Farming, like any business, has a bottom line. At the end of the season, a farmer has to be able to account for the farm’s costs, subtract from the revenue, and determine whether or not the farm is profitable. Due to the number of variables involved in farming, however, calculating the bottom line for a farm is harder than for most businesses. The emerging field of Big Data is creating new approaches to help farmers analyze their farm operations and ultimately make agriculture more efficient, profitable, and sustainable.
The CGIAR Platform for Big Data in Agriculture is helping lead the digital revolution for farming. This initiative centers around incorporating Big Data tools into the work of more than 8,000 agricultural researchers across a global network of CGIAR Research Centers. The Platform for Big Data is accelerating discoveries in agriculture that translate directly into valuable guidance for farmers. In particular, the CGIAR Platform has the potential to make significant strides in sustainable intensification, a field of research that deals directly with assessing farming systems to improve efficiency.
Defined by CGIAR as agricultural practices that increase crop yields without increasing the amount of farmland used (or otherwise increasing the negative environmental impact of agriculture), sustainable intensification has become an important topic in recent years. According to a projection by the United Nations, the world population will increase by nearly two billion people in the next thirty years. Current agricultural production will have to increase up to 60 percent to keep pace with this population growth.
Sustainable intensification has a critical role to play in developing an effective strategy for global food production for this future. “In the simplest terms, sustainable intensification means to have more with less,” says Dr. Bram Govaerts, a soil scientist and the Director for Strategic Innovations at the International Maize and Wheat Improvement Center (CIMMYT) within CGIAR. “It means more food with less impact on the environment. It means more production with fewer costs of production, whatever those costs may be—financial, social, environmental.”
This gets to the heart of the challenge in calculating a farmer’s bottom line. In addition to the financial costs of seeds, equipment, irrigation, fuel, repairs, and labor on a farm, there are also the environmental costs of farming. Many of these factors, like weather, pests, and disease, are beyond the farmer’s control. Others are locked into a feedback loop based on decisions that the farmer makes. For example, farmers must manage the balance of micronutrients, minerals, and biotic life in their soil as carefully as they manage the balance in their bank account.
It is challenging for a farmer to take all of these variables into account and determine whether or not they are managing their farm in the best way. This is where the CGIAR Platform for Big Data in Agriculture is useful. By bringing together traditional agricultural research with contemporary data science, the Platform is developing powerful analytical models that can handle large volumes of data for large numbers of variables. These models can break out a farming system into all of its complex components and interactions and give farmers a better sense their farm management.
Being able to accurately analyze farming systems with this kind of detail is fundamental for sustainable intensification. In Latin America, Dr. Govaerts is using Big Data tools to prove the impact of sustainable intensification on a massive scale. “We have a project where we are crowdsourcing data from 35,000 plots throughout Mexico. We are able to collect data about different approaches and the resulting yields at each of these sights,” says Dr. Govaerts. “For about 1,500 of these plots, we had data for all of the different practices that were used from planting to harvest.”
CIMMYT is collaborating with scientists at the International Center for Tropical Agriculture (CIAT), another CGIAR Research Center, to analyze this data and identify correlations between different farming practices and crop yields. This is taking the capacity of CGIAR’s research to a new level. “In a traditional experimental research plots, a scientist can maybe do five plots to compare different farming practices. Now we do 2,500 plots,” says Dr. Govaerts. The Platform then shares the findings back with farmers to improve their decisionmaking on their farms.
Govaerts is finding it much easier to convince farmers of the value of different farming practices with Big Data. “As an engineer, in order to bring about change, I was taught to use a push method—to go out and tell farmers to change their practices. Instead, we are trying to create pulls and incentives for the farmers. If you give them the right tools and the right data, the decision actually becomes easier than pushing the idea and trying to force a farmer to adopt it.”
In Africa, CGIAR researchers recently facilitate another example of sustainable intensification project in a farming community in Tanzania. The community is located in an arid region that receives less than 300 millimeters of rain per year. Through a social impact grant, a development organization was able to build a well in the community, enabling farmers to irrigate their land. CGIAR researchers realized, however, that the existing layout of the individual farming plots in the community would make it difficult for everyone in the community to have access to irrigation. In order for the well to be effective, the community would need to consolidate their individual plots and reorganize the land so that all of the plots were centered around the well.
The farmers were initially resistant to the idea because of the risk involved in changing the location of their agricultural operations. Dr. Ravic Nijbroek is a social scientist at CIAT based in Nairobi who worked on the Tanzania project. Nijbroek’s specialty is inclusive restoration, which he describes as the intersection of landscape restoration and collective action—getting groups of people to work together. “One significant barrier for changing a farmer’s agricultural system is the perception of risk. As a social scientist, I’m interested in understanding what makes people get over their risk aversion.”
For the farmers in Tanzania, CGIAR researchers were able to create an analytical model that could show the farmers the positive benefits of this change. Nijbroek explains: “If you have an analytical model and can demonstrate to farmers that they will triple production and reduce labor with irrigation, that’s an effective argument.” It took two years, but the farmers eventually agreed to try the altered layout of their individual farms. They now enjoy the benefits of increased productivity on their farms through irrigation on a smaller footprint of land. Nijbroek sees the potential of Big Data to develop analytical models for these community projects across larger areas to achieve more efficient, sustainable practices more quickly.
But Nijbroek is also aware of the limitations of Big Data to analyze all of the complexities of agricultural systems at this point. Last year, he visited a farmer and noticed that her tomato plants were overgrown with weeds. When he asked her why she didn’t weed the crop, the farmer pointed to the trees and explained that if she weeds the tomatoes then monkeys eat them. But if she leaves the weeds, the monkeys eat those instead, and she can harvest at least a few tomatoes. “These systems are complex, and we can’t account for every variable,” says Nijbroek. “We can model large groups of people and figure out generally what is going on, but when we zoom in, things start falling apart because there is just too much uncertainty. When Big Data can account for the monkeys, then we’re in business.”
Sustainable intensification is one area of agricultural research that could be transformed by Big Data. By leveraging these new digital tools for smallholder farmers, the CGIAR Platform for Big Data in Agriculture is providing a new perspective on farming that can lead to a greater understanding of the complex systems that make up the global food system.
