Assessment of heat production and methane emission using infrared thermography in lactating Holstein and Gyrolando-F1 (½ Holstein ½ Gyr) crossbreed cows

Infrared thermography (IRT) is a non-invasive and efficient tool to detect changes in the animal's body surface temperature, which is directly associated with the animal's energy loss. Methane emission represents a significant energy loss, particularly in ruminants, as well as heat production. Therefore, the aim of this study was to correlate skin temperature measured using IRT with heat production (HP) and methane emission in lactating Holstein and crossbred ½ Holstein x ½ Gyr (Gyrolando-F1) cows. Six Gyrolando-F1 and four Holstein cows, all primiparous, at mid lactation were used to evaluate daily HP and methane emission using indirect calorimetry in respiratory chambers. Thermographic images were taken at anus, vulva, ribs (on the right side), left flank, right flank, right front foot, upper lip, masseter and eye; IRT was performed every hour during 8 h after the morning feeding. Cows received the same diet ad libitum. Daily methane emission was positively correlated with IRT taken at the right front foot 1 h after feeding in Gyrolando-F1 cows (r = 0.85, P < 0.05) and with IRT taken at the eye 5 h after feeding in Holstein cows (r = 0.88, P < 0.05). HP was positively correlated with IRT taken at the eye 6 h after feeding in Gyrolando-F1 cows (r = 0.85, P < 0.05) and with IRT taken at the eye 5 h after feeding in Holstein cows (r = 0.90, P < 0.05). Infrared thermography was positively correlated with HP and methane emission in both Holstein and Gyrolando-F1 lactating cows, although the anatomical points and times of image acquisition yielding the greatest correlation coefficients varies among breeds.

Production in peatlands: Comparing ecosystem services of different land use options following conventional farming

Majority of Dutch peatlands are drained and used intensively as grasslands for dairy farming. This delivers high productivity but causes severe damage to ecosystem services supply. Peatland rewetting is the best way to reverse the damage, but high water levels do not fit with intensive dairy production. Paludiculture, defined as crop production under wet conditions, provides viable land use alternatives. However, performance of paludiculture is rarely compared to drainage-based agriculture. Here, we compared the performances of six land use options on peatland following a gradient of low, medium, and high water levels, including conventional and organic drainage-based dairy farming, low-input grasslands for grazing and mowing, and high-input paludiculture with reed and Sphagnum cultivation. For each land use option, we conducted environmental system analysis on model farm system defined by a literature based inventory analysis. The analysis used five ecosystem services as indicators of environmental impacts with a functional unit of 1-ha peat soil. Ecosystem services included biomass provisioning, climate, water, and nutrient regulation, and maintenance of habitat. Results showed that drainage-based dairy farming systems support high provisioning services but low regulation and maintenance services. Organic farming provides higher climate and nutrient regulation services than its conventional counterpart, but limited overall improvement due to the persistent drainage. Low-intensity grassland and paludiculture systems have high regulation and maintenance services value, but do not supply biomass provisioning comparable to the drainage-based systems. Without capitalizing the co-benefits of regulation and maintenance services, and accounting for the societal costs from ecosystem disservices including greenhouse gas emission and nitrogen pollution, it is not likely that the farmers will be incentivized to change the current farming system towards the wetter alternatives. Sustainable use of peatlands urges fundamental changes in land and water management along with the financial and policy support required.

Global contributions of milk to nutrient supplies and greenhouse gas emissions

Globally, ruminant production systems used to synthesize meat and milk differ tremendously in terms of their efficiency, productivity, and environmental impacts. Regardless of this variability, milk has been repeatedly identified as an essential source of nutrients for humans. The objective of this work was to characterize global contributions of fluid milk to human food and nutrient supplies, greenhouse gas emissions, and water withdrawal. Data were leveraged from the United Nations Food and Agriculture Organization to estimate global supplies of agricultural products. Trade of agricultural products and waste of those products, along with use of human food for livestock feed were accounted for before estimating human nutrient supplies. The contributions of milk to human-edible nutrient supplies were then enumerated in terms of their proportion of total nutrients supplied by the agricultural system and in terms of the human population's requirement for that nutrient. We identified that fluid milk provides over 10% of consumable supplies of vitamin B₁₂, vitamin A, riboflavin, and calcium available for humans globally. In terms of human nutrient requirements, milk provides sufficient vitamin B₁₂ to meet the needs of over 60% of the global population, riboflavin to satisfy 50% of the population, and calcium and phosphorus for over 35% of the population. Compared with other foods, milk ranked among the highest in terms of nutrient-to-calorie ratio for numerous amino acids, phosphorus, calcium, and riboflavin. Conditional dependencies were identified between greenhouse gas emissions and ruminant milk and meat, but not between water withdrawal and milk production. When evaluating the trade-offs in nutrient use versus nutrient provision for producing milk or producing all ruminant products, the production of ruminants worldwide was associated with both net increases and net decreases of several key nutrients. Continued work focusing on strategies to reduce the environmental impact of milk production will improve the utility of milk for feeding the growing global population.

Symposium review: Effective nutritional strategies to mitigate enteric methane in dairy cattle

Intensive research in the past decade has resulted in a better understanding of factors driving enteric methane (CH₄) emissions in ruminants. Meta-analyses of large databases, developed through the GLOBAL NETWORK project, have identified successful strategies for mitigation of CH₄ emissions. Methane inhibitors, alternative electron sinks, vegetable oils and oilseeds, and tanniferous forages are among the recommended strategies for mitigating CH₄ emissions from dairy and beef cattle and small ruminants. These strategies were also effective in decreasing CH₄ emissions yield and intensity. However, a higher inclusion rate of oils may negatively affect feed intake, rumen function, and animal performance, specifically milk components in dairy cows. In the case of nitrates (electron sinks), concerns with animal health may be impeding their adoption in practice, and potential emission trade-offs have to be considered. Tannins and tanniferous forages may have a negative effect on nutrient digestibility, and more research is needed to confirm their effects on overall animal performance in long-term experiments with high-producing animals. A meta-analysis of studies with dairy cows fed the CH₄ inhibitor 3-nitrooxypropanol (3-NOP) at the Pennsylvania State University showed (1) a consistent 28 to 32% decrease in daily CH₄ emissions or emissions yield and intensity; (2) no effect on dry matter intake, milk production, body weight, or body weight change, and a slight increase in milk fat concentration and yield (0.19 percentage units and 90 g/d, respectively); 3-NOP also appears to increase milk urea nitrogen concentration; (3) an exponential decrease in the mitigation effect of the inhibitor with increasing its dose (from 40 to 200 mg/kg of feed dry matter, corresponding to 3-NOP intake of 1 to 4.8 g/cow per day); and (4) a potential decrease in the efficacy of 3-NOP over time, which needs to be further investigated in long-term, full-lactation or multiple-lactation studies. The red macroalga Asparagopsis taxiformis has a strong CH₄ mitigation effect, but studies are needed to determine its feasibility, long-term efficacy, and effects on animal production and health. We concluded that widespread adoption of mitigation strategies with proven effectiveness by the livestock industries will depend on cost, government policies and incentives, and willingness of consumers to pay a higher price for animal products with decreased carbon footprint.

Full adoption of the most effective strategies to mitigate methane emissions by ruminants can help meet the 1.5 °C target by 2030 but not 2050

To meet the 1.5 °C target, methane (CH4) from ruminants must be reduced by 11 to 30% by 2030 and 24 to 47% by 2050 compared to 2010 levels. A meta-analysis identified strategies to decrease product-based (PB; CH4 per unit meat or milk) and absolute (ABS) enteric CH4 emissions while maintaining or increasing animal productivity (AP; weight gain or milk yield). Next, the potential of different adoption rates of one PB or one ABS strategy to contribute to the 1.5 °C target was estimated. The database included findings from 430 peer-reviewed studies, which reported 98 mitigation strategies that can be classified into three categories: animal and feed management, diet formulation, and rumen manipulation. A random-effects meta-analysis weighted by inverse variance was carried out. Three PB strategies—namely, increasing feeding level, decreasing grass maturity, and decreasing dietary forage-to-concentrate ratio—decreased CH4 per unit meat or milk by on average 12% and increased AP by a median of 17%. Five ABS strategies—namely CH4 inhibitors, tanniferous forages, electron sinks, oils and fats, and oilseeds—decreased daily methane by on average 21%. Globally, only 100% adoption of the most effective PB and ABS strategies can meet the 1.5 °C target by 2030 but not 2050, because mitigation effects are offset by projected increases in CH4 due to increasing milk and meat demand. Notably, by 2030 and 2050, low- and middle-income countries may not meet their contribution to the 1.5 °C target for this same reason, whereas high-income countries could meet their contributions due to only a minor projected increase in enteric CH4 emissions.

Review: Perspective on high-performing dairy cows and herds

Milk and dairy products provide highly sustainable concentrations of essential amino acids and other required nutrients for humans; however, amount of milk currently produced per dairy cow globally is inadequate to meet future needs. Higher performing dairy cows and herds produce more milk with less environmental impact per kg than lower performing cows and herds. In 2018, 15.4% of the world's dairy cows produced 45.4% of the world's dairy cow milk, reflecting the global contribution of high-performing cows and herds. In high-performing herds, genomic evaluations are utilized for multiple trait selection, welfare is monitored by remote sensing, rations are formulated at micronutrient levels, health care is focused on prevention and reproduction is managed with precision. Higher performing herds require more inputs and generate more waste products per cow, thus innovations in environmental management on such farms are essential for lowering environmental impacts. Our focus is to provide perspectives on technologies and practices that contribute most to sustainable production of milk from high-performing dairy cows and herds.

Life cycle cost analysis of dairy production systems in Southern Brazil

The intensification of milk production in Brazil in the past decade has imposed great stress on the environment. Therefore, it is very important to find a balance between economic, social, and environmental objectives. The paper assesses the economic costs by production systems: confined feedlot, semi-confined feedlot, and pasture in the south of the country. The economic assessment was realized on some investment analysis tools and the GHG emissions costs for the different production systems indicated. Our results show that hectare and the total area of rural properties were lower in the confined feedlot system, followed by the semi-confined feedlot system and pasture-based grazing system. However, the reduction of the need for feed inputs in the pasture system resulted in lower feeding costs when compared to the other systems. The hectare analysis suggested that the superior productivity of the semi-confined system conditioned higher emission costs in relation to the other systems. However, considered the total emissions of the systems, the pasture system obtained higher values, justified by the greater need for an area for production. The results showed that the higher the food efficiency of a system, the greater the profitability.

Sustainability of Four Dairy Farming Scenarios in an Alpine Environment: The Case Study of Toma di Lanzo Cheese

The dairy sector accounts for a large share of all European agricultural production, at the same time however, it is one of the most ascribed sector contributors to the environmental impact of agriculture, particularly for greenhouse gas emissions. Simultaneously, it is a strategic sector for the economy but generates increasing debate in the community regarding the social aspects mainly related to the use of resources and the food-feed competition of livestock involving the reduction of human-edible crops. In this general framework, this study aims to compare four different dairy farming scenarios characterized by different use of environmental resources in the Alpine area, considering as a case study the production of the Toma di Lanzo cheese (a traditional cheese produced in the mountainous regions of Piedmont-Northwest Italy). The study envisaged the integrated use of three methodologies: Life Cycle Assessment, Life Cycle Costing and the assessment of human-edible feed conversion efficiency to jointly analyze environmental, economic and social aspects. The main results of this research highlighted how the utilization of local breeds, which maximize the efficiency in the use of territory resources, such as grasslands in a mountain environment, allowed dairy production to reduce emissions, when compared to the high-input traditional breeding systems. Although the mountain livestock systems have several critical issues mainly linked to social factors such as low generational turnover, work schedules, modest life quality of families, it is however possible to earn more income than in lowland scenarios. At the same time, this production system allows the Toma di Lanzo cheese-making heritage to be preserved. This mountain pasture cheese, to which superior organoleptic and nutritional characteristics are attributable, when compared to cheeses from the valley floor, incorporates traditional values, a link to the territory and the transmission of knowledge. With reference to food-feed competition in livestock involving the reduction of the use of human-edible crops and feedstuffs in animal diets, we found that grazing and grass-based feeding systems were one of the most sustainable ways to produce milk.

Supervised Machine Learning to Assess Methane Emissions of a Dairy Building with Natural Ventilation

A reliable quantification of greenhouse gas emissions is a basis for the development of adequate mitigation measures. Protocols for emission measurements and data analysis approaches to extrapolate to accurate annual emission values are a substantial prerequisite in this context. We systematically analyzed the benefit of supervised machine learning methods to project methane emissions from a naturally ventilated cattle building with a concrete solid floor and manure scraper located in Northern Germany. We took into account approximately 40 weeks of hourly emission measurements and compared model predictions using eight regression approaches, 27 different sampling scenarios and four measures of model accuracy. Data normalization was applied based on median and quartile range. A correlation analysis was performed to evaluate the influence of individual features. This indicated only a very weak linear relation between the methane emission and features that are typically used to predict methane emission values of naturally ventilated barns. It further highlighted the added value of including day-time and squared ambient temperature as features. The error of the predicted emission values was in general below 10%. The results from Gaussian processes, ordinary multilinear regression and neural networks were least robust. More robust results were obtained with multilinear regression with regularization, support vector machines and particularly the ensemble methods gradient boosting and random forest. The latter had the added value to be rather insensitive against the normalization procedure. In the case of multilinear regression, also the removal of not significantly linearly related variables (i.e., keeping only the day-time component) led to robust modeling results. We concluded that measurement protocols with 7 days and six measurement periods can be considered sufficient to model methane emissions from the dairy barn with solid floor with manure scraper, particularly when periods are distributed over the year with a preference for transition periods. Features should be normalized according to median and quartile range and must be carefully selected depending on the modeling approach.

Applying a mechanistic fermentation and digestion model for dairy cows with emission and nutrient cycling inventory and accounting methodology

In mitigating greenhouse gas (GHG) emissions and reducing the carbon footprint of dairy milk, the use of generic estimates in inventory and accounting methodology at farm level largely ignores variation of on-farm GHG emissions. The present study aimed to implement results of an extant dynamic, mechanistic Tier 3 model for enteric methane (CH4) (applied in Dutch national GHG inventory) in order to capture variation in enteric CH4 emission, and in faecal N and organic matter (OM) digestibility, ultimately required to predict manure CH4 and ammonia emission. Tier 3 model predictions were translated into calculation rules that could easily be implemented in an annual nutrient cycling assessment tool including GHG emissions, which is currently used by Dutch dairy farmers. Calculations focussed on (1) enteric CH4 emission, (2) apparent faecal OM digestibility and (3) apparent faecal N digestibility. Enteric CH4 was expressed in CH4 yield indicated with the term emission factor (EF; g CH4/kg DM) for individual dietary components and feedstuffs. Factors investigated to cover predicted variation in EF value included the level of feed intake, the type of roughage fed (proportions of grass silage and maize silage) and the quality of roughage fed. A minimum number of three classes of roughage type (i.e. 0. 40% and 80% maize silage in roughage DM) appeared necessary to obtain correspondence between interpolated EF values from EF lists and Tier 3 model predictions. A linear decline in EF value with 1% per kg increase in DM intake is adopted based on model simulations. The quality of roughage was represented by the effect of maturity of harvested grass or of the whole plant maize at cutting, based on a survey of modelling as well as experimental work. Also, predictions were assembled for apparent faecal OM digestibility which could be used in national inventory and in farm accounting. Apparent faecal N digestibility (as a major determinant of predicted urinary N excretion) was predicted, to support current Dutch national ammonia emission inventory and to correct the level of N digestibility in farm accounting. Compared to generic values or values retrieved from the Dutch feeding tables, predicted OM and N digestibility and enteric CH4 are better rooted in physiological principles and better reflect observed variation under experimental conditions. The present results apply for conditions with fairly intensive grassland management in temperate regions.

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.

Impact of longevity on greenhouse gas emissions and profitability of individual dairy cows analysed with different system boundaries

Dairy production systems are often criticized as being major emitters of greenhouse gases (GHG). In this context, the extension of the length of the productive life of dairy cows is gaining interest as a potential GHG mitigation option. In the present study, we investigated cow and system GHG emission intensity and profitability based on data from 30 dairy cows of different productive lifetime fed either no or limited amounts of concentrate. Detailed information concerning productivity, feeding and individual enteric methane emissions of the individuals was available from a controlled experiment and herd book databases. A simplified GHG balance was calculated for each animal based on the milk produced at the time of the experiment and for their entire lifetime milk production. For the lifetime production, we also included the emissions arising from potential beef produced by fattening the offspring of the dairy cows. This accounted for the effect that changes in the length of productive life will affect the replacement rate and thus the number of calves that can be used for beef production. Profitability was assessed by calculating revenues and full economic costs for the cows in the data set. Both emission intensity and profitability were most favourable in cows with long productive life, whereas cows that had not finished their first lactation performed particularly unfavourably with regard to their emissions per unit of product and rearing costs were mostly not repaid. Including the potential beef production, GHG emissions in relation to total production of animal protein also decreased with age, but the overall variability was greater, as the individual cow history (lifetime milk yield, twin births, stillbirths, etc.) added further sources of variation. The present results show that increasing the length of productive life of dairy cows is a viable way to reduce the climate impact and to improve profitability of dairy production.

Effects of grass silage quality and level of feed intake on enteric methane production in lactating dairy cows

The objective of this study was to determine the effect of level of feed intake and quality of ryegrass silage as well as their interaction on enteric methane (CH) emission from dairy cows. In a randomized block design, 56 lactating dairy cows received a diet of grass silage, corn silage, and a compound feed meal (70:10:20 on DM basis). Treatments consisted of 4 grass silage qualities prepared from grass harvested from leafy through late heading stage, and offered to dairy cows at 96 ± 2.4 (mean ± SEM) days in milk (namely, high intake) and 217 ± 2.4 d in milk (namely, low intake). Grass silage CP content varied between 124 and 286 g/kg of DM, and NDF content between 365 and 546 g/kg of DM. After 12 d of adaptation, enteric CH production of cows was measured in open-circuit climate-controlled respiration chambers for 5 d. No interaction between DMI and grass quality on CH emission, or on milk production, diet digestibility, and energy, and N retention was found ( ≥ 0.17). Cows had a greater DMI (16.6 vs. 15.5 kg/d; SEM 0.46) and greater fat- and protein-corrected milk (FPCM) yield (29.9 vs. 25.4 kg/d; SEM 1.24) at high than low intake (both ≤ 0.001). Apparent total-tract nutrient digestibility was not affected ( ≥ 0.08) by DMI level. Total enteric CH production (346 ± 10.9 g/d) was not affected ( = 0.15) by DMI level. A small, significant ( = 0.025) decrease at high compared with low intake occurred for CH yield (21.8 ± 0.59 g/kg of DMI; -4%). Methane emission intensity (12.8 ± 0.56 g/kg of FPCM; -12%) was considerably smaller ( ≤ 0.001) at high intake as a result of greater milk yields realized in early lactation. As grass quality decreased from leafy through late heading stage, FPCM yield and apparent total-tract OM digestibility declined (-12%; ≤ 0.015), whereas total CH production (+13%), CH yield (+21%), and CH emission intensity (+28%) increased ( ≤ 0.001). Our results suggest that improving grass silage quality by cutting grass at an earlier stage considerably reduces enteric CH emissions from dairy cows, independent of DMI. In contrast, losses of N in manure increased for the earlier cut grass silage treatments. The small increase in DMI at high intake was associated with a small to moderate reduction in CH emission per unit of DMI and GE intake. This study confirmed that enteric CH emissions from dairy cows at distinct levels of feed intake depend on the nutritive value and chemical composition of the grass silage.

Relationships between milk mid-IR predicted gastro-enteric methane production and the technical and financial performance of commercial dairy herds

Considering economic and environmental issues is important in ensuring the sustainability of dairy farms. The objective of this study was to investigate univariate relationships between lactating dairy cow gastro-enteric methane (CH4) production predicted from milk mid-IR (MIR) spectra and technico-economic variables by the use of large scale and on-farm data. A total of 525 697 individual CH4 predictions from milk MIR spectra (MIR-CH4 (g/day)) of milk samples collected on 206 farms during the Walloon milk recording scheme were used to create a MIR-CH4 prediction for each herd and year (HYMIR-CH4). These predictions were merged with dairy herd accounting data. This allowed a simultaneous study of HYMIR-CH4 and 42 technical and economic variables for 1024 herd and year records from 2007 to 2014. Pearson correlation coefficients (r) were used to assess significant relationships (P<0.05). Low HYMIR-CH4 was significantly associated with, amongst others, lower fat and protein corrected milk (FPCM) yield (r=0.18), lower milk fat and protein content (r=0.38 and 0.33, respectively), lower quantity of milk produced from forages (r=0.12) and suboptimal reproduction and health performance (e.g. longer calving interval (r=-0.21) and higher culling rate (r=-0.15)). Concerning economic results, low HYMIR-CH4 was significantly associated with lower gross margin per cow (r=0.19) and per litre FPCM (r=0.09). To conclude, this study suggested that low lactating dairy cow gastro-enteric CH4 production tended to be associated with more extensive or suboptimal management practices, which could lead to lower profitability. The observed low correlations suggest complex interactions between variables due to the use of on-farm data with large variability in technical and management practices.