At the University of Georgia, a team of researchers is developing a robotic system of all-terrain rovers and unmanned aerial drones that can more quickly and accurately gather and analyze data on the physical characteristics of crops, including their growth patterns, stress tolerance and general health. This information is vital for scientists who are working to increase agricultural production in a time of rapid population growth.
While scientists can gather data on plant characteristics now, the process is expensive and painstakingly slow, as researchers must manually record data one plant at a time. But the team of robots developed by Li and his collaborators will one day allow researchers to compile data on entire fields of crops throughout the growing season.
The project addresses a major bottleneck that's holding up plant genetics research, said Andrew Paterson, a co-principal investigator. Paterson, a world leader in the mapping and sequencing of flowering-plant genomes, is a Regents Professor in UGA's College of Agricultural and Environmental Sciences and Franklin College of Arts and Sciences.
"The robots offer us not only the means to more efficiently do what we already do, but also the means to gain information that is presently beyond our reach," he said. "For example, by measuring plant height at weekly intervals instead of just once at the end of the season, we can learn about how different genotypes respond to specific environmental parameters, such as rainfall." | READ MORE
As farm acreage grows, it is virtually impossible to know every part of the field and to scout every acre. Remote sensing is simply defined as collecting field information remotely from a remote platform. Satellites, planes, UAVs/drones or equipment mounted platforms can provide a bird’s-eye view of the field to collect information and see field variability and patterns that you can’t readily detect as you walk across a field.
By Jeanette Gaultier, Provincial Weed Specialist
May 7, 2016 - Herbicides work best when weeds are small. Period. Exclamation mark. You get the gist...
There's perhaps no better example of this than cleavers. Take a quick flip through the Guide to Field Crop Protection and you'll notice that most herbicides with activity on cleavers only guarantee control/suppression of this weed when applied between the 1 to 4 whorl stage. Although this staging is most common, application timing may be limited to as few as 2 whorls or extend up to the 8 whorl stage, depending on the product. There are also herbicides that are somewhat ambiguous as to cleavers staging but research and experience have shown that, when it comes to herbicide application to cleavers, the smaller the better.
It makes sense then that a recent question on CropTalk Westman was: 'How do you stage cleavers?'
Whorled leaves, one of cleavers most distinctive features, results in a herbicide application staging unique to this weed. Staging cleavers is similar to other weeds with a few simple tweaks:
- Find the main stem. Identifying the main stem is an important step in staging crops and weeds. But this is often easier said than done with cleavers because of its creeping habit and similar sized branches. If you can't find the main stem, just be sure to pick the stem with the highest number of whorls present.
- Don't count the cotyledons. Only the true leaves count when staging plants. The cotyledons of cleavers are oval to oblong with a notch at the tip and are easy to distinguish from the true leaves.
- Each whorl counts. Unlike most other weeds, cleavers have a whorled leaf arrangement, with each whorl having ~4 to 8 leaves (usually 6). In this case, simply count each whorl along the main stem rather than each leaf (see figure & example below).
Henry Ford once said, “If I had asked people what they wanted, they would have said faster horses.”
Imagine the vision Henry Ford had for the automobile industry as he built the factories and components in 1908 that would become the vehicle assembly platform for the 20th century. Early automobiles were indeed “found on road dead” as the punchline of an old joke goes, and farmers would have been a segment of society that wanted to keep their horses. But the assembly line brought together the components and processes to create the future vehicles that people didn’t know they wanted.
At the time, few people understood how to build an assembly line for automobiles. Today, few people understand the technical components of precision agriculture. Some people view precision agriculture as driving straighter with bigger or faster equipment, while others envision farms with driverless tractors and swarms of robots tending each plant.
Agriculture is undergoing a period of technology convergence, and precision agriculture is the virtual assembly line of new tools and processes to enable more efficient operations and measurable results. Initially there were distinct segments, each providing services to agriculture such as manufacturing (equipment, seed, fertilizer, herbicide/fungicide), crop input retail, record keeping, grain merchants and consulting services. In the early days of tractors, there were hundreds of small manufacturers that consolidated into the dominant brands.
The ongoing growth and mergers of companies has resulted in farm service providers that participate in numerous segments to provide a bundle of interrelated services beyond their core businesses. Competition is a wonderful motivator that is currently directing billions of dollars into agriculture, and specifically precision agriculture, to disrupt the status quo. New alliances and partnerships are forming as companies strive to share development costs and secure channel access to reach farmers. Now there are over 100 companies offering precision agriculture services, ranging from tech startups to Fortune 500 companies, all striving to create the virtual assembly line for precision agriculture.
The platforms produced from this convergence are the apps, websites and cloud storage facilities that can utilize all the information and data collected by any sensor, device or equipment. Our imagination leaps to futuristic tools of The Jetsons or Star Trek, depending on your generation, but today’s technology is confusing because technology adoption takes time.
Progress tends to be a series of challenges that are overcome by a series of small innovations and new ideas. Equipment sensors can collect “as applied” and yield data, and alert the operator to hundreds of possible equipment fault codes. There are about 1100 active satellites orbiting the Earth and the remote sensing satellites gather massive amounts of data that is valuable for agriculture. Improved cellular and Internet services have enabled data to be sent to powerful cloud computer servers with specialized software that are available to rent at a fraction of the cost of buying your own computers. You can now stand in any field on the planet and hold a tremendous amount of site-specific field data in your hands.
Your smartphone or tablet may enable your great leap forward, but first you need to learn to navigate the platforms, websites and apps, just like you learned how to drive. I encourage you to try out the numerous websites and apps to see the features and options available.
The ultimate precision agriculture platform hasn’t been created yet, as companies are still gathering the parts and building the assembly platforms. More fieldwork is required to determine the correct stacking sequence for the data layers and how many years and layers of data are required. How many in-season images, soil tests or weather stations are required to collect sufficient data is still being debated. New products and services are being developed, but unlike the Model T, precision agriculture can tailor the service levels or products to each specific farm. Prices, features and options will vary just like your vehicle choices today.
Technology convergence has the potential to fill the needs of many stakeholders because the resulting software platform doesn’t cost much to operate and deliver through the Internet. It is difficult to determine what the most popular precision agriculture platform will look like in 2020 and who will own it, but farmers will have the most advanced tools to monitor their operations, their crops and the environment. Farmers will continue to rely on their experiences to make decisions every day and the measurement tools will be better.
Imagine if the “Internet of Things” was actually functioning on your farm to catalogue every action performed. The Internet of Things (IoT) is the network of devices, equipment and buildings that are connected with sensors and switches. Instead of wasting human time to record farm actions like when you seeded, changed rates and crop inputs, identified crop pests and updated field records, yield and moisture by area, the loads hauled and bins managed… what if the data was collected automatically by your tools?
That information alone is just a record of what you did. But aggregated over years and compared to thousands of farms, it will display patterns and management choices that are the most valuable. History has examples of countries and societies that forgot how to farm. Perhaps the adoption of reduced tillage practices would not have taken decades if better data was available? Benchmarking the actions and results to validate best practices is an old concept, but aggregated data can make it a powerful tool again as we discuss climate change and environmental stewardship.
The assembly line continues to be the most efficient method to produce most of the products in the world today. Imagine what we can produce with precision agriculture once we figure out how to operate its virtual assembly line efficiently.
Many crop growers know about the use of unmanned aerial vehicles (UAVs), or drones, for activities like crop scouting. But UAVs are also a great tool for detecting and tracking airborne spores, bacteria and other microorganisms that cause crop disease.
The resulting information can have such practical applications as helping in on-farm disease management decisions, contributing to early warning systems for major diseases, evaluating the effectiveness of disease eradication efforts, and tracking down the sources of disease outbreaks.
“The field of aerobiology, which is the study of the flow of life in the atmosphere, has lacked appropriate tools to get after organisms that are flying high in the sky. UAVs have really become an important tool in that arena,” David Schmale, an associate professor at Virginia Tech, says.
According to Schmale, the use of UAVs in aerobiology got off the ground through the work of United States Department of Agriculture (USDA) plant pathologist Tim Gottwald back in the 1980s. Schmale notes, “Tim Gottwald stuck a little rotating spore trap underneath the wings of a biplane, along with some little insect nets that he could remotely swing open, and he started buzzing peach and pecan orchards. His work was the pioneering work to get unmanned systems to track the movement of plant pathogens and also insects in the atmosphere. So he is the godfather and the real motivation behind all that we do.”
The Schmale Laboratory has been working on the use of UAVs in aerobiology for over a decade, making important strides forward in both the technical aspects of how to conduct this type of research and in discoveries about plant pathogens and their transport tens to hundreds of metres above farm fields, across thousands of kilometres. Depending on their study objectives, they can sample the entire microbial community along the UAV’s sampling path or they can tailor the sampler to selectively collect certain species. They can sample at a single altitude or multiple altitudes to find out where and how the microbes are moving. And they can sample at different times of the day and the year to learn about the timing of pathogen transport and deposition.
A key early advance at the lab was their development of a fixed-wing UAV (a UAV that looks like a little airplane) with its own onboard computer system. “Although technologies like autonomous systems are readily available today on most unmanned systems platforms, they were in their infancy about 10 years ago,” Schmale says.
“In this case, we had a small autopilot computer about the size of a cell phone that had been integrated into a UAV and allowed the UAV to follow prescribed paths through the atmosphere at really tight altitudes. That was really an important milestone for us in terms of engineering.”
And this engineering advance enabled important discoveries about pathogen movement.
Some of those discoveries involve Fusarium pathogens. “The genus Fusarium contains some very nasty plant and animal pathogens, and many of them produce mycotoxins. We have a really good selective medium for Fusarium that we can take for a ride on one of our aircraft, and we’ve collected all sorts of different Fusarium species,” Schmale explains.
“The first discovery was about a very important plant pathogen of wheat, barley and corn, Fusarium graminearum. We were able to show that isolates we had collected upwards of 40 to 300-odd metres above the surface of the earth were able to cause disease and produce mycotoxins.
“And one of the isolates produced a really unique toxin that we hadn’t discovered in any of our ground-based populations in Virginia. So this unique isolate was buzzing through the atmosphere over Virginia, perhaps from somewhere pretty far away, which was really exciting and had important implications for biosecurity efforts.”
These findings confirmed the long-distance spread of Fusarium graminearum spores and the potential for this type of transport to contribute to increased disease risk and to changes in Fusarium populations that could affect human health.
Surprisingly, the UAV samples from this research include many previously unknown Fusarium species. Schmale says, “One of the more striking aspects of that work is that about half of any given population that we’ve collected appears to represent new or understudied species. So, at least in terms of Fusarium, quite a bit remains to be discovered in the air. Many of these potentially new species could also be important pathogens that just haven’t yet been studied or uncovered in some agricultural system.”
A big part of the lab’s current work relates to the use of UAV sampling data to understand atmospheric dynamics and to help predict the regional-scale movement of airborne crop pathogens. One of Schmale’s engineering colleagues at Virginia Tech, Shane Ross, is modelling atmospheric features called Lagrangian coherent structures, or LCSs, which are like waves in the atmosphere. Schmale and Ross came up with the idea of using Fusarium sampling to track what the LCSs are doing as a way to confirm the modelling work. He notes, “We were the first to show that LCSs shuffle along Fusarium populations and modulate their movement over long distances in the atmosphere.”
The Schmale Lab is also studying the trajectories of airborne pathogens, seeking to identify their sources and destinations. As part of this, the researchers are doing release-recapture experiments, where they release identifiable spores in a field and find out where those spores land to determine pathogen movement patterns.
Monitoring fungicide resistance in Quebec
A new Canadian project will soon be using UAV sampling to monitor for fungicide resistance in Botrytis, an onion pathogen, in southern Quebec.
“We want to monitor if resistance is building up in the pathogen’s population in the region. We’ll use this information to provide the growers with information about which types of fungicide are no longer efficacious,” Bernard Panneton, who is leading the project, says. He is a research scientist at Agriculture and Agri-Food Canada’s Saint-Jean-sur-Richelieu Research and Development Centre, a horticultural research facility that specializes in field vegetable crops.
“In our research centre, there is a huge expertise in using ground-based samplers to monitor diseases in horticultural fields. During the last three years we had a project using ground samplers, placed about one metre above the ground and on towers up to 10 metres high, to monitor how spores from fungal diseases are emitted from a field and dispersed over the area and eventually go higher in the air and move away. We found that even at 10 metres above the ground, we can collect quite large samples if you do the sampling at the right time and in the right way,” he says.
To monitor for fungicide resistance, the researchers need information on what is happening at a regional level, so they want samples from higher than 10 metres. “With spore sampling, the higher up you are, the further back you see – the spores come from a longer distance,” Panneton notes. Plus they will need to sample large volumes of air. “When you are at some distance above the ground, above 40 or 50 metres, the density of spores is pretty low. So you have to sample for a long time with an efficient sampler to collect some spores on your sampler.”
UAV sampling can meet these needs – a UAV sampler can sample a much larger volume of air than a ground-based sampler, and it can sample the air at specific altitudes high above the ground.
Panneton’s research team will be using an octocopter, a little helicopter-like UAV with eight rotors. It has a small onboard computer with GPS, so the researchers can upload its flight path. “This technology is getting fairly cheap, and it is a bit easier to use than a fixed-wing UAV. With the fixed-wing type, you need a place to take off and land. With the octocopter, you don’t need a landing strip. And the electric motors are fairly easy to service.”
The project’s first step will be to develop the necessary technologies to conduct the Botrytis sampling. For example, the little octocopter is limited in terms of how much weight it can carry, so the researchers will have to develop a lightweight sensor.
They’ll also need to develop a way to plan the UAV’s flight paths to collect samples that will be representative of the region. Panneton says, “We will use a map showing where the onion fields are in the region plus forecasts of meteorological conditions to see where the wind is coming from. From this information, we will have to find a way to design a proper flight path so we increase the probability of collecting spores. We are hoping to detect fungicide resistance when the resistant proportion of the population is fairly low, about 10 per cent of the population. So we will need a fair amount of the spores to do that.”
Panneton plans to conduct the sampling in August when spore emission from the onion fields is at a maximum. “We think we can achieve a good sampling program with perhaps two flights at two different dates.”
The sky’s the limit
Looking ahead, Schmale and Panneton see intriguing possibilities for UAV sampling.
Panneton is excited by the ability of UAVs to work at different altitudes and scales. “I think there is a future for a multi-scale approach where first you look at a larger region to get an understanding of the overall pathogen situation. If you see that something is happening and it seems to be coming from a particular area, then you can fly right there and take a point sample to confirm your hypothesis. And this approach can also work for weed [pollen], insect pests and other things we can find in the air.”
On-the-go pathogen reporting is another potentially important possibility. The Schmale Laboratory has been experimenting with a portable biosensor to do this. “We were interested in being able to collect and analyze a sample in the atmosphere while the drone was flying and to communicate that analysis down to a ground control station, which is essentially a computer on the ground that is talking with the aircraft while it’s flying,” Schmale notes.
Unfortunately, the sensor they’re using costs about $30,000 so it’s not a practical option for most agricultural uses at present. “However, those sensor technologies will continue to decrease in size and hopefully cost,” he says. “For the future, it opens up many exciting applications like being able to do source tracking while you’re in the air, so essentially sniffing out the plume of an agent, and continuing to follow the concentration gradient until you find the source of that agent.”
Another potential application of UAV sampling is for on-farm disease monitoring. Schmale says, “Imagine you’re a potato grower with thousands of acres of potatoes and you are really worried about a particular pathogen that might be blowing into your potato fields from somewhere else. UAV sampling can do something that a ground sampler can’t do – it can sample a very, very large volume of air. So you can essentially sniff over your entire farm, collect a very large volume of air and determine whether or not a disease agent is there.”
At the Schmale Laboratory, the latest UAV research ventures are heading in a new direction: bioprecipitation. “Some of our recently funded work is focused on a rather narrow group of microorganisms [called microbial ice nucleators]. Some of these microbes reside in clouds, while others live on leaf surfaces and in the soil and become airborne. They express interesting proteins that allow water to freeze at higher temperatures and have been associated with global precipitation events,” Schmale explains.
“The idea that a microorganism can be determining whether or not it is going to rain, hail or snow is pretty exciting.” His research on these microbes could eventually lead to improved precipitation predictions, and perhaps even contribute to approaches to weather modification. For instance, some researchers are proposing the idea of planting crops that are hosts to these microbes as a way to increase precipitation in arid areas. “Potentially we could do things on our land surface to change the weather, which is an interesting concept and likely to be very important in the coming decades.”
As farm acreage grows, it is virtually impossible to know every part of the field and to scout every acre. Remote sensing is simply defined as collecting field information remotely from a remote platform. Satellites, planes, UAVs/drones or equipment mounted platforms can provide a bird’s-eye view of the field to collect information and see field variability and patterns that you can’t readily detect as you walk across a field.
Watching kids grow up, you don’t notice the subtle changes each week, but looking back over a few years of family pictures enables you to see dramatic changes. Pictures are also useful in agriculture to capture the moment and review the history.
Your farm actually has a tremendous imagery archive, although you probably have never seen it. Airplanes and satellites have been collecting imagery of your fields for years. In Alberta, air photos are available back to 1949 for most farmland. Landsat satellites started collecting multi-spectral imagery in 1972 and Landsat 8 continues building that 44-year archive. Google Earth was available in 2005 with a collection of true colour images of the Earth. The RapidEye satellite network was launched in 2009 with field detail and re-visit dates more suited to agriculture. Lethbridge based Ventus Geospatial was established as one of the first UAV/drone service providers in 2012, well ahead of the emerging U.S. market.
Technology advances have improved the camera and sensors to deliver amazing field detail every week of the growing season. Satellites and UAV/drone images can show excellent field detail. As a chemical rep, I took a lot of field pictures before the new smartphone apps could locate, store and share those important areas of interest. Now you can see layers of information on your tablet as you walk across the field to assess field areas with GPS precision.
Remote sensing is a broad discipline and I encourage you to build your background knowledge using Internet searches. For agriculture, you want to know some basic information when viewing imagery of your fields. Vegetation can be measured with different wavelengths of the electromagnetic spectrum that our eyes can’t see. Near-infrared (NIR) and normalized difference vegetation index (NDVI) values are accepted measurements of vegetation that contain much more information than true colour pictures. Ask: What is the resolution or pixel size? What platform collected the image using what sensors? What is the image date and relative crop stage? What type of image processing was used?
Orthorectification ensures the image scale is correct for the field, just as most fishermen know that the tilt and background references can make their fish look much larger in pictures.
I find most farmers are skeptical about remote sensing, field variability and vegetation differences until they see their own fields with NIR vegetation detail from the RapidEye satellites or UAV/drones. Each image platform has pros and cons pertaining to the resolution and cost of collecting this field information. High resolution UAV/drone imagery can become terabytes of data that require good software to stitch together multiple images and GPS coordinates to quantify the data and the clouds that limit satellite image capture. Even now, lack of farmer access to multi-spectral crop imagery remains a barrier, but as precision agriculture acres have grown, imagery costs have been reduced dramatically. RapidEye satellite imagery access can start at $0.50/acre and UAV/drone imagery is approaching $3 to $4 per acre. Satshot provides access to the imagery from numerous satellite networks along with information and imagery processing options.
A picture is worth a thousand words. One picture can identify issues in the growing season, but the power of imagery is it enables change detection on a massive scale. If nature and crop growth were predictable, we could just seed, spray and harvest on the same calendar dates each year. But farming isn’t that simple. The primary function of crop scouting is to determine anything unusual or different from the norm and adjust the timing of management actions to the crop growth. Remote sensing can assist with change detection by providing multiple images in the growing season and multiple years of images to compare a field.
Change detection with remote sensing can identify crop issues or differences in vegetation much faster and better than traditional methods. When crop issues are identified, it leads to questions: What is the field evidence telling me? What caused it? Was it seeding depth, germination issues, wireworms, cutworms, nutrient issues, drainage issues, irrigation issues or a combination of factors? Can we fix the problem? What actions are required? Will it pay off? What is the yield difference?
Knowledge always has a cost and it can’t all come from a book. Imagery provides the base knowledge to add layers of information for soils, topography, fertility, vegetation and yield. Precision agronomists have traditional agronomy skills and remote sensing knowledge to use precision agriculture tools. I encourage you to continue learning about precision agriculture technology and seek out good people to assist your farm decisions.
Everyone is talking about it, but what does data mean to agriculture? It all starts with digital field borders.
Every farmer knows his fields and where the field boundaries are located. Indeed, urban folks are sometimes in awe of how farmers keep track of every field when there are no trees and few landmarks in the wide-open Prairies.
The first step in any process is to identify and define the field. Driving the field boundaries to create digital field borders is an option if you don’t have access to precise GIS tools and good imagery. On large farms or complicated fields with coulees and ponds across the fields, it is cost prohibitive and time consuming to drive every non-crop boundary.
Precise field borders are the beginning of everything in precision agriculture. Some smartphone apps allow you to quickly draw your field borders on background imagery with your finger. This method is not accurate enough because your fingers sometimes won’t place the field boundary precisely. Your field boundary could be out 100 feet or eight feet depending on your skill, and the acreage measurement will be incorrect. The inaccurate field border will cause later issues with equipment guidance, VR prescriptions and sectional control applications.
Good technicians can define accurate digital field boundaries with a click of the mouse using GIS tools and high-resolution ortho-rectified imagery. Creating digital field borders only needs to be completed once, unless the field boundaries change when you remove fence lines or enlarge fields. Google Earth provides a nice imagery viewer but I have found errors where a collection of images weren’t stitched together accurately or the dated imagery doesn’t reflect the current field area.
For large farms, I suggest a field naming structure that makes sense to your farm staff and can be utilized in equipment controller formats, and shared with companies that provide services on your farm. I suggest a short field name, a legal land description and year the digital field border was created. This will accommodate adding, deleting and merging fields as rotation dictates or as your farm grows. Adding a file tree with multiple farms or sub-farms can aid the equipment operators to quickly select or identify fields in the equipment controller displays.
Field boundaries guide the trucks and can provide equipment guidance for your farm employees and service providers. Think of the digital field border as a “cookie cutter” for data. As with cookie dough, we take big batches of data and roll out the layers of data to make the final product. Data could be anything related to the field such as satellite imagery collected over the past 30 years or UAV/drone imagery collected earlier in the day. Additional layers of data can be soils information, sensor data or yield data files collected from multiple combines across hundreds of fields. The field borders cut through the data and grab only the data associated with the specific fields. This enables the analysis of a single field or batch processing by variety, crop type for the farm or county, or soil zone.
As a farmer, consider the information you have when you rent or buy a new field. Have you ever visited a snow-covered field to consider a new field decision? What data did you have for that field? Years of farming experience has always been a criteria to assess knowledge because that individual’s knowledge is a collection of experiences and information gathered over numerous years. One common trait is recalling past experiences for a field while monitoring current situations and determining the timing for future actions that are adaptive to each growing season. Farmers and agronomists know nature has a multitude of factors that affect crop growth and final yields.
Precision agriculture offers the data to look back in time instead of farming blind with limited field history. Sometimes the field history may have died with the farmer, but now a convergence of technology is enabling farmers and others to retrieve past information about agriculture. The technology pieces are ready and different companies possess different components of data. Equipment companies have built the hardware. Different levels of government have the EC maps for every irrigation field and soil maps for the country. Each satellite network has historic and in-season remote sensing for the entire Earth. Numerous weather station networks collect and archive weather data. Seed, fertilizer and chemical companies have years of research plot data. Crop insurance has detailed field information. Farmers have details on crop rotation, soil lab results, planting dates, fertilizer rates and final yields.
A lot of software programmers are focused on creating another app or game for smartphones. Imagine if more efforts were directed to feeding the world. The individual skills and data sets have been underutilized because they don’t offer a direct benefit or an easy way to see patterns in the data because agriculture is complex.
Growing food is the most valuable job on the planet, and technology wants to help you do it better. It all starts with digital field borders.
Variable rate nitrogen applications have the potential to save money and improve crop yields. But what is the best way to come up with variable rate management zones that provide economic benefits to the farmer? Could soil sensor maps be a practical data source for identifying meaningful management zones? Those are some of the questions Alberta researchers are answering through a major on-farm precision agriculture study.
The idea for this study was sparked a few years ago when Ken Coles, general manager of Farming Smarter, saw some electrical conductivity (EC) sensors at a precision agriculture conference. He was intrigued by the possibility of using these sensors as an alternative to grid soil sampling for mapping in-field soil variability. “The idea is that we can’t do grid soil sampling to the level of accuracy needed to manage variable rate inputs effectively, plus soil sampling is expensive. So if we can run a soil sensor over a field and get the same or better information, then maybe there is value in it,” he says.
Lewis Baarda, GIS analyst with Farming Smarter, compares the two approaches. He explains that a grid soil sampling system with one sample every five acres would provide 32 data points for a quarter section, and the lab analysis for nitrogen, phosphorus, potassium and sulphur would cost about $1,600. An EC sensor service could produce an EC map of a quarter section with about 50,000 data points for a cost of about $880. EC data tend to be good at predicting soil texture and soil moisture content.
But Coles wanted to do more than compare EC sensor maps and grid soil sampling maps for creating management zones; he wanted to evaluate if those zones were actually meaningful and useful for variable rate management. He says, “Creating management zones based on soil information and then creating a prescription map is not that hard. The challenging part is verifying whether your variable rate management is actually paying for itself. That is really what I wanted to do with this study.”
Coles also wanted to do the study as on-farm research, which added another level of variability. He notes, “Just finding the right co-operators to work with is challenging, and even when we have the right people, we still have human error issues or lack of priority issues. So, not only are we going into a complex environment where we have no control over the variables, but we are literally studying variability and we also have human and equipment and scale variability.”
Starting in 2012, he teamed up with Baarda and Muhammad (Adil) Akbar, precision agriculture specialist and research director with Farming Smarter, to conduct the study on 10 farm fields. The fields are located in southern Alberta, the Drumheller area and the Peace Region (in co-operation with the Smoky Applied Research and Demonstration Association).
Because of the study’s complex objectives, quite a few steps were required in the data collection and analyses for each field, including: conducting soil sensor mapping and grid soil sampling; determining how strongly the EC maps matched up with the soil sample data, yield maps and other data sources; delineating field zones based on these different data sources; conducting a nitrogen fertilizer rate/yield response trial; determining which zone map best predicted yield variability across the field; and determining which zone map provided the best basis for variable rate nitrogen applications.
With funding from Alberta’s Agricultural Initiatives Program, Farming Smarter was able to purchase two EC sensors: the EM38-MK2 and the Veris MSP3. The researchers hooked together the two sensors and pulled them across each field, using Farming Smarter’s onboard RTX-DGPS sensor for georeferencing and elevation recording.
“The EM38 has been around for a long time; they used to use it to map salinity and it’s quite effective for that,” Coles says. The EM38 does not require direct contact with the soil to take EC readings. It is pulled over the field’s surface and takes measurements every few seconds. It can measure EC at depths of 0.75 and 1.5 metres at the same time.
The Veris MSP3 is a mobile sensor platform with three sensors: EC, pH and organic matter. Its EC sensor requires soil contact so it has coulters that maintain soil contact as the equipment is pulled across the field. Like the EM38, this sensor measures EC at both 0.75 and 1.5 metres deep.
The research team scanned each field twice with the two EC sensors, usually in the spring and the fall.
The Veris organic matter sensor measures the soil’s optical reflectance, basically how dark or light the soil is, and those reflectance data are converted to organic matter content by Veris. The pH sensor directly measures soil pH using an on-the-go chemical test, taking a soil sample, testing it and then taking the next sample, while the Veris moves across the field. The pH and organic matter sensors provide fewer data points per field than the 50,000 points generated by the EC sensors.
Soil sampling followed a five-acre grid, with 32 samples for each 160-acre field. The samples were analyzed for nitrogen, phosphorus, potassium, sulphur, organic matter, pH, EC, moisture content and texture.
The co-operators provided yield data collected by their on-combine yield monitors. Baarda notes, “Although the standard practice is to use at least three to five years of yield maps to define productivity zones, in most cases it was a challenge to gather even three years with good spatial coverage.”
Coles adds, “Finding good yield map data is really difficult. There are many reasons for that. One reason is that people don’t save the data; they don’t take the time to transfer it to their computer. Another reason may be that they have two or three combines on the field at the same time, which makes it challenging to stitch the data together. Or it could be they didn’t calibrate it properly.”
For each field, the researchers created zone maps using five different data sources: EC sensor data; historical yield data from the co-operator; grid soil sample data; a visual depiction of the field’s main terrain features; and a composite of yield and EC sensor data. This composite method was included because an objective procedure called principal component analysis identified EC and yield as the two variables, among all the data collected, that best accounted for spatial variability in the 10 fields.
At each field, they conducted a replicated, randomized nitrogen fertilizer rate/yield response trial. The nitrogen fertilizer was applied at seeding. The specific nitrogen rates used in each trial depended in part on what the cooperating farmer wanted to do; usually fewer than five rates were used. The researchers measured the variations in grain yield response and determined the nitrogen rate/yield response curves.
Next, they laid each zone map over the yield response results and determined which of the five zone delineation methods worked best for predicting in-field yield variations and for predicting zones for variable rate nitrogen applications.
Highlights of results
As you can probably imagine, the study involved huge amounts of data that required complex analysis. Akbar, Baarda and Coles are currently finalizing the study’s report, and they hope to also publish some scientific papers.
In terms of the performance of the EC sensors, Baarda says, “Our EC data from the Veris and the EM38 were highly consistent with each other. Also, the spring EC map was always highly consistent with the fall EC map. We could almost take one EC layer and say that’s what the EC map is [for the field] because those patterns don’t change over time and they don’t change between the sensors.” The researchers also found that the EM38 was easier and
less costly to use than the Veris for mapping EC.
Overall, the EC sensor data tended to be strongly correlated with the soil sample data for sand and clay content and soil moisture content, although the strength of the correlations varied from field to field. So, EC sensor maps can give farmers a better understanding of the soil variability in their fields.
However, the EC sensor maps didn’t necessarily predict the spatial patterns in some of the other soil sample data, like nitrogen (N), phosphorus (P), potassium (K), sulphur (S), pH and organic matter. According to Baarda, scale issues could be a factor in the weakness of some of these correlations.
“We’re comparing about 30 data points from soil sampling to about 50,000 from the EC sensors. The [weaker] relationships can get obscured because of the different scales of the datasets. So, even though we don’t see a relationship to N, P, K and S, we can’t necessarily say that those macronutrients don’t correlate to EC. But we get a sense that they probably don’t correlate as strongly as we’d need to make a management response to them.” So the EC sensor maps are not a reliable way to directly estimate variable nutrient rates.
The study also showed grain yield could not be predicted directly from just the EC sensor maps. The correlations with yield were weak or did not exist. Various factors might have contributed to these poor correlations, including the challenges in obtaining good yield data.
Another key finding was the surprising amount of year-to-year variation in the yield patterns. “I think people have a sense that yield patterns are more static than they actually are. Some parts of those spatial patterns are consistent, but statistically those patterns change more than I would have thought,” Baarda says. “So it’s important to have at least three to five years of yield data; the more years you have, the more it helps to balance out the
The strength of the correlations among the various other data layers – such as elevation, yield, soil nutrients, soil texture, the pH sensor and the organic matter sensor – also varied from field to field.
Because of the field-to-field differences, the different zone delineation techniques had different levels of success depending on the field.
For predicting yield potential, the composite method – pairing up yield and EC sensor data – was the best of the five methods for delineating zones. “In 100 per cent of the instances, the composite method was the most successful in differentiating within-field zones of different yield potentials. So the zones created by pairing EC and yield were meaningful: they predicted where we would have high and low productivity based on the information we had before the growing season,” Baarda explains. “The other four delineation methods failed in differentiating any productivity zones in 20 to 30 per cent of the instances and had varying combinations of complete and/or partial success in the remaining instances.”
He adds, “Some of the composite method’s success is likely due to the use of multiple variables – hedging our bets so to speak. Some of its success also likely lies in the fact that we objectively identified yield and EC as key variables for zone delineation.”
None of the delineation methods were very successful in identifying zones that could be managed differently for
nitrogen in ways that would benefit the farmers economically.
According to Coles, the next step in this research would be to add more layers of data to the analysis, such as remote sensing data from satellites and data from other in-field sensors.
Some take-home messages
“Our big message is there is no single data layer that can be guaranteed to tell you what you need to know to variably manage inputs,” Baarda says. He emphasizes that zone management is a process – each field is unique and you have to be prepared to invest some time in understanding the field’s variability and figuring out what works best for that particular field.
If you’re interested in experimenting with variable rate applications, Coles recommends starting with just a few layers of data.
Baarda thinks an EC sensor map could be a good option for one of those layers. “Not only is EC mapping cheaper than grid soil sampling, but it has a longer ‘shelf life.’ In our experience, EC doesn’t tend to change over time, so a field could be mapped for EC once, and in most circumstances, that data would be relevant for a number of years.” Although the sensors don’t provide the data on nutrient levels that you can get from soil sample analysis, the sensor maps do indicate variations in other soil properties, especially soil texture.
If you want to use your yield monitor data in identifying management zones, then try to ensure the reliability of that data. For instance, be sure to download the yield data from your combine and save it so you can accumulate as many years of data as possible. If you’re using two separate combines on the same field, then consider calibrating them in the same way. If you’re calculating the average yield pattern for a field based on several years of data, exclude any years where the data are skewed because of some external factor, like hail damage on half of the field.
And no matter what data layers you use and what zone delineation method you test, Coles suggests that your on-farm study design should include the steps needed to allow an objective evaluation of whether or not your approach is actually helping you economically.
Coles concludes, “There are lots of people doing variable rate agriculture but very few who are effectively testing and verifying the success.” He adds, “More academic work in this area is sorely needed.”
The study was funded by the Alberta Canola Producers Commission, Alberta Barley and Farming Smarter.
Feb. 18, 2016 - BASF has introduced Compass Grower Advanced, the next generation in farm data management, to address Canadian growers' need for a comprehensive system to improve decision making, profitability and address consumer-driven food trends.
BASF and Affinity Management, a software and business solution provider, developed Compass Grower Advance in response to customer's requests for a one-stop, fully-integrated farm data management system that allows growers to plan crops, test profitability scenarios and improve decision making on their farm. This is confirmed by the findings of a November 2015 survey conducted by Ipsos Public Affairs of 500 large to mid-sized Canadian growers.
According to BASF, key findings of the Ipsos survey on farm data management include:
• Only 42 per cent of Canadian growers currently use a data management system
• Two-thirds of growers without a system rely on pen and paper
• Eighty-four per cent of growers surveyed say a fully integrated data management system would be valuable
• Growers rated the following as the top reasons that a farm data management system is valuable: (Note: those surveyed could provide more than one response, meaning totals do not add up to 100 per cent)
- Profitability scenarios – 41 percent
- Improved decision making – 39 per cent
- Crop planning – 37 per cent
- Organizational efficiency – 30 per cent
- Benchmarking performance compared to past years – 28 per cent
- Traceability – 21 per cent
• Seven in 10 growers would like a farm data management system that has mobile and integration capabilities
Based on the Microsoft platform, Compass Grower Advanced is fully mobile and integrates accounting, crop plans, grain inventories, crop input applications, soil sampling, yield maps, contracts and more into one comprehensive system.
February 17, 2016 - A new partnership between the Co-operative Retailing System (CRS) and GEOSYS International Inc. will bring the latest digital agriculture and satellite imagery technologies to farmers.
Co-op AG Team Agronomists at South Country Co-op will begin using GEOSYS’ Croptical monitoring application this spring. In the first year of the partnership, the application will be used to monitor the field health of a minimum of 120,000 acres in southern Alberta.
GEOSYS, founded 28 years ago and operating globally, is a pioneer in developing tools based on satellite imagery that improve agriculture business efficiency, including farming practices.
“At South Country Co-op, we’re proud to provide services that deliver more value to our farm members and customers,” says Mike Clement, General Manager of South Country Co-op. “Satellite imagery is a proven technology for improving agriculture. Our agronomists are knowledgeable, experienced and ready to help growers adopt this exciting technology.”
Keeping the finger on the pulse of every fieldCroptical provides agronomists with a powerful tool as they build impactful farm strategies for growers.The monitoring application uses daily satellite and weather-based data to produce detailed crop health readings with Normalized Difference Vegetation Index (NDVI) technology. Field NDVI growth during the season reveals where there are opportunities to push or protect yields and where agronomists and growers should focus their attention and smart-scout.
“First and foremost, we are dedicated to agriculture. It is at the core of everything we do,” says Damien Lepoutre, President of GEOSYS. “Our team is committed to delivering value to the grower and empowering agricultural development through digital technology.”
Throughout the 2016 growing season, South Country Co-op will work with Federated Co-operatives Limited (FCL) to evaluate Croptical for potential introduction to other Co-op Agro Centres in Western Canada.“The Co-op AG Team and the CRS are committed to innovation,” says Trish Meyers, Knowledge and Innovation Manager at FCL. “By partnering with world-renowned leaders in digital agriculture, we will deliver new services and value to our growers. The Co-op AG Team will build on its reputation in Western Canada as a source of local knowledge and expertise and work with growers to realize the full potential of new agricultural technology and data collection tools.”
About Federated Co-operatives Limited and the Co-operative Retailing System
Federated Co-operatives Limited (FCL), based in Saskatoon, is the 43rd largest company in Canada and the largest non-financial co-operative in Canada. FCL is a unique multi-billion dollar wholesaling, manufacturing, marketing and administrative co-operative owned by more than 200 autonomous retail co-operatives across Western Canada.
Together FCL and those local retail co-operatives form the Co-operative Retailing System (CRS). The CRS serves our members and communities with products and services that help build, feed and fuel individuals and communities from Vancouver Island to northwestern Ontario. Our total workforce of 24,500 employees serve 1.8 million active individual members and many more non-member customers at 2,500 retail locations in more than 500 communities. We are a different kind of business – we are locally invested, community-minded and offer lifetime membership benefits including patronage refunds, quality products, quality service and fair prices. More information is available at www.coopconnection.ca.
GEOSYS is the first global digital agriculture company founded by agronomists. With more than 28 years of industry experience and business in more than 50 countries, GEOSYS is the world leader in agricultural information and decision support tools based on satellite imagery, remote sensing, geographic information systems and data analytics. GEOSYS combines the most advanced agronomic research with information technologies to provide its clients the data, analysis and insights they need to make more informed decisions. Acquired by Land O’Lakes, Inc. in 2013, GEOSYS is headquartered in Minneapolis, Minnesota, with offices in France, Switzerland, Australia and Brazil.
Harvest is the time of year when farmers reap the rewards from a season of hard work, worry and risk. They treasure the perfect harvests when the weather co-operated and yields surpassed expectations. The worst years serve as useful reminders of the challenges of farming.
Crop yield is the measurement of crop production on a given area of land. It refers to the average of the field and is usually stated in bushels per acre or tonne/ton per acre. Average yield is the benchmark to compare with neighbours, assess management decisions and report for crop insurance. Average yield tells you only part of the story, since it is product of the area of maximum yields and the area of minimum yields within the field.
Prior to combine yield monitors, farmers relied on subjective assessments to determine the best management decisions for their geography. People tend to rely on subjective information from magazine articles, comments from neighbours and their judgment on what seemed to work last year. Field assessments and summer crop tours are also useful to compare and assess crop input options. Each of these is a source of information, but farmers really need accurate and measurable results to make informed decisions.
Years ago, I was launching a canola variety in a large strip-trial comparison with 10 other varieties. A top yielding variety was assigned as the “check variety” for the site. This check variety was replicated with a strip on each side of the field. My variety was located near the end of the site and it placed second when it lost the comparison by 4 bu/ac. While reviewing the raw data, I noticed a 6 bu/ac yield difference between the two strips of the check variety. From that moment on, I realized the impact that field variability can have on future management decisions.
Precision agriculture utilizes data and measurement techniques to enhance traditional decision-making. The data required depends on the questions you want answered. Yield data can consist of general information, such as historic average yields, or the average yield for a specific field. GPS coordinates can also define areas within a field where the combine collected yield data every second. Each type of data is valuable and can be useful to answer different questions.
Combine yield monitors
Combine yield monitors have been around for years, but many farms don’t take the steps to turn that data into valuable information. Logging yield data is easier than many people think. The combine operators must enter some variation of farm/field/crop type into the controller and indicate where to save the data. If farm/field/crop type is not entered, you may watch the controller display yields, but yield data is not saved. Many combine brands require a USB or compact flash card inserted into the yield monitor to store the data during combining. John Deere yield monitors have internal memory to store the data once the controller is set up.
Many operators don’t calibrate the combine yield monitor during harvest. So even though they watch yields during 200 hours of harvesting, they know the data is not accurate. Inaccurate yield data can still be useful because it can be corrected from a known number. For example, the truck weights or bin measurement might confirm the combine yield monitor was +4 bu/ac high (on average). Newer yield monitors can reformat a prior field’s yield data to the corrected values from a calibration. Post-harvest data analysis can also correct inaccurate yield values. The final option is to just keep the inaccurate yield data knowing it is +4 bu/ac overstated. Either way, you can still make decisions with inaccurate data just like farmers have been making decisions for generations with no data at all. But a quick combine calibration can accurately capture the year’s final results.
I encourage farmers to install a GPS receiver on every combine to provide a GPS signal to the yield monitor, enabling advanced yield data analysis. Combines usually collect yield data every one to three seconds depending on the combine model. The GPS coordinates aid the merging of yield data from multiple combines and even multiple combine brands in a field. Auto-steer or GPS guidance is an option on combines, but a basic GPS receiver can provide a GPS signal to the yield monitor at minimal expense.
Do-it-yourself yield software such as APEX, AFS, SMS, FarmWorks and Yield Editor is available to process your yield data into yield maps. If you didn’t process your own yield maps by Christmas, consider hiring an experienced technician to do it for you. Yield data is like a Christmas present; you really should open it.
During harvest 2015, a new record wheat yield of 16.5 t/ha (245 bu/ac) was achieved in England. The media article didn’t say what the minimum yield of the field was, but a maximum yield of 342 bu/ac was mentioned. What future decisions can you make on this information? World records are interesting but without more information and yield maps to review, the information is not that useful.
Yield maps identify where your opportunities are and where improvements can be made. The farmer, the equipment operators and anyone involved with the farm can review each field after harvest to identify learnings prior to the next crop year. Was there a crop input comparison or unintended crop input comparison in the field? Perhaps there was no difference in yield from the additional crop inputs, or you identify a +5 bu/ac difference that was not noticed during crop scouting. Reviewing past years’ yield maps and/or satellite imagery can also identify chronic yield differences within a field that Grandpa could probably tell you a story about. From my experience, reviewing combine yield maps will always identify something interesting.
Modern farm equipment, GPS and computers have enabled precision agriculture to easily prescribe seamless rate changes for almost any crop input. Satellite remote sensing can detect vegetation differences and compare the in-season vegetation to the past seasons’ vegetation. UAVs/drones are available to see your fields with a bird’s-eye view. During harvest, combines measure yield results every one to three seconds to enable accurate analysis and mapping. Grain charts can record the bushels and you can count the truckloads to verify final yields. Advanced bin monitoring ensures grain is stored securely to manage moisture and quality. Farmers can assess the results of any on-farm comparisons/check-strips to review all management decisions given the particular weather conditions for the growing season. The convergence of technologies is happening very quickly in agriculture.
Farmers are always trying new things and relying on their experiences to anticipate the weather and conditions to succeed. With or without accurate measurement, farmers will always judge if something worked or not. Historically, government agriculture research and industry professionals relied on solid efficacy data to promote their products and the retail channel had strong relationships with farmers. This combination of elements is what made the Canadian farmers so good.
All farmers practice precision agriculture to some degree. For instance, when you disc excess moisture spots in the field – that is site-specific crop management. During a supper dialogue, you realize that one spot was missed but you don’t want to go back for it. This becomes your check-strip comparison. But you don’t really plan to measure yields next year between the disced vs. non-disced areas. You don’t have an easy mechanism to track and measure this comparison. You already know that removing the ruts is worth it, based on your experience.
There are a lot of skeptics when it comes to variable rate technology (VRT). VRT started over a decade ago before the tools were available to easily measure results. VRT has proven it works well (when done correctly) in fields with variability. All fields have some degree of variability which shows on the combine yield monitors and in the vegetation differences visible from the sky.
Consider that most farmers and agronomists can’t answer relatively simple questions like: What was the best canola variety? How many more bushels per acre did the fungicide application provide? What was the correct rate of nitrogen that produced the highest economic return in the canola or wheat? What was the correct seeding rate? Part of the reason these questions are difficult to answer is that the correct answer varies by region for the soil characteristics and climatic conditions of that season. There are many complex factors and interactions that influence final crop yields. Anyone can have an educated guess, but all scientific research starts with a hypothesis framed as “If I do this, then this will happen.”
Observation and measurement is required to prove your hypothesis. If someone claims to be an expert, ask that person to accurately predict your crop yield prior to harvest. How well did anyone predict your yields over the past five years?
Virtually all product research conducted by companies consists of small-plot trials to minimize field variability and enable statistical analysis. Later in the process, larger strip trials use real farm equipment to assess real world conditions. Multiple sites are required across a wide area, and it is rare for any treatment to win every time. Was it the product treatment, or does that area on the left side always out-yield the right side of the field? Most farmers acknowledge the limitations to small-plots and strip-trial formats, but those are the standards for the industry. Prior to 2012, the Pest Management Regulatory Agency (PMRA) required efficacy data for product registrations, but now the Canadian marketplace is buyer beware, as the U.S. has always been. The reality is that little on-farm research is conducted and few farmers leave check-strips or comparisons within a field. Most farmers don’t have the time or tools to accurately measure yield and review results.
For those of you with perfectly uniform fields on your farm, precision agriculture can enable you to compare differences in product performance, and you can get paid to generate the data and yield results to go beyond hollow testimonials.
The main challenge to precision agriculture is the lack of knowledgeable people in this technology segment. There is a general shortage of young folk now that farmers comprise one per cent of the population, and no Canadian schools have degrees or diplomas in precision agriculture. Many experienced/mature farmers have no idea what the technology is capable of nor how to use it on the farm.
Even with current challenges, most farmers agree they will adopt precision agriculture in the future, but they have difficulty articulating what exactly it will look like. The beauty of technology is that it can transform what was once difficult or impossible into something that happens every day.
Dale Steele is a precision agronomist in southern Alberta. “Precision agronomist” is a fairly new term that includes geospatial technologies like GIS, GPS and remote sensing/imagery in traditional agriculture management activities. Dale always tries to see things from a farmer’s perspective, to ensure new technology is relevant to the farm.
Nov. 23, 2015 - Case IH announces the award-winning 2000 series Early Riser planter featuring an all-new row unit. Building on a tradition of Agronomic Design, these rugged new planters have been engineered to operate at higher speeds to deliver fast and uniform emergence. The new planter has already been recognized with the agricultural engineering community's prestigious AE50 award for 2016.
Trimble Triple Play Farmer EventTue Oct 24, 2017 @ 3:00PM - 05:00PM
Farms.com Precision Agriculture ConferenceWed Oct 25, 2017
Western Forum on Pest Management Wed Oct 25, 2017 @ 8:00AM - 05:00PM
Grain World Conference: Canada's Agricultural Outlook ConferenceTue Nov 14, 2017
Canadian Forage & Grassland Association Conference Tue Nov 14, 2017 @ 8:00AM - 05:00PM
Webinar: Managing western bean cutworm in corn and edible beansTue Nov 14, 2017 @ 3:00PM - 04:00PM