This article was published in Scientific American’s former blog network and reflects the views of the author, not necessarily those of Scientific American
As spring in the Northern Hemisphere arrives, farmers around the world are making decisions about what crops to plant and how to manage them. In the United States, farmers typically now have troves of data to help make these decisions—on how crops or livestock have performed in prior years, what nutrients their soils lack, and even how practices they haven’t yet tried have fared with their neighbors. They will also have access to inexpensive loans and insurance, because the banks and insurers providing these services have the data needed to design affordable products.
These data have a clear upside. They make farms more productive and help to grow the economic sectors that produce food. In a world stressed by climate change and natural resource constraints, these sectors have to become ever more lean and efficient, and still remain bountiful. In the U.S., the past five years have seen a series of bumper harvests for both corn and soybean. Some of that is from favorable weather, but a big part is generated by effectively using data to produce more food from the same amount of land, seed and fertilizer.
In the poorer parts of the world, however, the picture is much different. Many farmers live on the edge of poverty, guided only by their history with the land and their community’s traditions. Their skills and knowledge are impressive, but they suffer from a poverty of data. They rely for advice on technical advisors from governments and academic centers who often have very little knowledge of the local area.
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For seeds and fertilizers and other materials used in the field, they rely on companies that lack data on how their products will perform in the local conditions. And even if farmers are ready to change how they farm, they often lack access to affordable credit and insurance, because companies do not have data needed to sort out the financial risks of these investments.
Governments also lack the information needed to identify promising new solutions. About 10 years ago, East African officials and their development partners started to explore why so few smallholder dairy farmers capitalized on growing demand from urban consumers—even farmers who had ample land and water. Surveys of farmers in the region revealed poor access to veterinary care and breeding assistance. An effort to provide these services has helped farmers get more milk per cow—a relatively simple fix, but one obscured by a lack of data.
Data would matter little if farming was easy and the paths to productivity were obvious. But in reality, agriculture is a complex mix of many factors, including climate, biology, chemistry, physics, economics and culture—all of which vary from region to region. Most things that are tried by farmers, governments or nongovernmental organizations end up failing for reasons that were not anticipated. In this situation, being able to take risks and learn quickly are two of the most critical needs. And both of these require good data.
Fortunately, efforts to improve agricultural data are under way. The 50 x 2030 Initiative, launched last September on the sidelines of the United Nations General Assembly, is a commitment to fund an overhaul of agricultural data systems in 50 developing countries by the year 2030. Initiative staff, working with analysts in national statistics offices, the World Bank and the U.N. Food & Agriculture Organization, plan to work directly with farmers to compile detailed data on the crops and livestock farmers are tending, the practices they employ, and effective options for making them more productive and resilient. Kenya, Ghana and Sierra Leone hosted the launch .
While the thrust of 50 x 2030 is based on traditional surveys of farmers and fields, there are also exciting opportunities to incorporate satellite measurements. Satellite sensors have improved by leaps and bounds over the past decade, so that now a smallholder field is typically observed nearly every day by a satellite orbiting overhead.
There is the real possibility that in a few years, acquiring good data on agricultural fields throughout the world will be routine thanks to the power of satellites. But there is also a very real risk that this promise fails to materialize. If countries and donors only invest in the familiar, traditional ground surveys, then they will forego the power of new satellite technologies.
At the same time, if they too quickly leap towards relying on new “big data” approaches, and fail to invest in the ground surveys, then the satellite methods will likely not work very well. The proper interpretation of satellite data requires algorithms drawn from the data of previous years and, as noted previously, there is very little preexisting data in smallholder agriculture.
Farmers in the Global North should not be the only ones basking in the sunlight of reliable data. Now is the chance to simultaneously invest in quality, precisely located ground- and satellite-based methods. Relying only on traditional data collection won’t deliver the progress we need, nor will relying only on new fancier methods by themselves. Like a good seed in good soil, it is the combination that will flourish.