Impact of subclinical mastitis on greenhouse gas emissions intensity and profitability of dairy cows in Norway

Effects of extending dairy cow longevity by adjusted reproduction management decisions on partial net return and greenhouse gas emissions: A dynamic stochastic herd simulation study

Prolonging dairy cattle longevity is regarded as one of the options to contribute to more sustainable milk production. Because failure to conceive is one of the main reasons for culling, this study investigates how adjustments in reproduction management affect partial net return at herd level and greenhouse gas emissions per unit of milk, using a dynamic stochastic simulation model. The effects of reproduction decisions that extend cattle longevity on milk yield, calving interval and pregnancy rate were derived from actual performance of Dutch commercial dairy cows over multiple lactations. The model simulated lactations, calving, and health status events of individual cows for herds of 100 cows. Scenarios evaluated differed in the maximum number of consecutive AI attempts (4, 5, or 6 services), or the production threshold (20, 15, or 10 kg of milk/d) at which cows that failed to conceive are culled (reproductive culling). Annual partial net return was computed from revenues of sold milk, calves, and slaughtered cows, and the costs from feed consumption, rearing replacement heifers, AI services, and treatment for clinical mastitis and lameness. Greenhouse gas emissions were computed for feed production, enteric fermentation, and manure management, and were expressed as total CO2 equivalents (CO2-eq). Average age at culling increased with an increased maximum number of AI services. This increase was larger when going from a maximum of 4 to 5 AI attempts (108 d) than from a maximum of 5 to 6 attempts (47 d). Similarly, the average age at culling increased from 1,968 to 2,040 and 2,132 d when the threshold for reproductive culling decreased from 20, to 15 and 10 kg of milk/d, respectively. Average annual partial net return increased by 1.1% from €165,850 per 100 cows at a maximum of 4 AI to €167,670 per 100 cows at a maximum of 6 AI, and increased by 4.3% from €161,210 per 100 cows at a reproductive culling threshold of 10 kg/d to €168,190 per 100 cows at a threshold of 20 kg/d. Greenhouse gas emissions decreased by 1.2% from 0.926 to 0.915 kg CO2-eq per kg of fat- and protein-corrected milk (FPCM) with an increase in a maximum number of AI from 4 to 6 AI. Conversely, greenhouse gas emissions increased by 0.2% from 0.926 kg at a threshold of reproductive culling of 20 kg/d to 0.928 kg CO2-eq/kg FPCM at a threshold of 10 kg/d. Although lowering the threshold for reproductive culling has the potential to extend cattle longevity more than increasing the maximum number of AI services, only the increase in AI services benefits a farm's partial net return, while reducing greenhouse gas emissions.

Improve animal health to reduce livestock emissions: quantifying an open goal

Greenhouse gas (GHG) emissions from livestock production must be urgently tackled to substantially reduce their contribution to global warming. Simply reducing livestock numbers to this end risks impacting negatively on food security, rural livelihoods and climate change adaptation. We argue that significant mitigation of livestock emissions can be delivered immediately by improving animal health and hence production efficiency, but this route is not prioritized because its benefits, although intuitive, are poorly quantified. Rigorous methodology must be developed to estimate emissions from animal disease and hence achievable benefits from improved health through interventions. If, as expected, climate change is to affect the distribution and severity of health conditions, such quantification becomes of even greater importance. We have therefore developed a framework and identified data sources for robust quantification of the relationship between animal health and greenhouse gas emissions, which could be applied to drive and account for positive action. This will not only help mitigate climate change but at the same time promote cost-effective food production and enhanced animal welfare, a rare win-win in the search for a sustainable planetary future.

Quantifying multiple burdens of dairy cattle production diseases and reproductive inefficiency: current knowledge and proposed metrics

The economic burden of diseases and reproductive inefficiency in dairy cattle is evident and has been quantified. Dairy diseases and reproductive inefficiency are however associated with other issues as well, including animal welfare, environmental pressure, and public health risks. Quantifying these other issues is becoming important to help farmers making decisions. Quantification of the non-economic burdens of diseases and reproductive inefficiency is rare and lacks an overview of approaches and metrics. The first aim of this paper is to provide trends for associating diseases and reproductive inefficiency with economic and non-economic burdens of disease. The second aim is to provide a review of approaches and metrics used to quantify the non-economic burdens of disease and reproductive inefficiency. For the economic burden of diseases and reproductive performance, only an overview of the approaches used to quantify the burden is provided. The final aim is to propose approaches and metrics for future quantification of non-economic burdens caused by individual diseases. A literature search was conducted in Web of Science to identify scientific articles on mastitis, lameness, metabolic disorders and reproductive inefficiency in dairy cows. The search was restricted to articles published between 1 January 2010 and 31 December 2022 and resulted in 7,565 articles. The total number of articles that mentioned the economic, animal welfare, public health, and environmental burden was 1,253, 428, 291, and 77, respectively. An increase in the percentage of articles mentioning the economic, animal welfare, and public health burden is observed between 2010 and 2022. Despite the 2,049 articles that mentioned one of the burdens, the results showed that approximately 10% of the articles quantified one or more of these burdens. The economic burden of diseases and reproductive inefficiency has been quantified in 154 articles and very few articles quantified the non-economic burdens (9 articles for environment, 29 articles for public health and 2 articles for animal welfare). Eleven articles were identified that quantified multiple burdens, and in all these studies the economic burden was combined with a non-economic burden through a modeling approach (mainly simulation). We propose to link the non-economic burdens to biological simulation models, and thus develop bio-burden simulation models. Well-established approaches and metrics can be used to quantify economic, environmental, and public health burdens. For the economic impact, costs per cow per year can be assessed. A life cycle assessment can be performed for environmental impact and the public health impact can be assessed by a defined daily dose for antimicrobial use and disability-adjusted life years for zoonotic diseases. Regarding animal welfare, approaches and metrics to quantify the welfare impact of a diseased animal are not well established. For animal welfare, we propose a welfare-adjusted life years approach. The mentioned approaches and metrics are a proposal, and it is up to the scientific community to use them or, based on empirics and research experience, propose changes so that we will end up with robust approaches and metrics that enable us to compare research results and provide more evidence for animal health decision makers.

Improvement of mammary gland health in 81 'sentinel herds' in England and Scotland between 2012 and 2021

BACKGROUND

Achieving a reduction in mastitis in dairy cows is a common industry goal, but there is no recent peer-reviewed record of progress in the UK.

METHODS

A convenience sample of 125 herds in England and Scotland was recruited based on the quality of records in 2016, willingness to participate and representative geographical distribution. Individual cow somatic cell counts and clinical mastitis data from 2012 to 2021 were summarised annually, and temporal changes were analysed. Eighty-one herds had sufficient data for comparison between 2012 and 2021, for one or more parameters.

RESULTS

Over this period, the median incidence rate of clinical mastitis decreased from 40.0 to 21.0 cases per 100 cows per year (p < 0.001), with improvement in both lactation and dry period indicators. Lactation new infection rate calculated from individual cow somatic cell counts fell from 8.75% to 5.95% (p < 0.001), dry period new infection rate fell from 16.8% to 14.1% (p < 0.05) and proportion of cows over 200,000 cells/mL fell from 20.0% to 14.3% (p < 0.001).

LIMITATIONS

Data were necessarily from herds with good records and do not provide absolute values for the industry.

CONCLUSION

The findings reflect good progress over a 10-year period in a cohort of well-recorded herds and align with other national datasets.

Association between Udder and Quarter Level Indicators and Milk Somatic Cell Count in Automatic Milking Systems

Simple Summary

In dairy cattle herds milked by automatic systems, the absence of a human milker originates the need for control systems to monitor the milking process and cow conditions. Modern milking robots are equipped with a lot of sensors that, at each milking (2.5–3 times a day), record data on milk yield and quality, milking efficiency, cow welfare, and health with particular focus to udder conditions. Mastitis is one of the most frequent and serious diseases of dairy cow that negatively affects milk quality and yield, reduces animal welfare, and often implies the use of antimicrobial drugs. At the moment, the alerting systems for mastitis risk is generally based on monitoring milk electrical conductivity, color, and/or temperature, but these indicators have limited reliability. Other information gathered by automatic sensors, already implemented in commercial robots, could be useful to early detect mastitis. Using a multivariate approach, our study showed that the deviations over time of milk electrical conductivity, milk yield, and milk flow of single quarters in comparison with the whole udder are potential indicators, alone or in combination, for altered udder conditions. The results could be useful for the development of new algorithms more effective in the early detection of mastitis.

Abstract

Automatic Milking Systems (AMS) record a lot of information, at udder and quarter level, which can be useful for improving the early detection of altered udder health conditions. A total of 752,000 records from 1003 lactating cows milked with two types of AMS in four farms were processed with the aim of identifying new indicators, starting from the variables provided by the AMS, useful to predict the risk of high milk somatic cell count (SCC). Considering the temporal pattern, the quarter vs. udder percentage difference in milk electrical conductivity showed an increase in the fourteen days preceding an official milk control higher than 300,000 SCC/mL. Similarly, deviations over time in quarter vs. udder milk yield, average milk flow, and milking time emerged as potential indicators for high SCC. The Logistic Analysis showed that Milk Production Rate (kg/h) and the within-cow within-milking percentage variations of single quarter vs. udder milk electrical conductivity, milk yield, and average milk flow are all risk factors for high milk SCC. The result suggests that these variables, alone or in combination, and their progression over time could be used to improve the early prediction of risk situations for udder health in AMS milked herds.

Environmental impacts of implementing basket fans for heat abatement in dairy farms

Health and welfare impairments in dairy cows have been described to increase environmental impacts of milk production due to their negative effect on cow productivity. One of the welfare problems is heat stress, which is gaining importance even in temperate regions. While improving animal welfare may reduce emissions, the mitigation potential depends on the environmental costs associated with specific intervention measures. Taking abatement of heat stress as an example, the aim of the present study was to estimate the effect of implementing mechanical ventilation devices on the contribution potential of milk production to global warming (GWP), terrestrial acidification (TAP) and freshwater eutrophication (FEP). Environmental impacts of two modelled production systems located in alpine and lowland production areas of Austria were estimated before and after the implementation of basket fans, using life cycle assessment. Region-specific climate data were retrieved to determine the number of days with heat stress and to evaluate heat stress-induced productivity shortfalls in the baseline scenario (S_basic). In the intervention scenario with increased ventilation (S_vent), this decline was assumed to be eliminated due to the convective cooling effect of fans. For S_basic, mean GWP, TAP and FEP impacts were estimated at 1.2 ± 0.09 kg CO₂-, 21.1 ± 1.44 g SO₂- and 0.1 ± 0.04 g P-equivalents per kg milk, respectively. Independent from the production system, in S_vent, implementation of fans did not result in significant environmental impact changes, except for FEP of the alpine system (+5.9%). The latter reflects the comparatively high environmental costs of additional cooling regarding FEP (+2.3%) in contrast to GWP (+0.4%) and TAP (+0.1%). In conclusion, the estimated overall effects of mechanical ventilation on GWP, TAP and FEP of milk production were minor and the model calculations point to the potential of heat stress abatement to at least outweigh the environmental costs associated with fan production and operation. To confirm this trend, further assessments are needed, which should be based on primary data regarding the effectiveness of fan cooling to improve cow productivity, and on emission calculation schemes that are sensitive to environmental factors such as wind speed and temperature.

Short communication: Identifying key parameters for modelling the impacts of livestock health conditions on greenhouse gas emissions

Improved animal health can reduce greenhouse gas (GHG) emissions intensity in livestock systems while increasing productivity. Integrated modelling of disease impacts on farm-scale emissions is important in identifying effective health strategies to reduce emissions. However, it requires that modellers understand the pathways linking animal health to emissions and how these might be incorporated into models. A key barrier to meeting this need has been the lack of a framework to facilitate effective exchange of knowledge and data between animal health experts and emissions modellers. Here, these two communities engaged in workshops, online exchanges and a survey to i) identify a comprehensive list of disease-related model parameters and ii) test its application to evaluating models. Fifty-six parameters were identified and proved effective in assessing the potential of farm-scale models to characterise livestock disease impacts on GHG emissions. Easy wins for the emissions models surveyed include characterising disease impacts related to feeding.

Infectious Diseases, Livestock, and Climate: A Vicious Cycle?

Ruminant livestock are a significant contributor to global methane emissions. Infectious diseases have the potential to exacerbate these contributions by elevating methane outputs associated with animal production. With the increasing spread of many infectious diseases, the emergence of a vicious climate–livestock–disease cycle is a looming threat.

Financial and economic analyses of the impact of cattle mastitis on the profitability of Egyptian dairy farms

Aim:

This study aimed to evaluate and quantify the different factors affecting the costs of mastitis in cattle, to quantify the annual and monthly financial losses attributed to mastitis, and to estimate production losses using average linear scores found on The Dairy Herd Improvement Association somatic cell count (SCC) sheets and bulk tank SCC per lactation number.

Materials and Methods:

All data (bovine mastitis-associated costs and expenditures in Egyptian dairy herds) were analyzed using SPSS/PCT, 2001. A partial budget technique using spreadsheet software and the general linear model procedure was used to analyze the productive and financial measures.

Results:

Mastitis was present in 57.1% of cows (240/420), while clinical mastitis (CM) was present in 19% of them (80/420). The geometric mean of SCC/ml in bulk tank milk samples of 150 cattle dairy farms was 556.3×10³. The annual subclinical mastitis (SCM)-related economic loss was 21,933,258.6 LE, and the two most important cost components were the subsequent decrease in milk production and quality premium losses (93% and 7% of SCM costs, respectively). The quality premium loss was 1,369,602.1 LE. On the other hand, the annual economic loss due to decreased milk production as a result of SCM was 20,563,656.5 LE. The total cost of 80 CM cases, including the failure and preventive costs, was 1,196,871.4 LE, including 1,169,150.4 LE failure costs (106,336.0 LE in direct costs and 1,062,814.4 LE in indirect costs) and 27,721.0 LE preventive costs. The average cost per CM case was 28,760.9 LE, including veterinary time and consultation fees of 250.0 LE (1%), labor 562.5 LE (2%), premature culling 736,000.0 LE (77%), decreased milk production 4085.18 LE (13.7%), discarded milk 185.3 LE (1%), and drugs and treatments 328.9 LE (1%). The total costs of CM (expenditures) extra control and preventive measures, including the diagnosis of CM for 80 cows annually in 20 Egyptian dairy farms were 27,721.0 LE, representing 346.5 LE or 1% of the total cost of CM cases. The cost of monitoring and diagnostic measures was 8635.2 LE, representing 107.9 LE or 1% of the total cost of a case of CM.

Conclusion:

The method used for cost estimation, in this study, is highly adaptable to individual cattle farms and had a major role in assessing specific control and management measures. The concepts described in this paper help to improve our understanding of the full economic impact of clinical and subclinical mastitis in cattle in Egypt. Assessing the economic losses from mastitis to determine the economic costs and losses occurring in Egyptian dairy farms is critical for encouraging farmers to acknowledge the scale of the problem and implement effective management practices aimed at improving mastitis control and reducing the associated costs.

Dairy Cow Health and Greenhouse Gas Emission Intensity

The purpose of this review is to identify the main influencing factors related to dairy cow health as it impacts the intensity of greenhouse gas emissions considering known data presented in the literature. For this study, we define the emission intensity as CO2 equivalents per kilogram of milk. In dairy cows, a high dry matter (DM) intake (25 kg/d) leads to an higher absolute methane emission compared to a lower DM intake (10 kg/d). However, the emission intensity is decreased at a high performance level. The emissions caused by DM intake to cover the energy requirement for maintenance are distributed over a higher milk yield. Therefore, the emission intensity per kilogram of product is decreased for high-yielding animals with a high DM intake. Apart from that, animal diseases as well as poor environmental or nutritional conditions are responsible for a decreased DM intake and a compromised performance. As a result, animal diseases not only mean reduced productivity, but also increased emission intensity. The productive life-span of a dairy cow is closely related to animal health, and the impact on emission intensity is enormous. A model calculation shows that cows with five to eight lactations could have a reduced emission intensity of up to 40% compared to animals that have left the herd after their first lactation. This supports the general efforts to increase longevity of dairy cows by an improved health management including all measures to prevent diseases.

Can technology help achieve sustainable intensification? Evidence from milk recording on Irish dairy farms

This article explores the potential of a farm technology to simultaneously improve farm efficiency and provide wider environmental and social benefits. Identifying these 'win-win-win' strategies and encouraging their widespread adoption is critical to achieve sustainable intensification. Using a nationally representative sample of 296 Irish dairy farms from 2015, propensity score matching is applied to measure the impact of milk recording on a broad set of farm sustainability indicators. The findings reveal that the technology enhances economic sustainability by increasing dairy gross margin and milk yield per cow. Furthermore, social sustainability is improved through a reduction in milk bulk tank somatic cell count (an indicator of animal health and welfare status). Conversely, milk recording (as it is currently implemented) does not impact farm environmental sustainability, represented by greenhouse gas emission efficiency. While the study shows that milk recording is a 'win-win' strategy, ways of improving current levels of utilisation are discussed so that milk recording achieves its 'win-win-win' potential in the future.

The effects of improved performance in the U.S. dairy cattle industry on environmental impacts between 2007 and 2017

The U.S. dairy industry considerably reduced environmental impacts between 1944 and 2007, primarily through improved dairy cow productivity. However, although milk yield per cow has increased over the past decade, whole-system environmental impact analyses have not been conducted over this time period, during which environmental modeling science has improved considerably. The objective of this study was to compare the environmental impact of U.S. dairy cattle production in 2007-2017. A deterministic model based on population demographics, metabolism, and nutrient requirements of dairy cattle was used to estimate resource inputs, nutrient excretion, and greenhouse gas (GHG) emissions per 1.0 × 106 t (one million metric t or MMT) of energy-corrected milk (ECM) produced in 2007 and 2017. System boundaries extended from the manufacture and transport of cropping inputs to milk at the farm gate. Milk transport, processing, and retail were not included. Dairy systems were modeled using typical management practices, herd population dynamics, and production data from U.S. dairy farms. Cropping data were sourced from national databases. The resources required to produce 1.0 MMT ECM in 2017 were considerably reduced relative to those required in 2007, with 2017 production systems using 74.8% of the cattle, 82.7% of the feedstuffs, 79.2% of the land, and 69.5% of the water as compared to 2007. Waste outputs were similarly reduced, with the 2017 U.S. dairy industry producing 79.4%, 82.5%, and 85.7% of the manure, N, and P excretion, respectively. Dairy production in 2017 emitted 80.9% of the CH4 and 81.5% of the N2O per 1.0 MMT ECM compared to 2007. Enteric and manure emissions contributed the major proportion (80%) of GHG emissions per unit of milk, with lesser contributions from cropping (7.6%) and fertilizer application (5.3%). The GHG emissions per 1.0 MMT ECM produced in 2017 were 80.8% of equivalent milk production in 2007. Consequently, although total U.S. ECM production increased by 24.9% between 2007 and 2017, total GHG emissions from this milk production increased by only 1.0%. In line with previous historical analyses, the U.S. dairy industry has made remarkable productivity gains and environmental progress over time. To maintain this culture of continuous improvement, the dairy industry must build on gains made to date and demonstrate its commitment to reducing environmental impacts while improving both economic viability and social acceptability.

Estimating the impact of clinical mastitis in dairy cows on greenhouse gas emissions using a dynamic stochastic simulation model: a case study

The increasing attention for global warming is likely to contribute to the introduction of policies or other incentives to reduce greenhouse gas (GHG) emissions related to livestock production, including dairy. The dairy sector is an important contributor to GHG emissions. Clinical mastitis (CM), an intramammary infection, results in reduced milk production and fertility, increases culling and mortality of cows and, therefore, has a negative impact on the efficiency (output/input) of milk production. This may increase GHG emissions per unit of product. Our objective was to estimate the impact of CM in dairy cows on GHG emissions of milk production for the Dutch situation. A dynamic stochastic simulation model was developed to simulate the dynamics and losses of CM for individual lactations. Cows receive a parity (1 to 5+), a milk production and a calving interval (CI). Based on the parity, cows have a risk of CM, with a maximum of three cases in a lactation. Pathogens causing CM were classified as gram-positive bacteria, gram-negative bacteria, or other. Based on the parity and pathogen combinations, cows had a reduced milk production, discarded milk, prolonged CI and a risk of removal (culling and mortality) that reduce productivity of dairy cows and therefore increase GHG emissions per unit of product. Using life cycle assessment, emissions of GHGs were estimated from cradle to farm gate for processes along the milk production chain that are affected by CM. Processes included were feed production, enteric fermentation, and manure management. Emissions of GHGs were expressed as kg CO2 equivalents per ton of fat-and-protein-corrected milk (kg CO2e/t FPCM). Emissions of cows with CM increased on average by 57.5 (6.2%) kg CO2e/t FPCM compared with cows without CM. This increase was caused by removal (39%), discarded milk (38%), reduced milk production (17%) and prolonged CI (6%). The GHG emissions increased by 48 kg CO2e/t FPCM for cows with one case of CM, by 69 kg CO2e/t FPCM for cows with two cases of CM and by 92 kg CO2e/t FPCM for cows with three cases of CM compared with cows without CM. Preventing CM can be an effective strategy for farmers to reduce GHG emissions and can contribute to sustainable development of the dairy sector, because this also can improve the income of farmers and the welfare of cows. The impact of CM on GHG emissions, however, will vary between farms due to environmental conditions and management practices.