e-Cow: an animal model that predicts herbage intake, milk yield and live weight change in dairy cows grazing temperate pastures, with and without supplementary feeding

This animal simulation model, named e-Cow, represents a single dairy cow at grazing. The model integrates algorithms from threepreviously published models: a model that predicts herbage dry matter (DM) intake by grazing dairy cows, a mammary gland model thatpredicts 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 herbageallowance (HA; kg DM offered/cow per day), metabolisable energy and NDF concentrations in herbage and supplements, supplementsoffered (kg DM/cow per day), type of pasture (ryegrass or lucerne), days in milk, days pregnant, lactation number, BCS and LW atcalving, breed or strain of cow and genetic merit, that is, potential yields of milk, fat and protein. Separate equations are used to predictherbage intake, depending on the cutting heights at which HA is expressed. The e-Cow model is written in Visual Basic programminglanguage within Microsoft Excel R . The model predicts whole-lactation performance of dairy cows on a daily basis, and the main outputsare the daily and annual DM intake, milk yield and changes in BCS and LW. In the e-Cow model, neither herbage DM intake nor milkyield or LW change are needed as inputs; instead, they are predicted by the e-Cow model. The e-Cow model was validated againstexperimental data for Holstein–Friesian cows with both North American (NA) and New Zealand (NZ) genetics grazing ryegrass-basedpastures, with or without supplementary feeding and for three complete lactations, divided into weekly periods. The model was able topredict animal performance with satisfactory accuracy, with concordance correlation coefficients of 0.81, 0.76 and 0.62 for herbage DMintake, milk yield and LW change, respectively. Simulations performed with the model showed that it is sensitive to genotype by feedingenvironment interactions. The e-Cow model tended to overestimate the milk yield of NA genotype cows at low milk yields, while itunderestimated the milk yield of NZ genotype cows at high milk yields. The approach used to define the potential milk yield of the cowand equations used to predict herbage DM intake make the model applicable for predictions in countries with temperate pasture