Harvest (pronounced hahr-vist)
(1) The
season when ripened crops are gathered.
(2) The
crop or yield of one growing season.
(3) A
supply of anything gathered at maturity and stored.
(4) The
result or consequence of any act, process, or event.
(5) To gather
(a crop or the like); to reap.
(6) To
gain, win, or use (a prize, product, or result of any past act, process etc)
(7) To
catch, take, or remove (animals), especially for food.
(8) To
collect (any resource) for future use.
(9) In
epidemiological statistical analysis, as harvesting effect, a method used to
calculate the excess deaths suffered during certain events (and the subsequent
decrease in the expected normal mortality rate as the specific conditions
subside.
(10) To
extract an organ or tissue from a living or dead body, for the purposes of
fertilization, transplantation or research.
(11) In
modern paganism, a ceremony held on or around the autumn equinox, traditionally
the harvesting season.
Pre 950:
From the Middle English harvest &
hervest (autumn, one of the four
seasons; period between August and November), from the Old English hærfest (autumn, harvest-time; August), from
the Proto-West Germanic harbist, from
the Proto-Germanic harbistaz
(harvest-time, autumn, fall) (source also of the Old Saxon hervist, the Old Frisian & Dutch herfst & the Old Norse haust
(harvest)), from the primitive Indo-European kerp- (to gather, pluck, harvest).
It was cognate with the German Herbst
(autumn) and related to the Old Norse harfr
(harrow), the Old High German herbist
(autumn), the Latin carpere (to pluck),
the Ancient Greek karpos (fruit) and
the Sanskrit krpāna (shears). Curiously, the use in cell biology to refer
to the extraction of cell began in 1946, the same year it appears first to have
been applied to the hunting and gathering of wild animals. The earlier (and mostly dialectical) forms harvist, hervest, harst & hairst are all obsolete.
Lindsay Lohan with a pair of ratchet loppers, pruning cuttings for the potting shed, May 2015.
In the
Old & Middle English, it was primarily a season name, the sense of
the implied reference to the gathering of crops just something of tradition and
the specific, separate meaning (the time of gathering crops) dates only from
the mid-thirteenth century, the sense extended to the action itself and the
product of the action only after circa 1300.
Early in the sixteenth century, harvest assumed the now familiar meaning
exclusively and the borrowed autumn and repurposed fall supplied the season
name. Being more evocative, fall is
better than autumn. The figurative uses
began to emerge in the 1530s, use as an adjective documented early in the
sixteenth century. “Harvest home” which
included the “festival feast”, was a festive event celebrating the bring home
of the last of that season’s harvest and is first recorded in 1577. The harvest moon, dating from 1704, was that
which was full within a fortnight of the autumnal equinox. Harvestable & harvestless are adjectives;
harvestability and harvesting are nouns.
The harvester, agent noun
from harvest and noted since the 1590s, was “a reaper", a device used to
assist in and speed-up the gathering of certain crops and the variations were many. The first (vaguely) recognizable ancestor of the modern combine harvester was the generation of harvesters (the earliest of
which were horse-drawn and seem to have been in use since the 1820s although no
patent was issued until 1835) first sold in 1847 and advertised as machines for
the “reaping and binding field crops".
The combine harvester (often referred to as “combines” or “headers”, the
latter a reference to the bolt-on attachments optimized for particular crops)
is so named because it combines in one machine the four separate harvesting
operations, (1) reaping, (2) threshing, (3) gathering and (4), winnowing, the
(5) multi-function headers a more recent innovation. The tractor and the combine harvester are two
of the most revolutionary machines, partially responsible for huge increases in
agricultural production, equally dramatic reductions in the farm labour force
and the consequent acceleration of urbanization as a demographic trend.
The
Harvesting Effect
The harvesting effect (properly called mortality displacement) is a term from a process in epidemiological statistical analysis which maps and quantifies (1) a period where the human death rate significantly exceeds the predicted level and (2) a subsequent period when aggregate mortality is lower. A harvesting effect is almost always associated with external factors such as war, extreme climatic conditions, famines or epidemics & pandemics. Implicit in the model is the notion of a relationship of vulnerability between those who suffer an early death and the sudden change in external circumstances. For example, when wars occur, there’s inherently the possibility of an accelerated death toll among those most likely to be serving in the most dangerous aspects of military service (fit, healthy young men) whereas when societies are subjected to extremes of heat or cold, it’s the frail and elderly who are most vulnerable. The harvesting effect is a useful analytical tool because it can quantify the extent to which causation can be attributed: a subsequent drop in the mortality of a target population would suggest a high causal correlation because the heatwave, polar vortex or whatever, has in advance already harvested the expected victims. That is rationalized as accelerated mortality, those who died as a result of the event were old and frail and thus likely soon anyway to die. War-time and post-war data is interesting too for those studying not only the long-tail effects of physical injuries sustained in conflict but also those of mental illness caused by the trauma of the experience. Historians can also use the data, where it exists with a high degree of reliability, to track the extent to which the causalities of war were civilians, something which in the West rose and fell between antiquity and the modern era before spiking dramatically in the wars of the twentieth century.
The harvesting
effect is of great interest during and in the aftermath of pandemics and
epidemics. In the sombre world of public
health policy, the harvesting effect is noted as one of the factors which can
lead to pandemics and epidemics receding or even disappearing, the idea being
the disease having already harvested the susceptible; those who remain are the
strong who won’t succumb and the resistant who remain unaffected. As a statistical source, the raw data of
excess deaths is helpful too in determining the true death toll from a disease
like COVID-19. Difficult anyway in
developing countries where in non-pandemic conditions there’s often a high
proportion of deaths where a cause, even if known, isn’t recorded but in
countries with highly developed health systems, many factors can mean the data
is inaccurate. That includes social
stigma which in some countries apparently appears to some extent to have
attached to COVID-19; it was certainly a factor in the early, misleading count
of deaths from AIDS, the sudden spike in fatal pneumonia a sociological rather
than a medical phenomenon.
A number of
institutions accumulated the data-sets necessary to assess the true COVID-19
death toll and several, including the Financial
Times and The Economist,
collaborated to create the World Mortality Dataset (WMD) which contains both
their statistical analysis and some discussion of the results. At a time when the official global death toll
was around 4.8 million, the findings published on the WMD (a perhaps
unfortunate acronym) suggests a true number somewhere between 8 and 18.5
million. Using the same statistical
modelling, the death tolls for the previous four influenza pandemics (if happening
now), they put at 75 million (1918), 3.1 million (1957), 2.2 million (1968) and
0.4 million (2009). It certainly appears
the official toll is significantly understated but the WMD does caution the
usual caveats inhabit the margins: this is a composite of many data sets,
capturing not only COVID-19 deaths (strictly speaking) but also those with some
indirect association such as those suffering other conditions yet not able to
secure timely treatment because the pandemic displaced healthcare
resources. It would be difficult to
create a statistically robust formula to calculate relative contributions to
death by various factors. The method the
WMD use they represent as:
Excess
mortality = (A) Deaths directly caused by COVID infection
+ (B)
Deaths caused by medical system collapse due to COVID pandemic
+ (C)
Excess deaths from other natural causes
+ (D)
Excess deaths from unnatural causes
+ (E) Excess
deaths from extreme events: wars, natural disasters etc.
Running
the COVID-19 numbers also produced some interesting finding of general interest
in the field of public health. There
were some countries, those with natural geographic advantages and which applied
stringent control measures, in which actual mortality was lower than that
expected, the spreading virus (indirectly) turning the curve negative because
the policies enforced had the side-effect of effectively eliminating seasonal
influenza and its associated deaths.
The official COVID-19 death toll: 5,476,854 on Wednesday 5 January 2022, 13:42 GMT.
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