The main effects of, and the interactions between, stocking rate (SR), supplementation and genotype on dry matter (DM) intake, herbage utilisation, milk production and profitability of grazing dairy systems have been reviewed. The SR determines the average herbage allowance (HA) per cow and therefore has a major effect on herbage intake (HI) and on the productivity of grazing dairy systems. In this review, the effect of HA on HI is presented separately for two groups of studies: those that measured allowance at ground level and those that measured allowance at a cutting height of 3Á5 cm above ground level. HI and milk yield per hectare usually increase as SR increases. However, there is generally an associated reduction in HI and milk yield per cow because of the decrease in average HA at a higher SR. The dual objectives of adequate level of feeding per cow and high herbage utilisation per hectare can be achieved through the inclusion of supplements. The milk response to supplements depends mainly on the size of the relative energy deficit between potential energy demand and actual energy supply. The relative energy deficit determines both energy partitioning within the cow and substitution rate. The relative energy deficit is increased by either a high demand for energy within the cow or by a deficit of dietary energy available to meet the demand. Cows of different genotype differ in their potential for milk yield. Cows with high genetic potential for milk yield undergo higher relative energy deficits under grazing dairy systems, resulting in lower substitution rates, higher milk responses to supplements, but also lower body condition score, which, in turn, leads to lower reproductive performance. Whole-farm experiments in many countries have demonstrated that the inclusion of supplements, with a concomitant increase in SR, can have synergistic effects in improving the productivity of grazing dairy systems. Overall, the level of supplementation required per cow and the optimum SR depend on the genetic potential of the cow, the size of the responses to supplement, the value of milk and the costs of feeding supplements.

Milk yield per cow has continuously increased in many countries over the last few decades. In addition to potential economic advantages, this is often considered an important strategy to decrease greenhouse gas (GHG) emissions per kg of milk produced. However, it should be considered that milk and beef production systems are closely interlinked, as fattening of surplus calves from dairy farming and culled dairy cows play an important role in beef production in many countries. The main objective of this study was to quantify the effect of increasing milk yield per cow on GHG emissions and on other side effects. Two scenarios were modelled: constant milk production at the farm level and decreasing beef production (as co-product; Scenario 1); and both milk and beef production kept constant by compensating the decline in beef production with beef from suckler cow production (Scenario 2). Model calculations considered two types of production unit (PU): dairy cow PU and suckler cow PU. A dairy cow PU comprises not only milk output from the dairy cow, but also beef output from culled cows and the fattening system for surplus calves. The modelled dairy cow PU differed in milk yield per cow per year (6000, 8000 and 10 000 kg) and breed. Scenario 1 resulted in lower GHG emissions with increasing milk yield per cow. However, when milk and beef outputs were kept constant (Scenario 2), GHG emissions remained approximately constant with increasing milk yield from 6000 to 8000 kg/cow per year, whereas further increases in milk yield (10 000 kg milk/cow per year) resulted in slightly higher (8%) total GHG emissions. Within Scenario 2, two different allocation methods to handle co-products (surplus calves and beef from culled cows) from dairy cow production were evaluated. Results showed that using the 'economic allocation method', GHG emissions per kg milk decreased with increasing milk yield per cow per year, from 1.06 kg CO 2 equivalents (CO 2eq ) to 0.89 kg CO 2eq for the 6000 and 10 000 kg yielding dairy cow, respectively. However, emissions per kg of beef increased from 10.75 kg CO 2eq to 16.24 kg CO 2eq due to the inclusion of suckler cows. This study shows that the environmental impact (GHG emissions) of increasing milk yield per cow in dairy farming differs, depending upon the considered system boundaries, handling and value of co-products and the assumed ratio of milk to beef demand to be satisfied.

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This animal simulation model, named e-Cow, represents a single dairy cow at grazing. The model integrates algorithms from three previously published models: a model that predicts herbage dry matter (DM) intake by grazing dairy cows, a mammary gland model that predicts potential milk yield and a body lipid model that predicts genetically driven live weight (LW) and body condition score (BCS). Both nutritional and genetic drives are accounted for in the prediction of energy intake and its partitioning. The main inputs are herbage allowance (HA; kg DM offered/cow per day), metabolisable energy and NDF concentrations in herbage and supplements, supplements offered (kg DM/cow per day), type of pasture (ryegrass or lucerne), days in milk, days pregnant, lactation number, BCS and LW at calving, breed or strain of cow and genetic merit, that is, potential yields of milk, fat and protein. Separate equations are used to predict herbage intake, depending on the cutting heights at which HA is expressed. The e-Cow model is written in Visual Basic programming language within Microsoft Excel R . The model predicts whole-lactation performance of dairy cows on a daily basis, and the main outputs are the daily and annual DM intake, milk yield and changes in BCS and LW. In the e-Cow model, neither herbage DM intake nor milk yield or LW change are needed as inputs; instead, they are predicted by the e-Cow model. The e-Cow model was validated against experimental data for Holstein-Friesian cows with both North American (NA) and New Zealand (NZ) genetics grazing ryegrass-based pastures, with or without supplementary feeding and for three complete lactations, divided into weekly periods. The model was able to predict animal performance with satisfactory accuracy, with concordance correlation coefficients of 0.81, 0.76 and 0.62 for herbage DM intake, milk yield and LW change, respectively. Simulations performed with the model showed that it is sensitive to genotype by feeding environment interactions. The e-Cow model tended to overestimate the milk yield of NA genotype cows at low milk yields, while it underestimated the milk yield of NZ genotype cows at high milk yields. The approach used to define the potential milk yield of the cow and equations used to predict herbage DM intake make the model applicable for predictions in countries with temperate pastures.Keywords: dairy cow, grazing, milk yield, body lipid reserves, model ImplicationsThe e-Cow model predicts the performance of dairy cows of different genetic merit, grazing either ryegrass or lucernebased pastures, with or without supplementary feeding, thus being useful for conditions in different countries. The model is sensitive to genotype 3 environment interactions.The e-Cow model is useful for applied research, teaching and extension purposes, allowing a quick and practical understanding of the effects of feeding level, that is, pasture and supplements offered, and cow's genetic merit on herbage dry matter intake, milk yield and changes in body condition score and live weight...

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Organic farming is believed by many to be an environmentally friendly production system that promotes the use of local forage while strongly limiting the input of chemicals, including allopathic treatments. As organic dairy farming has grown, farmers have realised that many available conventional breeds of cow are not well adapted to the new situations and that more ‘robust’ cows, able to function well in the constraining organic environment, are needed to yield acceptable longevity and productivity. In this review paper, the current breed diversity in organic dairy farming is analysed with the aim of identifying the types of cow that would best fulfil organic breeding goals. Unlike the conventional sector, organic dairy farming is very heterogeneous and no single type of cow can adapt well to all scenarios. There are advantages and disadvantages to the use of existing breeds (rustic Holstein-Friesian, other rustic breeds and crosses), and strong genotype × environment interactions demand different strategies for very diverse situations. Organic dairy farms producing milk for systems that recompense milk volume would benefit from using higher milk yielding cows, and rustic Holstein-Friesian cows may be the best option in such cases. Although most Holstein-Friesian cows are currently selected for use in conventional systems, this situation could be reversed by the implementation of an organic merit index that includes organic breeding goals. Farms producing milk either for systems that recompense milk solids or for transformation into dairy products would benefit from using breeds other than Holstein-Friesian or their crosses. Organic farmers who focus on rural tourism, farm schools or other businesses in which marketing strategies must be taken into account could benefit from using local breeds (when possible) or other rustic breeds that are highly valued by consumers.

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...

Enhancing pasture persistence is crucial to achieve more sustainable grass-based animal production systems. Although it is known that persistence of perennial ryegrass is based on a high turnover of tillers during late spring and summer, little is known about other forage species, particularly in subtropical climates. To address this question, this study evaluated survival of grazed tall fescue tillers growing in a subtropical climate. We hypothesized that hard tactical grazing during winter to remove reproductive stems (designated as 'flowering control'), and nitrogen fertilization in spring, would both improve tiller survival over summer, and thus enhance tiller density. This was assessed in two experiments. In both experiments, few tillers appeared during late spring and summer and so tiller density depended on the dynamics of vegetative tillers present in the sward in spring. In Experiment 2, flowering control and nitrogen fertilization both enhanced the survival of that critical tiller cohort, but the effects were not additive. Responses were similar but not statistically significant in Experiment 1, which had a warmer, drier summer and lower overall survival rates. Unlike grasses in temperate environments, persistence of tall fescue in this subtropical site appeared to follow a 'vegetative pathway'; i.e., new tillers were produced largely in autumn, from vegetative tillers that survived the summer.