In collaboration with a University of Prince Edward Island (UPEI) engineer, Agriculture and Agri-Food Canada weed specialist Andrew McKenzie-Gopsill is turning to sensors, cameras and computer algorithms to detect the exact location of weeds in a field.
The digital technology will create a data base of images to identify weeds, essentially pinpointing only the areas where herbicide is required.
The technique could cut down herbicide use to a fraction of what it is now and could significantly reduce operating costs for growers.
Some hurdles remain to smooth out the sensor imaging, but the goal is to create field data that can be fed into software that farmers can purchase for use on their sprayers.
Initial equipment costs of around $20,000 could be recouped over a couple of years with the savings from reduced herbicide purchases.
Much like antibiotic resistance in human medicine, the number of weeds that are resistant to commonly used herbicides is on the increase.
Herbicides that were once worked well now offer limited control and the overuse of herbicides is a major factor in weed resistance to sprays.
McKenzie-Gopsill is now doing experiments to find out how resistant various commons weeds on PEI are to herbicides.
His research shows there is weed resistance to metribuzin, the active ingredient in the #1 herbicide used by potato growers.
Weeds collected from tests at AAFC Harrington Research Farm tolerated very high rates of metribuzin. Some fields where metribuzin was applied showed no weed control. This research has the potential to address this challenge while helping growers to continue to provide Canadians with healthy, high-quality food.
A new atomiser, specifically for sprout inhibitors during this 'storage period', has been designed.
The atomiser, known as the Synofog, uses a new technique - electro-thermal atomisation. The advantage of this new piece of apparatus is that it does not have an open flame. This ensures its safe use with all kinds of sprout inhibitors. READ MORE
Harvest management, in large part, is bruise management. Bruising also affects tuber quality significantly. In order to harvest potatoes with minimum tuber damage, growers need to implement digging, handling and storage management practices that maintain the crop quality for as long as possible after harvest.
Assuming all harvest and handling equipment are mechanically ready to harvest the crop with minimum bruising, there are several tips to preserve the quality of potatoes crop during harvest:
- Timely Vine Killing. Killing the vines when tubers are mature makes harvesting easier by reducing the total vine mass moving through the harvester. This allows an easier separation of tubers from vines.
- Timely Harvest. Potatoes intended for long term storage should not be harvested until the vines have been dead for at least 14 days to allow for full skin set to occur.
- Soil Moisture. Optimal harvest conditions are at 60-65% available soil moisture.
- Tuber Pulp Temperature. Optimal pulp temperatures for harvest are from 500F to 600F. Proper pulp temperature is critical; tubers are very sensitive to bruising when the pulp temperature is below 450F. If pulp temperatures are above 650F, tubers become very susceptible to soft rot and Pythium leak. Pulp temperatures above 70°F increase the risk of pink rot tremendously no matter how gently you handle the tubers if there is inoculum in the soil.
- Tuber Hydration. An intermediate level of tuber hydration results in the least bruising. Overhydrated tubers dug from wet soil are highly sensitive to shatter bruising especially when the pulp temperature is below 450F. In addition, tubers harvested from cold, wet soil are more difficult to cure and more prone to breakdown in storage. Slightly dehydrated tubers dug from dry soil are highly sensitive to blackspot bruising.
- Reducing Blackspot Bruising. Irrigate soil that is excessively dry before digging to prevent tuber dehydration and blackspot bruising.
- Bruise Detection Devices. Try to keep the volume of soil and tubers moving through the digger at capacity at all points of the machine. If bruising is noticeable, use a bruise detection device to determine where in the machinery the tubers are being bruised.
- Do not harvest potatoes from low, poorly drained areas of a field where water may have accumulated and/or dig tests have indicated the presence of tubers infected with late blight.
- Train all employees on how to reduce bruising. Harvester operators must be continually on the lookout for equipment problems that may be damaging tubers. Ideally, growers should implement a bruise management program that includes all aspects of potato production from planting through harvest.
- Harvest when day temperatures are not too warm to avoid tuber infections. Storage rots develop very rapidly at high temperatures and spread easily in storage. If potatoes are harvested at temperatures above 27o C and cool off slowly in storage, the likelihood of storage rots is increased. If warm weather is forecast, dig the crop early in the morning when it is not so warm.
March 31, 2016 – Two Agrifac Condor Endurance sprayers are heading to Alberta this week.
Manufactured in the Netherlands, the pendulum chassis eliminates boom movement makes sure that the weight distribution is equal on all four wheels. The chassis enables a 2,100 US gallon tank and booms up to 180 feet. Due to the design of the tank, no rest liquids stay behind, and the EcoTronicPlus display is the spray computer as well as the interface for machine settings. For more information on Agrifac and on the self-propelled sprayers, check out www.agrifac.com or www.agrifac.ca.
So far in the project, the drone has collected imagery from about 50 potato fields in New Brunswick. Photo courtesy of Bernie Zebarth, AAFC.
Drones, or unmanned aerial vehicles (UAVs), are becoming increasingly popular with crop growers as an easy way to take a look at their fields. Now, researchers are fine-tuning the use of imagery from drones as a more advanced tool for precision management of Canadian potato fields.
This work with drone imagery is part of a major five-year project to boost potato yields. “About three years ago, Potatoes New Brunswick and McCain Foods Canada came to me and said they were having issues in terms of potato productivity in Eastern Canada,” says Bernie Zebarth, a research scientist with Agriculture and Agri-Food Canada (AAFC) at the Fredericton Research and Development Centre. “The data across North America show a slow but steady average increase over time in potato yields, going up about five hundredweight per acre per year. But in Eastern Canada, that is not happening – the crop insurance data for New Brunswick suggest our yields are either stagnant or perhaps even decreasing slightly over time.
“That’s a serious concern for industry. For example, a lot of New Brunswick’s potato production is for French fries for export. To export, you have to be competitive. If you start losing yield, then you become less competitive. So they asked us to work with them to see what could be done about it.”
The project aims to increase yields by addressing the variability in productivity within potato fields. “The project has three main objectives. First, can we develop ways of mapping the variability in plant growth and yield in potato fields? Second, for the areas of the fields that are not performing well, can we identify why? And third, can we overcome those yield limitations?” Zebarth says.
“In Eastern Canada, precision agriculture is an exciting new area for us, and this is part of what we’re doing in the project,” he adds. So, they are trying out tools like drones and yield monitors to map in-field variability as a first step towards managing that variability through precision agriculture. According to Yves Leclerc, McCain’s director of agronomy for North America, managing in-field variability is key to improving yields and profitability for potato growers. “We need to understand profitability not only at the field level but also the subfield level, and manage fields at that level. It is no longer enough to manage a field based on the average conditions; we need to be more precise [to achieve a field’s full yield potential].”
The project’s initial phase, in 2013 and 2014, took place in New Brunswick only. For 2015 to 2017, the research is also taking place in Prince Edward Island and Manitoba. For the first two years, Potatoes New Brunswick, McCain Foods Canada, AAFC and New Brunswick’s Enabling Agricultural Research and Innovation program funded the project. With the expanded project, two additional agencies have come on board: the PEI Potato Board and the Manitoba Horticulture Productivity Enhancement Centre Inc. The project’s lead agency is Potatoes New Brunswick.
To try to remedy yield limitations, the project team is looking at a wide variety of practices such as compost applications, fumigation, fall cover crops, nurse crops, furrow de-compaction and, in Manitoba, variable rate irrigation.
“We’re looking at everything from drones and drone imagery, to the thousands of holes we’re digging to look at soil compaction, to compost applications, to soil salinity – everything. We want to make sure that there’s literally no stone unturned,” says Matt Hemphill, executive director of Potatoes New Brunswick. “There is no one-size-fits-all and no magic bullet in this process because we have such variability in soil and weather conditions.”
Drone imagery – advantages and hurdles
A drone with a specialized camera and advanced software can be used to map in-field variability. The drone flies over the field in parallel passes to capture the entire field in a series of overlapping images. The camera can be set up to capture particular wavelengths of light; for instance, near-infrared wavelengths may be of interest because healthy plants reflect more near-infrared light than stressed or dead plants. So a near-infrared image of a field could potentially be used to identify patches where the plants are stressed due to problems like disease or low nutrient levels.
“For instance, if we could use the imagery to identify the parts of a field that aren’t deficient in certain nutrients, then a potato producer wouldn’t need to waste time, energy and money in putting fertilizer products on those parts of the field,” Hemphill says. “Imagine, instead of broadcasting X number of tonnes per acre of lime across the whole field, you maybe only have to apply it to 25 per cent of the field. You can imagine how quickly those savings would add up. The same goes for other input costs.”
Zebarth explains that, before drones became available, the main way to map vegetation patterns was with satellite imagery, which has advantages and disadvantages compared to drone imagery. “One advantage of satellite imagery is that it’s calibrated [so the imagery data is easier to use in advanced analysis]. But there are two big problems with satellites. The first one is that you can’t control when they capture imagery of your field – they only fly over the field every so often, and they can’t see through the clouds. In New Brunswick, we have a lot of cloud cover. The other disadvantage of satellite imagery is its low resolution; a ‘pixel’, an individual point of information, represents an area of maybe five by five to 30 by 30 metres in size [on the ground]. So we’ve never used satellite imagery very much to look at crops here in the East.”
Drones avoid those disadvantages. The user can choose where and when to capture the imagery, as long the operator flies the drone safely and legally. (Anyone operating a drone in Canada must follow the rules set out in the Canadian Aviation Regulations and must respect all federal, provincial/territorial and municipal laws related to trespassing and privacy.) Cloud cover isn’t as much of an issue for drones because they fly below the clouds. And drone imagery is at a much higher resolution, about seven to 10 centimetres, depending on how high the drone is flown.
These advantages are making drones popular with crop growers. “People are already using drones, mostly for qualitative assessment. Drones are fantastic for that,” Zebarth notes. “One such application is to have a visual look at your field. For example, if you fly the drone when the crop is beginning to emerge, you can really see the field’s variability – where the crop is already emerging and doing well, where it is just emerging, and where it hasn’t emerged yet.”
Another qualitative application is to target field scouting. Zebarth explains, “In the image of the field, you might see a patch that looks different. The imagery won’t tell you what the problem is; it will just tell you something is there. So you can go out to the field and look at that patch to identify the problem.”
However, the project team wants to take drone imagery a step further. “We’d like to get to where it’s a more sophisticated tool,” Zebarth says. “We want to determine quantitative differences, like trying to develop relationships between the imagery and things like yield and leaf area index, and so on.”
McCain Foods has its own drone, camera and software for this type of geo-referenced field mapping, and it has trained two of its employees to operate the drone, one as a pilot and the other as a spotter. For the project, they are flying the drone over commercial potato fields in New Brunswick. So far, they’ve collected imagery for about 50 fields.
They fly the drone over each field several times during the growing season. The first flight is when the soil is still bare, so they can look for differences in soil moisture and drainage across the field. The subsequent flights are timed to capture the crop during early and late emergence, mid-season, and early and late senescence. Zebarth says, “So we’re looking at: do we have variation in the soil, the early canopy growth, and the canopy die-down.”
One hurdle in their quantitative use of drone imagery is to correctly stitch together all the individual images from the drone’s flight over the field. “The drone might take perhaps 50 to 100 different images of the field. Those images have to be pieced together, which is called ‘mosaicking.’ If you have a discrete object, like a house or a fence in the images, mosaicking is not too difficult because you can easily find that object in the different images and align them. But if all you have in the image are rows of potato plants, there is not much to align with,” Zebarth notes. “There is mosaicking software to do that, but it’s not perfect. So we’re working with one of the drone companies to figure out how to get the images almost perfectly lined up so you can get down to looking almost at the [individual] plants.”
A second hurdle relates to calibration. “The drone’s camera is actually measuring how bright the light is in different bands; it is taking pictures that have red, green and blue, like a regular camera, but it may also have near-infrared. So it gives you a number from one to 255 in each of those bands, but it is just a relative number. To calculate things like vegetation indices, such as the NDVI [normalized difference vegetation index], you can use a relative brightness to get a relative value of the NDVI. However, you have to convert it to a reflectance value to get a true value for the NDVI that you can compare across fields or measurement dates. That’s where it has to be calibrated,” Zebarth explains.
The researchers are making good progress with overcoming both of these hurdles. Plus, they are testing over 20 different vegetation indices to determine what each index is sensitive to and which ones work best for potato fields in New Brunswick.
“For instance, one index might be mostly sensitive to how much coverage there is of green leaves, whereas another one might be more sensitive to how much chlorophyll is in the leaves,” Zebarth says. They’ve already found that the NDVI, a common index for measuring vegetation cover, isn’t the best choice for potato crops. “For potatoes, the NDVI reading initially goes up as the canopy develops, but after the canopy reaches a certain density, the NDVI becomes insensitive.” Some of the other indices don’t have that drawback.
Once the researchers complete this work in the coming months, they’ll have a much better idea of how they can use the drone imagery. The information from this work could also help agronomists, crop advisors and growers with an interest in quantitative uses of drone imagery in potato production.
“In the long run, drone imagery is going to be a tool to add to our arsenal of tools, for sure,” Leclerc says.
Progress on agronomic findings
One of the project’s key agronomic findings so far is the degree of variability in potato fields. “We are seeing a lot more variability in our fields than we had expected. Although some fields are relatively uniform, other fields have pretty dramatic variation,” Zebarth says.
The results so far indicate that, in New Brunswick, much of the variability is due to the soil. “What we think has been going on is a gradual decline in soil health over decades,” Zebarth says.
The wet spring in 2013 emphasized some of this soil variability. He says, “When we visited the field sites, we would see places in some fields where there wasn’t a single plant. It looked like problems with poor drainage or loss of soil structure or low soil organic matter. So that is why a lot of the remedies that we’re trying are ways to improve soil health, like compost applications or changing crop rotations.”
Leclerc notes, “The biggest challenge is determining the underlying causes of the differences in productivity. In some cases it’s fairly easy to pinpoint, especially [with the wet conditions] in the project’s first year, but in other cases it is a lot more difficult. We are examining that aspect with very precise soil and subsoil analysis.”
Once a field’s variability is mapped and the causes of its differences in productivity are understood, then the field’s management zones can be defined and managed. “We can work on those management zones to improve the limitations, which are most likely soil-related. Or, if that cannot be done, the idea is to manage the different zones differently. So perhaps we might back off in terms of inputs on the lower productivity zones and reallocate those resources to the higher productivity zones, and look at changing the spacing perhaps on the higher productivity zones to take advantage of their higher yielding capabilities,” Leclerc says.
After the trials with the various management practices are completed, the project team will analyze the results to see which practices provide the most consistent benefits, how effective they are, and under what conditions they are most effective, and to determine which options make the most economic sense.
“At the end of the day, we need to look at the input costs and profitability,” Hemphill says. “The outcome needs to work for the growers and the processors in order for the industry to remain sustainable.”
For the past nine years, veteran automotive journalists have donated their time to act as judges in the only annual North American truck competition that tests pickup and van models head to head – while hauling payload and also towing.
The Canadian Truck King Challenge started in 2006, and each year these writers return because they believe in this straightforward approach to testing and they know their readers want the results it creates.
I started it (and continue to do it) for the same reason – that, and my belief that after 40 years of putting trucks to work I know what’s important to Canadians. Now, that’s a long list of qualifications, but in a nutshell it’s the concept that a truck can be pretty, but that alone is just not enough. It had also better do its job – and do it well.
This year, nine judges travelled from Quebec, Saskatchewan and across Ontario to the Kawartha Lakes Region where we test the trucks each year. All the entries are delivered to my 70-acre IronWood test site days before the judges arrive so we can prepare them for hauling and towing. In the meantime they are all outfitted with digital data collectors. These gadgets plug into the USB readers on each vehicle and transmit fuel consumption data to a company in Kitchener, Ont. (MyCarma) that records, compiles and translates those readings into fuel economy results that span the almost 4,000 test kilometers we accumulate over two long days.
These results are as real world as it gets. The numbers are broken into empty runs, loaded results and even consumption while towing. Each segment is measured during test loops with the trucks being driven by five judges – one after the other. That’s five different driving styles, acceleration, braking and idling (we don’t shut the engines down during seat changes).
The Head River test loop itself is also a combination of road surfaces and speed limits. At 17-kilometres long it runs on gravel, secondary paved road and highway. Speed limits vary from 50 to 80 km/h and the road climbs and drops off an escarpment-like ridgeline several times; plus it crosses the Head River twice at its lowest elevation. The off-road part of our testing is done on my own course at IronWood. Vans are not tested on the off-road course, though it’s noteworthy that the Mercedes Sprinter was equipped with a four-wheel drive system this year.
This is the third year that we have used the data collection system and released the final fuel consumption report that MyCarma prepares for the Truck King Challenge. It’s become one of our most anticipated results.
But how do we decide what to test? Well as anyone who’s bought a truck knows, the manufacturers never sleep, bringing something different to market every year. As the challenge looks to follow market trends, what and how we test must change each year too and the 2016 model year proved no different. We had a field of 14 contenders at IronWood this year covering four categories. They were as follows:
Full-size half-ton pickup truck
- Ford F-150, Platinum, 3.5L, V6 EcoBoost, gas, 6-speed Auto
- Ford F-150, XLT, 2.7L, V6 EcoBoost, gas, 6-speed Auto
- Chevrolet Silverado, High Country, 6.2L, V8, gas, 8-speed Auto
- Ram 1500, Laramie, 3L EcoDiesel, V6, diesel, 8-speed Auto
Mid-size pickup truck
- Toyota Tacoma, TRD Off-Road, 3.5L V6, gas, 6-speed Auto
- GMC Canyon, SLT, 2.8L Duramax, I-4 diesel, 6-speed Auto
- Chevrolet Colorado, Z71, 3.6L V6, gas, 6-speed Auto
Full-size commercial vans
- Ford Transit 250, 3.2L Power Stroke I-5 diesel, 6-speed Auto
- Mercedes Sprinter 2.0L BLUE-Tec I-4 diesel, 2X4
- Mercedes Sprinter 3.0L BLUE-Tec V6 diesel, 4X4
- Ram ProMaster 1500, 3.0L I-4 diesel, 6-speed Auto/Manual
Mid-size commercial vans
- Ram ProMaster City, SLT, 2.4L Tigershark I-4 gas, 9-speed Auto
- Nissan NV200, 2.0L I-4, gas, Xtronic CVT Auto
- Mercedes Metris, 2.0L I-4, gas, 7-speed Auto
These vehicles are each all-new – or have had significant changes made to them. However, this year, the Truck King Challenge decided to try something else new by offering a returning champion category.
This idea had been growing for a while and had everything to do with the engineering cycles that each manufacturer follows. Simply put, trucks are not significantly updated each year and to date we have only included “new” iron in each year’s competition. However, we started to think that just because a truck is in the second or third year of its current generational life shouldn’t make it non-competitive. Certainly if you watch the builders’ ads it doesn’t!
So, this spring we decided that for the first time the immediate previous year’s winner (in each category) would be offered the chance to send its current truck back to IronWood to compete against what’s new on the market.
This year the invitation was sent to the Ram 1500 EcoDiesel, Ford Transit 250 and Nissan NV200 – all previous winners that accepted the offer to return and fight for their crowns.
They, along with the new vehicles, took the tests over two days with the judges evaluating everything from towing feel to interior features.
The judges score each vehicle in 20 different categories; these scores are then averaged across the field of judges and converted to a score out of 100. Finally the “as tested” price of each vehicle is also weighted against the average (adding or subtracting points) for the final outcome.
And this year’s segment winners are...
- Full-Size Half-Ton Pickup Truck – Ram 1500 EcoDiesel – 82.97 per cent
- Mid-Size Pickup Truck – GMC Canyon Duramax – 76.30 per cent
- Full-Size Commercial Van – Ford Transit 250 – 73.90 per cent
- Mid-Size Commercial Van – Mercedes Metris – 75.69 per cent
The overall top scoring 2016 Canadian Truck King Challenge winner is the Ram 1500, Laramie, 3L EcoDiesel, V6 diesel, 8-speed Auto.
Congratulations to all the winners and to the two repeating champions – the Ram 1500 EcoDiesel and the Ford Transit 250.
Oct. 29, 2015, Charlottetown – After 10 years of operation in Stratford, P.E.I., HJV Equipment, the specialized agriculture equipment company that has a particular focus on potato equipment, plans to move across the Hillsborough Bridge to Charlottetown, writes The Guardian. | READ MORE
October 29, 2015, Charlottetown, PEI – As P.E.I.'s potato harvest winds up, there's a distinctly different look to many Island warehouses: they've installed shiny new metal detectors, at a cost of between $50,000 to half a million dollars each.
Last fall, steel needles and other sharp metal objects were detected in P.E.I. potatoes at processing plants and in bags sold throughout Atlantic Canada. A number of metal objects were found in potatoes again this spring. READ MORE
New, state-of-the-art PEI potato storage facility opensCavendish Farms has officially opened its new potato storage facility,…
P.E.I. potato board unveils new ad campaignThe PEI Potato Industry has released a 30 second commercial…
Examining potatoes' past could improve spuds of the futureExamining the ancestors of the modern, North American cultivated potato…
GMO potatoes provide improved vitamin A and E profilesResearchers from Ohio State University and the Italian National Agency…
Fields on wheels conference Fri Dec 15, 2017
South West Ag ConferenceWed Jan 03, 2018
Potato Expo 2018Wed Jan 10, 2018
Ag DaysTue Jan 16, 2018