You’d be forgiven for thinking of farming as old-fashioned, slow and a bit low-tech. Popular culture and commercials sell us a nostalgic image of the countryside. Singing farm-hands stand knee-deep in the same wholesome soil their branded potato chips came from. The camera pans to a family farm that embody honest and earthy old-fashioned values, as distant from our mobile screens and social media as Constable’s Hay wain and Wood’s American Gothic.
While this is all good for fast-food advertisers, it’s not at all accurate. Agriculture has always changed. From the first horse-drawn ploughs to the 20th century green revolution, farming has been striving to feed ever increasing populations more and more efficiently. This has meant that less and less unskilled labour has been needed in the fields, with more hands and brains to do more of everything else.
The next few years will be no exception in terms of change. Farms are undergoing a high-tech revolution, with processing power, GPS satellites, data science, drones, driverless vehicles and the Internet of Things all set to radically change the way farming is done. The application of these recent and new technologies to the rearing of crops and livestock has been variously called precision agriculture,smart agriculture,digital agriculture and farming 4.0. According to Richard Markwell, president of CEMA (the European association representing the European agricultural machinery industry),
It’s about bringing technology to farmers to ensure they can meet the challenge of producing more food, with less land, in a sustainable manner, at an affordable price in the supermarket.
Precision Agriculture uses automation to save labour, and to do things that would take humans too long, using sensors and data science to make farming more responsive to spatial variation (in soil conditions, in water availability and in the presence of pests). Use satellite images, drones and big data algorithms to get to know the soil at different locations in a field and you can choose the best crop variety for the specific location and only the necessary amount of fertiliser or pesticide. Put driverless technology into tractors and harvesters and you can replace heavy soil-compacting tractors with a fleet of small and nimble vehicles, crammed with sensors, and sending regular updates to a centralised database. It looks likely to be pretty user-friendly, with companies like Agricision developing iOS apps for the iPhone and iPad.
It’s not about putting people out of jobs; instead changing the job they do. The tractor driver won’t be physically in the tractor driving up and down a field. Instead, they will be a fleet manager and agricultural analysts, looking after a number of farming robots and meticulously monitoring the development of their crops.
Experts anticipate that each of these approaches will provide large improvements in yields and reductions in the monetary and environmental costs associated with the over-application of fertilisers, herbicides and pesticides. Goldman Sachs Research predicts that precision planting (the right seeds for each acre) alone could drive “a double digit improvement in yields”, and that precision farming could be a $240 billion market by 2050. CEMA‘s website states that “Data is the key ingredient for the European farming sector to become more productive and sustainable and remain competitive in a global environment”.
Given that we’ll need to feed almost ten billion people by 2050 and deal with the effects of climate change, the focus on high tech agriculture is certainly for the best – current practices may not be able to keep up. It’s an interesting time. The technology is still in its infancy, and agricultural retail communicators Crop Life estimate that fewer than 15% of US farmers are using data technologies to support decision making.
Weekly Blog Feature – Smart Farming
There’s a lot going on in high-tech agriculture at the moment. There’s lots of interesting science, new technology and new applications of existing technology. It will affect food security; large and small farms; farmers in developed and developing countries; arable, livestock and viticulture.
There’s so much going on (and I’d love to know more!) that I’ve decided to try to write a regular blog feature on it. I’ll be doing one per week – starting this week. My current ideas include drones in agriculture, precision agriculture investment, organic farming and applications in the developing world.
But, what I’d really love to hear are YOUR suggestions! Whether you’re a precision agriculture expert, or like me, curious to see whether a robot could control an English sheep-dog, please feel free to leave any suggestions in the comments. Oh, and remember to subscribe by email if you’re interested in future articles in the series.
Once nations, communities and cultures have passed into distant memory, records of births and deaths are often all that’s left. Dates of death on tombstones, birth registers and human remains don’t give much away about how our forebears lived, thought or looked, but they do allow us to at least guess changes in population throughout history. Increasing numbers of births in records and registers describe a growing population. Mass graves disclose sudden catastrophe, famine or war. These methods can’t provide absolute certainty or exact numbers and are only as good as the records or data available, but they do give overall patterns.
Even when physical remains are hard to come by, scientists can use differences and similarities in genetic data to reveal migrations, intermarriage and even the ups and downs in population size. Using genentic data to investigate the tumoltuous history of the Caribbean, Andrés Moreno-Estrada, Eden R. Martin, Carlos D. Bustamante and colleagues were able to uncover important historical details. These include the patterns of pre-Columbian native-American migration, the birth-places of slaves transported from Africa and the impacts of European colonization. Sadly, these approaches can’t uncover everything and the historical DNA test has its limits – particularly if the tribe or community in question was wiped out without any descendants or genetic data. Perhaps this isn’t too surprising as this uncertainty doesn’t just cloud the distant past. In 1950, the UN estimated the world population at 2.4 billion. But it was later realised that this was an underestimate. By 1995, an extra hundred million had been added in hindsight, and the UN estimated the 1950 world population to have been over 2.5 billion.
Given the difficulty of piecing together the numbers of the long dead, it’d be tempting to think that predicting the numbers of people as yet unborn would be utterly impossible. If we can’t predict the weather for more than a few weeks, how can anyone be bold enough to claim how many people will be living in 50 years time? Yet that’s exactly what demographers and forecasters spend their working lives attempting to do.
Thomas Malthus (1766 – 1834) was an English cleric and thinker who published An Essay on the Principle of Population in 1798. Malthus saw that all populations (of humans, animals or plants) had an innate tendency to grow prolifically. Given that plants produced more than enough seeds and that the parents of the late 1700s had more than enough children to replace the previous generation,
The germs of existence contained in this earth, if they could freely develope themselves, would fill millions of worlds in the course of a few thousand years.
This is what mathematicians call exponential growth. If a country has a million people of child-bearing age, and each woman/couple has four children, then in a generation’s time (historically, about 25 years) there’ll be two million (of child-bearing age). In 50 years there’ll be four. Then eight. After 200 years there’d be 256 million people. That’s approximately the difference in scale between the current populations of Estonia (1.3 million) and the USA (324 million). Families don’t even need to have four children for this to happen. If the average number of children is three rather than four, the population would grow hundredfold in three hundred years. Even with the seemingly moderate average of 2.4 children per couple, the population would be a 1000 times bigger in 1000 years. For historical comparison, the average global number of children per woman was 4.85 in the late 1960s, and is now around 2.35.
Malthus also thought it was certain that this couldn’t go on for ever. Our planet has finite resources it’s food producing capacity is ultimately limited. Our food production might increase with the population (more land could be cultivated more intensely), but eventually demand would outstrip supply, and growth would have to stop or reverse. Malthus saw two ways for population growth to slow towards this limit. Barring any intervention, populations would be stopped by positive checks such as famine, wars, poverty and epidemics (often compounded by food scarcity). On the other hand, while preventative checks such as sexual abstinence and later marriages caused some unhappiness, this was outweighed by the benefit they brought in avoiding catastrophe and allowing the smaller number of people to live in greater happiness. Malthus hoped that understanding these dynamics would help create a happier and more prosperous society. This would be achieved by emphasising the preventative checks to avoid the destructive positive ones. Despite these noble aims, Malthus’ legacy is not particularly happy. Immortalised in phrases like Malthusian catastrophe and Malthusian spectre, Malthus has posthumously been accused of having no children, with the poet Percy Blythe Shelley calling him a “eunuch and tyrant”, and later of having eleven daughters and therefore being hypocritical in advocating controls on populations (in fact he had three).
Compared to Malthus, the American demographer Warren Thompson isn’t very famous. No catastrophes are named after him, not even a Wikipedia page. Try googling “Warren Thompson”, and the first few hits will be a 19th century explorer, a boxing champion and a corporate lawyer. Despite this, Thompson’s theory of demographic transition is a bedrock of modern demographic theory. It is based on the observation that as societies have developed technologically over the past few hundred years, they follow a predictable series of transitions or stages. The specifics of these transitions may differ between countries, but the overall pattern is the same. At the first stage, birth rates and death rates are both high. Large families are the norm, but few survive long enough to reproduce. The result is a stable or slowly growing population. As industrialisation improves the food supply and sanitation, the death rate drops and life expectancies increase, although families remain large. The population grows rapidly. In the third stage, factors such as increased access to contraception, improved women’s education and urbanisation cause a drop in birth rates. Population growth slows. Birth rates drop to or below replacement levels (two and a bit children if child mortality is low) in the fourth stage, and the population size remains constant or begins to decline.
This pattern has been observed around the world across racial, cultural and religious divides. Many countries (e.g. the USA, Germany, Singapore, Iran and China) have passed through the transition and have low birth and death rates. Others, like Mexico, Egypt and the Philippines are in stage three with low death rates and falling birth rates. Some Sub-Saharan African countries badly affected by AIDS are still in stage 2. It is unknown whether birth rates will remain low in developed countries, or whether other factors will lead to higher birth rates.
Neither Malthus nor the demographic transition model aimed to predict the actual number of people at specific future dates. In contrast, organisations like the UN and WHO are in the business of making more concrete predictions. The UN started making predictions of the global population in the 1950s, and has been relatively successful. Investigating the reliability of forecasts dating back to the ’50s, Nico Keilman at Statistics Norway
found that earlier UN population projections underestimated global population growth, while later projections (from the 1960s onwards) of the 1990 world population were off by no more than about five per cent. On a smaller scale, the UK Government
Actuary’s Department’s (GAD) 1955 projections underestimated the UK population in 1995 by over five million (53 million rather than 58 million) as it didn’t foresee a continued decrease in death rates or the 1960s baby boom. In contrast, the 1965 projections overestimated the population in 2000 by over fifteen million as they assumed that people would continue to have large families (2.97 children in 1964) and didn’t anticipate the drop in birth rates.
So was Malthus right? In a way, no. Since the publication of An Essay on the Principle of Population, the world’s population has grown from just under a billion to over 7.3 billion, doubling almost three times in 200 years. This has only been possible because our capacity to produce food has increased. Malthus didn’t and couldn’t have predicted the technological developments such as the Green Revolution that led to farmland producing much larger amounts of food. Unfortunately, this may not last. Given the effects industrialised agriculture is having on soils and the potential impacts of climate change, the long-term sustainability of feeding almost 10 billion people is in some doubt. Perhaps we’ve already exceeded the Earth’s capacity and are living on borrowed time.
On the other hand, fears of population catastrophes have certainly contributed to the ongoing decline in global fertility (down to under 2.4 from almost 5 children per woman in 50 years). The fertility rate’s not quite fallen to replacement levels, and a lot of future growth will be due to the world’s youthful population ageing and increasing life expectancy. Nonetheless, the rapid drop in birth rates can be at least at least partially chalked down to action motivated by fear of a population crisis. Unfortunately much of this action has been coercive and impinged on individual freedom (e.g. forced sterilisation in India and forced abortions in China). However, this certainly hasn’t needed to be the case: improving education leads to later marriages and smaller families, while improving health-care and lowering infant mortality means people no longer feel they need to have large families for some of their children to survive to adulthood.
That governments and NGOs are now aware that some of the surest ways to reduce birth is through education and health-care is some of the best population news, with Malthus and his spectre indirectly helping provide better education and health care around the world.