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Microbial applications and agricultural sustainability: a simulation analysis of Dutch potato farms
Get PDFCONTEXT
Fertilisers and plant protection products are essential for the economic viability of arable agriculture, but their overuse leads to environmental problems. Microbial applications have been proposed as a solution to reduce these environmental problems in arable farming. Experimental results suggest that microbial applications can increase yields and reduce abiotic stresses with fewer fertilisers and plant protection products. However, the overall effects of microbial applications on farm economics, the environment and social dimensions have not been quantified yet.
OBJECTIVE
In this study, we assess the capacity of microbial applications to enhance the sustainability, including environmental, economic and social dimensions, of Dutch potato production.
METHODS
We model a baseline scenario and a microbial application scenario with Monte Carlo simulation, and compare the scenarios using a composite sustainability index. The microbial application scenario is based on data from a Delphi expert elicitation.
RESULTS AND CONCLUSIONS
The model indicates that, at present, microbial interventions do not contribute to the sustainability of Dutch potato production. In fact, the conventional baseline approach is more sustainable compared to the scenario involving microbial applications. In the microbial application scenario, cost per hectare of potato production is almost three times higher than in the baseline scenario, which are not covered by the 3.7% yield and 9.8% revenue increase. However, microbial applications can reduce CO2 emissions by 60% and active substances by 6.6%. Technological advancements are necessary to reduce costs per unit of production and increase environmental sustainability.
SIGNIFICANCE
This study explores the impact of microbial applications on agricultural sustainability amid uncertainty, emphasising the need to quantify their effects. This study highlights the importance of economically viable sustainable practices to incentivise farmers' adoption. While efficient microbial applications can reduce reliance on conventional pesticides and fertilisers, they currently cannot contribute to the Farm-to-Fork reduction goals. Future research should focus on cost-effective microbial applications for disease prevention. We provide four routes for further research on microbial applications
Verona and Vitruvius
Get PDFThis article considers the signature of an architect named Vitruvius on the Arco dei Gavi in Verona and the effect that this had on Renaissance architecture in the cit
Unprivileged groups are less served by green cooling services in major European urban areas
Get PDFHeat stress is the leading climate-related cause of premature deaths in Europe. Major heatwaves have struck
Europe recently and are expected to increase in magnitude and length. Large cities are particularly threatened
due to the urban morphology and imperviousness. Green spaces mitigate heat, providing cooling services
through shade provision and evapotranspiration. However, the distribution of green cooling and the
population most affected are often unknown. We revealed environmental injustice regarding green cooling
in fourteen major European urban areas. Vulnerable residents in Europe are not concentrated in the suburbs
but in run-down central areas that coincide with low-cooling regions. In all studied areas, lower-income
residents, tenants, immigrants and unemployed citizens receive below-average green cooling, while upper income
residents, nationals, and homeowners experience above-average cooling provision. The fatality risk
during extreme heatwaves may increase as vulnerable residents are unable to afford passive or active cooling
mitigation
China’s maritime militias, human rights, and the law of the sea: contested norms in a shifting international legal order
Get PDFWhile many have studied the international legal dimensions of South
China Sea disputes, few have explored the intersection of human rights
and the law of the sea. Yet, this perspective is crucial – especially given
China’s claims that its maritime militia fishermen, who controversially
advance its maritime strategy, have suffered human rights abuses. Using this issue to theorise the broader relationship between human rights
and the law of the sea, we examine Chinese maritime militias through
a comparative international law framework. On the one hand, existing
legal doctrine – including the United Nations Convention on the Law
of the Sea and its implementing agreement on the conservation and sustainable use of marine biodiversity of areas beyond national jurisdiction
– provides strong grounds to critique China’s maritime militia strategy. On the other hand, these militias can be framed as agents of China’s ‘ecological civilisation’ agenda, confronting climate change and advancing
human rights under the law of the sea. Ultimately, the Chinese maritime
militia question offers deep insights into intersecting legal regimes amid
global norm contestations
Abortion and women: intersects of religion, law, and society - a perspective from Muslim majority countries
No full textAbortion is one of the most contentious and polarizing issues in contemporary society, invoking deep ethical, moral, legal, and emotional responses (Minkoff et al, 2024; Lewis, 2024; Petchesky, 2024; Chang et al, 2023). The practice of terminating a pregnancy has been present throughout human history, but its regulation and societal acceptance have varied widely across cultures and epochs (Cohen et al, 2023; Huzaimah et al, 2023; Pagoto et al, 2024; Petchesky, 2024). This chapter seeks to explore the multifaceted dimensions of abortion, examining its historical evolution, the influence of religious and ethical perspectives, the legal landscapes that govern its practice, and the implications for women's rights and health with a special focus on Muslim majority countries
The absence of television: broadcasting and war in Britain, 1939-45
Get PDFThis chapter asks what the response of the BBC to the beginning of the Second World War reveals about British broadcasting, regarded as what the media theorist Raymond Williams (1974) called a technology and a cultural form. It investigates how the reasons for stopping BBC television and reconfiguring radio were intertwined with ideas about the cultural role of broadcasting as a socially inclusive medium, the technological development of radio and television technologies for military uses, and the ways that Britain deployed broadcasting in ways both similar to and different from other nations. After the discovery of the properties of electromagnetic waves and their implementation for radio and television at the end of the 19th century, the evolutionary paths of these technologies were by no means determined. That radio and television became separate domestic broadcast technologies was merely one line of development, as their roles in the Second World War show. Both civilian and military uses of radio thrived, while television was relatively marginal and mutated instead into its sister technology, radar. Competition between individual inventors, nations and corporations was harnessed to push technologies of transmission and reception very quickly, and the ideological character of the different warring powers produced radio and television cultures that were relatively distinct. The absence of television in Britain from 1939 to 1945 offers, paradoxically, an effective focus on the significance of broadcast media in the period
Sensitivity analysis for feature importance in predicting Alzheimer’s Disease
Get PDFArtificial Intelligence (AI) classifier models based on Deep Neural Networks (DNN) have demonstrated superior performance in medical diagnostics. However, DNN models are regarded as "black boxes" as they are not intrinsically interpretable and, thus, are reluctantly considered for deployment in healthcare and other safety-critical domains. In such domains explainability is considered a fundamental requisite to foster trust and acceptability of automatic decision-making processes based on data-driven machine learning models. To overcome this limitation, DNN models require additional and careful post-processing analysis and evaluation to generate suitable explainability of their predictions. This paper analyses a DNN model developed for predicting Alzheimer’s Disease to generate and assess explainability analysis of the predictions based on feature importance scores computed using sensitivity analysis techniques. In this study, a high dimensional dataset was obtained from Magnetic Resonance Imaging of the brain for healthy subjects and for Alzheimer’s Disease patients. The dataset was annotated with two labels, Alzheimer’s Disease (AD) and Cognitively Normal (CN), which were used to build and test a DNN model for binary classification. Three Global Sensitivity Analysis (G-SA) methodologies (Sobol, Morris, and FAST) as well as the SHapley Additive exPlanations (SHAP) were used to compute feature importance scores. The results from these methods were evaluated for their usefulness to explain the classification behaviour of the DNN model. The feature importance scores from sensitivity analysis methods were assessed and combined based on similarity for robustness. The results indicated that features related to specific brain regions (e.g., the hippocampal sub-regions, the temporal horn of the lateral ventricle) can be considered very important in predicting Alzheimer's Disease. The findings are consistent with earlier results from the relevant specialised literature on Alzheimer’s Disease. The proposed explainability approach can facilitate the adoption of black-box classifiers, such as DNN, in medical and other application domains
Improved filter-based feature selection using correlation and clustering techniques
Get PDFFeature engineering and feature selection are essential techniques to most data science and machine learning applications, in which, respectively, raw data are transformed into features and features are selected to provide the most effective subset of features for the application. Feature selection techniques are particularly useful when dealing with high-dimensional datasets that contain noisy and redundant data. An optimised feature subset could enhance the performance as well as the interpretability of the model. There are three types of feature selection methods, namely filter, wrapper and embedded techniques. Amongst these methods, the filter method is more efficient than the others as it is computationally less expensive and more generalised. This work presents two improved filter-based feature selection methods based on a correlation coefficient and clustering techniques. The first approach is based on feature correlation where the feature subset consists of features above a similarity threshold to identify a kind of neighbourhood for each feature. The second method uses clustering analysis on the correlation data to identify features that can be used to represent the entire cluster. The obtained feature subsets have been applied as pre-processing step for logistic regression and artificial neural networks. The performance of the proposed methods has been compared against the popular ReliefF feature selection method. The experimental analysis shows that the proposed feature selection methods provide an observable improvement in accuracy by choosing the most effective features
The moon and sixpence: a qualitative study of the professional identity development of highly-educated female teachers at primary/secondary schools in Beijing
Get PDFThis thesis focuses on how highly-educated female teachers (graduates with a
Master or doctoral degree) identify with their job at primary/secondary schools in
Beijing China as a product of Chinese involution (fierce competition by
credentialism and perceived currently by youngsters regarding themselves as
Chinese ‘involuted’ generation) and further impacted by related educational policy
like ‘Double reduction’ and Covid-19 pandemic. This research applies a Bourdieu’s theory of field and its post-structural feminist
extension to explore how factors, such as economic, social, institutional and gender
possibly influence identity formation, their own perception of their current roles at
school and their attitude towards teacher training and future career plans. The
research is embedded in the paradigm of constructivist in terms of ontology as well
as epistemology using Interpretative Phenomenological Analysis (IPA) on the basis. Thus, a small sampled sized group of female teachers who graduate with a Master
or PhD degree and work more than 6 years at primary/secondary schools were
selected and received three semi-structure interviews during data collection. The
data are analysed from lens of institutional, interpersonal and personal level to see
how policies, welfare, working hours, gender issues and teacher training and
development impact teachers’ identity. Findings reveal that all participants show a
decreasing passion towards work and hold relatively negative perception towards
their professional identity and future development. Inadequate economic income, long working hours, gender/age bias and decreasing social status embedded in
educational policies negatively shape teacher identity. Teacher identity in turn
re-shape these teachers’ perception towards the above influential factors. The study
also finds out that teacher identity is dependent not only on external factors but also
greatly on personal characters and experience in perceiving their professional
identity. This research aims to extend understanding of highly educated teachers’ working experience and bridge the gap of insufficient qualitative empirical studies
on highly-educated female teachers. It also aims to make a significant contribution
to knowledge by offering insights of these female teachers’ identity trajectories and
possible suggestions to local schools as well as administration of Education on
professional training and teachers' welfare
Low-intensity insect herbivory could have large effects on ecosystem productivity through reduced canopy photosynthesis
Get PDFOur current understanding of the effect of insect herbivory on ecosystem productivity is limited. Previous studies have typically quantified only the amount of leaf area loss or have been conducted during outbreak years when levels of herbivory are much higher than on average. These set-ups often do not take into account the physiological changes taking place in the remaining plant tissue after insect attack or do not represent typical, non-outbreak herbivore densities. Here, we estimate the effect of non-outbreak densities of insect herbivores on gross primary productivity in a temperate oak forest both through leaf area loss and through changes in leaf gas exchange. We first conduct a meta-analysis to assess evidence of herbivory-induced changes in photosynthesis in the literature. We then estimate how canopy primary productivity changes with decreasing and increasing levels of herbivory by using a canopy upscaling model and the average leaf-level effect based on the literature. The meta-analysis revealed a wide range of effects of herbivory on leaf photosynthesis, ranging from a reduction of 82 % to an increase of 49 %. On average, herbivory reduces the photosynthetic rate in the remaining leaf tissue by 16 % [6 %–27 %; 95 % CI]. The gross primary productivity of an oak stand under normal (5 % leaf area loss) levels of herbivory is estimated on average to be 13 % [5 %–21 %] lower than that of a non-herbivorized stand, once physiological changes in the intact plant tissue are considered. We propose that the effect of insect herbivory on primary productivity is non-linear and determined mainly by changes in leaf gas exchange and the pattern at which herbivory spreads through the canopy. We call for replicated studies in different systems to validate the relationship between insect herbivory and ecosystem productivity proposed here