Evaluating the effects of grass management technologies on the physical, environmental, and financial performance of Irish pasture-based dairy farms.

Grass management technologies (grass measuring devices and grassland management decision support tools) have been identified as important tools to improve the performance of pasture-based dairy farms. They have the potential to significantly improve the efficiency and sustainability of dairy systems by increasing milk production through enhanced pasture growth and utilization, which would reduce the need for supplementary feeds, along with increased output, therefore increasing farm profitability and environmental sustainability. Despite the several potential benefits of grass management technologies, there is a lack of empirical research around the effects of these technologies on the performance of pasture-based dairy systems. The current study aimed to fill this knowledge gap by using a 2018 nationally representative survey of Irish dairy farms and a propensity score matching approach to determine the effects of adopting grass management technologies on the physical, environmental, and financial performance of Irish pasture-based dairy farms. The findings showed that dairy farms utilizing grass management technologies had, on average, higher farm physical, environmental, and financial performance (in terms of grazed pasture use, total pasture use, length of the grazing season, milk yield, milk solids, greenhouse gas emissions per kilogram of fat- and protein-corrected milk, gross output, and gross margin) compared with dairy farms not utilizing these technologies. However, when controlling for selection bias, we can only attribute a positive causal effect of grass management technology adoption on the use of grazed pasture per cow, grazing season length, milk yield per cow, and milk solids per cow. This might be due to dairy farmers not yet using the technologies to their full potential, 2018 being an unusual year in terms of weather (and therefore not being able to capture the full range of farm performance benefits), or because grass management technologies need to be adopted in association with other technologies and practices to achieve their expected performance outcomes. Future research should include updated farm-level data to capture the weather and learning effects and so be able to determine the impact of grass management technologies on a wider range of performance indicators.

Modeling the economic impacts of mobility scores in dairy cows under Irish spring pasture-based management.

Moderate to severe forms of suboptimal mobility on dairy cows are associated with yield losses, whereas mild forms of suboptimal mobility are associated with elevated somatic cell count and an increased risk to be culled. Although the economic consequences of severe forms of suboptimal mobility (also referred as clinical lameness) have been studied extensively, the mild forms are generally ignored. Therefore, the aim of the current study was to determine the economic consequences associated with varying prevalence and forms of suboptimal mobility within spring calving, pasture-based dairy herds. A new submodel predicting mobility scores was developed and integrated within an existing pastured-based herd dynamic model. Using a daily timestep, this model simulates claw disorders, and the consequent mobility score of individual cows. The impact of a cow having varying forms of suboptimal mobility on production and reproduction was simulated. The economic impact was simulated including treatment costs, as well as the production and reproductive impacts of varying levels of suboptimal mobility. Furthermore, different genetic predispositions for mobility issues and their interaction with herd-level management associated with each level of suboptimal mobility were simulated. Overall, 13 scenarios were simulated, representing a typical spring calving, pasture-based dairy herd with 100 cows. The first scenario represents a perfect herd wherein 100% of the cows had mobility score 0 (optimal mobility) throughout the lactation. The remaining 12 scenarios represent a combination of (1) 3 different herd-management levels, and (2) 4 different levels of a genetic predisposition for suboptimal mobility. The analysis showed that a 17% decrease in farm net profit was achieved in the worst outcome (wherein just 5% of the herd had optimal mobility) compared with the perfect herd. This was due to reduced milk yield, increased culling, and increased treatment costs for mobility issues compared the ideal scenario.

Economic impact of different strategies to use sex-sorted sperm for reproductive management in seasonal-calving, pasture-based dairy herds.

To maximize efficiency, profitability, and societal acceptance of modern dairy production, it is important to minimize the production of male dairy calves with poor beef merit. One solution involves using sex-sorted sperm (SS) to generate dairy replacements and breeding all other cows to an easy-calving, short-gestation bull with good beef merit. We used the Pasture Based Herd Dynamic Milk Model to investigate the effect of herd fertility and use of SS on farm net profit in a herd of 100 cows. This was completed by simulating herds with differing fertility performance (good, average, poor), and differing farm reproductive management [conventional semen (CONV) or SS with varying pregnancy per artificial insemination (P/AI) relative to CONV (i.e., relative P/AI 100%, 85%, and 70%)]. As an additional consideration, the method of allocating SS to cows was also examined. The first option used SS on random heifers and cows (S). The second option used SS on heifers and targeted high-fertility cows (SSel). The final option was similar to SSel, but used a fixed-time artificial insemination (AI) protocol to facilitate AI on the farm mating start date (SSync). For CONV, dairy breed semen was used for AI until 50 animals were pregnant (50% chance of a female calf), whereas for S, SSel, or SSync the target number of animals successfully conceiving with SS was set at 28 (based on assumed 90% chance of a female calf from pregnancies derived from SS). Beef breed semen was used on all other dams. The results indicated that the biggest effect on farm net profit was not based on whether or not SS was used, but instead was most affected by the overall fertility performance of the herd. Total farm profit decreased by 10% between the good and average fertility herds, and decreased by a further 12% between the average and poor fertility herds. In almost all situations, when the relative P/AI with SS was ≥85%, use of SS led to an overall increase of the farm net profit. There was an economic benefit of using either SSel or SSync compared with S for the average and poor fertility herds but not for the good fertility herd, highlighting an interaction between SS P/AI and overall herd fertility as well as management practices. If the relative P/AI with SS was <70%, the use of SS led to a decrease in profitability in all simulations except for SSync, highlighting the importance of a good management strategy for use of SS. The findings in this study indicated that SS has significant potential to help facilitate greater integration between the dairy and beef production sectors, as well as increase farm profitability when used appropriately.

Greenhouse gas emissions and nitrogen efficiency of dairy cows of divergent economic breeding index under seasonal pasture-based management.

Greenhouse gas (GHG) emissions and nitrogen (N) efficiencies were modeled for 2 genetic groups (GG) of Holstein-Friesian cows across 3 contrasting feeding treatments (FT). The 2 GG were (1) high economic breeding index (EBI) animals representative of the top 5% of cows nationally (elite) and (2) EBI representative of the national average (NA). The FT represented (1) generous feeding of pasture, (2) a slight restriction in pasture allowance, and (3) a high-concentrate feeding system with adequate pasture allowance. Greenhouse gas and N balance models were parameterized using outputs generated from the Moorepark Dairy Systems model, a stochastic budgetary simulation model, having integrated biological data pertaining to the 6 scenarios (2 GG × 3 FT) obtained from a 4-yr experiment conducted between 2013 and 2016. On a per hectare basis, total system GHG emissions were similar for both elite and NA across the 3 FT. Per unit of product, however, the elite group had 10% and 11% lower GHG emissions per kilogram of fat- and protein-corrected milk and per kilogram of milk solids (MSO; fat + protein kg), respectively, compared with the NA across the 3 FT. The FT incorporating high concentrate supplementation had greater absolute GHG emissions per hectare as well as GHG per kilogram of fat- and protein-corrected milk and MSO. The elite group had a slightly superior N use efficiency (N output/N input) and lower N surplus (N input - N output) compared with the NA group. The high concentrate FT had an inferior N use efficiency and a higher N surplus. The results of the current study demonstrate that breeding for increased EBI will lead to a general improvement in GHG emissions per unit of product as well as improved N efficiency. The results also illustrate that reducing concentrate supplementation will reduce GHG emissions, GHG emissions intensity, while improving N efficiency in the context of pasture-based dairy production.

Economic assessment of Holstein-Friesian dairy cows of divergent Economic Breeding Index evaluated under seasonal calving pasture-based management.

The objective of this study was to investigate the economic performance of 2 genetic groups (GG) of Holstein-Friesian dairy cows of divergent Economic Breeding Index (EBI), evaluated within 3 contrasting spring-calving pasture-based feeding treatments (FT). The study was a simulated economic appraisal, using the Moorepark Dairy Systems Model, a stochastic budgetary simulation model integrating biological data obtained from a 4-yr experiment conducted from 2013 to 2016. The 2 divergent GG were (1) high EBI representative of the top 5% nationally (elite) and (2) EBI representative of the national average (NA). The 3 FT were reflective of slight restriction to generous feeding. The elite GG had the lowest replacement rate, and therefore had lower replacement costs and an older and more productive parity structure. The elite GG consistently had higher sales of milk (on average +3% or +18,370 kg of milk) and milk solids (milk fat plus protein yield; +8.7% or +4,520 kg) compared with the NA GG across the 3 FT scenarios. Milk income was consequently greater for elite versus NA (on average +9.5% or +€21,489) cows. Livestock sales were greater (on average +13.2% or +€4,715) for NA compared with elite cows. Baseline net farm profit and net profit/ha at a base milk price of 29.5 cents per liter (3.3% protein and 3.6% fat) were on average €31,156, and €772 greater for elite compared with NA cows across the 3 FT. Greater profitability achieved with elite cows in each of the FT investigated demonstrated the adaptability of high-EBI cows across different levels of feeding intensities in seasonal pasture-based feeding systems. Sensitivity analysis of varying milk price and concentrate cost did not result in a reranking of GG for farm profit. This study clearly demonstrates the power of a suitably constructed genetic-selection index together with a well-considered breeding program to deliver genetics capable of favorable change to farm physical performance and profit over a relatively short duration.

Associating mobility scores with production and reproductive performance in pasture-based dairy cows.

Lameness in dairy cows can have significant effects on cow welfare, farm profitability, and the environment. To determine the economic and environmental consequences of lameness, we first need to quantify its effect on performance. The objective of this study, therefore, was to determine the associations of various production and reproductive performance measurements (including milk, fat, and protein yield, somatic cell count, calving interval, cow death, or cow slaughter), and mobility scores in spring-calving, pasture-based dairy cows. We collected mobility scores (0 = good, 1 = imperfect, 2 = impaired, and 3 = severely impaired mobility), body condition scores, and production data for 11,116 cows from 68 pasture-based dairy herds. Linear mixed modeling was used to determine the associations between specific mobility scores and milk, fat and protein yield, and somatic cell count and calving interval. Binomial logistic regression was used to determine the association between mobility score and cow death, or slaughter. Significant yield losses of up to 1.4% of the average yield were associated with mobility score 2 and yield losses of up to 4.7% were associated with mobility score 3 during the early scoring period. Elevated somatic cell count was associated with all levels of suboptimal mobility during the late scoring period. Cows with a mobility score of 2 during the early scoring period were associated with longer calving interval length, whereas only cows with a mobility score of 3 during the late scoring period were associated with longer calving interval length. Cows with a mobility score ≥1 were more likely to be culled during both scoring periods. Our study, therefore, shows an association between specific mobility scores and production and reproductive performance in spring-calving, pasture-based dairy cows scored during the summer grazing period.

An economic comparison of pasture-based production systems differing in sward type and cow genotype.

The objective of this study was to compare the economic performance of 2 sward types [perennial ryegrass (PRG; Lolium perenne L.) sown with or without white clover (Trifolium repens L.)] grazed by 3 cow genotypes. Physical performance data were collected from a 4-yr systems experiment based at Clonakilty Agricultural College, Clonakilty, Co. Cork, Ireland. The experiment compared 2 sward types (PRG-only swards and PRG-white clover swards), with each sward type being grazed by cows from 3 genotypes [Holstein-Friesian (HF), Jersey × HF (JEX), and Norwegian Red × JEX (3-way)]. All systems were stocked at 2.75 cows/ha with fixed fertilizer applications and concentrate supplementation. The data supplied 6 production systems (2 sward types × 3 cow genotypes). The production systems were modeled using the Moorepark Dairy Systems Model (stochastic budgetary simulation model) under 2 scenarios, one in which land area was fixed and one in which cow numbers were fixed. The analysis was completed across a range of milk prices, calf prices, and reseeding programs. The analysis showed that in the fixed-land scenario with a milk price of €0.29/L, adding white clover to PRG swards increased profitability by €305/ha. In the same fixed-land scenario, JEX cows were most profitable (€2,606/ha), followed by 3-way (€2,492/ha) and HF (€2,468/ha) cows. In the fixed-cow scenario, net profit per cow was €128 greater for PRG-white clover swards compared with PRG-only swards. In this scenario, JEX was the most profitable per cow (€877), followed by HF (€855) and 3-way (€831). The system that produced the highest net profit was JEX cows grazing PRG-white clover swards (€2,751/ha). Regardless of reseeding frequency or variations in calf value, JEX cows grazing PRG-white clover swards consistently produced the highest net profit per hectare.

An examination of the effects of labor efficiency on the profitability of grass-based, seasonal-calving dairy farms.

The seasonality of grass-based, seasonal-calving dairy systems results in disproportionately higher labor demands during the spring, when cows are calving, than in the remaining seasons. This study aimed to (1) examine the relationship between labor efficiency and profitability; (2) investigate strategies to reduce the hours worked per day by the farmer, family, and farm staff in the spring by having certain tasks outsourced; and (3) quantify the economic implications of those strategies. Data from an existing labor efficiency study on Irish dairy farms were used in conjunction with economic performance data from the farms. Tasks that required the highest level of farm labor per day in the spring were identified and hypothetical strategies to reduce the farm hours worked per day were examined. A stochastic budgetary simulation model was then used to examine the economic implications of employing these strategies and the effects of their use in conjunction with a proportionate increase in cow numbers that would leave the hours worked per day unchanged. The strategies were to use contractors to perform calf rearing, machinery work, or milking. Contracting out milking resulted in the greatest reduction in hours worked per day (5.6 h/d) followed by calf rearing (2.7 h/d) and machinery work (2 h/d). Reducing the hours worked per day by removing those tasks had slight (i.e., <5%) negative effects on profitability; however, maintaining the farm hours worked per day while utilizing the same strategies and increasing herd sizes resulted in profitable options. The most profitable scenario was for farms to increase herd size while contracting out milking.

Review: Grass-based dairy systems, data and precision technologies.

Precision technologies and data have had relatively modest impacts in grass-based livestock ruminant production systems compared with other agricultural sectors such as arable. Precision technologies promise increased efficiency, reduced environmental impact, improved animal health, welfare and product quality. The benefits of precision technologies have, however, been relatively slow to be realised on pasture based farms. Though there is significant overlap with indoor systems, implementing technology in grass-based dairying brings unique opportunities and challenges. The large areas animals roam and graze in pasture based systems and the associated connectivity challenges may, in part at least, explain the comparatively lower adoption of such technologies in pasture based systems. With the exception of sensor and Bluetooth-enabled plate metres, there are thus few technologies designed specifically to increase pasture utilisation. Terrestrial and satellite-based spectral analysis of pasture biomass and quality is still in the development phase. One of the key drivers of efficiency in pasture based systems has thus only been marginally impacted by precision technologies. In contrast, technological development in the area of fertility and heat detection has been significant and offers significant potential value to dairy farmers, including those in pasture based systems. A past review of sensors in health management for dairy farms concluded that although the collection of accurate data was generally achieved, the processing, integration and presentation of the resulting information and decision-support applications were inadequate. These technologies' value to farming systems is thus unclear. As a result, it is not certain that farm management is being sufficiently improved to justify widespread adoption of precision technologies currently. We argue for a user need-driven development of technologies and for a focus on how outputs arising from precision technologies and associated decision support applications are delivered to users to maximise their value. Further cost/benefit analysis is required to determine the efficacy of investing in specific precision technologies, potentially taking account of several yet to ascertained farm specific variables.

Factors associated with profitability in pasture-based systems of milk production.

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.

Using models to establish the financially optimum strategy for Irish dairy farms.

Determining the effect of a change in management on farm with differing characteristics is a significant challenge in the evaluation of dairy systems due to the interacting components of complex biological systems. In Ireland, milk production is increasing substantially following the abolition of the European Union milk quota regime in 2015. There are 2 main ways to increase the milk production on farm (within a fixed land base): either increase the number of animals (thus increasing the stocking rate) or increase the milk production per animal through increased feeding or increased lactation length. In this study, the effect of increased concentrate feeding or an increase in grazing intensity was simulated to determine the effect on the farm system and its economic performance. Four stocking rates (2.3, 2.6, 2.9, and 3.2 cow/ha) and 5 different concentrate supplementation strategies (0, 180, 360, 600, and 900 kg of dry matter/lactation) resulting in 20 different scenarios were evaluated across different milk, concentrate, and silage purchase prices. Each simulation was run across 10 yr of meteorological data, which had been recorded over the period 2004 to 2013. Three models-the Moorepark and St Gilles grass growth model, the pasture-based herd dynamic milk model, and the Moorepark dairy systems model-were integrated and applied to simulate the different scenarios. Overall, this study has demonstrated that the most profitable scenario was a stocking rate of 2.6 cow/ha with a concentrate supplementation of 600 kg of dry matter/cow. The factor that had the greatest influence on profitability was variability of milk price.

Investment appraisal of automatic milking and conventional milking technologies in a pasture-based dairy system.

The successful integration of automatic milking (AM) systems and grazing has resulted in AM becoming a feasible alternative to conventional milking (CM) in pasture-based systems. The objective of this study was to identify the profitability of AM in a pasture-based system, relative to CM herringbone parlors with 2 different levels of automation, across 2 farm sizes, over a 10-yr period following initial investment. The scenarios which were evaluated were (1) a medium farm milking 70 cows twice daily, with 1 AM unit, a 12-unit CM medium-specification (MS) parlor and a 12-unit CM high-specification (HS) parlor, and (2) a large farm milking 140 cows twice daily with 2 AM units, a 20-unit CM MS parlor and a 20-unit CM HS parlor. A stochastic whole-farm budgetary simulation model combined capital investment costs and annual labor and maintenance costs for each investment scenario, with each scenario evaluated using multiple financial metrics, such as annual net profit, annual net cash flow, total discounted net profitability, total discounted net cash flow, and return on investment. The capital required for each investment was financed from borrowings at an interest rate of 5% and repaid over 10-yr, whereas milking equipment and building infrastructure were depreciated over 10 and 20 yr, respectively. A supporting labor audit (conducted on both AM and CM farms) showed a 36% reduction in labor demand associated with AM. However, despite this reduction in labor, MS CM technologies consistently achieved greater profitability, irrespective of farm size. The AM system achieved intermediate profitability at medium farm size; it was 0.5% less profitable than HS technology at the large farm size. The difference in profitability was greatest in the years after the initial investment. This study indicated that although milking with AM was less profitable than MS technologies, it was competitive when compared with a CM parlor of similar technology.

Development and evaluation of the herd dynamic milk model with focus on the individual cow component.

The herd dynamic milk (HDM) model is a dynamic model capable of simulating the performance of individual dairy animals (from birth to death), with a daily time step. Within this study, the HDM model is described and evaluated in relation to milk production, body condition score (BCS) and BCS change throughout lactation by comparing model simulations against data from published experimental studies. The model's response to variation in genetic potential, herbage allowance and concentrate supplementation was tested in a sensitivity analysis. Data from experiments in Ireland and France over a 3-year period (2009-11) were used to complete the evaluation. The aim of the Irish experiment was to determine the impact of different stocking rates (SRs) (SR1: 3.28 cow/ha, SR2: 2.51 cow/ha) on key physical, biological and economic performance. The aim of the French experiment was to evaluate over a prolonged time period, the ability of two breeds of dairy cows (Holstein and Normande) to produce and to reproduce under two feeding strategies (high level and low level) in the context of compact calving. The model evaluation was conducted at the herd level with separate evaluations for the primiparous and multiparous cows. The evaluation included the two extreme SRs for the Irish experiment, and an evaluation at the overall herd and individual animal level for the different breeds and feeding levels for the French data. The comparison of simulation and experimental data for all scenarios resulted in a relative prediction error, which was consistently <15% across experiments for weekly milk production and BCS. In relation to BCS, the highest root mean square error was 0.27 points of BCS, which arose for Holstein cows in the low feeding group in late lactation. The model responded in a realistic fashion to variation in genetic potential for milk production, herbage allowance and concentrate supplementation.

Relating the carbon footprint of milk from Irish dairy farms to economic performance.

Mitigating greenhouse gas (GHG) emissions per unit of milk or the carbon footprint (CF) of milk is a key issue for the European dairy sector given rising concerns over the potential adverse effects of climate change. Several strategies are available to mitigate GHG emissions, but producing milk with a low CF does not necessarily imply that a dairy farm is economically viable. Therefore, to understand the relationship between the CF of milk and dairy farm economic performance, the farm accountancy network database of a European Union nation (Ireland) was applied to a GHG emission model. The method used to quantify GHG emissions was life cycle assessment (LCA), which was independently certified to comply with the British standard for LCA. The model calculated annual on- and off-farm GHG emissions from imported inputs (e.g., electricity) up to the point milk was sold from the farm in CO2-equivalent (CO2-eq). Annual GHG emissions computed using LCA were allocated to milk based on the economic value of dairy farm products and expressed per kilogram of fat- and protein-corrected milk (FPCM). The results showed for a nationally representative sample of 221 grass-based Irish dairy farms in 2012 that gross profit averaged € 0.18/L of milk and € 1,758/ha and gross income was € 40,899/labor unit. Net profit averaged € 0.08/L of milk and € 750/ha and net income averaged € 18,125/labor unit. However, significant variability was noted in farm performance across each financial output measure. For instance, net margin per hectare of the top one-third of farms was 6.5 times higher than the bottom third. Financial performance measures were inversely correlated with the CF of milk, which averaged 1.20 kg of CO2-eq/kg of FPCM but ranged from 0.60 to 2.13 kg of CO2-eq/kg of FPCM. Partial least squares regression analysis of correlations between financial and environmental performance indicated that extending the length of the grazing season and increasing milk production per hectare or per cow reduced the CF of milk and increased farm profit. However, where higher milk production per hectare was associated with greater concentrate feeding, this adversely affected the CF of milk and economic performance by increasing both costs and off-farm emissions. Therefore, to mitigate the CF of milk and improve economic performance, grass-based dairy farms should not aim to only increase milk output, but instead target increasing milk production per hectare from grazed grass.

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

Paratuberculosis (also called Johne's 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 Johne's disease in ruminants and Crohn's 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.

Effect of exposure to Neospora caninum, Salmonella, and Leptospira interrogans serovar Hardjo on the economic performance of Irish dairy herds.

The objective of the current study was to quantify the effects of exposure to Salmonella, Neospora caninum, and Leptospira interrogans serovar Hardjo (L. hardjo) on dairy farm profitability and to simulate the effect of vaccination for Salmonella and L. hardjo on dairy farm profitability. The production effects associated with exposure to each of these pathogens in study herds were defined under 3 categories: (1) milk production effects, (2) reproduction effects (including culling), and (3) mortality effects. The production effects associated with exposure to Salmonella, N. caninum, and L. hardjo were incorporated into the Moorepark Dairy Systems Model. In the analysis, herds negative for exposure to Salmonella, N. caninum, and L. hardjo were assumed baseline herds, with all results presented relative to this base. In simulations examining the effect of vaccination for Salmonella and L. hardjo on farm profitability, vaccinated herds (vaccination costs included) were considered as baseline herds and results were presented relative to this base. Total annual profits in unvaccinated herds were reduced by €77.31, €94.71, and €112.11 per cow at milk prices of €0.24, €0.29, and €0.34/L, respectively, as a result of exposure to Salmonella. In the current study, herds positive for exposure to Salmonella recorded a 316-kg reduction in milk yield, whereas no association was detected between exposure to N. caninum or L. hardjo and milk production. Exposure to both N. caninum and L. hardjo was associated with compromised reproductive performance. Herds positive for exposure to N. caninum and Salmonella had greater rates of adult cow mortality and calf mortality, respectively. Vaccination for both Salmonella and L. hardjo was associated with improved performance in study herds. Exposure to N. caninum resulted in a reduction in annual farm profits of €11.55, €12, and €12.44 per cow at each milk price, whereas exposure to L. hardjo resulted in a reduction in annual farm profits of €13.83, €13.78, and €13.72 per cow at each milk price. Herds that tested positive for exposure to Salmonella and L. hardjo were compared with herds vaccinated for the respective pathogens. Herds vaccinated for Salmonella generated €67.09, €84.48, and €101.89 per cow more profit at each milk price compared with herds positive for exposure. Similarly, herds vaccinated for L. hardjo generated €9.74, €9.69, and €9.63 per cow more profit compared with unvaccinated exposed herds. However, herds that tested negative for exposure to Salmonella and L. hardjo generated additional profits of €10.22 and €4.09 per cow, respectively, compared with vaccinated baseline herds.

Investment appraisal of technology innovations on dairy farm electricity consumption.

The aim of this study was to conduct an investment appraisal for milk-cooling, water-heating, and milk-harvesting technologies on a range of farm sizes in 2 different electricity-pricing environments. This was achieved by using a model for electricity consumption on dairy farms. The model simulated the effect of 6 technology investment scenarios on the electricity consumption and electricity costs of the 3 largest electricity-consuming systems within the dairy farm (i.e., milk-cooling, water-heating, and milking machine systems). The technology investment scenarios were direct expansion milk-cooling, ice bank milk-cooling, milk precooling, solar water-heating, and variable speed drive vacuum pump-milking systems. A dairy farm profitability calculator was combined with the electricity consumption model to assess the effect of each investment scenario on the total discounted net income over a 10-yr period subsequent to the investment taking place. Included in the calculation were the initial investments, which were depreciated to zero over the 10-yr period. The return on additional investment for 5 investment scenarios compared with a base scenario was computed as the investment appraisal metric. The results of this study showed that the highest return on investment figures were realized by using a direct expansion milk-cooling system with precooling of milk to 15°C with water before milk entry to the storage tank, heating water with an electrical water-heating system, and using standard vacuum pump control on the milking system. Return on investment figures did not exceed the suggested hurdle rate of 10% for any of the ice bank scenarios, making the ice bank system reliant on a grant aid framework to reduce the initial capital investment and improve the return on investment. The solar water-heating and variable speed drive vacuum pump scenarios failed to produce positive return on investment figures on any of the 3 farm sizes considered on either the day and night tariff or the flat tariff, even when the technology costs were reduced by 40% in a sensitivity analysis of technology costs.

A mechanistic model for electricity consumption on dairy farms: definition, validation, and demonstration.

Our objective was to define and demonstrate a mechanistic model that enables dairy farmers to explore the impact of a technical or managerial innovation on electricity consumption, associated CO2 emissions, and electricity costs. We, therefore, (1) defined a model for electricity consumption on dairy farms (MECD) capable of simulating total electricity consumption along with related CO2 emissions and electricity costs on dairy farms on a monthly basis; (2) validated the MECD using empirical data of 1yr on commercial spring calving, grass-based dairy farms with 45, 88, and 195 milking cows; and (3) demonstrated the functionality of the model by applying 2 electricity tariffs to the electricity consumption data and examining the effect on total dairy farm electricity costs. The MECD was developed using a mechanistic modeling approach and required the key inputs of milk production, cow number, and details relating to the milk-cooling system, milking machine system, water-heating system, lighting systems, water pump systems, and the winter housing facilities as well as details relating to the management of the farm (e.g., season of calving). Model validation showed an overall relative prediction error (RPE) of less than 10% for total electricity consumption. More than 87% of the mean square prediction error of total electricity consumption was accounted for by random variation. The RPE values of the milk-cooling systems, water-heating systems, and milking machine systems were less than 20%. The RPE values for automatic scraper systems, lighting systems, and water pump systems varied from 18 to 113%, indicating a poor prediction for these metrics. However, automatic scrapers, lighting, and water pumps made up only 14% of total electricity consumption across all farms, reducing the overall impact of these poor predictions. Demonstration of the model showed that total farm electricity costs increased by between 29 and 38% by moving from a day and night tariff to a flat tariff.

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.

Applications and cost benefits of sexed semen in pasture-based dairy production systems.

Sexed semen technology is now commercially available in many countries around the world, and is primarily used in dairy cattle breeding. Sperm are sorted by flow cytometry on the basis of a 4% difference in DNA content between sperm containing X and Y chromosomes. Despite reliably producing a 90% gender bias, the fertility of the sexed semen product is compromised compared with conventional semen. The negative implications of the reduced fertility of sexed semen are amplified in seasonal systems of dairy production, as the importance of fertility is greater in these systems compared with year-round calving systems. A review of the literature indicates that conception rates (CR) to 1st service with frozen-thawed sexed semen are ~75% to 80% of those achieved with conventional frozen-thawed semen. Preliminary results from a large-scale field trial carried out in Ireland in 2013 suggest that significant improvements in the performance of sexed semen have been made, with CR of 87% of those achieved with conventional semen. The improved fertility of a sexed semen product that delivers a 90% gender bias has considerable implications for the future of breeding management in pasture-based dairy production systems. Sexed semen may facilitate faster, more profitable dairy herd expansion by increasing the number of dairy heifer replacements born. Biosecurity can be improved by maintaining a closed herd during the period of herd expansion. In a non-expansion scenario, sexed semen may be used to increase the value of beef output from the dairy herd. The replacement heifer requirements for a herd could be met by using sexed semen in the 1st 3 weeks of the breeding season, with the remaining animals bred to beef sires, increasing the sale value over that of a dairy bull calf. Alternatively, very short gestation sires could be used to shorten the calving interval. Market prices have a considerable effect on the economics of sexed semen use, and widespread use of sexed semen should be restricted to well managed herds that already achieve acceptable herd fertility performance.