Effective Mitigation Strategies Research Articles

Evaluation of effects of heat released from SOC decomposition on soil carbon stock and temperature

AbstractHeat released from soil organic carbon (SOC) decomposition (referred to as microbial heat hereafter) could alter the soil's thermal and hydrological conditions, subsequently modulate SOC decomposition and its feedback with climate. While understanding this feedback is crucial for shaping policy to achieve specific climate goal, it has not been comprehensively assessed. This study employs the ORCHIDEE‐MICT model to investigate the effects of microbial heat, referred to as heating effect, focusing on their impacts on SOC accumulation, soil temperature and net primary productivity (NPP), as well as implication on land‐climate feedback under two CO2 emissions scenarios (RCP2.6 and RCP8.5). The findings reveal that the microbial heat decreases soil carbon stock, predominantly in upper layers, and elevates soil temperatures, especially in deeper layers. This results in a marginal reduction in global SOC stocks due to accelerated SOC decomposition. Altered seasonal cycles of SOC decomposition and soil temperature are simulated, with the most significant temperature increase per unit of microbial heat (0.31 K J−1) occurring at around 273.15 K (median value of all grid cells where air temperature is around 273.15 K). The heating effect leads to the earlier loss of permafrost area under RCP8.5 and hinders its restoration under RCP2.6 after peak warming. Although elevated soil temperature under climate warming aligns with expectation, the anticipated accelerated SOC decomposition and large amplifying feedback on climate warming were not observed, mainly because of reduced modeled initial SOC stock and limited NPP with heating effect. These underscores the multifaceted impacts of microbial heat. Comprehensive understanding of these effects would be vital for devising effective climate change mitigation strategies in a warming world.

Outpatient Antibiotic Consumption Trends in Belgium: A Comparative Analysis of Reimbursement and Sales Data, 2013-2022

Background: Antimicrobial resistance (AMR) is a global public health concern, necessitating close and timely monitoring of antibiotic consumption (AMC). In Belgium, AMC surveillance traditionally relies on reimbursement data, excluding over-the-counter non-reimbursed or imported products and involving a time lag. This study investigates disparities in AMC between reimbursement data and retail data, providing insights into AMC variations. Additionally this study seeks to critically evaluate the validity and representativeness of the reimbursed data in accurately reflecting the true extent of AMC in the country. Method: Utilizing reimbursement data from the National Institute for Health and Disability Insurance (NIHDI) and retail data (IQVIA Sales data; www.iqvia.com) for systemic antibacterials (ATC Group J01), outpatient consumption was estimated for the period 2013-2022. Volume of antimicrobials was measured in Defined Daily Doses (DDDs - WHO ATC/DDD Index 2023), while population data were extracted from Eurostat. Relative differences (RDs) in DDDs per 1000 inhabitants per day (DID) were computed, and validated through correlation analysis (Pearson’s r) and Bland–Altman plots. Result: J01 antibacterial sales declined from 23.10 DID (2013) to 20.85 (2022). Non-linear decreases, notably during the Covid-19 pandemic (21.54 DID in 2019 to 16.69 in 2020), followed by a rebound to pre-pandemic quantities in 2022 were observed (Figure 1). Reimbursement NIHDI data slightly underestimated IQVIA sales, with RDs ranging from 2% (2013) to 9% (2022). Notable differences, especially in recent years were attributed to quinolone reimbursement criteria changes implemented by law in Belgium in 2018, reducing the reimbursed proportion from 99% (2017) to 35% (2022). ATC-3 level analysis revealed disparities in low-DID groups (J01B, J01E and J01G). Notably, a small proportion of amphenicols (J01B) were reimbursed ( < 1 0%), with a congestion relieving combination product of tiamphenicol (+ N-acetylcysteine; Fluimucil®) frequently bought and remaining unreimbursed. Overall and across ATC3 groups, the correlation between NIDHI and IQVIA estimates was almost perfect across years and the Bland–Altman plots showed high agreement. Conclusion: Reimbursement data are reliable for outpatient AMC monitoring with slightly lower estimates than retail data across most categories. The 2018 quinolone reimbursement criteria change highlights the necessity of incorporating retail data for accurate assessments in this specific category. The synergistic use of reimbursement and retail datasets is crucial for a comprehensive understanding of consumption patterns, supporting effective AMR mitigation strategies in Belgium.

Comparison of Perimeter Delineation Methods for Remote Sensing Fire Spot Data in Near/Ultra-Real-Time Applications

Forest fires cause extensive damage to ecosystems, biodiversity, and human property, posing significant challenges for emergency response and resource management. The accurate and timely delineation of forest fire perimeters is crucial for mitigating these impacts. In this study, methods for delineating forest fire perimeters using near-real-time (NRT) remote sensing data are evaluated. Specifically, the performance of various algorithms—buffer, concave, convex, and combination methods—using VIIRS and MODIS datasets is assessed. It was found that increasing concave α values improves the matching percentage with reference areas but also increases the commission error (CE), indicating overestimation. The results demonstrate that combination methods generally achieve higher matching percentages, but also higher CEs. These findings highlight the trade-off between improved perimeter accuracy and the risk of overestimation. The insights gained are significant for optimizing sensor data alignment techniques, thereby enhancing rapid response, resource allocation, and evacuation planning in fire management. This research is the first to employ multiple algorithms in both individual and synergistic approaches with NRT or ultra-real-time (URT) active fire data, providing a critical foundation for future studies aimed at improving the accuracy and timeliness of forest fire perimeter assessments. Such advancements are essential for effective disaster management and mitigation strategies.

Anthropogenic, environmental and temporal associations with vertebrate road mortality in a wildland-urban interface of a biodiverse desert ecoregion.

Road mortality adversely affects wildlife populations. As urbanization and infrastructure densities expand, transportation and wildlife management aim to mitigate wildlife-vehicle conflicts while conserving biodiversity. Roadways in aridland ecosystems can invariably and adversely impact wildlife differently from temperate and other biomes, yet these rapidly urbanizing regions are understudied as are urban-rural gradients. We conducted road-cruise surveys (n = 204; 2018-2023) to assess anthropogenic, environmental, and temporal factors associated with vertebrate roadkill across the wildland-urban interface of Arizona's biodiverse Sonoran Desert ecoregion-already subjected to increased human development and climate change. Of n = 2019 vertebrates observed, 28.5% were roadkill. Increasing urbanization levels were associated with reduced vertebrate abundance on roads and increased road-killed endothermic vertebrates. Traffic volume was strongly associated with reduced vertebrate abundance and increased roadkill; additive effects on roadkill began at approximately 20 vehicles. Daily low temperature and/or relative humidity were also associated with roadkill across vertebrate groups. We provide empirical evidence to understand wildlife-roadkill associations across expanding wildland-urban interfaces to inform effective roadkill mitigation and wildlife conservation management strategies in biodiverse aridland regions. We recommend that managers mitigate or avoid development in rural areas that possess high biodiversity, valuable waterways or migration corridors, and populations of vulnerable species.

Spatial distribution and risk assessment of microplastics in surface waters of the St. Lawrence Estuary

Development of effective prevention and mitigation strategies for marine plastic pollution requires a better understanding of the pathways and transport mechanisms of plastic waste. Yet the role of estuaries as a key interface between riverine inputs of plastic pollution and delivery to receiving marine environments remains poorly understood. This study quantified the concentration and distribution of microplastics (MPs) (50–3200 μm) in surface waters of the St. Lawrence Estuary (SLE) in eastern Canada. Microplastics were identified and enumerated based on particle morphology, colour, and size class. Fourier Transform Infrared (FTIR) spectroscopy was used on a subset of particles to identify polymers. Generalized linear models (Gamma distribution with log-link) examined the relationship between MP concentrations and oceanographic variables and anthropogenic sources. Finally, a risk assessment model, using MP concentrations and chemical hazards based on polymer types, estimated the MP pollution risk to ecosystem health. Mean surface MP concentration in the SLE was 120 ± 42 SD particles m−3; MP concentrations were highest in the fluvial section and lowest in the Northwest Gulf of St. Lawrence. However, MP concentrations exhibited high heterogeneity along the length and width of the SLE. Microplastics were elevated at stations located closer to wastewater treatment plant outflows and downstream sites with more agricultural land. Black, blue, and transparent fibers and fragments ≤250 μm were most commonly encountered. Predominant polymer types included polyethylene terephthalate, regenerated cellulose, polyethylene, and alkyds. While the overall risk to ecosystem health in the entire estuary was considered low, several stations, particularly near urban centres were at high or very high risk. This study provides new insights into the quantification and distribution of MPs and first estimates of the risk of MP pollution to ecosystem health in one of the world's largest estuaries.

Microplastic: Characteristics, exposure pathways, toxicity, and implication for human health

Microplastics particles ranging from 1 µm to 5 mm, have gained attention for their omnipresence in marine environments and potential health risks. Initially recognized in the 1990s, microplastics are classified as primary (designed for microscopic dimensions) or secondary (resulting from degradation). Exposure pathways include ingestion, inhalation, and dermal contact, with sources ranging from food and beverages to personal care products. These particles possess unique properties, facilitating their interaction with organic contaminants and potential bioaccumulation in marine life. Their small size allows them to infiltrate ecosystems, raising concerns about their impacts on human health. Studies suggest associations between microplastic exposure and health issues such as inflammatory bowel disease and neurodevelopmental. Microplastics exhibit toxicity through mechanisms like oxidative stress induction and disruption of neurotransmitter levels. They have been detected in human tissues, including the brain, raising concerns about potential neurological impacts. To comprehend the effects on health over time, additional research is required, including biopersistence and tissue accumulation. Regulatory measures and consumer awareness initiatives are crucial to mitigate microplastic pollution and minimize health risks. Strategies to reduce plastic production, enhance recycling, and develop microplastic removal technologies are vital for protecting both human health and the environment. In summary, microplastics pose significant health risks due to their widespread presence and potential toxicity. Understanding their impacts and implementing effective mitigation strategies are essential for safeguarding human health and environmental integrity.

In-depth Analysis of Business Risks in the Cattle Livestock Industry: Comprehensive Literature Review

Cattle farming is a vital sector in the livestock industry, but it is also faced with various complex business risks. This literature review aims to provide an in-depth understanding of the risks associated with cattle farming. Through a thorough analysis of various literature sources, we explore the main aspects that influence risk in the cattle industry, including external factors such as fluctuations in feed prices, changes in government policies, and market uncertainty, as well as internal factors such as livestock health, financial management, and operational efficiency. By thoroughly understanding these risks, cattle farmers can develop effective mitigation strategies to increase the resilience of their business amidst the challenges facing the cattle farming industry.

From Chilean saltpeter to modern agriculture: navigating nitrate toxicity in ruminants through compartmental modeling

Nitrate/nitrite poisoning is a significant issue in ruminant livestock health, with historical roots pre-dating the widespread use of nitrogen fertilizers. This review explores the various factors contributing to nitrate toxicity, including natural and anthropogenic sources, metabolic pathways, mechanisms of action, and the variability in reported data. The importance of compartmental modeling in under-standing nitrate metabolism dynamics is emphasized. These models provide a framework for simu-lating the complex processes involved in nitrate intake, conversion, absorption, distribution, and ex-cretion, ultimately informing effective mitigation strategies. The goal of this article is to provide a comprehensive overview of nitrate/nitrite poisoning in ruminants and highlight the role of compart-mental modeling in safeguarding animal health, optimizing agricultural practices, and ensuring food safety in the context of modern agriculture.

Characteristics of microplastics in tributaries of the upper Brahmaputra River along the Himalayan foothills, India

Microplastic (MP) pollution is a global concern, yet its presence in riverine environments has received limited research attention. This study aimed to evaluate MP levels and identify their potential sources in river surface water and nearby soil samples from two rural and remote rivers near the Jaldapara National Park in the foothills of Eastern Himalaya of India. The average MP levels in water and soil samples were 0.14 ± 0.11 pieces m−3 and 633.33 ± 124.72 pieces/kg d.w. respectively. The primary types of microplastics detected were fibres, followed by fragments, and films. MP sizes in water were larger than in soil samples. Blue, black, and red MPs were most abundant. Micro-Raman analysis revealed polyethylene was the dominant polymer type, followed by nylon, and polypropylene. Comparatively, pollution levels in the study area were relatively low when compared to other rivers worldwide. Understanding the sources and characteristics of microplastics are vital in formulating effective mitigation strategies and promoting responsible waste management practices. These findings provide valuable insights for policymakers, environmentalists, and indigenous communities to implement measures that can lead to curbing of plastic use and safeguard vulnerable riverine ecosystems from adverse impacts of MP pollution.

Quantifying the Impact of Risk Factors on Direct Compensation Property Damage in Canadian Automobile Insurance

This study presents a statistical analysis assessing the impact of various risk factors on direct compensation property damage (DCPD) claims in private passenger vehicle accidents. Using automobile insurance data in Ontario, Canada for the decade years period between 2003 and 2012, a statistical model of property damage was explored via a generalized linear binary logit mixed model and considered the imbalance between the classes of insureds. The results indicate that several risk factors have a significant impact on the likelihood of DCPD claims, including usage, training, outstanding loss, and incurred loss. The effects of these risk factors were observed under the weights — the number of trials used to generate each success proportion — in the different classes of insureds. The generalized linear mixed models (GLMMs) analysis provides a powerful tool for quantifying the impact of risk factors on binary outcomes, which are called DCPD claims and property damage (PD) claims covered by third-party liability (TPL) insurance. These models can also inform insurance underwriting and policy design, focusing on identifying the most significant risk factors. The performance metrics calculated by considering the class imbalance in binary outcomes verify the resulting model’s ability to accurately predict classes. The F1 score, an evaluation metric to measure the performance of classification, was calculated as 0.934. In addition, PR AUC, which is the area under the Precision-Recall (PR) curve, was computed as 0.953. These high scores indicate that the resulting model performs well in the classification. The other metrics also support the classification accuracy of this model. The findings of the analysis can help insurers better understand the underlying drivers of property damages and develop more accurate and effective strategies for risk mitigation. Furthermore, this study highlights the importance of developing class-specific risk assessment models to account for the imbalance across different classes.

Assessing the Impacts of land Use Change on Carbon Sequestration: A Comprehensive Analysis for Kochi City, India

Land use typologies play a pivotal role in determining the carbon sequestration capacity of urban ecosystems. Ashuman activities continue to reshape landscapes, understanding the impact of different land uses on carbon storageis crucial for developing effective climate change mitigation strategies for cities. This paper aims to analyse therelationship between land use typologies and carbon sequestration. Carbon sequestered in the selected city i.e., Kochiis quantified using the InVEST Carbon Storage and Sequestration model (Integrated Valuation of Ecosystem Servicesand Tradeoffs), coupled with remote sensing. The model uses input data on carbon pools including above-ground,below-ground, soil biomass, and dead carbon matter. The change in carbon sequestration with change in land usebetween 2003 and 2023 is calculated. The study reveals that there has been a reduction in wetland areas by 59 %which leads to 63% less sequestration of carbon highlighting the implication of land use on carbon sequestration.

CLIMATE CHANGES AND AGRICULTURE SECTOR IN INDIA: ISSUE AND CHALLENGES

Global climate change refers to the long-term shifts in weather patterns that affect different regions of the world. In contrast, "weather" describes the short-term variations in wind, temperature, and precipitation in a specific area. Agriculture is the sector most vulnerable to climate change within the Indian economy. Long-term climate shifts can impact agricultural productivity, growth rates, photosynthesis, transpiration, moisture availability, and both the quantity and quality of crops. According to the Intergovernmental Panel on Climate Change (IPCC), India's temperatures are expected to rise by 3-5°C by the end of the twenty-first century, which could lead to a reduction of 3-26% in net agricultural revenues. This poses a significant threat to global food security as it alters seasonal temperature patterns. A decline in agricultural output could result in increased prices and make food unaffordable for many. Without effective mitigation and adaptation strategies, agricultural revenues may drop by 12-40% in the coming years. This situation is particularly critical for an agrarian country like India, affecting livelihoods, economic development, and food and employment security. It is essential to identify and address the factors contributing to the rise in greenhouse gases and the degradation of soil and water resources. Urgent mitigation and adaptation measures are necessary to tackle the challenges posed by global climate change. Stakeholders must focus on how quickly Indian farmers can adjust their practices to cope with these changes and what policies or technological innovations could facilitate this adaptation. However, in a developing country like India, where many farmers lack financial resources and expertise, rapid adaptation may be challenging.

Analysis of the Data Presented by Tide Gauge Stations in Albania

This study focuses on the comprehensive monitoring of tide gauges along the coastal regions of the Republic ofAlbania, aiming to assess sea level variations and understand the dynamics of the coastal environment. Tide gaugesplay a crucial role in providing essential data for coastal management, climate change studies, and disasterpreparedness. The research involves the deployment and maintenance of tide gauges at strategic locations along theAlbanian coastline, coupled with an in-depth analysis of the collected data. The monitoring process includescontinuous data acquisition from tide gauges, incorporating advanced sensors and telemetry systems to ensure realtime and accurate measurements. The acquired data are analyzed to identify patterns, trends, and anomalies in sealevel variations. Additionally, the study investigates the influence of meteorological and oceanographic factors on tidegauge readings, enhancing the understanding of the complex interactions shaping coastal dynamics. Furthermore, theresearch explores the implications of observed sea level changes on coastal ecosystems, infrastructure, andcommunity resilience. It provides valuable insights into potential threats such as coastal erosion, saltwater intrusion,and flooding. The findings contribute to the development of effective adaptation and mitigation strategies to addressthe challenges posed by rising sea levels.

Evaluating the Influence of Sustainable Urban Disaster Prevention Planning Satisfaction on Residents’ Living Willingness: A Structural Equation Modeling Approach in Kaohsiung City

Rapid urban development and increasing climate-related challenges underscore the need for effective urban disaster mitigation strategies. This study, conducted in Kaohsiung City, Taiwan, uses a Structural Equation Model to assess how satisfaction with urban disaster prevention planning influences residents’ intentions to stay there. We found that satisfaction with sustainable urban disaster prevention increases residents’ willingness to remain and acts as a key mediator between satisfaction with public facilities and residential intentions. The methodology provides practical insights for urban planners and policymakers, highlighting that improving public facilities and effective disaster planning significantly strengthens residents’ attachment to urban environments. These findings suggest a pragmatic way to enhance urban sustainability and resilience by incorporating disaster preparedness into urban development plans.

The Role of Artificial Intelligence in Advancing Climate Change Research

Abstract: Climate change is one of the most pressing challenges of the 21st century. The complexity and scale of climate systems require advanced tools for effective analysis and prediction. Artificial Intelligence (AI), with its ability to process vast amounts of data and identify patterns, holds significant potential to enhance climate change research. This paper explores the various applications of AI in climate science, evaluates the current capabilities, and discusses future prospects and challenges. The integration of AI into climate research can lead to improved climate models, better predictions of extreme weather events, and more effective climate mitigation and adaptation strategies.

Effect of Seasonal Variation and Soil Parameters on Soil-Gas Radon Concentration in Ogbomoso, South Western Nigeria

Radon 222Rn, an inert natural radioactive gas, a daughter product in the 238U natural decay series, a source of alpha radiation. It accounts for about 55% of the total radiation from natural sources. Radon is known for its substantial variations in concentration due to both seasonal changes and variations in soil parameters. Soil, fractures in rocks and water are the main source of radon and they are the routes through which individual is exposed to it, in which this study is aimed at examining the effects of seasonal variation, soil parameters, on soil radon concentration. A total of hundred (100) in-situ soil radon measurements were carried out for both seasons (wet and dry) in Ogbomoso using an active electronic device RAD 7. Soil parameters were measured using a 4-in-1 digital metre. The obtained experimental data were subjected to a statistical tool (SPSS) Version 23, with statistical significance set at p < 0.01. The result showed that the effect of seasonal variation on soil-gas radon concentration was discovered to be maximum during the dry season with the radon concentration of 1900.01 Bq/m3 and minimum during the wet season with the radon concentration of 711.81 Bq/m3.The mean value of radon in soil-gas was found to be 553.48 Bq/m3 and 1366.34 Bq/m3 for wet and dry season respectively. Soil parameters considered in this work have notable influence on soil gas radon concentration in both seasons. Thus, providing valuable insight into the dynamics of radon migration in the soil. The results will help to develop effective mitigation strategies by policies makers and reducing the risk of radon related lung cancer in the study area.

Picocyanobacterial-bacterial interactions sustain cyanobacterial blooms in nutrient-limited aquatic environments

Cyanobacterial blooms (CBs) and concomitant water quality issues in oligotrophic/mesotrophic waters have been recently reported, challenging the conventional understanding that CBs are primarily caused by eutrophication. To elucidate the underlying mechanism of CBs in nutrition-deficient waters, the changes in Chlorophyll a (Chl-a), cyanobacterial-bacterial community composition, and certain microbial function in Qingcaosha Reservoir, the global largest tidal estuary storage reservoir, were analyzed systematically and comprehensively after its pilot run (2011–2019) in this study. Although the water quality was improved and stabilized, more frequent occurrences of bloom level of Chl-a (>20 μg L−1) in warm seasons were observed during recent years. The meteorological changes (CO2, sunshine duration, radiation, precipitation, evaporation, and relative humidity), water quality variations (pH, total organic carbon content, dissolved oxygen, and turbidity), accumulated sediments as an endogenous source, as well as unique estuarine conditions collectively facilitated picocyanobacterial-bacterial coexistence and community functional changes in this reservoir. A stable and tight co-occurrence pattern was established between dominant cyanobacteria (Synechococcus, Cyanobium, Planktothrix, Chroococcidiopsis, and Prochlorothrix) and certain heterotrophic bacteria (Proteobacteria, Actinobacteria, and Bacteroidetes), which contributed to the remineralization of organic matter for cyanobacteria utilization. The relative abundance of chemoorganoheterotrophs and bacteria related to nitrogen transformation (Paracoccus, Rhodoplanes, Nitrosomonas, and Zoogloea) increased, promoting the emergence of CBs in nutrient-limited conditions through enhanced nutrient recycling. In environments with limited nutrients, the interaction between photosynthetic autotrophic microorganisms and heterotrophic bacteria appears to be non-competitive. Instead, they adopt complementary roles within their ecological niche over long-term succession, mutually benefiting from this association. This long-term study confirmed that enhanced nutrient cycling, facilitated by cyanobacterial-bacterial symbiosis following long-term succession, could promote CBs in oligotrophic aquatic environments devoid of external nutrient inputs. This study advances understanding of the mechanisms that trigger and sustain CBs under nutritional constraints, contributing to developing more effective mitigation strategies, ensuring water safety, and maintaining ecological balance.

Causes of Climate Change: A Neglected Dimension

Climate change is one of the most disturbing challenges faced by humankind due to the profound threat it poses to human life and livelihood, affecting almost every aspect of life negatively. A comprehensive grasp of the causes of climate change is imperative for effective mitigation strategies. However, the most prevalent causes of climate change are advanced within the Western scientific perspective. This article aims to provide a cross-cultural comparison between the Islāmic and Western scientific perspectives on the causes of climate change. Based on the review of scientific literature and other relevant documents, the article argues that both the Islāmic and the secular perspectives on the impacts of climate change are in accord. However, the causes of climate change extend beyond the material scientific explanation. The mischief and sins of human beings on earth are fundamental causes that should not be ignored. The article concludes that mitigation efforts will remain one-sided unless these two dimensions of human activities are perceived as the causes of climate change.

Adoption of precision livestock farming technologies has the potential to mitigate greenhouse gas emissions from beef production

To meet the objectives of the Paris Agreement, which aims to limit the increase in global temperature to 1.5°C, significant greenhouse gas (GHG) emission reductions will be needed across all sectors. This includes agriculture which accounts for a significant proportion of global GHG emissions. There is therefore a pressing need for the uptake of new technologies on farms to reduce GHG emissions and move towards current policy targets. Recently, precision livestock farming (PLF) technologies have been highlighted as a promising GHG mitigation strategy to indirectly reduce GHG emissions through increasing production efficiencies. Using Scotland as a case study, average data from the Scottish Cattle Tracing System (CTS) was used to create two baseline beef production scenarios (one grazing and one housed system) and emission estimates were calculated using the Agrecalc carbon footprinting tool. The effects of adopting various PLF technologies on whole farm and product emissions were then modelled. Scenarios included adoption of automatic weigh platforms, accelerometer-based sensors for oestrus detection (fertility sensors) and accelerometer-based sensors for early disease detection (health sensors). Model assumptions were based on validated technologies, direct experience from farms and expert opinion. Adoption of all three PLF technologies reduced total emissions (kg CO2e) and product emissions (kg CO2e/kg deadweight) in both the grazing and housed systems. In general, adoption of PLF technologies had a larger impact in the housed system than in the grazing system. For example, while health sensors reduced total emissions by 6.1% in the housed system, their impact was slightly lower in the grazing system at 4.4%. The largest reduction in total emissions was seen following the adoption of an automatic weight platform which reduced the age at slaughter by 3 months in the grazing system (6.8%) and sensors for health monitoring in the housed system (6.1%). Health sensors also resulted in the largest reduction in product emissions for both the housed (12.0%) and grazing systems (10.5%). These findings suggest PLF could be an effective GHG mitigation strategy for beef systems in Scotland. Although this study utilised data from beef farms in Scotland, comparable emission reductions are likely attainable in other European countries with similar farming systems.

Tetracycline and fluoroquinolone antibiotics contamination in agricultural soils fertilized long-term with chicken litter: Trends and ravages

We investigated the potential accumulation of tetracyclines (TCs) such as chlortetracycline (CTC), oxytetracycline (OTC) and doxycycline (DC), and fluoroquinolones (FQs) like enrofloxacin (ENR) and ciprofloxacin (CIP) in chicken litter and agricultural soils fertilized over short-term to long-term (<1–30 yrs) with chicken litter in a poultry hub for the first time from Tamil Nadu, India. CTC, OTC, DC, CIP, and ENR were detected in 46–92 % of the selected chicken litter samples, with mean levels ranging from 2.90 to 23.30 μg kg−1. Higher concentrations of TCs and FQs were observed in freshly collected chicken litter from poultry sheds than in those stockpiled in cultivated lands. CTC was the prevalent antibiotic in chicken litter. The overall occurrence, as well as the ecological risks of TCs and FQs, changed over a 30-yr period. The accumulation of veterinary antibiotics (VAs) (in μg kg−1) in short-term (>1 yr) to medium-term (1–3 yrs) chicken litter-fertilized soils reached a maximum of 11.60 for CTC, 6.50 for OTC, 0.80 for DC, 3.70 for CIP, and 3.60 for ENR, but decreased in long-term (10–30 yrs) fertilized soils. Ecological risk assessment revealed a Risk Quotient (RQ) of ≤0.10 for CTC, OTC, and DC in all soils, while an average risk (RQ >0.10–<1.0) was evident with CIP and ENR in short-term and medium-term fertilized soils. Antibiotic resistance genes (ARGs), including tetA, tetB, qnrA, qnrB and qnrS were detected in most of the chicken litter samples and litter-fertilized soils. Thus, it is critical to develop and adopt effective mitigation strategies before applying chicken litter in farmlands to decrease VAs and ARGs, reducing their associated risks to public health and ecosystems in India considering ‘One Health’ approach. Future investigations on the occurrence of other VAs and ARGs in soils fertilized with poultry litter at regional scale are required for effective risk mitigation of the widely used VAs.