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Dairy farmers need on-site, real-time information regarding mastitis occurrence in order to implement appropriate management actions. Technological progress has enabled the use of sensor systems as the basis for mastitis management. In this chapter, we describe the current state of automated mastitis detection systems in dairy cattle and potential new developments in mastitis detection. Most developed detection systems are focused on clinical mastitis, with systems utilizing electrical conductivity, on-line California mastitis tests, L-lactate dehydrogenase and on-line somatic cell counting being commercially available. The results of these systems are sufficient for practical use but not ideal. In the search for improved automatic mastitis detection, research is focused on: 1) improving existing and developing new sensors, 2) improving existing algorithms and combining more data sources within the analysis, and 3) defining specific mastitis situations for custom-made management measures.

Mastitis, or udder inflammation, is one of the most prevalent and costliest diseases in dairy farming. Automatic milking systems, equipped with sensors measuring mastitis indicators, have been used commercially since the 1990s. The overall objective for this PhD project was to explore the potential applications for a decision support system in automatic milking systems supporting chronic mastitis decision-making. Paper I described that mastitis cases usually recover in somatic cell count within three to four weeks. Paper II found strong non-linearities between milk production and lactate dehydrogenase, somatic cell count, and electrical conductivity, combined with possible actionable thresholds based on the size of milk yield loss. Paper III showed that it was possible to forecast the progression of mastitis. Finally, Paper IV estimated the economic impact of different sensor-based mastitis management strategies to show which strategy tends to decrease the cost of mastitis and chronic mastitis the most. More specifically, it estimated the economic consequences of chronic mastitis cases to show the direct impact of management failure on the economic situation of a dairy farm. This thesis shows that it is possible to support management regarding chronic mastitis with sensors, and it provides the basis for a decision support system. This decision support system would be a system that could tell the farmer which cases of mastitis are chronic, are likely to become chronic, are associated with large milk production loss, and could tell the economic consequences of chronic mastitis cases.

Bovine viral diarrhoea (BVD) is a viral cattle disease that presents in most cattle-raising countries worldwide and is listed by the World Organisation for Animal Health as a notifiable disease. Infection of bovine viral diarrhoea virus (BVDV) negatively effects the production and economic performance of the dairy herds. Many countries and regions developed BVDV control or eradication programmes and are at different stages of BVDV control. Therefore, information required for decision making in these countries is different. The overall objective of this thesis was to determine the effects of BVDV infection on the production and economic performance of dairy herds in order to support decision making in countries with and without a systematic BVDV control programme. A systematic BVDV control programme is identified by three central elements: biosecurity, elimination of PI animals and surveillance, as opposed to control attempts without clear objectives and monitoring to assess progress. The Netherlands and China were studied as countries with and without a systematic BVDV control programme, respectively. This thesis covers methodologies from different scientific disciplines including veterinary epidemiology, economics, bio-economic simulation model. The results of this thesis showed that 1) participating in the BVDV control programme at the final stage did not significantly affect the production and economic performance of the herd; 2) The new introduction of BVDV had a negative, but on average a relatively small, effect on milk production, somatic cell count and calf mortality rate in BVDV-free herds participating in the Dutch BVDV-free programme; 3) The within-herd seroprevalence of BVDV in 3 large commercial dairy herds in North China was very high, ranging between 96.3% and 100.0%; and 4) BVDV introduction in a large-scale Chinese dairy herd caused large production and economic losses

The twofold aim of this paper is to provide an overview of the current state of resilience research with regard to climate change in the social sciences and propose a research agenda. Resilience research among social scientists is characterized by much more diversity today than a few decades ago. Different definitions and understandings of resilience appear in publications during the last 10 years. Resilience research increasingly bears the mark of social constructivism, a relative newcomer compared to the more long-standing tradition of naturalism. There are also approaches that are indebted to both "naturalism" and "constructivism", which, of course, come in many varieties. Based on our overview of recent scholarship, which is far from being exhaustive, we have identified six research avenues that arguably deserve continued attention. They combine naturalist and constructivist insights and approaches so that human agency, reflexivity, and considerations of justice and equity are incorporated into systems thinking research or supplement such research. Ultimately, we believe that the overarching challenge for future research is to ensure that resilience to climate change does not compromise sustainability and considerations of justice (including environmental, climate, and energy justice)..

Sensor technologies for mastitis detection have resulted in the collection and availability of a large amount of data. As a result, scientific publications reporting mastitis detection research have become less driven by approaches based on biological assumptions and more by data-driven modeling. Most of these approaches try to predict mastitis events from (combinations of) raw sensor data to which a wide variety of methods are applied originating from machine learning and classical statistical approaches. However, an even wider variety in terminologies is used by researchers for methods that are similar in nature. This makes it difficult for readers from other disciplines to understand the specific methods that are used and how these differ from each other. The aim of this paper was to provide a framework (filtering, transformation, and classification) for describing the different methods applied in sensor data–based clinical mastitis detection research and use this framework to review and categorize the approaches and underlying methods described in the scientific literature on mastitis detection. We identified 40 scientific publications between 1992 and 2020 that applied methods to detect clinical mastitis from sensor data. Based on these publications, we developed and used the framework and categorized these scientific publications into the 2 data processing techniques of filtering and transformation. These data processing techniques make raw data more amendable to be used for the third step in our framework, that of classification, which is used to distinguish between healthy and nonhealthy (mastitis) cows. Most publications (n = 34) used filtering or transformation, or a combination of these 2, for data processing before classification, whereas the remaining publications (n = 6) classified the observations directly from raw data. Concerning classification, applying a simple threshold was the most used method (n = 19 publications). Our work identified that w

Sensor technologies for mastitis detection have resulted in the collection and availability of a large amount of data. As a result, scientific publications reporting mastitis detection research have become less driven by approaches based on biological assumptions and more by data-driven modeling. Most of these approaches try to predict mastitis events from (combinations of) raw sensor data to which a wide variety of methods are applied originating from machine learning and classical statistical approaches. However, an even wider variety in terminologies is used by researchers for methods that are similar in nature. This makes it difficult for readers from other disciplines to understand the specific methods that are used and how these differ from each other. The aim of this paper was to provide a framework (filtering, transformation, and classification) for describing the different methods applied in sensor data–based clinical mastitis detection research and use this framework to review and categorize the approaches and underlying methods described in the scientific literature on mastitis detection. We identified 40 scientific publications between 1992 and 2020 that applied methods to detect clinical mastitis from sensor data. Based on these publications, we developed and used the framework and categorized these scientific publications into the 2 data processing techniques of filtering and transformation. These data processing techniques make raw data more amendable to be used for the third step in our framework, that of classification, which is used to distinguish between healthy and nonhealthy (mastitis) cows. Most publications (n = 34) used filtering or transformation, or a combination of these 2, for data processing before classification, whereas the remaining publications (n = 6) classified the observations directly from raw data. Concerning classification, applying a simple threshold was the most used method (n = 19 publications). Our work identified that with

Dairy cows are negatively affected by the introduction of bovine viral diarrhea virus (BVDV), and consequently, produce less milk. Existing literature on potential milk production losses is based on relatively outdated data and hardly evaluates milk production loss in relation to a new BVDV infection in a surveillance system. This study determined the annual and quarterly loss in milk production of BVDV introduction in 3,126 dairy herds participating in the Dutch BVDV-free program between 2007 and 2017. Among these herds, 640 were “breakdown-herds” that obtained and subsequently lost their BVDV-free status during the study period, and 2,486 herds obtained and retained their BVDV-free status during the study period. Milk yields before and after BVDV introduction were compared through annual and quarterly linear mixed models. The fixed variables for both models included herd type (breakdown-herd or free-herd), bovine viral diarrhea status (on an annual and quarterly basis), year, season, and a random herd effect. The dependent variable was the average daily milk yield on the test day. To define the possible BVDV-introduction dates, 4 scenarios were developed. In the default scenario, the date of breakdown (i.e., loss of the BVDV-free status) was assumed as the BVDV-introduction date. For the other 3 scenarios, the BVDV-introduction dates were set at 4, 6, and 9 mo before the date of breakdown, based on the estimated birth date of a persistently infected calf. In the default scenario, the loss in milk yield due to BVDV introduction occurred mainly in the first year after breakdown, with a reduction in yield of 0.08 kg/cow per day compared with the last year before breakdown. For the other 3 scenarios, the greatest yield reduction occurred in the second year after BVDV introduction, with a loss of 0.09, 0.09, and 0.1 kg/cow per day, respectively. For the first 4 quarters after BVDV introduction in the default scenario, milk yield loss was 0.14, 0.09, 0.02, and 0.08 kg/c

Transportation of flammable liquefied gas in tunnels presents a significant risk of an accidental loss of containment leading to an explosion with major consequences. Possible scenarios include a BLEVE, a non-reactive gas expansion explosion and a reactive gas explosion. Quantification of the risk and consequences associated with such events is central in the design of tunnels and routing of dangerous goods. TNO previously developed a Quantitative Risk Analysis (QRA) method, which combines a probability assessment with state-of-the-art explosion effect and consequence models. The current article extends this model to combine the dispersion of a flammable cloud with its probability of ignition and the resulting physical effects such as overpressure. The model assumes an increasing probability of ignition with both the number and the duration of vehicles present within the flammable cloud. Various case studies are considered to illustrate the effect of different ignition probability parameters. These cases deal with instantaneous and continuous LPG releases with varying release rates including the effect of ventilation. They clearly show the capability to quantify the ignition probabilities and gas explosion load. The combination of the gas dispersion, gas explosion and ignition probability models are needed to derive design loads for tunnels, to perform tunnel risk assessments, and to develop safety measures. These models form the backbone for quantitative risk assessments., Applied Mechanics