If agriculture is to continue to feed the world, it needs to become more like manufacturing, says Geoffrey Carr. Fortunately, that is already beginning to happen.
TOM ROGERS is an almond farmer in Madera County, in California’s Central Valley. Almonds are delicious and nutritious. They are also lucrative. Californian farmers, who between them grow 80% of the world’s supply of these nuts, earn $11 billion from doing so. But almonds are thirsty. A calculation by a pair of Dutch researchers six years ago suggested that growing a single one of them consumes around a gallon of water. This is merely an American gallon of 3.8 litres, not an imperial one of 4.5 litres, but it is still a tidy amount of H2O. And water has to be paid for.
Technology, however, has come to Mr Rogers’s aid. His farm is wired up like a lab rat. Or, to be more accurate, it is wirelessed up. Moisture sensors planted throughout the nut groves keep track of what is going on in the soil. They send their results to a computer in the cloud (the network of servers that does an increasing amount of the world’s heavy-duty computing) to be crunched. The results are passed back to the farm’s irrigation system—a grid of drip tapes (hoses with holes punched in them) that are filled by pumps.
The system resembles the hydroponics used to grow vegetables in greenhouses. Every half-hour a carefully calibrated pulse of water based on the cloud’s calculations, and mixed with an appropriate dose of fertiliser if scheduled, is pushed through the tapes, delivering a precise sprinkling to each tree. The pulses alternate between one side of the tree trunk and the other, which experience has shown encourages water uptake. Before this system was in place, Mr Rogers would have irrigated his farm about once a week. With the new little-but-often technique, he uses 20% less water than he used to. That both saves money and brings kudos, for California has suffered a four-year-long drought and there is social and political, as well as financial, pressure to conserve water.
Mr Rogers’s farm, and similar ones that grow other high-value but thirsty crops like pistachios, walnuts and grapes, are at the leading edge of this type of precision agriculture, known as “smart farming”. But it is not only fruit and nut farmers who benefit from being precise. So-called row crops—the maize and soyabeans that cover much of America’s Midwest—are being teched up, too. Sowing, watering, fertilising and harvesting are all computer-controlled. Even the soil they grow in is monitored to within an inch of its life.
Farms, then, are becoming more like factories: tightly controlled operations for turning out reliable products, immune as far as possible from the vagaries of nature. Thanks to better understanding of DNA, the plants and animals raised on a farm are also tightly controlled. Precise genetic manipulation, known as “genome editing”, makes it possible to change a crop or stock animal’s genome down to the level of a single genetic “letter”. This technology, it is hoped, will be more acceptable to consumers than the shifting of whole genes between species that underpinned early genetic engineering, because it simply imitates the process of mutation on which crop breeding has always depended, but in a far more controllable way.
Understanding a crop’s DNA sequence also means that breeding itself can be made more precise. You do not need to grow a plant to maturity to find out whether it will have the characteristics you want. A quick look at its genome beforehand will tell you.
Such technological changes, in hardware, software and “liveware”, are reaching beyond field, orchard and byre. Fish farming will also get a boost from them. And indoor horticulture, already the most controlled and precise type of agriculture, is about to become yet more so.
In the short run, these improvements will boost farmers’ profits, by cutting costs and increasing yields, and should also benefit consumers (meaning everyone who eats food) in the form of lower prices. In the longer run, though, they may help provide the answer to an increasingly urgent question: how can the world be fed in future without putting irreparable strain on the Earth’s soils and oceans? Between now and 2050 the planet’s population is likely to rise to 9.7 billion, from 7.3 billion now. Those people will not only need to eat, they will want to eat better than people do now, because by then most are likely to have middling incomes, and many will be well off. The Food and Agriculture Organisation, the United Nations’ agency charged with thinking about such matters, published a report in 2009 which suggested that by 2050 agricultural production will have to rise by 70% to meet projected demand. Since most land suitable for farming is already farmed, this growth must come from higher yields. Agriculture has undergone yield-enhancing shifts in the past, including mechanisation before the second world war and the introduction of new crop varieties and agricultural chemicals in the green revolution of the 1950s and 1960s. Yet yields of important crops such as rice and wheat have now stopped rising in some intensively farmed parts of the world, a phenomenon called yield plateauing. The spread of existing best practice can no doubt bring yields elsewhere up to these plateaus. But to go beyond them will require improved technology.
This will be a challenge. Farmers are famously and sensibly sceptical of change, since the cost of getting things wrong (messing up an entire season’s harvest) is so high. Yet if precision farming and genomics play out as many hope they will, another such change is in the offing. In various guises, information technology is taking over agriculture.
ONE way to view farming is as a branch of matrix algebra. A farmer must constantly juggle a set of variables, such as the weather, his soil’s moisture levels and nutrient content, competition to his crops from weeds, threats to their health from pests and diseases, and the costs of taking action to deal with these things. If he does the algebra correctly, or if it is done on his behalf, he will optimise his yield and maximise his profit.
The job of smart farming, then, is twofold. One is to measure the variables going into the matrix as accurately as is cost-effective. The other is to relieve the farmer of as much of the burden of processing the matrix as he is comfortable with ceding to a machine.
An early example of cost-effective precision in farming was the decision made in 2001 by John Deere, the world’s largest manufacturer of agricultural equipment, to fit its tractors and other mobile machines with global-positioning-system (GPS) sensors, so that they could be located to within a few centimetres anywhere on Earth. This made it possible to stop them either covering the same ground twice or missing out patches as they shuttled up and down fields, which had been a frequent problem. Dealing with this both reduced fuel bills (by as much as 40% in some cases) and improved the uniformity and effectiveness of things like fertiliser, herbicide and pesticide spraying.
Since then, other techniques have been added. High-density soil sampling, carried out every few years to track properties such as mineral content and porosity, can predict the fertility of different parts of a field. Accurate contour mapping helps indicate how water moves around. And detectors planted in the soil can monitor moisture levels at multiple depths. Some detectors are also able to indicate nutrient content and how it changes in response to the application of fertiliser.
All of this permits variable-rate seeding, meaning the density of plants grown can be tailored to local conditions. And that density itself is under precise control. John Deere’s equipment can plant individual seeds to within an accuracy of 3cm. Moreover, when a crop is harvested, the rate at which grains or beans flow into the harvester’s tank can be measured from moment to moment. That information, when combined with GPS data, creates a yield map that shows which bits of land were more or less productive—and thus how accurate the soil and sensor-based predictions were. This information can then be fed into the following season’s planting pattern.
Farmers also gather information by flying planes over their land. Airborne instruments are able to measure the amount of plant cover and to distinguish between crops and weeds. Using a technique called multispectral analysis, which looks at how strongly plants absorb or reflect different wavelengths of sunlight, they can discover which crops are flourishing and which not.
Sensors attached to moving machinery can even take measurements on the run. For example, multispectral sensors mounted on a tractor’s spraying booms can estimate the nitrogen needs of crops about to be sprayed, and adjust the dose accordingly. A modern farm, then, produces data aplenty. But they need interpreting, and for that, information technology is essential.
Platform tickets
Over the past few decades large corporations have grown up to supply the needs of commercial farming, especially in the Americas and Europe. Some are equipment-makers, such as John Deere. Others sell seeds or agricultural chemicals. These look like getting larger still. Dow and DuPont, two American giants, are planning to merge. Monsanto, another big American firm, is the subject of a takeover bid by Bayer, a German one. And Syngenta, a Swiss company, is being bid for by ChemChina, a Chinese one.
Business models are changing, too. These firms, no longer content merely to sell machinery, seed or chemicals, are all trying to develop matrix-crunching software platforms that will act as farm-management systems. These proprietary platforms will collect data from individual farms and process them in the cloud, allowing for the farm’s history, the known behaviour of individual crops strains and the local weather forecast. They will then make recommendations to the farmer, perhaps pointing him towards some of the firm’s other products.
But whereas making machinery, breeding new crops or manufacturing agrochemicals all have high barriers to entry, a data-based farm-management system can be put together by any businessman, even without a track record in agriculture. And many are having a go. For example, Trimble Navigation, based in Sunnyvale, at the southern end of Silicon Valley, reckons that as an established geographical-information company it is well placed to move into the smart-farming market, with a system called Connected Farms. It has bought in outside expertise in the shape of AGRI-TREND, a Canadian agricultural consultancy, which it acquired last year.
By contrast, Farmobile of Overland Park, Kansas, is a startup. It is aimed at those who value privacy, making a feature of not using clients’ data to sell other products, as many farm-management systems do. Farmers Business Network, of Davenport, Iowa, uses almost the opposite model, acting as a co-operative data pool. Data in the pool are anonymised, but everyone who joins is encouraged to add to the pool, and in turn gets to share what is there. The idea is that all participants will benefit from better solutions to the matrix.
Some firms focus on market niches. iTK, based in Montpellier, France, for example, specialises in grapes and has built mathematical models that describe the behaviour of all the main varieties. It is now expanding into California. Thanks to this proliferation of farm-management software, it is possible to put more and more data to good use if the sensors are available to provide them. And better, cheaper sensors, too, are on their way. Moisture sensors, for example, usually work by measuring either the conductivity or the capacitance of soil, but a firm called WaterBit, based in Santa Clara, California, is using a different technology which it says can do the job at a tenth of the price of the existing products. And a sensor sold by John Deere can spectroscopically measure the nitrogen, phosphorous and potassium composition of liquid manure as it is being sprayed, permitting the spray rate to be adjusted in real time. This gets round the problem that liquid manure, though a good fertiliser, is not standardised, so is more difficult than commercial fertiliser to apply in the right quantities.
Things are changing in the air, too. In a recapitulation of the early days of manned flight, the makers of unmanned agricultural drones are testing a wide range of designs to find out which is best suited to the task of flying multispectral cameras over farms. Some firms, such as Agribotix in Boulder, Colorado, prefer quadcopters, a four-rotored modern design that has become the industry standard for small drones, though it has limited range and endurance. A popular alternative, the AgDrone, built by HoneyComb of Wilsonville, Oregon, is a single-engine flying wing that looks as if it has escaped from a 1950s air show. Another, the Lancaster 5, from PrecisionHawk of Raleigh, North Carolina, vaguely resembles a scale model of the eponymous second-world-war bomber. And the offering by Delair-Tech, based in Toulouse, France, sports the long, narrow wings of a glider to keep it aloft for long periods.
Even an endurance drone, though, may be pushed to survey a large estate in one go. For a synoptic view of their holding, therefore, some farmers turn to satellites. Planet Labs, a firm in San Francisco, provides such a service using devices called CubeSats, measuring a few centimetres across. It keeps a fleet of about 30 of these in orbit, which it refreshes as old ones die by putting new ones into space, piggybacking on commercial launches. Thanks to modern optics, even a satellite this small can be fitted with a multispectral camera, though it has a resolution per pixel of only 3.5 metres (about ten feet). That is not bad from outer space, but not nearly as good as a drone’s camera can manage.
Satellite coverage, though, has the advantage of being both broad and frequent, whereas a drone can offer only one or the other of these qualities. Planet Lab’s constellation will be able to take a picture of a given bit of the Earth’s surface at least once a week, so that areas in trouble can be identified quickly and a more detailed examination made.
The best solution is to integrate aerial and satellite coverage. That is what Mavrx, also based in San Francisco, is trying to do. Instead of drones, it has an Uber-like arrangement with about 100 light-aircraft pilots around America. Each of the firm’s contracted planes has been fitted with a multispectral camera and stands ready to make specific sorties at Mavrx’s request. Mavrx’s cameras have a resolution of 20cm a pixel, meaning they can pretty much take in individual plants.
The firm has also outsourced its satellite photography. Its raw material is drawn from Landsat and other public satellite programmes. It also has access to these programmes’ libraries, some of which go back 30 years. It can thus check the performance of a particular field over decades, calculate how much biomass that field has supported from year to year and correlate this with records of the field’s yields in those years, showing how productive the plants there have been. Then, knowing the field’s biomass in the current season, it can predict what the yield will be. Mavrx’s method can be scaled up to cover entire regions and even countries, forecasting the size of the harvests before they are gathered. That is powerful financial and political information. NEWS FROMAROUND THE WORLD.
