The Future in Farming and Ranching
As the state’s land-grant university, NDSU has a unique role to play in precision agriculture.
Recently, Dakota Digital Review (DDR) interviewed Greg Lardy, Vice President for Agricultural Affairs at NDSU, about precision agriculture and the future of food production. As well, Lardy serves as the Dean of the College of Agriculture, Food Systems and Natural Resources and the Director of the North Dakota Agricultural Experimental Station, both at NDSU, and also as the Director of NDSU Extension.
DDR: What is precision agriculture?
VP Lardy: Precision agriculture, broadly defined, is a more precise management of crops, livestock and other agricultural processes through the use of various techniques that require substantial data to support decision-making processes. Ultimately, the goal is to more efficiently and effectively manage agricultural operations to improve sustainability, conserve natural resources and enhance profitability.
DDR: How much interest is there in precision agriculture in the agricultural community?
VP Lardy: It’s a major interest area for almost everyone involved in agriculture at the moment. They may not express it in terms of “precision agriculture,” but interest in the farming and ranching community about techniques that will allow them to better manage their operations is substantial. Precision agriculture means different things to different people, depending on what segment of agriculture with which you are involved. In the farming world, a lot of the interest is associated with some sort of machinery application.
In the livestock sector, the interest is really in breeding and genetics, with genomic-associated means of animal selection (genomic-enhanced EPDs [expected progeny differences]). Also, interest is significant in the area of individual animal management. Collection of data on individual animals (for example, health or feed consumption data) helps livestock producers provide more customized nutrition and care for livestock.
DDR: Give us some examples of current precision agriculture applications.
VP Lardy: Most major equipment manufacturers are using computer geospatial applications to control tractors, planters and combines already. The onboard computer is controlling the speed of operations; monitoring seeding rates, fertility applications and chemical application rates; and communicating with other machines in the field.
Through GPS linkages, many machines are equipped with features such as autosteer, which helps move the tractor or combine down the field by using satellite positioning to reduce the amount of overlap in a field. These functions ultimately save the farmer money, as well as reduce operator fatigue, which can help improve safety.
In the livestock sector, cattle producers are selecting animals based on what we call “genomic-enhanced EPDs.” This gives the rancher or livestock producer additional information about the type of progeny that a bull is expected to produce when mated with a particular cow. It gives ranchers a tool to more precisely predict the outcome of mating decisions. A huge amount of data goes into the computations necessary to develop these EPDs.
DDR: How is rural broadband service linked with precision agriculture?
VP Lardy: Many of these applications require substantial use of data of some sort. It might be data from machines, such as planters or combines, or other applications, such as sprayers overlaid with soil maps, or other geospatial applications. These are data-intensive operations, so access to sufficient bandwidth is going to be critical for our farming and ranching operations.
This is especially true for applications that are going to involve real-time decision-making in field operations. For instance, if we expect a machine to sense or monitor specific conditions in the field and then make a change in real time, rural connectivity is critical.
In addition, rural broadband and sufficient connectivity are becoming a critical component of rural health care, veterinary medicine, teaching and learning, telework and other basic services. If we expect rural places to be places where people can live and work, broadband connectivity is going to be critical to attracting and retaining a workforce, providing adequate health care and developing learning platforms for K-12 education, as well as further education for the broader population. Luckily, North Dakota is ahead of most states with respect to rural broadband and connectivity.
DDR: What are some of the challenges associated with precision agriculture?
VP Lardy: Because precision agriculture is directly linked to computers and computing power, the challenges largely revolve around the typical computer problems described in many other industries. Things such as data storage, data security, cloud analytics and data management, as well as data access, are just a few of the challenges.
Questions about who owns the data are also part of the dialogue. Farm machinery is collecting data as part of routine fieldwork in many applications now. Most equipment companies have policies in place, related to data access and ownership, stipulating that the farmer owns the data and that farmers can share their data with their professional management team or others whom the farmers designate.
Precision agriculture data present some unique geopolitical challenges for equipment manufacturers as well. For example, manufacturers might find themselves selling equipment to foreign governments that have different philosophies or policies related to data ownership than we have in the U.S.
The sheer volume of collected data also presents some unique challenges. Movement of large volumes of data to and from the cloud requires adequate connectivity. The data storage for these applications can be problematic and quite costly.
DDR: How far away are we from the totally autonomous farm?
VP Lardy: If you are talking about a farm with only robots and no people, we are a substantial amount of time away from that yet. We probably will measure this timeframe in decades. But if you are talking about having driverless tractors or other field implements doing autonomous operations of some sort in the field, we are getting very close.
We have a few hurdles that we need to get past, related to the technology. A driverless tractor is one thing, but we also need to remember that the tractor is almost always pulling some sort of field implement, such as a planter or tillage tool, which means the system also needs to be monitoring what is going on with the tool the tractor is pulling. This presents some unique challenges with monitoring, sensing and adjusting what the implement is doing.
In addition, we have a few regulatory and policy-related items that need to be addressed regarding autonomous operation. These regulatory and policy issues are probably most acute when it comes to movement of autonomous farm equipment on roadways. Currently, we do not have an adequate regulatory framework for these sorts of applications.
Because the tractor is such a utilitarian piece of equipment on a farm, we won’t see cabless tractors for a while. On a farm, a tractor performs many functions that will be difficult to fully automate and run autonomously. For example, the tractor might be equipped with a loader for moving materials on and off a truck, or it might have other equipment driven by the PTO (power take-off). These functions will be much more difficult to run in a fully autonomous fashion, because a substantial amount of human sensing, adjusting and acting happens when these sorts of equipment are used with a tractor.
DDR: What is the future of precision agriculture?
VP Lardy: The future will involve more and more applications that are, what is termed, “sense and act.” In other words, a sensor will collect some sort of data (it might be soil fertility, moisture conditions or some other parameter) and then implement a solution or act in a manner that requires some sort of computation before implementing a solution in the field.
This requires data to be uploaded from the sensors at the farm to a cloud application. The data then undergoes some sort of computation in the cloud, and then the solution is downloaded back to the machine for implementation of the final solution, as the machine moves about the field. To do this in real time will require better connectivity across the rural landscape than what currently exists in most locations.
Obviously, this is a data-intensive operation that requires adequate connectivity, cloud storage and data security, and the resolution of all sorts of other “computer” issues. Consequently, tractors and combines are no longer going to be just “mechanical machines” but instead will be complex mechanical machines with sophisticated computer technology as an integral part of the machine. This already is happening in many applications in North America.
Additional applications will include more and more work with sensors of some sort. These sensors are going to be part of the internet of things. They might be moisture meters in grain storage facilities to monitor optimal storage conditions, or some sort of device that monitors the feed intake of livestock, or other sensors that can monitor various aspects of animal health and then provide information or recommendations to the farmer or rancher.
We also are moving rapidly to systems that manage individual plants. In the past, a field of corn was managed as a field, maybe 40 or 80 or 160 acres.
A farmer might plant 30,000 to 40,000 corn plants per acre. In the past, that field would be planted to a single variety, be fertilized with the same fertility recommendations throughout the entire field, and the entire field would have the same recommendation for weed control. In the future, when we think of these applications, we are envisioning systems that actually manage each of these plants individually.
As an example, the farmer would be working with a solution that chooses a specific plant variety for a small piece of the field (maybe as small as a square meter). On that quarter section (160 acres), you might be managing 4.8 million to 6.4 million plants.
Several different varieties of corn or soybeans would be planted in this field, which would be specifically chosen based on soil type, yield potential or other parameters. Each plant would have an individual prescription for fertility and weed control, which also depends on soil type and expected yield. This gets to be a pretty complicated set of data to even envision, much less manage.
Livestock producers are also moving to individual animal management. For instance, on dairy or swine farms, the cows and sows are receiving a specialized ration and feeding regime that depends on productivity and expected production. Monitors and sensors track activity (much like pedometers), as well as feed consumption, water intake and other parameters, all in an effort to more appropriately manage the individual.
DDR: What is NDSU doing in the world of precision agriculture?
VP Lardy: As the state’s land-grant university, NDSU has a unique role to play in precision agriculture. Being a land-grant university means that NDSU has, as part of its mission, a role in teaching, research and extension services. I’ll mention what we are doing in each of these areas briefly.
Teaching: NDSU recently launched one of the first undergraduate degrees in Precision Agriculture, and our first student graduated from the program in May 2021. Enrollment in the major is growing rapidly, and we’ve seen a lot of interest from employers in hiring students trained in this field. Many employers in agricultural disciplines are looking for graduates who understand basic agricultural principles, such
as agronomy or soil science, but who also have
training and experience with data analysis, data management and analytical tools that use data to
drive recommendations or decision-making.
Research: NDSU, through the North Dakota Agricultural Experiment Station, has a major research thrust in precision agriculture. This effort is multidisciplinary and includes work with sensors and robotics, imagery and drones, as well as work that links disciplines related to production and utilization of agricultural commodities and food. The work includes strategic partnerships with governmental agencies, industry and commodity groups.
One of the areas we are really seeing the “big data” function come into play in research is in our plant and animal breeding programs. Our scientists deal with huge volumes of data, and it will just get bigger as we incorporate more genomic data into the selection procedures. The volume of data that is being handled is growing rapidly, so access to better data storage, analytics and security is going to become increasingly important. Using genomic information allows our scientists to select better varieties more rapidly, which speeds up the selection process and helps ensure that the variety is going to be better suited for our growing conditions.
Extension: NDSU, through NDSU Extension, carries out programming in the area of precision agriculture that helps farmers and ranchers utilize precision agriculture to be more efficient, conserve natural resources and be more economically viable in the long term. These efforts include educational programs that highlight technology applications, as well as provide solutions for some of the more complex problems in agriculture.
Precision agriculture, in its many forms, is becoming increasingly important to farmers and ranchers in North Dakota. It is a complex and rapidly evolving field that makes it very exciting for our students, faculty and stakeholders. We are happy to play a role in shaping the future. Ultimately, the synergy of our teaching, research and extension services mission delivers a product that will help address challenges associated with land use, resource allocation, food security and economic sustainability, not only for farmers and ranchers but also for consumers. ª
