Farming Practices Research Articles

A synergy of participatory design and indigenous knowledge to address rice (Oryza sativa L.) cultivation challenges of Sadivayal village, Tamil Nadu, India

ABSTRACT This research employed a participatory co-design approach to improve sustainable agricultural practices in Sadivayal village, Tamil Nadu. Smallholder tribal farmers faced challenges like pest vulnerability, middlemen influence, and limited awareness of sustainable rice production technologies. The COVID-19 pandemic worsened these issues, leading many farmers to lease their land to middlemen who practiced chemically intensive farming. This created confusion, as farmers were uncertain whether to let middlemen continue or switch to chemically intensive farming themselves, abandoning traditional methods. The study involved farmers in defining problems, planning interventions, and conducting treatment demonstrations, integrating indigenous and scientific knowledge. Three treatments including organic, chemically intensive, and integrated farming were tested on four indigenous rice varieties and one commercial variety (CO55). Results showed CO55 was preferred due to its high-yielding capacity (4622 kg ha−1) and drought avoidance due to short duration (121 days), while Karuppu Kavuni was valued for its pest and disease resistance. Seeraga Samba was the least preferred due to high pest susceptibility and poor yield (1828 kg ha-1). Farmers favored a combination of organic and inorganic practices, and the study has led to the adoption of CO55 (54%), Karuppu Kavuni (24%), and Mappilai Samba (12%) in the 2023 season. The study features the value of participatory approaches in addressing local challenges and promoting sustainable farming practices.

ANTIMICROBIAL RESISTANCE IN FOODBORNE PATHOGENS: GLOBAL EPIDEMIOLOGICAL INSIGHTS, MECHANISTIC CHALLENGES, AND CUTTING-EDGE DETECTION

Background: Foodborne pathogens’ AMR is considered one of the most significant risks to food safety and human health worldwide.This situation has been worsened by the misuse of antibiotics in livestock farming, making it hard to handle food borne illnesses andhigher rates of sickness and death. Objectives: The main objective of this work is to present a review of the epidemiological data on AMR in foodborne pathogens on a global level, to discuss the genetic and biochemical basis of resistance, and to analyze new approaches to diagnostics and prevention. Methods: The current literature was systematically reviewed to evaluate the incidence and factors related to AMR in these important pathogens; Salmonella spp., Escherichia coli, Campylobacter spp., and Listeria monocytogenes. The study also discusses additional and more sophisticated diagnostic techniques such as CRISPR-based systems, and non-conventional forms of treatment like bacteriophages and antimicrobial peptides. Results: The data indicates that AMR continues to emerge in foodborne pathogens owing to factors such as intensive farming practices and global food trade, and environmental exposure. Some of the advanced diagnostic systems include metagenomics and point-of-care that can quickly and accurately identify the genes that cause resistance. It is, therefore, evident that nontraditional therapies hold promise in containing AMR but they have to be developed to a greater extent. Conclusions: AMR in foodborne pathogens requires a multifaceted approach involving accurate diagnostics, novel therapeutics, and cross-country cooperation. For this reason, there is need to embrace One Health approach to reduce the effect of AMR on health, food and the environment.

Disaster Induced Agricultural Productivity in Coastal Regions of Bangladesh: A Stochastic Frontier Analysis Approach

The production and technical efficiency of the rice sector in Bangladesh’s coastal areas are vulnerable to a wide range of natural disasters and climate change issues. These regions face recurring threats from climatic events such as salinity intrusion, inundation, rising sea levels, and cyclones, which can significantly disrupt agricultural activities. In this paper, we conduct a comparative analysis of the rice producers’ technical efficiency based on their exposure to disaster-induced impacts over the past five years. Using the Stochastic Frontier Analysis, we examine factors influencing household-level productive efficiency in three coastal districts—Patuakhali, Cox’s Bazar, and Khulna. Our findings reveal that rice-producing households affected by natural disasters between 2014 and 2018 exhibited, on average, an 8.29 percentage point higher productive efficiency compared to unaffected households. When controlling for other confounding variables, such as household characteristics and external conditions, the efficiency gain rises to 19.8%. This suggests that households exposed to adverse climatic events may have adapted their farming practices to become more resilient, thus improving their productive efficiency in the long term. Moreover, our results indicate that a larger household size enhances efficiency by 6.7%. Households where rice farming is the primary occupation also tend to be more efficient, likely because of greater specialization and focus on improving agricultural practices. Finally, the age of the producer is positively associated with efficiency, reflecting the accumulation of farming experience and knowledge over time.

AI Driven Animal Farming and Livestock Management System

AI-Driven Animal Farming and Livestock Management System represents a pivotal advancement in agricultural technology, promising to revolutionize traditional farming practices by integrating artificial intelligence (AI) into livestock management. This abstract presents an overview of the system's multifaceted capabilities, emphasizing its role in guiding animal farmers towards optimal livestock care, enhancing marketing strategies, and offering a suite of advanced functionalities. At its core, this system employs sophisticated AI algorithms like Natural Language Processing (NLP) and Convolutional Neural Networks (CNNs) to provide personalized guidance to animal farmers, ensuring they adhere to best practices in livestock care. Through real-time monitoring and data analysis, the system offers actionable insights into nutrition, health, and reproduction management, thereby maximizing the well-being and productivity of livestock. Furthermore, the System incorporates innovative features tailored to streamline marketing efforts. By analyzing market trends, consumer preferences, and supply chain dynamics, the system enables farmers to make informed decisions regarding product positioning, pricing strategies, and distribution channels, thereby enhancing market competitiveness and profitability

Effects of antibiotic growth promoter and its natural alternative on poultry cecum ecosystem: an integrated analysis of gut microbiota and host expression

BackgroundIn-feed antibiotic growth promoters (AGPs) have been a cornerstone in the livestock industry due to their role in enhancing growth and feed efficiency. However, concerns over antibiotic resistance have driven a shift away from AGPs toward natural alternatives. Despite the widespread use, the exact mechanisms of AGPs and alternatives are not fully understood. This necessitates holistic studies that investigate microbiota dynamics, host responses, and the interactions between these elements in the context of AGPs and alternative feed additives.MethodsIn this study, we conducted a multifaceted investigation of how Bacitracin, a common AGP, and a natural alternative impact both cecum microbiota and host expression in chickens. In addition to univariate and static differential abundance and expression analyses, we employed multivariate and time-course analyses to study this problem. To reveal host-microbe interactions, we assessed their overall correspondence and identified treatment-specific pairs of species and host expressed genes that showed significant correlations over time.ResultsOur analysis revealed that factors such as developmental age substantially impacted the cecum ecosystem more than feed additives. While feed additives significantly altered microbial compositions in the later stages, they did not significantly affect overall host gene expression. The differential expression indicated that with AGP administration, host transmembrane transporters and metallopeptidase activities were upregulated around day 21. Together with the modulated kininogen binding and phenylpyruvate tautomerase activity over time, this likely contributes to the growth-promoting effects of AGPs. The difference in responses between AGP and PFA supplementation suggests that these additives operate through distinct mechanisms.ConclusionWe investigated the impact of a common AGP and its natural alternative on poultry cecum ecosystem through an integrated analysis of both the microbiota and host responses. We found that AGP appears to enhance host nutrient utilization and modulate immune responses. The insights we gained are critical for identifying and developing effective AGP alternatives to advance sustainable livestock farming practices.

Potential applications of Bacillus thuringiensis Berliner in agriculture, medicine and environment

Bacillus thuringiensis (Bt) is renowned for its insecticidal activity against a wide range of target pests. Bt formulations offer safe alternatives to chemical insecticides, effectively eliminating insects using toxins and enzymes such as chitinases and metalloproteases. This bacterium has transformed pest management through the development of genetically modified insect-resistant crops, providing targeted protection. Beyond pest control, Bt serves as an alternative to antibiotics, fertilizers, bioremediation agents and for nanomaterial synthesis. While its effectiveness in insect control contributes to sustainable farming practices, Bt further promotes plant growth as a biofertilizer and growth enhancer. Additionally, it plays various roles in medicine and environmental applications. Bacteriocins, proteins produced by Bt, exhibit high efficacy against pathogenic bacteria and demonstrate some fungicidal activity, offering potential applications in medicine and food preservation. Bt’s influence extends to environmental bioremediation, where it targets heavy metals and dyes. It is also involved in the synthesis of metal nanoparticles and exhibits anti-cancer activity by targeting various cancerous cells. Overall, Bt showcases a broad spectrum of activity across agriculture, medicine and environmental sectors, highlighting its potential to enhance crop productivity, improve human health and reduce environmental pollution.

Carbon Footprint of Organic Coffee: Peruvian case study

In Peru, organic agriculture has emerged as a promising avenue for creating prosperity through the production of premium, value-added goods. Among the array of agricultural exports from Peru, organic coffee stands out as a highly significant product, celebrated internationally for its exceptional quality. Thus, there is an opportunity to address this study from the environmental point of view by focusing on greenhouse gas emissions in coffee production and adopting the principles of a circular economy. This approach not only promises to improve farmers' overall performance but also holds the potential to mitigate the environmental repercussions related with their production processes.The aim of this research is to analyze the carbon footprint of organic coffee that is produced by small farmers who are members of a cooperative situated in the northern region of Peru. To achieve this objective, the above-mentioned environmental impact during the production of organic green coffee was calculated using the Life Cycle Assessment methodology. This research also seeks to discern about disparities between the practices of farmers who already possess their organic certification and those currently in the certification process as well as assesses the relative eco-efficiency of these production units. Besides the absence of significant differences between those who have their organic certification and those who apply organic practices in their fields but are in the process of certification, results show that the carbon footprint of 1 kg of green coffee placed in port is estimated at 0.90 kg CO2e.

Risk and protective factors for ASF in domestic pigs and wild boar in the EU, and mitigation measures for managing the disease in wild boar.

Five epidemiological aspects of ASF were evaluated using literature reviews, field studies, questionnaires and mathematical models. First, a literature review and a case-control study in commercial pig farms emphasised the importance of biosecurity and farming practices, including the spread of manure around farms and the use of bedding material as risk factors, while the use of insect nets was a protective factor. Second, although wild boar density is a relevant known factor, the statistical and mechanistic models did not show a clear and consistent effect of wild boar density on ASF epidemiology in the selected scenarios. Other factors, such as vegetation, altitude, climate and barriers affecting population connectivity, also played a role on ASF epidemiology in wild boar. Third, knowledge on Ornithodoros erraticus competence, presence and surveillance was updated concluding that this species did not play any role in the current ASF epidemic in affected areas of the EU. Available scientific evidence suggests that stable flies and horse flies are exposed to ASFV in affected areas of the EU and have the capacity to introduce ASFV into farms and transmit it to pigs. However, there is uncertainty about whether this occurs, and if so, to what extent. Fourth, research and field experience from affected countries in the EU demonstrates that the use of fences, potentially used with existing road infrastructure, coupled with other control methods such as culling and carcass removal, can effectively reduce wild boar movements contributing to ASF management in wild boar. Fences can contribute to control ASF in both scenarios, focal introductions and wave-like spread. Fifth, the use of gonadotropin-releasing hormone (GnRH) vaccines as an immune contraceptive has the potential, as a complementary tool, to reduce and control wild boar populations. However, the development of an oral GnRH vaccine for wild boar still requires substantial additional work.

Climate Change Effects on Crop Area Dynamics in the Cachar District of Assam, India: An Empirical Study

Climate change is a worldwide phenomenon that significantly impacts the area, production, and yield of crops. Changes in climate conditions have diverse effects on farming globally. For instance, an increase in temperature can make specific crops more vulnerable to pests. Similarly, a decrease in rainfall reduces water availability, affecting both irrigated and rainfed farming practices. This study aims to investigate climate change effects on crop area dynamics in the Cachar district of Assam, India, for a period spanning from 1981 to 2017. The time series ARDL (Autoregressive Distributed Lag) model is employed to analyze the relationship between climate factors and areas under different crops. As a pre-requisite condition for ARDL, the Augmented Dickey-Fuller (ADF) test is employed to check the order of integration of area under selected crops. The research reveals that the annual average temperature negatively affects the area dedicated to chickpeas, while annual average rainfall negatively impacts the areas allocated to rice and chickpeas. Conversely, annual average relative humidity has a significant positive impact on the area of these crops in the study region. Policymakers may consider strategies and policies for agriculture by encouraging the cultivation of crop varieties that are more resilient to climate change.

Experimental Design: Implementation of IoT-based drip irrigation to enhance the productivity of Cilembu sweet potato (Ipomoea batatas) cultivation

Effective irrigation methods are crucial for meeting plant water needs while preventing excessive evaporation, soil dryness, and water shortages. Conventional irrigation methods often result in inefficient water distribution, leading to a reduced agricultural productivity. This study aimed to enhance water use efficiency and improve crop yield by employing Internet of Things (IoT)-based drip irrigation systems. The system regulates water supply and monitor environmental conditions using low-power sensor nodes that measure soil moisture, air temperature, humidity, and light intensity. Field experiments were conducted to ascertain optimal water requirements by adjusting irrigation durations to 15, 30, 60, and 120 minutes and modifying water application based on soil moisture levels below 70%. Irrigation scheduling was performed at 6 am and 6 pm. The implementation of IoT-based drip irrigation in Cilembu sweet potato field resulted in a production yield of 44 tons/ha, marking a 45% productivity compared to conventional irrigation methods. Furthermore, water savings of 65% were achieved through the application of IoT-controlled drip irrigation. These results demonstrate the potential of IoT-based systems in optimizing water use and enhancing agricultural productivity, making it a valuable approach for sustainable farming practices.

Assessing impact of organic farming for achieving sustainable agriculture development of Rajasthan: Structure equation remodeling

This study evaluates the impact of organic farming practices on achieving sustainable agriculture development in Rajasthan using Structural Equation Modeling with data from 285 respondents. Employing Smart-PLS for analysis, the research utilizes convenient sampling across three districts in Rajasthan to explore the relationships between key constructs. The study tests several hypotheses to understand how organic farming practices, farmers' perceptions, and attitudes toward organic farming contribute to sustainable agriculture development. Results reveal that organic farming practices and attitudes towards organic farming significantly impact sustainable agriculture development. At the same time, farmers' perceptions contribute positively but to a lesser extent. These findings underscore the importance of adopting organic farming practices and cultivating positive attitudes among farmers to foster sustainable agricultural practices in the region. The study provides valuable insights for policymakers and stakeholders aiming to enhance sustainable agriculture in Rajasthan through targeted interventions and support for organic farming initiatives.

Cloud-based Internet of Things Approach for Smart Irrigation System

The agriculture sector is facing numerous challenges such as water scarcity, climate change, and low productivity due to outdated farming practices. In most of the countries, 75% of their GDP depends on the agricultural products. Hence at most care has to be taken to introduce modern technology in the agriculture sector to enhance its productivity and efficiency. This paper presents an IOT Based Smart Agriculture Monitoring System aimed to increase the agricultural productivity through automating and optimizing crop management. The first objective of the proposed system is to supply the water to the crops based on soil moisture and the surrounding temperature. It can be achieved through soil moisture sensor, DHT11 sensor, light sensitive module and Node MCU. All sensors are interfaced with Node MCU. The data collected through sensor will be processed by the processor. Whenever the soil moisture is less and temperature is more, the pump will be automatically switched on for irrigation. Apart from this the collected data from sensor is also transmitted to the cloud through Thingspeak. The same information can be accessed by the farmers to monitor the crops remotely and improve the productivity. By providing farmers with real time data on their crops, the system can help them make informed decisions regarding water and fertilizer usage, pest control, and harvesting times. This, in turn, can lead to increased crop yields, reduced costs, and improved profitability. With the increasing demand for food production and the need to address the challenges of climate change and food security, this project serves as a promising solution for sustainable agriculture.

A Review on: Rice Disease Prediction

Abstract: Rice disease prediction is an emerging technology that leverages machine learning and image processing techniques to forecast disease outbreaks in rice crops. Early and accurate prediction of diseases like rice blast, bacterial leaf blight, and sheath blight is critical for maintaining crop health, optimizing resource use, and enhancing yield quality. This paper explores the use of high-resolution imaging and data-driven models to identify early signs of disease in rice plants. By analyzing data from multiple sources, including aerial imagery and environmental sensors, these predictive systems enable targeted and timely interventions, reducing the spread of infections and minimizing the use of pesticides. The application of predictive technologies not only aids in sustainable farming practices by lowering environmental impact but also aligns with the goals of precision agriculture to support food security and increase productivity. As agricultural practices continue to evolve, predictive models for rice disease management offer a promising solution for addressing the challenges posed by crop diseases in a changing climate

Enhancing Crop Classification in Smart Agriculture Using Multiclass Deep Learning Models

Smart agriculture is increasingly recognized as a vital approach to addressing the challenges of modern farming, including the need for improved yield prediction and efficient resource management. A cornerstone of this innovation is the precise classification of crops, which directly impacts decision-making and resource allocation. In this study, we investigate the effectiveness of several state-of-the-art deep learning models—VGGNet, Sequential, Artificial Neural Network (ANN), and ResNet50—for multiclass crop classification. A diverse and extensive crop image dataset was employed to ensure a comprehensive evaluation. The methodology incorporates rigorous preprocessing steps to enhance data quality, followed by meticulous model training and validation to achieve reliable outcomes. Comparative analysis of these models reveals their relative strengths and limitations, providing insights into their applicability in different agricultural scenarios. The findings emphasize the transformative potential of deep learning in agriculture, enabling precise crop identification and early detection of health issues, thus supporting smarter, more sustainable farming practices. This work lays a foundation for integrating advanced AI solutions into agriculture, paving the way for increased efficiency and resilience in crop management systems.

Commodity Prediction: ML-Based Commodity Suggestion

The increasing unpredictability of climate conditions and the dynamic nature of global markets pose significant challenges to traditional agricultural practices. This study aims to develop a predictive model using machine learning and artificial intelligence to assist farmers in selecting profitable exotic crops. The model focuses on optimizing agricultural profitability by analyzing key factors such as weather conditions and region. Through the use of data-driven techniques, including a suggestion matrix and confusion matrix for recommendation, the model predicts the most suitable crops for specific regions. To ensure robust predictions, the pre-processing steps address critical issues such as missing values, outlier detection due to inconsistencies in state-scrapped data, and normalization to align with seasonal and harvesting cycles. Reliable weather data sources are also integrated to account for variance in climate conditions, which directly impacts crop yield. The proposed model, validated across various scenarios, aims to enhance agricultural efficiency by providing farmers with actionable insights, ultimately leading to higher yields and increased income through the cultivation of less-known, high-demand crops. This paper discusses the data pre-processing techniques, model validation strategies, and the practical implementation of machine learning algorithms in the agricultural sector, contributing to the advancement of smart farming practices.

Enhancing Inner Area Revaluation Through Optional Control Programmes for Infectious Bovine Rhinotracheitis and Ruminant Paratuberculosis Potentially Linked to Crohn’s Disease in Humans

Regulation (EU) 2016/429 introduces comprehensive guidelines for managing transmissible animal diseases, including zoonoses. The subsequent Commission Implementing Regulation 2018/1882 categorizes these diseases into five groups, each with specific responses, ranging from mandatory eradication to optional eradication or surveillance. Key regulatory priorities include enhanced animal traceability, biosecurity, wildlife pathogen control, sustainable farming practices, and minimizing the impact of diseases on public health, animal health, and the environment. These objectives align with the European Green Deal, the Farm to Fork Strategy, the One Health approach, and the ongoing revaluation of European Inner Areas. They, including the Molise Region in Italy, are often remote, face service accessibility challenges, and suffer from depopulation and farm abandonment. Nonetheless, they hold significant potential for agropastoral and agri-food activities that can support tourism, the commercialization of local products, and recreational pursuits. Implementing optional programmes for animal diseases and zoonoses not subject to mandatory eradication could help the farms of these areas to mitigate productivity losses due to diseases like Infectious Bovine Rhinotracheitis and Paratuberculosis. The latter is a suspected zoonosis potentially linked to Crohn’s disease in humans. Optional programmes could enhance economic returns, counteract depopulation, support animal welfare and pasture conservation, and reduce the risk of exposure to zoonotic diseases for residents and tourists attracted by the ecological appeal of these areas.

Solar-Driven Agri Bot: A Storage-Free, Eco-Friendly Farming Solution

This work presents an innovative solution for sustainable agriculture by utilizing solar power to directly drive its motor system without the need for energy storage. This bot is equipped with Light Dependent Resistors (LDRs) and an Arduino microcontroller, the bot adjusts motor operations based on light conditions for optimal power efficiency. A relay switch enables seamless switching between solar power and a backup battery, ensuring continuous operation in varying sunlight. The bot’s multi-motor design supports reliable movement, accommodating a range of agricultural tasks such as transporting loads, water spraying, and ploughing. Its trolley adds versatility for carrying goods, making it practical and multifunctional in the field. This eco-friendly system not only reduces energy waste but also minimizes costs and design complexity by foregoing energy storage. Offering a cost-effective, environmentally-friendly solution for modern agriculture, this bot demonstrates a valuable application of direct solar power in field operations, supporting greener, more sustainable farming practices.

Typology of Grasscutter (Thryonomys swinderianus) Farming in Southern Benin Two Decades after Its Domestication

Aims: The aim of the study was to provide information on the current characteristics of grasscutter farms in Benin to identify areas for improvement. Study Design: A survey was conducted among 100 grasscutter farmers in southern Benin, identifying three groups of farmers. Place and Duration of Study: Data were collected between August and October 2023 in the departments of Mono, Couffo, Atlantique, Ouémé, Plateau and Zou in southern Benin. Methodology: Data collection were carried out through a retrospective survey using the snowball sampling method. During the survey phase, farmers were selected based on the accessibility of their farms and their willingness to participate in the questionnaire. Data were analyzed using SAS software, and a multiple component analysis (MCA) was performed to identify the different groups of grasscutter farmers. Results: Three groups of farmers were identified. Women were absent in Group 1, less present in Group 2 (8.20%), and more represented in Group 3 (23.53%). The most common occupations among respondents across all three groups were in agriculture and livestock farming. However, farmers were less frequent in Group 3 (82.34%) compared to the other groups (100%) (p<0.05). Ground-enclosure farming was only reported in Group 1 (76.47%). In the other groups, farming is done on tiers in compartmentalized enclosures. The smallest herd size was observed in Group 3 (p<0.05). All respondents in groups 1 and 2 fed their animals with a mixture of forage and commercial feed, while 64.71% did so in Group 3 (p<0.05). Panicum maximum, Pennisetum purpureum, and Moringa oleifera are the most commonly used forages in grasscutter feeding. Grasscutters are sold live by all farmers in Groups 1 and 2 and by 88.24% in Group 3 (p<0.05). The age at the sale is similar in Groups 1 (4.70 months) and 2 (4.15 months) and higher in Group 3 (7.28 months) (p<0.001). The most frequent constraints in all three groups were difficulties in accessing forage and lack of financial resources. Conclusion: Harmonizing grasscutter farming practices could be a lever to promote grasscutter farming in Benin.

Morphology-based weed type recognition using Siamese network

Automatic weed detection and classification can significantly reduce weed management costs and improve crop yields and quality. Weed detection in crops from imagery is inherently a challenging problem. Because both weeds and crops are of similar colour (green on green), their growth and texture are somewhat similar; weeds also vary based on crops, geographical locations, seasons and even weather patterns. This study proposes a novel approach utilising object detection and meta-learning techniques for generalised weed detection, transcending the limitations of varying field contexts. Instead of classifying weeds by species, this study classified them based on their morphological families aligned with farming practices. An object detector, e.g., a YOLO (You Only Look Once) model is employed for plant detection, while a Siamese network, leveraging state-of-the-art deep learning models as its backbone, is used for weed classification. This study repurposed and used three publicly available datasets, namely, Weed25, Cotton weed and Corn weed data. Each dataset contained multiple species of weeds, whereas this study grouped those into three classes based on the weed morphology. YOLOv7 achieved the best result as a plant detector, and the VGG16 model as the feature extractor for the Siamese network. Moreover, the models were trained on one dataset (Weed25) and applied to other datasets (Cotton weed and Corn weed) without further training. The study also observed that the classification accuracy of the Siamese network was improved using the cosine similarity function for calculating contrastive loss. The YOLOv7 models obtained the mAP of 91.03 % on the Weed25 dataset, which was used for training the model. The mAPs for the unseen datasets were 84.65 % and 81.16 %. As mentioned earlier, the classification accuracies with the best combination were 97.59 %, 93.67 % and 93.35 % for the Weed25, Cotton weed and Corn weed datasets, respectively. This study also compared the classification performance of our proposed technique with the state-of-the-art Convolutional Neural Network models. The proposed approach advances weed classification accuracy and presents a viable solution for dataset independent, i.e., site-independent weed detection, fostering sustainable agricultural practices.

Natural Farming: A Study of Farmers' Perceptions and Practices

Natural Farming: A Study of Farmers' Perceptions and Practices