In an effort to improve the accessibility of our information to you our users, information on commodities, e.g. Almonds, Grapes, Tomatoes, Walnuts, etc., is included on a single page. As an example, acreage, forecast, nursery sales, and objective measurement reports for Almonds are on one page. These reports along with the Annual Statistical Reviews and the California Ag Commissioner's Data Summary Reports & Data are located in the Specialty and Other Release section of this website.
California: First in the Number of Certified Organic Farms and Total Sales
The 2016 Certified Organic Survey is a survey of all operations identified as having certified organic production. Certified
organic farms must meet the standards set out by USDA’s Agricultural Marketing Service’s (AMS) National Organic
Program (NOP) and be certified compliant by an approved agent of NOP. Results from the survey provide acreage, production, and sales data for a variety of certified organic crops and certified organic livestock.
In 2016, the United States had 14,217 certified organic farms that produced $7.6 billion in certified organic products.
California certified organic farms were 19 percent of the U.S. total, with 2,713 certified farms. Of the 1,069,950 acres of
certified land in California, 336,409 acres were cropland and 733,541 acres were pastureland/rangeland. California’s total
gross value of certified organic agricultural products sold, at $2.9 billion, was 38 percent of the total gross value of U.S.
certified organic sales. The top two certified commodities sold in California were broiler chickens with sales valued at
$294.9 million and milk from cows with sales valued at $277.7 million.
For more information on the 2016 Certified Organic Survey and related reports, please visit the NASS Organic.
Top 10 States by Total Certified Organic Product Sales: 2016 and 2015
It would be hard to overestimate the importance of NASS's work or its contribution to U.S. agriculture. Producers, farm organizations, agribusinesses, lawmakers, and government agencies all rely heavily on the information produced by NASS. Statistical information on acreage, production, stocks, prices, and income is essential for the smooth operation of Federal farm programs. It is also indispensable for planning and administering related Federal and State programs in such areas as consumer protection, conservation and environmental quality, trade, education, and recreation.
Moreover, the regular updating of information helps to ensure an orderly flow of goods and services among agriculture's producing, processing, and marketing sectors. Reliable, timely, and detailed crop and livestock statistics help to maintain a stable economic climate and minimize the uncertainties and risks associated with the production, marketing, and distribution of commodities. Farmers and ranchers rely on NASS reports in making all sorts of production and marketing decisions. The reports help them decide on specific production plans, such as how much corn to plant, how many cattle to raise, and when to sell.
NASS estimates and forecasts are greatly relied upon by the transportation sector, warehouse and storage companies, banks and other lending institutions, commodity traders, and food processors. Those in agribusiness who provide farmers with seeds, equipment, chemicals, and other goods and services study the reports when planning their marketing strategies. Analysts transform the statistics into projections of coming trends, interpretations of the trends' economic implications, and evaluations of alternative courses of action for producers, agribusinesses, and policy makers. These analyses multiply the usefulness of NASS statistics.
To seek a correction of information, please submit a written request as follows:
State that your request for correction of information is submitted under Section 515 of Public Law 106-554 or under USDA's Information Quality Guidelines Include your name, mailing address, fax number (or e-mail address), telephone number, and organizational affiliation, if any. Clearly describe the information you believe to be in error and want corrected. Include the name of NASS’ publication, report or information product, the date of its issuance, and a detailed description of the information you feel should be corrected. State in detail why you feel the information should be corrected and, if possible, recommend specifically how it should be corrected. Please clarify which USDA Information Quality Guidelines were not followed or were not sufficient. Provide documentary evidence, such as comparable data, which will help in our review.
Explain how you use the information and how you have been harmed by the alleged error.
We do not intend this guidance to be a set of legally binding requirements. However, we may be unable to meet your request in a timely fashion, or at all, if you omit one or more of these elements. We do not intend to imply that, as an individual, you have any rights to request amendment of your own records beyond those permitted by the Privacy Act of 1974 or other organization specific laws. Can you imagine trying to use an estimate of corn acreage from an area sample that completely disregarded land-use? Can you imagine using a hog estimate where the list sample was selected
with no reference to control data but was simply a random sample from the entire list frame?
Can we provide data users with reliable crop and livestock estimates based on these types of
sampling plans?
Of course, the answer to these questions is a resounding NO! We cannot allow crop estimates to
be based on area samples where a sizable portion of the sample could fall in nonagricultural land
like cities or rangelands. Likewise, we cannot rely on livestock estimates based on samples
where we would have no idea how many livestock producers would be interviewed or how large
their operation might be. These types of designs give very imprecise survey results and, thus, are
poor investments for the money spent on them.
One method of controlling these problems is to stratify the sampling frame (either list or area)
before the sample is selected. Stratification is the process of subdividing the population into
mutually exclusive categories, called strata. In the first example, all the land in each State is
divided into different categories creating land-use strata, such as cultivated land, agri-urban, and
rangelands. In the second example, hog operators on the list frame are classified by size of
operation.
Many probability samples that NASS employs have a stratified design. There are several
reasons why stratified sampling is used, along with several tradeoffs that we need to remember
as well. The biggest gain from stratification comes from the improvement in precision of the
indications. Here, we mean smaller standard errors from the stratified design versus larger
standard errors from a simple random sample over the entire population. Let's discuss how this
improvement happens by using corn acreage as an example.
The standard error from a simple random sample will depend on how much the reported corn
values, including both positive and zero, vary over all units in the sample. For a stratified
sample, the size of the standard error will depend on how much the reported corn values vary
within each stratum. As the stratification becomes more efficient at putting "like" operations into
the same strata, the smaller the standard error will get. Thus, when the stratification is welldesigned and the sample size is properly allocated to the strata, large reductions can be made in the standard errors.
Another advantage of stratified sampling comes from targeting different subgroups of the
total population. Extreme operators and specialty crop growers are examples of how targeted
groups can be identified and grouped into separate strata. This capability insures that these types
of operations can be "special handled" for certain situations. For example, if a large number of
positive rice reports are needed for a general crop survey, then specialty strata based on rice
acreage could be used.
An important requirement for creating a stratified sample is to have relatively up-to-date size
information about each unit in the population. NASS maintains a profile of each unit in the
population, called control data, which tells us what commodities are produced and how big the
operation is. If an operation has changed considerably, but the control data available are not
current, that operation will be misrepresented in the wrong stratum. Any survey data collected
for that operation will definitely be different from the other "like" operations enumerated in that
stratum. Consequently, the standard error would increase. Also, item imputation would also be
affected since imputed values for list records are computed within the stratum level.
Another stratification problem concerns prioritizing control items so an operation possessing
several control items used to create strata can be assigned to the "best" possible stratum. If an
operation has both a large number of beef cows and a large number of dairy cows, to which
stratum should that operation be assigned? Decisions concerning all these possible situations are
made prior to constructing the survey strata, so this assignment can be made when the population
is created.
From these discussions concerning the importance for control data, you can see that the basic
requirement for a stratified design is good quality control information. A basic relationship
exists: the better the control data, the better the stratified sample will perform. If poor control
data are used for sampling, more problems associated with misclassification will later be found
during summary and analysis.
If we have designed efficient strata for your surveys and you have maintained your list with
quality control data, the net result will be reliable survey results for commodity estimation work.
Stratification is a powerful and useful tool that affects many facets of your job. Stratification -
you can't live without it!
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