The Myth of the Global Cow

Posted by Martin Cohen

Data crunchers have started to attack farms on the basis of statistical creations such as ‘The Global Cow’. Of course, there’s no such thing. The sublimation of differences in concepts like the average cow, leaves cows and sheep who are helpfully and quietly grazing grass suddenly accused of inefficiently expropriating vast tranches of valuable land, while farmers keeping animals fed soya in sheds can be reinvented and presented as efficient and ‘climate friendly’. And yet summarised and simplified messages creatively abstracted from the data itself construct a global picture, skewed by preconceived ideas, and designed to influence policy decisions.

    • The idea of ‘the Earth's average temperature’ is also an exercise in mental gymnastics - which parts of the oceans are included - or of the atmosphere? Does it make sense to have hypothetical data points in uninhabited regions? Even NASA and the Met Office cannot agree. 

   • Food policy in particular always seem to consist of sharp, Manichean (good versus evil), divisions even as most things are nuanced and a matter of detail - and degree. Missing from both types of thinking is any acknowledgement that the experts behind the expert consensus are also political and ideological subjects, and the vast majority of respected science (or any research) is produced from a mainstream and shaped by the policy objectives of funders.   

But let’s just take up that idea of a ‘global cow’. Even small farms can be completely different in terms of differing habitats and differing good or really bad practices in one place. Last year I had a series of email exchanges with a Welsh couple in the Brecon Beacons (on the England/ Wales border) about their efforts to graze farm animals ‘sustainably’. The two explained how they have mountain grazing rights on the Brecon beacons and have cattle grazing an ancient hill fort, to preserve the archaeology from the incursion of scrub and to enhance the diversity of the grassland untouched by a plough for millennia, if at all. All their fields are natural pasture kept in a grazing rotation. One of the fields is an iron age enclosure and has never been ploughed in modern times! Yet now the call everywhere is to shun animal farming and rely solely on crops. 

The couple keep grassfed (Dexter breed, as in  the picture above) cattle and sheep and rare-breed pigs, all raised outdoors and supplemented  by a range of non-soya concentrates, and farm amazingly sustainably. They firmly believe that the sheer complexity of their farm demonstrates that the global environmentalist models about ‘Norm’ cannot possibly map onto reality anywhere on the planet. 

Instead, their farm is a case study in how the new ‘plant-based food’ movement risks upturning delicate relationships between humans and nature but also a more anthropological study in how apparently deeply-entrenched attitudes towards long-established activities and traditions can be rapidly changed by elite groups using sophisticated control of public information.

Staring Statistics in the Face

By Thomas Scarborough

George W. Buck’s dictum has it, ‘Statistics don’t lie.’ Yet the present pandemic should give us reason for pause. The statistics have been grossly at variance with one another.

According to a paper in The Lancet, statistics ‘in the initial period’ estimated a case fatality rate or CFR of 15%. Then, on 3 March, the World Health Organisation announced, ‘Globally, about 3.4% of reported COVID-19 cases have died.’ By 16 June, however, an epidemiologist was quoted in Nature, ‘Studies ... are tending to converge around 0.5–1%’ (now estimating the infection fatality rate, or IFR).

Indeed it is not as simple as all this—but the purpose here is not to side with any particular figures. The purpose is to ask how our statistics could be so wrong. Wrong, rather than, shall we say, slanted. Statistical errors have been of such a magnitude as is hard to believe. A two-fold error should be an enormity, let alone ten-fold, or twenty-fold, or more.

The statistics, in turn, have had major consequences. The Lancet rightly observes, ‘Hard outcomes such as the CFR have a crucial part in forming strategies at national and international levels.’ This was borne out in March, when the World Health Organisation added to its announcement of a 3.4% CFR, ‘It can be contained—which is why we must do everything we can to contain it’. And so we did. At that point, human activity across the globe—sometimes vital human activity—came to a halt.

Over the months, the figures have been adjusted, updated, modified, revised, corrected, and in some cases, deleted. We are at risk of forgetting now. The discrepancies over time could easily slip our attention, where we should be staring them in the face.

The statistical errors are a philosophical problem. Cambridge philosopher Simon Blackburn points out two problems with regard to fact. Fact, he writes, 'may itself involve value judgements, as may the selection of particular facts as the essential ones'. The first of these problems is fairly obvious. For example, ‘Beethoven is overrated’ might seem at first to represent a statement of fact, where it really does not. The second problem is critical. We select facts, yet do so on a doubtful basis.

Facts do not exist in isolation. We typically insert them into equations, algorithms, models (and so on). In fact, we need to form an opinion about the relevance of the facts before we even seek them out—learning algorithms not excepted. In the case of the present pandemic, we began with deaths ÷ cases x 100 = CFR. We may reduce this to the equation a ÷ b x 100 = c. Yet notice now that we have selected variables a, b, and c, to the exclusion of all others. Say, x, y, or z.

What then gave us the authority to select a, b, and c? In fact, before we make any such selection, we need to 'scope the system'. We need to demarcate our enterprise, or we shall easily lose control of it. One cannot introduce any and every variable into the mix. Again, in the words of Simon Blackburn, it is the ‘essential’ facts we need. This in fact requires wisdom—a wisdom we cannot do without. In the words of the statistician William Briggs, we need ‘slow, maturing thought’.

Swiss Policy Research comments on the early phase of the pandemic, ‘Many people with only mild or no symptoms were not taken into account.’ This goes to the selection of facts, and reveals why statistics may be so deceptive. They are facts, indeed, but they are selected facts. For this reason, we have witnessed a sequence of events over recent months, something like this:

• At first we focused on the case fatality rate or CFR
• Then we took the infection fatality rate into account, or IFR
• Then we took social values into account (which led to some crisis of thought)
• Now we take non-viral fatalities into account (which begins to look catastrophic)

This is too simple, yet it illustrates the point. Statistics require the wisdom to tell how we should delineate relevance. Statistics do not select themselves. Subjective humans do it. In fact, I would contend that the selection of facts in the case of the pandemic was largely subconscious and cultural. It stands to reason that, if we have dominant social values, these will tend to come first in our selection process.

In our early response to the pandemic, we quickly developed a mindset—a mental inertia which prevented us from following the most productive steps and the most adaptive reasoning, and every tragic death reinforced this mindset, and distracted us. Time will tell, but today we generally project that far more people will die through our response to the pandemic than died from the pandemic itself—let alone the suffering.

The biggest lesson we should be taking away from it is that we humans are not rational. Knowledge, wrote Confucius, is to know both what one knows, and what one does not know. We do not know how to handle statistics.