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Correlates and stability of recovery capital among persons in long-term recovery from drug addiction
No full textIntroduction: Recovery capital is defined as the sum of one’s personal, social, and community resources to initiate and maintain addiction recovery. Available evidence points to recovery capital as a valuable predictor of long-term recovery, but less is known about the empirical underpinings of recovery capital as a construct, its stability over time and the factors that are related to building recovery capital
Methods: In a convenience sample of persons in recovery from a drug addiction in the United Kingdom, the Netherlands, and Belgium (n=367), we measured recovery capital using the Brief Assessment of Recovery Capital (BARC-10) as well as various other substance use variables and recovery indicators at baseline and 12 months later. Using linear model analyses, we established the relationship between recovery capital at baseline and changes in recovery capital and several predictor variables, differentiating between persons in early (5 years) recovery.
Results: Recovery capital was high, but reduced slightly over the 12-month observation period. More perceived social support and higher quality of life were associated with more recovery capital at baseline, while recent use of illicit substances and poorer mental health were related with lower levels of recovery capital. Greater reductions in recovery capital at follow-up were predicted by less social support and lower BARC-10 scores at baseline. Individuals in early recovery had lower recovery capital than those in sustained and stable recovery.
Conclusion: These findings highlight several areas for supporting individuals in addiction recovery to increase their recovery resources and tackle recovery barriers
Development, validation, and application of mechanistic models to predict population-level effects of chemical pollutant mixtures
No full textAnthropogenic chemicals are essential for modern society, but many of these man-made chemicals enter the environment one way or another. For the last 10 years, the chemical industry in Europe has doubled in production and is expected to double again in the next 10 years. Ecological risk assessment aims to estimate the concentration of harmful substances in the environment and the associated effects on the ecosystem. Based on the estimated concentrations and the predicted effects, potential risks for adverse effects to the environment are identified. Effect assessment nowadays is performed using standardized toxicity tests, with individual organisms, exposed to single substances, in strictly controlled laboratory conditions. However, the environment is complex, as we have individuals living together in populations, exposed to mixtures of chemical substances, under varying environmental conditions. Mechanistic effect models have gained increasing interest from the scientific community, as they can extrapolate effects across biological levels, i.e., from sub-organismal to the population or community level. Yet, applications of mechanistic effect models for mixture toxicity in a population context are limited. The aim of the current thesis is to demonstrate the use of mechanistic models to predict population-level effects of mixtures. Focus is on the freshwater crustacean Daphnia magna (the water flea), two metals (copper and zinc), and four organic priority substances listed under the Water Framework Directive (pyrene, dicofol, alfa-hexachlorocyclohexane, and endosulfan).
A generic individual-based model (IBM) implementation of the dynamic energy budget (DEB) theory was used to predict mixture toxicity effects to D. magna populations. Toxic stress was predicted using DEB-TKTD (extension of DEB including toxicokinetic-toxicodynamic processes) for sub-lethal effects and GUTS-RED-SD (reduced version of the general unified threshold model for survival assuming stochastic death) for lethal effects. A mixture toxicity implementation was developed, based on the general statistical models for mixture toxicity used in risk assessment. Two mixture toxicity approaches in mechanistic effect models can be considered: independent action or damage addition.
In a first case (Chapter 2), we extrapolate effects observed at the individual level to relevant population-level effects of mixtures. The model was applied for mixtures of copper and zinc. The DEB- TKTD and GUTS sub-models were calibrated based on data from a standard 21-day chronic reproduction test (endpoints: growth, reproduction, and survival over time). A population experiment with mixtures of copper and zinc was performed. The DEB-IBM, assuming independent action for mixture toxicity, was able to reproduce the effects observed in the population experiment. Using the DEB-IBM, the observed trends were explained. The absence of zinc effects was explained through population-level compensation mechanisms. The increased mortality due to zinc is compensated by a decrease in starvation-related mortality. For copper, the switch from copper-induced mortality to starvation-related mortality explained the recovery over time observed in the experiment. Based on standard toxicity data at the individual level, mixture toxicity effects at the population were predicted.
Based on the DEB-TKTD theoretical model, we hypothesize that combinations of physiological modes of action (PMoAs) in DEB-TKTD can lead to diverging effects at the population level. As a matter of fact, the PMoA will determine how the energy from food is redistributed within the population under chemical stress. We used DEB-IBM to design a population experiment, testing specific combinations of substances based on their inferred PMoAs (Chapter 3). We tested combinations of four organic substances: pyrene, dicofol, alfa-hexachlorocyclohexane (α-HCH), and endosulfan. An independent validation of mixture toxicity effects at the population level was performed with blind predictions, calibrated on individual-level effects of single substances only. Strong correlation was found between data and predictions during the constant exposed phase, the recovery phase after, and the pulsed acute phase. However, the recovery after the acute phase was not well predicted, meaning the model is unreliable in situations with high lethality. Overall, the independent action approach correctly predicted the observed mixture effects in the population experiment. The damage addition model was tested for the HCH-endosulfan mixture, but overpredicted the effects. Interestingly, synergisms (compared to statistical independent action) were observed in the population experiment that were correctly predicted by the DEB-IBM. We initially hypothesized that increased or decreased effects can occur due to the linking of DEB energy flows within the population. Overall, DEB-IBM was better in predicting mixture toxicity at the population level than current statistical models used in risk assessment. The two cases have shown the validity and relevance of mechanistic population models for mixture toxicity risk assessment. Application of these models for regulatory risk assessment is currently limited. We envision applications of mechanistic population models in current European regulations that encompass the risk assessment of chemicals, such as REACH, PPP, and BPR (Chapter 4). In this chapter, three example applications are highlighted. In a first example, mechanistic population models are used as predictive tools for the risk assessment of chemicals. Look-up tables and flowcharts were developed. A second example discusses the use of DEB-IBM as refinement tool for laboratory-to-field extrapolations. The effect of food density in combination with lethal and and sub-lethal effects to D. magna populations was investigated. As final example, DEB-IBM was linked to FOCUS (a dedicated exposure model that predicts the fate of pesticides in the environment) to predicted realistic effects of pesticide mixtures to D. magna populations. A realistic example was developed with a water body contaminated with endosulfan and funguren (a copper pesticide) due to pesticide application on nearby fields. The predicted surface water concentrations from FOCUS were linked with DEB-IBM. In addition, the DEB-IBM predictions were compared to a traditional dose-response curve analysis and predictions with a TKTD model.
Good model documentation and accessibility is required to increase model transparency and reliability. An extensive description of the model, following the TRACE (transparent and comprehensive model ‘evaludation’) documentation, is provided (Appendix E).
We conclude that mechanistic population models can be used for prospective and predictive risk assessment of chemical mixtures. More so than predicting effects, mechanistic population models can also give information and understanding of the driving forces of mixture toxicity within a population context. However, there is still a lack of guidance on the ‘standardized’ use of these models. More applications and communication of results would help increase acceptance of mechanistic population models for regulatory risk assessment. With this thesis we have shown that mechanistic population models can bridge multiple uncertainty gaps that were previously unaddressed in ecological risk assessment: the divide between individuals and populations, between single substances and mixtures of substances, and between constant controlled exposure conditions and dynamic exposure conditions
Lean management in sustainable South African horticulture : practice, performance, and determining factors
No full textOriginally developed by the Toyota Motor Corporation for use in automotive manufacturing, lean is an integrated system of management designed around the principle of maximizing value whilst minimizing waste. Value is defined by the customer, where it is understood that the definition of value is everything the customer is willing to pay for. This is paired with a comprehensive approach to reducing waste, where waste is defined as any step, action or use of resources which does not provide additional value to the customer. The lean system is comprised of a broad set of practices, tools, and methodologies, meant to be applied in an integrated manner. When implemented correctly, the lean system has shown a remarkable potential to improve operational performance, in terms of cost suppression, improved quality, and greater consistent in delivery. successes of lean have seen the system adopted across a broad band of productive contexts. In recent years, an interest in lean management has emerged from within various within agricultural primary production. Researchers highlight that industrialization of agriculture is driving demand for improved operational systems. This is the current situation in South African fruit horticulture, where farmers and cooperative groups have been exploring the application of lean and continuous improvement methodologies to drive operational performance.
This doctoral research was selected in response to the growing interest within South African fruit horticultural sector. Here the identified need is for a lean framework specifically tailored for application in the fruit primary production context. Research into lean agriculture remains limited, and lean frameworks adapted for application in primary production remain as a gap in the extant literature. This study sought to address this gap through several research contributions. These include a case study unpacking performance determinants of lean application in fruit horticulture, adaptation of a lean framework for this context, and the utilisation of that framework to assess lean practice patterns and performance benefits across a sample of 132 fruit farming estates. Here, the analysis supports that lean practice adoption by fruit farmers may generate performance benefits across the economic, environmental, and social dimensions. Finally, the contributions are configured into am house-of-lean framework, which considers the specific needs of the fruity primary production context. This framework provides a structured set of considerations for the application of lean in fruit farming, to support fruit growers in realizing the full potential benefits of the system. The results of this study represent a novel contribution to the scientific literature, and the learnings herein should find interest to consultants and practitioners in the agricultural domain seeking deeper insight into the application of lean management practices and/or the development of capability to enhance sustainable performance outcomes. To the academic community, this study will optimistically act to extend the legitimacy of and generate interest in furthering the development of lean management theories and frameworks within the agricultural domain
Site-specific seeding using proximal and remote sensors data fusion
No full textTraditional agronomic management applies a uniform rate seeding (URS) density throughout the field assuming that the entire field is equally productive. This is a misconception since most agricultural soils are spatially and temporally heterogeneous, and hence a unique seed rate never be optimal throughout a field. Consequently, the URS allows to grow an improper number of plant populations in differently fertile zones in a field, which raises inter-crop competitions and reduces crop yield and thus production profit. Planting density also is linked with the application rate of other farming inputs, and therefore, the non-optimal seeding rates may increase production costs associated with fertilizer and pesticide application, and affect the soil-water environment. Ideally, in-field heterogeneity should be managed properly by implementing precision farming technology. Site-specific seeding (SSS) is such a precision agricultural operation that can ensure an optimized application of seeding rate for each fertility zone by considering the in-field soil variations.
The aim of this study is to develop an optimized SSS approach based on the fusion of multiple soil and crop quality indicators estimated with state-of-the-art proximal and remote sensing technologies for maximizing crop yield and production margin. In the literature, there are two methods for implementing SSS i.e., map-based and sensor-based. The map-based SSS system allows adjusting seeding rates depending on the management zone (MZ) map delineated with reference soil and/or crop information. The sensor-based system adjusts seed rates based on an on-line measured soil fertility index (SFI), which is then used as input for real-time calculation and implementation of the recommended seeding rate. The SSS has been often practiced using map-based technology, frequently relying on yield or soil electrical conductivity (EC) data while ignoring many other important soil-related information. Hence, this study intended to move forward the map-based SSS using MZ map delineated with the fusion of multiple soil and crop data layers. It also focuses on identifying the key yield-limiting factors and thus proposing a set of proxies in the delineation of the MZ map accurately as well as in the development of an effective SFI. As the traditional laboratory-based analytical methods are slow and limited to low-resolution estimation of MZ proxies, this study intends to propose the best sensor and/or a combination of sensing technologies for estimating the key yield-limiting factors for delineating the MZ map. Moreover, the early works did not follow a systematic approach to optimize seed rate and they assigned the seed rate per MZ arbitrarily. Therefore, this research proposes an approach of SSS rate optimization. Most importantly, to date no research about sensor-based SSS is available, and hence this thesis will develop, for the first time, a sensor-based SSS technology. As a very limited studies evaluated SSS, the agronomic and economic benefits of SSS have not been largely explored. They showed inconsistent economic profits, which hinder the SSS adoption by end-users.
Therefore, this study also focuses on evaluating the agronomic and economic performance of SSS in comparison with the URS for two major crops i.e., maize and potato.
Multiple field experiments were conducted for evaluating the agronomic and economic benefits of map-based SSS for potato and maize in two cropping seasons. An on-line visible and near-infrared spectroscopy (vis-NIRS), electromagnetic induction (EMI) sensor, and Sentinel-2 image data were used for delineating three types of MZ maps, which are used for designing SSS treatments: 1)SSS based on apparent electrical conductivity (ECa) measured with an EMI sensor(EMI-SSS), 2) fusion of on-line vis-NIRS estimated soil properties with normalised difference vegetation index (NDVI) retrieved from Sentinel-2 based SSS (visNIRSen-SSS), and 3) another fusion-based SSS integrating data from EMI, on-line vis-NIRS, and Sentinel-2 retrieved NDVI data (EvisNIRSen-SSS). Five treatments of seed rate distributed over the MZ maps were tested to compare the performance of the “Kings” and “Robin Hood” methods for recommending the seed-to-seed spacing for both seed and consumption potatoes and seed rates for maize. The “Kings” method recommends the highest seeding rate for the highest fertile MZ and vice-versa while the “Robin Hood” follows the opposite principle by feeding most to the least fertile MZ. After all, the agro-economic performance of these five treatments was compared with those of URS. Results showed that the “Kings” approach outperformed the “Robin Hood” method both for potatoes and maize production. The SSS resulted in higher yield and gross margin by up to 597 € ha-1 in contrast to URS. The enhanced gross margin mainly emerged from increased crop yield instead of savings on the seed costs. The ECa-based SSS and the visNIRSen-SSS performed equally with a mild variation over the test sites. Since the EvisNIRSen-SSS revealed the highest gross margin for potato, the fusion of data obtained from EMI, vis-NIRS and Sentinel-2 seemed to be the most effective approach for MZ delineation in designing and implementing of the map-based SSS. Higher improvement in yield and the gross margin was observed in potato (380 to 597 € ha-1) than maize production (93 € ha-1). The highest profitability of SSS of up to 56.50 % was observed in the field with the lowest productivity. Despite increasing, crop yield and gross margin by SSS were statistically insignificant except in one test field with consumption potatoes.
Since crop yields frequently illustrated high-positive correlations with soil pH, OC, P, K, Mg and MC, these were identified as key fertility indicators in developing the SFI that was used as input for the sensor-based SSS. They were also considered as the key yield-limiting factors and used as proxies for MZ delineation needed for map-based SSS. The on-line vis-NIRS sensor can estimate all these MZ proxies accurately with high-spatial resolution, hence is highly recommended for realizing the two methods of SSS.
A fully automated sensor-based SSS system was developed and validated. It consists of hardware and software integrating an on-line vis-NIRS soil sensor, an on-line SFI prediction model, a seed rate calculation algorithm, and a variable rate planter machine. While the on-line soil sensor was set on the front end of a tractor, the maize planter actuates the seed rate according to the SFI measured by the on-line sensor. The system was tested in one field with silage maize when it revealed its potential for increasing yield by 1.4 t ha-1 and thus the gross margin by 91 € ha-1, compared to the URS. The study proved that the vis-NIRS sensor was the ideal sensing technology to assess soil fertility and MC for accurate calculations of seeding rate recommendations.
In conclusion, SSS is found to be a promising precision agriculture solution to increase crop productivity and thus the economic margin by scientifically managing in-field heterogeneity through optimizing the input seeding rates according to the yield potential of different zones of a field. To succeed in SSS application, soil pH, OC, P, K, Mg and MC should be included in the MZ delineation for map-based SSS and in SFI determination for sensor-based application. This will allow for a correct seed rate calculation, which should then be implemented according to the “Kings” method (e.g., feeding the rich). It is highly suggested in future works to include soil clay and elevation data as MZ proxies and in the SFI derivation. The study shows interesting economic and agronomic benefits that could be harvested when SSS is adopted by farmers. It is therefore recommended to adopt SSS in the commercial farming system. This study has become a reference and thus opened a window for further development and implementation of SSS for other crops
Optimising the use of barley straw in tropical ruminant diets
No full textDue to the decline in grazing land, the degradation through overgrazing, and the expansion of arable cropping in tropical countries, the contribution of crop residues for animal feeding becomes increasingly important. The overall aim of this dissertation was to improve total barley biomass utilization for food and feed use through the dual-purpose evaluation of barley varieties for mixed livestock-barley production in Ethiopia highlands
Aesthetic experiences and artistic expressions in climate change education
No full textThis chapter introduces a conceptual framework for studying the value of working with arts in climate change education and illustrates and discusses what it allows us to grasp in empirical investigations. The authors discuss how pragmatist theory can be employed to study the educative potential of using arts in climate change teaching. They present three theoretical models: a transactional theory of learning in which aesthetic encounters and experiences as drivers for inquiry take central stage; four crucial conditions for artistic engagement; and a dramaturgical perspective on the art of teaching as a practice that involves ‘scripting’, ‘staging’, and ‘performance’. These models are subsequently applied to an empirical case: a university course on modern English literature focusing on climate fiction. The analyses show how the teacher’s didactic work, using artistic artifacts (climate fiction literature) and artistic practices (creative storytelling), resulted in aesthetic experiences, elicited by aesthetic encounters with the phenomenon of the climate crisis through the content of the course, which lead to outcomes as different as new cognitive insights, artistic transformation of the self, and artistic self-expression in creative writings. The dramaturgy of the teaching practice facilitated ‘teachable moments’ and critical and creative inquiry into problematic situation related to climate change that deeply touched the participants, dismantled previously hold expectations, and redirected thought, action, and inquiry. The authors conclude by discussing how the presented framework can inform much-needed research on how climate change teaching can foster critical and creative inquiry through which new ways of being and living with others can emerge
Interpreters’ multimodal management of rapport : does video remote interpreting have an impact? A quantitative approach
No full textGiven the rise of video remote interpreting (VRI), it is surprising that there is limited research on interpreters’ multimodal management of interpersonal relations in this interpreting mode. This study addresses this gap by investigating how interpreters manage rapport challenges in onsite interpreting (OSI) and VRI. It provides a quantitative analysis of interpreters’ use of embodied resources, verbal resources, and strategies when conveying rapport challenges in both modalities. The article analyses 28 video recordings (14 OSI and 14 VRI) involving professional interpreters and role-players in the context of a reception centre for asylum seekers. The interactions were coded using a coding scheme based on Spencer-Oatey’s Rapport Management Theory. The findings indicate that interpreters use significantly fewer verbal and embodied resources to manage rapport challenges in VRI in comparison to OSI. The study also shows that interpreters in VRI employ fewer mitigating strategies, which might be attributed to the increased sense of security and physical distance provided by the modality. These findings highlight the impact of VRI on interpreters’ multimodal management of rapport challenges and seem to suggest that interpreters possibly adapt their strategies based on the affordances of the interpreting method
How management accounting and control practices can contribute to solving sustainability-related grand challenges
No full textThis chapter discusses the role of management accounting and control practices in the transitioning toward a more sustainable state. While management accounting practices can be designed to facilitate managers’ decision-making regarding sustainability issues, management control practices can influence organizational members’ and stakeholders’ decision-making by aligning their behavior with the organization’s sustainability objectives and related strategies. The aim of this chapter is to provide an overview of such sustainability management accounting and control practices. Particular attention is paid to practices that may influence sustainability performance beyond organizational boundaries or direct the efforts of stakeholders outside organizational boundaries, as such boundary-transcending practices may contribute to solving sustainability-related grand challenges