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Precision dairy tools (PDT) can provide timely information on individual cow's physiological and behavioral parameters, which can lead to more efficient management of the dairy farm. Although the economic rationale behind the adoption of PDT has been extensively discussed in the literature, the socio-psychological aspects related to the adoption of these technologies have received far less attention. Therefore, this paper proposes a socio-psychological model that builds upon the theory of planned behavior and develops hypotheses regarding cognitive constructs, their interaction with the farmers' perceived risks and social networks, and their overall influence on adoption. These hypotheses are tested using a generalized structural equation model for (a) the adoption of automatic milking systems (AMS) on the farms and (b) the PDT that are usually adopted with the AMS. Results show that adoption of these technologies is affected directly by intention, and the effects of subjective norms, perceived control, and attitudes on adoption are mediated through intention. A unit increase in perceived control score is associated with an increase in marginal probability of adoption of AMS and PDT by 0.05 and 0.19, respectively. Subjective norms are associated with an increase in marginal probability of adoption of AMS and PDT by 0.009 and 0.05, respectively. These results suggest that perceived control exerts a stronger influence on adoption of AMS and PDT, particularly compared with their subjective norms. Technology-related social networks are associated with an increase in marginal probability of adoption of AMS and PDT by 0.026 and 0.10, respectively. Perceived risks related to AMS and PDT negatively affect probability of adoption by 0.042 and 0.16, respectively, by having negative effects on attitudes, perceived self-confidence, and intentions. These results imply that integrating farmers within knowledge-sharing networks, minimizing perceived risks associated with these technologies, and enhancing farmers' confidence in their ability to use these technologies can significantly enhance uptake. [ABSTRACT FROM AUTHOR]

Understanding consumers' purchase behaviors is fundamental to the success of the dairy industry. With its economic importance, the Chinese market is critical to dairy producers in most countries around the world. However, understanding consumers in this market is particularly challenging, as these consumers often have a different relationship with dairy products than consumers elsewhere in the world, given the country's historical dairy-related scandals. This special relationship can be characterized by what consumer behavior researchers call "high involvement," indicating that Chinese dairy consumers often attempt to reduce the level of risk associated with buying dairy products. Surprisingly, although this relationship affects several important aspects of purchase behavior, examination of the concept of involvement in the dairy sector has not been widely considered. Of note, there is no understanding of how Chinese consumers vary in their involvement levels and their implications on their dairy purchase behaviors. Nor have there been involvement-based insights provided on how dairy companies can position their marketing strategy to suit the needs of these consumers better. Thus, this study proposes a new approach to understanding Chinese consumer dairy decisions by introducing "consumer involvement" as a segmentation tool through which individuals' behaviors can be predicated according to their involvement profile. Based on an online survey administered in Shanghai using 1,073 dairy consumers, principal component analysis confirmed involvement with dairy is a multidimensional construct with the following 4 factors: pleasure value, symbolic value, risk importance, and risk probability. A 2-step cluster analysis identified 4 consumer clusters based on their involvement profile: face-concerned dairy lover, carefree dairy consumer, cautious dairy lover, and confused dairy consumer. According to a one-way ANOVA test and cross-tabulation with χ2 test, these consumer segments behave differently in relation to the extensiveness of decision making, cue utilization, trust of information sources, and consumption behavior. The outcomes in this paper further explain why efforts to restore consumer trust for dairy products do not work among some consumers, as individuals may exhibit diverse attitudes toward such information due to their heterogeneous involvement levels. The study also provides suggestions for market practitioners and organizations to develop effective target market strategies and policies according to different consumer clusters. [ABSTRACT FROM AUTHOR]

Enteric fermentation from dairy cows is a major source of methane. Significantly and rapidly reducing those emissions would be a powerful lever to mitigate climate change. For a given productivity level, introducing fodder with high sources of n-3 content, such as grass or linseed, in the feed ration of dairy cows both improves the milk nutritional profile and reduces enteric methane emissions per liter. Changing cows' diet may represent additional costs for dairy farmers and calls for the implementation of payments for environmental services to support the transition. This paper analyzes 2 design elements influencing the effectiveness of a payment conditioned toward the reduction of enteric methane emissions: (1) the choice of emission indicator capturing the effect of farmers' practices, and (2) the payment amount relative to the additional milk production costs incurred. Using representative farm-level economic data from the French farm accountancy data network, we compare enteric methane emissions per liter of milk calculated with an Intergovernmental Panel on Climate Change Tier 2 method, to baseline emissions from a Tier 3 method accounting for diet effects. We also quantify the additional milk production costs of integrating more grass in the fodder systems by estimating variable cost functions for different dairy systems in France. Our results show the relevance of using an emission indicator sensitive to diet effects, and that the significance and direction of the additional costs for producing milk with a diet containing more grass differ according to the production basin and the current share of grasslands in the fodder crop rotation. We emphasize the importance of developing payments for environmental services with well-defined environmental indicators accounting for the technical problems addressed, and the need to better characterize heterogeneous funding requirements for supporting a large-scale adoption of more environment-friendly practices by farmers. [ABSTRACT FROM AUTHOR]

A dairy farmer's decision to cull or keep dairy cows is likely a complex decision based on animal health and farm management practices. The present paper investigated the relationship between cow longevity and animal health, and between longevity and farm investments, while controlling for farm-specific characteristics and animal management practices, by using Swedish dairy farm and production data for the period 2009 to 2018. We used the ordinary least square and unconditional quantile regression model to perform mean-based and heterogeneous-based analysis, respectively. Findings from the study indicate that, on average, animal health has a negative but insignificant effect on dairy herd longevity. This implies that culling is predominantly done for other reasons than poor health status. Investment in farm infrastructure has a positive and significant effect on dairy herd longevity. The investment in farm infrastructure creates room for new or superior recruitment heifers without the need to cull existing dairy cows. Production variables that prolong dairy cow longevity include higher milk yield and an extended calving interval. Findings from this study imply that the relatively short longevity of dairy cows in Sweden compared with some dairy producing countries is not a result of problems with health and welfare. Rather, dairy cow longevity in Sweden hinges on the farmers' investment decisions, farm-specific characteristics and animal management practices. [ABSTRACT FROM AUTHOR]

The objective of this cross-sectional study was to investigate the relationship between management practices and antimicrobial use in heifer calves on Canadian dairy farms. Questionnaires on calf management practices, herd characteristics, and calf treatment records were administered on 147 dairy farms in 5 provinces during annual farm visits in a multiyear, nationwide research project (Canadian Dairy Network for Antimicrobial Stewardship and Resistance: CaDNetASR). Questions focused on the calf caregiver, calving pen, colostrum management, milk feeding, grouping, bedding management, and age when male calves were sold. Antimicrobial treatment records were collected on each farm from either an electronic herd management system or paper-based records. Newborn heifers born in the last 12 mo were identified retrospectively and followed to 60 d of age, with antimicrobial treatments and dates of sale or death extracted for further analysis. A multivariable linear regression model was developed with the natural log of the number of antimicrobial treatments per calf-year as the dependent variable, and categorized calf management practices and farm characteristics as the independent variables. A complete data set of records on 7,817 calves was retrieved from 74 farms based on completeness of calf records. A total of 2,310 calves were treated at least once with an antimicrobial, and 7,307 individual antimicrobial treatments were recorded. Among the reasons for antimicrobial use, respiratory disease (54%) was most common, followed by diarrhea (20%), presence of a fever (3%), and umbilical disease (2%). Florfenicol (33% of recorded treatments), penicillin (23%), and trimethoprim-sulfamethoxazole (18%) were commonly used, whereas fluoroquinolones (4%), and ceftiofur (1%) were used less commonly. Farms (31%) commonly had 0–1.0 antimicrobial treatments/calf-year (median: 2.2 treatments/calf-year; interquartile range: 0.64–6.43 treatments/calf-year). Defined daily dose (DDD) per calf-year was calculated based on the Canadian bovine standards. Among the 74 farms, florfenicol (1.35 DDD/calf-year) and macrolides (0.73 DDD/calf-year) were used most, whereas ceftiofur (0.008 DDD/calf-year) was the lowest. The final multivariable linear regression model indicated that farms that fed transition milk had fewer than half the number of antimicrobial treatments per calf-year than those who did not feed transition milk. The number of antimicrobial treatments per calf-year in preweaning calves was low on many farms, and there was low use of highly important drugs for human medicine. The effect of feeding transition milk should be investigated regarding potential effects on antimicrobial use and disease prevention. [ABSTRACT FROM AUTHOR]

Lameness is one of the major health problems on dairy farms, which seriously affects dairy cow welfare and increases the risk of premature culling. Accuracy lameness detection ensures timely treatment of hooves and improves the level of health management on dairy farms. Most of the existing lameness detection methods detect one dairy cow, and it is difficult to detect lameness in multiple dairy cows at the same time. In this paper, a lameness detection system based on instance segmentation is constructed to extract the lameness features of multiple dairy cows and automatically detect lameness. First, an improved SOLOv2 network is designed to reduce the semantic gap between low-level and high-level features and improve the precision of dairy cow segmentation. Second, individual matching of dairy cows in different video frames is performed using the Hungarian algorithm. Third, the Canny algorithm is used to extract the neck and back contour features and key gait features of dairy cows. Finally, a deep learning model is constructed, and multiple lameness features are fused to detect the lameness of dairy cows. To evaluate the performance of the constructed system, 172 videos were randomly selected from 246 videos as training videos, and the remaining 74 videos were selected as the test videos. The lameness detection accuracy of the constructed system was 98.65%. The experimental results showed that the constructed system can extract the lameness features of multiple dairy cows at the same time and accurately detect the lameness of dairy cows. [ABSTRACT FROM AUTHOR]

Globally, climate change is a challenge for the dairy sector and its effects are expected to have important consequences on the environmental performance of the dairy products value chains. At the same time, this sector significantly contributes to global warming and other environmental impacts. This paper addresses this twin challenge from a life cycle perspective, i.e. covering from dairy farms, dairy factory, distribution and retail, to consumption. To do so, literature reviews were done on the contribution of the sector to climate change and on the biophysical impacts of climate change on the dairy sector in the near term in Europe. Both reviews were linked to qualitatively analyse the interaction and connect in a matrix the biophysical impacts caused by the effects of climate change on the environmental performance of the sector. Not surprisingly, dairy farms were identified as the major contributor to the total greenhouse gas emissions across the dairy value chains but also as the most vulnerable stage to climate change. Depending on the region, the dairy sector will face opportunities but also threats such as significant cows' heat stress, crop cultivation variability, on-farm water availability, cows' diseases, crop pests' pressure and product safety risk, which is associated with product losses and waste. Measures will be needed to mitigate them but with an environmental cost. The clear definition of the dairy sector-climate change interaction is the starting point to begin preparing this sector for a near-future under climate change conditions. • Climate change effects influences the dairy sector's environmental performance. • Dairy farm stage is the main contributor to climate change and the most vulnerable. • The European dairy sector will experience unequally the opportunities and threats. • Climate change will raise food safety risk, raw milk losses and dairy product waste. [ABSTRACT FROM AUTHOR]

• Model which simulates dairy farm electricity use in 15-min intervals. • Validated on 3 dairy farms of varying size. • Suitable for studying renewable energy integration and demand side management. • Reliable tool for decision support for dairy operations. The objective of this paper was to define, validate and demonstrate a model capable of accurately simulating dairy farm electricity consumption across varying herd and parlour sizes, to facilitate research investigating renewable energy systems (RES) and demand side management (DSM). The Farm Electricity System Simulator (FESS) was developed using grey-box modelling techniques utilizing empirical data for parameter tuning. Empirical data were gathered from nine spring calving, pasture based dairy farms located in the Republic of Ireland. A k-means clustering analysis was conducted, separating the farms into three, near homogenous groups, from which representative farms were selected. FESS was trained using 12 months of data from three representative farms using the repeat hold out method for data partitioning with 75 % of data used for training and 25 % used for validation. An optimisation algorithm was used to minimize the error during model training. Through cross-validation, FESS achieved a root mean squared error (RMSE) of 7.65 kWh, mean absolute percentage error (MAPE) of 7.10 %, mean percentage error (MPE) of −0.86 % and a relative prediction error (RPE) of 7.56 % for total daily electricity consumption. Across the three farms, the simulated outputs of FESS achieved an average R2 value of 0.72, demonstrating good agreement with observed data. FESS's utility was demonstrated by analysing the effects of different electricity pricing structures and on-site solar photovoltaic electricity generation on total farm energy costs. We concluded that FESS simulated on-farm electricity consumption with sufficient accuracy for the intended application. FESS accurately simulated dairy farm electricity consumption across three dairy farms of different herd and parlour sizes while evaluating the effects of demand side management and renewable generation on farm electricity consumption and costs. [ABSTRACT FROM AUTHOR]

The dairy sector has been poised for fierce competitiveness due to the commercialization of the dairy sector and the presence of a global market size of 893 billion dollars. It has received a new dimension with the challenge of environmental sustainability. Considering all these challenges, the present research paper studies the major growth factors and the sustainable approach to dairy development in an emerging economy like India. Fifteen major dairy-developed states of India have contributed 90% of the milk production of the country examined from 2001 to 2019. The significance of key growth factors for dairy farming and the dairy value chain is assessed. The opportunity for sustainable dairying is examined. The authors have found that the growth factors of dairy farming and dairy value chain are significant by 1% through the fixed effect (FE) model and random effect (RE) model respectively. Indian dairy farming practices and their approach create optimism about environmental sustainability. The present study has observed a series of operational activities that are conducive to the triple bottom approach. With that, this study has mentioned strategies that can steer growth momentum and support sustainable Indian dairy development in the coming times. [Display omitted] • Three key areas, namely, dairy farming, dairy value chain and environmentally friendly practices for sustainable dairy development. • Two main types of variables (internal and external growth factors) are involved in the development of the dairy farming. • Strategies are formulated to improve milk productivity. • In line with the triple bottom line approach, a series of operational activities are suggested. [ABSTRACT FROM AUTHOR]

The present study analysed the importance of individual variables and different thematic blocks of production areas, management, and herd infectious disease status on cow persistence, characterised by herd on-farm mortality rate (MR), culling rate (CR), and mean age of culled cows (MAofCC) applying multiblock partial least squares (mbPLS) analysis. This study included 120 free-stall dairy herds with ≥ 100 cows. Data on the previous year's predominant cow housing system and management practices were collected, and on-farm measurements and cow scoring were performed. Bulk tank milk (BTM) and heifer blood samples (10 samples per herd) were collected and analysed for antibodies against the selected pathogens. In total, 172 variables were aggregated into 14 thematic blocks. The annual CR, MR, and MAofCC values were calculated for each herd. Thematic blocks with significant impact on cow persistence (included herd MR, CR and MAofCC) were 'infectious diseases' (block importance index out of all blocks = 13.6%, 95% CI 10.3; 20.5), 'fertility management' (16.3%, 95% CI 6.8; 26.9), 'lactating cow management' (11.5%, 95% CI 6.4; 17.8), 'milking' (11.3%, 95% CI 3.2; 17.1), 'herd characteristics' (10.1%, 95% CI 6.3; 14.2), 'close-up period management' (9.7%, 95% CI 2.7; 15.7), 'calving management' (7.9%, 95% CI 3.1; 11.4) and 'disease management' (7.3%, 95% CI 0.2; 12.0). Variable categories with the highest importance in explaining composite outcome including herd MR, CR and MAofCC were rear-end and udder lesions in ≥ 20% of the cows, BTM and heifers seropositive to bovine respiratory syncytial virus, vaccination against bovine herpesvirus 1, twice daily milking and herd location in Northwest region. Larger herd size, higher levels of milk yield, and rearing predominantly Holstein breed cattle were herd factors associated with poorer cow persistency. Grazing cows and having semi-insulated barns were associated with lower CR and MR, respectively. Heat detection and farm pregnancy testing strategies were significant factors in the fertility block. Using disposable dry papers for teat cleaning and not using any wet teat-cleaning tools were risk factors for high MR. A robotic milking system was protective for increased herd MR and CR. A high pre-calving body condition score and poor rear body cleanliness of ≥ 30% of cows were associated with inferior herd persistency outcomes. Calving in group pens with deep litter bedding was associated with a lower CR. Multiblock PLS model is innovative tool that helped to identify most influential farming areas but also single risk factors associated with cow persistency described by multiple parameters. [ABSTRACT FROM AUTHOR]

In China, people have a great demand for milk in their daily life. However, the process of dairy farming could produce greenhouse gases, resulting in global warming. Therefore, this paper analyzes the green total factor productivity of dairy cow (GDC) of China's 27 provinces in different scales and regions from 2006 to 2018, in order to improve the production efficiency of dairy cows and reduce pollution emissions. Based on Slack Based Measure (SBM) model and three-level Meta-frontier Malmquist Luenberger (MML) index, this paper analyzes the GDC from aspect of scale structure efficiency, technical efficiency and management efficiency. The following conclusions are drawn: (1) From 2006 to 2018, China's GDC has been in a state of fluctuation, which showing an upward trend on the whole, with an average annual growth rate of 2.45 %. In particular，TE increased by 0.40 %, ME increased by 2.48 %, and SE decreased by 0.44 %. (2) In terms of scale, the GDC of middle-scale is the highest, followed by large-scale, and then the GDC of small-scale is the lowest. It is obvious that the SE of small-scale and middle-scale is low, and the TE of large-scale is low. (3) In terms of regional distribution, the GDC in the western region is the highest, followed by eastern region, and then the central region. It should note that SE is low in the eastern and central regions, and TE is low in the western region. This paper provides theoretical guidance for the development of low-carbon dairy farming in China. [ABSTRACT FROM AUTHOR]

This paper explores the associations between a set of mainly health and productivity related animal welfare indicators in dairy farming calculated based on herd recordings, and short-term economic efficiency. To explore this association, we merged a dataset with animal welfare indicators and a dataset with farm economic data from 459 Norwegian farms. Gross margin including direct payments together with milk quota, farmland and number of animals and were used to calculate economic efficiency. The analysis was performed in two steps. First, Data Envelopment Analysis (DEA) was used to rank the farms according to efficiency. Second, the machine learning method boosting was applied to analyze the associations between economic efficiency and the different welfare indicators. The findings show that for most welfare indicators, at indicator values below the national average a positive association exists between increasing indicator values and economic efficiency. For some indicators such as for example Fertility, Calves and Youngstock, this positive association continues also beyond the national average. However, for other indicators such as for example Claw and Udder health, a curvilinear relationship exists with a turning point from where the efficiency is negatively associated with increasing indicator values. Possible explanations for the associations between economic efficiency and animal welfare indicators are outlined. [ABSTRACT FROM AUTHOR]

As elsewhere in Western Europe, large scale intensive agriculture dominates the landscape of the Netherlands. Grassland for dairy production occupies more than a quarter of its land surface. The high production intensity on conventionally farmed grassland leads to poor habitat quality, resulting in sharp declines in bird and insect numbers. Nature-inclusive agriculture (NIA) comprises innovations in farm management, technology and resource use that have the potential to address farmland biodiversity decline, but few Dutch farmers implement these. We aim to analyze the adoption of NIA practices in the Dutch dairy sector. Specifically, we study the influence of the dominant agri-food regime on the innovation system for NIA. Innovation Systems Analysis was performed to identify the various structural barriers which hinder adoption. Our study used a multi-method design in which data on NIA in the Dutch dairy sector was collected via a literature study, four workshops and a focus group discussion. We identified five key blocking mechanisms that hinder adoption of nature-inclusive agriculture in the Dutch dairy sector: (1) insufficient economic incentives for farmers, (2) limited action perspective of many dairy farmers in the Netherlands, (3) lack of a concrete and shared vision for NIA, (4) lack of NIA-specific and integral knowledge and (5) regime resistance, which moreover is connected to each of the previous blocking mechanisms. Our analysis shows that one of the empirical novelties of this paper is that these blocking mechanisms are strongly interlinked in the Dutch dairy sector, thereby perpetuating a situation of lock-in. We conclude that in order to accelerate adoption of nature-inclusive farming practices, problems need to be addressed in conjunction with one another, and therefore holistic approaches are key. A second important conclusion is that in order to foster growth of the innovation system around NIA, the focus should not only be on innovation, but also on transforming current regimes, in particular the currently dominant economic paradigms of growth and yield maximization. By unraveling strongly interlinked blocking mechanisms, this paper provides intervention points to accelerate the transition towards NIA in the Netherlands. These intervention points are not only located within the innovation system, but should preferable be sought for in the broader structures and institutions of the dominant agri-food regime. [Display omitted] • Uptake of nature-inclusive practices in the Dutch dairy sector is currently low. • Through Innovation Systems Analysis we identified five blocking mechanisms hindering uptake. • These include lack of financial incentives and shared visions, limited action perspectives and knowledge transfer problems • The productivist agriculture paradigm is a recurring blocking mechanism requiring institutional change • Change needs to come from incumbents, requiring policies aimed at regime transformation [ABSTRACT FROM AUTHOR]

Regenerative agriculture is a farming approach that uses soil health as the entry point to contribute to multiple objectives, such as improved nutrient cycling and climate regulation. To reach these objectives farmers can apply different practices. The objectives and practices, however, are not equally relevant or applicable for every farming system and local context. The main objective of this paper, therefore, was to find out how tailor-made solutions towards regenerative agriculture can be identified and evaluated as such that they result in meaning-full advice for farmers. In this study a well-established modelling framework to redesign farming systems was applied to three typical but diverse Dutch farming systems. The modelling framework combined the models Soil Navigator and FarmDESIGN to simultaneously assess five soil functions at field-level and general sustainability indicators (e.g. greenhouse gas emissions) at farm-level. We applied the modelling framework to an arable farm on clay soil, a dairy farm on peat soil, and a mixed farm on sand soil. We subsequently explored a multitude of tailor-made solutions composed of combinations of practices for these farming systems, each showing solutions that contributed in varying degrees towards the objectives of regenerative agriculture. In total, we created 4000 alternative solutions per case-study farm. For all farming systems, environmental performance was improved in the solutions dominated by the use of regenerative practices. For example, for the arable, the dairy, and the mixed case-study farm, greenhouse gas emissions were reduced by 50% (from 4 to 2 Mg CO 2 eq. ha−1), 6% (from 30 to 28 Mg CO 2 eq. ha−1), and 23% (from 21 to 16 Mg CO 2 eq. ha−1), respectively, while maintaining soil functionality at high capacity for four out of the five soil functions. This overall improvement in environmental performance due to the application of regenerative practices, also resulted in reduced farm profitability for all case-study farms by on average 50%. We discuss that a mechanism to incentivize farmers for their tailor-made contribution to regenerative agriculture is for stakeholders to shift focus from solely primary productivity to also other ecosystem services. This study contributes to the wider implementation of regenerative agriculture, by showing which regenerative objectives and farming practices can contribute to the transition towards regenerative agriculture in contrasting contexts. The modelling framework that is used, can underpin regenerative management for farmers and other stakeholders to help, for example, the valorization of multiple regenerative objectives in business models. [Display omitted] • This study explored a novel modelling framework to redesign farms towards regenerative agriculture. • This framework can be used to analyze a multitude of tailor-made solutions for diverse farming systems. • From this framework farmers can select the solutions that fit their local context and intrinsic motivations best. • For our case-study farms, environmental performance improved when regenerative management practices increased. • This improvement in environmental performance, however, came at the expense of farm profitability. [ABSTRACT FROM AUTHOR]

The concept of creating shared value was first advanced by Harvard Business School and adopted by companies to develop sustainability strategies to maximize their positive impacts on society. Eco-efficiency is a sustainability concept and a prevalent operational decision support tool that assesses the environmental performance of a system as a function of its value. This paper proposes a novel multidimensional value eco-efficiency framework based on a life cycle perspective that broadens the value dimension assessment in keeping with the concept of creating shared value. The framework is tested and implemented in a case study in the Canadian dairy industry. It uses a systemic approach to compare the eco-efficiency performance of 11 multifunctional Greek yogurt systems delivering various co-products with different functions and economic values across the value chain. It makes it possible to identify the trade-offs between the environment and various value dimensions (economic or functional) based on stakeholders' interests. The results show that the scenarios that create the most economic value for the Greek yogurt processors do not perform as well for the other stakeholders along the value chain or in terms of overall environmental performance. By developing a multi-criteria value assessment, this enhanced eco-efficiency framework brings consistency in covering the same scope of analysis between value creation and environmental impacts. More specifically it breaks with the industry's prevailing value creation philosophy and value measurement in eco-efficiency analysis, which is usually based on cost-effectiveness and profit maximization for one stakeholder at the expense of the others in the value chain. Ultimately, it contributes to align companies' efforts in improving their eco-efficiency with sustainability goals and minimize the risk of burden shifting. [Display omitted] • Novel eco-efficiency framework to measure shared value creation. • Multidimensional value indicators to assess trade-offs among the value chain actors. • Case study on 11 Greek yogurt production systems in the Canadian dairy sector. • New metrics to better align company efforts with global sustainability objectives. [ABSTRACT FROM AUTHOR]