Development of agriculture in Russia and Belarus is based on the practical implementation of "smart" systems in agriculture based on the use of modern wireless, intelligent technologies and Internet of Things. This review presents research articles (mainly, in Russian) published in the period of 2013 – 2022 on the use of cloud technologies and Internet of Things for the development of agriculture in Russia and Belarus. An analysis of the use of cloud technologies and Internet of Things in the modern world is given on the basis of research articles and reviews published in English in the period of 2017 – 2022. The main directions of digitalization of modern agriculture are listed. The uses of cloud technologies and Internet of Things in agriculture are described along with promising directions for further research and applications.

This research investigates the potential benefits of integrating machine learning algorithms and IoT sensors in modern agriculture. The focus is on optimizing crop production and reducing waste through informed decisions about planting, watering, and harvesting crops. The paper discusses the current state of machine learning and IoT in agriculture, highlighting key challenges and opportunities. It also presents experimental results that demonstrate the impact of changing labels on the accuracy of data analysis algorithms. The findings recommend that by analyzing wide-ranging data collected from farms, including real-time data from IoT sensors, farmers can make more informed verdicts about factors that affect crop growth. Eventually, the integration of these technologies can transform modern agriculture by increasing crop yields while minimizing waste. In our studies, we achieve a classification accuracy of 99.59% using the Bayes Net algorithm and 99. 46% using Naïve Bayes Classifier, and Hoeffding Tree algorithms. Our results indicate that we achieved high accuracy results in our experiments in order to increase crop growth.

As Alberta increases solar power generation, land use conflicts with agriculture increase. A solution that enables low-carbon electricity generation and continued (in some cases increased) agricultural output is the co-locating of solar photovoltaics and agriculture: agrivoltaics. This study reviews policies that impact the growth of agri-voltaics in Alberta. Solar PV-based electricity generation is governed by three regula-tions based on system capacity. In addition, agrivoltaics falls under various legisla-tions, frameworks, and guidelines for land utilization. These include Land Use Frame-work, Alberta Land Stewardship Act, Municipal Government Act, Special Areas Dis-position, Bill 22 and other laws/policies all of which are reviewed in the context of agrivoltaics. Several policies are recommended to support rapid diffusion of agrivolta-ics. First, open access research into agrivoltaics, which not only will help optimize agrivoltaic systems for the region, but also coupled with public education is expected to galvanize social acceptability of large-scale PV deployment. Clearly defining and categorizing agrivoltaic technology, developing agrivoltaic standards, making agri-voltaic technology-friendly regulations/frameworks and developing programs and pol-icies to incentivize agrivoltaic deployment over conventional PV will all accelerate dif-fusion. Through these measures, Alberta can achieve conservation and sustainability in food and energy sector while simultaneously addressing the renewable energy and climate-related goals.

Poor households are more likely less resilient under climate change, risks of productive assets, social-related shocks, and decline of land productivity. The ability to deal with household resilience against poverty under the uncertain condition of risk is limited in the highlands of Ethiopia. The study aims to identify determinants of household resilience to livelihood insecurity under the crop-livestock mixed farming systems in Goncha district, Northwest highlands of Ethiopia. Primary data were collected by conducting face-to-face interviews among 280 households using structured questionnaire. Descriptive statistics, Kruskal-Wallis test and structural equation modeling were used to analyse the data. The results disclosed that sustainable management of the farming systems, cultivation of more fertile farmland, saving performance, diversification of income-earning activities, intensification of livestock husbandry practices, access to irrigation, and familiarity with practical technologies were found to be significant determinants at p<0.001 to household resilience of smallholder farmers. Social network development and tree plantation were explained household resilience to livelihood insecurity at P<0.01 and P<0.1 significant levels, respectively. The study concluded that scaling up sustainable management of the farming system and practical technologies, enhancing saving behavior, promoting income diversification, and intensifying agroforestry are significant for household resilience to livelihood insecurity of smallholders across agro-ecologies.

Farm fragmentation is the occurrence of numerous and often discontinuous land parcels associated with a single farm. Farm fragmentation is considered to be a defining feature of Northern Ireland’s (NI) agricultural landscape, influencing agricultural efficiency, productivity, and the spread of livestock diseases. Despite this, the full extent of farm fragmentation in cattle farms is not well understood, and little is known of how farm fragmentation either influences, or is influenced by, different animal production types. This study describes and quantifies farm fragmentation metrics for cattle enterprises in NI, presented separately for dairy and non-dairy production types. We find that 35% of farms consist of five or more fragments, with larger farms associated with greater levels of farm fragmentation, fragment dispersal and contact with contiguous farms. Moreover, this was particularly evident in dairy farms, which were over twice the size of farms associated with non-dairy production types, with twice as many individual land parcels and twice as many fragments. We hypothesise that the difference in farm fragmentation and farm size between dairy and non-dairy production types is associated with the recent expansion of dairy farms after the abolition of the milk quota system in 2015, which may have driven the expansion of dairy farms via the acquisition of land. The high levels of land fragmentation, fragment dispersal and contiguous contact observed in NI cattle farms may also have important implications for agricultural productivity and epidemiology alike. Whilst highly connected pastures could facilitate the dissemination of disease, highly fragmented and parcellised land could also hamper productivity via diseconomies of scale, such as preventing the increase of herd sizes or additionally, adding to farm costs by increasing the complexity of herd management.

Climate risk is one of the confronting factors in Indian agriculture. To overcome this distrust, a large number of sensors can be installed in the fields,The extensive IoT platform can process the data sent by these sensors.The Data stream can be processed in real-timeusing Fuzzylogic, to offer smart solution. Network coding can enhance throughput and security. Thus reducing human interaction and improve efficiency.

Climate risk is one of the confronting factors in Indian agriculture. To overcome this distrust, a large number of sensors can be installed in the fields,The extensive IoT platform can process the data sent by these sensors.The Data stream can be processed in real-timeusing Fuzzylogic, to offer smart solution. Network coding can enhance throughput and security. Thus reducing human interaction and improve efficiency.

The aim of the instant study was to evaluate relative inline progesterone dynamic changes according to parity and status of reproduction and to estimate the relationship with productivity in dairy cows by inline milk analysis system (IMAS) Herd Navigator. According to a progesterone assay, cows were divided into three periods: postpartum, after insemination, and pregnancy. In the first stage of the postpartum period (0-29 days), the progesterone level in milk was monitored every 6 days. The second stage of the postpartum period (30-65 days) lasted until cows were inseminated. In the third period (0-45 days) after cows were inseminated, progesterone scores were distributed according to whether or not cows became pregnant. The stability of progesterone dynamics was monitored in the last study period (45-90 days). For milk progesterone detection, the fully automated real-time progesterone analyzer Herd Navigator (Lattec I/S. Hillerød. Denmark) was used in combination with a DeLaval milking robot (DeLaval Inc., Tumba, Sweden). The highest progesterone concentration in multiparous cows ranged from 1.08% (11-17 days postpartum) to 34.89% higher than that in cows of the first parity. The lowest progesterone concentrations in the milk of all cows were estimated during the first 5 postpartum days and between 18 and 23 days after calving. Peak milk progesterone concentrations were evaluated in the first stage of the experiment on days 24-29 after calving. In the 30-65-day period after calving, the level of milk progesterone was 2.02-2.08 times higher than that in the 24-29-day postpartum period. After insemination, the level of progesterone in milk increased by 10.77-22.54% compared with the level from cows on days 30-65 after calving. A higher (12.88%) concentration of progesterone in milk was evaluated in multiparous cows compared with that from cows of the first parity. In pregnant cows, milk progesterone within 0-45 days after insemination was 23.88% (in multiparous cows) and 32.54% (in primiparous cows) higher than that in non-pregnant cows. On days 31–35 after insemination, pregnant cows had higher milk progesterone levels, which can predict pregnancy success. According to our study results, we can suggest that an inline progesterone concentration determined by inline milk analysis system Herd Navigator and changes in its dynamics correlate with different reproductive statuses and milk yield of cows. Pregnant cows 11–15 days after insemination have higher milk progesterone levels, what positively, associated with a successful pregnancy.

The demand for precise indoor localization services is steadily increasing. Among various methods, fingerprint-based indoor localization has become a popular choice due to its exceptional accuracy, cost-effectiveness, and ease of implementation. However, its performance degrades significantly as a result of multipath signal attenuation and environmental changes. In this paper, we propose an indoor localization method based on fingerprints using self-attention and long short-term memory (LSTM). By integrating a self-attention mechanism and LSTM network, the proposed method exhibits outstanding positioning accuracy and robustness in diverse experimental environments. The performance of the proposed method is evaluated under two different experimental scenarios, which involve 2D and 3D moving trajectories, respectively. The experimental results demonstrate that our approach achieves an average localization error of 1.76 m and 2.83 m in the respective scenarios, outperforming the existing state-of-the-art methods by 42.67% and 31.64%.

As an established and widely available infrastructure, wireless local area networks (WLANs) have emerged as a viable option for indoor localization of both mobile and stationary users. In planning a mobile communications network and radio system design, the critical role of coverage prediction becomes evident, empowering network operators to optimize cellular networks and elevate the overall customer experience. Moreover, WLANs present several challenges that must be fulfilled when it comes to localization based on Wi-Fi signals to get a proper coverage prediction map. This paper presents a study based on application of the extra trees regression (ETR) for indoor localization by using coverage prediction maps. The aim of the proposed method is to accurately estimate a user’s position within a radio environment map (REM) area using collected received signal strength indicator (RSSI) values. The proposed scheme investigates several machine learning (ML) regression algorithms for localization, where the training dataset is obtained from the REM by using the nearest neighbors method. Parameter tuning is conducted to optimize the performance of the ETR scheme by using 10-fold cross-validation. In the numerical results, we first demonstrate the effectiveness of utilizing ML regression techniques for generating coverage maps, which enables accurate estimation of the Wi-Fi signal strength in indoor environments. Then, we showcase the superior performance of the proposed ETR-based method compared to several other ML schemes for indoor localization using the REM. ML algorithms, including decision tree regression and the ETR, are compared to evaluate the system model. Based on error metrics, the proposed ETR-based approach exhibits the best performance among the evaluated techniques. The combination of coverage map generation and localization using regression techniques offers a powerful approach for analyzing the radio frequency (RF) environment in indoor spaces.

Elevated indoor CO2 levels might have adverse effects on human health. However, the introduction of outdoor air to lower indoor CO2 concentrations results in significant HVAC energy consumption. Aligning with office hours and the natural light cycle, the utilization of photosynthesis in living walls offers an energy-efficient and sustainable solution for the mitigation of high CO2 levels in office spaces. This study experimentally investigates the impacts of the carbon fixation pathways, light intensity, and substrate moisture content on the CO2 removal rate of living walls at the room scale. Furthermore, the fresh air energy-saving effects of living walls under different scenarios are accurately simulated in EnergyPlus. The results demonstrate that choosing C3 plants over CAM plants in living walls yields higher CO2 removal efficiency. In a 30-m2 office room accommodating 2-3 occupants, living walls can reduce the demand for fresh air by 12.3%-27.8% and decrease fresh air energy consumption by 11.2%-28.2%. The city with the highest energy savings has energy savings that are 4.5 times greater than those of the city with the lowest energy savings. The findings of this research promote the application and development of living walls, thus providing a viable solution for improving indoor air quality.

The current COVID19 pandemic has raised huge concerns for outdoor air quality due to expected lungs deterioration. These concerns include the challenges in the scalable prediction of harmful gases like carbon dioxide, iterative/repetitive inhaling due to mask and environmental temperature harshness. Even in the presence of air quality sensing devices, these challenges lead to failed planning and strategy against respiratory diseases, epidemics, and pandemics in severe cases. In this work, a dual time-series with bi-cluster sensor data-stream-based novel optimized regression algorithm was proposed with optimization predictors and optimization responses that use automated iterative optimization of the model based on the similarity coefficient index. The algorithm was implemented over SeReNoV2 sensor nodes data, i.e. multi-variate dual time-series of environmental and US Environmental Protection Agency standard sensor variables for air quality index measured from air quality sensors with geospatial profiling. The SeReNoV2 systems were placed at four locations that were 3 km apart to monitor air quality and their data was collected at Ubidots IoT platform over GSM. Results have shown that the proposed technique achieved a root mean square error (RMSE) of 1.0042 with a training time of 469.28 seconds for normal and RMSE of 1.646 in the training time of 28.53 seconds for optimization. The estimated R-Squared error of 0.03 with Mean-Square Error for temperature 1.0084 ᵒC and 293.98 ppm for CO2 was observed. Furthermore, the Mean-Absolute Error (MAE) for temperature 0.66226 ᵒC and 10.252 ppm for CO2 at a prediction speed of ~5100 observations/second for temperature 45000 observations/second for CO2 due to iterative optimization of the training time 469.28 seconds for temperature and 28.53 seconds for CO2 was very promising in forecasting COVID19 countermeasures before time.

With the rapid development of smart phones, tablets and their operative systems, many positioning enabled sensors have been built into these devices. Users can now accurately fix their location according to the function of GPS receivers. For indoor environments, as in the case we are studying, WiFi based positioning is preferred to GPS due to the attenuation or obstruction of signals. This paper deals with the automatic classification of customers in a Sports Shop Center on the basis of their movements around the shop's premises. To achieve this goal, we start by collecting (x,y) coordinates from customers while they visit the store. Consequently, any costumer's path through the shop is formed by a list of coordinates, obtained with a frequency of one measurement per minute. Then, a guess about the full trajectory is constructed and a number of parameters about these trajectories is calculated before performing an Unsupervised Learning Clustering Process. As a result, we can identify several types of customers, and the dynamics of their behavior inside the shop. This information is of great value to the company, to be used both in the long term and also in short periods of time, monitoring the current state of the shop at any moment, identifying different types of situation appearing during restricted periods, or predicting customer flow conditions

Among others, road traffic, industrial emissions, commercial activities, smoking and cooking are considered as major contributing factors for the increasing levels of pollutants in atmosphere. High levels of potentially toxic metals and microbes in atmosphere, especially in indoor air, may pose serious threat to human health. Therefore, concentration and associated health risks of potentially toxic trace metals (Cd, Cr, Cu, Fe, Mn, Pb, and Zn) and their risk to human health, and microbial load in indoor air was assessed using air condition (AC) filter dust samples collected from 5 locations representing residential, agricultural and industrial settings of Eastern Province, Kingdom of Saudi Arabia. The levels of trace metals varied considerably among sampling areas, with the highest levels of Cr and Cd recorded in the Industrial-area sites followed by the Agricultural and Urban-Residential sites. The highest levels of Pb and Fe were found in the Agricultural area sites followed by the Industrial and Urban-Residential area sites. The metals in dust sample, especially Cd, Cr and Pb, showed a considerable health risk through dermal pathway. Among the sites, the highest hazard quotient for these metals was found for Al-Qatif-Industrial areas sites and among the metals it was the highest for Cd. The cancer risk from the metals contained in AC filter dust was negligible. Samples collected from Agricultural and Industrial area sites were substantially contaminated with bacteria and fungi, respectively. Bacterial contaminants were mostly Gram Negative, with considerable antibiotic resistance and haemolytic activity. Thus, indoor air quality as assessed by AC filter dust depicted that a considerable health risk could be posed by the trace heavy metals and microorganisms for a long-term exposure. Furthermore, this study demonstrated that AC filters dust could be a unique and reliable test sample for the assessment of indoor environment.

Indoor dusts are collectively formed from anthropogenic and atmospheric activities. Particle matter 10 (PM10) is inhalable and causes significant inflammation by interaction with the pulmonary epithelial barrier. The mediators involved in bronchial epithelial cells response to dust are remined unknown. The air-liquid interface of our lung on chip model was exposed to indoor dust collected from highly polluted houses in Delhi, India. The media were collected after 4 days and cytokine levels were measured. We found that the concentration of IFN, IFNγ, Interleukin-6 (IL-6), IL1b, TNFa, and Granulocyte monocyte colony stimulating factor (GM-CSF) were significantly increased after exposure to indoor dust. IFN type I pathways were a major response from dust exposure. Further investigation is needed to determine the mechanism of action and targets of dust in bronchial epithelial cells.

The problem of real-time estimation of occupancy of buildings (number of people in various zones at every time instant) is relevant to a number of emerging applications that achieve high energy efficiency through feedback control. The measurement of CO2 concentration can be considered an important indicator that allows to define the occupation of closed and crowded spaces. Interesting cases can be school buildings and other buildings used in civil and residential (shopping centres, hospitals, etc.). This paper, starting from an experimental analysis in different classrooms of a University campus in real operating conditions, in different period of the year, proposes a possible correlation between CO2 concentration and the occupancy profile of the spaces. The acquired data are used to present some graphical correlations and to understand the most important variables or combination of them. Starting from an accurate analysis of the data, attempts are made to define a preliminary estimation method through the development of a mathematical models of occupancy dynamics in a building, which show interesting results.

This research work uses a simplified approach to combine location information from beacons propagation signal interaction with a mobile device with local building information to give real-time location and guidance to a user inside a building. This is an interactive process with visualisation information that can help user’s orientation inside unknown buildings and the data stored from different users can provide useful information about users movements inside a public building. Beacons installed on the building at specific pre-defined position emit signals that give a geographic position with an associated imprecision, related with Bluetooth’s range. This uncertainty is handled by building layout and users’ movement in a developed system that maps users’ position, gives guidance and store user movements. This system is based on an App (Find Me!) for Android OS (Operating System) which captures the Bluetooth Low Energy (BLE) signal coming from the beacon(s) and shows, through a map, the location of the user ‘s smartphone and guide him to the desired destination. Also, the beacons can deliver relevant context information. The application was tested by a panel of new and habitual campus users against traditional wayfinding alternatives yielding navigation times about 30% smaller, respectively.

This paper attempts to alter a prevailing assumption that buildings use energy to an understanding that in fact, people use energy. Therefore, to successfully accelerate the transition to a low-carbon society and economy more emphasis should be on motivating people and increasing their awareness by making them energy conscious building users and therefore active players in the energy transition process. In this context, this paper provides insights from the Horizon 2020 MOBISTYLE project. It demonstrates research and development approaches, highlights the main project objectives, and presents findings of an ethnographic (qualitative) study of users’ habits, practices, and needs. The aim of the project is to motivate behavioural change by raising consumer awareness through the provision of attractive personalized information on user’s energy use, indoor environment and health, all enabled by an integrated information and communication technology (ICT) service. In this context, the anthropological people-centred approach is integrated into the MOBISTYLE approach putting users at the centre of the ICT tools development process. The main quantitative objective of the project is a reduction of energy use for at least 16 % prompted by the provision of combined information and feedback systems on energy, indoor environmental quality (IEQ) and health. The most relevant motivational factors and key performance indicators (KPIs) for encouraging a more energy conscious and healthy lifestyle were defined by means of a people-centred approach, adopting anthropological inquiries in different settings. Information about users’ lifestyles and their needs was collected in focus groups with potential users in five case studies, located in different European Union (EU) countries. Behaviour change is achieved through awareness campaigns, which encourage users to be pro-active about their energy consumption and to simultaneously improve health and well-being.

Indian tobacco farm laborers are primarily women and children working for very low wages. The aim of this study was to explore occupational and reproductive health challenges faced by women tobacco farm labors in Mysore District, India. We conducted interview and six focus group discussions among 41 women tobacco farm laborers. Codes and themes were generated based on deductive and inductive approaches using the socio-ecological model. Participants reported symptoms of green tobacco sickness including headaches, back pain, gastric problems, weakness, and allergies during menstruation, pre-natal, and post-natal periods. Participants had poor awareness about the health effects of tobacco farming and there were gender inequalities in wages and use of personal protective equipment. Participants received support from family and commu-nity health workers during their pregnancy and post-natal period. Women reported wanting ma-ternity benefits from the tobacco board, monetary support, and nutritional supplements. There is a need for health education about the environmental dangers of tobacco among farm laborers, and more supportive policies for women farmworkers during pregnancy and post-natal periods.

In recent years, organic agriculture has gained more popularity, yet its approach to food production and its potential impact on consumers’ health and various environmental aspects remain to be fully discovered. The goal of organic farming practices is to maintain soil health, sustain ecological systems, maintain fairness in its relationship with the environment and protect the environment in its entirety. Various health benefits have been associated with higher consumption of organic foods. This review identified some of these health benefits including a reduction in obesity and body mass index (BMI), improvements in blood nutrient composition as well as a reduction in maternal obesity and pregnancy-associated preeclampsia risk. Furthermore, organic food consumption can reduce the development of non-Hodgkin lymphoma and colorectal cancers. Upon reviewing existing literature regarding the nutritional value of organic foods, it was found that organic food contained higher levels of iron, magnesium, and vitamin C. However, the evidence available to draw definitive generalizations remains limited. In this review, we provided essential insights to support sustainable organic farming and highlighted the potential of organic food consumption that could play a pivotal role in positively impacting human health.

Climate change is expected to affect the agricultural sector in ways that are often unclear to predict. If in the short- medium-terms farmers may adapt to climate change by adjusting agricultural practices, in the long-term these adjustments may become insufficient, forcing farmers to change their farming systems. The extent and direction in which these farming system transitions will occur is still a subject underexplored in the literature. We propose a new framework to explore the marginal effect of climate change on the choice of the farming system, while controlling the effect of other drivers known to also influence farming system choice. Using a spatial-explicit farming system choice-model developed by a previous study in an extensive agricultural region of southern Portugal, we applied a space-for-time approach to simulate the effect of climate change on the future dynamics of the farming systems in the study area. Results suggest that climate change will force many farmers to change the farming system in a foreseeable future. The extent of the projected changes in farming systems is likely to trigger significant social, economic, or environmental impacts, which should require early attention from policy makers.

This study proposes an image recognition method to assist swine farm managers in collecting health data related to pig feeding and excretion. Analyzing the correlation between sow health data and indoor air quality in the pigsties revealed significant influences of air quality indicators on sow health. Increased levels of TVOC, CO2, and temperature were found to negatively affect feeding health, while increased temperature, humidity, and PM10 were found to negatively impact excretion health. These findings provide a basis for evaluating variables when constructing future sow disease prediction models. Analyzing the correlation between pig health status and air quality can help swine farm managers improve sow production environments, reduce disease risks, enhance production efficiency, and provide valuable insights for future research on disease prediction models.

Rice farming households having limited capital do various combinations of the capital to get diversified livelihoods in continuing their lives. The purpose of this study was to analyze the effect of the household capital of rice farmers on livelihood diversification in Indramayu District. Survey method with data sources from 214 rice farming households taken by proportional simple random sampling technique. Data analysis used the partial least square method. The results found that the household capital of rice farmers has a positive and significant effect on the livelihood diversification. Government policy recommendations were determined in order of priority are physical capital with the help of agricultural tools and machinery, natural capital by anticipating climate change, financial capital by increasing support for capital sources, social capital by social networks, and human capital by improving farming skills.

Chemical acaricides are widely used to control ticks and tick borne pathogens on cattle. However, prolonged and indiscriminate use of these chemicals inevitably leads to selection of resistant ticks. In-vitro bioassays (adult and larval immersion tests) were conducted to assess amitraz and del-tamethrin resistance in Rhipicephalus (Boophilus) microplus populations from communal farms of the King Sabata Dalindyebo municipality of South Africa. Data generated on percentage inhibi-tion of oviposition (% IO) revealed that all the tick populations assessed showed resistance (% IO≤ 95%) to at least one of the acaricides. All six tick populations assessed for efficacy (% IO≥ 95% at the DD) with deltamethrin were resistant (% IO≤ 95%) and only one of six tick populations as-sessed for efficacy with amitraz was susceptible. Based on resistance ratios, the adult immersion test detected amitraz and deltamethrin resistance in three and five of the six tick populations re-spectively. With the larval immersion test, deltamethrin and amitraz resistance (Larval mortality < 90 % at the DD) was detected in all four, and three of four R. (B.) microplus populations assessed respectively. This data is critical for the design of an effective and sustainable tick control strategy on the communal farms.

Unmanned Aerial Vehicles (UAV) are increasingly being used in a variety of domains and precision agriculture is no exception. Precision agriculture is the future of agriculture and will play a key role in long-term sustainability of agricultural practices. This paper presents a survey of how image data collected using UAVs has been used in conjunction with ma-chine learning techniques to support precision agriculture. Numerous agricultural applications including classification of crop types and trees, crops detection, weed detection, cropland cover, and segmentation of farming fields are discussed. A variety of supervised, semi-supervised and unsupervised machine learning techniques for image-based preci-sion agriculture are compared. The survey showed that for traditional machine learning approaches, Random Forests performed better than Support Vector Machines (SVM) and K-Nearest Neighbor Algorithm (KNN) for crop/weed classification. And, while Convolutional Neural Networks (CNN) have been used extensively, the U-Net-based models out-performed conventional CNN models for classification and segmentation tasks. Among the Single Stage Detectors (SSD), YOLO series performed relatively well. Two-Stage Detectors like R-CNN, FPN, and Mask R-CNN generally tended to outperform SSDs. Vision Trans-formers (ViT) showed promising results amongst transformer-based models which did not generally perform better than CNNs. Finally, Generative Adversarial Networks (GANs) have been used to address the problem of smaller datasets and unbalanced data

A comparative study of organic and conventional farming systems was conducted in almond orchards to determine the effect of management practices on their fungal and bacterial communities. Soils from two orchards under organic (OM) and conventional (CM), and nearby nonmanaged soil (NM) were analyzed and compared. Several biochemical and biological parameters were measured (soil pH, electrical conductivity, total nitrogen, organic material, total phosphorous, total DNA, and fungal and bacterial DNA copies). Massive parallel sequencing of regions from fungal ITS rRNA and bacterial 16S genes was done to characterize their diversity in the soil. We report a larger abundance of bacteria and fungi in soils under OM, with a more balanced fungi:bacteria ratio, compared to bacteria-skewed proportions under CM and NM. The fungal phylum Ascomycota corresponded to around the 75% relative abundance in the soil, whereas for bacteria, the phyla Proteobacteria, Acidobacteriota and Bacteroidota integrated around 50% of their diversity. Alpha diversity was similar across practices, but beta diversity was highly clustered by soil management. Linear discriminant analysis effect size (LEfSE) identified bacterial and fungal taxa associated to each type of soil management. Analyses of fungal functional guilds revealed 3-4 times larger abundance of pathogenic fungi under CM compared to OM and NM treatments. Among them, the genus Cylindrocarpon was more abundant under CM and Fusarium under OM.

Since the second half of the second millennium AD, water management among the Chagga people of Kilimanjaro in Tanzania has involved community collaboration in the construction, ownership and management of water infrastructure. Chagga settlement on the lower slopes of Kilimanjaro transformed the landscape significantly to reflect an agrarian society characterised by decentralised forms of socio-political and economic organisation. Such organization involved conception, construction, and post-construction management of water distribution systems, constituting high-level socio-political complexity. The study employs ethnography, archaeological surveys and GIS to document water infrastructures on the lower slopes of Kilimanjaro. We conclude that community collaboration was key in management of the water infrastructure and by extension, agriculture, which sustained Chagga and chiefdoms for centuries.

Crop-type mapping is an important intermediate step for cost-effective crop management at the field level, as an overview of all fields with a particular crop type can be used for monitoring or yield forecasting, for instance. Our study used a data set with 2400 fields and corresponding satellite observations from the federal state of Bavaria, Germany. The study classified corn, winter wheat, winter barley, sugar beet, potato, and winter rapeseed as the main crops grown in Upper Bavaria. We additionally experimented with a rejection class "Other", which summarised further crop types. Corresponding Sentinel-2 data included the normalised difference vegetation index (NDVI) and raw bands from 2016 to 2018 for each selected field. The influence of raw bands compared to NDVI was analysed and the classification algorithms, i.e. support vector machine (SVM) and random forest (RF), were compared. The study showed that the use of an index should be critically questioned and that raw bands provided a wider spectral bandwidth, which significantly improved the mapping of crop types. The results underline the use of RF with raw bands and achieved overall accuracies (OA) of up to 92%. We also predicted crop types in an unknown year with significantly different weather conditions and several months before the end of the growing season. Thus, the influence of climate anomalies and the accuracy depending on the time of prediction were assessed. The crop types of a test site and year without labels could be determined with an OA of up to 86%. The results demonstrate the usefulness of the proof-of-concept and its readiness for use in real applications.

This research was specifically aimed at assessing the influence of sulfur in triple-super phosphate (TSP) on wheat yield. From the results, wheat showed response to sulfur (S) from gypsum (in 67%); and nitrogen (N) from urea in about 100% (of 24 sites). Based on this N was found to be the most limiting element to wheat production followed by sulfur, and then by phosphorus. TSP is tested to contain agronomically up to 2-6% by weight of S. However, wheat didn’t show response to S impurity supplied in the form of TSP. Though, not statistically significant, it is observed that there have always been yield increments by certain percent due to S from TSP in 8 out of 10 target sites, which is depicted in the increasing trends of yield response curves. From this it is learnt that, the benefits of the accidental/incidental application of such high analysis fertilizers can be many-folds in the quality attributes of wheat, if the soils of such investigation at the same time would contain significant amount of organic matter (OM). Indeed, such analysis would be vital in varietal specific nutrient requirement studies in precision-farming and/or in categorizing soils into fertility gradients and fertilizer recommendation domains.

Vertical farming (VF) is a potential solution for the production of high-quality, accessible, and climate-friendly nutrition for growing urban populations. However, to realize VF’s potential as a sustainable food source, innovative technologies are required to ensure that VF can be industrialized on a massive scale and extended beyond leafy greens and fruits into the production of food staples or row crops. A major obstacle to the economic and environmental sustainability of VF is the lighting energy consumed. While technological advances have improved the energy efficiency of VF lighting systems, there has been insufficient research into biostimulation as an approach to reduce energy needs. We conducted a controlled trial to investigate the application of a phycocyanin-rich Spirulina extract (PRSE) as a biostimulant in hydroponically grown, vertically farmed lettuce (Lactuca sativa and Salanova®). PRSE application reduced the time from seeding to harvest by 6 days, increased yield by 12.5%, and improved quality including color, taste, texture, antioxidant flavonoid levels and shelf life. Metagenomic analysis of the microbial community of the nutrient solution indicated that PRSE increased the overall bacterial diversity including raising the abundance of Actinobacteria and Firmicutes and reducing the abundance of potentially pathogenic bacteria. This preliminary study demonstrates that microalgae-derived biostimulants may play an important role in improving the economic and environmental sustainability of VF.

Unmanned aerial vehicles (UAVs) are one of the most promising innovative technologies invented in recent years to promote precision agriculture and smart farming. UAVs can not only reduce labor requirements but also increase production output, reduce the use of pesticides, and protect the environment. However, previous studies on agricultural UAVs have mostly focused on technical problems such as software and hardware design. Few studies have examined users’ behaviors in the implementation process. On the basis of Orlikowski and Gash’s technological frames, this study explored the participants’ cognition and expectation of farmers, pesticide, sprayers, and agriculture officials, who are three key groups of stakeholders involved in the application of UAVs to pesticide spraying, regarding agricultural UAVs and examined how the conflicts between their cognition and expectation influenced the choice of using pesticide spraying UAVs. The conclusions of this study contributed to supplement the content and broaden the scope of application of technological frame theory and provided a crucial reference for the promotion of agricultural UAVs in practice.

This paper pioneers a novel exploration of environmental impacts in livestock farming, with a focus on pig farming's intersection with climate change and sustainability. It emphasizes the transformative potential of data-driven Artificial Intelligence (AI) methodologies, specifically the Internet of Things (IoT) and multimodal data analysis, in promoting equitable and sustainable food systems. The study observes five pigs, aged 86 to 108 days, using a tripartite sensor that records heart rate, respiration rate, and accelerometer data. The unique experimental design alternates between periods of isolation during feeding and subsequent pairing, enabling the investigation of stress-induced changes. Key inquiries include discerning patterns in heart rate data during isolation versus paired settings, fluctuations in respiration rates, and behavioral shifts induced by isolation or pairing. The study also explores potential detection of gait abnormalities, correlations between pigs' age and their gait or activity patterns, and the evolution of pigs' walking abilities with age. The paper scrutinizes accelerometer data to detect activity changes when pigs are paired, potentially indicating increased stress or aggression. It also examines the adaptation of pigs to alternating isolation and pairing over time, and how their heart rate, respiration rate, and activity data reflect this process. The study considers other significant variables, such as time of day and isolation duration, affecting the pigs' physiological parameters. Sensor data is further utilized to identify behavioral patterns during periods of feeding, isolation, or pairing. In conclusion, this study harnesses IoT and multimodal data analysis in a groundbreaking approach to pig welfare research. It underscores the compelling potential of technology to inform about overall pig welfare, particularly stress levels and gait quality, and the power of data-driven insights in fostering equitable, healthy, and environmentally conscious livestock production systems.

In the wake of rapid advancements in artificial intelligence (AI) and sensor technologies, a new horizon of possibilities has emerged across diverse sectors. Livestock farming, a domain often sidelined in conventional AI discussions, stands at the cusp of this transformative wave. This paper delves into the profound potential of AI and sensor innovations in reshaping animal welfare in livestock farming, with a pronounced emphasis on a human-centric paradigm. Central to our discourse is the symbiotic interplay between cutting-edge technology and human expertise. While AI and sensor mechanisms offer real-time, comprehensive, and objective insights into animal welfare, it's the farmer's intrinsic knowledge of their livestock and environment that should steer these technological strides. We champion the notion of technology as an enhancer of farmers' innate capabilities, not a substitute. Our manuscript sheds light on: Objective Animal Welfare Indicators: An exhaustive exploration of health, behavioral, and physiological metrics, underscoring AI's prowess in delivering precise, timely, and objective evaluations. Farmer-Centric Approach: A focus on the pivotal role of farmers in the adept adoption and judicious utilization of AI and sensor technologies, coupled with discussions on crafting intuitive, pragmatic, and cost-effective solutions tailored to farmers' distinct needs. Ethical and Social Implications: A discerning scrutiny of the digital metamorphosis in farming, encompassing facets like animal privacy, data safeguarding, responsible AI deployment, and potential technological access disparities. Future Pathways: Advocacy for principled technology design, unambiguous responsible use guidelines, and fair technology access, all echoing the fundamental principles of human-centric computing and analytics. In essence, our paper furnishes pioneering insights at the crossroads of farming, animal welfare, technology, and ethics. It presents a rejuvenated perspective, bridging the chasm between technological advancements and their human beneficiaries, resonating seamlessly with the ethos of the Human-Centric Intelligent Systems journal. This comprehensive analysis thus marks a significant stride in the burgeoning domain of human-centric intelligent systems, especially within the digital livestock farming landscape, fostering a harmonious coexistence of technology, animals, and humans.

Air pollution and the increasing production of greenhouse gases has prompted greater use of renewable energy sources; the EU has set the use of green energy at 32 percent by 2030. With this in mind, in the last 10 years the demand for pellets in Italy has more than doubled making Italy the second largest consumer in Europe. The quality of the pellets burned in stoves is crucial to indoor and outdoor pollution. Among other parameters, moisture and ash are used to classify pellets according to EN ISO 17225:2014. This work involved the analysis of the organic and inorganic fraction of both some finished products on the Italian market and some raw materials (e.g., wood chips) sampled according to the technical standard EN 14778:2011. The analytical results showed the presence of some substances potentially harmful to human health such as formaldehyde, acetone, toluene and styrene for the organic fraction and nickel, lead and vanadium for the inorganic fraction. The chemometric approach showed that it is the inorganic fraction which is most responsible for the diversification of the samples under study. The detection of some substances may be a warning bell about the impact of such materials both for the environment and for the human health.

In recent years, since the propagation channel characteristics have been effectively used for applications such as motion sensing, position detection, etc. A great deal of attention is attracted to channel sounding methods easy to utilize using low-cost devices. This paper presents a device-free indoor location estimation method using spatio-temporal features of radio propagation channels using the 2.4-GHz band 3-by-3 MIMO channel sounder developed using commodity wireless LANs. The measurement results demonstrated a reasonable performance of the proposed method with small number of antennas.

Since the recent legalization of medical and recreational use of cannabis (Cannabis sativa L.) in many regions worldwide, there has been high demand for research to improve yield and quality. With the paucity of scientific literature on the topic, this study investigated the relationships between light intensity (LI) and photosynthesis, inflorescence yield, and inflorescence quality of cannabis grown in an indoor environment. After growing vegetatively for 2 weeks under a canopy-level photosynthetic photon flux density (PPFD) of ≈ 425 μmol·m-2·s-1 and an 18-h light/6-h dark photoperiod, plants were grown for 12 weeks in a 12-h light/12-h dark ‘flowering’ photoperiod under canopy-level PPFDs ranging from 120 to 1800 μmol·m-2·s-1 provided by light emitting diodes. Leaf light response curves varied both with localized (i.e., leaf-level) PPFD and temporally, throughout the flowering cycle. Therefore, it was concluded that the leaf light response is not a reliable predictor of whole- plant responses to LI, particularly crop yield. This may be especially evident given that dry inflorescence yield increased linearly with increasing canopy-level PPFD up to 1800 μmol·m-2·s-1, while leaf-level photosynthesis saturated well below 1800 μmol·m-2·s-1. The density of the apical inflorescence and harvest index also increased linearly with increasing LI, resulting in higher-quality marketable tissues and less superfluous tissue to dispose of. There were no LI treatment effects on cannabinoid potency, while there were minor LI treatment effects on terpene potency. Commercial cannabis growers can use these light response models to determine the optimum LI for their production environment to achieve the best economic return; balancing input costs with the commercial value of their cannabis products.

Background: Low-income families often depend on fuels such as wood, coal, and animal dung for cooking. Such solid fuels are highly polluting and are a primary source of indoor air pollutants (IAP). We examined the association between solid fuel use (SFU) and acute respiratory infection (ARI) among under-five children in Afghanistan and the extent to which this association varies by socioeconomic status (SES) and gender. Materials and Methods: This is a cross-sectional study based on de-identified data from Afghanistan’s first standard Demographic and Health Survey conducted in 2015. The sample consists of ever-married mothers with under-five children in the household (n=27,565). We used mixed-effect Poisson regression models with robust error variance accounting for clustering to examine the associations between SFU and ARI among under-five children after adjusting for potential confounders. We also investigated potential effect modification by SES and sex. Additional analyses were conducted using an augmented measure of the exposure to IAP accounting for both SFU and the location of cooking/kitchen (High Exposure, Moderate, and No Exposure). Results: Around 70% of households reported SFU, whereas the prevalence of ARI was 17.6%. The prevalence of ARI was higher in children living in households with SFU compared to children living in households with no SFU (adjusted prevalence ratio [aPR]= 1.10; 95%CI: 0.98, 1.23). We did not observe any effect modification by SES or child sex. When using the augmented measure of exposure incorporating the kitchen’s location, children highly exposed to IAP had a higher prevalence of ARI compared to unexposed children (aPR 1.17; 95% CI: 1.03, 1.32). SES modified this association with the strongest associations observed among children from the middle wealth quintile. Conclusion: The findings have significant policy implications and suggest that ARI risk in children may be reduced by ensuring clean cookstove as well as clean fuels and acting on the socio-environmental pathways.

Automatic methods for constructing navigation routes do not fully meet all requirements. The aim of this study was to modify the methodology for generating indoor navigation models based on the Medial Axis Transformation (MAT) algorithm. The simplified method for generating corridor axes relies on the Node-Relation Structure (NRS) methodology. The axis of the modeled structure (corridor) is determined based the points of the middle lines intersecting the structure (polygon). The proposed solution involves a modified approach to the segmentation of corridor space. Traditional approaches rely on algorithms to construct Triangulated Irregular Networks (TINs) by Delaunay triangulation or algorithms for generating Thiessen polygons known as Voronoi diagrams (VDs). In this study, both algorithms were used in the segmentation process. The edges of TINs intersect structures. Selected midpoints on TIN edges, which are located in the central part of the structure, are used to generate VDs. Polygon VDs segment corridor structures. The identifiers or structure nodes are the midpoints on TIN edges rather than the calculated centroids. The generated routes are not zigzag lines, and they approximate natural paths. The main advantage of the proposed solution is its simplicity which can be attributed to the use of standard tools for processing spatial data in a geographic information system.

Food and nutrition insecurity, affecting 30% of the global population in 2020, poses a significant challenge to achieving Sustainable Development Goal (SDG) 2 - Zero Hunger. Urbanization, particularly in sub-Saharan African cities e.g. Lagos, Nigeria, exacerbates these issues, impacting resources and contributing to informal settlements. There is emphasis on innovative solutions in urban farming, with Recirculating Aquaculture Systems (RAS) emerging as one of the promising approach due to its efficiency in space and capital utilization. However, maintaining optimal conditions in RAS, crucial for SDG 2 and 11, requires robust water quality monitoring. This study explores the availability of digital tools for water quality monitoring in small-scale urban RAS, evaluating handheld devices and IoT sensors' reliability through t-test statistical method. The results aim to guide practitioners in selecting effective monitoring tools, contributing valuable insights for sustainable aquaculture in urban areas, particularly in sub-Saharan Africa, where access to affordable digital solutions is pivotal for success and can attract youth to agri-food technologies.

Cambodia faces the challenge of managing excess water during the wet season and insufficient water during the dry season. This harms human life and endangers aquatic and natural resources, agricultural practices, and food security. In order to ensure the well-being of both people and food security, water governance is crucial. However, Cambodia's water governance is hindered by various obstacles, including sectoral and centralized influences, top-down and large-scale strategies, a lack of coordination among relevant agencies, and limited involvement of local communities. This study delves into water governance across different sectors, from centralized to community-based natural resources management to tackle these challenges. Through analyzing literature and case studies of farmer water user communities (FWUC), community fisheries (CFis), and community fish refuges (CFRs) in three Mekong Delta provinces in Cambodia, the study concludes that although water governance has improved, it has resulted in a decline in fishery resources from rivers and water bodies and an increase in water conflicts among farmers and sectors in the face of climate change. To enhance water governance in Cambodia, it is critical to integrate it at the district level. This will promote sustainable water use and management across the country and pave the way for a brighter future.

This study assesses the perception and vulnerability of the farming communities to climate change in the southwestern parts of Ethiopia. Data were collected from 442 households in four districts: Jimma Arjo, Bako Tibe, Chewaka, and Sekoru. The vulnerability of the farming communities was assessed using the households’ livelihood vulnerability index. A total of 40 indicators were applied to calculate household livelihood vulnera-bility to climate change, which were categorized into five major capitals: natural, social, financial, physical, and human. The household percep-tions of climate change results showed that there existed a statistically significant relationship between climate change perceptions and changes in rainfall pattern (75.6%, p<0.001), temperature pattern (69.7%, p<0.001), drought (41.6%, p=0.016), flood (44.1%, p=0.000), and occurrence of early (53.2%, p<0.001) and late rain (55.9%, p<0.001). The results showed that households in Sekoru district were the most vulnerable (0.61), while Jimma Arjo district were less vulnerable (0.47) to the effect of climate change. The vulnerability of the households in the study areas is mainly related to the occurrence of drought, lack of much-needed infrastructure facilities and weak institutional support. Links with the financial organization are also lacking among the household. The findings of this study will support policymakers to design climate change adap-tation strategies to combat climate change impacts. To support disaster risk management on the one hand and increase the resilience of vulnera-ble societies to climate change on the other hand, we recommend a detailed assessment in the remaining districts of the region.

Data centres, though a necessary part of modern society, are being stigmatised for consuming vast amounts of electricity for their operational and cooling needs. Due to Ireland’s reliance on fossil fuels to meet the increased energy demand of data centres, the data centres are contributing significantly to Ireland’s total carbon emissions. As much of this energy is expelled from data centres as waste heat energy, the potential for recycling some of this wasted heat energy was explored using environmentally friendly systems from recent publications. The recovered waste heat energy was applied in a vertical farming system, and the benefits of this waste heat to the vertical farm were analysed and quantified in two scenarios. Using conservative estimates, it was predicted that each vertical farm could be between 5-23% the size of the data centre and produce enough food to feed between 14-61 adults their daily calorie needs, and between 13-58 people their daily fresh produce requirements, depending on the scenario applied. For a more accurate prediction, each vertical farm would have to be assessed on a case-by-case basis. However, there was not enough data available on Irish data centres to perform these calculations.

The urban population in developing countries, especially sub-Saharan Africa, is rapidly increas-ing. As towns and cities grow, so does the demand for fish protein. While flow-through aqua-culture can provide fresh, healthy and nutritious fish protein, it is plagued by extensive land re-quirement as well as effluent discharge, thus, unsuitable for city regions. Alternatively, small-scale Recirculating Aquaculture System (RAS) could improve food and nutritional security (FNS), livelihoods as well as reduce environmental degradation in urban areas despite land and water constraints. The question however remains - what are the key technical, business and managerial issues, surrounding small-scale RAS in urban farming? This study reviews the RAS prototype of the Sustainable Aquaponics for Nutritional and Food Security in Urban Sub-Saharan Africa (SANFU) II project based on mass balance and stock density, relevant for fish survival and/or availability as well as net cash-flow analyses. The results suggest that small-scale RAS are technically and financially viable only with family labor having proper aquaculture monitoring and management skills. Furthermore, access to adequate equipment and inputs as well as electricity for the recirculating system are crucial. Urban innovation actors will adopt RAS if operations are profitable given that family labor is employed.

Carbon farming is a capable strategy for more sustainable production of food and other related products. It seeks to produce the diverse array of natural farming methods and marketable products simultaneously. In agroforestry system, carbon sequestration is done by incorporating carbon dioxide (CO2) into plant biomass via photosynthesis. Carbon is, thus, stored in reserves of above-ground biomass, such as timber or branches, and below-ground biomass such as roots, or organic carbon in the soil. In addition to the significance of carbon sequestration in climate change mitigation, soil organic carbon (SOC) is an imperative indicator for the soil health as well as fertility. The change in SOC can explain whether the land use pattern degrades or improves the soil fertility. SOC, found in the soil in the form of soil organic matter (SOM), helps to improve soil health either directly or indirectly. Its direct consequence is related to the process of mineralization. Further, agroforestry is highly capable of generating huge amounts of bio-mass. In fact, agroforestry is believed to be particularly suitable for replenishment of SOC. Therefore, efforts should be made to convince farmers for their resource-use efficiency and soil conserving ability in order to get maximum benefits out of agriculture. According to food and agriculture organization (FAO,) agriculture, forestry, and other land use practices account for 24% of global greenhouse gas (GHG) emissions, and total global livestock emissions of 7.1 gigatons of CO2-equivalent per year, representing 14.5% of total anthropogenic GHG emissions. Agroforestry system that deliberately integrates trees and crops with livestock in the agricultural production could potentially increase carbon sequestration and decrease GHG emission from the terrestrial ecosystems, thus, helping in global climatic change mitigation. This study, therefore, aimed at clarification about carbon farming, modifications in carbon cycle and carbon sequestration during agricultural development in addition to benefits of agroforestry.

The world's growing population is highly dependent on animal agriculture. Animal products provide nutrient-packed meals that help to sustain individuals of all ages in communities across the globe. As the human demand for animal proteins grows, the agricultural industry must continue to advance its efficiency and quality of production. One of the most commonly farmed livestock is poultry and their significance is felt on a global scale. Current poultry farming practices result in the premature death and rejection of billions of chickens on an annual basis before they are processed for meat. This loss of life is concerning regarding animal welfare, agricultural efficiency, and economic impacts. The best way to prevent these losses is through the individualistic and/or group level assessment of animal on a continuous basis. On large-scale farms, such attention to detail was generally considered to be inaccurate and inefficient, but with the integration of Artificial Intelligence (AI) assisted technology individualized and per-herd assessments of livestock are possible and accurate. Various studies have shown cameras linked with specialized systems of AI can properly analyze flocks for health concerns, thus improving the survival rate and product quality of farmed poultry. Building on the recent advancements, this review explores the aspects of AI in the detection, counting and tracking of the poultry in commercial and research-based applications.

The European Union green deal has proposed the “organic farming action plan” to render this farming system more sustainable for climate mitigation and adaptation and to meet the United Nations Sustainable Development Goals (UN-SDGs). While this policy instrument is fundamental to reach sustainable agriculture, there is still no agreement on what sustainable agriculture is and how to measure it. This opinion paper proposes an ecosystem-based framework on the crop life-cycle to determine the balance between economic, social and environmental pillars of sustainability to support decision-making.

For cereals grown under organic conditions, information on levels of free asparagine (free Asn) as a precursor to acrylamide (AA) formation, is almost completely lacking. This study investigated the impact of organically grown cereal species and cultivars of winter wheat (Triticum aestivum), winter spelt (Triticum aestivum ssp. spelta), winter rye (Secale cereale), winter einkorn (Triticum monococcum) and winter emmer (Triticum dicoccum) on the level of free Asn with simultaneous consideration of grain yields and flour qualities over three growing seasons (2005–2006, 2006–2007 and 2007–2008) in Southwest Germany. Additionally, the relation of free Asn and AA was investigated. Heritability revealed how strongly the level of free Asn was linked to the genotype. In this context free Asn of species and cultivars grown at a second location in Southern Germany were analysed. The level of free Asn was significantly influenced by species and within species by cultivars. Rye was found to exhibit the highest free Asn amount (52 mg 100 g⁻¹), followed by einkorn (32 mg 100 g⁻¹), emmer (16 mg 100 g⁻¹) wheat (10 mg 100 g⁻¹) and spelt (8 mg 100 g⁻¹), which showed the overall lowest free Asn content. Hence, replacing rye with spelt in food products would lead to an 85% reduction of free Asn in raw material. Within species, cultivars differed in their levels of free Asn by up to 67% for wheat, 55% for spelt and 33% for rye. Year also had a significant impact as almost all samples were significantly higher in their level of free Asn in 2008 compared to 2006 and 2007. Rye was most significantly affected by year as the level of free Asn varied by up to 32% between years. In contrast, wheat and spelt were only affected minimally by year. A high heritability was found for wheat (0.79) and spelt (0.91) concerning locations in 2008, meaning that the level of free Asn is mainly determined by the genotype and less influenced by environmental conditions. In contrast, heritability was low for wheat (0.23) but high for spelt (0.71) and rye (0.67) regarding years. As for organically grown cereals the relation between free Asn and AA formation was never investigated before. Correlation of both parameters was calculated. There was also a close correlation between free Asn and AA. Across species and years, the amount of free Asn correlated with the AA content in heated flour with R² = 0.69***. Thus, free Asn can serve as an indicator for AA formation during processing. In conclusion, the level of free Asn can be highly influenced by proper selection of species and cultivars under organic growing conditions.

Stingless bee beekeeping provides new opportunities to improve the incomes of many households in Malaysia through the sale of honey and other bee products. While Heterotrigona itama is one of the most commonly cultured species of stingless bees, its behavior is not very well understood. Hence, we conducted this study to investigate the behavior of H. itama in exploiting food sources by ascertaining the nectar sugar concentration preferred by the bee. We also aimed to determine the preferred distance of food source from the bee hive. Our results suggest that H. itama prefers high sugar concentrations of 35% and above, and they would fly up to 7 m from the hive to collect food. We discuss how nectar concentration and food distance influence the number of bees exploiting food sources and the overall foraging pattern of H. itama.

Indoor comfort is a critical determinant of the holistic educational experience, significantly influencing the well-being, concentration, and performance of students within the confines of school environments. Despite the widespread implementation of heating, ventilation, and air conditioning (HVAC) systems in educational institutions, achieving satisfactory thermal conditions, acceptable acoustics, and optimal daylight levels for occupants continues to pose a considerable challenge. In this review, we specifically delves into the parameters characterizing Italian school environments, recognizing their direct impact on students’learning and concentration. The primary objectives of this review are conducting a thorough examination of the current acustic conditions in Italian schools. The initial phase of our search strategy involved retrieving data from key databases, namely, Scopus, Web of Science, ScienceDirect, PubMed, and Google Scholar. Following the elimination of duplicate articles (n = 289), a total of 135 distinct papers remained for analysis. A critical review of the existing literature is presented based on these criteria, offering guidelines and recommendations for forthcoming studies. The critical review underscores various shortcomings in the current design, deployment, and documentation of multi-domain studies, signaling the need for enhanced quality in future research efforts.

In this study, we propose an augmentation method for machine learning based on relabeling data in caregiving and nursing staff indoor localization with Bluetooth Low Energy (BLE) technology. Indoor localization is used to monitor staff to patient assistance in caregiving and to gain insights into workload management. However, improving accuracy is challenging when there is a limited amount of sensor data available for training. In this paper, we propose a data augmentation method to reuse the Received Signal Strength (RSS) from different sensors by relabeling to the sensors with less samples resolving data imbalance. Standard deviation and Kullback-Leibler divergence between minority and majority class are used to measure signal pattern to find matching sensors to relabel. By matching sensors between classes, two variation of relabeling are implemented specifically full and partial matching. The performance is evaluated using real world dataset we collected for five days in a nursing care facility installed with 25 BLE beacons. Random Forest model is utilized for location recognition, and performance is compared using the weighted F1-score to account for class imbalance. By increasing the sensor data with our proposed relabeling method for data augmentation, we achieved higher minority class F1-score as compared to augmentation with Random Sampling, Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN). Our proposed method utilizes collected sensor data by leveraging majority class samples. Full matching demonstrated a 6 to 8% improvement from the original baseline overall weighted F1-score.

Advances towards smart ecosystems showcases Internet of Things (IoT) as a transversal strategy to improve energy efficiency in buildings, to enhance their comfort and environmental conditions, and to grow knowledge about building behavior, its relationships with the users and the interconnections among themselves and with the environmental and ecological context. EU estimates that 75% of the building stock is inefficient and aged with +40 years old. Although many buildings have some kind of system for regulating their indoor temperature, only a small subset provides integrated Heating, Ventilation, and Air Conditioning (HVAC) systems; within that subset, only a low percentage includes smart sensors, and only a minimum of that percentage integrates those sensors into IoT ecosystems. This work proposes several contributions. On the one hand, to understand the built environment as a set of interconnected systems that constitute a complex framework where IoT ecosystems are key enabling technologies to improve energy efficiency and Indoor Air Quality (IAQ) by filling the gap between theoretical simulations and real measurements. On the other hand, to understand IoT ecosystems as cost-effective solutions where: acquire data through connected sensors, analyze information in real-time, and build knowledge to make data-driven decisions. Furthermore, data set is public for third-party use to contribute scientific community to their research studies. Thus, this paper also contributes with a detailed functional scheme of IoT ecosystem deployed in 3 buildings of University of Zaragoza (Spain) with +200 geolocated wireless sensors with +100 representative spaces. The obtained results, through real installations with IoT as learning factory, show several learned lessons (about building complexity; energy consumption, costs and savings; and IAQ and health improvement) and contribute, as a proof-of-concept, with a proposal of prediction of building performance based on both correlations (between CO2 and occupancy) and neural networks (applied to CO2 and temperature). In summary, in a real context of economic restrictions, complexity, higher energy costs, social vulnerability and climate change, IoT-based strategies, as proposed in this work, highlight as an open, modular and interoperable approach to move towards smart communities (buildings, cities, regions, etc.) by improving energy efficiency and environmental quality (indoor and outdoor) with low cost, quick implementation, and low impact on users within great challenges for growth, interconnection, climate change and sustainability.

Indoor localization (IL) is a significant topic of study with several practical applications. The area of IL has evolved greatly in recent years due to the introduction of numerous technologies such as WiFi, Bluetooth, cameras, and other sensors. Despite the growing interest in this field, there are numerous challenges and drawbacks that must be addressed to develop more accurate and sustainable systems for IL and its real-life applications. This review study gives an in-depth look into IL, covering the most promising artificial intelligence-based and hybrid strategies that have shown excellent potential in overcoming some of the limitations of classic methods. In addition, the paper investigates the significance of high-quality datasets and evaluation metrics in the design and assessment of IL algorithms. Furthermore, this overview study emphasizes the crucial role that machine learning techniques, such as deep learning and transfer learning, play in the advancement of IL. A focus on the importance of IL and the various technologies, methods, and techniques that are being used to improve it. Finally, The survey highlights the need for continued research and development to create more accurate and scalable techniques that can be applied across a range of industries, such as evacuation-egress routes, hazard-crime detection, smart occupancy-driven energy reduction and asset tracking and management.

Vernacular architecture constitutes a rich source of information and ancestral knowledge that can become a key resource for sustainable development through its passive design strategies, which effectively respond to local climatic and weather conditions using locally sourced materials for the construction of its supporting structure and enveloping elements, as well as spatial organization and the incorporation of a buffer area (patio) that optimize the use of renewable resources. This qualitative study analyzes a traditional housing typology with a central patio located in the Historic Center of Azogues city, Ecuador, to evaluate its interior thermal comfort through in-situ monitoring in the different building spaces. Using the open-source software Open Studio and EnergyPlus, a simulation model was built to assess the annual thermal performance of the house. Field records were used to verify the effectiveness of the strategies that respond to the climatic conditions of the area. The analysis carried out on the passive strategies used in the selected house with regard to natural ventilation, solar protection, and thermal insulation, which depend on various aspects of the building, such as its location, internal space arrangement, design of openings (doors and windows), among others. The thermal simulations reveal that the traditional house located in the Historic Center of Azogues city is well adapted to the local climate, although interior thermal comfort is not entirely satisfactory.

At present, indoor localization is one of the core technologies of location-based services (LBS), and there exist numerous scenario-oriented application solutions. Visual features, as the main semantic information to help people understand the environment and thus occupy the dominant part, many techniques about indoor scene recognition are widely adopted. However, the engineering application problem of cell phone indoor scene recognition and localization has not been well solved due to insufficient semantic constraint information of building map and the immaturity of building map location anchors (MLA) matching positioning technology. To address the above problems, this paper proposes a cell phone indoor scene recognition and localization method with building map semantic constraints. Firstly, we build a library of geocoded entities for building map location anchors (MLA), which can provide users with "immersive" real-world building maps on the one hand and semantic anchor point constraints for cell phone positioning on the other. Secondly, using the improved YOLOv5s deep learning model carried on the mobile terminal, we recognize the universal map location anchors (MLA) elements in building scenes by cell phone camera video in real-time. Lastly, the spatial location of the scene elements obtained from the cell phone video recognition is matched with the building MLA to achieve real-time positioning and navigation. The experimental results show that the model recognition accuracy of this method is above 97.2%, and the maximum localization error is within the range of 0.775 m, and minimized to 0.5 m after applying the BIMPN road network walking node constraint, which can effectively achieve high positioning accuracy in the building scenes with rich MLA element information. In addition, the building map location anchors (MLA) has universal characteristics, and the positioning algorithm based on scene element recognition is compatible with the extension of indoor map data types, so this method has good prospects for engineering applications.

Radon gas represents the major contributor to human health risk from environmental radiological exposure. In confined spaces radon can accumulate to relatively high levels so that mitigation actions are necessary. The Italian legislation on radiation protection has set a reference value for the activity concentration of radon at 300 Bq/m3. In this study, measurements of the annual radon concentration in 62 bank buildings, spread on Campania region (Southern Italy), were carried out. Using CR-39 solid-state nuclear track detectors, radon level was assessed in 136 confined spaces (127 at underground and 9 at ground floors), frequented by workers and/or the public. The survey parameters considered in the analysis of the results were: floor types, wall cladding materials, number of openings, door/window opening duration for air exchange. Radon levels were found to be between 17 and 680 Bq/m3, with an average value of 130 Bq/m3 and a standard deviation of 120 Bq/m3. About 7% of the results gave a radon activity concentration above 300 Bq/m3. The analysis showed that the floor level and air exchange have the most significant influence. This study highlighted the importance to assess the indoor radon levels, even in particular environments, to protect public and workers by radon-induced effects on health.

The amount of outdoor particles that indoor environments receive depends on the particle infiltration factors (Fin), peculiar of each environment, and on the outdoor aerosol concentrations and size distributions. The respiratory doses received, while residing indoor, will change accordingly. This study aims to ascertain to what extent such doses are affected by the vertical distance from the traffic sources. Particle number size distributions have been simultaneously measured at street level and at about 20 m height in a street canyon in downtown Rome. The same Fin have been adopted to estimate indoor aerosol concentrations, due to the infiltration of outdoor particles and then the relevant daily respiratory doses. Aerosol concentrations at ground floor were more than double than at 20 m height and richer in ultrafine particles. Thus, although aerosol infiltration efficiency was on average higher at 20 m height than at ground floor, particles more abundantly infiltrated at ground level. On a daily basis, this involved a 2.5-fold higher dose at ground level than at 20 m height. At both levels, such doses were greater than those estimated over the period of activity of some indoor aerosol sources, therefore they represent an important contribution to the total daily dose.

We propose an iterative scheme for feature-based positioning using a new weighted dissimilarity measure with the goal of reducing the impact of large errors among the measured or modeled features. The weights are computed from the location-dependent standard deviations of the features and stored as part of the reference fingerprint map (RFM). Spatial filtering and kernel smoothing of the kinematically collected raw data allow efficiently estimating the standard deviations during RFM generation. In the positioning stage, the weights control the contribution of each feature to the dissimilarity measure, which in turn quantifies the difference between the set of online measured features and the fingerprints stored in the RFM. Features with little variability contribute more to the estimated position than features with high variability. Iterations are necessary because the variability depends on the location, and the location is initially unknown when estimating the position. Using real WiFi signal strength data from extended test measurements with ground truth in an office building, we show that the standard deviations of these features vary considerably within the region of interest and are neither simple functions of the signal strength nor of the distances from the corresponding access points. This is the motivation to include the empirical standard deviations in the RFM. We then analyze the deviations of the estimated positions with and without the location-dependent weighting. In the present example the maximum radial positioning error from ground truth are reduced by 40% comparing to kNN without the weighted dissimilarity measure.

This study presents a novel approach for automating the body condition scoring of dairy cows, leveraging advancements in 3D imaging technology and deep neural networks. The primary objective was to design and implement a system capable of accurately and efficiently assessing the body condition of dairy cows, a critical metric in livestock management for optimizing health and productivity. To achieve this, a 3D camera was employed to capture detailed point cloud data, reconstructing the three-dimensional morphology of individual cows. The obtained data were then fed into a deep neural network, specifically tailored for the task of ranking body condition. The neural network was trained on a diverse dataset of annotated body condition score representing varying degrees of body condition, ensuring robust performance across different physiological states. The results demonstrate the efficacy of the proposed system in automatically and objectively scoring the body condition of dairy cows. The automated process not only expedites the assessment but also reduces the subjectivity associated with manual scoring methods. This innovative approach holds promise for improving the efficiency of dairy farm management by providing timely and accurate body condition assessments. The integration of 3D imaging and deep learning techniques paves the way for future advancements in precision livestock farming, contributing to enhanced animal welfare, optimized production, and sustainable agriculture practices.

Lentil (Lens culinaris Medik.) is an essential legume crop providing healthy and nutritious food for people in low- to middle-income countries, worldwide. Lentil roots support symbiotic interactions with soil rhizobia species fostering nitrogen fixation; however, assemblage and diversity of the complete microbial rhizosphere community and the effect of seed genotype and origin remains largely unexplored. In this study we examined with metagenomic analysis, the effects of seed origin on the rhizosphere’s communities in samples of the famous Greek lentil landrace Eglouvis derived from different local farmers and farming systems including a Gene Bank sample, in comparison to a commercial variety. The landrace exhibited higher rhizosphere microbiome diversity compared to the commercial variety for all indexes. A core microbiome comprised of 158 taxa was present in all samples, while higher number of unique bacterial taxa was recorded in the landrace samples compared to the commercial cultivar. Noteworthy, landrace samples originated from organic farming had more than double unique taxa compared to conventional counterparts. The study revealed higher diversity of N2 fixers and archaea, Crenarchaeota and Thaumarchaeota, in landrace samples and particularly in those derived from organic farming, underpinning the distinct recruiting efficiency of beneficial soil microbes by the landrace.

The hard bottom layer of paddy field has a great influence on the driving stability and operation quality and efficiency of intelligent farm machinery, and the continuous improvement of unmanned precision operation accuracy and operation efficiency of paddy field operation machin-ery is the support to realize unmanned rice farm. In this paper, in view of the complicated hard bottom layer situation of unmanned operation farm machinery driving is difficult to realize to quantify the local characteristics of hard bottom layer of paddy field, the unmanned rice direct seeding machine chassis is used to operate the operation field and collect the hard bottom layer information simultaneously, and the data processing method of automatic calibration of sensor installation error, abnormal value rejection and 3D sample curve denoising of contour trajectory is designed; a hard bottom layer surface profile evaluation method based on the local sliding surface roughness is proposed. The local characteristics of the hard bottom layer were quantified, and the quantified results of the local characteristics of the hard bottom layer in the test plots showed that the mean value of the local roughness was 0.0065, 68.27% was distributed in the variation range of 0.0042~0.0087, and 99.73% was distributed in the variation range of 0~0.0133. Based on the test field data, the surface roughness features are verified to describe the variability of representative working conditions such as transport, downfield, operation and trapping of unmanned operation of intelligent farm machinery. The method of quantifying the hard-bottom local features of farm machinery driving can provide feedback on the local environmental features of intelligent farm machinery driving at the current position, and provide a reference basis for the design optimization of unmanned system for improving the quality of intelligent farm machinery operation.

One of the stated goals of the Common Agricultural Policy reforms has been to provide a fairer distribution of payments across and within Member States, but little progress has been accomplished, with about 20% of farmers receiving 80% of the total amount of Direct Payments. The structural factors that underlie this inequity, notably the preponderance of specific types of farming systems, are investigated in this research. A logit model was developed using Agricultural Census data at the Commune level, using the evolution of the percentage of farmers receiving Direct Payments in Portugal as the dependent variable. The findings reveal that the local importance of arable crops (cereals) and cattle farming systems, as well as the existence of larger farms and younger farmers, all contributed to farmers' increasing access to DP between 2009 and 2019. In traditional Mediterranean farming systems, however, access to DP has been restricted to a smaller proportion of farmers. However, there appears to have been some redistribution in the previous two CAP programming cycles, from bigger to smaller farmers, older to younger farmers, and from olives, cereals, and cattle to other types of production, notably vineyards.

Nations of the world have seen unprecedented changes in climate variables in recent decades. But it is unclear to what extent climate change has impacted and will impact food systems in some developing regions, and how policymakers can frame an approach to encouraging adaptation and advancing climate-smart agriculture. Many studies attempting to link agroecology to climate change adaptation do so without understanding the potential of Agroecology not only to mitigate climate change – which is the weak response – but to reverse its impact and ‘climate proof’ our food systems. By modeling the near and far future impacts of climate change on crop production, we showed how climate will impact crop production under two crop production systems (agroecology and non-agroecology production systems). The overarching aim is to derive sustainable development strategies and lessons for policymakers and climate researchers - essential components of environment and Agricultural development. Using case studies from Nigeria, we observed that transitioning to agroecology, even at the farm level also transforms farm designs, thereby affecting their overall food and nutrition status. The result showed that the use of agroecology management practices not only reduces the impact of climate change in the near future but will also lead to increased crop yield in the future. The finding suggests that to feed the over 400 million projected population of Nigeria by 2050, the use of agroecological practices will be a better alternative to the conventional farming methods. To advance the use of agroecological farming methods, governments at every level in Nigeria need to mainstream organic agriculture in national government policies. This is important as it will not only address climate change impacts but also hunger and poverty.

Indoor crop cultivation systems such as vertical farms or plant factories necessitate artificial lighting. Light spectral quality can affect plant growth and metabolism and, consequently, the amount of biomass produced and the value of the produce. Conflicting results on the effects of light spectrum in different plant species and cultivars make it critical to implement a singular lighting solution. In this study we explored the response of green and red leaf lettuce cultivars (’Aquino’, CVg, or ‘Barlach’, CVr, respectively) to long-term blue-enriched light application (WB). Plants were grown for 30 days in a growth chamber with optimal environmental condi-tions (temperature: 20°C, relative humidity: 60%, ambient CO2, Photon Flux Density (PFD) of 260 µmol m-2 s-1 over an 18-h photoperiod). At 15 days after sowing (DAS) white spectrum LEDs (WW) were compared to WB (λPeak = 423 nm) maintaining the same PFD of 260 µmol m-2 s-1. At 30 DAS, both lettuce cultivars resulted adapted to the blue light variant, though the adaptive re-sponse was specific to the variety. Rosette weight, light use efficiency and maximum operating efficiency of PSII photochemistry in the light, Fv/Fm’, were comparable between the two light treatments. Significant light quality effect was detected on stomatal density and conductance (20% and 17% increase under WB, respectively, in CVg) and, on the modified anthocyanin re-flectance index (mARI) (40% increase under WB, in CVr). Net photosynthesis response was gen-erally stronger in CVg compared to CVr; e.g. net photosynthetic rate, Pn, at 1000 µmol m-2 s-1 PPFD increased from WW to WB by 23% in CVg, compared to 18% in CVr. Results obtained suggest the occurrence of distinct physiological adaptive strategies in green and red pigmented lettuce cultivars to adapt to the higher proportion of blue light environment.

Well-intentioned regulations to protect Canada’s most productive farmland restrict large-scale so-lar photovoltaic (PV) development. The recent innovation of agrivoltaics, which is the co-development of land for both PV and agriculture, makes these regulations obsolete. Burgeoning agrivoltaics research has shown agricultural benefits including increased yield for a wide range of crops, plant protection from excess solar energy and hail, improved water conservation while maintaining agricultural employment and local food supplies. In addition, the renewable electricity generation decreases greenhouse gas emissions while increasing farm revenue. As Canada in general, and Ontario in particular, is at a strategic disadvantage in agricultural without agrivoltaics, this study investigates the policy changes necessary to capitalize on the benefits of using agrivoltaics in Ontario. Land use policies in Ontario are reviewed. Then, three case studies (peppers, sweet corn and winter wheat) are analyzed for agrivoltaic potential in Ontario. These results are analyzed in conjunction with potential policies that would continue to protect the green-belt of the Golden Horseshoe, while enabling agrivoltaics in Ontario. Four agrivoltaic policy areas are discussed: increased research and development, enhanced education/public awareness, mechanisms to support Canada’s farmers converting to agrivoltaics and using agrivoltaics as a potential source of trade surplus with the U.S.

The demand for animal products is expected to continue to rise, which requires the development of efficient livestock farming systems. Environmental, societal and economic concerns regarding this industry are however accumulating, addressing the large resource demand, pollutants and greenhouse gas emissions and health concerns that the livestock industry is responsible for. Precision livestock farming systems allow the continuous automatic monitoring of various physiological, behavioural and phenotypic parameters of animals in order to increase productivity and animal welfare while controlling and minimizing the environmental impact. There is a high potential for digital farming to be the solution for responsibly and ethically feeding the growing and urbanizing population. However, many problems and concerns are still present in this developing industry and remain relatively unaddressed, starting with the ethical aspects in regard to the animal, including its objectification, human-animal relationships and welfare and ending with the societal implications of this digitalization. Concrete frameworks, inter-disciplinary studies and global legislation need to be put in place in order to ensure the safety and protection of the animals, farmer and society. Here, implications of digital farming for the animals, farmers, society and the planet are critically reviewed with the future outlook of digital farms.

Salmon farming has been blamed for the collapse of the sea trout (Salmo trutta) fishery in Loch Maree on Scotland’s west coast despite the absence of any direct evidence. Stocks of west coast demersal marine fish, especially around the Clyde Estuary have also declined over a similar time span. The decline of these marine fish stocks can be attributed to the removal of the “three-mile fishing limit” in 1984 by UK Government legislation. Sea trout inhabit the same inshore waters as targeted demersal fish and can be caught as by-catch. Comparisons of the decline of demersal species and the sea trout from Loch Maree and the west coast show a high degree of correlation. Stocks of whiting (Merlangius merlangus) from inshore waters have found to consist of small fish which mirrors the stock makeup of the Loch Maree sea trout stock.

The United Arab Emirates (UAE) is significantly dependent on desalinated water and groundwater resource, which is expensive and highly energy intensive. Despite the scarce water resource, only 54% of the recycled water was reused in 2015. In this study, an “Oasis” complex comprised of Sustainable Farming Compartments (SFCs) was proposed for reusing treated wastewater to decrease the ambient temperature of the SFC via an evaporative cooling system. A prototype SFC with half the original scale (width = 1.8 m, depth = 1.5 m, front height = 1.2 m back height = 0.9 m) was designed, built, and tested in an environmentally controlled laboratory and field site to evaluate the feasibility and effectiveness of the SFC under the climatic conditions in Abu Dhabi. Based on the experimental results, the temperature drops obtained from the SFC in the laboratory and field site were 5 ̊C at initial relative humidity of 60% and 7- 15 ̊C at initial relative humidity of 50%, respectively. An energy simulation using dynamic numerical simulations was performed in comparison to the results of the experiment. The energy-based dynamic simulation shows good agreement with the experimental results. The total power consumption of the SFC system was approximately three and a half times lower than that of an electrical air conditioner.

This comprehensive critical review shows how green buildings and energy optimization solutions improve indoor air quality as well as environmental quality (IEQ) and human health. Electricity-efficient green buildings create a better indoor atmosphere and save electricity. Green buildings improve occupant well-being through IAQ, thermal comfort, acoustics, and lighting. Renewable energy, green construction materials, and intelligent technologies increase sustainability and reduce environmental impact. Environmental hazards, architectural design, and societal variables can improve indoor air quality and subsequently reduce health risks linked with poor air quality. The evaluation highlights the pros and cons of low-cost IAQ sensors and equipment. These devices provide valuable insights, but calibration and validation using reference equipment are necessary to ensure accuracy and reliability. Architects, engineers, environmentalists, and policymakers are essential in creating green buildings with high IEQ. Prioritizing occupant comfort, health, and productivity ensures a sustainable and healthy future for future generations. However, continual monitoring and research are needed to improve green building practices and overcome construction problems. Intelligent models for continual on-field calibration of low-cost sensors may improve performance. Green buildings benefit occupants and the environment by optimizing energy use and using IAQ monitoring technologies.

To date, only a few studies focused on carbon monoxide (CO) production during waste composting; all targeted on CO inside piles. Here, the CO net emissions from compost piles and the assessment of worker’s occupational risk of exposure to CO at large-scale composting plants are shown for the first time. CO net emissions were measured at two plants processing green waste, sewage sludge, or undersize fraction of municipal solid waste. Effects of the location of piles (hermetised hall vs. open yard) and turning (before vs. after) were studied. Higher CO net emission rates were observed from piles located in a closed hall. The average CO flux before turning was 23.25 and 0.60 mg‧m-2‧h-1 for hermetised and open piles, respectively, while after – 69.38 and 5.11 mg‧m-2‧h-1. The maximum CO net emissions occurred after the compost was turned (1.7x to 13.7x higher than before turning). The top sections of hermetised piles had greater CO emissions compared to sides. Additionally, 5% of measurement points of hermetised piles switched to ‘CO sinks’. The 1-h concentration in hermetised composting hall can reach max. ~50 mg CO∙m-3 before turning, and &gt;115 mg CO∙m-3 after, exceeding the WHO thresholds for a 1-h and 15-min exposures, respectively.

With metal oxide semiconductors being a promising candidate for accurate indoor air quality assessments, multiple drawbacks of the gas sensors prevent their widespread use. Examples include poor selectivity, instability over time, and sensor poisoning. Complex calibration methods and advanced operation modes can solve some of those drawbacks. However, this leads to long calibration times, which are unsuitable for mass production. In recent years, multiple attempts to solve calibration transfer have been made with the help of direct standardization, orthogonal signal correction, and many more methods. Besides those, a new promising approach is transfer learning from deep learning. This article will compare different calibration transfer methods, including direct standardization, piecewise direct standardization, transfer learning for deep learning models, and global model building. The machine learning methods to calibrate the initial models for calibration transfer are feature extraction, selection, and regression (established methods) and a custom convolutional neural network TCOCNN. It is shown that transfer learning can outperform the other calibration transfer methods regarding the root mean squared error, especially if the initial model is built with multiple sensors. It was possible to reduce the number of calibration samples by up to 99.3 % (from 10 days to approximately 2 hours) and still achieve an RMSE for acetone of around 18 ppb (15 ppb with extended individual calibration) if six different sensors were used for building the initial model. Furthermore, it was shown that the other calibration transfer methods (direct standardization and piecewise direct standardization) also work reasonably well for both machine learning approaches, primarily when multiple sensors are used for the initial model.

Many engineered approaches have been proposed over the years for solving the hard problem of performing indoor localisation using smartphone sensors. However, specialising these solutions for difficult edge cases remains challenging. Here we propose an end-to-end hybrid multimodal deep neural network localisation system, MM-Loc, relying on zero hand-engineered features, learning them automatically from data instead. This is achieved by using modality-specific neural networks to extract preliminary features from each sensing modality, which are then combined by cross-modality neural structures. We show that our choice of modality-specific neural architectures is capable of estimating the location with good accuracy independently. But for better accuracy, a multimodal neural network fusing the features of early modality-specific representations is a better proposition. Our proposed MM-Loc solution is tested on cross-modality samples characterised by different sampling rates and data representation (inertial sensors, magnetic and WiFi signals), outperforming traditional approaches for location estimation. MM-Loc elegantly trains directly from data unlike conventional indoor positioning systems, which rely on human intuition.

Smart buildings concept aims at the use of the smart technology to reduce energy consumption as well as improvement of the comfort conditions and users’ satisfaction. It is based on the use of smart sensors to follow both outdoor and indoor conditions as well as software for the control of comfort and security devices. The optimal control of the energy devices requires software for indoor temperature forecasting. This paper presents an ANN – based model for the indoor temperature forecasting. The model is developed using data recoded in an old building of the engineering school Polytech’Lille. Data covered both indoor and outdoor conditions. Analysis of the relevance of the input parameters allowed to develop a simplified forecasting model of the indoor temperature that uses only the outdoor temperature as well as the history of the façade temperature as input parameters. The paper presents successively, data collection, the ANN concept used in the temperature forecasting, and finally the ANN model developed for the façade and indoor forecasting. It shows that an ANN-based model using outdoor and façade temperature sensors provides a good forecasting of the indoor temperature. This model could be used for the optimal control of buildings energy devices.

The mitigation of NLOS (non-line-of-sight) propagation conditions is one of main challenges in wireless signals based indoor localization. When RFID localization technology is applied in applications, RSS fluctuates frequently due to the shade and multipath effect of RF signal, which could result in localization inaccuracy. In particularly, when tags carriers are walking in LOS (line-of-sight) and NLOS hybrid environment, great attenuation of RSS will happen, which would result in great location deviation. The paper proposes an IMU-assisted (Inertial Measurement Unit) RFID based indoor localization in LOS/NLOS hybrid environment. The proposed method includes three improvements over previous RSS based positioning methods: IMU aided RSS filtering, IMU aided LOS/NLOS distinguishing and IMU aided LOS/NLOS environment switching. Also, CRLB (Cramér-Rao Low Bound) is calculated to prove theoretically that indoor positioning accuracy for proposed method in LOS/NLOS mixed environment is higher than position precision of only use RSS information. Simulation and experiments are conducted to show that proposed method can reduce the mean positioning error to around 3 meters without site survey.

This paper presents an improved indoor localization system based on RF and ultrasonic signal which we named SNSH system. This system composes of a transmitter mounted in a mobile target, and a series of receiver nodes which are managed by a coordinator. By measuring Time Delay of Arrival (TDoA) of RF and ultrasonic signal from the transmitter, distance from target to receiver node is calculated and sent to the coordinator through CAN network, and all the information are gathered in PC to estimate 3D position of the target. Sine wave detector and dynamic threshold filter are applied to provide excellent accuracy of range measurement from TDoA result, and multilateration algorithms is realized to optimize coordinate determination accuracy. Specifically, Linear Least Square and Non-linear Least Square techniques are implemented to contrast their performances in target coordinate estimation. RF signal encoding/decoding time, time delay in CAN network and math calculating are carefully considered to ensure optimal system performance and get ready for field application. Experiments show that sine wave detector algorithm has greatly improved range measurement accuracy, with mean error at 2.2mm and maximum error at 6.7 mm, for distance below 5m. In addition, 3D position accuracy is greatly enhanced by multilateration methods, with mean error in position stay under 15mm, and 90% confident error values at 23mm for LLS, 20mm for NLS. Overall system update period has been verified in real system operation, with maximum rate at 25ms, and compared with other existing researches.

Indoor positioning based on the received signal strength (RSS) of the WiFi signal has become the most popular solution for indoor localization. In order to realize the rapid deployment of indoor localization systems, solutions based on crowdsourcing have been proposed. However, compared to conventional methods, crowdsourced RSS values are more erroneous and can result in large localization errors. To mitigate the negative effect of the erroneous measurements, a graph-based semi-supervised learning (G-SSL) method is used to exploit the correlation between the RSS values at nearby locations to estimate an optimal RSS value at each location. Before using the G-SSL method, the Linear Regression (LR) algorithm is proposed to solve the device diversity problem in crowdsourcing system. Since the spatial distribution of the APs is sparse, the Compressed Sensing (CS) method is applied to precisely estimate the location of the APs. Based on the location of the APs and a simple signal propagation model, the RSS difference between different locations is calculated and used as an additional constraint to improve the performance of G-SSL. Furthermore, to exploit the sparsity of the weights used in the G-SSL, we use the CS method to reconstruct these weights more accurately and make a further improvement on the performance of the G-SSL. Experimental results show improved results in terms of the smoothness of the radio map and the localization accuracy.

This paper describes the first results of an Image Recognition Based Location (IRBL) for mobile application focusing on the procedure to generate a Database of range images (RGB-D). In an indoor environment, to estimate the camera position and orientation, a prior spatial knowledge of the surrounding is needed. In order to achieve this objective a complete 3D survey of two different environment (Bangbae metro station of Seoul and E.T.R.I. building in Daejeon – Republic of Korea) was performed using LiDAR (Light Detection And Ranging) instrument and the obtained scans were processed in order to obtain a spatial model of the environments. From this, two databases of reference images were generated using a specific software realized by the Geomatics group of Politecnico di Torino (ScanToRGBDImage). This tool allow to generate synthetically different RGB-D images) centered in the each scan position in the environment. Later, the external parameters (X, Y, Z, ω, φ, κ) and the range information extracted from the DB images retrieved, are used as reference information for pose estimation of a set of acquired mobile pictures in the IRBL procedure. In this paper the survey operations, the approach for generating the RGB-D images and the IRB strategy are reported. Finally the analysis of the results and the validation test are described.

The integration of Artificial Intelligence (AI) in dairy farm management through biometric facial recognition for cows is a significant stride in livestock management. This review critically evaluates the evolution, applications, and challenges of AI-driven biometric facial recognition in dairy farming. It emphasizes the role of this technology in enhancing individual monitoring of dairy cows, providing accurate health, behavior, and productivity tracking. Originally derived from human facial recognition systems, this approach utilizes distinctive bovine facial features for essential, non-invasive, real-time monitoring in large-scale operations. The progression of AI from elementary pattern recognition to advanced Convolutional Neural Networks (CNNs) and deep learning models marks a shift toward data-driven farming. This study addresses key challenges such as environmental variability, data collection hurdles, ethical concerns, and technological limitations. It also contrasts various AI models, spotlighting their unique strengths and practical utility in dairy farming scenarios. Despite these challenges, facial recognition technology holds promise for improving farm efficiency, animal welfare, and sustainable practices, highlighting the need for continuous research and development. The review concludes by advocating for future research focused on environmental adaptability, ethical AI application, cross-breed compatibility, and integration with other farming technologies. Ultimately, it underscores AI's transformative potential in modernizing dairy farming towards a more data-oriented, responsible agricultural future.

As practiced in Africa, urban agriculture (UA) has been examined in the literature and by architecture and urban planning experts, revealing both advantages and disadvantages for health. While the recognized disadvantages are not negligible, the literature focuses mainly on the positive outcomes about food security and improved socio-economic conditions for farmers. However, little research has focused on farmers' perceptions of the effect of this practice on their psychological well-being. This study seeks to delve into this aspect by surveying 733 farmers in Dar es Salaam and Greater Lomé to map UA's distribution, assess its benefits and challenges, study its psychosocial associations, and demonstrate how architectural design and urban planning can enhance its spatial distribution and overall benefits. The standardized Mental Health Continuum-Short Form (MHC-SF) and non-standardized questionnaires are used to evaluate their responses. Using logistic regression and spatial analysis techniques with Python and QGIS 3.32.2-Lima, the analysis uncovers that farmers attribute improved psychological well-being to their involvement in UA. The study found that farmers' happiness due to UA varies by location within a city, with those on the outskirts generally more satisfied than those in urban centers. Additionally, women farmers reported greater happiness than men farmers, highlighting UA's role in empowering women. The research underscores UA's positive association with practitioners' psychological health in Africa and advocates for its integration into urban planning to promote equitable and beneficial urban spaces.

In the livestock industry, maintaining the health of animals is very important, and the use of technology has greatly helped this. One of the existing challenges has been that most of the available sensors and electronic processing devices are designed to fit the human body, and the use of these devices for livestock faces limitations due to the difference in the structure of the body and skin of animals and humans. Reliable solutions tailored for animal husbandry can be achieved by modifying the physical and software structure of existing devices. In this article, we have presented the current situation, by reviewing the research done and the existing technology in the field of animal husbandry. The aim is to obtain comprehensive parameters for monitoring livestock health by leveraging available information and exploring new methods based on the Internet of Things (IOT), Artificial Neural Networks (ANN), and Artificial Intelligence (AI). We take a proactive approach by utilizing available technologies based on sound-based diagnostic techniques. Initially, we propose utilizing piezoelectricity and amplifying received sound using a diaphragm attached to a collar. Subsequently, we discuss the results of relevant tests that can be conducted using the phonocardiography system. For the next phase of research and designing a more advanced livestock health monitoring system, we suggest employing an ultrasonic system to measure heart rate and blood pressure for the next phase of research and designing a more advanced livestock health monitoring system. This system, coupled with a piezoelectric system to make electric energy from the pulse of the veins, could be subcutaneously implanted for continuous monitoring

In the context of globalization One Health enables and concurs to the achievement of Global Health. One Health approach involves wide knowledge and understanding of its critical factors, next to an adequate implementation, also in farm animal sector. Moreover, animal farming and breeding shows particularities depending on country. Therefore, well-defined and specific strategies are still needed to be developed and adapted to the current conditions found in various countries for ensuring a well implementation, thus driving to better and healthier global outcomes and security. This paper will explore the One Health status in Romania, highlighting the current progress registered until now and the future perspectives based on its potential, in the context of the livestock farming framework and challenges. Our findings may help to disclose the challenges of putting in practice One Health in Romania, alongside to the pathways showing potential to enhance sustainability in livestock farming.

There is an intense need to optimize agrivoltaic systems. This article describes the invention of a new testing system: the parametric open source cold-frame agrivoltaic system (POSCAS). POSCAS is an adapted gardening cold-frame used in cold climates as it acts as a small greenhouse for agricultural production. POSCAS is designed to test partially transparent solar photovoltaic (PV) modules targeting the agrivoltaic market. It can both function as a traditional cold frame, but it can also be automated to function as a full-service greenhouse. The integrated PV module roof can be used to power the controls or it can be attached to a microinverter to produce power. POSCAS can be placed in an experimental array for testing, agricultural and power production. It can be easily adapted for any type of partially transparent PV module. An array of POSCAS systems al-lows for testing agrivoltaic impacts from the percent transparency of the modules by varying the thickness of a thin film PV material or the density of silicon-based cells, and various forms of optical enhancement, anti-reflection coatings and solar light spectral shifting materials in the back sheet. All agrivoltaic variables can be customized to identify ideal PV designs for a given agricultural crop.

Traditional agricultural extension services rely on extension workers, especially in countries with large agricultural areas. In order to increase adoption of sustainable agriculture, the recommendations given by such services must be adapted to local conditions and be provided in a timely manner. The AgroTutor mobile application was built to provide highly specific and timely agricultural recommendations to farmers across Mexico and complement the work of extension agents. At the same time, AgroTutor provides direct contributions to the United Nations Sustainable Development Goals, either by advancing their implementation or providing local data systems to measure and monitor specific indicators such as the proportion of agricultural area under productive and sustainable agriculture. The application is freely available and allows farmers to geo-locate and register plots and the crops grown there, using the phone’s in-built GPS, or alternatively, on top of very high-resolution imagery. Once a crop and some basic data such as planting date and cultivar type have been registered, the app provides targeted information such as weather, potential and historical yield, financial benchmarking information, data-driven recommendations as well as commodity price forecasts. Farmers are also encouraged to contribute in-situ information, e.g., soils, management, and yield data. The information can then be used by crop models, which, in turn, would send tailored results back to the farmers. Initial feedback from farmers and extension agents has already improved some of the app’s characteristics. More enhancements are planned for inclusion in the future to increase the app’s function as a decision support tool.

We hypothesized possibility that inline registered reticulorumen pH can be as biomarker of cows reproduction and health status. Aim of this study was to evaluate the relationship of reticulorumen pH with biomarkers from automatic milking system (AMS) and some blood parameters and determinate reticulorumen pH as biomarker of cows reproduction and health status. According to cows reproductive status the cows were classified as belonging to the following four groups: 15-30 d. postpartum; 1-34 d. after insemination; 35 d. after insemination (non-pregnant); 35 d. after insemination (pregnant). According reticulorumen pH assay experimental animals were divided into four classes: 1) pH<6.22 (5.3% of cows), 2) pH - 6.22-6.42 (42.1% of cows), 3) pH - 6.42-6.62 (21.1% of cows), 4) pH >6.62 (10.5% of cows). Rumination time, body weight, milk yield, milk fat – protein ratio, milk lactose, milk somatic cell count (SCC), milk electrical conductivity of all quarters of udder were registered with the help of Lely Astronaut® A3 milking robots. The pH, temperature of the contents of cow reticulorumens and cow activity were measured using specific smaX-tec boluses. Blood gas parameters were analyzed using a blood gas analyzer (EPOC, Canada). We found that pregnant cows has higher reticulorumen pH during insemination time, comparing with non-pregnant. Cows with lower reticulorumen pH has lowest milk fat – protein ratio, and lactose concentration, and highest SCC. Cows with lowest reticulorumen pH has lowest blood pH. With increase reticulorumen pH, increases blood potasium and hematocrit, decreases CO2, saturation and sodium.

Organic farming has positive, impact on environment, soil health, and healthy food quality. Worldwide demand for organic foods is increasing by leaps and bounds in recent years. The present investigation was undertaken during 2014 to 2018 to evaluate the effect of cowpea (Vigna unguiculata) co-culture with maize (Zea mays L.) on productivity enhancement over prevailing maize-fallow system, and to assess the feasibility of inclusion of short duration winter crops after maize with appropriate residue management practices on productivity and soil health. The experiment comprised of six cropping systems in main plot and three soil moisture conservation (SMC) measures options in sub plot. Results indicated that the inclusion of second crop in place of fallow and cowpea co-culture with maize increased average maize grain yield by 6.2 to 23.5% as compared to that of maize-fallow (MF). Use of maize stover mulch (MSM) + weed biomass mulch (WBM) increases maize grain yield by 19.1 and 6.5% over those of MSM and no mulch (NM), respectively. Various soil moisture conservation (SMC) measures had significant (p=0.05) effect on crop yields and water productivity. Double cropping system had significantly (p=0.05) higher amount of soil available NPK, soil organic carbon (SOC), microbial biomass carbon (MBC) and dehydrogenase activity (DHA) at 0-15 cm and at 15-30 cm depth than those under MF. The SWC measures of MSM+WBM had significantly higher available N, SOC, and MBC by 5.5, 4.8 and 8.1% than those under NM, respectively. Correspondingly, soils under MSM and MSM+WBM had 2.24 and 2.99% lower bulk density (ρb) in 0-15 cm and 2.21 and 2.94% lower ρb in 15-30 cm than that of NM. The energy use efficiency (EUE) was significantly higher under MCV (7.90%) over rest of the cropping sequences. MSM+WBM and MSM recorded 25.1 and 16.6% higher net energy over NM, respectively. The net return (INR 159.99×103/ha) and B:C ratio (2.86) were significantly higher with MCV system followed by MCR cropping sequence. MSM+WBM had significantly higher net return (INR 109.44×103/h), B:C ratio (2.46) over those under MSM (INR 97.6×103/h) and NM (INR 78.61×103/h). Overall the cowpea co-culture with maize and inclusion of short cycle winter crops along with MSM+WBM in maize-based cropping systems was found productive in terms of crop and water, profitable, energy efficient and sustained the soil health.

This paper proposes an Integrated Fire Management (IFM) framework that can be used to support communities and resource managers in finding effective and efficient approaches to prevent damaging fires, as well as maintain desirable fire regimes in Kenya. Designing and implementing an IFM approach in Kenya calls for a systematic understanding of the various uses of fire and the underlying perceptions and traditional ecological knowledge of the local people. The here proposed IFM framework allows an evaluation of the risks posed by fires, while balancing them with their beneficial ecological and economic effects, and thus developing effective fire management approaches. A case study of the proposed IFM framework was conducted in Gathiuru Forest that is part of the larger Mt. Kenya Forest Ecosystem. Focus group discussions were held with key resource persons, primary and secondary data on socio-economic activities were studied, fire and weather records were analyzed and the current fire management plans were consulted. Questionnaires were used to assess how the IFM is implemented in the Gathiuru Forest Station. The results show that the proposed IFM framework is scalable and can be applied in places with fire-dependent ecosystems as well as in places with fire-sensitive ecosystems in Kenya. The effectiveness is dependent on the active participation, formulation and implementation of the IFM activities by the main stakeholder groups (Kenya Forest Service (KFS), Kenya Wildlife Service (KWS), and the Community Forest Associations (CFA)). The proposed IFM framework helps in implementing cost-effective approaches to prevent damaging fires and maintain desirable fire regimes in Kenya.

This article concerns the adoption of small-scale irrigation farming as a climate-smart agriculture practice and its influence on household income in the Chinyanja Triangle. Chinyanja Triangle is a region that experiences mid-season dry spells and an increase in occurrences of drought due to low and erratic rainfall patterns which is attributed largely to climate variability and change. This poses high agricultural production risks, which aggravate poverty and food insecurity. For this region, adoption of small-scale irrigation farming as a climate-smart agriculture practice is very important. Through a binary logistic and ordinary least squares regression, the article determines factors that influence the adoption of small-scale irrigation farming as a climate-smart agriculture practice and its influence on income among smallholder farmers. The results show that off-farm employment, access to irrigation equipment, access to reliable water sources and awareness of water conservation practices, such as rainwater harvesting have a significant influence on the adoption of small-scale irrigation farming. On the other hand, the farmer’s age, distance travelled to the nearest market and nature of employment negatively influenced the adoption of small-scale irrigation farming decisions. Ordinary least squares regression results showed that the adoption of small-scale irrigation farming as a climate-smart agriculture practice has a significant positive influence on agricultural income. We therefore conclude that to empower smallholder farmers to quickly respond to climate variability and change, practices that will enhance adoption of small-scale irrigation farming in the Chinyanja Triangle are critical as this will significantly impact on agricultural income.

There are several challenges on how to attain energy efficiency while maintaining balance among factors affecting energy consumption such as power rating and temperature setpoints of cooling units, room temperature and humidity, and indoor air quality (IAQ). A real-time energy and IAQ monitoring system were installed in an educational building to profile the operational power consumption of inverter-based air condition unit (ACU) installed in each room. Polynomial curve and neural fitting regression analysis were applied to the real-time power consumption, indoor temperature and humidity, and carbon dioxide (CO2) level data. The derived models were able to provide the stabilizing indoor thermal and air conditions of the room to reach steady state ACU power consumption. These collected data and calculated parameters can be used to define rules in automating control of cooling appliances for an efficient energy utilization. The regression approaches, using real-time data, have determined the influence of indoor heat conditions and carbon dioxide levels on ACU power consumption. These parameters were utilized to establish consistent values for temperature, humidity, and carbon dioxide levels under stable settings of inverter-based air conditioning units.

Sport centers are significant energy consumers. This article outlines the engineering design for a comprehensive energy performance upgrade of the indoor sport hall in Arkalochori, Greece, and presents the projected results. The indoor sport hall constitutes a major sport facility in the mainland of Crete, hosting a broad cluster of sport municipal activities and the official basketball games of the local team in the 2nd national category. Having being constructed in the mid ‘90s, the facility exhibits very low thermal performance, with considerably high U-factors for all constructive elements (from 4 to 5 W/m2∙K), still use of diesel oil for indoor space heating and domestic heat water production and ineffective, old lamps and luminaries covering the lighting needs of the facility. The energy performance upgrade of the indoor sport hall was holistically approached, through all possibly applicable passive and active measures: insulation of opaque surfaces and replacement of openings, installation of new stone wool panels, installation of heat pumps for indoor space conditioning, elimination of diesel oil for any final energy use, installation of a solar-combi system for domestic hot water production, upgrade of all indoor and outdoor lighting equipment, installation of solar tubes on the main sport hall roof for natural lighting and installation of a photovoltaic plant on the same roof for the compensation of the remaining electricity consumption. With the proposed measures, the indoor sport hall is upgraded to zero energy facility. The payback period of the investment was calculated at 26 years, on basis of the avoided energy resources procurement cost. This work has been funded by the Horizon 2020 project with the acronym “NESOI” and was awarded with the public award of the “Islands Gamechanger” competition of the NESOI project and the Clean Energy for EU Islands initiative.

There is a need for cost-effective and non-invasive methods of monitoring feeding behavior in livestock operations, considering the significant impact of feed costs on economic efficiency and assisting in detecting health issues of group-fed animals. This paper proposes using deep learning-based computer vision techniques to detect pen-fed beef cattle feeding behavior using Mask Region-based Convolutional Neural Network (RCNN). A deep learning model was pre-trained on the Common Objects in Context (COCO) dataset to generate cattle instance segmentation. Manually defined feed bunk polygons are compared with these segmentation masks to derive feeding time for each bunk. A full day’s worth of video data and the corresponding physical sensor data are collected for the experiment. By benchmarking the computer vision detected data with physical ground truth over random time segments from morning to evening (thus various lighting conditions), the optimal thresholds for Mask RCNN are determined to be 0.7 for bounding boxes and 0.1 for masks. Using these parameters. The reports suggest that the computer vision system achieved a precision of 87.2% and a recall of 89.1%, signifying precise detection of feeding events. Our study, to the best of our knowledge, was one of the first investigations of instance segmentation on feeding time sense, which combines deep learning methods with traditional computer vision logistics, reporting on feeding time data collection and processing, camera testing and adjustment, and performance evaluation. Future research directions include computer vision applied in feed grading and animal re-identification for individual production analysis.

CGT is composed of fiber residues, leaves, dust particles, soil and others derived during the ginning and yarn spinning process at processing industries,. In the cotton spinning industrial clusters, farmers are using CGT) as one of the alternative roughage feed for their sheep, mainly during forage shortages in the summer months. To have baseline information on farmers using gin and factors driving them to choose CGT as a roughage source, which need to be identified for future planning on the usage of CGT in sheep feeding. Considering the above facts, the present study was undertaken to assess socio-personal characteristics, managemental practices associated with farmers using cotton gin in the feeding of sheep and also identify the factors driving the choice of the CGT as the primary source of roughage in the cotton spinning industry cluster of Tamil Nadu. For this, a survey among 80 sheep farmers was carried out using a pre-tested in-terview schedule. The collected data was analysed using descriptive statistics and logit regres-sion. The results indicated that the majority of male, aged, and large-land farmers were involved in practicing CGT feeding during the summer and also indicated the non-availability of green fodder during the summer season. The coarse type of CGT preferred more than the fine type of CGT trash in that area due to quality perception and price. The replacement level of CGT as roughage ranged from 33 to 75% of the total roughage requirement per day. About 88 percent-age of farmers were highly satisfied with the results of using CGT and they also expressed that the presence of foreign particles and dust were the major problems with using CGT. The choice of CGT as primary roughage among sheep farmers was primarily influenced by selective farming contexts, namely landholding, access to labour, and feeding practices of other livestock with cotton gin. Further, research has to be focused on to improve the quality of CGT in the future, as it is being utilised largely by sheep farmers.

European Protected Areas (PAs) are facing today complex and highly diverse challenges. Farm management structures have changed over time and traditional low-intensity farming systems have become unprofitable leading to either abandonment or intensification of farming practices. These changes have contributed to the environmental degradation of biodiversity rich agricultural landscapes and the loss of cultural knowledge and traditions. The interrelations developing at various levels between agricultural land use, nature and landscape conservation, and socio-economic activities influence the sustainable development and management of PAs. The development of certification and labelling schemes for high quality agri-food products with a PA logo is a rural development process that involves multiple functions which could generate environmental improvements and positive socio-economic changes. The differentiation of the agricultural products of PAs, through labelling and information, can be a proactive market-based instrument to support the local economy, promote environmentally sound practices, raise environmental awareness, and preserve farmland biodiversity in protected areas. This paper review is an attempt to gather and analyze in depth the findings of the existing studies focusing on the relationship between the European PAs, farming systems and certification/labelling schemes of agri-food products with a PA logo. Academic research on the subject is limited but provides valuable insights. The findings can serve as a starting point for discussions and reveal opportunities for further research to better understand the interrelations and additional effects emerging from the labelling/certification of PAs’ high quality agri-food products.

Canada has committed to reducing greenhouse gas (GHG) emissions by increasing the non-emitting share of electricity generation to 90% by 2030. As solar energy costs have plummeted, agrivoltaics (co-development of solar photovoltaic (PV) systems and agriculture) provide an economic path to these goals. This study quantifies agrivoltaic potential in Canada by province using geographical information system analysis of agricultural areas and numerical simulations. Systems modeled would enable conventional farming of field crops to continue (and potentially increase yield) by using bifacial PV for single-axis tracking and vertical system configurations. Between a quarter (vertical) to more than one third (single axis tracking) of Canada’s electrical energy needs can be provided solely by agrivoltaics using only 1% of current agricultural lands. These results show that agrivoltaics could be a major contributor to sustainable electricity generation and provide the ability for Canada to render the power generation sector net zero/GHG emission free. It is clear that the potential of agrivoltaic-based solar energy production in Canada far outstrips current electric demand and can thus be used to electrify and decarbonize transportation, heating, expand economic opportunities by powering the burgeoning computing sector, and export green electricity to the U.S. to help eliminate their dependence on fossil fuels.

Degraded soils causing from natural and human affects are universal in arid and semi-arid regions all over the world. Bentonite and humic acid (BHA) are increasingly being tested to remediate these degraded lands with potential benefits on crop production and soil health. The objective of this paper was to determine the residual effects four to five years after a one-time BHA application at six rates on (i) dynamic changes in soil properties, and (ii) oat crop productivity parameters, in a dryland farming ecosystem. With increasing rates of one-time BHA application, soil profile water storage displayed a piecewise linear increase plus plateau, whereas soil electrical conductivity, pH and bulk density were all reduced significantly (P < 0.05) in the 0-20 cm and 20-60 cm layers. The improved soil environments gave rise to an increased activity of soil enzymes urease, invertase and catalase that respectively reached the peak values of 97%, 37% and 32% at the rates of 21 to 24 Mg BHA ha-1. These conversely boosted soil nutrient turnover, leading to a 40% higher soil available P. Compared with the control treatment, application of BHA at the estimated optimum rate (roughly 24 Mg ha-1) increased grain yield by 20%, protein yield by 62%, water use efficiency by 41%, and partial factor productivity of N by 20%. Results of this study showed for the first time that a one-time BHA application would be a new and effective strategy to combat land degradation, drought, and promote a sustainable soil micro-ecological environment in dryland agroecosystem under a varying climate scenario.

This paper considered the issue of agricultural fields boundary recognition in satellite images. A novel algorithm based on the aggregated history of vegetation index data obtained via open satellite data, Sentinel-2, was proposed. The proposed algorithm included several basic steps, namely the detection of parcel regions on aggregated index data; the calculation of aggregated edge maps; the segmentation of parcel regions using the edges obtained; the computation of connected components and their contour extraction. In this paper, we showed that the use of aggregated vegetation index data and boundary maps allow for much more accurate agricultural field segmentation compared to the instant vegetation index approach. The quality of segmentation within regions of Russia and the Ukraine was estimated. The dataset that was used and Python implementation of the proposed algorithm were provided.

: The agriculture sector is one of the backbones of many countries’ economies and its processes have been changing in order to enable technological adoption to increase productivity, quality, and sustainable development. In this research, we present a theoretical reflection through a scientific mapping of the adoption of precision techniques and breakthrough technologies in agriculture, the so-called Precision Agriculture (PA) and Agriculture 4.0 (A4.0). To do this, we used 4,694 documents from the Web of Science database to perform a Bibliometric Performance and Network Analysis (BPNA) of the literature with the support of the PICOC protocol and the SciMAT software. Our findings present 22 strategic themes related to Digital Agriculture (DA) such as Internet of Things (IoT) and Climate-smart Agriculture (CSA) among others, and the thematic network structures of the motor themes and the thematic evolution structure of the field of the study over time. In addition, our results discuss the main challenges and opportunities of DA. Our findings have the potential to provide insights for practitioners and researchers in decision-making and pave the way for future works.

Growing acreage and changing consumer preferences cause increasing interest in the cereal products originating from organic farming. Lack of results of objective test, however, does not allow drawing conclusions about the effects of cultivation in the organic system and comparison to currently preferred conventional system. Field experiment was conducted in organic and conventional fields. Thirty modern cultivars of winter wheat were sown. They were characterized for disease infection including Fusarium head blight, seed sowing value, the amount of DNA of the six species of Fusarium fungi as well as concentration of ergosterol and trichothecenes in grain. The intensity Fusarium head blight was at a similar level in both systems. However, Fusarium colonization of kernels expressed as ergosterol level or DNA concentration was higher for the organic system. It did not reflect in an increased accumulation of trichothecenes in grain, which was similar in both systems, but sowing value of organically produced seeds was lower. Significant differences between analyzed cropping systems and experimental variants were found. The selection of the individual cultivars for organic growing in terms of resistance to diseases and contamination of grain with Fusarium toxins was possible. Effects of organic growing differ significantly from the conventional and grain obtained such way can be recommended to consumers. There are indications for use of particular cultivars bred for conventional agriculture in the case of organic farming, and the growing organic decreases plant stress resulting from intense fertilization and chemical plant protection.

Enclosed public spaces are the hotspots for airborne disease transmission. To measure and maintain indoor air quality in terms of airborne transmission, an open source, low cost and distributed array of Particulate Matter Sensors has been developed and named as Dynamic Aerosol Transport for Indoor Ventilation or DATIV system. This system can use multiple Particulate Matter Sensors (PMS) simultaneously and can be remotely controlled using a Raspberry Pie based operating system. The data acquisition system can be easily operated using the GUI within any common browser installed on a remote device such as a PC or Smartphone with corresponding IP address. The software architecture and validation measurements are presented together with possible future developments.

In urban areas like Chicago, daily life extends above ground level due to prevalence of high-rise buildings where residents and commuters live and work. This study examines the variation in fine particulate matter (PM2.5) concentrations across building stories. PM2.5 levels were measured using PurpleAir sensors, installed between April 8 and May 7, 2023, on floors one, four, six, and nine of an office building in Chicago. Additionally, data were collected from a public outdoor PurpleAir sensor on the fourteenth floor of a condominium located 800 meters away. Results show that outdoor PM2.5 concentrations peak at 14 meters height, then decline by 0.11 μg/m3 per meter elevation, especially noticeable from midnight to 8 AM under stable atmospheric conditions. Indoor PM2.5 concentrations increase steadily by 0.02 μg/m3 per meter elevation, particularly during peak work hours, likely caused by greater infiltration rates at higher floors. Both outdoor and indoor concentrations peak around noon. We find that indoor and outdoor PM2.5 are positively correlated, with indoor levels consistently remaining lower than outside levels. These findings align with previous research suggesting decreasing outdoor air pollution concentrations with increasing height. The study informs decision-making by community members and policymakers regarding air pollution exposure in urban settings.

Every day around 93% of children under the age of 15 (1.8 billion children) breathe outdoor air that is so polluted it puts their health and development at serious risk. Due to the pandemic, however, ventilation of buildings using outdoor air has become an important safety technique to prevent the spread of COVID-19. With the mounting ev-idence suggesting that air pollution is impactful to human health and educational out-comes, this contradictory guidance may be problematic in schools with higher air pol-lution levels, but keeping kids COVID-19 free and in school to receive their education is now more pressing than ever. To understand if all schools in an urban area are ex-posed to similar outdoor air quality and if school infrastructure protects children equally indoors, we installed research grade sensors to observe PM2.5 concentrations in indoor and outdoor settings to understand how unequal exposure to indoor and out-door air pollution impacts indoor air quality among high- and low-income schools in Salt Lake City, Utah. Based on this approach, we found that during atmospheric inver-sions and dust events, there was a lag ranging between 35 to 73 minutes for the out-door PM2.5 concentrations to follow a similar temporal pattern as the indoor PM2.5. This lag has policy and health implications and may help to explain the rising concerns re-garding reduced educational outcomes related to air pollution in urban areas. These data and resulting analysis show that poor air quality may impact school settings, and the potential implications with respect to environmental inequality.

It is unclear which noise exposure time window and noise characteristics during nighttime are most detrimental for sleep quality in real life settings. We have conducted a field study with 105 volunteers wearing a wrist actimeter to record their sleep during seven days, together with concurrent outdoor noise measurements at their bedroom window. Actimetry recorded sleep latency increased by 5.6 minutes (95% confidence interval: 1.6 to 9.6 minutes) per 10 dB(A) increase in noise exposure during the first hour after bedtime. Actimetry assessed sleep efficiency was significantly reduced by 2-3 percent per 10 dB(A) increase in measured outdoor noise (Leq, 1h) for the last three hours of sleep. For subjectively reported sleepiness, noise exposure during the last hour prior to wake up was most crucial with an increase in the sleepiness score of 0.31 units (95% CI: 0.08 to 0.54) per 10 dB(A) Leq,1h. Associations for estimated indoor noise were not more pronounced than for outdoor noise. Considering noise events in addition to equivalent sound pressure levels (Leq) only marginally improved the statistical models. Our study provides evidence that matching the nighttime noise exposure time window to the individual’s diurnal sleep-wake pattern results in a better estimate of detrimental nighttime noise effects on sleep. We found that noise exposure at the beginning and the end of the sleep is most crucial for sleep quality.