Info technology 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 two-fold. 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 application.

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.