Soil health and nutrient management
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Research outcomes: Soil health is improved with a resulting positive impact on the environment and yield growth. Improved reputation and relationship between industry and environmental groups.
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Item 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, GusActivity 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.Item 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 JohnOver 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).Item Item Development of commercial molecular biological assays for improved sugarcane soil health and productivity : final report 2018/009(Sugar Research Australia Limited) Magarey, R; McKay, AProject research has shown that the DNA-based molecular assays for Pachymetra chaunorhiza, Pratylenchus zeae and Meloidogyne species quantify soil populations in field samples, confirming research undertaken in project 2016047, and has also confirmed the importance of sampling strategy and storage for obtaining representative data. Some further research is needed with P. chaunorhiza to ensure the accuracy of the molecular assay; the focus should be on sample storage conditions, amongst other things. Soil samples were processed from soil health projects, plant breeding selection trials and industry samples sent to the Tully soil assay laboratory.Item Effect of harvest time on N-fertiliser requirements in the Wet Tropics : ASSCT extended-abstract paper(ASSCT, 2019) Skocaj, DM; Schroeder, BL; Park, G; Salter, BItem Is magnesium deficiency a causal agent of sugarcane Yellow Canopy Syndrome? : ASSCT peer-reviewed paper(ASSCT, 2019) Tippett, O; Olsen, DJ; Ostatek-Boczynski, ZYellow Canopy Syndrome (YCS) is a disorder affecting sugarcane in the Australian industry, the cause of which is unknown. This paper reviews YCS research focusing on magnesium imbalance as a possible cause of the condition. Four studies were undertaken to evaluate the role of Mg in YCS incidence and severity. In Trial 1 sugarcane leaves were collected at multiple locations in the Burdekin and Herbert with samples taken from sugarcane blocks with both YCS symptomatic and asymptomatic plants. Despite adequate soil-Mg, leaf-Mg concentrations were significantly lower (p?0.05) in leaves 2, 3, 4, 5 and 6 of YCS symptomatic plants in both regions suggesting an imbalance of this critical nutrient. Trial 2 measured Mg concentrations in sugarcane leaves before, during, and after YCS symptom expression. Symptomatic cane showed decreased leaf-Mg concentrations, but this returned to normal levels once the cane recovered. Trial 3 treated YCS symptomatic cane with foliar and soil applications of Mg in an attempt to mitigate the condition. Neither treatment resulted in alleviation of the YCS symptoms. Trial 4 treated sugarcane with foliar-Mg and soil-Mg prior to onset of symptoms. Despite elevating the Mg concentration in leaves, these pre-symptomatic treatments did not prevent YCS expression and plants exhibited YCS symptoms similar to that of the untreated control. We conclude that YCS affected cane is associated with reduced leaf Mg concentrations, but it is unlikely that this is the cause of YCS per se, as concentrations were well above critical thresholds for plant health. YCS occurs independently of Mg and low Mg is an indirect effect rather than a cause. Given that disruption to plant nutrient balance has been described as a symptom of some plant diseases, we speculate that these findings suggest a biotic causal agent.Item Nitrogen accumulation in biomass and its partitioning in sugar cane grown in the Burdekin : ASSCT peer-reviewed paper(ASSCT, 2016) Connellan, JF; DeutschenbaurNitrogen is a key component of metabolic processes in plants and due to its mobile nature in soils is often a limiting factor in achieving maximum yield in commercial sugarcane crops grown in Australia. Demand for N depends upon a crop’s yield potential which is determined by climate, crop age and class and management practices (Muchow and Robertson, 1994). Determining the correct amount of nitrogen required to achieve maximum cane yield while minimising losses to the environment is a difficult task; however developing a basic understanding of nitrogen accumulation in biomass and the rate at which it accumulates will provide useful insights for agronomists, industry advisors and farmers. There have been few studies into the accumulation of nitrogen in the above-ground biomass of sugarcane in Australia. Wood et al. (1996) investigated the accumulation of N in the above ground biomass of two cultivars (Q117, Q138) and confirmed earlier findings from work in South Africa conducted by Thompson (1988), that most of the N was taken up in the first six months following planting/ratooning. In a recent review, Bell et al. (2014) reported that greater than 90% of the total above-ground N uptake occurs in the 200 day period after planting/ratooning. Few studies have been conducted into the accumulation of nitrogen in below ground biomass (roots and stool) of sugarcane in Australia. Bell et al. (2014), summarised the limited data collected to date and suggested that N in stool and root accumulates at about 20 kg N/ha/year while a further 10 kg N/ha/year accumulates in root material down to 60 cm. The objective of this study was to gain an insight into nitrogen accumulation in the above and below ground biomass of sugarcane and its partitioning in crops grown under irrigation in the Lower Burdekin region of Australia.Item Does ratoon age impact on N-fertiliser requirements in the Wet Tropics? : ASSCT poster paper(ASSCT, 2019) Skocaj, DM; Schroeder, BL; Park, G; Hurney, APItem Aspects of temporal N management in sugarcane in sub-tropical Queensland : ASSCT peer-reviewed paper(ASSCT, 2019) Panitz, JH; Schroeder, BL; Skocaj, DM; Salter, BThe proximity of the Australian sugar industry to the Great Barrier Reef (GBR) has resulted in ongoing concerns about elevated concentrations of the dissolved inorganic nitrogen (DIN) in the near-reef environments due to sugarcane production practices on-farm. Although the nitrogen (N) guidelines within the SIX EASY STEPS nutrient-management program are generally appropriate, scope exists for fine-tuning of N application rates for specific circumstances. In particular, enhanced-efficiency fertilisers (EEFs), such as urea coated with 3,4-dimethylpyrazole phosphate (DMPP-urea) and polymer-coated urea (PC-urea), offer promise potentially to improve nitrogen-use efficiency (NUE) in sugarcane production and reducing DIN losses to the GBR. Temporal N-management strategies using these EEFs were assessed within a randomised complete-block field trial conducted in a sub-tropical environment on a well-drained soil supported by a concurrently run shorter-term pot experiment. There were no significant yield responses to applied N, split applications or use of EEFs in the trial in either the plant or first-ratoon crops. Rainfall measured during these seasons would not have resulted in excessively wet conditions at the trial site and may have contributed to the lack of responses to EEFs. Increased N-uptake by the crop, due to the use of N strategies away from the standard practice (i.e. by using EEFs or split applications of urea), improved NUE values based on crop N, but this did not always translate into any improvements in yield. The highest partial net returns in the plant and first-ratoon crop corresponded to the control treatments. Urea applied at 120 kg N/ha in a single application resulted in the next best partial net returns in both crops. This appeared to be the most appropriate strategy to minimise risk to growers. The cost of EEF fertilisers negatively affected the partial net returns, with DMPP-coated urea being more affordable than the poly-coated urea. The results of the pot experiment that included two sugarcane cultivars supported these outcomes. Further work, across seasons (dry, wet and 'normal'), is needed to evaluate more fully the potential of EEFs for use in specific circumstances.