Micronutrient survey and Scoping Study Project 16

Transcription

1 Grains Research and Development Corporation More Profit from Crop Nutrition II Micronutrient survey and Scoping Study Project 16 A national survey of the extent of micronutrient (Boron, Copper, Manganese, Molybdenum, Zinc) deficiencies across all regions and agroecological zones with analysis against environmental and management metadata by Robert Norton, International Plant Nutrition Institute. 54 Florence St, Horsham, Victoria, This work is not to be cited without the permission of the author. 1

2 Contents: Summary... 3 Limitations Conclusions from the survey Summary of research areas identified Areas for future investment Priority order and costing Mapping and tabulation of areas at risk from B, Cu, Mn and Zn deficiency Grain micronutrient concentration on a regional basis a preliminary survey Review of past research on micronutrient responses in crops Using soil properties to infer micronutrient deficiency The Australian Soil Classification and soil orders significant to the grains industry Soil properties of ASC Orders Micronutrient soil test values within particular ASC orders or sub- orders Grain micronutrient concentration within particular ASC orders or sub- orders Grain micronutrient concentration and soil properties Response by cultivar Response by geographic region Response by Australian Soil Order 8. Discussion and synthesis Summary Acknowledgements Selected References Appendices Appendix 1. Soil orders within the Australian Soil Classification Appendix 2: Counts of each Australian soil orders within each grain zone as derived from the NVT database Appendix 3: Soil properties and Australian soil orders derived from the the South Australian soil survey database contributed to ASRIS (courtesy of D Maschmendt) Appendix 4. Critical DTPA Zn concentrations using Brennan (1992) formula, Critical DTPA Zn concentration = *pHCa %Clay% *OrganicC%. These values were derived from data within the South Australian Soil Survey dataset Appendix 5: Number of soil samples for NVT used in developing Table 11 and Table 12 in the text Appendix 6. Wheat grain coefficients of variation (%) for each micronutrient by a) Australian Soil Classification Soil Order, b) Australian Soil Classification Order Soil Order with alkaline soils separated, c) Agroecological zone or d) National Variety Testing System zone Appendix 7. Canola grain coefficients of variation (%) for each micronutrient by a) Australian Soil Classification Soil Order, b) Australian Soil Classification Order Soil Order with alkaline soils separated, c) Agroecological zone or d) National Variety Testing System zone Appendix 8. Box plots for B, Cu, Mn, Mo and Zn wheat and canola grain micronutrient concentrations segragated on Australia Soil Classification Order and suborders Appendix 9 GM canola and Grain Mn concentrations This work is not to be cited without the permission of the author. 2

3 Summary: The approach taken in this report was to assess each Australian Soil Classification soil order for its properties related to the primary risk of micronutrient deficiency. This risk was then assessed against reports in the literature, a survey of soil test data and a survey of grain nutrient concentration. A summary is shown in the table below, with green indicating low risk of deficiency, yellow moderate risk and pink high risk. Some parts of the matrix are uncertain. Soil Order B Cu Mn Mo Zn Calcarosol Low Mod High + Low High Chromosol Mod Uncertain Low Mod (C)*** Mod Dermosol Low Mod Low Low High Ferrosol Uncertain** Low Low Low Mod Kandosol High Mod Mod Mod (C) Mod Kurasol Uncertain Mod Uncertain Uncertain Uncertain Podosol Uncertain Uncertain Uncertain Uncertain Uncertain Sodosol Mod Mod Low Low Mod Tenosol High Low Low High (C) Mod Vertosol Low Low Mod Low Mod VertosolAlk Low Uncertain High + Low High * Where free calcium carbonate is present; ** Probably low. *** For Canola It is clear that the major risk is from Zinc deficiency and while Copper deficiency is not widely indicated, there are uncertainties about the risks associated with that micronutrient. The major investments should still focus on these two micronutrients Summary risk tables for each GRDC region were developed based on the relative area of each soil class and modified these could be further modified environment. These were used for reference and discussion, and identified areas of uncertainty. For the Northern Region the main issues appear to be with Zinc (Zn) on Kandosols, Vertosols and Sodosols. There is uncertainty about Copper (Cu) generally. For the Southern Region, the main soil types of Calcarosols, Sodosols and Vertosols have high risk of Zn deficiency, while Manganese (Mn) is likely to be a significant risk on these soils if they contain more than about 60% free calcium carbonate. For the Western Region, Kandosols and Tenosols are more significant than the eastern states, although Sodosols is are the major soil order, and low Zn is seen on these soils. The acid soil types such as more strongly acidic Tenosols are likely to be at risk of Molybdenum (Mo) deficiency, while Mn deficiency is moderately likely Kandosols and Tenosols. Additional tables for each agroecological zone could be developed as communication tools. Limitations: In general, the use of soil tests and grain testing to assess micronutrient supply are not conclusive guides to the risk of deficiency, so the assessments made are somewhat subjective based on the weight of the soil tests, grain tests, soil properties and the literature. The Australian Soil Classification (ASC) Orders assigned in this report are based on imperfect information about the true ASC Order, and so groupings based on ASC Order are not definitive for the soil test or grain nutrient concentration data. The digital mapping used to link a NVT site to ASC This work is not to be cited without the permission of the author. 3

4 Order was indicative and the expected ASC order was based on that indication plus soil properties known from the soil test (texture, ph, EC). The NVT sites used in the survey may not be representative of the whole regions characterised, as the NVT sites and the Western Australia (WA) Farm Focus paddocks were not selected as average paddocks. Conclusions from the survey: Even under good fertiliser management, low Zn, Mn and B grain concentration are common in many cropping regions. Further research aims to link these deficiencies to particular soil types so that growers can make a reasoned assessment of the risk of micronutrient deficiency for their production system. A collation of field and laboratory experiments on micronutrient response has supported the general view of risk of micronutrient deficiency as: B responses for canola on Tenosols. Cu responses for wheat on Chromosols and Sodosols. Mn response for wheat on Calcarosols, and for pulses on Chromosols, Sodosols and Tenosols. Mo responses on very acid soils generally (Tenosols, Kandosols) Zn responses for wheat on Chromosols and Calcarosols. Soil properties can be used to infer a prima facie risk of micronutrient deficiency. Maps of the ASC Soil Orders are already available through digital platforms, so the opportunity is to link current mapping the risk assessments based on soil order rather than remapping risk. Sodosols and Kandosols are significant cropping soil types in all regions, while Kurasols and Tenosols are important in WA and Kurasols also in northwest New South Wales and southwest Queensland. Vertosols and Calcarosols generally are more important in the eastern states. The soil types assessed as important from the Australian Soil Resource Information System (ASRIS) were consistent with the distribution of soil types estimated from the NVT database, although data from WA is scarce. Based on the soil properties, crop are most likely to be at risk of deficiency on particular soils are; B deficiency on Kurasols, Podosols, and Tenosols, Cu deficiency on Kurasols Managenese deficiency on Calcarosols. Mo deficiency on Kurasols, Podosols, and Tenosols, Zn deficiency on Calcarosols, Soil test values from the NVT database indicate that: 6% of B soil test values were below critical and they are lowest on Kurosols and Tenosols and highest on Ferrosols and Calcarosols. 34% of soil B tests were less than 0.5 mg kg - 1 and 11% were less than 0.25 mg kg - 1 in the Western Australia data set particularly on Chromosols and Tenosols. 2% of Cu soil test values were below critical, and they were lowest on Chromosols and Sodosols and highest on Tenosols and Vertosols. 21% of Mn soil test values (24% WA) were less than 5 mg kg - 1, and re below critical, but Mn soil test values are extremely variable on all soil types. Values were lowest on Caclarosols, Tensols This work is not to be cited without the permission of the author. 4

5 and alkaline Vertosols. The high variability is likely a reflection of the low reliability of this soil test. 15% of Zn (1% in WA) soil test values were below critical, and they were lowest on Dermosols and Tenosols, and also low on alkaline Vertosols. The grain micronutrient survey indicates that there are: 38% of wheat grain samples had B < 1.0 mg kg - 1 and lower values were recorded on Ferrosols. 3% of wheat grain samples had Cu <0.2 mg kg - 1 and the lowest values were recorded on Tenosols. Wheat grain Mn levels was less than 10 mg kg - 1 in 1% of samples, but grain Mn is an unreliable indicator of soil Mn status. Wheat grain Mo levels were less tha 0.02 mg kg - 1 on 23% of wheat samples, mainly from Kandosols and some Chromosols. Low wheat grain Zn levels are seen on Calcarosols, Dermosols, Kandosols and Sodosols, but not on Vertosols. 15% of the wheat tested had levels less than 15 mg kg - 1. Canola has a a much higher B and Mo concentration than wheat, as well as a higher Zn concentration, although it also appears more efficient than wheat at accessing Zn in particular in all soils. Canola grain Cu levels were about 30% lower than Cu levels in wheat. Summary of Research Areas Identified Boron Because B is mobile in the soil and does not have a long persistence in the soil (residual value), decisions on management of B will be likely made on an annual basis so either soil or tissue tests will be an important management tool for areas at risk of B deficiency. There is a need to address B deficiency for canola on Tenosols mainly around delivery strategies including timing and products. Low solubility B sources such as B- phosphates, B- frits or B- silicates could be considered. In terms of developing better risk profiles, the mapping initiated in WA (Wong et al. 2005) could be extended to the Southern and Northern GRDC zones. There are tissue tests developed for wheat and canola that are used diagnostically, but the data to support the critical levels of 5-10 mg kg - 1 for wheat is based on a NSW DPI AgFact Sheet (Dear and Weir 2004). There are no critical values for canola although they are likely to be higher than those for wheat. Copper It would seem that Cu is widely used by growers, in anticipation of deficiencies. However, the true extent of Cu deficiency is yet to be defined within cropping systems. Field agronomists using visual symptoms often diagnose Cu deficiency, but better use of tissue tests could be made in diagnosis and validation of the critical values for tissue tests would increase diagnostic confidence. The long residual nature of applied Cu on soils in eastern Australia should be established, as this will largely determine if management approaches should be tactical or strategic. Options for growers are to use either base fertilizer supplemented with Cu or to use in- crop foliar applications. There are few published experimental comparisons of these management strategies from the eastern states and well- documented case studies could assist growers with this decision. It is likely that soil type could This work is not to be cited without the permission of the author. 5

6 have a major effect on the most appropriate approach, but there are few guidelines on which this assessment could be made. Unresolved issues around Cu (similar to Zn) relate to higher yielding environments and an improved understanding of the interaction of N and Cu with modern high yielding cultivars. Under high N conditions, Cu is less able to be remobilized so late Cu deficiency may occur (Hill et al. 1978). This may have a genetic aspect, but the timing suggests that applications of Cu before flowering may be more important than earlier applications under high yielding conditions. The combined Zn and Cu demand could be considered as a single project. Manganese The use of foliar Mn sources, or of better formulations of Mn, such as with new or novel chelating agents, would seem to be opportunities to more efficiently address this micronutrient. The focus of the research on Mn would be in the VicSA Mallee, and in WA, particularly on pulse and canola crops, with a view to comparing strategic and tactical approaches to Mn management. Molybdenum Declining soil ph due to product removal suggests that there is an increased risk of Mo deficiency on soils that were once near neutral in ph. Increasing lime use will reduce some of these risks, but for soils on the ph brink (4.8?) there is a need to evaluate options for product delivery and formulation. At these ph values, molybdenum will be a cheaper option than liming but liming will provide a longer term solution to the deficiency. Zinc Comparisons of strategic with tactical application of Zn may require investigation, and to aid in this decision better information on the residual values of different formulations and presentations may be needed. Zinc nutrition has been shown to interact with liming, root pruning herbicides and high soil P concentration, and the impacts of these factors require further consideration, which may include the development of alternative delivery strategies or product formulations. Unresolved issues around Zn (simular to Cu) relate to in higher yielding environments and an improved understanding of the interaction of N and Zn with modern high yielding cultivars. The combined Zn and Cu demand could be considered as a single project. Other Issues There are good images available to growers to identify micronutrient deficiency (eg IPNI, Nutrient Deficiency Images App) and these should be incorporated into materials currently being developed as part of the diagnostic agronomy project. It would be useful to link the risk assessments made here based on ASC Soil Order to the SoilMapp App, to enable growers to check if the observations are likely to be micronutrient deficiency on their soil type. A second part to this identification is a strategy to confirm the diagnosis using tissue testing, and the data on tissue tests in Reuter and Robinson, along with sampling protocols should be linked to the images. This then becomes part of an extension package, linking risk (ASC Soil Order) to symptoms (Image database) to diagnosis (tissue tests). The quality of seed retained on- farm or from commercial seed production can be affected by low Zn, Cu and Mn and late sprays to enhance seed vigour may be a good option, even if there is no particular yield advantage gained. Grain nutrient content (i.e. content per seed) has been found to be more important than than grain nutrient concentration. This work is not to be cited without the permission of the author. 6

7 The levels of Zn in Australian wheat grain are low on world standards, and are significantly below the Harvest Plus target of >30 mg kg - 1 which aims to meet human health needs for those on grains based diets. There is no price premium for high Zn grain, but supplying such grain is a significant humanitarian issue. Selenium (Se) is important for human health and although not an objective of this project, a summary of grain Se content can be made available to GRDC. This work is not to be cited without the permission of the author. 7

8 Mapping and tabulation of areas at risk from B, Cu, Mn, Zn deficiency. Project background Micronutrients or trace elements have been proposed as the next major nutrient limitation to improved grain production. This of course assumes that the other limitations, biotic and abiotic, are adequately addressed and that micronutrients are in short supply. The overall approach in this one- year project is to assess the areas at risk from each of the target micronutrients (Boron, Copper, Manganese, Molybdenum and Zinc) for the major grain crops grown. The study area is the grain producing regions of Australia for each agroecological zone (except the Atherton, Burdekin, Tasmania and the Ord zones). Australian scientists were some of the first to identify widespread micronutrient deficiencies in South Australia (SA) and Western Australia (WA). Applications of these nutrients in particular have been shown to give significant yield increases (responses) in many situations and large areas of land in those two states in particular have become productive by addressing the gross deficiencies seen during agricultural development. The science has been world class and much of the research is preserved in the literature. This research has penetrated into good farming practice with applications of micronutrients a routine part of many growers fertiliser program. So now, there are few places where gross deficiencies are seen, and generally the responses are smaller than responses when the land was cleared for agriculture. It is difficult to provide an estimate of the current responses, but where seen they are generally in around 10 to 15%, although if across a large area this would be significant to both the grower and the industry. The assumption made by some growers is that high yielding crops which have macronutrient demands met, now require additional micronutrients and many suppliers of micronutrient supplements are keen to promulgate this assumption. On the other hand, there has been little active micronutrient research in the eastern states in particular, so advisors and growers may not readily appreciate risks. Widespread deficiencies in WA have been actively researched over recent decades, tapering off in the past few years and the future of this effort is uncertain. A particular problem in assessing situations where micronutrient applications will result in yield increases is that none of the tools available are reliable indicators. Very wet or very dry conditions and soil type can affect potential response. The availability of residual applied nutrients, especially for Zn and Cu, can alter this potential response, as can lime and tillage practices. Root pruning herbicides, such as the ALS- inhibitors, can reduce root access to micronutrients, and there are also genotypic differences in micronutrient efficiency that affect potential yield response. Soil tests have been used diagnostically but generally only have a low to moderate predictive capacity depending on soil type, crop species, environmental conditions and past management. The usual extractant for Zn, Cu and Mn is DTPA which complexes the free cations for analysis. Hot- water extractable B is the most common test for B availability. While critical soil test values have been developed for Zn, B and Cu, Mo and Mn do not have routine tests with reliable calibrations. Plant tissue tests have been developed to assess micronutrient status in growing crops as a guide to in- season remedial action. In general, when the correct tissue is taken at the right time, these tests are reliable diagnostic tools, even though there are some problems with transient deficiencies due to soil conditions, or where plant roots are yet to access deeper micronutrient reserves. However, tissue tests are not as widely used as soil tests possible because of the difficulty of sampling, the This work is not to be cited without the permission of the author. 8

9 need for timely turnaround in analysis and slightly different protocols for different micronutrients (whole plants, youngest fully expanded leaf, petioles, etc.). There would also seem to be an opportunity for a post- factum assessment of field micronutrient concentration by assessing grain concentrations. It is clear that grain concentration is related to the uptake and then remobilization of micronutrients and the latter factor is an important link between soil supply and grain concentration. Yield dilution is also a factor likely to affect grain micronutrient concentration, as are the synergistic or antagonistic relationships among nutrients, such as where high soil P can reduce Zn uptake, or where high N can reduce Cu remobilization. Geology and pedogenesis play important parts in determining soil micronutrient concentration and many cases the amount of plant available micronutrient is a consequence of the surrounding chemistry, in particular soil ph and soil organic carbon, which alter the solubility of the redox products and/or interact to produce organic ligand complexes. The metallic cations (Cu, Mn and Zn) are also relatively immobile and their availability can be reduced in minimum tillage systems. Boron, because it is undissociated in neutral to acid soils, will move readily with leachates and can become positionally unavailable on particular soils. Mo availability is strongly affected by ph, also iron and aluminium complex Mo and makes it less available under acid soils. Cobalt is not essential for plants but is required by livestock and is important for the bacteria in legume nodulation. Selenium, like fluorine and iodine, is not essential for plants but is important in human and animal nutrition. Other micronutrients, such as Cadmium (Cd) can be unacceptable contaminants in food. Selenium (Se) is present at variable levels in grains and while not an essential nutrient for plants, is essential for animals including humans. The MPCN2 project #16 aims to assemble these types of data to make assessments of micronutrient deficiency risks for B, Cu, Mn, Mo and Zn based on particular soil properties. The project comprises several components based on the weight of evidence of the risk of micronutrient deficiency. The lines of evidence collected in this project are: 1. Grain micronutrient concentrations on a regional basis. 2. Review of past research on micronutrient responses. 3. Using soil properties to infer micronutrient availability. 4. The ASC, and soil orders important to the grains industry. 5. Soil properties of ASC Orders. 6. Micronutrient soil test values within particular ASC orders or sub- orders. 7. Grain micronutrient concentration within particular ASC orders or sub- orders. 8. Summary of ASC orders or sub- orders at risk of micronutrient deficiency. o Soil Orders and agroecological zones most at risk o Crop species most at risk. o Management issues modifying risk. Summary and synthesis This work is not to be cited without the permission of the author. 9

10 1. Grain micronutrient concentration on a regional basis a preliminary survey. Grain micronutrient concentration is sensitive to changes in the supply of micronutrients from the soil and so provides an assessment of the amount plant available nutrient over the whole season. While it is clear that there are significant differences in concentration due to yield dilution and intra- specific variation in nutrient acquisition, micronutrient levels in grain, along with other data, can provide a weight of opinion on the risk of deficiency. Despite the limitations, spatial and temporal patterns in barley micronutrient concentrations could be related generally to areas with recognised micronutrient deficiencies (Reuter 1994). In 2009, IPNI undertook a survey of wheat grain nutrient concentrations using the wheat NVT sites from southeastern Australia. This was essentially to review macronutrient off- take figures for wheat as used in nutrient budgets, but the analyses also provided data on the spatial and temporal pattern of grain micronutrient concentrations. Wheat grain samples of two varieties (Yitpi and Gladius) were collected from the 2008 and 2009 seasons from 70 sites across 12 agro- ecological regions in southeastern Australia. The grain was sourced from National Variety Trial (NVT) sites, managed using commercial best practice. Site details are in the full report (Norton 2012). It should be noted that many of the South Australian sites received a base fertilizers with Zn. Grain samples from each site were randomly selected from the harvested grain sub- sample, dried, weighed and processed for nutrient analysis by Inductively Coupled Plasma Optical Emission Spectrometer (ARL 3580 B, Appl. Res Lab. SA, Ecublens, Switzerland) and results are reported on a dry grain basis. This report will only present data for Al, B, Cu, Fe, Mn and Zn. Grain Mo and Co concentrations in the survey were generally less than the analytical limits (<0.4 mg kg - 1 ) for assays using ICP- OES so are not reported here. As there were no replicated samples from each site in each year, the data were assessed using a one- way analysis of variance to compare nutrient densities using either site (70 site years) or varieties (Gladius and Yitpi). In addition, the data set from SA was used to compare annual nutrient concentrations in 2008 and Table 1. P values for the F test in one- way analyses of variance for regions, cultivar or year from the NVT data set analysed. Al B Cu Fe Mn Zn Regions Cultivars Year (SA only) Table 1 shows significant regional and differences for all grain micronutrient levels tested and temporal differences for B, Cu, Mn and Zn. There were significant genotypic differences between Yitpi and Gladius, with the former Zn concentration 25.0±0.8 mg kg - 1 and the latter 21.1±0.9 mg kg - 1. Similarly, grain Al concentrations were 3.4±0.5 mg kg - 1 for Yitpi and 5.2±0.5 mg kg - 1. Even though micronutrient concentration could not be significantly correlated with soil ph (data reported in Norton 2011), regional micronutrient concentrations (Table 2) reflect the soil conditions that are common in those regions. Alkaline soils that are common in the SA Murray Mallee, SA Lower EP and the Vic Mallee, had the lowest grain Fe, Mn and Zn levels. Conversely high Al, Fe and Mn levels could be expected on acid soils, and is seen in the NSW South West, NSW South East and Vic North Central. There were also high grain Al concentrations in the SA Murray Mallee and the SA This work is not to be cited without the permission of the author. 10

11 Upper EP where alkaline soils are dominant, and Brautigan et al. (2012) reported high available Al levels in these soils. Table 2. Mean and standard deviations of micronutrients (Al, B, Cu, Fe, Mn and Zn) for wheat samples from the 2008 and 2009 NVT sites for Yitpi and Gladius. All values are for dry grain (0% moisture concentration). Colour indicates low (green), medium (yellow) or high (red) risk of deficiency from the data. Region Site Years Al mg kg - 1 B mg kg - 1 Cu mg kg - 1 Fe mg kg - 1 Mn mg kg - 1 Zn mg kg - 1 NSW South East 4 3.5± ± ± ± ± ±2.4 NSW South West 4 5.1± ± ± ± ± ±2.4 SA Lower EP 6 1.6± ± ± ± ± ±2.0 SA Mid North 7 3.1± ± ± ± ± ±1.8 SA Murray Mallee 9 7.6± ± ± ± ± ±1.6 SA South East 5 1.1± ± ± ± ± ±2.2 SA Upper EP ± ± ± ± ± ±1.4 SA Yorke Penn ± ± ± ± ± ±2.0 Vic. Mallee 8 4.5± ± ± ± ± ±1.7 Vic. North Central 2 3.4± ± ± ± ± ±3.4 Vic. North East 2 5.1± ± ± ± ± ±3.4 Vic. Wimmera 5 4.6± ± ± ± ± ±2.2 Critical Value Mean 4.3± ± ± ± ± ±7.3 Grain Zn concentration varied widely across the survey area, and mean grain levels were lower than a desirable levels indicated by Cakmak et al. (1999). Grain Zn levels also varied between regions on a per grain basis, and ranged from around 100 ng Zn kernel to 1600 ng Zn kernel - 1 with around 18% of samples having less than 500 ng Zn kernal - 1. Compared to the critical levels in Reuter and Robinson (1997), none of the grain samples had Cu levels less than 1 mg kg - 1, 8% of samples were < 25 mg Mn kg - 1 and 60% of samples were < 2.0 mg B kg - 1. Conclusion Even under good fertiliser management, low Zn, Mn and B grain concentration are common in many cropping regions. Further research aims to link these deficiencies to particular soil types so that growers can make a reasoned assessment of the risk of micronutrient deficiency for their production system. This work is not to be cited without the permission of the author. 11

12 2. Review of past research on micronutrient responses in crops. It is beyond the resources available, and the scope of this report, to provide a full review and summation of the literature on micronutrient deficiencies in crops in Australia. Recent excellent reviews are already available for Australia (e.g. Holloway et al. 2008) and internationally (e.g. Bell and Dell 2008). Here is reported a collation of field and laboratory experiments on micronutrient responses in crop species to assess the veracity of a particular micronutrient response on particular soil types. The data were collected from some published research as well as field research from various farming systems groups across Australia. These reports are summarised for each micronutrient investigated, the crop species grown and the soil types used. Ninety- four field experiments were selected and summarised in Table 3. This approach was taken so that experiments that did not give a significant response were included, because reports showing positive responses are more likely to be published than a nil response. Table 3. Nutrient experiments and states, or by nutrient and crop. B Cu Mn Mo Zn By State NSW SA 3 8 Qld 1 1 Vic WA By Crop Barley 2 1 Canola Pulses 6 2 Wheat By Australian Soil Classification Soil Order Calcarosol Chromosol Ferrosol 1 Kandosol Oxisol 1 Sodosol Tenosol Vertosol Table 4. Wheat experiments with particular micronutrients on various Australian soil orders, with the number that showed a significant difference and the number of experiments in response to the added micronutrient. ASC Order B Cu Mn Mo Zn Calcarosol - 0/3 2/3-1/6 Chromosol - 5/ /5 Ferrosol /1 Sodosol - 4/6 0/1-11/16 Vertosol 0/1 0/ /8 The vast majority of these field experiments were undertaken on wheat and Zn and most of these were on Vertosols and Sodosols, which probably reflect that these are the major crop grown on This work is not to be cited without the permission of the author. 12

13 these soils. Tables 4, 5 and 6 show the number of experiments reported and the number of those experiments that showed a significant response to the supplementary micronutrients in wheat, canola and pulses respectively Table 5. Canola experiments with particular micronutrients on various Australian soil orders, with the number of experiments and the number that showed a significant difference in response to the added micronutrient. ASC Order B Cu Mn Mo Zn Calcarosol /2 Chromosol 0/1 0/1-0/1 0/1 Kandosol 0/2-2/2 2/2 - Sodosol 0/1 0/1-0/1 0/1 Tenosol 2/4-1/1 1/1 - Table 6. Pulse experiments with particular micronutrients on various Australian soil orders, with the number of experiments and the number that showed a significant difference in response to the added micronutrient. ASC Order B Cu Mn Mo Zn Chromosol - - 3/3-0/1 Oxisols /1 - Sodosol - - 3/3 - - Tenosol - - 1/1 - - Vertosol /1 Based on these data, there is strong evidence of: B responses for canola on Tenosols. Cu responses for wheat on Chromosols and Sodosols. Mn response for wheat on Calcarosols, and for pulses on Chromosols, Sodosols and Tenosols. Mo responses on very acid soils generally (Tenosols, Kandosols) Zn responses for wheat on Chromosols and Calcarosols. There are other data sources such as the micronutrient field responses recorded by the fertilizer companies such as CSBP (WA) and Incitec Pivot (Eastern Australia). Incitec Pivot Fertilizers reports from its database that it has 248 experiments on Zn, 243 on Cu, 116 on Mn, 41 on Mo and 6 on B. These experiments are from 1971 until now and cover Queensland, New South Wales, Victoria and South Australia, and many of the experiments are base dressings compared with and without Zn. CSBP also reports trials from 24 sites from 2007 to 2012 about experiments with Cu (7 sites), Mn (8 sites), Zn (8 sites), Mo (2 sites) and B (2 sites). Many of these experiments are spatially referenced and these could provide additional data to validate the frequency of responses in Tables 4 to 6. These data are corporately held and negotiations would need to be undertaken to access these experiments. The literature on field micronutrient experiments in Australia is voluminous with over 70 years of active research in Australia, particularly in WA and SA. It would take a large co- ordinated effort to collate this research and present it in a way similar to the macronutrient data presented through the Better Fertilizer Decisions for Crops (BFDC) database. It is highly likely that much of the data, even though published in the literature, may not have the appropriate metadata for interrogation, as was noted in the collation of macronutrient responses presented in the Better Fertilizer Decisions for This work is not to be cited without the permission of the author. 13

14 Crops project. Consideration of the source, rate, timing and placement of micronutrients would make this compilation more illustrative than integrative. The value of compiling and presenting the specific research trials is questionable, as much of the previous research has already been summarised in extension publications produced by state agricultural agencies. These data, mostly available electronically, represents a valuable source of information for growers and advisors. Conclusion This collation of field and laboratory experiments on micronutrient response has supported the general view of risk of micronutrient deficiency presented in part 1 of this report. B responses for canola on Tenosols. Cu responses for wheat on Chromosols and Sodosols. Mn response for wheat on Calcarosols, and for pulses on Chromosols, Sodosols and Tenosols. Zn responses for wheat on Chromosols and Calcarosols. Although not specifically reviewed here, there do not appear to be many reports of large (40% or greater) responses to micronutrients in modern cropping systems, despite their high productivity. This work is not to be cited without the permission of the author. 14

15 3. Using soil properties to infer micronutrient deficiency The literature often refers to areas at risk of micronutrient deficiency in terms of soil types or particular soil properties. A summary of these regions is given below: B deficiency New South Wales tablelands and southern slopes, derived from granite or sandstone. Acidic sandy soils of Western Australia Yellow and Bleached Orthic Tenosols, Brown Chromosols. Cu deficiency Coastal and inland calcareous soils of marine origin. High organic matter soils. Acid weathered soils derived from granites and sandstones. Calcareous and siliceous sands of southeastern SA and western Victoria. Mn deficiency Calcareous soils of the Eyre and York Peninsula soils >80% calcium carbonate. Calcareous (alkaline) and acidic sandy (low inherent levels) soils of Western Australia. Gravels in an area South of Moora to Katanning and east from Corrigin to Dumbleyung in WA. Neutral to alkaline soils of the Mallee area [north of Esperance] in WA. Victorian northwestern alkaline soils? Regions of low Mn often lie within regions of low Zn and Cu in Vic, SA and WA. Mo deficiency Highly weathered acidic soils in Adelaide Hills, Kangaroo Island, lower Eyre Peninsula. Highly weathered acidic soils in southwestern WA. Southern tablelands of NSW, central Victoria, and coastal lowlands of Queensland. Mo may be deficient in soils that have acidified through time by the removal of agricultural products. Unlikely where Cu, Mn and Zn are low. Zn deficiency Podzolic sands, lateritic podsolic sands, yellow earths, calcareous sands. Acidic sands (SA, Western Victoria, WA). Sand over clay (WA). Neutral to alkaline Vertosols. Grey and red- brown calcareous sands and sandy loams, shallow grey and red calcareous soils, alkaline grey, red and brown clays, heavy grey and black cracking clays. Use of lime and gypsum for amelioration of acid soils. Low Cu and Zn are often contiguous. Using soil properties from soil surveys to infer risk of nutrient status through the development of pedotransfer functions (Bouma, Adv. Soil Sci, 9, ) is a strategy to derive the unknown from the known. These functions can then be used in a soils inference system as a set of rules (McBratney et al. 2002, Geoderma, 109, 41-79). If such inferences can be drawn, this provides the basis of mapping data such as the risk of micronutrient deficiency using the current soil survey data within the ASRIS database. Because many of the soils properties are available as digital mapping sets, such an approach is attractive providing that reliable pedotransfer functions can be developed. This work is not to be cited without the permission of the author. 15

16 South Australia has also developed soil maps showing the distribution of potential micronutrient deficiencies (Reuter et al. 1988, Trace Elements in South Australian Agriculture, Department of Agriculture, South Australia, Technical Report N0 139). CSBP has used its soil test database to develop spatial and temporal trend maps for macronutrients (N, P, K and S) in WA but has not extended this methodology to micronutrient risk (Neuhaus et al. 2012, ASA meeting paper 7996). However, the Neuhaus paper reports that the database has an annual input of 10,000 to 15,000 tissue- testing samples that includes the micronutrients, so that there is the potential to estimate risk of micronutrient deficiency across WA. Wong et al. (2005) reported that soil properties could be used to estimate the risk of B deficiency in soils in southwest WA. This approach considered the relationship between soil B levels (CaCl 2 extractable) and soil properties using reference soil data, and then verifying those estimates against field and glasshouse studies. They conclude that a similar approach may be of value for mapping other deficiencies of relevance to agriculture. The risk of B toxicity has also been mapped for southwest WA using essentially the same soil properties (Lacey and Davis, 2009, Department of Agriculture Farm Note 388). Based on the extensive literature available, inherent micronutrient availability can be estimated from soil texture, soil ph, organic carbon concentration and other parameters, as summarized in Table 7. This approach is quite subjective; it does not necessarily have associated functions to quantify these relationships and develop into more objective criteria or critical values. Despite that, these factors should provide prima facie evidence about the risk of micronutrient availability. Table 7. A summary of soil and climatic factors affecting micronutrient availability. B Cu Mn Mo Zn ph > ph < Sand content High organic C content high P content water- logged soil drought compaction indicates increased availability, - indicates reduced availability. So based on these criteria, the following approximations can be made: Boron Deficiency is most likely on acidic, sandy soil with low organic matter (low ph, low WHC). Adequate supply (to toxic) is likely on alkaline, low organic matter soils. Very mobile and subject to leaching. Short residual availability due to a high leaching potential. Copper Deficiency is most likely on alkaline and sandy soils, with a high organic matter concentration (high ph, low WHC, high OM). Adequate supply under acidic, and wet compacted heavy soils. Not readily leached. Long to very long residual availability with organic ligand complexing (>5 years). This work is not to be cited without the permission of the author. 16

17 Manganese Deficiency is most likely on well drained, alkaline and dry soils (high ph, low WHC, also cold, wet soils). Adequate supply is most likely on acidic, waterlogged soils with high organic matter. Can leach as present as Mn 2+ or MnO 2+. Short residual availability. Molybdenum Deficieny is most likely on acid sandy soils with poor P history (low ph, low WHC). Adequate supply on alkaline heavy soils. Not usually long lasting declines to ~50% after 2 years, depending on soil ph. Zinc Deficiency is most likely on alkaline sands with a high P concentration (high ph, low WHC). Adequate supply is most likely on heavy, acidic, high organic matter soils. Residual availability is moderate (3-5 years) on alkaline soils. Conclusion Soil properties can be used to infer a prima facie risk of micronutrient deficiency. This work is not to be cited without the permission of the author. 17

18 4. The Australian Soil Classification and soil orders significant to the grains industry. The Australian Soil Classification (ASC) (Isbell, 2002) is based on profile properties and arrangement of horizons and diagnostic layers (such as pans). It built on earlier classifications, and has developed a framework in which soils can be organized and described. It is based on particular diagnostic attributes, horizons, or materials, and draws laboratory data on soil physical and chemical properties into the classification. There are 13 Soil Orders, within which soil types are further differentiated by Suborder then Great Group then Subgroup. A summary of this classification is given in Appendix Table 1. The ASC at Soil Order has been mapped and is available through the Australian Soil Resource Information System (ASRIS) and also as part of the application SoilMapp. The latter provides growers and advisors with the opportunity to identify soil order (at least) using georeferencing. In some cases, soil properties that assist with classification relate to micronutrient availability and so are interpretable from the soil classification, particularly at the Order, Great Group and Subgroup levels. As the properties of layers used to differentiate soil types are often not the surface layers, the influence of the diagnostic layer on the availability of micronutrients in the profile as a whole is a source of uncertainty. The ASC has a great utility as the inherent properties of some soils are much better described using it rather than the older Northcote system. For example, what was considered a red- brown earth (Dr) with Northcote soil classification could be a Chromosol (non- sodic, non- acidic subsoil), a Calcarosol (calcic throughout) or a Sodosol (sodic subsoil). In terms of defining micronutrient availability, the ASC assists with identifying soils that have properties that are likely to be of importance. Table 8. Areas of each Australian Soil Class by land use. Data were derived from the Australian Natural Resource Audit (ANRA, 2001). Native Pasture (Mha) Dryland Farmed (Mha) % of dryland farming Irrigat. (ag & hort) (Mha) Australian Soil Order Natural (Mha) Other (Mha) Total (Mha) Calcarosol % Chromosol % Dermosol % Ferrosol % Hydrosol % Kandosol % Kurosol % Organosol % Podosol % Rudosol % Sodosol % Tenosol % Vertosol % Table 8 indicates that the main agricultural soils are Sodosols (40%), Vertosols (13%), Kandosols (12%), Chromosols (10%), Calcarosols (9%) and Tenosols (7%), which together make up over 90% of the soils used for dryland agriculture. Even so, within each agroecological zone, particular soils may This work is not to be cited without the permission of the author. 18

19 be of importance, even though they are only a small percentage of the soils within a state. For example, in Queensland, Ferrosols are a significant agricultural soil and many of the 2.33 Mha would be used agriculturally, although this soil type makes up only a small proportion of national soils. Similarly, Orthic Tenosols are not widely used for cropping other than in WA where they are common cropping soils. In terms of agricultural use, Rudosols, Hydrosols, Organosols and Anthroposols will not be considered in this assessment, as they are not likely to be widespread in the grain growing areas. Kurasols (both Brown and Red) are generally found in high rainfall zones ( mm) along coastal and subcoastal regions. They are generally used for grazing or native hardwood forests. Table 9. Percentage of soil orders within each agroecological zone of the Australian cropping regions (excluding the Burdekin). Data were derived from the ASRIS database. Grand Agroecological Calcar Chrom Derm Ferr Kand Kuro Pod Sodo Teno Vert Total Zone osol osol osol osol osol sol osol sol sol osol (km 2 ) NENSW- SEQ ,7392 NWNSW- SWQ ,6712 Qld Central ,1186 Northern * NSWCentral ,2780 NSWVicSlopes ,238 SAMidNorthLEP ,541 SAVicWimmera ,428 VicHRZ ,614 VicSAMallee ,319 Southern * WACentral ,709 WANorthern ,079 WASandplain/WA Mallee ,921 Western * Grand Total ,834,820 *Percentages do not sum to 100 as the areas exclude some ASC Orders. The data in Table 9 is derived from ASRIS soil maps using shape files designed around the Natural Resource Management regions, and these regions can be joined to form the agroecological zones as defined b GRDC. The values for each soil order were calculated from the Digital Soil Atlas (Principal Profile Form classification), translated to ASC order following Ashton and McKenzie, 2001, (both from Hydrosols, Organosols, Rudosols, areas of rock and lakes have been excluded from the summary table (Table 9). Across Australia, Sodosols, Kandosols, Vertosols and Calcarosols are the dominant soils within the agroecological zones. However, some soils have a high regional significance such as the Kandosols of NWNSW- SWQ or in the central and northern AEZ of Western Australia. Calcarosols are important soils in the SAMidNorthLEP, SAVicWimmera and the VicSAMallee as well as in the WA Sandplain/WAMallee. Chromosols are regionally important in the SAMidNorthLEP and to a lesser extent in the NSWSlopes. Some of the less widespread soils are Dermosols, Ferrosols, Kursaols and Podosols, although growers with these soil types on their property would still need some assurance on the risk of micronutrient deficiency, even though it may not be important to the grains industry as a whole. The NVT soil test database was used to assess the relative agricultural significance of particular ASC orders for the cropping industries, although data from WA is under- represented in these data. The This work is not to be cited without the permission of the author. 19

20 geo- references for each site between 2008 and 2011 were used in conjunction with ASRIS mapping information to estimate the ASC order for each site in the NVT database. The ASC order suggested from ASRIS was checked against the soil test values (ph, EC, texture, colour) for the site, and where the classification was uncertain the site was excluded from the analysis. Typically, uncertainty occurred where a Calcarosol, Sodosol or Chromosol were presented as options. The major ASC orders used for the NVT trials were Vertosols (35%), Sodosols (28%) and Calcarosols (16%). Dermosols and Ferrosols were regionally significant in northeastern NSW and Kandosols were most represented in southeastern NSW and northeastern Victoria (Appendix 2). This NVT dataset analysed did not contain many WA soils, because the data presented did not have soil micronutrient tests performed on them. Conclusions Maps of the ASC Soil Orders are already available through digital platforms, so the opportunity is to link current mapping the risk assessments based on soil order rather than remapping risk. Sodosols and Kandosols are significant cropping soil types in all regions, while Tenosols are important in WA and Kursaols also in Northwest NSW/SW Queensland. Vertosols and Calcarosols generally are more important in the eastern states. The soil types assessed as important from ASRIS were consistent with the distribution of soil types estimated from the NVT database, although data from Western Australia is scarce. ASRIS Australian Soil Classification Soil Order, level 3 map. (ASRIS database) This work is not to be cited without the permission of the author. 20

21 5. Soil properties of ASC Orders The South Australian soil database used to populate the ARSIS detailed soil descriptions was made available and interrogated to estimate the actual soil properties and their ranges for each ASC order (Table 10) and suborder (Appendix Table 3). Unfortunately there were no Ferrosols in that dataset. Because the soils described are part of a national descriptive system, the properties derived from the South Australian dataset should reflect the soil properties of the same soil orders from other states. Table 10. Means (± standard deviation) for selected soil properties of the ASC soil orders derived from the South Australian ASRIS soil descriptions. ASC Soil ph (L1) Soil ph (L3) Clay % (L1) OC% (L1) Ks (L1) CEC (L1) Calcarosol 7.8± ±0.2 18±9 1.7±1.6 88±123 17±10 Chromosol 5.5± ±1.0 14±7 2.1±1.3 67±81 9±4 Dermosol 6.5± ±1.2 29±9 2.5±1.5 21±7 19±8 Ferrosol* Kandosol 6.4± ±1.4 15±8 1.8±1.0 55±41 11±5 Kurosol 4.8± ±0.6 12±4 2.9±1.4 68±64 8±3 Podosol 4.8± ±0.5 5±1 2.0± ±21 6±2 Sodosol 6.1± ±1.0 14±7 1.7±1.1 72±84 9±5 Tenosol 5.8± ±1.3 11±6 1.4± ±157 7±4 Vertosol 7.1± ±0.6 44±4 2.7±1.5 12±5 35±10 * No data on Ferrosols from this database. Based on the soil test database, Table 11 indicates the probability of particular soil orders having conditions indicated in Table 3. Table 11. Probability (%) of a soil within a given ASC order having ph and % clay concentrations falling within the indicated reanges that affect micronutrient availability. ph L1 ph L3 Clay % L1 ASC <5.5 >7.5 <5.5 >7.5 <5% >15% Calcarosol Chromosol Dermosol Ferrosol* Kandosol Kurosol Podosol Sodosol Tenosol Vertosol * No data on Ferrosols from this database. The probability of a soil order having combinations of properties will better indicate risk of deficiency occurring in an Order, and we calculated for high ph and low clay in L1, low ph and low clay in L1, and low ph and low clay in Li and low ph in L3 (Table 11). It was necessary to include L3 ph as Wong et al (2005) found subsoil ph >7 was a strong indicator adequate B in WA. Generally there was good agreement with previously estimated risk classes, with some exceptions. In particular, it appears that Chromosols and Sodosols should have a moderate rating for B and Mo This work is not to be cited without the permission of the author. 21

22 potential deficiency, while the risk of Podosols being deficient in Cu, Mn, Zn was over estimated. Analysis of field trials and direct analysis of micronutrient availability will provide less equivocal links between soil type and deficiency risk. Table 12. Selected soil propeties (± standard deviations) of each ASC Order and the risk of those soils having a high, medium or low risk of micronutrient deficiency. Colour indicates low (green), medium (yellow) or high (red) risk of micronutrient deficicency from the data. ASC Soil ph Clay % OC% B Cu Mn Mo Zn Calcarosol 7.8±0.2 18±9 1.7±1.6 Low Mod High Low High Chromosol 5.5±0.9 14±7 2.1±1.3 Mod Mod Low Mod Mod Dermosol 6.5±1.0 29±9 2.5±1.5 Low Mod Low Low Mod Ferrosol * * * * * * * * Kandosol 6.4±1.1 15±8 1.8±1.0 Low Mod Mod Low Mod Kurosol 4.8±0.7 12±4 2.9±1.4 High High Low High Low Podosol 4.8±0.7 5±1 2.0±0.8 High Mod Low High Mod Sodosol 6.1±1.0 14±7 1.7±1.1 Low Mod Low Low Mod Tenosol 5.8±1.1 11±6 1.4±0.6 High Low Low High Mod Vertosol 7.1±0.8 44±4 2.7±1.5 Low Mod Mod Low Mod Conclusions Based on the soil properties, crop are most likely to be at risk of deficiency on particular soils are; B deficiency on Kurasols, Podosols, and Tenosols, Cu deficiency on Kurasols managenese deficiency on Calcarosols. Mo deficiency on Kurasols, Podosols, and Tenosols, Zn deficiency on Calcarosols, This work is not to be cited without the permission of the author. 22

23 6. Micronutrient soil test values within particular ASC orders or sub- orders. Even though soil tests for micronutrients are not reliable and robust indicators of neither available micronutrient supply, nor the response to added fertilizer, existing data on soil test levels will be interogated for each soil type collected from the NVT soil test database supplied. Standard soil tests for Cu, Zn and Mn (0-10 cm as DTPA extracted) and B (0-60 cm or cm as either hot water or hot CaCl 2 extracted) were matched against the georeferenced location of the NVT site. From this dataset, 1356 site/years of soil tests was compiled with soil ph Ca, soil organic carbon %, soil texture, exchangable cations and the micronutrient analytes. Each site was assigned an ASC soil order based on the data on the ASRIS database and the georeference provided for the site. The tentative classifications were then checked using soil texture, soil ESP, soil ph and soil colour. The data from Queensland provided ph W (ph in water) were converted to soil ph Ca using an algorithm derived from paired data within the NVT dataset. There were few data in this set from Western Australia. Table 13. Soil test critical values and interpretive comments. Data taken from Peverill, Sparrow & Reuter (1999) and Rayment & Lyons (2011). Soil report are values from commercial soil testing labs. Micronu Crop Extractant Critical Value How assessed Region trient (R&H Method) B Barley Hot Water O/S Wheat Hot Water 0.12 ph6 O/S Soil Report CaCl 2 (12C) Cu Barley AmmOxalate % of RY* WA Lupin AmmOxalate % of RY WA Canola AmmOxalate 0.35 Non limiting WA DTPA (12C2) 0.2 Non limiting WA Wheat AmmOxalate >0.9 - WA DTPA (12C2) Probability WA DTPA (12C2) <0.2 90% RY SA DTPA (12C2) <1.4/0.7 - SA N higher DTPA (12C2) <0.2 - SA DTPA (12C2) 0.3 (poor) C/N Qld Soil Report DTPA (12C2) Mn Barley CaCl 2 (12C) <10 Non- limiting Vic Canola CaCl 2 (12C) >20? Acid Soil Pots Barley CaCl 2 (12C) <10 Non- limiting Vic Wheat CaCl 2 (12C) <10 Non- limiting Vic Lupin DTPA (12C2) 2-4 Lower in sands WA Soil Report DTPA (12C2) 6-50 Not given Zn >ph7 DTPA (12C2) General <ph7 DTPA (12C2) General Barley DTPA (12C2) %RY SA Mallee Field Pea DTPA (12C2) %RY Mallee Wheat DTPA (12C2) ph/clay/oc WA Wheat DTPA (12C2) 0.8 SA Canola DTPA (12C2) % RY Pot WA Soil Report DTPA (12C2) ?? 0.13 Sand 0.55 Clay These data were then used to assess the mean and standard devations of each of the assigned ASC Orders for B, Cu, Mn and Zn showed large variations within and between regions and different This work is not to be cited without the permission of the author. 23

24 micronutrients show different frequencies of low soil test values. These data were used as they are one of the few publicly available georeferenced soil test datasets for the grains industry. Table 14. Soil test values (0-10 cm) by region as taken from the NVT soil test database Values given are the means and standard errrors for each analyte. Low, medium and high risk are indicated by green, yellow and red cell shading. Colour indicates low (green), medium (yellow) or high (red) risk of micronutrient deficiency from the data. State Region ph (CaCl 2 ) HWS B (mg kg - 1 ) DTPA Cu (mg kg - 1 ) DTPA Mn (mg kg - 1 ) DTPA Zn (mg kg - 1 ) NSW N/E 7.0± ± ± ± ±0.4 N/W 7.0± ± ± ± ±0.3 S/E 5.3± ± ± ± ±0.3 S/W 5.7± ± ± ± ±0.5 Qld CQ 7.5± ± ± ± ±0.7 SEQ 7.1± ± ± ± ±1.1 SWQ 7.4± ± ± ± ±0.7 SA Lower EP 7.1± ± ± ± ±3.8 Mid North 6.9± ± ± ± ±3.8 Murray Mallee 7.4± ± South East 7.1± ± ± ± ±0.5 Upper EP 7.7± ± Yorke P 7.4± ± Vic Mallee 7.4± ± ± ± ±2.2 North Central 5.8± ± ± ± ±0.7 North East 5.2± ± ± ± ±0.6 South West 5.4± ± ± ± ±1.4 Wimmera 7.7± ± ± ± ±0.6 WA Agzone1 5.3± ± ± ± ±1.3 Agzone2 4.9± ± ± ± ±1.0 Agzone3 5.1± ± ± ± ±1.0 Agzone4 5.2± ± ± ± ±01.9 Agzone5 6.0± ± ± ± ±1.3 Agzone6 5.4± ± ± ± ±2.1 Means All zones 6.3± ± ±1.0 24±43 1.0±2.9 Table 13 gives some typical critical soil test values for wheat from the literature. In compiling this table, it is recognised that there are large soil type differences but the values given are at the lower end of the critical ranges given. Some significant issues for these interpretative values are that the critical Cu soil test is higher under high N supply than low N supply. It is also recognised that some pedotransfer functions have been used to estimate B risk (Wong et al. 2005) while Brennan noted that a critical DTPA Zn level required the incorporation of ph, clay concentration and organic carbon levels, although the actual impact on critical values shows that different ASC Orders will have critical Zn DTPA values from 0.16 to 0.32 (Appendix 4). It has also been proposed that ph could be used to modify critical Mn levels, and this could also incorporate clay and organic carbon conents. Soil Mn testing is particularly problematic as soil drying can alter values and even with appropriate drying the interpretive values are not reliable (Uren 1999). It is also noted that while the literature cited in Table 13 indicates CaCl 2 extractable Mn is a useful test, the current commercial laboratory test is a DTPA extract. A critical value of 5 could be This work is not to be cited without the permission of the author. 24

25 used although the only data contributing to this value is from overseas (Graham et al. 1988, p94). Rogers and Swift concluded that neither DTPA Mn nor CaCl 2 were reliable tests for Mn responses in moderately acid soils. It has been reported that lupins (Brennan 1996) and lentils (Brennan and Bolland 2003) are likely more at risk of Mn deficiency than cereals. Ammonium oxalate was suggested as a reliable extractant for Cu in Western Australia where a paddock samples could be compared to an uncleared scrub/vegetation area, but this test is now rarely used in that state. Ammonium oxalate soil extractable Cu is not listed in Rayment and Lyons (2011). Despite those issues, critical values for wheat could be derived from Table 13 as 0.5 mg B kg - 1, 0.2 mg Cu kg - 1, 5 mg Mn kg - 1 and 0.2 mg Zn kg - 1. These values can be compared to the means and standard errors in Table 14, and where this critical value is a standard deviation below the mean, then around 12% of the values would indicate a high risk in that region. However, because many of the datasets are skewed (ie not normally distributed), the proportion of low soil tests can be quite low even if the estimated standard errror is relatively large. Figure 1 shows the probability of exceedance plots for soil test B, Cu, Mn and Zn. Based on the critical values given in Table 13 and applied in Table 15, only 2% of the samples assessed had DTPA Cu levels less than 0.2 mg kg - 1, and 6% had HWS- B levels less than 0.5 mg kg - 1. However 15% of samples had DTPA- Zn more less 0.2 mg kg - 1 and 38% of samples had DPTA- Mn less than 10 mg kg - 1. Another soil database for southeastern Australia of ~30,000 soil tests, showed that 92% of soil DTPA Zn were more than 0.2 mg kg - 1, 93% were more than 0.2 mg kg - 1 DTPA Cu and 78% were more than 0.5 mg kg - 1 HWS B (unpublished). Figure 1. Cumulative probability plots for (a) DTPA- Mn and (b)hws- B, DTPA- Cu and DTPA- Zn soil test values (all mg kg - 1 ) from the NVT soil test database a) b) This work is not to be cited without the permission of the author. 25

26 Using the site location and the ASRIS database, a soil type was estimated for each NVT site within the dataset. The data were then used to assess the mean and standard error for each the soil micronutrient test values. These data are presented in Table 15, using the same critical values as for Table 13, and the risk of low micronutrient deficiency based on soil test data from each soil type is shown. This data set does not contain many soil populations from WA as few of those soil tests included micronutrient status, so data on Podosols is lacking and is scarce for Kurasols. There were also few Ferrosols in the data set used, so these may not be representative of the more general Ferrosol values. While soil test critical values can be best interpreted in terms of soil properties, the indicators above suggest that for cereals, soil test B levels are generally very variable, with some sites showing very high B, but generally risk of low soil B was low on Calcarosols and Ferrosols. Cu soil tests indicate a moderate to high risk on all except Ferrosols (limited data) and Kandosols. Sodosols and Chromosols are a low risk for still Cu for cereals, although low values were seen on Tenosols. Manganese soil test levels were lowest on Calcarosols and Tenosols as expected from the literature, and highly variable on other soils. Zn soil test values are also highly variable and only on the Dermosol and Tenosols was risk of Zn deficiency in cereals considered high, while risk of Zn deficiency on alkaline Vertosols was considered a moderate risk. The B soil test values in Table 15 are for the top 10 cm and this does not necessarily provide indicate the levels of B deeper in the soils which may be accessible by plant roots later in the season. Also, because B is mobile, following wet years B may leach beyond the 10 cm layer. The NVT data indicated that there was little relationship between top 10 B and cm B (r 2 = 0.049, n = 175) so that the soil test B values in the topsoil would not necessarily reflect B supply to the crop. Soil test B in the deeper soil profile was not measured on all soils in all years. Where recorded, Calcarosols had the highest mean deep B values although this was very variable, while the lowest values were from Tenosols and Chromosols. Nuttall and Armstrong (2010) used soil ECse (r 2 =0.81) or ESP (r 2 =0.81) to predict soil B concentration from a set of soil tests from South Australia. Similar, but somewhat weaker relationships were seen in a similar date set from Victoria. Presumably the inverse of these regressions could then be used to derive soil estimates in a similar way to the methods of Wong et al. (2005). Table 15. Mean values (± standard errors) of soil ph, Hot water soluble B, DTPA extractable Cu, DTPA extractable Mn and DTPA extractable Zn in the top 10 cm, from NVT soil tests Colour indicates low (green), medium (yellow) or high (red) risk of micronutrient deficiency from the data. ph (CaCl 2 ) (0-10 cm) HWS B (0-10 cm) (mg kg - 1 ) HWS B* (10-60 cm) (mg kg - 1 ) DTPA Cu (0-10 cm) (mg kg - 1 ) DTPA Mn (0-10 cm) (mg kg - 1 ) DTPA Zn (0-10 cm) (mg kg - 1 ) ASC Order Calcarosol 7.3± ± ± ± ± ±0.7 Chromosol 5.6± ± ± ± ± ±0.3 Dermosol 6.8± ± ± ± ±0.6 Ferrosol 6.6± ± ± ± ±0.9 Kandosol 5.3± ± ± ± ± ±0.4 Sodosol 6.7± ± ± ± ± ±0.4 Tenosol 6.0± ± ± ± ± ±0.9 Vertosol (ph<7.0) 6.3± ± ± ± ± ±0.4 Vertosol (ph>7) 7.8± ± ± ± ±0.3 Critical Value ** 0.2 Means 6.7± ± ± ± ±2.0 % below Critical 22% 6% 2% 21% 15% * HWS deep B soil tests taken from the 2012 soil data. ** Value may be higher, see text for discussion. This work is not to be cited without the permission of the author. 26

27 High levels of soil B, leading to subsoil toxicity, are likely where annual rainfall is less than 450 mm, on clay and clay loam soils with an alkaline (ph Ca >7.5) and sodic (ESP>6%) subsoils, so for Calcarosols, Sodosols and some Vertosols, it would seem that there is low probability of B deficiency. Soil micronutrient data from the Western Australian NVT database were not included as there were few values. However, a data set from the Department of Agriculture and Food Western Australian (DAFWA) was accessed and interrogated in the same way as the data set from the NVT sites. The DAFWA data included a range of soil properties analytes from 184 focus paddocks and the 2012 data reported in Table 16. The focus paddocks were selected as better areas in well- managed paddocks and many paddocks had micronutrient fertilizers applied recently. Each site had an ASC order identified as well as micronutrient levels. In general, soil test values for B and Mn were lower in WA than the eastern states. It may be that the way the paddocks and soil types were selected has skewed the data to higher values than would normally be found in the cropping regions of WA. Table 16. Mean values of soil ph, Hot water soluble B, DTPA extractable Cu, DTPA extractable Mn and DTPA extractable Zn in the top 10 cm (± standard errors), from DAFWA Focus Paddocks survey, Colour indicates low (green), medium (yellow) or high (red) risk of micronutrient deficiency from the data. ph (CaCl 2 ) (0-10 cm) Number of Samples HWS B (0-10 cm) (mg kg - 1 ) DTPA Cu (0-10 cm) (mg kg - 1 ) DTPA Mn (0-10 cm) (mg kg - 1 ) DTPA Zn (0-10 cm) (mg kg - 1 ) ASC Order Calcarosol 6.1± ± ± ± ±0.5 Chromosol 5.1± ± ± ± ±0.2 Dermosol 5.7± ± ± ± ±0.4 Ferrosol Kandosol 5.5± ± ± ± ±0.1 Kurasol Sodosol 5.1± ± ± ± ±0.1 Tenosol 5.5± ± ± ± ±0.2 Vertosol (ph<7.0) 5.7± ± ± ± ±0.3 Vertosol (ph>7) Critical Value/s 0.25/ / Means 5.3± ± ± ± ±0.8 % below Critical 67% 34% < 0.5 2% 41% < 10 1% 11% < % < 5 Wong et al. (2005) suggested that low soil B (less than 0.3 to 0.5 mg kg - 1 HWS B 0-10 cm) could be estimated using a combination of soil properties. Risk was high where soil clay concentrations were <5%, reaching zero at 15% clay. The risk was also related to soil ph Ca, with a high risk below 4.5 and a low risk above 6.0. Geology (sandstone) and subsoil ph (<7) were associated with higher risk, and these parameters were used to generate risk maps across southwestern Western Australia. Given the data present in the ASRIS database, similar estimates could be made for other regions in Australia using the same procedure. Based on the soil properties for each of the ASC soil orders in Table 6, a risk assessment of would suggest that Kurosols and Podosols were very likely to be at risk of B deficiency, while Calcarosols and Vertosols were unlikely (Table 16). This approach was used to populate the data in Table 16, which indicates a high risk for Kurosols and Podosols. This work is not to be cited without the permission of the author. 27

28 It is generally recognized that canola has a higher B demand than cereals, based on differences in tissue or grain B concentrations rather than on the assessment of yield responses of wheat and canola. There are few experimental results to test the hypothesis that the higher grain B content indicates a high nutrient demand. Brennan (1992) used soil ph, clay concentration and organic carbon concentration to develop wheat critical levels for Zn, and the crticial values in Tables 14 and 16 for the Zn soil test values are 0.21 for Chromosols, Kandosols, Sodosols and Tenosols, 0.25 for Calcarosols and Dermosols and 0.32 for Vertosols. Brennan (2010) used pot expeirments to estimate a critical DTPA Zn test value for canola of 0.38 mg kg - 1, which was higher than the 0.21 mg kg - 1 established on the same soil type for cereals. Brennan and Bolland (2004) estimated the critical soil test values for Cu for canola as being lower than for wheat, and proposed that canola is better able to access soil Cu. Table 17. Assessment of the risk of B deficiency based on soil properties of particular Australian Soil Classification orders, using the methods of Wong et al. (2005). Colour indicates low (green), medium (yellow) or high (red) risk of micronutrient deficiency from the data (± standard errors). Estimate Soil ph Clay % from ASC Soil ph (L1) (L3) (L1) Table 12 Calcarosol 7.8± ±0.2 18±9 Low Chromosol 5.5± ±1.0 14±7 Mod Dermosol 6.5± ±1.2 29±9 Low Kandosol 6.4± ±1.4 15±8 Low Kurosol 4.8± ±0.6 12±4 High Podosol 4.8± ±0.5 5±1 High Sodosol 6.1± ±1.0 14±7 Low Tenosol 5.8± ±1.3 11±6 High Vertosol 7.1± ±0.6 44±4 Low The soil test values indicated in Table 14 suggest that many soils are at risk of Mn deficiency but this needs to be considered in the light of the reliability of the soil test for Mn. Uren (1999) suggested that it was impossible to diagnose Mn status on the basis of soils test alone because of the rapid changes in Mn availablity in soils in response of changes in water logging status and soil ph. It is of interest that the data from the NVT soil tests (Table 13) suggests there are low Mn levels in areas generally considered to be at risk ie the lower south west of South Australia, the lower Eyre Pennisula and the south west of Western Australia or just use WA. No soil data for Mo was available from the NVT soil test database as the soil test is considered unreliable because of the very small quantities to be assayed, the difficulty of getting a representative sample and a risk of contamination during collection and laboratory handling (Brennan and Bruce 1999). Soil Mo availability Is heavily influneced by soil ph, and when this is above 5.3 (ph Ca ) adequate solution molybdate (form taken up by plants) is present to meet plant demand. Molybdate uptake does compete with sulfate transporters and so high sulfate levels can induce Mo deficiency. A small data set (89 samples) was collected and analysed for CaCl 2 extractable Mo from the 2012 survey. The mean soil Mo content was 202 (±178) μg/kg and no trend with ph or soil type association could be assessed from these data. There are no critical values established for soil Mo in the literature. Therefore, little can be drawn from these results. This work is not to be cited without the permission of the author. 28

29 Conclusions Soil test values from the NVT database indicate that: 6% of B soil test values were below critical and they are lowest on Kurosols and Tenosols and highest on Ferrosols and Calcarosols. 34% of soil B tests were less than 0.5 mg kg - 1 and 11% were less than 0.25 mg kg - 1 in the Western Australia data set particularly on Chromosols and Tenosols. 2% of Cu soil test values were below critical, and they were lowest on Chromosols and Sodosols and highest on Tenosols and Vertosols. 21% of Mn soil test values (24% WA) were less than 5 mg kg - 1, and re below critical, but Mn soil test values are extremely variable on all soil types. Values were lowest on Caclarosols, Tensols and alkaline Vertosols. The high variability is likely a reflection of the low reliability of this soil test. 15% of Zn (1% in WA) soil test values were below critical, and they were lowest on Dermosols and Tenosols, and also low on alkaline Vertosols. This work is not to be cited without the permission of the author. 29

30 7. Grain micronutrient concentration within particular ASC orders or sub- orders. Grain micronutrient concentration can be used as a post- factum assessment of nutrient supply although the strength of the relationship depends on cultivar, yield and how well retranslocated the nutrient is from the plant to the grain. Reuter and Robinson (1999) summarised the results of many experiments to the mid s that investigated the critical ranges of grain micronutrient concentration for a range of species. For Copper, critical values in Reuter and Robinson were between 1.0 and 1.5 mg kg - 1 for Australia, and somewhat higher (2.5) in Canada. Hill et al. (1979) suggested that Cu concentration was unsatisfactory as an indicator of Cu status, but other authors suggest that grain Cu does is related to soil Cu supply (Nambiar, 1976; King and Alston, 1975; Karamanos et al., 1984; Brennan et al., 1986; Brennan, 1992). Soil nitrogen fertility, amongst a range of other management factors can also affect grain Cu concentration. Graham et al. (1987) did note that there are some differences within and between species, when grown in controlled conditions, and those authors looked to introgress rye chromosomes into cereals to improve efficiency. That work has not progressed far though as the traits involved appear complex and will need to address the significant issue of Cu homeostasis noted by Brown and Bassil (2011). Canadian data indicates that canola seed concentrations less than 3 mg kg - 1 are deficient (Reuter and Robinson, 1999). Zinc grain concentration has been widely studied because of the important role it plays in human nutrition. Grain Zn responds to applied Zn as well as in response to soil N and P supply and root diseases, There is also a significant genetic component to this efficiency and many traits have been identified as contributing (Sageghzadeh and Rengel, 2011). Several studies have suggested that a critical grain Zn concentration is 10 to 15 mg kg - 1 with ranges in Zn adequate crops usually above 20 mg kg - 1 (Reuter and Robinson 1999). While that grain Zn concentration may be agronomically adequate, for people of grains based diets, concentrations of 30 mg kg - 1 is considered the target. For canola, there are no data on critical grain Zn concentrations, although Yanusa et al. (2008) reported canola grain Zn concentrations of around 50 mg kg - 1 but this did not change with added Zn from coal fly ash, while Assadi et al. (2010) reported grain Zn concentrations of mg kg - 1. Data from a long term fertilizer experiment at Dahlen showed canola Zn concentration of 34 mg kg - 1, while Grewal et al indicated grain Zn levels ranged from 35 to 53 mg kg - 1 in a range of canola germplasm and differences in seed Zn could be related to better early vigour. Grewal and Graham indicated that low vigour occurred where seed Zn was around 25 mg kg - 1. Boron grain concentrations have been proposed as a reliable measure to determine the potential toxicity of particular soils to B (Hall 2010), and B accumulation in wheat also has a strong genetic basis. B is predomiantely taken up in the xylem stream as undissociated boric acid (H 3 BO 3 ), but in strongly alkaline soils the borate ion (H 2 BO 3 - ) may be present. There is evidence that B is also phloem mobile hence it can be retranslocated into grain. Holloway and Alston (1992) indicated that an adequate wheat grain B concentration was 2 mg kg - 1, although there is more than a two- fold genotypic variation with grain B concentration when wheat is grown on soils with B- toxic subsoils (Paull et al., 1992). A value of 1 mg kg - 1 could be considered to be deficient, although the diagnostic value of this concentration has not been confirmed. Boron efficient cultivars have been reported for canola based on responses in low B soils from both glasshouse and field experiments (Stangoulis et al. 2001). Bell and Frost (2002) altered canola grain B by manipulating B supply during growth, and found that low grain B in canola (<10-20 mg kg - 1 ) had a strong effect on seed viability. However, critical grain B concentration could not be derived from the study. Data from a long term fertilizer experiment at Dahlen showed canola B concentration of 12 mg kg - 1. This work is not to be cited without the permission of the author. 30

31 Managanese is an abundant element in the earths crust, but plant availability is more often mediated by the ph and waterlogging conditions of the soil. Toxicity is often a larger problem than deficiency, particularly on acid and waterlogged soils where Mn 2+ becomes the dominant Mn species in the soil solution. The literature suggests that wheat grain Mn concentrations less than about 20 mg kg - 1 indicate low supply (Reuter and Robinson 1999), and Mn grain concentrations can be quite variable with Burjan et al (2012) reporting large regional Mn differences (13 mg kg - 1 to 57 mg kg- 1 ) for winter wheat in Hungary. There are few reports of the critical Mn concentration in canola, but Assadi et al. (2010) reported canola Mn concentrations of 36 mg kg - 1. Data from a long term fertilizer experiment at Dahlen suggests a canola Mn concentration of 48 mg kg - 1. However, the conclusion from Graham et al. (1985) was that Mn was poorly retranslocated so grain is not a reliable indicator of soil Mn status. There are very few reports of grain Mo concentration and so little can be said about the value of that data set, other than to set some benchmarks for grain concentration. The soil Mo analyses are not reliable and in most cases, using soil ph ca <4.5 is a benchmark for low Mo supply (taken up as MoO 4 2- rather than a particular soil test critical concentration. Data in Reuter and Robinson (1999) suggests that wheat grain Mo concentrations of 0.16 to 0.20 are adequate, but there are no reports of canola Mo grain critical concentrations, although Yanusa et al. (2008) reported canola grain concentrations of around 2 mg kg - 1 which increased to around 8 mg kg - 1 with added Mo derived from coal fly ash. Bell (1995) estimated canola Mo concentration as 0.6 mg kg - 1 (1.5 mg kg - 1 in meal). Competition for root uptake transporters between Mo and S are well known, so that high sulfate levels in the soil can reduce Mo uptake. There is some evidence of intraspecific differences in Mo efficiency and it is also clear that seed Mo concentration increases with soil supply, because selecting seed with high micronutrient concentration appears a viable strategy for improving Mo nutrition in low Mo situations (Brown and Bassil 2011). A critical value of 0.1 mg kg - 1 was used for wheat but this is a subjective estimate based on the reported sufficiency values. To investigate the range of values of grain micronutrient concentrations, wheat and canola samples from around 200 sites used in the National Variety Testing program were collected. These sites are georeferenced and ASC soil order identified, along with the soil test taken for the NVT experiments. A composited sample of grain was taken from these sites, and common varieties across the major sites sampled. This resulted in 309 wheat samples from 137 sites, and the varieties chosen were Elmore (82), Gascoyne (71), Gregory (52) and Scout (104). Two hundred and ninety canola samples from 55 sites were selected, and the varieties sourced were triazine tolerant types (ATR Cobbler (55), CBJunee (58)), conventional types (AV Garnet (7), Hyola50 (18)), imidiazolinine tolerant types (Hyola45CL (39), Pioneer 44Y84 (43)) and glyphosate tolerant types (GT Cobra (44), Hyola505RR (2), Pioneer 45Y22 (24)). All site management details for the NVT experiments are available through the NVT database, and management practices are based on regional best practices for fertilizer use, sowing times, seeding rates and crop protection strategies. Some sites received Zn supplemented fertilizers but no additional micronutrients were used. Grain from the wheat and canola NVT series was analysed for nutrient concentration using ICP- OES. Analytes reported from this analysis are Al, B, Ca, Cd, Co, Cr, Cu, Fe, K, Mg, Mn, Mo, Na, Ni, P, Pb, S, Se, Ti and Zn, although this technique has detection limits for Cd, Co, Mo and Se often below grain concentrations. For the heavy metal analytes, ICP- MS has appropriate detection limits. This report will focus on grain B, Cu, Mn, Mo and Zn concentrations, although the other analytes may be of interest in other projects where grain macronutrient concentrations are of investigated. This work is not to be cited without the permission of the author. 31

32 Similar to the 2010 nutrient concentration report, because the data set developed was not balanced, nor were there replicated samples from each site in each year, the data were assessed using a one- way analysis of variance to compare nutrient densities using either variety (4 for wheat, 9 for canola), Australian Soil Classification, Australian Soil Classification with alkaline class for Vertosols separated, Agroecological zones (13) or NVT region. Table 18 shows that there are significant species, cultivar, regional and soil type differences for all grain micronutrient concentrations in wheat and canola. All micronutrients, except Mo, levels could be related to ASC Order. If the alkaline sublclass of the Vertosols was included, a weak relationship could be seen for Mo in the dataset for wheat. Because of the unbalanced nature of the dataset, it is not possible to assign relative strengths to each of the categorizations tested, but they were all significant at p< Table 18. P values for the F test in one- way analyses of variance for regions, cultivar or year from the NVT data set analysed. Factor B Cu Mn Mo Zn Species wheat/canola Wheat Cultivars ASC ASC+Alk AEZ Region Canola Cultivar Type ASC ACS+Alk AEZ Region a) Grain micronutrient concentration and soil properties Soil micronutrient test value, soil organic carbon or soil ph Ca was regressed against grain micronutrient data from the NVT sites. Regressions were fitted to asssess the relationships between these variables and the coefficients of determination are given in Table 18. Soil test values were poorly related to grain micronutrient concentrations for Cu in both wheat and canola, but for wheat high soil test Zn was significantly related to high grain Zn only in wheat. Grain B in wheat was related to deep soil B concentration. Higher soil ph could be related to higher grain Cu for wheat, but not for canola which could indicate that canola is better able to access soil Cu than wheat, even though the grain concentrations for both species are similar. Soil organic carbon concentration could not be related significantly to any of the wheat or canola micronutrient concentrations. Grain Mn concentration in canola was related to higher soil test values and lower soil ph, and this relationship was suggested as being useful in estimating Mn- deficient soils (Sims and Johnston 1991). While the coefficient of dertmination between CaCl 2 extractable Mo and canola grain Mo was significnant, the relation was negative with high Mo soil test values give lower grain Mo values. Even though some of these relationships were statistically significant, the predictive powers are low, but it does indicate weak relationships in some cases between soil test and grain micronutrient concentration. The conclusion from these data is that multiple soil properties not just a single index may be a better indicator of grain micronutrient concentration. This work is not to be cited without the permission of the author. 32

33 The prediction of grain B, Cu and Zn were estimated using multiple linear regression with soil test value, soil ph, soil organic C and soil EC as the variables. While the coefficient of multiple determination was improved compared to simple linear regressions and significant for all except B in canola (Table 19), it is beyond the scope of this report to investgiate these relationships. However, this does assist in substantiating that micronutrient grain concentrations are related to more than just simple factors derived from individual factors. Precise clay concentrations were not available in the soil test database so could not be included in this analysis but this factor may have assisted with improving the relationships, such as identified for B (Wong et al. 2005) and Zn (Brennan 1992). Table 19. Coefficients of determination (r 2 ) between grain micronutrient concentration and either soil test value, soil ph or soil organic carbon. Crop Soil Test Test value ph Ca OC% Wheat Grain B HWS- B (0-60 cm) Grain Cu DTPA Cu (0-10 cm) Grain Mn DTPA Mn (0-10 cm) Grain Zn DTPA Zn (0-10 cm) Grain Mo CaCl 2 - ex Mo Canola Grain B HWS- B (0-60 cm) Grain Cu DTPA Cu (0-10 cm) Grain Mn DTPA Mn (0-10 cm) Grain Zn DTPA Zn (0-10 cm) Grain Mo CaCl 2 - ex Mo Table 20. Multiple linear regression relationships between grain micronutrient concentration, soil test value, soil ph, soil organic carbon and soil EC, with the coefficients of multiple determination (R 2 ). Constant Soil Test Soil ph Ca Soil OC% Soil EC R 2 Value (0-10) (0-10) (0-10) Wheat B 1.00** 0.10** 0.07* ** (ns) Wheat - Cu 1.41** 0.28** 0.34** 0.12(ns) ** Wheat - Zn 14.70** 3.03** 0.43 (ns) 0.55 (ns) ** Canola - B 10.93** (ns) 0.21** (ns) 0.73 (ns) Canola Cu 1.32** 0.29** 0.35** 0.14 (ns) ** Canola Zn 14.62** 2.72** 0.37 (ns) 0.94 (ns) * b) Response by cultivar There were significant genotypic differences in Cu concentration among the cultivars tested, with the concentrations being highest in Gregory (4.50±0.15 mg kg - 1 ) and lowest in Elmore (3.47±0.12 mg kg - 1 ) and Scout (3.57±0.11 mg kg - 1 ), with Gascoyne (3.82±0.13 mg kg - 1 ) intermediate. Unlike the previous analysis (part 1 of this report), there were no differences in grain Zn concentration seen between cultivars with the mean grain Zn concentration (20.8±5.9 mg kg - 1 ). The cultivar and herbicide tolerance differences for canola were clearly seen in all micronutrients except Mo. Appendix 9 presents some interpretations for canola by herbicide types. c) Response by Geographic Region This work is not to be cited without the permission of the author. 33

34 The mean wheat grain values recorded in each GRDC AEZ are shown in Table 20. The values for Zn are lowest in the Southern regions, while Cu levels are lowest in the WASandplain AEZ. Values collected from the survey in 2008/2009 (Table 2) are in the same ranges as the values collected in the 2012 survey for the Southern Region. When considered on the basis of NVT region, which is the same as the divisions for Table 2, then similar patterns occur (See appendix Table 8). Table 21. Wheat grain micronutrient concentration and standard errors arranged by agroecological zones. Agroecological Zone B mg kg - 1 se* of B Cu mg kg - 1 se* of Cu Mn mg kg - 1 se* of Mn Zn mg kg - 1 se* of Zn NSWNEQldSE NSWNWQldSW QldCentral NSWVicSlopes NSWCentral SAMidnorthLYP SAVicMallee SAVicWimmera VicHRZ WACentral WAEastern WANorthern WASandplain Grand Total * se = standard error of the mean. d) Response by Australian Soil Order Because the different regions and soil types show significant differences for micronutrient concentrations among the relevant groupings, the data collected was interrogated for the variability shown within the groups used. The mean coefficient of variation (CV) for each nutrient and each grouping was calculated and these data indicate that groupings within soil types, with or without the inclusion of high alkalinity group in the Vertosols did slightly reduce the CV. Grouping within Agroecological Zone reduced the CVs for Mn, B and Zn when compared to the CV for soil type groupings, but not for Mo or Cu in wheat. There were little differences for the different ways to categorise the canola grain data. The NVT zones had the largest number of categories but within zone variability for micronutrient concentration was generally higher than the other groupings. Because of the differences in variability within the difference categorisation strategies, the micronutrient concentrations will be presented based on Agroecological Zone with alkaline classes noted. Similarly, because generally the genotypic effects were small, the values presented will be means for the cultivars collected. The data in Table 22 and the intervals shown in Figure 2 show that B levels are generally low but are less than 1.0 mg kg - 1 in wheat grain occur in Dermosols and Tenosols. Grain Cu values are lowest on Tenosols and low on some Calcarosols and Chromosols, although very few samples are below 1.5 mg kg - 1. Wheat grain Mo levels do show considerably more variation within soil groups, mainly because soil ph can be quite variable within many of the all soil classes, although all are above the critical grain value reported by Brennan (2006), although that value is an order of magnitude lower than the This work is not to be cited without the permission of the author. 34

35 critical values suggested in Reuter and Robinson. As would be expected though, it was lowest on acid Calcarosols, Chromosols and Dermosols, but it was also low on the alkaline Vertosols. Table 22. Wheat grain micronutrient concentration for each of 11 soil orders and suborders. Colour indicates low (green), medium (yellow) or high (red) risk of micronutrient deficiency from the data. ASCSO B Cu Mn Mo Zn mg kg - 1 mg kg - 1 mg kg - 1 mg kg - 1 mg kg - 1 Calcarosol 2.1± ± ± ± ±0.9 Chromosol 1.4± ± ± ± ±1.1 Dermosol 1.0± ± ± ± ±3.0 Ferrosol 1.7± ± ± ± ±3.0 Kandosol 1.2± ± ± ± ±0.9 Sodosol 1.3± ± ± ± ±0.5 Tenosol 1.1± ± ± ± ±1.3 Vertosol 1.2± ± ± ± ±0.8 VertosolAlk 1.6± ± ± ± ±0.8 Critical Value Mean* 1.4± ± ± ± ±0.4 % Below Critical 38% 3% 1% 23% 15% *standard error is derived from a data analysis using all wheat and canola data. Figure 3 is the cumulative probability plots of grain micronutrient concentration, showing that 38% and 15% of wheat sample grain B and Zn was less than 1 mg kg - 1 or 15 mg kg - 1 respectively, while less than 4% of samples had Cu or Mn concentrations less than 1.5 or 20 mg kg - 1 respectively. Figure 3. Cumulative probability plots for (a) wheat grain B and Cu and (b) wheat grain Mn and Zn (all mg kg - 1 ) from the NVT grain survey a) Wheat B and Cu. b) Mn and Zn Grain Zn levels on alkaline Calcarosols, Dermosols, all Sodosols and Kandosols were significantly lower than for Chromosols and Ferrosols, Tenosols and all Vertosols. Some of the NVT experiments, particularly those from South Australia, routinely use Zn supplemented fertilizers which may explain the higher values on some Calcarosols and Vertosols. Table 23. Canola grain micronutrient concentration for each of 11 soil orders and suborders. Colour indicates low (green), medium (yellow) or high (red) risk of micronutrient deficiency from the data. This work is not to be cited without the permission of the author. 35

36 ASCSO B Cu Mn Mo Zn mg kg - 1 mg kg - 1 mg kg - 1 mg kg - 1 mg kg - 1 Calcarosol 12.2± ± ± ± ±1.2 Chromosol 11.8± ± ± ± ±0.9 Dermosol 13.1± ± ± ± ±3.5 Ferrosol 12.1± ± ± ± ±3.5 Kandosol 11.3± ± ± ± ±1.8 Sodosol 11.8± ± ± ± ±0.7 Tenosol 12.1± ± ± ± ±2.0 Vertosol 12.2± ± ± ± ±1.2 VertosolAlk 12.5± ± ± ± ±1.9 Critical Value 10? 3.0? 0.3? 25 Mean* 12.0± ± ± ± ±0.4 >>wheat ~wheat? >wheat ~wheat *standard error is derived from a data analysis using all wheat and canola data. The interpretation of the canola data (Table 23) is less clear than that for wheat, mainly because of uncertainty about the interpretive levels for this crop. The literature does indicate that canola has a higher B demand and concentration than wheat, and a somewhat higher Mo demand. Canola is also reported to be more efficient than wheat at accessing Cu (Brennan and Bolland 2004) and Zn (Brennan and Bolland 2006). Even though the literature and this grain survey shows a 6 fold higher B concentration in canola compared to wheat (Table 22) and Table 232 indicate that there were fewer soil classes where there was low grain B in canola. Mo levels in canola were almost twice those in wheat, Grain Cu was lower in canola than wheat even though it is known that canola is able to access Cu more efficiently than wheat (Brennan and Bolland 2004). Canola grain Cu levels tended to be lower on alkaline Calcarosols and Vertosols, while wheat grain Cu levels tended to be higher on those soils, although the significance of this is not clear. The canola grain Mn concentrations were significantly lower than wheat, although these levels were relatively high in both species. Grain Zn concentrations in canola were almost twice the concentrations in wheat, and it is recognised that canola is more efficient than wheat at accessing soil Zn (Brennan and Bolland 2006). Those authors indicated that because canola has more Zn than wheat, although Cu levels are similar, the drawdown of soil Zn would occur more rapidly under canola than wheat, so that the residual value of applied Zn would be less for canola than for wheat. This work is not to be cited without the permission of the author. 36

37 Figure 2. Interval plots for B, Cu, Mn, Mo and Zn wheat grain micronutrient concentrations segragated on Australia Soil Classification Order and suborders. a) Wheat grain B concentrations (mg kg - 1 ) f) Canola grain B concentrations (mg kg - 1 ) b) Wheat grain Cu concentrations (mg kg - 1 ) g) Canola grain Cu concentrations (mg kg - 1 ) c) Wheat grain Mn concentrations (mg kg - 1 ) h) Canola grain Mn concentrations (mg kg - 1 ) d) Wheat grain Mo concentration (mg kg - 1 ) i) Canola grain Mo concentrations (mg kg - 1 ) e) Wheat grain Zn concentrations (mg kg - 1 ) j) Canola grain Zn concentrations (mg kg - 1 ) This work is not to be cited without the permission of the author. 37

38 Conclusions The grain micronutrient survey indicates that there are: 38% of wheat grain samples had B < 1.0 mg kg - 1 and lower values were recorded onferrosols. 3% of wheat grain samples had Cu <0.2 mg kg - 1 and the lowest values were recorded on Tenosols. Wheat grain Mn levels was less than 10 mg kg - 1 in 1% of samples, but grain Mn is an unreliable indicator of soil Mn status. Wheat grain Mo levels were less tha 0.02 mg kg - 1 on 23% of wheat samples, mainly from Kandosols and some Chromosols. Low wheat grain Zn levels are seen on Calcarosols, Dermosols, Kandosols and Sodosols, but not on Vertosols. 15% of the wheat tested had levels less than 15 mg kg - 1. Canola has a a much higher B and Mo concentration than wheat, as well as a higher Zn concentration, although it also appears more efficient than wheat at accessing Zn in particular in all soils. Canola grain Cu levels were about 30% lower than Cu levels in wheat. This work is not to be cited without the permission of the author. 38

39 8. Discussion and synthesis The lines of evidence for the risk of micronutrient deficiency assessed in this report were information from the literature, the importance of inherent soil properties, soil test values, and grain micronutrient concentration. None of these factors alone will adequate inform growers of the complete risk, as the data considered does not include prior nutrient management practices, and therefore the residual value of prior micronutrient applications. Nor does it consider season influences such as drought, waterlogging, both of which can affect nutrient forms and the ability of crops to access soil nutrients. Below are summaries of each line of evidence for each soil type and micronutrient, along with opportunities for future investment in this area of nutrient management. Boron The soil test data showed that only 6% of samples had low topsoil B, but 83% of wheat grain samples had low grain B. The combination of the data presented in Tables 12, 15, 16 and 20 is given in Table 24. Based on these data it would suggest that the soil orders with the highest risk of B deficiency are Kurasols, Podosols and Tenosols. This is consistent with the responses seen in Western Australia as well as in some of the New South Wales tablelands in section 2 of the report. Despite the low grain B levels seen commonly, a higher reliability is placed on deep B tests as an indication of deficiency. These values are variable but would generally be expected to be adequate for many soils. Table 24. Combined risk assessment of low B supply for Australian Soil Classification soil orders. Soil HWS B** HSW B** ASC Grain B*** Properties* (0-10) (10-60) Summary Reliability Mod Low High Low Calcarosol Low Low Low Low Low Chromosol Mod Low Mod Low Mod Dermosol Low Low - High Low Ferrosol - High - Mod Uncertain Kandosol Low High Low Mod High Kurasol High Uncertain Podosol High Uncertain Sodosol Low Low Mod Mod Mod Tenosol High Mod Mod High High Vertosol Low Low Low Mod Low VertosolAlk Low Low - Low Low * From Tables 12 & 17. **From Table 16. ***From Table 22. Kurasols are 8% of the VicHRZ, but not common in any other agroecological zones. Podosols are most common in Western Australia (Northern and Sandplain) but are of a relatively small area. Tenosols are the dominant soil type in the WANorthern, WACentral and WASandplain/Mallee, as well as in the VicSA Mallee. What is not consistent is the indication of B deficiency on Chromosols, which tend to be common on the NSWVic Slopes, and while to soil tests are low, the general soil properties, the literature and the grain B concentrations suggest a moderate to high risk. This work is not to be cited without the permission of the author. 39

40 Because B is mobile, it may become positionally unavailable in wet years due to leaching. Plant tissue tests may or may not reflect this availability if the roots have not reached the deeper parts of the soil. Other factors that will contribute to B risk are recent liming which results from aluminium hydroxides complexing soluble B, or from drought which reduces the volume of soil explored by the roots. In terms of crops, canola is more at risk than cereals even though a recent survey across Western Australia (Brennan et al. 2010) concluded that B was less important than either Mo or Mn. The data on comparative responses of pulses is equivocal, with reports from India (Wankhade et al. 1996) suggesting that chickpea had a higher demand for B than cereals, while a pot experiment in Western Australia indicated that lupin showed either a toxic response or no response to B applied at rates that did not affect canola (Bell et al. 2002). It is known that low B can reduce black gram yields by 40-60% (Rerkasem et al. 1988) but not on wheat or soybean in the same experiment. Bell and Dell (2008) indicated that critical tissue concentrations for wheat are an order of magnitude less than canola, which in turn was 30% less than black and green gram. From these data, it could be concluded that crops most at risk are canola, then wheat, with lupins and lentils less susceptible and chickpea uncertain. Areas for Future Investment Because B is mobile in the soil and does not have a long persistence, decisions on management of B will be likely made on an annual basis so either soil or tissue tests will be an important management tool for areas at risk of B deficiency. Research gaps to address B deficiency for canola on Tenosols are mainly around delivery strategies including timing and products. Low solubility B sources such as B phosphates, Boron frits or boro- silicates could be considered. In terms of developing better risk profiles, the mapping initiated in Western Australia (Wong et al. 2005) could be extended to the Southern and Northern GRDC zones. There are tissue tests developed for wheat and canola that are used diagnostically, but the data to support the critical levels of 5-10 mg kg - 1 for wheat is based on a NSW DPI AgFact Sheet (Dear and Weir 2004). There are no critical values for canola although they are likely to be higher than those for wheat. Copper The soil test data showed that only 2% of samples had low topsoil DTPA- Cu, and around 3% of wheat grain samples had low grain Cu. The combinations of risk assessments by Australian Soil Order for Cu are presented in Table 25. The lines of evidence from the grain concentrations seem unrelated to soil tests and the overall conclusions would be that Cu is well supplied in the regions tested. Ferrosols and Dermosols appear to be low risk, while Calcarosols and Kandosols are moderate to high risk. It was expected that the Calcarosols and alkaline Vertosols would be at higher risk than the non- alkaline forms, but the grain Cu data does not support that hypothesis. The data from Western Australia shows that less than 2% of the 184 paddocks tested had low DTPA Cu. The lowest values were on Tensosols and Chromosols from that data set. The observations support the experimental data that Sodosols and Chromosols are at risk, but the data for Sodosols suggests only a low- moderate risk. Vertosols seem quite variable in both soil test value and grain concentration. There is little to substantiate the low supply for Chromosols and Sodosols despite the experimental record, which may reflect either (or both) inappropriate soil test or grain concentration critical values. This work is not to be cited without the permission of the author. 40

41 Sodosols are the dominant soil type in all except the Northern Region, and Calcarosols are most common in the Southern Region. The risk assessment for Sodosols suggests a moderate, while Calcarosols have a moderate to low risk. These data, and indeed the experimental record, indicates that there is least certainty about the risk of Cu deficiency across the Northern and Southern GRDC regions. The expansion of grain production into the high rainfall zones where Chromosols are common suggests that greater attention could be given to the management of Cu on those soils, particularly where soil organic matter can be high after long pasture phases. The effect of soil organic matter on the availability of Cu makes the interpretation of any soil test for Cu unreliable across soil types. The low reliability of DTPA Cu as a soil test (Brennan and Bolland 2006) indicates that tissue testing for Cu may be a better option than attempting to calibrate this test across a range of soil types. Even though Hill et al. (1978) indicated grain Cu was an unreliable assessment of supply, McDonald (2006) identified that grain Cu concentration at maturity was well related to whole shoot nutrient concentration at tillering for wheat and barley, which suggests that the analysis of grain could provide a post- harvest assessment of Cu status of the paddock. Given the fact that applied Cu has a long residual activity in most soils (Brennan 2006) it may be that grain Cu monitoring presents a more reliable tool to assess paddock nutrient status rather than soil testing, even though it is after the immediate crop. Table 25. Combined risk assessment of low Cu supply for Australian Soil Classification soil orders. ASC Soil Properties* DTPA Cu** Grain Cu*** Summary Reliability Mod Low Mod Calcarosol Mod Low Low Mod Chromosol Mod Low Low Uncertain Dermosol Mod Low Low Mod Ferrosol - Low Low Low Kandosol Mod Low Low Mod Kurosol High Low - Mod Podosol Mod - - Uncertain Sodosol Mod Low Low Mod Tenosol Low Low Low Low Vertosol Mod Low Low Low VertosolAlk Mod+ Low Low Uncertain * From Table 12. **From Table 16. ***From Table 22. Grain Cu concentration of canola was about 30% higher than wheat in this survey, but the diagnostic criteria indicated in Reuter and Robinson suggest similar critical values for each of <3 mg kg - 1 for canola, which was around the mean value seen, which would seem to support that canola is better able to access Cu than wheat, while albus lupins were even more efficient (Brennan and Bolland 2004), but field peas somewhat less efficient (Brennan and Bolland 2004). So, the management of Cu should probably focus around the cereal phase of the rotation, and that would be the most appropriate time for interventions to be made. Unresolved is the residual nature of applied Cu on soils in eastern Australia, although it could be expected to be the longest of the micronutrients. This work is not to be cited without the permission of the author. 41

42 Areas for future investment The true extent of Cu deficiency is yet to be defined within cropping systems. Field agronomists using visual symptoms often diagnose Cu deficiency, but better use of tissue tests could be made in and validation of the critical values for tissue tests would increase diagnostic confidence. The long residual nature of applied Cu on soils in eastern Australia should be established, as this will largely determine if management approaches should be tactical or strategic. Options for growers are to use either base fertilizer supplemented with Cu or to use in- crop foliar applications. There are few published experimental comparisons of these management strategies from the eastern states and well documented case studies could assist growers with this decision. It is likely that soil type could have a major effect on the most appropriate approach, but there are few guidelines on which this assessment could be made Unresolved issues around Cu relate to higher yielding environments and an improved understanding of the interaction of N and Cu with modern high yielding cultivars. Under high N conditions, Cu is less able to be remobilized so late Cu deficiency may occur (Hill et al. 1978). This may have a genetic aspect, but the timing suggests that later applications of Cu may be more important than earlier applications under high yielding conditions. Manganese The soil test data showed that only 21% of samples had low topsoil DTPA- Mn, but only 4% of wheat grain samples had low grain Mn. The combinations of risk assessments by Australian Soil Order for Mn are presented in Table 26. These data suggest that Calcarosols are at moderate to high risk of Mn deficiency, while Tenosols showed the lowest soil Mn test values. The low reliability of both the soil test values and the grain Mn concentrations mean that soil properties are the only reliable guide to the risk of Mn deficiency. This conclusion is also clear from the literature. Soil types with either the presence of calcium carbonate or on highly leached soils with low native Mn levels, are key features. The literature indicates that Mn deficiency will occur on calcareous soils on the Eyre and York Pennisula or on calcareous soils in Western Australia. There is little mention of the deficiency elsewhere, other than on acidic and sandy soils or Western Australia, although Holloway et al. (2008) conjecture if the supracalcic and lithocalcic calacarosols in the Victorian Mallee are likely to be responsive. The research evidence supports the likelihood of low Mn availability for cereals and pulses on Calcarosols where free lime is obvious. Indeed, the development of large areas of the Lower Eyre Peninsula was largely a consequence of the identification and correction of this deficiency (Holloway et al. 2008). There is also good evidence for lupins and lentils having a higher Mn demand than cereals and poor lupin seed quality has been associated with low seed Mn levels (Walton 1978). Brennan (2011) identified Mn deficiencies for canola in Western Australia, but the data on Mn demand by canola is not clear. It is known that there are genetic differences among wheat and barley genotypes for Mn efficiency (Tong et al. 1997), and that canola shows variation in response to Mn toxicity. Therefore, it seems reasonable to expect that similar differences in Mn efficiency could be present in canola. Liming soils beyond ph 5.5 can reduce the amount of available Mn (and B) in the period following lime application, so care needs to be taken around this practice. Rates of Mn application in the Lower Eyre Peninsula and Western Australia are around 3-4 kg Mn ha - 1, although Holloway et al. (2008) mentions rates up to 20 kg Mn ha - 1. Manganese applications tend to have very little residual value. This work is not to be cited without the permission of the author. 42

43 Table 26. Combined risk assessment of low Mn supply for Australian Soil Classification soil orders. ASC Soil* Properties DTPA Mn** Grain Mn*** Summary Reliability Mod Low Low Calcarosol High High Low High + Chromosol Low Low Low Low Dermosol Low Low Low Low Ferrosol * Low Low Low Kandosol Mod Low Low Mod Kurosol Low - - Uncertain Podosol Low - - Uncertain Sodosol Low Low Low Low Tenosol Low High Low Low Vertosol Mod Low Low Mod VertosolAlk Mod Low Low High + * From Table 12. **From Table 16. ***From Table where soils have more than 60% calcium carbonate Areas for future investment The use of foliar Mn sources, or of better formulations of Mn, such as with new chelating agents, would seem to be opportunities to more efficiently address this micronutrient. The focus of the research on Mn would be in the VicSA Mallee, and in Western Australia, particularly on pulse and canola crops, with a view to comparing strategic and tactical approaches to Mn management. Molybdenum 23% of wheat grain samples had Mo concentrations less than mg kg - 1. The CaCl 2 - extractable soil test data for Mo was limited, as it is a special requirement in the suite of soil tests offered by the major laboratories. Brennan and Bruce (1999) indicated that ammonium oxalate or hot water extractions have also been studied. Table 18 shows that soil test values and grain micronutrient concentrations were not related in wheat, and poorly related in canola. It would be unlikely that commercial soil tests for Mo would be developed mainly because the analysis requires detection using ICP- MS because of the low levels of the analyte in the samples (Rayment and Lyons 2011, Method 12E1). The most reliable indicator of Mo deficiency is the soil ph Ca, especially if it is lower than 5.5, and high soil phosphorus status is also reported to enhance Mo availability. The data in Table 24 summarises the distribution of risk factors by soil type, including an assessment of the proportion of each soil type with ph less than 5.5. Soils least at least risk are Calcarols and Vertosols, while Chromosols, Tenosols and Kurosols are most at risk. Kurasols are minor soils in all agroecological zones, but Tenosols are important in the WACentral, WANortherm and the VicSAMallee zones. Inherent Mo deficiencies in Western Australia have been reported on newly cleared land (Doyle et al. 1965), as well as more recently where soil acidification is occurring (Brennan et al. 2004). Brennan (2011) has also identified Mo responses in canola across parts of the Western Australian zone, and it would be expected that canola is more at risk than cereals. Pulse crops have a higher Mo demand than cereals and low Mo will also reduce N fixation in legumes. This work is not to be cited without the permission of the author. 43

44 Table 27. Combined risk assessment of low Mo supply for Australian Soil Classification soil orders. Soil* % Soils** Wheat Grain Canola Grain Properties ph<5.5 Mo*** Mo**** Summary Reliability Good Good Low Low? Calcarosol Low 0 Low Low Low Chromosol Mod 51 Low High Mod (C) Dermosol Low 22 Low Low Low Ferrosol * - Low Mod Low Kandosol Low 22 Low Mod Mod (C) Kurosol High Uncertain Podosol High Uncertain Sodosol Low 28 Low High Low Tenosol High 39 Low High High (C) Vertosol Low 7 Low Low Low VertosolAlk Low 0 Low Low Low * From Table 12. **From Table 16. ***From Table 22. ****From Table 23. Liming can reduce the risk of Mo deficiency and this would be the primary course of action recommended as it also reduces the risk of Mn and aluminium toxicity. However, interventions with Mo supplemented fertilizers, seed dressings or foliar sprays may present a lower cost option if Mo deficiency is the main nutrient of concern. Despite the analytical challenge of measuring low Mo concentrations, the development of robust tissue Mo standards could be valuable in providing growers improved diagnostics. The Department of Agriculture and Food Western Australia, indicates that for wheat, the critical concentration in the youngest fully emerged leaf is between 0.03 and 0.05 mg kg - 1. Adequate vegetative tissue values for canola were reported in Reuter and Robinson as mg kg - 1 compared to wheat at mg kg - 1, so that if critical values were to be developed, they would be somewhat higher than the values expected for wheat. Areas for future investment Because Mo is likely to be more important for canola than cereals, some focused fieldwork to investigate and calibrate tissue test critical values on acid soils would assist with diagnosis. However, diagnostic tests from tissue Mo are generally difficult because the detecting limits require more expensive analyses. It is also important to consider Mo relative to soil ph and liming. While liming will increase Mo availability, it is an expensive adjustment if no other factors are limiting (e.g. Al or Mn toxicity). At moderately low ph, say around 4.8, in the short term supplementary Mo may be a cheaper alternative than liming. The relationship between critical soil ph and Mo availability could be determined to more precisely identify where Mo supplements could be used rather than liming in the soil ph range of 4.7 to 5.2. Spatial and temporal patterns of soil ph may make the determination of these limits difficult. This work is not to be cited without the permission of the author. 44

45 Zinc The soil test data showed that 15% of samples had low topsoil DTPA- Zn, but 55% of wheat grain samples had low grain Zn. These values may be high because many of the sites, especially in South Australia, had supplementary zinc supplied in the base fertilizer. Based on this risk assessment, all soil orders evaluated seem to have a moderate to high risk of Zn deficiency, and wheat grain analysis does show low Zn concentrations. Soil tests are quite variable and show low Zn, but the relationship between grain Zn and soil test Zn is weak, even with the inclusions of other soil factors such as ph and organic C. The inclusion of clay concentration may improve this relationship but that factor was not available in the data analysed. Many of the Zn recommendations in the eastern states have been developed for alkaline cropping soils, and as yet there are few published reports of Zn responses in the high rainfall zones. It is also well known that soil applied Zn gives a moderate (3-5 years) residual response, but much of that work has been on alkaline soils, but little is documented on residual values on other soils. Information from a range of researchers working in this area suggests that chronically Zn deficient sites are not common, and that while Zn responses are often seen in terms of increased grain Zn concentration, significant yield benefits are less common (Peck et al. 2008). The highest risk of Zn deficiency is seen with wheat on Calcarosols, Dermosols, Kandosols, Sodosols and alkaline Vertosols. There is some evidence of Zn responses on canola in the literature, but the critical values suggest that canola is more efficient than wheat at accessing soil Zn. Table 28. Combined risk assessment of low Zn supply for Australian Soil Classification soil orders. Soil* % Soils** Grain DTPA Zn** Properties ph>7.5 Zn*** Summary Reliability Mod Mod Mod Mod Calcarosol High 89 Mod Low High Chromosol Mod 1 Low Low Mod Dermosol Mod 11 Low High High Ferrosol - - Low Low Mod Kandosol Mod 7 Low Mod Mod Kurosol Low Uncertain Podosol Mod Uncertain Sodosol Mod 7 Low Mod Mod Tenosol Mod 5 Low Low Mod Vertosol Mod - Low Low Mod VertosolAlk High 100 Mod Low High * From Table 12. **From Table 16. ***From Table 22. Zinc has been considered the classic deficiency on alkaline soils such as Vertosols and Calcarosols and the data here substantiates that observation. Growers would routinely use Zn supplements either added to base fertilizer or as foliar sprays with rates of 1-2 kg Zn ha - 1. The guidelines for the use of these products are widely known and it seems routinely used. Critical soil test values need to be reinforced with growers and advisors, as some literature suggests critical DTPA Zn as high as 1.0 mg kg - 1, while the literature indicates a range of 0.2 to 0.4 mg kg - 1. This work is not to be cited without the permission of the author. 45

46 Areas for future investment Comparisons of strategic with tactical application of Zn may require investigation, and to aid in this decision better information on the residual values of different formulations and presentations may be needed. The differential timing of Zn, Cu and Mn sprays is also a general issue, and for crops retained for on- farm or commercial seed production, late sprays to enhance seed vigour may be a good option, even if there is no particular yield advantage gained. Levels of Zn in Australian wheat are low on world standards, and are significantly below the Harvest Plus target of >30 mg kg - 1 which aims to meet human health needs for those on grains based diets. There is no price premium for high Zn grain. High P soils will also reduce Zn availability so in high areas with a strong P application history such as the high rainfall zones, the use of alternative strategies or formulations could be considered. This work is not to be cited without the permission of the author. 46

47 Summary The approach taken in this report was to assess each ASC soil order for its properties related to micronutrient availability. This risk was then assessed against reports in the literature, a survey of soil test data and a survey of grain nutrient concentration. The use of the ASC was chosen because of the digital soil maps available now and this provides a way for growers to know the soil orders and suborders, and then to use these to assess risk. Table 29. Soil order, their relative areas and risks for the GRDC Northern Region. Soil Order %North B Cu Mn Mo Zn Region Calcarosol 3 Low Mod High + Low High Chromosol 5 Mod Uncertain Low Mod (C)*** Mod Dermosol 1 Low Mod Low Low High Ferrosol 1 Uncertain** Low Low Low Mod Kandosol 33 High Mod Mod Mod (C) Mod Kurasol 1 Uncertain Mod Uncertain Uncertain Uncertain Podosol 0 Uncertain Uncertain Uncertain Uncertain Uncertain Sodosol 18 Mod Mod Low Low Mod Tenosol 5 High Low Low High (C) Mod Vertosol Low Low Mod Low Mod 26 VertosolAlk Low Uncertain High + Low High * Where free calcium carbonate is present; ** Probably low. *** For Canola From a GRDC Investment perspective, the ASC already provides maps of the distribution of soil orders within the agroecological zones, so it was considered redundant to try to remap these data. Instead, using the lines of evidence developed, a summary table for each GRDC region is provided below (Tables 29, 30, 31). These show the relative distribution of each ASC within the regions and the likely risk of micronutrient deficiency for B, Cu, Mn, Mo, and Zn. While there is little data on micrnonutrient risk on Kurasols, Ferrosols and Podosols, because they are less than 3% of cropping soils nationally, investment may be better directed to more important soil orders. In summary, for the Northern Region (Table 29), main issues appear to be with Zn on Kandosols, Vertosols and Sodosols, There is uncertainty about Cu generally. Table 30. Soil order, their relative areas and risks for the GRDC Southern Region. Soil Order %South B Cu Mn Mo Zn Region Calcarosol 30 Low Mod High + Low High Chromosol 7 Mod Uncertain Low Mod (C) Mod Dermosol 3 Low Mod Low Low High Ferrosol 0 Uncertain** Low Low Low Mod Kandosol 3 High Mod Mod Mod (C) Mod Kurasol 2 Uncertain Mod Uncertain Uncertain Uncertain Podosol 1 Uncertain Uncertain Uncertain Uncertain Uncertain Sodosol 32 Mod Mod Low Low Mod Tenosol 9 High Low Low High (C) Mod Vertosol Low Low Mod Low Mod VertosolAl 10 Low Uncertain High + Low High This work is not to be cited without the permission of the author. 47

48 * Where free calcium carbonate is present; ** Probably low. *** For Canola For the Southern Region (Table 30), the main soil types of Calcarosols, Sodosols and Vertosols have high risk of Zn deficiency, while Mn is likely to be a significant risk on these soils if the soils have more than about 60% free calcium carbonate. For the Western Region (Table 31), Kandosols and Tenosols become more significant, although Sodosols are the main soil orders, and low Zn is seen on these soils. The acid soil types such as Tenosols are likely to be at risk of Mo deficiency, while Mn deficiency is moderately likely on Kandosols and Tenosols. Table 31. Soil order, their relative areas and risks for the GRDC Western Region. Soil Order %West B Cu Mn Mo Zn Region Calcarosol 8 Low Mod High + Low High Chromosol 7 Mod Uncertain Low Mod (C) Mod Dermosol 0 Low Mod Low Low High Ferrosol 0 Uncertain** Low Low Low Mod Kandosol 18 High Mod Mod Mod (C) Mod Kurasol 1 Uncertain Mod Uncertain Uncertain Uncertain Podosol 2 Uncertain Uncertain Uncertain Uncertain Uncertain Sodosol 34 Mod Mod Low Low Mod Tenosol 22 High Low Low High (C) Mod Vertosol Low Low Mod Low Mod 0 VertosolAlk Low Uncertain High + Low High * Where free calcium carbonate is present; ** Probably low. *** For Canola Communication Strategy The risk classifications identified here with some refinement through feedback from researchers and advisors - could be added to the Soil Map App with colour coding indicating potential risks for each micronutrient for the ASC Order. It would also be important to add in appropriate management issues that would increase or decrease this risk, as summarised in Table 7. Liming, time since the most recent micronutrient applications, and rainfall are all significant. Once a risk has been identified, strategies for diagnosis should be promoted, indicating what should be done to confirm a suspected deficiency. There are clear and reliable guidelines for tissue testing for micronutrients in a range of crops. Critical values in Reuter and Robinson are now almost 15 years out of date, but it would seem that the fundamental values still hold. Updating the data for grain crops in Reuter and Robinson and republishing these in electronic forms possibly linked to nutrient deficiency images would be a useful project. A key part of this would be to ensure that reliable sampling protocols were in place so that the right tissues are collected at the right time Clearly there are well- defined tissue testing guidelines for most of these micronutrients in many crops, and these critical values, along with appropriate sampling strategies (timing and tissue taken) could be more widely promulgated. This work is not to be cited without the permission of the author. 48

49 Many of the interventions have been investigated, but new products and alternative methods of presentation may be in the pipeline of fertilizer research companies. Substantiation of these options would be needed to inform growers and advisors, but these data are not necessarily available even for products currently on the market including the miracle types. This work is not to be cited without the permission of the author. 49

50 Acknowledgements The late Doug Reuter provided many resource documents and interesting discussion points in developing this report. I am deeply indebted to him for his enthusiasm, interest and support during this project. Many of us will sorely miss Doug and his wise counsel, but he has left a huge footprint on Australian agriculture. Robert Edis provided guidance and input into the data analyses and interpretation. David Maschmendt, South Australia Soil Survey for providing access to the South Australian soil survey database that enabled Australian Soil Classes to be linked to soil properties. Alan Bedggood, Neil Sutton and Greg Bowie from NVT for providing access to the NVT network and coordinated the interaction with regional service providers across Australia, as well as supplying soil test data from the NVT network. I would also acknowledge the valuable discussions with Mike Wong, Richard Bell, Geoff Anderson, Simon Speirs, Mark Conyers, Nigel Wilhelm, Ken Peverill, Ross Brennan, and Glenn McDonald. Geoff Anderson and Martin Harris kindly made the Western Australia focus paddock soil test data available. Teresa Fowles and Lyndon Palmer from Waite Analytical Services undertook the grain analyses. This project was supported by the Grains Research and Development Corporation, as part of the More Profit for Crop Nutrition 2 research initiative. As with much of science, this work has built on the expertise world leading micronutrient researchers including Professor Robin Graham, Professor Zed Rengel, Dr Ross Brennan, Dr Richard Bell and many others. This work is not to be cited without the permission of the author. 50

51 Selected References It is beyond the scope of this report to provide a fully referenced literature review, but below is a selection of refereed journal articles and books relating to micronutrient research in Australia. General References Bell RW Temporary nutrient deficiency a difficult case fo diagnosis and prognosis by plant analysis. Communications in Soil Science and Plant Analysis, 31, Bell RW, Dell B Micronutrients for sustainable food, feed, fibre and bioenergy production. International Fertilizer Industry Association, Paris, France. Brown P, Bassil E Overview of the acquisition and utilization of B, chlorine, Cu, Mn, Mo, and nickel by plants, and the prospects for improvement in micronutrient use efficiency. In The Molecular and Physiological Basis of Nutrient Use Efficiency in Crops, First Edition, (MJ Hawkesford and P Baraclough (Eds)). John Wiley and Sons, West Sussex, UK. Burjan Z, More M, Kovacs B, Gyrori Z The effect of different growing areas on the Cu, Mn and Zn concentration of winter wheat. European Chemical Bulletin, 1, Hazelton P, Murphy B, Interpreting soil test results. CSIRO Publishing. Holloway R, Graham RD, Stacey SP Micronutrient Deficiencies in Australian Field Crops. In BJ Alloway (ed) Micronutrient deficiencies in Global Crop Production, Springer Science. McDonald GK Effects of soil properties on variation in growth, grain yield and nutrient concentration of wheat and barley. Australian Journal of Experimental Agriculture, 46, Mortvedt JJ, Giordano PM, Lindsay WL. (eds) Micronutrients in Agriculture. Soil Science Society of America, Madison, Wisconsin. Hamon RE, McLaughlin MJ, Lombi E.(eds) Natural attenuation of trace element availability in soils.(society of Environmental Toxicology and Chemistry, Pensacola, FA, USA. Nicholas DJD, Egan AR Trace elements in Soil- Plant- Animal Systems. Academic Press. Norton, 2011, NVT Grains Analysis, EN Price G (ed) Australian Soil Fertility Manual. CSIRO Publishing, Collingwood, Victoria, Australia. Reuter DJ and Robinson JB (Eds), Plant Analysis: an interpretation manual. CSIRO Publishing. Shi R, Y Zhang, Chen X, Sun Q, Zhang F, Romheld V, Z C. (2010) Influence of long- term fertilization of micronutrient density in grain of winter wheat. Journal of Cereal Science, 51, Schultz JE, French RJ Mineral concentration of herbage and grain of Halberd wheat in South Australia. Australian Journal of Experimental Agriculture and Animal Husbandry. 16, Smith FW, JF Loneragan Interpretation of Plant Analysis: Concepts and Principles, In (Eds). DJ Peverill KI, LA Sparrow, DJ Reuter Soil analysis: an interpretation manual. CSIRO Publishing, Collingwood, Victoria, Australia. Boron Asad, A, Bell RW, Dell B, Huang L External B requirements for canola in B buffered solution culture. Annals of Botany, 80, Bell RW Boron. In Soil analysis an interpretation manual'. (Ed~ Kl Peverill. LA Sparroww, DJ Reuter) pp (CSIRO Publishing Coliingwood. Vic.) Bell RW, Diagnosis and prediction of B deficiency for plant production. Plant and Soil, 193, Bell RW, Frost K, Wong M, Brennan R B should we be worried about it. Crop Updates 2002 ( accessed 22 May 2013) Bell RW, L McLay, D Plaskett, B Dell, and JF Loneragan Germination and vigour of black gram (Vigna mungo (L.) Hepper) seed from plants grown with and without Boron. Australian Journal of. Agricultural. Research 40: Brennan Rl. Adcock KG Incidence of B toxicity in spring barley in southwestern Australia. Journal of Plant Nutrition 27, Brennan RF, Bell RW, Frost K Risks of B toxicity in canola and lupin by forms of B application in acid sands of southwestern Australia. Journal of Plant Nutrition (accepted). Cartwright B, Zarcinas SA, Spouncer LR Toxic concentrations of B on a a red- brown earth at Gladstone. South Australia. Australian Journal of Soil Research Dear BS, Weir RG B deficiency in pastures and field crops. NSW Agriculture AgFacts P1 AC1 8pp. Hall D B. Department of Agriculture and Food Western Australia This work is not to be cited without the permission of the author. 51

52 Huang L, Ye ZQ, Bell RW The importance of sampling young leaves for the diagnosis of B deficiency in canola (Brassica napus L.). Plant and Soil Rerkasem B, Netsangtip R, Bell RW, Loneragan JF, Hiranburana N Comparative species responses to B on a Typic Tropaqualf in Northern Thialand. Plant and Soil, 106, Stangoulis JCR, Grewal HS, Bell RW, Graham RD B efficiency in oilseed rape. 1 Genotypic variation demonstrated in field and pot grown Brassica napus and Brassica juncea. Plant and Soi, 225, Stangoulis, J, Tate M, Graham R, Bucknal M, Palmer L, Boughton B, Reid R The Mechanism of B Mobility in Wheat and Canola Phloem. Plant Physiology. 153, Wei YZ, Bell RW, Yang Y, Xue JM, Wang K, Huang L (1998) Prognosis of B deficiency in oilseed rape (Brassica napus L.) by plant analysis. Australian Journal of Agricultural Research Wong MTF, Bell RW, Frost K Mapping B deficiency risk in soils of southwest Western Australia using a weight of evidence model. Australian Journal of Soil Research 43: Copper Brennan RF Effectiveness of some Cu compounds applied as foliar sprays in alleviating Cu deficiency of wheat grown on Cu- deficient soils in Western Australia. Australian Journal of Experimental Agriculture, 30, Brennan RF, Robson AD, Gartrell JW The effect of successive crops of wheat on the availability of Cu fertiliser to plants. Australian Journal of Agricultural Research 37, Brennan RF, Best E Cu. In Peverill, K. I., Sparrow, L. A., Reuter DJ,(Eds.), Soil Analysis: An Interpretation Manual (pp ). CSIRO, Melbourne, Australia. Bolland MDA, Brennan RF Phosphorus, Cu and Zn requirements of no- till wheat crops and methods of collecting soil samples for soil testing. Australian Journal of Experimental Agriculture 46(8) Brennan RF, Bolland MDA Comparing soil and tissue testing of Cu for early growth of wheat. Communications in Soil Science and Plant Analysis 37: Brennan RF, Bolland MDA Comparing Cu requirements of field pea and wheat grown on alkaline soils. Australian Journal of Experimental Agriculture, 44, Brennan RF, Bolland MDA Comparing Cu requirements of canola, albus lupin, durum wheat and spring wheat grown on alkaline soils. Australian Journal of Experimental Agriculture 44: Brennan RF Long- term residual value of Cu fertiliser for production of wheat grain. Australian Journal of Experimental Agriculture 46, Gartrell JW The residual effectiveness of Cu fertiliser for wheat in Western Australia. Australian Journal of Experimental Agriculture and Animal Husbandry 20, Grundon NJ Effectiveness of soil dressings and foliar sprays of Cu sulphate in correcting Cu deficiency of wheat (Triticum aestivum) in Queensland. Australian Journal of Experimental Agriculture and Animal Husbandry 20, Hill J, Robson AD, Loneragan JF The effects of Cu and nitrogen supply on the distribution of Cu in dissected wheat grains. Australian Journal of Experimental Agriculture, 30, Loneragan JF, Robson AD, Graham RD Cu in Soils and Plants. Academic Press. Robson AD, Reuter DJ Diagnosis of Cu deficiency and toxicity. In Loneragan, J. F., Robson, A. D., Graham, R. D. (Eds.), Cu in Soils and Plants (Chapter 13, pp ). Academic, Sydney, Australia. Manganese Brennan RF, Bolland MDA Application of fertilizer Mn doubled yields of lentil grown on alkaline soils. Journal of Plant Nutrition, 26(6): Brennan RF, Longnecker NE Effects of the concentration of Mn in the seed in alleviating Mn deficiency of Lupinus angustifolius L. Australian Journal of Experimental Agriculture, 41: Brennan RF Effectivieness of different sources of Mn foliar sprays in alleviating Mn deficiency of Lupinus angustifolius grown on Mn deficient soils in western Australia. Journal of Plant Nutrition, 19, Brennan RF Lupin grain yields and fertilizer effectiveness are increased by banding Mn below the seed. Australian Journal of Experimental Agriculture 39: Brennan RF Mn. In Moore, G. (Ed.), Soil Guide. A Handbook for Understanding and Managing Agricultural Soils (pp ). Agriculture Western Australia, Bulletin No Brennan RF, Gartrell JW, Adcock KJ Residual value of Mn fertiliser for lupin grain production. Australian Journal of Experimental Agriculture 41(8) This work is not to be cited without the permission of the author. 52

53 Crosbie J, Longnecker NE, Robson AD Seed Mn affects the early growth of lupins in Mn- deficient conditions. Crop and Pasture Science, 45, Graham RD, Davies WJ, Ascher, JS The critical concentration of Mn in field grown wheat. Australian Journal of Agricultural Research, 36: Graham RD, Hannam RJ, Uren NC. (eds) Mn in Soils and Plants. Developments in Plant and Soil Sciences, Volume 33. Kluwer Academic Press. Khabaz- Saberi H, Graham R Improvement of screening for Mn efficiency by producing seed with similar Mn concentrations in different genotypes and genetic stocks. Journal of Plant Nutrition. 25, Longnecker N, Crosbie J, Davies F, Robson AD Low seed Mn concentrations and decreased emergence of Lupinus angustifolius. Crop Science, 36, Marcar NE, Graham RD Effect of seed Mn concentration on the growth of wheat (Triticum aestivum) under Mn deficiency. Plant and Soil. 96, Pearson JN, Rengel Z, Graham R Transport of Zn and Mn to developing wheat grains. Physiologia Plantarum, 95: Pearson JN, Rengel Z Distribution and remobilization of Zn and Mn during grain development in wheat. Journal of Experimental Botany, 45: Pearson JN, Rengel Z Uptake and distribution of 65Zn and 54Mn in wheat grown at sufficient and deficient levels of Zn and Mn 1. During vegetative growth. Journal of Experimental Botany, 46: Reuter DJ, Heard TG, Alston AM Correction of Mn deficiency in barley crops on calcareous soils I. Manganous sulphate applied at sowing and as foliar sprays. Australian Journal of Experimental Agriculture and Animal Husbandry, 13: Reuter DJ, Heard TG, Alston AM Correction of Mn deficiency in barley crops on calcareous soils 2. Comparison of mixed and compound fertilizers. Australian Journal of Experimental Agriculture and Animal Husbandry, 13: Tong Y, Rengel Z, Graham, R Interactions Between Nitrogen and Mn Nutrition of Barley Genotypes Differing in Mn Efficiency. Annals of Botany. 1/01/1997, Vol. 79 Issue 1, p p. Uren NC Mn. In Peverill, K. I., Sparrow, L. A., Reuter DJ,(Eds.), SoilAnalysis: An Interpretation Manual (Chapter 19, pp ). CSIRO, Melbourne,Australia. Walton GH The effect of Mn on seed yield and the split seed disorder of sweet and bitter phenotypes of Lupinus angustifolius and L. cosentinii. Australian Journal of Agricultural Research, 29, Wilhelm NS, Graham RD, Rovira AD (1988) Application of different sources of Mn sulfate decreases take- all (Gaeumannomyces graminis var. tritici) of wheat grown in a Mn deficient soi. Australian Journal of Agricultural Research 39, Molybdenum Brennan RF, Bolland MDA, Bowden JW Potassium deficiency, and Mo deficiency and aluminium toxicity due to soil acidification have become problems for cropping sandy soils in southwestern Australia. Australian Journal of Experimental Agriculture, 44: Brennan RF, Bolland MDA Increased Concentration of Mo in Sown Wheat Seed Decreases Grain Yield Responses to Applied Mo Fertilizer in Naturally Acidic Sandplain Soils. Journal of Plant Nutrition. 30, Brennan RF, Bolland MDA Foliar spray experiments identify no B deficiency but Mo and Mn deficiency for canola grain production on acidified sandy gravel soils in southwestern Australia. Journal of Plant Nutrition, 34, Brennan RF, Bruce RC Mo. In Peverill, K. I., Sparrow, L. A., Reuter DJ,(Eds.), Soil Analysis: An Interpretation Manual (pp ). CSIRO, Melbourne, Australia. Brennan RF Residual value of Mo trioxide for clover production on an acidic sandy podzol. Australian Journal of Experimental Agriculture, 42(5): Doyle RJ, Parkin RJ, Smith JAC, Gartrell JW Mo increases cereal yields on wheat belt scrub plain. Journal of Agriculture of Western Australia, 27: Riley MM Mo deficiency in wheat in Western Australia. Journal of Plant Nutrition, 17, Zinc Bolland MDA, Brennan RF Phosphorus, Cu and Zn requirements of no- till wheat crops and methods of collecting soil samples for soil testing. Australian Journal of Experimental Agriculture 46(8) This work is not to be cited without the permission of the author. 53

54 Brennan RF, Armour JD, Reuter DJ, Diagnosis of Zn deficiency. In Zn in soil and plants. (Ed. AD Robson) pp (Kluwer Academic Publisher: Dordrecht) Brennan RF, Bolland MDA, Bell RW Increased risk of Zn deficiency in wheat on soils limed to correct soil acidity. Australian Journal of Soil Research 43: Brennan RF, Bolland MDA, Siddique KHM Responses of cool season grain legumes and wheat to soil- applied Zn. Journal of Plant Nutrition 24, Brennan RF, Bolland MDA Relative effectiveness of soil applied Zn for four crop species. Australian Journal of Experimental Agriculture 42, doi: /ea01154 Brennan RF, Bolland MDA Residual values of soil- applied Zn fertilizer for early vegetative growth of six crop species. Australian Journal of Experimental Agriculture 46: Brennan RF, Bolland MDA Estimating the long- term residual value of Zn oxide for growing wheat in a sandy duplex soil. Australian Journal of Agricultural Research 58: Brennan RF, Bolland MDA Quantifying the residual value of Zn for canola production due to the removal of Zn in shoots and grain and continued reaction of Zn with soil. Journal of Plant Nutrition. 33: Brennan RF, Gartrell JW Reaction of Zn with soil affecting its availability to subterranean clover. I. The relationship between critical concentrations of extractable Zn and properties of Australian soils responsive to applied Zn. Australian Journal of Soil Research 28: Brennan RF, McGrath, JF The vertical movement of Zn on sandy soils in southern Western Australia. Australian Journal of Soil Research, 26: Brennan RF Reactions of Zn with soil affecting its availability to subterranean clover. II. Effect of soil properties on the relative effectiveness of applied Zn. Australian Journal of Soil Research 28: Brennan RF Effectiveness of Zn sulfate and Zn chelate as foliar sprays in alleviating Zn deficiency of wheat grown on Zn- deficient soils in Western Australia. Australian Journal of Experimental Agriculture, 31, Brennan RF Effectiveness of Zn sulfate and Zn chelate as foliar sprays in alleviating Zn deficiency of wheat grown on Zn- deficient soils in Western Australia. Australian Journal of Experimental Agricultural Research, 31: Brennan RF The relationship between critical concentration of DPTA- extractable Zn from the soil or wheat production and properties of southwestern Australian soils respective to applied Zn. Communications in Soil Science and Plant Analysis 23: Brennan RF Availability of previous and current applications of Zn fertiliser using single superphosphate for the grain production of wheat on soils of southwestern Australia. Journal of Plant Nutrition 19, Brennan RF Zn. In Soil guide: a handbook for understanding and managing agricultural soils. Agriculture Western Australia Bulletin no (Ed. G Moore) pp (Department of Agriculture: South Perth) Brennan RF Residual value of Zn fertilizer for production of wheat. Australian Journal ofexperimental Agriculture 41: Brennan RF Residual value of Mo for wheat production on naturally acidic soils of Western Australia. Australian Journal of Experimental Agriculture, 46, Graham RD, Asher JS, Hynes SC Selecting Zn- efficient cereal genotypes for soils of low Zn status. Plant and Soil, 146, Graham RD, Rengel Z Genotypic variation in Zn uptake and utilization by plants. In: Zn in Soils and Plants, ed. A. D. Robson, pp Dordrecht, the Netherlands: Kluwer Publishers. Lindsay, W. L., and W. A. Norvell Development of a DTPA soil test for Zn, iron, Mn and Cu. Proceedings of the Soil Science Society of America 42: Grewal HS, Graham RD Seed Zn concentration influences early vegetative growth and Zn uptake in oilseed rape (Brassica napus and Brassica juncea) genotypes on Zn- deficient soil. Plant and Soil, 192, Grewal HS, Stangoulis, JCR, Potter TD, Graham, RD Zn efficiency of oilseed rape (Brassica napus and B- juncea) genotypes. Plant and Soil, 191, Khan HR, McDonald GK, Rengel Z Response of chickpea genotypes to Zn fertilization under field conditions in South Australia and Pakistan. Journal of Plant Nutrition, 23, Khan HR, McDonald GK, Rengel Z Zn fertilization improves water use efficiency, grain yield and seed Zn concentration in chickpea. Plant and Soil, 249, This work is not to be cited without the permission of the author. 54

55 McDonald G K, Genc Y, Graham RD A simple method to evaluate genetic variation in grain Zn concentration by correcting for differences in grain yield. Plant and Soil, 306, Oliver DP, Wilhelm NS, Tiller KG, McFarlane JD, Cozens GD Effect of soil and foliar applications of Zn on cadmium concentration in wheat grain. Australian Journal of Experimental Agriculture 37, Peck AW, McDonald GK, Graham RD Zn nutrition influences the protein composition of flour in bread wheat (Triticum aestivum). Journal of Cereal Science, 47, Rasouli- Sadaghiani, M., Sadeghzadeh, B., Sepehr, E., Rengel, Z. 2011, 'Root exudation and Zn uptake by barley genotypes differing in Zn efficiency', Journal of Plant Nutrition, 34, pp Rengel Z, Graham RD Importance of seed Zn concentration for wheat growth on Zn- deficient soil 2.Grain yield. Plant and Soil, 173: Rengel Z, Graham RD Importance of seed Zn concentration for wheat growth on Zn- deficient soil 1.Vegetative growth. Plant and Soil, 173: Robson AD. (ed) Zn in Soils and Plants. Developments in Plant and Soil Sciences, Volume 55. Kluwer Academic Press. Sadeghzadeh B and Regnel Z, Zn in soils and crop nutrition. In The Molecular and Physiological Basis of Nutrient Use Efficiency in Crops, First Edition, (MJ Hawkesford and P Baraclough (Eds)). John Wiley and Sons, West Sussex, UK. Wheal M, Rengel Z, Graham R Chlorsulfuron Reduces Extension of Wheat Root Tips in Low- Zn Solution Culture. Annals of Botany 81, p Zhu Y- G, SE Smith, FA Smith Zn (Zn)- phosphorus (P) Interactions in Two Cultivars of Spring Wheat (Triticum aestivum L.) Diffring in P Uptake Efficiency. Annals of Botany, 88, This work is not to be cited without the permission of the author. 55

56 Appendix 1. Soil orders within the Australian Soil Classification Australian Soil Classification (ASC) Great Soil Groups Northcote Factual Key Calcarosols: soils that are usually calcareous throughout the soil profile (often highly calcareous). Chromosols: soils with a strong texture contrast between the topsoil and subsoil. Subsoils are not strongly acid and are not sodic. Dermosols: soils with structured subsoils that lack a strong textural contrast between the topsoil and subsoil. Ferrosols: soils that have subsoils that contains a high concentration of free iron oxide and which lack a strong texture contrast between the topsoil and subsoil. Kandosols: soils that lack a strong texture contrast between the topsoil and subsoil, having at best a weakly- structured subsoil and not calcareous throughout. Kurosols: soils with a strong texture contrast between the topsoil and strongly acid subsoil. These soils can have high levels of magnesium, sodium and aluminium in the subsoil. Organosols: soils dominated by organic material. Podosols: soils with a subsoil dominated by the accumulation of compounds of organic matter, aluminium and/or iron. Rudosols: includes soils with little pedological organisation. These soils are usually 'young' in the sense that soil forming factors have little time to pedologically modify parent rocks or sediments. The component soils can vary widely in texture and depth; many are stratified and some are highly saline. Sodosols: soils with strong texture contrast between topsoil and subsoil horizons. These soils are not strongly acid but are sodic and have an ESP greater than 6. Tenosols: soils with generally weak pedological organisation in the subsoil. Vertosols: clay soils with shrink- swell properties that exhibit strong cracking when dry. Some of these soils also show gilgai microrelief. Solonised brown soils, grey- brown and red calcareous soils, mallee soils, highly calcareous sands, lithosols, rendzina Non- calcic brown soils, some red- brown earths, red and brown podzolic soils, red and brown duplex soils Red gradational soils, prairie soils, chocolate soils, some brown, red and yellow podsolic soils, kraznozem, rendzina, chenozem, terra rossa. Krasnozem, euchrozem, chocolate soils Red, yellow and grey earths, calcareous red earths, earthy sands, brown podsolic soils or lithosols Many podsolic soils and soloths Neutral to alkaline, and acid peats Podsols, humus podsols, and peaty podsols, lateritic podsols Lithosols, alluvial soils, calcareous and siliceous sands, some solonchaks, deep gravelly soils Solodized solenetz and solodic soils, some soloths and red- brown earths Lithosols (shallow stony soil), siliceous and earthy sands, alpine humus soils and some alluvial soils, some terra rossa, brown earths Black earths, black, grey, brown and red (cracking) clays Gc1, Gc2, Um1, Um5 soils Many forms of duplex (D) soils Wide range of Gn3 soils, some Um4 soils Gn3, Gn4, Uf5, Uf6 soils Gn2, Um5 soils Many strongly acid duplex soils Organic soils Many Uc2, some Uc3, Uc4 soils Uc1, Um1, Uf1 soils Many duplex soils Many Uc and Um classes Ug5 soils This work is not to be cited without the permission of the author. 56

57 Appendix 2: Counts of each Australian soil orders within each grain zone as derived from the NVT database. Row Labels Calca rosol Chrom osol Derm osol Ferro sol Kand osol Sodo sol Teno sol Verto sol Total Agzone1 1 1 Agzone Agzone Agzone Agzone6 2 2 NSW NE NSW NW NSW SE NSW SW Q CQ Q SE Q SW SA LEP SA Mallee SA Mid North SA SE SA UEP SA YP Vic Mallee Vic NCent Vic NE Vic SW Vic Wimmera Grand Total This work is not to be cited without the permission of the author. 57

58 Appendix 3: Soil properties and Australian soil orders derived from the the South Australian soil survey database contributed to ASRIS (courtesy of D Maschmendt). Soil ph (L1) Soil ph (L3) Clay% (L1) OC% (L1) Ks (L1) CEC (L1) ASC Order & Suborder Mean StdD Mea n StdD Mea n CA BD CQ CV DA EL FB FJ CH AA AB AC AE DE AA AB AD AE KA AA AB AC AD KU AA AB AC PO AL AM EJ SO AA AB AC AD AE TE AW CY GZ IL IN IO IP VE AA AB AD AE AM Grand Total StdD Mea n StdD Mea n StdD Mea n StdD This work is not to be cited without the permission of the author. 58

59 Appendix 4. Critical DTPA Zn concentrations using Brennan (1992) formula, Critical DTPA Zn concentration = *pHCa %Clay% *OrganicC%. These values were derived from data within the South Australian Soil Survey dataset. ASC Soil Order Average of Critical Zn StdDev of Critical Zn Calcarosol Chromosol Dermosol Kandosol Kurasol Podosol Sodosol Tenosol Vertosol Mean Appendix 5: Number of soil samples for NVT used in developing Table 11 and Table 12 in the text. ASC Soil Order NSW QLD SA VIC WA Grand Total Calcarosol Chromosol Dermosol Ferrosol Kandosol Sodosol Tenosol Vertosol Vertosol Alk Grand Total This work is not to be cited without the permission of the author. 59

60 Appendix 6. Wheat grain coefficients of variation (%) for each micronutrient by a) Australian Soil Classification Soil Order, b) Australian Soil Classification Order Soil Order with alkaline soils separated, c) Agroecological zone or d) National Variety Testing System zone. a) ASC Order B Cu Mn Mo Zn Calcarosol 6% 4% 5% 13% 5% Chromosol 11% 7% 6% 21% 5% Dermosol 42% 14% 18% 51% 18% Ferrosol 24% 12% 14% 40% 13% Kandosol 10% 5% 5% 17% 5% Sodosol 6% 3% 3% 11% 3% Tenosol 16% 9% 7% 23% 6% Vertosol 5% 2% 3% 13% 2% Mean 15% 7% 8% 42% 7% b) ASC+Alk B Cu Mn Mo Zn Calcarosol 6% 4% 5% 13% 5% Chromosol 11% 6% 5% 20% 5% Dermosol 41% 14% 17% 50% 18% Ferrosol 24% 12% 14% 39% 13% Kandosol 9% 5% 5% 19% 5% Sodosol 6% 3% 3% 11% 3% Tenosol 16% 9% 7% 22% 6% Vertosol 9% 3% 4% 15% 3% VertosolAlk 7% 3% 4% 23% 4% Mean 14% 7% 7% 24% 7% c) AEZ B Cu Mn Mo Zn NSWCentral 9% 6% 4% 18% 6% NSWNEQldSE 9% 3% 3% 13% 3% NSWNWQldSW 12% 5% 4% 16% 5% NSWVicSlopes 7% 4% 3% 13% 4% QldCentral 18% 5% 6% 539% 5% SAMidnorthLYP 6% 5% 6% 18% 5% SAVicMallee 6% 5% 5% 12% 5% SAVicWimmera 6% 5% 6% 23% 4% VicHRZ 12% 5% 4% 106% 6% WACentral 11% 5% 5% 21% 4% WAEastern 20% 13% 9% 70% 9% WANorthern 26% 11% 7% 27% 7% WASandplain 21% 16% 8% 62% 8% Mean 13% 7% 6% 72% 5% This work is not to be cited without the permission of the author. 60

61 d) NVT Region B Cu Mn Mo Zn Agzone1 26% 12% 6% 21% 7% Agzone2 14% 7% 5% 15% 5% Agzone3 22% 9% 9% 128% 7% Agzone4 13% 9% 6% 29% 6% Agzone5 19% 16% 15% 48% 9% Agzone6 24% 17% 7% 30% 7% CQ 16% 5% 6% 402% 4% NENSW 10% 5% 4% 9% 5% NSW NW 10% 5% 4% 6% 5% NSW SE 10% 5% 4% 12% 6% NSW SW 7% 5% 4% 12% 5% SA Mallee 14% 10% 12% 22% 11% SA SE 12% 8% 10% 12% 5% SA UEP 10% 9% 9% 13% 8% SALEP 10% 12% 15% 17% 8% SAMN 14% 8% 8% 31% 7% SAYP 8% 8% 9% 26% 8% SEQ 23% 8% 7% 1719% 6% SWQ 11% 4% 4% 976% 4% Vic NC 12% 8% 6% 338% 9% Vic NE 15% 7% 5% 118% 7% Vic SW 18% 7% 5% 72% 7% Vic Wimmera 6% 6% 6% 666% 6% VicMallee 7% 7% 7% 17% 8% Mean 14% 8% 7% 198% 7% This work is not to be cited without the permission of the author. 61

62 Appendix 7. Canola grain coefficients of variation (%) for each micronutrient by a) Australian Soil Classification Soil Order, b) Australian Soil Classification Order Soil Order with alkaline soils separated, c) Agroecological zone or d) National Variety Testing System zone. a) ASC Order B Cu Mn Mo Zn Calcarosol 3% 4% 3% 8% 8% Chromosol 2% 2% 3% 10% 8% Dermosol 7% 9% 8% 20% 61% Ferrosol 7% 8% 8% 26% 113% Kandosol 3% 5% 4% 13% 16% Sodosol 1% 2% 2% 6% 7% Tenosol 5% 5% 5% 23% 16% Vertosol 2% 3% 3% 6% 10% Mean 4% 5% 4% 14% 30% b) ASC+Alk B Cu Mn Mo Zn Calcarosol 4% 4% 3% 9% 9% Chromosol 2% 2% 3% 10% 8% Dermosol 7% 8% 8% 20% 60% Ferrosol 7% 7% 8% 25% 113% Kandosol 3% 5% 4% 13% 16% Sodosol 2% 2% 2% 7% 8% Tenosol 5% 5% 5% 23% 16% Vertosol 3% 3% 3% 6% 11% VertosolAlk 5% 5% 5% 11% 20% Mean 4% 5% 5% 14% 28% c) AEZ B Cu Mn Mo Zn NSWCentral 7% 8% 7% 13% 99% NSWNEQldSE 5% 5% 4% 8% 60% NSWNWQldSW 8% 8% 7% 6% 70% NSWVicSlopes 2% 3% 3% 6% 9% QldCentral 6% 11% 7% 13% 22% SAMidnorthLYP 4% 4% 4% 10% 10% SAVicMallee 5% 4% 4% 6% 6% SAVicWimmera 4% 5% 5% 14% 19% VicHRZ 5% 4% 5% 7% 15% WACentral 3% 2% 3% 9% 7% WAEastern 8% 7% 9% 36% 45% WANorthern 5% 4% 4% 22% 20% WASandplain 4% 4% 4% 18% 13% Mean 5% 5% 5% 13% 30% This work is not to be cited without the permission of the author. 62

63 d) NVT Region B Cu Mn Mo Zn Agzone1 5% 4% 4% 24% 29% Agzone2 3% 2% 3% 10% 9% Agzone3 3% 3% 4% 22% 10% Agzone4 8% 7% 9% 37% 38% Agzone5 5% 5% 6% 36% 23% Agzone6 4% 4% 4% 18% 10% Lower EP 4% 5% 5% 16% 12% Mallee 4% 3% 4% 9% 27% Mid North 4% 4% 4% 17% 20% NENSW 4% 4% 4% 11% 62% North Central 4% 4% 5% 11% 20% North East 4% 4% 5% 14% 13% NWNSW 3% 4% 3% 5% 22% S/W 5% 4% 4% 12% 31% SASE 4% 4% 4% 11% 5% SENSW 2% 3% 3% 7% 11% South West 4% 6% 5% 18% 20% Upper EP 8% 8% 6% 13% 12% Wimmera 5% 4% 5% 10% 14% Yorke P 6% 7% 5% 19% 15% Mean 4% 4% 5% 16% 20% This work is not to be cited without the permission of the author. 63

64 Appendix 8. Box plots for B, Cu, Mn, Mo and Zn wheat and canola grain micronutrient concentrations segragated on Australia Soil Classification Order and suborders. a) Wheat grain B concentrations (mg kg- 1) f) Canola grain B concentrations (mg kg- 1) b) Wheat grain Cu concentrations (mg kg- 1) g) Canola grain Cu concentrations (mg kg- 1) c) Wheat grain Mn concentrations (mg kg- 1) h) Canola grain Mn concentrations (mg kg- 1) d) Wheat grain Mo concentration (mg kg- 1) i) Canola grain Mo concentrations (mg kg- 1) e) Wheat grain Zn concentrations (mg kg- 1) j) Canola grain Zn concentrations (mg kg- 1) This work is not to be cited without the permission of the author. 64

65 Appendix 9 GM canola and Grain Mn contents Appendix figure 9.1 is a box- plot of the values for all micronutrients and canola cultivars evaluated. The mean grain Mn concentration for glyphoshate tolerate types was 37.3±0.8 mg kg - 1, which was similar to the Mn concentration for triazine tolerant types (37.3±0.6 mg kg - 1 ), significantly higher than Mn in conventional types (34.9±1.4 mg kg - 1 ) and significantly lower than the imidiazoline tolerant types (40.6±0.7 mg kg - 1 ). These data indicate that the glyphosate construct in the glyphosate tolerant types has not compromised their ability to access Mn. Appendix Figure 9.1. Box and whisker plots of (a) grain B, (b) grain Cu, (c), grain Mn, and (d) grain Zn concentration of seven canola cultivars from the National Variety Testing program a) Grain B concentrations of seven canola b) Grain Cu concentrations of seven canola cultivars cultivars c) Grain Mn concentrations of seven canola d) Grain Zn concentrations of seven canola cultivars cultivars This work is not to be cited without the permission of the author. 65