Key Environmental Indicators Research Articles

Need for harmonized long-term multi-lake monitoring of African Great Lakes

To ensure the long-term sustainable use of African Great Lakes (AGL), and to better understand the functioning of these ecosystems, authorities, managers and scientists need regularly collected scientific data and information of key environmental indicators over multi-years to make informed decisions. Monitoring is regularly conducted at some sites across AGL; while at others sites, it is rare or conducted irregularly in response to sporadic funding or short-term projects/studies. Managers and scientists working on the AGL thus often lack critical long-term data to evaluate and gauge ongoing changes. Hence, we propose a multi-lake approach to harmonize data collection modalities for better understanding of regional and global environmental impacts on AGL. Climate variability has had strong impacts on all AGL in the recent past. Although these lakes have specific characteristics, their limnological cycles show many similarities. Because different anthropogenic pressures take place at the different AGL, harmonized multi-lake monitoring will provide comparable data to address the main drivers of concern (climate versus regional anthropogenic impact). To realize harmonized long-term multi-lake monitoring, the approach will need: (1) support of a wide community of researchers and managers; (2) political goodwill towards a common goal for such monitoring; and (3) sufficient capacity (e.g., institutional, financial, human and logistic resources) for its implementation. This paper presents an assessment of the state of monitoring the AGL and possible approaches to realize a long-term, multi-lake harmonized monitoring strategy. Key parameters are proposed. The support of national and regional authorities is necessary as each AGL crosses international boundaries.

Review of the monograph by N.G. Gadzhiev, S.A. Konovalenko, M.N. Trofimov, “Theoretical aspects of the formation and development of ecological economics in Russia. Moscow, Infra‐M Publ., 2022, 169 p.”

The monograph reviewed is devoted to the place and role of ecology and environmental safety in ensuring the sustainable socio‐economic development of society. In the conditions of the obligatory transition of the economies of the leading countries of the world from an industrial type to a new form of "green economy" aimed at ensuring the preservation of ecological systems and the maximum reduction of damage to the biodiversity of ecological systems, the Russian Federation faces the task of forming a new course of socio‐economic development of society focused on the preservation of the potential of nature and ecology at a level enabling the support of the viability of society, flora and fauna in the foreseeable future and in the long term. The authors outline the role and importance of environmental safety in the system of ensuring the economic security of the state, examine the concept of the ideology of Global Commons in ensuring sustainable socio‐economic development of society, analyse the problems and prospects for the implementation of the Green Course of Russia programme, and examine special aspects of environmental audit, accounting and control, damage assessment in the field of ecology. Special attention is paid to the forecast of the dynamics of key environmental indicators for the medium term. The main directions of increasing the effectiveness of the mechanism for ensuring environmental safety in a market economy are described.

Main elements of the Rail Cargo Hungaria Zrt’s environmental concept

In this research the efforts of Rail Cargo Hungaria Zrt. were studied on the reduction of main environmentally harmful factors induced by the company. The objective was to determine the key environmental indicators. The fields of examination were the greenhouse gas (GHG) emission, the use of energy, the noise level, the production of different type of wastes. Based on the results, it can be said that RCH is committed to environmental protection and has an energy conscious management.

Trace metals, polycyclic aromatic hydrocarbons and polychlorinated biphenyls in the surface sediments from Sanya River, China: Distribution, sources and ecological risk

The urban inland river ecosystems are now facing comprehensive pollution and governance pressures. Up to now, few works related to the multiple pollution assessment of trace metals, polycyclic aromatic hydrocarbons (PAHs) and polychlorinated biphenyls (PCBs) for the urban inland river sediments have been reported in China. Our study investigated the spatial distribution, ecological risk and potential sources of trace metals, PAHs and PCBs in surface sediment collected from 20 sampling sites of Sanya River, Hainan Province, China. The pollution status and potential ecological risk of trace metals were evaluated using the contamination indexes including geoaccumulation index (Igeo), individual potential ecological risk (Eri), potential ecological risk index (RI) and pollution load index (PLI). Considering the carcinogenicity and toxicity of PAHs and PCBs to human health and the ecological environment, we also analyzed the distributions, sources and adverse biological effects of PAHs and PCBs according to the sediment quality guidelines (SQGs), principal component analysis (PCA) and other source analysis. This study revealed that the surface sediments in Sanya River were extremely slight pollution and showed a very low ecological risk according to Igeo, Eri, PLI and RI results for trace metals. Besides, PAHs and PCBs pollution detected may not pose considerable adverse biological effect to ecological environment in a foreseeable period on the basis of comprehensive research results. The overall surface sediments quality of the Sanya River not seem to pose a serious pollution and ecological risk based on the evaluation results of multiple pollution factors. The study provided detailed information on the multiple pollution status and location of surface sediments, one of the key environmental indicators of international tourism cities, in the Sanya River, which would be useful for the water quality improvement of Sanya River and the environmental remediation of the other coastal ecosystems from different regions.

Scenario Analysis of Changes in the Food Supply Chain

The aim of this study is to identify possible alternative scenarios in food production and consumption that would lead to a more balanced supply of locally produced food and a reduction in the environmental impact of agricultural production. The analysis of food consumption and agricultural land distribution in Slovenia showed that a significantly larger proportion of land area is used for food production for animals than for humans. Several potential changes in the distribution of agricultural land, in milk consumption and production, in the use of fertilizers and in people's eating habits towards lower consumption of red meat were identified. Analysis of the scenarios showed the potential to increase food self-sufficiency by 15 %, reduce greenhouse gas emissions by 35 %, and reduce nitrogen intake by 30 % by 2050. Reducing red meat consumption could shift intensive feed-based livestock production to grass-based production and produce more plant-based food for humans. This could increase the amount of local food and make food production less harmful to the environment. Preliminary research has shown that there are several influencing factors in the food supply chain that have conflicting effects on key food supply parameters and environmental indicators. It is therefore necessary to move towards a systematic multi-criteria optimization of the food supply chain in order to achieve the objectives of the European and national strategies on agriculture and climate change.

Towards a Single Integrative Metric on the Dynamics of Social-Environmental Systems

Integrating the dynamics and interconnections of natural and human system properties into a single measure would make it simpler to reliably and repeatedly assess and compare different social-environmental systems (SES). We propose a novel metric to assess the magnitudes and variations in SES dynamics by integrating longitudinal gross domestic product, population, and ecosystem net primary production. We use annual public data across the Asian Drylands Belt (ADB) from 1992 through 2016 for 18 political entities as our testbed for assessing the efficacy of the metric. We perform cross-comparisons with existing natural and social science metrics to demonstrate the validity of the proposed metric, including the Human Development Index and the Palmer Drought Severity Index. The new metric demonstrates notable and meaningful differences in trends among the political entities that reflect major social, economic and environmental events over the 25-year period. It provides unique perspectives about the three pillar components (social, economic and environmental systems) in each of the 18 political entities (PE) of the ADB. The metric also shows meaningful associations with key economic and environmental indicators and great potential for broader application and evaluation, given additional testing in other countries, regions, and biomes.

Greenhouse gas mitigation strategies and opportunities for agriculture

AbstractTo cope with increasing demands for food, feed, and energy along with environmental challenges due to climate change, the agricultural sector has a unique opportunity to meet sustainable development goals set by the United Nations through innovative and regenerative agriculture practices that enhance agricultural productivity, ecosystem services, and human well‐being simultaneously. Among many sustainability metrics to measure agriculture's impacts on sustainability and contribution to its improvements, we focus on the greenhouse gas (GHG) emissions from the agricultural sector as a key environmental indicator to assess several GHG mitigation practices from the perspective of life‐cycle analysis applied to agriculture. We first analyze the key factors contributing to farming GHG emissions and then identify a range of GHG mitigation strategies, such as optimizing farm fertilizer/chemical inputs, manufacturing low‐carbon fertilizer/chemical, reducing on‐farm energy/fuel consumption, and increasing soil carbon stocks. Furthermore, we elaborate on how these strategies can be successfully implemented to different scales of farming through policies and incentives and better quantification and verification schemes for effective policies and incentives. Finally, we present the holistic evaluation of agricultural GHG emissions in terms of landscape management approaches and provide ecosystem services to address social and economic issues.

Missing Information Reconstruction of Land Surface Temperature Data Based on LPRN

Temperature is the main driving force of most ecological processes on Earth, with temperature data often used as a key environmental indicator to guide various applications and research fields. However, collected temperature data are limited by the hardware conditions of the sensors and atmospheric conditions such as clouds, resulting in temperature data that are often incomplete. This affects the accuracy of results using the data. Machine learning methods have been applied to the task of completing missing data, with mixed results. We propose a new data reconstruction framework to improve this performance. Using the MODIS LST map over a span of 9 years (2000–2008), we reconstruct the land surface temperature (LST) data. The experimental results show that, compared with the traditional reconstruction method of LST data, the proportion of effective pixels of the LST data reconstructed by the new framework is increased by 3%–7%, and the optimization effect of the method is close to 20%. The experiment also discussed the influence of different altitudes on the data reconstruction effect and the influence of different loss functions on the experimental results.

Life cycle assessment for the determination of key environmental impact indicators in soilless tomato culture and mitigation potential

ISHS III International Symposium on Soilless Culture and Hydroponics: Innovation and Advanced Technology for Circular Horticulture Life cycle assessment for the determination of key environmental impact indicators in soilless tomato culture and mitigation potential

The potential for photovoltaic-powered pumped-hydro systems to reduce emissions, costs, and energy insecurity in rural China

• A micro-PV-PH system is proposed in rural China. • The technical feasibility of a PV-PH installation in rural areas is evaluated. • Techno-economic indicators are used to investigate the potential of the PV-PH system. • This research provides a theoretical foundation for the promotion of the PV-PH system. Homes in rural China often have access to water wells for domestic and agricultural use. Given the significant difference in height between the well water and the roof of the house, such an arrangement could be used as a solar-powered pumped-hydro energy generation system. These systems could both limit increases in carbon emissions from coal-powered electricity while alleviating energy insecurity, while providing significant savings to households. The potential for these systems in rural regions across Shaanxi province is investigated by assessing eight key environmental and socioeconomic indicators. The find is that positive impacts across almost all indicators with generally rapid investment payback periods of 6.4–8.1 years, annual net income increases of 314.2 CNY −541.6 CNY per year per household. This research laid a theoretical foundation for the promotion of PV-PH system in Shaanxi Province.

Aeromonas hydrophila as an environmental indicator to detect TiLV-infected tilapia under coinfection threat

As an emerging infectious agent threatening freshwater ecosystems and tilapia aquaculture, tilapia lake virus (TiLV) has expanded its geographical range over last five years. Seeking a way to alleviate loss of tilapia production caused by TiLV is an urgent need for commercial aquaculture. Recently, Aeromonas hydrophila (AH) was found to be concurrent with TiLV causing serious issues. However, our understanding of how coinfection of AH and TiLV impact on disease transmission dynamics remains limited, particularly in real farming ecosystems, where difficulty of direct experimentation makes inference challenging. Here we applied epidemiological models to fit mortality data of tilapia single- or co-infected with TiLV and AH to estimate key transmission parameters that govern virus-bacteria interactions among infection states. Dose–response analysis was performed to assess the effects of AH concentration on TiLV disease transmissibility and mortality. Results showed that basic reproduction number ( R 0 ) for TiLV transmission in population that infected with TiLV followed by higher load of AH could be reached ~11. Our dose-response model reveals that AH concentration in fish pond can be a key environmental indicator to drive epidemiology-based measure as well as to assess exacerbating mortality risk of TiLV-infected tilapia under coinfection. The model may therefore be useful for guiding the future development of control measures from an epidemiological perspective to mitigate the impact of TiLV disease on tilapia.

Optimum machine learning algorithm selection for forecasting vegetation indices: MODIS NDVI & EVI

Vegetation as a key environmental indicator both influences and is influenced by other factors. Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) are quantitative measurements of vegetation that follow a monthly trend. Machine learning algorithms are now widely used to forecast several environmental indicators, including vegetation indices. In this study, Moderate Resolution Imaging Spectroradiometer (MODIS) dataset MOD13A2.006 (2001–2018) was used to extract NDVI and EVI values. These quantitative values were used to forecast vegetation indices for 2019 and scored on their performance. Four supervised machine learning algorithms: Support Vector Regression, Random Forest, Linear & Polynomial Regression were tested. The models forecasted data with 5.73%–1.51% error in NDVI and 6.99%–4.33% error in EVI. However, sudden loss and sudden gain in NDVI or EVI midyear could not be forecasted with these four algorithms in some cases. There was an observed upwards linear trend in the data suggesting increasing vegetation cover.

Coupling landscape-scale diagnostics surveys, on-farm experiments, and simulation to identify entry points for sustainably closing rice yield gaps in Nepal

CONTEXTRice is the primary staple food crop in Nepal, contributing 20% of the agricultural gross domestic product and more than 50% of the total calories in the national diet. Nevertheless, the productivity of rice (3.36 t ha−1) is the lowest in South Asia region. OBJECTIVEThe objective of this study was to employ a mixed-methods approach to characterize and decompose yield gaps (YGs) in the context of identifying sustainable intensification pathways for rice production in Nepal. METHODSMethodologies include: a) landscape-scale crop diagnostic survey on crop management, field attributes, and productivity outcomes combined with gridded soil and daily weather data to decompose rice yield gaps into constituent factors with machine learning diagnostics; b) with survey data, computation of key performance indicators to identify factors associated with productivity, profitability, and resource use efficiencies; c) complementary multi-location on-farm experiments (2011–2017) evaluating new agronomic management practices; and d) dynamic simulation (ORYZA3) to derive estimates of rice yield potential. RESULTS AND CONCLUSIONSAnalysis of survey data suggests an exploitable YG of 2.57 t ha−1 (40%) and the total YG of 4.85 t ha−1 (55%) indicating substantial scope for increasing rice yields in Nepal. Frequency of irrigation, amount of late-season rainfall, soil type, amount of early-season rainfall, presence of water stress, soil pH, and nitrogen (N) and phosphorus (P) fertilizer rates are the principal determinants of productivity outcomes in descending ranked order. Efficiency metrics suggest rice farmers in the study region make good use of fertilizer inputs, but since application rates are very low (e.g. most farmers apply <20 kg P ha−1) unsustainable mining of soil nutrients is likely common. Farmers in the top 10% of the yield distribution had lower greenhouse gas emission intensities (−43%), increased water productivity (+66%), and higher use efficiencies of N and P fertilizers (+28% and + 20%, respectively), suggesting that yield intensification can be achieved without tradeoffs with key environmental performance indicators. On-farm experiments conducted over several seasons support insights from surveys by demonstrating that major gains in rice yield (1.86 t ha−1) and profitability (US$ 243 ha−1) are achievable through the adoption of good agronomic practices. SIGNIFICANCEThrough a mixed methods approach, our results suggest that adoption of integrated 'good agronomic practices' can close YGs and improve food security outcomes associated with the rice-based agricultural systems of Nepal while simultaneously preserving or enhancing key sustainability and livelihood objectives.

Ранговый классификатор состояний природной среды

This paper discusses the main features associated with the development and research of the device based on the methods of intelligent technology for assessing the state of the natural environment. It should be noted that natural and technical objects, as well as the processes occurring in them, are characterized by high complexity and dynamism, and a significant part of these processes has not yet been fully studied and formalized. Therefore, one of the most important areas of data analysis in this area is the use of artificial neural networks in information and measurement systems. In the works of scientists from various countries, the high efficiency of the use of artificial neural networks in solving individual data processing problems in the classification of environmental conditions is shown. The proposed approach is based on methods of nonparametric statistics using rank criteria and will allow for intelligent analysis of data on key environmental indicators, such as hydrometeorological data on the level of pollution and composition of air, soil, maximum permissible emissions of harmful substances, environmental monitoring of anomalies, and others. Static, dynamic, integral, and generalized models of classification of environmental conditions are presented. Further research plans suggest evaluating the impact of sample size on statistical sensitivity, statistical stability, and areas of confident/uncertain recognition, as well as building a decision support system for detecting the G-effect, and considering an adaptive approach to constructing an evaluation matrix.

Integrating Environmental Impact and Ecosystem Services in the Process of Land Resource Capitalization—A Case Study of Land Transfer in Fuping, Hebei

The contradiction between human and land has always been a problem in the process of development and utilization of land resources. Under such circumstances, relevant government agencies put forward the management concept of land resource capitalization. As an effective policy to implement the conception of land resource capitalization, land transfer is of great significance to reforming rural land systems and liberating productivity in poor areas of Tai-hang Mountain in Hebei. However, how to integrate environmental impact and value evaluation of ecosystem services of land transfer in the process of resource capitalization deserves our attention. This paper takes the land transfer of Fuping, Hebei in Tai-hang Mountain as an example, combined with life cycle assessment (LCA), life cycle cost assessment (LCC), and the methods of value evaluation of ecosystem services to quantify the changes of environmental loads, economic costs, and ecosystem services in the whole process and different stages of land resource capitalization. Moreover, through the sensitivity analysis of key environmental indicators, the possibility of restricting environmental costs is explored. This paper studies land transfer from the direction of the cross-discipline and provides a new idea for land resource management.

The need to decelerate fast fashion in a hot climate - A global sustainability perspective on the garment industry

Controversy exists regarding the scale of the impacts caused by fast fashion. This article aims to provide a robust basis for discussion about the geography, the scale and the temporal trends in the impacts of fast fashion because the globalisation of the fashion industry means original, peer-reviewed, quantitative assessments of the total impacts are relatively rare and difficult to compare. This article presents the first application of Eora, a multiregional environmentally extended input output model, to the assessment of the impacts of clothing and footwear value chain. We focus on the key environmental indicators of energy consumption, climate and water resources impacts, and social indicators of wages and employment.The results of the analysis indicate that the climate impact of clothing and footwear consumption rose from 1.0 to 1.3 Gt carbon dioxide equivalent over the 15 years to 2015. China, India, the USA and Brazil dominate these figures. The trends identified in this and the other indicators represent small increases over the study period compared to the 75% increase in textile production, meaning that the impacts per garment have improved considerably. On the other hand, the climate and water use impacts are larger as a proportion of global figures than the benefits provided via employment and wages. Our analysis of energy consumption suggests most of the per-garment improvement in emissions is the result of increased fashion-industrial efficiency, with a lesser role being played by falling carbon intensity among energy suppliers. While both the social benefits and environmental impacts per mass of garment appear to have decreased in recent times, much greater improvements in the absolute carbon footprint of the fashion industry are attainable by eliminating fossil-fueled electricity supplies, and by eliminating fast fashion as a business model.

Environmental Key Performance Indicators for Sustainable Evaluation in Automotive Industry: A Focus Group Study

Regarding the significant share of the automotive industry in both producing economic value added and employment, this sector is dominated in the industrial development approach. On the other side, different societies have focussed on the emissions, energy consumption and also waste production of road transportation as important risk threatening environmental health. So, the supply side is highly expected to deal with the expectations of stakeholders relating to sustainability concerns and related risks. To evaluate the implementation of environmental policies in the direction of sustainability and respond to the sustainability and environmental concerns of stakeholders, similar organisations need a uniform framework for performance appraisal. The present study aimed to identify, select and validate the environmental key performance indicators for sustainability assessment of the automotive industry to create a framework for selecting the indicators and using them as a uniform method for better comparison of the performance of car companies in the field of environmental sustainability. In this research, key environmental performance indicators are studied in accordance with sustainability evaluation in the vehicle industry and final indicators introduced using SMARTL criteria. The results showed that 41 indicators included in five categories of waste and waste water, soil contaminators and solid waste, carbon production and energy consumption and management are introduced according to ISO14031 model. Evaluating and monitoring these indicators, defined based on the UN Sustainable Development Goals 2030, can both reflect the quantitative improvement in the environmental performance. Additionally, using SMARTL key environmental performance indicators, the organization is able to comprehensively manage the environmental performance in accordance with environmental and sustainable development goals and policy. Using a uniform method for evaluating and monitoring the environmental performance allows us to compare the environmental performance of similar companies in line with sustainable development policies and creates a competition that allows the organisation to always increase its performance in a positive way.

Scenario analysis and sensitivity exploration of the MEDEAS Europe energy-economy-environment model

Today's decision-makers rely heavily on Integrated Assessment Models to guide the decarbonisation of the energy system. Uncertainty is embedded in the assumptions these models are built upon. Unless those uncertainties are adequately assessed, using Integrated Assessment Models for policy design is unadvised. In this work we run Monte Carlo simulations with the MEDEAS model at European Union scale to assess how the uncertainties on the main drivers of the transition affect key socioeconomic and environmental indicators. In addition, One-at-a-time sensitivity exploration is performed to grade the contribution of a set of model parameters to the uncertainty in the same key indicators. The combination of the uncertainties in the model drivers magnify the uncertainty in the model outputs, which widens over time. Parameters affecting sectorial and households' energy efficiency and households' transport energy use ranked amongst the most impacting ones on simulation results.

Neanderthal plant use and stone tool function investigated through non-pollen palynomorphs analyses and pollen washes in the Abri du Maras, South-East France

This pioneering research performed at the Abri du Maras (Ardèche, South-East France) explores the potential of non-pollen palynomorphs (NPP) to provide new insights into the relationships between Neanderthals and plant resources. Remains of endophytic fungi found during the analysis are key indicators of the local presence of plants, which could have potentially been used by Neanderthal groups. The recording of high concentrations of Chaetomium-type (HdV-7A) in archaeological contexts from localized areas within the shelter suggests the local presence of decayed plants near an in situ fireplace. This could point to the use of grasses or other types of plants from aquatic or marshy areas such as reed and rushes for a surface preparation (bedding?) near the fireplace. Our research has also applied for the first time a combined study of archaeological sediments and pollen washes of artefacts following a microspatial approach in the study of Middle Palaeolithic archaeological contexts. One artefact (1079), identified as a sidescrapper, provided positive results, with the recording of high concentrations of Diporotheca webbiae-type (HdV-143). We could identify the host plant of the fungal signature documented on the tool surface as alder roots by comparing archaeobotanical results with NPP analyses in modern surface samples. Following this, we have proposed the use of this Middle Palaeolithic sidescrapper for processing this plant by Neanderthal groups. Marsh and riparian ecosystems were thus attractive for Neanderthals occupying the Abri du Maras. This study clearly shows that NPP must be considered as key taphonomical, environmental and cultural indicators in archaeobotanical analyses. It also underlines the contribution of combined studies of archaeological sediments and pollen washes of artefacts following a microspatial approach to a better understanding of Middle Palaeolithic lithic tools and plant use relationships.

Environment-, health-, performance- and welfare-related parameters in pig barns with natural and mechanical ventilation

A multifactorial approach using environmental, performance, health and welfare parameters was used to investigate the numerous associations of ventilation throughout three consecutive fattening batches (08/2015 to 12/2016) in a farrow-to-finish commercial pig farm in Belgium. Two fattening pig units were used, unit A (1256 pigs) with mechanical ventilation and unit B (1264 pigs) with natural ventilation. Animal genetics, nutrition, stocking density and health management were the same for both units. Key environmental indicators were monitored in real-time (temperature, humidity, CO2 and NH3) and the daily prevalence of respiratory disease cases was recorded to monitor the temporal expression of disease over time within a farm environment. The welfare status of the animals was assessed twice per production round (batch) with a simplified version of the Welfare quality® protocol. Serological tests for the most prevalent respiratory infectious agents (Mycoplasma hyopneumonniae, swine influenza virus (subtypes H1N1, H1N2 and H3N2), Actinobacillus pleuropneumoniae serotypes 1-2-9-11 and porcine circovirus type 2) were performed during the first, second and final third of each fattening period. Finally, key performance parameters were calculated (average daily growth, feed conversion ratio) and upon slaughter lungs from pigs from unit A (n: 782) and from unit B (n: 544) were assessed for the presence of lung lesions, pleurisy and fissures. To identify the associations of ventilation on the aforementioned parameters, statistical models were run that also included other factors (when applicable) namely production batch, season, age and sampling.Overall, the use of natural ventilation was associated with a less optimal environment with regards to thermal comfort (p < 0.001), CO2 (p < 0.001) and NH3 (p < 0.001). A higher daily prevalence of respiratory disease cases was seen in the naturally-ventilated unit (p < 0.001). Concerning the sero-prevalence of the infectious agents tested, the odds to have a positive H1N1 sample were 3.17 higher in the naturally-ventilated unit (p = 0.003). From the visual assessment of the lungs no statistically significant associations were seen between ventilation type and the presence of lesions, fissures or pleuritis. Yet, the lung lesion score was expected to be lower in the naturally-ventilated unit (p = 0.010). Regarding performance parameters, feed conversion ratio and average daily gain were overall better in the mechanically-ventilated unit (descriptive results). Finally, a better welfare score was seen in the mechanically-ventilated unit in all three production batches (descriptive results).In conclusion, the mechanically-ventilated farm was associated with better environmental conditions for the fattening pigs. Yet, further research is needed to reach definite causal claims.