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grdc.com.au
27TH JULY 2017
GRAINS RESEARCH UPDATE PALLAMALLAWA
DRIVING PROFIT THROUGH RESEARCH
AGENDA
Time Topic Speaker(s)
9:00 AM Welcome
9:10 AM Landing the chickpea crop safely ‐ strategies for late season
disease management
Kevin Moore (NSW DPI)
9:40 AM Harvest timing decisions in canola ‐ seed colour change as a key driver; yield and quality impacts of harvest delays
Rick Graham (NSW DPI)
10:05 AM Bugology ‐ what's new and topical?
Russian wheat aphid
Aphid thresholds in canola
Rutherglen bug damage and management
Altacor® and resistance management
Melina Miles (DAF Qld)
10:40 AM Morning tea
11:10 AM Autonomous tractors ‐ platforms and applications under
development
Craig Baillie (USQ)
11:40 AM On the go protein sensors ‐ using real‐time protein data for
more profitable marketing aggregations and nitrogen decisions
Phil Clancy (Next Instruments)
12:00 PM Precision agriculture and new technology adoption, what we do at home
Broughton Boydell (“Marinya” Pallamallawa), Mike Smith (“Tarnee” Gurley)
12:45 PM Lunch
1:35 PM Spray additive impacts on coverage and drift and interactions with nozzle types. Canopy penetration and optimising nozzle and pressure settings
Chris O’Donnell (UQ)
2:05 PM Spray drift ‐ how do we stop it?
Identifying acceptable spray conditions, drift mitigation strategies and when to stop spraying
Product volatility and formulation differences ‐ how important are they?
Boom spray setups to minimise spray drift while maintaining efficacy ‐ examples and discussion for glyphosate phenoxy mixes
Bill Gordon (Nufarm)
2:40 PM Groundcover, stubble type and management impact on fallow
efficiency
Rachel Norton (NGA)
3:05 PM Close
Pallamallawa Grains Research Update
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Contents
Minimising risk of disease in 2017 chickpea crops ......................................................................................... 3 Kevin Moore, Nicole Dron, Kristy Hobson, Kurt Lindbeck, Mark Richards and Sean Bithell
Improving harvest management decisions in canola – implications of seed colour change and windrow timing on yield and seed quality .............................................................................................................................. 9
Rick Graham, Leigh Jenkins, Kathi Hertel, Rohan Brill, Don McCaffery and Neroli Graham
Insect pest management update Canola aphids, Russian wheat aphid, and rutherglen bug ......................... 17 Melina Miles, Adam Quade, Paul Grundy & Richard Lloyd
Developments in autonomous tractors ....................................................................................................... 26 Craig Baillie, Craig Lobsey, Cheryl McCarthy, Dio Antille, Alex Thomasson, Zhe Xu and Salah Sukkarieh
On‐the‐go protein sensors: Using real‐time protein data for more profitable marketing aggregations and nitrogen decisions ...................................................................................................................................... 32
Phillip Clancy
Precision agriculture and new technology adoption, what we do at home .................................................. 39 Broughton Boydell and Mike Smith
Recent work with spray adjuvants .............................................................................................................. 40 Chris O’Donnell and Andrew Hewitt
Spray application tips and tactics ................................................................................................................ 42 Bill Gordon
10 tips for reducing spray drift .................................................................................................................... 47 Bill Gordon
Groundcover and stubble type impact on fallow efficiency ......................................................................... 49 Brendan Burton
Compiled by Independent Consultants Australia Network (ICAN) Pty Ltd.
PO Box 718, Hornsby NSW 1630 Ph: (02) 9482 4930, Fx: (02) 9482 4931, E‐mail: [email protected] Follow us on twitter @GRDCNorth or Facebook: http://www.facebook.com/icanrural
DISCLAIMER
This publication has been prepared by the Grains Research and Development Corporation, on the basis of information available at the time of publication without any independent verification. Neither the Corporation and its editors nor any contributor to this publication represent that the contents of this publication are accurate or complete; nor do we accept any omissions in the contents, however they may arise. Readers who act on the information in this publication do so at their risk. The Corporation and contributors may identify products by proprietary or trade names to help readers identify any products of any manufacturer referred to. Other products may perform as well or better than those specifically referred to.
CAUTION: RESEARCH ON UNREGISTERED PESTICIDE USE
Any research with unregistered pesticides or unregistered products reported in this document does not constitute a recommendation for that particular use by the authors, the authors’ organisations or the management committee. All pesticide applications must be in accord with the currently registered label for that particular pesticide, crop, pest, use pattern and region.
Varieties displaying this symbol beside them are protected under the Plant Breeders Rights Act 1994.
® Registered trademark
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Minimising risk of disease in 2017 chickpea crops
Kevin Moore1, Nicole Dron1, Kristy Hobson1, Kurt Lindbeck2, Mark Richards2 and Sean Bithell1
NSW DPI 1Tamworth and 2Wagga Wagga
Key words
Chickpea, Ascochyta, Botrytis, Phytophthora, Sclerotinia, waterlogging, management
GRDC codes
DAN00176, DAN00212, DAN00172, DAN00177
Take home message
2016 conditions were very conducive to Ascochyta, Botrytis, Phytophthora and Sclerotinia diseases in chickpea crops throughout the GRDC Northern Region.
Large amounts of inoculum of these pathogens will be available to infect 2017 chickpea crops.
Strategies described in this paper will reduce the risk of these diseases; the more strategies employed, the greater the benefit for chickpea growers in 2017.
Background
Following high incidences of diseases (Ascochyta, Phytophthora, Sclerotinia and Botrytis) in 2016 chickpea crops throughout NSW and Queensland, there will be large amounts of inoculum to infect 2017 chickpea crops.
This paper describes strategies that will reduce the risk of each of these diseases. Some of these strategies are based on local and international field experiments; others are based on observations of reduced disease in 2016 crops. The more strategies employed, the greater the benefit for chickpea growers in 2017 and beyond.
Ascochyta blight, AB, Asco (fungus Phoma rabiei previously called Ascochyta rabiei)
Ascochyta inoculum will be present in four forms:
1. Ascochyta infected chickpea residue being discharged out the back of headers or spread by floods and surface water;
2. Seed internally infected by the fungus (a consequence of pod infection);
3. Seed contaminated externally with infected chickpea residue during harvest and handling;
4. Volunteer chickpea plants infected over summer and autumn.
The following will reduce the occurrence and impact of Ascochyta Blight in 2017 chickpea crops.
Grow varieties with improved AB resistance (experiment/observation): These varieties will have less disease and require fewer fungicide sprays.
Burn cereal stubble (this holds AB inoculum, observation): Infected chickpea residue discharged during harvest of 2016 crops blows onto paddocks that are intended for chickpeas in 2017; most of these will have had a cereal crop in 2016 (or 2015).
Remove volunteers (observation): Volunteer chickpea plants infected with Ascochyta will provide inoculum even if the volunteer plants are killed with herbicide. Controlling volunteers early will restrict their size and limit the amount of inoculum they can produce.
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Treat all planting seed (experiment): Proper treatment of seed with a registered fungicide will control both internally borne Ascochyta and external contamination.
Sow later in planting window (experiment/observation): This reduces the number of infection events.
Wider rows 66cm+ (experiment/observation): Wide rows improve airflow through the crop leading to more rapid drying after a rain event or dew. They also delay canopy closure and improve penetration of fungicides later in the season.
Tyne openers rather than disc (observation): 2016 observations of less Ascochyta where crops had been sown with tynes is thought to reflect burial and movement of Ascochyta inoculum away from the emerging seedlings.
Double crop sorghum, cotton (experiment/observation): Stress and high biomass favour Ascochyta. 2016 crops double cropped into sorghum or cotton residue were less affected by waterlogging and did not produce the biomass of chickpeas sown into winter cereal or long fallow paddocks.
Fungicide before 1st post emergent rain event, even PBA Seamer (experiment/observation): 2016 crops that had an early preventative Ascochyta fungicide had less disease than crops that were not sprayed until after the disease was detected. Even though PBA Seamer is rated resistant to Ascochyta, growers are urged to apply a preventative fungicide because: (a) the large amount of inoculum will increase disease pressure, (b) it safeguards against changes in the Ascochyta pathogen that are more aggressive or virulent on PBA Seamer and (c) it insures against contamination of PBA Seamer crops with plants of varieties with lower or no Ascochyta resistance eg PBA HatTrick , PBA Boundary or Kyabra (varietal purity is still a major issue in our chickpea industry).
Phytophthora root rot, PRR (fungus‐like Oomycete Phytophthora medicaginis)
Phytophthora inoculum will be present in three forms:
1. Chickpea plants that had PRR in previous seasons (up to 10years back);
2. Other hosts e.g. medics, lucerne, and other leguminous plants including sulla (Hedysarum spp) and sesbania (Sesbania spp) in which Phytophthora can survive and multiply;
3. Soil and water containing PRR infected material and survival structures (oospores, chlamydospores).
The following will reduce the risk of PRR in 2017 chickpea crops.
Avoid PRR high risk paddocks where annual or perennial medics have been a component of pastures and where PRR has occurred in the past chickpea or lucerne; the oospores of Phytophthora medicaginis can survive for more than 10 years.
Avoid paddocks with areas prone to waterlogging although the conditions which induce waterlogging may not occur every year.
Avoid paddocks exposed to water flow from previous chickpea or medics areas; PRR infected material and survival structures can be spread though water movement to neighbouring paddock/s.
Metalaxyl‐based seed dressings are registered for PPR, but they are relatively expensive and provide only 6‐8 weeks protection after sowing.
Grow a variety with the highest level of resistance, particularly in medium‐high risk situations, such as where medics, chickpea or lucerne crops have been grown in the past 5‐6 years.
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Sclerotinia stem and basal rot (fungi Sclerotinia sclerotiorum, S. minor)
In the GRDC northern region, Sclerotinia spp infect chickpea plants two ways (a) Sclerotia germinate directly in or on soil and invade the plant through root or basal stem tissue, producing Sclerotia on and within the basal stem tissues, (b) Sclerotia germinate indirectly, produce apothecia at ground level and these release air borne ascospores (carpogenic germination) that infect plant parts higher in the canopy. In most seasons we only see direct germination because carpogenic germination needs cool moist conditions. In August/September 2016, Sclerotinia disease was very common in chickpea crops in north western NSW and southern QLD due to high levels of canopy leaf wetness and favourable temperatures. Importantly, every case of Sclerotinia involved carpogenic germination ie infection at mid canopy meaning that the Sclerotia formed on and inside the chickpea stems would have been captured during harvest. This led to problems at receival because the cylindrical Sclerotia formed inside the stems resembled ryegrass ergots and some loads were rejected or docked. Sclerotinia inoculum will be present in several forms:
4. Sclerotia spread by floods and surface water;
5. Sclerotia admixed with chickpea seed and introduced into 2017 chickpea paddocks during planting;
6. Sclerotia in canola residue in paddocks intended for chickpea in 2017; large Sclerotia can survive for up to 10 yr;
7. Sclerotia in weed hosts in paddocks intended for chickpea in 2017;
8. Sclerotia already present in paddocks with a history of broadleaf crops and recent Sclerotinia outbreaks.
The following will reduce the risk of Sclerotinia in 2017 chickpea crops.
Grow varieties with lowest susceptibility: Sclerotinia basal rot was assessed in field trials at Wagga Wagga in 2014 and 2016 which led to the following tentative ratings:
Very susceptible: PBA Maiden
Susceptible: Ambar , Genesis TM 090, Neelam , PBA Slasher , PBA Striker , PBA Monarch
Moderately susceptible: PBA Boundary , PBA HatTrick , PBA Seamer
Avoid paddocks with a history of Sclerotinia. Paddocks with a history of Sclerotinia will already have a population of viable sclerotia before the crop is sown and these are a disease risk. A frequent history of the disease also indicates that the environment is also most likely favourable for Sclerotinia to develop. Be aware that even adjoining paddocks can be at risk, due to movement of air‐borne ascospores of the Sclerotinia fungus.
Avoid paddocks with a history of canola. Canola is a very good host for Sclerotinia stem rot. Experience in southern NSW has shown that the number of sclerotia in the soil can build up very quickly when canola is frequent in the cropping rotation.
Avoid paddocks with a history of broadleaf weeds. The collective host range of the Sclerotinia fungi (Sclerotinia sclerotiorum, S. minor) exceeds 400 plant species, mostly broadleaf plants. Weeds can be important in maintaining sclerotial populations in paddocks, even when the frequency of broadleaf host crops in the rotation is low. Broadleaf weeds such as capeweed, shepherds purse and variegated thistle are just some common hosts for Sclerotinia.
Sow within the planting window. Observations from field trials at Wagga Wagga suggest that early sown chickpea is more prone to developing symptoms of Sclerotinia infection; this includes both direct infection and canopy infection from air‐borne spores. Plots sown within the recommended sowing window developed significantly less disease. Dense crop canopies from an early sowing also favour Sclerotinia stem rot later in the season.
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Botrytis seedling disease, BSD (fungus Botrytis cinerea)
BSD and Botrytis Grey Mould, BGM are caused by the same fungus, Botrytis cinerea, but they are very different diseases. BSD is a seed‐borne disease that can occur at any temperature and under any conditions. BSD can ONLY occur if pods of chickpea crops from which the seed came were affected by BGM. BSD is readily controlled with the standard chickpea seed treatments. BSD inoculum will be present in two forms:
9. Seed from pods infected with B. cinerea during a prior BGM outbreak.
10. Primary infections of BSD (ie from B. cinerea infected seed); primary infections lead to secondary infection of initially healthy seedlings through root contact.
The following will reduce the risk of BSD in 2017 chickpea crops.
Treat all planting seed: Field trials conducted in 2011 at Moree, Narrabri and Breeza using two B. cinerea infected seed lots from the 2010 BGM epidemic, showed treating chickpea seed with registered seed dressings controlled BSD, improved crop establishment and increased yield but proper coverage and rate were essential.
Avoid using B. cinerea infected seed: Even though seed treatment controls BSD, Botrytis infected seed will have lower vigour than non‐infected seed.
Botrytis grey mould, BGM (fungus Botrytis cinerea)
BGM is an air‐borne foliar disease active ONLY when temperatures warm up towards spring (ca 15°C). It is more prevalent in the warmer regions of the north, where significant crop losses can occur in wet winters and springs as occurred in 2016. BGM is controlled with foliar fungicides; seed treatment is ineffective. Testing chickpea seed from the 2016 harvest at Tamworth has found that half the seed lots tested to date (December 2016) are internally infected with Botrytis. Not treating this seed will lead to BSD (but will have no impact on BGM in 2017). Botrytis cinerea is ubiquitous, has a wide host range (over 138 genera in 70 families) and is a good saprophyte, meaning it can survive, grow and sporulate on just about any dead plant tissue. The fungus readily produces air borne spores and some isolates form sclerotia. This means that inoculum of BGM is always present and if conditions favour BGM, it will occur irrespective of what has happened earlier in the chickpea season.
The following will reduce the risk of BGM in 2017 chickpea crops.
Paddock selection: Avoid planting chickpeas next to paddocks where BGM was an issue the previous season. As for Ascochyta blight, chickpeas should be grown as far away from paddocks in which BGM was a problem as is practically possible. However, under conducive conditions, this practice will not guarantee that crops will remain BGM free, because of the pathogen’s wide host range, ability to colonise dead plant tissue, and the airborne nature of its spores.
Sow later: If long‐term weather forecasts suggest a wetter‐than‐normal 2017 season (La Nina) consider sowing in the later part of the planting window as this will reduce biomass production; BGM is favoured by dense canopies.
Wider rows 66cm+: Wide rows improve airflow through the crop leading to more rapid drying after a rain event or dew. They also delay canopy closure and improve penetration of fungicides later in the season.
Foliar fungicide: In areas outside central QLD, spraying for BGM is not needed in most years. However, in seasons and situations favourable to the disease, a preventative spray of a registered fungicide immediately prior to canopy closure, followed by another application 2 weeks later will assist in minimising BGM development in most years. If BGM is detected in a district or in an individual crop particularly during flowering or pod fill, a fungicide spray should
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be applied before the next rain event. None of the fungicides currently registered or under permit for chickpea BGM have eradicant activity, so their application will not eradicate established infections. Consequently, timely and thorough application is critical.
Waterlogging
Waterlogging (WL) and other stresses can reduce resistance and efficacy of management. Plants exposed to environmental stresses have altered architecture, metabolism and elongation; these reduce the plant’s ability to maintain resistance and re‐shoot post disease infection. This was evident across chickpea crops in 2016 with increased severity of AB on resistant lines (including PBA HatTrick , MR) when under WL stress. Preventative fungicide spray application on stressed, disease prone areas is critical to reducing yield loss.
There are currently no released varieties with significantly improved waterlogging (WL) tolerance; further studies are currently being carried out to exploit potential for improvement in conjunction with PRR resistance. During the 2016 season the northern growing region reported significant crop losses due to PRR. Surveying and quantitative PCR testing of soil samples collected from a number sites across this region concluded that both PRR and waterlogging were involved in plant death at various growth stages. Differentiating WL and PRR crop damage is difficult, often WL is incorrectly identified as PRR.
Distinguishing WL from PRR
Water logging symptoms occur 1‐3 days post flooding compared to a minimum of 7 days for PRR. Both WL and PRR can have similar above ground symptoms with red/yellowing, wilting, and death of plants. However, WL affected plants often succumb too quickly to have red/yellowing or lower leaf drop as always occurs with PRR. PRR symptoms may be delayed if temperatures are cool and soil moist. Waterlogging is a result of lack of oxygen to roots; PRR is the result of an organism killing roots. Waterlogging is thus more common and pronounced during warmer periods because (i) warm water cannot hold as much oxygen as cold water, and (ii) during warm weather, plants grow faster and thus need more oxygen.
Affected whole plants need to be examined shortly after prolonged rain or flood event (1‐3 days). Are the plants easy to pull out? PRR affected plants have little to no lateral roots and offer no resistance when pulled. Initially, WL affected plants have intact lateral roots and will not pull easily. Note: waiting longer than7 days to do the pull test on WL affected plants allows opportunistic soil fungi to decay lateral roots leading to misidentification as root rot.
PRR infected plants may have dark brown or black lesions on the tap root. Often such lesions extend above ground level forming a slightly sunken canker with a distinct junction with healthy stem tissue above.
Plants are most susceptible to WL at flowering and early pod fill unlike PRR which can occur at all growth stages.
The following will reduce the risk of waterlogging in 2017 chickpea crops.
Avoid poorly drained paddocks and those prone to waterlogging.
Sow later if the weather forecast for 2017 predicts a wetter‐than‐normal early‐to‐mid season. Evidence suggests that in chickpea and other crops early vigour associated with plants in the early vegetative phase will re‐shoot and recover root growth more efficiently reducing plant death.
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Additional Information
Further information on chickpea disease management can be found at the Pulse Australia website www.pulseaus.com.au and in the NSW DPI 2017 Winter Crop Variety Sowing Guide.
Acknowledgements
This research is made possible by the significant contributions of growers through both trial cooperation, field access and the support of the GRDC; the authors most gratefully thank them and the GRDC. Thanks to Woods Grains, Goondiwindi, Glen Coughran, “Beefwood”, Moree and Joe Fleming, “Parraweena”, Blackville for providing seed for the trials. We also thank agronomists for help with the crop inspections and submitting specimens, Paul McIntosh, Pulse Australia for industry liaison and chemical companies who provide products for research purposes and trial management.
Contact details
Kevin Moore NSWDPI, Department of Primary Industries, Tamworth, Ph: 02 6763 1133 Mb: 0488 251 866 Fx: 02 6763 1100 Email: [email protected]
Varieties displaying this symbol are protected under the Plant Breeders Rights Act 1994.
TM Registered Trademark
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Improving harvest management decisions in canola – implications of seed colour change and windrow timing on yield and seed quality
Rick Graham1, Leigh Jenkins2, Kathi Hertel3, Rohan Brill4, Don McCaffery5 and Neroli Graham1 1NSW DPI, Tamworth; 2NSW DPI, Trangie; 3NSW DPI, Narrabri; 4NSW DPI, Wagga Wagga; 5NSW DPI, Orange
Key words
Canola, seed colour change, windrow timing, seed size, seed yield, oil concentration
GRDC code
CSP00187: Optimised Canola Profitability
Take home message
Seed colour change (SCC) in canola occurs later on branches compared to primary stems.
Research examining the partitioning of yield between the primary stem and branches demonstrated that branches contribute more than 60% of total yield.
Windrowing at the commencement of SCC; before 40% SCC on the primary stem; was shown to reduce yield by up to 55% and oil concentration by 7.7% units.
Results demonstrated significant yield and quality penalties associated with early windrow timing at the commencement of SCC and before 40% to 60% SCC on the primary stem, with potential for increases in yield and seed oil concentration, at the upper end of current industry guidelines (approximately 60% SCC) for windrowing canola crops.
Relying solely on SCC from the primary stem for windrowing decisions could result in seed development being underestimated with potential negative impacts on seed size, oil concentration and yield potential.
This study indicates that SCC should ideally be measured on a whole plant basis and not based solely on assessment from the primary stem.
Background
Windrowing is a widely adopted harvest management practice of canola (Brassica napus L.) production in Australia. Current Australian industry guidelines based on research conducted in the 1970’s and 1980’s, and supported by Hocking and Mason (1993), recommend that canola is ready to windrow when 40% to60% of seeds on the main (primary) stem change colour from green to red, brown or black. Over the past decade however, with the adoption of hybrid canola varieties and changes in farming practices, including lower plant populations and perceived changes to plant architecture, there has been increased discussion about how best to determine seed colour change (SCC; Street, 2014). Whilst there have been requests for further research into windrow timing (WT), particularly in central and northern NSW (Hertel, 2013). The main concerns relate to the proportion of yield contained on the branches (secondary racemes) versus the primary or main stem (primary racemes) and the effect of the differential rate of seed maturity on yield and seed quality parameters.
In 2015 research commenced as a component of the ‘Optimised Canola Profitability’ project (CSP00187; GRDC co‐investment with NSW DPI), to examine the relationship between SCC, seed yield and quality parameters, with the aim of assisting growers to make more informed decisions about canola harvest management in northern NSW, and potentially across Australia. This paper outlines
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preliminary findings from SCC experiments carried out in 2015 and discusses in detail results from a more comprehensive set of experiments conducted in 2016.
Research in 2015
Initial experiments investigating the relationship between SCC and windrow timing (WT) on yield and seed quality parameters were conducted at Tamworth in northern NSW and Trangie in central western NSW. Treatment timings were targeted at 25%, 50%, 75% and 100% SCC. For consistency between experiments, across sites, SCC was defined as when ‘a minimum of two‐thirds of the surface area of an individual seed changed colour from green to brown, red or black’. SCC is regarded as the main determinant of WT, whilst there is also need for a clear definition of what constitutes SCC, to enable the development of robust guidelines around WT. This is important, as the view within the canola industry as to when the optimum time to windrow a crop is based on SCC, and those views vary considerably (Hertel, 2012)
Results
Trangie 2015
The Trangie experiment examined the partitioning of seed from pods on the main stem and branches of two hybrid canola varieties Pioneer® 44Y89 (CL) and Hyola® 575CL, over four windrow timings. In this experiment SCC occurred later on branches compared to the primary stem (Table 1). When looking at yield for Hyola® 575CL in response to WT at Trangie, it was observed that at the early WT 1 (average 32% SCC) there was a 0.71 t/ha or 23% decline in yield compared to WT 3 (average 67% SCC) (Table 1). There was also a small but significant decrease in oil concentration of 2.3% points (data not shown).
Table 1. Actual percentage (%) seed colour change (SCC) on the primary stem and branches at four target SCC timings at Trangie in 2015.
Variety Component 25% SCC 50% SCC 75% SCC 100% SCC
44Y89 (CL) Primary stem
65 96 100 100
Branch 44 86 93 99
Average 55 91 97 100
Hyola® 575CL Primary stem
39 84 98 100
Branch 24 50 58 96
Average 32 67 78 98
When assessing the breakdown of yield components; primary stem vs. branches; results showed that branches contributed around 70% of total seed yield averaged across WT’s, variety and target plant densities. There were also varietal differences and variations due to plant density, with the branch component of yield increasing at the lower target plant density of 15 plants/m2 (Figure 1).
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Figure 1. Mean yield contribution (%) of primary stem vs. branches for two target plant densities (15 plants/m2 and45 plants/m2) at Trangie in 2015.
An important observation from this experiment was how rapidly SCC occurred, Hyola® 575CL for example, went from 32% SCC to 78% SCC in six days. Another significant observation was the differential rate of seed maturity between the two varieties, although both reached 50% flowering at the same time. Pioneer® 44Y89 (CL) was more advanced and progressed more rapidly than Hyola® 575CL at any targeted WT.
Tamworth 2015
In a parallel set of experiments at Tamworth, similar results were observed. When assessing the breakdown of primary stem vs. branch yield components for example, it was shown that branches contributed 70% and 73% of total seed yield for Hyola® 575CL and Pioneer® 44Y89 (CL) respectively, averaged across WT’s. Unlike Trangie however, where SCC for WT 1 was relatively advanced, SCC at Tamworth was only 8% and 13% SCC respectively for Hyola® 575CL and Pioneer® 44Y89 (CL). As a result, yield and oil concentration penalties for early WTs were more pronounced. Pioneer® 44Y89 (CL) for example experienced a 1.01 t/ha or 30% yield penalty from WT 1 (13% SCC) vs WT 2 (82% SCC). The oil concentration in Hyola® 575CL was reduced by 7.7% units; 36.4% in WT 1 (8% SCC) compared to 44.2% (62% SCC) in WT 3 (Figure 2). These results highlight the need to accurately determine SCC and hence WT, as there is a 1.5% premium or deduction paid for each 1% units above or below 42% oil content in canola.
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Figure 2. Seed oil concentration (%) and SCC of Hyola® 575CL at four windrow timings at Tamworth in 2015.
Summary of findings ‐ 2015
Overall, these preliminary findings highlighted the importance of correct WT and the need to accurately determine SCC. Windrowing earlier than the current recommendation (i.e. <40% SCC), resulted in significant reductions in yield and oil concentration at both Tamworth and Trangie in 2015. Seed from the primary stem contributed only around 30% of the total yield. Results from Trangie showed that SCC occurred later on the branches compared to the primary stems meaning that if a grower was to rely solely on the primary stem to estimate SCC, overall seed development is being underestimated and likely to impact negatively on yield and quality parameters.
Research in 2016
In 2016, a series of experiments were conducted to further investigate the effect of SCC and WT on canola seed size, yield and seed quality parameters. Replicated field experiments were conducted at sites located at ‘Tarlee’ near Edgeroi on the north west plains of NSW, Tamworth Agricultural Institute in north west NSW and Trangie Agricultural Research Centre in the central west of NSW.
Experiments were sown on 6 May at Tamworth and Trangie, and 10 May at Edgeroi, and were managed using best management practices to limit biotic stresses and nutritional constraints. The same two hybrid canola varieties as used in 2015; Pioneer® 44Y89 (CL) and Hyola® 575CL; with similar flowering times but different maturity ratings, were sown in experiments conducted at Tamworth and Trangie, however only one variety Pioneer® 44Y87 (CL) was sown at Edgeroi. WTs were conducted at 2─3 day intervals (i.e. Monday, Wednesday and Friday) from the start of SCC on the primary stem up until 100% SCC on branches, alleviating difficulties associated with trying to achieve target SCC and hence windrow timings, as was experienced in the 2015 experiments.
Yield and seed quality components were determined by threshing 3 x 1 m hand‐cuts taken from the three middle rows at either end of experimental plots, giving 6 m of total cut area. Six plants sampled from the middle rows, three from each end of a plot, were threshed to determine the proportion of yield from branches and the primary stem, with seed moisture percentage and thousand seed weight (TSW) at actual SCC and SCC for contribution to yield (primary stem vs. branches) was recorded. Seed quality parameters measured included oil concentration (adjusted to 6% moisture), determined by near‐infrared spectroscopy (NIR) with an Infratec® 1241 Grain Analyser.
The following results focus on overall effects of WT and SCC on canola yield and oil concentration, rather than on varietal differences.
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Results
Seed colour change (SCC)
SCC and WT treatments commenced on 4 October at Edgeroi, 7 October at Trangie and 14 October at Tamworth. Consistent with the 2015 Trangie findings, SCC occurred earlier on the primary stem compared to the branches. WT averaged across the two varieties at Tamworth showed that when SCC on the stem was at 61%, branches were only at 20% SCC (Windrow Timing 7, Figure 3). Similarly at Edgeroi and Trangie where SCC occurred earlier than Tamworth, the primary stem was more advanced compared to branches at key WTs. At Trangie for instance, when the primary stem was at 84% SCC (Windrow Timing 7), branches were only at 43% SCC, likewise at Edgeroi when the primary stem was at 80% SCC, branches were at 52% SCC (data not shown). The results from Tamworth also illustrated how rapidly SCC can occur, with SCC on the primary stem progressing in a five day period from 18% to 61% SCC (windrow timing 5 to timing 7) (Figure 3).
Figure 3. Seed colour change (%) primary stem vs. branches over time as determined by windrow timings at Tamworth in 2016. (Vertical line approximates 60% SCC on the primary stem ~ WT 7).
Seed size
Changes in seed size (TSW) across WT can be used as an indicator of both reaching physiological maturity and yield potential over time. At Tamworth (Figure 4a) and Trangie (Figure 4b) differences in TSW on the primary stem vs. branches was greatest during the earlier WTs, reflecting differences in SCC and maturity. This would be expected given that canola seeds mature progressively up the primary stem and from the lower branches to the upper branches, with changes in seed colour indicative of declining metabolic activity and increasing seed maturity (Hertel, 2012).
Importantly, the optimum TSW for branches occurred at a later WT than current industry recommendations which are based solely on SCC on the primary stem. This is of relevance given that branches contributed 63% and 78% of potential yield at Trangie and Tamworth respectively. A similar pattern of TSW development on branches vs. the primary stem was also observed at Edgeroi (data not shown).
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a Tamworth b Trangie
Figure 4. Changes in seed size (TSW) on primary stem vs. branches over time as determined by
windrow timing for Tamworth (a) and Trangie (b) in 2016.
Yield
Windrowing at the start of SCC at Trangie (WT 1 SCC ~6% on primary stem) resulted in a 1.34 t/ha decline in yield compared to windrowing at 60% SCC on the primary stem (~WT 6), a yield loss of 48% (Figure 5). When looking at the breakdown of yield contribution of the primary stem vs. branches, it was observed that stems only contributed 37% of the total yield at Trangie, averaged across WTs (data not shown). At Edgeroi, yield based on 40─60% SCC on the primary stem ranged from 1.70 to 2.35 t/ha, with yield peaking at 2.42 t/ha (SCC primary stem 89%, branches 65%). The yield penalty at Edgeroi for windrowing at the start of SCC (approximately 6% SCC on primary stem) vs. industry guidelines (40% to 60% SCC primary stem) was 0.6─1.3 t/ha, a potential yield loss of 55% (data not shown). At Tamworth, the penalty for early windrowing at the start of SCC versus industry recommendations was 1.20 t/ha, a potential yield loss of 32% (data not shown). In all three experiments delaying WT to where SCC on the primary stem was >60% either resulted in significant (P<0.001) increases in yield at Edgeroi and Trangie, or trended towards a yield increase at Tamworth.
Figure 5. Effect of windrow timing/seed colour change on seed yield (t/ha) at Trangie in 2016. (Vertical line approximates 60% SCC on the primary stem ~ WT 6).
Oil concentration
There were significant (P< 0.001) oil concentration penalties for windrowing at early stages of SCC. At Tamworth, there was a 14% decline or a 6.5% unit reduction in oil concentration (38.9% vs. 45.4%) when windrowing at the start of SCC versus at ~40% SCC on the primary stem (Figure 6). There was also an increase in oil concentration at Tamworth where SCC was >60% on the primary stem, with
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increases in oil concentration of 0.6─2.0% units. At Edgeroi there was a significant (P = 0.001) decrease in oil concentration with early versus >40% SCC on the primary stem, with oil concentration declining by 3.9% units (Figure 7). Similarly at Trangie, there was a significant (P<0.001) decline in oil concentration of 4%units (early versus >40% SCC), whilst there was an increase of 1.1─1.9% units in oil concentration, with delayed WTs of >60% SCC (data not shown).
Figure 6. Effect of windrow timing (SCC) on oil concentration (%) at Tamworth in 2016 (Vertical line approximates 40% SCC on the primary stem ‐ WT 6).
Figure 7. Effect of windrow timing (SCC) on oil concentration (%) at Edgeroi in 2016 (Vertical line approximates ~40% SCC on the primary stem ‐ WT 6).
Summary
Results from these experiments highlight the importance of correct WT and the need to accurately determine SCC. Importantly, it was observed from the partitioning of seed from the primary stem and branches that SCC occurred later on branches compared to the primary stem. This is highly relevant when you consider the breakdown of the yield contribution of the primary stem vs. branches, where stems contributed only 22% to 37% of the total yield averaged across WTs at Tamworth and Trangie in 2016. These findings show that by relying solely on SCC from the primary stem for windrowing decisions overall seed development can be underestimated with potential negative impacts on seed size, oil concentration and yield.
Results clearly demonstrate the substantial yield and oil concentration penalties associated with early windrow timings at the start of SCC and before 40% to 60% SCC on the primary stem. Potential yield and oil concentration benefits are obtained from delayed WTs at the upper end of current industry guidelines (> 60% SCC). Given the significance of the proportion of yield contributed by the branches as opposed to the primary stem there appears to be a need to reconsider the method of
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how SCC is determined. This study using hybrids indicates that SCC should ideally be measured on a whole plant basis and not based solely on the primary stem. Research in 2017 has included an open‐pollinated (OP) variety into experiments to investigate if there are any differences between hybrids and OP varieties. In order to develop robust industry guidelines around WT based on SCC, there is also a need for clear definitions as to what actually constitutes SCC.
Acknowledgements
This project is a component of the collaborative ‘Optimised Canola Profitability’ project (CSP00187) between GRDC, NSW DPI and CSIRO in partnership with SARDI, CSU, MSF and BCG. We gratefully acknowledge the assistance of NSW DPI technical staff with this work including Rod Bambach, Stephen Morphett, Jan Hosking, Jim Perfrement, Michael Dal Santo (Tamworth); Jayne Jenkins, Scott Richards, Liz Jenkins, Joanna Wallace (Trangie); Leah Rood‐England, Mitch Whitten, Joe Morphew, and Brooke McAlister (Narrabri). The research undertaken as part of this project is made possible by the significant contributions of growers through both trial cooperation and the support of the GRDC. The authors would like to thank them for their continued support and in particular Peter White and Alex Murray, ‘Tarlee’, Edgeroi.
References
Hertel K (2012) Better canola technology update, Module 7: Harvest management. http://www.australianoilseeds.com/__data/assets/pdf_file/0016/9142/MODULE_7_‐_Harvest_Management_Kathi_Hertel_‐_V2_Sep_2012.pdf
Hertel K (2013) Canola: the economics and physiology of the timing of windrowing. https://grdc.com.au/Research‐and‐Development/GRDC‐Update‐Papers/2013/02/Canola‐the‐economics‐and‐physiology‐of‐the‐timing‐of‐windrowing
Hocking PJ, Mason L (1993) Accumulation, distribution and redistribution of dry matter and mineral nutrients in fruits of canola (oilseed rape), and effects of nitrogen fertilizer and windrowing. Aust. J. Agric. Res. 44, 1377‐88
Street M (2014) To windrow or not to windrow in 2014? This is the question, but if so, when? https://grdc.com.au/Research‐and‐Development/GRDC‐Update‐Papers/2014/02/To‐windrow‐or‐not‐to‐windrow‐in‐2014‐‐This‐is‐the‐question‐but‐if‐so‐when
Contact details
Rick Graham NSW Department Primary Industries Ph: 0428 264 971 Fx: 02 6763 1222 Email: [email protected]
Reviewed by Dr Steven Simpfendorfer, NSW Department of Primary Industries Loretta Serafin, NSW Department of Primary Industries
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Insect pest management update Canola aphids, Russian wheat aphid, and rutherglen bug
Melina Miles, Adam Quade, Paul Grundy & Richard Lloyd, QDAF, Toowoomba
Key words
Cabbage aphid, canola, Rutherglen bug, Russian wheat aphid
GRDC code
DAQ00196
Take home message
Canola is most susceptible to yield and oil loss with infestations of aphids from bolting. This is earlier than previously thought, but still requires at least 10 to 14 days of infestation to significantly impact on the crop.
Russian wheat aphid (RWA) has not yet been detected north of Rankin Springs in the Riverina district of NSW. Vigilance is required to ensure early detection of any RWA outbreaks in the northern region this winter.
Rutherglen bug (RGB)will impact on the yield and viability of sorghum. Thresholds are available; early grain fill is the most susceptible crop stage. At extremely high densities, RGB control with insecticides can be challenging. Be aware that some commonly used products have limited residual efficacy and may not provide sufficient control to prevent crop loss.
Research to commence on options for minimising the movement of RGB from canola stubble into neighbouring summer crop.
Aphid thresholds in canola
Over the past 3 years, DAF entomologist Paul Grundy has been undertaking research to determine the susceptibility of canola to cabbage aphid infestations during flowering and seed set. The initial work, simulating late infestations on the terminals of filling/maturing racemes, showed that canola was compensating for terminal damage and/or the affected terminal pods were not contributing significantly to yield. Recent trials (2016) have shown that cutting stimulates atypical compensation, and that bagging better simulates aphid impact, which is to prevent normal development of racemes (Table 1).
Table 1. Differences in the plant response to simulated aphid damage. Cutting racemes vs constraining growth by bagging racemes. Trial conducted in dryland canola at Allora, SE Qld, 2013.
Cutting to simulate aphid damage Bagging racemes to simulate aphid damage
Treatment Yield (t/ha) Treatment Yield (g/plot)
Control 2.07 a Control (unbagged) 1293 a
10% of terminals removed
1.93 a 1. Raceme covered first flower 988 b
50% of terminal removed
1.98 a 2. Raceme covered first flower +7 days 989 b
90% of terminal removed
2.01 a 3. Raceme covered first flower +14 days 949 b
Treatments followed by the same letter are not significantly different (P<0.05).
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In 2014, trials at 8 locations on the Liverpool Plain produced similar results, with significant yield loss only occurring when the plant was forced to replace all the reproductive structures removed at early flowering (Figure 1).
Figure 1. Aggregate treatment yields across 8 sites near Spring Ridge NSW, 2014, expressed as a percentage of the control.
The conclusion of this research was that the widely used best‐bet threshold of 10% to 20% of racemes infested during late flowering‐pod set, is probably conservative and could be revised up. The compensatory ability of the crop allows time for biological control by aphid parasitoids, lacewings, hoverflies and ladybeetles to take place during the first weeks of flowering without significant crop loss. If natural enemies were ineffective in supressing aphid populations, spraying on increasing level (20% to 25% of racemes infested) would be unlikely to result in irrecoverable crop damage. Very late infestations of aphids have very limited damage potential as the associated disruption mainly effects flowers/pods that are unlikely to contribute to final yield.
In 2016, replicated trials at Wellcamp near Toowoomba were undertaken with infestations of aphids, rather than simulated damage. This approach provided an opportunity to examine both the impact of aphids on crop growth and oil content. Conditions for crop compensation were excellent with approximately 300 mm of rain between June and September. Plots were infested with aphids prior to bolting to get racemes infested as early as possible. In the early sown trial (sown 9 April, infested 16 July) the infestations did not establish before bolting, but were well established through flowering. Infestation in the later sowing (sown 17 May, infested 10 August) resulted in successful early infestation so flowering racemes were heavily infested from early flowering. Infestations resulted in every plant having some aphids established in each treated plot. Greater impacts on both the yield and oil content were observed in the later sown trial (Figure 2).
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Figure 2. Very early infestation of flowering racemes with cabbage aphid for a 10 day period can reduce both yield and oil content. Result of replicated trials, Wellcamp 2016.
What the 2016 trial results mean for canola aphid management recommendations
The series of trials over the past 3 years have indicated that infestations in early flowering result in greater impacts than later infestations, and this work reinforces this.
The most susceptible period is the 2 to 4 weeks in which the plant is bolting and starting to flower, this is when crop monitoring for aphids should start – and which is probably earlier than it is done currently. To simplify sampling, assess aphid infestation in terms of % plants infested, rather than % racemes infested.
As significant yield and oil content declines did not occur with infestations of 10‐14 days, there is time to assess the rate at which the aphid infestation is growing, and the potential impact of natural enemies and weather on infestations.
At this stage, a threshold of 20% of plants infested is proposed. Further work is required to validate this approach.
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Russian Wheat Aphid (RWA) as a pest in the northern grains region
RWA is considered a high priority pest by the grains industry because of its potential to cause significant yield losses in wheat and barley if not well managed. Triticale and rye are also susceptible to crop loss, but oats are considered relatively tolerant.
The RWA is more damaging to cereals that the aphid species we already have in Australia. We don’t yet know how susceptible Australian varieties are to RWA. GRDC is investing in the development of resistant cultivars for Australia. In the meantime, international experience with RWA has been that in seasons following outbreaks, yield losses are generally lower as growers are better equipped to detect and manage infestations, and natural enemies establish and contribute to the suppression of populations. In South Australia and Victoria in 2016, infested crops that were treated to control RWA, recovered to grow and yield normally.
It is inevitable that RWA will establish in the northern grains region, but we don’t know when we will start to see it in crops. Given how widely distributed RWA is in SA and Victoria, it seems likely that RWA has been in Australia for some time prior to the 2016 outbreak. An outbreak is most likely in the event of favourable conditions for RWA populations to both oversummer, build up in winter and move into early crops in autumn. The environmental conditions that favoured the outbreak in the southern region were a wet summer (summer hosts), a warm autumn and early sowing of winter cereals (favour rapid population growth and movement from weed hosts to crops). In addition to crop hosts, non‐crop and pasture grass species in the genera Poa, Bromus, Hordeum, Lolium, and Phalaris may also host RWA. It remains to be seen whether RWA over‐summer on grass hosts or crops (e.g. sorghum, maize, millet, canary) in the cropping landscape.
In the 2017 season, it is important that crops are monitored more frequently than they might usually be to ensure early detection of RWA, should it occur.
Sample for RWA in the same way you would sample for other cereal aphids. Concentrate on the margins of the field and in areas of the paddock that are stressed (e.g. dry, wet, root disease). Look for both symptoms (see description below) and the presence of aphids.
The following thresholds are recommended:
Emergence to tillering ‐ 20 RWA per plant; and
Tillering onwards (Z30‐59) – 10 aphids per tiller.
If control of RWA is warranted, there is a current Emergency Use Permit APVMA PER82792 is for chlorpyrifos and pirimicarb. Pirimicarb will kill aphids, but not the beneficial insects in the crop. If possible, use this option first to preserve beneficials which may then suppress further outbreaks.
Leaf symptoms caused by RWA infestations – what to look for in the field
RWA induce striking symptoms in wheat and barley, unlike the oat and corn aphid which produce no obvious symptoms. Within a week or so of being infested with RWA, plants will start to exhibit symptoms. Plant damage is in response to direct aphid feeding, so only the leaves and/or tillers infested show symptoms.
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1. White streaking of the leaves. Some varieties show reddening.
2. Rolled leaves. RWA colonies shelter inside the rolled leaves.
Figure 3. Symptoms of Russian wheat aphid
Some of these symptoms are similar to those caused by wheat streak mosaic virus (WSMV) and phenoxy herbicide damage in cereals –close examination of symptomatic plants to determine the presence of RWA is recommended.
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Distinguishing RWA in the field
Use a hand lens to check for key features (Figure 4), specifically for the absence of siphunculi and the double tail (cauda), characteristic of RWA.
Figure 4. Features to look for in a Russian wheat aphid
Rutherglen bug (RGB)
RGB movement from canola stubble – damage to establishing summer crop
To date little, if any, research has been focused on this phenomenon. QDAF entomologists have done some preliminary work, which has provided a little insight. This issue is a high priority research area for 2017.
RGB nymphs (all developmental stages) and adults typically move en masse from canola stubble as it dries down in October‐November. The movement out of the canola stubble is in all directions and appears to be an exodus rather than directed movement towards summer crops. In southern Australia, the same movement occurs and is of little consequence as there are no summer crops. Similarly in central NSW, the benefit that RGB provide by feeding on and killing canola regrowth, volunteers and weeds is appreciated by growers as a benefit.
RGB moving into summer crop will congregate on seedlings and the sheer impact of feeding results in plant dehydration and death. Repeated spraying of crop edges slows the movement of the RGB further into the summer crop, but does not prevent the death of plants in rows closest to the canola. The movement from the canola stubble can continue for weeks.
The number of RGB moving has also made barrier strips, ploughed strips and water‐filled channels ineffective in stopping the movement. The absence of effective, long residual products that can be applied to bare earth between canola stubble and summer crops is a major constraint in having a simple management option.
There are 3 key approaches being considered for research to resolve this problem.
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1. Preventing the build‐up of RGB populations in canola stubble. Understanding the ecology and behaviour of RGB in the canola crop to determine when eggs are being deposited (in the soil). One of the key questions is around the potential impact of RGB to canola. Knowing what impact RGB has on other crops (sunflower and sorghum) during grain fill, it is possible that there may be benefits to the canola crop from controlling RGB during flowering‐harvest.
2. Effective options to minimise the movement of RGB between canola stubble and summer crop. In the absence of effective bare‐earth residual options, we are considering the potential of trap crops to slow, and possibly stop the movement of RGB. Neonicotinoid seed treatments are essentially a way to deliver a highly effective residual insecticide to RGB. We expect that it may be possible to have migrating RGB populations stop, feed and then die in a trap crop planted between the canola stubble and the susceptible summer crop. The trap crop would be maintained only for the period during which RGB were moving. This approach is completely novel, and will be trialled for the first time in the spring of 2017.
3. Reducing the RGB populations within canola fields. Evaluating options to reduce the size of the population moving out of the stubble through the application of less disruptive options within the canola field. For example, the use of diatomaceous earth, biopesticides, spray oils, swathing insecticide (rather than complete coverage) are options to be trialled for reducing the impact of whole‐field treatment with broad‐spectrum insecticides as is being done currently.
RGB in sorghum
In the spring‐summer of 2016‐17, the northern grains region experienced a major outbreak of Rutherglen bug (RGB, Nysius vinitor).
Until 2007, RGB was not considered a major pest of sorghum although it has been recognised as a major pest of sunflowers for a long time. Possibly, RGB were controlled in sorghum when crops were treated for midge. However, in 2007, extremely high infestations of RGB in early sorghum crops resulted in damage; heads with low percentage of seed set. In 2007‐08, QDAF entomologists undertook research to understand the impact of RGB in sorghum.
In 2016‐17, RGB populations were extremely high, and they persisted in crops for weeks. At the same time, heatwave conditions were affecting sorghum crops. In combination, the RGB and the heat resulted in many sorghum crops with low yields and poor quality. In this paper, we describe RGB damage, so it can be distinguished from heat damage.
As if this wasn’t enough, control of RGB proved to be a challenge for some. Questions were asked about the efficacy of products and the possibility that RGB were resistant to some insecticides. In response to the challenges with effective control of RGB in 2016‐17, QDAF entomologists screened a large number of insecticides to evaluate both contact and residual efficacy. The results of this work, as it relates to RGB control in sorghum, is presented here.
RGB damage and thresholds in sorghum
Rutherglen bug females need to feed on developing seed before they can start to produce eggs. Consequently, damage to sorghum at the susceptible crop stages (flowering – soft dough) is caused by adults only. Nymphs are generally present in sorghum crops as the grain starts to colour.
Adult feeding results in seed that does not fill, or seed that is discoloured (dark red) with black feeding spots (Figure 5A, B). The seed is not typically ‘beaked’ as occurs with head stress (Figure 5C).
The major impact of RGB feeding is poor seed set and low grain weights. RGB are particularly damaging in seed production where their feeding also results in greatly reduced germination rates of resulting seed.
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Figure 5. Typical RGB damage to sorghum (A) poor seed set at the top of the panicle similar in appearance to midge damage, (B) reddening and spotting on the seed. (C) heat‐damaged sorghum
seed, note the reddening, but absence of black feeding marks.
Based on trial work conducted in 2007‐08, the proposed economic thresholds for RGB in sorghum are presented in Table 2. The thresholds reflect the susceptibility of the grain whilst filling, and the lack of impact once the grain is filled and reaches physiological maturity. These results show that treating large populations of RGB in sorghum close to harvest has no benefit in terms of yield, but may reduce the likelihood of problems with large populations at harvest (blockages, moisture, live insect contamination).
Table 2. Action thresholds for Rutherglen bug in sorghum.
Stage of development Threshold (RGB per head)
Flowering and milky dough 20‐25
Soft dough 30‐50
Physiological maturity (black layer) to harvest No impact on yield*
*large populations of RGB in the crop at harvest may be a contaminant and
occasionally cause blockages because of the increased moisture from the RGB
in the grain.
A
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Chemical control of RGB
Screening trials (laboratory based Potter Tower bioassay) showed that alphacypermethrin, deltamethrin and dimethoate were highly effective in killing RGB when sprayed directly onto adults. Please note – these products are currently registered for control of RGB in other crops, but not in sorghum.
However, when RGB populations are large it is unlikely that commercial application will get good direct contact on all individuals. This is likely to be more problematic when large populations of adults and nymphs are targeted. Therefore, the residual efficacy of the insecticides becomes important (how toxic they are when RGB make contact with dry residues on leaves/stems/heads). Again the same suite of products was tested in a lab test with RGB introduced to leaves that had been treated with each of the products at 3 and 7 days after spraying. The number of RGB adults alive/dead was assessed after 24 hours of exposure to the treated leaves. Results showed the residual efficacy of the treatments varies, with rapid deterioration of alphacypermethrin after 3 days, and further loss of efficacy of alphacypermethrin and low rate dimethoate after 7 days. These results support the anecdotal observations of many agronomists and growers in the field this past summer.
Acknowledgements
The research undertaken as part of this project is made possible by the significant contributions of growers through both trial cooperation and the support of the GRDC, the author would like to thank them for their continued support.
Contact details Melina Miles Queensland Department of Agriculture and Fisheries PO Box 102, Toowoomba. Qld 4350 Ph: 0407 113 306 Email: [email protected]
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Developments in autonomous tractors
Prof Craig Baillie1, Dr Craig Lobsey1, Dr Cheryl McCarthy1, Dr Dio Antille1, Prof Alex Thomasson1,2, Dr Zhe Xu3 and Prof Salah Sukkarieh3
1National Centre for Engineering in Agriculture, University of Southern Queensland 2Texas A&M 3Australian Centre for Field Robotics, University of Sydney
GRDC code
USQ00022
Key words
Auto steer, tractors, future research, autonomous, platforms, applications
Take home message
Technology underpinning autonomous tractors is relatively advanced and has been developed by the major tractor manufacturers. Growers are currently utilising many of these technologies and realising their benefits now.
There is an opportunity for the early release of autonomous tractors in Australia by engaging with machinery OEMs (original equipment manufacturers; i.e. John Deere and CNH) to incubate technology before world wide release.
Technology companies are emerging that provide aftermarket solutions to make current model tractors autonomous (here and now).
Disruptive technology concepts in automation / robotics are pre commercial or in the early stages of commercialisation.
Introduction
The development of autonomous and intelligent technologies provides a mechanism to increase the precision of crop management and realise additional potential from precision agriculture. This means that the right management strategy can be implemented in the right place, at the right time. The introduction of these technologies will also provide opportunities to lever and enhance past developments and improvements in cropping systems and agronomy.
While there is much interest in the development of new automation and robotic technologies, multi‐national machinery manufacturers have already developed a number of incubated autonomous, agricultural technologies with near commercial potential. Australia provides a significant opportunity in the refinement and commercial release of these technologies and is relatively advanced in comparison to North America and Europe in the practical application of precision agriculture technologies.
Methodology
A review of autonomous tractor developments and enabling technologies was undertaken to inform future engagement with equipment manufacturers and with the view of accelerating the release and adoption of autonomous tractors for the Australian Grains Industry. The review involved commercial developments undertaken by the six largest tractor manufacturers (i.e. original equipment manufacturers, OEMs) and included John Deere, Case New Holland, AGCO, CLAAS, Same Deutz‐Fahr and Kubota. This work also examined the status of research on autonomous tractor technologies,
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the current status of intellectual property in this space, aftermarket technology developments, fully autonomous tractor concepts and emerging technologies.
Results and discussion
OEM developments
Autonomous vehicle enabling technologies are relatively advanced in agriculture and informed by both the potential for efficiency gains from more precise operation of tractors and the laborious, systematic nature of machine operation in agriculture. The agricultural operating environment is also conveniently suited to automation with vehicles operating in large, well defined open areas that are relatively free of obstruction and personnel.
A number of technologies have been developed over the last two decades to improve the operational efficiency and productivity of tractors (e.g. guidance, drive‐by‐wire, continuously variable transmissions). Technologies have also been developed to improve management of crops using precision agriculture (PA) techniques (e.g. precision seeding, variable rate application, yield monitoring). Machine to machine communications are improving logistics and coordination between multiple vehicles and new telematics solutions are now available that provide in‐field management of tractor operations and monitoring of vehicle performance. These technologies are routinely used today and there is a range of products commercially available or in active development. While these technologies are being developed as products to augment human operation of the tractor, they form key technological elements of full tractor autonomy (i.e. a pathway to autonomy).
Each of the 6 major tractor manufacturers has made significant developments that inform key components of an autonomous tractor and essentially a technology pathway to autonomy. Currently this technology has been developed to augment human operations which are currently accessible via features or product offerings on tractors commercially sold and available to Australian farmers including the following:
hands free tractor / implement guidance including Global Navigation Satellite System (GNSS; eg GPS and GLONASS) and vision based solutions;
variable rate control including functionality where the tractor and implement (i.e. seeding, spraying, fertilising etc.) operate in concert;
machine optimisation via constantly varying transmissions and adaptive control to environmental conditions and work requirements;
path planning / automated machine operations which includes auto turn and auto control of implement functionality for example, raising and lowering, starting and stopping operations (i.e. seeding, spraying, fertilising etc.);
sensing and perception which includes interaction / awareness of the machine and i) the surrounding environment, ii) the crop and iii) the task being undertaken; and
telematics and infield communications for remote control of tractor operations and uploading of task related data.
Table 1 provides a summary of these features which indicate each of the 6 major tractor manufacturers has made significant developments that inform key components of an autonomous tractor and essentially a technology pathway to autonomy. Currently this technology has been developed to augment human operations which are currently accessible via features or product offerings on tractors commercially sold and available to Australian farmers.
Table 1. Technology and product development for the six major tractor manufacturers relating to autonomy
Technology Pathway to Autonomy
Tractor Manufacturer
John Deere CNH AGCO CLAAS SAME Deutz‐Fahr Kubota
Automated Tractor Guidance
Variable Rate Technology
Drive by wire functionality
Performance Optimisation
Operation & path planning ‐
Machine to machine communication ‐ ‐
Sensing ‐ perception ‐ ‐
Sensing ‐ process monitoring ‐ ‐
Telematics ‐
Infield coms. and data infrastructure ‐ ‐ ‐
In addition, the 2 major tractor manufacturers including John Deere and CNH have released operational concept vehicles to gauge consumer interest and the application of this technology in commercial farming operations and environments. In comparison AGCO the third largest tractor manufacturer has developed a semi‐autonomous concept which includes leader (i.e. manned) / follower (unmanned) technology, while Kubota has announced plans to develop autonomous driver technology.
In comparison AGCO, CLAAS and Deutz have developed sensors for both perception and process monitoring as key enablers of autonomous tractor developments which are presented as independent technologies. It would appear that John Deere and CNH are the most advanced of the large OEMs in terms of having potential to accelerate a product to market.
Figure 1. John Deere concept autonomous vehicle.
Figure 2. Case IH autonomous tractor concept
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Aftermarket solutions
In addition to the major tractor manufacturers there is the emergence of autonomous technology providers such as ASI and Precision Makers that provide 3rd party “bolt on” solutions. Notably ASI has worked in partnership with John Deere (early autonomous tractor developments) and more recently CNH. Precision Makers has equipped a Fendt tractor in Australia (Beefwood, Moree NSW) for autonomous commercial operations. The emergence of these 3rd party technology providers is akin to the initial release of GPS autosteer technology in Australia by Beeline Technologies before the major tractor manufacturers. This provides an additional avenue for Australian producers to access autonomous tractor technology as an aftermarket solution.
Early adopters of aftermarket solutions are potentially exposed to compatibility issues with OEM technology and development cycles in offering a robust retrofit product (presuming this is in isolation to OEMs). The experience with GPS autosteer indicates that third party suppliers of autonomous technology however are more likely to release product before the major tractor manufacturers (in the absence of coordinated industry engagement). This provides an additional option for Australian agricultural industries to access autonomous tractor technology notwithstanding the potential for exposure to developmental risks which would need to be determined.
Disruptive technologies
Early start‐ups and disruptive technologies is another potential area in which autonomous tractors / platforms may have some bearing on autonomy in agriculture outside of the work by the large OEMs. Swarm Farm based in Central Queensland is a notable example and the most relevant to Australian agricultural industries at present. Swarm Farm is an emerging service based business that provides autonomous platforms on a contract operator basis that is currently centred on spray applications. The service based business model means clients are less exposed to technology risk. As the technology matures departure from the service based model is envisaged to on‐sell technology to early adopters who have demonstrated technical ability to use the technology and incorporate into their farming operations. In the interim the adoption / uptake of the technology is based on the initial growth of the current service based business model.
Further research and development
Apart from the commercial application of existing technologies, key areas of research include interaction of autonomous vehicle with unstructured environments (i.e. terrain); sensing for navigational control and environmental awareness and path planning to optimise machinery operations. Other key areas include machine intelligence for oversight of operations and perception relating to machine / environmental / human interaction. A revision of registered intellectual property relating to autonomous tractor developments identifies machine to machine communications and sensing and perception as a primary focus. This is suggestive of future developments in the commercialisation pipeline.
Conclusions
Tractor companies are relatively advanced in terms of the development of technologies which inform autonomous tractor concepts. Of the 6 major tractor manufacturing companies John Deere and CNH appear to be the most advanced. Other tractor manufacturers including AGCO, CLAAS, Same Deutz‐Fahr and Kubota have developed technologies which also inform various components of autonomous tractor functionality such as guidance, perception, machine optimisation and sensors for process monitoring.
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31 In addition to the large tractor manufacturers is the emergence of technology companies providing aftermarket solutions. Similar to the advent of GPS auto steer, these companies provide an independent opportunity for farming enterprises to engage in autonomous tractor technology. The compatibility and field readiness of these technologies requires further investigation although it is noted that there are some examples of deployment under commercial operating conditions.
Emerging / disruptive technologies also feature as a potential opportunity for autonomous technologies on farm and a number of companies have developed platforms that are pre commercial or in the early stages of commercial release. In comparison to the large tractor manufacturers the accelerated roll out of these technologies will be tempered by early adopter interest in new technology and farming systems concepts as well as the establishment of dealer network and support services.
Acknowledgement of GRDC
The research undertaken as part of this project is made possible by the significant contribution of growers through the support of GRDC. The authors would like to thank them for their continued support. This paper is an extract from the final report for project USQ00022 – Future FARM Theme 3 Intelligent Infrastructure Phase 1 Review.
Contact details
Craig Baillie University of Southern Queensland Mb: 0428 750 060 Email: [email protected]
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On‐the‐go protein sensors: Using real‐time protein data for more profitable marketing aggregations and nitrogen decisions
Phillip Clancy, Next Instruments
Keywords
nitrogen (N) uptake and availability, protein mapping, yield mapping, nitrogen removal maps, variable rate nitrogen fertilization, near infrared analysis
Take home messages
Protein mapping provides an additional layer of information that complements yield mapping. It enables growers to better identify and diagnose the possible cause(s) of lost revenue in their paddocks due to sub optimal yield and protein premium payments.
Introduction
Precision Agriculture or Precision Farming has provided significant improvements in productivity and profitability in farming. Auto steering and controlled traffic applications can be shown to have relatively quick returns for the investment. However, yield mapping has not been able to deliver the same return and as such variable rate fertilization applications have not really provided the expected benefits to growers. While yield mapping relates nitrogen availability and uptake, but is it only provides only half the story. Real‐time protein mapping provides the rest of the story and when yield and protein are combined, the farmer has the tools to determine where and by how much their crops are under performing. This paper introduces an Australian developed technology, an on the go protein sensor, that provides real‐time protein mapping as grains are stripped in the paddock. The paper provides several examples of where growers have been able to use this new layer of data to increase their yield and profitability.
Description
The CropScan 3000H On Combine Analyser has been developed by Next Instruments, an Australian company that specialises in Near Infrared analysers for farmers. The CropScan 3000H consists of three components; Remote Sampling Head, NIR Spectrometer and Touch Screen PC, see Figure 1.
Figure 1. Three components: Remote Sampling, Head, NIR Spectrometer, Touch Screen PC
Figure 2 shows a schematic of how the system is configured and connected on the combine harvester.
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Figure 2. Schematic of the CropScan 3000H setup on a combine harvester
As grain travels up the clean grain elevator, seeds drop into the remote sampling head mounted to the side of the elevator. The grains are held by a flap at the bottom of the head. NIR light passes through the grains and collected by a fibre optic cable that transmits the light back to the NIR spectrometer located in the cabin. The NIR spectrometer generates the NIR spectrum and sends it to the Touch Screen PC where the calibrations models are applied for protein, moisture and oil. The results are displayed on the PC screen in the form of tabular data and real‐time field maps. Figures 3 and 4 show examples of the screen displays. As the combine’s bin fills with grain the bin averages are displayed so that the combine operator can decide to segregate the grain into separate field bin, or the selectively strip different parts of the paddock to optimise the protein grade for the bin load.
Figure 3. Screen display of protein and moisture in wheat
Figure 4. Real‐time Protein map
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When the out loading auger is extended from the combine, a window pops up on the screen to allow the operator to record the tonnage for the bin load, the bin averages for protein, oil and moisture, and the storage location or the truck where the grain will be placed, Figure 5 shows the pop up window used by the operator to record each bin load. When completed, the data is posted to the Cloud where it can be accessed by a Smart Phone, Tablet or PC.
Figure 5. Bin load record window
Making better decisions
There are two applications for an on‐the ‐go protein sensor, ie, CropScan 3000H:
Tactical: 1) In paddock blending of grain to maximize protein grade payments.
2) In paddock segregation and storage to maximize protein grade payments.
3) On farm segregation and storage into on farm silos.
Strategic: 1) Protein/Nitrogen/Yield Optimization to find the “Sweet Spot”.
2) Reduction in Nitrogen Fertilizer or better use of Nitrogen Fertilizer.
Case Study 1: Tactical 1 ‐ In Paddock Blending
Mark and Jordan Hoskinson farm 8000 hectares at Kikora, NSW. They installed a CropScan 3000H into their John Deere combine in 2014. In one large wheat field, Jordan quickly realised that there was a 4% to 5% variation in protein across the field. He started to strip grain from one section of the field where the protein content was up to 15%. As the bin filled he would monitor the bin average for protein as displayed on the PC. He would monitor the tonnage in the bin and when it reached 5 tonne, he then stripped grain from another section where the protein was 9‐11%, thereby blending the grain based on protein. He would monitor the bin average for the protein as he filled the bin with the lower protein wheat. When the average reached 13.5%, he would go to the field bin and out load the grain. He stripped this field over several days and reported that they trucked every load to the GrainCorp silos at Kikora and had every load accepted as APH1 grade (minimum 13.5% protein). At that time APH1 was paying $30 per tonne more than APW grade. The Hoskinson’s reflected that in previous years this field had produced a mixture of low and high protein grades. Overall, the ability to blend in the fields across their farm had generated an estimated $40,000 in higher payments as compared to previous years. Figure 6 shows the Protein Map for the field mentioned above.
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Figure 6. Protein map for wheat
Case Study 2: Tactical 2 ‐ In Paddock Segregation
Figure 7 shows a protein map of a field on the Eyre Peninsula, SA. Mr Wilmont used the used 2 x 55 tonne field bins to segregate wheat into the field bins based on protein being above or below 11.5%, ie, ASW and APW grades. He reported that he was able to blend from the two field bins into trucks so that all loads delivered to the Kimba silos went APW or higher grade. The table below shows a calculation of the additional revenues he made by segregating grain based on protein.
Figure 7. Protein map for a wheat field in Kimba, SA
Table 1. Calculation of additional profit from in paddock blending
Zones Hectares x yield (t/ha)
Tonnes of grain
Price ($) No blending ($)
In field blending ($)
ASW 89 x 4 356 159 56,604
APW 211 x 3.6 760 171 129,892 235,392
H2 223 x 3.2 714 186 132,730 132,730
Total 319,225 368,121
Additional profit
48,896
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Case Study 3: Tactical 3 ‐ On Farm Segregation and Storage
Luke Follett, Pindara, Euston, NSW grows approximately 6000 tonne of wheat, barley and lentils. He operates 2 CASE 8130 combines and he installed a CropScan 3000H onto one of his combines in 2014. Luke has 6 x 1000 tonne silos on farm where he segregates his wheat into ASW, APW, AHI and AH2 grades. Luke uses the local AgFarm agent to market his wheat to domestic users. The CropScan 3000H software calculates the bin average for protein for each bin load and then posts the bin average to the internet in real‐time. The combine operator uses the bin average data to segregate the loads into specific field bins based on grade. Trucks are filled from the field bins and taken back to the farm’s silo complex. The protein, moisture and weight of every truck load that is stored into each silo is received from the internet and stored in the farm’s PC.
Fabian Devereaux, AgFarm, has access to Luke’s data by signing into the CropNet web site. He can see what is stored in each silo as shown in figure 8 and 9. Fabian can then market the grain in each silo based on the running average with confidence that the protein will meet the buyer’s requirements.
Figures 8 and 9. Plots of protein and moisture of wheat stored in silos 2 and 5
Luke advised that AgFarm were able to secure an additional $5 per tonne for 1000 tonne of wheat from the buyer in guarantee that the average was >12% protein. He also commented that all truck deliveries passed inspection by the buyer. Since Euston is over 400km from Melbourne, rejected truck loads have in previous years cost him thousands of dollars in penalties and/or returned loads.
Strategic 1 ‐ Protein/Nitrogen/Yield Optimization to find the “Sweet Spot”
Figure 10 shows the plot of yield Vs nitrogen applied (kg/ha) and the protein%.
The optimum protein level for growing wheat or barley is between 11% and 12%. At this protein level, the yield should be approximately 4tonne per hectare if there is sufficient nitrogen and moisture in the soil and there are no micro nutrient deficiencies. The objective should be to achieve the highest yield at the highest protein premium. Nitrogen needs to be available at both the tillering stage and the reproductive stage in order to achieve optimum yield at the correct protein level, ie, the “Sweet Spot”.
Figure 10. Protein/Nitrogen/Yield Optimization Plot
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37 There are four scenarios that relate Yield and Protein.
1. Low yield and low protein indicate that Nitrogen is the limiting factor.
2. High yield and low protein indicates that there was sufficient Nitrogen at the time of planting
but insufficient Nitrogen to fully develop the plant.
3. Low yield and high protein indicates insufficient Nitrogen in the soil at the emergence and
tillering stages. High protein shows that there was Nitrogen available at the flowering and grain
filling stages but that the tiller count was low and therefore yield could not be recovered.
4. High yield and high protein indicates that the plant has reached its’ full potential and reached
the “Sweet Spot”.
Case Study 4: How to use a Protein Maps for better Nitrogen management
Michael Ayres, Injecta, SA, points out “The yield map correlates directly to soil performance and the protein map is a very good proxy for plant performance. The nitrogen data is what makes everything else fit together, ie, productivity and performance. “
Figures 11 and 12 show the protein and yield maps for a property in Manangatang, Victoria. The maps show a typical inverse relationship between yield and protein, ie, following the ‘dilution theory’. On the left hand side of the field, the yield is low and the protein is high. On the right hand side, the yield is high and the protein is low. If only the yield data was looked at, then the data suggests the left hand side was Nitrogen deficient, however the high protein data shows that there must have been Nitrogen available in the flowering and grain filling stages. The yeld map does not provide the complete story. The protein map shows that the problem with yield was that there was insufficient Nitrogen available in the emergence and tillering stages but there was enough Nitrogen for the growth stages. On the right hand side the yield is higher than the left hand side. However the Protein map shows that on the right hand side the average protein level was less than 10% or ASW grade. If more Nitrogen had been applied on the right hand side and the protein level had been increased to above 10.5%, i.e. APW grade, then the farmer could have realised an extra $15‐20 per tonne or approximately $5900 to $7880. If the yield was also increased across the paddock by .5t/ha then another $2880 in revenues could have been achieved. The message is that this farmer was leaving approximately $10,000 in the paddock because he did not achieve the optimum yield and protein grades across the paddock.
Farmer: Leeton Ryan Location: Manangatang, VIC Field: Ellertons Paddock Size: 315ha Yield Av: 2.5t/ha Protein Av: 10.2% ASW Grade
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Figure 11. Protein Map for Wheat
Figure 12. Yield Map for Wheat
Conclusion
Farming is about converting water into grain. When there is sufficient water available, then the farmer’s task is to optimize the yield and to get the best price for the grain. By combining yield and protein maps, farmers can more completely understand the variations that occur across the paddock. Farmers are then able to use variable rate fertilisation applications to achieve the optimum yield across fields, and real‐time protein data can help to determine where in the field Nitrogen should be applied and at what rate.
Contact details
Phillip Clancy Next Instruments Ph: 02 9771 5444 Email: [email protected]
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Precision agriculture and new technology adoption, what we do at home
Broughton Boydell (“Marinya” Pallamallawa) & Mike Smith (“Tarnee” Gurley)
Contact details
Broughton Boydell “Marinya” Pallamallawa Ph: 0428 549 257
Mike Smith “Tarnee” Gurley Ph: 0427 546 816
Notes:
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Recent work with spray adjuvants
Chris O’Donnell and Andrew Hewitt, University of Qld
Key words
Spray adjuvants, spray quality, coarsener, guar, spray angle, vmd
Take home message
Polymeric adjuvants are good spray coarseners.
Adjuvants are defined as any substance that is added to a spray solution that enhances or modifies the action of a pesticide. Adjuvants are often claimed to cause a synergistic effect with the tank mix. Synergy means there is an interaction between two substances that causes a resultant effect that is greater than the sum of the two individual effects from those substances.
An operational notice was issued by the Australian Pesticides and Veterinary medicines Authority (APVMA) in March 2010 that requires new pesticides to be assessed for the potential risk of spray drift. This allows pesticide labels to prescribe mandatory no‐spray buffer zones at the time of spray application. The Environmental Protection Agency in the USA implemented similar policy at about the same time. Both agencies have incorporated a mechanism that allows chemical applicators who use verified Drift Reducing Technologies (DRTs) to access smaller downwind buffer distances.
Changing the tank mix with the addition of an adjuvant that coarsens the droplet spectrum is one DRT that has been adopted and there has been a rapid increase in the number of products being brought to market. Many “emulsion” type adjuvants have shown to be good at coarsening the droplet spectra of many different types of hydraulic nozzles but the polymeric adjuvants that are based on guar gum have shown the most success. Earlier polymeric adjuvants suffered adverse shear effects from recirculating pumping that caused the adjuvant to quickly break down and lose its effectiveness. Guar gum is not affected by shear effects but does require careful mixing and sometimes it also requires standing time to become fully hydrated.
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0
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0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 2500 2600 2700 2800 2900 3000 3100 3200 3300 3400 3500Droplet Diameter / µm
Trt
23
Active Adjvt
51 cm laser to nozzle51 cm laser to nozzle
Nozzle
TTI 11004TTI 11004
Dv0.1µm271380
Dv0.5µm633857
Dv0.9µm10841374
<100%0.350.09
<141%1.410.62
<150%1.700.77
<200%4.452.10
Figure 1. The addition of a polymeric adjuvant “Intact” (blue line) at 0.5% to a standard tank mix (red line) shows that the Dv0.1, Dv0.5 and the Dv0.9 were increased by 40%, 33% and 26%, respectively. The volume percentage below 100 microns was decreased by 74%. This trend has been evident in
every one of the several hundred tank mixes that have been tested.
These guar‐gum based adjuvants have increased viscosity which can decrease the spray angle of the nozzle. For example, the TTI 11002 nozzle which has a swath width of 180 cm when sprayed from a boom height of 60 cm above the target, has the swath reduced to 125 cm with the addition of 0.5% of polymeric adjuvant “Intact” to the spray tank. Spray booms with nozzles spaced at 50 cm ensure that the spray pattern has a double‐overlap at the top of the canopy but this assumes that the full 110 degree angle has been generated. Adjuvants that decrease the spray angle will probably require the nozzle spacing on the boom to be adjusted accordingly in order to ensure that spray operators will still achieve the double‐overlap.
Contact details
Chris O’Donnell The University of Queensland Gatton, Qld Phone: 07 5460 1350 Email: [email protected]
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Spray application tips and tactics
Bill Gordon, Nufarm Australia
Key words
Spray application, spray coverage
Take home message
Product choice and rate, timing and total application volume will normally have the largest impacts on the efficacy achieved from any spray job. The sprayer setup and operating parameters can also have a significant effect on the outcome by affecting the spray coverage on the target, as well as the drift potential. Assessing spray coverage is a simple process that can help to improve the sprayer setup.
Introduction
Throughout the season there are a number of situations where spray applications are made to very different types of targets, often with products that have different types of translocation. This variation generally requires a change in nozzle type or orifice size and the operating parameters to achieve a change in total application volume and/or spray quality.
After adjusting the sprayer setup, being able to determine where the spray droplets are landing allows the operator to change the sprayer setup to improve the coverage for particular spray jobs. Often this requires that the operator is able to assess the impact of changes to the set up on where the droplets land.
A starting point – how many setups should the operator have?
As a general guide, the main spray jobs, application volumes and typical spray qualities required by an operator are covered in table 1. This highlights the fact that often more than 2 sets of nozzles are required to cover all situations.
For each type of spray application there may be subtle variations in the sprayer setup or operation that can be made to improve the coverage. This paper discusses some of the practical considerations the operator should take into account when choosing the set up for various spray jobs throughout a typical season, including the factors influencing spray coverage and how to compare setups and operating parameters for continual improvement.
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43 Table 1. Typical spray quality and total application volume for different situations
Typical Application Volume
Medium Spray Quality
(lower drift risk areas)
Coarse Spray Quality
Extremely Coarse Spray Quality
(higher drift risk areas)
Lower range
50 -60 L/ha (Low stubble load)
to 70-80 L/ha
(High stubble load)
*Only where permitted on label:
Fully translocated herbicides
Small to medium sized targets.
Fallow Spraying
Fully translocated herbicides such as Glyphosate and Group I herbicides,
Fully translocated herbicides, medium targets,
Very sensitive areas or NIGHT SPRAYING
Higher range
70-80 L/ha (Low stubble load)
to
100 + L/ha
(High stubble load/ dense crop canopy)
*Only where permitted on label:
Contact type products.
Small targets.
In crop spraying.
Penetration and coverage in large & broadleaf crops.
Good stubble penetration.
Pre-emergent’s.
Fully Translocated herbicides,
Some contact herbicides at the higher application volumes.
Water soluble Pre-emergent’s.
Medium sized targets with fully translocated summer fallow herbicides.
Very sensitive areas or NIGHT SPRAYING
*note, the arrows indicate that one nozzle may be able to do more than one type of application, provided the spraying speed, application volume and operating pressure are suitable
Suggestions to improve fallow applications
The following points have been included to provide a guide or starting point for the sprayer setup. Some of the important things to consider include:
Total application volume: For fully translocated products typically volumes above 50L/ha for a coarse spray quality in low stubble environments, and above 70L/ha in heavy stubble environments. Typically this volume should be increased by 10 to 20 L/ha when using an extremely coarse spray quality. For contact type products (translaminar) the total application volume should be above 70L/ha in low stubble environments, and up to 100 L/ha in heavy stubble situations.
Spray quality: For small vertical targets (grasses) operating at the small end of the coarse spectrum will normally provide good retention of droplets on a range of weed types, however using coarser spray qualities may also be useful for many broadleaf weeds. Often operating at the small end of the coarse spectrum will provide a good balance for a range of targets and products. This is normally suitable for daytime conditions, but may not reduce the spray drift potential if considering spraying at night.
Nozzle spacing: Using narrow nozzle spacings, e.g. 25cm compared to 50cm, can improve deposition into standing stubble. However before deciding to plumb the machine this way ensure that the orifice size and spray quality will be suitable if operating with nozzles that have smaller orifice sizes.
Boom Height: Operating at heights above that required for a double overlap at top of the stubble or weed (whichever is the taller) will reduce coverage and increase drift potential. Increasing boom height from 50cm above the target to 70cm above the target can increase the airborne fraction of spray by up to 4 times.
Adjuvant selection: adjuvants should always be chosen to increase efficacy, however many adjuvants have the potential to change the spray quality and drift potential in unexpected ways. Most non‐ionic wetter 1000 type products can more than double the drift potential from some air
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inducted coarse nozzles. For fallow spraying it is important to select adjuvants that do not increase the drift potential of the spray application.
Spraying speed: Reducing spraying speed can reduce dust and wheel tracks, will improve penetration into stubble and crop canopies and can reduce shadowing.
Nozzle design: There are a number of nozzle designs that have twin patterns, where one pattern is angled forward and the other angled backwards. Generally twin nozzles are best utilised for increasing deposition onto vertical targets, which may also increase stubble interception. Twin nozzles are best operated at lower spraying speeds, commonly less than 16 km/h.
Risk assessment: Before any spray application it is important to fully assess any risks including the weather conditions, sensitive areas and volatility risk.
Pre‐emergent herbicides
Most applications of pre‐emergent herbicides will benefit from using coarser spray qualities to increase penetration through stubble and by increasing the total application volume, however volumes above 150 L/a generally do not provide further significant improvements in efficacy.
For products with relatively low water solubility, such as trifluralin and pendimethalin, avoiding the tie up of product onto stubble is critical to maximising herbicide contact with the soil. When using a conventional nozzle spacing of 50cm, a VC spray quality or larger (such as XC) set to produce a double overlap at the top of the stubble can minimise retention on stubbles, however the uniformity of the spray deposit onto the soil surface will be more variable than compared to a coarse spray quality due to the lower number of droplets produced.
For reasonably water soluble products such as atrazine, simazine and metalochlor, interception by the stubble may have a smaller impact on efficacy, provided a reasonable rainfall event can wash the product back onto the soil. Where rainfall is anticipated, the more water soluble products may be applied in a lower total application volume, typically above 70‐80 L/ha.
Generally reducing spraying speeds will improve the penetration into stubble and improve the evenness of the application. Narrower nozzle spacings can also be of benefit, provided the spray quality and boom height are suitable.
Alternately, many operators have plumbed machines with nozzle spacings to match the crop row width. Where nozzles are positioned in the centre of the inter‐row gap between standing stubble lines, the nozzle height may be lowered to obtain an overlap close to the base of the stubble. This may improve soil contact and reduce interception by the stubble, provided spraying speeds and wind speeds do not excessive.
Early season grass sprays in‐crop
Droplet retention on small, vertical grasses is usually optimised when using a medium spray quality (where permitted on label), however in a heavy standing stubble, the smaller droplet sizes tend to increase the amount of product deposited onto the stubble. Generally a spray quality at the smaller end of the coarse spectrum (towards medium) combined with total application volumes above 70‐80 L/ha will provide a reasonable outcome.
Where operators typically operate at higher spraying speeds, or with larger than coarse droplets, they may notice increased shadowing of small weeds behind stubble. Where this is occurring, slowing down would help, but ensuring that each new job is driven in the opposite direction to the last can also improve overall level of control, particularly when a ‘double knock’ strategy is employed.
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45 When using a Group A product, always ensure that an appropriate adjuvant is used, through a nozzle that will not significantly alter the spray quality with the addition of the adjuvant (see table 2). Also ensure the water quality is suitable by testing for bicarbonate levels before the application.
Table 2. Variation in the Dv0.5 (VMD) produced by selected low drift nozzles* operated at 3.0 bar, expressed as the standard deviation +/‐ from the Dv0.5 (VMD)1 in micrometers (μm) for 3 spray
solutions
1Dv0.5 or VMD is the droplet size (diameter in micrometers or μm) at which half of the spray volume produced by the nozzle will exist as droplets smaller than this size, and the other half will exist as dropets larger than this size.
*note the range of nozzles listed in this table does not include all of the nozzles tested by J Connor Ferguson
Broadleaf sprays in crop
Fully translocated products such as the Group I herbicides should be applied with a coarse spray quality or larger at application volumes above 60 L/ha. Where a product with contact activity is used the application volume should be increased to 80 L/ha or more.
Spray Solution water clopyralid pinoxaden + methylated oil
Average Standard Deviation
Nozzles Tested (all nozzles were
operated at 3.5 bar)
Standard Deviation + / ‐ micrometers (μm)
7.33 4.69 4.45 5.49
Bellericay Bubblejet ABJ 110‐015
28.62 26.01 14.52 23.05
Bellericay Bubblejet ABJ 110‐02
9.60 5.11 3.54 6.08
TeeJet AITTJ60‐110‐02 8.40 8.72 9.78 8.97
TeeJet AIXR 110‐015 5.44 10.28 9.06 8.26
TeeJet AIXR 110‐02 19.63 16.40 12.80 16.28
Hypro Guardian Air 110‐015
15.92 14.27 10.61 13.60
Hypro Guardian Air 110‐02
6.14 8.17 8.73 7.68
Lechler IDK 120‐02 4.64 6.35 4.84 5.28
Lechler IDKT 120‐02 6.32 8.23 4.29 6.28
Hardi Minidrift MD‐110‐02
4.16 3.73 3.10 3.66
Hardi Minidrift Duo‐110‐02
5.23 2.30 3.53 3.68
TeeJet TTI 110‐015 13.04 10.51 14.04 12.53
TeeJet TTI 110‐02 5.39 8.71 12.25 8.78
Teejet TTJ60‐110‐02 41.71 11.69 5.83 19.74
Hypro ULD 120‐015 7.75 14.11 8.54 10.13
Hypro ULD 120‐02 7.63 3.39 3.89 4.97
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Late season applications into dense canopies
Late season fungicide and insecticide applications, along with pre‐harvest desiccation, typically require that the droplets are able to penetrate into the canopy. The size of the canopy and the architecture of the plants will greatly influence how far droplets can penetrate into the canopy.
Leaf type, leaf shape and leaf surface all affect how well droplets will be retained. Droplet retention on most cereals and large grass type crops will be improved by using a medium spray quality (where permitted on label), however penetration through a canopy may be increased using a coarse spray quality. Droplet retention on many broadleaf crops may allow for good retention when using coarse spray qualities.
With a standard boom sprayer there are only a limited number of things the operator can do to potentially improve the penetration into the canopy, those include:
Reducing the spraying speed
Increasing the application volume
Manipulating the spray quality, and
Utilise a narrower nozzle spacing
To make a greater impact on penetration into the canopy generally requires the use air assistance to help transport droplets into the canopy. While the addition of air into the equation can add another layer of complexity to the sprayer setup, it can also provide large improvements in canopy penetration when correctly setup and adjusted.
To assess which variations in the sprayer setup and operating parameters can actually improve the penetration into dense canopies, useful tools include water sensitive paper (WSP) and the SNAPCARD app, which can help the operator to determine where improvements are being made.
Consider assessing spray deposits to improve your spray coverage
Using tools such as water sensitive paper will allow the operator to look at where the droplets are landing and to compare various sprayer setups to see which ones provide the best coverage.
Often it is difficult to see small improvements in coverage. A tool that can measure the level of spray deposit can assist when trying to evaluate changes. The SNAPCARD app allows spray operators to measure the spray deposits onto water sensitive paper by indicating a ‘percent coverage.’ Taking regular measurements and recording this information allows for continual improvements in the sprayer setup for different types of spray applications.
Further reading
https://grdc.com.au/GrowNotesSprayApplication
Contact details
Bill Gordon Nufarm Australia Limited 11 Hienneman Road, Wellcamp, Q.4350 Ph: 0418 794 514 Email: [email protected]
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10 tips for reducing spray drift
Bill Gordon, Nufarm Australia
Key words
spray drift reduction, spray application
Take home message
Spray operators need to understand and adjust the variables they have direct control over to minimise the risk of off target movement of spray. Where they are not able to control factors, such as the weather, they must access reliable forecast information to plan when to apply products at times of lowest risk.
1. Choose all products in the tank mix carefully, this includes the choice of active ingredient, the
formulation type and the adjuvant used. Tools are now available to assess the impact of tank
mix on the percentage of drift prone droplets produced.
2. Understand the products mode of action and coverage requirements on the target– read the
crop protection product label and technical literature for guidance on spray quality, buffer (no‐
spray) zones and wind speed requirements.
3. Select the coarsest spray quality that will provide an acceptable level of control. Be prepared to
increase application volumes when coarser spray qualities are used, or when the delta T value
approaches 10 to 12. Use water sensitive paper and the SnapCard® app to assess the impact of
coarser spray qualities on coverage at the target.
4. Always expect that surface temperature inversions will form later in the day, as sunset
approaches, and they are likely to persist overnight and beyond sunrise on many occasions. If
the spray operator cannot determine that an inversion is not present, then no spraying should
occur.
5. Use weather forecasting information to plan the application. Bureau of Meteorology (BOM)
meteograms and forecasting websites can provide information on likely wind speed and
direction for 5 to 7 days in advance of the intended day of spraying. Pay close attention to
variations between predicted maximum and minimum temperatures above 5 to7 degrees
Celsius, delta T values below 2, low overnight wind speeds (less than 11 km/h) and predictions
of dew or frost as these all indicate the likely presence of a surface inversion.
6. Only start spraying after the sun has risen more than 20 degrees above the horizon and the
wind speed has been above 4 to 5 km/h for more than 20 to 30 minutes, with a clear direction
that is away from adjacent sensitive areas.
7. Set the boom height to achieve double overlap of the spray patterns. With a 110 degree nozzle
using a 50cm nozzle spacing, this is 50cm above the top of the stubble or crop canopy. Boom
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height and stability is critical, use height control systems for wider booms or reduce the
spraying speed to avoid boom bounce.
8. Avoid high spraying speeds, particularly when ground cover is minimal. Spraying speeds above
16 to 18 km/h with trailing rigs, and above 20 to 22 km/h with self‐propelled sprayers greatly
increase losses due to affects at the nozzle and the aerodynamics of the machine.
9. Be prepared to leave unsprayed buffers when the label requires, or when the wind direction is
towards sensitive areas. For ground application of non‐volatile products using a coarse spray
quality (or larger) during daylight hours and wind speeds between 3 and 20 km/h a 300m
downwind buffer is generally sufficient, however you should always refer to the spray drift
restraints on the product label. Smaller spray qualities will require larger buffers.
10. Continually monitor the weather conditions at the site of application. Always measure and
record the wind speed, wind direction, temperature and relative humidity at the start of
spraying and at the end of every tank, according to the label requirements. Label ‘no‐spray’
zones and downwind buffer distances are based on wind measurements at 2m above the
ground.
Contact details
Bill Gordon Spray Application Specialist ‐ Nufarm Australia Email: [email protected] Mobile: 0418 794 514
® Registered trademark
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Groundcover and stubble type impact on fallow efficiency
Brendan Burton, Northern Grower Alliance
Key words
Soil water, fallow efficiency, groundcover, stubble type, plant available water
GRDC code
NGA0004
Take home message
The impetus for this trial work came following three consecutive dry seasons from 2013‐2015 where little to no winter crop was planted in the Walgett district. Growers and consultants wanted proof of concept that going beyond current no‐till farming practices to increase the percentage groundcover can improve fallow efficiency and safe guard soil moisture against evaporative losses.
Current soil water models show upper most limit at 100% groundcover. NGA set out to determine how much additional soil water could be accumulated by going beyond 100% groundcover, by applying additional straw to the system and hence increasing the thickness layer of straw on the soil surface.
Small plot trials initiated by NGA in both 2014 and 2015 indicated that the addition of extra ground cover can increase the depth of accumulated soil water by up to 50‐60cm when compared to standard standing stubble, with reduced evaporation losses considered the most likely cause.
While stubble load is important for accumulating soil water, the type of stubble appears to have a major impact on fallow efficiency. Results from a trial site at Macalister demonstrated that barley stubble had double the fallow efficiency compared to faba bean stubble given the same length of fallow period and rainfall.
Background
Following two consecutive years of drought during 2013 and 2014, with little to no winter crop planted in the western districts of northern NSW, growers and consultants from the Walgett area made maximising soil water accumulation their top priority during the NGA’s local research group meetings. Consequently, two small plot trials were established in 2014 to begin looking at the effect of applying additional groundcover to the current standing stubble in an attempt to quantify how much additional soil water could be gained by applying additional groundcover in the form of baled straw.
In addition to the small plot stubble cover trials, there was interest from the Liverpool Plains Local Research Group in comparing the fallow efficiency of different stubble types. Consultants believed that stubble type had a major impact on how quickly the soil moisture profile could be refilled after harvest. Consequently, NGA evaluated a range of different stubble types following harvest to determine their fallow efficiency.
2014 small plot trials
Two trials were established in 2014. Both trials had the same six treatments with four replicates. Each treatment plot was 2m x 2m in size with a 2m x 2m buffer area between each treatment plot. Chicken wire was used to hold additional straw in place during the trial period.
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Table 1. 2014 small plot trials
Trt Product Bullarah Walgett
% Groundcover
Mulch layer thickness (cm)
% Groundcover
Mulch layer thickness (cm)
1 UTC. Standing stubble. 33 4.4t DM/ha
0 31 4.6t DM/ha
0
2 Bare ground. Stubble removed 7 0 7 0
3 Additional 5t/ha straw added to existing stubble
94 1 88 1
4 Additional 10t/ha straw added to existing stubble
99 3 98 2
5 Additional 20t/ha straw added to existing stubble
100 9 100 4
6 Additional 40t/ha straw added to existing stubble
100 11 100 5
One of the trial sites was setup at Bullarah on the 13th January 2014, where the paddock had already been fallowed for a period of 14 months, following wheat in 2012. Initial gravimetric soil samples (to 120cm depth) were taken at trial initiation to ensure soil moisture across the trial site was uniform. Starting soil water was calculated as 11mm of plant available water (PAW). Final gravimetric soil samples were taken in May 2015, approx. 16 months after trial initiation. Total rainfall received during this period was 551mm.
Figure 1. An additional 53mm of PAW was stored where 5t/ha of straw was applied compared to standing stubble alone. Additional 40t/ha straw resulted in extra 87mm PAW compared to the
standard standing stubble. Based on water use efficiency (WUE) (wheat) of 15kg/mm/ha, an extra 87mm of PAW could result in an additional 1.3t/ha of grain.
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Figure 2. An extra 19mm of PAW accumulated where additional 5t/ha straw added, compared to the standing stubble. Where 40t/ha straw was applied, an extra 37mm of PAW stored compared to standing stubble. Based on WUE for wheat this could equate to an extra 550kg/ha of grain.
2015 small plot trials
An additional two small plot trials were established in 2015 following the promising initial results. The 2015 trials had eight treatments in total by four replicates. Each treatment plot was 2m x 2m in size with a 2m x 2m buffer area between each treatment plot. Sheets of galvanised mesh were placed on top of those plots where additional straw was added to keep the mulch layer in place during the trial period.
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Table 2. 2015 small plot trials
Trt Product Bullarah Mungindi
% Groundcover
Mulch layer thickness (cm)
% Groundcover
Mulch layer thickness (cm)
1 Stubble removed, surface left rough/cracked
9
5.4t DM/ha
0 8
3.1t DM/ha
0
2 Stubble removed, surface raked level & smooth
5 0 3 0
3 Standing stubble, stubble cut to 50% height
28 0 24 0
4 UTC. Standing stubble 34 0 37 0
5 Additional 5t/ha straw added to existing stubble
89 0.5 97 3
6 Additional 10t/ha straw added to existing stubble
100 1 100 5
7 Additional 20t/ha straw added to existing stubble
100 3 100 11
8 Additional 40t/ha straw added to existing stubble
100 5 100 18
Capacitance probes were also installed at both sites in two reference plots to monitor water infiltration and soil water accumulation over time. The capacitance probes were installed in a single plot of treatment four (standard standing stubble), and treatment eight (additional 40t/ha of straw added to the existing standing stubble).
One of the 2015 trials was established on the same property from the previous year at Bullarah, but in a different paddock on the 9th Nov 2015, some two weeks after the wheat harvest on the 25th Oct 2015. Initial gravimetric soil water samples were taken to confirm soil water uniformity across the trial site. Stubble height was ~25‐30cm.
Total rainfall of ~550mm was received during the trial period from Nov 2015 until the 27th Oct 2016. There was no significant treatment affect at this site. The site received 42mm of rainfall during the fortnight between harvest and trial initiation. Capacitance probe results, following an early January rainfall of ~100mm, indicated that soil moisture had reached a depth of >40cm in both the standing stubble and 40t/ha straw treatments. There were no differences observed between treatments in subsequent assessments using moisture probes, EM38 or in final gravimetric soil water.
The second 2015 site was setup up on the 10th Nov 2015 approx. 30km north west of Mungindi, and similar to Bullarah had recently been harvested. Initial gravimetric soil water samples were taken to confirm trial site uniformity. Stubble height was recorded at 30‐35cm. A total of 544mm of rainfall was received during the trial period from the 10th Nov 105 until the 11th Oct 2016.
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Figure 3. An additional 24mm of PAW was stored where an extra 5t/ha of straw had been added to the standing stubble. I.e. Treatment 4 versus Treatment 5.
Assumptions for soil bulk density and crop lower limits have been made for this particular site to calculate plant available water. Values for bulk density and crop lower limits were taken from the nearest soil‐characterised site at Thallon. While the absolute figures may not be correct, the relative treatment differences remain.
Figure 4. Standing stubble. January rainfall wet the top 20cm, but was rapidly lost due to evaporation during a drier March/April. An extra 100mm of rainfall was required in the standing
stubble compared to 40t/ha straw to get the same moisture level down to 60cm.
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Figure 5. Standing stubble plus an extra 40t/ha straw added. Following the January rainfall, the surface moisture layers remained wet. The mulch layer appeared to reduce evaporative losses, and
consequently the soil profile filled more quickly.
Fallow efficiency x stubble type
Following the harvest of winter trials in 2015 at a P thornei trial site at Macalister, NGA collected Dual EM data on 27th Nov 2015 from plots recently harvested. Data was collected with a minimum of four readings per plot (6m long x 2m wide plot dimensions). Dual EM data was recorded at four separate depths: 50cm, 100cm, 150cm and 300cm. During data collection, the Dual EM emitter/receiver was suspended approx. 30cm above ground level naturally altering the depth to which the Dual EM measured. The corresponding depths of measurement were now 20cm, 70cm, 120cm and 270cm.
In order to convert the Dual EM data into millimetres of plant available water a calibration curve had to be developed. Fortunately, the paddock had a previous soil characterisation carried out so figures for bulk density and crop lower limits were used. Specific plots within the trial area that were known to have different water contents were initially measured using the Dual EM and accurately marked for later soil sampling. Physical soil samples were taken from the identical location to the Dual EM readings to obtain the gravimetric soil water percentage. The appropriate bulk density and crop lower limits were applied to the calculation to determine the PAW down to a depth of 120cm.
Following the first Dual EM run, data for each plot was collected and averaged for later analysis. This data provided an indication to the differences in soil water remaining in the profile following each crop, and differences within the same crop type when moving from low to high Pt levels.
A second Dual EM run on the 1st September 2016 determined the quantity of soil water accumulated during the fallow period. A total of 342mm of rainfall was received during this ten month fallow period. Further reference points were recorded with the Dual EM and sampled to add additional points to the calibration curve.
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Figure 6. Accuracy of converting dual EM data into PAW is highly dependent on sampling a range of points with different soil moisture levels, and having good soil characterisation data.
Figure 7. Stacked column graph indicates the PAW at the two dual EM timings for each stubble type. The yellow column illustrates the PAW remaining in the soil after harvest, i.e. start of fallow PAW. The blue column represents the PAW at the end of the fallow, with the number inside the blue
column representing the gain in mm of PAW during the fallow period. The number above the blue column is the fallow efficiency percentage for each stubble type. A total of 342mm of rainfall was
received during the fallow period.
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It appears the range in fallow efficiency figures are influenced by stubble type, the amount of stubble, how easily the stubble is broken down, and whether the stubble is standing or lying flat on the soil surface. For example, both the faba bean and chickpea stubble began the fallow period with 4.3t and 3.4t DM/ha respectively, but by the end of the fallow period very little stubble remained. The low C:N ratio of these legume stubbles allow rapid microbial breakdown, which may be a possible cause of the relatively low fallow efficiency compared to the cereal and canola stubbles.
Conclusion
Accumulating groundcover to beyond 100 % appears to offer significant benefits in both capturing and retaining soil water. NGA’s trial work has demonstrated that real benefits in the accumulation of soil water can occur once 100 % ground cover is reached, which generally coincides with approx. 10t of dry matter per hectare. Trial results have demonstrated that the amount of soil water is related to the amount of groundcover, with significant gains occurring once 100 % groundcover is reached and the thickness of mulch layer on the soil surface accumulates. These gains are most likely the result of reduced evaporation losses from the soil surface, particularly the top 20cm.
The challenge for growers now is how to achieve such levels of stubble cover and still maintain the ability to plant the following crop through high levels of trash. Further work needs to be done to determine whether the same benefit is achieved depending on whether the stubble is standing or lying flat on the ground. Consequently, a large scale trial was initiated in the Walgett district in December 2016 evaluating the impact of stubble height and standing versus lying flat on soil water accumulation.
The use of Dual EM technology has provided an efficient and cost effective way of providing an objective measure of soil water. However, in order to obtain meaningful results from EM soil water monitoring devices accurate soil characterisation data, and a good spread of soil water contents are required to produce a useful calibration curve.
Following the Dual EM results from our Macalister Pt site, it would appear that canola/mustard are extremely efficient in extracting soil moisture from the profile during the growing season. This can explain the difficulty growers face in trying to refill the profile following such crops.
In terms of fallow efficiency of different stubble types, legume based stubble appears to be less efficient in capturing soil water compared to cereal/canola stubble. The type, amount and whether the stubble is standing or lying flat appears to effect the overall fallow efficiency of each stubble type.
Acknowledgements
The research undertaken as part of this project is made possible by the significant contributions of growers through both trial cooperation and the support of the GRDC, the author would like to thank them for their continued support.
Contact details
Brendan Burton Northern Grower Alliance PO Box 78, Harlaxton 4350 Mob: 0428 979 170 Email: [email protected]
