Article taken from Agronomist & Arable Farmer, October 2017.
Last year The Economist focused one of its Technology Quarterly sections of the future of agriculture.
It said that one way to view farming is as a branch of matrix algebra - and maybe they were on to something, when you consider the role that big data is now playing in farm decision-making, as well as in technical tasks such calculating seed rates 'on the fly'.
The Economist said: ""A farmer must constantly juggle a set of variables, such as the weather, his soil's moisture levels and nutrient content... if he does the algebra correctly, or it is done on his behalf, he will optimise his yield and maximise his profit.""
Certainly drones, sensors, weather stations and detailed soil tests are invaluable sources of information to help deliver farm potential. However, the burgeoning megabytes of data will only deliver yield and profit if interpreted and used to change farm practice, whether that's rationing a dairy cow or applying fertiliser variably.
Digging into big data can tell some important stories about where yield is coming from, what's holding it back and, increasingly, can identify when or where a crop should be harvested first. Examining data over a number of years can point to a chronic problem, such as collapsed drains or rotational issues such as potato cyst nematode (PCN).
Our tendency in farming is to add in more gadgets, which is fine, but you need to make sure you master the basics first.
A very neat gadget that one of my colleagues, Chris Martin, used was a chip fitted to a tractor to measure a tractor's fuel consumption.
The chip recorded differences in resistance as the tractor worked the soil following some Agrovista cover crop trials. Alongside soil tests, it was a really useful tool to measure any
changes, such as compaction, in the soil structure depending on the cover crop mix.
In Scotland, we've been monitoring high-value seed potato crops using a drone to give us a complete picture of what's happening in a crop. Even the fittest potato agronomist working 24/7 can't see the whole of every field, but a drone can.
In Morayshire in early June, we had 105mm of rain in just four days; it was so intense that farmers lost part of their fields - the persistence of the rainfall also prevented early blight sprays from being applied when they were needed. The drone quickly gave us 'eyes' on the situation.
Maps we've generated have picked up stunted areas of growth - early indicator of rhizoctonia - and other signs of stress. This can be particularly beneficial in spotting problems like PCN or rhizoctonia early, as well as for logging fields with problems that we'd need to be aware of before growing another potato crop.
The other challenge with big data is that the systems gathering the information often speak different languages. There are some really good advances to resolve this, including greater collaboration between manufacturers, offering open source data protocols and investment in cloud services that process data from disparate sources.
Agrovista's cloudbased agronomy management system, Axis, is designed to help growers cope with the vast amounts of digital information created on their farms as the adoption of decision-support and precisionfarming systems grows. Users can control and map data simply and quickly, allowing the many pieces of the precision farming jigsaw to fall into place.