Agricultural Law Weekly Review—September 1, 2016

Written by M. Sean High – Staff Attorney

The following information is an update of recent, local, state, national, and international legal developments relevant to agriculture:

Big Data: DOJ Sues to Prevent Deere’s Acquisition of Precision Planting  

On August 31, 2016, the United States Department of Justice (DOJ) brought an anti-trust action in the United States District Court for the Northern District of Illinois Eastern Division to prevent Deere & Company’s (Deere) from acquiring Precision Planting LLC (Precision Planting), a subsidiary of Monsanto Company (U.S.Department of Justice v. Deere & Co., Case No. 1:16-cv-08515).  According to the filed complaint, Deere and Precision Planting currently account for 86% of all high-speed precision planting system sales in the United States and as a result, DOJ asserted “the proposed acquisition likely would lessen competition substantially, and tend to create a monopoly…in violation of Section 7 of the Clayton Act, 15 U.S.C. § 18.”

Labor: California Governor Presented with Bill Removing Agricultural Worker Overtime Exemption

On August 30, 2016, California Governor Edmund G. (“Jerry”) Brown was presented with legislation which would require California agricultural producers to pay overtime to agricultural employees (AB 1066).  If signed by the Governor, AB 1066 “would remove the exemption for agricultural employees regarding hours, meal breaks, and other working conditions, including specified wage requirements, and would create a schedule that would phase in overtime requirements for agricultural workers…over the course of 4 years.” The proposed legislation would also allow agricultural producers with 25 or fewer employees an additional 3 years to phase in the overtime requirements.  

HPAI: Avian Influenza Found in Wild Duck in Alaska

On August 26, 2016, the United States Department of Agriculture (USDA) Animal Plant Health Inspection Service (APHIS) “confirmed the presence of highly pathogenic H5N2 avian influenza (HPAI) in a wild mallard duck from a state wildlife refuge near Fairbanks, Alaska.”  According to APHIS, this recent discover is the first reported case of H5N5 HPAI—in either wild or commercial birds—in the United States since June 2015. 

Water: DEP Extends NPDES General Permit for Point Source Discharges to Waters of the Commonwealth of Pennsylvania from the Application of Pesticides

On August 27, 2016, the Pennsylvania Department of Environmental Protection (DEP) published notice in the Pennsylvania Bulletin that the department was “extending for 12 months, the availability of the current [National Pollutant Discharge Elimination System—NPDES] General Permit for Point Source Discharges to Waters of the Commonwealth of Pennsylvania from the Application of Pesticides (PAG-15)” (46 Pa.B. 5640).  According to DEP,  “[t]he existing PAG-15 in effect at this time will expire on October 28, 2016…[and] [p]ersons that are operating under the PAG-15 General Permit may continue to operate until October 28, 2017, or the expiration date of coverage identified on the permit coverage approval page, whichever is later.

Inspection: FSIS Announces Request to Renew Voluntary Interstate Shipment of Meat and Poultry Products Program

On August 26, 2016, the United States Department of Agriculture (USDA) Food Safety and Inspection Service (FSIS) published notice in the Federal Register “announcing its intention to renew the approved information collection regarding the voluntary cooperative interstate shipment program” (81 FR 58903).  According to FSIS, the program in question is a voluntary cooperative inspection program, administered by FSIS, “under which State-inspected establishments with 25 or fewer employees are eligible to ship meat and poultry products in interstate commerce.” The comment period for the published notice ends October 25, 2016. 

Raw Milk: Utah Dept. of Health Links Salmonellosis Outbreak to Raw Milk

On August 30, 2016, the Utah Department of Health issued a news release announcing that State health officials were investigating nine illnesses associated with the consumption of raw milk purchased at Heber Valley Milk in Wasatch County, Utah.  According to the news release, on August 23, 2016, “a raw milk sample collected at the dairy by a Utah Department of Agriculture and Food inspector was positive for Salmonella Saintpaul.” The news release also stated that recent testing of raw milk samples collected from Heber Valley Milk  have not tested positive for salmonella, and as a result, the dairy has been allowed to resume sales.

Beef: AMS Proposes Amendment to the Beef Promotion and Research Rules and Regulations

On August 23, 2016, the United States Department of Agriculture (USDA) Agricultural Marketing Service (AMS) published notice in the Federal Register of a “proposed rule [that] would amend the Beef Promotion and Research Order (Order) established under the Beef Promotion and Research Act of 1985 (Act) to add six Harmonized Tariff System (HTS) codes for imported veal and veal products and update assessment levels for imported veal and veal products based on revised determinations of live animal equivalencies” (81 FR57495).  The comment period for the proposed rule closes October 24, 2016.

Agriculture Big Data Legal Issues and Protections: Part 6 – Possible Legal Protections

Written by M. Sean High – Staff Attorney

Contract Protections

The contract between a farmer and an agricultural Big Data company provides the first line of defense for any farmer seeking to determine how their agricultural information may be used. 

As with all contracts, it is important to understand the contractual terms prior to signing the agreement.  Once the contract is signed, the farmer will be bound by the terms of the agreement. 

If a farmer has certain concerns regarding how their agricultural information will be used, those concerns should be spelled out in the contract.  For example, a farmer could specifically prohibit agricultural Big Data companies from providing commodities traders or rival farmers with their individual agricultural information.  If an agricultural Big Data company were to violate such an agreement, the farmer would have the ability to seek damages for a breach of contract.

Pennsylvania Trade Secrets Law

Many farmers worry about unauthorized individuals gaining access to their individual agricultural information.  A statue that could possibly offer protection to Pennsylvania farmers’ is the Pennsylvania Uniform Trade Secret Act (PUTSA) (12 Pa.C.S.A. §§ 5301-5308). 

Enacted in 2004, PUTSA defines a trade secret as information that has economic value from not being generally known and that the owner of the information takes reasonable steps to maintain the secrecy of the information (12 Pa.C.S.A. §5302).  PUTSA makes it a crime for anyone to “misappropriate” a trade secret through improper means or to disclose or use a trade secret without the consent of the owner (12 Pa.C.S.A. § 5302).  If a trade secret is misappropriated, the courts in Pennsylvania have the ability to grant a harmed party: (1) injunctive relief to stop the violation of the owners’ rights and to maintain the secrecy of the information (12 Pa.C.S.A. § 5303); (2) damages (12 Pa.C.S.A. § 5304); and (3) attorney’s fees (12 Pa.C.S.A § 5305).    

To determine what information qualifies as a trade secret, the Pennsylvania courts will look to the following factors: “(1) the extent to which the information is known outside of the company’s business; (2) the extent to which the information is known by employees and others involved in the company’s business; (3) the extent of the measures taken by the company to guard the secrecy of the information; (4) the value of the information to the company and its competitors; (5) the amount of effort or money the company spent in developing the information; and (6) the ease or difficulty with which the information could be acquired or duplicated legitimately by others.” (Bimbo Bakeries USA, Inc., v. Botticella, 613 F.3d 102 (C.A.3 Pa. 2010)). 

Therefore, if it can be established that agricultural Big Data information qualifies as a trade secret, a Pennsylvania farmer may have the ability to bring a civil action against an offending party.  Furthermore, while PUTSA is specific to Pennsylvania, forty states and the District of Columbia have also enacted similar legislation.   

Federal Economic Espionage Act

While Pennsylvania farmers may have state trade secret protection through PUTSA, the federal Economic Espionage Act (EEA) Protection of Trade Secrets (18 U.S. Code §§1831 – 1839) also offers the federal government the potential to criminally prosecute those that steal trade secrets. 

Enacted in 1996, EEA defines a trade secret as all “types of financial, business, scientific, technical, economic, or engineering information...if the owner therein has taken reasonable measures to keep such information secret; and the information derives independent economic value, actual or potential, from not being generally known to, and not being readily ascertainable through proper means by, the public” (18 U.S. Code § 1839).  Under EEA, anyone that “steals, or without authorization appropriates, takes, carries away, or conceals, or by fraud, artifice, or deception obtains such information” can be fined up to $5,000,000 and/or imprisoned up to 10 years (18 U.S. Code § 1832).

Providing that it can be established that a agricultural Big Data information meets the trade secret definition, and that the theft of that trade secret “is related to a product or service used in or intended for use in interstate or foreign commerce, to the economic benefit of anyone other than the owner,” federal prosecutors could possibly bring criminal charges (18 U.S. Code § 1832).

Agriculture Big Data Legal Issues and Protections: Part 5 - Legal Concerns Regarding the Freedom of Information Act

Written by M. Sean High – Staff Attorney

Some in the agricultural community have expressed concern that depending on where the agricultural Big Data is stored, this private information could be subject to public exposure under the Freedom of Information Act (FOIA).

Under FOIA, citizens have the right to access information from the federal government.  It is important to note, however, that FOIA only applies to government information and not private information. 

A citizen’s FOIA right to information includes “‘agency records’ maintained by ‘agencies’ within the executive branch of the federal government, including government corporations, government controlled corporations, and independent regulatory agencies.” While FOIA does not provide a definition for “‘agency records,’ in United States Dep’t of Justice v. Tax Analysts, 492 U.S. 136, the Supreme Court set forth a two-part test to determine what constitutes agency records pursuant to FOIA: (1) records that are either created or maintained by an agency, and (2) under agency control at the time the FOIA request is made.”

Recently, in the case American Farm BureauFederation v. United States Environmental Protection Agency, Civil No. 13–1751 ADM/TNL (2015), District Judge Ann D. Montgomery ruled that EPA (responding to a FOIA request submitted by environmental groups) could disclose agency records regarding the names, addresses, and GPS locations of certain farms keeping animals in close confinement.  As a result, farmers are now fearful that if at some point federal law requires that a federal agency maintain records for aggregated agricultural Big Data information, this currently private information will become susceptible to FOIA.

FOIA, however, does offer specific protections that exempt the disclosure of information that is a trade secret, commercial, or financial.  While FOIA does not provide a definition of the term “trade secret,” federal courts have generally regarded information to be “commercial or financial” if that information relates to business or trade.”

Agriculture Big Data Legal Issues and Protections: Part 4 - Legal Concerns Regarding the Use of Big Agricultural Data

Written by M. Sean High – Staff Attorney

Though agricultural Big Data offers the prospect of higher yields and greater profitability, many farmers are fearful of the potential consequences that could follow after their individual information has been submitted for aggregation and analysis.

Concerns regarding data ownership

Contracts between farmers and agricultural Big Data companies “are generally a license agreement whereby the farmer retains ownership of the information, [however,] most also give the companies free rein to conduct studies and use the data tocreate highly profitable products.” As a result, once a farmer’s individual agricultural information is transmitted to the agricultural Big Data companies and aggregated, the aggregated information is most likely owned by the agricultural Big Data companies.

Today, many large seed companies, “such as…Monsanto and DuPont are now as much data-technology companies as they are makers of…seeds.” As a result, farmers have raised conflict of interest concerns due to the seed companies’ financial incentive to encourage the planting of more their product.  In response, many agricultural Big Data companies have included farmer protections in their policy documents regarding the ownership and privacy of agricultural information.  These policy statements, however, are not legally binding agreements and may be subject to future revision.

Concerns regarding Wall Street

Farmers have also indicated concern that their agricultural data could fall into the hands of Wall Street commodities traders who would then use the information to make bets on futures contracts.  The concern is that the overall effect of these bets would lower futures-contract prices early in the growing season; thereby deny farmers the opportunity to make a profit through selling futures-contracts that lock-in higher crop prices.  

Concerns regarding other farmers

Finally, farmers have expressed fear that their information could be used by other farmers competing to rent the same farmland.  The concern is that through the use of information relating to crop yields, rival farmers will see untapped potential in rented farmland.  Farmers fear that this knowledge will cause competition for the rented farmland and ultimately result in higher land rents. 

Agriculture Big Data Legal Issues and Protections: Part 3 - What are the Benefits of Agricultural Big Data?

Written by M. Sean High – Staff Attorney

To create agricultural Big Data, relevant crop information is collected from individual farms and aggregated with similar information from other farms.  The aggregated information is then combined with “highly detailed records of historic weather patterns, topography and crop performance,” to create models and simulations that attempt to predict future conditions and help farmers make decisions that will improve yields and productivity.  As a result, instead of merely blanketing fields with arbitrary amounts of seed, water, and fertilizer, farmers are able to selectively apply these inputs to specifically targeted portions of the land.

Some agricultural Big Data companies have asserted that farmers utilizing agricultural Big Data can eventually increase their average corn harvest by an additional 40 bushels per acre.  While the majority of farmers currently employing agricultural Big Data have only seen corn yields increase by 5-10 bushels per acre, agricultural Big Data companies maintain that the higher yields will eventually be realized once additional information is gathered from more farmers and pooled.

Though the interest in agricultural Big Data is a relatively recent phenomenon, much of the agricultural information currently being utilized was available to farmers in the mid-1990s.  Lack of development of this agricultural information was primarily a result of underpowered computer processors and high data storage costs.  Today, however, the average smartphone has considerably more processing power than the top-of-the-line computers in the mid-1990s, and fees associated with most types of data storage are relatively low.  

By utilizing the availability of modern computer processing and data storage, researchers are now able to aggregate and analyze agricultural information to now find previously hidden patterns and signal—“those needles in the haystack.”  Importantly, because agricultural Big Data enables researchers to create predictive models that are based on actual farming results, as greater numbers of farmers permit their agricultural information to be collected and aggregated, researchers will be able to uncover additional needles in the haystack and provide increasingly more accurate predictions.

Agriculture Big Data Legal Issues and Protections: Part 2 - What is Agricultural Big Data?

Written by M. Sean High - Staff Attorney

The term agricultural Big Data generally refers to the collection, aggregation, and analysis of incredibly large amounts of agricultural information.  This available agricultural information is so vast that it is difficult to work with and therefore cannot be processed according to traditional methods.  As a result, agricultural Big Data requires advanced computer software and innovative analysis techniques.  The ultimate goal of this collection and analysis is to provide farmers with a tool to increase production through a precise and efficient use of resources.

The first step in the agricultural Big Data process is the collection of agricultural information from individual farms. 

Recent developments in farming practices have served to provide an incredible wealth of agricultural information.  Today, it is common practice for farmers to employ Global Positioning System (GPS) satellites to guide their tractors and combines.  Farmers that utilize this technology simply sit in the equipment cabs and monitor the progress of the machinery from computer tablets.  As a result, many farmers have been freed form the tedious task of steering and are now able to plant significantly straighter rows.  

Significantly, the same machinery currently used to guide farm equipment also has the potential to collect soil and crop information.  These highly developed tractors and combines are able to display in real time, on the same computer tablets utilized for steering, detailed planting and harvesting information regarding  where every seed is placed and what the current yields are.  Importantly, this information can also be recorded and collected for later analysis and use.   

In addition to information collected from tractors and combines, information may also be gathered through the use of sensors placed in fields that measure the temperature and humidity of the soil and surrounding air.  Furthermore, crop maturity may be monitored from images acquired through the use of satellite imagery.

An area that offers significant potential for crop monitoring and information collection is through the use of drones.  Drones are flying devices that do not have an “onboard pilot, use global positioning satellites (GPS) for guidance, and establish a microwave (“wifi”) data link to a control station on the ground.” These unmanned aircrafts are able to effectively cover large areas and collect vast amounts of agricultural information through the use of mounted cameras (one of which usually has infrared detection).” Relatedly, as a result of the ever increasing use of drones, in December 2015, the Federal Aviation Administration established new regulations regarding drone registration. 

Agriculture Big Data Legal Issues and Protections: Part 1 - Background

Written by M. Sean High - Staff Attorney

In recent years, the term “Big Data” has been used with increased frequency.  In general, Big Data refers to the modern practice of collecting and using computers to process incredibly large amounts of information for a designed purpose.  A common example of this would be when online companies collect information, based on social media activities, in order to present likely consumers with targeted advertisements.

By processing the collected information, Big Data promises businesses the potential to increase profits through a more efficient use of their limited resources.  In the example of online advertisement, recording social media activities and habits allow businesses to present consumers with products they are inclined to purchase and not with those they are unlikely to buy.  By employing this approach, advertising dollars are concentrated where they are likely to have the greatest affect.  While the monitoring of social media activities may offer businesses a significant marketing tool, it also raises questions regarding control of the collected data and the personal privacy of those being observed.

Recently, Big Data has become widely associated with agricultural production.  Proponents of agricultural Big Data assert that better understanding of agricultural information (such as that related to crop production) will allow farmers, like other businesses, to more efficiently use their limited resources (such as land, water, seed, and fertilizer).  Others in the agricultural community have been reluctant to embrace agricultural Big Data because of concerns over control of the information collected and loss of personal privacy; the same apprehensions associated with the monitoring of social media activities.

While most farmers have heard of the term agricultural Big Data, large numbers of them do not fully understand how agricultural Big Data affects (or potentially affects) their own agricultural operations.  Nevertheless, farmers are now being approached by companies offering to sell their agricultural Big Data services.  Because these farmers are being asked to decide on whether or not to utilize agricultural Big Data, it is now necessary that they understanding the meaning of the term agricultural Big Data; that they comprehend the key legal issues regarding agricultural Big Data; and that they become aware of the potential legal protections available to those who decide to utilize agricultural Big Data.

Big Data Big Questions Part II

Written by Stephen Kenney

In Part I of “Big Data Big Questions” we discussed Dr. Shannon Ferrell’s testimony before the House Agricultural Committee regarding agricultural big data.  Part I focused on his testimony about the existing intellectual property law and how it applies to agricultural data.  Part II will highlight the potential issues he raised concerning agricultural data collection.

Dr. Ferrell spoke of the many threats that have been realized in the disclosure of personally identifiable information (PII) to outside parties.   Some of those realized threats included the loss of credit card data in targeted hacks of Adobe Systems, Sony, Home Depot and Target among others.   These anecdotes contribute to the concern over the safety of agricultural data.   

According to Dr. Ferrell, most producers are concerned about their data being accessed when it is routed through a cellular signal to be disclosed to the service provider.  Most of the data that is sent to service providers is in the form of telematics data; raw data that has information concerning crop production and GIS information about the farm.  The first protection of this data comes in the form of the cellular signal.  Virtually all cellular signals are encrypted.  The transmitted data could only be stolen by a sophisticated hacker unless he had the decryption key.     Hackers are normally attracted to information that can easily be converted into financial value such as credit card information.  It is more difficult to convert agricultural data into financial gain quickly.   Thus systems storing agricultural data are less likely to be attacked.  Nevertheless, this data could be a more appealing target if the farmer’s vendor account information is linked to their data. 

Dr. Ferrell stated that another major concern for producers is the misuse or inadvertent disclosure of data by the recipient of the data.  One major concern would be the disclosure of that data to regulatory agencies.  There is little law on whether a government agency could simply request data from a service provider and attain it.  Another concern would be whether an opposing party in litigation or potential litigation could coax the service provider into disclosing a producer’s data, even if the disclosure is not legally required.  Dr. Ferrell concluded that, “Ultimately there are no laws defining an inherent privacy right in agricultural data.”  There are protections in place for healthcare data (HIPPA), financial data (Gramm-Leach Bliley Act and Fair Credit Reporting Act), and personal information held by the federal government (Privacy Act of 1974).  There are large categories of agricultural data that do not fall within a protected area.

Big Data Big Questions Part 1

Written by Stephen Kenney

On October 28, 2015, the House Agriculture Committee held a public hearing to discuss the impacts of big data on agriculture.  The hearing focused on the opportunities and challenges of managing and utilizing big data to improve yield production.  Shannon Ferrell, associate professor and faculty teaching fellow at Oklahoma State University Department of Agricultural Economics, spoke on the legal issues concerning big data in agriculture.

Dr. Ferrell explained the possible legal framework that will be used to analyze agricultural data.  Data is most akin to intellectual property.  Intellectual property is made up of four different categories including: trademark, patent, copyright, and trade secret. Trademark is irrelevant to the data discussion on its face.  Trademark is defined in the United States code as “any word, name symbol, or device or combination thereof” used to identify a particular good or product.  Data is not a particular word, symbol, device, or combination of the three so it cannot be a trademark.

Dr. Ferrell went into more detailed analysis of patent, copyright, and trade secret law in relation to agricultural data.  He concluded that data could not be defined under patent laws because patent law protects “inventions.”  Raw data cannot satisfy the definition of invention which generally means that the invention must be capable of performing its intended purpose, be different from existing knowledge in the field, and non-obvious (“if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains”).

The analysis of copyright also concluded that agricultural data did not fall into this legal category.  Dr. Ferrell cited a Supreme Court case Fiest Publications Inc. v. Rural Telephone Service Company, where the Court held that the Copyright Act does not protect individual facts.  The author must add some type of creative component to ensure the intellectual property falls under the Copyright laws.  Agricultural data is just a collection of facts relevant to the agricultural process so it is not copyrightable in itself, but it could lead to copyrightable works.  A report that summarizes the data and includes recommendations for further action could possibly be copyrightable.

The final conclusion was the big data most likely falls under trade secret protections.  He came to this conclusion by focusing on the definition of “trade secret” under the Uniform Trade Secrets Act which all but three states have adopted.  Dr. Ferrell noted that the definition makes clear that “information….pattern(s), [and] compilation(s)” can be protected by trade secret law.  Data is inherently a compilation of information.

The data must also be proven to have economic value from not being known to other parties and be subject to reasonable efforts to maintain the secret.”  The argument for the data having economic value is that farm data such as “planting rates, harvest yields, or outlines of fields and machinery paths must have economic value because such information is not generally known.”  The owner of a trade secret is required to take reasonable steps to ensure that the information does not become generally known.  “Reasonable steps” almost certainly requires that there be “some form of agreement in place between the disclosing party and the receiving party regarding how the receiving party must treat the received information.”  Dr. Ferrell ultimately concluded that the trade secret option provides the “best doctrinal fit” among the traditional intellectual property forms.