Sugar Research Australia eLibrary

SRA's eLibrary provides online full-text access to a growing collection of research reports and electronic publications authored by SRA researchers and staff or resulting from research funded by SRA.

Most documents in the eLibrary are freely available to any reader. Some documents are restricted, for example, where copyright restrictions prevail. To request access to restricted documents, contact library@sugarresearch.com.au . Content in the eLibrary is categorised into communities. Within each community, content is organised into collections. The collections broadly classify the content within the eight Key Focus Areas that SRA currently invests in.

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Now showing 1 - 4 of 4

Recent Submissions

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Australian sugarcane industry soil health benchmarking in Central Queensland: Increasing profit and transforming soil health practices through cooperative industry research, extension and adoption
(2021-12-13) Manatsa, Gus
Activity 1: Measure changes in soil health under a range of farming practices: potential soil health indicators, benchmarks & measurements recommended to enable grower/ industry demonstration of performance improvement through the implementation of IFS practices (i.e., cover cropping, organic amendments, row spacing, controlled traffic, minimal till). Over two years, ten paired sites were established across the three mill areas of the Central Region to determine the soil health, root health and business impact of transitioning to an Improved Farming System (IFS). Long-term IFS sites, of at least ten years, were matched with nearby sites using conventional farming practices. Physical, chemical, and biological soil parameters were measured, along with root development testing, to determine variation between the sites within each pair and therefore the long-term impact of implementing IFS practices. This work is building the evidence required to assist the industry to determine the best set of soil health indicators for the Central region. Combined results from the Central region indicate that microbial biomass, pH and soil compaction are positively impacted by improved farm management systems. Some measures that seemed to show very strong trends in the first year were more mixed in the second year, notably effective rooting depth. Soil texture emerged as a major influence on results, making it difficult to assess the effects of improved management practices in some cases. Root biomass averaged substantially higher in the IFS treatment, possibly reflecting a combined influence of other soil health factors. Activity 2: Innovative soil health/ IFS extension: regional synthesis of solution-based soil health messages to improve production, profit and sustainability through development, training in and implementation of the SRA Soil Health Toolkit (SHET). This project was an industry partnership of the Central cane growing region of Queensland. Collaboratively, the partners, led by Farmacist and SRA, ground-truthed potential soil health indicators and benchmarks for varying soil types and farming systems of the region. This work was needed so that growers could have increased confidence in soil, plant and root sampling data, to inform their decision making and build a greater understanding of how IFS practices deliver production, profit & sustainability outcomes, in addition to improved resilience to climatic variability and extreme weather. The development of the Soil Health Extension Toolkit (SHET) provided a way for local service providers to build their own knowledge in possible Central region soil health indicators, whilst working alongside “champion” growers keen to trial the tests included in the SHET and use the data to help inform the soil constraints most impacting their yield potential, and importantly, where to progress their investigations through further in-depth testing.
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A Common Approach to Greenhouse-Gas Accounting for Australian Agriculture: Project Overview & Non-Technical Summary
(2023-04) Cowie, Annette
This document accompanies the Methods and Data Guidance (Sevenster et al., 2023) and Common Terminology (Cowie et al., 2023) documents to provide a non-technical description of the project that led to the development of those documents, and an executive summary of the key technical decisions in the Methods and Data Guidance document. It is intended for industry decision makers without expert knowledge of greenhouse gas (GHG) accounting, and to be read in conjunction with the two technical documents. The need for a common approach to GHG accounting across agricultural sectors was identified in a stakeholder workshop in December 2019 with participants representing most Rural Research and Development Corporations (RDCs), the National Farmers Federation and sector-level peak bodies, federal and state government, AFI, Rabobank and expert consultants. As sector-level reporting was starting to become important (e.g. Mayberry et al. 2018), the lack of clear methodological guidance for this type of GHG accounting was clear. A collaborative project was developed, initially by the Climate Research Strategy for Primary Industries (CRSPI) collaboration and then by Agricultural Innovation Australia (AIA), who commissioned CSIRO and a large team of subcontractors to conduct an interactive, collaborative process to develop such guidance with broad support from both agricultural sectors and technical experts. The scope of the project was to develop a consistent common framework for agriculture GHG baseline accounting at sector level (i.e. a Common Approach). Implementation of the framework was not part of the project and is up to each sector individually. While many stakeholders contributed to the development of the Common Approach there is no obligation or commitment on any party to implement it. The Common Approach is a state-of-the-art, best practice guidance for sector-level GHG accounting and can be seen as aspirational; guiding improvements in data collection and GHG reporting over time across Australia’s agricultural sectors.
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A Common Approach to Greenhouse-Gas Accounting for Australian Agriculture: Methods and Data Guidance
(2023-04) Cowie, Annette
A common approach for GHG accounting across agricultural sectors is essential to enhance consistency, transparency and confidence in sector-level GHG reporting. Internationally, there are approaches and tools that influence Australian farmers via market access criteria or product labelling, which do not always adequately reflect the reality of Australian farming. A common approach to GHG accounting will allow Australian agriculture to control the representation and communication of climate impacts and mitigation. This Methods and Data Guidance provides a common framework for greenhouse gas (GHG)accounting of Australian agricultural activities at the sector level. The process that was followed to develop this framework is described in the Project Overview and Non-Technical Summary (Sevenster et al., 2023). It describes how GHG accounting can be conducted to generate a transparent and trusted inventory of GHG emissions based on: - a consistent set of principles - a modular approach to account for differences between agricultural sectors - general guidance on data - consistent terminology and language. Agricultural sectors, in the context of this document, refer to individual commodities (or commodity groups such as “grains”), as distinguished by the system of levies applied to primary production. They include forestry and fisheries. No existing standards or protocols exist for this context, which is the reason this guidance document was generated. Nevertheless, where possible and appropriate, the approaches and method choices recommended in this framework draw on relevant guidance from the following frameworks primarily: - Australian National Greenhouse Gas Inventory (NGGI) - ISO 14044:2006 Environmental management — Life cycle assessment — Requirements and guidelines (ISO, 2006) - ISO 14067:2018 Greenhouse gases — Carbon footprint of products — Requirements and guidelines for quantification (ISO, 2018) - guidance provided by the Livestock Environmental Assessment and Performance (LEAP) Partnership (FAO, 2016) - sector-specific guidance for product or corporate accounting, such as IDF (2022). In addition, guidance for corporate accounting provided by the Greenhouse Gas Protocol (GHG-P)(GHG-P, 2015), guidance for product accounting provided by GHG-P(GHG-P, 2011), the Product Environmental Footprint (PEF) scheme (EU, 2021), and guidance from the ILCD Handbook (ILCD, 2010) is referenced for some aspects of the goal and scope principles (2.1).
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A Common Approach to Greenhouse-Gas Accounting for Australian Agriculture: Common Terminology for GHG Accounting
(2023-04) Cowie, Annette
This document is an extended glossary of terms used in or relevant to the project A Common Approach to Sector-Level GHG Accounting for Australian Agriculture, including abbreviations. It accompanies the Methods and Data Guidance (Sevenster et al., 2023a) and Project Overview and Non-Technical Summary (Sevenster et al., 2023b) reports. Definitions have been sourced from authoritative literature, particularly the Intergovernmental Panel on Climate Change (IPCC) glossary, International Organization for Standardization (ISO) standards, and specific policies and schemes, such as the Emissions Reduction Fund (ERF) and the United Nations Framework Convention on Climate Change (UNFCCC). Abbreviations are included where in common use. Additional relevant information is included in the glossary entries to aid comprehension and to indicate relevance for Australian agricultural systems.
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Seeing is believing: managing soil variability, improve crop yield, and minimising off site impacts using digital soil mapping
(2020-12-31) Triantafilis, Honorary Associate Professor John
Over 70 % of sugarcane industry operates next to the Great Barrier Reef (GBR). Farmers are under pressure to improve practices to minimise off-farm pollution, while at the same time improve fertiliser (e.g. lime) and amelioration (e.g. gypsum) efficiency to minimise yield variation. While the biggest driver of variation is rainfall, differences in soil condition affect yield and farmers need to know its variation. For example, knowledge about soil cation exchange capacity (CEC – cmol(+)/kg) is important because it is a measure of how many exchangeable (exch.) cations (i.e. calcium [Ca], magnesium [Mg]) can be retained on soil surfaces and because it influences soil stability, nutrient availability, pH and reaction to fertilisers. If no action is taken to map soil and manage different soil condition, opportunities to sustainably improve application of fertilisers and ameliorants in a cost-effective way will be foregone as well as an opportunity to make a meaningful, economically viable contribution to reducing impacts of sugarcane growing on the GBR. The six-easy-steps (6ES) nutrient and ameliorant guidelines were developed to minimise in-field variation and reduce losses of inputs to the GBR. However, there was and is no practical way for farmers to apply the 6ES guidelines given there is no in-field data to enable its application. This project aimed to undertake case studies in four sugarcane growing areas to enable precision agriculture via the use of a digital soil map (DSM). A DSM requires collection of digital data, such as proximally sensed electromagnetic (EM) induction and gamma-ray spectrometry (ϒ-ray) and coupling this to soil data via mathematical models. The study areas, where a DSM approach was taken, include Mossman, Herbert, Burdekin and Proserpine. The results show that a DSM approach is valid with the potential to implement the 6ES nutrient and ameliorant guidelines to enable precise application of lime, gypsum and other fertilisers demonstrated via various case studies. They are provided here in brief and in summarised form in Section 5. All published papers or submitted manuscripts are provided in the same order and appear in the Appendices. In the Mossman area (see Section 5.1), a DSM approach was used to characterise soil condition in terms of topsoil (0-0.3 m) soil organic carbon (SOC, %) variation, with the DSM able to be used to apply the 6ES nutrient management guidelines (Schroeder et al., 2010) with varying N application rates for different levels of SOC to achieve a district yield potential of 120 t/ha after a bare fallow (Wang et al., 2021). In various areas (see Section 5.2), the DSM approach could be used to predict topsoil (0-0.3 m) clay content across any of six study sites in the Mossman, Herbert, Burdekin, and Proserpine districts. The site-specific approach to making DSM of topsoil clay was optimal, however site-independent (universal calibration) and a spiking approach give almost as good prediction agreement and accuracy (Arshad et al., 2021). In the Herbert (see Section 5.3), a DSM approach was used to characterise soil condition in terms of topsoil (0–0.3 m) and subsoil (0.6–0.9 m) CEC (cmol(+)/kg) variation, with the topsoil DSM able to be used to apply the 6ES nutrient management guidelines (Sugar Research Australia, 2013) with varying lime application rates for different levels of CEC (Li et al., 2018). In the Herbert (see Section 5.4), a DSM approach was used to identify zones by clustering digital data (i.e. EM and -ray data). The DSM was more accurate in predicting topsoil (0-0.3 m) and subsoil (0.6–0.9 m) chemical (e.g. CEC, exch. Ca and Mg and ESP) properties. The 6ES guidelines of Schroeder et al. (2009) were applicable to ameliorate topsoil ESP; the latter shown to influence yield percentage (Dennerley et al., 2018). In the Herbert (see Section 5.5), a wavelet transform of the digital data (i.e. EM and -ray data) was used to enable prediction of topsoil (0-0.3 m) ESP. The DSM, using all the wavelet transformed digital data (i.e. elevation, EM and -ray data) gave the most accurate predictions. The 6ES guidelines of Schroeder et al. (2006) to manage ESP through variable rates of gypsum was also demonstrated (Li et al., 2021a). Sugar Research Australia Final Report 2017/014 4 In the Herbert (see Section 5.6), a DSM approach was again used to identify zones by clustering digital data (i.e. EM and -ray data). The DSM was more accurate in predicting topsoil (0-0.3 m) and subsoil (0.6–0.9 m) chemical (e.g. CEC, exch. Ca and Mg) properties than a traditional texture map or field delineations. The 6ES guidelines of Schroeder et al. (2009) were applicable for these properties (Arshad et al., 2019). In the Burdekin (see Section 5.7), a DSM approach was used to predict topsoil (0-0.3 m) exch. Ca and Mg. The DSM was more accurate than a traditional map (Li et al., 2019a) and useful for applying lime and magnesium, respectively, using 6ES guidelines (Schroeder et al., 2009). In terms of calibration, 30 samples were enough to predict exch. Ca with 40 for exch. Mg (Li et al., 2019b). In Proserpine (see Section 5.8), a DSM was developed to predict topsoil (0-0.3 m) ESP. A map generated using ordinary kriging of 120 soil samples was satisfactory, but, a minimum of 100 samples was required. When digital data was used to value add to soil data, Cubist-RK outperformed OK with only 60 samples required. The 6ES guidelines of Schroeder et al. (2009) were applicable to ameliorate topsoil ESP (Li et al., 2021b). In Proserpine (see Section 5.9), a DSM was developed to predict topsoil (0-0.3 m) and subsoil (0.9-1.2 m) CEC. Topsoil prediction required 80 calibration samples whereas for subsoil only 30 were needed. Using both digital gave best results although -ray used alone slightly better than EM. Small transect spacing (i.e. 5 m) was recommended for topsoil, but larger spacing OK for subsoil (i.e. 5 – 60 m). The 6ES guidelines of Proserpine (Calcino et al., 2010) were applicable to ameliorate topsoil CEC (Zhao et al., 2020). Given the results presented in this Final Report and the published research, it can be concluded that the DSM approach can be applied to map various topsoil and subsoil physical (e.g. clay, silt and sand) and chemical (i.e. CEC, Exch. Ca, Exch. Mg and ESP) properties at the field and multi-field scale in different sugarcane growing districts. The final DSM can be used to apply the 6ES nutrient and ameliorant guidelines in the four sugarcane growing areas investigated and including Mossman, Herbert, Burdekin, and Proserpine. In terms of operational aspects, the following key conclusions can be made; i) Various soil physical (e.g. clay, silt and sand) and chemical (i.e. CEC, Exch. Ca, Exch. Mg and ESP) properties can be mapped using a DSM approach, but regardless of modelling technique, the number of soil samples required to make a calibration was approximately the same (i.e. 1 sample per hectare) regardless of the soil property (i.e. topsoil Exch. Ca and Mg and ESP) or study area. ii) Mathematical methods such as LMM are useful when digital data are correlated with soil data, with hybrid methods of machine learning (i.e. Cubist) and regression kriging (Cubist-RK) useful when correlations were statistically significant but not as strong and if residuals were spatially auto-correlated. Alternatively, wavelet analysis can also be useful to predict soil properties (i.e. topsoil ESP) where there was no direct relationship with digital data but a relationship with scale specific variation in digital data (i.e. ϒ-ray, EM and DEM). Moreover, fuzzy k-means or k-means clustering can be used to make management zones from -ray and EM data when the digital data is not directly correlated to the soil data of interest and produce superior predictions than traditional soil texture maps and or using field delineations to predict soil properties. iii) Digital data of elevation, ϒ-ray and EM were best used in combination rather than alone, regardless of which modelling technique was considered (e.g. LMM, Cubist-RK and wavelet analysis). In terms of the density of digital data transect spacing, the smaller the spacing the better (i.e. transect every 7.5 m) with a maximum transect spacing of 30 m allowing large areas to be measured in a day (~ 400 ha).