L. Shalloo

The influence of strain of Holstein-Friesian cow and feeding system on greenhouse gas emissions from pastoral dairy farms

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 farms 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.

Stochastic simulation of the cost of home-produced feeds for ruminant livestock systems

(Received 3 November 2010; revised 1 June 2011; accepted 23 June 2011; first published online 22 July 2011)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 (homeproduced feed). Unpredictable year-to-year variation in crop yields and input prices were identified asquantifiablemeasuresofriskaffectingfeedcost.Stochasticanalysiswasusedtostudytheimpactofyieldandinputprice risk on the variability of feed cost for eight feeds grown in Ireland over a 10-year period. Intensively grazedperennial ryegrass was found to be the lowest cost feed in the current analysis (mean cost E74/1000 UniteFourragere Viande (UFV)). Yield risk was identified as the greatest single factor affecting feed cost variability. Atmeanpricesandyields,purchasedrolledbarleywasfoundtobe3%lesscostlythanhome-producedspring-sownbarley. However, home-produced spring barley was marginally less risky than purchased barley (coefficient ofvariation (CV) 0·063 v. 0·064). Feed crops incurring the greatest proportion of fixed costs and area-dependentvariable costs,including bunkergrass silage, were the mostsensitive to yield fluctuations. The mostenergy input-intensive feed crops, such as grass silage, both baled and bunker ensiled, were deemed most susceptible to inputpricefluctuations.Maizesilagewasthemostriskyfeedcrop(CV0·195),withpotentialtobeboththecheapestandthe most expensive conserved feed.INTRODUCTIONForlivestockfarmers,oneofthemostimportantgroupsof management decisions is that relating to feedprovision. McCall & Clark (1999) identified feed costas the primary issue determining the choice of dairysystem in North Eastern USA and New Zealand, whilein Australia Archer et al. (1999) described feed cost asthe greatest input cost group in any animal productionsystem. Feed cost accounts for 0·70–0·75 of all vari-able costs incurred on Irish cattle and sheep farms(Connollyetal.2010).Furthermore,fixedcostsassoci-atedwithfeedproductionandutilization,suchassilos,fencing, buildings and machinery, are an additionalconsideration when costing alternative feeds (Fluck PShallooetal.2004;Belascoetal.2009;Finneranetal.2010b).Computer models have been extensively usedto model the interactions between biological andmanagement variables influencing crop production(McCown et al. 1996; Shaffer et al. 2000; Jones et al.2003; Dobos et al. 2004). Fewer studies used models

A case study of the carbon footprint of milk from high-performing confinement and grass-based dairy farms

Life-cycle assessment (LCA) is the preferred methodology to assess carbon footprint per unit of milk. The objective of this case study was to apply an LCA method to compare carbon footprints of high-performance confinement and grass-based dairy farms. Physical performance data from research herds were used to quantify carbon footprints of a high-performance Irish grass-based dairy system and a top-performing United Kingdom (UK) confinement dairy system. For the US confinement dairy system, data from the top 5% of herds of a national database were used. Life-cycle assessment was applied using the same dairy farm greenhouse gas (GHG) model for all dairy systems. The model estimated all on- and off-farm GHG sources associated with dairy production until milk is sold from the farm in kilograms of carbon dioxide equivalents (CO2-eq) and allocated emissions between milk and meat. The carbon footprint of milk was calculated by expressing GHG emissions attributed to milk per tonne of energy-corrected milk (ECM). The comparison showed that when GHG emissions were only attributed to milk, the carbon footprint of milk from the Irish grass-based system (837 kg of CO2-eq/t of ECM) was 5% lower than the UK confinement system (884 kg of CO2-eq/t of ECM) and 7% lower than the US confinement system (898 kg of CO2-eq/t of ECM). However, without grassland carbon sequestration, the grass-based and confinement dairy systems had similar carbon footprints per tonne of ECM. Emission algorithms and allocation of GHG emissions between milk and meat also affected the relative difference and order of dairy system carbon footprints. For instance, depending on the method chosen to allocate emissions between milk and meat, the relative difference between the carbon footprints of grass-based and confinement dairy systems varied by 3 to 22%. This indicates that further harmonization of several aspects of the LCA methodology is required to compare carbon footprints of contrasting dairy systems. In comparison to recent reports that assess the carbon footprint of milk from average Irish, UK, and US dairy systems, this case study indicates that top-performing herds of the respective nations have carbon footprints 27 to 32% lower than average dairy systems. Although differences between studies are partly explained by methodological inconsistency, the comparison suggests that potential exists to reduce the carbon footprint of milk in each of the nations by implementing practices that improve productivity.

Development and application of an economic ranking index for perennial ryegrass cultivars

Economic values in euros (€) were calculated for traits of economic importance in Irish grass-based systems. The economically important traits selected were spring, midseason, and autumn grass dry matter (DM) yield (€/kg of DM per ha), grass quality (€ per unit DM digestibility), first- and second-cut silage DM yield (€/kg per ha), and sward persistency (€/% change in persistency per ha per yr). The economic value for each trait was calculated by changing the trait of interest while keeping all other traits constant, using the Moorepark Dairy Systems Model. Herd parameters (including cow numbers and calving pattern), milk production, energy demand, supplementary feeds, and land area were readjusted to calculate the economic value for the trait of interest. The base scenario assumed fixed cow numbers with 40 ha of land available, with full costs included. The results for the base scenario show the economic values were: €0.15/kg of DM spring yield, €0.03/kg of midseason yield, €0.10/kg of DM autumn yield, the quality value was €0.001, €0.008, €0.010, €0.009, €0.008, and €0.006 per unit change in DM digestibility/kg of DM yield for the months of April, May, June, July, August, and September, respectively; €0.036/kg of DM first-cut silage; €0.024/kg of DM second-cut silage; and -€4.961 per 1% decrease in persistency/ha per yr. Sensitivity of the economic values to changes in milk price and scenario were tested. The economic values were applied to experimental production data collected over 3 yr for 20 perennial ryegrass cultivars to establish the total economic merit for each cultivar and then to rank each cultivar based on its economic performance. Rank correlations between the base and alternative scenarios ranged from 0.90 to unity. This indicates that the economic values are reliable regardless of system, intensity, or price. The total merit index will identify the cultivars that can make the greatest economic contribution to a grass-based production system.

Development and application of a processing model for the Irish dairy industry

A processing-sector model was developed that simulates (i) milk collection, (ii) standardization, and (iii) product manufacture. The model estimates the product yield, net milk value, and component values of milk based on milk quantity, composition, product portfolio, and product values. Product specifications of cheese, butter, skim and whole milk powders, liquid milk, and casein are met through milk separation followed by reconstitution in appropriate proportions. Excess cream or skim milk are used in other product manufacture. Volume-related costs, including milk collection, standardization, and processing costs, and product-related costs, including processing costs per tonne, packaging, storage, distribution, and marketing, are quantified. Operating costs, incurred irrespective of milk received and processing activities, are included in the model on a fixed-rate basis. The net milk value is estimated as sale value less total costs. The component values of fat and protein were estimated from net milk value using the marginal rate of technical substitution. Two product portfolio scenarios were examined: scenario 1 was representative of the Irish product mix in 2000, in which 27, 39, 13, and 21% of the milk pool was processed into cheese (€ 3,291.33/t), butter (€ 2,766.33/t), whole milk powder (€ 2,453.33/t), and skim milk powder (€ 2,017.00/t), respectively, and scenario 2 was representative of the 2008 product mix, in which 43, 30, 14, and 13% was processed into cheese, butter, whole milk powder, and skim milk powder, respectively, and sold at the same market prices. Within both scenarios 3 milk compositions were considered, which were representative of (i) typical Irish Holstein-Friesian, (ii) Jersey, and (iii) the New Zealand strain of Holstein-Friesian, each of which had differing milk constituents. The effect each milk composition had on product yield, processing costs, total revenue, component values of milk, and the net value of milk was examined. The value per liter of milk in scenario 1 was 24.8, 30.8, and 27.4 cents for Irish Holstein-Friesian, Jersey, and New Zealand strain of Holstein-Friesian milk, respectively. In scenario 2 the value per liter of milk was 26.1, 32.6, and 28.9 cents for Irish Holstein-Friesian, Jersey, and New Zealand strain of Holstein-Friesian milk, respectively.

Invited review: The economic impact and control of paratuberculosis in cattle

Paratuberculosis (also called Johnes disease) is a chronic disease caused by Mycobacterium avium ssp. paratuberculosis (MAP) that affects ruminants and other animals. The epidemiology of paratuberculosis is complex and the clinical manifestations and economic impact of the disease in cattle can be variable depending on factors such as herd management, age, infection dose, and disease prevalence, among others. Additionally, considerable challenges are faced in the control of paratuberculosis in cattle, such as the lack of accurate and reliable diagnostic tests. Nevertheless, efforts are directed toward the control of this disease because it can cause substantial economic losses to the cattle industry mainly due to increased premature culling, replacement costs, decreased milk yield, reduced feed conversion efficiency, fertility problems, reduced slaughter values, and increased susceptibility to other diseases or conditions. The variability and uncertainty surrounding the estimations of paratuberculosis prevalence and impact influence the design, implementation, and efficiency of control programs in diverse areas of the world. This review covers important aspects of the economic impact and control of paratuberculosis, including challenges related to disease detection, estimations of the prevalence and economic effects of the disease, and the implementation of control programs. The control of paratuberculosis can improve animal health and welfare, increase productivity, reduce potential market problems, and increase overall business profitability. The benefits that can derive from the control of paratuberculosis need to be communicated to all industry stakeholders to promote the implementation of control programs. Moreover, if the suspected link between Johnes disease in ruminants and Crohns disease in humans was established, significant economic losses could be expected, particularly for the dairy industry, making the control of this disease a priority across dairy industries internationally.

The economic impact of cow genetic potential for milk production and concentrate supplementation level on the profitability of pasture based systems under different EU milk quota scenarios

A 3-year study was set up to evaluate the influence of cow genetic potential for milk production and concentrate supplementation level on profitability of pasture based systems of milk production. In each of the 3 years, 96 cows were used in a three (genotype) x 3 (levels of concentrate supplementation) randomized block design. Cows were categorized based on their pedigree index (PD) for milk production (PDMILK) into low (LP; PDMILK less than 100 kg), medium (MP; PDMILK 100-200 kg) and high (HP; PDMILK 200-300 kg). Concentrate supplementation levels were 376, 810 and 1540 kg per cow per lactation, identified as low (LC), medium (MC) and high (HC) concentrate respectively. Three milk production scenarios were investigated using the Moorepark Dairy Systems Model (MDSM) which included: EU milk quota applied at farm level with current costs and prices (Sl), EU quota applied at farm level with projected future costs and prices (S2), and EU milk quota applied at industry level (quota purchasing possible) with projected future costs and prices (S3). The effect of variation in milk price, concentrate price and opportunity cost of land were modelled using stochastic budgeting. The results suggest that where EU milk quota is applied at farm level (S1 and S2), the optimum system of milk production is where margin per unit of output is maximized. When milk quota is applied at industry level (S3) the optimum system will be where margin per cow will be maximized. The results also suggest that the optimum system for cows with lower genetic potential for milk production is low level of concentrate supplementation, while cows with higher genetic potential for milk production is high level of concentrate supplementation.

A model of nitrogen efficiency in contrasting grass-based dairy systems.

Nitrogen (N) efficiency is one of the key drivers of environmentally and economically sustainable agricultural production systems. An N balance model was developed, evaluated, and validated to assess N use efficiency and N surplus and to predict N losses from contrasting grass-based dairy production systems in Ireland. Data from a 5-yr study were used to evaluate and validate the model. Grass-based and high-concentrate production systems combined with 3 divergent strains of Holstein-Friesian (HF) dairy cows-high-production North American (HP), high-durability North American (HD), and New Zealand (NZ)-were evaluated. As concentrate input increased, N surplus per hectare increased and N use efficiency per hectare decreased (23 and 10%, respectively). When the N required to rear replacement animals to maintain the production system was considered, the N surplus of the HP genetic strain was greater (156 kg of N/cow) than that of the HD (140 kg of N/cow) or the NZ (128 kg of N/cow). The model estimated N leaching of 8.1mg of NO(3)-N/L, similar to that measured by others at the same site. The model creates awareness of methods and indicators available to assess the most suitable and environmentally sustainable grass based dairy production systems.

Effect of fertility on the economics of pasture-based dairy systems

There are significant costs associated with reproductive inefficiency in pasture-based dairy herds. This study has quantified the economic effect of a number of key variables associated with reproductive inefficiency in a dairy herd and related them to 6-week calving rate for both cows and heifers. These variables include: increased culling costs, the effects of sub optimum calving dates, increased labour costs and increased artificial insemination (AI) and intervention costs. The Moorepark Dairy Systems Model which is a stochastic budgetary simulation model was used to simulate the overall economic effect at farm level. The effect of change in each of the components was simulated in the model and the costs associated with each component was quantified. An analysis of national data across a 4-year period using the Irish Cattle Breeding Federation database was used to quantify the relationship between the 6-week calving rate of a herd with survivability (%), calving interval (days) and the level of AI usage. The costs associated with increased culling (%), calving date slippage (day), increased AI and intervention costs (0.1 additional inseminations), as well as, increased labour costs (10%) were quantified as €13.68, €3.86, €4.56 and €29.6/cow per year. There was a statistically significant association between the 6-week calving rate and survivability, calving interval and AI usage at farm level. A 1% change in 6-week calving rate was associated with €9.26/cow per annum for cows and €3.51/heifer per annum for heifers. This study does not include the indirect costs such as reduced potential for expansion, increased costs associated with failing to maintain a closed herd as well as the unrealised potential within the herd.

Extended lactations in a seasonal-calving pastoral system of production to modulate the effects of reproductive failure

This study was conducted to determine whether extending the calving interval (CI) to 24 mo would be an alternative to culling and replacing cows that had failed to become pregnant. Forty-six nonpregnant lactating cows were assembled in November 2004 and assigned to receive either 3kg (low) or 6kg (high) of concentrate supplement and a basal diet of grass silage and maize silage over the winter period (13 wk). Cows returned to pasture in late March and received 1kg of concentrate/d until dry-off (milk yield <5 kg/d). Cumulative milk production was calculated from calving to the end of November 2004 (12-mo CI) and from the start of December 2004 until dry off in 2005 (extended lactation part of 24-mo CI). High winter feeding resulted in greater milk production over the winter confinement (20.0+/-0.3 vs. 17.8+/-0.3 kg/d for high and low winter feeding, respectively) and had a carryover effect during the remainder of the 24-mo CI period (5,177 vs. 4,686kg; SEM=173kg). At the end of the study, cows were ranked on cumulative milk solids and separated into 3 groups (R1, R2, and R3). During the 24-mo CI, milk yields were 7,287, 6,267, and 5,273kg (SEM=308kg) in yr 1, and 5,738, 4,836, and 4,266 (SEM=241kg) in yr 2 for R1, R2, and R3, respectively. Eighty-five percent of the cows became pregnant during the breeding season of yr 2, with a conception rate to first service of 52%. An economic analysis of different ranks with a 12-mo CI, a 24-mo CI, and an annualized herd effect, which compared an efficient spring calving system with 30% recycled cows in R1 and 10% recycled cows in R3, was carried out. Farm profit was reduced by 60% and 65% at a milk price of 22.3 euro-cents (c)/L with the corresponding values of 17% and 30% for a milk price of 30 c/L, respectively, when R1 and R3 systems were compared with an efficient spring milk (12-mo CI) production system. Within a spring system where 30% and 10% of R1 and R3 animals were subjected to extended lactations, the profit difference was reduced compared with an efficient spring system, The results indicated that lactations with a 24-mo CI may be a viable alternative to culling nonpregnant cows and be economically more suited to higher producing cows.