Cranfield University

Controlled atmosphere (CA) is used to extend the postharvest life of climacteric fruit by suppressing respiration and delaying ethylene mediated ripening. However, sudden exposure to low oxygen conditions can induce hypoxic stress, triggering metabolic and hormonal disruptions that negatively impact fruit quality. This study aimed to understand the mechanisms underlying ethylene sensitivity under low oxygen conditions through the application of a novel approach to CA, called Graduated Controlled Atmosphere (GCA), in ‘Piccolo’ cherry tomato. Compared to standard CA, GCA treatment resulted in greater suppression of respiration and improved firmness retention, indicating reduced physiological stress and slower cell wall degradation. Gene expression analysis revealed downregulation of NCED1 , ACS , and ACO genes under GCA, indicating delayed ethylene-associated transcriptional activity. These hormonal adjustments were also reflected in lower abscisic acid (ABA) concentrations, implying a more stable ripening trajectory. Besides hormonal modulation, GCA-treated fruit exhibited alterations in primary metabolism. Sucrose accumulation and changes in malate levels under GCA conditions suggest a shift in energy metabolism, consistent with improved hypoxia tolerance. However, a notable trade-off was observed in reduced lycopene accumulation, potentially due to lower oxidative signalling and shared precursors between carotenoid and ABA biosynthesis. These findings demonstrate that GCA promotes a more controlled physiological and molecular response to hypoxic storage by reducing stress-associated metabolic and hormonal activity. GCA, as an advanced postharvest strategy, enhances texture retention and may reduce quality losses during storage. This work provides new mechanistic insights into hypoxia adaptation in fruit and supports the use of gradual atmosphere modification to optimise CA protocols. • GCA reduces hypoxic stress and preserves firmness in cherry tomato storage. • GCA suppresses NCED1 , ACS , and ACO expression under hypoxic conditions. • GCA alters sucrose and malate metabolism, enhancing hypoxia tolerance.

The assessment and quantification of agricultural sustainability remain disjointed with many methodological frameworks used by the research community. Defining ‘indicators’ as measurable variables used to assess targeted areas of sustainability, this study provides a systematic review of studies simultaneously employing indicators across social, economic, and environmental pillars of sustainability to assess the farm-level performance of European arable agriculture. Based on strict search criteria, 36 publications were identified, employing 22 different frameworks and multiple unique indicator combinations. Overall, the analysis demonstrates that whilst many existing assessment structures are similar in their overall approach, there are large differences in the total number of indicators used and a common trend towards a higher number of environmental, rather than economic or social indicators. Across the three pillars, common indicators included the treatment and accessibility of labour, production efficiency, environmental care during production, and impacts on soil quality, suggesting that improvements in these areas on farms are considered to support their sustainability. The review also identified trade-offs between the broad applicability and site specificity of sustainability assessment frameworks, suggesting that a minimal set of indicators incorporating these key areas could be used across frameworks to support comparisons. This review identifies research gaps, summarises conflicting methodologies, and provides scientific reference and guidance for the evaluation of the sustainability of European agricultural systems.

Ice accretion poses a threat to fixed-wing aerial vehicles as it alters the wings' shape and thus degrades the aerodynamic performance. In manned aircraft, the icing detection system assists the pilot and utilises dedicated sensors. However, in unmanned aerial vehicles (UAVs), onboard icing detection can generally only be achieved using standard sensors in conjunction with dynamical models, because dedicated sensors are rarely available. In this paper, we propose two approaches based on the particle filter for both icing detection and accurate state and aerodynamic parameter estimation in the presence of icing, with different levels of severity. The first approach uses the observation likelihood for icing hypothesis testing with a complement of the Gaussian kernel to compute icing probability. The second approach uses a discrete jump approach based on a Bernoulli process and a subset of particles to test the icing hypothesis for faster icing detection by estimating changes in icing-related aerodynamic parameters. Using both approaches, the simulation results demonstrate improved estimation accuracy compared to an extended Kalman filter (EKF), under both moderate and severe icing conditions. With adequate tuning, the proposed approaches show potential for indirect icing detection in UAVs. They also enable the computation of icing severity and provide a more accurate and reliable estimate of the icing probability compared to the EKF.

Shareholder activists vary in investment style, expertise, time horizon, incentives, and engagement mode. This study examines four activist types, namely, Exclusive, Substantial, Limited, and Non-specialists, classified by degree of specialism. Using a U.S. sample of 3,903 activist campaigns (2008–2021), we analyze how specialism shapes campaign demands, tactics like Wolf Packs, and outcomes. We find that higher activist specialism significantly influences campaign themes and increases the likelihood of campaign success. Market reactions to campaign announcements are positively associated with specialism, with Exclusive specialists generating the highest abnormal returns. However, analysis of long-term shareholder returns and operating performance reveals a reversal of these initial gains over the three-year post-campaign period. Exclusive specialists underperform Non-specialists in long-term shareholder value and operating performance, though they outperform other specialist categories in limiting value deterioration. The reversal from short-term gains to long-term losses appears driven by specialists' preference for campaign themes that yield immediate payoffs but undermine long-term value. In contrast, fewer specialised activists often secure partial success through messy compromises that also erode long-term shareholder value. Robustness tests almost fully confirm the validity of these findings.

Climate decision-making increasingly requires tools that can translate complex climate science into easy-to-use information. We develop two open-source, complementary interactive dashboards, designed to support climate understanding across metrics-based assessment and analysis of temperature trajectories under a range of scenarios. The Climate Metrics Decision Dashboard (CMDD) provides a comprehensive yet simple framework for exploring a wide range of climate metrics spanning agriculture, aviation, precipitation, economy, and sea level rise. CMDD is designed to support informed interpretation of diverse metrics without requiring deep domain expertise. It’s a smart guide to navigating the world of climate metrics. It helps researchers, policymakers, and practitioners identify which metric best fits their goals, whether it’s tracking emissions, comparing warming impacts, or assessing progress toward sustainability targets. Instead of getting lost in technical jargon, CMDD helps to learn, compare, and choose all in one place. The dashboard includes thorough descriptions of metrics, guided workflows, recommendations, and accounting of both short-lived and long-lived climate pollutants, enabling users to assess their implications for climate-relevant outcomes.Taking a similar approach, the FaIR Climate Explorer offers an accessible interface to the FaIR2.2 simple climate model, allowing users to simulate global temperature responses under different Shared Socioeconomic Pathway (SSP) scenarios. By abstracting model complexity behind an intuitive dashboard, the tool enables users with no prior familiarity with FaIR to explore scenario-driven temperature outcomes. Together, these dashboards demonstrate how interactive, user-centric tools can lower barriers to climate analysis while supporting both metrics-based evaluation and scenario-driven temperature exploration. They highlight the potential of dashboard-based approaches to enhance transparency, usability, and decision relevance in climate science and policy contexts.

Managed realignment is an effective solution in coastal management. This typically involves breaching existing coastal defences, allowing flooding of previously protected land and creation of intertidal habitat, and relocation of the line of actively maintained defences inland. In the UK, creation of intertidal habitat by managed realignment is recommended by strategic plans, yet the uptake of schemes is not keeping pace to meet self-selected targets. The underlying reasons for this slow uptake are complex, span multiple interacting disciplines and are not fully understood. A critical aspect relates to the long-term sustainability and success of the scheme. We explore here how the response of managed realignment to climate drivers leading to intended and unintended consequences intersect with community perceptions.We focus on a case study in the UK (Hesketh Out Marsh in the Ribble Estuary) where we integrate community co-production with quantitative modelling and long-term environmental datasets. We bring together outcomes from co-creating a shared understanding of the managed realignment system with stakeholders and the local community, with results from downscaled hydrodynamic modelling of the Ribble estuary under present and future sea level, and with LiDAR and Sediment Erosion Table datasets for Hesketh Out Marsh.Our results show that the managed realignment have both positive and negative influences on the overall social-ecological system. Hydrodynamic modelling results show significant spatial variability in the effect of the managed realignment scheme, which is amplified by sea level rise. In some areas, managed realignment is beneficial but in others it is not. The newly created saltmarsh is slowly accreting, which is beneficial against sea level rise and its long-term viability, but impairs drainage of its terrestrial hinterland. Workshops with local stakeholders revealed entrenched and conflictual perceptions of the process, goals, and effectiveness of the managed realignment scheme. Altogether, this demonstrates the complexity inherent to managed realignment social-ecological systems. Transdisciplinary approaches are critical to better incorporate this complexity into management approaches by enabling to bring together multiple voices and knowledges and to co-create a clearer, more complete shared understanding of the system.

Integrating physical, socio-economic, and technological perspectives is indispensable for addressing climate mitigation challenges. While directly coupling state-of-the-art Earth System Models (ESMs) and Integrated Assessment Models (IAMs) offers a way to explore feedbacks between these domains, doing so with full-complexity models remains computationally prohibitive. This is particularly true for cost-effective intertemporal optimization IAMs due to fundamental operational differences: while ESMs perform forward simulations, such IAMs optimize over time. Consequently, direct coupling would require numerous computationally intensive iterations to converge, a complication further compounded by the stochastic nature of ESMs.To overcome the barriers to coupling ESMs and IAMs, we employ their reduced-complexity representations (i.e., emulators). We couple an IAM emulator representing 9 distinct IAMs (Xiong et al. 2025) with an ESM emulator, FaIR, representing 66 ESM configurations (Smith et al. 2024a). Using this coupled ESM-IAM emulator framework in an optimization setting, we calculate cost-effective pathways that achieve the temperature targets of the Paris Agreement with and without overshoot.Our preliminary results indicate that the uncertainty ranges for such pathways are significantly larger than previously estimated. Our results also have implications for target setting; we show how pathways differ when IAMs optimize directly for a temperature target – a capability IAMs traditionally lack. Instead, IAMs typically rely on temperature proxies, such as carbon budgets (or their corresponding carbon price pathways), which do not necessarily provide an accurate representation of the temperature target. Furthermore, this study offers advanced insights into the dynamics of climate-economy interactions, providing a roadmap for future efforts to couple full-complexity models. ReferencesXiong, W., Tanaka, K., Ciais, P., Johansson, D. J. A., & Lehtveer, M. (2025). emIAM v1.0: an emulator for integrated assessment models using marginal abatement cost curves. Geosci. Model Dev., 18(5), 1575-1612. doi:10.5194/gmd-18-1575-2025Smith, C., Cummins, D. P., Fredriksen, H. B., Nicholls, Z., Meinshausen, M., Allen, M., . . . Partanen, A. I. (2024). fair-calibrate v1.4.1: calibration, constraining, and validation of the FaIR simple climate model for reliable future climate projections. Geosci. Model Dev., 17(23), 8569-8592. doi:10.5194/gmd-17-8569-2024