Emphasis on resilience in dairy cattle breeding: Possibilities and consequences.

This study aimed to investigate dairy cattle breeding goals with more emphasis on resilience. We simulated the consequences of increasing weight on resilience indicators and an assumed true resilience trait (TR). Two environments with different breeding goals were simulated to represent the variability of production systems across Europe. Ten different scenarios were stochastically simulated in a so-called pseudogenomic simulation approach. We showed that many modern dairy cattle breeding goals most likely have negative genetic gain for TR and promising resilience indicators such as the log-transformed, daily deviation from the lactation curve (LnVAR). In addition, there were many ways of improving TR by increasing the breeding goal weight of different resilience indicators. The results showed that adding breeding goal weight to resilience indicators, such as body condition score and LnVAR, could reverse the negative trend observed for resilience indicators. Loss in the aggregate genotype calculated with only current breeding goal traits was 12 to 76%. This loss was mainly due to a reduction in genetic gain in milk production. We observed higher genetic gain in beef production, fertility, and udder health when breeding for more resilience, but from an economical point of view, this was not high enough to compensate for the reduction in genetic gain in milk production. The highest genetic gain in TR was obtained when adding the highest breeding goal weight to LnVAR or TR, both with 0.29 genetic standard deviation units. The indicators we used, body condition score and LnVAR, can be measured on a large scale today with relatively cheap methods, which is crucial if we want to improve these traits through breeding. Economic values for resilience have to be estimated to find the most optimal breeding goal for a more resilient dairy cow in the future.

Possibilities for a specific breeding program for organic dairy production.

Organic dairy production differs from conventional dairy production in many aspects. However, breeding programs for the 2 production systems are the same in most countries. Breeding goals (BG) might be different for the 2 production systems and genotype × environment interaction may exist between organic and conventional dairy production, both of which have an effect on genetic gain in different breeding strategies. Other aspects also need to be considered, such as the application of multiple ovulation and embryo transfer (MOET), which is not allowed in organic dairy production. The general aim of this research was to assess different environment-specific breeding strategies for organic dairy production. The specific aim was to study differences in BG weights and include the effect of genotype × environment interaction, MOET, and the selection of breeding bulls from the conventional environment. Different scenarios were simulated. In the current scenario, the present-day situation for dairy production in Denmark was emulated as much as possible. The BG was based on a conventional dairy production system, MOET was applied in both environments, and conventional bulls could be selected as breeding bulls in the organic environment. Four alternative scenarios were simulated, all with a specific organic BG in the organic breeding program but differences in the usage of MOET and the selection of conventional bulls as breeding bulls. Implementation of a specific BG in organic dairy production slightly increased genetic gain in the aggregate genotype compared with the breeding program that is currently implemented in organic dairy production. Not using embryo transfer or only selecting breeding bulls from the organic environment decreased genetic gain in the aggregate genotype by as much as 24%. However, the use of embryo transfer is debatable because this is not allowed according to current regulations for organic dairy production. Assessing genetic gain on trait levels showed that a significant increase for functional traits was possible compared with the current breeding program in the organic environment without a decrease in genetic gain in the aggregate genotype. This difference on trait level was even more present when selection of conventional bulls as breeding bulls in the organic environment was not possible. This finding is very relevant when breeding for the desired cow in organic dairy production.

Genomic selection improves the possibility of applying multiple breeding programs in different environments.

One joint breeding program (BP) for different dairy cattle environments can be advantageous for genetic gain depending on the genetic correlation between environments (rg). The break-even correlation (rb) refers to the specific rg where genetic gain with 1 joint BP is equal to the genetic gain of 2 environment-specific BP. One joint BP has the highest genetic gain if rg is higher than rb, whereas 2 environment-specific BP have higher genetic gain if rg is lower than rb. Genetic gain in this context is evaluated from a breeding company's perspective that aims to improve genetic gain in both environments. With the implementation of genomic selection, 2 types of collaboration can be identified: exchanging breeding animals and exchanging genomic information. The aim of this study was to study genetic gain in multiple environments with different breeding strategies with genomic selection. The specific aims were (1) to find rb when applying genomic selection; (2) to assess how much genetic gain is lost when applying a suboptimal breeding strategy; (3) to study the effect of the reliability of direct genomic values, number of genotyped animals, and environments of different size on rb and genetic gain; and (4) to find rb from each environment's point of view. Three breeding strategies were simulated: 1 joint BP for both environments, 2 environment-specific BP with selection of bulls across environments, and 2 environment-specific BP with selection of bulls within environments. The rb was 0.65 and not different from rb with progeny-testing breeding programs when compared at the same selection intensity. The maximum loss in genetic gain in a suboptimal breeding strategy was 24%. A higher direct genomic value reliability and an increased number of genotyped selection candidates increased genetic gain, and the effect on rb was not large. A different size in 2 environments decreased rb by, at most, 0.10 points. From a large environment's point of view, 1 joint BP was the optimal breeding strategy in most scenarios. From a small environment's point of view, 1 joint BP was only the optimal breeding strategy at high rg. When the exchange of breeding animals between environments was restricted, genetic gain could still increase in each environment. This was due to the exchange of genomic information between environments, even when rg between environments were as low as 0.4. Thus, genomic selection improves the possibility of applying environment-specific BP.

Simulating consequences of choosing a breeding goal for organic dairy production.

In Denmark, Finland, and Sweden, the Nordic Total Merit index is used as the breeding selection tool for both organic and conventional dairy farmers based on common economic models for conventional dairy farming. Organic farming is based on the principles of organic agriculture (POA) defined by the International Federation of Organic Agriculture Movements. These principles are not set up with an economic point of view, and therefore it may be questionable to use a breeding goal (BG) for organic dairy production based on economic models. In addition to economics and the principles of organic agriculture, it is important to look at farmers' preferences for improving BG traits when setting up a BG for organic farming. The aim of this research was to set up, simulate, and compare long-term effects of different BG for organic and conventional dairy production systems based on economic models, farmers' preferences, and POA, with particular emphasis on disease resistance or on roughage consumption and feed efficiency. The BG based on economic models and on farmers' preferences were taken from previous studies. The other BG were desired gains indices, set up by means of a questionnaire about relatedness between the POA and BG traits. Each BG was simulated in the stochastic simulation program ADAM. The BG based on POA, with particular emphasis on disease resistance or on roughage consumption and feed efficiency, caused favorable genetic gain in all 12 traits included in this study compared with 6 traits for the other BG. The BG based on POA, with particular emphasis on disease resistance or on roughage consumption and feed efficiency, were very different from BG for organic and conventional production based on economic models and farmers' preferences in both simulated genetic change and correlations between BG. The BG that was created based on the principles of organic agriculture could be used as a specific index for organic dairy farming in Denmark, but this index was economically not very sustainable. Hence, an intermediate breeding goal could be developed by breeding companies to address both economics and the principles of organic agriculture.

Organic dairy farmers put more emphasis on production traits than conventional farmers.

The overall aim of this research was to characterize the preferences of Danish dairy farmers for improvements in breeding goal traits. The specific aims were (1) to investigate the presence of heterogeneity in farmers' preferences by means of cluster analysis, and (2) to associate these clusters with herd characteristics and production systems (organic or conventional). We established a web-based survey to characterize the preferences of farmers for improvements in 10 traits, by means of pairwise rankings. We also collected a considerable number of herd characteristics. Overall, 106 organic farmers and 290 conventional farmers answered the survey, all with Holstein cows. The most preferred trait improvement was cow fertility, and the least preferred was calving difficulty. By means of cluster analysis, we identified 4 distinct clusters of farmers and named them according to the trait improvements that were most preferred: Health and Fertility, Production and Udder Health, Survival, and Fertility and Production. Some herd characteristics differed between clusters; for example, farmers in the Survival cluster had twice the percentage of dead cows in their herds compared with the other clusters, and farmers that gave the highest ranking to cow and heifer fertility had the lowest conception rate in their herds. This finding suggests that farmers prefer to improve traits that are more problematic in their herd. The proportion of organic and conventional farmers also differed between clusters; we found a higher proportion of organic farmers in the production-based clusters. When we analyzed organic and conventional data separately, we found that organic farmers ranked production traits higher than conventional farmers. The herds of organic farmers had lower milk yields and lower disease incidences, which might explain the high ranking of milk production and the low ranking of disease traits. This study shows that heterogeneity exists in farmers' preferences for improvements in breeding goal traits, that organic and conventional farmers differ in their preferences, and that herd characteristics can be linked to different farmer clusters. The results of this study could be used for the future development of breeding goals in Danish Holstein cows and for the development of customized total merit indices based on farmer preferences.

Combined mechanistic and genetic modelling to benchmark body reserve traits as proxies of dairy cows' lifetime efficiency in grass-based production systems.

Improving lifetime lactation efficiency of dairy cows by selection is difficult due to the complexity of this trait and the existence of genotype-by-environment interactions. This study aimed at assessing the relevance of traits derived from body reserves as lifetime efficiency indicators under contrasting nutritional environments. Given the absence of large-scale datasets covering a panel of feeding regimes, phenotypes were simulated for populations of 20 000 dairy cows using a mechanistic bioenergetic model. Ten phenotypes were computed for third-lactation cows. Analysed phenotypes comprised total milk production, lactation efficiency, BW at calving (BWcalv), DM intake (DMI) and interval between first insemination and conception. Five traits described levels and changes of body reserves at different periods during lactation. Lifetime lactation efficiency was computed for all cows (Life_Eff). Three nutritional environments were defined considering a grass-based production system with seasonal calving: a high non-limiting scenario (HS) mimicking ad libitum access to feed and two limiting environments with moderate (MS) and low (LS) feed offer. Variance components were estimated for all traits within and between environments using REML. Heritabilities estimated for milk production, lactation efficiency, BWcalv and DMI were moderate in the different environments (0.27-0.35 ± 0.04). The heritability of body reserve levels and dynamics were moderate in the HS and MS scenarios (0.23-0.30 ± 0.03) and lower in the LS scenario (0.14-0.25 ± 0.03). The heritability of Life_Eff was low in the HS environment (0.07 ± 0.01) and slightly increased in the limiting environments. All genetic correlations estimated between environments were moderate to high (≥0.66 ± 0.07), suggesting low to moderate genotype-by-environment interactions. Estimated genetic correlations were moderate between Life_Eff and body reserve levels (from 0.39 to 0.51 ± 0.08) and moderate but negative between Life_Eff and change in body reserves traits (-0.27 to -0.37 ± 0.09) in the HS environment. The genetic correlations between Life_Eff and body reserve levels increased to higher values in the limiting environments. In contrast, genetic correlations between Life_Eff and the changes in body reserves were closer to zero. In conclusion, this study showed that body reserve levels were relevant proxies of lifetime irrespective of the environment. In contrast, changes in body reserves that reflected energy mobilisation in early lactation were less informative about lifetime efficiency in environments with severe feed restrictions.

Exploring syndemic vulnerability across generations: A case study of a former fishing village in the Netherlands.

This qualitative case study uses a life-course approach to explore syndemic vulnerability in a former fishing village in the Netherlands. Building on four years of fieldwork in a low-income neighborhood, we explored salient themes between and across families and generations. Elderly community members (>65 years) were interviewed to map village history and explore how contextual factors have affected family life, health, and wellbeing since the 1940s. We systematically traced and compared processes leading to or from syndemic vulnerability by studying seven families across three generations. Adults with at least one of clustering diseases, their parents (when possible), and their children participated in semi-structured life-course interviews. A complex interaction of endemic social conditions, sociocultural normative processes, learned health behaviors, and disheartening life events shaped families' predispositions for a syndemic of psychological distress, cardiometabolic conditions, and musculoskeletal pain. Educational attainment, continued social support, and aspirational capabilities emerged as themes related to decreasing syndemic vulnerability. This study demonstrates that syndemic vulnerability is potentially intergenerational and reveals the need for culturally sensitive and family-focused syndemic interventions. Future longitudinal research should focus on unravelling the pathogenesis of the clustering of psychological distress, cardiometabolic conditions, and musculoskeletal pain among young people.

Negotiating ADHD: Pragmatic medicalization and creolization in urban India.

Although a growing number of studies have demonstrated differences in responses to ADHD-like behaviours, very few studies have focused on theorizing diversity in the way ADHD is framed and approached globally. To contribute to the study of medicalization in a global context, this study examines the discursive field in which care professionals explain and treat ADHD among children in metropolitan India and addresses the need for an analytic framework to grasp the variations in the way ADHD is understood and approached. Building on the concepts of pragmatic medicalization and creolization, we study ADHD discourses in India asking 'What is at stake' and 'What matters most'? In this mixed methods study, 64 care professionals regularly involved in assessing ADHD-like behaviour completed an online Q-sort, and 21 professionals participated in face-to-face interviews. The Q-data were subjected to factor analysis. The interviews were analyzed using qualitative content analyses. Our study identified six distinct ADHD discourses, which showed that care professionals combine explanatory and treatment models. Professionals adapt their explanations and treatments of ADHD to parents' worries regarding academic performance, family prestige, stigma and side effects of allopathic medicine. Our findings indicate that an awareness of local concerns and adjustments to structural opportunities can diversify how ADHD-like behaviour is framed and responded to. This study demonstrates that medicalization operates between the emerging institutions of care and the everyday concerns of families and care professionals and reveals the need to examine conflicting stakes as drivers of diverse responses to ADHD diagnosis and treatment in India and the rest of the world.

The Evaluation of a Family-Engagement Approach to Increase Physical Activity, Healthy Nutrition, and Well-Being in Children and Their Parents.

Prevention programs often are directed at either parents or children separately, thereby ignoring the intergenerational aspect of health and well-being. Engaging the family is likely to improve both the uptake and long-term impact of health behavior change. We integrated an intergenerational approach into a frequently used shared assessment tool for children's care needs. The current study's aim was 2-fold: to monitor this family-engagement tool's effects on both children and their parents' health behaviors and well-being, and to examine the different dynamics of health behavioral change within a family. Method: We followed 12 children ages 10-14 years and their parents for 12 weeks using an explanatory mixed-methods design comprising interviews, questionnaires, and an n-of-1 study. During home visits at the beginning and end of the study, we interviewed children and their parents about their expectations and experiences, and measured their height and weight. Furthermore, we collected secondary data, such as notes from phone and email conversations with parents, as well as evaluation forms from professionals. In the n-of-1 study, families were prompted three times a week to describe their day and report on their vegetable intake, minutes of exercise, health behavior goals, and psychosomatic well-being. The interviews, notes, and evaluation forms were analyzed using qualitative content analyses. For the n-of-1 study, we performed multi-level time-series analyses across all families to assess changes in outcomes after consulting the family-engagement tool. Using regression analyses with autocorrelation correction, we examined changes within individual families. Results: Five child-mother dyads and three child-mother-father triads provided sufficient pre- and post-data. The mean minutes of children's physical activity significantly increased, and mothers felt more energetic, but other outcomes did not change. In consultations related to overweight, the family-engagement tool often was used without setting specific or family goals. Conclusions: The family-engagement approach elicited positive effects on some families' health and well-being. For multifaceted health problems, such as obesity, family-engagement approaches should focus on setting specific goals and strategies in different life domains, and for different family members.

Psychological distress, cardiometabolic diseases and musculoskeletal pain: A cross-sectional, population-based study of syndemic ill health in a Dutch fishing village.

BACKGROUND: Disease clustering is a growing public health concern and is increasingly linked to adverse socioeconomic conditions. Few population-based studies have focussed on interaction between non-communicable diseases. In this cross-sectional study, we examine clustering of, and synergistic interactions between, frequently occurring non-communicable diseases in Katwijk, a former fishing village in the Netherlands. Additionally, our study identifies contextual variables associated with these clusters of non-communicable diseases. METHODS: In a survey among adults (>19 years) living in the former fishing village Katwijk, Netherlands, were asked about non-communicable diseases, psychological distress, self-rated health scores and contextual factors, eg, socio-demographic, psychosocial and health behavior characteristics. Interaction was measured on the additive and the multiplicative scale. We used generalized ordered logistic regression analysis to examine associations with contextual variables. RESULTS: Three disease clusters were found to be most prevalent among the study participants (n = 1408). Each cluster involved a combination of frequently occurring conditions in this population: psychological distress (n = 261, 19%), cardiometabolic diseases (n = 449, 32%) and musculoskeletal pain (n = 462, 33%). These three diseases interact synergistically on the additive scale to increase the odds of reporting a low self-rated health. None of the disease clusters showed a statistically significant positive interaction on a multiplicative scale. Multiple contextual factors were associated with these disease clusters, including gender, loneliness, experiencing financial stress, and a BMI≥30. CONCLUSION: Our findings imply that psychological distress, cardiometabolic diseases and musculoskeletal pain synergistically interact, leading to a much lower self-rated health than expected. Several contextual factors are related to this interaction emphasizing the importance of a multicomponent, ecological approach.