Protein mapping – getting more bang for your fertiliser buck

Author: | Date: 23 Feb 2022

Take home messages

  • Wheat grain protein concentrations of less than 11.5% generally indicate that nitrogen (N) supply was insufficient for a crop to meet its water limited yield potential.
  • If this ‘rule-of-thumb’ is applied across a landscape, a spatially referenced wheat grain protein concentration map is analogous with an ‘N adequacy’ map.
  • This layer can be used in conjunction with targeted deep N soil sampling as a basis for site-specific N inputs to reduce both instances of yield loss due to N undersupply and adverse environmental/economic consequences associated with N oversupply.
  • Research conducted in 2019/2020 across five paddocks (511.4ha) in southern NSW supported the use of wheat protein mapping as a basis for site-specific N.


Fertiliser prices have put the use of variable rate technology front and centre of input decisions for the 2022 season. Nitrogen is a dynamic nutrient in the environment, and as such, grower confidence in utilising the right precision ag information to support the variable rate decision making has been of high importance. The use of cereal grain protein mapping as part of site-specific N fertilisation strategy has shown promising results as a valuable precision ag layer for improved decision making. By using grain protein as an indicator, fields can be assessed to where yield gains can be achieved. Results will be presented from paddock scale research conducted in 2019-20 by EM Ag Consulting that examined relationships between soil mineral nitrogen (SMN) levels and grain protein concentration across five paddocks in southern/central NSW.

Theoretical background to cereal grain protein based site-specific N

For many decades it has been recognised that a consistent relationship exists between cereal grain yield and cereal grain protein concentration according to N supply (for example, Russell 1963). This relationship consists of increasing grain yield and protein concentrations with greater N supply up to a certain point, after which grain yield begins to plateau while protein concentration continues to increase. At very high N levels, a decline in yield often occurs (Holford et al. 1992).

The point at which N supply has been optimised for maximum grain yield is termed the ‘critical grain protein concentration’ and is around 11.2–12.0% in most Australian hard white wheats through studies conducted in southern/central NSW (Brill et al. 2013; Sandral et al. 2018) and South Australia/Victoria (G. McDonald, review published in Unkovich et al. 2020).

While critical grain protein concentrations will vary between varieties and across seasonal conditions (Fowler 2003), a simplified ‘rule-of-thumb’ interpretation under favourable (non-drought) conditions can be summarised as:

  • protein <11.5% = insufficient N supply to meet yield potential
  • protein 11.5–12.5% = adequate/optimum N supply to achieve yield potential
  • protein >12.5% = surplus N to crop requirement, possibly some yield penalty (Figure 1).

Figure 1. A generalised representation of the relationship between yield and grain protein concentration in wheat with increasing N supply.

Figure 1. A generalised representation of the relationship between yield and grain protein concentration in wheat with increasing N supply. Labels refer to grades in the Australian wheat classification system.

If we apply this rule-of-thumb spatially across a management area grown to a single wheat variety, a georeferenced map of wheat protein concentration is analogous to an ‘N adequacy’ map, that is, it serves to distinguish areas of the paddock that had insufficient, ideal or surplus N according to their site-specific yield potentials.

Ground-truth soil testing at the start of the following season can be used to test this assumption and quantify out-of-season mineralisation. A good approach to determining the placement of soil tests is to divide the paddock into zones based on combinations of yield and protein results from the previous harvest. This process provides useful insights into not only N dynamics but also where non-N related constraints may warrant further investigation. These concepts are summarised in Table 1.

Table 1: Within-paddock combinations of cereal yield, protein attributes and their properties.



Residual N levels


High Yield/ 
High Protein

  • Optimum scenario
  • Yield potential achieved, no major limitations
  • This area of the paddock may have higher mineralisation potential

Likely moderate to high, however soil test to confirm (particularly if crop N demand was higher than budgeted)

Determine rates based on soil test results and according to high yield potential

High Yield/ 
Low Protein

  • Sub-optimal N management
  • Yield could have been even higher
  • N deficiency likely occurred later in the season, as sufficient N was available   to produce biomass/tillers
  • Could indicate ‘tired’ areas with lower mineralisation potential (e.g.,   historically high N removal/low SOM)

Likely low  (assume post-harvest residual SMN was negligible, so levels are dependent on out-of-season mineralisation)

Increase N rates relative to paddock average in following season/s to support higher yields and build SMN

Low Yield/
High Protein

  • Non-N related problem
  • Further N additions would not have increased yield
  • If protein is very high, yield penalties from oversupply of N likely occurred
  • Most commonly related to lack of moisture supply (for example, shallow or hostile subsoils, around trees), however may be another constraint such as pH, P

Likely high  (mining of N may be advised to reduce yield penalties associated with N oversupply)

If the constraint cannot be amended, reduce N inputs relative to paddock average permanently to match lower yield potentials

Low Yield/ 
Low Protein

  • Sub-optimal N management
  • Yield potential was not met
  • It is unclear if other constraints exist   that would continue to limit yield with higher N inputs
  • If N deficiency is the primary cause, SMN was likely low for the whole season

Likely low

Start by increasing N to determine the non-N constrained yield potential, then manage according to results

A major advantage of a protein-based variable rate (VR) N approach over currently available alternatives is that it combines both the supply and demand elements of the N balance equation. For example, low protein areas within a paddock may occur either due to low N supply (for example, differences in carryover N, mineralisation, fertiliser inputs) OR higher yield potential (for example, due to the dilution of protein by higher yield; Simmonds 1995). Regardless of which factor is responsible (or both), the management decision will involve increasing N rates in the following season.

In this sense, the protein layer is also accounting for temporal variability of N dynamics by providing a retrospective assessment of the whole season, net N balance, rather than a ‘snapshot in time’ as occurs with data layers such as spectral indices or grid soil mapping.

Another advantage is the benefit afforded by the plant providing an indication of N adequacy according to the conditions it experienced, that is, the plant available N. This circumvents a limitation of soil testing where mineral N may be present within the profile however the plant may not be able to access it (for example, if subsoil hostilities prevent root access). In a similar manner, if subsoil conditions are favourable and the plant is able to access deeper SMN, this will be reflected by the plant’s protein concentration however may be missed by an arbitrary soil sampling depth cut-off.

Getting started

Many growers have access to yield maps, and with an increase in growers having an on-combine grain analyser, these growers have access to quantity and quality metrics across their fields which can support subsequent crop fertiliser decision making.

A protein based site-specific N strategy might be a good approach for a grower if they:

  • are predominantly located on soil types not prone to losses (that is, free draining with good nutrient holding capacity) and
  • have within-paddock variability in factors such as texture/CEC/OC%/PAWC, productivity (N removal) and/or management histories (for example, amalgamated paddocks, previous inputs).

At present, the cost of a harvester mounted grain analyser is approximately AUD $25,000 + GST and installation (Next Instruments ‘CropScan 3300H’ unit). This cost will be spread over a number of seasons. The unit can also be removed and reinstalled if a new harvester is purchased. There will also be costs related to data management and interpretation if the grower cannot or does not wish to do this themselves.

After completing the first harvest, a good strategy is to pick a few of the most variable paddocks to focus on. If a grower isn’t comfortable implementing a VR application straight away, they may prefer to use N-rich and/or N-poor strips to test the impact of variable N rates on their soils. If doing so, strips should be designed so they pass through several zones (for example, low/high protein, soil types, management histories). Paddocks being cropped to a second cereal crop (for example, wheat on wheat) will be of most value for reviewing the results of strip trials and/or the success of VR N applications.

Setting rates

Due to fluctuations that occur in critical grain protein concentrations between seasons and some varieties, start-of-season soil sampling will remain an essential step to determining actual N rates. Soil sampling will also act as a ground-truthing step to test assumptions regarding patterns of carryover SMN and to test any unusual areas.

Where consistent protein zones are present, soil sampling should cover off on each of the major protein/yield combinations (see Table 1), aiming to get an idea of the paddock average and the spread (range) of SMN values.

Over a number of seasons, implementing this strategy should reduce the spatial variability of protein concentrations, ideally converging around 11.5–12.5% if the base rates chosen have been appropriate. It is likely that the most ‘bang for buck’ to be gained implementing this strategy will occur in the early stages, by eliminating very low (highly constrained) and very high N zones.

It is important to remember that in paddocks where yield potential varies greatly due to factors other than N (for example, relatively fixed factors such as PAWC), a successful outcome will not be where yield becomes even, but rather where yield is optimised in all areas according to their site-specific yield potentials.

In all cases, ongoing monitoring of cereal protein per cent results and annual deep soil sampling should serve as constant feedback to ensure N decision-making approaches are performing well.

Results and discussion

A snapshot of the 2019/2020 research project undertaken across five sites in southern NSW by FarmLink Research is presented. The research sought to examine within-paddock N variability patterns and test assumptions around the correlation of SMN with various parameters, including protein concentration. Selected findings are presented below. The full research report can be accessed at Farmlink.  (Moffitt 2021).

Considerable within-paddock variability of start-of-season (Feb–Mar 2020) SMN was observed at four of the five sites, where the range of values (max – min) was greater than 140kg N/ha, and the standard deviation was greater than 20kg N/ha (Table 2). At the fifth site (Ardlethan), where the average SMN was much lower (46kg N/ha ± 11kg N/ha SD), the range of SMN was 43kg N/ha.

When examining the relationship between start-of-season (Feb–Mar 2020) SMN and various other attributes, 2019 grain protein per cent displayed the most consistent and strongest correlation compared to all other layers examined (Figure 2a). This consistently positive relationship was significant at four out of the five sites.

Figure 2. 0-60cm oil Mineral N (kg N/ha; sampled Feb–Mar 2020) versus 2019 cereal harvest results, (a) Grain Protein Concentration and (b) Dry Yield. (Girral = barley, rest = wheat). Each point represents one grid site (n = 425).

Figure 2. 0-60cm oil Mineral N (kg N/ha; sampled Feb–Mar 2020) versus 2019 cereal harvest results, (a) Grain Protein Concentration and (b) Dry Yield. (Girral = barley, rest = wheat). Each point represents one grid site (n = 425).

Importantly, at each of the four significantly correlating sites, areas of the paddock with the lowest protein per cent coincided reasonably well with areas of low SMN.

Previous management history appeared to be a key driving factor of N variability for at least three sites, with noticeable differences observed between areas that were previously fenced separately, despite some of these changes being made up to 15 years prior.

Across the five sites, there was a general trend of increasing strength of correlation between SMN and protein per cent as the average SMN level increased. This may be explained by considering that N supply levels have to be high in comparison to N demand in order for there to be substantial residual (carryover) SMN. If crop demand is much higher than supply, SMN may be drawn down across the paddock and residual N will be correspondingly low. In this situation, protein per cent may still vary, as overall N supply may have differed spatially throughout the season.

Due to the uncertainty around this result, a strip trial experiment was implemented in 2020 to explore whether the grid soil mapping results or 2019 protein per cent layer would have been the best basis for site-specific N in 2020. The site was grown to a second season of wheat, with 80kg/ha urea applied as a flat rate and two 160kg/ha urea N-rich strips applied at 140m width.

Results demonstrated a significant positive correlation between 2019 protein per cent and 2020 protein per cent for both the N-rich strip areas (n = 15, r = 0.81, P<0.001) and non N-rich strip areas (n = 40, r = 0.73, P<0.0001; Figure 3a). A significant positive correlation was also observed between 2019 protein per cent and 2020 yield for the non N-rich strip areas (r = 0.83, P<0.0001; Figure 3b) while no significant correlations were observed between 2020 start-of-season SMN and 2020 yield or protein.

An average yield increase of 564kg/ha and protein increase of 1.7% was observed for the N-rich strip cells when compared to their immediately adjacent non N-rich cells.

Figure 3. 2019 wheat (cv. LancerA) grain protein (left) and 2020 wheat (cv. SpitfireA) dry yield at Ardlethan, with locations of N-rich strips shown.

Figure 3. 2019 wheat (cv. Lancer) grain protein (left) and 2020 wheat (cv. Spitfire) dry yield at Ardlethan, with locations of N-rich strips shown. Note the greater yield response to additional N in areas of lower 2019 protein per cent.

These results suggest that given a fixed N budget, applying additional fertiliser to the lowest protein per cent areas of the paddock would have produced the greatest overall yield increase. Therefore, it appears that the adoption of a VR N strategy in 2020 based on the 2019 protein per cent pattern would likely have resulted in a more profitable outcome at this site than using grid soil mapping results or management zones where soil tests were used to directly determine rates.


The results of this project and experiences working with growers collecting and utilising harvester protein data have demonstrated that considerable potential exists for protein-based site-specific N strategies to drastically improve N management in our cropping systems, when used in conjunction with annual soil sampling and an appropriate N rate calculation method.

The success of protein-based site-specific N strategies appears to be linked to the major advantage of this approach whereby the crop itself indicates the N adequacy it experienced over the sum of the whole season. This circumvents many of the challenges of site-specific N management which have either limited the quality/efficacy of some VR N approaches (that attempt to provide simple solutions to a complex problem) or have limited the uptake of other VR N approaches (that are too complex/laborious to be practical). The high spatial resolution of this data and relatively low cost when compared to alternative approaches (for example, intensive soil sampling) is another major advantage.

While protein maps cannot be used to guide N management decisions in the season of their collection, this method should be considered more of a ‘whole-system’ approach to N management, with the aim of incrementally building (and/or mining) background SMN levels to match site-specific yield potentials across the farming operation over a number of seasons. This approach has considerable synergy with the concept of ‘N banking’ (Hunt et al. 2021; Meier et al. 2021) which aims to decouple N input decisions from seasonal demand by ‘topping up’ N levels each year to a pre-defined target that would be considered non-limiting in most seasons.

By using these two methods in conjunction (on soils that are not prone to losses), growers are armed with a simple, yet targeted strategy to both reduce/eliminate areas of yield loss due to N deficiency and reduce instances of N oversupply which are environmentally, agronomically and economically undesirable. This approach also has logistical benefits in that N rates and VR input maps can be determined/created quite early in the season (following the return of deep N soil test results). This has obvious benefits for financial budgeting and planning however also means that these decisions can be made well ahead of time rather than at a potentially stressful period before a rain event if relying on mid-season remotely sensed imagery, for example.

Given the immense potential productivity and environmental benefits of improved site-specific N management, considerable scope exists for follow up research to address the abovementioned challenges and explore the applicability of these methods in other regions and soil types.


This research component of this paper was undertaken in partnership between FarmLink Research and Precision Agriculture. It was supported by the Department of Agriculture, Water and the Environment through funding from the Australian Government’s National Landcare Program.Research was further supported by Charles Sturt University where the author holds an Adjunct Research Fellow position.

Thank you to Eva Moffitt, EM Ag Consulting for supporting and providing the research and Mat Clancy (Next Instruments T/A CropScanAg) for technical support.


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Contact details

Edward Scott
CropScanAg Solutions
Adelaide SA
0403 313 741

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