Description and evaluation of the Farmax Dairy Pro decision support model

Bryant, Jeremy; Ogle, Graeme; Marshall, Paul R.; Glassey, C. B.; Lancaster, J.A.S.; García, S. C.; Holmes, C. W.

doi:10.1080/00288231003606054

Cited by 88 publications

(58 citation statements)

References 25 publications

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“…The accuracy of prediction was moderate for LW and BCS, when considering the CCC (Table 1). These levels of accuracy of prediction for DM intake and milk yield are similar or higher than those reported for other models for grazing dairy systems, such as Farmax Dairy Pro (Bryant et al, 2010), GrazeIn (Delagarde et al, 2011) and the WFM (Beukes et al, 2008). However, it is important to notice that the validation of the model was conducted for annual outputs, and the accuracy of the model for short-term predictions (weekly or monthly basis) remains to be tested.…”

Section: Model Validationsupporting

confidence: 66%

“…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%

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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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Section: Model Validationsupporting

confidence: 66%

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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“…Production and financial costs of clover soil disease were analysed using Farmax Dairy Pro decision support models (Bryant et al 2010) of the Southland, Canterbury, and Waikato-BOP Monitor Dairy farms of Year 2012/13. The model assumed that removing clover soil disease constraints would increase average clover yield by 28.5 %.…”

Section: Economic Modelling Of Clover Disease Costmentioning

confidence: 99%

Cost of root disease on white clover growth in New Zealand dairy pastures

Wakelin

Eslami

Dake³

et al. 2016

Australasian Plant Pathol.

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The cost to clover growth of soil-borne root disease was measured in ten New Zealand dairy pasture soils. The average increase clover growth (weight) after soil pasteurisation was 28.5 %, but ranged from a 64 % increase (Whataroa soil) to a decrease of 11.9 % (Ruakura soil). The economic cost of reduced clover growth was determined using the Farmax Dairy Pro decision support system. In Southland and Canterbury, clover root disease was estimated to cost $750 and $715 ha −1 year −1 respectively, and in the Waikato region $1506 ha −1 year −1 . DNA-based testing of the soils detected the presence of diverse fungal, oomycete and nematode pathogen populations. A significant linear relationship was found between reduction in clover growth and group F Pythium spp. (P = 0.0177). The DNA-based assay indicated that Aphanomyces trifolii, a root pathogen of subterranean clover, may be present in the dairy-pasture soils. As this pathogen is currently not recognised as present in New Zealand, a definitive determination of its presence is required. Based on the high economic costs of diseases, the control of soil-borne root pathogens in New Zealand pasture is a direct means to increase profitability.

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“…Nevertheless, output validation indicates a model's ability to adequately describe reality and thus is an important activity that practitioners should seek to practice, where possible. Table 3 presents unpublished validation output that compares output from a detailed optimisation model of a New Zealand dairy farm model (the Integrated Dairy Enterprise Analysis (IDEA) model) and output from FARMAX -a system-level model that farmers and consultants use to guide production decisions on New Zealand dairy farms (Bryant et al 2010). The FARMAX modelling is based on survey data obtained from farmers in the Waikato region, but provides an integrated set of data for comparison with output from the optimisation model (A. Adler, pers.…”

Section: Model Validationmentioning

confidence: 99%

A process for the development and application of simulation models in applied economics

Doole¹,

Pannell²

2012

Aus J Agri & Res Econ

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Simulation models are widely used in applied economics to improve understanding of how a system could behave under different conditions. However, the potential degree to which such models can influence decision making depends on their ability to provide an adequate description of the important elements of a given problem. A systematic and robust procedure for the development and application of such models in agricultural, ecological, environmental and natural resource economics is presented. This process is based on the authors' experience across a broad range of model types and applications and extensive literature review. The practical impact of simulation models is argued to be greater where stakeholders and technical experts are consulted extensively throughout the modelling process.

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Description and evaluation of the Farmax Dairy Pro decision support model

Cited by 88 publications

References 25 publications

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

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

Cost of root disease on white clover growth in New Zealand dairy pastures

A process for the development and application of simulation models in applied economics

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