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Modelling pasture-based automatic milking system herds: grazeable forage options.


Previous research conducted at Camden, NSW, Australia over two years showed that the software modelling framework, Agricultural Production Systems Simulator (APSIM) can be used effectively to simulate and validate yield and nutrient-use efficiencies of an annual cycle of a triple-crop complementary forage rotation (CFR) of maize (grown for silage), forage rape and field peas (Islam and Garcia, 2010). Many researchers (Carberry et al., 1996; Chauhan, 2010) also reported that modelling approaches can assist in arriving at initial estimates of various productive parameters of crops or crops in rotations based on historic climate data and soil characteristics without having to conduct expensive field experiments. These evidences indicate that new research questions can be tested using APSIM or that various treatment options can be screened to prioritize treatments for field experiments. Thus there is a good prospect of APSIM to be used as a tool to screen forage options that can supply grazeable forages for a large herd (400 to 800 cows) throughout the year in an automatic milking system (AMS) dairy.

Pasture-based AMS systems generally have larger herds which pose unique challenges to AMS (Jago et al., 2007) compared to herds housed indoors. Jago et al. (2004) and Jago and Kerrisk (2011) reported that the long walking distances associated with pasture-based large herds milked with AMS reduces cow trafficking and milking frequency. The potential exists to minimize average walking distances if the volume of grazeable forages grown in close vicinity to the dairy is increased. Incorporation of principles of a complementary forage system (Farina et al., 2011) and high yielding forages may provide an opportunity to grow more feed in a small area compared to traditional pastures (e.g. 17 t dry matter [DM]/ha) (Garcia et al., 2007a) thereby reducing average walking distances. Analysis of two years of historical data from a pasture-based AMS research herd has indicated the number of extended milking intervals (>16 h intervals between consecutive milkings) was increased when cows were grazing paddocks in excess of 800 m from the dairy. The extended milking intervals were also associated with a reduced milk accumulation rate in the udder (Lyons et al., 2014).

Recent developments with AMS technology have resulted in installation, testing and co-development of a prototype robotic rotary (RR; Automatic Milking Rotary, DeLaval AMR, Tumba, Sweden) which is expected to have the capacity to milk up to 800 cows when it is commercialized (Kolbach et al., 2012). The RR has the potential to be used with either batch milking or voluntary cow traffic. If the RR is to be used with voluntary cow traffic, it will likely be adopted with herds much larger in size than any existing pasture-based AMS. With the imminent commercialization of the RR (DeLaval AMR, Sweden) it is important for research to address some of the potential challenges that may be associated with voluntary trafficking of large pasture-based herds. One of those challenges is the longer distances to pasture that will be inevitable with the larger herds. Developing robust management practices within an AMS system is important to ensure that farmers have the best opportunity to be successful when AMS is adopted on-farm (Donohue et al., 2010). One approach is to increase the amount of grazeable forages within a 1-km radius of the dairy.

Strategic selection of high yielding grazeable forages in different seasons that have high growth and/or re-growth potential in summer (e.g. maize, soybean, sorghum) or in autumn-winter (e.g. forage rape, ryegrass) has the potential to dramatically increase the yield of forages compared to current levels of pasture utilization (14 to 20 t DM/ha; Stockdale, 1983; Garcia et al., 2007b). Previous research has shown that sowing soybean in late-spring and forage rape in late-summer may provide 8 to 10 t DM/ha (Islam and Garcia, unpubl. results) and 11 to 14 t DM/ha (Garcia et al., 2008; Islam and Garcia, 2012; Islam et al., 2012a) respectively in three harvests. Ryegrass (short season) can also provide 13 to 14 t DM/ha in 7 to 8 harvests (Neal et al., 2009). However, yield of these forages and water requirements may vary due to climatic variations such as El-Nino Southern Oscillation (ENSO) events (Chauhan, 2010) which need to be investigated. It was hypothesized that using these forages in rotations individually, or by intercropping or over-sowing may increase grazeable forage supply for AMS herds substantially. Various work reported advantages of intercropping in terms of yield, nutrient-use efficiency and in controlling weeds, pests and diseases (Ofori and Stern, 1987; Caviglia et al., 2004).

Therefore, a modelling study was undertaken in order to investigate the potentials of APSIM to screen forages that can be grown to supply a high yield of grazeable forages for large AMS herds. The expected output of this study was to gather information on different options of forage crops for AMS farms potential yield and optimum growing season of such forages and alleviate risks. The main objective of this APSIM modelling was to develop sustainable forage option technologies tailored to AMS dairy farming systems.


Simulation model

The model used in this study (APSIM version 7.0) is a crop simulation model (Keating et al., 2003) consisting of modules that incorporate aspects of soil nitrogen (N), water, crop residues, crop growth and development, and their interactions within a cropping system that is driven by daily weather data. It calculates the potential yield, which is the maximum yield reached by a crop in a given environment that is not limited by pests, diseases, frost damage, weeds and lodging, but is limited by temperature, solar radiation, N and water supply (Asseng et al., 2008). The model is calibrated, tested and validated in different locations and is extensively used in Australia and internationally (Robertson et al., 2005; Anwar et al., 2009; Chauhan, 2010; Pembleton et al., 2011) to simulate yield of many grain and forage crops individually and in rotations with various input rates for N and water under various sowing management strategies. The above mentioned publications reported their assessed model predictions against field trials using a range of soil, crop, climatic and management data and have concluded that the APSIM model is able to adequately simulate crops and forages growth. More recently, the capability of the model to simulate triple-crop annual rotations has been confirmed (Islam and Garcia, 2010).

Site, soil and climate

The chosen study site was Camden, located (150.70[degrees]E, 34.05[degrees]S) in New South Wales, Australia. Camden is the location of Australia's first and only pasture-based AMS research herd. Annual average rainfall of this site was 737.7 mm (1900 to 2010), with a diverse annual range from 324 (1944) to 42 mm (1950). Rainfall is generally quite evenly dispersed through the 12-month period. On average, rainfall in spring (September to November), summer (December to February), autumn (March to May) and winter (June to August) accounted for 24.1%, 27.8%, 20.8%, and 28.6% of the annual rainfall, respectively. Mean annual minimum and maximum temperature were 10.7[degrees]C and 23.3[degrees]C, respectively and mean average radiation was 16.9 MJ/[m.sup.2].

Simulations were conducted using the APSIM model with historical weather data from 1900 to 2010, which was downloaded from the enhanced climate database SILO ( The model used inputs of minimum and maximum temperatures, rainfall, solar radiation and vapour pressure deficit on a daily time step every year to simulate growth and yield of forages used in the rotation. The soil of the site corresponded to brown chromosols and black vertisol (Isbell, 2002), which was loam over clay, with texture ranging from loam on surface layers (0 to 15 cm) to heavy clay from 30 to 120 cm and to light clay below 120 cm. Soil properties in the simulated site are given in Table 1.

Simulation scenarios and model criteria

The aim of this study was to investigate and identify forage crops options with regard to their suitability for AMS herds through APSIM simulations. These crop rotations were intended as potential options that may provide forages throughout the year and enable field researchers to make tailored decisions on which forage option(s) may be suitable for AMS herds.

In order to investigate impact of different forage options that can supply maximum forages to AMS herds throughout the year, three main rotational forage options simulations were carried out. These were: i) maize, ii) soybean, and iii) sorghum as summer forages each followed by forage rape over-sown or intercropped with ryegrass to complete a year-round rotation. There are no existing modules to simulate forage rape and ryegrass using APSIM. However, previous work (Islam and Garcia, 2010) have indicated that the canola and weed modules of APSIM can closely simulate yields of forage rape and ryegrass, respectively. Whilst Sorghum does exist in the APSIM module, its simulated re-growth is limited. However, it was found that SweetSorghum modules in APSIM simulations generate re-growth, which was more representative of re-growth achieved in the field. As a result the modules of APSIM maize, canola, weed and SweetSorghum were used to simulate yields of maize, forage rape, ryegrass and sorghum, respectively.

Each of the forage crops were simulated over the years from 1900 to 2010. Long term average values of monthly simulated growth rate (kg/ha/d) for each forage in the rotation were used to calculate total simulated yield. Risks associated with ENSO events particularly due to El-Nino were also assessed. Data dispersion among years or differences due to climate was used for risk assessment or probability of not achieving a certain target.

Irrigation was non-limiting in all simulations and efficiency of irrigation was set at 1. Initial surface residue was set at 00 kg/ha and carbon: nitrogen (C:N) ratio of initial residue was 80. Details of agronomic principles used in each of the three rotation simulations are provided below and presented in Table 2 and 3.

Maize followed with forage rape and ryegrass (oversown with forage rape) : Maize was sown in simulations in four different sowing dates with a fixed rate N fertilizer (135 kg/ha) applied at the time of sowing. Simulated sowing dates were 10 October, 20 October, 20 November and 20 December. All maize crop sowing treatments were grazed (harvested in simulations) at Zadoks growth scale 8 (Zadoks et al., 1974). After the final grazing of maize, forage rape was sown respectively in maize sowing treatments on 15 February, 20 February, 28 February and 7 March in the four maize sowing treatments respectively. Ryegrass was over-sown with forage rape immediately after first grazing and second grazing simulations respectively after first two and last two sowing dates (Table 2). Final grazing of forage rape in all simulations occurred on 13 August to allow high growth of ryegrass. Forage rape was grazed when simulated yield reached 4.5 t/ha or over. Three to four grazings were achieved from forage rape. A total of 70 kg N/ha was applied to forage rape at the time of sowing, 90 kg N/ha after the 1st grazing and 70 kg N/ha thereafter after each subsequent grazing (Islam and Garcia, 2012). Thus, a total of 300 kg N/ha was applied to forage rape. On the other hand, ryegrass was grazed when the simulated yield reached 2 t/ha or over. No additional N was applied to ryegrass since 300 kg N/ha applied to forage rape by default was in fact used by forage rape-ryegrass. The final grazing of ryegrass was made a day before the next sowing of maize to start the rotation again.

Soybean followed with forage rape and ryegrass (oversown with forage rape) : Soybean was also sown in simulations on four different sowing dates, but no N fertilizer was applied at the time of sowing or thereafter. Sowing dates were 15 October, 15 November, 30 November and 15 December. In all sowing treatments, soybeans were harvested when they reached yields exceeding 4.5 t/ha or over. In most simulations soybeans were grazed three times. Final grazings of soybeans were carried out on 19 February, 14 March, 24 March and 30 March, respectively for the above mentioned sowing dates. Forage rape was sown one day after grazing of soybean in all sowing treatments. Ryegrass was over-sown with forage rape immediately after first grazing of forage rape in all sowings of forage rape. A total of 280 kg N/ha was applied to forage rape; 70 kg N/ha at the time of sowing and 70 kg N/ha after each harvest. No N fertilizer was applied to specifically to ryegrass as it was planted as a companion to the forage rape. The grazing rules for forage rape and ryegrass were the same as described in maize rotations above. The final grazing of each of the forage rape-ryegrass was made a day before the next sowing of soybean to start the rotation again.

Sorghum followed with forage rape and ryegrass (intercropped) : SweetSorghum was sown in simulations on four sowing dates (1 November, 15 November, 30 November and 15 December). The final grazing date of sorghum (in all sowing date simulations) was 30 April.

A fixed final grazing date applied to sorghum treatment simulations was set in recognition of the high frost risk after 30 April. The 1 November sowing date simulation achieved two to three grazings whereas the last sowing simulation (15 December) was able to achieve a maximum of only two grazings prior to 30 April. Whilst the final grazing date of 30 April was suited to the sorghum, it resulted in the optimum sowing date for the forage rape-ryegrass being compromised. Nitrogen fertilizer was applied at 40 kg N/ha after each sorghum grazing. Therefore, in the first three sowing treatments, N fertilizer was applied up to three times (total 120 kg/ha), but only twice in the last sowing treatment (total 80 kg/ha).

The forage rape-ryegrass was sown on 1 May in all sorghum simulations. The final grazings were carried out on 31 October, 14 November, 29 November and 14 December to coincide with the 4 sowing dates for the subsequent season of sorghum (Table 2). In all treatments, only two grazings of forage rape was possible. Nitrogen was applied at 140 kg/ha in all sowing simulations; 70 kg N/ha at the time of sowing and 70 kg N/ha after the 1st grazing of forage rape. No N fertilizer was applied directly to the ryegrass in the simulation. Grazing management of forage rape and ryegrass was the same as described for these forages in the maize and soybean rotations (see previous sections).

Approaches used for model validation

Local yield data of individual forages (used in rotation simulations) generated in field experiments at Camden were used to simulate the growing conditions observed in the field (Table 4). In addition, our experiences on field experiments (Garcia and Fulkerson, 2005; Garcia et al., 2006; 2007a, b; 2008; Farina et al., 2011; Islam and Garcia, 2012) on annual cycle of a triple-crop CFR including simulations and validation of CFR using a range of N (0 to 523 kg/ha) and water (0 to 14 ML/ha) (Islam and Garcia 2010) and experimental data on triple-crop CFR rotations have created a level of confidence within the authors that APSIM is a suitable tool allowing the simulation and screening of potential grazeable forage rotations for AMS herds. However, it should be mentioned that the forage screening simulation rotations for AMS herds used in this study have not been tested in any field studies nor have they been reported.


Comparison of modelled and actual yield of individual forages used in the rotation

Actual historical yield data of individual forages (used in the rotation simulations) was predicted relatively moderately ([R.sup.2] = 0.58) by the simulated yield of the respective individual forages (Figure 1; Table 4).

Grazeable forage options and simulated yields from different rotation options

Maize and forage rape-ryegrass rotations provided simulated yields of a total of 28.2, 27.8, 26.0, and 25.3 t DM/ha grazeable forages respectively for 10 October, 20 October, 20 November and 20 December sowing treatments (Table 5). There was a consistent trend of maize yield declining as the sowing date progressed from 10 October to 20 December. As sowing dates for forage rape over-sown with ryegrass progressed the simulated yield of forage rape decreased from 10.0 to 8.2 t DM/ha but this coincided with ryegrass yields increasing from 5.8 to 6.5 t DM/ha. The increased ryegrass yields were insufficient to counteract the decrease in forage rape yields resulting in overall simulated yields of late sowing of forage rape-ryegrass combinations decreasing from 15.8 t DM/ha to 14.7 t DM/ha. As a result of decrease of yields of both maize and forage rape-ryegrass, total simulated grazeable forage yields from this rotation also decreased due to late sowing (Table 5).

Total grazeable simulated yields from soybean-based rotations were 22.9, 22.9, 22.4, and 21.5 t DM/ha respectively for 15 October, 15 November, 30 November and 15 December sowing (Table 5). The pattern of decrease in soybean (9.5 to 7.6 t DM/ha) and forage rape (8.3 to 7.9 t DM/ha), and increase in ryegrass (5.1 to 6.0 t DM/ha) in soybean-based rotations due to late sowing was similar to the maize-based rotations. However, simulated yields of both forage rape and ryegrass in soybean-based rotations were lower compared to those obtained from maize-based rotations.

As a consequence of the limited number of simulated grazings of sorghum (particularly in late sowings) combined with the late sowing of forage rape-ryegrass, total simulated grazeable forage yields were 19.3, 19.1, 19.1, and 16.9 t DM/ha respectively for 15 October, 15 November, 30 November and 15 December sowing of sorghum in sorghum-based rotations (Table 5). Late sowing reduced simulated grazeable sorghum yields substantially.

Standard deviations (based on 111 years) of maize, soybean and sorghum based rotations ranged from 1.5 to 1.7, 0.9 to 1.1, and 0.9 to 1.7 t DM/ha, respectively (Table 5), which indicates that under non-limiting inputs yield differences between years would be minimal from these rotations.

Growth rates of forages in different forage options, forage supply and critical periods

Simulated monthly and annual (average of all months) mean daily growth rates of different forages in different rotations are presented in Table 6 (maize rotations), Table 7 (soybean rotations) and Table 8 (sorghum rotations). Growth rates of forages in maize-based rotations were higher compared to forages in other rotations.

There were two common features of grazeable forage supplies in all rotations. Firstly, all rotations (except Sg4) were able to supply grazeable forages to a varied degree for approximately eight months of the year. Secondly, the rest of the four months of the year were critical periods when grazeable forage supplies were not available. These critical periods were approximately two months after sowing of summer forage crops followed by a period of two months after sowing of forage rape in the rotation.

Impact of El-Nino Southern Oscillation events on simulated yields and irrigation requirements

There were 56 neutral (normal), 29 La-Nina and 26 El-Nino years in 111 years and irrigation and temperature related risks associated with El-Nino years was assessed. Average daily maximum temperature (23.7[degrees]C) and radiation (17.2 MJ/[m.sup.2]) was slightly higher in El-Nino years compared to neutral years (23.2 and 17.0 respectively), but radiation was lower in La-Nina (16.6 MJ/[m.sup.2]) compared to neutral years (Table 9). Under non-limiting irrigation water and N fertilizer, total DM yield for a particular simulation was similar between neutral and El-Nino or La-Nina years (Table 10). Irrigation water requirements could increase by up to 18%, 16%, and 17% respectively in those rotations in El-Nino years compared to neutral years (Table 10). On the other hand, irrigation requirement could increase by up to 25%, 23%, and 32% in maize, soybean and sorghum based rotations in El-Nino years compared to La-Nina years. However, irrigation requirement could decrease by up to 8%, 7%, and 13% in maize, soybean and sorghum based rotations in La-Nina years compared to neutral years (Table 10).


Model comparison (evaluation) with individual forages used in field studies and in simulations

The main objective of this APSIM modelling was to explore sustainable forage option technologies tailored to AMS dairy farming systems. However, one of the challenges associated with dealing with modelled yields in practice might occur due to the lack of confidence of the users on whether these data on simulated yields are realistic or not. In order to gain confidence on these simulated yields it is valuable to justify whether such yields can be explained realistically based on agronomic principles and supported (validated) by field data.

In fact, the basis and confidence of organizing simulation forage rotations for AMS herds was based on our experiences from both field experiments and simulation trials on forage rotations at the Camden site. Agronomical evidences came from our research at Camden, which consistently showed that yields in excess of 40 t DM/ha can be harvested from a triple-crop CFR (maize for silage-forage rape-field peas) under non-limiting N and water inputs (Garcia et al., 2008; Farina et al., 2011; Islam and Garcia, 2012). Our simulations using APSIM also revealed that 39 t DM/ha simulated yield (under non-limiting N and water) may be achievable from the same triple-crop CFR not only from Camden, but also from Hunter Valley and North Coast sites (Islam and Garcia, 2010). These evidences suggest that the dynamic simulation of the APSIM model successfully represented key agronomic, physiological and production system behavior that have occurred in the experimental paddocks or fields. Therefore, it is expected that the simulated forage yields from similar type of forage rotations as those carried out in the present study might be realistic.

In addition, we have also demonstrated that APSIM was able to validate ([R.sup.2] = 0.81) actual yield of total CFR grown under a range of N (from 0 to 523 kg/ha) and water (0 to 14 ML water/ha) (Islam and Garcia, 2010). This evidence further suggests that the APSIM model is underpinned by sound scientific-based principles and biologically explainable for forage crop rotations and may be used to explore various other forage rotations as in the present study for the benefit of the livestock industry.

Finally, our measured actual yields of individual forages (used in rotations in this study) from various field trials and then simulation of same forages with the same agronomic principles (e.g. sowing date) and inputs (e.g. N and water rates and timing) used in the field trials revealed that simulated yields were reasonably well correlated to actual forage yields ([R.sup.2] = 0.58). Therefore, although the simulation rotation scenarios used in this study were not validated using field experiments per se, closely matched simulated and actual yields of individual forages used in the rotations in this study indicate that simulated yields obtained from these forage rotations might be realistic. Nonetheless, one limitation of the validation is that none of the simulations including intercropping of forage rape and ryegrass used in this study was validated using data generated from field studies.

Grazeable forages for automatic milking system herds and critical periods of supply

The main objective of this study was to identify a suitable forage option that can provide high yield as well as supply grazeable forages for AMS herds throughout the year. The simulated yields indicate that maize-based rotations have the potential to provide between 25.3 and 28.2 t DM/ha followed by soybean-based rotations (between 21.5 and 22.9 t DM/ha). Simulated sorghum based rotations supplied lower total forage yields; however, in practice more forage supply may be achieved which will be discussed later. The results presented here indicate that maize-based rotations may have the potential to supply 49% to 66% higher yields of grazeable forages for AMS compared to traditional well managed pastures (17 t DM/ha; Garcia et al., 2007b). This in turn may reduce the grazing area at a similar rate required to grow pastures for AMS herds. Therefore, maize-based rotations of this simulation study may have considerable potential to reduce average walking distances of a large AMS herd and consequently may decrease milking interval and increase milk yield. Similarly, there is a good prospect of soybean-based rotations to increase cow-traffic for a large AMS herds as grazeable forage yields from these rotations increased by 27% to 35% compared to pastures.

In order to maximise grazing (both grazed yield and grazing days) from these rotations, some principles of grazing may need to follow. For example, grazing of maize can commence when the crop reaches 5 t DM/ha (or more), which is generally between 45 and 55 days after sowing. The window of opportunity for grazing of maize may last for up to 40 days after which the crop will likely be too mature to graze (crop ends at Zadoks growth stage 8). Similarly, the first grazing of soybean should be expected to commence approximately 55 days after sowing and all areas of the crop should be grazed before initiation of flowering to ensure full potential is harnessed with high re-growth. The subsequent re-growth of soybean should be expected to be grazed after a similar interval. For sorghum, first grazing may be started approximately 50 to 60 days after sowing and then 30 to 40 days after each grazing.

Grazing of forage rape-ryegrass will by default be synchronized as the two forages are intercropped. Some yield may be compromised if grazing must occur at less than ideal maturity for either the forage rape or the ryegrass. Therefore, planning the grazing schedule would be expected to play a significant role in maximization of yield and DM intake from these forages.

Overall, our simulations showed that most rotations used in this study may be able to supply grazeable forages for AMS herds for at least 8 months of the year. The most critical periods when forage supply may not be available were approximately 2 months immediately after sowing of summer forages such as maize, soybean or sorghum and similarly 2 months after sowing of forage rape. Unfortunately these two critical periods have the potential to coincide with periods of reduced pasture supply on the wider farm area particularly for some of the sowing date simulations. One possible solution could be to stagger the sowing date of the forage crops to extend the viable grazing periods thereby increasing the potential to minimise the critical periods in discussion. Another approach could be growing forages that are grazeable during the critical 4 months. Maize or other forages may also be grown and conserved (preferably in more distant paddocks) and may be offered during those critical times. In situations where these approaches are not valid, purchased feed may be necessary to fill the feed gap during these critical periods.

Growth of forages in different forage option rotations

Our simulated results suggest that the decision on which of the crops should be grown in the rotation in summer to supply forages for AMS herd might influence the total output. Simulation results showed that high forage yield may be achieved from maize-based compared to other rotations. This high potential yield of maize-based rotations was due to a higher yield of both summer and autumn-winter crops compared to soybean and sorghum.

The physiological basis for this high yield of maize is well established. Dry matter production is a function of light interception and the efficiency of use of absorbed radiation (Andrade et al., 1993). The erectophile leaves of maize compared to horizontal leaves of soybean allow a more uniform distribution of the incoming radiation (Awal et al., 2006). This is an advantage for canopy photosynthesis and radiation use efficiency of maize at a high leaf area index. Radiation use efficiency of maize (2.77 g/MJ) is higher than soybean (1.74 g/MJ) (Andrade, 1995). As a result, the photosynthetic system of maize was more efficient than soybean, which enables maize to develop more biomass.

Simulated yields of forage rape alone (when over-sown or intercropped with ryegrass) in all forage options were lower (4.7 to 10.0 t DM/ha) than reported yields (11 to 14 t DM/ha; Garcia et al., 2008; Neal et al., 2009; Islam and Garcia, 2012). This yield reduction of forage rape when over-sown/intercropped with ryegrass was expected. However, the combined yields of forage rape and ryegrass in many simulated options out-yielded sole forage rape yields reported in the literature. In addition to higher yield of forage rape-ryegrass, ryegrass may provide an opportunity for extended grazing for AMS herds up to spring and early summer. Several authors (Ofori and Stern, 1987; Caviglia et al., 2004) reported that intercropping often out-yielded sole crop due to more efficient use of resources, reduced incidence of weeds, insect, pests and diseases. Echarte et al. (2011) suggested that the more efficient use of resources in intercropping may occur because the component crops use the resources either at different times or acquire resources from different depth and areas of the soil or aerial environment. Caviglia et al. (2004) reported that such efficiency of resources contributes to improve environment by reducing the likelihood of runoff and deep drainage.

The reported decrease in simulated yields of soybean and sorghum observed with the late sowing dates was in line with that of maize suggesting all three chosen summer forage crops underperform with late sowing. Simulated yields of soybean were similar to the actual yield (8.7 t DM/ha) found in our study at the same location (Islam and Garcia, unpubl. results). However, simulated yields of sorghum were much lower than actual yields previously reported at the same location (17 to 18 t DM/ha; Neal et al., 2009). These researchers harvested sorghum 5 to 6 times from spring to autumn. However, in our simulations a maximum of 3 harvests were possible. Therefore, it is likely that higher yields may be achieved from this (sorghum and forage rape-ryegrass) rotation in practice compared to that obtained from the current simulations. In addition to the impact of compromised sowing and grazing dates of the sorghum-based rotations in simulations, another explanation of lower simulated yield might be due to the use of SweetSorghum cultivar instead of traditional sorghum crop in the simulations as the model cannot perform multiple harvests when traditional sorghum was used.

It should be noted that soybean forage options may require some extra precautions in practice during grazing for AMS herds. Firstly, grazing heavily may reduce or even eliminate the chance of re-growth thereby dramatically reducing the overall yields of the soybean forage crop. Secondly, grazing allocation of more than one-fourth of the total dry matter intake of cow's requirement might cause health hazards such as bloat. Similar caution might also be necessary during periods of forage rape grazing (Fulkerson et al., 2008).

Impact of El-Nino Southern Oscillation events on simulated yields and irrigation requirements

These results indicate that irrigation requirements could increase up to 18% in El-Nino, but decrease up to 13% in La-Nino compared to neutral years. This increase in irrigation in El-Nino years resulted in little or no gain in yield in most of the simulated scenarios, which is in agreement with the results of Chauhan (2010). High and low irrigation requirements in El-Nino and La-Nina years, respectively might be attributable to daily average radiation interception and maximum temperature which were higher in El-Nino and lower in La-Nino years compared to neutral years for only a small gain in yield in El-Nino years. This result also agrees with Chauhan (2010), who reported higher solar radiation and maximum temperature in El-Nino years despite similar minimum temperature across ENSO events.

Overall, although the impact of ENSO events were minimal on simulated yields, their impact on irrigation water requirements was significant. As both El-Nino and La-Nina occur on average one in every four year (based on data of 111 years from 1900 to 2010), forecasting tools can potentially be valuable tool in determining water requirement for a particular rotation and year in response to differences in rainfall, so that one can plan optimal area required to supply feed for AMS herds under irrigation for a given amount of irrigation water allocation in El-Nino and La-Nino years.


The results of our study indicate that the APSIM model has the potential to screen grazeable forage options for AMS herds. APSIM simulations also showed that there is a considerable potential to increase grazeable forages (16.9 to 28.2 t DM/ha) for AMS herds compared to current levels of pasture yield (17 t DM/ha) using strategic forage rotations. The results also showed that these simulations may indicate periods of reduced forage availability including critical periods which would require management. One major implication of this study is that APSIM models may assist in devising preferred forage options in order to maximise grazeable forage yield for AMS herds. It may create an opportunity to grow more forage in small areas around the AMS which in turn will minimise walking distance and milking interval and thus increase milk production. Ultimately this should also help to reduce purchased feed and cost of production. Yield variability of the simulated forages in the rotations were minimal between years under non-limiting inputs, but irrigation water requirements were higher in El-Nino years compared to La-Nina and neutral years. Overall, APSIM can be used as a valuable tool to assist in devising preferred grazeable forage options in a production system such as for AMS herds. It may also provide decision support by identifying critical periods when alternatives are needed and risk assessment during climatic uncertainty. Further work should be conducted regarding system fitness of the forage crops to the wider farm and herd. This would ensure that a more integral understanding of the full system could be developed including balancing the diet and need for supplementary feeds.


The authors thank the Dairy Research Foundation for its support of the Dairy Science Group and the investors of the FutureDairy project (Dairy Australia, NSW Department of Primary Industries, The University of Sydney, and DeLaval). The authors also acknowledge Dr K. Pembleton, University of Tasmania for his suggestion in constructing sorghum-based simulations.


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Caviglia, O. P., V. O. Sadras, and F. H. Andrade. 2004. Intensification of agriculture in the south-eastern Pampas I. Capture and efficiency in the use of water and radiation in double-cropped wheat-soybean. Field Crops Res. 87:117-129.

Chauhan, Y. S. 2010. Potential productivity and water requirements of maize--peanut rotations in Australian semiarid tropical environments--A crop simulation study. Agric. Water Manag. 97:457-464.

Donohue, R. H., K. L. Kerrisk, S. C. Garcia, D. A. Dickeson, and P. C. Thomson. 2010. Evaluation of two training programs aimed to improve early lactation performance of heifers in a pasture based automated milking system. Anim. Prod. Sci. 50:939-945.

Echarte, L., A. Della Maggiora, D. Cerrudo, V. H. Gonzalez, P. Abbate, A. Cerrudo, V. O. Sadras, and P. Calvino. 2011. Yield response to plant density of maize and sunflower intercropped with soybean. Field Crops Res. 121:423-429.

Farina, S. R., S. C. Garcia, and W. J. Fulkerson. 2011. A complementary forage system whole-farm study: forage utilization and milk production. Anim. Prod. Sci. 51:460-470.

Fulkerson, W. J., A. Horadagoda, J. S. Neal, I. Barchia, and K. S. Nandra. 2008. Nutritive value of forage species grown in the warm temperate climate of Australia for dairy cows: Herbs and grain crops. Livest. Sci. 114:75-83.

Garcia, S. C. and W. J. Fulkerson. 2005. Opportunities for future Australian dairy systems: A review. Aust. J. Exp. Agric. 45:1041-1055.

Garcia, S. C., W. J. Fulkerson, R. Nettle, S. Kenny, and D. Armstrong. 2007a. FutureDairy: A national multidisciplinary project to assist dairy farmers to manage challenges--methods and early findings. Aust. J. Exp. Agric. 47:1025-1031.

Garcia, S. C., J. L. Jacobs, S. L. Woodward, and D. A. Clark. 2007b. Complementary forage rotation: A review. In: Meeting the Challenges for Pasture Based Dairying, Proceedings of the Australian Dairy Science Symposium (Eds. D. F. Chapman, D. A. Clark, K. L. Macmillan, and D. P. Nation). National Dairy Alliance, Melbourne, Australia. pp. 221-239.

Garcia, S. C., W. J. Fulkerson, and S. U. Brookes. 2008. Dry matter production, nutritive value and efficiency of nutrient utilization of a complementary forage rotation compared to a grass pasture system. Grass Forage Sci. 63:284-300.

Isbell, R. F. 2002. Australian Soil Classification. CSIRO Publishing, Collingwood, Victoria, Australia.

Islam, M. R. and S. C. Garcia. 2009. Forage option to increase forage and water productivity in autumn-winter. 14th Dairy Sci. Symp., The University of Sydney, Sydney, Australia. pp. 114-116.

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Kolbach, R., K. L. Kerrisk, S. C. Garcia, and N. Dhand. 2012. Attachment accuracy of a novel prototype robotic rotary and investigation of two management strategies for incomplete milk quarters. Comput. Electron. Agric. 88:120-124.

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crop and nitrogen fertilizer in Malawi. Field Crops Res. 91:91-105.

Stockdale, C. R. 1983. Irrigated pasture productivity and its variability in the Shepparton Region of northern Victoria. Aust. J. Exp. Agric. Anim. Husb. 23:131-139.

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Submitted May 21, 2014; Revised Aug. 31, 2014; Accepted Sept. 9, 2014

M. R. Islam *, S. C. Garcia, C. E. F. Clark, and K. L. Kerrisk

Dairy Science Group, Faculty of Veterinary Science, The University of Sydney, Camden, NSW 2570, Australia

* Corresponding Author: M. R. Islam. Tel: +61-2-9036-7750, Fax: +61-2-4655-2374, E-mail:

Table 1. Soil properties at the Camden study site used in
simulation modules in APSIM

Depth          BD       SAT       DUL     AirDry      LL      PAWC
(cm)         (g/cc)   (mm/mm)   (mm/mm)   (mm/mm)   (mm/mm)   (mm)

0 to 15       1.29     0.48      0.29      0.05      0.10     29.7
15 to 30      1.63     0.36      0.29      0.12      0.16     20.6
30 to 60      1.40     0.44      0.39      0.24      0.24     46.2
60 to 90      1.44     0.43      0.38      0.23      0.23     44.7
90 to 120     1.55     0.39      0.34      0.23      0.23     33.0
120 to 150    1.68     0.34      0.29      0.21      0.21     23.7
150 to 180    1.72     0.32      0.27      0.21      0.21     18.6

Depth         Organic       pH       SW    N[O.sub.3]-N
(cm)         carbon (%)   (water)   (%)      (kg/ha)

0 to 15         1.37        6.3     9.5        0.19
15 to 30        0.43        6.3     15.5       0.24
30 to 60        0.33        6.8     23.8       0.42
60 to 90        0.24        7.7     22.8       0.43
90 to 120       0.15        8.2     22.7       0.46
120 to 150      0.11        8.3     20.9       0.50
150 to 180      0.11        8.2     21.0       0.52

APSIM, Agricultural Production Systems Simulator; BD, bulk
density; SAT, saturated water content; DUL, drained upper limit
of soil water content; LL, lower limit of soil water content;
PAWC, plant available water capacity; SW, soil water;
N[O.sub.3]-N, nitrate-nitrogen.

Table 2. Sowing dates and rates and timing of N fertiliser and
harvesting dates of forages in the rotations used in simulations

                                        Summer forages

Simulations   Sowing      N application (A) timing and rate (kg/ha)
(1)            date

                          A1             A2              A3

                       At sowing    Date    Rate    Date    Rate

M1            10-Oct      135

M2            20-Oct      135

M3            20-Nov      135

M4            20-Dec      135

S1            15-Oct       0                 0

S2            15-Nov       0                 0

S3            30-Nov       0                 0

S4            15-Dec       0                 0

Sg1           1-Nov       40       7-Jan     40    25-Feb    40

Sg2           15-Nov      40       7-Jan     40    25-Feb    40

Sg3           30-Nov      40       15-Jan    40    25-Feb    40

Sg4           15-Dec      40       15-Jan    40    25-Feb    0

                                                 spring forages

Simulations   Harvest (2)        Sowing date      N application
(1)                                              (A) timing and
                                                  rate (kg/ha)


                            Forage     Rye-       Date    Rate
                             rape    grass (3)

M1              14-Feb      15-Feb    28-Apr     16-Feb    70

M2              19-Feb      20-Feb    28-Apr     21-Feb    70

M3              27-Feb      28-Feb    26-Jun     27-Feb    70

M4               6-Mar      7-Mar     19-Jul     7-Mar     70

S1              19-Feb      20-Feb     1-Apr     21-Feb    70

S2              14-Mar      15-Mar     1-May     16-Mar    70

S3              24-Mar      25-Mar    25-May     26-Mar    70

S4              30-Mar      31-Mar    31-May     1-Apr     70

Sg1             30-Apr      1-May      1-May     2-May     70

Sg2             30-Apr      1-May      1-May     2-May     70

Sg3             30-Apr      1-May      1-May     2-May     70

Sg4             30-Apr      1-May      1-May     2-May     70

                       Autumn-winter-spring forages

Simulations      N application (A) timing and rate (kg/ha)

                   A2            A3            A4

              Date   Rate   Date   Rate   Date    Rate

M1            27-     90    26-     70     26-     70
              Apr           Jun            Aug

M2            27-     90    26-     70     26-     70
              Apr           Jun            Aug

M3            27-     90    26-     70     26-     70
              Apr           Jun            Aug

M4            19-     90    19-     70     19-     70
              May           Jul            Sep

S1             1-     70     1-     70     1-      70
              Apr           Jun            Aug

S2             1-     70     1-     70    1-Sep    70
              May           Jul

S3            25-     70    25-     70     25-     70
              May           Jul            Sep

S4            31-     70    30-     70     30-     70
              May           Jul            Sep

Sg1            5-     70    15-     70
              Jul           Sep

Sg2            5-     70    15-     70
              Jul           Sep

Sg3            5-     70    15-     70
              Jul           Sep

Sg4            5-     70    15-     70
              Jul           Sep

Simulations        Harvest (4)     Total N
(1)                                (kg/ha)

              Forage     Rye-
               rape    grass (3)

M1            13-Aug      9-         435

M2            13-Aug      19-        435

M3            13-Aug      19-        435

M4            13-Aug      1-         435

S1            14-Oct      14-        280

S2            14-Oct      14-        280

S3            14-Oct      29-        280

S4            14-Oct      29-        280

Sg1           31-Oct      31-        330

Sg2           14-Nov      14-        330

Sg3           29-Nov      29-        330

Sg4           14-Dec      14-        290

(1) M, S, and Sg represents maize, soybean and sorghum
respectively each followed by a intercrop of forage rape-ryegrass
(weed) rotation and 1, 2, 3, and 4 against each of M,S, and Sg
represents as sowing 1, sowing 2, sowing 2, and sowing 4

(2) Maize harvested at Zadoks growth stage 8.

(3) Weed as ryegrass.

(4) Forage rape harvested at pre-graze cover of [greater than or
equal to] 4.5 t DM/ha and ryegrass as weed harvested at pre-graze
cover of [greater than or equal to] 2 t DM/ha.

Table 3. Agronomy and management rules used in simulations (1)
during the periods from 1900 through 2010

Parameters                   Maize           Soybean

Cultivar                  Pioneer 3527        Davis
Sowing characteristics
  Plants/sq.m.                 10               30
  Row spacing (cm)             65               17
  Sowing depth (cm)           4.5              3.0
Harvesting               Zadoks scale 8   When crop
                           or day 59;       reached 4.5
                           harvest          t DM/ha;
                           height 20 cm     harvest
                                            height 35 cm

Parameters                  Sorghum as       Forage rape as
                           SweetSorghum          Canola

Cultivar                    Sugargraze           Early
Sowing characteristics
  Plants/sq.m.                  16                 80
  Row spacing (cm)              65                 17
  Sowing depth (cm)            3.0                2.0
Harvesting               When crop          When crop
                           reached 4.5        reached 4.5
                           t DM/ha;           t DM/ha;
                           harvest height     harvest height
                           35 cm              35 cm

Parameters                 Ryegrass
                            as weed

Cultivar                     Late
Sowing characteristics
  Plants/sq.m.                100
  Row spacing (cm)            17
  Sowing depth (cm)           1.5
Harvesting               When crop
                           reached 2
                           t DM/ha;
                           height 5 cm

(1) Irrigation water was non/limiting as automatic irrigation in
simulation was on; efficiency of irrigation was set at 1; initial
surface residue was set at 1,000 kg/ha and C:N ratio of initial
residue was 80; 0.90 removed from all crops when harvested at

Table 4. Actual and simulated (1) data used for validation
of the model

                       Crop         Compo-    Actual
                                              Sowing date

Islam and          Soybean only               19/11/2010
  Garcia           Soybean only               19/11/2010
  (2014) (d)       Soybean only               19/12/2008
                   Soybean only               19/11/2010
                   Soybean only               19/11/2010
                   Soybean only               19/11/2010
                    Maize only                22/02/2009
                    Maize only                 2/02/2010
Islam and          Maize-forage     Maize     22/02/2009
  Garcia (2009)     rape (FR)
                                      FR      22/02/2009
Islam and         Maize-Ryegrass    Maize     22/02/2009
Garcia (2013)                      Ryegrass   22/02/2009
                     Maize-FR       Maize      2/02/2010
                                      FR       2/04/2010
Garcia et al.     Maize-FR-Maple      FR      20/02/2004
  (2008) (b)           pea
Pancha and         Soybean only               21/11/2007
  (2008) (d)
Horadagoda and     Soybean only               12/12/2008
  (2011) (d)


                    Final      Irrigation    N (a)
                   harvest                   kg/ha

Islam and         14/04/2011      Full      240 (c)
  Garcia          14/04/2011       0        240 (c)
  (2014) (d)      20/02/2009      Full      240 (c)
                  14/04/2011      Full      240 (c)
                  14/04/2011       0        240 (c)
                  14/04/2011      Full      240 (c)
                  23/04/2009      Full        101
                  1/04/2010       Full        135
Islam and         23/04/2009      Full        101
  Garcia (2009)
                  24/09/2009      Full        300
Islam and         23/04/2009      Full        101
Garcia (2013)     24/09/2009      Full        100
                  1/04/2010       Full        140
                  12/10/2010      Full        250
Garcia et al.     24/09/2004      Full        270
  (2008) (b)
Pancha and        2/02/2008       Full         0
  (2008) (d)
Horadagoda and    22/02/2009      Full        27
  (2011) (d)

                         Actual        Simulated

                  Yield    Cultivars    Plants/
                  (t DM/                 sq.m.

Islam and          8.3     Intrepid       30
  Garcia           5.8     Intrepid       30
  (2014) (d)       5.1     Intrepid       30
                   7.7     Warrigal       30
                   6.8     Warrigal       30
                   8.7       Zeus         30
                   5.6       31H50        13
                   7.0       31H50        13
Islam and          5.5       31H50        12
  Garcia (2009)
                   12.4     Goliath       80
Islam and          5.9       31H50        12
Garcia (2013)      5.4      Surrey        100
                   6.9       31H50        12
                   11.8     Goliath       80
Garcia et al.      11.4     Goliath       80
  (2008) (b)
Pancha and         7.9     Intrepid       80
  (2008) (d)
Horadagoda and     9.1     Intrepid       80
  (2011) (d)


                   Yield       Row          Simulated
                  (t DM/    spacing, m      cultivars
                  ha) (1)

Islam and          10.2        0.17           Davis
  Garcia            7.8        0.17           Davis
  (2014) (d)        6.2        0.17           Davis
                   10.2        0.17           Davis
                    7.8        0.17           Davis
                   10.2        0.17           Davis
                    4.2        0.65       Pioneer 3527
                    5.0        0.65       Pioneer 3527
Islam and           5.0        0.65       Pioneer 3527
  Garcia (2009)
                   10.3        0.17      Early (Canola)
Islam and           4.0        0.65       Pioneer 3527
Garcia (2013)       6.6        0.17      As weed (Early)
                    4.8        0.65       Pioneer 3527
                   11.0        0.17      Early (Canola)
Garcia et al.      10.0        0.17      Early (Canola)
  (2008) (b)
Pancha and          7.5        0.17           Davis
  (2008) (d)
Horadagoda and      7.8        0.17           Davis
  (2011) (d)

FR, forage rape; CFR, complementary forage rotation.

(1) Every simulation covered periods from 1900 through 2010.

(a) Rates of N fertiliser was the same as observed except for
ryegrass which was simulated without N.

(b) FR was grown in a rotation of CFR over three years.

(c) DAP (Di-ammonium phosphate) contained 18% N, 20% P, and 2.2%
sulphur (Hi-fert, Melbourne, Victoria).

(d) Unpublished results.

Table 5. Simulated forage yields (t DM-ha) in rotations of maize,
soybean and sorghum sown in summer followed by forage rape
(over-sown or intercropped) with ryegrass

Rotations   Simulations              Simulated forage yields
                (1)                         (t DM/ha)

                          M/S/Sg       Forage rape-ryegrass
                           (1)               intercrop

                                   Forage   Ryegrass   Total

Maize           M1         12.4     10.0      5.8      15.8
                M2         12.1     9.5       6.2      15.7
                M3         11.3     8.5       6.2      14.7
                M4         10.6     8.2       6.5      14.7
Soybean         S1         9.5      8.3       5.1      13.4
                S2         9.4      8.0       5.5      13.5
                S3         9.0      7.9       5.5      13.4
                S4         7.6      7.9       6.0      13.9
Sorghum         Sg1        10.2     4.6       4.5       9.1
                Sg2        9.7      4.6       4.8       9.4
                Sg3        9.1      5.0       5.1      10.1
                Sg4        6.8      4.7       5.4      10.1

Rotations   Simulations   Total simulated
                (1)       forages (t DM/
                            ha/yr) (SD)

Maize           M1          28.2 (1.7)
                M2          27.8 (1.6)
                M3          26.0 (1.7)
                M4          25.3 (1.5)
Soybean         S1          22.9 (0.9)
                S2          22.9 (1.0)
                S3          22.4 (0.9)
                S4          21.5 (1.1)
Sorghum         Sg1         19.3 (1.3)
                Sg2         19.1 (0.9)
                Sg3         19.2 (0.9)
                Sg4         16.9 (1.7)

DM, dry matter; SD, standard deviation.

(1) M, S, and Sg represents maize, soybean and sorghum
respectively each followed by an intercrop of forage
rape-ryegrass rotation. Sowing dates and all other agronomic
principles can be seen in Table 2.

Table 6. Simulated monthly mean daily growth rate (kg DM/ha/d) of
forages in maize and forage rape oversown with ryegrass at
different sowing dates ([M.sup.1])

                        M1                       M2

              Maize   Forage    Rye    Maize   Forage    Rye
                       rape    grass            rape    grass

Jan           56.6      0        0     135.2     0        0
Feb             0      1.3       0       0      0.7       0
Mar             0      30.3      0       0      20.6      0
Apr             0     121.7      0       0     116.4      0
May             0      81.8    17.2      0      79.5    17.6
Jun             0      23.3    26.6      0      35.9    29.5
Jul             0      44.4    26.5      0      39.7    24.2
Aug             0      25.1    34.4      0      19.7    36.3
Sep             0       0      56.1      0       0      55.1
Oct            1.4      0      30.0     0.2      0      39.8
Nov           57.1      0        0     21.7      0        0
Dec           291.9     0        0     240.2     0        0
Grand total   34.2     27.3    15.7    33.4     26.0    16.7

                        M3                       M4

              Maize   Forage    Rye    Maize   Forage    Rye
                       rape    grass            rape    grass

Jan           265.0     0        0     53.6      0        0
Feb           67.3      0        0     257.4     0        0
Mar             0      11.3      0     36.6     4.8       0
Apr             0      85.6      0       0      52.9      0
May             0      78.7      0       0      97.2      0
Jun             0      41.0      0       0      52.1      0
Jul             0      52.3    10.3      0      44.2     2.3
Aug             0      10.3    39.7      0      18.4    30.6
Sep             0       0      52.1      0       0      43.7
Oct             0       0      67.1      0       0      66.0
Nov            0.3      0      33.0      0       0      69.7
Dec           37.4      0        0      0.5      0       1.8
Grand total   30.9     23.2    16.5    27.7     22.4    17.5

(1) M represents sowing dates of forages. Sowing dates and all
other agronomic principles can be seen in Table 2.

Table 7. Simulated monthly mean daily growth rate (kg DM/ha/d) of
forages in soybean and forage rape oversown with ryegrass at
different sowing dates ([S.sup.1])

                          S1                       S2

                Soy-    Forage    Rye    Soy-    Forage    Rye
                bean     rape    grass   bean     rape    grass

Jan             102.3     0        0     70.9      0        0
Feb             50.6     0.7       0     115.9     0        0
Mar               0      18.2      0     30.5     1.7       0
Apr               0     105.2    26.1      0      27.2      0
May               0      26.4    28.8      0      99.3    12.7
Jun               0      4.1     15.7      0      20.3    17.5
Jul               0      33.8    34.1      0      5.4     37.9
Aug               0      68.3    34.2      0      53.2    19.9
Sep               0      2.7     20.7      0      52.8    50.0
Oct              0.9     12.1     7.6      0      3.1     22.7
Nov             53.3      0        0      1.4      0      19.7
Dec             102.8     0        0     88.7      0        0
Grand average   25.4     22.7    14.0    25.1     22.0    14.8

                          S3                       S4

                Soy-    Forage    Rye    Soy-    Forage    Rye
                bean     rape    grass   bean     rape    grass

Jan             123.7     0        0     101.4     0        0
Feb             53.8      0        0     57.2      0        0
Mar             90.2      0        0     89.5      0        0
Apr               0      12.5      0       0      7.4       0
May               0      60.7     1.5      0      49.8      0
Jun               0      72.4     9.5      0      79.8     6.8
Jul               0      4.3     18.2      0      12.7    18.5
Aug               0      12.0    42.1      0      9.7     44.8
Sep               0      80.9    24.5      0      79.4    24.3
Oct               0      17.6    53.0      0      21.9    56.1
Nov               0       0      31.6      0       0      45.5
Dec             27.4      0        0      2.3      0        0
Grand average   24.2     21.6    14.8    20.6     21.6    16.1

(1) S represents sowing dates of forages. Sowing dates and all
other agronomic principles can be seen in Table 2.

Table 8. Simulated monthly mean daily growth rate (kg DM/ha/d) of
forages in sorghum and forage rape intercropped with ryegrass at
different sowing dates (Sg (1))

                  Sg1                      Sg2

          Sor-   Forage    Rye    Sor-    Forage    Rye
          ghum    rape    grass   ghum     rape    grass

Jan       86.2     0        0     110.9     0        0
Feb       85.4     0        0     63.6      0        0
Mar       48.2     0        0     57.2      0        0
Apr       47.6     0        0     43.3      0        0
May        0      4.7     13.1      0      4.7     13.1
Jun        0      18.9    54.5      0      19.0    54.6
Jul        0      8.8      8.6      0      8.9      8.8
Aug        0      47.1     9.1      0      47.9     9.7
Sep        0      54.2    39.9      0      56.1    40.2
Oct        0      17.6    20.6      0      13.2    20.4
Nov       3.4      0        0      0.4     2.4      9.9
Dec       64.7     0        0     41.3      0        0
Average   27.6    12.5    12.0    26.1     12.6    12.9

                   Sg3                      Sg4

          Sor-    Forage    Rye    Sor-    Forage    Rye
          ghum     rape    grass   ghum     rape    grass

Jan       131.7     0        0     43.7      0        0
Feb       43.3      0        0     108.9     0        0
Mar       82.6      0        0     33.2      0        0
Apr       32.1      0        0     34.9      0        0
May         0      4.7     13.1      0      4.7     13.1
Jun         0      18.4    54.5      0      17.5    54.4
Jul         0      8.5      8.7      0      7.3      8.2
Aug         0      47.0     9.7      0      43.8     8.2
Sep         0      55.6    40.4      0      52.4    39.8
Oct         0      15.1    20.4      0      18.6    21.1
Nov         0      13.7    20.3      0      3.3     19.9
Dec        9.6      0        0      0.7     5.3     11.0
Average   24.8     13.5    13.7    17.8     12.7    14.5

(1) Sg represents sowing dates of forages. Sowing dates and all
other agronomic principles can be seen in Table 2.

Table 9. Mean total rainfall, daily solar radiation, maximum and
minimum temperatures during simulated periods from 1900 to 2010
in neutral, La-Nina and El-Nino years

ENSO (1)        No. of   Rainfall     Radiation       Max temp.
events          years      (mm)     (MJ/[m.sup.2])   ([degrees]C)

Neutral           56      672.0          17.2            23.7
La-Nina           29      813.1          16.6            23.2
El-Nino           26      729.2          17.0            23.2
Average/total    111      738.1          16.9            23.4

ENSO (1)         Min temp.
events          ([degrees]C)

Neutral             10.7
La-Nina             10.7
El-Nino             10.8
Average/total       10.7

(1) El-Nino Southern Oscillation.

Table 10. Impact of El-Nino Southern Oscillation (ENSO) events on
simulated yields, irrigation and total water requirement in
different forage rotations

Simulations (1)    Years     M/S/Sg (1)   Forage   Ryegrass     Total
                                           rape               (t DM/ha)

M1                El-Nino       12.4       10.6      5.9        28.9
                  La- nina      11.7       10.0      5.7        27.4
                  Neutral       12.2       10.0      5.8        28.0
M2                El-Nino       12.5       9.5       6.2        28.2
                  La- nina      11.7       9.5       6.1        27.3
                  Neutral       12.2       9.5       6.1        27.8
M3                El-Nino       11.0       8.6       6.3        25.9
                  La- nina      10.8       8.4       6.1        25.3
                  Neutral       11.3       8.5       6.2        26.0
M4                El-Nino       10.7       8.1       6.6        25.4
                  La- nina      10.6       8.2       6.5        25.3
                  Neutral       10.6       8.2       6.5        25.3
S1                El-Nino       9.6        8.3       5.2        23.1
                  La- nina      9.5        8.5       5.1        23.1
                  Neutral       9.3        8.4       5.1        22.8
S2                El-Nino       9.4        7.8       6.0        23.2
                  La- nina      9.4        8.2       5.3        22.9
                  Neutral       9.5        8.0       5.4        22.9
S3                El-Nino       9.2        8.0       5.7        22.9
                  La- nina      8.9        7.8       5.3        22.0
                  Neutral       9.0        7.9       5.6        22.5
S4                El-Nino       7.7        7.8       6.1        21.6
                  La- nina      7.3        7.8       6.0        21.1
                  Neutral       7.7        8.0       6.0        21.7
So1               El-Nino       10.8       4.5       4.4        19.7
                  La- nina      9.8        4.7       4.4        18.9
                  Neutral       9.9        4.6       4.4        18.9
So2               El-Nino       9.9        4.6       4.8        19.3
                  La- nina      9.6        4.6       4.8        19.0
                  Neutral       9.6        4.7       4.8        19.1
So3               El-Nino       9.2        5.1       5.2        19.5
                  La- nina      9.0        4.9       5.1        19.0
                  Neutral       9.0        4.9       5.1        19.0
So4               El-Nino       7.0        4.8       5.5        17.3
                  La- nina      6.9        4.6       5.4        16.9
                  Neutral       6.4        4.6       5.4        16.4

Simulations (1)    Years     Irrigation   Total water
                                (mm)         (mm)

M1                El-Nino      639.6         1,312
                  La- nina     510.7         1,324
                  Neutral      545.5         1,275
M2                El-Nino      632.8         1,305
                  La- nina     510.7         1,324
                  Neutral      552.4         1,282
M3                El-Nino      673.8         1,346
                  La- nina     554.5         1,368
                  Neutral      579.1         1,308
M4                El-Nino      671.5         1,344
                  La- nina     540.8         1,354
                  Neutral      567.2         1,296
S1                El-Nino      698.5         1,371
                  La- nina     567.5         1,381
                  Neutral      609.7         1,339
S2                El-Nino      731.5         1,404
                  La- nina     589.3         1,402
                  Neutral      633.1         1,362
S3                El-Nino      722.3         1,394
                  La- nina     588.4         1,402
                  Neutral      622.6         1,352
S4                El-Nino      683.9         1,356
                  La- nina     553.0         1,366
                  Neutral      595.8         1,325
So1               El-Nino      525.4         1,197
                  La- nina     400.1         1,213
                  Neutral      451.6         1,181
So2               El-Nino      493.7         1,166
                  La- nina     384.9         1,198
                  Neutral      434.6         1,164
So3               El-Nino      484.8         1,157
                  La- nina     367.5         1,181
                  Neutral      422.2         1,151
So4               El-Nino      433.3         1,105
                  La- nina     326.8         1,140
                  Neutral      369.8         1,099

(1) M, S, and Sg represents maize, soybean and sorghum
respectively each followed by an intercrop of forage
rape-ryegrass rotation. Sowing dates and all other agronomic
principles can be seen in Table 2.
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Author:Islam, M.R.; Garcia, S.C.; Clark, C.E.F.; Kerrisk, K.L.
Publication:Asian - Australasian Journal of Animal Sciences
Article Type:Report
Geographic Code:8AUST
Date:May 1, 2015
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