DairyWise, A Whole-Farm Dairy Model

Schils, R.L.M.; Haan, M.H.A. de; Hemmer, J.G.A.; Dasselaar, A. van den Pol–van; Boer, J.A. de; Evers, A.G.; Holshof, G.; Middelkoop, J.H. van; Zom, R.L.G.

doi:10.3168/jds.2006-842

Cited by 118 publications

(86 citation statements)

References 20 publications

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“…For example, simulated losses (N and P) and whole farm balances of N and P (Rotz et al, 2006) and plume measurement of CH 4 emissions (Hensen et al, 2006) have been used to verify those components in whole farm simulations. When actual farm data are not available or quantifiable, the model may be best evaluated by comparing with other farm models (Schils et al, 2007a). When models developed independent of each other are found to predict similar results, this supports the validity of the models.…”

Section: Model Evaluation and Uncertaintymentioning

confidence: 65%

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

Prado

Crosson

Olesen

et al. 2013

Animal

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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.Keywords: adaptation, farm-models, greenhouse gases, mitigation, ruminants ImplicationsWhole-farm models are valuable tools to study the feedback and feedforward interactions between mitigation of greenhouse gas (GHG) emissions and adaptation to climate change for ruminant-based production systems. All of the processes affecting GHG emissions and farm productivity involve the cycling of C and N within the farm, most of which will be affected by climate change. However, not all farm models include such responses to climatic drivers, and there is a need for further development and testing of the models under diverse climatic conditions. Modelling of the complex interactions between farm components and the environment, including the socio-economic aspects, is instrumental for providing strategic direction to the development of climate and food-related policies. Despite the contribution of nontemperate livestock systems to total global GHG emissions, there is a dearth of specific farm models to assess GHG emissions from these regions. Modelling of soil carbon (C) fluxes is still largely absent or very simplified in many studies even though soil C has the potential to be the largest source or sink of C for grassland-based livestock systems. IntroductionAccording to The Food and Agriculture Organization (FAO) (Steinfeld et al., 2006) livestock production systems contribute about 18% of global anthropogenic greenhouse gas (GHG) emissions. Livestock is also currently one of the fastest growing agricultural subsectors in developing countries (Thornton, 2010), with projections indicating that by 2050 worldwide animal production is expected to increase by 80% compared with 2005 (Alexandratos andBruinsma, 2012). Although demand for non-ruminant meat has been increasing more rapidly than that for ruminant meats and, consequently, the importance of grasslands in livestock production and trade has been declining, increased milk and beef demand, potentially produced via grassland-based systems, is expected to increase (Fiala, 2008;Thornton, 2010). It is thus projected that the global annual growth rate of beef until 2050 will be 1.2%, clos...

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Section: Model Evaluation and Uncertaintymentioning

confidence: 65%

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

Prado

Crosson

Olesen

et al. 2013

Animal

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show abstract

“…Some of these models work at a herd level (Larcombe, 1990;Freer et al, 1997;Shalloo et al, 2004;Schils et al, 2007;Vayssiè res et al, 2009), whereas other models work at an individual animal level (Beukes et al, 2008;Bryant et al, 2010). These latter models account for genetic differences between cows, but they were designed to be used under specific conditions, that is, grass-based dairy systems and their environmental conditions under which the breeding values were estimated.…”

Section: Introductionmentioning

confidence: 99%

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

Baudracco

López‐Villalobos

Holmes

et al. 2013

Animal

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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 3 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.Keywords: dairy, grazing, whole-farm, model, stochastic ImplicationsThe e-Dairy model was designed to predict the biophysical and economic performance of grazing dairy systems, with some key variables allowed to behave stochastically, which enables the risk associated with different feed management strategies to be evaluated. The e-Dairy model can be used for different types of grazing dairy systems, that is, ryegrass-or lucerne-based systems with and without supplementation and for cows of different genetic merit. This paper combines, within a whole-farm model, advances from previous models that predic...

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“…In addition, in a crop-livestock farm, nitrous oxide (N 2 O) emissions from fertiliser use should also be accounted for (see, e.g. Schils et al 2007 ;Petersen et al 2009 ;Veysset et al 2010 ) . Carbon dioxide (CO 2 ) emissions from energy consumption pose an additional source of GHGs.…”

Section: Ghg Emissionsmentioning

confidence: 99%

Evaluating Socio-economic and Environmental Sustainability of the Sheep Farming Activity in Greece: A Whole-Farm Mathematical Programming Approach

Sintori

Tsiboukas

Zervas

2012

Methods and Procedures for Building Sustainable Farming Systems

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Ruminant livestock farming is an important agricultural activity, mainly located in less favoured areas. Furthermore, ruminants have been identi fi ed as a signi fi cant source of GHG emissions. In this study, a whole-farm optimisation model is used to assess the socio-economic and environmental performance of the dairy sheep farming activity in Greece. The analysis is undertaken in two sheep farms that represent the extensive and the semi-intensive farming systems. Gross margin and labour are regarded as socio-economic indicators and GHG emissions as environmental indicators. The issue of the marginal abatement cost is also addressed. The results indicate that the semi-intensive system yields a higher gross margin/ewe (179 €) than the extensive system (117 €) and requires less labour. The extensive system causes higher emissions/kg of milk than the semi-intensive system (5.45 and 2.99 kg of CO 2 equivalents, respectively). In both production systems, abatement is achieved primarily via reduction of the fl ock size and switch to cash crops. However, the marginal abatement cost is much higher in the case of the semi-intensive farms, due to their high productivity.

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DairyWise, A Whole-Farm Dairy Model

Cited by 118 publications

References 20 publications

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

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

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

Evaluating Socio-economic and Environmental Sustainability of the Sheep Farming Activity in Greece: A Whole-Farm Mathematical Programming Approach

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