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Lameness in dairy cows is a clinical sign resulting from multiple diseases that affects animal welfare. The aim of this study was to evaluate the consistency of lameness prevalence estimations between farm managers and locomotion scoring conducted by trained observers, in confined and grazing dairy systems. The study was conducted on 18 dairy farms in Argentina. The locomotion of the lactating cows was scored by trained observers using a four-point visual scale from 0 to 3. Farm managers were interviewed about the number of lame cows in the herd. The consistency of lameness prevalence detected by the farm manager and the observers was assessed by computing the Lin's concordance and correlation coefficient. The comparison of grazing systems versus confined systems on lameness prevalence was analyzed using a generalized mixed model, assuming a binomial distribution for the errors. On average, farm managers estimated a lower prevalence of lameness (p < 0.01) compared with the trained observers; 2.24% and 7.06%, respectively. Based on the estimations from trained observers, we could not detect differences (p = 0.19) in lameness prevalence between confined and grazing systems.

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Milking cows once a day (OAD) is a herd management practice that may help to reduce working effort and labour demand in dairy farms. However, a decrease in milk yield per cow occurs in OAD systems compared with twice a day (TAD) systems and this may affect profitability of dairy systems. The objective of this study was to assess productive and economic impact and risk of reducing milking frequency from TAD to OAD for grazing dairy systems, using a whole-farm model. Five scenarios were evaluated by deterministic and stochastic simulations: one scenario under TAD milking (TADAR) and four scenarios under OAD milking. The OAD scenarios assumed that milk yield per cow decreased by 30% (OAD30), 24% (OAD24), 19% (OAD19) and 10% (OAD10), compared with TADAR scenario, based on experimental and commercial farms data. Stocking rate (SR) was increased in all OAD scenarios compared to TADAR and two levels of reduction in labour cost were tested, namely 15% and 30%. Milk and concentrate feeds prices, and pasture and crop yields, were allowed to behave stochastically to account for market and climate variations, respectively, to perform risk analyses. Scenario OAD10 showed similar milk yield per ha compared with TADAR, as the increased SR compensated for the reduction in milk yield per cow. For scenarios OAD30, OAD24 and OAD19 the greater number of cows per ha partially compensated for the reduction of milk yield per cow and milk yield per ha decreased 21%, 15% and 10%, respectively, compared with TADAR. Farm operating profit per ha per year also decreased in all OAD scenarios compared with TADAR, and were US$684, US$161, US$ 303, US$424 and US$598 for TADAR, OAD30, OAD24, OAD19, OAD10, respectively, when labour cost was reduced 15% in OAD scenarios. When labour cost was reduced 30% in OAD scenarios, only OAD10 showed higher profit (US$706) than TADAR. Stochastic simulations showed that exposure to risk would be higher in OAD scenarios compared with TADAR. Results showed that OAD milking systems might be an attractive alternative for farmers who can either afford a reduction in profit to gain better and more flexible working conditions or can minimise milk yield loss and greatly reduce labour cost.

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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.

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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®. 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.

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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 CO2 equivalents (CO2eq) to 0.89 kg CO2eq for the 6000 and 10 000 kg yielding dairy cow, respectively. However, emissions per kg of beef increased from 10.75 kg CO2eq to 16.24 kg CO2eq 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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