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Agricultural 4.0: Its Implementation Toward Future Sustainability
Abstract
Agriculture 4.0 refers to systems that employ drones, robotics, Internet of Things (IoT), vertical farms, artificial intelligence (AI), and solar energy. Through the integration of digital technology into farming practices, companies are able to increase yields, reduce costs, experience less crop damage, and minimize water, fuel, and fertilizer usage. For the consumer, this equals cheaper and better quality food. However, there are some of the complex challenges the crop production industry, along with diminishing production profit margins for farmers. To grow the food and to provide the world needs, crop production systems need innovative solutions to produce more in an environmentally, economically, and socially viable way. So, while the conceptual framework, intentions, and the scope revolving around Agriculture 4.0 are thought provoking and exciting at the first instance, its successful implementation is the main challenge in many countries all over the world.
... Artificial Intelligence (AI) in the context of anaerobic digestion can offer many benefits. AI-based systems, such as machine learning algorithms and artificial neural networks, can help to monitor and control the plant's equipment, such as its digesters and pumps, as well as the process's variables, such as temperature, pressure, and pH [6]. This helps to reduce the risk of a process failure and maximize the efficiency of the plant [7]. ...
... Internet of things (IoT) systems can also provide access to realtime data, allowing for fine-tuning processes [8]. While security, safety, and privacy issues, as well as ethical, moral, and legal debates surrounding the use of AI, it's potential to improve productivity, particularly in the energy sector, is undeniable [6]. Rural energy developers and clean energy practitioners are expected to benefit the most from the increased usage of AIbased and IoT systems. ...
... The potential of combining AI and IoT technology to improve performance and expand the use of agricultural and bioenergy systems is promising [6], [8], [11]. As such, AI and IoT systems are being adopted by biomethane producers to optimize operations and improve efficiency. ...
This study explored the use of artificial intelligence (AI) and the Internet of Things (IoT) to boost biogas production from organic waste using anaerobic digesters. An artificial neural network (ANN) genetic algorithm (GA) model predicted biogas yields and optimized production. Sensors and IoT technologies monitored factors affecting biogas production, achieving an efficiency of 78.2%. The ANN GA model showed strong accuracy, with a correlation coefficient of 0.85. AI and IoT can significantly increase biogas yield and efficiency, though challenges and limitations exist. This research highlights the potential of these technologies in biogas production.
... In order to keep up with the growing demand for food and the expanding population, farmers must enhance their productivity and performance [12]. To achieve this goal, they need to rely on new technologies based on the Agriculture 4.0 standard and adopt innovative techniques to optimize their livestock farms [13]. These technologies can enable the implementation of smart and efficient management strategies through realtime automatic monitoring [14] and the use of advanced techniques such as artificial intelligence. ...
The increasing demand for food has led to the adoption of precision livestock, which relies on information and communication technology to promote the best practices in meat production. By automating various aspects of the industry, precision livestock allows for increased productivity, more effective management strategies, and decision-making. The paper proposes a methodology that uses deep learning techniques to automatically classify cattle behavior using accelerometer sensors embedded in collars. The work aims to enhance the efficiency and productivity of the industry by improving the classification of cattle behaviors, which is essential for farmers and barn managers to make informed decisions. We tested three different classification techniques to classify rumination, movement, resting, feeding, salting and other cattle behaviors and we achieved promising results that can contribute to a better understanding and management of cattle behavior in the livestock industry.
... This could also be stated in terms of its applicability to the farm sector, taking as a starting point the discussion on the use of the Internet of Things (IoT) suggested by Soufi et al. [13]. These researchers presented the concept of smart farm exploring, in an incremental way and the sharing of data in so-called Agriculture 4.0, while Yahya [14] extends this same concept (A4.0) to the use of drones, robots, artificial intelligence (AI), and solar energy. Thus, the introduction of solar energy in a rural environment transforms it into a production unit that, in turn, is also a consumer, i.e., a "prosumer", capable of adapting its consumption according to the energy generated and buying or selling the surplus according to its specific needs [15]. ...
Rural farms constitute a vital component of a country’s agricultural landscape, traditionally reliant on energy installations known for their reliability yet notorious for their energy-intensive and inefficient characteristics. While the smart farm concept, integrating renewable energy sources and resource management technologies, has seen widespread adoption in domestic and industrial sectors, rural farms have been slower to embrace these innovations. This study presents a groundbreaking solution, deployed on a rural farm in Portugal, resulting in an impressive 83.24% reduction in energy consumption sourced from the grid. Notably, this achievement translates to a substantial reduction in CO2 emissions, aligning with the growing need for environmentally sustainable farming practices. The technical intricacies of this pioneering solution are comprehensively described and juxtaposed with other scientific case studies, offering valuable insights for replication. This initiative represents a vital first step towards the integration or combination of conventional farming with photovoltaic energy production, exemplified by agrivoltaic systems. In conclusion, this research showcases the potential for rural farms to significantly enhance energy efficiency and financial viability, thereby contributing to a more sustainable and cost-effective agricultural sector. These findings serve as a model for similar endeavors, paving the way for a greener and more economically viable future for rural farming practices.
... One of the application sectors in which AI can be a valuable tool (Spanaki et al., 2021;Yahya, 2018) is agriculture (Mhlanga, 2021). Looking at the studies analysing AI applications in Agri-food, it is possible to recognise an evolutionary path moving from specific applications to an all-encompassing approach (Rejeb et al., 2022). ...
Purpose
This study analyses the literature on artificial intelligence (AI) and its implications for the agri-food sector. This research aims to identify the current research streams, main methodologies used, findings and results delivered, gaps and future research directions.
Design/methodology/approach
This study relies on 69 published contributions in the field of AI in the agri-food sector. It begins with a bibliographic coupling to map and identify the current research streams and proceeds with a systematic literature review to examine the main topics and examine the main contributions.
Findings
Six clusters were identified: (1) AI adoption and benefits, (2) AI for efficiency and productivity, (3) AI for logistics and supply chain management, (4) AI for supporting decision making process for firms and consumers, (5) AI for risk mitigation and (6) AI marketing aspects. Then, the authors propose an interpretive framework composed of three main dimensions: (1) the two sides of AI: the “hard” side concerns the technology development and application while the “soft” side regards stakeholders' acceptance of the latter; (2) level of analysis: firm and inter-firm; (3) the impact of AI on value chain activities in the agri-food sector.
Originality/value
This study provides interpretive insights into the extant literature on AI in the agri-food sector, paving the way for future research and inspiring practitioners of different AI approaches in a traditionally low-tech sector.
... It is characterized by the use of technologies in agricultural production processes, production chains, and in the industrialization of products with the purpose of achieving increased efficiency in production (Rose & Chilvers, 2018;Raj, 2021). This paradigm proposes monitoring the entire production process and the use of more advanced technologies to make this possible (Yahya, 2018). ...
Faced with the challenges posed by the effects of climate change, a decrease in resources in agro-ecosystems and strengthening of the sustainability of agricultural communities, the Fourth Industrial Revolution puts the Internet of Things (IoT) at the service of the agricultural sector as an alternative solution to configure resilient production systems. This chapter presents an empirical study of an agricultural IoT framework deployed in a food production system to collect microclimate data in plantations, transmission through wireless networks, storage, and analysis of the dataset with computational algorithms to support farmers in data-driven decision processes. The agricultural IoT framework enables the configuration of flexible, data-driven agricultural production systems focused on productivity improvement, labor optimization, input use efficiency, and agro-ecosystem sustainability.
... Building resilient and sustainable farming systems is the ultimate goal of the agri-food production system. To that end, information and communication technology (ICT) is pervading the agri-food sector while passing through the Agriculture (or Agri-Food) 4.0 revolution [1]. In such a context, new words have been coined (such as smart farming or precision agriculture) as a result of the application of the Internet of Things (IoT) and machine learning (ML) algorithms. ...
This work aims at providing a concise review of various agri-food models that employ
fractional differential operators. In this context, various mathematical models based on fractional
differential equations have been used to describe a wide range of problems in agri-food. As a result of
this review, we found out that this new area of research is finding increased acceptance in recent years
and that some reports have employed fractional operators successfully in order to model real-world
data. Our results also show that the most commonly used differential operators in these problems are
the Caputo, the Caputo–Fabrizio, the Atangana–Baleanu, and the Riemann–Liouville derivatives.
Most of the authors in this field are predominantly from China and India.
... Along with the times, technological advances and agricultural machinery are increasingly expensive, making it increasingly difficult for farmers to get optimal harvest (Yahya, 2018). Therefore, this electric cultivator is a new breakthrough in increasing production output that is expected to help people in Indonesia, especially farmers. ...
Building or designing an electric cultivator requires a comprehensive study by paying attention to each main component, namely the chassis. The chassis serves as a place to attach the constituent components and holds the weight of the overall components contained in the tool. A good machine frame will increase the workability of the machine because the components that make up the cultivator are in the right layout. A good chassis design is needed to improve the performance of the electric cultivator. This study aims to design and simulate the strength analysis of the electric cultivator frame. This research consists of several stages, namely literature review, frame design, chassis strength simulation and chassis cultivator capability analysis. From the results of the analysis concluded that technically this tool is classified as safe with a loading condition of 18 kg and a support on its axis. However, it is still recommended that before production, the tool design must be re-optimized. Especially at the joints of the upper and lower frames near the pillow block. This is because this cross section is a critical area because it has a large equivalent stress value, has a small life cycle, and has a low safety factor. Keywords: Design, Electric cultivator, Simulation
... The technological revolution has reshaped the agricultural technology framework with a strong focus on AI (Spanaki et al., 2022). The transformation of this field into a modern and data-driven sector stemmed from the need to ensure food security for large populations and environmental sustainability (Yahya, 2018). The original industrial model of conventional agriculture was found to be unsustainable and flawed (Miranda et al., 2019). ...
Purpose
Agricultural technologies (agri-techs) have focused on developing the AI perspective of human-AI interaction rather than human perceptions and responses. A lack of understanding of their employees’ behavioral responses when interacting with advanced technologies can lead to unexpected problems in the future. Drawing on the theoretical perspective of advanced user engagement, this paper examines the impact of five different technostressors on user engagement and, consequently, user experience.
Design/methodology/approach
For data collection, 464 participants from the U.S. and Asian (Singaporean) agri-tech sectors were interviewed via an electronic survey.
Findings
The U.S. study showed that techno-overload, techno-complexity, and techno-uncertainty were positively related to user engagement (t = 2.609; t = 6.998, and t = 6.013, respectively), whereas techno-invasion and techno-uncertainty were negatively correlated with user engagement (t = –2.167 and t = –3.119, respectively). The Singapore study showed that techno-overload, techno-complexity, and techno-invasion were negatively related to user engagement (t = –2.185, t = –2.765; t = –5.062, respectively), while techno-insecurity and techno-uncertainty surprisingly showed nonlinear correlations with user engagement. In both studies, user engagement is positively related to user experience (t = 2.009 for the U.S. study and t = 2.887 for the Singapore study).
Originality/value
First, this paper provides agri-techs with a modern framework to better predict the behavioral responses of their employees when managing AI. Second, this paper expands the equation of change in the discipline of change management by introducing the dimension of readiness.
... To meet the rising demand for date fruit in global markets, the producers face many challenges, including the necessity to have enough high-quality yields to attain self-sufficiency and to improve their export capacity. Moreover, the date palm industry must be emerged to meet the agriculture 4.0 standards which is characterized with the automatization of processes, adaptation to climate changes and the reasonable allocation of resources [8]. ...
Date palm cultivation is considered one of the most important levers in the economy of many countries, especially in North Africa and the Middle East. This tree and the fruits it produces are of great importance in terms of their nutritional and medicinal value, as well as their uses in some biochemical applications. In the last decade, artificial intelligence and its applications in the field of precision agriculture constituted a fertile field for research. Consequently, the date palm sector is emerging with new and modern technologies to satisfy global sustainability standards. This article provides an overview of studies on the application of artificial intelligence (AI) in the date palm agriculture industry throughout the past decade (2012--2021). After applying exclusion criteria, the scoping review was constructed to answer four (4) predetermined research questions by analyzing 43 publications. Based on the defined research questions, the analysis of the examined literature included the yearly and geographical distribution of papers, the most widely adopted algorithms, and the research trends in the field of AI applications for date palm cultivation and production. Investigations have indicated that AI is underutilized in certain applications such as yield estimation and diseases and pest control and management. Nonetheless, intelligent systems using machine vision and artificial intelligence are evolving to improve the date palm agricultural industry. This article explains the importance of artificial intelligence in the date palm agricultural industry and presents insight for future study in this field.
... Farmers can learn and gain knowledge about their crops and yields with the help of AgriTech, and also AgriTech assists to increase the rate of production (Pham & Stack, 2018). Along with human intelligence, AI can identify the methods to improve farming land condition and change it which in turn influences to achieve sustainability (Yahya, 2018). The rate of production, resource allocation, and proactive action can be achieved by applying AgriTech (Fountas et al. 2015). ...
... Perkotaan yang jauh dari sumber bahan baku pangan juga menjadi alasan pentingnya perlu dikembangkan pertanian cerdas di perkotaan. Terdapat sebuah inovasi pertanian yang memanfaatkan teknologi penanaman dapat dilakukan dengan berbagai cara seperti vertical farming (menggunakan media pot/paralon yang disusun secara vertical), polibag, hidroponik, aeroponik, tabu lampot dan lain-lain(Krishnan et al., 2020; Vertical Farming : Karna Yang Horizontal Sudah Terlalu Mainstream -Kmd Tp Uh, n.d.;Yahya, 2018). ...
Pembangunan perumahan di perkotaan yang sangat pesat, cenderung tidak mempertimbangkan faktor konservasi lingkungan dengan memberikan sumbangsih ruang terbuka hijau yang terbatas. Pekarangan merupakan lahan yang potensial untuk dikembangkan menjadi lahan pertanian produktif terutama untuk pemenuhan kebutuhan pangan yang bergizi bagi si pemilik. Keterbatasan lahan bukanlah hal yang menjadi hambatan untuk mengaktualisasi potensi nilai ekonomi. Lahan di lingkungan perumahan yang cenderung terbatas dapat dioptimalkan untuk ditanami tanaman yang memiliki nilai ekonomi tinggi seperti tanaman pangan, tanaman hias, tanaman obat dan tanaman penyuplai oksigen dalam jumlah besar. Salah satu model optimalisasi lahan terbatas untuk mendukung pertanian sederhana di lingkungan perumahan yaitu model smart vertical farming. Metode pelaksanaan kegiatan antara lain dengan memberikan pelatihan teknik vertical farming, praktik budidaya vertical farming, pembuatan model smart vertical farming dan cara melakukan perawatan. Paparan pelatihan dan praktik disampaikan oleh narasumber dari bidang agribisnis, sedangkan pemodelan smart vertical farming disampaikan oleh narasumber dari bidang teknik informatika. Opini yang berkembang di masyarakat bahwa menerapkan konsep vertical farming adalah sesuatu yang mahal dan rumit, nyatanya tidak semuanya benar. Dengan bermodalkan bibit tanaman, paralon atau botol bekas, maka masyarakat dapat membuat model pertanian vertikal. Model tersebut akan menjadi lebih efisien apabila menerapkan konsep pertanian cerdas berbasis sensor dalam pemantauan dan pengelolaan tanaman. Hasil dari kegiatan baik pelatihan, praktik, maupun pembuatan model smart vertical farming sangat diterima dengan baik oleh masyarakat.
... Production scales differ from mass-production to small-scale-systems for private households. Innovative technologies are at the center of this development, UVlamps (LED or mercury) and systems for Controlled-Environment Agriculture combined with a close monitoring of the plant's growth process [96][97][98]. ...
Western Europe’s agrifood systems are highly developed, extremely complex, and dependably produce food for billions. Securing their functionality is imperative whilst dealing with varieties of major challenges and opportunities in the future. Multiple stakeholders are involved in system transitions; therefore, synthesizing views from different scientific disciplines is essential for a robust trend analysis. Through workshops with a variety of experts, extensive research, followed by close monitoring over 5 years, we identified trends that will influence the shape of the evolving agrifood systems. Based on this, we determined which trends need addressing by agrifood research to secure the system’s future functioning. We detected nine trends with 50 sub-topics that will shape the future of Western European agrifood systems, of which 5 are classified as macro- and 4 as micro-trends. Our second objective was to improve the efforts of the stakeholders in- and outside of the agrifood area to secure functioning and further improvement through giving a comprehensive overview. This contributes to enhanced strategies for sustainable and resilient agrifood systems that produce sufficient affordable nutritious food for a planetary health diet, and hence, supporting successful implementation of selected goals from the 2030 Agenda for Sustainable Development and the European Green Deal.
... According to the UN Food and Agriculture Organization, by 2050, the world would need to increase the production of food by 70% and this is because the high increase in population, dwindling agricultural areas, and the depletion of scarce natural resources [23]. As a result of decreasing yields in various main crops and decreasing supplies of natural resources such as fresh water and arable land, the situation has become worse. ...
... Uncertainty and risk are on the rise, scarce resources and their cost, climate change, and fluctuating market conditions present a bottleneck that limits decision making for both farmers and different stakeholders i.e., policymakers (Lammers et al, 2018;Meike, 2019;Arvanitis et al., 2020;Hrusteck, 2020). The ever-changing trends have resulted in a new era in which digital technologies achieve more than human capabilities, even in activities that were previously unattainable through automation, such as those activities that involve pattern recognition in changing or uncertain environments (Yahya, 2018). Therefore, digital transformation becomes inevitable for any business that has growth, expansion, quality, and sustainability as its core and central purpose. ...
With the ever-increasing dilemma experienced by the agricultural sector, due to the ever-increasing and continuous demand for food, feed, and fiber amidst population growth and declining conditions that favor agricultural productivity, there is a need to develop means of producing agricultural products more sustainably through highly efficient and effective models. Digital technologies in the agriculture sector, present a ray of hope with tools
such as Geographical Information System (GIS), Remote Sensing (RS), Artificial Intelligence (AI), Precision Agriculture (PA), etc. The incorporation of digital technologies in agriculture may help solve the problem of global warming and the ever-increasing population associated with declining levels of natural resources. Hence, digital agriculture has the potential to revolutionize sustainable crop production substantially, to achieve the goal of ‘zero hunger’. Therefore, the present review highlights some of the digital technology tools and their role in sustainable crop production if utilized efficiently.
... Battery-free sensor nodes are equipped with energy harvesting devices, which can obtain energy from the surrounding environment for power supply, such as wind energy [4], solar energy [5], radio frequency signals [6], etc., while eliminating the demand to replace batteries. The potential application fields of battery-free WSNs are very wide, such as agricultural environmental monitoring [7], natural disaster prevention [8], smart manufacturing [9], and so on, especially in the application scenarios where the batteries of sensors are not easy to replace. However, the characteristics of energy harvesting and storage also bring greater challenges to wireless communication in battery-free WSNs. ...
The emergence of battery-free wireless sensor networks (benefiting from the ability to collect energy from the surroundings) has broken through the energy and lifetime limitations of traditional wireless sensor network systems, but also brings challenges to the sharing of network resources. In the multi-channel wireless communication environment, in particular, how to coordinate the communication time and occupied channels of a large number of sensor nodes from the perspective of optimizing the global network has become a research problem that must be solved. To reduce the transmission delay and the usage of wireless channels, a new multi-channel data aggregation scheduling method based on the chaotic firework algorithm is proposed in this paper. With the help of the generation function of feasible solutions, one scheduling set and a firework individual can be rapidly converted to each other. By the operations of firework explosions, the Gaussian mutation, and chaotic exploration, a sub-optimal scheduling set could be found during an acceptable time period. Finally, simulation results show that the new scheduling method has advantages in aggregation delay and occupied channels when compared with existing methods.
... Therefore, an effort should be made to improve the use efficiency of water and energy for food production. To tackle these serious problems, the plan of "Agriculture 4.0" was launched in 2015 (Chuang, Wang, & Liou, 2020;Yahya, 2018). The development of this plan is urgent and necessary because crop growth and yield is severely disturbed by natural disasters such as typhoons every year. ...
Precision agriculture control systems count on reliable and accurate microclimate forecasts to maintain environmental suitability for crop growth. However, IoT devices adopted to monitor microclimate are expensive to people in developing countries. This study proposed a hybrid deep learning model (ConvLSTM*CNN-BP) without using IoT data to produce accurate multi-horizon and multi-factor (greenhouse internal temperature, relative humidity, and photosynthetically active radiation) forecasts simultaneously. The proposed model fused a convolutional-based long short term memory neural network (ConvLSTM), a convolutional neural network (CNN), and a backpropagation neural network (BPNN). Model construction involved an ensemble of gridded 6-hour-ahead meteorological forecasts from the STMAS-WRF model and 3-hour-ahead greenhouse internal temperature simulated by a physically-based model at a 10-min scale. Another deep learning model (CNN*LSTM*Stacked LSTM-BP) using IoT data was established for comparison purpose. The experimental results on two greenhouses located in Central Taiwan indicated that the proposed model (non-IoT) and the benchmark model (with IoT) produced similar forecast performances on greenhouse internal temperature, relative humidity, and photosynthetically active radiation. Moreover, the proposed model with illustrious abilities of noise removal and feature extraction could provide satisfactory forecast accuracy. The proposed deep learning approach hits a milestone in multi-horizon and multi-factor forecasting on microclimate, which significantly supports farmers, especially in developing countries, in reducing the installation and maintenance costs of IoT devices for monitoring purpose.
... Agriculture 4.0 refers to systems that employ digital technologies, such as drones, robotics, Internet of Things, and artificial intelligence [11]. Specifically, drone applications are expected to become more advanced and reliable, and affordable [12]. ...
... According to some researchers, the agriculture industry is suffering from an insufficient application of responsible innovation (Rose and Chilvers, 2018). As a result, the researchers are calling for a system referred to as Responsible Agriculture 4, which incorporates drones, IoT, robotics, vertical farms, AI, and solar and wind power linked to microgrids (Davies and Garrett, 2018;Mustapha et al., 2021;Yahya, 2018). When it comes to the productivity of agriculture, factors such as the cost of energy for cultivation are equally significant (Ceylan, 2020). ...
Parallel to the rising debates over sustainable energy and artificial intelligence solutions, the world is currently discussing the ethics of artificial intelligence and its possible negative effects on society and the environment. In these arguments, sustainable AI is proposed, which aims at advancing the pathway toward sustainability, such as sustainable energy. In this paper, we offered a novel contextual topic modeling combining LDA, BERT and Clustering. We then combined these computational analyses with content analysis of related scientific publications to identify the main scholarly topics, sub-themes and cross-topic themes within scientific research on sustainable AI in energy. Our research identified eight dominant topics including sustainable buildings, AI-based DSSs for urban water management, climate artificial intelligence, Agriculture 4, convergence of AI with IoT, AI-based evaluation of renewable technologies, smart campus and engineering education and AI-based optimization. We then recommended 14 potential future research strands based on the observed theoretical gaps. In addition to its theoretical contribution to scientific research on sustainable artificial intelligence in energy management, the research utilizes a novel topic modeling method in exploring scientific texts and identifying challenges and possible solutions. A variety of solutions was incorporated, including huggingface tool or elbow method, to address these challenges.
... Thus, an intelligent energy management system must include three main elements: (i) internal communication network, (ii) intelligent control systems and (iii) local automation. A smart farm [6,7] must include a central controller, a set of equipment with the capability of remote control and monitoring interface. Communication between users and devices can be made via the internet or mobile phone [8,9]. ...
Rural farms are typically energy-intensive facilities with relatively low energy efficiency. In this sector, the introduction of renewable energies and integrated resource management technologies has been slower than in the domestic and industrial sector. The introduction of renewable energy sources was an important step in the past, but they are currently insufficient, as they do not allow for adequate energy management. The development of new solutions with integrated energy control is especially attractive for these installations as they present the least limitations in terms of space and adaptation to new technologies.
This work describes a solution that was developed and implemented in a farm located in central Portugal. The results show that 83.2% reduction in energy from the grid can be achieved, with 5527 kg CO2 savings, and the return on investment (of € 32,434) is about 8 years. However, this period can be shortened if evolutionary options are taken, such as upgrading to electric driven agricultural equipment.
... However, we believe that the broader benefits for adaptive management when reproducing the impacts of restoration practices at the field level outweigh the costs associated with these models . The advent of digital transformation of agricultural and conservation practices generates more agricultural data from farmers, and thus the likelihood to bring this framework to fruition is higher than ever before (Yahya, 2018). ...
The present study aims to provide guidelines with respect to the implementation of a watershed modelling framework that can support adaptive management in the Canadian side of the Lake Erie basin. Our primary objective is to highlight the establishment of a multimodel ensemble as an appealing strategy that can address a wide range of conceptual and operational uncertainties typically underlying any modelling exercise. We identify three ensemble strategies that have different implications for the granularity of the analysis, empirical knowledge and data input demands to constrain the individual models, and required timeframe to operationalize them. The first option partly resembles the watershed modelling work in the Maumee River watershed, where the characterization of the watershed attributes and functioning that modulate nutrient loading will be based on multiple independent SWAT applications with different process characterizations. Before implementing SWAT, we propose the use of SPARROW —a data-driven model— to delineate hot-spots, examine landscape predictors for nutrient mobilization and retention, and use these results to inform the SWAT models. Alongside the SPARROW and SWAT applications, the second option considers two additional models (HYPE, HSPF) that are conceptually sound, have been applied in the area, and can increase the structural and functional diversity of the ensemble of catchment models by offering alternative representations of fundamental processes/functions associated with the water cycle, soil erosion, biogeochemical cycles, and soil-plant interactions. The third option introduces local (farm, urban or natural vegetation) and edge-of-field monitoring and modelling (EPIC or APEX tools) to refine the spatial resolution and design in-field conservation practices that mitigate nonpoint source pollution from agriculture and other sources. Our study also pinpoints data monitoring needs to elucidate critical unknowns of the watershed functioning, such as the role of legacy phosphorus (P), the causes and consequences of the increasing long-term trends in dissolved reactive P loading, the challenges in reproducing spring-freshet or event-flow conditions, and the dynamic characterization of water/nutrient cycles under the non-stationarity of a changing climate. The strong linkages between environmental processes and management decisions that occur along the watershed-lake continuum require the presence of a holistic model ensemble to provide rigorous BMP efficiency assessment and simulation of the human-environment system responses to current and future watershed nutrient loads, land uses, climatic forcing, and socioeconomic conditions. The proposed model ensemble framework can be used as an integrative tool for ecosystem policy analysis and design of proper management action plans in Lake Erie and elsewhere.
... According to some researchers, the agriculture industry is suffering from an insufficient application of responsible innovation [75]. As a result, the researchers are calling for a system referred to as Responsible Agriculture 4.0, which incorporates drones, IoT, robotics, vertical farms, AI, and solar and wind power linked to microgrids [76][77][78]. When it comes to the productivity of agriculture, factors such as the cost of energy for cultivation are equally significant [79]. ...
Parallel to the rising debates over sustainable energy and artificial intelligence solutions, the world is currently discussing the ethics of artificial intelligence and its possible negative effects on society and the environment. In these arguments, sustainable AI is proposed, which aims at advancing the pathway toward sustainability, such as sustainable energy. In this paper, we offered a novel contextual topic modeling combining LDA, BERT, and Clustering. We then combined these computational analyses with content analysis of related scientific publications to identify the main scholarly topics, sub-themes, and cross-topic themes within scientific research on sustainable AI in energy. Our research identified eight dominant topics including sustainable buildings, AI-based DSSs for urban water management, climate artificial intelligence, Agriculture 4, the convergence of AI with IoT, AI-based evaluation of renewable technologies, smart campus and engineering education, and AI-based optimization. We then recommended 14 potential future research strands based on the observed theoretical gaps. Theoretically, this analysis contributes to the existing literature on sustainable AI and sustainable energy, and practically, it intends to act as a general guide for energy engineers and scientists, AI scientists, and social scientists to widen their knowledge of sustainability in AI and energy convergence research.
... Lezoche et al. [107], Yahya [108], and Zambon et al. [109] discussed the challenges faced by agriculture along the farming supply chain in order to implement operative guidelines coming from Industry 4.0. Specifically, the impacts deriving from each Industry 4.0 pillar were analyzed [58]. ...
Global population growth, climate change, and food safety are some of the challenges that the agri-food sector is current facing. Addressing these in a sustainable way requires the adoption of sustainable-oriented innovations capable of developing new knowledge and reshaping production methods and business models. Digitalization and technological evolution in the agri-food sector, culminating within Agriculture 4.0 (A4.0), represent the latest revolution, as testified by the extant body of knowledge. The lack of a systematic model to map the body of knowledge in A4.0 and how it addresses the agri-food sustainability challenges represents the input of our review. We propose a systematic literature review to discover the research routes in A4.0, increasing the understanding of this paradigm as agri-food sustainability enabler. A taxonomy, composed of 566 terms grouped in seven categories representative of the extant research routes, is proposed: the names assumed by agri-food digitalization process, the main aims and issues of A4.0, the features of the agricultural sector, the parameters monitorable and calculable through A4.0, data, and technologies. It emerged that A4.0 absolve the role of enabler in sustainability agri-food providing to the company a set of data and technological sustainable oriented innovations able to solve, according to an interdisciplinary approach, environmental, social, and economic challenges. The findings identify research gaps and theoretical and practical implications for a wide range of stakeholders, such as engineering managers, agri-food companies, researchers, or companies involved in the web content analysis. Limitations and future research route complete the study.
... The introduction of Information Communication Technologies (ICT) have provided new ways, and techniques of disseminating information among people in the society and has led to developments in all sectors of Nigerian economy. Mobile phones as part of the inventions of ICT, has brought significant changes in the agriculture sector [1]. The use of mobile phones by farmers has helped them in getting recent information on ways to improve their agricultural techniques from crop plantation to marketing of farm produce to the consumers. ...
Effective market information systems help to reduce information asymmetries , increase competitiveness, and improve efficiency in the marketing network. Thus, lack of dissemination of market information and the bargaining capability of the traders across the agricultural supply chain is a major concern among small holder farmers in Nigeria. The advent of mobile phones serves as a great tool for awareness and information dissemination to people. A Mobile Based Market Information System is presented in this study. It serves as a means of enhancing farmers marketing strategy by providing market visibility for transacting business both within and outside the region. The proposed system employs Google map API which shows the location of the registered markets. The system was implemented using JavaScript, PHP/MySQL and Phonegap/Cordova. XAMMP database server was used for data management. The application was tested and validated by 50 respondents, the results returned high acceptance rate, high perception on usage and ease of use.
... However, most typically 'Agriculture 4.0′ is understood as the "evolution in agriculture and agricultural engineering from Precision Farming to connected, knowledge-based farm production systems" (CEMA 2017). Extending, therefore, the concept of Industry 4.0 to the agricultural sector, we note that "Digital Farming is structurally similar to the concept of Industry 4.0" (Yahya 2018). Nevertheless, the critical distinguishing parameters of the agricultural production processes are quite different from the more typical industrial processes because agriculture is heavily determined by natural and biological factors, and therefore the 'physical' artefact itself should be taken under careful consideration. ...
This chapter presents the challenges of agricultural 4.0 supply chains which are identified as being sector heterogeneity, farm size, validation and collaborationcollaboration, safety and security, investment costs, and design and compatibility.
... However, most typically 'Agriculture 4.0′ is understood as the "evolution in agriculture and agricultural engineering from Precision Farming to connected, knowledge-based farm production systems" (CEMA 2017). Extending, therefore, the concept of Industry 4.0 to the agricultural sector, we note that "Digital Farming is structurally similar to the concept of Industry 4.0" (Yahya 2018). Nevertheless, the critical distinguishing parameters of the agricultural production processes are quite different from the more typical industrial processes because agriculture is heavily determined by natural and biological factors, and therefore the 'physical' artefact itself should be taken under careful consideration. ...
This chapter discusses the sustainabilitysustainability aspect of agricultural 4.0 supply chains. It starts with the definitions of sustainability, triple bottom linetriple bottom line, and ASC sustainability. The chapter concludes with a discussion of the concept of sustainability performance and an explanation of its importance in relation to agriculture 4.0agriculture 4.0. The efficient and effective use of agriculture 4.0 applications could lead to increased sustainability and increase sustainability performance therefore this aspect is important to be understood.
... However, most typically 'Agriculture 4.0′ is understood as the "evolution in agriculture and agricultural engineering from Precision Farming to connected, knowledge-based farm production systems" (CEMA 2017). Extending, therefore, the concept of Industry 4.0 to the agricultural sector, we note that "Digital Farming is structurally similar to the concept of Industry 4.0" (Yahya 2018). Nevertheless, the critical distinguishing parameters of the agricultural production processes are quite different from the more typical industrial processes because agriculture is heavily determined by natural and biological factors, and therefore the 'physical' artefact itself should be taken under careful consideration. ...
This chapter starts with an introduction of the agricultural supply chains and then the need for value addition is explained. The chapter aims to introduce the reader to the key issues that ASCs are facing in relation to food loss, food safety, food insecurity and accessibility, increased demand for food, decreasing natural resources, raw materials scarcity, and global food crisis. By discussing these issues areas of improvement emerge which highlight the need for technological solutions such as agriculture 4.0.
... However, most typically 'Agriculture 4.0′ is understood as the "evolution in agriculture and agricultural engineering from Precision Farming to connected, knowledge-based farm production systems" (CEMA 2017). Extending, therefore, the concept of Industry 4.0 to the agricultural sector, we note that "Digital Farming is structurally similar to the concept of Industry 4.0" (Yahya 2018). Nevertheless, the critical distinguishing parameters of the agricultural production processes are quite different from the more typical industrial processes because agriculture is heavily determined by natural and biological factors, and therefore the 'physical' artefact itself should be taken under careful consideration. ...
In this chapter the opportunities of agricultural 4.0 supply chains are presented. These include real-time data analysis and decrease of operational costs, increase in revenue and production flexibility, improvement in sustainabilitysustainability and enables circular economy, enhanced reliability and uptime, self-optimisation and quality of service, and improved infrastructure.
... However, most typically 'Agriculture 4.0′ is understood as the "evolution in agriculture and agricultural engineering from Precision Farming to connected, knowledge-based farm production systems" (CEMA 2017). Extending, therefore, the concept of Industry 4.0 to the agricultural sector, we note that "Digital Farming is structurally similar to the concept of Industry 4.0" (Yahya 2018). Nevertheless, the critical distinguishing parameters of the agricultural production processes are quite different from the more typical industrial processes because agriculture is heavily determined by natural and biological factors, and therefore the 'physical' artefact itself should be taken under careful consideration. ...
This chapters starts through the explanation of the industry 4.0 concepts, its evolution, and its different technological applications. As part of Industry 4.0 a wide range of technological applications are discussed which are: big data analytics, autonomous robots, simulation, horizontal and vertical system integration, IoT, the cloud, additive manufacturing, augmented reality. Then the concept of agriculture 4.0 and its emergence are introduced, and this is followed by the evolution of agricultural technologies.
... However, most typically 'Agriculture 4.0′ is understood as the "evolution in agriculture and agricultural engineering from Precision Farming to connected, knowledge-based farm production systems" (CEMA 2017). Extending, therefore, the concept of Industry 4.0 to the agricultural sector, we note that "Digital Farming is structurally similar to the concept of Industry 4.0" (Yahya 2018). Nevertheless, the critical distinguishing parameters of the agricultural production processes are quite different from the more typical industrial processes because agriculture is heavily determined by natural and biological factors, and therefore the 'physical' artefact itself should be taken under careful consideration. ...
This chapter discusses the different technological applications of Agricultural Supply Chains 4.0. By doing so a wide range to technological applications are explained which are: smart farming applications, smart devices and platforms, IoT, temperature control applications, blockchain applications, tracking and tracing technologies, autonomous land farming robots, autonomous aerial farming robots and smart monitors.
... However, most typically 'Agriculture 4.0′ is understood as the "evolution in agriculture and agricultural engineering from Precision Farming to connected, knowledge-based farm production systems" (CEMA 2017). Extending, therefore, the concept of Industry 4.0 to the agricultural sector, we note that "Digital Farming is structurally similar to the concept of Industry 4.0" (Yahya 2018). Nevertheless, the critical distinguishing parameters of the agricultural production processes are quite different from the more typical industrial processes because agriculture is heavily determined by natural and biological factors, and therefore the 'physical' artefact itself should be taken under careful consideration. ...
This chapter starts with the definition of circular economy then it discusses the need for circular economy in ASCs. Then the relationship between corporate social responsibility, circular economy, agriculture 4.0 and sustainability are discussed. In relation to that the principles of circular economy are explained. The chapter concludes with a discussion about circular economy and agriculture 4.0.
... However, most typically 'Agriculture 4.0′ is understood as the "evolution in agriculture and agricultural engineering from Precision Farming to connected, knowledge-based farm production systems" (CEMA 2017). Extending, therefore, the concept of Industry 4.0 to the agricultural sector, we note that "Digital Farming is structurally similar to the concept of Industry 4.0" (Yahya 2018). Nevertheless, the critical distinguishing parameters of the agricultural production processes are quite different from the more typical industrial processes because agriculture is heavily determined by natural and biological factors, and therefore the 'physical' artefact itself should be taken under careful consideration. ...
This chapter discusses the data sharing issues in ASCs and how the agriculture 4.0 applications have transformed the operations of the ASC entities. The latter are discussed in relation to all the different ASC entities which are farmers, processors, distributors, retailers, and consumers.
... Rural areas and agriculture are facing many challenges; globalization, climate change, other environmental threats to name a few, are making imperative the need to build more resilient futures and more sustainable farms in economically, socially and environmentally (Scaramuzzi et al., 2020). Sustainable agriculture and investing in innovations, like Agriculture 4.0 (Dung and Hiep, 2017) may result to increased yields and reduction of inputs, better environmental footprint and overall better economic figures driven by data and analytics (Yahya, 2018). To this end Agriculture 4.0 (in the early 2010s) has brought the development of various technologies and a new promotion of Precision Agriculture (PA) (Kovács and Husti, 2018). ...
The main aim of this paper is narrowing the innovation divide on entrepreneurship and the effective application of sustainable Precision Agriculture (PA). For this purpose, the Business Model Canvas (BMC) analysis was carried out for farms belonging to different crop sectors in Greece. The goal is to present how the use of BMC methodology analyses the entrepreneurial formula of farms that have successfully implemented PA technologies. Furthermore, it can be a new tool towards "Agriculture 4.0" to support companies that could invest in PA in order to grow economically and, at the same time, decrease the environmental impact. To succeed this, 5 BMC analyses were performed and SWOT analysis to investigate barriers, drivers, benefits, and impact, according to the entrepreneur's perception, towards the implementation of PA technologies. Finally, there was evaluation research of the BMC experience from both the interviewed farmers' and interviewers' point of view. The results of this analysis showed that the samples of the five cases have a common attitude regarding PA. On the one hand, the barriers highlighted where the lack of knowledge, lack of support systems and high investment costs. On the other hand, the benefits where higher quality productivity and profitability and lower environmental and control costs. As for the drivers and the expected impact, all farmers agreed on the development of a more environmentally friendly sustainable agriculture with reduced costs. Finally, this paper clarifies how the "human factor" is the element key to motivate farmers to adopt PA practices.
Artificial Intelligence (AI) plays a vital role in the agriculture sector. Its use in the agriculture industry to improve farming practices has increased over time. The uniqueness of AI in agriculture is its potential to transform conventional agricultural practices, opening the doors to greater productivity, sustainability, and, ultimately, a more secure global food supply. However, there are obstacles that limit the application of AI in this industry. Through a well-organized literature review, the study identified nine barriers that hinder the implementation of AI. To finalize the barriers for further investigation, the Delphi approach was employed. The barriers were analysed through modified total interpretive structural modelling (m-TISM) technique and categorized into 4 clusters using the Matrice d’impacts croisés multiplication appliquée á un classment (MICMAC) analysis. Lack of skilled workforce and extreme climatic conditions are major driving barriers that prevent effective AI adoption. Based on the findings, the study puts forward three propositions. Timely action on the recommendation can help mitigate the concerns and benefit the stakeholders in the agriculture sector.
Chapter 9
Adoption of Climate Smart Agriculture (CSA) Technologies in Sri Lanka: Scope, Present Status, Problems, Prospects, Policy Issues, and Strategies
El capítulo parte de un recorrido histórico sobre los acontecimientos
de la Biotecnología Ambiental y se discuten varios conceptos esenciales. Se
mencionan varias aplicaciones, tales como: biomarcadores, biosensores,
biocombustibles, biorremediación, biotransformación. Además, se describe las
principales herramientas biotecnológicas aplicadas al monitoreo de la
contaminación ambiental y a procesos de biorremediación, de las cuales se
toman en cuenta las consideraciones técnicas, las estrategias de recuperación y
mitigación. Se destaca una de las herramientas de biorremediación como es la
fitorremediación, que abarca: fitoextracción, rizofiltracción, fitoestabilización,
rizodegradación, fitodegradación y fitovolitización. Asimismo, se proyectan las
investigaciones y los proyectos más destacados en el contexto de América
Latina. Además, se da a conocer algunas herramientas biotecnológicas para
promover la agricultura sustentable con el uso de microorganismos como control
biológico, y uso de biofertilizantes. Por último, se presentan aplicaciones para la
mitigación del impacto ambiental generado por la actividad petrolera como las
técnicas de inyección de CO2 en reservorios subsuperficiales. También se
mencionan las aplicaciones en la producción de nuevas fuentes de energía
sustentable y tendencias biotecnológicas en el desarrollo de biomateriales
alternativas al cuero y al plástico, en el que se detalla el procesamiento de
micelios para la industria textil. En conclusión, esta guía sirve de apoyo para los
estudiantes que estén interesados en conocer la asignatura de Biotecnología
Ambiental o que estén cursando dicha asignatura
NIL
Food production must be greatly increased to support the world population, which is expected to reach 9.7 billion by 2050. As a result, agriculture must be modernized and improved in order to significantly increase food productivity; unless otherwise, the 2nd United Nations Sustainable Development Goal, Zero Hunger, will be difficult to achieve. In addition to this, humans are also dealing with issues such as diminishing natural resources and lands, global warming, unpredictable weather patterns and the effects of coronavirus disease. The COVID-19 pandemic had a significant impact on agricultural food productivity, making food security a major concern around the world. Thus, the purpose of this work is to explore the possibility to enhance the existing practices in agricultural sector through the mechanization, automation and the application of advanced technologies, particularly the research works from Malaysian Agricultural Research and Development Institute (MARDI) into the new era of Agriculture 4.0, which are adopting the Industry 4.0 (IR4.0) technologies to reduce the dependency of the labor force and the period of production and increase the productivity and food quality.
Seaweeds are important component in the marine ecosystem. In the global scenario, about 221 species are having commercial utility but only 10 species are being commercially cultivated and has a market value of 11.7 billion US$. Among the 10 species, Eucheuma sp. (35%), Laminaria japonica (27%), Gracilaria sp. (13%), Undaria pinnadifida (8%), Kappaphycus alvarezii (6%), and Porphyra sp. (4%), have a major share in global seaweed biomass production. Seaweeds are the only resources for commercially important phycocolloids such as agar, carrageenan, and alginic acid production. In 2015, seaweed’s phycocolloids production was 93,035 tons wt and had a market value of 1058 million US$. Hectare level cultivation of K. alvarezii (carrageenan yielding seaweeds) can sequester 643.80 tons CO2/ha/yr, whereas Gracilaria edulis and Gracilaria debilis (agar yielding seaweeds) can sequester 10.71 tons CO2/ha/yr. Seaweeds are an excellent biosorbent for the removal of heavy metal ions. Seaweed biochar, an effective adsorbent for wastewater treatment systems. For bioremediation of eutrophicated water, green seaweeds Ulva sp., Cladophora coelothrix, and Cladophora parriaudii; red seaweeds Porphyra sp. and Gracilaria sp. are used. Seaweed has high protein content as it is being used by many of the countries like Japan, China, Korea, Malaysia, Thailand, Indonesia, Philippines, and other South East Asia. Seaweeds like Ulva sp., Enteromorpha sp., Caulerpa sp., Codium sp., Monostroma sp., Sargassum sp., Hydroclathrus sp., Laminaria sp., Undaria sp., Macrocystis sp., Porphyra sp., Gracilaria sp., Eucheuma sp., Laurencia sp., and Acanthophora sp. are used in the preparation of soup, salad and curry, salad vegetable or as garnish material for fish. Ascophyllum, Ecklonia, and Fucus are the general species sold as soil additives and functioned as both fertilizer and soil conditioner. Red seaweeds K. alvarezii, G. edulis, a green seaweed Caulerpa spp. Ulva spp., etc., have been commercially exploited for biostimulant production and increase in crop yield was found in the range of 8–25% over control.
In this work, we will study the 1D transport of contaminants in a saturated porous medium which can be presented by different phenomena, such as advection, diffusion, and reaction. The system of the three equations linked together is considered a difficult system to solve since each equation has its stability and convergence condition. Therefore, our objective is to develop a new strategy that will allow us to solve this kind of problem and obtain more effective results, to compare the two procedures of this approach method and specify the best procedure for modeling this type of system. The method utilized here is the operator splitting method, which is a good method to solve these kinds of complicated models. The main idea behind this strategy is to split down a complex problem into smaller subsystems, known as division sub-problems, and solve each one individually using the appropriate numerical method. The effects of operator splitting methods on the solution of advection-diffusion-reaction are examined, within the context of this works two operator splitting methods, Lie-Trotter and Strang-Marchuk splitting methods were used and comparisons were made through various decomposition rate. Obtained results were compared with analytical solutions to the problems and available methods in the literature. It is seen that the Lie-Trotter splitting method has lower error norm values than the Strang-Marchuk splitting method. But, the Lie-Trotter splitting method produces accurate results for very small values of the numerical result for an application concerning the transport of a contaminant will be presented to enhance the value of our results, and prove the efficiency of the LTM (Lie-Trotter Method).
The objective of this research is to apply Quality 4.0 (Q4.0) concept in Agriculture 4.0 (A4.0) to digitize the traditional quality management (QM) system and demonstrate the effectiveness of zero-defect manufacturing (ZDM) in the agricultural part manufacturing industry. An autonomous quality management system was developed based on the ZDM system using the Industrial Internet of Things (IIoT). Both traditional and autonomous quality management systems were evaluated using six-sigma quality indicators and machining and inspection cost analysis. The ZDM resulted in a significant improvement in the quality of CARD148 manufacturing, increasing the manufacturing process from a low level of sigma to a high level of sigma (0.75 to 5.10 sigma). The component rejection rate was reduced by a high percentage, leading to significant economic benefits and a significant reduction in machining cost. The process yield was also increased to a high percentage. The developed ZDM was found to be consistent in improving the quality of the turning process, with notable increases in tool life and reduction in inspection cost. The total component cost was reduced significantly, while the PPM value increased notably. While this study focuses on agriculture-related manufacturing organizations, the developed ZDM has potential for other machining industries to improve sigma levels, particularly in industries such as automotive and medical.
Due to the sudden increased demand for food for the ever-increasing population, there has been a surge in the presence of adulterants and toxicants in the food items available for the consumers. Nanoscience and nanotechnology are new frontiers of this century, and food nanotechnology is an arising innovation. The expansion in the development and usage of nanotechnology connotes wide utilization of nanomaterials particularly in the food area with varieties of expected advantages in the territories of food safety and quality, micronutrients and bioactive fixing conveyance, food handling, and processing. Nanotechnology may allow us to develop nanosensors that can help us to detect adulterants and toxicants in food, especially in their applications for food quality and security. They can be fused in the compound and natural sensors empowering the plan of fast and touchy gadgets to evaluate newness and distinguish allergens, poisons, or pathogenic pollutants. The future part of these nanosensors will turn out to be significantly more significant as the food lab is confronted with the expanding strain to lessen cost, time, and multifaceted nature. Along these lines, this part will zero in on one of the multiparous advances of nanotechnology in the field of an arrangement of non-defilement food. In this chapter, we discuss the potential and applications of nanosensors for the development and availability of adulterant and toxic-free food.
The 21st-century agricultural industry is witnessing negative impacts of climate change, land and water scarcity, and more recently, a global COVID-19 pandemic. Consequently, the socioeconomic sustainability of current and future food-supply systems appears to be threatened. To combat issues due to water shortage across agricultural applications, artificial intelligence (AI)-based solutions are appearing as viable alternatives. This review demystifies AI in pre, during, and post-agronomic stages owing to increasing agricultural efficiency amidst decreasing water availability. The potentials of AI are primarily reviewed in a sustainable agricultural water management context, given emerging agricultural applications of artificial neural networks, machine-learning, deep-learning, remote-sensing, digital-image processing, and robotics. From the results of the systematic review, advanced opportunities have been identified, such as real-time assistance to farmers amidst natural disasters and predictive analysis of agromarket-price forecasting. Though challenges exist while transferring technology from experimental to real environments, the review concludes with the promising prospect of AI-powered applications.
Makalah ini mengulas peran Lembaga Keuangan Mikro Islam (LKMI) dalam mewujudkan ketahanan pangan masyarakat pedesaan. Ketahanan pangan yang didefinisikan sebagai kondisi aman dari kelaparan atau ketersediaan makanan yang cukup, kemudahan akses dalam memperoleh makanan yang berkualitas dan bergizi, menjadi isu kajian yang terus menarik perhatian para pakar ekonomi. Hal itu karena ketahanan pangan merupakan salah satu indikator kesejahteraan dan bahkan strategi dalam menyelesaikan masalah kemiskinan. Problematika ini terus dialami masyarakat pedesaan khususnya, karena lemahnya dalam permodalan yang berdampak pada tingkat daya beli pangan. Akan tetapi pada aspek lain memberikan keuntungan bagi lembaga mikro konvensional dalam memanfaat situasai ini dengan meningkatkan suku bunga dan mengekang masyarakat menengah ke bawah. Maka, hadirnya LKMI dengan asas ta’awun melalui pembiayaan syariah dan program pemberdayaan di tengah masyarakat pedesaan layak untuk dikaji ulang karena menjadi solusi bagi masyarakat atas hambatan permodalan. Dengan menggunakan pendekatan kualitatif yang bersifat library research terhadap literatur yang berkaitan dengan keuangan mikro, dianalisa secara empiris, komparatif dan pemberian rekomendasi dengan beberapa karya teoritis, akhirnya makalah ini menyimpulkan bahwa LKMI memiliki peran dalam mewujudkan ketahanan pangan pada masyarakat pedesaan yaitu pengembangan ekonomi, adopsi teknologi baru, mekanisme pertanian yang lebih baik, peningkatan produktivitas, peningkatan standar hidup petani dan pemberantasan kemiskinan.
Agriculture is interrelated with other economic sectors both upstream and downstream, and in this way, agricultural activities follow the tendencies which are often verified in their surrounding dynamics. In these frameworks, it is presumed that farming activities influence the food sector, as well as the fact that the food chains and the evolution of industry impact upon the dynamics in the agricultural sector. From this perspective, it is important to analyse the interrelationships between agriculture and the other sectors in Era 4.0. Considering these contexts, the main objective of this research is to highlight the main insights from the literature concerning Agriculture 4.0 and the interrelationships between agriculture with Food 4.0 and Industry 4.0. For this purpose, a systematic review based on bibliometric analysis was carried out, considering documents from the Scopus database. The results obtained show that the contributions from Era 4.0 for the farming sector are diverse, from the IoT (Internet of Things) to robotics, and may support efficiency improvements (crucial towards improving sustainability) in the sector.
Agriculture 4.0 is the usage of advanced technologies for ensuring sustainability and efficient use of resources in agriculture, which can be accepted one of the biggest challenges of mankind. With Agriculture 4.0 technologies, it is aimed to raise productivity, reduce waste and costs. To ensure that agriculture 4.0 technologies are adopted by farmers as soon as possible, the perspective and perception of farmers to these technologies should be analyzed firstly. For this purpose, we propose a decision-making framework to measure farmers' view of Agriculture 4.0 technologies and to perform a prioritization study based on the perception of use among these technologies. Multicriteria decision analysis is also utilized to deal with all qualitative and quantitative factors in the decision process. Within the scope of this study, interval-valued spherical fuzzy numbers are used to model the vagueness in the process in the best manner and to be able to reflect the uncertainty arising from the usage of linguistic variables in the decision process. The SWARA and MAIRCA multicriteria decision making (MCDM) methods, which have been used frequently in the literature and are applied very successfully in MCDM problems, have been firstly extended by spherical fuzzy sets (SFSs) and the advantages for these methods have been utilized within the framework of fuzzy logic. The proposed method allows decision-makers to mirror their hesitations in decision-making using a linguistic evaluation scale established upon interval-valued SFSs. A comparative analysis based on the ordinary fuzzy sets is also performed for the obtained results and the clear superiority of the proposed approach is presented. In addition, the robustness of the model is tested with sensitivity analysis. After these analyzes it is obtained that this paper provides a unique perspective to the literature due to its originality in both the subject and adopted fuzzy methodology.
Biochar, is one of the thermo-chemical conversion products generated from lignocellulosic biomass via conversion techniques including gasification, pyrolysis, torrefaction, hydrothermal liquefaction, and carbonization. Modified biochar has received great attention in the catalytic process due to the great physicochemical properties and catalytic activities. Hence, this work presents a review on the current conversion techniques in transforming lignocellulosic biomass waste into biochar, which mainly focuses on gasification and pyrolysis. Additionally, comparison on the conversion techniques in terms of benefits, drawbacks, and limitations such as environmental factor, costing and safety aspect are discussed. Moreover, this review highlights the modification techniques of biochar and compares the physical properties of the pristine and modified biochar. Likewise, the biochar characterization techniques such as FT-IR, XRD, TGA, and TPD are reviewed. Subsequently, the applications of biochar-derived catalyst are studied in the production of biodiesel, syngas, and biogas, as well as NOx reduction. Besides that, the performance of biochar-derived catalyst and conventional catalyst are compared. The analysis showed that both catalysts give comparable catalytic activities. Hence, the biochar-derived catalyst generated from lignocellulosic biomass waste can be an alternative heterogeneous catalyst to replace conventional catalyst if more in-depth researches are performed. Lastly, the current challenges and limitations are discussed, and the impacts of the fourth industrial revolution on the biomass industry are highlighted.
The challenge of Nigeria’s food insecurity in the era of the Covid-19 pandemic, insecurity, climate change, population growth, food wastage, etc., is a demanding task. This study addresses Nigeria’s food insecurity challenges by adopting agriculture 4.0 and commercial farming. Using data from six digital libraries, the Nigerian Bureau of Statistics, and other internet sources, we conducted a Systematic Literature Review (SLR using PRISMA) on Nigeria’s agriculture, food security, and agriculture 4.0. Our results show Nigeria’s current agricultural state, threats to food security, and modern digital agriculture technologies. We adapted our SLR findings to develop an implementation framework for agriculture 4.0 in solving Nigeria’s food insecurity challenge in the post-Covid-19 era. Our proposed framework integrates precision agriculture in Nigeria’s food production and the necessary enabling digital technologies in the agri-food supply chain. We analyzed the critical implementation considerations during each agri-food supply chain stage of farming inputs, farming scale, farming approach, farming operation, food processing, food preservation/storage, distribution/logistics, and the final consumers. This study will help researchers, investors, and the government address food security in Nigeria. The implementation of agriculture 4.0 will substantially contribute to SDG 2 (zero hunger), SDG 3 (good health and well-being), and SDG 8 (decent work and economic growth) of #Envision 2030 of the United Nations, for the benefit of Nigeria, Africa, and the entire world.
This volume of essays builds upon renewed interest in the long-run global development of wealth and inequality stimulated by the publication of Thomas Piketty's Capital in the Twenty-First Century . It brings together an international team of leading economic historians and economists to provide an overview of global developments in the theory and reality of inequality and its salience in the modern world order.
The contributors take stock of the key concepts involved in contemporary debates - capital, wealth and income distribution, economic development, private and collective assets, financialization, global liberalisation - and evaluate the evidence for both common and contrasting historical trends in national statistical data sources. To the developed economies upon which Piketty drew are added contributions covering Latin America, Africa, India and Japan, providing a global perspective upon a global phenomenon.
The book seeks to provide readers with a deeper awareness and understanding of the significance of inequality in economic development, the varying pace and nature of economic change around the world, and the manner in which this process of change affects the distribution of incomes and wealth in diverse economies. The collection marks an important step in the process of developing Piketty's analytical framework and empirical material, overcoming some of their limitations and helping to cement a lasting place for inequality in the future agenda of economics and economic history.
Betel nut or betel palm (Areca catechu Linn.) has become a major cash crop in southern and central Taiwan since the mid-1980s. Many slopeland fruit orchards and forests have been converted to betel nut plantations. The total area of betel nut plantations has increased over the past 30 years, reaching a peak of 56 542 ha in 1997. The public and conservation groups frequently express great concern over the potential negative hydrological impacts of betel nut plantations on steep slopes. This investigation in central Taiwan examines the effects of hillslope betel nut plantations on hydrological processes. Differences in hydrological characteristics between plots planted with betel nut trees and those with other ground cover types were evaluated at two study sites. The tall, single-layer canopy and wide spacing between planted betel nut trees led to low interception losses, high throughfall and high net rainfall. Plots planted with betel nut trees had lower infiltration, higher surface runoff and higher erosion than forested sites. These hydrological characteristics can be related to factors such as crown cover, soil organic content and soil porosity in betel nut plantations. Streamflow data from three gauged watersheds with different proportions of total area planted with betel nut showed that where greater proportions of total area were planted with betel nut trees, there were higher annual streamflow/rainfall ratios, higher specific peak flows, steeper recessions and higher peak flow/baseflow ratios. The results from this study suggest that, in general, betel nut trees are less desirable from the soil and water conservation viewpoints than natural forests.
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