Providing food security while preserving natural resources and reducing the use of production factors (land, labor, and capital), is a critical challenge for EU agricultural sectors in the 21st ...century. Sustainable Development goals adoption has increased the need for eliminating Greenhouse gas emissions across all EU sectors, while production should remain stable or even increase. For this purpose, Window Data Envelopment Analysis (DEA) has been selected as a benchmarking technique, in order to assess input use efficiency of agricultural sectors of EU countries for the 2005–2019 period. Moreover, three-year projections (until 2022) have been calculated in order to acquire future efficiency scores. Emphasis has been given on the selection of alternative window widths, examining their influence on calculating efficiency scores for both projected and actual dataset. From a methodological point of view, this paper aims to highlight the assumption of zero technological change within Window DEA frames and present their differences. At the same time, results indicate that Estonia (1.000), the Netherlands (0.999) and Slovenia (0.999) are the most efficient countries in terms of input use efficiency, while Finland, UK, and Hungary (0.670, 0.755 and 0.771) score the least. Countries of central Europe (Hungary, Czech Rep., Croatia, Slovakia, and Austria) should redesign their agricultural strategies, so as to achieve the nine objectives of the upcoming CAP (2021–2027).
Nowadays, the sustainability of Greek dairy cattle farms is questionable due to low competitiveness and high GHG emissions. In this context, the BIOCIRCULAR project, funded by the EYDE ETAK, ...developed a series of alternative practices focusing on precision agriculture principles. However, the adoption of any practice from farmers is not a given, and depends on several determinants. Hence, the objective of this study is to examine farmers’ adoption decisions regarding precision-agricultural practices in Greek dairy production systems, as well as the economic and environmental impacts of this adoption. In order to achieve this, a bio-economic model was developed based on mathematical programming methods. The proposed model simulates a large number of dairy cattle farms with or without crop production, including different management strategies and their relevant costs, and provides an environmental assessment of the adopted practices based on GHG emissions. Moreover, in order to analyze farmers’ adoption decisions, different policy measures, linked to various environmental outcomes, were examined. The results highlighted that the adoption of precision-agricultural practices led to significantly better economic and environmental outcomes. Furthermore, it was found that different levels of incentives can be efficiently targeted to encourage the adoption of new feeds and, more broadly, to secure the sustainability of the sector.
Several digital technologies are available to facilitate the transition toward a circular supply chain infrastructure. Small-Medium Enterprises (SMEs) should assess their readiness and measure their ...performance to select the most appropriate digital technology. This study explores how well-established digital technologies such as Cyber-Physical Systems (CPS), the Internet of Things (IoT), Cloud Manufacturing (CM), and Big Data Analytics (BDA) impact circular supply chain infrastructure in SMEs. Questionnaires have been distributed to collect employees’ preferences concerning the circular supply chain management criteria (profit, innovation, sustainability, and optimization). The responses have been organized into three clusters using Principal Component Analysis (PCA). A fuzzy Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) technique is adopted to evaluate these technologies since it constitutes a reliable managerial tool when vagueness impacts the smooth operation of the supply chain. Results indicate the ranking order of the investigated digital technologies (CPS>IoT>CM>BDA) as well as the circular benefits and the supply chain attributes imparted upon implementing these technologies. Such benefits and attributes are provided to assess the impact of these digital technologies on a circular economy. Lastly, the perspective of the selection process affected by other factors, such as the enterprise’s extroversion level and its internal structure, are discussed.
Nowadays, China’s food consumption structure is shifting from being survival-oriented to health-oriented. However, the food industry is still facing a research and development (R&D) dilemma. ...Scientific evaluation of an enterprise’s R&D performance can help to reduce the investment risk of R&D and promote economic benefits. This study implements the dynamic data envelopment analysis (DDEA) technique to measure and evaluate the level of R&D performance in the Chinese food manufacturing industry. Twenty-eight listed companies were selected for the study, considering the time period from 2019 to 2021. After constructing a system of inputs, outputs and carry-over indicators, overall and period efficiency scores were obtained. The results reveal that the overall level of R&D in the industry is relatively low (0.332). Average efficiency scores across years were estimated as 0.447, 0.460, 0.430 for 2019, 2020, and 2021, respectively. Lastly, this study considers the actual business situation of the industry and makes suggestions for improvement from the perspective of enterprises and the government; these anticipate aiding the food manufacturing industry to improve the performance management of R&D activities.
Safeguarding natural resources and energy is essential to ensure food security for future generations. Given the increase of published papers in the agricultural field applying Data Envelopment ...Analysis (DEA), this review seeks to address the special requirements of this methodology when implemented in the agricultural sector as well as to classify papers under sustainability aspects (economic, environmental, social). More specifically, 120 papers from Scopus and Web of Science databases were included in this review by using PRISMA methodology, and they were tested in the following groups (i) General information, (ii) DEA implementation, (iii) DEA extensions, (iv) Data type, (v) Data collection and processing, and (vi) Sustainability dimensions. Results indicate that there is a great need for weights use when performing DEA in the agricultural sector, to acquire results with greater explanatory power. Moreover, systematic data collection of multiple factors could lead to the implementation of complex methodologies, providing feasible solutions to the involved stakeholders. Lastly, the social aspect is the least represented dimension out of the three aspects of sustainability, indicating the need for the integration of social factors in such analyses, especially when DEA is used to create a policy framework in a specific area.
The kiwi is a highly insect-pollinated dependent crop and is the cornerstone of the Greek agricultural sector, rendering the country as the fourth biggest kiwi producer worldwide, with an expected ...increase in national production the following years. This extensive transformation of the Greek arable land to Kiwi monocultures in combination with a worldwide shortage of pollination services due to the wild pollinators' decline raises questions for the provision of pollination services, and consequently, for the sustainability of the sector. In many countries, this shortage of pollination services has been addressed by the installation of pollination services markets, such as those in the USA and France. Therefore, this study tries to identify the barriers towards the implementation of a pollination services market in Greek kiwi production systems by conducting two separate quantitative surveys, one for beekeepers and one for kiwi producers. The findings showed a strong basis for further collaboration between the two stakeholders, as both of them acknowledge the importance of pollination services. Moreover, the farmers' willingness to pay and the beekeepers' willingness to receive of the beekeepers regarding the renting of their hives for pollination services were examined.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
This paper seeks to underline the driving factors of farmers’ engagement in Participatory Research Projects (PRPs). This is a critical issue for formulating efficient and effective technology ...transfer channels, essential for improving the operational status of agricultural holdings. A survey was conducted on a sample of 326 Greek arable crops farmers. An explanatory framework consisting of three major factor categories and 11 variables was developed. A logistic regression analysis empirically tests the effect of the variables on the participation of farmers in PRP. Furthermore, the relative importance of variables and factors is extracted with the Shapley–Owen decomposition analysis. The results show that Farmers’ Willingness and Social Influences are the factors that mostly affect their decision to engage in a PRP. The farmers’ ability consisting of socioeconomic and demographic variables has a small effect on their decision-making process. The estimated effects can help decision-makers to shape and prioritize more targeted policies for farmers’ engagement in research. Additionally, this paper sets the basis for shifting research from simple estimations of the effect of variables on farmers’ decision-making, to a more comprehensive estimation that also accounts for the strength of these relationships. The paper fills a gap in the literature of studies on farmers’ decisions for participating in PRPs, by developing and testing an explanatory framework which also accounts for the relative importance of each factor/variable.
Raising Climate Change (CC) awareness is crucial for mitigating the negative effects of this phenomenon, as well as for ensuring a sustainable future for upcoming generations. This study assesses and ...compares the climate change awareness level of Mediterranean university students in France and Greece. Emphasis was given to three research questions regarding knowledge, information sources, and awareness level. A questionnaire was used to determine the knowledge level and awareness of 641 university students in France and Greece. Principal Component Analysis was used to determine the main factors influencing the above-mentioned aspects, as was an independent sample t-test to compare the sample's awareness level and socio-economic characteristics. Based on a modified climate change awareness index developed by the researchers, most students were found to have a low level of awareness of climate change. However, there is evidence that students are willing to undertake specific eco-friendly initiatives. Additionally, one of the study's key findings is that students' knowledge and awareness of climate change varies according to socio-demographic characteristics such as gender, age, income level, and academic field. Nationality was proved to be a significant factor influencing belief in CC and willingness to take action. Incentives for both formal and informal information should be provided by policymakers to ensure theoretical background as well as climate change-related actions, in order to raise climate change awareness across younger generations.
Many organisations are composed of multiple departments connected either in series or in parallel, which may be further decomposed into a number of functions arranged in a hierarchical structure. ...Several researchers have successfully used appropriate Data Envelopment Analysis (DEA) modelling techniques to assess complex structures. However, to our knowledge, no-one has yet examined the case of measuring and evaluating a parallel network structure combined with a hierarchical one. This paper discusses the development of a multi-function parallel system with embedded hierarchical network structures. A linear additive decomposition DEA model and a non-linear multiplicative aggregation DEA model are proposed as alternatives to evaluate the operating performance of such a structure. The system, the sub-systems, and the efficiencies of their internal units, as well as their relationships, are identified. The system efficiency of the additive model is shown to be greater than or equal to that of the multiplicative model. To verify the applicability of our proposed models, we consider a hypothetical example of the measurement and evaluation of the performances of several Business Schools across a number of universities. Other envisaged areas of application of our structure could include supporting the evaluation of the supply chain management of a firm, or the determination of the most desirable ship design considering maintenance issues.
•Developed a parallel system with embedded hierarchical network structures.•Introduced additive decomposition and multiplicative aggregation DEA models.•Several properties of such a system are analysed.•The newly developed models are applied to a higher education example.
Highlights: • AI in weed management potentials for transforming agricultural ecosystems. • AI influence in economic, social, technological, and environmental dimensions. • AI's role in enhancing food ...safety by reducing pesticides residues. • Digital literacy as a crucial enabler empowering stakeholders to use AI effectively.Abstract: In the face of increasing agricultural demands and environmental concerns, the effective management of weeds presents a pressing challenge in modern agriculture. Weeds not only compete with crops for resources but also pose threats to food safety and agricultural sustainability through the indiscriminate use of herbicides, which can lead to environmental contamination and herbicide-resistant weed populations. Artificial Intelligence (AI) has ushered in a paradigm shift in agriculture, particularly in the domain of weed management. AI's utilization in this domain extends beyond mere innovation, offering precise and eco-friendly solutions for the identification and control of weeds, thereby addressing critical agricultural challenges. This article aims to examine the application of AI in weed management in the context of weed detection and the increasing impact of deep learning techniques in the agricultural sector. Through an assessment of research articles, this study identifies critical factors influencing the adoption and implementation of AI in weed management. These criteria encompass factors of AI adoption (food safety, increased effectiveness, and eco-friendliness through herbicides reduction), AI implementation factors (capture technology, training datasets, AI models, and outcomes and accuracy), ancillary technologies (IoT, UAV, field robots, and herbicides), and the related impact of AI methods adoption (economic, social, technological, and environmental). Of the 5821 documents found, 99 full-text articles were assessed, and 68 were included in this study. The review highlights AI's role in enhancing food safety by reducing herbicide residues, increasing effectiveness in weed control strategies, and promoting eco-friendliness through judicious herbicide use. It underscores the importance of capture technology, training datasets, AI models, and accuracy metrics in AI implementation, emphasizing their synergy in revolutionizing weed management practices. Ancillary technologies, such as IoT, UAVs, field robots, and AI-enhanced herbicides, complement AI's capabilities, offering holistic and data-driven approaches to weed control. Additionally, the adoption of AI methods influences economic, social, technological, and environmental dimensions of agriculture. Last but not least, digital literacy emerges as a crucial enabler, empowering stakeholders to navigate AI technologies effectively and contribute to the sustainable transformation of weed management practices in agriculture.
PDF
