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Feed Grain Partnership Project Report

Harvest Grain Receival Segregation

September 2013

A project completed for the Feed Grain Partnership Denis McGrath – Seedvise

and John Spragg – JCS Solutions

Denis McGrath Seedvise Pty Ltd PO Box 8178, Newtown, Vic 3220, Australia T: 0408 688 478 | F: 03 4206 7015 E: [email protected] John Spragg JCS Solutions Pty Ltd 32 Grantham Cres, Berwick Vic 3806, Australia T: 0402 831 843 | F: 03 9769 7174 E: [email protected]

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FGP Project Report -‐ Harvest Grain Receival Segregation

Harvest Grain Receival Segregation Project Report

EXECUTIVE SUMMARY This project commissioned by the Feed Grain Partnership evaluated the practicalities of establishing alternate feed grain receival standards at grain receival sites in Australia. Grain Grower & Grain Accumulator Findings • Continued use of GTA segregations for H2 and APW milling wheat realises

the greatest value for growers and grain marketers. • Trial results demonstrated the potential to provide growers and feed grain

end users additional value through segregating ASW, SFW and FED1 grades into high and low protein grades with linked payment differentials.

• Segregation of wheat into high and low Pig DE and/or Broiler AME would require greater variation than seen within this project.

• Grain accumulators have capacity using protein to segregate grain at delivery and define energy content post receival and segregation to allow targeted marketing to monogastric or ruminant markets.

• Grain accumulators would be encouraged to offer specialist feed grain segregations if these segregations attracted more grain to their grain sites.

• The use of AusScan tests as a basis to segregate grain is currently limited due to these tests not being accredited by the National Measurement Institute (NMI).

End User Findings • Based on the trial site, existing GTA receival standards provide a mechanism

that segregates grain based on protein content. • Test weight within the range of samples tested (71-‐83kg/hl) was found not

to be correlated with available energy. • Where grain is segregated based on GTA receival standards, nutritionists

place greater significance on adjusting feed formulations for grain protein content.

• Assuming there is sufficient variation, nutritionists formulating feeds for monogastric feeding recognise Pig Faecal DE, Broiler AME and protein as being the most important measures of grain value and potential for segregation. Ruminant nutritionists place most emphasis on total starch, protein and NDF, with Cattle ME having a lesser relevance.

• Within an “average” grain production year, there is reduced variation in wheat quality for grain received at one site as measured using the AusScan NIR predictions for Pig faecal DE, Broiler AME and Cattle and Sheep ME.

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• A desktop exercise provided limited support in segregating ASW and lower grades into high and low protein and high and low Pig DE and/or Broiler AME.

• The data for starch and NDF identified a reasonable level of variation across wheat received. Ruminant nutritionists have identified these two parameters as offering greater potential for wheat segregation than Cattle or Sheep ME.

• The dairy and beef industries should further evaluate the use of total starch and NDF as well as grain fermentation to better define grain quality for ruminant feeding.

Recommendations 1. Use of the AusScan technology is best suited for:

• Use in conjunction with grain protein testing and segregation. • Post receival analysis for Pig DE and/or Broiler AME and grain marketing

to end use market segments. • Short supply chain use – grower to end users or via a trading agent. • Fine tuning of feed formulations to take account of grain variation from

year to year and between different growing regions.

2. Information from this project, together with other PGLP research findings need be written into fact sheets. This material being for provision to the feed grain supply chain to promote the AusScan technology use in grain segregation and short supply chain grain marketing arrangements.

3. A pilot study to be implemented with a regional grain receival operator with an NIR instrument being located at sample receival with real time data used to implement a segregation using the AusScan calibrations in conjunction with GTA receival standards.

4. The dairy and beef feedlot industries review the merits of completing research work on grain starch fermentation and its impact on animal performance. The intent is to develop potential rapid analysis systems to better define grain quality for ruminant feeding.

5. Consult with NMI to assess use of grain receival and grower payments utilising AusScan technology.

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INTRODUCTION The Feed Grain Partnership (FGP) in 2012 conducted an Animal Nutritionists and Plant Breeder Forum to consider a number of factors relating to the supply of grain to the feed grain market sector. Participants of this forum questioned the ability of the current Grain Trade Australia (GTA) grain receival standards to meet the needs of Australia’s domestic feed industry. This project commissioned by the Feed Grain Partnership evaluates the practicalities of establishing alternate feed grain receival standards at strategically located grain receival sites in key feed producing regions of Australia. Project Objectives

1. To capture the grain quality measurements in wheat samples delivered into a major grain receival site.

2. Complete a desk top analysis to segregate the grain received from these wheat samples using revised feed grain quality standards.

3. Value the wheat based on the revised feed grain quality standards. 4. Compare the grower and end user value of the actual and desk top grain

segregations. 5. If the results of (4) are positive identify any practical limitations

associated with establishment of the revised feed grain standards as industry feed grain standards.

METHODOLOGY This project was run with the co-‐operative support of:

• Australian Bulk Alliance, Grain Receival Sites Contact -‐ Aaron Matheson • Southern Quality Producers Co-‐operative -‐ Contact: Robert Ford • Southern Quality Producers Grain Pty Ltd -‐ Contact: Ben Fleahy

1. Sample collection ABA Werneth grain receival site collected 549 individual wheat delivery samples from the 2012 harvest. Data measured and recorded for each sample was:

• Grade • Variety • Tonnes delivered • Test weight • Protein • Moisture • Screenings

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2. AusScan Testing

Samples were sent to NSW DPI Wagga Wagga Agriculture Research Institute where each sample was scanned by NIR FOSS 6500 and AusScan results predicted. 3. Data collation

Results were collated based on both receival site testing and AusScan testing, with results graphed and summary findings identified. Correlation between GTA and AusScan results were completed. 4. Nutritionist Evaluation

The collated results report was provided to selected Australia animal nutritionists with a series of questions asked seeking their opinion on the relevance of the GTA versus AusScan test results. Evaluation included assessment of the relative value of wheat segregated by either the GTA or AusScan test results. 5. Desktop Analysis

Data was used to complete an analysis looking at alternate segregation options and the relative value compared to the existing GTA segregations.

RESULTS

Sample Testing Samples collected came from 549 individual loads of wheat representing 15,970 tonnes.

1. Quality based on existing segregation using GTA receival standards Table 1. Average segregation results using GTA receival standards and AusScan predictions Grade Average

No. Deliveries

Delivered Tonnes

Test Weight Kg/hl

Protein % as is

Moisture %

Screenings %

Broiler AME MJ/kg

Pig DE MJ/kg

Cattle ME

MJ/kg

Starch %

H2 24 715 78.2 12.0 12.5 3.1 13.0 14.2 12.8 70.1 APW1 109 3547 79.3 11.0 11.2 2.3 12.8 14.1 12.8 72.7 ASW1 222 6340 78.7 9.3 10.9 2.3 12.9 14.0 12.7 72.0 SFW1 160 5199 76.5 8.5 10.6 2.7 13.0 14.0 12.5 72.3 FED1 5 169 74.1 9.2 11.9 2.2 12.5 13.9 12.6 73.3

AusScan Predicted

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Table 2. 2012 GTA wheat receival standards

Grade Test Weight Min Kg/hl

Protein Min %

N X 5.7 @ 11% Moisture Basis

Moisture Max %

Screenings Max %

H2 74.0 11.5 12.5 5.0 APW1 74.0 10.5 12.5 5.0 ASW1 74.0 N/A 12.5 5.0 SFW1 70.0 N/A 12.5 10.0 FED1 62.0 N/A 12.5 15.0

The largest difference between the grades was protein, with higher milling grades having higher protein. Due to all wheat received at the Werneth site being high test weight and low screenings, the major GTA parameter driving the segregations was protein content. Figures shown below provide individual sample results for each wheat segregation.

All deliveries of Revenue wheat variety were segregated into SFW1 or FED1 as this is a feed wheat variety. All other wheat varieties were milling wheat varieties and could be segregated into any grade. For the grades receiving the majority of samples (APW1, ASW1 and SFW1) total starch is not related to grade. There is a trend for H2 and FED1 wheat to be lower in total starch, although from a smaller number of deliveries.

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The average test weight for SFW1 and FED1 is lower than higher milling grades.

There is no consistent difference in available energy between grades.

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2. Quality based on all wheat samples i.e. no GTA grade segregation Screenings variation – there was a low level of screenings found, typical of heavier grain with minimal small grains.

Test Weight variation – there were few light weight grains, only two deliveries were below 70kg/hl.

Crude Protein variation was large from 6.5 to 13%. Results are lower than expected; data is supported through use of both AusScan protein measurement and the on-‐site NIR that also predicted a large variation in protein.

y = 1.2714x -‐ 2.7544 R² = 0.91596

5.00

7.00

9.00

11.00

13.00

15.00

17.00

19.00

5 7 9 11 13 15 17 19

AusScan Result % DMB

Site result % as is

Crude Protein Correlation AusScan vs Site

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Commentary relating to the growing conditions and resulting low protein results is:

• Werneth is traditionally a low protein area. • Protein level of the grain was impacted by the previous 2 wet summers

using up available soil nitrogen. • Some growers scrimping on nitrogen input costs. • No pulse crops grown in south west Vic. • Reasonable yields diluting the grain protein level.

Total Starch variation – 10% spread matches the PGLP database, however average is 72.2% and higher than PGLP average 66.1%. The higher starch content is possibly the result of a better finish in the growing region, also indicated by lower protein and typically higher starch grain. The PGLP database includes low test weight, high screenings and weather damaged wheat samples.

NDF Variation -‐ the range in NDF is 8.9 – 15.6% on DMB. The AusScan reference sample database includes more high NDF samples, with the top 20% of samples being above 18.6%. The lower NDF levels correspond with the higher starch

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results.

3. AusScan measurements across all wheat samples ME Cattle variation within 0.7MJ/kg is low, calibration accuracy +/-‐ 0.34MJ/kg. Average is 12.7MJ/kg and almost identical to PGLP wheat cattle ME average at 12.72MJ/kg.

Pig Faecal DE variation 0.8MJ/kg, also relatively small variation, calibration accuracy +/-‐ 0.27MJ/kg Average is 14.0MJ/kg which is above the PGLP wheat average of 13.85MJ/kg.

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Broiler AME variation is greater at almost 2MJ/kg, calibration accuracy +/-‐ 0.40MJ/kg Average is 12.9MJ/kg and just above the PGLP average at 12.7MJ/kg.

4. Relationships between measured data Test weight vs starch -‐ there is no relationship. Test weights are all above 70kg/hl. In PGLP research it was found that only lighter test weight grains had lower starch – these were frost damaged and/or high screenings wheat samples. PGLP found that when test weight falls below 62kg/hl there is a relationship between test weight and starch content. Test weight of 62kg/hl co-‐incides with the GTA standard for FED1 wheat. All the wheat samples in this project were well above these test weight levels.

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Test Weight vs Protein – there is seen to be no relationship

Protein vs Starch The data shows no relationship and may be due to all the samples being high in starch. Typically low test weight and high screenings corresponds with increased protein and lower starch content. There were none of these types of grains received during 2012.

Cattle ME vs Starch – there is no relationship and agrees with previous PGLP findings.

5. Varietal Differences

Average measurements for each variety where greater than 1,000 tonnes received.

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Segregation Tonnes Test

Weight kg/hl

Protein %

Moisture %

Screenings %

Broiler AME MJ/kg

Pig DE

MJ/kg

Cattle ME

MJ/kg

Starch %

Bolac 4520 78.5 10.2 10.7 2.6 12.9 14.1 12.7 71.2 Derrimut 1295 78.6 9.6 11.3 2.0 12.7 14.0 12.8 71.5 Kelalac 1308 79.4 9.9 10.9 2.2 12.8 14.1 12.8 73.2 Lincoln 1470 80.1 10.1 11.4 1.9 13.0 14.2 12.9 73.4 Revenue 4013 76.2 8.4 10.6 2.8 13.0 14.0 12.5 72.8 Yitpi 1336 78.4 9.7 10.8 2.4 12.9 14.1 12.8 72.8

Available energy results are not greatly different between varieties. Revenue is a feed winter wheat variety and provided a marginally lower test weight and protein content. The other varieties are all milling wheats. Lincoln provided the highest test weight and starch content together with higher protein, Broiler AME and Pig DE. It is however a milling variety with declining production volume due to increased pre-‐harvest sprouting in wet harvest years.

Nutritionist Evaluation Feedback on the collated results and their evaluation was received from the following nutritionists:

• Tim Harrington – Ridley AgriProducts • Greg Connors – Ridley AgriProducts • David Henman – Rivalea • Ian Sawyer – Feedworks • Ken Bruerton – Protea Park Nutrition • Steve Little – C & S Little • Tony Edwards – ACE Consulting • Todd Middlebrook – Weston Animal Nutrition • Robert van Barneveld – Barneveld Nutrition

A summary of responses to each of the questionnaire questions is provided below. Some nutritionist working in both ruminant and monogastric nutrition provided responses for both animal feeding groups. Question 1. Would you alter feed formulations based on using each of the existing grade segregations? Existing segregation based on GTA receival standards:

Grade Average Delivered Tonnes

Test Weight Kg/hl

Protein %

Moisture %

Screenings %

H2 715 78.2 12.0 10.2 3.1 APW1 3547 79.3 11.0 11.2 2.3 ASW1 6340 78.7 9.3 10.9 2.3 SFW1 5199 76.5 8.5 10.6 2.7

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FED1 169 74.1 9.2 11.9 2.2 Responses:

• Protein – yes, all other measures – no. • Only on crude protein. • The protein differentials alone would require us to reformulate all our feeds

as a 1% change in protein may mean a large shift in amino acid specifications which is the key focus for us.

• Yes recoup value from protein differential, especially during summer dairy feeding.

• Yes. • Yes especially formulating for higher protein cattle feeds. • Yes on protein, all these samples would be ascribed "normal" energy values. • Yes but only in relation to protein. • Yes – protein differences would be enough to facilitate a change.

Question 2. What value difference exists between the five grades based on ASW1 being $260/tonne and all other raw materials being available at current supply prices. Average of all responses is shown below, all nutritionists provided a value based on the variation in protein between grades. Comments indicated that the major difference in value came from protein content.

GTA Quality Grades

Nutritionists ‘Feed” Grain

Value

Werneth Actual Grower Price

2012/13 Harvest H2 $272.20 $282

APW1 $267 $277 ASW1 $260 $260 SFW1 $256.40 $247.50 FED1 $257.40 $229.50

Questions 3. The wheat samples have been scanned using the AusScan NIR prediction equations. Assuming there was an alternate segregation criteria, which measurements (including both physical and AusScan measurements) would you like to see used in creating wheat storage segregations that can be used for later feed formulation and use? Indicate the priority score for each measurement for use in segregation (0 lowest and 10 highest)

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Ruminant Nutritionist Priority Scores

Monogastric Nutritionist Priority Scores

Question 4. Are there any combination of measurements that you believe could be used to segregate wheat?

• Starch, protein, moisture is the most useful combination • Assuming there is a strong correlation between the species energy values

then AME, DE & ME could be grouped together. • Pig Faecal DE and Protein measurements. • From a cow perspective…this is ALL pretty good wheat. Even the Fed1

has really good starch and quite acceptable ME. I rate starch and NDF well above ME as a segregation.

• For poultry and pig customers Protein with AME or DE. • No. • The two key parameters that underpin the nutritional value of grain are

energy and protein. Combining these two parameters to achieve segregation requires keeping this simple or the number of separate bins and subsequent utilization of the specific parcels becomes quite complicated. A Key factor here is having sufficient quantities within each category to warrant the segregation. An example segregation could be:

1 2 3 4 5 6 AveTest Weight 4 6 1 1 2 1 2.5Protein 8 9 7 10 9 10 8.8Moisture 5 8 3 5 2 5 4.7Screenings 3 1 5 5 1 5 3.3ADF 7 2 7 8 3 8 5.8Crude Fibre 2 0 0 1 1 1 0.8NDF 9 3 9 3 3 3 5.0Cattle ME 6 7 8 1 10 1 5.5Total Starch 10 10 10 10 5 10 9.2

1 2 3 4 5 AveTest Weight 6 5 2 5 1 3.8Protein 10 7 9 10 10 9.2Moisture 8 7 2 8 5 6.0Screenings 8 7 1 4 5 5.0ADF 3 7 3 5 8 5.2Broiler AME 10 10 10 8 10 9.6Crude Fibre 5 5 1 4 1 3.2NDF 5 7 3 6 3 4.8Pig Faecal DE 10 10 10 10 8 9.6

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Faecal DE – MJ/kg High Low Protein -‐ % High + + Low + +

• Starch/ADF/NDF for ruminants. • Ileal DE and Faecal DE for pigs.

Question 5. If an alternate system of grain segregation was in use, and assuming it can be segregated through to feed mill use, what added value in $/tonne would be derived through the supply chain in the different use of the wheat? Value high versus low segregations and compare against existing GTA based gradings.

1. Broiler AME above or below 12.8MJ/kg

Segregation Tonnes Test Weight kg/hl

Protein %

Moisture %

Screenings %

Broiler AME

MJ/kg

Pig DE

MJ/kg

Cattle ME

MJ/kg

Starch %

Low Broiler AME Wheat 7019 77.9 9.7 11.8 2.2 12.6 14.0 12.7 72.7

High Broiler AME Wheat 8950 78.2 9.5 10.1 2.7 13.1 14.1 12.7 71.7

• Broiler AME difference of 12.6 verses 13.1. This is the only parameter worth considering, as the difference in test weight, protein, moisture, screening, starch, etc are all less than the standard errors that come from sampling and testing.

• There is no fixed value to this energy difference as it depends on the cost of energy from other sources (other grains, oil or tallow, full fat oil seeds, etc) and also on the energy density specification of the diet it is being applied in. For example, in a broiler grower diet set at say 12.8 MJ ME/kg the two wheats costed at $250/tonne result in a difference in feed cost of around $8/tonne. As this relates to a 50% inclusion level and only a 0.5 MJ difference in AME, it values the AME differences at $32/MJ ($16/tonne grain). However, if the diet was set at 13.2 MJ ME/kg then the energy differentiation in the wheat becomes more valuable -‐ in this instance a

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feed cost difference around $11/tonne which corresponds to $44/MJ AME ($22/tonne grain).

• Difference in value is $6.50, given almost equal protein. The grain grower could get some of this but it is also likely that the broiler producers would just go for the better quality stuff and leave the low ME wheat to find a home elsewhere.

• $4.80/T to the point of the finished feed. • In a broiler grower diet the cost advantage of 0.5MJ (12.6 > 13.1) could be

as much as $30/t when there is a wheat inclusion of around 60%. Then on top of this there would be a bird response to a more accurately formulated diet

2. Pig DE above and below 14.0MJ/kg


Weight kg/hl

Protein %

Moisture %

Screenings %

Broiler AME

MJ/kg

Pig DE

MJ/kg

Cattle ME

MJ/kg

Starch %

Low Pig DE Wheat 5697 76.9 8.6 11.3 2.4 12.7 13.9 12.6 71.7

High Pig DE Wheat 10272 78.8 10.1 10.6 2.5 13.0 14.1 12.7 72.4

• This segregation would be barely worth the effort as the difference between the 2 categories is less than the error involved in its DE measurement.

• Each extra MJ DE /kg is worth about $20 -‐ $30/tonne of grain while each additional % unit of protein is worth $4-‐5/tonne grain (between say 8 -‐ 14% -‐ outside this range the relationship distorts). So the segregation of energy content probably takes priority (as long as the range is greater than 0.5 MJ DE/kg as this is not that much higher than the accuracy of the measurement +/-‐0.3 MJ DE/kg.

• Appears a small difference at first glance but at fat prices approaching $1000/t its value in the wheat is $6.5/t.

• Due to introduction of xylanase enzymes the advantage associated with the differential in Pig Faecal DE and Broiler AME is now related to the cost of applying the enzyme which is in the vicinity of $3 per tonne of wheat.

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• Looking at a pig grower diet with 60% wheat a shift in DE of 0.2MJ (13.9 > 14.1) could reduce diets costs by as much as $4-‐5/t.

3. Cattle ME above and below 12.65MJ/kg


Weight kg/hl

Protein %

Moisture %

Screenings %

Broiler AME

MJ/kg

Pig DE

MJ/kg

Cattle ME

MJ/kg

Starch %

Low Cattle ME Wheat 6123 76.7 8.4 10.3 2.9 13.0 14.0 12.5 71.8

High Cattle ME Wheat 9845 78.9 10.2 11.1 2.2 12.8 14.1 12.8 72.4

• This segregation would be barely worth the effort as the difference

between the 2 categories is less than the error involved in its measurement.

• Don’t have enough confidence these ME predictions are accurate or meaningful on small dataset to create the regression equations used to derive the ME calibrations.

• At $250 / tonne, high ME wheat is worth approx. $7 more per tonne than the low ME wheat.

4. Total starch content

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Weight kg/hl

Protein %

Moisture %

Screenings %

Broiler AME

MJ/kg

Pig DE

MJ/kg

Cattle ME

MJ/kg

Starch %

Low Starch Wheat 6599 77.5 9.6 10.3 2.7 13.0 14.0 12.7 70.2

High Starch Wheat 9370 78.5 9.6 11.2 2.3 12.8 14.1 12.7 73.6

• The difference in starch is of no consequence unless it is correlated to

energy value. In this instance the energy values are not different so there is no point segregating on the basis of starch.

• For ruminants such small increment would be relatively minute in turn with the relative savings. A year of greater magnitude would obviously have a greater effect.

• 20% starch in a dairy diet from 5.7kg of low starch wheat vs 5.43 kg high starch= 5% difference = $12.50/t based on $250/t grain.

Question 6. Is there any additional aspects of the Werneth data that you believe could be better analysed? Is there any other analysis or measurement that would better define wheat’s nutritional content and use in animal feeding?

• The critical aspect is does the grain support animal performance at the expected levels. Thus the index of feed intake and DE levels is a critical measure that may further define the impact of wheat on performance.

• The analysis needs to show that the variation observed is within the parameters of the calibration. For example the majority of the wheat lies in the range of 13.9 to 14.2 and thus a 0.3 unit variation and the accuracy of the calibration is 0.27 units and we therefore assume that the majority of the wheat will support expected performance. The outliers to this “normal range” need to be identified and show that performance of the pig or other animal is influenced by this variation and why?

• The AusScan analysis is a huge improvement on previous categorisation and is probably all that is needed (but should include moisture, fat and ash).

• The more parameters, the need for more expensive facilities to segregate in order to control which is unlikely to be accommodated by grain storage service providers.

• The more information available, the better the capacity of the nutritionist to make a judgement. It comes down to the timeliness and accuracy of the analysis.

Question 7. Is there any further feed grain research you would like to have undertaken to improve your ability to source grain that provides maximum value to your ration formulation?

• Rather than Cattle ME, it would be more useful to look at NE according to its use in terms of maintenance, lactation, growth etc.

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• NIR calibrations that can accurately predict anti nutritional factors such as kafirin in sorghum, NSP’s in cereals and phytic acid in cereals and vegetable protein meals would be of benefit.

• NIR Calibration to predict starch rate of fermentation for use in addition to total starch content would be good, as a risk parameter. Possibly protein rumen solubility.

• Mycotoxin is important to both pigs and poultry and this has been a problem in recent harvests, especially with the SFW and FED grades

• Grains total starch content plus its speed of microbial fermentation in the rumen. Sorghum calibrations characterizing lower digestibility (high kafirin, tannins, starch characteristics) may be useful in northern regions.

• The complication here is that the feeding value of the grain is very much influenced by how it is prepared (grind size), what it is fed with, its inclusion rate, how the feed is processed (pelleting, extrusion) the use of enzymes, and how it is fed -‐ quite independent of the apparent nature of the grain. I doubt NIR will ever accommodate all these nuances. The question of rate of digestion of starch and other components is to some extent covered by the DE Intake Index in the pig calibrations. However it also introduces the concept of nutrient asynchrony which can influence nutrient utilisation but since this involves more than just the inherent rate of digestion of the grain components it may be too complicated to quantitate by NIR. A measure of the rate of starch fermentation in ruminants (particularly dairy cows) would be useful as would be an estimate of "effective NDF" but as this is partly a function of grind size it is really beyond the scope of NIR analysis of the base grain. Similarly in sorghum the digestibility is a function of grind size which is independent of the NIR assay but knowing the kafarin content or starch characteristics may help to define the optimum grind size or the likely response to specific enzyme supplementation.

• Sorghum kafirin content, ruminant fermentation rate, grain starch content, improved confidence in Cattle ME predication.

• I would like to see practical and cost-‐effective implementation of the AusScan calibrations (which the Pork CRC is currently working on) and further research to improve these calibrations.

Desk Top Segregation Exercise Scenario 1 Using the Werneth AusScan results, grain deliveries were run through a potential segregation based on the combination of protein and Pig DE. This involved defining segregation into four receival standards as shown below. This scenario does not utilise any GTA receival standards and makes no allowance for segregation of wheat into milling grades.

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13.60

13.70

13.80

13.90

14.00

14.10

14.20

14.30

14.40

14.50

0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00 18.00

Pig Faecal DE -‐ M

J/kg

Protein % DMB

Low DE Low protein Low DE High Protein

High DE Low protein High DE High protein

Segregation TonnesTest

Weight kg/hl

Protein %

Moisture %

Screenings %

Broiler AME

MJ/kg

Pig DE MJ/kg

Cattle ME

MJ/kg

Starch %

Low DE Low Protein 4021 76 8.1 11.0 2.6 12.8 13.9 12.6 71.8Low DE High Protein 1676 78 9.8 11.8 2.0 12.5 13.9 12.8 71.5High DE Low Protein 2428 78 8.5 9.6 3.0 13.3 14.1 12.6 71.7High DE High Protein 7843 79 10.6 10.9 2.3 12.9 14.1 12.8 72.6

1. High DE > 14MJ/kg & High protein > 9% 2. High DE > 14 MJ/kg & Low protein < 9% 3. Low DE < 14 MJ/kg & High protein > 9% 4. Low DE < 14 MJ/kg & Low protein < 9% Above or below 14MJ Pig DE was set based on the average predicted result. Similarly above or below 9% protein was used in the segregation exercise. The following diagram shows individual test results falling into each segregation.

The volume of grain and the average results for each of the four segregations are shown below.

Due to the high level of variation in protein there is seen to be significant segregation variation based on protein values. However the high and low Pig DE segregations provided only low variation, 0.2MJ/kg, between high and low DE grains. It is seen that the high DE segregations are also higher in Broiler AME. The PGLP research has previously identified a positive correlation between Pig DE and Broiler AME.

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The relative value of this segregation to the pig industry is two fold: 1. Increased feed formulation accuracy and realising an added 0.2MJ/kg DE and 1.5 – 2% protein value within feed formulations. The disclaimer is however the relative accuracy of the Pig DE prediction and ability to segregate based on DE when the overall amount of variation in DE is small. The high DE segregation is very effective in removing more extreme low energy grains into a low DE segregation. 2. Increased pig performance consistency through reduced variation in feed nutrient content. The low protein and low Pig DE grain segregation is not low in Cattle ME and through the majority of the year the southern Australian dairy industry is not seeking higher protein grains. A large negative aspect of this segregation option is lost value from the H2 and APW milling wheat grades and the price premium paid to growers. Based on the Werneth data this option does not appear to be viable, as any added premium in marketing the high protein and available energy grains does not compensate for lost value from removing the milling wheat segregations. Scenario 2 This scenario works on retaining the higher value milling grain segregations, ie. H2 and APW with premiums paid to growers at delivery. Grain falling below APW grade is segregated into high and low protein with growers being paid a protein premium, in this case set as above 9% protein. Post receival the low protein wheat is further segregated based on Broiler AME content. Thus there are two feed grain segregations shown below as Pig/Broiler and Dairy in addition to the H2 and APW segregations.

This scenario results in lower protein and available energy wheat being removed from the segregation aimed at pig and poultry end users. The resulting grain is higher in protein average 9.3%, Broiler AME 13.0MJkg and Pig DE 14.0MJ/kg than grain segregated for marketing to the dairy industry end users. The advantages offered to pig and poultry end users is the removal of lower protein and energy grains from supply and the potentially significant impact

Segregation TonnesTest

Weight kg/hl

Protein %

Screenings %

Broiler AME MJ/kg

Pig DE MJ/kg

Cattle ME

MJ/kg

Starch %

H2 715 78.2 12.0 3.0 13.0 14.2 12.8 70.1APW1 3547 79.3 11.0 2.2 12.8 14.1 12.8 72.7

Pig/Broiler 8017 78.0 9.3 2.5 13.0 14.0 12.7 72.1Dairy 3690 76.8 8.1 2.6 12.7 13.9 12.6 72.4

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these grains have on pig and bird performance. The grains segregated between pig/broiler and dairy are shown below.

Under this scenario the benefits delivered to the supply chain are: Segregation Parameters Benefits Milling wheat grades – H2, APW

GTA receival standards – min. protein and test weight, max screenings

Growers receive market premium for milling grades

High Protein feed Min protein Growers receive ASW price plus protein bonus

Pig/Broiler Min Broiler AME or Pig DE

Accumulator gains price premium above ASW Feed mill pig/broiler end user gains more consistent higher protein and energy wheat

Dairy Below min protein and energy

Accumulator gains ASW price

Scenario 2 segregation provides flexibility in allowing segregation post receival, with the availability of different segregation silos or bunkers allowing greater splits based on protein and energy.

12.00

12.50

13.00

13.50

14.00

14.50

6 7 8 9 10 11 12 13

Brolier AM

E MJ/kg

Protein %

Pig/Broiler

Dairy

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DISCUSSION Variation in Available Energy Pig DE results for the 549 wheat samples is shown below, with comparative data from the AusScan reference grain sample database. It can be seen that the average Pig DE result at 14.0 MJ/kg is in agreement with the AusScan reference sample database mean of 13.9 MJ/kg. The Werneth wheat Broiler AME mean result 12.9MJ/kg is higher than the AusScan database value at 12.57MJ/kg. The range in Pig DE and Broiler AME is lower than that measured within the AusScan reference samples database. The AusScan database includes more extreme wheat samples including frosted, drought affected and weather damaged grains that were measured as low energy grains. There is limited variation in available energy to allow wheat segregation based on AusScan Pig DE or Cattle ME. The greatest variation in available energy was found with Broiler AME. The range in samples tested is: Werneth Samples AusScan in vivo database Pig DE 13.6 – 14.6 12.7 – 15.1 Broiler AME 11.4 – 14.2 11.1 – 14.0 Cattle ME 11.9 – 13.0 12.2 – 13.1

From an animal nutrition and performance perspective, segregation based on available energy would guard against low energy grain being received into higher energy grain storage. Based on this project test results and the feedback from nutritionists there may be value in a wheat segregation for high and low

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available energy for pigs and broilers. When combined with high and low protein content the result would be the segregation of lower protein and energy grains that would be marketed to the ruminant industries and higher protein and energy grains marketed to the pig and poultry industries. Protein A large variation in protein content was found in the Werneth samples and this was the main point of interest from nutritionists with respect to modifying feed formulations or implementing grain segregation. Protein in wheat is recognised as being highly variable and not consistent with variation in starch content. The deposition of protein and starch during crop growth does not always occur at the same time with rate and duration working as independent events. Normally protein deposition peak deposition occurs earlier than starch deposition. When high temperature or lack of moisture during grain fill occurs, often higher protein and lower starch grain content occurs. The wheat samples in this project were all high in starch and many samples were low in protein. This is more typical of insufficient nitrogen across the growing season, although other mineral nutrition can also impact on nitrogen uptake by the wheat plant. It is of note that the existing GTA segregation provided an effective mechanism for protein segregation as it includes a measure for protein. The value derived from the additional protein in animal feed formulations is relatively low compared to the milling wheat segregations paid. The nutritionists were reasonably consistent in valuing additional protein at around $5/protein unit based on current raw material prices. This price differential does not justify segregation of milling quality wheat for animal feeding based on protein content relative to segregation based on GTA receival standards. Other parameters – starch for ruminants The data for starch and NDF identified a reasonable level of variation across the wheat samples. The ruminant nutritionists have identified these two parameters as offering greater potential for wheat segregation than Cattle or Sheep ME. The results of this work are consistent with that of Jolly et al (2011 and 2012 GRDC progress reports). In their analysis of NVT wheat samples they have found that grain tested from the 2010 growing year, total starch ranged from 67.8 – 78.8% (Figure below) and although there was no difference between varieties there was a significant difference between locations (Figure below) in starch concentration. The major difference between the NVT growing locations was identified to be rainfall.

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Starch vs Wheat variety Starch vs Growing location

Comparing starch and NDF results between the 2010 and 2011 growing years (Jolly 2011 and 2012) is shown below. It is seen that both starch and NDF varies between growing years. Total Starch 2010 Total Starch 2011

NDF 2010 NDF 2011

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From the Werneth segregation project work, the level of variation in starch and NDF content is greater than the Cattle or Sheep ME content of the grain. This has also been identified in the Jolly data and is also previously reported within the PGLP research program. The dairy and beef feedlot sectors have not embraced the use of AusScan cattle ME predictions. Preferring to continue use of more traditional ME prediction via the Weende method based on laboratory analysis of protein, fat, fibre and ash. Although the AusScan calibrations are based on in vivo digestibility research and take account of the inherent characteristics of the grain affecting digestibility, there remains a reluctance to accept the technologies use. In the FGP nutritionists and breeders meeting held in 2012, alternate quality measurement that suited the dairy industry were discussed and a system potentially including starch was defined. The feedback from ruminant nutritionists in this project has also identified the importance of NDF. There would seem to be considerable opportunity for the dairy and beef feedlot sectors to further investigate this variation and its effect on animal performance. Research looking at grain fermentation and rate or extent of starch digestion could provide valuable information and provision of data that could lead to more suitable NIR calibrations for these industries. It is noted that the in vitro fermentation work being completed by the University of Melbourne has been used to characterize different grain samples, this being used in work comparing red and white wheats (Dairy Australia funded project) as well as sorghum grain fermentation rates (GRDC funded project in progress). It would seem a logical research progression for the dairy and beef industries to further evaluate the use of total starch and NDF as well as grain fermentation to better define grain quality for ruminant feeding. At this stage there is not sufficient performance data to link variability in grain starch and NDF to either beef or dairy production. Although the added value of knowing starch content is seen as beneficial in fine tuning dairy nutrition, the actual added value this delivers needs to be validated through research feeding experiments. Variation between region and year This project was intentionally undertaken to study the amount of variation within a region based on deliveries to a common receival site in one harvest period. The data generated was seeking to establish the added value that use of the AusScan NIR calibrations provided to delivery of an alternate segregation for wheat use in animal feeding. The assumption was made that there would be sufficient variation in predicted energy content to justify an alternate segregation process. The data generated identified low level variation in Pig DE and to a lesser extent Broiler AME relative to the in vivo testing accuracy. Other reports generated

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from the PGLP research has looked at grain variation between years and locations: 1. ABB Case study compared wheat and barley between 2004/05 and 2005/06. Statistical analysis of the ABB case study data from 04/05 and 05/06 demonstrated significant variation in PGLP measures between these two harvest periods. The 04/05 and 05/06 crop growing conditions were very different with ABARE data showing 04/05 crop yields being significantly reduced due to lower rainfall and crop moisture availability. The 04/05 harvest was characterised by a dry finish which resulted in poor grain fill. The data set supports the expected result of seeing higher available energy for pigs and poultry when crops are favoured with better agronomic conditions, conversely poor cropping years result in barley which is better suited to ruminant feeding due to there being no significant decline in Ruminant ME. The ABB Case study looked at comparisons between different receival silo sites and concluded that it is possible within each year to define silo cells where grain is more suitable for different end use market applications. ABB Case study looked at variation within one silo site with a theoretical segregation exercise completed where deliveries were segregated based on AusScan predictions. The difference between the segregation options was not great, even though the difference between the highest and lowest individual deliveries at the site were significant. For example the range in wheat Broiler AME is 0.8MJ/kg. However due to averaging of low and high grains the differences between the segregated high AME and low AME wheat is reduced to 0.4MJ/kg. The ABB case study concluded that variation at site level may be insufficient to allow segregation which provides resulting grain supplies with sufficient variation to capture livestock industry benefits. The benefits derived from segregation are potentially less than the logistical cost of providing segregation. 2. Grainsearch PGLP Case Study – using AusScan test results, variation for different feed wheat varieties was minimal when the wheat is grown under the same conditions. Variation resulting from agronomic conditions provides potentially greater variation to PGLP nutritional quality than grain variety. PGLP nutritional quality variation for the same wheat variety grown on different properties within a region is minimal. The conclusion based on this project and previous PGLP work is that the existing GTA receival standards for various milling wheat and feed wheat segregations provides greater value than a move to segregation solely to meet the feed wheat market using AusScan analysis. This is principally due to the premium prices obtained by growers in milling wheat grades and the limited variation in available energy content at silo site level. Rather than using available energy testing to segregate all wheat delivered, the better option is seen to be a hierarchical segregation process utilising a combination of existing GTA and AusScan testing.

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Additional Nutritionist Feedback Feedback from the nutritionists consulted in this project raised additional concern regarding segregation of grain based on available energy content with respect to: • Large end users will have difficulty segregating the supply chain to capture

energy values from farm to mill. i.e. unless they can utilise higher energy or protein wheat in isolation from other wheat deliveries they may be unable to take advantage of the additional nutrient content.

• Traders and growers would have increased price risk in forward selling grain based on NIR-‐energy (or other parameter) which they can’t control.

• Major users will end up with “average” grain AME (or other parameter) as the supply chain works with multiple growers, traders, forward contracts, storage co-‐mingling and swaps by storage providers between sites.

• Small end users may be able to capitalise on NIR-‐energy (or other parameter) through a direct alignment with individual growers.

Additional comments from a leading pig nutritionist: From a pig nutritionist perspective the results are as expected in that wheat taken in from a single district with a similar genetic makeup will elicit a similar performance from the pig. However outliers to this need to be identified and either not purchased or a further treatment applied to them to get a similar performance response. The value of separation is low in relationship to DE. Feedback on results achieved by the Bulk Handling, Grain Marketing and Grower Co-‐operative companies that supported the collection of samples for testing in this project. These companies were very supportive of this project as the domestic feed grain industry is a major user of grain produced in southwest region of Victoria. Representatives from these companies expressed a strong interest in better understanding the grain quality parameters important to the monogastric and ruminant animal based industries so they can better meet the needs of this important market segment. The project results were considered to be encouraging as they identified and valued a number of feed grade quality traits that are important to the various feed industries. The companies involved were encouraged to engage more with the end users of feed grains and to possibly invest in grain testing equipment (e.g. AusScan) to provide them the capability to segregate grains based on specific feed grain qualities. The key driver for the bulk handler is to maximize the quantity of grain delivered into their grain receival site. A bulk handler, particularly those located in a major feed grain use region, would look positively towards establishing special feed

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grain segregations, for a grain marketer or end user, if the segregations created value and encouraged growers to deliver their grain to this site. A possible impediment to the establishment of special feed grade segregations based on AusScan analysis (e.g. Broiler AME measurements) is that these tests are not currently accredited by the National Measurement Institute (NMI). The existing GTA parameters including protein are meeting NMI requirements. The AusScan available energy calibrations are not approved by NMI and cannot be used to determine grower payments. Value and Cost of Segregation -‐ actual price versus alternate segregations Based on the desktop segregation scenario 2 described earlier, value on Pig/Broiler versus Dairy wheat segregations have been estimated based on an ASW wheat price of $260/tonne. The higher protein 1.2% and Broiler AME 0.3MJ/kg provides an additional $15/tonne value relative to the dairy wheat segregation. This is where protein is valued at $5/% unit and Broiler AME $30/MJ.

Segregation Value $/tonne Pig/Broiler 275

Dairy 260 This added value needs to be split between the supply chain participants, a potential split is as follows:

• Value to grower in added protein > 9% $3/tonne • Value to accumulator to run segregation $8/tonne • Value to feed mill/end user in grain use $4/tonne plus for end user

potential improved or more consistent livestock performance. The actual prices paid to growers are heavily discounted where less than ASW grades segregations occur. For the Werneth site relative to ASW, the reduced grower payment for SFW1 is $12.50/tonne and FED1 $30.50/tonne. Based on the AusScan test results, these segregations are expected to perform as well as the ASW segregation. This discounted value delivers to either/or the bulk handler, marketer and end user additional value at the expense of the grain grower. It would seem that grain growers supplying feed grains have potentially the greatest to gain from AusScan technology adoption. The value derived from use of available energy in grain segregation is expected to be greater in more variable grain production years. Having a 0.5MJ/kg difference delivers $15/tonne and if 1% higher protein $5/tonne for a total $20/tonne higher value. The costs for grain accumulators in running a segregation include:

• The need for NIR equipment capable of running the AusScan calibrations at the receival point.

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• Technology licensing fees for AusScan use. • Staff capable of controlling segregations, no different to any segregation

system. • Availability of silos or bunkers allowing segregation post receival. • Capability to control out loading to ensure the correct grain is outloaded

correctly. For end users including feed mills to take advantage of higher protein and energy grains requires them to be capable of using grain in isolation from other wheat receivals to allow rations to be reformulated.

Recommendations 1. Use of the AusScan technology is best suited for:

• Use in conjunction with grain protein testing and segregation. • Post receival analysis for Pig DE and/or Broiler AME and grain marketing

to end use market segments. • Short supply chain use – grower to end users or via a trading agent. • Fine tuning of feed formulations to take account of grain variation from

year to year and between different growing regions.

2. Information from this project, together with other PGLP research findings need be written into fact sheets. This material being for provision to the feed grain supply chain to promote the AusScan technology use in grain segregation and short supply chain grain marketing arrangements.

3. A pilot study should be implemented with a regional grain receival operator with an NIR instrument being located at sample receival with real time data used to implement a segregation using the AusScan calibrations in conjunction with GTA receival standards. i.e. real world technology demonstration.

4. The dairy and beef feedlot industries should review the merits of completing research work on grain starch fermentation and its impact on animal performance. The intent is to develop potential rapid analysis systems to better define grain quality for ruminant feeding. 5. Consultation is needed with NMI to assess grain receival and grower payments utilising AusScan technology.

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