Optimising grain yield and grazing potential of crops across Australia s high-rainfall zone: a simulation analysis. 2. Canola

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1 CSIRO PUBLISHING Crop & Pasture Science, 215, 66, Optimising grain yield and grazing potential of crops across Australia s high-rainfall zone: a simulation analysis. 2. Canola Julianne M. Lilley A,C, Lindsay W. Bell B, and John A. Kirkegaard A A CSIRO Agriculture Flagship, GPO Box 16, Canberra, ACT 261, Australia. B CSIRO Agriculture Flagship, PO Box 12, Toowoomba, Qld 435, Australia. C Corresponding author. Julianne.Lilley@csiro.au Abstract. Recent expansion of cropping into Australia s high-rainfall zone (HRZ) has involved dual-purpose crops suited to long growing seasons that produce both forage and grain. Early adoption of dual-purpose cropping involved cereals; however, dual-purpose canola (Brassica napus) can provide grazing and grain and a break crop for cereals and grass-based pastures. Grain yield and grazing potential of canola (up until bud-visible stage) were simulated, using APSIM, for four canola cultivars at 13 locations across Australia s HRZ over 5 years. The influence of sowing date (2-weekly sowing dates from early March to late June), nitrogen (N) availability at sowing (5, 15 and 25 kg N/ha), and crop density (2, 4, 6, 8 plants/m 2 ) on forage and grain production was explored in a factorial combination with the four canola cultivars. The cultivars represented winter, winter spring intermediate, slow spring, and fast spring cultivars, which differed in response to vernalisation and photoperiod. Overall, there was significant potential for dual-purpose use of winter and winter spring cultivars in all regions across Australia s HRZ. Mean simulated potential yields exceeded 4. t/ha at most locations, with highest mean simulated grain yields ( t/ha) in southern Victoria and lower yields ( t/ha) in central and northern New South Wales. Winter cultivars sown early (March mid-april) provided most forage (>2 dry sheep equivalent (DSE) grazing days/ha) at most locations because of the extended vegetative stage linked to the high vernalisation requirement. At locations with Mediterranean climates, the low frequency (<3% of years) of early sowing opportunities before mid-april limited the utility of winter cultivars. Winter spring cultivars (not yet commercially available), which have an intermediate phenology, had a longer, more reliable sowing window, high grazing potential (up to 18 DSE-days/ha) and high grain-yield potential. Spring cultivars provided less, but had commercially useful grazing opportunities (3 7 DSE-days/ha) and similar yields to early-sown cultivars. Significant unrealised potential for dual-purpose canola crops of winter spring and slow spring cultivars was suggested in the south-west of Western Australia, on the Northern Tablelands and Slopes of New South Wales and in southern Queensland. The simulations emphasised the importance of early sowing, adequate N supply and sowing density to maximise grazing potential from dual-purpose crops. Additional keywords: APSIM, cultivar, frost, heat, phenology, nitrogen. Received 22 August 214, accepted 2 January 215, published online 31 March 215 Introduction Until recently, brassicas grown in Australian mixed-farming systems (livestock and crop) were either winter fodder rapes sown in autumn and grown for spring and summer forage, or spring canola (Brassica napus), also sown in autumn, and harvested in early summer for oilseed (Dove and Kirkegaard 214). Dual-purpose cropping is the farming practice in which crops are used for forage before excluding stock and allowing the crop to recover and produce grain yield. Dual-purpose cropping has long been successful in wheat and other cereals, and more recently has been expanded to include canola (Dove and Kirkegaard 214). Kirkegaard et al. (28) showed that forage of dual-purpose canola has a high nutritive value (mean in vitro digestibility.8; crude protein content 2.4% dry matter). The Journal compilation CSIRO 215 concept of dual-purpose canola was tested in Australia unsuccessfully in the 197s (Dann et al. 1977); more recently, however, Kirkegaard et al. (28) demonstrated success with mid-maturity spring canola cultivars originally bred for grain production. Subsequently, best management guidelines for dualpurpose canola were developed (GRDC 29; Sprague et al. 21, 213; Kirkegaard et al. 212) by utilising a range of vigorous, blackleg-resistant, mid-season cultivars. The practice has been rapidly adopted in existing mixed-farm enterprises in the medium-rainfall areas of southern Australia (45 65 mm) (Kirkegaard et al. 212; McCormick et al. 212; Bell et al. 214). In the Pacific Northwest of the USA, farming systems involving cattle grazing winter canola as a dual-purpose crop have also been investigated (Neely 21; Walsh 212). However, we found no

2 35 Crop & Pasture Science J. M. Lilley et al. other published studies of dual-purpose canola. Renewed interest in dual-purpose canola in the Australian high-rainfall zone (HRZ, >5 mm) was generated by the need for a suitable rotation crop for disease and weed control in long-season wheat crops, which were expanding in this region, and a recognition of the yield potential of wheat and canola in the HRZ (Zhang et al. 26). The release of a several new, vigorous, herbicide-tolerant hybrid spring and winter canola cultivars well suited for dual-purpose use offers opportunities to expand dual-purpose canola cropping in the HRZ. In Australia, crop productivity is maximised by optimising crop phenology such that crops sown following autumn rains avoid sensitive stages of reproductive development during the period of significant frost risk in early spring but mature before the onset of significant drought and heat stress. Grazing can delay flowering (Kirkegaard et al. 212), and this can be manipulated to minimise the risk of frost damage to early-sown crops. Field experiments reported by Kirkegaard et al. (28, 212) and Sprague et al. (214, 215) showed that canola could recover well from grazing by sheep, provided stock were removed before elongating buds were grazed. Dual-purpose cropping capitalises on the excessive early vegetative growth of early-sown crops to provide forage for animals without compromising the canopy development required for grain production (Kirkegaard et al. 212; McCormick et al. 212, 213). These studies demonstrated significant opportunities to capture grazing benefits without significant yield penalties by selecting appropriate cultivars for the location and sowing date, and grazing within the guidelines developed (Dove and Kirkegaard 214). Canola typically provides less grazing than cereals and has a higher seed value; therefore, attention to grazing management is required to avoid the higher risks of economic loss. However, Bell et al. (214) have estimated significant economic returns from grazing canola. With the large production and economic benefits possible from dual-purpose canola, the scope to expand its use from the current areas of adoption in the medium-rainfall zone to new regions of the HRZ warrants further investigation. Recently released, well-adapted long-season winter cultivars requiring a period of vernalisation are most commonly used for dual-purpose cropping in southern New South Wales (NSW) and Victoria (Riffkin et al. 212; Sprague et al. 215); however, these phenology types may be poorly suited to other HRZ environments within Australia. A broad range of mid-season spring cultivars are also widely adapted and grown within Australia. A better understanding of the contribution of phenology type to dualpurpose productivity in HRZ systems is required. The sensitivity of dual-purpose grazing opportunities to agronomic management such as fertiliser application and crop density has received limited attention. Simulation modelling provides a powerful way of investigating many of these factors over a range of climatic conditions and enables the likely effects of key agronomic management decisions on crop growth, forage production and yield to be examined. Although simulation analysis has been used widely to examine performance of grain-only crops, there are few cases where it has been used to explore the potential of dualpurpose crops. Moore (29) used simulation to examine potential dual-purpose productivity of a limited range of wheat cultivars in southern Australia. McCormick et al.(215) also used simulation modelling to examine the potential and agronomic management of dual-purpose canola at Wagga Wagga, NSW, in the medium-rainfall area of southern NSW. Their analysis showed that grazing was possible at Wagga Wagga in 53% of years, that crops could be grazed until early to mid-july providing 4 1 dry stock equivalent grazing days (DSEdays)/ha, and that subsequent regrowth was sufficient to achieve a yield equivalent to that of the ungrazed crop. We conducted a national simulation analysis across Australia s HRZ to quantify the frequency of sowing opportunities, the grain yield and the grazing potential of canola of a range of phenology types and to determine how this is influenced by sowing date, nitrogen (N) availability and crop density. These simulation analyses extrapolate from, and are supported by, field experimental studies on canola crops conducted across a range of environments in Australia s HRZ (Zhang et al. 24; Kirkegaard et al. 212; Riffkin et al. 212; Christy et al. 213; Sprague et al. 214, 215). In this paper, we also report Agricultural Production System Simulator (APSIM) parameters for four cultivars, including a winter and winter spring intermediate type, and identify the optimal flowering window for canola for a range of locations in the HRZ. Methods This paper reports a simulation analysis of canola grain yield and grazing potential similar to that reported for wheat in Bell et al. (215, this issue). The majority of the design of the simulation analysis was identical to that of the wheat analysis and is described briefly here, with detail provided where differences between the two studies exist. Simulation analysis design The study was conducted at the same set of 13 locations across Australia s HRZ as presented in Bell et al.(215). The locations were chosen to capture regional climatic and edaphic conditions that will influence the performance of dual-purpose crops (Bell et al. 215, fig. 2 therein). Mean annual rainfall of 5 8 mm represents the wetter parts of Australia s croppingzone,and values for each site are shown in Bell et al. (215, table1 therein). The soil and crop modules from APSIM ( info/) were configured in combination with the GRAZPLAN animal models ( to simulate the effects of sowing date, crop phenology type, crop nitrogen (N) availability and crop density on both grazing and grain yield from dual-purpose crops across all 13 locations (Holzworth et al. 214). The APSIM-Canola model predictions of grain yield and crop biomass have been validated across a range of environments (Robertson et al. 1999a, 22;Farréet al. 22; Robertson and Holland 24; Robertson and Kirkegaard 25; Wang et al. 212;McCormicket al. 215) and further validation was not undertaken here. Root-mean-square deviation for APSIM-Canola model predictions compared with measured yields ranges from.3 to.5 t/ha for grain yield in these previous evaluations. Local parameterised soils at each location were chosen to represent moderate to good cropping soils in each of the regions (Bell et al. 215, table 1 therein). Single-year simulations (i.e. with reset of initial conditions

3 Optimising grain and grazing from canola in the HRZ Crop & Pasture Science 351 eachyear)wererunforeachof5years(1959to29)of climate data for Bureau of Meteorology stations extracted from the SILO database (Jeffrey et al. 21) for each of the 13 locations. At all locations, a factorial simulation analysis was conducted with nine sowing dates simulated at 2-weekly intervals from 8 March to 28 June, and four cultivars of different phenology types, representing slow winter, winter spring intermediate, slow spring and fast spring types (Table 1). These phenology types were chosen because they represent the full range of canola options available in Australia. APSIM phenology parameters were developed to describe the flowering response of the cultivars (see Cultivar parameterisation below). The factorial analysis included three levels of N availability at sowing (5, 15 and 25 kg N/ha) and four plant densities (2, 4, 6 and 8 plants/m 2 ). Based on a sensitivity analysis, optimum N availability (25 kg N at sowing) and plant density (6 plants/m 2 ) scenarios were chosen as the standard crop-management scenario to determine the potential water-limited yields. Table 1. Combinations of sowing dates, cultivars, soil nitrogen (N) at sowing and plant density used in factorial simulation analysis of dual-purpose cropping opportunities across locations in Australia s high-rainfall zone Bold values for soil N at sowing and plant density are those presented for standard crop management scenarios when sensitivity to these factors are not being explored Sowing dates: 8 March, 22 March, 5 April, 19 April, 3 May, 17 May, 31 May, 14 June, 28 June Phenology type, cultivars: Slow winter, Taurus; winter spring, CBI46; slow spring, 46Y78; fast spring, Hyola 5 Soil N at sowing (kg N/ha): 5, 15, 25 Plant density (plants/m 2 ): 2, 4, 6, 8 Cultivar parameterisation Phenology parameters for cultivars were derived from observed values by using the optimisation process described in Robertson et al. (22). Observed data (sowing date and flowering date) were collected from up to 16 locations across Western Australia (WA), South Australia (SA), Tasmania, Victoria and New South Wales (NSW) for the four cultivars (Table 2). The optimisation process used daily values of daylength and temperature between sowing and the start of flowering to account for the response of a cultivar to vernalisation, photoperiod and thermal time. The APSIM parameters derived were TT juv (thermal time for the juvenile phase at zero vernalisation, degree-days) and TT base (thermal time at a base photoperiod of 1.8 h). These two parameters characterise the sensitivity of the rate of phenological development of the cultivar to vernalisation and photoperiod, respectively, and interact with the effect of thermal time on phenological development. A third parameter, which also interacts with TT juv, captures the underlying vernalisation requirement of winter types, and the parameter was set to a value of 5 days for slow winter and winter spring intermediates, whereas spring types have a value of 25 days. Crop and animal management setup Soil water content was set on 1 February each year at 6% of plantavailable water with the profile filled from the top (dry at the bottom) to mimic conditions following a winter crop sown in the previous year. Therefore, soil-water content at sowing varied across seasons in a realistic way as a consequence of subsequent seasonal rainfall and evaporation processes between 1 February and the specified sowing date. To provide simulated outcomes for all sowing dates and years, the top 3 cm of the profile was wet to field capacity on the specified sowing dates to ensure crop establishment. This eliminated the separate effect of sowing opportunity (presented in Bell et al. 215) on simulation outcomes for different sowing dates. The sowing opportunity Table 2. Summary of locations and number of sowing dates used to develop phenology model parameters for the four cultivars Location Latitude, Number of sowing dates for: Data courtesy of: Longitude Taurus CBI46 46Y78 Hyola 5 Canberra, ACT 35.3, S Sprague, J Kirkegaard Naracoorte, SA 36.96, T Potter Wagga, NSW 35.13, S Sprague, J Kirkegaard Young, NSW 34.32, S Sprague, J Kirkegaard Esperance DRS, WA 33.6, M Seymour Geraldton, WA 28.8, M Seymour South Perth, WA 31.93, M Seymour Spring Ridge, NSW 31.39, L Bell Goulburn, NSW 28.8, S Sprague, J Kirkegaard Delegate, NSW 37.4, N Spoljaric Lake Bolac, Vic , P Riffkin Inverleigh, Vic. 38.8, P Riffkin Hamilton, Vic , P Riffkin Warialda, NSW 29.54, L Bell Armidale, NSW 3.5, L Bell Coonabarabran, NSW 31.27, L Bell Total

4 352 Crop & Pasture Science J. M. Lilley et al. analysis described in Bell et al. (215) relies solely on meteorological data and is not altered by crop species, and is appropriate to this study. Soil nitrate content at sowing was set at 5, 15 or 25 kg N/ha to investigate the effect of N availability. At bud-visible stage (APSIM stage 4.9), all crops were topdressed with a further 1 kg N/ha. Grazing was simulated using sheep at a stocking rate of 25 DSE/ha. Stock were introduced when the crop reached a biomass of 1 kg/ha and removed either (1) when crop green biomass fell to 4 kg/ha, or (2) before the crop reached budvisible stage, beyond which yield development is known to be sensitive to grazing. Stock were not introduced until crop biomass reached 15 kg/ha for winter canola at Hamilton, Delegate, Cressy, Young and Armidale. This modification was made because at these sites the combination of high stocking rate and low biomass at the start of grazing meant stock were regularly removed owing to low biomass. This shortened the grazing period and underestimated the potential grazing in these environments. In reality, grazing is more flexible in terms of stock numbers, multiple grazing and timing; however, simple rules were required for this comparative analysis. There has been little testing of the ability of the current APSIM-Canola model to simulate regrowth following defoliation. Consequently, we circumvented the need to simulate the effect of grazing on grain yield by simulating grazing management in ways shown repeatedly to have little impact on yield of canola (Kirkegaard et al. 212; Sprague et al. 214, 215); that is, stock were removed before bud-visible stage. Potential grain yield was then determined from simulations of ungrazed crops, thus assuming no effect of grazing on grain yield or phenology. Frost and heat stress limitations Currently, the APSIM-Canola model does not account for the effects of heat or frost stress events on flower or grain survival during the sensitive period around flowering and early grain growth. In our factorial simulation analysis, some sowing date cultivar combinations resulted in this sensitive phenological period coinciding with periods of high risk of frosts or heat stress, resulting in over-prediction of grain yield. Bell et al. (215) outline our method to estimate the impact on yield of frost and heat stress during sensitive phenological stages of the crop across the factorial of sowing dates and crop phenology types. Temperature and phenological stage criteria, and an associated yield reduction per day of stress were established for canola and are shown in Table 3. These criteria reproduced similar relationships between yield reduction and temperature stress observed for heat by Morrison and Stewart (22) and for frost by Takashima et al. (213). The yield reductions associated with the occurrence of frost or heat events presented in Table 3 were applied to simulated potential yields and compared favourably with observed yields at Delegate, Canberra and Young. The optimal period for a canola crop to commence flowering was determined by an analysis of the frequency of low- and hightemperature events at each site and related to the frost- and heatsensitive periods during flowering and grain-filling (described in Table 3), and is presented for three sites in Fig. 2. Because canola is an indeterminate crop with new flowers (and therefore new grain) appearing over ~6 weeks, the effect of heat and frost stress on new flowers and grain must be accounted for. Therefore, climatic conditions for the 6 weeks of flowering (heat effect on flowers) and the period 2 8 weeks after first flower appearance (frost effect on early grainfill) were determined and attributed to the date of the start of flowering. In this way, the extended duration of the flowering period was accounted for in the optimal window for first flower appearance. The duration of this optimal window was limited to 25 days. Results Cultivar parameterisation Figure 1 presents the relationship between observed and the predicted days to flowering derived from the parameter optimisation process for the four cultivars. Time between sowing and flowering ranged from 8 to 189 days across the range of cultivars, locations and sowing dates, with the two spring cultivars flowering most quickly and the winter cultivar, Taurus, flowering most slowly. Minimal bias was observed, with the data for each of the cultivars falling near the 1 : 1 line. RMSD values were days, which was 5 1% of the mean period from sowing to flowering. The APSIM parameters derived in this process and presented in Table 4 represent the essential range of differences in vernalisation, photoperiod and thermal time responses of the four phenology types examined. Sowing opportunity and risks for diverse dual-purpose phenology types The analysis of the optimal period for first flower appearance showed that all sites experienced some level of frost risk during June and July and a risk of heat stress from late September (Fig. 2; data shown for three sites). However, considerable variation Table 3. Minimum and maximum temperature criteria for frost and heat stress during phenologically sensitive stages and estimated resulting yield reductions Yield reductions were calculated for each day and accumulated (multiplicatively), so that increasing numbers of stress events resulted in cumulative reductions in the yield. The extended duration of flowering in canola is accounted for by the ~6-week duration of the sensitive period Stress Level Daily temp. (min. max.) Sensitive period Yield reduction per day Frost Moderate 28C to8c 14 8 degree-days after first 2% Severe < 28C flower (early pod-filling period) 1% Heat Mild 38C to338c 63 degree-days after first 1% Moderate 338C to368c flower (flowering period) 18% Severe >368C 35%

5 Optimising grain and grazing from canola in the HRZ Crop & Pasture Science (a) Taurus RMSD = 8.5 (b) CBI46 RMSD = Predicted days to flowering (c) 46Y78 RMSD = 1.1 (d) Hyola 5 RMSD = Observed days to flowering Fig. 1. Simulated v. observed flowering dates (days after sowing) for four cultivars: (a) Taurus, (b) CBI46, (c) 46Y78, (d) Hyola 5. The solid line is the 1 : 1 line. Root mean square deviation (RMSD) from the 1 : 1 line is also shown. Table 4. Details of APSIM-Canola phenology parameters that influence the period up until floral initiation for four simulated cultivars Higher values of vernalisation sensitivity and days of vernalisation both extend the period of low-temperature vernalisation required to initiate reproductive development. Cultivars with higher photoperiod sensitivity values are less sensitive to daylength Cultivar: Taurus CBI46 46Y78 Hyola 5 Phenology-type: Slow winter Winter spring Vernalisation sensitivity (tt_emergence) Photoperiod sensitivity (y_tt_end_of_juvenile) Days of vernalisation (cumvd_emergence) existed across sites in the HRZ with respect to the probability of frost or heat effects on grain yield. For example, at a site such as Kojonup (Fig. 2a) the risk of frost was low, but the risk of heat stress reducing grain yield increased sharply as flowering was delayed. At mild sites such as Hamilton (Fig. 2b), the risk of either frost or heat effects on yield was low, whereas at Young (Fig. 2c), the probability of both frost and heat effects on grain yield was much higher and the optimal first flower window was more sharply defined. The optimal period for first flower appearance is summarised for all 13 sites in Table 5. Opening of the flowering window ranged from 26 July at Esperance, WA, to 18 September at Delegate, NSW, due to differences in climate. The window for first flower appearance ranged from 17 to 25 days in length across the HRZ, decreasing for sites where the opening occurred later. The shaded zone in Table 5 shows those cultivar sowing time combinations that achieve the optimal flowering date, and for each case, the likelihood of a sowing opportunity in the window (based on the analysis presented in Bell et al. 215) is shown as a percentage. At all locations, the likelihood of a sowing opportunity occurring increased as the season progressed, resulting in a greater possibility of sowing spring cultivars

6 354 Crop & Pasture Science J. M. Lilley et al (a) Kojonup Optimal flowering window Mean frequency of frost stress risk during flowering (b) Hamilton (c) Young Optimal flowering window Jun 28-Jun 5-Jul 12-Jul 19-Jul 26-Jul 2-Aug 9-Aug 16-Aug 23-Aug 3-Aug 6-Sep 13-Sep 2-Sep 27-Sep Mean frequency of heat stress risk during flowering Optimal flowering window 4-Oct 11-Oct 18-Oct 25-Oct Date of start of flowering Fig. 2. Frequency of frost events occurring during early grain-filling and heat-stress events occurring during flowering for given first-flower appearance dates and predicted optimal window for first-flower appearance at three locations in Australia s high-rainfall zone. Lines show mean number of severe frost (< 28C min. temp.: dark grey, long dashes), moderate frost (<8C min. temp.: moderate grey, short dashes) and mild frost (<28C min. temp.: light grey, dotted) frost events, and number of heat-stress events (>38C max. temp.: solid black line) that occur during the sensitive window (see Table 3). than winter cultivars. Optimally, winter canola was sown during March and into April at some sites. As sowing windows moved progressively later, winter cultivars became less optimal and faster cultivars were more appropriate. The analysis indicates that the optimal sowing window for all of the cultivars studied ceased by the end of May at all sites. At the Mediterranean (WA, SA) sites, the opportunity for sowing a slow winter cultivar in its optimal window was 2 14%, although the likelihood of a sowing opportunity for a winter spring intermediate was considerably higher at 3 41% (Table 5). The only other location where the opportunity to sow a slow winter cultivar was <4% was in the more northern sites of Quirindi and Pittsworth. The probability of a sowing opportunity for a winter spring cultivar in its optimal window was >7% for all sites in the south-eastern part of Australia s HRZ. For most sites, the likelihood of a sowing opportunity in the optimum

7 Optimising grain and grazing from canola in the HRZ Crop & Pasture Science 355 Table 5. Summary of predicted safe window for first flower appearance, the corresponding optimal sowing window (grey), average of simulated grazing days, potential grain yield and grain yield adjusted for frost and heat effects for four canola cultivars at 13 locations in the Australian high-rainfall zone with the probability of a sowing opportunity in the sowing window (%) are shown The four phenology types represent slow winter (e.g. Taurus), winter spring intermediate (e.g. CBI46), slow spring (e.g. 46Y78), fast spring (e.g. Hyola 5) cultivars. Data presented are for a crop density of 6 plants/m 2 with 25 kg N/ha available at sowing and 1 kg N/ha added post-grazing L ocation Optimal window Phenology-type Sowing window intervals Mean predicted Mean frost heat- Mean pot. for first flower grazing days limited yield yield Mar. Mar. Apr. Apr. May May May June June (DSE-days/ha) (t/ha) (t/ha) Kojonup 18 Aug. 9 Sept. Slow winter 14% % S low spring 8% % Esperance 26 July 2 Aug. Slow winter 7% % % % Cummins 3 27 Aug. Slow winter 12% % % % Naracoorte 11 Aug. 3 Sept. Slow winter 2% % % % Hamilton 1 21 Sept. Slow winter 57% % % % Inverleigh 25 Aug. 15 Sept. Slow winter 57% % % % Bairnsdale 14 Aug. 8 Sept. Slow winter 54% % % % Cressy 16 Sept. 3 Oct. Slow winter 84% % % % Delegate 18 Sept. 6 Oct. Slow winter 78% % % % Young 27 Aug. 18 Sept. Slow winter 43% % % % Quirindi 8 Aug. 2 Sept. Slow winter 28% % % % Armidale 26 Aug. 19 Sept. Slow winter 67% % % % Pittsworth 27 July 21 Aug. Slow winter 28% % % %

8 356 Crop & Pasture Science J. M. Lilley et al. window for a spring cultivar was >8%, with the exceptions of Esperance, Quirindi and Pittsworth, where the likelihood was >5%. The risk of frost or heat damage despite being sown in the optimal window varied across sites. For example, at Kojonup and Inverleigh, the long-term average yield loss was 2 3%, whereas at Pittsworth, Quirindi and Young, yield losses averaged 1 13%. At other sites, losses were typically 5 7%. Dual-purpose productivity Average predicted grazing days and grain yield of the four cultivars sown in their optimal sowing window are summarised in Table 5 for the 13 locations. The analysis shows considerable variation in dual-purpose canola productivity among locations across the zone. The winter cultivars were predicted to produce DSE-days/ha depending on location, and spring cultivars up to 83 DSE-days/ha. Grain yields of t/ha were simulated for all cultivars sown in their optimal window. Early-sown, longer season winter cultivars tended to have the highest long-term average yield at each site. When the appropriate cultivar was chosen for each sowing date, later sowing reduced average grain yield by only.5 t/ha per week delay in sowing (range..8 t/ha per week) (Table 5). Simulated grain yields Figure 3 shows mean simulated grain yield (adjusted for effects of frost and heat) for each sowing date for each location. Yields of winter and winter spring types were highest for the earliest sowing dates and declined as sowing date was delayed. At the WA and SA sites (Fig. 3a d), yield of the winter spring cultivar tended to be higher than that of the winter type. The winter cultivar rarely outyielded the winter spring cultivar except when sown on 8 March at the Armidale, Cressy and Hamilton locations. At all sites, yield of the two spring cultivars was reduced (by frost) relative to the winter cultivars at the early sowing dates (before the end of April) and reached an optimum from sowing around the end of April. Exceptions were at Inverleigh and Hamilton, where the optimum yield of the spring cultivars was achieved from sowing later, in mid late May. At most sites, spring cultivars outyielded winter cultivars when sown after mid late April; however, the yields of all four cultivars were similar from late May onwards at Hamilton, Inverleigh, Bairnsdale, Cressy, Delegate and Armidale. Long-term average yield declined after this optimum sowing date in all cultivars, due to the cumulative impacts on yield of factors such as (i) lower biomass from shorter crop duration, (ii) heat stress during flowering and grain-filling, and (iii) terminal water deficit (Robertson et al. 1999b). Figure 4 demonstrates the significant seasonal variability in grain yield that could be expected for canola crops at three selected locations in the HRZ. At Young, there was a high degree of seasonal variability for all cultivars and sowing dates, presumably due to the combination of the greater frost and heat risk (see Fig. 2) and more variable rainfall. For sowing dates up to mid-may, the yield of the optimal cultivar was within 1 t/ha of the median in 5% of years, and 1th decile yields were ~2 t/ha less than the median. Yields of up to 1 t/ha above the median were observed in some years for optimally sown crops. At Hamilton, yield was much more stable, with a yield range of <1 t/ha simulated for the highest yielding cultivar for sowing dates up to mid-may, and variability only increasing for sowings during June. At Kojonup, there was little variability for optimally sown spring types, although variability increased and the median yield decreased when sown after late-april. For winter cultivars, the earliest sowing had the highest yield and lowest variability. Median yield declined and variability increased rapidly for all cultivars as sowing was delayed beyond the optimal dates. Simulated grazing days Simulations demonstrated significant grazing opportunities from canola crops across the HRZ, particularly from earlysown winter types (Fig. 5). The three locations presented were generally similar within each of the four cultivars; however, inter-seasonal variability of grazing was greater for early-sown winter cultivars at Hamilton and Young than at Kojonup. Winter cultivars offered the highest grazing potential with a long period of grazing before the crop reached budvisible (up to 1 days in some environments). Across all environments, an average of 198 DSE-days/ha was simulated for the slow winter types sown in March or early April. In some locations (e.g. Quirindi), an average of >25 DSE-days/ha was simulated (Table 5). Even when sown on the same date, significantly more grazing was obtained from winter cultivars than from the spring cultivars (Fig. 5). The greater vernalisation requirement of the winter cultivars delays reproductive development, extending the period of vegetative growth, which lengthens the safe grazing window. However, in most of the HRZ environments simulated here, early-sown slow spring cultivars could also be grazed for a significant period, providing 5 DSE-days/ha (Table 5). The fast-developing spring cultivars offered the lowest grazing potential, typically ~3 55 DSE-days/ha. Variability in grazing days (Fig. 5) was low relative to the variability simulated for grain yield (Fig. 4). Most variability was seen in early-sown winter cultivars at Hamilton and Young, where variable autumn growing conditions resulted in variability in biomass. In some years, stock were removed in the simulation because crop biomass fell to the destocking threshold of 4 kg/ha long before the crop reached bud-visible, whereas in more favourable years, higher crop growth rates kept up with grazing and hence stock were not removed until bud-visible. This was despite the higher threshold for commencement of grazing (15 v. 1 kg/ha) at these sites. For later sowing dates, stock were consistently removed because of the critical phenological stage (rather than biomass threshold) so that duration of grazing was less variable. Sensitivity of grazing potential to crop density and nitrogen supply Crop N supply had a significant effect on the amount of forage predicted for dual-purpose canola at all locations. Figure 6 shows the simulated response of the four cultivars to available N at sowing at three representative locations. Increasing N availability had the greatest effect on availability of grazing forage of winter and winter spring types, little effect on the slow spring type, and no effect on the fast spring type. The effect on winter cultivars was greatest for earlier sowing dates (March and April), after which grazing potential declined and increased

9 Optimising grain and grazing from canola in the HRZ Crop & Pasture Science (a) Kojonup (b) Esperance (c) Cummins (d) Naracoorte (e) Hamilton (f ) Inverleigh Frost-heat adjusted grain yield (kg/ha) (g) Bairnsdale (j) Young (h) Cressy (k) Quirindi (i) Delegate (l) Armidale (m) Pittsworth 29 Jun 1 Mar 16 Mar 31 Mar 15 Apr 3 Apr 15 May 3 May 14 Jun 29 Jun Mar 16 Mar 31 Mar 15 Apr 3 Apr 15 May 3 May 14 Jun 29 Jun 1 Mar 16 Mar 31 Mar 15 Apr 3 Apr 15 May 3 May 14 Jun Fig. 3. Simulated long-term average grain yields (adjusted for frost and heat limitation) of four canola cultivars sown at 2-weekly intervals at 13 locations across Australia s high-rainfall cropping zone. The four phenology types were: slow winter (e.g. Taurus, *), winter spring intermediate (e.g. CBI46, *), slow spring (e.g. 46Y78, &), and fast-spring (e.g. Hyola 5, &). Data presented are for a crop density of 6 plants/m 2 with 25 kg N/ha at sowing and 1 kg N/ha added post-grazing. rate of N at sowing had little impact. For example, for the earliest sowing date at Young, the average grazing productivity of the slow winter type was 45 DSE-days/ha at 5 kg N/ha at sowing; this increased to 14 DSE-days/ha at 15 kg N/ha, and to 23 DSE-days/ha at 25 kg N/ha (Fig. 6i). Similar interactions of N supply and sowing date on grazing from winter cultivars were observed at other locations. There was little response of the grazing potential of spring cultivars to N availability at sowing. The reduced responsiveness to N in spring cultivars was due to lower biomass production during the shorter window for grazing. In the spring cultivars, grazing potential was less for the very early March sowing dates than the April sowing dates because the crop progressed rapidly to flowering in the very long days, whereas the grazing window was longer for April-

10 358 Crop & Pasture Science J. M. Lilley et al. 6 5 Kojonup Hamilton Young (a) Winter slow (e) Winter slow (i) Winter slow Frost-heat adjusted grain yield (kg/ha) (b) Winter spring (f ) Winter spring ( j ) Winter spring (c) Spring slow (g) Spring slow (k) Spring slow (d) Spring fast (h) Spring fast (l) Spring fast 8-Mar 22-Mar 5-Apr 19-Apr 3-May 17-May 31-May 28-Jun 8-Mar 22-Mar 5-Apr 19-Apr 3-May 17-May Sowing date 31-May 28-Jun 8-Mar 22-Mar 5-Apr 19-Apr 3-May 17-May 31-May 28-Jun Fig. 4. Variability in simulated frost heat-adjusted grain yields of four canola cultivars sown at 2-weekly intervals at three locations across Australia s highrainfall cropping zone: (a d) Kojonup, Western Australia; (e h) Hamilton, Victoria; and (i l) Young, New South Wales. The four phenology-types were: slow winter (e.g. Taurus), winter spring intermediate (e.g. CBI46), slow spring (e.g. 46Y78), and fast spring (e.g. Hyola 5). Boxes depict the 25th, 5th, 75th percentile and whiskers the 1th and 9th percentile of simulated yields over 5 years ( ). Data presented are for a crop density of 6 plants/m 2 with 25 kg N/ha at sowing and 1 kg N/ha added post-grazing. sown crops, which were not exposed to the very long days and did not reach bud-visible stage so quickly. Plant density had a significant influence on grazing potential of dual-purpose canola over the range of densities simulated (2 8 plants/m 2 ) (Fig. 7). Grazing potential of spring cultivars was lower than of winter cultivars at all plant densities, but at some sites such as Hamilton and Young, grazing potential of high-density spring canola (8 plants/m 2 ) was similar to that of low-density winter canola (2 plants/m 2 ) (Fig. 7). High densities of spring canola could compensate for the short duration of grazing by producing more biomass per unit area. For example, increasing plant density by 2 plants/m 2 typically provided an additional 2 25 DSE-days/ha of grazing for all cultivars. This response diminished at >6 plants/m 2, particularly for spring types. Discussion Grain and grazing potential of canola crops across the HRZ At all of the HRZ locations studied in this simulation analysis there were opportunities to sow the full range of canola phenology types to achieve significant forage for grazing (>5 DSEdays/ha) and high grain yields ( t/ha). Winter cultivars

11 Optimising grain and grazing from canola in the HRZ Crop & Pasture Science Kojonup Hamilton Young (a) Winter slow (e) Winter slow (i) Winter slow (b) Winter spring (f ) Winter spring ( j ) Winter spring Grazing days (DSE.day/ha) (c) Spring slow (g) Spring slow (k) Spring slow (d) Spring fast (h) Spring fast (l) Spring fast 8-Mar 22-Mar 5-Apr 19-Apr 3-May 17-May 31-May 28-Jun 8-Mar 22-Mar 5-Apr 19-Apr 3-May 17-May Sowing date 31-May 28-Jun 8-Mar 22-Mar 5-Apr 19-Apr 3-May 17-May 31-May 28-Jun Fig. 5. Variability in simulated sheep grazing days (DSE-days/ha) from four canola cultivars sown at 2-weekly intervals at three locations across Australia s high-rainfall cropping zone: (a d) Kojonup, Western Australia; (e h) Hamilton, Victoria; and (i l) Young, New South Wales. The four phenology-types were: slow winter (e.g. Taurus), winter spring intermediate (e.g. CBI46), slow spring (e.g. 46Y78), and fast spring (e.g. Hyola 5). Boxes depict the 25th, 5th, 75th percentile and whiskers the 1th and 9th percentile of simulated yields over 5 years ( ). Data presented are for a crop density of 6 plants/m 2 with 25 kg N/ha at sowing and 1 kg N/ha added post grazing. sown at the earliest opportunity provided most grazing (9 25 DSE-days/ha) and the highest average grain yields of t/ha (Table 5). Fast-maturing spring cultivars sown later had little grazing potential; however, choosing the optimal cultivar for any given sowing opportunity maximised potential yield and minimised risk (Fig. 5). The long-term average grain yield declined only marginally (.5 t/ha per week delay in sowing) across the range of sowing dates, provided an appropriate phenology type for the sowing date was selected (Table 5). Kirkegaard et al.(21) also reported little difference in the ungrazed grain yield of winter canola sown in early April and spring canola sown in mid-april at two sites in south-eastern Australia, and Robertson et al. (1999b) reported a yield change of 1% to +4% per week delay in sowing date. The indeterminate flowering habit of canola allows an extended duration and plasticity in the phase of yield potential determination during late reproductive development (Gomez and Miralles 211). In addition, the cooler and wetter environments of the Australian HRZ allow a longer flowering and grain-filling duration and these factors combined to maintain high yield potential. The small decline in canola yield across sowing dates reported here contrasts

12 36 Crop & Pasture Science J. M. Lilley et al Kojonup (a) Winter slow Hamilton (e) Winter slow Young (i) Winter slow (b) Winter spring (f ) Winter spring (j) Winter spring 2 Grazing days (DSE.days/ha) (c) Spring slow (g) Spring slow (k) Spring slow (d) Spring fast (h) Spring fast (l) Spring fast Mar 16-Mar 31-Mar 15-Apr 3-Apr 15-May 3-May 29-Jun 1-Mar 16-Mar 31-Mar 15-Apr 3-Apr 15-May 3-May 29-Jun 1-Mar 16-Mar 31-Mar 15-Apr 3-Apr 15-May 3-May 29-Jun Sowing date Fig. 6. Effect of available nitrogen at sowing (hollow, 5 kg N/ha; grey, 15 kg N/ha; black, 25 kg N/ha) on sheep grazing days (DSE-days/ha) from four canola cultivars sown at 2-weekly intervals at three locations across Australia s high-rainfall cropping zone: (a d) Kojonup, Western Australia; (e h) Hamilton, Victoria; and (i l) Young, New South Wales. The four phenology-types were: slow winter (e.g. Taurus), winter spring intermediate (e.g. CBI46), slow spring (e.g. 46Y78), and fast spring (e.g. Hyola 5). Data presented are for a crop density of 6 plants/m 2 with 25 kg N/ha at sowing and 1 kg N/ha added post grazing. with the larger decline reported for wheat at the same set of sites (.45 t/ha per week delay; Bell et al. 215). The difference between wheat and canola due to indeterminacy may be smaller in low- and medium-rainfall areas because drought and heat stress limit the duration of grain-filling and hence yield. Sowing at the earliest opportunity clearly maximises forage production (Fig. 5, Table 5), a finding consistent with the experiments reported by Kirkegaard et al. (212) and Sprague et al. (215). Those authors attributed the reduction in grazing forage with later sowing to the shorter duration of biomass accumulation before and during grazing up to bud elongation. Later sowing also delayed growth into a colder period, causing slower growth rates during the vegetative period. Here, we estimated that for the winter cultivar sown at Young, median grazing declined by 2 DSE-days/ha for each week delay in sowing date (Fig. 5i). Grazing was also reduced in the less productive spring types by ~3 65 DSE-days/ha per week delay in sowing, depending on location. At some locations such as Hamilton and Young, early-sown winter cultivars had highly variable forage production. This was

13 Optimising grain and grazing from canola in the HRZ Crop & Pasture Science 361 Grazing days (DSE.days/ha) (a) Kojonup (b) Hamilton (c) Young removed until the phenology rule was enacted, producing a long period of predicted grazing. In seasons with less favourable conditions for early growth, stock were removed much sooner, when the minimum biomass threshold (4 kg/ ha) was reached, and as a consequence the grazing period was shorter. In reality, grazing of long-season crops is frequently conducted in a much more flexible manner, either through adjustments to stocking rate to match herbage on offer, or by a second grazing when crop biomass recovers. Our simulation rules did not permit such flexible grazing scenarios and probably predicted higher variability in grazing days than would be observed in reality. Kirkegaard et al. (21) and Sprague et al. (214, 215) reported that in the HRZ, early-sown winter cultivars (sown March early April) provided an extended grazing period (May August) with high grain yields (3 5 t/ha) and little impact of grazing on yield unless it was delayed into September. This extended grazing period provides much greater flexibility for the livestock grazing enterprise than the use of spring cultivars, where the safe period for grazing is shorter if yield loss is to be avoided (Sprague et al. 215). Our simulation analysis showed that for spring cultivars, the earliest sowing dates (March) provided less grazing than April sowings (Fig. 5) because of rapid onset of flowering in response to long days and warmer autumn conditions. This shorter safe period for grazing reduced the available grazing forage; however, the simulation did not account for the delayed phenological development caused by grazing (Kirkegaard et al. 212), which in reality may lengthen the grazing period. In the HRZ, the main benefit of sowing canola early was the increased grazing potential, whereas the grain yield benefit was small. By contrast, sowing date significantly influenced both yield and grazing potential in wheat (Bell et al. 215) Sowing density (plants/m 2 ) Fig. 7. Effect of plant sowing density on sheep grazing days (DSEdays/ha) from four canola cultivars at three locations across Australia s high-rainfall cropping zone: (a) Kojonup, Western Australia; (b) Hamilton, Victoria; and (c) Young, New South Wales. The four phenology-types were: slow winter (e.g. Taurus, *), winter spring intermediate (e.g. CBI46, *), slow spring (e.g. 46Y78, &), and fast spring (e.g. Hyola 5, &). Data presented are for 25 kg N/ha at sowing and 1 kg N/ha added post grazing and the mean of all sowing times. largely an artefact of the simulation rules, which were set the same for all 13 sites across the Australian HRZ. In favourable seasons, biomass growth was substantial and stock were not Implications for integration of dual-purpose canola into high-rainfall cropping systems This analysis supports commercial experience, where currently available spring canola cultivars sown early (e.g. March in NSW, or mid-april in Naracoorte) are prone to premature flowering and high frost risk. Although well-timed grazing of early-sown spring cultivars could be used to delay flowering into the appropriate window to avoid frost (Kirkegaard et al. 28), success would require very careful grazing management to avoid significant yield penalties if grazing occurred after bud elongation, and to facilitate crop recovery (McCormick et al. 212, 213; Dove and Kirkegaard 214). By contrast, longer season winter and winter spring cultivars have a more suitable phenology for dual-purpose use in farming systems in the HRZ and provide the flexibility of an earlier and extended sowing window. The longer safe grazing period and the capacity for good yield recovery have been clearly demonstrated for the longer season winter types in numerous experiments (Sprague et al. 215) and are predicted by this simulation analysis to be achievable across a range of seasons and HRZ regions. There is little difference in grain yield or grazing value between winter and winter spring types in the earliest sowing window (Figs 3 and 4). In addition, in the Mediterranean and northern locations, the probability of a sowing opportunity for a winter cultivar was small. Growers purchasing seed in advance, before the

14 362 Crop & Pasture Science J. M. Lilley et al. sowing opportunity is known, may find that winter spring cultivars provide a more flexible option. The development and release of well-adapted winter spring intermediate cultivars would be of great advantage at many sites in the HRZ, as previously proposed by Kirkegaard et al. (21), Christy et al. (213) and Sprague et al. (215). Some winter spring cultivars are in the final phases of commercial testing and release is expected in 215. Regional differences in dual-purpose cropping opportunities were significant. For example, at Delegate, the grazing value was less than half that at many other locations because low autumn and winter temperatures reduced growth from midautumn to late spring. At sites with long, cool growing seasons (e.g. southern Victoria and Tasmania), sowing before 8 March may increase the potential grazing value of a dual-purpose crop. Paridaen and Kirkegaard (215) demonstrated potential in these environments for biennial dual-purpose winter canola, sown in the previous spring and grazed in summer and autumn before locking up to produce grain yield. In southern sites where fewer early-march sowing opportunities exist and later sowings result in limited grazing due to cold autumn and winter temperatures, spring sowing may provide more reliable crop establishment. Realising the potential of dual-purpose cropping may be problematic in some HRZ regions, because of fragmentation of suitable arable land and a lack of skills and infrastructure for cropping on livestock farms (Bell et al. 214; Dove and Kirkegaard 214). Share-farming arrangements with cropping contractors can surmount these issues, such as in the Goulburn area of NSW where significant land-use change has occurred through integration of dual-purpose crops (GRDC 213). Our analysis suggests similar productive potential (15 25 DSE-days/ha and grain yields of >4 t/ha from canola) for dualpurpose cropping in the Northern Tablelands of NSW, where the practice has not yet been adopted. Nitrogen nutrition and plant density both influenced forage production and grazing potential. Our analysis showed that a plant density of 6 plants/m 2 and soil N levels at sowing of kg N/ha for winter types and 5 15 kg/ha for spring types maximised grazing biomass (Figs 4 and 5). The analysis demonstrates that careful attention to establishing a good plant population and early N management of the crop can have significant impact on the profitability of dual-purpose cropping. APSIM model performance and issues for further research The yields reported in this study are potential yields given the water and N supply to the crop. We recognise that APSIM does not capture all of the production limitations that occur at various sites, such as waterlogging, hot dry winds, or pests and diseases. The adjustments made to yield to correct for the significant effects of heat or frost stress were essential to this study to simulate realistically the yield of canola sown on dates that resulted in flowering outside of the optimal window. The stress factors applied to reduce grain yield were tested against a limited dataset and further refinement of these relationships is an area of further research. Nevertheless, the range of forage and grain yields predicted were within the range of those achieved experimentally. Simulated grain yields were in the range reported in experiments exploring diverse canola cultivars in southern Victoria and SA (Christy et al. 213), NSW (Kirkegaard et al. 212; Sprague et al. 215) and WA (Zhang et al. 24; Sprague et al. 215). However, the N applied in these simulations was not sufficient to support grain yields as high as reported by Christy et al. (213) (up to 7.5 t/ha). This is a further example of the limitation of imposing uniform management rules at all 13 sites with divergent climate and soil type. The range in simulated grazing biomass (3 25 DSEdays/ha) was directly comparable with the range reported in field experiments in south-eastern NSW (8 26 DSE-days/ha) (Kirkegaard et al. 28, 21, 212; Sprague et al. 214, 215) and in WA and northern NSW (Sprague et al. 215). The early work on APSIM-Canola of Robertson et al.(22) and Farré et al. (22) did not include parameters for winter canola types, and minimal investment has been made in developing parameters to describe canola cultivars released during the last decade. Wang et al. (212) parameterised three Chinese winter-canola cultivars, and developed parameters of a similar magnitude to the parameters developed in this study for the winter cultivar, Taurus. Christy et al. (213) also developed APSIM parameters for two cultivars in common with our study (Hyola 5 and Taurus); however, they used a small dataset from a small geographical range and their parameters differed significantly from the parameters derived in this study. New parameters developed for modern cultivars described four broad phenology types for the purpose of this study. Robertson et al. (22) achieved a closer fit (RMSD days) between observed and predicted flowering date than was achieved with this dataset (RMSD days; Fig. 1); however, RMSD values were in the same range (4 1%) in both studies when expressed as a percentage of the mean days to flowering. Most phenology data included in the study of Robertson et al. (22) were collected for developing phenology parameters for APSIM, whereas our dataset was derived from observation of agronomic experiments. Further research to refine parameters describing phenological and growth characteristics of a broader range of modern cultivars is likely to improve the accuracy of simulations. This study assumed that grain yields of grazed crops were equivalent to those of ungrazed crops. Although there is considerable experimental evidence that grazing in the safe window does not affect grain yield, it is known that delays in phenological development can occur, depending on the duration and severity of grazing (Kirkegaard et al. 212). Further development of the simulation models to capture the effects of defoliation on phenology and the dynamics of regrowth would improve the ability to explore the impact of grazing on yield across a range of environments and seasons. This simulation analysis provided important insights into the importance of several management factors for optimisation of productivity of dual-purpose canola in the HRZ. Further improvements to APSIM and location-specific refinements of management rules in the analysis would provide further insights.