A stochastic budgetary simulation model of a dairy farm was developed to allow investigation of the effects of varying biological, technical, and physical processes on farm profitability. The model integrates animal inventory and valuation, milk supply, feed requirement, land and labor utilization, and economic analysis. A key model output is the estimated distribution of farm profitability, which is a function of total receipts from milk, calves, and cull cows less all variable and fixed costs (including an imputed cost for labor). An application of the model was demonstrated by modeling 2 calving patterns: a mean calving date of February 24 (S1) and a mean calving date of January 27 (S2). Monte Carlo simulation was used to determine the influence of variation in milk price, concentrate cost, and silage quality on farm profitability under each scenario. Model validation was conducted by comparing the results from the model against data collected from 21 commercial dairy farms. The net farm profit with S1 was 53,547 euros, and that with S2 was 51,687 euros; the annual EU milk quota was 468,000 kg, and farm size was 40 ha. Monte Carlo simulation showed that the S1 scenario was stochastically dominant over the S2 scenario. Sensitivity analyses showed that farm profit was most sensitive to changes in milk price. The partial coefficients of determination were 99.2, 0.7, and 0.1% for milk price, concentrate cost, and silage quality, respectively, in S1; the corresponding values in S2 were 97.6, 2.3, and 0.1%. Validations of the model showed that it could be used with confidence to study systems of milk production under Irish conditions.

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The global dairy industry needs to reappraise the systems of milk production that are operated at farm level with specific focus on enhancing technical efficiency and competitiveness of the sector. The objective of this study was to quantify the factors associated with costs of production, profitability, and pasture use, and the effects of pasture use on financial performance of dairy farms using an internationally recognized representative database over an 8-yr period (2008 to 2015) on pasture-based systems. To examine the associated effects of several farm system and management variables on specific performance measures, a series of multiple regression models were developed. Factors evaluated included pasture use [kg of dry matter/ha and stocking rate (livestock units/ha)], grazing season length, breeding season length, milk recording, herd size, dairy farm size (ha), farmer age, discussion group membership, proportion of purchased feed, protein %, fat %, kg of milk fat and protein per cow, kg of milk fat and protein per hectare, and capital investment in machinery, livestock, and buildings. Multiple regression analysis demonstrated costs of production per hectare differed by year, geographical location, soil type, level of pasture use, proportion of purchased feed, protein %, kg of fat and protein per cow, dairy farm size, breeding season length, and capital investment in machinery, livestock, and buildings per cow. The results of the analysis revealed that farm net profit per hectare was associated with pasture use per hectare, year, location, soil type, grazing season length, proportion of purchased feed, protein %, kg of fat and protein per cow, dairy farm size, and capital investment in machinery and buildings per cow. Pasture use per hectare was associated with year, location, soil type, stocking rate, dairy farm size, fat %, protein %, kg of fat and protein per cow, farmer age, capital investment in machinery and buildings per cow, breeding season length, and discussion group membership. On average, over the 8-yr period, each additional tonne of pasture dry matter used increased gross profit by €278 and net profit by €173 on dairy farms. Conversely, a 10% increase in the proportion of purchased feed in the diet resulted in a reduction in net profit per hectare by €97 and net profit by €207 per tonne of fat and protein. Results from this study, albeit in a quota limited environment, have demonstrated that the profitability of pasture-based dairy systems is significantly associated with the proportion of pasture used at the farm level, being cognizant of the levels of purchased feed.

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The objective of this study was to compare the biological and economic efficiency of a seasonal pasturebased spring calving system of milk production on a high-rainfall, heavy-clay soil [Kilmaley (KMY)] to that on a lower-rainfall free-draining soil [Moorepark (MPN)] in Ireland. The physical performance data were obtained from a 3-year study (1998)(1999)(2000) carried out at both sites. Analysis of the system of milk production at the two sites was undertaken using the Moorepark Dairy System Model. Herbage dry-matter production was greater at the MPN site with a greater proportion being produced between 1 September and 1 May. On average, over the 3 years, the system of milk production at the MPN site had a higher stocking rate (2AE34 vs. 1AE89 cows ha )1 ), higher milk production per cow (6421 vs. 5781 kg per cow), longer grazing season (250 vs. 149 d) and a higher proportion of the diet of the herd from grazed grass (0AE70 vs. 0AE40) than at KMY. Economic analysis showed that, in a 468 100 kg European Union milk quota scenario, the profitability at the MPN site was 28 417 greater than at the KMY site. At similar milk production per cow it was 19 138 greater. Monte Carlo simulation showed that the MPN site was stochastically dominant over the KMY site. Sensitivity analyses showed that farm profit was most sensitive to changes in milk price. The results also indicated that milk production in the future may not be sustainable economically on high-rainfall, heavy-clay soils in Ireland.

This study explores drivers and resulting changes in the structure and technical efficiency of Irish dairy farms from 2005 to 2018 (covering pre‐ and post‐milk quotas) during which milk production increased by 54%. Over this period, farm structure changed dramatically (fourfold increase in farmers milking >100 cows) and farmers improved technical efficiency and profitability and reduced the greenhouse gas emission intensity of milk produced. The impact of the adoption of key technologies at farm level and the contribution of strategic direction nationally (the Irish Government's Food Harvest 2020 Strategy) influencing this development are explored as are future sector challenges.

SUMMARYAn agro-economic simulation model was developed to facilitate comparison of the impact of management, market and biological factors on the cost of providing ruminant livestock with feed grown on the farm (home produced feed). Unpredictable year-to-year variation in crop yields and input prices were identified as quantifiable measures of risk affecting feed cost. Stochastic analysis was used to study the impact of yield and input price risk on the variability of feed cost for eight feeds grown in Ireland over a 10-year period. Intensively grazed perennial ryegrass was found to be the lowest cost feed in the current analysis (mean cost E74/1000 Unité Fourragère Viande (UFV)). Yield risk was identified as the greatest single factor affecting feed cost variability. At mean prices and yields, purchased rolled barley was found to be 3% less costly than home-produced spring-sown barley. However, home-produced spring barley was marginally less risky than purchased barley (coefficient of variation (CV) 0·063 v. 0·064). Feed crops incurring the greatest proportion of fixed costs and area-dependent variable costs, including bunker grass silage, were the most sensitive to yield fluctuations. The most energy inputintensive feed crops, such as grass silage, both baled and bunker ensiled, were deemed most susceptible to input price fluctuations. Maize silage was the most risky feed crop (CV 0·195), with potential to be both the cheapest and the most expensive conserved feed.

The purpose of this study was to model the effect of 3 divergent strains of Holstein-Friesian cows in 3 pasture-based feed systems on greenhouse gas (GHG) emissions. The 3 strains of Holstein-Friesian compared were high-production North American (HP), high-durability North American (HD), and New Zealand (NZ). The 3 feed systems were a high grass allowance system (MP, control); high stocking rate system (HS); and high concentrate supplementation system (HC). The MP system had an overall stocking rate of 2.47 cows/ha and received 325 kg of dry matter concentrate per cow in early lactation. The HS system had a similar concentrate input to the MP system, but had an overall stocking rate of 2.74 cows/ha. The HC system had a similar overall stocking rate to the MP system, but 1,445 kg of dry matter concentrate was offered per cow. A newly developed integrated economic-GHG farm model was used to evaluate the 9 milk production systems. The GHG model estimates on-farm (emissions arising within the farm's physical boundaries) and production system (incorporating all emissions associated with the production system up to the point milk leaves the farm gate) GHG emissions. Production system GHG emissions were always greater than on-farm emissions, and the ranking of the 9 systems was usually consistent under both methods. The exception was the NZ strain that achieved their lowest GHG emission per unit of product in the HC system when indirect emissions were excluded, but their lowest emission was in the HS system when indirect emissions were included. Generally, the results showed that as cow strain changed from lower (HD and NZ) to higher genetic potential (HP) for milk production, the GHG emission per kilogram of milk solids increased. This was because of a decline in cow fertility in the HP strain that resulted in a higher number of nonproductive animals, leading to a lower total farm milk solids production and an increase in emissions from nonproductive animals. The GHG emission per hectare increased for all strains moving from MP to HS to HC feed systems and this was associated with increases in herd total feed intake. The most profitable combination was the NZ strain in the HS system and this combination resulted in a 12% reduction in production system GHG emission per hectare compared with the NZ strain in the HC system, which produced the highest emissions. This demonstrates that grass-based systems can achieve high profitability and decreased GHG emissions simultaneously.