The concentrated ills of concentrated agribusiness

A review of Barons: Money, Power, and the Corruption of America’s Food Industry.

Also published on Resilience.

If you are a government-approved American hog farmer, you drive: a) a dusty pickup truck, from your barn to your local small-town feed store; b) a huge articulated tractor, through your thousand-acre fields of corn and soybeans; c) a private jet, which you fly from your midwestern corporate headquarters to a second or third home in Florida.

Barons, by Austin Frerick, published by Island Press, March 2024.

If you’ve read Austin Frerick’s new book Barons (Island Press, March 2024), you’ll pick the private jet. The hog farmer won’t drive to a small-town feed store, because small towns in agricultural areas are losing most of their businesses. The hog farmer won’t use a big tractor to till fields of corn and soybeans; as a hog specialist who raises no grain, he or she will buy feed “inputs” from big grain farmers who raise no animals.

But as two prominent US Department of Agriculture secretaries advocated, farmers should “get big or get out”. And a hog farmer who has really “got big” will want that private jet, either to get to a second home on the Gulf Coast or to make quick trips to Washington to lobby for subsidies and tax breaks.

In his highly readable book, Frerick describes the businesses of barons who dominate seven sectors of the US food industry. In the process he illuminates much in recent American history and goes a long way towards diagnosing environmental ills, socio-economic ills, and the ill health of so many food consumers.

Although two of the barons, Cargill Inc. and JAB Holding Company, are well over a hundred years old, all seven barons have seen explosive growth in the 40 years since the US government switched to very lax anti-trust regulations. Except for JAB (a little-known Luxembourg-based company that has recently swallowed coffee supply chains around the world), all the highlighted barons are US-based, and all are very much involved in international trade.

One of the companies is neither a grower, processor, nor retailer of food – its core businesses are in marketing and in owning and licensing genetics. Driscoll’s is the major brand of strawberries and several other berries sold in supermarkets in the US as well as in Canada. (Frerick writes that they control about one-third of the US berry market.) The company buys from 750 growers in two dozen countries, employing more than one hundred thousand people. The growers work to Driscoll’s specifications, but Driscoll’s has no legal responsibility to those hundred thousand workers.

Now that American consumers have learned to buy fresh – albeit nearly tasteless – fruit twelve months of the year, it’s essential for Driscoll’s to have suppliers in countries with different seasons. This has other business advantages, Frerick writes: “the Driscoll’s model is based on shifting farming out of the country to companies that don’t need to worry about US minimum wage laws or environmental regulations.”

For two of the barons profiled, most of the production as well as most of the environmental damage occurs closer to home. Jeff and Deb Hansen, who own that private jet from the opening paragraph, rule an empire known as Iowa Select which brings five million pigs to market each year. “Today,” French writes, “Iowa raises about one-third of the nation’s hogs, about as many as the second-, third-, and fourth-ranking states combined.”

Dairy barons Sue and Mike McCloskey own a vast complex in Indiana called Fair Oaks Farms. Besides being an (indoor) home to 36,000 dairy cows, and the midwest’s largest agri-tourism destination, Fair Oaks produces about 430,000 gallons of manure every day.

The huge hog, chicken, dairy or beef operations favoured by the current rules of the game share this problem – they produce far more manure than can be safely used to augment local soils. The result, in many locations across the country, is polluted groundwater, runoff that disrupts river and lake ecosystems – and an overpowering stench for residents unlucky enough to live just downwind.

For workers in the hog, dairy, berry, slaughter, and grocery businesses profiled by Frerich, working conditions are often dangerous and the pay is low. The book reflects on Upton Sinclair’s century-old classic The Jungle, in which immigrant workers toil for meagre wages in filthy and dangerous Chicago slaughterhouses. In the decades after Sinclair’s book became a runaway bestseller, workers unionized and working conditions and wages in slaughterhouses improved dramatically. Today, however, many of the unions have been defeated, many slaughterhouses have moved to small towns where there is little other opportunity for employment, and most workers once again are new immigrants who have little ability to fight back against employers.

The most widely recognized name in Barons is Walmart. The mega-retailer is far and away the largest grocer in the US. As such, there are obvious advantages in buying products in huge, uniform quantities – in short, products that barons in the hog, dairy, grain, and berry sectors are ideally suited to provide. It matters not whether these products are truly nutritious. What matters is whether the products are cheap and, in line with WalMart’s directives to suppliers, cheaper year after year. Still, French explains, not cheap enough for WalMart’s own employees to afford – WalMart employees in many states require government assistance just to feed their families.

Barons is not a long book – under 200 pages, not including the footnotes – but Frerick covers a lot of ground. He does not spend a lot of time discussing solutions, however, beyond some very good ideas sketched briefly in the Conclusion. Still, for people not already deeply familiar with industrial agribusiness and its associated environmental, labour, health and political ills, Barons is a compelling read.

Image at top of page: “State of the art lagoon waste management system for a 900 head hog farm,” photo by Jeff Vanuga for the United States Department of Agriculture, public domain, accessed on Wikimedia Commons.

Watching work

Bodies, Minds, and the Artificial Intelligence Industrial Complex, part five
Also published on Resilience.

Consider a human vs computer triathlon. The first contest is playing a cognitively demanding game like chess. The second is driving a truck safely through a busy urban downtown. The third is grabbing packages, from warehouse shelves stocked with a great diversity of package types, and placing them safely into tote boxes.

Who would win, humans or computers?

So far the humans are ahead two-to-one. Though a computer program passed the best human chess players more than 25 years ago, replacing humans in the intellectually demanding tasks of truck-driving and package-packing has proved a much tougher challenge.

The reasons for the skills disparity can tell us a lot about the way artificial intelligence has developed and how it is affecting employment conditions.

Some tasks require mostly analytical thinking and perceptual skills, but many tasks require close, almost instantaneous coordination of fine motor control. Many of these latter tasks fall into the category that is often condescendingly termed “manual labour”. But as Antonio Gramsci argued,

“There is no human activity from which every form of intellectual participation can be excluded: Homo faber cannot be separated from homo sapiens.”1

All work involves, to some degree, both body and mind. This plays a major role in the degree to which AI can or cannot effectively replace human labour.

Yet even if AI can not succeed in taking away your job, it might succeed in taking away a big chunk of your paycheque.

Moravec’s paradox

By 2021, Amazon had developed a logistics system that could track millions of items and millions of shipments every day, from factory loading docks to shipping containers to warehouse shelves to the delivery truck that speeds to your door.

But for all its efforts, it hadn’t managed to develop a robot that could compete with humans in the delicate task of grabbing packages off shelves or conveyor belts.

Author Christopher Mims described the challenge in his book Arriving Today2. “Each of these workers is the hub of a three-dimensional wheel, where each spoke is ten feet tall and consists of mail slot-size openings. Every one of these sorters works as fast as they can. First they grab a package off the chute, then they pause for a moment to scan the item and read its destination off a screen …. Then they whirl and drop the item into a slot. Each of these workers must sort between 1,100 and 1,200 parcels per hour ….”

The problem was this: there was huge diversity not only in packaging types but in packaging contents. Though about half the items were concealed in soft poly bags, those bags might contain things that were light and soft, or light and hard, or light and fragile, or surprisingly heavy.

Humans have a remarkable ability to “adjust on the fly”. As our fingers close on the end of a package and start to lift, we can make nearly instantaneous adjustments to grip tighter – but not too tight – if we sense significant resistance due to unexpected weight. Without knowing what is in the packages, we can still grab and sort 20 packages per minute while seldom if ever crushing a package because we grip too tightly, and seldom losing control and having a package fly across the room.

Building a machine with the same ability is terribly difficult, as summed up by robotics pioneer Hans Moravec.

“One formulation of Moravec’s paradox goes like this,” Mims wrote: “it’s far harder to teach a computer to pick up and move a chess piece like its human opponent than it is to teach it to beat that human at chess.”

In the words of robotics scholar Thrishantha Nanayakkara,

“We have made huge progress in symbolic, data-driven AI. But when it comes to contact, we fail miserably. We don’t have a robot that we can trust to hold a hamster safely.”3

In 2021 even Amazon’s newest warehouses had robots working only on carefully circumscribed tasks, in carefully fenced-off and monitored areas, while human workers did most of the sorting and packing.

Amazon’s warehouse staffers still had paying jobs, but AI has already shaped their working conditions for the worse. Since Amazon is one of the world’s largest employers, as well as a major player in AI, their obvious eagerness to extract more value from a low-paid workforce should be seen as a harbinger of AI’s future effects on labour relations. We’ll return to those changing labour relations below.

Behind the wheel

One job which the artificial intelligence industrial complex has tried mightily to eliminate is the work of drivers. On the one hand, proponents of autonomous vehicles have pointed to the shocking annual numbers of people killed or maimed on highways and streets, claiming that self-driving cars and trucks will be much safer. On the other hand, in some industries the wages of drivers are a big part of the cost of business, and thus companies could swell their profit margins by eliminating those wages.

We’ve been hearing that full self-driving vehicles are just a few years away – for the past twenty years. But driving is one of those tasks that requires not only careful and responsive manipulation of vehicle controls, but quick perception and quick judgment calls in situations that the driver may have seldom – or never – confronted before.

Christopher Mims looked at the work of tuSimple, a San Diego-based firm hoping to market self-driving trucks. Counting all the sensors, controllers, and information processing devices, he wrote, “The AI on board TuSimple’s self-driving truck draws about four times as much power as the average American home ….”4

At the time, tuSimple was working on increasing their system’s reliability “from something like 99.99 percent reliable to 99.9999 percent reliable.” That improvement would not come easily, Sims explained: “every additional decimal point of reliability costs as much in time, energy, and money as all the previous ones combined.”

Some of the world’s largest companies have tried, and so far failed, to achieve widespread regulatory approval for their entries in the autonomous-vehicle sweepstakes. Consider the saga of GM’s Cruise robotaxi subsidiary. After GM and other companies had invested billions in the venture, Cruise received permission in August 2023 to operate their robotaxis twenty-four hours a day in San Fransisco.5

Just over two months later, Cruise suddenly suspended its robotaxi operations nationwide following an accident in San Francisco.6

In the wake of the controversy, it was revealed that although Cruise taxis appeared to have no driver and to operate fully autonomously, things weren’t quite that simple. Cruise founder and CEO Kyle Vogt told CNBC that “Cruise AVs are being remotely assisted (RA) 2-4% of the time on average, in complex urban environments.”7

Perhaps “2–4% of the time” doesn’t sound like much. But if you have a fleet of vehicles needing help, on average, that often, you need to have quite a few remote operators on call to be reasonably sure they can provide timely assistance. According to the New York Times, the two hundred Cruise vehicles in San Francisco “were supported by a vast operations staff, with 1.5 workers per vehicle.”8 If a highly capitalized company can pay teams of AI and robotics engineers to build vehicles whose electronics cost several times more than the vehicle itself, and the vehicles still require 1.5 workers/vehicle, the self-driving car show is not yet ready for prime time.

In another indication of the difficulty in putting a virtual robot behind the wheel, Bloomberg News reported last month that Apple is delaying launch of its long-rumored vehicle until 2028 at earliest.9 Not only that, but the vehicle will boast no more than Level-2 autonomy. CleanTechnica reported that

“The prior design for the [Apple] vehicle called for a system that wouldn’t require human intervention on highways in approved parts of North America and could operate under most conditions. The more basic Level 2+ plan would require drivers to pay attention to the road and take over at any time — similar to the current standard Autopilot feature on Tesla’s EVs. In other words, it will offer no significant upgrades to existing driver assistance technology from most manufacturers available today.”10

As for self-driving truck companies still trying to tap the US market, most are focused on limited applications that avoid many of the complications involved in typical traffic. For example, Uber Freight targets the “middle mile” segment of truck journeys. In this model, human drivers deliver a trailer to a transfer hub close to a highway. A self-driving tractor then pulls the trailer on the highway, perhaps right across the country, to another transfer hub near the destination. A human driver then takes the trailer to the drop-off point.11

This model limits the self-driving segments to roads with far less complications than urban environments routinely present.

This simplification of the tasks inherent in driving may seem quintessentially twenty-first century. But it represents one step in a process of “de-skilling” that has been a hallmark of industrial capitalism for hundreds of years.

Jacquard looms, patented in France in 1803, were first brought to the U.S. in the 1820s. The loom is an ancestor of the first computers, using hundreds of punchcards to “program” intricate designs for the loom to produce. Photo by Maia C, licensed via CC BY-NC-ND 2.0 DEED, accessed at flickr.

Reshaping labour relations

Almost two hundred years ago computing pioneer Charles Babbage advised industrialists that “The workshops of [England] contain within them a rich mine of knowledge, too generally neglected by the wealthier classes.”12

Babbage is known today as the inventor of the Difference Engine – a working mechanical calculator that could manipulate numbers – and the Analytical Engine – a programmable general purpose computer whose prototypes Babbage worked on for many years.

But Babbage was also interested in the complex skeins of knowledge evidenced in the co-operative activities of skilled workers. In particular, he wanted to break down that working knowledge into small constituent steps that could be duplicated by machines and unskilled workers in factories.

Today writers including Matteo Pasquinelli, Brian Merchant, Dan McQuillan and Kate Crawford highlight factory industrialism as a key part of the history of artificial intelligence.

The careful division of labour not only made proto-assembly lines possible, but they also allowed capitalists to pay for just the quantity of labour needed in the production process:

“The Babbage principle states that the organisation of a production process into small tasks (the division of labour) allows for the calculation and precise purchase of the quantity of labour that is necessary for each task (the division of value).”13

Babbage turned out to be far ahead of his time with his efforts to build a general-purpose computer, but his approach to the division of labour became mainstream management economics.

In the early 20th century assembly-line methods reshaped labour relations even more, thanks in part to the work of management theorist Frederick Taylor.

Taylor carefully measured and noted each movement of skilled mechanics – and used the resulting knowledge to design assembly lines in which cars could be produced at lower cost by workers with little training.

As Christopher Mims wrote, “Taylorism” is now “the dominant ideology of the modern world and the root of all attempts at increasing productivity ….” Indeed,

“While Taylorism once applied primarily to the factory floor, something fundamental has shifted in how we live and work. … the walls of the factory have dissolved. Every day, more and more of what we do, how we consume, even how we think, has become part of the factory system.”14

We can consume by using Amazon’s patented 1-Click ordering system. When we try to remember a name, we can start to type a Google search and get an answer – possibly even an appropriate answer – before we have finished typing our query. In both cases, of course, the corporations use their algorithms to capture and sort the data produced by our keystrokes or vocal requests.

But what about remaining activities on the factory floor, warehouse or highway? Can Taylorism meet the wildest dreams of Babbage, aided today by the latest forms of artificial intelligence? Can AI not only measure our work but replace human workers?

Yes, but only in certain circumstances. For work in which mind-body, hand-eye coordination is a key element, AI-enhanced robots have limited success. As we have seen, where a work task can be broken into discrete motions, each one repeated with little or no variation, it is sometimes economically efficient to develop and build robots. But where flexible and varied manual dexterity is required, or where judgement calls must guide the working hands to deal with frequent but unpredicted contingencies, AI robotization is not up to the job.

A team of researchers at MIT recently investigated jobs that could potentially be replaced by AI, and in particular jobs in which computer vision could play a significant role. They found that “at today’s costs U.S. businesses would choose not to automate most vision tasks that have “AI Exposure,” and that only 23% of worker wages being paid for vision tasks would be attractive to automate. … Overall, our findings suggest that AI job displacement will be substantial, but also gradual ….”15

A report released earlier this month, entitled Generative Artificial Intelligence and the Workforce, found that “Blue-collar jobs are unlikely to be automated by GenAI.” However, many job roles that are more cerebral and less hands-on stand to be greatly affected. The report says many jobs may be eliminated, at least in the short term, in categories including the following:

  • “financial analysts, actuaries and accountants [who] spend much of their time crunching numbers …;”
  • auditors, compliance officers and lawyers who do regulatory compliance monitoring;
  • software developers who do “routine tasks—such as generating code, debugging, monitoring systems and optimizing networks;”
  • administrative and human resource managerial roles.

The report also predicts that

“Given the broad potential for GenAI to replace human labor, increases in productivity will generate disproportionate returns for investors and senior employees at tech companies, many of whom are already among the wealthiest people in the U.S., intensifying wealth concentration.”16

It makes sense that if a wide range of mid-level managers and professional staff can be cut from payrolls, those at the top of the pyramid stand to gain. But even though, as the report states, blue-collar workers are unlikely to lose their jobs to AI-bots, the changing employment trends are making work life more miserable and less lucrative at lower rungs on the socio-economic ladder.

Pasquinelli puts it this way:

“The debate on the fear that AI fully replaces jobs is misguided: in the so-called platform economy, in reality, algorithms replace management and multiply precarious jobs.”17

And Crawford writes:

“Instead of asking whether robots will replace humans, I’m interested in how humans are increasingly treated like robots and what this means for the role of labor.”18

The boss from hell does not have an office

Let’s consider some of the jobs that are often discussed as prime targets for elimination by AI.

The taxi business has undergone drastic upheaval due to the rise of Uber and Lyft. These companies seem driven by a mission to solve a terrible problem: taxi drivers have too much of the nations’ wealth and venture capitalists have too little. The companies haven’t yet eliminated driving jobs, but they have indeed enriched venture capitalists while making the chauffeur-for-hire market less rewarding and less secure. It’s hard for workers to complain to or negotiate with the boss, now that the boss is an app.

How about Amazon warehouse workers? Christopher Mims describes the life of a worker policed by Amazon’s “rate”. Every movement during every warehouse worker’s day is monitored and fed into a data management system. The system comes back with a “rate” of tasks that all workers are expected to meet. Failure to match that rate puts the worker at immediate risk of firing. In fact, the lowest 25 per cent of the workers, as measured by their “rate”, are periodically dismissed. Over time, then, the rate edges higher, and a worker who may have been comfortably in the middle of the pack must keep working faster to avoid slipping into the bottom 25th percentile and thence into the ranks of the unemployed.

“The company’s relentless measurement, drive for efficiency, loose hiring standards, and moving targets for hourly rates,” Mims writes, “are the perfect system for ingesting as many people as possible and discarding all but the most physically fit.”19 Since the style of work lends itself to repetitive strain injuries, and since there are no paid sick days, even very physically fit warehouse employees are always at risk of losing their jobs.

Over the past 40 years the work of a long-distance trucker hasn’t changed much, but the work conditions and remuneration have changed greatly. Mims writes, “The average trucker in the United States made $38,618 a year in 1980, or $120,000 in 2020 dollars. In 2019, the average trucker made about $45,000 a year – a 63 percent decrease in forty years.”

There are many reasons for that redistribution of income out of the pockets of these workers. Among them is the computerization of a swath of supervisory tasks. In Mims words, “Drivers must meet deadlines that are as likely to be set by an algorithm and a online bidding system as a trucking company dispatcher or an account handler at a freight-forwarding company.”

Answering to a human dispatcher or payroll officer isn’t always pleasant or fair, of course – but at least there is the possibility of a human relationship with a human supervisor. That possibility is gone when the major strata of middle management are replaced by AI bots.

Referring to Amazon’s 25th percentile rule and steadily rising “rate”, Mims writes, “Management theorists have known for some time that forcing bosses to grade their employees on a curve is a recipe for low morale and unnecessarily high turnover.” But low morale doesn’t matter among managers who are just successions of binary digits. And high turnover of warehouse staff isn’t a problem for companies like Amazon – little is spent on training, new workers are easy enough to find, and the short average duration of employment makes it much harder for workers to get together in union organizing drives.

Uber drivers, many long-haul truckers, and Amazon packagers have this in common: their cold and heartless bosses are nowhere to be found; they exist only as algorithms. Management-by-AI, Dan McQuillan says, results in “an amplification of casualized and precarious work.”20

Management-by-AI could be seen, then, as just another stage in the development of a centuries-old “counterfeit person” – the legally recognized “person” that is the modern corporation. In the coinage of Charlie Stross, for centuries we’ve been increasingly governed by “old, slow AI”21 – the thinking mode of the corporate personage. We’ll return to the theme of “slow AI” and “fast AI” in a future post.


1 Antonio Gramsci, The Prison Notebooks, 1932. Quoted in The Eye of the Master: A Social History of Artificial Intelligence, by Matteo Pasquinelli, Verso, 2023.

2 Christopher Mims, Arriving Today: From Factory to Front Door – Why Everything Has Changed About How and What We Buy, Harper Collins, 2021; reviewed here.

3 Tom Chivers, “How DeepMind Is Reinventing the Robot,” IEEE Spectrum, 27 September 2021.

4 Christopher Mims, Arriving Today, 2021, page 143.

5 Johana Bhuiyan, “San Francisco to get round-the-clock robo taxis after controversial vote,” The Guardian, 11 Aug 2023.

6 David Shepardson, “GM Cruise unit suspends all driverless operations after California ban,” Reuters, 27 October 2023.

7 Lora Kolodny, “Cruise confirms robotaxis rely on human assistance every four to five miles,CNBC, 6 Nov 2023.

8 Tripp Mickle, Cade Metz and Yiwen Lu, “G.M.’s Cruise Moved Fast in the Driverless Race. It Got Ugly.” New York Times, 3 November 2023.

9 Mark Gurman, “Apple Dials Back Car’s Self-Driving Features and Delays Launch to 2028”, Bloomberg, 23 January 2024.

10 Steve Hanley, “Apple Car Pushed Back To 2028. Autonomous Driving? Forget About It!”, 27 January 2024.

11 Marcus Law, “Self-driving trucks leading the way to an autonomous future,” Technology, 6 October 2023.

12 Charles Babbage, On the Economy of Machinery and Manufactures, 1832; quoted in Pasquinelli, The Eye of the Master, 2023.

13 Pasquinelli, The Eye of the Master.

14 Christopher Mims, Arriving Today, 2021.

15 Neil Thompson et al., “Beyond AI Exposure: Which Tasks are Cost-Effective to Automate with Computer Vision?”, MIT FutureTech, 22 January 2024.

16 Gad Levanon, Generative Artificial Intelligence and the Workforce, The Burning Glass Institute, 1 February 2024.

17 Pasquinelli, The Eye of the Master.

18 Crawford, Kate, Atlas of AI, Yale University Press, 2021.

19 Christopher Mims, Arriving Today, 2021.

20 Dan McQuillan, Resisting AI: An Anti-Fascist Approach to Artificial Intelligence,” Bristol University Press, 2022.

21 Charlie Stross, “Dude, you broke the future!”, Charlie’s Diary, December 2017.


Image at top of post: “Mechanically controlled eyes see the controlled eyes in the mirror looking back”, photo from “human (un)limited”, 2019, a joint exhibition project of Hyundai Motorstudio and Ars Electronica, licensed under CC BY-NC-ND 2.0 DEED, accessed via flickr.

A fascinating, flawed look at limits

A review of The Wizard and The Prophet

Also published at

Charles C. Mann has written consecutive bestsellers of popular history writ large. His 1491 surveyed the civilizations of the pre-Columbian Americas, while 1493 looked at how post-Columbian America has affected the whole world.

The Wizard and the Prophet, by Charles C. Mann, 2018, 616 pages

The Wizard and the Prophet at first glance shows Mann at work on a smaller canvas, comparing the life’s work of two American scientists in the mid-20th century.

Though Norman Borlaug and William Vogt both studied agricultural resources their career trajectories could hardly have been more different. Mann uses the contrast as a framework for a sweeping discussion of the biggest environmental questions facing our generations.

In the process he transforms Borlaug into “The Wizard” and Vogt into “The Prophet’’, superheroes who have, in Mann’s telling, guided the two major currents in environmental thinking ever since. Thus “The Wizard” and “The Prophet” are tapped for analyses of subjects which, for all we know, neither Borlaug nor Vogt actually thought about.

Always lurking in the background are the questions with which Mann opens the book: is it possible to feed, clothe, and shelter 10 billion people on this planet, or are we moving towards inevitable environmental collapse?

The real Norman Borlaug was born to a poor Iowa farm family and he yearned to escape the backbreaking work in the fields. After earning a degree in plant pathology he found himself immersed in even more tedious manual labour in a dusty, eroded, wind-blown patch of dirt outside Mexico City. His goal was to find a variety of wheat that would resist the blight known as rust.

Borlaug planted eight thousand wheat varieties the first season and came up with exactly four rust-resistant varieties. But he eventually developed strains of “dwarf” wheat that not only resisted rust, but which did not blow over in the wind and which responded well to artificial fertilizers. This development became known as the “Green Revolution”, and earned Borlaug a Nobel Peace Prize. He continued to work nearly up to his death in 2009 at the age of ninety-five, with advocacy for genetic engineering a theme of his later writings.

William Vogt was publicly lionized long before Borlaug came to fame, yet he too did his key research in an unglamorous setting: the guano-caked islands off Peru’s coast. For half a century the nitrogen-rich excrement of Guanay cormorants had been a key resource for world agriculture. Peru’s government wanted to know: why did the population of cormorants sometimes crash, and could they safeguard the marvellous output of fertilizer?

While Borlaug’s work rewarded a rigorous focus on detail, Vogt approached his task with the wide-angle lens of ecology. He tied cormorant populations to the ups and downs of the anchovetas which fed the birds; the plankton which fed the anchovetas; and the alternately warm or cold ocean currents of El Niño or La Niña which fed or starved the plankton. The maximum numbers of cormorants as well as their periodic crashes, Vogt reported, were set by nature’s own limits, and it would be foolhardy to push against those limits.

Vogt extended this message of limits in his 1948 book Road to Survival. He believed too much consumption is ecologically disastrous, and this consumption is based on both population growth and the quest for continuing economic growth. Road to Survival was a runaway best-seller.

Trending to infinity

Mann’s story-telling skills shine when he’s narrating the life and times of Borlaug, Vogt and the colourful characters they worked with. When The Wizard and the Prophet embarks on a 200-page tour of today’s many global ecology challenges, Mann’s discursions are fascinating but the quality is uneven.

An overview of world agriculture contrasts the Green Revolution with small-scale “organic” approaches. Yet Mann winds up that chapter without posing an obvious question. The artificial fertilizers required by Green Revolution crops are based on an energy-intensive process with natural gas as a feedstock, but can we be confident we have affordable resources to maintain, let alone double, current fertilizer production?

Through most of the book Mann recognizes the value in Vogt’s arguments for limits as well as Borlaug’s success in at least temporarily pushing those limits. That even-handedness is gone in his chapter on energy supply. Responding to the fear that fossil fuel resources might soon run short, Mann espouses Cornucopianism with an enthusiasm that would make a tar-sands tycoon blush.

In Mann’s reading of history the mere thought of “peak oil” has produced such infelicities as 75 years of war and tyranny in the Middle East. Though in some mere physical sense fossil fuel reserves must be limited, Mann argues, they are economically infinite – and economics trumps physics. That may be “counterintuitive”, he admits, “but more than a century of experience has shown it to be true.” If a trend lasts 100 years, apparently, we should feel confident it will be sustained for all time.

His chapter on climate change has more grounding in science and reason, but is badly dated. He relies on the 2014 report of the Intergovernmental Panel on Climate Change, a necessarily conservative consensus review of thousands of reports published in prior years, which gave a likely range of global temperature increases from 1.5° to 4.5° Celsius.

Mann uses the IPCC’s temperature range and probability estimates to conclude “Very roughly speaking, this translates into a one-out-of-six chance that nothing much will happen – and a one-out-of-six chance of complete disaster.” When Stewart Brand used a similar one-in-six analogy in his 2009 book Whole Earth Discipline it was somewhat plausible. But since that time, measured global warming has been consistently outrunning the IPCCs cautious projections, many climatologists warn that we’ve already passed any chance of keeping global warming to less than 2°C, and the possible outcomes now run along a spectrum of biospheric  and civilizational catastrophes.

Vogt’s 1948 Road to Survival was a bestseller, but by the mid-1960s he found it hard to get a hearing in major media. Borlaug’s 1970 Nobel Prize was the first of a series of accolades that continued for the next 40 years. (Photo of statue in US Capitol building by Architect of the Capitol)

While Borlaug was influential to the end of his long life Vogt’s career flamed out early. In the 1950s he turned to population control as the single overriding issue, leading to a stormy tenure  at the helm of Planned Parenthood. Publishers and book buyers lost interest in his writings and he slid into despair. In 1968 – two years before Borlaug won his Nobel Prize – Vogt was gone, dead by his own hand.

Had he lived another fifty years to see 7 billion people trying to secure a subsistence on a planet already suffering from climate change, it’s hard to imagine that he would have regained hope.


Photos at top: Norman Borlaug in Mexico, 1964, photo from Centro Internacional de Mejoramiento de Maíz y Trigo. William Vogt, 1940, promotional photo from Compañia Administradora del Guano

Super-size that commodity

Also published at

A review of ‘A Foodie’s Guide to Capitalism’

Don’t expect a whole lot of taste when you sit down to a plateful of commodities.

That might be a fitting but unintended lesson for foodies who work through the new book by Eric Holt-Giménez. A Foodie’s Guide to Capitalism will reward a careful reader with lots of insights – but it won’t do much for the taste buds.

While A Foodie’s Guide is lacking in recipes or menu ideas, it shines in helping us to understand the struggles of the men and women who work in the farms and packing plants. Likewise, it explains why major capitalists have typically shown little interest in direct involvement in agriculture – preferring to make their money selling farm inputs, trading farm commodities, or turning farm products into the thousands of refined products that fill supermarket shelves.

Fictitious commodities

Karl Polanyi famously described land, labour and money as “fictitious commodities”. Land and labour in particular come in for lengthy discussion in A Foodie’s Guide to Capitalism. In the process, Holt-Giménez also effectively unmasks the myth of the free market.

“Markets have been around a long time,” he writes, “but before the nineteenth century did not organize society as they do today.” He shows how capitalism in England arose concurrently with vigorous state intervention which drove people off their small farms and into the industrial labour pool. Meanwhile overseas both the slave trade and settler colonialism were opening critical parts of global markets, which were anything but “free”.

Nevertheless the takeover of food production by capitalism has been far from complete.

“Today, despite centuries of capitalism, large-scale capitalist agriculture produces less than a third of the world’s food supply, made possible in large part by multibillion-dollar subsidies and insurance programs. Peasants and smallholders still feed most people in the world, though they cultivate less than a quarter of the arable land.” (Holt-Giménez, A Foodie’s Guide To Capitalism, Monthly Review Press and FoodFirst Books, citing a report in GRAIN, May 2014)

There are a lot of reasons for this incomplete transition, but many are related to two of the “fictitious commodities”. Let’s start with land.

While land is the most important “means of production” in agriculture, land is of course much more than that. For people throughout history, land has been home, land has been the base of culture, land has been sacred. Even today, people go to great lengths to avoid having their lands swallowed up by capitalist agriculture – especially since this transition typically results in widespread consolidation of farms, leaving most former farmers to try to earn a living as landless labourers.

Autumn colours in the Northumberland Hills north of Lake Ontario, Canada

Likewise labour is much more than a commodity. An hour of labour is a handy abstraction that can be fed into an economist’s formula, but the labourer is a flesh-and-blood human being with complex motivations and aspirations. Holt-Giménez offers a good primer in Marxist theory here, showing why it has always been difficult for capitalists to extract surplus value directly from the labour of farmers. He also builds on the concept of the “cost of reproduction” in explaining why, in those sectors of farming that do depend on wage labour, most of the wage labourers are immigrants.

Before people can be hired at wages, they need to be born, cared for as infants, fed through childhood, provided with some level of education. These “costs of reproduction” are substantial and unavoidable. A capitalist cannot draw surplus value from labour unless some segment of society pays those “costs of reproduction”, but it is in the narrow economic self-interest of capitalists to ensure that someone else pays. Consider, for example, the many Walmart employees who rely on food stamps to feed their families. Since Walmart doesn’t want to pay a high enough wage to cover the “cost of reproduction” for the next generation of workers, a big chunk of that bill goes to taxpayers.

In industrialized countries, the farm workers who pick fruit and vegetables or work in packing plants tend to be immigrants on temporary work permits. This allows the capitalist food system to pass off the costs of reproduction, not to domestic taxpayers, but to the immigrants’ countries of origin:

“the cost of what it takes to feed, raise, care for and educate a worker from birth to working age (the costs of reproduction) are assumed by the immigrants’ countries of origin and is free to their employers in the rich nations, such as the United States and the nations of Western Europe. The low cost of immigrant labor works like a tremendous subsidy, imparting value to crops and agricultural land. This value is captured by capitalists across the food chain, but not by the worker.” (Holt-Giménez, A Foodie’s Guide to Capitalism)

Farmstead in the Black Hills, South Dakota, USA

The persistence of the family farm

In the US a large majority of farms, including massive farms which raise monoculture crops using huge machinery, are run by individual families rather than corporations. Although they own much of their land, these farmers typically work long hours at what amounts to less than minimum wage, and many depend on at least some non-farm salary or wage income to pay the bills. Again, there are clear limitations in a capitalist food system’s ability to extract surplus value directly from these hours of labour.

But in addition to selling “upstream” inputs like hybrid and GMO seeds, fertilizers, pesticides and machinery, the capitalist food system dominates the “downstream” process of trading commodities, processing foods, and distributing them via supermarket shelves. An important recent development in this regard is contract farming, which Holt-Giménez refers to as “a modern version of sharecropping and tenant farming”.

A large corporation contracts to buy, for example, a chicken farmer’s entire output of chickens, at a fixed price:

“Through a market-specification contract, the firm guarantees the producer a buyer, based on agreements regarding price and quality, and with a resource-providing contract the firm also provides production inputs (like fertilizer, hatchlings, or technical assistance). If the firm provides all the inputs and buys all of the product, it essentially controls the production process while the farmer basically provides land and labor ….”

The corporation buying the chickens gets the chance to dominate the chicken market, without the heavy investment of buying land and buildings and hiring the workforce. Meanwhile farmers with purchase contracts in hand can go to the bank for operating loans, but they lose control over most decisions about production on their own land. And they bear the risk of losing their entire investment – which often means losing their home as well – if the corporation decides the next year to cancel the contract, drop the price paid for chicken, or raise the price of chicken feed.

Contract farming dominates the poultry industry in the US and the pork market is now rapidly undergoing “chickenization”. Holt–Giménez adds that “The World Bank considers contract farming to be the primary means for linking peasant farmers to the global market and promotes it widely in Asia, Latin America, and Africa.”

Farm field in springtime, western North Dakota, USA

Feeding a hungry world

In North America the conventional wisdom holds that only industrial capitalist agriculture has the ability to provide food for the billions of people in today’s world. Yet on a per hectare basis, monoculture agribusiness has been far less productive than many traditional intensive agricultures.

“Because peasant-style farming usually takes place on smaller farms, the total output is less than capitalist or entrepreneurial farms. However, their total output per unit of land (tons/hectare; bushels/acre) tends to be higher. This is why, as capitalist agriculture converts peasant-style farms to entrepreneurial and capitalist farms, there is often a drop in productivity ….”

Marxist political-economic theory provides a useful basis for Holt-Giménez’ explorations of many aspects of global food systems. Among the topics he covers are the great benefits of the Green Revolution to companies marketing seeds and fertilizers, along with the great costs to peasants who were driven off their lands, and potentially catastrophic damages to the ecological web.

But an over-reliance on this theory, in my opinion, leads to an oversimplification of some of our current challenges. This is most significant in Holt-Giménez’s discussions of the overlapping issues of food waste and the failure to distribute farm outputs fairly.

In recent decades there has been a constant surplus of food available on world markets, while hundreds of millions of people have suffered serious malnutrition. At the same time we are often told that approximately 40% of the world’s food goes to waste. Surely there should be an easy way to distribute food more justly, avoid waste, and solve chronic hunger, no?

Yet it is not clear what proportion of food waste is unavoidable, given the vagaries of weather that may cause a bumper crop one year in one area, or rapid increases in harvest-destroying pests in response to ecological changes. It is easy to think that 40% waste is far too high – but could we reasonably expect to cut food waste to 5%, 10% or 20%? That’s a question that Holt-Giménez doesn’t delve into.

On the other hand he does pin food waste very directly on capitalist modes of production. “The defining characteristic of capitalism is its tendency to overproduce. The food system is no exception.” He adds, “The key to ending food waste is to end overproduction.”

Yet if food waste is cut back through a lowering of production, that in itself is of no help to those who are going hungry.

Holt-Giménez writes “Farmers are nutrient-deficient because they don’t have enough land to grow a balanced diet. These are political, not technical problems.” Yes, access to land is a critical political issue – but can we be sure that the answers are only political, and not in part technical as well? After all, famines predated capitalism, and have occurred in widely varying economic contexts even in the past century.

Particularly for the coming generations, climatic shifts may create enormous food insecurities even for those with access to (formerly sufficient) land. As George Monbiot notes in The Guardian this week, rapid loss of topsoil on a world scale, combined with water scarcity and rising temperatures, is likely to have serious impacts on agricultural production. Facing these challenges, farming knowledge and techniques that used to work very well may require serious adaptation. So the answers are not likely to be political or technical, but political and technical.

These critiques aside, Holt-Giménez has produced an excellent guidebook for the loose collection of interests often called “the food movement”. With a good grasp of the way capitalism distorts food production, plus an understanding of the class struggles that permeate the global food business, foodies stand a chance of turning the food movement into an effective force for change.