYS corn yield indications for an extended sample period. Figure 1 is taken directly from the Wang et al. article, and it presents: i) the OYS indications for the corn objective yield region and each report release month over 1993–2009; ii) the AYS yield indications for the same corn objective yield region and each report release month over 2001–2009; and iii) the December Agricultural Survey (DAS) corn yield indication for each year over 1996–2009. The objective region yield is simply the weighted-average yield for the objective yield states (10 since 2005) and the DAS is very close to the final yield estimates released in January. After viewing the chart, it is probably more understandable why NASS has been so cautious in disclosing this data previously. The magnitude of the bias in both yield indications is striking. The coarseness of the scales prevents precise measurements, but the upward bias in the OYS, regardless of the release month, varies from around 10 to 15 bushels per acre (using the DAS as the benchmark). The downward bias in the AYS in August ranges from about 10 to 20 bushels, and then generally declines in September and October and is usually, but not always, fairly small in November. The pattern of bias in the AYS yield indications make sense, in that farmer-reported yields should become more accurate as the growing season progresses and harvest eventually is completed.

What this means from a practical standpoint is that the ASB is confronted with substantial bias in its two most important yield indications. For example, based on Figure 1, the final DAS objective region yield for corn in 2009 was about 175 bushels and the August OYS yield indication was around 190 bushels and the AYS yield indication was around 155 bushels, a spread of 35 bushels. A more recent paper by Nandram, Berg, and Barboza (2014) provides precise measurements. They report that the average biases for the OYS objective region corn yield indications relative to the DAS over 1993–2010 were 13.96, 11.62, 13.13, 14.99 and 14.97 bushels for August, September, October, November and December, respectively (note that we assume the bias estimates were stated in bushels but the article does not explicitly state the units). The average differences between the AYS indications and the DAS indications were respectively –12.26, –13.42, –9.81, and –4.10 bushels for August, September, October, and November. These imply AYS/OYS yield indicator spreads that average 26.22, 25.04, 22.94, and 19.09 bushels. It is important to keep in mind that these spreads are not symmetric around zero, as the AYS bias diminishes substantially by November but the OYS bias does not. Assuming these bias estimates are similar to the estimates available to the ASB when making published estimates in recent years, we can use the averages to provide a rough estimate of the OYS and AYS corn yield indications for the objective region in August 2016. The published objective region corn yield this year was 182 bushels, which combined with the bias estimates suggest an OYS yield of about 196 bushels and an AYS yield of about 170 bushels.

From this discussion, it should be obvious that the corn yield indications have to be adjusted for bias by the ASB in order to make useful published estimates. The key is the consistency of the bias across time. Figure 1 suggests there is some variation in the bias for a given report month across years and this likely contributes to the errors in the published corn yield estimates. Nonetheless, there appears to be a strong degree of consistency in the bias from year-to-year and this can be used to efficiently “de-bias” the AYS and OYS yield indications applying standard statistical techniques. The larger question is why the biases exist in the first place. Nandram, Berg, and Barboza (2014) conjecture that:
One possible reason for the bias of the OYS indications is that the measurement process leads to a systematic overestimation of the plant density in a field. Two potential reasons for the biases in the AYS indications are pessimism on behalf of the farmers and exclusion of farms in the largest size stratum from the sampling frame. Large farms are conjectured to have higher average yields than moderately sized farms because of greater investment in advanced technology. (p. 510)

There is also a large literature (e.g., Poate, 1988) that formally evaluates the accuracy of so-called “crop cutting” yield estimates, and the evidence shows that such techniques have a tendency to over-estimate actual yields for a variety of reasons, including variability in population density. So, it is not surprising that the OYS yield indicator is biased upwards. NASS has also been aware of the bias in OYS indications for decades and has conducted extensive research to better understand the underlying reasons for the bias, apparently without great success (Warren, 1985). The underlying reasons for the downward bias in the AYS yield indicator are hard to pinpoint beyond a general conservatism among farmers about yield prospects before harvest. Given the dramatic shrinkage in the AYS bias through November and the fact that the same farms are surveyed each month for a given year, it is unlikely that under-sampling large farm operations is one of the main reasons for the AYS bias, as suggested by Nandram, Berg, and Barboza (2014).

In light of the level and variability of the bias in AYS and OYS yield indications, it is sensible for the ASB to seek out additional non-survey information when generating published yield estimates. The Wang et al. (2011) article provides helpful information in this regard:

Each month (August through December), estimates are produced by the ASB with the primary objective of providing the most accurate projection or end-of-season estimate given the survey indications and standard errors, in addition to other factors. When projecting corn yield, information on crop maturity, planting date, weather conditions, and other auxiliary factors is also taken into consideration by the ASB. (p. 85)

Since two of the co-authors of this paper at the time of its publication were NASS employees, we take this as authoritative evidence that the ASB uses “auxiliary” information when generating published estimates. While the details of exactly what type of auxiliary information the ASB uses when making corn estimates is not available, several recent articles by provide important clues. The stated purpose of the Wang et al. (2011), Adrian (2012), and Nandram, Berg, and Barboza (2014) articles is to basically formalize the decision-making process of the ASB in making corn yield forecasts. In order to do so, the models in the articles incorporate a variety of auxiliary information, including trend yields, planting progress, July temperature and precipitation, and crop condition ratings, which presumably reflect variables actually used by the ASB in the estimation process. Another type of auxiliary information used by the ASB in recent years is remote-sensing satellite data (Adrian, 2012; Johnson, 2014), such as the Normalized Difference Vegetative Index (NDVI), a measure of biomass density.

In sum, NASS has an active research program to try to formalize and improve the ASB methodology (e.g., Adrian, 2012; Wang et al., 2011; Nandram, Berg, and Barboza, 2014; Cruze, 2015). This provides important clues about the types of non-survey information used by the ASB, but we still don’t have any information about the weights placed on this information in ASB deliberations. There is obviously some concern within NASS about the subjective weights placed on the survey and non-survey information given discussions found in the articles cited above. For example, Adrian (2012) notes that the emphasis placed on different information, “will inevitably vary from person to person and depend upon the composition of the Board.” Our view is that an element of subjective expert judgement will likely always be a necessary component of a process as complex and difficult as forecasting crop yields. We concur with the late Bruce Gardner, who made this trenchant observation after sitting through ASB lock-up deliberations for an early 1990s Crop Production report:

“A NASS board in Washington then assesses all the indicators of yield, including the estimates of a month earlier. This is not done using a pre-specified formula–in which case a computer could replace the NASS board–but through a consensus of the Board members based on their experience and the full information before them.” (Gardner, 1992, p. 1068)

ASB Track Record

When viewing the details of the ASB forecasting methodology, especially the magnitude of the bias in the AYS and OYS yield indications, it is easy to lose sight of the big picture. That is, the ASB is tasked with combining the AYS and OYS yield indications with other relevant information in order to make the best possible published estimates of corn yield, and it is the accuracy of the final published estimates that ultimately matters. So, we updated our earlier evaluation (farmdoc daily, August 29, 2014) of the historic accuracy of USDA published August forecasts of the U.S. average corn yield relative to the “final” yield estimate released in January (we say “final” because January estimates are sometimes revised based on the Agricultural Census conducted every five years). The differences between the forecasts and the final estimates in percentages over 1990–2015 are presented in Figure 2. When interpreting the errors, note that a positive error implies an under-estimate on the part of USDA and a negative error implies an over-estimate. The errors associated with the USDA corn yield forecasts are occasionally very large, such as 1993 and 1995. These examples of large errors are not surprising due to the unusual weather events that occurred in those years. It is interesting to note that USDA corn yield forecast errors in 2012 were extremely small, with the August forecast exactly equal to the final estimate, and thus, having a zero forecast error. This is surprising given the magnitude of drought conditions that prevailed in 2012 and the difficulty of forecasting corn yields under these circumstances. Accuracy was also exceptional in 2015, when the forecast error was only –0.2 percent. The plot suggests a clear downward trend in USDA corn forecast errors over time.

Forecast performance after 2011 has been particularly impressive.

It is important to emphasize that the USDA track record of forecasts evaluated here is based on “real-time” forecasts. In other words, this is the record of actual forecasts produced and released to the public over time. As Tetlock and Gardner (2015) observe in their best-selling book Superforecasting:

The Art and Science of Prediction, measurement and evaluation of a large sample of real-time quantitative forecasts is the only scientifically-valid way of determining true forecasting skill.

Recently, there has been a considerable media coverage of several new “big data” firms that claim to produce superior corn yield forecasts to that of the USDA (e.g., Brokaw, 2016; Woyke, 2016). What is not emphasized is that the evidence for these claims is based on historical simulations of model performance, or “backcasting,” a much lower and less rigorous hurdle. These firms may actually be able to beat the USDA, but real-time track records will be required to convincingly prove the point. Until then, the available evidence indicates it is hard to beat the accuracy of USDA corn yield forecasts.


The Agricultural Statistical Board (ASB) of the National Agricultural Statistics Service (NASS), is responsible for determining the official published corn yield estimates of the USDA. While there is considerable information available about the survey methodology used by NASS to generate yield indications, until recently, very little information has been publically available about the nature of the corn yield indications, the non-survey information, if any, used by the ASB to supplement the survey indications, or how the ASB reaches a collective consensus. Several publications have appeared in the scientific literature during the last several years that help open up the ASB “black box.” These publications present for the first time historical data on the separate Agricultural Yield Survey (AYS) and the Objective Yield Survey (OYS) corn yield indications and the results are striking. The upward bias in the OYS corn yield indications, regardless of the release month, varies from around 10 to 15 bushels per acre, while the downward bias in the AYS in August ranges from about 10 to 20 bushels, and then generally declines in September and October and is usually, but not always, fairly small in November. Given the level and variability of the bias in AYS and OYS yield indications, it is sensible for the ASB to seek out additional non-survey information when generating published yield estimates. The recent publications indicate that the ASB incorporates a variety of auxiliary information, including trend yields, planting progress, July temperature and precipitation, crop condition ratings, and satellite imagery data. Unfortunately, we still don’t have any information about the weights placed on this information in ASB deliberations. While it is important to better understand all aspects of the NASS corn yield forecasting methodology, and ASB procedures in particular, in the end what matters is the accuracy of the final published estimates.
On that score, the evidence suggests USDA corn yield forecasts are still hard to beat.

Why USDA's Ear Weight is Unlikely to Change

June 17 | USDA NASS Weekly Crop Progress Report

Statistical Methodology

Weekly Crop Progress Report Survey Procedures: Crop progress and condition estimates are based on survey data collected each week from early April through the end of November. The non-probability crop progress and condition surveys include input from approximately 4,000 respondents whose occupations provide them opportunities to make visual observations and frequently bring them in contact with farmers in their counties. Based on standard definitions, these respondents subjectively estimate the progress of crops through various stages of development, as well as the progress of producer activities. They also provide subjective evaluations of crop conditions.

Most respondents complete their questionnaires on Friday or early Monday morning and submit them to the National Agricultural Statistics Service (NASS) Field Offices in their States by mail, telephone, fax, e-mail, or through a secured internet website. A small number of reports are completed on Thursday, Saturday, and Sunday. Regardless of when questionnaires are completed, respondents are asked to report for the entire week ending on Sunday. For reports submitted prior to the Sunday reference date, a degree of uncertainty is introduced by projections for weekend changes in progress and condition. By the end of the 2015 season, over 90 percent of the data were being submitted through the internet website. As a result, the majority of all data are submitted on Monday morning, significantly reducing projection uncertainty.

Respondents are sent written reporting instructions at the beginning of each season and are contacted periodically to ensure proper reporting. Terms and definitions of crop stages and condition categories used as reporting guidelines are available on the NASS website at

Estimating Procedures: Reported data are reviewed for reasonableness and consistency by comparing with data reported the previous week and data reported in surrounding counties for the current week. Field Offices summarize the reported data to district and State levels, weighting each county’s reported data by NASS county acreage estimates. Summarized indications are compared with previous week estimates, and progress items are compared with earlier stages of development and historical averages to ensure reasonableness. Weather events and respondent comments are also taken into consideration. State estimates are submitted to the Agricultural Statistics Board (ASB) along with supporting comments, where they are compared with surrounding States and compiled into a National level summary by weighting each State by its acreage estimates.

Revision Policy: Progress and condition estimates in the Crop Progress report are released after 4:00 pm ET on the first business day of the week. These estimates are subject to revision the following week.

USDA's Crop Yield Forecasting Method

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 Wednesday (tomorrow/today). 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 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.

The Final Days of the USDA Report Data

Tuesday the Department of Agriculture will release one of its most anticipated reports of the year. It began collecting data from farmers at the beginning of this month. The crop acreage data is compiled, encrypted and transferred to Washington, D.C.

USDA contacts more than 80,000 farmers across the United States in March. It asks them a series of questions. One in the series is about which crops and how many acres of each they expect to plant this season. The agency sends all those farmers a letter to do this. Those not responding get a phone call, and then if they still don’t respond receive a face-to-face visit. The collection was completed Wednesday March 18th. Last Friday the Illinois and Missouri National Agricultural Statistics Service staffs, if the schedule went as Mark Schleusener expected, should have been reviewing the information.

Quote Summary - The last few days before publication there is an analysis period. Friday morning we are going to look at a balance sheet. We’ll add up all the corn, soybeans, wheat, hay, etcetera, and CRP. In Illinois the total is pretty constant across years with the mix of crop acres changing from one year to the next. So, we’ll make estimates on acreage in each, add them up, and compare it to previous years to see if the sum of the parts makes sense. We’ll do that Friday morning and then submit our estimates in an encrypted file to our Washington, D.C. headquarters. There will be more analysis done under secure conditions and the report comes out March 31.

This analysis is done by National Agricultural Statistic Service staff. Schleusener says the staff is primarily gifted in two area; statistics and agriculture. And he says the sum of those two qualifications is what’s required to do a good job for NASS. Schleusener serves at the NASS Illinois State Statistician.

Quote Summary - So, we are looking at what the number shows. What comes out of the computer, and how that compares to previous surveys and other factors. For instance, this balance sheet approach is a way to make sure we don’t go off-the-rails by being a little bit too high on each crop and a lot too high overall. The balance sheet makes sure we don’t go in that direction.

It gives the analysts a chance to see errors before the Prospective Plantings figures are reported up the chain or out the door. The Prospective Plantings report will be released in Washington, D.C. at noon eastern time Tuesday March 31, 2015.

The March 31 Grain Stocks Report

The reports USDA releases March 31 will set the tone of agricultural trade for three months in Chicago.

Once every quarter the National Agricultural Statistics Service takes a census of the available bushels of corn, soybeans, and wheat. It is called the Grain Stocks report. It is not exactly a survey, but rather more of an actual accounting, in his case of what’s stored in Illinois, says NASS State Statistician Mark Schleusener, “…to measure the whole supply of grains and oilseeds USDA NASS does on farm surveys. Those are done with producers to find out what they have in their grain storage bins. Off farm storage tallies bushels in the mills and the elevators using a census as of March 1. All commercial storage facilities are contacted”.

Nationwide more than 9000 commercial storage facilities are contacted for the census side of the Grain Stocks report. The survey side - that done with farmers - is sent to more than 80,000 producers with an 80 percent response rate. The goal is to get a very accurate accounting of the bushels available for use.

Where the bushels are stored changes across the season. December 1 it is stored on farm. Through the winter months these bushels slowly move to the elevators and mills and eventually, in the case of corn, the bushels are shipped down the river for export, or fed to livestock, or turned into ethanol. The bushels are used.

If you add what’s used to what’s left - the Grain Stocks number - the sum should be the total available supply for the year. However, tracking the middle usage number for corn - bushels fed to livestock - isn’t possible. That’s why USDA calls this number Feed & Residual. This season it is supposed to be 5.3 billion bushels. The question is how much of that 5.3 billion has already been consumed. There in lies the guess says University of Illinois Ag Economist Darrel Good.
Quote Summary - If the most recent pattern is being followed this year and USDA’s 5.3 billion bushel usage for the year is correct, then use for the first half the year should total 3.9 billion bushels with 1.7 of that used in the second quarter. If that is the case, the total use during the second quarter would have been 3.75 billion bushel and leave March 1 stocks at 7.45 billion.
On-the-other-hand, if the usage pattern is more like it was prior to 2010, there could be another 200 or 300 million bushels of corn accounted for in the Grain Stocks figure because it hasn’t yet been consumed. It will still be consistent with a 5.3 billion bushel usage figure for the year.

The Grain Stocks report for corn has a wide range then of acceptable figures from around 7.4 to 7.7 billion bushels. It makes the Grain Stocks number not so important, and puts a great deal more weight on the Prospective Plantings report to be released on the same date, March 31.

How USDA NASS Counts Acres

USDA has just wrapped up its survey of more than 80,000 U.S. farmers. The agency uses the information to develop the March 31st acreage forecast.

In the spring USDA’s National Agricultural Statistics Service division contacts farmers in hopes of learning how much of each crop they expect to plant. The agency contacts farmers across the United States. Corn and soybean farmers are of particular interest. This year more than 4000 Kansas farmers were tapped, along with around 3700 in Nebraska and about 3000 in each of the Dakotas, Iowa, and Illinois. Another 2000 farmers each were contacted in Indiana and Ohio.

Quote Summary - Our goal is to make sure we are measuring small, medium, and large farms. So, we use what’s called a stratified sample.

That’s NASS Illinois State Statistician Mark Schleusener.

Quote Summary - That is a fancy way of saying for the biggest farms, we are going to talk to all of them; for the large, but not biggest we will talk with one out of three of those and for the medium, maybe one out of ten; and for the smaller farms we might measure one out of twenty-five of those.

Each farmer surveyed is asked how many acres they operate. How much of that land they intend to plant to corn or soybeans, and how much might already be in wheat. They’re also asked about oats, sorghum, and hay. The response rate goal, and usually achievement, is an amazing eighty percent.

Quote Summary - Yes, our goal is an 80% response rate on all surveys and we use several methods of data collection. Every producer in the sample receives a letter with a planting intentions questionnaire. The letter also has instructions for reporting to a secure internet website. These are both inexpensive ways of gathering data. The people that do not respond will be called. If this doesn’t work then someone will make a farm visit for a face to face. Both these methods are more effective, in general, but also are more expensive.

The biggest problem NASS faces when taking the acreage survey is that farmers usually haven’t yet made all their planting decisions. The agency knows this and is satisfied with best estimates. The individual reports are confidential by law and the data collected is exempt from legal processes.

The data can be aggregated at the county, state, and national level. Computers flag any large acreage changes at the individual level so that an analyst can check for a data entry error or make a follow up call. The state statisticians review the total number of crop acres for any major changes - total crop acres generally remain constant - and then submit the estimates in an encrypted file to USDA NASS in Washington, D.C. There more analysis is done and the final report is produced for release March 31.

USDA Updates Cash Rents by County


In recent weeks, two sources released cash rent information for Illinois. The U.S. Department of Agriculture released county average cash rents for 2014. The Illinois Society of Professional Farm Managers and Rural Appraisers released 2014 and expected 2015 cash rents for professionally managed farmland. Expected 2015 rents point to decreasing cash rent levels on professionally managed farmland. Whether or not other cash rents follow professionally managed cash rents down is an open question.

Average Cash Rents in Illinois

The National Agricultural Statistical Service (NASS) - an agency of the U.S. Department of Agriculture - released 2014 average rents per county on September 5, 2014. A number of counties do not have cash rents reported, likely because statistically reliable rents could not be obtained with survey responses.

As can be seen in Figure 1, there is a considerable range in cash rents across Illinois. Four counties had average cash rents over $300 per acre: Logan ($308 per acre), Piatt ($303 per acre), Sangamon ($302 per acre), and Ogle ($300 per acre). Except for Ogle County, these high-rent counties are located in central Illinois. The five counties with the lowest cash rents are Johnson ($80 per acre), Williamson County ($92 per acre), Perry ($106 per acre), Saline ($107 per acre), and Franklin ($108 per acre). These counties with the lowest cash rents are located in southern Illinois. Generally, average cash rent levels are related to productivity, with counties having more productive farmland have higher cash rents than those counties with less productive farmland (farmdoc daily, September 10, 2013).


Overall, 2014 average cash rents were higher in 2014 than 2013. According to NASS, the average rent in Illinois increased from $224 per acre in 2013 to $234 per acre in 2014, an increase of 5%. This continued a string of years of large increases. Since 2006, average state rents in Illinois have increased from $132 per acre in 2006 to $234 per acre in 2014, an increase over this eight year period of 77%.

Professional Cash Rents Levels

The Illinois Society of Professional Farm Managers and Rural Appraiser released results of its annual mid-year survey. This survey asked for 2014 and expected 2015 cash rents on professionally managed farmland. These rents, along with 2013 cash rents from a previous survey, are shown in Table 1. Average rent levels are shown for four classes of farmland productivity:

Excellent - expected corn yields are over 190 bushels per acre
Good - expected corn yields are between 170 and 190 bushels per acre,
Average - expected corn yields are between 150 and 170 bushels per acre, and
Fair - expected corn yields are below 150 bushels per acre.

Average cash rents decreased between 2013 and 2014. For excellent quality farmland, cash rents decreased from $396 per acre to $374 per acre in 2014, a decrease of $14 per acre.

Decreases for professionally managed farmland stands in contrast to average cash rents, which increased from $224 per acre in 2013 to $234 per acre in 2014. Farm managers follow agricultural markets, likely much more closely than land owners without management. As a result, farm managers likely set rents closer to those suggested by market conditions. Cash rents on professionally managed farmland increased faster than average cash rents between 2006 and 2013, when returns rose as a result of higher prices. Now that prices have decreased from levels experienced during 2009 through 2013, farm managers are lowering cash rents. On farmland, not managed there may be considerably more lagged relationship between changes in returns and changes in rent levels.

On professionally managed farmland, cash rents likely will continue to decline into 2015. For all quality classes, Society members indicated that rents would be lower in 2015. For excellent quality farmland, for example, cash rents are projected to decrease from $374 per acre in 2014 to $338 per acre in 2014, a decrease of $36 per acre (see Table 1). If the decrease occurs, cash rents would decrease by about 10%.

There is a considerable range in cash rents for similar productivity farmland within a small geographical area, with some rents above the average by $100 and other rents below the average by $100. Below average cash rents could continue to increase to "catch up" with average levels. At the same time, above average cash rents could decrease, as indicated by results from the Illinois Society. These two forces could counter each other, leading to stable or maybe even increasing average cash rent levels.

Projections are for much lower returns in 2014 and 2015 return (farmdoc daily, July 8, 2014). Even with decreases in cash rents projected by the Illinois Society, farmer returns would be projected to decrease because returns have decreased more than cash rents.


Rents on professionally managed farmland could decrease in 2015. Other above average cash rents could decrease as well. However, below average cash rents may remain stable or increase. Overall, rent decreases likely will not cover decreases in lower returns projected for 2014 and 2015.