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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 × 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.

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®. 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.

The somatotropic axis [including growth hormone (GH), GH receptor, and insulin-like growth factor (IGF)-I] is uncoupled in high-producing cows in early lactation so that the liver fails to respond to GH and produces less IGF-I. This uncoupling was implicated in the process of nutrient partitioning, enabling high milk production. Different genetic selection goals may affect functional components of the somatotropic axis. Thus, the somatotropic axis was examined in diverse genetic strains of dairy cows [North American Holstein 1990 (NA90), New Zealand Holstein-Friesian 1990 (NZ90), and New Zealand Holstein-Friesian 1970 (NZ70)] that were managed similarly within a pasture-based system but were offered feed allowances commensurate with their genetic ability to produce milk. The NA90 cows produced more milk (26.2 +/- 0.3, 24.1 +/- 0.3, and 20.1 +/- 0.4 kg/d, for NA90, NZ90, and NZ70, respectively), but had lower milk fat percentages (4.28 +/- 0.03, 4.69 +/- 0.03, and 4.58 +/- 0.04 kg/d for NA90, NZ90, and NZ70, respectively) compared with both NZ strains. Milk protein percentages (3.38 +/- 0.02, 3.52 +/- 0.02, and 3.29 +/- 0.03 kg/d for NA90, NZ90, and NZ70, respectively) were greater for NZ90 cows. During early lactation (wk 2 to 6), the total net energy produced in milk was greater in NA90 compared with NZ90 or NZ70 cows, but total net energy in milk after wk 6 was equivalent for NA90 and NZ90 cows. The greater milk production in early lactation in NA90 cows was associated with lower body condition scores (BCS; 1 to 10 scale; 4.0 +/- 0.1) elevated blood GH concentrations (1.6 +/- 0.1 ng/mL), and low blood IGF-I concentrations (14.8 +/- 1.1 ng/mL), indicating an uncoupled somatotropic axis. In comparison, the NZ70 cows retained a coupled somatotropic axis during early lactation, maintaining greater BCS (4.6 +/- 0.1), lower blood GH (0.7 +/- 0.1 ng/mL), and greater blood IGF-I (21.9 +/- 1.2 ng/mL). The degree of uncoupling in NZ90 cows was intermediate between the other 2 strains. Additional feed allowance failed to change blood IGF-I concentrations in NA90 cows but increased IGF-I concentrations in NZ90 cows (20.9 +/- 1.4 and 13.2 +/- 1.4 ng/mL for the high and low feed allowance, respectively). Furthermore, additional feed allowance in NZ90 cows lessened BCS loss in early lactation, but did not affect BCS loss in NA90 cows. Functional components of the somatotropic axis differed for the respective strains and were consistent with strain differences in milk production, BCS, and feed allowance.

Effects of milking cows once daily throughout lactation at high stocking rates (17% more cows/ha than for those milked twice daily) were studied in 2 Friesian and 2 Jersey herds during 3 lactations. Cows were allocated to 2 herds within breed and were milked either once or twice daily, based on age, genetic merit, and previous performance. Cows remained in their original herd and were milked at the same milking frequency during all lactations. Culled cows (20% per year) were replaced by 2-yr-old heifers. Yields of milk, lactose, protein, and fat were measured every 2 wk by commercial herd test. Cubic splines (5 knots) were used to approximate the lactation curve for each cow-yr to provide estimates of performance for each day of lactation. Yields of milk were greater for Friesian and Jersey cows milked twice daily (4,751 +/- 89 and 3,067 +/- 81 kg/cow) than for cows milked once daily (3,329 +/- 80 and 2,431 +/- 75 kg/cow), respectively. Cows milked once daily had lesser total and peak yields of milk, lactose, protein, and fat than cows milked twice daily. Friesians had greater total and peak yields than Jerseys. Peak production for all milk components occurred earlier in lactation for cows milked once daily than twice daily (d 24 to 39 vs. 32 to 44). Three measures of persistency of lactation were considered for each cow with 2 measures (Pers1 and Pers2) indicating that cows milked twice daily had better persistency than those milked once daily. Ranking of herds in persistency tended to match the ranking based on total yields. Measures of persistency (Pers1 and Pers2) were positively related to total yield in the Jerseys milked once daily and negatively related to peak yield in the Friesians. The third persistency measure (Pers3) ranked once-daily Jerseys first and twice-daily Friesians last, and was negatively correlated with total yield in the Friesian herds and negatively correlated with peak yield in all herds. For most performance measures, cows milked twice daily had better total yields and persistency than cows milked once daily.

The aim of this study was to evaluate the profitability of dairy herds under three mating systems involving the Holstein-Friesian, Jersey, and Ayrshire breeds. Mating systems were straight breeding and rotational cross-breeding using two or three breeds. A deterministic model was developed to simulate the nutritional, biological, and economic performance of dairy herds under New Zealand conditions. Expected performances per cow were obtained using estimates of breed group and heterosis effects, age effects, and age distribution in the herd. Requirements for dry matter in feed were estimated per cow for maintenance, lactation, pregnancy, and growth of the replacements. Stocking rate was calculated by assuming 12,000 kg of dry matter utilized annually per hectare. Productivity per hectare was calculated as performance per cow multiplied by stocking rate. Profitability was the difference between income (sale of milk and salvage value of animals) and costs (related to the number of cows in the herd and the land area farmed). Under current market values for milk and meat, all of the rotational crossbred herds showed superior profitability to the straightbred herds (Holstein-Friesian x Jersey, NZ$505/ha; Holstein-Friesian x Jersey x Ayrshire NZ$493/ha; Jersey x Ayrshire, NZ$466/ha; Holstein-Friesian x Ayrshire, NZ$430/ha; Jersey, NZ$430/ha; Holstein-Friesian, NZ$398/ha; and Ayrshire, NZ$338/ha). Changes in the value for fat relative to protein affected profitability more significantly in herds using the Jersey breed, and changes in the value for meat affected profitabiity more significantly in herds using the Holstein-Friesian and Ayrshire breeds. Results suggested that, under New Zealand conditions, the use of rotational crossbreeding systems could increase profitability of dairy herds under the conceivable market conditions.

The effects of selection and crossbreeding on the New Zealand dairy industry net income were evaluated with a deterministic model over a 25 yr planning horizon. Several mating strategies involving Holstein-Friesian, Jersey and Ayrshire cattle were evaluated. These strategies were straight breeding, upgrading to Holstein-Friesian, upgrading to Jersey, upgrading to Ayrshire, use of the best bulls irrespective of breed and two- and three-breed rotational crossbreeding. Industry productions of milk, fat, protein, and lactose were calculated assuming that 12,000 kg of dry matter per hectare was utilized from 1,224,911 hectares of pasture. Profitability was the difference between income (international sale of whole milk powder, casein, butter, and beef from salvage animals) and costs (farm expenses, milk collection, manufacture, and marketing). Casein and whole milk powder were valued at NZ$8.345 and NZ$3.306/ per kilogram, respectively, over 25 yr. Butter was valued at NZ$2.995/kilogram for base year production levels and NZ$0.45/kilogram for marginal increases in production. Upgrading to Holstein-Friesian resulted in the highest industry net income (NZ$1119 million) followed by straight breeding (NZ$1086 million) and two-breed rotational Holstein-Friesian x Jersey (NZ$1076 million). However, if the marginal value of extra butter production was assumed equal to the average base value, then upgrading to Jersey resulted in the highest industry net income (NZ$1185 million) followed by two-breed rotational Holstein-Friesian x Jersey (NZ$1177 million) and use of the best bulls (NZ$1173 million). Future costs and prices of dairy products have major impact on mating strategies.

A deterministic model was developed to evaluate the concurrent effects of selection and crossbreeding on the rate of genetic gain and productivity of New Zealand dairy cattle over 25 yr. Selection was based on an index, which included estimated breeding values for mature cow live weight and lactation yields of milk, fat, and protein. Mating strategies involving Holstein-Friesian, Jersey, and Ayrshire breeds were evaluated. Effects of heterosis and age were included to calculate live weight and yields of milk, fat, and protein per cow. Feed requirements were estimated for maintenance, lactation, and pregnancy and for replacement heifers. Stocking rate was calculated by assuming 12,000 kg of dry matter annually utilized per hectare. Upgrading to either Jersey or Holstein-Friesian increased the number of potential bull mothers and resulted in genetic gains of 0.27 genetic standard deviations/yr for both options. Rotational crossbreeding of Holstein-Friesian x Jersey decreased the number of potential bull mothers and resulted in a genetic gain of 0.24 genetic standard deviations/yr. Upgrading to Jersey resulted in the least increase in milk (5%) and the greatest increase in fat (16%) and protein (27%) per hectare with a small decrease in stocking rate (0.4%). Upgrading to Holstein-Friesian reduced the stocking rate by 11% and increased production of milk, fat, and protein per hectare by 10, 8, and 21%, respectively. Rotational crossbreeding of Holstein-Friesian x Jersey resulted in greater production per hectare than the intermediate production between upgrading to Jersey and upgrading to Holstein-Friesian. Crossbreeding can be used in combination with selection to exploit the effects of heterosis while maintaining genetic diversity to cover changes in market conditions.