I. R. Johnson

DairyMod and EcoMod: biophysical pasture-simulation models for Australia and New Zealand

DairyMod and EcoMod, which are biophysical pasture-simulation models for Australian and New Zealand grazing systems, are described. Each model has a common underlying biophysical structure, with the main differences being in their available management options. The third model in this group is the SGS Pasture Model, which has been previously described, and these models are referred to collectively as ‘the model’. The model includes modules for pasture growth and utilisation by grazing animals, water and nutrient dynamics, animal physiology and production and a range of options for pasture management, irrigation and fertiliser application. Up to 100 independent paddocks can be defined to represent spatial variation within a notional farm. Paddocks can have different soil types, nutrient status, pasture species, fertiliser and irrigation management, but are subject to the same weather. Management options include commonly used rotational grazing management strategies and continuous grazing with fixed or variable stock numbers. A cutting regime simulates calculation of seasonal pasture growth rates. The focus of the present paper is on recent developments to the management routines and nutrient dynamics, including organic matter, inorganic nutrients, leaching and gaseous nitrogen losses, and greenhouse gases. Some model applications are presented and the role of the model in research projects is discussed.

Climate change effects on pasture systems in south-eastern Australia.

Climate change projections for Australia predict increasing temperatures, changes to rainfall patterns, and elevated atmospheric carbon dioxide (CO2) concentrations. The aims of this study were to predict plant production responses to elevated CO2 concentrations using the SGS Pasture Model and DairyMod, and then to quantify the effects of climate change scenarios for 2030 and 2070 on predicted pasture growth, species composition, and soil moisture conditions of 5 existing pasture systems in climates ranging from cool temperate to subtropical, relative to a historical baseline. Three future climate scenarios were created for each site by adjusting historical climate data according to temperature and rainfall change projections for 2030, 2070 mid-and 2070 high-emission scenarios, using output from the CSIRO Mark 3 global climate model. In the absence of other climate changes, mean annual pasture production at an elevated CO2 concentration of 550 ppm was predicted to be 24-29% higher than at 380 ppm CO2 in temperate (C-3) species-dominant pastures in southern Australia, with lower mean responses in a mixed C-3/C-4 pasture at Barraba in northern New South Wales (17%) and in a C-4 pasture at Mutdapilly in south-eastern Queensland (9%). In the future climate scenarios at the Barraba and Mutdapilly sites in subtropical and subhumid climates, respectively, where climate projections indicated warming of up to 4.4 degrees C, with little change in annual rainfall, modelling predicted increased pasture production and a shift towards C-4 species dominance. In Mediterranean, temperate, and cool temperate climates, climate change projections indicated warming of up to 3.3 degrees C, with annual rainfall reduced by up to 28%. Under future climate scenarios at Wagga Wagga, NSW, and Ellinbank, Victoria, our study predicted increased winter and early spring pasture growth rates, but this was counteracted by a predicted shorter spring growing season, with annual pasture production higher than the baseline under the 2030 climate scenario, but reduced by up to 19% under the 2070 high scenario. In a cool temperate environment at Elliott, Tasmania, annual production was higher than the baseline in all 3 future climate scenarios, but highest in the 2070 mid scenario. At the Wagga Wagga, Ellinbank, and Elliott sites the effect of rainfall declines on pasture production was moderated by a predicted reduction in drainage below the root zone and, at Ellinbank, the use of deeper rooted plant systems was shown to be an effective adaptation to mitigate some of the effect of lower rainfall.

Simulating pasture growth rates in Australian and New Zealand grazing systems

DairyMod, EcoMod, and the SGS Pasture Model are mechanistic biophysical models developed to explore scenarios in grazing systems. The aim of this manuscript was to test the ability of the models to simulate net herbage accumulation rates of ryegrass-based pastures across a range of environments and pasture management systems in Australia and New Zealand. Measured monthly net herbage accumulation rate and accumulated yield data were collated from ten grazing system experiments at eight sites ranging from cool temperate to subtropical environments. The local climate, soil, pasture species, and management (N fertiliser, irrigation, and grazing or cutting pattern) were described in the model for each site, and net herbage accumulation rates modelled. The model adequately simulated the monthly net herbage accumulation rates across the range of environments, based on the summary statistics and observed patterns of seasonal growth, particularly when the variability in measured herbage accumulation rates was taken into account. Agreement between modelled and observed growth rates was more accurate and precise in temperate than in subtropical environments, and in winter and summer than in autumn and spring. Similarly, agreement between predicted and observed accumulated yields was more accurate than monthly net herbage accumulation. Different temperature parameters were used to describe the growth of perennial ryegrass cultivars and annual ryegrass; these differences were in line with observed growth patterns and breeding objectives. Results are discussed in the context of the difficulties in measuring pasture growth rates and model limitations.

Interannual variation in pasture growth rate in Australian and New Zealand dairy regions and its consequences for system management

The profitability of dairy farms in Australia and New Zealand is closely related to the amount of pasture dry matter consumed per hectare per year. There is variability in the pasture growth curve within years (seasonal variation) and between years (interannual variation) in all dairy regions in both countries. Therefore, the biological efficiency of production systems depends on the accuracy and timeliness of the many strategic and tactical decisions that influence the balance between feed supply and demand over an annual cycle. In the case of interannual variation, decisions are made with only limited quantitative information on the range of possible pasture growth outcomes. To address this limitation, we used the biophysical simulation model ‘DairyMod’ to estimate mean monthly herbage accumulation rates of annual or perennial ryegrass-based pastures in 100 years (1907–2006) for five Australian sites (Kyabram in northern Victoria, Terang in south-west Victoria, Ellinbank in Gippsland, Elliott in north-west Tasmania and Vasse in south-west Western Australia) and in 35 years (1972–2006) for three sites in New Zealand (Hamilton in the Waikato, Palmerston North in the Manawatu and Winchmore in Canterbury). The aim was to evaluate whether or not a probabilistic approach to the analysis of pasture growth could provide useful information to support decision making. For the one site where annual ryegrass was simulated, Vasse, the difference between the 25th and 75th percentile years was 20 kg DM/ha.day or less in all months when pasture growth occurred. Irrigation at Kyabram and Winchmore also resulted in a narrow range of growth rates in most months. For non-irrigated sites, the 25th–75th percentile range was narrow (10–15 kg DM/ha.day) from May or June through to September or October, because plant available soil water was adequate to support perennial ryegrass growth, and the main source of interannual variability was variation in temperature. Outside of these months, however, variability in growth was large. There was a positive relationship between total annual herbage accumulation rate and mean stocking for four southern Australian regions (northern Victoria, south-west Victoria, Gippsland and Tasmania), but there was evidence of a negative relationship between the co-efficient of variation in pasture growth and stocking rate. The latter suggests that farmers do account for risk in pasture supply in their stocking rate decisions. However, for the one New Zealand region included in this analysis, Waikato, stocking rate was much higher than would be expected based on the variability in pasture growth, indicating that farmers in this region have well defined decision rules for coping with feed deficits or surpluses. Model predictions such as those presented here are one source of information that can support farm management decision making, but should always be coupled with published data, direct experience, and other relevant information to analyse risk for individual farm businesses.

The challenges - and some solutions - to process-based modelling of grazed agricultural systems

Pastoral systems are characterised by a number of features that are absent in arable cropping systems. These features include: (i) pastures are biologically diverse so interactions between plant species must be considered; (ii) economic return requires the inclusion of the animal as an additional trophic level; (iii) interaction between the grazing animal and the pasture is complex, influenced by the environment, plant species and animal behaviour and this creates feedbacks that can result in vicious cycles; (iv) animals spatially transfer substantial amounts of nutrients both randomly and systematically and this creates or exacerbates soil variability; and (v) whole farm management is both more complex and more important to system function in grazed compared to arable systems and it is harder to capture in simulation models. These challenges complicate the process-based modelling of pastoral systems and present significant obstacles to model developers and users.Here we discuss these challenges, describe the range of solutions used by different models and discuss the strengths and weaknesses of these solutions. We have placed particular emphasis on the analysis of a range of possible solutions with the point of view that diversity between and within models is important to provide the flexibility needed for future uses.We find that for most challenges there is a diversity of solutions incorporated into the models and that there is the potential to capture additional diversity, if needed, from other models. We note an apparent lack of development in the modelling of extreme events such as very high temperatures, systematic animal-mediated nutrient transfers, pests, weeds and gene-environment interactions in pastoral simulation models and suggest that these subject areas should receive more attention. We review the major challenges for simulating pastoral farming systems.Simple solutions can transfer the burden of conceptualisation from developer to user.The more complex solutions are probably not suitable for routine use.Modelling the effects of systematic nutrient transfers requires additional attention.Modelling of pests, diseases and gene-environment should receive more attention.

The single heterogeneous paddock approach to modelling the effects of urine patches on production and leaching in grazed pastures

Despite the fact that urine patches within grazed paddocks are the primary source of N leaching, virtually all pastoral simulation models assume a uniform spatial return of urinary-N to the soil. This simple spatial averaging might not be appropriate if the aim of the modelling is to explore leaching losses because of the non-linearity caused by the high N concentration in urine patches. Here we describe the single heterogeneous paddock (SHP) approach to modelling the dynamics of N in pastoral systems. We also examine the potential for manipulating rate parameters in a simpler uniform-return model (URM) to compensate for the lack of explicit description of urine patches. Comparison of simulation results from the URM and SHP showed some differences in the patterns of production and a substantial difference in leaching. Depending on soil and climate simulated, there was 5–30% higher pasture production in the URM because simulated leaching in the URM was 5–85% of that simulated by the SHP. Examination of the ratio of the outputs from the two models revealed that the differences in pasture production and N fixation in the URM could probably be corrected with a change in parameter values. This was not true of leaching where there was considerable variation and skew in the ratios, so at the very least, any correction factor would be highly soil and climate specific. We suggest that models of grazed grass–legume systems can probably adequately simulate production with a simple URM but that the simulation of leaching requires an explicit representation of the heterogeneous urine return. The SHP approach is one methodology for this but this has implications for model and software complexity and for model run-time duration.

A generic model of growth, energy metabolism, and body composition for cattle and sheep.

A generic daily time-step model of animal growth and metabolism for cattle and sheep is described. It includes total BW as well as protein, water, and fat components, and also energy components associated with the growth of protein and fat, and activity costs. Protein decay is also incorporated, along with the energy costs of resynthesising degraded protein. Protein weight is taken to be the primary indicator of metabolic state, and fat is regarded as a potential source of metabolic energy for physiological processes such as the resynthesis of degraded protein. Normal weight is defined as maximum protein and the associated fat component so that if the BW of the animal exceeds the normal value, all excess weight is in the form of fat. It is assumed that the normal fat fraction increases from birth to maturity. There are relatively few parameters, all of which have a reasonable physiological interpretation, which helps simplify choosing parameters for different animal types and breeds. Simulations for growing and mature cattle and sheep in response to varying available ME are presented and comparisons with empirical curves reported in the literature for body composition are in excellent agreement.

A model of milk production in lactating dairy cows in relation to energy and nitrogen dynamics

A generic daily time-step model of a dairy cow, designed to be included in whole-system pasture simulation models, is described that includes growth, milk production, and lactation in relation to energy and nitrogen dynamics. It is a development of a previously described animal growth and metabolism model that describes animal body composition in terms of protein, water, and fat, and energy dynamics in relation to growth requirements, resynthesis of degraded protein, and animal activity. This is further developed to include lactation and fetal growth. Intake is calculated in relation to stage of lactation, pasture availability, supplementary feed, and feed quality. Energy costs associated with urine N excretion and methane fermentation are accounted for. Milk production and fetal growth are then calculated in relation to the overall energy and nitrogen dynamics. The general behavior of the model is consistent with expected characteristics. Simulations using the model as part of a whole-system pasture simulation model (DairyMod) are compared with experimental data where good agreement between pasture, concentrate and forage intake, as well as milk production over 3 consecutive lactation cycles, is observed. The model is shown to be well suited for inclusion in large-scale system simulation models.

Northern Australian pasture and beef systems. 2. Validation and use of the Sustainable Grazing Systems (SGS) whole-farm biophysical model

The Sustainable Grazing Systems (SGS) model is a biophysical, mechanistic whole-farm model that simulates pasture production based on climate and soil data. While the SGS model has been extensively used for southern temperate systems, the model has yet to be evaluated for use in the tropical rangeland systems of Australia. New pasture parameter sets were developed in SGS to represent groups of grasses with the following common characteristics: (1) 3P grasses represented tropical rangeland grasses that were perennial, palatable and productive, and (2) annual tropical grasses that include both productive and less productive grass species. Fifteen years of data from the long-term Wambiana grazing trial ~70 km south-west of Charters Towers, Queensland, were used to validate the model. The results showed that SGS is capable of representing northern Australian beef systems with modelled outputs for total standing dry matter and steer liveweight in agreement with the year-to-year variation in measured data over three different soil types and two stocking rates. Recommendations for further model development are made, such as incorporating fire, tree growth and the use of urea supplementation in the model. Further testing is required to verify that the new pasture parameter sets are suitable for other regions in northern Australia.

ASKBILL as a web-based program to enhance sheep well-being and productivity

ASKBILL is a web-based program that uses farm measurements, climate data and information on genetics to predict pasture growth, animal performance and animal health and climate risks. The program uses several biophysical models, which are customised by user inputs, localised daily weather updates and a dynamical probabilistic 90-day climate forecast to enhance sheep well-being and productivity. This approach can minimise the requirement for manual, auto and remote measurements, thus reducing labour requirements and complexity. In this article, the animal growth model provides an example of a biophysical model used to provide predictions. This is an energy-based model and the model parameterisation is designed to be physiologically meaningful and able to be customised for the genetic merit of the animal using a growth coefficient that calibrates growth of body components and energy requirements. A key feature of the animal growth model is its forecast projections, which are based on an ensemble of simulations. The model can estimate supplementary feeding rates required to achieve target liveweights and body condition scores and stocking rates required to achieve target pasture levels. The model can be customised for a farm and its livestock and is updated daily in response to climate data. This dynamic feature enables it to provide early stage alerts to users when animal production targets are unlikely to be met.