The prediction of both food intake and milk production constitutes a major issue in ruminants. This article presents a model predicting voluntary dry matter intake and milk production by lactating cows fed indoors. This model, with an extension to predict herbage intake at grazing presented in a second article, is used in the Grazemore decision support system. The model is largely based on the INRA fill unit system, consisting of predicting separately the intake capacity of the cows and the fill value (ingestibility) of each feed. The intake capacity model considers potential milk production as a key component of voluntary feed intake. This potential milk production represents the energy requirement of the mammary gland, adjusted by protein supply when the protein availability is limiting. Actual milk production is predicted from the potential milk production and from the nutritional status of the cow. The law of response of milk production is a function of the difference between energy demand and actual energy intake, modulated by protein intake level. The simulation of experimental data from different feeding trials illustrates the performance of the model. This new model enables dynamic simulations of intake and milk production sensitive to feeding management during the whole lactation period.

Decision support tools to help dairy farmers gain confidence in grazing management need to be able to predict performance of grazing animals with easy‐to‐obtain variables on farm. This paper, the second of a series of three, describes the GrazeIn model predicting herbage intake for grazing dairy cows. The model of voluntary intake described in the first paper is adapted to grazing situations taking account of sward characteristics and grazing management, which can potentially affect intake compared to indoor feeding. Rotational and continuously stocked grazing systems are considered separately. Specific effects of grazing management on intake were quantified from an extensive literature review, including the effect of daily herbage allowance and pre‐grazing herbage mass in rotational grazing systems, sward surface height in continuously stocked grazing systems, and daily time at pasture in both grazing systems. The model, based on iterative procedures, estimates many interactions between cows, supplements, sward characteristics and grazing management. The sensitivity of the prediction of herbage intake to sward and management characteristics, as well as the robustness of the simulations and an external validation of the GrazeIn model with an independent data set, is presented in a third paper.

GrazeIn is a model for predicting herbage intake and milk production of grazing dairy cows. The objectives of this paper are to test its robustness according to a planned arrangement of grazing and feeding scenarios using a simulation procedure, and to investigate the precision of the predictions from an external validation procedure with independent data. Simulations show that the predicted effects of herbage allowance, herbage mass, herbage digestibility, concentrate supplementation, forage supplementation and daily time at pasture are consistent with current knowledge. The external validation of GrazeIn is investigated from a large dataset of twenty experiments representing 206 grazing herds, from five research centres within Western Europe. On average, mean actual and predicted values are 14AE4 and 14AE2 kg DM d )1 for herbage intake and 22AE7 and 24AE7 kg d )1 for milk production, respectively. The overall precision of the predictions, estimated by the mean prediction error, are 16% (i.e. 2AE3 kg DM d )1 ) and 14% (i.e. 3AE1 kg d )1 ) for herbage intake and milk production, respectively. It is concluded that the GrazeIn model is able to predict variations in herbage intake and milk production of grazing dairy cows in a realistic manner over a wide range of grazing management practices, rendering it suitable as a basis for decision support systems.

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.