Abstract. Efficient and effective nutrient management decisions are critical to profitable and sustainable milk production on modern Australian dairy farms. Whole-farm nutrient balances are commonly used as nutrient management tools and also for regulatory assessment on dairy farms internationally, but are rarely used in Australia. In this study, nitrogen (N), phosphorus (P), potassium (K), and sulfur (S) imports and exports were measured during a standardised production year on 41 contrasting Australian dairy farms, representing a broad range of geographic locations, milk production, herd and farm size, reliance on irrigation, and soil types. The quantity of nutrients imported varied markedly -with feed and fertiliser generally the most substantial imports -and were principally determined by stocking rate and type of imported feed. Milk exports were the largest source of nutrient exports. Nitrogen balance ranged from 47 to 601 kg N/ha.year. Nitrogen-use efficiency ranged from 14 to 50%, with a median value of 26%. Phosphorus balance ranged from -7 to 133 kg P/ha.year, with a median value of 28 kg P/ha. Phosphorus-use efficiencies ranged from 6 to 158%, with a median value of 35%. Potassium balances ranged from 13 to 452 kg K/ha, with a median value of 74 kg K/ha; K-use efficiency ranged from 9 to 48%, with a median value of 20%. Sulfur balances ranged from -1 to 184 kg S/ha, with a median value of 27 kg S/ha; S-use efficiency ranged from 6 to 110%, with a median value of 21%. Nitrogen, P, K and S balances were all positively correlated (P < 0.001) with stocking rate and milk production per ha. Poor relationship between P, K and S fertiliser inputs and milk production from home-grown pasture reflected the already high soil fertility levels measured on many of these farms. The results from this study demonstrate that increasing milk production per ha will be associated with greater nutrient surpluses at the farm scale, with the potential for greater environmental impacts. We suggest that simplified and standardised nutrient balance methodologies should be used on dairy farms in Australia to help identify opportunities for improvements in nutrient management decisions and to develop appropriate industry benchmarks and targets.

An improved ability to predict pasture dry matter (DM) yield response to applied phosphorus (P), potassium (K) and sulfur (S) is a crucial step in determining the production and economic benefits of fertiliser inputs and the environmental benefits associated with efficient nutrient use. The adoption and application of soil testing can make substantial improvements to nutrient use efficiency, but soil test interpretation needs to be based on the best available and most relevant experimental data. This paper reports on the development of improved national and regionally specific soil test–pasture yield response functions and critical soil test P, K and S values for near-maximum growth of improved pastures across Australia. A comprehensive dataset of pasture yield responses to fertiliser applications was collated from field experiments conducted in all improved pasture regions of Australia. The Better Fertiliser Decisions for Pastures (BFDP) database contains data from 3032 experiment sites, 21918 yield response measures and 5548 experiment site years. These data were converted to standard measurement units and compiled within a specifically designed relational database, where the data could be explored and interpreted. Key data included soil and site descriptions, pasture type, fertiliser type and rate, nutrient application rate, DM yield measures and soil test results (i.e. Olsen P, Colwell P, P buffering, Colwell K, Skene K, exchangeable K, CPC S, KCl S). These data were analysed, and quantitative non-linear mixed effects models based upon the Mitscherlich function were developed. Where appropriate, disparate datasets were integrated to derive the most appropriate response relationships for different soil texture and P buffering index classes, as well as interpretation at the regional, state, and national scale. Overall, the fitted models provided a good fit to the large body of data, using readily interpretable coefficients, but were at times limited by patchiness of meta-data and uneven representation of different soil types and regions. The models provided improved predictions of relative pasture yield response to soil nutrient status and can be scaled to absolute yield using a specified maximal yield by the user. Importantly, the response function exhibits diminishing returns, enabling marginal economic analysis and determination of optimum fertiliser application rate to a specific situation. These derived relationships form the basis of national standards for soil test interpretation and fertiliser recommendations for Australian pastures and grazing industries, and are incorporated within the major Australian fertiliser company decision support systems. However, the utility of the national database is limited without a contemporary web-based interface, like that developed for the Better Fertiliser Decisions for Cropping (BFDC) national database. An integrated approach between the BFDP and the BFDC would facilitate the interrogation of the database by advisors and farmers to generate yield response curves relevant to the region and/or pasture system of interest and provides the capacity to accommodate new data in the future.

Accurate definition of the sulfur (S) soil test–crop grain yield increase (response) relationship is required before soil S test measurements can be used to if there are likely to be responses to S fertilisers. An analysis was done using the Better Fertiliser Decision for Crops (BFDC) National Database using a web application (BFDC Interrogator) to develop calibration relationships between soil S tests (KCl-40 and MCP) using a selection of sampling depths and grain relative yields (RY). Critical soil test values (CSTV) and critical soil test ranges (CSTR) were defined at RY 90%. The ability of the KCl-40 extractable S soil test to predict grain yield response to applied S fertiliser was examined for wheat (Triticum aestivum L.) grown in Western Australia (WA), New South Wales (NSW), and Victoria and canola (Brassica napus L.) grown in WA and NSW. A smaller dataset using MCPi-extractable S was also assessed. The WA-grown wheat KCl-40 S CSTV, using sampling depth to 30 cm for soil types Chromosols (Coloured), Chromosols (Sesqui-Nodular), Kandosols (Grey and Yellow), Tenosols (Brown and Yellow), and Tenosols (Grey, Sesqui-Nodular), was 2.8 mg kg–1 with an associated CSTR 2.4–3.2 mg kg–1 and a correlation coefficient (r) 0.87. Similarly, KCl-40 S CSTV was defined using sampling depth to 10 cm for these selected soil types and for wheat grown on Vertosols in NSW. The accuracy of the KCl-40 S CSTV for canola grown in WA was improved using a sampling to a depth of 30 cm instead of 10 cm for all soil types. The canola KCl-40 S CSTV using sampling depth to 30 cm for these soil types was 7.2 mg kg–1 with an associated CSTR 6.8–7.5 and an r value 0.70. A similar KCl-40 S CSTV of 7.0 mg kg–1 was defined using a sampling depth of 10 cm, but the CSTR was higher (6.4–7.7 mg kg–1) and the r value lower (0.43). A lower KCl-40 S CSTV of 3.9 mg kg–1 or 31.0 kg ha–1 using a sampling depth of 60 cm was defined for canola grown in NSW using a limited number of S-rate calibration treatment series. Both MCPi (r = 0.32) and KCl-40 (r <0.20) soil S test–NSW canola response relationships using a 0–10 cm sampling depth were weak. The wheat KCl-40 S CSTR of 2.4–3.2 mg kg–1 can be used widely on soil types where soil sulfate is not leached during the growing season. However, both the WA canola CSTR of 6.4–7.2 mg kg–1 using a sampling depth of 30 cm and NSW canola CSTR of 25–39 kg ha–1 or 3.1–4.9 mg kg–1 using a sampling depth of 60 cm can be considered in regions outside of WA and NSW.

Australian grain production depends on applied fertiliser, particularly nitrogen (N) and phosphorus (P), and to a lesser extent potassium (K) and sulfur (S). Despite this dependence, soil testing is used sparingly as a tool to underpin fertiliser decisions. Some grain producers typically conduct soil tests at least once every 3 years on a selection of individual fields, but it is broadly understood that many grain producers use soil testing rarely or not at all. The choice by many grain producers not to support fertiliser decisions by soil testing relates to several factors. One key factor has been a perception that soil test interpretation criteria, previously published separately before collation by K. I. Peverill, L. A. Sparrow, and D. J. Reuter, may be biased or unreliable. The current paper provides an overview of research findings, presented in this special edition of Crop & Pasture Science, describing a national approach to the collation of all available and statistically valid N, P, K, and S response trials for cereal, oilseed, and pulse crops in Australia. It provides an overview of the process adopted to make this single national dataset available to both the grains and fertiliser industries. The process to build adoption has formed an integral component of the approach, as calibration data derived from the national database are being used to underpin soil test interpretation as part of fertiliser recommendations made through Fertcare to grain producers in Australia.

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