Modelling nitrogen uptake by sugarcane crops to inform design of controlled release fertiliser for synchrony of N supply and demand
High nitrogen (N) input to support sugarcane productivity has been associated with low N use efficiency. Controlled release fertilisers (CRF) have gained interest for their potential to reduce N losses through better synchronisation of N release with crop N demand. However, there is almost no experimental data on N release from CRF specific to conditions experienced in sugarcane soils and the limited data on sugarcane N uptake patterns typically come from short-term experiments under specific conditions. This paper presents N uptake patterns of sugarcane crops in response to varying seasonal climate conditions by extending field observation data using APSIM modelling. The objectives were to characterise the seasonal variability in N uptake patterns and to explore what this may mean for the design of CRF N release patterns for Australian sugarcane systems. The results show that large variations were found in observed above-ground biomass and biomass N accumulation. Both, however, showed some consistency during the early growing period across sites. During the early stages of growth, the simulated variation in above-ground biomass, N accumulation and total N uptake were quite small and as a consequence the simulated N uptake pattern was quite well defined and relatively insensitive to seasonal climatic differences. In terms of CRF design, these simulation results provide an indication of the required release patterns, the length of a potential delay in release and subsequent release rate from CRF.
Keywords: Sugarcane, nitrogen uptake pattern, controlled release fertiliser, APSIM
Sugarcane is a dominant crop in the tropics and sub-tropics of Australia. High nitrogen (N) input to support sugarcane productivity has, however, been associated with low N use efficiency due to N losses (Thorburn et al. 2015; Verburg et al. 2015). The Australian sugarcane industry is under pressure to reduce N losses and increase N use efficiency (Bell, 2014). Better matching of N demand through improved synchrony of N supply (from fertiliser and soil) and crop N demand are seen as key solutions to improving N use efficiency and reducing N losses (Bell and Moody, 2014). Controlled release fertilisers (CRF) have gained interest in sugarcane industry for precisely this reason (Kirsten et al., 2016, 2017). Through better synchronisation of N release with crop N demand, they have been shown to increase nitrogen uptake efficiency, increase yields, and reduce losses via leaching, runoff, volatilization, or denitrification (Shaviv and Mikkelsen, 1993; Shaviv, 2001; Kirda et al., 2005; Chu et al., 2007; Grant et al., 2012; Zhu et al., 2012; Shao et al., 2013; Ye et al., 2013).
The most popular CRFs are polymer coated fertilisers, where composition of polymer coating determines release character (shape of release curve and rate of N release). Most of these have been referred to as ‘linear release’ types. Shaviv et al. (2001) described release from these products as a three stage release process consisting of (1) an adsorption stage during which water enters the coated granule, but no release occurs, (2) a linear release stage while water penetration into granule and solid fertiliser is dissolving, maintaining a constant osmotic pressure and hence N release, and (3) a declining stage starting as the concentration inside begins to decrease when all solid fertiliser has dissolved. Each of these stages are affected by temperature, so the ultimate release pattern and time will depend on local soil temperatures.
Studies have documented that application of CRF significantly increases the yield and NUE of maize (Chu et al., 2007; Shao et al., 2013; Guo et al., 2016), rice in North China Plain (Ye et al., 2013), sugarcane in Hawaii (Isobe, 1971; Isobe, 1972). However, there are almost no studies into the required release patterns to achieve synchrony and the limited data on N uptake patterns typically come from short-term experiments under specific conditions (e.g. Keating et al., 1999). Different N demand patterns may require different release patterns to achieve synchronisation. Hauck (1985) commented that ‘because uptake and use patterns vary considerably among different plant species grown under similar conditions of N supply, it is unlikely that any single pattern of N release from a material will satisfy the N requirements of all cropping situations.’ Therefore a product that suits one crop, may not suit another, and hence care must be taken when extrapolating experimental findings from one crop to another. In order to inform the required synchrony there is, therefore, a need to systematically analyse N uptake patterns as a function of soil, crop and management factors as well as consider the effect of seasonal climate variability (Verburg et al., 2015). This is difficult to achieve through experimentation alone, but could be accomplished in conjunction with modelling.
In recent years, the Agricultural Production Systems Simulator (APSIM) model (Keating et al., 2003; Holzworth et al., 2014), which has a well-tested sugarcane module (Keating et al., 1999), has been used in many applications concerning the water and nutrient balance of a range of cropping systems including sugarcane. APSIM has been used in a range of applications to simulate the impact of different management systems on sugarcane yield and N loss via deep drainage, denitrification, runoff and sediment loss (Keating et al., 1997; Verburg et al., 1998; Stewart et al., 2006; Thorburn et al., 2010; Thorburn et al., 2011; Biggs et al., 2013). The Keating et al. (1999) paper provided simulations of a number of experimental datasets on crop biomass and biomass N accumulation, thus it forms a good starting point for a systematic analysis of N uptake by sugarcane crop.
In this study, selected single year datasets and simulations from Keating et al. (1999) were extrapolated using historical climate data and scenario modelling. The objectives of the analysis were: 1) to characterise the seasonal variability in N uptake patterns, 2) to compare the simulated N uptake patterns with a three-stage, conceptual release pattern commonly attributed to polymer coated fertilisers (Shaviv, 2001), and 3) to explore what this may mean for the design of CRF release patterns for Australian sugarcane systems.
- Materials and methods
- Data sources
The experimental data for Australian sugarcane cropping systems were obtained from the Sugarbag database developed in the 1990s (Robertson et al., 1996) which contains a number of N accumulation datasets. Seventeen of these datasets were simulated as part of the model development (Keating et al., 1999). They are still part of the validation dataset of APSIM and checked with every update of the model. Most of these datasets were obtained from experiments under high water and N input conditions. Nine of the 17 datasets containing both biomass and biomass N measurements for plant or ratoon crops from five different locations were selected for this analysis to capture the key climatic differences within the Australian sugarcane region. APSIM also provided the best descriptions of measured above-ground biomass and biomass N for these datasets. Full details on model performance were presented by Keating et al. (1999). Details of experimental treatments for the nine datasets are provided in Table 1. Long term historical climate data (1958 – 2013) were obtained for representative meteorological stations located closest to each of the experimental sites from the SILO climate data archive (Jeffrey et al., 2001).
- Modelling analysis
The modelling analysis consisted of four steps as described below:
Step 1: Simulation of single year datasets with actual experimental management and fertiliser N and irrigation inputs. These were the same as the original simulations included in Keating et al. (1999) as updated in the validation set for APSIM v7.7.
Step 2: Simulation of the same single year datasets using management rules to mimic actual management (to ensure that the simulated dynamics of above-ground biomass and biomass N matched the measured data from the experiment as close as possible), but defined in more general terms to allow extrapolation to other years in step 3. In most of the experiments irrigation and high N application were used to maintain a high yield. Irrigation schedules in the original simulations (step 1) matched the specific dates in the experimental trials. As there could be big differences in climate and soil water conditions for the same day in different years from 1958 to 2013, a general management schedule was designed using either the amount of available soil water (e.g. 150 and 80 mm soil water for Ayr and Bundaberg) or the amount of rainfall in the preceding days (e.g. less than 18, 15 and 15 mm rainfall in past 3 days for Ingham, Harwood and Grafton, respectively) as a trigger for irrigation.
Step 3: Extrapolation to 55 additional seasons. In these simulations, sugar cropping system was modelled with the same planting/ratooning day and with the same length of growing season as that in the experiment, but started in different years (1958 to 2013; 56 repeated seasons) with the general management rules for each site derived in step 2. Above-ground biomass and biomass N accumulation were simulated to characterise the system’s productivity and N accumulation in response to seasonal climate variation. In addition total N uptake, which includes N accumulated in below-ground roots, was predicted.
Step 4: Scenario modelling with same management across all sites (Harwood (H), Grafton (G), Ingham (I), Ayr (A) and Bundaberg (B)), and extrapolation to 56 seasons. In these simulations, sugar cropping system was modelled with early (1-May/1-Aug) and late (1-Aug/1-Nov) planting/ratooning time scenarios, but started in different years (1958 to 2013; 56 repeated seasons) with high irrigation and N application. All simulations were conducted with the same cultivar ‘q117_fum’ and with planting depth of 100mm. The growing periods of plant and ratoon crops were set to 15 and 12 months respectively, to represent the typical sugarcane management practice. Above-ground biomass and biomass N accumulation were simulated to characterise the system’s productivity and N accumulation in response to seasonal climate variation. In addition total N uptake, which includes N accumulated in below-ground roots, was predicted.
Step 5: Scenario modelling similar to that in Step 4 but for rainfed or limited irrigation instead of high irrigation water input. Region specific irrigation scenarios were set up based on the work by Biggs et al. (2013) and Thorburn et al. (2011) who developed these scenarios in consultation with sugarcane industry. Irrigation water amount was limited to an annual maximum of 2100 mm for Ayr, 375 mm for Bundaberg, and 200 mm for Harwood and Grafton. The sugarcane cropping system in Ingham was rainfed except for a few years (2%) when plant crop had not reached the sprouting stage 40 days after planting then 20 mm irrigation was applied to avoid crop failure. The amount of water per irrigation was 110 mm for Ayr, 37.5 mm for Bundaberg, Harwood and Grafton applied to the soil surface.
- Analysis of model output
Nitrogen uptake curves provide guidance on the ideal synchrony for N supply. Conceptually the N release from polymer coated controlled release fertilisers has been presented as a three-stage process (Shaviv, 2001; Shaviv et al., 2003). There is a water absorption stage during which there is no N release, then a linear N release stage, and finally a first order declining N release stage. (Fig 1a).
The 3-stage release model was synchronised with the predicted N uptake patterns to obtain seasonal estimates of the stage 1 lag and linear stage 2 rate. The simulated N uptake patterns from step 3, 4 and 5 were fitted with a sigmoidal function. The maximum slope of this function was used to derive a rate of N release for the linear stage 2 as well as the corresponding length of the stage 1 lag (see Fig 1b). As this does not capture a small amount of early N uptake, early N requirement that needs to be met by alternative means by end of stage 1 (before stage 2 release commences) was also determined.
- Variations and consistencies in observed above-ground biomass and biomass N
Above-ground biomass and N accumulation in above-ground biomass followed, in general, a sigmoidal pattern (Figure 2). Nitrogen accumulation ceased well before above-ground biomass reached its plateau. Large variations were found in observed above-ground biomass (Figure 2a and b) and biomass N (Figure 2c and d), both from site to site and from plant crop to ratoon crop. After 12 to 15 months of growth, above-ground biomass varied from 4400 g/m2 to about 7300 g/m2for plant crop (Figure 2a), and from 4100 g/m2 to 6800 g/m2 for ratoon crop (Figure 2b). Aboveground biomass N ranged from 25 g N/m2 to about 37 g N/m2 for plant crop (Figures 2c), and from 18 g N/m2 to 27 g N/m2 for ratoon crop (Figure 2d). Datasets 5 came from experiment that included a hilling up treatment (partial filling up of the furrow in which the cane is planted) at 138 days after planting. The aboveground biomass as well as biomass N of dataset 5 were relatively small for a period after hilling up treatment (Figure 2a and c).
Above-ground biomass and N accumulation in biomass did, however, appear to be fairly consistent during the early growing period across sites although patterns for plant and ratoon crops were different. The growth rate of above-ground biomass was most rapid between 80 and 280 days after planting and between 70 and 250 days after ratooning. The N accumulation rate appeared to be most rapid for a period of 80 – 120 days occurring between 80 and 280 days after planting and between 70 and 160 days post ratooning.
- APSIM simulations of above-ground biomass and biomass N in experimental datasets
In general, the APSIM simulated time-course of above-ground biomass (Figure 3), either using original management from the experimental trial or general rule-based management, agreed well with the observations among all the selected datasets conducted under high N and water input conditions. Except for dataset 1, simulated results using general rule-based management nearly coincided with that using original management. For dataset 1, simulated above-ground biomass using general rule-based management was closer to the measured data due to reduced irrigation frequency in early growing period as compared with the original management.
During the early growing stages of sugarcane, simulated N accumulation in above-ground biomass (Figure 4), either using original management or general management, also closely followed the measurements. The simulated results using general management nearly coincide with that using original management for at least the first 200 days after planting or ratooning.
Successfully simulating the above-ground biomass and N accumulation in above-ground biomass using general rule-based management provided a basis for extrapolation of the experimental results (biomass, N in biomass) as well as prediction of total N uptake in 56 seasons from 1958 to 2013.
- APSIM simulated ranges of aboveground biomass, N accumulation in biomass and N uptake from 56 different seasons (1958 – 2013, results from step 3)
During the early stages of growth (100 – 150 days after planting or ratooning), simulated seasonal climate variations in above-ground biomass (Figure 3), N accumulation (Figure 4) and total (above-and below-ground) N uptake (Figure 5) were quite small. For example, at 100 days after planting/ratooning, predicted aboveground biomass and N accumulation across all sites were from 202 ± 39 g/m2 (mean ± standard deviation, dataset 5) to 1020 ± 129 g/m2 (dataset 3) and from 2.1 ± 0.4 g N/m2 to 10.4 ± 1.2 g/m2, respectively, along with total N uptake from 1.9 ± 0.4 g N/m2 to 11.4 ± 1.4 g/m2. As a consequence the simulated N uptake pattern was quite well defined and relatively insensitive to seasonal climatic differences.
All simulated N uptake patterns exhibited a period of no uptake followed by a relatively sudden transition to a rapid linear increase period. The lengths of these periods were different from site to site and from plant to ratoon crop. Following the linear period, N uptake continued at a slower and more variable rate, causing the simulated ranges to increase with time.
- Information for design of CRF release patterns
- Seasonal variability in early N requirement, stage 1 lag and linear N rate for stage 2
Generally, the amount of early N requirement that by end of stage 1 (before stage 2 release commences) showed smaller seasonal variation (Figure 6a), as compared with predicted differences between datasets. This reflected effects of climate and management such as planting/ratooning time on early N requirement. For plant crops, average values were about 1.57 g/m2 with variation of 0.43 g/m2 for most of the datasets except for dataset 5 where the average value was about 4.1 g/m2 with variation of 0.97 g/m2 days. For the ratoon crops, average values were more consist at about 1.63 g/m2 with variation of 0.43 g/m2.
Variation in the predicted stage 1 lag (Figure 6b) and stage 2 linear uptake rate (Figure 6c) for a synchronized 3-stage CRF product was considerable, as a combination effect of seasonal climate and management such as planting/ratooning time and season length. The stage 1 lag for CRF was around 90 days for plant crop except for datasets 5, and 75 days for ratoon crop. Datasets 5, which had hilling up management applied, had a relatively early start to N uptake (Figure 6a), but a longer stage 1 lag (Figure 6b). Stage 2 rates were more variable but with still significant site to site differences too. Average linear N uptake rates were between 0.2 g/m2 and 0.35 g/m2 across sites and were similar for plant and ratoon crops.
- Implications for design of CRF release patterns (results from step 4 and 5)
With same management and high irrigation (from step 4) across all sites (Harwood (H), Grafton (G), Ingham (I), Ayr (A) and Bundaberg (B)), the amount of early N requirement during stage 1 lag showed clear patterns (Figure 7a and d). In addition, the predicted range and average of early N requirement is clearly higher for the early sowing crops than that for late sowing crops. For early sowing crops, average N uptake value of plant crops (2.9 g N/m2) was higher than that of ratoon crops (2.1 g N/m2). For the late sowing crops, average values were more consist at 0.9 g N/m2, and similar for plant and ratoon crops.
The predicted stage 1 lag duration for a synchronized 3-stage CRF product (Figure 7b and 7e) is clearly longer for the plant crops compared with the ratoon crops. Moreover, there is pretty strong location effect for the plant crop and slightly smaller effect for the ratoon. For early sowing plant crops, the stage 1 lag for CRF were about 100 days for Ingham and Ayr from north, and increased to about 145 days for Harwood and Grafton from south. For the early sowing ratoon crops, the stage 1 lag were about 75 days for Ingham and Ayr from north, and about 92 days for Harwood and Grafton from south. Besides that, the delay is clearly shorter for the late planting/ratooning crop compared with the early planting/ratooning crop. For late sowing plant crops, the stage 1 lag for CRF were about 70 days for Ingham and Ayr from north, and increased to about 100 days for Harwood and Grafton from south. For the late sowing ratoon crops, the stage 1 lag increased slightly from north to south and with average 55 days across sites.
The predicted average stage 2 linear N uptake rate (Figure 7c and f) for a synchronized 3-stage CRF product was around 0.2 to 0.32 g N/m2 and the variation was small. There also appears to be a tendency for the rapid N uptake at the southern side (Harwood and Grafton) to be slightly faster. For early sowing crops, average linear N uptake rates were around 0.3 g N/m2 with variation of less than 0.1 g N/m2 across sites and were similar for plant and ratoon crops. For late sowing crops, average linear N uptake rates for both plant and ratoon crops were slightly smaller than early sowing crops, and average values for plant crops were slightly larger than that for ratoon crops.
With limited irrigation (from step 5) across all sites, predicted amount of early N uptake during stage 1 lag (Figure 8a and d) were smaller as compared to that in Fig 7 with high irrigation. Compared to the seasonal variation on early N requirement, the site to site difference was small. Moreover, the stage 1 delay for a synchronized 3-stage CRF product (Figure 8b and e) shows same trends as with high irrigation (Fig 7b and e). For early planting/ratooning crops, the predicted stage 1 lag duration (Figure 8b) is about 20 days shorter (on average) compared with that under high irrigation condition (Fig 7b). For the late planting/ratooning crops, the predicted stage 1 lag duration (Figure 8e) were similar as with high irrigation (Fig 7e). In addition, the seasonal variation of predicted stage 1 delay, for both early and late planting/ratooning crops, were clearly bigger than that with fully irrigation at each site.
With limited irrigation (from step 5) across all sites, the predicted average stage 2 linear N uptake rate (Figure 8c and f) for a synchronized 3-stage CRF product was around 0.12 to 0.3 g N/m2 and the variation was much bigger (more than 0.15 g/m2 across sites) compared with that under high irrigation (Fig 7c and f). For early sowing crops, average linear N uptake rates for both plant and ratoon crops were slightly smaller than late sowing crops, and average values for ratoon crops were slightly larger than that for plant crops. For late sowing crops, average linear N uptake rates were around 0.22 g/m2 and were similar for plant and ratoon crops.
Accomplishing synchronisation requires an understanding of crop N demand patterns and how these are influenced by seasonal conditions, soil properties and cropping systems management. While there has been some study on the N demand patterns, a systematic assessment of the N uptake patterns as they respond to seasonal climate conditions is still lacking. This study provides an example of such analysis for sugarcane cropping systems in Australia, through combination of field measurement data and APSIM modelling. The results generated in this paper enable the quantification of the N uptake in response to seasonal conditions under high water and N input. They also allow to design of N release pattern from CRF for plant and ratoon crops along with different timing of planting.
This study analysed the N uptake patterns of plant and ratoon sugarcane crops from 5 sites in response to seasonal climate conditions by extending field observation data using APSIM modelling. The simulated results provide early information that may inform the design of CRF for Australian sugarcane systems. Our results suggest that N release from CRF may on average be delayed by 100 and 75 days from north (Ingham and Ayr) for early sowing plant and ratoon crops respectively, and by 75 and 55 days from north (Ingham and Ayr) for late sowing plant and ratoon crops respectively. This could be further delayed from south site (Harwood and Grafton).
This result is, however, sensitive to the model’s ability to predict early crop N requirements, including by the roots. One point of note is that most data on crop N accumulation only consider the aboveground biomass. Root N requirements are less well understood. Some of the N that is taken up will be in the roots. A recent study on below ground N accumulation in three different varieties of plant crops without N fertiliser by Connellan and Deutschenbaur (2016) showed that at 200 and 365 days the amounts of root N content were small (11% (10.3 kgN/ha) and 15% (15.2 kgN/ha) of the above-ground biomass N respectively). In another study from Brazil (Otto et al., 2014) the authors collected data on root and shoot biomass and N in biomass over the first ratoon crop, and found that the amount of root biomass (< 1.2 t/ha) and root N (< 5 kgN/ha) was fairly constant and small compared with the shoots biomass (> 30 t/ha) and biomass N (> 100 kgN/ha). In APSIM sugar mode (Keating et al., 1999; Marin et al., 2015), root biomass is produced in proportion to the daily aboveground biomass production and affected by water stress. The proportion decreases from a maximum of 0.30 at emergence and asymptotes to 0.20 at flowering. The simulated root biomass N was about 12 kgN/ha at 200 days, and was comparable with the observations by Connellan and Deutschenbaur (2016) in the same crop growing region. Anecdotal evidence in the industry suggests, however, that some early N may be required. This may be supplied by accumulated soil mineral nitrogen or by combined N supply in planting mix or through use of blend of CRF with urea. Plant crops is already supplied as part of planting fertiliser mix which typically contains 30 kg N/ha. Further work is, therefore, needed to confirm early N uptake predictions.
If N release from CRF is targeted at the rapid N uptake period, the linear release stage could possibly be delayed by 100 days from north (Ingham and Ayr) to 145 days from south (Harwood and Grafton) for early planting crops (Figure 7b) and by 75 days from north to 92 days from south for early ratooning crops, provided initial soil N, nitrogen in planting mix or N mineralisation can supply the relatively small N demand prior to this period (subject to confirmation the root N demand). For late planting/ratooning crops the delay is between 55 and 100 days which is depended by site and crop class. The simulations also characterised the N uptake rate during the linear uptake period under high water and N input conditions, which could inform the release rate for CRF stage 2. Our results show that effect of timing of planting on linear N uptake rate was very small. This study shows the maximum linear N release rate from CRF (Fig 7) where the crop demand for water and/or N was satisfied.
Under limited irrigation water conditions where crop experienced water stress and growth and N uptake was limited, the predicted stage 2 N release rate could be reduced considerably (Fig 8c and f). The reduced extent was depended on the severity of water stress. The late sowing crops which received more rainfall and experienced less water stress and relative higher temperature, grew faster and had a steeper N uptake curve thus would need faster N release rate compared with early sowing crops. It should be noted that the predicted linear stage 2 rates reflect an average over the fast uptake period. Nitrogen release from CRF product is very temperature dependant (Verburg et al., 2016) and here these average rates are also the cumulative effect of variations in temperature within the season.
Our results show that N uptake ceased well before total aboveground biomass reaches its plateau, which agreed well with the experimental findings by Wood et al. (1996) and Kingston et al. (2008). Simulated results from 56 repeated seasonal climate conditions show that the variability in N uptake patterns differed from site to site, and from plant to ratoon crop. Narrow ranges during the early growth period allows design of CRF, but wider ranges later leave uncertainty about the total amount of N to be added. The simulated variability in patterns and responses to seasonal climate were caused by a combination of factors including crop class, crop age, genotype as well as management. A number of Australian and overseas studies have indicated that uptake of N by sugarcane is affected by crop class (plant or ratoon cane), crop age, genotype as well as seasonal and management effects that affect biomass accumulation (Bell, 2014). Further research is needed to explore the effects of these factors on N uptake patterns including the root N demand and prediction of seasonal total N requirement using seasonal climate forecasting (Skocaj et al., 2013; Everingham et al., 2016), and prediction of seasonal stage 2 rate given variability in Fig 8.
The simulation modelling based systems approach helped explore impact of different time of planting/ratooning on the N uptake patterns of the sugarcane crops system. It enabled the quantification of N uptake patterns in response to soil, crop and management factors as well as seasonal climate variability. Our modelling results allows to quantify the lag period of N uptake and the linear N uptake rate at the rapid N uptake period for the design of CRF. The modelling approach can be used to design CRF product that mimics crop response to environmental conditions more closely by considering location and time of planting/ratooning.
A simulation based systems approach enabled the quantification of N uptake patterns of sugar systems in response to crop and management factors as well as the characterisation of the impacts from seasonal climate variability. The approach used and the results generated in this paper can help to develop N release patterns for CRF to synchronize N release with crop N demand. Seasonal variations of stage 1 lag and stage 2 linear rate were small at a location, and became more variable if under water stress. This indicates that location and management effects can be much larger. Hence optimal synchrony cannot be achieved with a ‘one size fit all’ CRF fertiliser across industry given Australian sugarcane system has such a large range in climate and spread in planting/ratooning dates.
Bell MJ and Moody P (2014) Fertilizer N use in the sugarcane industry – an overview and future opportunities. In ‘A review of nitrogen use efficiency in sugarcane. SRA Research Report’. (Ed MJ Bell) pp.305-320. (Sugar Research Australia: Brisbane).
Bell MJ (Ed.) (2014) A review of nitrogen use efficiency in sugarcane. SRA Research Report. Sugar Research Australia, Brisbane.
BellConnellan, JF and Deutschenbaur J (2016) Nitrogen accumulation in biomass and its partitioning in sugarcane growth in the Burdekin. Proc. Aust. Soc. Sugar Cane Technol, pp. 72-80.
Biggs, J.S., Thorburn, P.J., Crimp, S., Masters, B., Attard, S.J., 2013. Interactions between climate change and sugarcane management systems for improving water quality leaving farms in the Mackay Whitsunday region, Australia. Agriculture, ecosystems & environment 180, 79-89.
Everingham, Y., Sexton, J., Skocaj, D., Inman-Bamber, G., 2016. Accurate prediction of sugarcane yield using a random forest algorithm. Agronomy for Sustainable Development 36, 1-9.
Grant, C.A., Wu, R., Selles, F., Harker, K.N., Clayton, G.W., Bittman, S., Zebarth, B.J., Lupwayi, N.Z., 2012. Crop yield and nitrogen concentration with controlled release urea and split applications of nitrogen as compared to non-coated urea applied at seeding. Field Crops Research 127, 170-180.
Guo, L., Ning, T., Nie, L., Li, Z., Lal, R., 2016. Interaction of deep placed controlled-release urea and water retention agent on nitrogen and water use and maize yield. European Journal of Agronomy 75, 118-129.
Holzworth, D.P., Huth, N.I., deVoil, P.G., Zurcher, E.J., Herrmann, N.I., McLean, G., Chenu, K., van Oosterom, E.J., Snow, V., Murphy, C., Moore, A.D., Brown, H., Whish, J.P.M., Verrall, S., Fainges, J., Bell, L.W., Peake, A.S., Poulton, P.L., Hochman, Z., Thorburn, P.J., Gaydon, D.S., Dalgliesh, N.P., Rodriguez, D., Cox, H., Chapman, S., Doherty, A., Teixeira, E., Sharp, J., Cichota, R., Vogeler, I., Li, F.Y., Wang, E., Hammer, G.L., Robertson, M.J., Dimes, J.P., Whitbread, A.M., Hunt, J., van Rees, H., McClelland, T., Carberry, P.S., Hargreaves, J.N.G., MacLeod, N., McDonald, C., Harsdorf, J., Wedgwood, S., Keating, B.A., 2014. APSIM – Evolution towards a new generation of agricultural systems simulation. Environmental Modelling & Software 62, 327-350.
Isobe M (1971) Sulphur-coated urea performance encouraging. Hawaiian Sugar Planters’ Assn. Agron. Rept. p.9
Isobe M (1972) Controlled release fertilizers for sugarcane. Technology for a better community. Hawaiian Sugar Technologists 1971 reports. p.83-87.
Jeffrey, S.J., Carter, J.O., Moodie, K.B., Beswick, A.R., 2001. Using spatial interpolation to construct a comprehensive archive of Australian climate data. Environmental Modelling & Software 16, 309-330.
Keating, B.A., Carberry, P.S., Hammer, G.L., Probert, M.E., Robertson, M.J., Holzworth, D., Huth, N.I., Hargreaves, J.N.G., Meinke, H., Hochman, Z., McLean, G., Verburg, K., Snow, V., Dimes, J.P., Silburn, M., Wang, E., Brown, S., Bristow, K.L., Asseng, S., Chapman, S., McCown, R.L., Freebairn, D.M., Smith, C.J., 2003. An overview of APSIM, a model designed for farming systems simulation. European Journal of Agronomy 18, 267-288.
Kingston, G., Anink, M., Allen, D., 2008. Acquisition of nitrogen by ratoon crops of sugarcane as influenced by waterlogging and split applications. Proc Conf Aust Soc Sugar Cane Technol, pp. 202-211.
Kirda, C., Topcu, S., Kaman, H., Ulger, A.C., Yazici, A., Cetin, M., Derici, M.R., 2005. Grain yield response and N-fertiliser recovery of maize under deficit irrigation. Field Crops Research 93, 132-141.
Marin, F.R., Thorburn, P.J., Nassif, D.S.P., Costa, L.G., 2015. Sugarcane model intercomparison: Structural differences and uncertainties under current and potential future climates. Environmental Modelling & Software 72, 372-386.
Otto, R., Franco, H.C.J., Faroni, C.E., Vitti, A.C., de Oliveira, E.C.A., Sermarini, R.A., Trivelin, P.C.O., 2014. The Role of Nitrogen Fertilizers in Sugarcane Root Biomass under Field Conditions. Agricultural Sciences 5, 1527.
Robertson, M.J., Muchow, R.C., Prestwidge, D.B., 1996. The Sugarbag database system: Enhancing the ability of field experimentation to overcome constraints to sugarcane production. In Wilson JR, Hogarth DM, Campbell JA and Garside AL (eds) Sugarcane: Research Towards Efficient and Sustainable Production. CSIRO Division of Tropical Crops and Pastures, Brisbane. 1996, pp. 19-21. ISBN 0 643 05941 5.
Shao, G., Li, Z., Ning, T., Zheng, Y., 2013. Responses of photosynthesis, chlorophyll fluorescence, and grain yield of maize to controlled-release urea and irrigation after anthesis. Journal of Plant Nutrition and Soil Science 176, 595-602.
Shaviv, A., Raban, S., Zaidel, E., 2003. Modeling controlled nutrient release from polymer coated fertilizers: Diffusion release from single granules. Environmental science & technology 37, 2251-2256.
Skocaj, D.M., Everingham, Y.L., Schroeder, B.L., 2013. Nitrogen Management Guidelines for Sugarcane Production in Australia: Can These Be Modified for Wet Tropical Conditions Using Seasonal Climate Forecasting? Springer Science Reviews 1, 51-71.
Stewart, L.K., Charlesworth, P.B., Bristow, K.L., Thorburn, P.J., 2006. Estimating deep drainage and nitrate leaching from the root zone under sugarcane using APSIM-SWIM. Agricultural Water Management 81, 315-334.
Thorburn, P.J., Biggs, J.S., Attard, S.J., Kemei, J., 2011. Environmental impacts of irrigated sugarcane production: Nitrogen lost through runoff and leaching. Agriculture, ecosystems & environment 144, 1-12.
Thorburn, P.J., Biggs, J.S., Collins, K., Probert, M.E., 2010. Using the APSIM model to estimate nitrous oxide emissions from diverse Australian sugarcane production systems. Agriculture, ecosystems & environment 136, 343-350.
Thorburn, P.J., J.S. Biggs, E.A. Meier, M. Empson, J. Palmer, K. Verburg, and D.M. Skocaj (2015). Increasing nitrogen use efficiency in Australian sugarcane crops: Insights from simulation modelling. Chapter 6, p.180-225. In: A review of nitrogen use efficiency in sugarcane. Research Report. Sugar Research Australia.
Verburg, K., Hochman, Z., Probert, M., Keating, B., 1998. Soil acidification prediction and quantification using APSIM-SWIM. Agronomy*/Growing a Greener Future. Proceedings Ninth Australian Agronomy Conference, Wagga Wagga, pp. 789Á/790.
Verburg, K. T.G. Harvey, T.H. Muster, L.E. Brennan McKellar, P.J. Thorburn, J.S. Biggs, L.P. Di Bella and W. Wang (2015). Use of enhanced efficiency fertilisers to increase fertiliser nitrogen use efficiency in sugarcane Chapter 7 Part I, p.226-292. In: A review of nitrogen use efficiency in sugarcane. Research Report. Sugar Research Australia.
Verburg K, Zhao Z, Biggs JS, Thorburn PJ (2016) Controlled release fertilisers – Lessons from a review and early results characterising release, synchrony and nitrogen losses. International Sugar Journal, 118 (1414), 764-770 (reprinted from Proceedings of the Australian Society of Sugar Cane Technologists 38, 159–169.)
Verburg K, Biggs JS, Zhao Z, Thorburn PJ (2017) Potential production and environmental benefits from controlled release fertilisers – Lessons from a simulation analysis. Proceedings of the Australian Society of Sugar Cane Technologists 39 (in press).
Wood, A.W., Muchow, R.C., Robertson, M.J., 1996. Growth of sugarcane under high input conditions in tropical Australia. III. Accumulation, partitioning and use of nitrogen. Field Crops Research 48, 223-233.
Ye, Y., Liang, X., Chen, Y., Liu, J., Gu, J., Guo, R., Li, L., 2013. Alternate wetting and drying irrigation and controlled-release nitrogen fertilizer in late-season rice. Effects on dry matter accumulation, yield, water and nitrogen use. Field Crops Research 144, 212-224.
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