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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.

The introduction of legumes in grass-based swards provides some economic and agronomic advantages, often allowing an increase in the performance of grazing ruminants. The aim of this study was to obtain a better quantification of the nutritional benefits to dairy cows after introducing white clover into swards of perennial ryegrass (PRG), using two ages of regrowth. Four treatments were studied in a 2 ✕ 2 factorial design with two sward types and two ages of regrowth. The swards were either a pure perennial ryegrass sward with nitrogen (N) fertilization, or a perennial ryegrass/white clover mixture (GC) with no N fertilization. The regrowth ages were 19 and 35 days (treatments: PRG19, PRG35, GC19 and GC35). The proportion of clover in the GC swards was on average 420 g/kg dry matter (DM). Twelve late-lactation Holstein cows, fistulated at the rumen and duodenum, were used according to a 4 ✕ 4 Latin-square design with four 11-day periods. The pasture was strip-grazed with 12 kg DM per cow of herbage above 5 cm offered daily in all the treatments.The effects of sward type and regrowth age were often additive, in particular for herbage intake and milk yield. Herbage organic matter (OM) intake, duodenal non-ammonia N (NAN) flow and milk yields were higher on the GC swards and lower on the older regrowths. Finally, the performance of the cows was similar on the PRG19 and GC35 treatments. The OM digestibility of the selected herbage as well as the duodenal nitrogen flux per kg digestible OM intake was not affected by the sward type. Ruminal fermentations were more intense with mixed swards and the youngest regrowths. The daily grazing time and the daily pattern of grazing activities were modified by the type of sward and by regrowth age. The average OM intake rate was higher on the GC swards than on the PRG swards. In this study, the nutritional advantage of introducing white clover into swards of perennial ryegrass was related to an increase in herbage intake and not to any improvement in the nutritive value of the sward.

In order to establish the response of dairy cow performance to concentrate supplementation in contrasting grazing conditions and for cows differing in milk yield at turn-out, three experiments were conducted. Each year, two levels of herbage allowance were studied in interaction with four (experiment 1) or three (experiments 2 and 3) levels of concentrate on two groups of 30 to 40 mid-lactation Holstein cows producing between 20 and 46 kg milk at turnout. Amount of concentrate and herbage allowance ranged from 0 to 6 kg fresh weight and from 12 to 22 kg dry matter (DM) per cow per day respectively. The supplementation led to average responses, per kg DM concentrate, of 104 kg milk, +66 g/day body-weight gain, +0·19 g/kg milk protein and -0·57 g/kg milk fat. These responses remained linear up to 4 or 6 kg according to the years and treatments. The response to the concentrate did not vary with the milk yield or composition at turn-out. The increase in the herbage allowance from 12 to 16 kg DM per cow per day (experiment 1) improved milk yield (+1·2 kg/day) and milk protein (+0·7 g/kg) while the increase from 16 to 22 kg DM (experiments 2 and 3) had less effect (+0·5 kg/day milk yield and +0·4 g/kg milk protein). There was no clear interaction between concentrate supplementation and herbage allowance. Under the usual conditions of spring pasture, with cows in mid lactation, the use of a constant level of concentrate at grazing proves to be a technique of some interest.

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.

Grazed pasture, which is the cheapest source of nutrients for dairy cows, should form the basis of profitable and low-input animal production systems. Management of high-producing dairy cows at pasture is thus a major challenge in most countries. The objective of the present paper is to review the factors that can affect nutrient supply for grazing dairy cows in order to point out areas with scope for improvement on managing variations in nutrient supply to achieve high animal performance while maintaining efficient pasture utilisation per hectare (ha). Reviewing the range in animal requirements, intake capacity and pasture nutritive values shows that high-producing cows cannot satisfy their energy requirements from grazing alone and favourable to unfavourable situations for grazing dairy cows may be classified according to pasture quality and availability. Predictive models also enable calculation of supplementation levels required to meet energy requirements in all situations. Solutions to maintain acceptable level of production per cow and high output per ha are discussed. Strategies of concentrate supplementation and increasing use of legumes in mixed swards are the most promising. It is concluded that although high-producing cow cannot express their potential milk production at grazing, there is scope to improve animal performance at grazing given recent developments in our understanding of factors influencing forage intake and digestion of grazed forages.

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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.