Exploring inclusion in UK agricultural robotics development: who, how, and why?

The global agricultural sector faces a significant number of challenges for a sustainable future, and one of the tools proposed to address these challenges is the use of automation in agriculture. In particular, robotic systems for agricultural tasks are being designed, tested, and increasingly commercialised in many countries. Much touted as an environmentally beneficial technology with the ability to improve data management and reduce the use of chemical inputs while improving yields and addressing labour shortages, agricultural robotics also present a number of potential ethical challenges – including rural unemployment, the amplification of economic and digital inequalities, and entrenching unsustainable farming practices. As such, their development is not uncontroversial, and there have been calls for a responsible approach to their innovation that integrates more substantive inclusion into development processes. This study investigates current approaches to participation and inclusion amongst United Kingdom (UK) agricultural robotics developers. Through semi-structured interviews with key members of the UK agricultural robotics sector, we analyse the stakeholder engagement currently integrated into development processes. We explore who is included, how inclusion is done, and what the inclusion is done for. We reflect on how these findings align with the current literature on stakeholder inclusion in agricultural technology development, and suggest what they could mean for the development of more substantive responsible innovation in agricultural robotics

Web-based spatial decision support system for optimum route to forest fires: A case of Viphya plantations

Efficient access to fire incidents is crucial for successful firefighting operations. This study aimed at developing a web-based spatial decision support system (SDSS) to determine optimal routes to forest fires and risk zones in the Viphya Plantations, Malawi. The system integrates remote sensing analysis to identify fire risk zones and a web-based SDSS to suggest optimal response routes. Remote sensing data was used to map areas prone to forest fires based on factors such as land use/cover type, terrain, and anthropogenic activities. These risk zones were incorporated into the GIS routing decision support system, enabling the generation of optimal routes from fire stations to fire risk zones and reported fire cases. System testing demonstrated the SDSS's capability to provide optimum routing options targeting fire risk hotspots and reported incidents within the plantations. The SDSS facilitated the identification of optimal routes to mitigate transportation costs and provided insights into spatial patterns of fire vulnerability, revealing areas that may be inaccessible within the optimal timeframe. This highlighted the necessity of establishing additional fire stations in high-risk regions to enhance rapid response times. The web-based SDSS proved to be an effective decision support tool for optimizing resource allocation and improving emergency response coordination for fighting forest fires in the Viphya Plantations

Exploring Granular Filter Media in Sustainable Drainage Systems (SuDS) for Stormwater PollutantAdsorption: A Pilot Study

Granular filter media are integral to sustainable drainage systems (SuDS) for their efficiency in removing pollutants from urban runoff. This study focuses on understanding the filtration processes within these media by combining a pilot experimental study with a modeling approach. The experimental study involved characterizing the physical and hydraulic properties of various granular filter media materials, including sand, pea-gravel, gravel, and geotextile membranes. Three laboratory-scale stormwater filtration rigs were tested to evaluate the filter me-dia's pollutant removal capacity and hydraulic performance. This work presents a phenomenological model that predicts the spatial variation in the concentrations of stormwater and urban runoff substances, specifically nitrate ions (NO 3-), phosphate ions (PO 4 3-), chemical oxygen demand (COD), and suspended solids, by studying their concentration profiles. The stormwater quality model was used to predict the concentration profiles for stormwater with an average inflow consisting of 2.9 mg/L nitrates, 3.4 mg/L phosphate ions, 225 mg/L COD, and 3.3 mg/L of suspended solids. The predicted outlet concentrations matched well with measured experimental data. The results showed that adding geotextile membranes to a granular filter significantly improves its ability to adsorb dissolved species for stormwater applications. This research highlights the importance of understanding the physical and hydraulic properties of granular filter media and their impact on stormwater pollutant removal efficiency. The developed model can assist in the design and optimization of stormwater treatment systems by predicting the performance of different filter media materials, allowing for informed decision-making and improved system functionality

Microclimate, an important part of ecology and biogeography

AbstractBrief introduction: What are microclimates and why are they important?Microclimate science has developed into a global discipline. Microclimate science is increasingly used to understand and mitigate climate and biodiversity shifts. Here, we provide an overview of the current status of microclimate ecology and biogeography in terres-trial ecosystems, and where this field is heading next.Microclimate investigations in ecology and biogeography: We highlight the latest research on interactions between microclimates and organisms, including how micro-climates influence individuals, and through them populations, communities and entire ecosystems and their processes. We also briefly discuss recent research on how or-ganisms shape microclimates from the tropics to the poles.Microclimate applications in ecosystem management: Microclimates are also impor-tant in ecosystem management under climate change. We showcase new research in microclimate management with examples from biodiversity conservation, forestry and urban ecology. We discuss the importance of microrefugia in conservation and how to promote microclimate heterogeneity.Methods for microclimate science: We showcase the recent advances in data acqui-sition, such as novel field sensors and remote sensing methods. We discuss micro-climate modelling, mapping and data processing, including accessibility of modelling tools, advantages of mechanistic and statistical modelling and solutions for computa-tional challenges that have pushed the state-of-the-art of the field.What's next?We identify major knowledge gaps that need to be filled for further ad-vancing microclimate investigations, applications and methods. These gaps include spatiotemporal scaling of microclimate data, mismatches between macroclimate and microclimate in predicting responses of organisms to climate change, and the need for more evidence on the outcomes of microclimate management

Wildflower strips in polytunnel cherry orchard alleyways support pest regulation services but do not counteract edge effects on pollination services

Sweet cherry (Prunus avium) production relies on modern growing practices like polytunnel coverings to improve yields but this may interrupt arthropod-mediated ecosystem services. The distribution of beneficial arthropods (natural enemies and flower visitors) and the ecosystem services they provide may be affected under polytunnel systems, especially at orchard edges. Across 10 commercial cherry orchards grown in polytunnels, we explored how wildflower strips mitigated edge effects on beneficial arthropods and pest regulation and pollination services. In each orchard, we established a standard wildflower strip (SWS; single cut at the end of the season) and an actively managed wildflower strip (AMWS; regularly cut at 20 cm height) between tree rows and compared this to a conventional control strip (CS). We recorded natural enemies in alleyways and cherry trees post-cherry anthesis (flowering) and flower visitors during and post-cherry anthesis at different distances from the orchard edge (2017-2019). In 2019, we deployed insect prey bait cards in trees to measure pest regulation services and recorded fruit quality (2017-2019) and fruit set (2018-2019) to measure pollination services. Distance from the orchard edge did not affect natural enemy density or diversity in any year or under any alleyway treatment, but pest regulation services decreased towards orchard centres with CS (by 33.0% reduction). Flower visitor density (-34% individuals) and diversity declined with distance from the edge during cherry anthesis. For post-cherry anthesis, marginal negative edge effects were observed for flower visitor density and diversity and behaviour. Overall, fruit set decreased towards the orchard centre while fruit quality increased. Our results suggest that wildflower strips are an effective tool to mitigate edge effects on pest regulation services but have limited effects on flower visitors and pollination

Antibiotic use in first opinion equine practice in the United Kingdom: Serial point prevalence surveys in 17 practices

Background: Monitoring antibiotic use (AMU) is a key component of antimicrobial stewardship. Published data on AMU in first opinion equine practice are limited. Objectives: To document AMU in first opinion equine practices. Study design: Repeated point prevalence surveys. Methods: AMU was recorded one day every month for 12 consecutive months in 17 equine practices. Results: Two hundred and fifty-two horses were prescribed antibiotics across 2273 consultations (excluding routine appointments) (11.1%; 95% CI 9.8%–12.4%). Median number of consultations per practice was 121 (IQR 112–159; range 27–303). Across 17 practices, the proportion of horses receiving antibiotics varied by practice from 0% to 26.4%. Commonest indications for AMU included cellulitis (66; 26.8%), wounds (46; 18.7%), surgical prophylaxis (36; 14.6%), respiratory infection (27; 11.0%) and skin infection (20; 8.1%). Commonest antibiotics prescribed were potentiated sulphonamides (109; 43.6%), oxytetracycline (58; 23.2%), procaine penicillin (40; 16.0%) and doxycycline (36; 14.4%). 45.0% of oxytetracycline use was for surgical prophylaxis. 44.8% of procaine penicillin use was for cellulitis. 28.6% of ‘other antimicrobial’ use was for pyrexia of unknown origin. Use of antibiotics differed significantly depending on the underlying diagnosis (p< 0.001). Median antibiotic dose rates were: potentiated sulphonamides 30 mg/kg (IQR 27–75; range 10–75; n= 96); procaine penicillin 19 mg/kg (IQR 15–23; range 7–30; n= 35); oxytetracycline 6 mg/kg (IQR 5–6; range 4–30; n= 55); doxycycline 10 mg/kg (IQR 10–20; range 7–30; n= 34). Main limitations: Weight of horses were often estimated. Duration of antibiotic courses was not recorded. Conclusions: Antibiotics were prescribed in 11% of nonroutine consultations. Commonest indication for AMU was cellulitis. Potentiated sulphonamides, oxytetracycline and procaine penicillin were the commonest prescribed drugs. Critically important antibiotic use was infrequent. Dose rates varied, but median values were generally appropriate

Drought resilience: water resources and agricultural settings

Drought resilience and water resources, the terms that are becoming increasingly used todav. are gaining prominence particularly within the agricultural industry. Greater efforts are required to mitigate devasting drought effects on farmers, their livelihood and the vulnerabilities that they create for the wider food sector

In-depth simulation of rainfall–runoff relationships using machine learning methods

Measurement inaccuracies and the absence of precise parameters value in conceptual and analytical models pose challenges in simulating the rainfall–runoff modeling (RRM). Accurate prediction of water resources, especially in water scarcity conditions, plays a distinctive and pivotal role in decision-making within water resource management. The significance of machine learning models (MLMs) has become pronounced in addressing these issues. In this context, the forthcoming research endeavors to model the RRM utilizing four MLMs: Support Vector Machine, Gene Expression Programming (GEP), Multilayer Perceptron, and Multivariate Adaptive Regression Splines (MARS). The simulation was conducted within the Malwathu Oya watershed, employing a dataset comprising 4,765 daily observations spanning from July 18, 2005, to September 30, 2018, gathered from rainfall stations, and Kappachichiya hydrometric station. Of all input combinations, the model incorporating the input parameters Qt−1, Qt−2, and R̄t was identified as the optimal configuration among the considered alternatives. The models' performance was assessed through root mean square error (RMSE), mean average error (MAE), coefficient of determination (R2), and developed discrepancy ratio (DDR). The GEP model emerged as the superior choice, with corresponding index values (RMSE, MAE, R2, DDRmax) of (43.028, 9.991, 0.909, 0.736) during the training process and (40.561, 10.565, 0.832, 1.038) during the testing process