Comparison of carbon footprint and water scarcity footprint of milk protein produced by cellular agriculture and the dairy industry

The International Journal of Life Cycle Assessment - This paper studies the carbon footprint and water scarcity footprint (WSF) of a milk protein, beta-lactoglobulin, produced by cellular...     

Variation in carbon footprint of milk due to management differences between Swedish dairy farms

To identify mitigation options to reduce greenhouse gas (GHG) emissions from milk production (i.e. the carbon footprint (CF) of milk), this study examined the variation in GHG emissions among dairy farms using data from previous CF studies on Swedish milk. Variations between farms in these production data, which were found to have a strong influence on milk CF, were obtained from existing databases of 1051 dairy farms in Sweden in 2005. Monte Carlo (MC) analysis was used to analyse the impact of variations in seven important parameters on milk CF concerning milk yield (energy-corrected milk (ECM) produced and delivered), feed dry matter intake (DMI), enteric CH4 emissions, N content in feed DMI, N-fertiliser rate and diesel used on farm. The largest between-farm variations among the analysed production data were N-fertiliser rate (kg/ha) and diesel used (l/ha) on farm (CV = 31% to 38%). For the parameters concerning milk yield and feed DMI, the CV was approximately 11% and 8%, respectively. The smallest variation in production data was found for N content in feed DMI. According to the MC analysis, these variations in production data led to a variation in milk CF of between 0.94 and 1.33 kg CO2 equivalents (CO2e)/kg ECM, with an average value of 1.13 kg CO2e/kg ECM. We consider that this variation of ±17%, which was found to be based on the used farm data, would be even greater if all Swedish dairy farms were included, as the sample of farms in this study was not totally unbiased. The variation identified in milk CF indicates that a potential exists to reduce GHG emissions from milk production on both the national and farm levels through changes in management. As milk yield and feed DMI are two of the most influential parameters for milk CF, feed conversion efficiency (i.e. units ECM produced/unit DMI) can be used as a rough key performance indicator for predicting CF reductions. However, it must be borne in mind that feeds have different CF due to where and how they are produced.     

基于生命周期评价的上海市水稻生产的碳足迹

碳足迹是指由企业、组织或个人引起的碳排放的集合。参照PAS2050规范并结合生命周期评价方法对上海市水稻生产进行了碳足迹评估。结果表明:(1)目前上海市水稻生产的碳排放为11.8114 t CO2e/hm2,折合每吨水稻生产周期的碳足迹为1.2321 t CO2e;(2)稻田温室气体排放是水稻生产最主要的碳排放源,每吨水稻生产的总排放量为0.9507 t CO2e,占水稻生产全部碳排放的77.1%,其中甲烷(CH4)又是最主要的温室气体,对稻田温室气体碳排放的贡献率高达96.6%;(3)化学肥料的施用是第二大碳排放源,每吨水稻生产的总排放量为0.2044 t CO2e,占水稻生产总碳排放的16.5%,其中N最高,排放量为0.1159 t CO2e。因此,上海低碳水稻生产的关键在降低稻田甲烷的排放,另外可通过提高氮肥利用效率,减少氮肥施用等方法减少种植过程中碳排放。     

Evaluating the carbon footprint of Chilean organic blueberry production

Purpose

Chile is the second largest blueberry producer and exporter worldwide. At the global level, there is a lack of information by means of field data about greenhouse gas emissions from organic cultivation of this fruit. This study obtains a resource use inventory and assesses the cradle-to-farm gate carbon footprint (CF) of organic blueberry (Vaccinium corymbosum) production in the main cultivation area of Chile in order to identify CF key factors and to provide improvement measures.

Methods

The method used in this study follows the ISO 14040 framework and the main recommendations in the PAS 2050 guide as well as its specification for horticultural products PAS 2050-1. Primary data were collected for three consecutive production seasons from five organic Chilean blueberry orchards and calculations conducted with the GaBi 4 software. Agricultural factors such as fertilizers, pesticides, fossil fuels, electricity, materials, machinery, and direct land use change (LUC) are included. Only three orchards present direct LUC.

Results and discussion

The direct LUC associated with the conversion from annual crops to perennial crops is a key factor in the greenhouse gas removals from the orchards. When accounting for direct LUC, the CF of organic blueberry production in the studied orchards ranges from removals (reported as negative value) of ?0.94 to emissions of 0.61 kg CO₂-e/kg blueberry. CF excluding LUC ranges from 0.27 to 0.69 kg CO₂-e/kg blueberry. The variability in the results of the orchards suggests that the production practices have important effects on the CF. The factors with the greatest contribution to the greenhouse emissions are organic fertilizers followed by energy use causing, on average, 50 and 43 % of total emissions, respectively.

Conclusions

The CF of the organic blueberry orchards under study decreases significantly when taking into account removals related to LUC. The results highlight the importance of reporting separately the greenhouse gas (GHG) emissions from LUC. The CF of blueberry production could be reduced by optimizing fertilizer application, using cover crops and replacing inefficient tractors and large irrigation pumps. The identification of improvement measures would be a useful guide for changing grower practices.

     

Applying water scarcity footprint methodologies to milk production in Finland

The International Journal of Life Cycle Assessment - Food production without consuming scarce local freshwater resources in an unsustainable way needs to be ensured. A robust method to assess water...     

Candidate genes for milk production traits in Portuguese dairy sheep

In this study, the influence of the β-lactoglobulin, prolactin and α_S1-casein variants on milk production traits of three Portuguese sheep breeds, including Serra da Estrela, White Merino and Black Merino, was analyzed. The genetic variants of each marker were identified using PCR-RFLP. β-Lactoglobulin genotype AA was associated with lower milk yield in Serra da Estrela and Merino ewes. This marker also affected milk fat content in Serra da Estrela and protein content in Merino. First results regarding the influence of the sheep prolactin gene on milk production traits are reported. In the Serra da Estrela breed, ewes carrying prolactin genotype AA were significantly associated with lower milk yield, but this influence of prolactin genotypes on milk yield was not detected in Merino. Prolactin genotypes were also significantly associated with milk fat and protein contents in the Serra da Estrela breed. A suggestive effect of the α_S1-casein locus on milk yield was detected in Serra da Estrela, but no associations were found between the variants of this marker with milk fat and protein content.     

An appraisal of carbon footprint of milk from commercial grass-based dairy farms in Ireland according to a certified life cycle assessment methodology

Purpose

Life cycle assessment (LCA) studies of carbon footprint (CF) of milk from grass-based farms are usually limited to small numbers of farms (<30) and rarely certified to international standards, e.g. British Standards Institute publicly available specification 2050 (PAS 2050). The goals of this study were to quantify CF of milk from a large sample of grass-based farms using an accredited PAS 2050 method and to assess the relationships between farm characteristics and CF of milk.

Materials and methods

Data was collected annually using on-farm surveys, milk processor records and national livestock databases for 171 grass-based Irish dairy farms with information successfully obtained electronically from 124 farms and fed into a cradle to farm-gate LCA model. Greenhouse gas (GHG) emissions were estimated with the LCA model in CO₂ equivalents (CO₂-eq) and allocated economically between dairy farm products, except exported crops. Carbon footprint of milk was estimated by expressing GHG emissions attributed to milk per kilogram of fat and protein-corrected milk (FPCM). The Carbon Trust tested the LCA model for non-conformities with PAS 2050. PAS 2050 certification was achieved when non-conformities were fixed or where the effect of all unresolved non-conformities on CF of milk was?<?±5 %.

Results and discussion

The combined effect of LCA model non-conformities with PAS 2050 on CF of milk was <1 %. Consequently, PAS 2050 accreditation was granted. The mean certified CF of milk from grass-based farms was 1.11 kg of CO₂-eq/kg of FPCM, but varied from 0.87 to 1.72 kg of CO₂-eq/kg of FPCM. Although some farm attributes had stronger relationships with CF of milk than the others, no attribute accounted for the majority of variation between farms. However, CF of milk could be reasonably predicted using N efficiency, the length of the grazing season, milk yield/cow and annual replacement rate (R ²?=?0.75). Management changes can be applied simultaneously to improve each of these traits. Thus, grass-based farmers can potentially significantly reduce CF of milk.

Conclusions

The certification of an LCA model to PAS 2050 standards for grass-based dairy farms provides a verifiable approach to quantify CF of milk at a farm or national level. The application of the certified model highlighted a wide range between the CF of milk of commercial farms. However, differences between farms’ CF of milk were explained by variation in various aspects of farm performance. This implies that improving farm efficiency can mitigate CF of milk.     

Genetic merit for milk production and reproductive success in dairy cows

The effect of genetic merit for milk production traits - fat, protein and milk yield - in dairy cows on milk production, body condition, blood metabolites, reproductive hormones, feed intake and reproductive performance was studied over a period of 2 years. Cows were grouped into two categories, based on calculated pedigree indices using multiple-trait across country evaluation (MACE). Cows of high genetic merit (HGM, n = 48 in year 1 and n = 46 in year 2) had a mean predicted difference +/- standard deviation for milk production of 475 +/- 76kg. The cows of medium genetic merit (MGM, n = 48 in both years) had a mean predicted difference for milk production of 140 +/- 68kg.The cows calved between January and April, and were offered grass silage ad libitum plus 9kg concentrates per cow per day, irrespective genetic merit, from calving to turnout in March, when they were subjected to one of three grazing systems. Cows were available for rebreeding from late April until late July of each year.High genetic merit cows had higher milk production, incurred greater body condition loss between calving and first service and had lower plasma glucose and insulin-like growth factor-1 (IGF-1) concentrations than medium genetic merit cows. Furthermore, HGM cows had lower first and second service and overall conception rates, and required more services per conception than the MGM cows.Cows that did not conceive to first service were retrospectively compared to those that conceived to first service within each genetic merit group. There were no significant differences between the HGM cows that did not conceive to first service and those that conceived to this service in terms of milk production, body condition score change between calving and first service, feed intake at first service, or in plasma concentrations of glucose, non-esterified fatty acids (NEFA) or IGF-1. Medium genetic merit cows that did not conceive to first service lost more body condition between calving and first service than did those that conceived to this service.In the present study, HGM cows had higher milk production and reduced reproductive performance in comparison with MGM cows. However, reproductive performance was not associated with milk production, feed intake or plasma concentrations of glucose, NEFA or IGF-1 between calving and first service, since there were no significant differences in these variates between high or medium genetic merit cows that did not conceive to first service and those that conceived to this service. Therefore, these variates are unlikely to be useful predictors of reproductive performance, under the conditions of the present study.     

Sensitivity of the carbon footprint of New Zealand milk to greenhouse gas metrics

Milk production is responsible for emitting a range of greenhouse gases (GHGs), mainly carbon dioxide (CO₂), nitrous oxide (N₂O) and methane (CH₄). In Life Cycle Assessments (LCA), the Global Warming Potential with a time horizon of 100 years (GWP100) is used almost universally to aggregate emissions of individual gases into so-called CO₂-equivalent emissions that are used to calculate the overall carbon footprint of milk production. However, there is growing awareness that, depending on the purpose of the LCA, metrics other than GWP100 could be justified and some would give a very different weighting for the short-lived gas CH₄ relative to the long-lived gases CO₂ and N₂O when calculating the carbon footprint. Pastoral dairy production systems at different levels of intensification differ in the balance of short- and long-lived GHGs associated with on- and off-farm emissions. Differences in the carbon footprint of different production systems could therefore be highly sensitive to the choice of GHG metric. Here we explore the extent to which alternative GHG metric choices would alter the carbon footprint of New Zealand milk production at different levels of intensification at national, regional and individual farm scales and compared to the carbon footprint of milk of selected European countries. We find that the ranking of different production systems and individual farms in terms of their carbon footprint is relatively robust against the choice of GHG metric, despite significant differences in their utilisation of pastures versus supplementary off-farm feed, fertiliser use and energy consumption at various stages of farm operations. However, there are instances where alternative GHG metric choices would fundamentally change the conclusions of LCA of different production systems, including whether a move towards higher or lower input systems would increase or decrease the average carbon footprint of milk production in New Zealand. Greater transparency about the implications of alternative GHG metrics for LCA, and the often inadvertent and implicit value judgements embedded in these metrics, would help ensure that policy decisions and consumer choices based on LCA indeed deliver the climate outcomes intended by end-users.     

Genetic mapping of loci responsible for milk production traits in dairy cattle

The review presents a definition of loci controlling quantitative traits (quantitative trait loci, QTLs) and localization of all currently known QTLs responsible for milk production traits in dairy cattle. The QTL number and chromosome localization are verified, with special reference to chromosomes 1, 3, 6, 14, 20, and 23. In a number of cases, close location of QTLs for mastitis and for milk production traits was found. Some aspects of QTL pleiotropy and epistasis are discussed and mapping methods of major QTLs are listed. Original Russian Text Sc M.G. Smaragdov, 2006, published in Genetika, 2006, Vol. 42, No. 1, pp. 5–21.     

Spatial and technological variability in the carbon footprint of durum wheat production in Iran

Purpose

The purpose of this study was to quantify the spatial and technological variability in life cycle greenhouse gas (GHG) emissions, also called the carbon footprint, of durum wheat production in Iran.

Methods

The calculations were based on information gathered from 90 farms, each with an area ranging from 1 to 150 ha (average 16 ha). The carbon footprint of durum wheat was calculated by quantifying the biogenic GHG emissions of carbon loss from soil and biomass, as well as the GHG emissions from fertilizer application and machinery use, irrigation, transportation, and production of inputs (e.g., fertilizers, seeds, and pesticides). We used Spearman’s rank correlation to quantify the relative influence of technological variability (in crop yields, fossil GHG emissions, and N₂O emissions from fertilizer application) and spatial variability (in biogenic GHG emissions) on the variation of the carbon footprint of durum wheat.

Results and discussion

The average carbon footprint of 1 kg of durum wheat produced was 1.6 kg CO₂-equivalents with a minimum of 0.8 kg and a maximum of 3.0 kg CO₂-equivalents. The correlation analysis showed that variation in crop yield and fertilizer application, representing technological variability, accounted for the majority of the variation in the carbon footprint, respectively 76 and 21%. Spatial variation in biogenic GHG emissions, mainly resulting from differences in natural soil carbon stocks, accounted for 3% of the variation in the carbon footprint. We also observed a non-linear relationship between the carbon footprint and the yield of durum wheat that featured a scaling factor of ?2/3. This indicates that the carbon footprint of durum wheat production (in kg CO₂-eq kg^?1) typically decreases by 67% with a 100% increase in yield (in kg ha^?1 year^?1).

Conclusions

Various sources of variability, including variation between locations and technologies, can influence the results of life cycle assessments. We demonstrated that technological variability exerts a relatively large influence on the carbon footprint of durum wheat produced in Iran with respect to spatial variability. To increase the durum wheat yield at farms with relatively large carbon footprints, technologies such as site-specific nutrient application, combined tillage, and mechanized irrigation techniques should be promoted.

     

Genome-wide associations for investigating time-dependent genetic effects for milk production traits in dairy cattle

Phenotypic variation in milk production traits has been described over the course of a lactation as well as between different parities. The objective of this study was to investigate whether variation in production is affected by different loci across lactations. A genome-wide association study (GWAS) using a 50-k SNP chip was conducted in 152 divergent German Holstein Friesian cows to test for association with milk production traits over different lactations. The first four lactations were analysed regarding milk yield, fat, protein, lactose, milk urea nitrogen yield and content as well as somatic cell score. Two approaches were used: (i) Wilmink curve parameters were used to assess the genetic effects over the course of a lactation and (ii) test-day yield deviations (YD) were used as a normative approach for a GWAS. The significant effects were largest for markers affecting curve parameters for which there was a statistical power <0.8 of detection even in this small design. While significant markers for YDs were detected in this study, the power to detect effects of a similar magnitude was only 0.11, suggesting that many loci may have been missed with this approach in the present design. Furthermore, all significant effects were specific for a single lactation, leading to the conclusion that the variance explained by a certain locus changes from lactation to lactation. We confirm the common evidence that most production traits vary in the degree of persistency after the peak as a result of genetic influence.     

Effect of milk production on the incidence of double ovulation in dairy cows

To determine the effect of parity and milk production on the incidence of double ovulation, the synchronization of ovulation, using GnRH and prostaglandin F2 alpha followed by timed AI (Ovsynch), was initiated at a random stage of the estrous cycle in lactating Holstein cows (n = 237). Ovulatory response at 48 h after the second GnRH injection and conception rate at 28 d post AI were determined by transrectal ultrasonography. Ovulation was synchronized in 84% of cows receiving the Ovsynch protocol. Of the synchronized cows, 14.1% exhibited a double ovulation and 47.6% conceived. Conception rate tended to be greater (P = 0.08) for cows exhibiting double (64.0%) rather than single ovulation (45.2%). To determine the effect of milk production on the incidence of double ovulation, cows were classified into low (< or = 40 kg/d) or high (> 40 kg/d) milk production groups based on the average milk production of 40.5 +/- 0.8 kg/d collected 2 d before AI. Although the incidence of double ovulation tended to increase linearly (P = 0.09) with increasing parity, the incidence of double ovulation was nearly 3-fold greater (P < 0.05) for cows in the high (20.2%) than the low (6.9%) milk production group. Furthermore, the increase in the incidence of double ovulation with parity apparently occurred because, within a parity group, the proportion of cows with high milk production was greater for the older cows. Twinning rate of cows that calved (n = 58) was 5.2%. In a secondary objective, cows were retrospectively classified as cystic or normal based on ultrasonographic ovarian morphology at the time of the second GnRH injection. Incidence of ovarian cysts was 11%, and the synchronization and conception rate of cows classified as cystic was 73.1 and 36.8%, respectively, which did not differ from that of normal cows. We conclude that milk production is the primary factor affecting the incidence of double ovulation in lactating dairy cows and may explain the effect of parity on twinning rate. In addition, Ovsynch appears to be an effective method for establishing pregnancy in lactating dairy cows with ovarian cysts.     

Soil organic matter accounting in the carbon footprint analysis of the wine chain

Purpose

Concerns about global warming led to the calculation of the carbon footprint (CF) left by human activities. The agricultural sector is a significant source of greenhouse gas (GHG) emissions, though cropland soils can also act as sinks. So far, most LCA studies on agricultural products have not considered changes in soil organic matter (SOM). This paper aimed to: (1) integrate the Hénin–Dupuis SOM model into the CF study and (2) outline the impacts of different vineyard soil management scenarios on the overall CF.

Methods

A representative wine chain in the Maremma Rural District, Tuscany (Italy), made up of a cooperative winery and nine of its associated farms, was selected to investigate the production of a non-aged, high-quality red wine. The system boundary was established from vineyard planting to waste management after use. The functional unit (FU) chosen for this study was a 0.75-L bottle of wine, and all data refer to the year 2009. The SOM balance, based on Hénin–Dupuis’ equation, was integrated and run using GaBi4 software. A sensitivity analysis was performed, and four scenarios were developed to assess the impact of vineyard soil management types with decreasing levels of organic matter inputs.

Results and discussion

SOM accounting reduced the overall CF of one wine bottle from 0.663 to 0.531 kg CO₂-eq/FU. The vineyard planting sub-phase produced a loss of SOM while, in the pre-production and production sub-phases, the loss/accumulation of SOM was related to the soil management practices. On average, soil management in the production sub-phase led to a net accumulation of SOM, and the overall vineyard phase was a sink of CO₂. Residue incorporation and grassing were identified as the main factors affecting changes in SOM in vineyard soils.

Conclusions

Our results showed that incorporating SOM accounting into the wine chain’s CF analysis changed the vineyard phase from a GHG source to a modest net GHG sink. These results highlighted the need to include soil C dynamics in the CF of the agricultural product. Here, the SOM balance method proposed was sensitive to changes in management practices and was site specific. Moreover, we were also able to define a minimum data set for SOM accounting. The EU recognises soil carbon sequestration as one of the major European strategies for mitigation. However, specific measures have yet to be included in the CAP 2020. It would be desirable to include soil in the new ISO 14067—Carbon Footprint of Products.     

The number of loci that affect milk production traits in dairy cattle

We have used the results of an experiment mapping quantitative trait loci (QTL) affecting milk yield and composition to estimate the total number of QTL affecting these traits. We did this by estimating the number of segregating QTL within a half-sib daughter design using logic similar to that used to estimate the "false discovery rate" (FDR). In a half-sib daughter design with six sire families we estimate that the average sire was heterozygous for approximately 5 QTL per trait. Also, in most cases only one sire was heterozygous for any one QTL; therefore at least 30 QTL were likely to be segregating for these milk production traits in this Holstein population.     

碳足迹核算的国际标准概述与解析

各种层面上的碳足迹核算在全球气候变化控制领域得到了越来越多的关注。但是,这些关于碳足迹核算的相关国际标准繁多,彼此之间的关系复杂,不利于研究领域和工业界对这些标准进行应用与交流,限制了碳足迹核算的发展进度与深度。对目前已有的国际主要碳足迹核算标准及生命周期评价标准进行了整理,梳理出这些国际标准的一些基本特征,绘制了国际标准之间的关系图;并进一步从生命周期评价步骤的角度出发,解析了各种国际标准在这些阶段上的相关内容,以及每一个阶段上各标准相关规定中的不同特点及逻辑关系。对促进我国碳足迹核算相关研究与实践工作具有一定的理论与现实参考意义。