Whole-farm Model Research Articles

E-Dairy: a dynamic and stochastic whole-farm model that predicts biophysical and economic performance of grazing dairy systems

A whole-farm, stochastic and dynamic simulation model was developed to predict biophysical and economic performance of grazing dairy systems. Several whole-farm models simulate grazing dairy systems, but most of them work at a herd level. This model, named e-Dairy, differs from the few models that work at an animal level, because it allows stochastic behaviour of the genetic merit of individual cows for several traits, namely, yields of milk, fat and protein, live weight (LW) and body condition score (BCS) within a whole-farm model. This model accounts for genetic differences between cows, is sensitive to genotype × environment interactions at an animal level and allows pasture growth, milk and supplements price to behave stochastically. The model includes an energy-based animal module that predicts intake at grazing, mammary gland functioning and body lipid change. This whole-farm model simulates a 365-day period for individual cows within a herd, with cow parameters randomly generated on the basis of the mean parameter values, defined as input and variance and co-variances from experimental data sets. The main inputs of e-Dairy are farm area, use of land, type of pasture, type of crops, monthly pasture growth rate, supplements offered, nutritional quality of feeds, herd description including herd size, age structure, calving pattern, BCS and LW at calving, probabilities of pregnancy, average genetic merit and economic values for items of income and costs. The model allows to set management policies to define: dry-off cows (ceasing of lactation), target pre- and post-grazing herbage mass and feed supplementation. The main outputs are herbage dry matter intake, annual pasture utilisation, milk yield, changes in BCS and LW, economic farm profit and return on assets. The model showed satisfactory accuracy of prediction when validated against two data sets from farmlet system experiments. Relative prediction errors were <10% for all variables, and concordance correlation coefficients over 0.80 for annual pasture utilisation, yields of milk and milk solids (MS; fat plus protein), and of 0.69 and 0.48 for LW and BCS, respectively. A simulation of two contrasting dairy systems is presented to show the practical use of the model. The model can be used to explore the effects of feeding level and genetic merit and their interactions for grazing dairy systems, evaluating the trade-offs between profit and the associated risk.

Whole-farm models to quantify greenhouse gas emissions and their potential use for linking climate change mitigation and adaptation in temperate grassland ruminant-based farming systems

The farm level is the most appropriate scale for evaluating options for mitigating greenhouse gas (GHG) emissions, because the farm represents the unit at which management decisions in livestock production are made. To date, a number of whole farm modelling approaches have been developed to quantify GHG emissions and explore climate change mitigation strategies for livestock systems. This paper analyses the limitations and strengths of the different existing approaches for modelling GHG mitigation by considering basic model structures, approaches for simulating GHG emissions from various farm components and the sensitivity of GHG outputs and mitigation measures to different approaches. Potential challenges for linking existing models with the simulation of impacts and adaptation measures under climate change are explored along with a brief discussion of the effects on other ecosystem services.

Potential impacts of negative associative effects between concentrate supplements, pasture and conserved forage for milk production and dairy farm profit

A case study and whole-farm modelling approach was used to examine the potential impacts of negative associative effects on milk production and economic performance of two dairy farms in northern Victoria. The two case studies differed in herd and farm size, calving pattern, forages grown and use of labour, but both had production systems based on grazed pasture, grain fed in the dairy at milking and conserved hay fed out in the paddock. The feeding system of each farm was altered by implementing a partial mixed ration (PMR), where cows grazed once a day and received supplements in a well formulated mix once a day. Negative associative effects between feeds were included in the biophysical modelling by deriving a relationship from published studies between declining neutral detergent fibre digestibility and increasing grain intake. Before applying a PMR system, both farms were profitable and earning competitive rates of return after tax, with mean real internal rate of return higher than 5%, and positive mean annual operating profit and mean net present value, at a discount rate of 5%. Feeding a PMR enabled both farms to increase profitability and internal rate of return, particularly if milk production was increased as well, but only when associative effects were less than those in the feeding system based on grain fed in the dairy and hay in the paddock. Increased profitability was also associated with higher standard deviation in annual operating profit, internal rate of return and net present value, in other words risk increased under the PMR feeding system, as the businesses would be more vulnerable to fluctuating supplementary feed prices.

Evaluation of mitigation strategies for nitrate leaching on pasture-based dairy systems

Dairy farms are under pressure to increase productivity while reducing environmental impacts. We used the DairyNZ Whole Farm Model (WFM) and APSIM to evaluate the effect of mitigation strategies within an efficient farm (EF) in the Waikato region, NZ, on these targets. Mitigation strategies compared with the baseline farm (BF) included the use of fewer more efficient cows, low nitrogen (N) feed supplements, loafing pads, less N fertiliser and nitrification inhibitor (DCD). To encompass climate affects three different years with average, high and low annual rainfall were modelled. The WFM predicted number of urinations and urinary N loads deposited during individual grazing events were used as an input for APSIM to simulate N leaching from urine patches, as well as from non-urinated areas. Results were aggregated to obtain total N leached on a paddock and farm scale. For all 3years, farm averaged N leaching was lower, by 20–55%, in the EF compared with the BF farm. DCD reduced leaching in two of the 3years by 12% and 15%. N leaching was lowest for N deposited in the wet year and highest for the dry year. Milk production was consistently greater for the EF compared to the BF, with an increase in milksolids (MS)/ha ranging from 8% in the wet, to 17% in the dry year.

Validating a Whole Farm Modelling with Stakeholders: Evidence from a West African Case

Strategic and tactical decision support tools can assist farmers to make farm management decisions. The challenge facing researchers is to construct decision support tools that lead to the successful adoption of the strategies selected. This article describes and assesses the use of a whole-farm model in West African savannah regions. Farmers and advisors were involved in the choice and design of models, the definition and analysis of scenarios, the design of alternative strategies, and the validation of models. The validation process included a short and mid-term analysis of the use and usefulness of the model for farmers. During the validation process, farmers demonstrated that they were able to define and characterize iterative scenarios permitting the performance of their farm to be improved. They also had a good understanding of the outputs of the model, even several months after they had used the model. They declared having changed some technical or management practices and having acquiried calculation and management skills. This type of process offers an alternative to conventional advisory methods based on advisor expertise.

Understanding the role of spring grazing of lucerne in the Central Otago environment

The Lucerne for Lambs Sustainable Farming Fund project used on-farm data and whole-farm modelling to help farmers understand the impact of increasing the amount of grazing dryland lucerne on Central Otago farms. On-farm results from 2 years showed that spring stocking rate on lucerne-based pasture averaged 10 ewes and their lambs per hectare compared with 3 ewes and their lambs on dryland grass-based pasture. Lamb growth rate was approximately 50 g/day higher on lucerne-based pastures, resulting in a total lamb production of 358 kg liveweight/ha at weaning compared with 108 kg liveweight/ha on grass-based pastures. Whole-farm modelling showed that increased profitability could be achieved by the addition of significant areas of grazing lucerne. Profitability increased by $119/ha on a 5000 ha traditional merino property. Profitability of a 522 ha partially irrigated valley floor property running crossbred ewes was increased by $249/ha. Using lucerne for spring grazing also reduced the impacts of both winter and summer droughts in the variable conditions of Central Otago. Keywords: dryland, lamb growth, lucerne, modelling, profitability, stocking rate, whole farm systems.

MELODIE: a whole-farm model to study the dynamics of nutrients in dairy and pig farms with crops

In regions of intensive pig and dairy farming, nutrient losses to the environment at farm level are a source of concern for water and air quality. Dynamic models are useful tools to evaluate the effects of production strategies on nutrient flows and losses to the environment. This paper presents the development of a new whole-farm model upscaling dynamic models developed at the field or animal scale. The model, called MELODIE, is based on an original structure with interacting biotechnical and decisional modules. Indeed, it is supported by an ontology of production systems and the associated programming platform DIESE. The biotechnical module simulates the nutrient flows in the different animal, soil and crops and manure sub-models. The decision module relies on an annual optimization of cropping and spreading allocation plans, and on the flexible execution of activity plans for each simulated year. These plans are examined every day by an operational management sub-model and their application is context dependent. As a result, MELODIE dynamically simulates the flows of carbon, nitrogen, phosphorus, copper, zinc and water within the whole farm over the short and long-term considering both the farming system and its adaptation to climatic conditions. Therefore, it is possible to study both the spatial and temporal heterogeneity of the environmental risks, and to test changes of practices and innovative scenarios. This is illustrated with one example of simulation plan on dairy farms to interpret the Nitrogen farm-gate budget indicator. It shows that this indicator is able to reflect small differences in Nitrogen losses between different systems, but it can only be interpreted using a mobile average, not on a yearly basis. This example illustrates how MELODIE could be used to study the dynamic behaviour of the system and the dynamic of nutrient flows. Finally, MELODIE can also be used for comprehensive multi-criterion assessments, and it also constitutes a generic and evolving framework for virtual experimentation on animal farming systems.

Review of whole-farm economic modelling for irrigation farming

The main objective of this paper is to review the progress that has been made in South Africa with respect to whole-farm economic modelling over the past 2 decades. Farming systems are complex and careful consideration to the stochastic dynamic nature of irrigation farming processes and their linkages with the larger water system is necessary when conducting whole-farm modelling. Both simulation and optimisation approaches to whole-farm modelling have been developed. Simulation is able to realistically model key performance indicators for decision-making while taking cognisance of the stochastic dynamic nature of irrigation agriculture. Normally only a few predefined scenarios are considered and these do not include decisions regarding allocation of water between competing farm uses of water. Optimisation models take the opportunity cost of water into account while optimising water use between multiple crops. Simplifications of the soil-cropwater subsystem are necessary to optimise agricultural water use between activities which are differentiated by crop, irrigation technology and soil at whole-farm level. Appropriate use of crop simulation models to provide input for mathematical programming models holds promise but needs to be weighed against the extra time needed to validate models and generate the required information. Research is necessary to determine the value of considering water as a stock resource compared to a situation where water use is optimised without considering water as a stock resource. Optimisation results indicated that it is profitable to irrigate larger areas with water saved from deficit irrigation and increasing irrigation efficiency. Relatively little research was done to demonstrate the externalities caused by irrigation farming under the current water policy. Future research should focus on developing integrated hydro-economic modelling frameworks that will incorporate irrigation externalities. Modelling decision-making by means of a single-attribute utility function is unsatisfactory and more research is necessary to improve understanding of the decision-making process to enhance whole-farm modelling frameworks that will assist farmers in making tactical decisions.

The whole-farm benefits of controlled traffic farming: An Australian appraisal

Controlled traffic farming (CTF) uses a range of technologies to confine traffic-induced compaction to permanently defined tramlines within a farm’s cropping area. CTF concentrates and improves trafficability whilst simultaneously supporting soil structure improvement between tramlines, thereby raising crop yields and offering other advantages such as reduced overlap that saves on crop inputs. This study uses whole-farm modelling to assess the profitability and role of CTF in different farming systems in Australian dryland agriculture. Farming system scenarios with and without the CTF are compared. Stepwise analysis, combined with sensitivity analysis, reveals the characteristics of CTF that most affect its value. Results indicate that the most valuable aspect of the technology is its beneficial impact on the yield and quality of crops grown on soils most subject to compaction. Hence, on farms dominated by these soils and where their faming system emphasizes cropping, CTF forms an especially valuable role. For a typical farm in the study region, employing conservative measures, farm profit increases by around 50% through use of CTF. Hence, CTF represents a remarkably profitable innovation for farming systems, offering input savings and output increases.

Integrated participatory modelling of actual farms to support policy making on sustainable intensification

On the tropical island of La Réunion, population growth, increasing demand in food products, agricultural densification, and the resulting pressure on the environment are representative of what is expected to happen in the majority of the world’s regions in the next decades. Crop-livestock integration is a possible solution for the sustainable intensification of farming systems. GAMEDE, a whole-farm model, was designed and used with six representative dairy farmers on the island to ex-ante assess differences in farm sustainability of various degrees of crop-livestock integration and to support discussions with farmers about these options. The model details the dynamics of the main biophysical and decisional processes affecting labour, gross margin, and energy and nutrient flows within the farm. We propose a method based on typology, modelling and participatory techniques to support policy making. All its methodological stages integrate both quantitative and qualitative data. The large majority of farm model implementation cases reported in the literature refers to constructed synthetic farms. However, in our case, actual farm simulation was particularly useful for capturing farmers’ expert knowledge and providing insights into how agro-ecosystems are really managed. This approach enabled taking farm diversity into account in defining relevant interventions. The reliability of extrapolations and recommendations for policy formulation based on farm-level simulation were verified by a rigorous evaluation of the representativeness of the farm sample, crossing expert data with data stemming from a multi-variate analysis. Our research indicates that actual farms can also be typical.

Using a whole farm model to determine the impacts of mating management on the profitability of pasture-based dairy farms

An approach to assessing likely impacts of altering reproductive performance on productivity and profitability in pasture-based dairy farms is described. The basis is the development of a whole farm model (WFM) that simulates the entire farm system and holistically links multiple physical performance factors to profitability. The WFM consists of a framework that links a mechanistic cow model, a pasture model, a crop model, management policies and climate. It simulates individual cows and paddocks, and runs on a day time-step. The WFM was upgraded to include reproductive modeling capability using reference tables and empirical equations describing published relationships between cow factors, physiology and mating management. It predicts reproductive status at any time point for individual cows within a modeled herd. The performance of six commercial pasture-based dairy farms was simulated for the period of 12 months beginning 1 June 2005 (05/06 year) to evaluate the accuracy of the model by comparison with actual outcomes. The model predicted most key performance indicators within an acceptable range of error (residual < 10% of observed). The evaluated WFM was then used for the six farms to estimate the profitability of changes in farm “set-up” (farm conditions at the start of the farming year on 1 June) and mating management from 05/06 to 06/07 year. Among the six farms simulated, the 4-week calving rate emerged as an important set-up factor influencing profitability, while reproductive performance during natural bull mating was identified as an area with the greatest opportunity for improvement. The WFM presents utility to explore alternative management strategies to predict likely outcomes to proposed changes to a pasture-based farm system.

Life cycle assessment of greenhouse gas emissions from beef production in western Canada: A case study

A life cycle assessment (LCA) was conducted to estimate whole-farm greenhouse gas (GHG) emissions from beef production in western Canada. The aim was to determine the relative contributions of the cow–calf and feedlot components to these emissions, and to examine the proportion of whole-farm emissions attributable to enteric methane (CH 4). The simulated farm consisted of a beef production operation comprised of 120 cows, four bulls, and their progeny, with the progeny fattened in a feedlot. The farm also included cropland and native prairie pasture for grazing to supply the feed for the animals. The LCA was conducted over 8 years to fully account for the lifetime GHG emissions from the cows, bulls and progeny, as well as the beef marketed from cull cows, cull bulls, and progeny raised for market. The emissions were estimated using Holos, a whole-farm model developed by Agriculture and Agri-Food Canada. Holos is an empirical model, with a yearly time-step, based on the Intergovernmental Panel on Climate Change methodology, modified for Canadian conditions and farm scale. The model considers all significant CH 4, N 2O, and CO 2 emissions and removals on the farm, as well as emissions from manufacture of inputs (fertilizer, herbicides) and off-farm emissions of N 2O derived from nitrogen applied on the farm. The LCA estimated the GHG intensity of beef production in this system at 22 kg CO 2 equivalent (kg carcass) −1. Enteric CH 4 was the largest contributing source of GHG accounting for 63% of total emissions. Nitrous oxide from soil and manure accounted for a further 27% of the total emissions, while CH 4 emissions from manure and CO 2 energy emissions were minor contributors. Within the beef production cycle, the cow–calf system accounted for about 80% of total GHG emissions and the feedlot system for only 20%. About 84% of enteric CH 4 was from the cow–calf herd, mostly from mature cows. It follows that mitigation practices to reduce GHG emissions from beef production should focus on reducing enteric CH 4 production from mature beef cows. However, mitigation approaches must also recognize that the cow–calf production system also has many ancillary environmental benefits, allowing use of grazing and forage lands that can preserve soil carbon reserves and provide other ecosystems services.

Identifying the value of pasture improvement using wholefarm modelling

The economic value of pasture improvement in a farming system in a Mediterranean-type environment is assessed using farm modelling. Bioeconomic modelling combined with sensitivity analysis is used to explore how new annual legume species may impact on farm profit and land use. If introduced on all suitable soils the new pastures lead to a 26% increase in farm profit with an additional 12% of farm area being switched into pasture to support more livestock. However, stocking rates decline slightly, the enterprise mix becomes less diversified and several rotational changes are required. If the new pastures are only introduced on particular soils then farm profit increases by a lesser percentage and the pathways to additional profit vary, with both the pasture area and stocking rate either increasing or decreasing. To extract maximum profit from pasture improvement on any particular land management unit often requires management changes on other land management units, apart from those on which pasture improvement initially takes place. The economic value of pasture improvement is found to depend on the portfolio of pastures available for improvement and the nature and mix of the farm’s land management units.

A model of growth, pregnancy and lactation in the red deer

SUMMARYA model of the growth, pregnancy and lactation of red deer was developed for incorporation into a whole-farm systems model in order to improve the understanding of venison supply systems. The model estimates the level of metabolic demand for a deer, which depends on the maximum capacity of its tissues to use energy. A function that takes account of satiation signals and rumen capacity is used to convert the metabolic demand into an estimate of the deer's forage intake demand, which can be used as an input into a foraging model. The actual energy intake of the deer is subsequently used to predict live weight (LW), body condition score, foetal growth and gestation length in pregnant hinds, and milk yield in lactating hinds. In order to make these predictions, the model requires inputs that include values for mean daily temperature, mean daily wind speed, day length and season, as well as pasture quality. Values for model parameters were obtained from the literature, rather than by fitting to data, and model predictions were then compared with measurements obtained in independent trials.In simulations, the model predicted that 152-day-old stags and hinds, weighing, respectively, 44 and 48 kg, would grow to, respectively, 106 and 90 kg when 517 days old, compared with trial results of, respectively, 103 and 84 kg. Predictions for the weight of pregnant hinds, gestation length and calf birth weight compared well with an experiment for hinds on a high plane of nutrition but poorly for hinds on medium and low planes. Weekly predictions of hind LWs for days 132–230 of pregnancy had respective residual means of 0·08, 6·2 and 8·5 kg, and respective residual standard deviations of 1·33, 4·6 and 5·2 kg for the high, medium and low nutritional planes. Predicted gestation length for high, medium and low planes of nutrition were, respectively, 231·5, 238·0 and 242·0 days compared with experimental values of, respectively, 231·3, 234·7 and 239·2 days, while predicted birth weights were, respectively, 8·5, 8·3 and 8·9 kg compared with measured values of, respectively, 8·4, 9·5 and 9·3 kg. Predicted calf growth from birth to 14 weeks agreed well with data (residual mean and standard deviation being 0·04 and 1·15 kg, respectively).The existing software structure of the whole-farm model dictated that the deer model use the Euler method with a fixed, daily time step. Therefore, the model was constructed using difference (rather than differential) equations and used a traditional, energy-balance method for predicting growth. This empirical approach tacitly imposed a standard body composition and standard metabolic rate for adults, with values corresponding to well-fed deer. This does not cater for variation in body composition and metabolic activity, and in retrospect, caused the model to perform poorly for the medium and low nutritional regimes.

Simulation of Nitrous Oxide Emissions from Dairy Farms to Assess Greenhouse Gas Reduction Strategies

Farming practices can have a large impact on the net emission of greenhouse gases, including carbon dioxide (CO2), methane, and nitrous oxide (N2O). The primary sources of N2O from dairy farms are nitrification and denitrification processes in soil, with smaller contributions from manure storage and barn floors. Emissions from all greenhouse gas sources are interrelated, so strategies to reduce emissions from one source can affect emissions from another. Therefore, a comprehensive whole-farm evaluation is needed, which can be cost-effectively achieved through computer simulation. The Integrated Farm System Model (IFSM), a process-based whole-farm model, was extended to simulate sources of N2O and other greenhouse gas emissions. A module was added to simulate N2O emissions from croplands using relationships from the previously established DAYCENT model, and relationships were incorporated to predict emissions from slurry storage and free-stall barn floors. The new module was found to predict N2O emissions consistent with reported values from specific experiments and previously estimated whole-farm emissions. The model also predicted sensitivity to soil texture and soil water content similar to experimental data and DAYCENT model predictions, which further verified this most important component of N2O emissions. Simulations illustrated the impact of management practices on a representative farm in central Pennsylvania. Reducing the use of inorganic fertilizer by accounting for manure nitrogen (N) reduced N2O emission from the farm by 6% and reduced the net farm emission of all greenhouse gases in CO2-equivalent units by 1%. Adding a mulch cover crop to corn land reduced N2O emission by 34% with a 7% reduction of all greenhouse gases. Use of a top-loaded manure storage tank prevented formation of a surface crust, which eliminated storage N2O emission with little effect on net farm emission of all greenhouse gases. This extended whole-farm model provides a tool for evaluating proposed N2O reduction strategies along with their effects on other greenhouse gas emissions, other N and phosphorus losses, and farm profitability.

The Australian Farm Forestry Financial Model

Summary The Australian Farm Forestry Financial Model (AFFFM) has been developed as a tool for the financial appraisal of farm forestry investments. It is a whole-farm financial model developed in modules using Visual Basic as the programming language, and provides estimates of key financial criteria including net present value, land expectation value and internal rate of return. It also provides details of the effects of forestry options on cash flows and the business cash position. Included in the AFFFM are data sets for plantation establishment and maintenance costs, growth rates and potential timber products of a number of tree species, for three regions. This paper outlines the scope of the AFFFM including its structure, assumptions, input parameters and key variables. The paper also reports the process by which the model was validated through replication of previous studies, the development of case studies and testing by undergraduate students.

Simulation of Carbon Dioxide Emissions from Dairy Farms to Assess Greenhouse Gas Reduction Strategies

Farming practices can have a large impact on the soil carbon cycle and the resulting net emission of greenhouse gases including carbon dioxide (CO2), methane, and nitrous oxide. Primary sources of CO2 emission on dairy farms are soil, plant, and animal respiration, with smaller contributions from microbial respiration in manure. Strategies designed to reduce emissions from one source can cause an increase in emissions from another source. Therefore, a comprehensive whole-farm evaluation is needed, which can be cost-effectively met through computer simulation. The Integrated Farm System Model (IFSM), a process-based whole-farm model, was extended to simulate the carbon cycle. Relationships were added to represent photosynthetic fixation, soil and plant respiration, animal respiration, and emissions from manure storage and barn floors. The new module was verified to predict the mass of carbon present in soil pools at the end of annual simulations and to predict CO2 emissions within expected emission ranges for both specific sources and overall farm emissions. A farm-level carbon balance was used to further verify that predicted emissions were reasonable across a variety of production strategies. Farm simulations illustrated that changes in cropping practices affected emissions from all farm sources, with a primary effect on the assimilation of CO2 in feed production. For a representative farm in central Pennsylvania, use of more alfalfa in place of corn production caused a 6% increase in net farm greenhouse gas emission in CO2-equivalent units, while replacing non-permanent grassland with corn production reduced the net emission by 16%. Changing from a Holstein herd to Jersey animals with animal numbers increased to produce the same amount of milk affected most emission sources, with a net impact of increasing the net greenhouse gas emission by 20%. Incorporation of greenhouse gas emission modules in IFSM provides a more comprehensive tool for evaluating the overall farm-level environmental and economic impacts of management scenarios used to reduce emissions.

An exploratory tool for analysis of forage and livestock production options

The Grain &amp; Graze Whole-Farm Model was developed as a simple modelling tool to identify better strategies to improve the income of farmers and overcome grassland degradation. Using information on farm structure, crop and forage production systems, livestock production systems and variable costs involved in all enterprises, maximum whole-farm gross margins are obtained for an optimum or a prescribed mix of enterprises. The incorporation of production systems for different rainfall scenarios enables climatic risks and water use efficiencies of different enterprises to be investigated. Model simulations demonstrated the potential improvements that could be achieved in dollar water use efficiency ($WUE), by changes in management and/or changes in enterprise. The design of the model makes it a valuable tool for evaluating new systems, as it easy to develop new crop, pasture and livestock systems. Innovative farming systems such as pasture cropping and alley farming are included in the model.

Simulation of Methane Emissions from Dairy Farms to Assess Greenhouse Gas Reduction Strategies

As a sector, agriculture is reported to be the second greatest contributor to atmospheric methane (CH4) in the U.S., emitting 31% of the total emission. Primary sources of CH4 on dairy farms are the animals and manure storage, with smaller contributions from field-applied manure, feces deposited by grazing animals, and manure on barn floors. The Integrated Farm System Model (IFSM) was expanded to include simulation of CH4 emissions from all farm sources along with modules predicting other greenhouse gas (GHG) emissions. The new CH4 module incorporated previously published relationships and experimental data that were consistent with our modeling objectives and the current structure of IFSM. When used to simulate previously reported experiments, the model was found to predict enteric fermentation and slurry manure storage emissions similar to those measured. In simulating a representative 100-cow dairy farm in Pennsylvania, the model predicted a total average annual emission of 21 Mg CH4. This included annual emissions of 142 kg CH4 per cow from the Holstein herd and 6.4 kg CH4 per m3 of slurry manure in storage, which were consistent with previously summarized emission data. To illustrate the use of the expanded whole-farm model, potential CH4 reduction strategies were evaluated. Farm simulations showed that increasing the production and use of forage (corn silage) in animal diets increased CH4 emission by 16% with little impact on the global warming potential of the net farm emission of all GHGs. Use of grazing along with high forage diets reduced net farm GHG emission by 16%. Using an enclosed manure storage and burning the captured biogas reduced farm emission of CH4 by 32% with a 24% reduction in the net farm emission of GHG. Incorporation of GHG emission modules in IFSM provides a tool for estimating whole-farm emissions of CH4 and evaluating proposed reduction strategies along with their impact on net GHG emission and other environmental and economic measures.

Comparison of two pasture growth models of differing complexity

Two pasture growth models that shared many common features but differed in model complexity were refined for incorporation into the Integrated Farm System Model (IFSM), a whole-farm model that predicts effects of weather and management on hydrology, soil nutrient dynamics, forage and crop yields, milk or beef production, and farm economics. Major differences between models included the explicit representation of roots in the more complex model and their effects on carbon partitioning and growth. The simple model only simulated aboveground processes. The overall goal was to develop a model capable of representing forage growth and ecosystem carbon fluxes among multiple plant species in pastures while maintaining a relatively simple model structure that minimized the number of required user inputs. Models were compared to observed yield data for 12 site-years from three experiments in central Pennsylvania, USA. Both models underestimated observed yield by 6% when averaged across site-years. However, the simple model provided a better fit to the one-to-one line between observed and simulated yield than did the complex model. The models also showed similar relationships between yield and gross primary productivity (GPP), despite the fact that the complex model was specifically developed to optimize simulation of GPP. The simple model predicted much greater shoot respiration and carbon partitioning to above ground plant tissues, but less shoot senescence than the complex model. Published data on the proportion of GPP consumed in aboveground or total plant respiration exhibit a wide range of values, making it impossible to determine which model provided the best representation of respiration rates and, thus, of the entire carbon budget.