Farming in Silico

Jack Caulfield
October 18th 2017

Agriculture has been with us since the dawn of civilization, and since then all kinds of machinery have helped make the process physically easier. But farming is more than just manual labor. For many farmers, the hard part is not planting and harvesting, but knowing when, where and how to plant in order to get the best yield. Thankfully, there’s an app for that too: growing crops in silico - on a computer screen.

No, we’re not talking about FarmVille or Harvest Moon. In silico is a new method agricultural scientists have come up with for optimizing crop growth. It begins with the collection of data from real crops in the field and from samples in the lab. The data collected are then compiled and examined for patterns and correlations, scientists look at which crops performed best and worst, and what circumstances seem to account for the disparity.

The data are then converted into a supercomputer simulation - the silicon part - which models how crops are likely to perform under any given circumstances. Researchers can after adjust each factor individually: how do sugarcane plants do when planted symmetrically and exposed to bright sunlight? How about in the shade? What if they were planted in a different pattern? Usually, afarmer experimenting with different methods has to wait until harvest to find out whether they worked, and even then cannot pinpoint exactly what made the difference. The supercomputer’s calculations take just one day.

The innovation arrives at a time when we must take our food production very seriously. Scientists expect the global population to increase rapidly in the coming decades, and this expanding population needs to be fed. What will the future of agriculture look like? If in silico catches on, we could see innovative new farming approaches tested out in the virtual world before they are tried out for real. Maybe we still have things to learn from the virtual dimension about tasks we've been performing for thousands of years.

Source: Scientific American


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