The objective of this GRDC / SPAA funded project is to increase the level of adoption of PA 'beyond guidance' by broadacre farmers. The project specifically aims to increase the level of adoption of variable rate (VR) by growers in the project to 30% by 2013.This goal will be achieved by demonstrating how to use PA tools to growers at a regional level and by increasing the skills of growers and industry in PA to a level where they can then use PA tools in their farming systems to achieve economic, environmental and social benefits.

There was a visual response to P applied at sowing and up until after Z31. This did not translate into yield at harvest. There was an inital response to varying seeding rate but this was not obvious at Z31 and did not translate into a yield response. The results may have been a reflection of the dry spring and the time of application. The addition of P therefore resulted in the loss of money. There was no visual response to applying N in the N rich strip in either zone. DSN results and yield targets assessed using weather prediction and the French and Schultz model were therefore used to determine a single rate of N for all plots in both zones of 3.5 t/ha. The harvest results showed no response in the N rich strip but as these were only 80 metres wide and had additional N at Z31, the result may not be a reliable guide to whether there was a response at the site. There was no response to N at harvest. Overall the yield was disappointing and well below the target and the quality was also below the target.

• Commercial viability has been difficult to demonstrate.
• Field trials demonstrated that the H Senor was able to accurately classify annual ryegrass in several broadleaf crops, including canola, faba bean, lentil and field pea.
• Other grass weeds such as brome grass and wild oats were also correctly identified as grasses in these crops.
• Broad leaf weeds were accurately classified in cereal crops, however this was more difficult due to the overlapping nature of the cereal crops at the time when the broadleaf weeds had emerged.
• Classification accuracy was lower for detection of grass weeds in cereal crops and broadleaf weeds in broadleaf crops, and these scenarios were not pursued for that reason.  

• Commercial viability has been difficult to demonstrate.
• Field trials demonstrated that the H Senor was able to accurately classify annual ryegrass in several broadleaf crops, including canola, faba bean, lentil and field pea.
• Other grass weeds such as brome grass and wild oats were also correctly identified as grasses in these crops.
• Broad leaf weeds were accurately classified in cereal crops, however this was more difficult due to the overlapping nature of the cereal crops at the time when the broadleaf weeds had emerged.
• Classification accuracy was lower for detection of grass weeds in cereal crops and broadleaf weeds in broadleaf crops, and these scenarios were not pursued for that reason.  

• Commercial viability has been difficult to demonstrate.
• Field trials demonstrated that the H Senor was able to accurately classify annual ryegrass in several broadleaf crops, including canola, faba bean, lentil and field pea.
• Other grass weeds such as brome grass and wild oats were also correctly identified as grasses in these crops.
• Broad leaf weeds were accurately classified in cereal crops, however this was more difficult due to the overlapping nature of the cereal crops at the time when the broadleaf weeds had emerged.
• Classification accuracy was lower for detection of grass weeds in cereal crops and broadleaf weeds in broadleaf crops, and these scenarios were not pursued for that reason.  

• Commercial viability has been difficult to demonstrate.
• Field trials demonstrated that the H Senor was able to accurately classify annual ryegrass in several broadleaf crops, including canola, faba bean, lentil and field pea.
• Other grass weeds such as brome grass and wild oats were also correctly identified as grasses in these crops.
• Broad leaf weeds were accurately classified in cereal crops, however this was more difficult due to the overlapping nature of the cereal crops at the time when the broadleaf weeds had emerged.
• Classification accuracy was lower for detection of grass weeds in cereal crops and broadleaf weeds in broadleaf crops, and these scenarios were not pursued for that reason.  

The trial went well in 2011 because the manure coverage was good. Michael was disappointed with the results from the deep rip treatments with or without lime, because there was no significant yield difference compared to the control and hence no economic gain. However applying chicken manure at 2t/ha resulted in an increase in yield (especially from the most acidic sand rises) with a considerable economic gain/return. Therefore, as a result of these trials Michael is going to implement a strategic manure application programme with the objective of increasing his paddocks yield and profitability while reducing the impact of the sand rises. Fellow growers in the area have embraced the trial outcomes and most of them are keen to adopt a similar strategy in their cropping system since chicken manure is widely available in the area.

• Commercial viability has been difficult to demonstrate.
• Field trials demonstrated that the H Senor was able to accurately classify annual ryegrass in several broadleaf crops, including canola, faba bean, lentil and field pea.
• Other grass weeds such as brome grass and wild oats were also correctly identified as grasses in these crops.
• Broad leaf weeds were accurately classified in cereal crops, however this was more difficult due to the overlapping nature of the cereal crops at the time when the broadleaf weeds had emerged.
• Classification accuracy was lower for detection of grass weeds in cereal crops and broadleaf weeds in broadleaf crops, and these scenarios were not pursued for that reason.  

• Commercial viability has been difficult to demonstrate.
• Field trials demonstrated that the H Senor was able to accurately classify annual ryegrass in several broadleaf crops, including canola, faba bean, lentil and field pea.
• Other grass weeds such as brome grass and wild oats were also correctly identified as grasses in these crops.
• Broad leaf weeds were accurately classified in cereal crops, however this was more difficult due to the overlapping nature of the cereal crops at the time when the broadleaf weeds had emerged.
• Classification accuracy was lower for detection of grass weeds in cereal crops and broadleaf weeds in broadleaf crops, and these scenarios were not pursued for that reason.  

There was no visual response to applying N or yield. The N rich strips were also not obvious at the time of applying the N to the plots. This may have been a result of the moderate level of N in all zones, the dry spring and the time of application. From the DSN results, 20-60 kg/ha of N would have commended in August in the various zones to achieve the yield targets that were considered achievable at that time. The quality of the wheat produced was low (ASW due to low protein) indicating that insufficient N was applied. The economic analysis showed that the addition of N, lost money. The N rich strips were a useful guide to assessing that there would be no response to applying additional of N but not for assessing differing responses in different zones. It has not been possible to assess if satellite map were a useful guide to possible N response.

 There was a visual response to P applied at sowing which resulted in additional yield at harvest. Responses were obtained to up to 10 kg/ha of P. The added net income was calculated as $44 /ha or a $3 return for every $1 spent. There was no visual response to applying N. This may have been a result of the dry pring and the time of application. There was no response to N at harvest.   The co-operator was please that he noted a visual response to applying P that resulted in higher yields. The ack of N response was probably due to the dry weather in early October.

The aim of the trial was to investigate the use of variable rate liquid & urea applications and their impacts on yield & grain protein. The liquid N application of 120l/ha achieved 2.8T/ha yield and increased protein to 13% which was $199/ha higher return than the 120kg/ha of Urea treatment. The 120kg/ha Urea treatment yielded 2.6t/ha and was graded as H2 because of a protein level of 12.6%. In this case it was economic to use the liquid N rate of 120l/ha. There were no other economic impacts from this trial work.

We focussed on measuring paddock variability rather than implementing an actual variable rate trial. Soil pH mapping proved to be a valuable step towards variable rate lime with significant variability found, pH(CaCl2) varying from 4.0 to 7.1 across a 50ha paddock. A variable rate management approach to pH on this paddock would deliver a 75% saving of lime.

There has been widespread use of continuous cereal in the Mallee over the past decade, and with many drought years throughout this period fertiliser inputs have generally been greater than crop demand. As a consequence, soil phosphoruslevels are often well above the critical level. By identifying soils and conditions where continuous cereal systems perform best and where inputs can be most effectively targeted, there is an opportunity to reduce input costs and increase profitability. This trial used PA methods to identify soil types for targeted soil sampling to achieve a greater understanding of soil phosphorus levels in different soil zones. Yield mapping was then used to identify if and where fertiliser applied to the crop impacted grain yield.

• Commercial viability has been difficult to demonstrate.
• Field trials demonstrated that the H Senor was able to accurately classify annual ryegrass in several broadleaf crops, including canola, faba bean, lentil and field pea.
• Other grass weeds such as brome grass and wild oats were also correctly identified as grasses in these crops.
• Broad leaf weeds were accurately classified in cereal crops, however this was more difficult due to the overlapping nature of the cereal crops at the time when the broadleaf weeds had emerged.
• Classification accuracy was lower for detection of grass weeds in cereal crops and broadleaf weeds in broadleaf crops, and these scenarios were not pursued for that reason.  

• Commercial viability has been difficult to demonstrate.
• Field trials demonstrated that the H Senor was able to accurately classify annual ryegrass in several broadleaf crops, including canola, faba bean, lentil and field pea.
• Other grass weeds such as brome grass and wild oats were also correctly identified as grasses in these crops.
• Broad leaf weeds were accurately classified in cereal crops, however this was more difficult due to the overlapping nature of the cereal crops at the time when the broadleaf weeds had emerged.
• Classification accuracy was lower for detection of grass weeds in cereal crops and broadleaf weeds in broadleaf crops, and these scenarios were not pursued for that reason.  

• Commercial viability has been difficult to demonstrate.
• Field trials demonstrated that the H Senor was able to accurately classify annual ryegrass in several broadleaf crops, including canola, faba bean, lentil and field pea.
• Other grass weeds such as brome grass and wild oats were also correctly identified as grasses in these crops.
• Broad leaf weeds were accurately classified in cereal crops, however this was more difficult due to the overlapping nature of the cereal crops at the time when the broadleaf weeds had emerged.
• Classification accuracy was lower for detection of grass weeds in cereal crops and broadleaf weeds in broadleaf crops, and these scenarios were not pursued for that reason.  

• Commercial viability has been difficult to demonstrate.
• Field trials demonstrated that the H Senor was able to accurately classify annual ryegrass in several broadleaf crops, including canola, faba bean, lentil and field pea.
• Other grass weeds such as brome grass and wild oats were also correctly identified as grasses in these crops.
• Broad leaf weeds were accurately classified in cereal crops, however this was more difficult due to the overlapping nature of the cereal crops at the time when the broadleaf weeds had emerged.
• Classification accuracy was lower for detection of grass weeds in cereal crops and broadleaf weeds in broadleaf crops, and these scenarios were not pursued for that reason.  

This demonstration has shown that there are many tools available to help farmers identify paddock zones. In this case, the farmer was easily able to identify roughly each zone, however from the yield map it is apparent that using the yield maps, to get those zones 100%, slight changes were required. There was little advantage in applying fertiliser in any of the zones, however, emergence issues may have confounded some of these responses. Both P tests (Colwell and DGT) indicated that Zone 2 was responsive to P. There appeared to be a slight increase in yield to P. This was indicated visually during the season, though the difference was thought to be greater and unlikely to be significant. Most importantly, this demonstration has highlighted that you don’t need to invest heavily to practice PA and VR. The farmer identified 80-90% of the variation with the help of other tools. Those tools will prove very important when the zones as less pronounced or in paddocks that have been recently acquired.

• Commercial viability has been difficult to demonstrate.
• Field trials demonstrated that the H Senor was able to accurately classify annual ryegrass in several broadleaf crops, including canola, faba bean, lentil and field pea.
• Other grass weeds such as brome grass and wild oats were also correctly identified as grasses in these crops.
• Broad leaf weeds were accurately classified in cereal crops, however this was more difficult due to the overlapping nature of the cereal crops at the time when the broadleaf weeds had emerged.
• Classification accuracy was lower for detection of grass weeds in cereal crops and broadleaf weeds in broadleaf crops, and these scenarios were not pursued for that reason.  

• Commercial viability has been difficult to demonstrate.
• Field trials demonstrated that the H Senor was able to accurately classify annual ryegrass in several broadleaf crops, including canola, faba bean, lentil and field pea.
• Other grass weeds such as brome grass and wild oats were also correctly identified as grasses in these crops.
• Broad leaf weeds were accurately classified in cereal crops, however this was more difficult due to the overlapping nature of the cereal crops at the time when the broadleaf weeds had emerged.
• Classification accuracy was lower for detection of grass weeds in cereal crops and broadleaf weeds in broadleaf crops, and these scenarios were not pursued for that reason.  

The objective of this GRDC / SPAA funded project is to increase the level of adoption of PA 'beyond guidance' by broadacre farmers. The project specifically aims to increase the level of adoption of variable rate (VR) by growers in the project to 30% by 2013.This goal will be achieved by demonstrating how to use PA tools to growers at a regional level and by increasing the skills of growers and industry in PA to a level where they can then use PA tools in their farming systems to achieve economic, environmental and social benefits.

The objective of this GRDC / SPAA funded project is to increase the level of adoption of PA 'beyond guidance' by broadacre farmers. The project specifically aims to increase the level of adoption of variable rate (VR) by growers in the project to 30% by 2013.This goal will be achieved by demonstrating how to use PA tools to growers at a regional level and by increasing the skills of growers and industry in PA to a level where they can then use PA tools in their farming systems to achieve economic, environmental and social benefits. The grower mentioned the usefulness of the Greenseeker map to provide a visual overview of how the crop was growing and where nitrogen was being used. This has helped with understanding how different soil types perform and allow extraction of nitrogen during a growing season.

Farmers in this situation can be confident that since the NDVI and the N sensor measurements complement one another, the low producing areas can be farmed differently to the high producing areas. In order to maximise profits on farm, growers ought to consider varying inputs according to productive potential of their paddocks. Soil types vary considerably throughout the Upper North and can also be quite variable within paddocks. Considerable cost benefits can be made over time by using variable rate technology in paddocks with high variability. While the upfront costs might be large, the investment quickly pays itself off, giving farmers increased confidence in using the technology. Some training is often required when using new monitors and equipment however regular use and familiarity of products improves ease of operation.

Variable rate fertiliser application has a real and profitable place in the Mallee when combined with good paddock science. The precision technology gives the ability to examine the yield data for many effects including other possible influencing factors such as elevation, slope aspect or orientation to the sun, and probably many others. Grain yield was recorded continuously during harvest and logged against GPS position. From the resultant yield map it was possible to isolate treatment strips and EM38 zones within these strips and determine the yield for individual strips and therefore treatments, and also where these treatments intersect with the paddock zones. Trial was statistically analysed and Table 1 above shows there was no significant difference between treatments.

Variable rate fertiliser application has a real and profitable place in the Mallee when combined with good paddock science. The precision technology gives the ability to examine the yield data for many effects including other possible influencing factors such as elevation, slope aspect or orientation to the sun, and probably many others.

• Commercial viability has been difficult to demonstrate.
• Field trials demonstrated that the H Senor was able to accurately classify annual ryegrass in several broadleaf crops, including canola, faba bean, lentil and field pea.
• Other grass weeds such as brome grass and wild oats were also correctly identified as grasses in these crops.
• Broad leaf weeds were accurately classified in cereal crops, however this was more difficult due to the overlapping nature of the cereal crops at the time when the broadleaf weeds had emerged.
• Classification accuracy was lower for detection of grass weeds in cereal crops and broadleaf weeds in broadleaf crops, and these scenarios were not pursued for that reason.  

The objective of this GRDC / SPAA funded project is to increase the level of adoption of PA 'beyond guidance' by broadacre farmers. The project specifically aims to increase the level of adoption of variable rate (VR) by growers in the project to 30% by 2013.This goal will be achieved by demonstrating how to use PA tools to growers at a regional level and by increasing the skills of growers and industry in PA to a level where they can then use PA tools in their farming systems to achieve economic, environmental and social benefits.

The objective of this GRDC / SPAA funded project is to increase the level of adoption of PA 'beyond guidance' by broadacre farmers. The project specifically aims to increase the level of adoption of variable rate (VR) by growers in the project to 30% by 2013.This goal will be achieved by demonstrating how to use PA tools to growers at a regional level and by increasing the skills of growers and industry in PA to a level where they can then use PA tools in their farming systems to achieve economic, environmental and social benefits.

The objective of this GRDC / SPAA funded project is to increase the level of adoption of PA 'beyond guidance' by broadacre farmers. The project specifically aims to increase the level of adoption of variable rate (VR) by growers in the project to 30% by 2013.This goal will be achieved by demonstrating how to use PA tools to growers at a regional level and by increasing the skills of growers and industry in PA to a level where they can then use PA tools in their farming systems to achieve economic, environmental and social benefits.

The Victorian Mallee has experienced more than a decade of below average rainfall (including 3 droughts). This has placed an enormous amount of financial pressure on the farm businesses. By reducing up-front expenditure on inputs such as fertiliser, provided there is no yield penalty, delayed applications can significantly reduce risk. Delayed applications also allow for rates/inputs to be refined to suit the season.

Agronomists and farmers throughout the Glenthompson region appreciate the presence of potassium deficinancy, however little has been done to understand the spatial distribution across the landscape. This demonstration trial has utilised a range of spatial technologies, strategic soil sampling and local knowledge to determine opportunites for variable rate potash.

The Rand site was sown on the 12th May 2010 with 8 treatments of various amounts of seed and P fertilizer. The variety was Lincoln and the sowing fertilizer was MAP. The site had grown canola in 2009 and had been summer sprayed for weeds. It was direct drilled into the canola stubble. The site had an N rich strip of 50hg/.ha of N applied about 5 weeks prior to the assessment in July 2010.

The objective of this GRDC / SPAA funded project is to increase the level of adoption of PA 'beyond guidance' by broadacre farmers. The project specifically aims to increase the level of adoption of variable rate (VR) by growers in the project to 30% by 2013.This goal will be achieved by demonstrating how to use PA tools to growers at a regional level and by increasing the skills of growers and industry in PA to a level where they can then use PA tools in their farming systems to achieve economic, environmental and social benefits.

The objective of this GRDC / SPAA funded project is to increase the level of adoption of PA 'beyond guidance' by broadacre farmers. The project specifically aims to increase the level of adoption of variable rate (VR) by growers in the project to 30% by 2013.This goal will be achieved by demonstrating how to use PA tools to growers at a regional level and by increasing the skills of growers and industry in PA to a level where they can then use PA tools in their farming systems to achieve economic, environmental and social benefits. The grower mentioned the importance of correctly identifying the GPS coordinate locations to get reliable soil type information. Yield maps provided showing areas of the paddock with high and low potential assisted this process. Soil analysis verified the trends produced by the yield map.

It was hypothesised that the benefits of the growth regulants would be related to the amount of crop growth. It was expected they would have a greater beneficial impact where the crop was identified as being thick and bulky, according to the aerial imagery and that the effects would be less or negative where crop growth was less and possibly already constrained by other factors such as nutrition. If this hypothesis were correct, crop imagery could be used to target growth regulants to areas where a positive response is most likely. There was lower NDVI at the southern end of Ronnies paddock, and this is where a negative yield response was observed, indicating the hypothesis may be correct, however the link is not strong.

Yield differences between treatments were highly significant. Differences observed between the growth regulant treatments and nil were up to 0.5t/ha along the trial strip. The yield differences were not significant at the northern end of the trial strips, this is where localised waterlogging was observed in the trial and crop growth was reduced before the growth regulants were applied, as observed in the crop spec data.

As the climate is changing to warmer conditions and the atmosphere contains more CO2 (also called CO2 fertilisation), crops grow more quickly and mature earlier. It is important to be aware of these effects on crops so that the risk of frost or heat shock reducing crop yield can be better managed. Even though the climate is warming, it is not clear whether the occurrences of frosts are reducing. In fact, there is anecdotal evidence that frost severity and frequency are not changing, which could be because there is reduced moisture in the atmosphere. If this is the case, it will have implications for sowing time and variety choice. Through the use of PA and VR, growers are able to sow different varieties on more frost prone areas. Without this technology, growers would either need to sow different zones separately, reducing efficiencies, or treat the paddock as a whole, which may see reduce yields and risk in various zones. To demonstrate the advantages of this strategy, over the past two seasons, BCG and SPAA have undertaken paddock scale trials. Two demonstrations were undertaken to compare two varieties of different maturity at two different sowing times.

During 2010, trials were carried out on a number of paddocks near Dookie, Vic and Yarrawonga, VIC to explore the relationship between in-season crop reflectance, crop biomass and crop nitorgen content. In-season crop reflectance was measured using CropCircle snesors, which use NDVI (Normalised Difference Vegetation Index). The ultimate goal is to assess the suitability of the data to assist in making decisions on crop nitrogen requirements as the season unfolds. In this reprt one of the paddocks at Dookie is used as an example.

Picking out the ryegrass from the cereal crops in spring did not prove to be effective.The ryegrass tiller count data was converted into a contoured map and divided into three zones for each paddock. Once again we compared the manually drawn zones and found a relatively good correlation but not good enough to mplement patch ryegrass management with confidence. A dual boom A more detailed analysis of previous yield maps when farmers determine their ryegrass patches would certainly improve the accuracy of their zone drawings. Any farmer wanting to implement patch ryegrass management should also consider btaining imagery at a time of the year when ryegrass is more visible such as prior to sowing or around crop establishment as these maps would more accurately define the weed patches with less crop effect.

When comparing the soil pH map against the EM38 map we found that there were some similar trends, especially in the alkaline zone in the south east of the paddock which lines up partially to the high EM38 readings (clay soil). However, the difference between the two maps is significant in terms of creating lime The greatest lime savings through the soil pH mapping approach have occurred on paddocks which are considered to be in a maintenance phase. That is, paddocks which have had a reasonable lime history and only require strategic lime applications. Savings on these paddocks tends to range between 35 – 65%. Other farmers who have previously implemented variable rate lime are also using the grid soil pH mapping service to measure how effective the variable rate application was. The subsequent map is then used to apply a maintenance rate of lime.

Soil testing prior to sowing revealed that chemical properties were highly variable between zones (see Table 1 in attached Trial report). For example, over a distance of 100 m between the midslope and the swale, soil organic carbon levels nearly doubled from 0.22 to 0.40. Topsoil phosphorus levels also increased from just above the marginal level of 20 (Colwell) on the midslope to nearly 30 (Colwell) on the swale. 

Averaged across all sampling locations, protein levels increased by 0.6% in the 50kg/ha of urea treatment and by 1% in the 100kg/ha of urea treatment relative to the nil in-crop fertiliser strip. This increase in protein was enough to improve grain quality from Australian Standard White (ASW) to Australian Premium White (APW) at four of the six sampling locations. On the day of harvest at the nearest grain receiver site (Yelta), the ASW silo cash price was $127/t and APW was $158/t. At a urea cost of $620/t, the application of 50 kg/ha of urea costs $31/ha (fertiliser cost only). Therefore, at the four sites where grain quality was improved, a small profit was made (15 – 55 $/ha) with the application of 50 kg/ha of fertiliser. However, at one third of the protein sampling sites, a loss of $31/ha was incurred as there was no difference in grain quality between the treatments.

While all soil types responded to nitrogen, the response and profitability of nitrogen application varied between soil zones. Using the Urea/SOA treatment as an example, for the loam and swale soil types, the additional profit compared to no in-season N was approximately $50/ha. However, applying additional fertiliser to the dune soil was twice as profitable (approximately $100/ha). As input costs were standard across all soil types (i.e. an N Rich strip with the equivalent rate of N was used for all treatments), profitability was driven by the increase in yield. As fertiliser efficiency was below its potential for the loam and swale soils (and at potential for the dune soil), it is possible that the same yield response could have been achieved with less fertiliser, therefore these two soil zones could have been more profitable if less nitrogen was applied.

• Commercial viability has been difficult to demonstrate.
• Field trials demonstrated that the H Senor was able to accurately classify annual ryegrass in several broadleaf crops, including canola, faba bean, lentil and field pea.
• Other grass weeds such as brome grass and wild oats were also correctly identified as grasses in these crops.
• Broad leaf weeds were accurately classified in cereal crops, however this was more difficult due to the overlapping nature of the cereal crops at the time when the broadleaf weeds had emerged.
• Classification accuracy was lower for detection of grass weeds in cereal crops and broadleaf weeds in broadleaf crops, and these scenarios were not pursued for that reason.  

• Commercial viability has been difficult to demonstrate.
• Field trials demonstrated that the H Senor was able to accurately classify annual ryegrass in several broadleaf crops, including canola, faba bean, lentil and field pea.
• Other grass weeds such as brome grass and wild oats were also correctly identified as grasses in these crops.
• Broad leaf weeds were accurately classified in cereal crops, however this was more difficult due to the overlapping nature of the cereal crops at the time when the broadleaf weeds had emerged.
• Classification accuracy was lower for detection of grass weeds in cereal crops and broadleaf weeds in broadleaf crops, and these scenarios were not pursued for that reason.  

The objective of this GRDC / SPAA funded project is to increase the level of adoption of PA 'beyond guidance' by broadacre farmers. The project specifically aims to increase the level of adoption of variable rate (VR) by growers in the project to 30% by 2013.This goal will be achieved by demonstrating how to use PA tools to growers at a regional level and by increasing the skills of growers and industry in PA to a level where they can then use PA tools in their farming systems to achieve economic, environmental and social benefits.

The objective of this GRDC / SPAA funded project is to increase the level of adoption of PA 'beyond guidance' by broadacre farmers. The project specifically aims to increase the level of adoption of variable rate (VR) by growers in the project to 30% by 2013.This goal will be achieved by demonstrating how to use PA tools to growers at a regional level and by increasing the skills of growers and industry in PA to a level where they can then use PA tools in their farming systems to achieve economic, environmental and social benefits.

A yield increase is apparent in figures 2 (circled) and 3 (see attached Trial report) at the ripped only treatment in the eastern plot, however this is not seen in the western plots and does not stand out in graph 1. Results presented in Graph 1 generally show only slight differences with the greatest response observed in the gypsum only treatment on the eastern plots. This soil was highly sodic at 10cm and as could be expected the gypsum would make a quicker impact on sodicity, thus allowing for improved drainage that may have improved crop production. Gypsum would take longer to impact on subsoil sodicity and may show a response after several years of data are collected

The objective of this GRDC / SPAA funded project is to increase the level of adoption of PA 'beyond guidance' by broadacre farmers. The project specifically aims to increase the level of adoption of variable rate (VR) by growers in the project to 30% by 2013.This goal will be achieved by demonstrating how to use PA tools to growers at a regional level and by increasing the skills of growers and industry in PA to a level where they can then use PA tools in their farming systems to achieve economic, environmental and social benefits.

The results indicate that the land owner may see economic benefit (as per Gross Margin) from the uptake of variable rate technology in paddocks with varying soil types such as this trial paddock.

The objective of this GRDC / SPAA funded project is to increase the level of adoption of PA 'beyond guidance' by broadacre farmers. The project specifically aims to increase the level of adoption of variable rate (VR) by growers in the project to 30% by 2013.This goal will be achieved by demonstrating how to use PA tools to growers at a regional level and by increasing the skills of growers and industry in PA to a level where they can then use PA tools in their farming systems to achieve economic, environmental and social benefits. Fertiliser rates were selected after discussing with farmer that the lower yielding zones would never produce yields comparable to medium and high yielding zones, due to the low fertility and poor water and nutrient holding capacity of the soil. The farmer believed that that fertiliser was being wasted on the sand zones of his farm, and wanted to see if cutting fertiliser rates would be detrimental to yield in this zone.

NDVI satellite imagery was collected to establish dry matter production difference in 2011. The crop was sown as brown manure peas and therefore there were no yield results. Yield data will be collected in 2012 as part of the trial program.

In 2010, at Wass brothers’ property in Nyngan, a very wet season caused the normally poor performing areas to perform just as well as the good areas in this paddock regardless of additional nitrogen application. In other paddocks across the farm, there were significant yield differences across each paddock. However, in the previous years Wass’s have always wanted to apply N and have had mixed results possibly because of soil and water variability. Most seasons they have areas that burnout and areas which could use a higher application of N and give a better yield. In 2011, Hayden intended to try to understand what was driving these differences, and yield maps are a way of spatially determining different zones. Therefore, this trial was set up.

The objective of this GRDC / SPAA funded project is to increase the level of adoption of PA 'beyond guidance' by broadacre farmers. The project specifically aims to increase the level of adoption of variable rate (VR) by growers in the project to 30% by 2013.This goal will be achieved by demonstrating how to use PA tools to growers at a regional level and by increasing the skills of growers and industry in PA to a level where they can then use PA tools in their farming systems to achieve economic, environmental and social benefits.

Because there was no effect of fertiliser on the yield it meant any potential benefit from VR was greatly reduced. This can likely be put down to the high levels of soil N recorded at the start of 2011. The trial does suggest that this paddock was over fertilised as there was no significant loss of yield from a 40kg/ha reduction in urea in either trial.

There was no response shown to varying nitrogen rates across the 4 yield zones on this paddock. This could be due to the adequate levels of nitrogen present before seeding as show in the 0-10 and 10-60cm soil test results and also reinforced with the NDVI esults, this would stem from good fertiliser history and rotation from the Harkness’s.This could give confidence that rates could be reduced on the lower yielding areas without risk of impacting on yield and consequently increasing gross margins across the paddock by saving wasted inputs on poorer performing soil types and leaving rates higher on better performing areas to maximise returns when seasons are more favourable.

The trial has indicated that the application of the high rate UAN (140L/ha) over the entire paddock still proved to be a more profitable than applying the low rate over the entire paddock, however the overall gross margin benefits are not as visible due to the low yielding sand zone only occupying 9ha (13%) of the paddock. However these results confirm that the adoption of PA is an economically sound decision, indicating that the grower could make a saving of $48/ha if that extra nitrogen was not applied to the low yielding area via the use of variable rate fertiliser application. The trial results have solidly indicated that the overall gross margin would be profitably affected not having to spend the extra $62/ha on fertiliser. The gain of $53/ha in the high yield zone with high rate N (140L) is a substantial increase in profit over the low rate (60L) showing there is benefits to adding N in these zones.

Applying nitrogen on the sand soil zones has consistently delivered sound economic returns. The highest ROI achieved in the sand west zone ($123.20) and sand east zone ($37.85) was in the 115 and 130kg urea/ha treatments respectively. Applying nitrogen in the clay zone however failed to deliver an economic eturn and this has been realised in this paddock before. This trial highlighted that variable rate nitrogen is highly applicable to paddocks with high degrees of spatial variability in order to maximise yield and/or return on investment in individual paddock zones.

The NDVI map has a strong correlation to the final yield, which indicates that the variable rate nitrogen did not achieve the goal of levelling out crop yield. This may be due to the dry finish to the season. This result does however confirm the concept of using in-season satellite imagery (typically late July, early August) o manage final yield through a variable rate intervention.

The objective of this GRDC / SPAA funded project is to increase the level of adoption of PA 'beyond guidance' by broadacre farmers. The project specifically aims to increase the level of adoption of variable rate (VR) by growers in the project to 30% by 2013.This goal will be achieved by demonstrating how to use PA tools to growers at a regional level and by increasing the skills of growers and industry in PA to a level where they can then use PA tools in their farming systems to achieve economic, environmental and social benefits.

With good soil phosphorus reserves ‘P replacement’ is a safe and viable option that will help farmers manage their inputs and maintain soil fertility in high yielding areas of the paddock.

The objective of this GRDC / SPAA funded project is to increase the level of adoption of PA 'beyond guidance' by broadacre farmers. The project specifically aims to increase the level of adoption of variable rate (VR) by growers in the project to 30% by 2013.This goal will be achieved by demonstrating how to use PA tools to growers at a regional level and by increasing the skills of growers and industry in PA to a level where they can then use PA tools in their farming systems to achieve economic, environmental and social benefits.

The objective of this GRDC / SPAA funded project is to increase the level of adoption of PA 'beyond guidance' by broadacre farmers. The project specifically aims to increase the level of adoption of variable rate (VR) by growers in the project to 30% by 2013.This goal will be achieved by demonstrating how to use PA tools to growers at a regional level and by increasing the skills of growers and industry in PA to a level where they can then use PA tools in their farming systems to achieve economic, environmental and social benefits.

On upper Eyre Peninsula few people use EM38 mapping to support the use of VRT. Currently yield maps and paddock knowledge are the most commonly used methods for determining production zones within paddocks. The demonstrations show that increasing fertiliser rates did not have an effect on yields but yields did follow EM38 mapped soil zones.

Additional N did not have an economic advantage in this season. Paddock and demonstration site suffered from the dry 6 weeks period mid season. Stem rust devastated potential 3 ton crop reducing yield by 50%. This combination resulted in poor yields, low test weight and high screenings. Good soil showed improved protein over deeper sand regardless of extra N applied. Extra N marginally improved protein but not enough economically to apply more.

• Additional N did not have an economic advantage in this season.
• Paddock and demonstration site suffered from the dry 6 weeks period mid season.
• Stem rust devastated potential 3 ton crop reducing yield by 50%. This combination resulted in poor yields, low test weight and high screenings.
• Good soil showed improved protein over deeper sand regardless of extra N applied.
• Extra N marginally improved protein but not enough economically to apply more.

Birchip: The site was located in a paddock with moderate subsoil limitations (40 to 70cm layer had chloride reading of 1800ppm, and an EC of 0.6dS/m). The site had a previous legume history and was high in soil available N (prior to sowing the soil contained 230 kg available N/ha to 1m depth).

Kooloonong: The site was located on a Mallee land system, on a flat to sloping rise with a sandy loam soil to a depth of 40cm and at greater depth a sandy clay loam to at least 130cm. The soil has no subsoil limitations but because it is light and sandy, leaching of nutrients can be a problem. 2010 had a growing season rainfall of 329mm (decile 10).

The objective of this GRDC / SPAA funded project is to increase the level of adoption of PA 'beyond guidance' by broadacre farmers. The project specifically aims to increase the level of adoption of variable rate (VR) by growers in the project to 30% by 2013.This goal will be achieved by demonstrating how to use PA tools to growers at a regional level and by increasing the skills of growers and industry in PA to a level where they can then use PA tools in their farming systems to achieve economic, environmental and social benefits.

DBA Aurora and Mace showed similar ability to compete with weeds in 2014. Using higher seeding rates improved weed control. Increased crop seeding rate resulted in reduced crop screenings in 2014

This study has shown that substantial savings can be made with the use of this technology. It essntially allows the farmer to have a zero tolerance on summer weeds, without having to apply economical thresholds. The study also identified that the savings will not always result. If there is consistently, 60-70% ground cover, the economical returns may not be as great. This is due to the way the technology is designed. Often in relatively high densities, two sensors would come on to spray one weed, hence twice the amount of water. This is most likely the cause of more water being used in the areas with the High density. Overall, the Weed Seeker technology has the potential to reduce chemical costs and increase the efficiency of spray applications.

• Commercial viability has been difficult to demonstrate.
• Field trials demonstrated that the H Senor was able to accurately classify annual ryegrass in several broadleaf crops, including canola, faba bean, lentil and field pea.
• Other grass weeds such as brome grass and wild oats were also correctly identified as grasses in these crops.
• Broad leaf weeds were accurately classified in cereal crops, however this was more difficult due to the overlapping nature of the cereal crops at the time when the broadleaf weeds had emerged.
• Classification accuracy was lower for detection of grass weeds in cereal crops and broadleaf weeds in broadleaf crops, and these scenarios were not pursued for that reason.  

The objective of this GRDC / SPAA funded project is to increase the level of adoption of PA 'beyond guidance' by broadacre farmers. The project specifically aims to increase the level of adoption of variable rate (VR) by growers in the project to 30% by 2013.This goal will be achieved by demonstrating how to use PA tools to growers at a regional level and by increasing the skills of growers and industry in PA to a level where they can then use PA tools in their farming systems to achieve economic, environmental and social benefits.

• Commercial viability has been difficult to demonstrate.
• Field trials demonstrated that the H Senor was able to accurately classify annual ryegrass in several broadleaf crops, including canola, faba bean, lentil and field pea.
• Other grass weeds such as brome grass and wild oats were also correctly identified as grasses in these crops.
• Broad leaf weeds were accurately classified in cereal crops, however this was more difficult due to the overlapping nature of the cereal crops at the time when the broadleaf weeds had emerged.
• Classification accuracy was lower for detection of grass weeds in cereal crops and broadleaf weeds in broadleaf crops, and these scenarios were not pursued for that reason.  

• Commercial viability has been difficult to demonstrate.
• Field trials demonstrated that the H Senor was able to accurately classify annual ryegrass in several broadleaf crops, including canola, faba bean, lentil and field pea.
• Other grass weeds such as brome grass and wild oats were also correctly identified as grasses in these crops.
• Broad leaf weeds were accurately classified in cereal crops, however this was more difficult due to the overlapping nature of the cereal crops at the time when the broadleaf weeds had emerged.
• Classification accuracy was lower for detection of grass weeds in cereal crops and broadleaf weeds in broadleaf crops, and these scenarios were not pursued for that reason.