GRAZPLAN: Decision support systems for Australian grazing enterprises—II. The animal biology model for feed intake, production and reproduction and the GrazFeed DSS

Organic agriculture is developing worldwide, and organic rabbit production has developed within this context. It entails raising rabbits in moving cages or paddocks, which enables them to graze grasslands. As organic farmers currently lack basic technical information, the objective of this article is to characterize herbage intake, feed intake and the growth rate of rabbits raised on grasslands in different environmental and management contexts (weather conditions, grassland type and complete feed supplementation). Three experiments were performed with moving cages at an experimental station. From weaning, rabbits grazed a natural grassland, a tall fescue grassland and a sainfoin grassland in experiments 1, 2 and 3, respectively. Rabbit diets were supplemented with a complete pelleted feed limited to 69 g dry matter (DM)/rabbit per day in experiment 1 and 52 g DM/rabbit per day in experiments 2 and 3. Herbage allowance and fiber, DM and protein contents, as well as rabbit intake and live weight, were measured weekly. Mean herbage DM intake per rabbit per day differed significantly ( P < 0.001) between experiments. It was highest in experiment 1 (78.5 g DM/day) and was 43.9 and 51.2 g DM/day in experiments 2 and 3, respectively. Herbage allowance was the most significant determinant of herbage DM intake during grazing, followed by rabbit metabolic weight (live weight 0.75 ) and herbage protein and fiber contents. Across experiments, a 10 g DM increase in herbage allowance and a 100 g increase in rabbit metabolic weight corresponded to a mean increase of 6.8 and 9.6 g of herbage DM intake, respectively. When including complete feed, daily mean DM intakes differed significantly among experiments ( P < 0.001), ranging from 96.1 g DM/rabbit per day in experiment 2 to 163.6 g DM/rabbit per day in experiment 1. Metabolic weight of rabbits raised on grasslands increased linearly over time in all three experiments, yielding daily mean growth rates of 26.2, 19.2 and 28.5 g/day in experiments 1, 2 and 3, respectively. The highest growth rate was obtained on the sainfoin grassland despite lower concentrate supplementation. Thus, it seems possible to reduce complete feed supplementation without reducing animal performance. This possibility requires improving our knowledge about organic rabbit production systems and especially grazing and animal health management.

Section: Discussionmentioning

confidence: 99%

Herbage intake regulation and growth of rabbits raised on grasslands: back to basics and looking forward

Martin¹,

Duprat²,

Goby³

et al. 2016

“…Agreil and Meuret (2004) proposed predicting feeding choices on garrigue rangeland from a functional description of the vegetation, which they divide into items inducing 'big bites' and items inducing 'small bites'. Their conceptual model contrasts with some previous models, which predicted that the animals would first graze the best vegetation items, either the top layer of the sward (Baumont et al, 2004) or the most digestible plant parts (Freer et al, 1997). On a daily time scale, it is thought compatible with the model of , which predicts selective intake according to 'biomass 3 digestibility s ' of edible plant parts.…”

Section: Indicators To Support Grazing Management Decisions In Heteromentioning

confidence: 69%

“…Simulation models of agro-pastoral systems to investigate grazing dynamics in rangelands and evaluate management practices A few simulation models have tackled the problem of intake in highly heterogeneous vegetations, either by empirically introducing a shrub component (Armstrong et al, 1997a and1997b) or by considering a wider array of plant species (Freer et al, 1997). In both cases, the abundance and digestibility of the plant material were the main criteria for diet selection.…”

Section: Indicators To Support Grazing Management Decisions In Heteromentioning

confidence: 99%

Rangeland utilization in Mediterranean farming systems

Jouven

Lapeyronie

Moulin

et al. 2010

In the countries surrounding the Mediterranean basin, most of the semi-natural grazing lands are covered by rangelands. Rangelands can be defined as highly heterogeneous natural vegetation communities with high conservation value, growing in harsh environments (poor soils, unfavourable climatic conditions). In the recent socio-economic context, traditional livestock grazing practices that enabled one to reconcile rangeland preservation and animal production no longer apply, especially because they require labour that has become scarce and costly. The consequence is rangeland degradation, due to underutilization in Southern Europe, and overutilization in Northern Africa. We analysed issues raised by rangeland utilization in livestock farming systems of the Mediterranean basin. Based on a review of the scientific literature about rangeland utilization in this area, we argue that the best way to reconcile animal production and rangeland preservation would be to promote management practices allowing animals to express their adaptative capacities in feeding behaviour and productive response. In order to propose management practices adapted to extensive and simplified systems, we conclude that research efforts should focus on: (i) proposing a functional characterization of vegetation heterogeneity at the scale of the vegetation community, (ii) validating the criteria determining animals' foraging behaviour on Mediterranean rangelands, (iii) developing and using simulation models to test management strategies against seasonal and long-term variability in climatic conditions and (iv) evaluating the potential of modern technologies for improving rangeland utilization.

“…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

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...