Evaluation of factors associated with bulk milk somatic cell count and total plate count in Indonesian smallholder dairy farms

Increasing milk quality in smallholder dairy farms will result in a greater quantity of milk being delivered to milk collection centers, an increased milk price for farmers and consequently an improved farmers’ livelihood. However, little research on milk quality has been performed on smallholder farms in Southeast Asia. The objective of this study was to identify risk factors associated with somatic cell count (SCC) and total plate count (TPC) in Indonesian smallholder dairy farms. One dairy cooperative in West Java, Indonesia was selected based on its willingness to participate. All 119 member farmers in the cooperative, clustered in six groups, were interviewed and a bulk milk sample from all farms was collected in April 2022. Risk factors associated with dairy farms’ SCC and TPC were investigated using multivariable population-averaged generalized estimating equations (GEE) models. The mean geometric SCC and TPC from these farms were 529,665 cells/mL of milk and 474,492 cfu/mL of milk, respectively. Five risk factors including manure removal frequency, receiving mastitis treatment training, washing the udder using soap, number of workers, and ownership of the pasture area were associated with SCC. Two risk factors, manure removal frequency and dairy income contribution, were associated with TPC. These findings can therefore be used as a starting point to improve udder health and milk quality in Indonesia and other countries where smallholder farmers play a significant role in milk production

Risk management and its role in enhancing perceived resilience capacities of farms and farming systems in Europe

In facing future challenges, risk management (RM) is essential for European farming systems (FS). This article synthesises lessons learned on RM based on a farm survey, interviews with farmers, and focus groups involving a range of FS actors. In contrast to previous literature, we broaden the definition of RM to include strategies that target long-term structural challenges, as well as expanding the level of analysis from the farm to the FS level. The results were consistent across the different methods. We found that farmers mainly worry about economic challenges: in particular long-term pressures. We also found that European farmers have implemented diverse RM strategies in the past 5 years, and that no single strategy has been applied by the vast majority of farmers. In line with perceptions of future challenges, there is a demand for the reorientation of RM strategies towards long-term pressures, rather than short-term shocks. FS actors were found to perceive RM as enhancing resilience capacities, especially adaptability. The results of interviews distinguished between major learning strategies and the attributes of farmers for enhancing robustness, adaptive, or transformative capacities. Focus group discussions revealed that the future development of RM strategies requires contributions by all FS actors

Evaluation of factors associated with bulk milk somatic cell count and total plate count in Indonesian smallholder dairy farms

Increasing milk quality in smallholder dairy farms will result in a greater quantity of milk being delivered to milk collection centers, an increased milk price for farmers and consequently an improved farmers’ livelihood. However, little research on milk quality has been performed on smallholder farms in Southeast Asia. The objective of this study was to identify risk factors associated with somatic cell count (SCC) and total plate count (TPC) in Indonesian smallholder dairy farms. One dairy cooperative in West Java, Indonesia was selected based on its willingness to participate. All 119 member farmers in the cooperative, clustered in six groups, were interviewed and a bulk milk sample from all farms was collected in April 2022. Risk factors associated with dairy farms’ SCC and TPC were investigated using multivariable population-averaged generalized estimating equations (GEE) models. The mean geometric SCC and TPC from these farms were 529,665 cells/mL of milk and 474,492 cfu/mL of milk, respectively. Five risk factors including manure removal frequency, receiving mastitis treatment training, washing the udder using soap, number of workers, and ownership of the pasture area were associated with SCC. Two risk factors, manure removal frequency and dairy income contribution, were associated with TPC. These findings can therefore be used as a starting point to improve udder health and milk quality in Indonesia and other countries where smallholder farmers play a significant role in milk production

A framework to assess the resilience of farming systems

Agricultural systems in Europe face accumulating economic, ecological and societal challenges, raising concerns about their resilience to shocks and stresses. These resilience issues need to be addressed with a focus on the regional context in which farming systems operate because farms, farmers’ organizations, service suppliers and supply chain actors are embedded in local environments and functions of agriculture. We define resilience of a farming system as its ability to ensure the provision of the system functions in the face of increasingly complex and accumulating economic, social, environmental and institutional shocks and stresses, through capacities of robustness, adaptability and transformability. We (i) develop a framework to assess the resilience of farming systems, and (ii) present a methodology to operationalize the framework with a view to Europe’s diverse farming systems. The framework is designed to assess resilience to specific challenges (specified resilience) as well as a farming system’s capacity to deal with the unknown, uncertainty and surprise (general resilience). The framework provides a heuristic to analyze system properties, challenges (shocks, long-term stresses), indicators to measure the performance of system functions, resilience capacities and resilience-enhancing attributes. Capacities and attributes refer to adaptive cycle processes of agricultural practices, farm demographics, governance and risk management. The novelty of the framework pertains to the focal scale of analysis, i.e. the farming system level, the consideration of accumulating challenges and various agricultural processes, and the consideration that farming systems provide multiple functions that can change over time. Furthermore, the distinction between three resilience capacities (robustness, adaptability, transformability) ensures that the framework goes beyond narrow definitions that limit resilience to robustness. The methodology deploys a mixed-methods approach: quantitative methods, such as statistics, econometrics and modelling, are used to identify underlying patterns, causal explanations and likely contributing factors; while qualitative methods, such as interviews, participatory approaches and stakeholder workshops, access experiential and contextual knowledge and provide more nuanced insights. More specifically, analysis along the framework explores multiple nested levels of farming systems (e.g. farm, farm household, supply chain, farming system) over a time horizon of 1-2 generations, thereby enabling reflection on potential temporal and scalar trade-offs across resilience attributes. The richness of the framework is illustrated for the arable farming system in Veenkoloniën, the Netherlands. The analysis reveals a relatively low capacity of this farming system to transform and farmers feeling distressed about transformation, while other members of their households have experienced many examples of transformation

D5.3 Resilience assessment of current farming systems across the European Union

For improving sustainability and resilience of EU farming system, the current state needs to be assessed, before being able to move on to future scenarios. Assessing sustainability and resilience of farming systems is a multi-faceted research challenge in terms of the scientific domains and scales of integration (farm, household, farming system level) that need to be covered. Hence, in SURE-Farm, multiple approaches are used to evaluate current sustainability and resilience and its underlying structures and drivers. To maintain consistency across the different approaches, all approaches are connected to a resilience framework which was developed for the unique purposes of SURE-Farm. The resilience framework follows five steps: 1) the farming system (resilience of what?), 2) challenges (resilience to what?), 3) functions (resilience for what purpose?), 4) resilience capacities, 5) resilience attributes (what enhances resilience?). The framework was operationalized in 11 case studies across the EU. Applied approaches differ in disciplinary orientation and the farming system process they focus on. Three approaches focus on risk management: 1) a farm survey with a main focus on risk management and risk management strategies, 2) interviews on farmers’ learning capacity and networks of influence, and 3) Focus Groups on risk management. Two approaches address farm demographics: 4) interviews on farm demographics, and 5) AgriPoliS Focus Group workshops on structural change of farming systems from a (farm) demographics perspective. One approach applied so far addresses governance: 6) the Resilience Assessment Tool that evaluates how policies and legislation support resilience of farming systems. Two methods address agricultural production and delivery of public and private goods: 7) the Framework of Participatory Impact Assessment for sustainable and resilient farming systems (FoPIA-SURE-Farm), aiming to integrate multiple perspectives at farming system level, and 8) the Ecosystem Services assessment that evaluates the delivery of public and private goods. In a few case studies, additional methods were applied. Specifically, in the Italian case study, additional statistical approaches were used to increase the support for risk management options (Appendix A and Appendix B). Results of the different methods were compared and synthesized per step of the resilience framework. Synthesized results were used to determine the position of the farming system in the adaptive cycle, i.e. in the exploitation, conservation, release, or reorganization phase. Dependent on the current phase of the farming system, strategies for improving sustainability and resilience were developed. Results were synthesized around the three aspects characterizing the SURE-Farm framework, i.e. (i) it studies resilience at the farming system level, (ii) considers three resilience capacities, and (iii) assesses resilience in the context of the (changing) functions of the system. (i) Many actors are part of the farming system. However, resilience-enhancing strategies are mostly defined at the farm level. In each farming system multiple actors are considered to be part of the system, such as consultants, neighbors, local selling networks and nature organizations. The number of different farming system actors beyond the focal farmers varies between 4 (in French beef and Italian hazelnut systems) and 14 (large-scale arable systems in the UK). These large numbers of actors illustrate the relevance of looking at farming system level rather than at farm level. It also suggests that discussions about resilience and future strategies need to embrace all of these actors. (ii) At system level there is a low perceived capacity to transform. Yet, most systems appear to be at the start of a period in which (incremental) transformation is required. At system level, the capacity to transform is perceived to be relatively low, except in the Romanian mixed farming system. The latter may reflect a combination of ample room to grow and a relatively stable environment (especially when compared to the past 30 to 50 years). The relatively low capacity to transform in the majority of systems is not in line with the suggestion that most systems are at the start of (incremental) transformation, or, at least, reached a situation in which they can no longer grow. Further growth is only deemed possible in the Belgium dairy, Italian hazelnut, Polish fruit and Romanian mixed farming systems. (iii) System functions score well with regard to the delivery of high-quality and safe food but face problems with quality of rural life and protecting biodiversity. Resilience capacities can only be understood in the context of the functions to be delivered by a farming system. We find that across all systems required functions are a mix of private and public goods. With regard to the capacity to deliver private goods, all systems perform well with respect to high-quality and safe food. Viability of farm income is regarded moderate or low in the livestock systems in Belgium (dairy), France (beef) and Sweden (broilers), and the fruit farming system in Poland. Across all functions, attention is especially needed for the delivery of public goods. More specifically the quality of rural life and infrastructure are frequently classified as being important, but currently performing bad. Despite the concerns about the delivery of public goods, many future strategies still focus on improving the delivery of private goods. Suggestions in the area of public goods include among others the implementation of conservation farming in the UK arable system, improved water management in the Italian hazelnut system, and introduction of technologies which reduce the use of herbicides in Polish fruit systems. It is questionable whether these are sufficient to address the need to improve the maintenance of natural resources, biodiversity and attractiveness of rural areas. With regard to the changing of functions over time, we did not find evidence for this in our farming systems

Shake a tail feather: the evolution of the theropod tail into a stiff aerodynamic surface

Theropod dinosaurs show striking morphological and functional tail variation; e.g., a long, robust, basal theropod tail used for counterbalance, or a short, modern avian tail used as an aerodynamic surface. We used a quantitative morphological and functional analysis to reconstruct intervertebral joint stiffness in the tail along the theropod lineage to extant birds. This provides new details of the tail's morphological transformation, and for the first time quantitatively evaluates its biomechanical consequences. We observe that both dorsoventral and lateral joint stiffness decreased along the non-avian theropod lineage (between nodes Theropoda and Paraves). Our results show how the tail structure of non-avian theropods was mechanically appropriate for holding itself up against gravity and maintaining passive balance. However, as dorsoventral and lateral joint stiffness decreased, the tail may have become more effective for dynamically maintaining balance. This supports our hypothesis of a reduction of dorsoventral and lateral joint stiffness in shorter tails. Along the avian theropod lineage (Avialae to crown group birds), dorsoventral and lateral joint stiffness increased overall, which appears to contradict our null expectation. We infer that this departure in joint stiffness is specific to the tail's aerodynamic role and the functional constraints imposed by it. Increased dorsoventral and lateral joint stiffness may have facilitated a gradually improved capacity to lift, depress, and swing the tail. The associated morphological changes should have resulted in a tail capable of producing larger muscular forces to utilise larger lift forces in flight. Improved joint mobility in neornithine birds potentially permitted an increase in the range of lift force vector orientations, which might have improved flight proficiency and manoeuvrability. The tail morphology of modern birds with tail fanning capabilities originated in early ornithuromorph birds. Hence, these capabilities should have been present in the early Cretaceous, with incipient tail-fanning capacity in the earliest pygostylian birds

Impact of COVID-19 on farming systems in Europe through the lens of resilience thinking

CONTEXT Resilience is the ability to deal with shocks and stresses, including the unknown and previously unimaginable, such as the Covid-19 crisis. OBJECTIVE This paper assesses (i) how different farming systems were exposed to the crisis, (ii) which resilience capacities were revealed and (iii) how resilience was enabled or constrained by the farming systems’ social and institutional environment. METHODS The 11 farming systems included have been analysed since 2017. This allows a comparison of pre-Covid-19 findings and the Covid-19 crisis. Pre-Covid findings are from the SURE-Farm systematic sustainability and resilience assessment. For Covid-19 a special data collection was carried out during the early stage of lockdowns. RESULTS AND CONCLUSIONS Our case studies found limited impact of Covid-19 on the production and delivery of food and other agricultural products. This was due to either little exposure or the agile activation of robustness capacities of the farming systems in combination with an enabling institutional environment. Revealed capacities were mainly based on already existing connectedness among farmers and more broadly in value chains. Across cases, the experience of the crisis triggered reflexivity about the operation of the farming systems. Recurring topics were the need for shorter chains, more fairness towards farmers, and less dependence on migrant workers. However, actors in the farming systems and the enabling environment generally focused on the immediate issues and gave little real consideration to long-term implications and challenges. Hence, adaptive or transformative capacities were much less on display than coping capacities. The comparison with pre-Covid findings mostly showed similarities. If challenges, such as shortage of labour, already played before the crisis, they persisted during the crisis. Also, the eminent role of resilience attributes was confirmed. In cases with high connectedness and diversity we found that these system characteristics importantly contributed to dealing with the crisis. Also the focus on coping capacities was already visible before the crisis. We are not sure yet whether the focus on short-term robustness just reflects the higher visibility and urgency of shocks compared to slow processes that undermine or threaten important system functions, or whether they betray an imbalance in resilience capacities at the expense of adaptability and transformability. SIGNIFICANCE Our analysis indicates that if transformations are required, e.g. to respond to concerns about transnational value chains and future pandemics from zoonosis, the transformative capacity of many farming systems needs to be actively enhanced through an enabling environment

The resilience of European farm: a qualitative and quantitative assessment

European farms face numerous complex and interrelated economic, environmental, social, and institutional shocks and stresses. In addition, farms face unanticipated crises, such as the COVID-19 pandemic. The impact of these shocks and stresses may limit farmers’ access to credit, constrain opportunities to invest, and reduce their willingness to continue farming. This may threaten the delivery of several farm functions, including food production, biodiversity, and the maintenance of natural resources. Resilient farms successfully cope with shocks and stresses and secure the delivery of desired farm functions. This likely requires adaptation and transformation. To this end, the European Commission calls for a better operationalisation and assessment of farm resilience.The general objective of this thesis is to assess the resilience of European farms. Three building blocks are used to assess farm resilience: (i) understanding shocks and stresses, (ii) assessing the resilience capacities of robustness, adaptability, and transformability, and (iii) evaluating the performance of farm functions over time. These building blocks are investigated by perceived and indicator-based resilience assessments, which provide complementary insights. Perceived resilience assessments contribute to a better understanding of decision-making under risk and uncertainty. Indicator-based resilience assessments have a more objective character, allowing researchers to assess farm resilience using secondary datasets.Chapter 2 connects risk theory and resilience thinking using survey data from 916 Dutch farmers. This chapter explores how risk perceptions, risk preferences, and risk management strategies are related to perceived robustness, adaptability, and transformability. The results of the Partial Least Squares Structural Equation Model (PLS-SEM) reveal the importance of a diverse portfolio of risk management strategies. More diverse risk management portfolios are associated with higher perceived adaptability and, in some cases, with higher perceived transformability. This underlines the importance of studying combinations of risk management strategies instead of optimising single strategies. Less risk-averse farmers perceive themselves as better able to adapt and transform while the relationship between risk-aversion and perceived robustness is heterogeneous across farms. Furthermore, higher perceived robustness, adaptability, and transformability are related to farmers who perceive themselves as more resilient.Chapter 3 explores how learning and social networks contribute to farm resilience in terms of robustness, adaptation, and transformation. A combination of qualitative (semi-structured interviews, focus groups, expert interviews) and quantitative methods (farmer survey) is used to study the resilience of Dutch arable farmers from the Veenkoloniën and Oldambt. The results indicate that social networks and learning primarily enable farmers to adapt and, in some cases, contribute to robustness and transformation. Several strategies that enhance each of the resilience capacities are identified. Robustness-enhancing strategies are to build bonding social capital, strengthen financial management skills, and acquire agricultural knowledge. Adaptation-enhancing strategies include building bonding and bridging social capital and being an early adopter of innovation. Transformations are enhanced by the following strategies: building linking social capital from formal networks, learning radically new ideas, and critically reflecting on the status quo.Chapter 4 assesses farm resilience in nine European countries. This chapter quantifies the resilience capacities of robustness, adaptation, and transformation. It uses the Farm Accountancy Data Network (FADN) panel dataset to study changes in inputs and outputs over time. Several indicators for each resilience capacity are aggregated into composite indicators. This chapter investigates which farm(er) characteristics and policy instruments affect the resilience capacities by estimating a correlated random effects fractional probit model combined with a control function approach. The results reveal that resilience-enhancing strategies are heterogeneous across regions and farm types. In most European regions, decoupled direct payments constrain robustness, while rural development payments enhance robustness. Both decoupled direct payments and rural development payments do not affect adaptation and transformation in most European regions.Chapter 5 investigates if decoupled direct payments are an effective policy instrument to ensure short and long-term farm viability. The FADN panel dataset that contains farm-level data from eleven European countries is used. Dynamic correlated random effects probit models are estimated. A control function is employed to account for endogeneity caused by the non-random assignment of decoupled direct payments. The results indicate that 74.5% of the European farms is short-term viable, while less than half of the farms are long-term viable (42.5%). Decoupled direct payments increase the probability to be short-term viable in Southern and Eastern European countries while having no effect or even decrease the probability to be short-term viable for farms from Western and Northern European countries. Additionally, decoupled direct payments decrease the probability of being long-term viable in almost all countries.Chapter 6 synthesises the results and identifies three common themes: (i) moving from risk analysis to resilience thinking deepens the understanding of farmer behaviour under shocks and stresses, (ii) assessing the contribution of risk management by adopting a portfolio view on risk management rather than focussing on single risk management strategies enhances the understanding of resilience, and (iii) reiterating the need to assess farm income as it contributes to multiple facets of farm resilience. Furthermore, Chapter 6 introduces policy and business implications. Policy instruments are suggested to foster a shift towards diverse risk management portfolios, build social capital through social networks and learning, facilitate the adoption of innovations, and focus on paying farmers for public good provision and eco-schemes. Business implications arise for farmers, other supply chain actors, innovation platforms, and banks and other credit suppliers. Farmers are recommended to adopt diverse risk management portfolios and be open to learn from their formal and informal networks. Food supply chain actors are recommended to foster cooperation and learning with farmers by creating joint innovation programmes. To enhance resilience, innovation platforms could host network events to facilitate social learning between farmers and their social network actors; for instance, concerning developments in precision agriculture and other innovations. For banks and other credit suppliers, being able to identify resilient and viable farms is important to grant loans to the least risky farms.&nbsp