Editor’s Note: Joseph Byrum is senior R&D and strategic marketing executive in Life Sciences – Global Product Development, Innovation, and Delivery at Syngenta. This is the second article in a three-part series about agricultural data as a commodity and currency, this time focusing on farmers.
Farmers are always searching for ways to better manage factors outside their control. Droughts, heat waves, regional disease outbreaks and fluctuations in commodity prices can undermine even the most carefully crafted management plans. When data provide the insight farmers need to deal with unexpected crises, the data hold a great deal of value and they become currency.
Often, not a week goes by without a farmer receiving some kind of solicitation from a company promising to boost yield by harnessing “high value” data. But these sales pitches tend to overlook the farmer’s practical needs. As discussed in the previous article in this series, the term “data” is somewhat ambiguous, containing several possible meanings. For the industry as a whole, data can refer generally to all information regarding a particular geographic location that is either in digital form, or that would need to be converted to a machine-readable format.
The farmer has a slightly different perspective on three main types of data. First, there are the historical geographic data that farmers themselves have either inherited or generated over time while managing and observing their fields. These are the farm maps, land elevation and contour maps, historic weather and rainfall tables, as well as country-level information pertaining to weather and water availability. These could also include information relating to general soil conditions for the country and information relating to historic episodes of pathogens, pests, and disease. The category includes historical third-party environmental data generated on the farm.
The second type of data is production specific. It refers to data collated by the farmer and his service providers about specific crop production on a seasonal basis, such as soil classification maps, chemical grids, historic per acre chemical and fertilizer application, historic crop and variety use, harvest monitor information and yield performance based on millimeters of rain. This information is collected by precision agricultural equipment and remote sensing technology as well as the physical observations of the farmer, his agronomist, and service providers.
Join Us! Sign up for our next fund here.
The third type is on-going environmental data, which includes general environmental data that, in most cases, relates to broader geographic zones than just one farm. These data are not generated on the farm or by the farmer, and they include data collected by government services gathering country level satellite and weather data, rainfall data, country level performance of crop yield, as well as any other information that would impact on the overall stability of production in the boarder environment of the farming region. These data might be collected on a broad basis by finance and insurance companies and fertilizer companies. There is some overlap with the first two types of data, as the farmer could record the same information as it relates to the farm.
The nuances in categorizing data enable data to be valued as a currency. While “data” is an increasingly visible topic of conversation, our industry tends to focus on the second and third types. Far less attention is paid to capturing the historical field and environmental data for each geographic zone. That’s unfortunate, because the long-term information is vital to achieving a more complete scientific understanding of a given growing situation.
Why does the first type of data get so little respect? Perhaps an overconfidence in digital algorithms leads some to underestimate the value of farm-specific data. It also takes an incredible amount of effort to capture, record and process historical growing data, so it can seem like more trouble than it’s worth. The older the data, the more likely it is that they must be converted into digital format, or if the data are available, the format is likely incompatible with other data sources.
It can be a real headache, but it’s worthwhile to gather this information. A better understanding of the importance of historical, farm-specific data will help promote the commoditization of data and their potential currency value.
To be useful, correlate the data
Farmers who want to extract as much value from their field data as possible can experience a great deal of frustration. If they happened to, as a test, order a half-dozen, very expensive chemical analysis grids each from a different, reputable firm, they would wind up with a half-dozen datasets in incompatible formats. The results received would not line up with one another, leaving the farmer bewildered, unable to make sense of how it all fits together.
As with most scientific endeavors, the meaning of agricultural trends is written across the span of decades. The more historical data that are captured, the clearer the picture that emerges, and the more solid are the conclusions that may be drawn.
The industry can help nudge matters in the right direction by developing unified standards that encourage collection of these data so that they can be “refined” and verified as scientifically sound.
Properly refined data can give farmers a competitive edge by mitigating risk and assisting them in navigating an increasingly complex value chain. What could be more valuable than spotting that anomaly, the tell-tale sign of a serious problem, before it’s too late? Risk mitigation is the greatest source of value for data, from the famers’ perspective.
Data will become truly valuable, and effective at risk mitigation, when they systematically represent proven scientific methodology, and when they are in a format that allows consistent and accurate use. Farmers realize data can have great value, but growers and industry have to work together to turn that data into a tangible commodity.
This brings us to the most tangled and controversial aspects of data. Who owns the data? Who can benefit from field-specific data? The next article in this series will explore these issues and the roles farmers, policymakers and industry will play in realizing the promise of commoditized agricultural data.
Image credit: United Soybean Board on Flickr. “Agronomist & Farmer Inspecting Weeds” using an iPad.