Blog

Labeling Food Processes: The Good, the Bad and the Ugly

That's the title of an interesting new article in the journal Applied Economic Perspectives and Policy by Kent Messer, Marco Costanigro, and Harry Kaiser.  Here's the abstract:

Consumers are increasingly exposed to labels communicating specific processing aspects of food production, and recent state and federal legislation in the United States has called for making some of these labels mandatory. This article reviews the literature in this area and identifies the positive and negative aspects of labeling food processes. The good parts are that, under appropriate third-party or governmental oversight, process labels can effectively bridge the informational gap between producers and consumers, satisfy consumer demand for broader and more stringent quality assurance criteria, and ultimately create value for both consumers and producers. Despite the appeal of the “Consumer Right to Know” slogan, process labeling also can have serious unintentional consequences. The bad parts are that consumers can misinterpret these labels and thus misalign their personal preferences and their actual food purchases. The ugly parts are that these labels can stigmatize food produced with conventional processes even when there is no scientific evidence that they cause harm, or even that it is compositionally any different. Based on this review of the literature, we provide three policy recommendations: (i) mandatory labeling of food processes should occur only in situations in which the product has been scientifically demonstrated to harm human health; (ii) governments should not impose bans on process labels because this approach goes against the general desire of consumers to know about and have control over the food they are eating, and it can undermine consumer trust of the agricultural sector; and (iii) a prudent policy approach is to encourage voluntary process labeling, perhaps using smart phone technology similar to that proposed in 2016 federal legislation related to foods containing ingredients that were genetically engineered.

Technology and evolving supply chains in the beef and pork industries

That's the title of a new article in Food Policy written by Josh Maples, Darrell Peel, and me.  The paper will ultimately be part of a special issue on technology and supply chains. 

Here is part of the lead in.

The structural shifts in the beef and pork industries have occurred alongside (and perhaps because of) technological innovation and its effect on the flow of these meats through their respective supply chains. Technology innovations have been a major factor in the changing economics of the beef and pork industries. Improved nutrition, growth promotion technologies, better genetics, and economic conditions have all played a role in livestock becoming more efficient (Lusk, 2013). The values of improved technologies between 1977 and 2012 in the beef and pork sectors have been estimated at $11 billion annually and $7.6 billion annually, respectively (Lusk, 2013).

We discuss the nature and causes of different market structure in the beef and pork industries.

Even with the presence of marketing agreements, the beef industry is easily the least vertically integrated of the big three protein industries (Ward, 1997). The key reasons for this revolve around the aforementioned asset specificity as well as the biological makeup of cattle. There is a greater incentive to vertically integrate or engage in contracting in livestock industries in which genetic changes can be made more rapidly (Ward, 1997). The biological production cycle is about two years for cattle, which is twice as long as that of hogs and the genetic base of cattle is relatively diverse and is not narrowing (Ward, 1997). Alternatively, market coordination has allowed the pork industry genetic base to narrow toward the most efficient hogs for production. The number of hogs marketed today is 29 percent greater than in 1959 from a breeding stock that is 39 percent smaller (Boyd and Cady, 2012). Geographical concentration also plays an important role. During the cow-calf stage, cattle are scattered throughout the U.S. due to the required land and forage needed while hog production is centered in the Midwest (and more recently the Southeast) near the heaviest corn-producing states. These factors create significant barriers to integration in the beef industry.

And, we discuss the impacts of various technologies on the industries.  Here's a segment on effects of pharmaceutical innovations in the cattle industry.

Vaccinations, parasite control, ionophores, antibiotics, growth promotant implants (often referred to as growth-promoting hormones), and beta-agonists have been the most widely-used of these innovations (Arita et al., 2014 ; APHIS, 2013). The productivity and economic impacts of these technologies are large. Lawrence and Ibarburu (2007) estimated that the cumulative direct cost savings of the technologies was over $360 per head for cattle over the lifetime of an animal while Capper and Hayes (2012) estimated that the increased cost of U.S. beef production without growth enhancing technologies would be the equivalent of an 8.2 percent tax on beef. Elam and Preston (2004) discussed each of these technologies at length in their summary of the technological impact in the beef industry. They found that growth implants increase rate of gain by 15–20 percent and improve feed efficiency 8–12 percent. Growth-promoting hormone implants are believed to be used on approximately 90 percent of cattle in U.S. feedlots (Johnson, 2015). Elam and Preston (2004) also found ionophores increase average daily gain by 1–6 percent and improve feed efficiency by 6–8 percent. Lawrence and Ibarburu (2007) used a meta-analysis approach to find estimates for the farm level economic value of these five technologies in the beef industry. They estimated that beta agonists improve feedlot average daily gain by 14 percent and that the combination of implants, ionophores, antibiotics and beta-agonists account for a 37 percent increase in average daily gain in feedlots. These increases in feed and gain efficiency have direct effects on the profitability per animal. Lawrence and Ibarburu (2007) estimated that sub-therapeutic antibiotics impact cattle profitability by $5.86 per head, ionophores have an $11–$13 impact, and the use beta-agonists impacts per head profitability by $13.02 per head. The use of growth promoting implants has the largest impact on cattle profitability at between $68 and $77 per head ( Lawrence and Ibarburu, 2007; Wileman et al., 2009).

Testing Public Knowledge about Food and Agriculture

Kudos to Bailey Norwood and Susan Murray at Oklahoma State who have keep the Food Demand Survey (FooDS) alive and well.  In the August 2017 edition of FooDS, they asked a series of questions related to consumers knowledge about food.  The results are fascinating.  

First, they sought to weigh in on the claim that went viral last month: that 7% of consumers thought chocolate milk came from brown cows.  In this month's FooDS, they found only 1.6% of the American public held this belief when given various options for how chocolate milk is made.

Read the whole report for more interesting findings, such as:

  • 23% of respondents thought gluten as a preservative or additive to make bread whiter;
  • 79% correctly knew how soy milk is made;
  • 18% think the sun revolves around the earth (yes, you read that right);
  • 95%+ correctly identified broccoli as a vegetable and beef as coming from cows;
  • only 28% correctly knew that Trump likes his steaks well done (a plurality thought he likes it medium rare);
  • 15% thought I was the secretary of agriculture (no I didn't put them up to this; Sonny Perdue was picked by 37% of respondents and Michael Pollan by 29%)
  • 99% of respondents said they took their answers to the previous questions seriously.  

National Academies Town Hall

Last week I gave a short talk at a Town Hall held at the National Academy of Science Building in Washington, D.C. in relation to the Science Breakthroughs 2030 project aimed at identifying strategies for food and agricultural research.  

You can see all the presentations here.  Or, if you just want to see my comments and provocations entitled "Importance of Understanding Behavioral Responses to Food and Health Policies", the video is embedded below.

How Expenses Vary with Farm Size

I've been a bit surprised at the number of comments and questions I continue to receive about this article I wrote for the New York Times almost a year ago.

Here are the opening sentences from the piece:

There is much to like about small, local farms and their influence on what we eat. But if we are to sustainably deal with problems presented by population growth and climate change, we need to look to the farmers who grow a majority of the country’s food and fiber.

Large farmers — who are responsible for 80 percent of the food sales in the United States, though they make up fewer than 8 percent of all farms, according to 2012 data from the Department of Agriculture — are among the most progressive, technologically savvy growers on the planet. Their technology has helped make them far gentler on the environment than at any time in history. And a new wave of innovation makes them more sustainable still.

Common questions I tend to get are "who are these large farms" and "do large farms use more or less fertilizer or chemicals than small farms?"  On the first question, I simply rely on USDA's classification of farms based on gross sales (which is where the above 80% from 8% originates).  The second types of questions are much more difficult to answer as there isn't great data easily accessible on the matter.

However, I recently ran across this USDA, National Agricultural Statistics Service (NASS) publication that reports farm expenses for different sized farms (again, where size is determined by gross sales).  These data are part of the Economic Research Service (ERS), Agricultural Resource Management Survey (ARMS).  Using the 2016 data in this publications, I created the following charts to help provide some perspective on how relatively small, medium, and large farms allocate their spending.

Here are relatively small farms.

The spending of relatively medium-sized farms is illustrated below.

Finally, here are graphics on spending by the largest farms.

A few comments on the comparisons are in order.  First, as indicated by the share of spending on livestock, poultry, and feed, there are different types of farms across size categories, so it's a bit like comparing apples to oranges.  The largest farms are most likely dairies, feedlots, or hog/poultry operations.  The proportion of crop output (as a share of total output) is likely higher for small and medium sized farms.  What we'd like to compare are small crop farms to large crop farms, but that data wasn't easily obtainable.   

The figures show that three categories of spending (as a share of total spending) fall as farms sizes increase: farm improvement and construction, tractors and trucks, and taxes and interest. This relates to some of what I argued in the NYT piece:  

But increased size has advantages, especially better opportunities to invest in new technologies and to benefit from economies of scale. Buying a $400,000 combine that gives farmers detailed information on the variations in crop yield in different parts of the field would never pay on just five acres of land; at 5,000 acres, it is a different story.

On two of the issues which people worry about the most - chemicals and fertilizers - these expenses tend to increase (again, as a share of total expenses) as size goes from small to medium than falls when going from medium to large.  However, some of this change is almost certainly due to the different mix of crops vs. livestock in the different size categories, so it's difficult to draw much of a conclusion from these data.   

Finally, I'll note the small sized categories of farm (less than $10,000 in gross sales) lose money on average.  Why?  Because, by definition, they're  bringing in less than $10,000 in revenue, but they're spending $13,755.  These farms need to generate at least $3,755 in additional annual value per farm to the farm owners, to their patrons, or to their neighbors that isn't reflected in market price for their activities to yield a net benefit to society.