By cultivating data, producers can access the benefits of collective wisdom.
One of the most overused terms right now on the Internet of things is “big data.” My definition of big data, condensed from multiple sources (and my own experience), is being overwhelmed by the great big amount of data available to you.
The more common definition of big data is presented in a more practical way: big data is extremely large amounts of data from multiple sources that can be harnessed to make
outcomes better for everyone.
Big data on the farm can come from many sources: from records of our planting dates and what we’ve sprayed, to historic records of input expenses, to how often we turn on a piece of equipment. Data from Internet searches on our smartphones and tablets also factors into big data. It’s been many years since companies like Google started monitoring what we click, view and search. Over 60 per cent of all Internet searches are now made from handheld devices.
Never in the history of livestock production has so much information been available on the Internet, from published material to knowledge shared between experts and peers.
Recently, I was working on spreadsheet and could not figure out what function I needed to get the result I wanted. Thirty years ago, solving the problem would have involved digging into the 400-plus page manual or attending a night class with the hope that the curriculum, the teacher or a fellow student would be able to help.
That night, I placed a query on an Internet chat room bulletin board and, within 45 minutes, I had over 10 replies. One of them had the 17 keystrokes that were exactly what I needed.
Suddenly, it hit me: in today’s world, there is no longer a need to learn and memorize everything. Rather, we only have to know how and where to find the necessary information.
The ability to navigate this new Information Age goes back to what we learned, or didn’t learn, in school. I am of an older vintage – the last of the baby boomers. Many times while in school, I heard phrases like “It can’t be done.” This attitude became part of my generation’s thinking.
But take the time to listen to millennials or younger kids in school today. More often than not, the phrase they believe and have ingrained as part of their personal knowledge base is “We just haven’t figured that out yet.” There’s a new confidence and I see this new mantra as a fundamental societal shift in how we approach any problem.
The next generation of farmers will use sophisticated software programs, even artificial intelligence, to drive their decision-making. Through the Internet and global networking tools, this generation will also be more empowered to reach out to the global agricultural community to seek answers. They will not repeat the mistakes of others.
The sophisticated software programs in self-driving cars are a perfect example. The software adheres to a clear set of instructions, but also learns and remembers. Because the vehicle is connected to the Internet (this is the ‘Internet of things’), what its software has experienced and learned is passed on.
This information is passed on not only to the team developing the next self-driving car but also to other selfdriving cars. Mistakes are not repeated. Everyone benefits from the actions and reactions of individual self-driving cars – even the generation of self-driving cars that has not been created yet.
In our facilities today, the sources of data that are quickly and readily available, without having to do any significant amount of work to gather this information, are staggering.
How much feed did that sow eat in the Electronic Sow Feeder? What is the ambient air temperature both inside and outside the gestation barn? Can I manipulate the feed ration at today’s feed prices to keep my sows comfortable at 60 F (15.6 C) versus 68 F (20 C) and will it reduce the overall cost of energy in the barn, reducing the carbon footprint?
What are the data points or tools utilized in these scenarios?
There are the SowChoice Electronic Sow Feeder and the barn’s automated environmental controls, which monitor temperature, humidity, hydrogen sulfide and carbon dioxide.
An app or website provides the current price of the feed components and the current price for heating energy. This data on feed and heating can be loaded into an evaluation program by scanning a QR code or through an automated link from your supplier. The SowChoice feeder could then make feeding decisions based on historical data or preprogrammed parameters established by farm management, or a mixture of the two data sources.
Similarly, we can further integrate both individual and herd genetic/productivity information and have the software make other decisions based on parameters and goals we’ve identified.
Perhaps we want the system to maximize pigs per sow per year (PSY) or perhaps, in low price cycles, it will be least cost alternatives. Or, in the event of expansion, perhaps we want to select gilts in the finishing barn based on genetic potential for pounds (or kilograms) of pork per sow. Maybe we want to add in how data from pressure plates in an autosorter relates to better feet and legs … the list goes on and on.
Add in the big data from dozens or hundreds of other swine operations and, like self-driving cars, we producers will avoid the age-old trial-and-error grind. Instead, we’ll learn from each other and access the benefits of collective and historical wisdom.
Five years ago, PigCHAMP and SowChoice Systems teamed up to create the first Electronic Sow Feeder that was fully integrated with swine management software or Powered by PigCHAMP.
That was Phase 1 of a long-term commitment to integrate multiple data sources into the day-to-day operation of a swine unit.
Phase 2 involves analyzing over 40 potential inputs to calculate things like the most effective feed ration for each individual sow to maximize productivity, and to improve animal welfare and sow longevity, while minimizing the cost of production and reducing the farm’s environmental footprint. The 40 inputs range from amino acids, digestible fiber and nutrient management requirements, to ambient air temperature, static or dynamic loose housing configurations, and the genetic evaluation of the herd (including the sows’ current weight, back fat measurements and predicted litter size).
In the near future, it’s hoped that the results of this in-depth daily learning can be linked between many farms using the SowChoice Electronic Sow Feeder Powered by PigCHAMP.
All farmers will be able to learn what is most effective in other locations. They won’t have to strive on alone, stuck in an (almost) bygone era of trial and error and limited opportunities for improvement.
Phase 3 will involve precision feeding of market hogs, giving producers the ability to not only maximize pounds (or kilograms) per square foot but to do it in the most cost-effective manner with the least environmental impact.
Perhaps producers will even be able to tailor the fat/protein composition of the hog to a specific market requirement.
To begin to accomplish these goals, we need to do what farmers do best: cultivate, sow, reap and cultivate! Again, and again, and again.
It is easier and easier to gather and store huge amounts of data, but the possession of lots of data points is not enough. Data must be continually cultivated, examined, analyzed and reaped for rewards. Then, data must be broken down and added to. The collection and analysis process must be renewed, improved and repeated.
Data collection, management and analysis is the new way of farming, attracting the best and brightest who want to use technology to assist in decision making.
Technology must – and will – not be a barrier to our next generation of farmers, but rather will be a partner in success.
Don’t forget: knowledge cultivates innovation.