Yield mapping — quality results require preparation · to gathering meaningful and accurate information. key messages by Sam Trengove, Trengove Consulting. PA in Practice II Using

PA in Practice II Using precision agriculture technologies: a guide to getting the best results

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Yield mapping — quality results require preparation

The collection and analysis of yield information at harvest is the fi rst investment into PA for most growers. But quality of a yield map is only as good as the data collected and the rigour applied to the mapping process.

‘Rubbish in equals rubbish out’ is the equation for a wasted year of yield data. As harvest is the only opportunity to gather real yield data it

is vital to use the most effi cient and accurate processes.

The following information summarises the key actions to consider before harvest gets underway.

Off to a good start

Start with an empty data card — save a copy of any data from previous years and then clean the card before harvest.

Consistent paddock names — use the same name for each paddock each year. Make sure the spelling is correct as many programs are case sensitive. Enter paddock details into the yield monitor before harvest. Set up the data card with paddock names and crop types in the mapping software (although this information can be entered into the monitor when in the paddock).

When starting a new paddock select the correct paddock name so data is recorded to the correct location. Paddock boundaries that have been set up in the mapping software can also be exported to some of the newer yield monitors, this allows an auto fi eld recognition feature to be used, the paddock name is selected automatically based on the machine’s GPS position.

All in working order — check that the fl ow and moisture sensors are working properly before harvest gets underway. If these are not working properly then everything that follows is compromised and may be a waste of time.

Check the fl ow sensor defl ector plate for wear and tear — it should be free of holes. Ensure nothing is jammed behind the plate and that cables are free from damage and connected properly.

Flow sensors may need cleaning during harvest, especially in paddocks with large numbers of snails.

Careful calibration counts

Before harvesting each grain type, calibrate the fl ow sensor. Aim for an accuracy of fi ve per cent or better.

A side benefi t of a well-calibrated yield monitor is being able to accurately fi ll trucks within their legal weight requirements.

Avoid recalibration within a paddock, this is especially important if there are multiple harvesters working in a single paddock, as it can introduce step changes in the yield map.

• The quality of a yield map is only as good as the quality of information collected.

• A logical fi le management system and consistent recording methods allow data to be found quickly.

• Ensure the yield monitor is calibrated and in good working order before harvest gets underway.

• Calibrate the yield monitor against post-harvest delivery volumes for greater information accuracy.

• Harvester set-up is critical to gathering meaningful and accurate information.

key messages

by Sam Trengove, Trengove Consulting

PA in Practice II Using precision agriculture technologies: a guide to getting the best resultsPA in Practice II Using precision agriculture technologies: a guide to getting the best results

27

Recalibration during the season might be necessary where crop conditions have changed signifi cantly due to rainfall or some other event. If this is the case, note when, where and why the calibration was changed, and record the new calibration fi gures.

Calibrations can also be fi ne-tuned after harvest in the yield mapping software using whole paddock yields (see data cleaning, page 28), however this is less accurate than calibrations with small loads harvested in more uniform crop conditions.

Where two harvesters are operating, try and calibrate under similar crop load conditions so they have the same fl ow rates of grain passing through the fl ow sensor. For example, if one harvester is calibrated at harvesting 40 tonnes per hour, then try and calibrate the second harvester in similar crop conditions.

Refer to the yield monitor manual for details on calibration.

Harvester set-up

It is critical to set the width of cutting correctly. For grain harvesting, the cutter bar width is the width of the harvester comb. If harvesting windrowed crops it is the width of the windrower comb.

Maintaining a constant cutting width with an autosteer system or accurate driving will improve the accuracy of the yield data.

Some of the newer yield monitors have an overlap control feature, which is the equivalent of ‘autosection’ control, where the monitor alters the recorded cut width based on the machine position in relation to previous passes. This will cut the recorded comb width down where there is only a fraction of a comb width left to fi nish a paddock or at the ends of some runs where the headland is on an angle. This feature may need to be turned on manually.

Ensure the fl ow delay in the monitor is set accurately — the stop height for recording is set just above crop height, the comb is raised to disengage recording when not in the crop and, for newer monitors, the overlap control is enabled. This will help to reduce high and low data points at the start and end of runs, which can be time-consuming to clean from the dataset.

Card check and back-up

If the yield monitor does not create an on-the-go yield map on screen, it is essential to load data onto the mapping software to confi rm data is being logged correctly.

Download and store data regularly (every couple of days) — once a season is defi nitely not often enough. Create a new folder for each year’s fi les and copy a back-up of the raw data fi les straight from the card to that folder. A hierarchal fi ling system (see Figure 1) will assist in the rapid retrieval of the correct data.

Regularly copy and back-up data to an external hard drive, USB, CD or DVD. Issues occur with any electronic equipment, software packages change and growers change brands. Backing up data is the best way to be protected against issues such as these.

Yield mapping software generally has a back-up function. It is a good idea to copy and backup before data is processed by the mapping software.

Make sure all data is downloaded and backed-up at the end of harvest.

Figure 1. Logical yield data file structure

Cutting width: Maintaining a constant cutting width will improve overall data accuracy. PHOTO: DAVE GOODEN

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Clean data yields better mapsCleaning yield data is an important step in order to remove any erroneous data points (for example, extremely high or low yields).

Not every data point is appropriate for mapping — some may be misplaced due to GPS signal error, others are low or even zero readings at the start and end of rows when the comb is not at full capacity. Other data may be abnormally high when the harvester slows, or when there is a blockage in the system.

Excessively low and high yielding points can be removed by setting a minimum and maximum yield fi lter. Observing the histogram or normal distribution of the yield values will help determine what the minimum and maximum values should be to remove these outliers.

Often there are erroneous points that fall within the normal distribution of yields and these cannot be removed via a simple yield fi lter without deleting correct yield data that should be kept.

These are often associated with the start and ends of passes when the harvester is not at full capacity and the grain fl ow is not representative of the crop being harvested.

The simplest way to remove these points is by manually selecting and deleting them, however this can be a time-consuming job depending on the distribution of yields, the shape of the paddock and how the paddock was harvested.

Fortunately the major software brands have features to help with simple cleaning. Newer headers now have a form of ‘section control’ where the header automatically adjusts for less-than-full comb widths.

Free software packages also are available, which offer additional data-cleaning options. The most useful of these is a combination of: Field Operations Viewer and Yield Editor. Both packages are available to download from the internet:

Field Operations Viewer: www.mapshots.com/FODM/fodd.asp

Yield Editor: www.ars.usda.gov/services/software/download.htm?softwareid=20#downloadForm

Field Operations Viewer is basically a translation package that reads data from different brands of yield mapping programs and brings them into one format.

Yield Editor does the actual cleaning and is straight-forward to use.

Using dataAfter yield data is cleaned it can easily be imported into most software packages and used to create management zones or assess results from VRA programs.

Note: The maximum number of yield brackets should be eight or less otherwise maps can become too messy and confusing.

If using the SPAA recommended key, high yields should be blue and low yields red (see Figure 1).

The average value for the paddock should be pale green.

Half tonne increments allow paddocks with more variability to have more colours to show the variability, whereas a paddock that is relatively uniform might only have two or three.

Some growers prefer to put the whole farm on the same scale so different paddocks can be compared on the same scale.

Figure 1. SPAA yield mapping key

For more on cleaning yield data, go to the SPAA website: www.spaa.com.au and use the search function to fi nd relevant articles.

for more informationi


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Post-harvest calibration

Check yield monitor calibration accuracy after harvest by comparing total deliveries (do not forget stored or grain kept for seed) with the total yield recorded on the yield monitor.

Mapping software can produce a summary of crop yields and total tonnes harvested for each paddock based on the yield monitor.

If there are discrepancies between the yield monitor and total deliveries the data can be adjusted with a post-harvest calibration. This is done by entering into the software the correct amount of harvested grain or by scaling the data.

The scaling factor is calculated by dividing the total physical tonnes harvested by the total tonnes recorded by the yield monitor.

If the calibration is not accurate, yield maps still identify areas of higher and lower yield but this will not be suffi ciently accurate for making nutrient removal, gross margin or water use effi ciency maps.

If data is to be cleaned with the yield mapping software then refer to the yield mapping software manual or help guide.

If using alternative software the data will need to be exported, most commonly as ‘comma separated values’ (.csv), ‘text’ (.txt) or ‘shape’ (.shp, but also creates .shx and .dbf fi les at the same time that must be stored in the same fi le location).

farmer feedback

Ashley started yield mapping during the 1990s when the John Deere header he bought came equipped with a yield monitor. However it was several years before he could work out how to practically use the data produced.

“At fi rst it was frustrating trying to convert the yield maps into useful zone maps as it was diffi cult to determine and validate if the data provided a true representation of the paddock,” Ashley said.

But according to Ashley, the technology has improved signifi cantly and three mapping programs later, he is now generating high-quality yield maps. The current John Deere 9770 header uses its own yield-mapping program called Apex.

Ashley employs the services of Minlaton-based PA consultant Peter Treloar, who assists with data entry using a VA Gateway program and develops the yield maps and variable rate fertiliser and sowing maps.

Setting up the header is simple according to Ashley.

“The technology is already inbuilt, but I always test the program after the fi rst day of harvesting to ensure data is being collected,” he said.

Ashley also backs up all raw data to at least two separate locations to reduce the risk of it being lost.

Ashley started yield mapping

Ashley Wakefi eld, Urania, SA

Calibrate post harvest: Calibrate yield monitors after harvest by comparing total deliveries against total yield recorded on the monitor. PHOTO: ASHLEY WAKEFIELD

yield mapping

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Changing the GPS projection

GPS data is generally gathered based on longitude and latitude in decimal degrees using the WGS84 American standard. However, some mapping software requires data to be in eastings and northings with units in metres.

To make this change the software will require the Universal Transverse Mercator (UTM) zone to be entered. The user is usually prompted to enter this and it is normally only required to be entered once.

Australia is covered by UTM zones 49 to 56 (see Figure 2). Some properties may fall across the boundary of two zones, if this occurs choose one zone and stick with it for all your properties provided they do not straddle the UTM boundaries by very large distances.

Figure 2. Australian Universal Transverse Mercator

49

108 114 120 126 132 138 144 150 156

50 51 52 53 54 55 56

(BOUNDING LONGITUDES)

(AMG ZONE NUMBERS)

farmer feedback

Mark and Steve Day collect yield maps using the GreenStar™ system and according to Mark successful yield mapping relies on:

• Being organised — have data cards loaded and ready before harvest starts

• Having one person set the machine and any contractors’ machines — check everything is working before you start harvesting.

• Ideally using contractors and machines that are the same as their own. For the Days this means late model John Deere harvesters with 12m fronts. This makes for effi cient harvesting — header fronts are always full, block cutting is always consistent and parallel, and yield data is compatible. It also creates accurate maps with minimal errors from overlaps and recording double-ups.

• Collecting data every two days and download for data integrity.

• Calibrating the monitor once and then leaving it. Apply after-harvest calibrations with software to fi ne tune. Do not try and calibrate every paddock.

Mark and Steve Day, Lockhart, NSW

• Making use of the technology’s features, such as overlap control particularly when working along side contractors and or employees.

• Creating a raw data fi le before unloading the information off the card.

“The need for data cleaning is often limited after harvest provided all the functions of the system have been working during harvest,” Mark said.

“Largely it is just a matter of adjusting the calibration fi gure after exact tonnages are known.”

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Sam Trengove is an agronomist in South Australia, where he coordinates the GRDC PA Grower Groups. Sam is also a member of the SPAA committee.

m: 0428 262 057e: [email protected]

about the author

Interpolating data

To turn all the data points and spaces between them into a yield surface the data needs to be interpolated.

The benchmark method for interpolation, and the method preferred by researchers, is to krige data, however commercial software most commonly uses inverse distance weighting as the method of interpolation.

Practical experience suggests that in most situations the default setting in SMS and APEX software produce suffi ciently accurate maps for most grain production uses of yield maps.

VESPER is a shareware program (http://sydney.edu.au/agriculture/pal/software/vesper.shtml) that kriges the data and places it on a standard grid.

Creation of a grid is a key step for multi-layer comparisons and analysis. This step is inbuilt as part of the multi-layer analysis process in yield mapping software.

If using VESPER the grid will need to be created and the same grid (size and location) for a paddock should be used to display and integrate all data collected for that paddock.

For grain crops a fi ve to 10m grid is appropriate and for block kriging a 25 to 30m block.

The default grid setting in SMS is 15.24m and in APEX the default grid size changes with paddock size. Therefore, modifi cation is recommended.

After yield map interpolation in VESPER, the results need to be displayed. Ideally, this is done using GIS software. If using yield-mapping software then display is not an issue — it occurs throughout the whole process.

SMS and APEX both use inverse distance weighting rather than kriging for map interpolation. In SMS the maximum search setting is 22.86m (75 feet), however, this is not always appropriate.

Based on CSIRO calculations, for a 13m front harvesting at 10km/h, 34m would be more appropriate and for a 9m front harvesting at 6km/h 21m would be more appropriate.

Saving the changes

Generally, yield-mapping software saves changes as they are made, but it is always worth checking.

If the data is exported from the mapping software for cleaning and kriging, make sure every step is saved. It is sensible to retain raw data and save processed data to a separate folder using the same paddock name but coded to indicate the data has been manipulated.

Short video tutorials on many of these processes have been produced to support the GRDC PA Groups Project. Contact Sam Trengove to source copies. PA

yield mapping

farmer feedback

Adam fi rmly believes in the benefi ts of yield mapping and says everyone who can, should be doing it.

“It costs nothing to do and the information is valuable even if you don’t act on it for a few years,” he said.

Adam collects harvest data annually via his Autofarm A5 autosteer, a CNH yield monitor on a CR960 header together with a NIR tech protein monitor, which Adam says has failed to perform well.

“The actual measurement of protein is fi ne, but getting a consistent sample has proven diffi cult due to the sampler not working properly.”

When setting up for yield mapping Adam recommends keeping things simple and consistent.

“I generally don’t try and calibrate within 5% or even 10% of actual yield. It’s the patterns that are important not the actual numbers.”

Adam’s maps are developed using the AgLeader SMS Advanced software, which he has found can readily import data from different systems and sources and export it to a range of different hardware devices.

“I don’t clean maps that much, however it is necessary at times to interpolate across missed areas but seeking a professional to do this is often best,” Adam said.

Adam Inchbold, Yarrawonga, Victoria

For more information on yield mapping or data management go to: www.spaa.com.au and search under the ‘Resources’ tab for the latest information and links.

for more informationi

areas but seeking a

PHOTO: DANIEL ADAMS

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farmer feedback

The Goodens collect their harvest data via the New Holland PLM software to develop yield maps.

“The AutoFarm system easily interacts with the New Holland system, so collecting yield data has been easy,” David said.

“At the start of harvest we clear last year’s data and add our current paddocks to the list. We rotate between two cards during harvest and regularly back up the data to an external hard drive.”

“It’s also important when running two harvesters to make sure both machines are calibrated the same — having similar machines makes it easier.”

Several years of yield data is helping David and his family build a platform for zone management but the Gooden’s will wait until they have enough data to make valid decisions about VRA for lime, phosphorus, sulphur and nitrogen.

“We are starting to notice the impact elevation and soil type can have on crop yields in different rainfall years and frost events,” David said.

“Yields also tend to fl ip-fl op depending on rainfall and spring conditions.”

PA has enabled the Gooden’s to readily conduct farm-scale trials and to examine some of the paddock variations. So far some of these trials have included variety trials, a Prosaro® demo in canola, nitrogen and phosphorus rate trials, with plots ranging from 5 to 20 hectares.

Residue management

The Goodens believe residue management is as important as yield mapping at harvest, and according to David all paddock activity, whether it is spreading fertiliser, sowing seed, managing wheel tracks or crop residue, must be done evenly across the fi eld.

“We aim to spread crop residues as wide as the 13.5m header front and minimise trash rows behind the harvester,” David said.

David, Jason and Adam Gooden, Lockhart NSW

“Uneven spreading can reduce the success of inter-row sowing due to uneven germination, reduced activity with pre-emergent herbicides and blockages at sowing.”

Crops are cut at 300mm and residues spread using a MAV Redekop residue spreader, which the Goodens retro-fi tted to the header during 2005.

“The spreader was uncomplicated and simple to fi t and has generally worked well, but in below-average-rainfall years, when stubbles are thin and dry, spreading widths have been reduced,” David said.

The Goodens have also found spreading widths vary signifi cantly between day and night harvesting.

“Balancing harvest speed and cutting height is all about weighing up when the next rain is coming and deciding whether the paddock will be mulched after harvest, David said.

While stubble consistency varies each season the Goodens aim to keep as much of it standing and well anchored as possible to facilitate inter-row sowing. To this end, only the chaser bin runs off the tramlines during pick-up and the dispersal of the sheep fl ock means stubbles are no longer grazed. The recent run of wet summers has caused a few challenges with stubble breaking down and becoming loose, but techniques such as adding coulters to the seeder and mulching those paddocks where crops were lodged have helped.

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33PA in Practice II Using precision agriculture technologies: a guide to getting the best results

Pre-harvest checklist

✓ Logging data on the memory card

• Before harvest, copy all fi les to a computer and delete them from the card to free up space — start harvest with an empty data card.

• Make sure the fi les are backed up.

• Use the same paddock names each year.

• Enter paddock details into the yield monitor before harvest.

• Select the correct name when starting a new paddock.

• Keep data clean by lifting the comb where there is no grain passing, or on overlaps, or where there is less than half a full cutter bar width.

✓ Downloading and storing the yield data

• Download the PC card to yield-mapping software regularly during harvest.

• Check the data can be viewed on the PC.

• If satisfi ed, download data correctly — the card can be wiped to free up space (memory).

• Back up regularly (daily).

• Use a consistent storage method so you don’t lose fi les.

Yield monitor checks✓ Check the fl ow sensors — if these are not working properly everything else is a waste of time.

• Check the defl ector plate for wear and tear.

• Check for jammed objects behind the plate.

• Check cables are free from damage and connected properly.

✓ Calibrating the fl ow sensor

• Calibrate at the start of harvest.

• Calibrate at several speeds to manipulate a range of fl ow rates past the sensor and try to keep a full comb.

• Calibrate for different crops.

• Avoid recalibrating within a paddock.

Calibration can be fi ne-tuned in software but is a time-consuming job.

✓ Calibration basics — check (including switches):

• height

• vibration

• speed/distance (and elevator speed)

• moisture

• grain fl ow

• manual.

Header checks

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trial results

Yield mapping technology can be a useful way to compare crop varieties on-farm. This is particularly useful for growers in regions that

are a considerable distance from relevant National Variety Trials (NVTs), such as the trials on Mark Venning’s property, Crystal Brook, South Australia.

A SPAA split paddock trial on Mark’s property during 2007 compared PugsleyA and Clearfi eld JNZA side by side. The trial highlighted the poor performance of Clearfi eld JNZA in a dry spring.

Mark sowed the two varieties at a rate of 100kg/ha on 6 May 2007. With 114mm of GSR and identical fertiliser inputs (40kg/ha DAP, 80kg/ha urea) the varieties had the same opportunities for production.

The crop was harvested during late November 2007 and yield differences were assessed with a CNH yield monitor. Yield differences between the varieties were quite obvious from the yield map (see Figure 1) and Table 1 for actual t/ha.

Note: Yield results from a single year of data can be misleading and should be compared against longer-term variety trials (see Table 2). To add more scientifi c rigour to this data set a paired t-test could be employed. PA

Table 1. Yield comparison between PugsleyA and Clearfield JNZA (2007)

Variety Yield (t/ha)

PugsleyA 1.34

Clearfi eld JNZA 0.72

Table 2. Average long-term (2005 to 2011) performance of PugsleyA vs Clearfield JNZA in NVT, Mid North SA

Variety Yield (t/ha)

PugsleyA 3.62

Clearfi eld JNZA 3.30

Yield mapping allows convenient comparisons

Figure 1. Yield map of trial paddock (2007)

Farmer: Mark Venning

Location: Crystal Brook, South Australia

Crop type and varieties: Wheat (Clearfi eld JNZA and PugsleyA)

Sowing date: 6 May 2007

Sowing rate: 100kg/ha

Fertiliser rate: 40kg/ha DAP, 80kg/ha urea

2007 rainfall: 317mm (2007 GSR): 114mm

Trial information

Crystal Brook, South Australia


The full report can be downloaded from:www.spaa.com.au

full reporti

35PA in Practice II Using precision agriculture technologies: a guide to getting the best results

protein sensing

Protein sensing for selective harvesting still requires refinement

Given the availability of on-the-go protein sensing and the desire of processors for grain lots with minimal variation, selective harvesting makes good philosophical sense for both growers and processors.

However, successful implementation will depend on an ability to robustly defi ne zones with respect to both yield and protein (or other quality attributes), changes

to harvest and storage logistics, improvements to on-the-go protein sensing technology, especially in terms of calibration and sampling frequency and suffi cient price premiums to make selective harvesting profi table.

Opportunities abound

While grain growers have focused on using PA technology predominantly for targeted use of inputs, such as fertiliser, the wine industry has focused on using PA to selectively harvest according to quality parameters, such as colour (anthocyanin content).

Selective harvesting has allowed grape growers to pick and allocate fruit to different product streams in order to maximise overall value. This approach is highly profi table both where the selective harvest has focused on variation within single blocks and where similar parcels have been identifi ed in different blocks and combined so as to deliver a suffi cient tonnage for separate processing to be commercially viable.

Given the availability of on-the-go protein sensing technology and the price premiums paid according to grain quality parameters, researchers were keen to explore the potential for grain growers and processors to adopt similar selective harvesting strategies and so extract greater value from the process of grain growing.

• The use of protein sensing technology has potential for the grains industry, but the technology still needs work to make it more reliable and easy to use.

• Instrument calibration requires particular attention.

• As the data density from protein sensing is much lower than for yield monitoring, identifi cation of ‘protein zones’ is less robust than identifi cation of yield zones. This may make it diffi cult to stream grain to segregations narrow enough to deliver signifi cant price premiums.

key messages

by Rob Bramley, CSIRO

PHOTO: DANIEL ADAMS


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Better barley batches

Researchers focused on barley crops across three cropping regions of South Australia — Lower-North, Yorke Peninsula and Eyre Peninsula.

Production at Lower-North and Eyre Peninsula was concentrated on malting barley, while at Yorke Peninsula, the focus was on feed barley for premium pig production.

Results from pre-sowing high-resolution soil surveys, carried out using both EM38 and gamma-radiometrics were used in conjunction with pre-existing yield data to identify management zones, which might form the basis for selective harvesting (given a starting assumption, that patterns of variation in yield and protein were similar).

Mid-season (about GS31) crop performance data was collected using remote and proximal sensing. At all sites, growers supplied yield monitors for yield mapping.

On-the-go sensing

An AccuHarvest on-header grain analyser was fi tted to the headers at Lower-North and Eyre Peninsula to assess protein on-the-go during harvest. At Yorke Peninsula, a Cropscan on-header analyser was used. Both systems used near-infrared (NIR) transmission as the basis of protein sensing.

Figure 1. Identification of zones (seasons 2006-2011) in a 35ha Lower North paddock growing barley (B), canola (C), peas (P) or wheat (W)

Normalised yield data (mean=0) were used for this analysis. The number shown at the bottom right of each map is the 95% confi dence interval (CI) for that map and is used to assess the between zone differences in the zone maps. Higher values in 2006 and 2010 refl ect missing data in part of the paddock. Zone averages followed by different letters are signifi cantly different (p<0.05). The legend to the map at bottom right shows 2011 data only: the zone averages corresponding to other years are similar to those reported for the map immediately to the left. Zone colours are approximately matched to the yield map legend.

On-the-go: The availability of on-the-go protein sensing has potential for the grains industry. PHOTO: DANIEL ADAMS


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protein sensing

Figure 2. Crop performance at the Lower-North site during 2011

Grain samples were also collected immediately before harvest and analysed for their protein content in the lab.

Figure 1 shows yield data collected pre-project during 2006-2010 and during 2011 in the 35ha Lower-North paddock. EM38 soil survey data was collected during March 2011 and the results of clustering these data in various combinations into management zones are shown.

The patterns of yield variation were stable across the six-year period and closely match the pattern of soil variation.

In turn, the patterns of soil variation closely matched topographic variation — higher EM38 values corresponded to lower-lying areas where the soils also have higher clay contents (this data not shown here): yields tended to be higher in these areas, presumably due to greater soil water availability. Discussion with the Lower-North grower supported the delineation of two zones.

During 2011, yield at Lower-North varied from 2.1–5.5t/ha (with an average yield across the paddock of 4.2t/ha), while grain protein varied from 7.0–13.2% (average across 150 samples was 9.5%).

Unsurprisingly, protein variation was spatially structured (there was a pattern visible rather then the variation just being random or ‘noisy’). There was a marked difference between the patterns of variation in grain protein when these were determined using sensor data collected on-the-go, compared with those derived from pre-harvest grain samples (see Figure 2).

Note: Crop performance as measured by NDVI (proximal sensing at GS31), yield monitoring and grain protein measured using either laboratory analysis of samples collected by hand or on-the-go sensing using an ‘AccuHarvest’ sensor. In the top row of maps, data have been classifi ed on the basis of 20th percentiles. The bottom row of maps depicts the same data with a more conventional classifi cation. The map at bottom right shows the results of clustering the yield and protein data (hand sampling) — two-zone solution.

Sample variation: Patterns of protein variation across the paddock differed between pre-harvest grain samples and sensor data at harvest. PHOTO: LEIGHTON WILKSCH


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The protein map derived from the pre-harvest grain samples aligned well with existing understandings of paddock variation (see Figure 1, page 36). The same could not be said of the map derived from the sensor data.

When the yield map was clustered with the grain-sample protein map, the result (bottom right map in Figure 2, page 37) was similar to the zone maps and yield:protein relationships shown in Figure 1.

A similar analysis using protein sensor data did not match the patterns evident in Figure 1. Similarly disappointing sensor results were obtained at the Eyre Peninsula site, where the same type of sensor (AccuHarvest) was used.

In contrast, there was a much better alignment in the patterns of protein variation between sensor-derived and hand-sampled data at the Yorke Peninsula site (Cropscan sensor), even though grain samples at this site were collected about three weeks before harvest, immediately before wind-rowing (see Figure 3).

Lessons from the data

The relatively low protein at the Lower-North site suggests that additional nitrogen may have resulted in a yield response, as did a nitrogen-rich strip, which can be seen in the yield map, close to the northern edge of the paddock (see Figure 2).

It could also be argued that Figure 2 provides little support for selective harvesting in this paddock, during the 2011 cropping season, given that both zones had average protein levels within the range of the malt segregation (9–12%). However, as Figure 2 shows, even though the paddock made malt grade on average, signifi cant areas had protein below the accepted minimum for malting.

Under present practice, this grain would be hidden as a result of it being ‘shandied’ (mixed) with that from elsewhere. However, maltsters are like winemakers in regarding the uniformity of a parcel of grain (or grapes) as a key element of its quality, in addition to attributes such as its protein (or anthocyanin) content.

One consequence of a change to selectively harvesting grains could be a narrowing of the segregations to meet demands for greater uniformity, for which greater price premiums can be achieved than are presently offered.

At the Eyre Pensinsula site during 2010, even though the entire paddock made malt grade, ‘micro-malts’ made from samples collected from different yield zones exhibited trends in malt quality attributes that were consistent with trends in yield and protein variation.

The possible economics of this issue are worth examining.

Figure 3. Protein variation (2011) in two Yorke Peninsula paddocks of feed barley (60.6ha in total).

Protein variation was assessed using on-the-go sensing during harvest (CropScan sensor) or laboratory analysis of samples collected immediately before wind-rowing, three weeks earlier. As the paddocks were sown to different varieties, the data underpinning the maps in the centre and right columns were normalised on a per paddock basis to remove variety-specifi c effects.


39

This work was jointly funded by CSIRO, under the auspices of the Sustainable Agriculture Flagship, the collaborating growers and Australia’s grain growers through the Grains Research and Development Corporation (GRDC).

Note: The mention of trade names in this paper does not infer endorsement (or otherwise) from CSIRO, the GRDC or the authors.

acknowledgments

farmer feedback

Although Ashley Wakefi eld, Urania, South Australia has had limited success in fi nding a correlation between protein and yield using his protein monitor, he believes it could have some applications in assessing the nitrogen requirements of crops.

“Generally a higher yield means reduced protein therefore it may be possible to apply more urea and nitrogen to those higher-yielding areas to boost protein levels,” Ashley said.

But Ashley believes the main benefi t of monitoring levels of grain protein is in providing assurance that the nitrogen fertiliser program is not limiting maximum crop yield potential.

For example, during 2011 Ashley applied an extra application of nitrogen to several hard wheat crops, based on the seasonal outlook and soil moisture and increased protein by 1% achieving an extra $25-$35/tonne.

“There may also be an opportunity to mix wheat with different protein levels to achieve a higher grade,” Ashley explained.

“For example if I had two paddocks, one averaging 12% protein and one averaging 11%, I could mix the grain together to achieve a higher grade.”

But Ashley warns that the price differential would need to be suffi cient to justify the extra labour and time at harvest.

Ashley bought his NIR Technologies on-the-go protein monitor in 2002, but it took several years before he produced a protein map due to problems with the grain sampling section of the unit.

According to Ashley, it is similar to the protein and moisture meters found in silos and the technology has basically been installed in a header to measure protein and produce protein maps.

Ashley Wakefi eld, Urania, SA

For more information on protein monitoring go to: www.spaa.com.au and search under the ‘Resources’ tab for the latest information and links.

for more informationiDr Rob Bramley is Principal Research Scientist – Precision Agriculture and site leader at CSIRO Waite Campus in Adelaide. He has worked in PA research and development since 1996 in the wine, sugar and grain industries.

m: 0417 875 803e: [email protected]

about the author

Calibration concerns

A key issue during this work has been that protein sensing is presently substantially less robust than yield monitoring.

The area of instrument calibration needs particular attention. Even after factory reconditioning following the 2010 harvest, it still took more than a week of careful work in the laboratory before researchers were confi dent that the AccuHarvest sensors were suffi ciently well calibrated to be used in the fi eld.

Figures 2 and 3 raise concerns as to the temporal stability of the calibrations, and there were times during the 2011 season when a lack of faith in the numbers being logged led to the sensors being switched off by harvester operators.

The density of the data set collected was also a concern along with its impact on 95% map confi dence intervals (CI). Assuming that the sensor takes a ‘good’ reading on its fi rst attempt, the process of fi lling the sensor chamber, taking the reading and then emptying the chamber takes around 6–7 seconds. So when operating optimally, data are logged at approximately seven-second intervals, although logging intervals are often considerably longer. In contrast, a yield monitor typically logs yield every 1 or 2 seconds.

Given the task the protein sensor has to carry out, one could argue that its performance is impressive. However, this low data density contributes to high CI values (in the protein maps shown in Figures 2 and 3, CI was greater than 1% protein). Such high CIs are the reason why zone average protein contents have not been signifi cantly different.

Since the malt segregation is only 3% protein wide (9–12%), it is clearly problematic for a protein map CI to approach half the tolerable range.

This work suggests that, without marked improvements to sensing technology, it will not be possible to stream grain to narrower segregations. PA

had limited success in fi nding

therefore it may be possible to apply more urea

protein sensing

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Documents

Yield mapping — quality results require preparation · to gathering meaningful and accurate information. key messages by Sam Trengove, Trengove Consulting. PA in Practice II Using