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.

Tomás Elías González Benitez

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 fertilizer 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 soy beans that cover much of America’s Midwest—are being teched up, too. Sowing, watering, fertilizing 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.

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Tomás Elías González Benitez

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 Organization, 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 skeptical 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 centimeters 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 fertilizer, herbicide and pesticide spraying.

Bugs in the system

Bacteria and fungi can help crops and soil

MICROBES, though they have a bad press as agents of disease, also play a beneficial role in agriculture. For example, they fix nitrogen from the air into soluble nitrates that act as natural fertilizer. Understanding and exploiting such organisms for farming is a rapidly developing part of agricultural biotechnology.
At the moment, the lead is being taken by a collaboration between Monsanto and Novozymes, a Danish firm.
This consortium, called BioAg, began in 2013 and has a dozen microbe-based products on the market. These include fungicides, insecticides and bugs that liberate nitrogen, phosphorous and potassium compounds from the soil, making them soluble and thus easier for crops to take up. Last year, researchers at the two firms tested a further 2,000 microbes, looking for species that would increase maize and soy bean yields. The top-performing strains delivered a boost of about 3% for both crops.
In November 2015 Syngenta and DSM, a Dutch company, formed a similar partnership. And earlier that year, in April, DuPont bought Taxon Biosciences, a Californian microbes firm. And hopeful start-ups abound. One such is Indigo, in Boston. Its researchers are conducting field tests of some of its library of 40,000 microbes to see if they can alleviate the stress on cotton, maize, soyabeans and wheat induced by drought and salinity. Another is Adaptive Symbiotic Technologies, of Seattle. The scientists who formed this firm study fungi that live symbiotically within plants. They believe they have found one, whose natural partner is panic grass, a coastal species, which confers salinity-resistance when transferred to crops such as rice.

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The big prize, however, would be to persuade the roots of crops such as wheat to form partnerships with nitrogen-fixing soil bacteria. These would be similar to the natural partnerships formed with nitrogen-fixing bacteria by legumes such as soyabeans. In legumes, the plants’ roots grow special nodules that become homes for the bacteria in question. If wheat rhizomes could be persuaded, by genomic breeding or genome editing, to behave likewise, everyone except fertilizer companies would reap enormous benefits.

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 fertilizer.

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.


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Tomás Elías González Benitez

Tomás Elías González Benitez

Tomás Elías González Benitez

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