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With the facilitation of agricultural modernization, China's agricultural energy consumption continues to rise. This paper empirically tests the agricultural energy rebound effect and its influencing factors in China during 1999–2020. It is believed that the improvement of energy efficiency has not led to the expected energy-saving effect in agriculture due to the "Jevons paradox" of energy efficiency and energy consumption. The average energy efficiency of China's agricultural energy consumption was 0.446, and the average energy rebound effect was 68.9 %. The level of agricultural mechanization, the effective irrigated area of farmland and agricultural investment have a positive impact on the rebound effect of agricultural energy, and the direct effects are 0.062, 0.008 and 0.064, respectively, while agricultural labor force and energy price have a negative impact on the rebound effect of agricultural energy. In particular, technology spillover and scale spillover led by agricultural technology progress have strong negative space spillover effect. As it is argued, 1 % of agricultural technology progress can reduce 3.5 % of agricultural energy rebound effect in surrounding areas. In order to reduce the energy rebound effect, it is suggested to increase the subsidies to agricultural labors and rationally formulate regional differentiated energy regulation policies. • The internal mechanism of rebound effect is expounded from the view of producers and consumers. • The average energy efficiency of China's agricultural energy consumption was 68.9 % during 1999–2020. • The determinants of the agricultural energy rebound effect were further discussed by considering the spatial spillover effect. [ABSTRACT FROM AUTHOR]

• Stepwise improved biophysical model is reusable for reduced and extended cases. • The studied food web models are sensitive to initial concentrations of plankton. • Model improvement requires parallel measurements to decrease sampling errors. • Programmable Process Structure serves as an extensible method to support reuse. • Reusability check contributes to the development of scaling-up and planning models. Given the increasing importance of data- and model-driven design and control in food production systems, this paper addresses the need to improve the reproducibility, replicability, and reusability of datasets, models, and modeling frameworks. While sensor data and machine learning-based control and operation of agricultural and aquacultural systems face reproducibility and replicability challenges, reusability is becoming critical for computational model-based design and planning of complex processes. This study evaluates the reusability of an existing pond aquaculture model and outlines a systematic, stepwise approach for reusability enhancements. The suggested methodology starts with an established reference model of a typical production fishpond and improves its reusability through pilot-scale experiments, covering key aspects of pond farming technologies. The reference model is subjected to stepwise reusability checks using measured data from the respective pilot units, progressing from simpler (reduced) to more complex (extended) cases. Each step concludes with the necessary parameter or sub-model refinements, which remain unchanged in subsequent steps. The refined model is validated with the measured data from other pilot experiments. This process can be repeated until satisfactory results are obtained. The resulting model is then tested to scale up a production pond model using limited case-specific input data. In addition, a hypothetically modified scenario is studied to address discrepancies between measured and simulated data. [ABSTRACT FROM AUTHOR]

This paper presents a study focused on the analysis of phytosanitary treatment sales in the Souss Massa region of Morocco. The objective of the study is to predict the sales of agricultural products, particularly crop protection solutions, aiming to optimize supply chain operations and meet customer demand effectively. Data for this study are collected from multiple sources, including the Enterprise Resource Planning (ERP) system called Microsoft Dynamics AXAPTA used by a leading agricultural company operating in the region. Information such as the date of sale, farming type, climate, and specific sales locations within the Sous Massa region is gathered. Machine learning techniques are applied for forecasting. Various regression models, including the Gradient Boosting Regressor algorithm, are employed to determine the most accurate predictor. Evaluation of the models reveals promising results, with a Mean Absolute Error (MAE) of 0.0035 and a Root Mean Square Error (RMSE) of 0.0066. The results obtained by applying various regression models, including the Gradient Boosting Regressor algorithm, demonstrate promising prediction scores. These findings contribute to the field of sales prediction in the agricultural industry while considering the impact of climate conditions, farming practices, and regional factors. [ABSTRACT FROM AUTHOR]

• Amaranthus palmeri growth stage recognition was possible with YOLO architectures. • YOLO v8-X achieved the highest mAP@[0.5:0.95] for growth stage recognition of 47.34% • A. palmeri single-class outperformed 8-class recognition by over 20% (v7-original) • The first high-quality, benchmarking, growth stage dataset is provided. Many advanced image-based precision agricultural technologies for plant breeding, field crop research, and site-specific crop management hinge on the reliable detection and phenotyping of plants across highly variable morphological growth stages. Convolutional neural networks (CNNs) have shown promise for image-based plant phenotyping and weed recognition, but the ability to recognize growth stages, often with stark differences in appearance, is uncertain. Palmer amaranth (Amaranthus palmeri) is a particularly challenging weed plant in cotton (Gossypium hirsutum) production; due to high genetic diversity, it exhibits highly variable plant morphology across growth stages over a growing season, as well as between plants at a given growth stage. This paper investigates eight-class growth stage recognition of A. palmeri in cotton as a challenging detection case study for You Only Look Once (YOLO) architectures. In total, 26 architecture variants from YOLO v3, v5, v6, v6 3.0, v7, and v8 are compared on an eight-class growth stage dataset of A. palmeri. The highest mean average precision (mAP@[0.5:0.95]) for recognition of all growth stage classes was 47.34% achieved by v8-X, with inter-class confusion across visually similar classes. With all growth stages grouped as a single class, performance increased, with a maximum mAP@[0.5:0.95] of 67.05% achieved by v7-Original. Single-class recall of up to 81.42% was achieved by v5-X, and precision of up to 89.72% was achieved by v8-X. Class activation maps (CAM) were used to understand model attention on the complex dataset. Fewer classes, grouped by visual or size features improved performance over the ground-truth eight-class dataset. Successful growth stage detection highlights the substantial opportunity for improving plant phenotyping and weed recognition technologies with open-source object detection architectures. Additionally, the first open-access, benchmarking growth stage dataset ' Palmer amaranth growth stages – 8 (PAGS8) ' is presented, which is available here: https://weed-ai.sydney.edu.au/datasets/5c78d067-8750-4803-9cbe-57df8fae55e4. [ABSTRACT FROM AUTHOR]

Integrated Pest Management (IPM) technologies offer effective solutions to reduce the negative effects of crop pests while considering human and environmental health. However, disseminating these technologies faces several barriers, with one of the most significant being the lack of farmer awareness regarding their availability, deployment, and uptake. Digital tools are perceived as a new form of leverage for overcoming these barriers. This study analyzes current IPM digital tools and their potential to boost farmers' awareness of the deployment and adoption of IPM technologies. From a software engineering perspective, this study aims to emphasize the critical functionalities and limitations of various IPM dissemination tools. It provides valuable insights to improve the adoption process and streamline the dissemination of IPM technologies. Through a systematic search in Google, Scopus and Web of Science for journal articles, over 32 dissemination tools were identified. The study thoroughly assesses these tools and identifies 5 main limitations hindering their regular use, especially in developing countries. Among the most significant limitations are the inadequate representation of tools developed in developing countries, lack of agroecological customization, and insufficient offline functionalities. Building on these findings, a user-centered design is employed to propose a software architecture for a novel Decision Support System (DSS) tailored to farmers and experts. The architecture comprises a local database for offline access, a mapping engine for data visualization, a conversation module with a triangulation engine for knowledge sharing, and an agroecology engine for technology recommendation based on an agroecological classification of the user's landscape. Drawing from the review, identified limitations, and the proposed architecture, we illustrate how the resulting novel DSS is anticipated to improve the dissemination of IPM technologies. • First Literature Review that explicitly focuses on web-based Decision Support Systems (DSS) for Integrated Pest Management (IPM) since 2015. • In this literature review, 340 published papers up to April 2023 were retrieved, of which 32 primary studies were selected for a detailed analysis. • The main functionalities and modules of current web-based IPM DSS are discussed. • Five critical limitations are identified, providing insight for future DSS development and paving the way for further research in the field. • A novel three-tier software architecture is proposed as a road map for the development of innovative tools to enhance the dissemination rate of IPM technologies. [ABSTRACT FROM AUTHOR]

It is still an open question how to assess the contribution of digitalisation in agriculture to the United Nations' Sustainable Development Goals, and how digitalisation then can be done in a responsible way. A socio-cyber-physical system (SCPS) concept can help this analysis, but little experience exists with its operationalisation and application, and its integration with the Responsible Research and Innovation approach. To address this gap, this paper has a twofold purpose: a) operationalise the SCPS concept within an integrated assessment framework adaptable to multiple levels of analysis, contexts, and purposes (e.g. ex-ante, ongoing, ex post evaluation) to shed light on impacts of digitalisation in relation to SCPS entities, relationships, and activities; b) apply the designed framework in 21 multi-stakeholder platforms (Living Labs), which were established to explore needs and expectations in specific subjects relevant for European agriculture, forestry and rural areas. Impacts were assessed through interviews (158 respondents), focus groups (378 participants), online surveys (273 respondents), and other secondary data. The findings indicate that the SCPS framework enables elucidating relationships between digital and broader sustainable development goals and needs, and can sharpen earlier assessments, going beyond a pessimistic or optimistic dichotomy associated to digitalisation by specifying effects and trade-offs in terms of enabling, disenabling, boosting and depleting impacts of digital agriculture. However, the framework being comprehensive and open to emerging socio-cyber-physical interactions, makes that Livings Labs doing participatory impact assessments struggled with the complexity and multiple dimensions of the topic. The paper provides both conceptual and operational knowledge to set up impact evaluations of responsible digitalisation in agriculture and outline concepts that can help anticipating the consequences and trade-offs. [Display omitted] • The paper outlines concepts for anticipating the consequences and trade-offs of digitalisation. • Application of a socio-cyber-physical framework to assess digitalisation impacts in 21 Living Labs. • Presenting a typology of enabling, disenabling, boosting and depleting impacts of digitalisation. • This framework and typology enable impact evaluations for responsible digitalisation in agriculture. • Responsible digitalisation means working in parallel on design, access, and system complexity. [ABSTRACT FROM AUTHOR]

The agricultural sector is undergoing several transitions through "smart-farming" technologies. To make this innovation responsible, it is critical to support technological innovation at different stages of innovation with customized strategies for the individual technology. What are the critical support functions at different stages of technological innovation in agriculture? Four technologies are analysed: Automated Milking Systems (AMS), a digital fencing system for the virtual herding of goats, a technology for drone-based observation and management in agriculture and forestry, and round baler silage systems. These are analysed as sociotechnical constructions of multiple stakeholders, with heterogeneous pathways to societal acceptance and practical usage on the farm. To provide information about how such technology can be created in a responsible manner, the paper suggests a Balanced Readiness Level assessment (BRLa) to highlight five dimensions for technological maturity: technological, market, regulatory, social acceptance, and organizational maturity. Through this approach, the findings show that each phase of the technological development benefits from specifically targeted support and that support functions should not be underestimated in order to get technology to a higher level of acceptance. Agriculture is facing challenges that demand transition of the sector. Supporting invention to stimulate innovation is important; supporting implementation is also important, but identifying the supporting functions that can have an impact at the societal level is crucial to stimulating transition of the system. This paper clarifies the variations of the supporting functions. [Display omitted] • Exploring critical support functions at different stages of technological innovation in agriculture • Balanced Readiness Level assessment (BRLa) highlight technology, market, regulation, social acceptance, and organization • Technology is socio-technically constructed by multiple stakeholders negotiating technological innovation • Crucial to identify the supporting functions that have an impact to stimulate the system's transition at the macro-level [ABSTRACT FROM AUTHOR]

The aim of this paper is to improve our understanding of the roles of individuals and the importance of their social contexts in shaping the dynamics of technological diffusion in the agricultural sector. When justifying the different rates of innovation adoption, existing literature reviews overemphasize either the drivers of technological changes enacted by farmers' agentic behaviour or the cognitive processes of individual farmers and their social contexts (structures). However, they continue to have a fragmented view of how local social systems and the agentic behaviour of individual actors influence the evolution of technological regimes, and they lack the ability to describe a purposeful interplay between agency and structures. We present an integrative review of the most relevant papers published in the last 20 years and discuss the impact of structures and agency emerging from local social systems on the local innovation process and, as a result, the evolution of technological regimes. The identified macro categories describe the main processes affecting individuals' abilities to mobilize and manage local resources for innovation, allowing us to critically assess the stock of previous developments from a new perspective and identify novel research avenues. • The last two decades of innovations in the agricultural sectors are reviewed. • Three macro categories emerged. • Agency, structure, and their interplay constitute the core of the integrative review. • A future research agenda is proposed. [ABSTRACT FROM AUTHOR]

Climate change has had a significant impact on global agricultural production. Hence, studying the impact of climate change on the efficiency of agricultural production has become crucial in helping major agricultural regions around the world adopt novel technologies to improve the adaptability of agricultural production. The efficiency of agricultural production can also be improved, and global food safety can be ensured at the same time. This paper used data collected from 43 countries around the world from 1992 to 2018. The data envelopment analysis-Malmquist model was used to estimate agricultural productivity with and without the presence of climate factors, and data comparison and analysis were performed among years and regions. The results show that climate factors can overestimate agricultural productivity. On the whole, climate factors have a positive impact on agricultural production in Sub-Saharan Africa and Latin America and a negative impact in other areas. In addition, the impact of climate change on technical progress is greater than the impact on technical efficiency. According to the results, this paper finds that relevant departments should pay attention to the development of agricultural technology under climate change and should carry out suitable management based on geographical location and climate conditions. In summary, the research methods and results of this article provide a reference for the progress of agricultural production under global climate change. • Data collection from 43 countries around the world. • A comparative analysis of agricultural production between developed and underdeveloped regions. • The impact of climate change on technical progress is remarkable. [ABSTRACT FROM AUTHOR]

• Technical properties of various wireless communication protocols are compared. • Inconsistent information about the properties in different sources are underlined. • Utilization of the protocols in six major application areas are investigated in detail. • Trending protocols and applications are discussed by analyzing recent papers. IoT based smart agriculture systems are important for efficient usage of lands, water, and energy resources. Wireless communication protocols constitute a critical part of smart agriculture systems because the fields, in general, cover a large area requiring system components to be placed at distant locations. There are various communication protocols with different features that can be utilized in smart agriculture applications. When designing a smart agriculture system, it is required to carefully consider the features of possible protocols to make a suitable and optimal selection. Therefore, this review paper aims to underline the specifications of the wireless communication protocols that are widely used in smart agriculture applications. Furthermore, application-specific requirements, which may be useful during the design stage of the smart agriculture systems, are highlighted. To accomplish these aims, this paper compares the technical properties and investigates the practical applications of five different wireless communication protocols that are commonly used in IoT applications: ZigBee, Wi-Fi, Sigfox, NB-IoT, and LoRaWAN. In particular, the inconsistencies in the technical properties of these protocols reported in different resources have been highlighted and the reason for this situation has been discussed. Considering the features offered by the protocols and the requirements of smart agriculture applications, the appropriateness of a particular protocol to a particular smart agriculture application is examined. In addition, issues about cost, communication quality, and hardware of the five protocols have been mentioned. The trending technologies with high potential for the future applications of smart agriculture have been introduced. In this context, the relation of the technologies like aerial systems, cellular communication, and big data analytics with wireless have been specified. Finally, the leading protocol and the smart agriculture application area have been highlighted through observing the year-based distribution of the recent publications. It has been shown that usage of LoRaWAN protocol has become more widespread in recent years. [ABSTRACT FROM AUTHOR]

Summary It has now become almost a stylized fact that sustained agricultural growth is central to rapid poverty reduction and economic development. Yet, world poverty is largely concentrated in the agrarian societies, which have the potential for agricultural productivity growth. This is particularly true for Sub-Saharan African countries, where the gaps between potential and actual yields remain high. Minimizing this gap through the promotion of modern inputs—such as fertilizer and modern seeds—has been at the core of almost all development strategies in Ethiopia. Among other initiatives, the country has promoted microfinance institutions and member-owned financial cooperatives to alleviate credit constraints of the smallholder farmers. This paper analyzes the impacts of these institutions. Using household survey data and a propensity-score-matching technique, we examine the effects that institutional financial services have on farmers’ adoption of agricultural technology in Ethiopia. The results suggest that access to institutional finance has a significant positive impact on both the adoption and extent of technology use. However, when impacts are disaggregated by type of financial institution and farm size, heterogeneities are observed. In particular, financial cooperatives have a greater impact on technology adoption than microfinance institutions, and the results appear to vary depending on farm size and types of inputs. The paper concludes with implications for policies to promote adoption of modern agricultural inputs. [ABSTRACT FROM AUTHOR]

Food security remains a top development priority and global concern. It is enshrined in the 2030 Agenda for Sustainable Development in Sustainable Development Goal two. Food security is also a core component of the human development and capability paradigm, since food access and entitlements are critical for reinforcing essential human capabilities. In introducing this special issue, this paper argues that agriculture is central to improving food security and reducing poverty in Africa. It suggests that realizing the potential of agriculture in Africa requires rapid increases in land productivity and increases in agricultural yields. A science-based approach that integrates gender and sustainability is critical to meet this goal, through the design and implementation of policies that improve the availability farm inputs and farm technology. The paper concludes by introducing the papers in this special issue. [ABSTRACT FROM AUTHOR]

The paper presents economic efficiency of work in the horticultural production in the selected farms which carry out the integrated production. At the average, one man-hour was paid 0.09 PLNk·man-hour-1. Taking into consideration the use structure in the investigated objects, the intensity of production organization was determined. The production was varied (it was within the intensity range of 213-393 points) and the differences resulted directly from the participation of the grain cultivation area and currant plantation in particular farms. A field work mechanization degree is, inter alia, a factor that influences the labour inputs level. Thus, an annual use of farm tractors was also determined. It was 400 hours. The paper covers farms associated in the formalized producer's group. [ABSTRACT FROM AUTHOR]

Applying big data technology to intelligent marketing of agricultural products is the development trend of agricultural product production and marketing information service, and is an important measure to improve farmers' income and realize targeted poverty alleviation. After analyzing the existing problems of intelligent marketing of agricultural products in the era of big data, this paper constructs the innovation mode of intelligent marketing technology of agricultural products in the era of big data from the collection, storage and analysis technology of big data of intelligent marketing of agricultural products, the construction of the big data center of intelligent marketing of agricultural products and the construction of the public platform of intelligent marketing information service of agricultural products Can be applied in marketing. [ABSTRACT FROM AUTHOR]

With increasing population, the demand for agricultural productivity is rising to meet the goal of "Zero Hunger". Consequently, farmers have optimized the agricultural activities in a sustainable way with the modern technologies. This integration has boosted the agriculture production due to high potentiality in assisting the farmers. The impulse towards the technological advancement has revived the traditional agriculture methods and resulted in eco-friendly, sustainable, and efficient farming. This has revolutionized the era of smart farming which primarily alliance with modern technologies like, big data, machine learning, deep learning, swarm intelligence, internet-of-things, block chain, robotics and autonomous system, cloud-fog-edge computing, cyber physical systems, and generative adversarial networks (GAN). To cater the same, a detailed survey on ten hot-spots of smart farming is presented in this paper. The survey covers the technology-wise state-of-the-art methods along with their application domains. Moreover, the publicly available data sets with existing research challenges are investigated. Lastly, the paper concludes with suggestions to the identified problems and possible future research directions. • Current ten hot spots of smart farming BD, ML, DL, SI, IoT, BC, cloud-fog-edge, and GAN. • Covering the technology wise methods and current trends. • The available agricultural data sets, research gap, and existing research challenges. • Identified problems and possible future solutions. [ABSTRACT FROM AUTHOR]

New technological solutions can encourage lower household waste production and higher levels of waste separation. This paper focuses on analyzing the role of different behavioral factors, such as empowerment and pro-environmental behavior (PEB), have on citizens' intention to use a novel household waste management and separation system and how these interact with the financial incentives typically applied in this area, pay-as-you-throw (PAYT) and save-as-you-throw (SAYT). The proposed model was tested in Portugal using the structural equation modeling approach. Survey data from 400 respondents found that empowerment plays a vital role in adopting an innovative waste management system. The research discerns pro-environmental behavior (PEB) both as an antecedent and a moderator between system use and empowerment, system use and behavioral intention, and also between system use and financial incentives. We discovered that for people with low perceived PEB, PAYT actually reduces the use of the new waste management system, while SAYT can increase the use of the system. Furthermore, increasing the empowerment of users in the system can work exceptionally well at encouraging consumers that already have a high level of PEB. The paper concludes with a discussion section about the developed framework's application and implication in the waste management sector. This study is valuable for understanding how citizens will adopt a new waste management system and essential for encouraging citizens to engage in recycling behavior regularly. • Explores citizens' use of a novel household waste management system (WMS). • Pro-environmental behavior was identified as an antecedent and a moderator. • Empowerment boosts system's use for people with high pro-environmental behavior. • Pay-as-you-throw reduces system's use if pro-environmental behavior is low. • Save-as-you-throw increases system's use if pro-environmental behavior is low. [ABSTRACT FROM AUTHOR]

In the era of Big data, data-driven farming is changing the agricultural businesses thanks to the use of modern technologies such as the Internet of Things (IoT) sensors, drones, and farm monitoring. IoT devices produce a massive amount of precious agri-data, which are collected and analyzed in real-time using innovative application tools. This combination of technology, known as "Smart farming", helps various stakeholders in the agri-ecosystem to monitor crops in real-time, as well as maximize productivity and profitability in farm and business operations with the minimum efforts. Although many Smart farming solutions have been introduced, both from industry and academia, universal applicability of these approaches for other farms, unfortunately, is not feasible. Most of these solutions are based on a home-made non-standard Big data processing architecture. In this paper, we propose WALLeSMART, a cloud-based Smart farming management system, applied to the Wallonia region of Belgium. The framework introduces a general architecture to address the challenges of acquisition, processing, storing, and visualization of very large amounts of data, both in batch and real-time basis. An initial prototype has been developed and tested with various farms showing prominent results. [ABSTRACT FROM AUTHOR]

Precision dairy monitoring involves the use of technologies to measure physiological, behavioral, and production indicators on individual animals to detect events of interest. Estrus, disease, and calving detection are common applications, although estrus detection is the most tested and used. Many precision dairy monitoring technologies (PDMT) are commercially available and are being used in research and on farms. As a result, a common question from researchers and producers alike is, "what PDMT should I buy?" The answer to this question is inherently complicated because it depends on many factors, some of which researchers have yet to explore. The objective of this paper is to examine the less quantitively researchable aspects of PDMT adoption and use on-farm. This will be done through 3 lists of 5, determined from published theory and my own experience. First, the 5 main factors that influence adoption of an innovation: (1) relative advantage, (2) compatibility, (3) complexity, (4) trialability, and (5) observability. Each of these factors is at play to a different extent in the 5 adopter categories: (1) innovators, (2) early adopters, (3) early majority, (4) late majority, and (5) laggards. From my experience and research, the top PDMT are those that improve (1) farm efficiency; (2) farm economics; (3) decision-making; (4) animal welfare; and (5) producer happiness. Implementing PDMT on a farm is an enormous and potentially expensive decision. As this part of the industry continues to progress, the potential for different PDMT is endless. Sound research and producer feedback are imperative to ensuring that PDMT continue to improve and become more widely adopted. [ABSTRACT FROM AUTHOR]

The massive growth of wireless communications in recent years is mostly due to new connectivity demands and advances in technology development of low power) transceivers. An example of the unique demands is the increasing exchange of data in Internet services, which has led to wireless network deployment for data transmissions. The coordination of the IoT devices, smart systems, and agriculture can contribute directly to the development of the farmer's practices by building their farm more intelligent and digital. However, enhancing farming practices requires inspecting farm equipment and farmer's experiences, which can be analyzed through the interconnectedness of IoT objects to collect farm data over the Internet to launch smart digital agriculture. It is challenging to control all farming processes (especially in real-time), this remaining as the main limitation of traditional farming. In this work, we focus on how wireless sensors can play a vital role in smart farm systems and allow processing the large amount of data generated in batches or real-time to analyze it, retrieve insights from it, and create a Smart Digital Farm. This paper proposes hierarchical-logic mapping and deployment algorithms to tackle the problem of poor network connectivity and sensing coverage in random IoT deployment. [ABSTRACT FROM AUTHOR]

The most arduous and arduous task of building a well-off society in an all-round way is mainly in the countryside. Eliminating rural poverty is an urgent problem to be solved. The development of rural e-commerce through the Internet has great potential. From the perspective of precise poverty alleviation, this paper proposes to build a e-commerce precise poverty alleviation system based on geographic information. It introduces in detail the technical framework and development route of the system, the design of detailed functional modules and key technologies, hoping to provide reference for peers. [ABSTRACT FROM AUTHOR]

To promote green agricultural production, the government often provides two types of subsidies: the Green Technology Operation Subsidy (GOS), and the Green Technology Investment Subsidy (GIS). This paper examines the impact of government subsidy policies on a two-tier agricultural supply chain in which the farmer can choose to self-invest in green technologies or outsource them to the service provider. Our results indicate that outsourcing is more conducive to higher green technology inputs than self-directed investment in most cases and can generate higher profits for small-scale farmers; however, as the land scale increases, farmers prefer to invest in green technologies by themselves. In addition, if the farmer decides to self-invest in green technologies, the farmer prefer the GIS (GOS) policy when the land scale is small (large). Further analysis shows that if the farmer decides to outsource green technologies, the farmer's profit under the two subsidy policies is equal when the land scale is small, while the farmer benefits more from the GOS policy when the land scale is large. Finally, from the perspective of promoting agricultural green production, the GIS policy is always better than the GOS policy, regardless of whether the farmer outsource green technologies. • Investigate the production decision of farmers when outsourcing green technologies. • Outsourcing generates higher profits for small-scale farmers than self-investment. • Farmers benefits more from green technology operation subsidy when land scale is large. • Outsourcing is in most cases more effective than self-investment in increasing green technology inputs. • Green technology investment subsidy yields higher levels of agricultural green production. [ABSTRACT FROM AUTHOR]

The waste heat recovery technology of data center has attracted the attentions for energy conservation and emission reduction in recent years. This paper presents a new scheme which uses heat recovery technology to link the data center and the agricultural sector. The detailed system design, technical validation, and environmental and economic analysis show that the project can fully absorb a large amount of waste heat in the data center, and use the air source heat pump (ASHP) to transport heat to an ecological farm, reducing the consumption of electric energy and promoting the development of agriculture and animal husbandry. In a case study of China Brain Green Data Center, the dynamic payback period is about 3.4 years, and the cumulative net present value will reach $666 k. In terms of environmental impact, the proposed ASHP-based complementary utilization scheme can save 676 MWh of electricity for the data center and 88 MWh of electricity for the ecological farm in each year, which is equivalent to reducing coal by 230 tons and reducing CO 2 emissions by 168 tons. Therefore, from the perspective of economic and environmental benefits, this new complementary waste heat utilization technology realizes a win-win development. [ABSTRACT FROM AUTHOR]

Central kitchen engineering for industrial production of prepared dishes is gaining prominence in response to urbanization, socioeconomic shifts, evolving family structures, and COVID-19 impacts. These dishes, including staple foods, convenience items, fresh-cut vegetables, and sides, cater to the demand for convenient, diverse, and health-conscious options. Central kitchen engineering significantly enhances production efficiency, reduces labor and space requirements, and advances food industrialization. However, central kitchen engineering still faces challenges such as conceptual clarity, outdated technology, sensory quality decline, inconsistent standards, and heightened nutrition and safety concerns. To enhance quality and safety, research efforts are currently focusing on innovative and emerging technologies. Innovations cover cooking/reheating techniques, cooling/freezing methods, sterilization, packaging, natural extracts, and fat substitutes. The current state, research progress, and innovations in prepared dishes are critically analyzed in this paper, offering insights into future directions. By addressing challenges and presenting growth opportunities, this study aims to guide both academic researchers and industry practitioners, fostering progress and innovation in this dynamic field. Panoramic view of the prepared dish industry based on central kitchen engineering: the comprehensive amalgamation of raw material primary processing at the forefront, agricultural product deep processing in the middle, proceeding with catering and food logistics, and food market consumption and supply at the rear end, along with future opportunities. [Display omitted] • Urbanization, evolving families, and COVID-19 drive prepared dishes' popularity. • Critically analyze challenges in processing, from outdated technology to safety concerns. • Research and production tackles challenges with innovative and emerging technologies. • Roadmap for prepared dish progress is proposed for greater safety, convenience and nutrition. [ABSTRACT FROM AUTHOR]

The adoption of soil and water conservation (SWC) technology is a key strategy for reducing global land degradation and improving agricultural productivity. This study uses survey data from households in the Loess Plateau in 2017 to evaluate the impact of social interaction on the decision-making process of SWC adoption by farmers. It measures the mediating effect of information acquisition and the moderating effect of internet use and deploys the probit model, general decomposition (KHB) model, and moderated effect model for analysis. The study found that the level of SWC adoption by farmers in the Loess Plateau of China remains relatively low, with only 57.3% of farmers adopting SWC, whereas social interaction increases the possibility of farmers adopting SWC by 10.0%. Accordingly, the paper argues that social interaction can encourage farmers to adopt SWC by improving their ability to acquire information, while internet use can positively moderate the impact of social interaction on farmers' adoption of SWC. Furthermore, the study found the positive impact of social interaction on SWC adoption is more pronounced for farmers in larger villages and those who join cooperative societies. Therefore, the research results provide a new perspective for promoting SWC in China, emphasizing the importance of enhancing social interaction improving farmers' ability to acquire information, and accelerating internet use. • Social interaction plays a crucial role in promoting SWC adoption. • Improving farmers' ability to acquire information can promote SWC adoption. • Internet use can moderate the impact of social interaction on SWC adoption. [ABSTRACT FROM AUTHOR]

• Develop a probability model for the poor farmers to escaping poverty. • Analyze three poverty alleviation schemes conducted by the pro-poor enterprise. • Examine the compensation strategy of different poverty alleviation schemes. • Provide valuable insights into the supply chain and poverty alleviation policy. In this paper, we examine the minimum procurement price and government subsidies for the successful implementation of three Poverty Alleviation Schemes (PASs): Contract-Only Scheme (CO), Contract with Compensation Scheme (CS), and Capacity-building with Training (CT). We consider an agricultural supply chain with a pro-poor enterprise and a group of impoverished farmers who grow, harvest, and sell the product to the pro-poor enterprise, and the government decides to subsidize the pro-poor enterprise for poverty alleviation. The price of the agricultural product and the harvest output are uncertain and negatively correlated. We consider a new type of poverty alleviation objective: the poor farmer escapes poverty if their profit exceeds the poverty line (PEP) and establishes a higher probability of escaping poverty (EPP). We use real coffee and tea prices to capture the skewness of the distribution of product prices in a numerical analysis. We find that (i) the impoverished farmers would benefit from PASs, but the pro-poor enterprise would incur risks because both parties share the uncertainty of harvest output in PASs; (ii) the local government should provide subsidies to the pro-poor enterprise to implement PASs better; (iii) CT and CS have a lower bound for the procurement price as well as local government compensation; and (iv) if the product price exhibits a left-skewed distribution, then the EPP becomes crucial, and a higher procurement price is required to increase the EPP. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSN) are pivotal for real-time monitoring in diverse sectors through the Internet of Things (IoT). However, current wireless communication protocols face challenges in integrating Precision Agriculture (PA) management systems with Artificial Intelligence (AI). PA optimizes nutrient supply and pesticide use, enhancing crop yield and product quality. Despite these advantages, structural changes in WSNs, including deployment, coverage, scalability, and energy consumption, hinder their potential. This paper conducts a comparative study of WSNs in agriculture, focusing on wireless protocols, energy harvesting, and efficiency procedures. The research delves into challenges faced by WSNs in agriculture, emphasizing the need for power-saving agricultural management strategies to ensure sustained, effective long-term monitoring. The study addresses integration issues, explores agricultural benefits, and proposes solutions to enhance WSN performance in dynamic field environments. [ABSTRACT FROM AUTHOR]

A consortium managed project in the Upper Indus Basin of Gilgit-Baltistan , Pakistan, supported the creation of women's collective farming. The component of the project related to water resource management for agriculture was implemented in Hunza district, where in three sites, the communities encouraged women farmers to engage in the pilot interventions related to innovative water resource management technologies and improved farming practices to overcome climate-induced issues for farmers. This paper analyzes the situation of women as collective farmers and its contributions to women's agency. The study used a gender analysis framework, and data was collected through quantitative and qualitative research methodologies in the project site areas. The study's findings show that climate change has created issues more for women farmers being at the forefront in handling and tackling the impacts through various measures. It shows that collective farming and networking is an appropriate tool for women to challenge norms and stereotypes that hinder them from being part of the decision-making at various levels. Furthermore, it helps women to minimize drudgery by sharing burdens, using appropriate technology, and sharing their successes and issues while working together. Based on the findings, this study suggests that policy decisions to implement such initiatives at a more macro level will help involve women at various levels of decision-making, minimize their drudgery, expand the choices of women farmers, and overall contribute to the agency. [ABSTRACT FROM AUTHOR]

[Display omitted] • An advanced control method has been proposed for the boost PFC converter for smart farming lighting application. • Enhanced second-order generalised integrator filter-based quasi type-1 PLL is developed as the grid detection scheme. • Nonlinear proportional-integral controllers are applied for the voltage and current control loops. • Comparative results across several practical challenging test scenarios showed the effectiveness of the proposed method over conventional counterparts. Controlled environment farming has become an attractive solution to increase the food production with limited use of resources. LED lights are often used in this type of farming to increase plant yield. LED lights are dc load, whereas the grid provides ac power. This necessitates the use of power factor correction (PFC) converter as an interface between the grid and the lights. This paper proposes an innovative control method for the boost PFC converter, where an advanced phase-locked loop scheme is developed that can eliminate measurement dc offset and provide harmonic robust estimation of the grid voltage fundamental component. The applied nonlinear control uses the existing two-loop control architecture. Based on a baseline PI controller, a nonlinear function is added to make the controller react faster when it is far from the reference and vice-versa. Comprehensive simulation studies are conducted to assess the performance of the proposed method under various challenging test scenarios. Compared to the baseline method, the proposed technique achieved 42 % ∼ 65 % reduction in total harmonic distortion depending on the test cases, which makes the technique a suitable candidate for improving the operational efficiency and, consequently, the running cost of a smart farming lighting system on an industrial scale. Results show the effectiveness of the proposed method over the conventional counterpart. [ABSTRACT FROM AUTHOR]

Sustainability-Oriented Innovation (SOI) is recognized as a way to address agrifood system sustainability challenges. Because of its complexity, SOI requires inter-organizational collaboration between actors within and around value chains. Since farming practices account for a large part of the environmental impact of food products, farmer involvement in SOI processes is key. However, there is a lack of evidence on the degree of farmer engagement in inter-organizational collaboration, as well as the diversity of partners involved. Moreover, our understanding of the heterogeneity of collaboration in relation to the sustainability dimensions of the innovation SOI processes is limited. The objectives of our research are twofold. First, to propose a novel conceptualization of SOI, converging the sustainability specificities of the innovation process and the characteristics of the collaboration supporting the innovation process. Second, to unpack heterogeneous forms of collaboration in SOI with attention to farmer engagement in these forms. We developed the SOI framework and analyzed SOI processes in the fruit and vegetable sector across Europe. A multiple correspondence statistical analysis was carried out based on data from more than one hundred value chains that have implemented a SOI process. A hierarchical clustering analysis was performed to reveal patterns of collaboration in SOI. The results show different patterns of collaborative SOI in European fruit and vegetable chains. The SOI differ as regards to three specific dimensions: the diversity, the direction, and the distribution; and they rely on various collaboration forms. Seven clusters are analyzed and discussed, with specific attention to the farmer engagement in the collaboration forms. We differentiated between 1) Intermediary-led farm & food innovation with farmer collaboration; 2) Intermediary-led short food supply chain innovation with farmer collaboration; 3) Processor-led green food product innovation without farmer collaboration; 4) Farmer-led sustainable agricultural practices innovation with a collaborative network; 5) Farmer-led coupled innovation with downstream integration; 6) value chain actor-led coupled innovation with farmer collaboration; 7) Retailer-led distribution innovation without farmer collaboration. The relationship between diverse collaboration forms and SOI dimensions is discussed. Revealing the heterogeneity of collaboration forms in SOI processes is important for developing sound policies. Given the significant role of farmers in sustainable agrifood system transitions, this paper reveals various levels of farmer engagement in collaborative SOI processes in value chains. Results have implications for mission-oriented policies since they allow a more precise and targeted approach to agrifood chains within the broader agrifood system transformation. [Display omitted] • Sustainability-oriented innovation (SOI) has three dimensions: direction, diversity, and distribution • SOI requires collaboration among multiple actors within and around the value chain • SOI is characterised in 74 food value chains in Europe; seven clusters reveal the heterogeneity of collaboration • The relationship between heterogeneity of collaboration and SOI dimensions provides insights for targeted innovation policy [ABSTRACT FROM AUTHOR]

Due to the increasing world population, demand for food products have created the need to modernize and intensify agricultural operations through precision agriculture. The Internet of Things offers a wide variety of solutions for precision agriculture, but implementing it in the agriculture field imposes challenges to hardware and data communication in the network. Importantly, the sensor nodes have to be reliable for long periods with the limited available battery power. In this connection, the goal of this paper is to propose an efficient data collection algorithm which improves the network lifetime of the wireless sensor network (WSN), decreases the energy hole issue and reduces the data collection delay. In the proposed work, the rendezvous points (RPs) in the network are selected based on the energy level of the nodes, data packet density and the distance to the RPs. Then the mobile sink (MS) moves to the RPs in the shortest possible path to collect the data from the data-powered points. A hybrid meta-heuristic algorithm called WOAXGWO is proposed to find the shortest path for the MS. To assess the performance, the network's lifetime, energy consumption, and latency are considered. Simulation findings show the effectiveness of the proposed algorithm and are validated on a coconut farm to demonstrate its reliability. • WOAXGWO algorithm is proposed to alleviate premature convergence. • Proposed data collection algorithm first determines the optimal set of RPs. • Mobile sink determines the shortest path to visit all the RPs and reduces the delay. • The proposed work is implemented in real-time coconut farm to prove its reliability. [ABSTRACT FROM AUTHOR]

Indoor vertical farming can address future food insecurity by reducing land use and environmental dependence positively mitigating the environmental impacts associated with conventional agriculture. However, its high energy consumption and environmental impact raise concerns. This paper explores the use of Concentrating Solar Power (CSP) to power vertical farming in Iraq's arid regions, using abundant solar resources to reduce environmental impact and energy costs. The study evaluates the feasibility and benefits of integrating CSP technologies into sustainable agricultural practices, considering factors like implementation costs, energy production, and water usage. The study utilized the system advisor model (SAM) to carry out the techno-economic assessment by relocating existing commercial CSP plant in Seville, Spain to six Iraqi locations, following a comprehensive technical site assessment. The energy production level, capacity factor (CF), and levelized cost of electricity (LCOE) of the initial Spanish plant were lower than five of the six selected locations. Haditha and Ar Rutbha cities have the highest annual energy output, 123 GWh and 122 GWh, respectively, surpassing Seville's original plant output, which at 110 GWh performed less than Iraq locations. Next, comparisons are made between conventional farming and vertical farming (VF) cultivation. The study focuses on Romaine lettuce as the benchmark crop due to its prevalence in vertical farms and its suitability for arid climates. VF shows the potential to achieve yields between 41 and 68 kg/m2/y, 63 to 104 times higher than conventional farming in Western Iraq. VF's year-round cultivation with artificial lighting results in 83 times greater energy demands, requiring 25 kWh/kg/y. The findings emphasize the potential of CSP technologies to support sustainable agriculture in Iraq's arid regions. These findings demonstrate the possibility of utilizing solar power for sustainable agriculture in Iraq, offering valuable insights for policymakers and stakeholders seeking cost-effective and energy-efficient solutions. • Concentrating Solar Power and Vertical farming for sustainable energy in arid regions was investigated. • Techno-economic analysis shows the cost-effectiveness of CSP technologies in selected Iraqi locations. • CSP-powered vertical farming model has the potential to reduces land use, improves food security, and reduces the environmental impact of conventional farming. [ABSTRACT FROM AUTHOR]

This paper surveys the supportive technologies currently available for ground mobile robots used for autonomous mapping in agriculture. Unlike previous reviews, we describe state-of-the-art approaches and technologies aimed at extracting information from agricultural environments, not only for navigation purposes but especially for mapping and monitoring. The state-of-the-art platforms and sensors, the modern localization techniques, the navigation and path planning approaches, as well as the potentialities of artificial intelligence towards autonomous mapping in agriculture are analyzed. According to the findings of this review, many examples of recent mobile robots provide full navigation and autonomous mapping capability. Significant resources are currently devoted to this research area, in order to further improve mobile robot capabilities in this complex and challenging field. • We review hardware technologies for autonomous robotic mapping in agriculture. • Modern localization and mapping approaches for mobile robotics are discussed. • Path planning strategies for autonomous navigation in agriculture are analyzed. • The role of artificial intelligence for robotic mapping and monitoring is explored. • Future trends and challenges for robotic mapping in agriculture are highlighted. [ABSTRACT FROM AUTHOR]

The proposal of the Data Act containing 'harmonised rules on fair access and use of data' was released on 23 February 2022. It is a horizontal framework for data access to unlock competition and innovation. The emerging Digital Agriculture sector is one of the non-personal data-driven industries that can be affected by this legislative intervention. Therefore, it is highly relevant to investigate the possible implications of the horizontal Data Act proposal from the sectoral perspective. This paper aims to map the prominent data access problems in this emerging sector, explain the needed solutions for the sectoral challenges, demonstrate what the Data Act brings, and discuss to what extent this horizontal framework is helpful to overcome the sectoral problems connected to the ambiguities on agricultural data access. Thus, this piece also identifies the remaining issues that may need to be addressed by a possible follow-up sectoral regulation and competition law. [ABSTRACT FROM AUTHOR]

In smart farming, information and communication technologies such as IoT are driving the agricultural industry towards improved efficiency and productivity while reducing human intervention. As agricultural systems become increasingly connected to the Internet, they provide many new opportunities but also introduce new challenges. Connecting such systems to a network exposes them to cyberattacks and vulnerabilities; therefore, security and privacy are important challenges that should be addressed. Specifically, because of the distinct characteristics of smart farming such as environmental and farm conditions, security solutions suitable for smart farming are indispensable. In this paper, we propose a location verification (LV) protocol as a security solution for autonomous and intelligent moving devices such as drones and robots deployed in smart farming. Moreover, as an application of the proposed LV protocol, we propose a blockchain-based secure drone rental mechanism for smart farming. We present a security analysis in which the proposed LV protocol and rental mechanism are secure against man-in-the-middle, impersonation, modification, replay, and relay attacks, with the rental mechanism providing mutual authentication. Through a performance evaluation of the LV protocol in terms of the communication overhead, computation cost, and storage cost and a performance evaluation of the blockchain for drone rental with regard to throughput and latency, we confirm the feasibility and efficiency of the LV protocol and drone rental mechanism in the smart farming. • A location verification protocol for agricultural drones and robots is proposed. • A blockchain-based drone rental mechanism without human intervention is introduced. • Both the protocol and the mechanism are secure against various cyberattacks. • Performance results show their feasibility and efficiency in smart farming. [ABSTRACT FROM AUTHOR]

This paper focuses on the user rights regimes that have governed access to the marine resources of the Faroe Islands, which in the case of the fisheries are primarily fish stocks, and, in the case of salmon farming, primarily clean and healthy water masses. Examining the application of different user rights arrangements, we were able to observe a gradual improvement in the economic efficiency of the production activity for salmon farming, where strong operating exclusivity played a pivotal role in strengthening user rights. Similarly, the pelagic fisheries and the distant-waters demersal fisheries governed by ITQs have witnessed improved economic efficiency, although the strength of their user rights weakened over the last decade because of diminishing exclusivity and security of their rights. At the same time, the coastal demersal fisheries governed by ITEs continued to be marked by overcapacity and low fish stock levels despite the presence of strong exclusivity. These findings indicate that the design of user rights must also relate to specific management differences in marine resource exploitation under consideration. [ABSTRACT FROM AUTHOR]

Agriculture farms such as crop, aquaculture and livestock have begun the implementation of Internet of Things (IoT) and artificial intelligence (AI) technology in improving their productivity and product quality. However, microalgae farming which requires precise monitoring, controlling and predicting the growth of microalgae biomass has yet to incorporate with IoT and AI technology, as it is still in its infancy phase. Particularly, the cultivation stage of microalgae involves many essential parameters (i.e. biomass concentration, pH, light intensity, temperature and tank level) which require precise monitoring as these parameters are important to ensure an effective biomass productivity in the microalgae farming. Besides, the conventional practices in the current process equipment are still powered by electricity, thus further development by integrating IoT into these processes can ease the production process. Further to that, many researchers has studied the machine learning approach for the identification and classification of microalgae. However, there are still limited studies reported on applying machine learning for the application of microalgae industry such as optimising microalgae cultivation for higher biomass productivity. Therefore, the implementation of IoT and AI in microalgae farming can contribute to the development of the global microalgae industry. The purpose of this current review paper focuses on the overview microalgae biomass production process along with the implementation of IoT toward the future of smart farming. To bridge the gap between the conventional and microalgae smart farming, this paper also highlights the insights on the implementation phases of microalgae smart farming starting from the infant stage that involves the installation and programming of IoT hardware. Then, it is followed by the application of machine learning to predict and auto-optimise the microalgae smart farming process. Furthermore, the process setup and detailed overview of microalgae farming with the integration of IoT have been discussed critically. This review paper would provide a new vision of microalgae farming for microalgae researchers and bio-processing industries into the digitalisation industrial era. • Implementation of IoT technology helps to monitor microalgae farming remotely. • Microalgae industries should collaborate with local companies to implement IoT. • Lack of machine learning studies on the application of microalgae farming • Machine learning support IoT in data optimization and predict production forecast • Challenges are internet access, lack of profession, cybersecurity, industrial scale. [ABSTRACT FROM AUTHOR]

• This paper showcases the potential of information and communication technologies in traditional agriculture, as well as the issues to be encountered when they are applied to farming practices. • The challenges of robotics, IoT devices, and machine learning, as well as the roles of machine learning, artificial intelligence, and sensors used in agriculture, are all described in detail. In addition, drones are under consideration for conducting crop surveillance as well as for managing crop yield optimisation. • Additionally, whenever appropriate, global and state-of-the-art IoT-based farming systems and platforms are mentioned. We perform a detailed study of the recent literature in each field of our work. • From this extensive review, we conclude that the current and future trends of artificial intelligence (AI) and identify current and upcoming research challenges on AI in agriculture. The digitalization of data has resulted in a data tsunami in practically every industry of data-driven enterprise. Furthermore, man-to-machine (M2M) digital data handling has dramatically amplified the information wave. There has been a significant development in digital agriculture management applications, which has impacted information and communication technology (ICT) to deliver benefits for both farmers and consumers, as well as pushed technological solutions into rural settings. This paper highlights the potential of ICT technologies in traditional agriculture, as well as the challenges that may arise when they are used in farming techniques. Robotics, Internet of things (IoT) devices, and machine learning issues, as well as the functions of machine learning, artificial intelligence, and sensors in agriculture, are all detailed. In addition, drones are being considered for crop observation as well as crop yield optimization management. When applicable, worldwide and cutting-edge IoT-based farming systems and platforms are also highlighted. We do a thorough review of the most recent literature in each area of expertise. We conclude the present and future trends in artificial intelligence (AI) and highlight existing and emerging research problems in AI in agriculture due to this comprehensive assessment. [ABSTRACT FROM AUTHOR]

This paper focuses on the issue of how rising wages of agricultural labor affect pesticide application. Using survey data of 3399 rice farmers from 12 provinces in southern China between 2018 and 2019, this paper estimates the effect of wages of agricultural labor on pesticide application by employing the control function (CF) approach. The results suggest that a 10% rise in labor wages corresponds to a 6.88% increase in pesticide dosage and a 4.46% increase in pesticide expenses. Furthermore, this paper analyzes the heterogeneous effects of rising labor wages on pesticide dosage. The results reveal that the positive effect of labor wages on pesticide dosage is weaker for farmers who receive rice subsidies and have cooperative membership but it is stronger for farmers with more rice cultivation experience and those who use more chemical fertilizers. According to these findings, this study proposes policy implications for reducing pesticide application in terms of developing agricultural technologies, encouraging farmers to join cooperatives, and calling on governments to increase crop subsidies. • Focusing on how rising wages of agricultural labor affect pesticide application. • There is a positive relationship between wages of agricultural labor and pesticide application. • Rice subsidies and cooperative membership can weaken the positive relationship. • Rice cultivation experience and chemical fertilizer use can strengthen the positive relationship. [ABSTRACT FROM AUTHOR]

Nowadays, agricultural precision management (APM) has become an important way to transform the agricultural industry and realize the sustainable development of the agricultural supply chain. However, there is no relevant research based on the agricultural supply chain to explore the problems in the process of APM currently. There is also no literature to construct a problem identification model to provide guidance for corporate practice and provide suggestions for the government to issue encouraging policies. In order to fill the gaps, this paper adopts grounded theory method, interviews 24 business managers who carry out APM through a combination of personal in-depth interviews and focus group interviews, uses continuous comparative analysis ideas, and continuously refines and revises theories, and innovatively proposes an identification model of APM based on supply chain. It found that the four main categories of supply chain coordination, production resource support, scientific and technological equipment and structural system construction are the problems that hinder APM. Based on the problem identification model, this paper puts forward suggestions for the better development of APM from two aspects: corporate practice and government policy. [ABSTRACT FROM AUTHOR]

• A goal point determination method is proposed for the problem of selecting the optimal path point by Pure Pursuit. • An evaluation function is established to select the optimal goal point based on tractor position prediction model. • The performance of the proposed path tracking algorithm is studied by field test. The development of precision agriculture requires the implementation of automatic navigation technology for agricultural machinery. Path tracking control as one of the key steps in automatic navigation technology has great research value. Pure Pursuit algorithm as a popular control algorithm for automatic navigation technology for agricultural machinery, the look-ahead distance is the key to the tracking effect, while the calculation of look-ahead distance has the problem that many influencing factors are difficult to be accurately described by mathematical expressions, which leads to the difficulty to select a suitable goal point tracking path. In order to solve the above problems, this paper proposes a path tracking algorithm for agricultural machinery based on the optimal goal point. The algorithm simulates the look-ahead behavior of the driver and searches for optimal goal point in the look-ahead area according to the evaluation function. The objective of the study is to minimize the lateral error and heading error to achieve the adaptive optimization of the goal point. Finally, the feasibility of the algorithm proposed in this paper was verified in simulations and bumpy field tests under various different conditions, with tracking errors reduced by more than 20% compared to the pure pursuit algorithm. The tracking accuracy is significantly improved. [ABSTRACT FROM AUTHOR]

Due to its low global share of direct energy consumption and greenhouse gas emissions (1–2%), the implications of technological transitions in the agricultural and forestry sector on the energy system have been overlooked. This paper introduces the Agriculture and Land Use Sector module part of the ModUlar energy System Environment (MUSE), a novel energy system simulation model. The study presents a generalisable method that enables energy modellers to characterise agricultural technologies within an energy system modelling framework. Different mechanisation processes were characterised to simulate intensification/extensification transitions in the sector and its wider implications in the energy and land use system aiming at providing reliable non-energy outputs similarly to those found in dedicated land use models. Additionally, a forest growth model has been integrated to explore the role of reforestation alongside decarbonisation measures in the energy system in achieving carbon mitigation pathways. To illustrate the model's capabilities, Brazil is used as case study. Outputs suggest that by 2030 under a 2 °C mitigation scenario, most of Brazil agricultural production would move from 'transitional' to 'modern' practices, improving productivity and reducing deforestation rates, at the expense of higher energy and fertiliser demand. By mid-century Brazil has the potential to liberate around 24.4 Mha of agricultural land, where large-scale reforestation could have the capacity to sequester around 5.6 GtCO 2 , alleviating mitigation efforts in the energy system, especially reducing carbon capture and storage technology investments in the industry and power sector. • A novel energy-oriented agriculture and land use systems model has been developed. • Mechanisation levels are characterised using cluster analysis and linear optimisation. • Role of reforestation alongside measures in the energy sector in Brazil are studied. • By 2050, modern mechanisation could represent 54–63% of agricultural production. • Brazil has the potential to sequester around 5.6 GtCO 2 through reforestation. [ABSTRACT FROM AUTHOR]

Particularly for developing regions like Sub Saharan Africa, energy intensity is a critical policy issue and usually affected by four challenges: substitution between energy and other factors, technological change, changes in energy composition, and changes of economic growth. This paper examines energy intensity, output elasticity, energy-capital-labor substitution possibility, and factors contribution rates. The study applies a Translog production approach with data spanning 1990–2014 and further, applies ridge regression technique as a robust rectification to achieve unbiasedness in the findings. There is expected evidence of inverse relationship: higher energy intensity in lower per capita economy, evidenced by upward energy tariffs with lower energy elasticity. Factors are inelastic and have average elasticity substitution possibility for capital-energy with their inferential rebound effects' challenges. However, other combinations indicate complementarity at the current development stage. We further, observe that if higher energy intensity not managed, would translate into higher CO 2 intensity, because 99% of total energy is sourced from hydrocarbons of 51% and 48% of biomass. Based on the findings, policymakers and implementors are encouraged to integrate and optimize the whole energy system towards improving energy efficiency through increasing renewable energy composition from the current 1% to at least 10% whiles increasing quality-energy from nonrenewable. • Translog production with Ridge Regression analysis on energy intensity. • Higher energy intensity in lower per capita economy, and low energy elasticity. • The presence of energy substitution has implication for rebound effect on efficiency. • 99% of total energy consumption originates from hydrocarbons and biomass. • Energy poverty and inefficiency as a result of low energy technology innovations. [ABSTRACT FROM AUTHOR]

The emerging bioeconomy requires vast amounts of biomass that current value chains cannot provide. Novel biomass value chains therefore need to be developed. To date, only few novel value chains emerged and additional research on their development is much needed. This paper presents the results of an exploratory case-study analysis of a corn stover value-chain development process in two regions: Ontario (Canada) and Flanders (Belgium). Applying an integrated analytical framework and comparing the results with literature, we identified a number of barriers for novel value-chain development and state seven concrete actions that value-chain actors can take to overcome these barriers. These recommendations can be used by policy makers wishing to facilitate the development of novel biomass value chains in their region; by farmers and industrial actors working to establish new avenues to valorize currently underutilized biomass sources and create additional revenues; and by researchers seeking to disseminate and implement their knowledge about novel technologies as well as socio-economic insights. • Involve all stakeholders in advisory committee to ensure a value-chain perspective. • Independent and competent intermediary is needed to guide value-chain development. • Decide common objective first, but recognize individual actors' goals and concerns. • Non-disclosure agreements and time contribute to creation of trust in value chain. • Transparency about research results is crucial for novel value-chain development. [ABSTRACT FROM AUTHOR]

Under the strategy of rural vitalization, talent has become one of the main bottlenecks restricting the rural industries vitalization. Based on the analysis of the contradictory problem between the supply and demand of new professional farmers cultivation, this paper constructs a basic-elements set of new professional farmer cultivation, defines the contradictory problems, and solves the contradictory problems through extension transformation, and finally forms a new professional farmers cultivation supply models to promote the rapid development of rural vitalization. [ABSTRACT FROM AUTHOR]

In this article we explore whether and how the dynamics of access shape the scaling of modern agricultural technologies. It is based on the experience of an agricultural research for development (AR4D) project called CASCAPE, which aims to validate and scale agricultural best practices for smallholder farmers in Ethiopia. The socio-political dynamics of external interventions are often taken for granted contextual factors in AR4D projects. By contrast, this article takes this context as the point of departure for its analysis. The aim of this in-depth case study is to unpack the concept of access as condition for scaling of agricultural technologies. We identify and analyse the mechanisms that determine access to the various components of a malt barley technology package which was introduced in two highland communities in southern Ethiopia (and later 'scaled' to a range of other communities). Our research approach is technographic, implying that we consider the technology to contain both material and social components. The findings suggest that social and clan-based exchange mechanisms (such as clan-based loyalty, reciprocity and vertical accountability) are often rendered invisible even though they are of critical importance in governing access to the material and social components of modern agricultural technologies. Ignoring this socio-political context in the malt barley interventions resulted in an unintended scaling effect in terms of widening the social and economic gap between a few better off farmers and a larger group of poor farmers. The paper thus provides evidence that the socio-political dynamics of access to technology can have an important influence on its wide spread application and may complicate efforts to scale the uptake of technology. Paying more attention to such processes would help to improve the effectiveness of AR4D efforts. • Scaling and adoption arise from a web of interactions in farming communities • Ignoring the socio-political context may result in perpetuating the status quo • Tackling social exclusion requires addressing the constrained agency of the poor • Mechanisms that affect access determine the success or failure of scaling [ABSTRACT FROM AUTHOR]

Agricultural extension to improve yields of staple food crops and close the yield gap in Sub-Saharan Africa often entails general recommendations on soil fertility management that are distributed to farmers in a large growing area. Site-specific extension recommendations that are better tailored to the needs of individual farmers and fields, and enabled by digital technologies, could potentially bring about yield and productivity improvements. In this paper, we analyze farmers' preferences for high-input maize production supported by site-specific nutrient management recommendations provided by an ICT-based extension tool that is being developed for extension services in the maize belt of Nigeria. We use a choice experiment to provide ex-ante insights on the adoption potentials of site-specific extension services from the perspective of farmers. We control for attribute non-attendance and account for class as well as scale heterogeneity in preferences using different models, and find robust results. We find that farmers have strong preferences to switch from general to ICT-enabled site-specific soil fertility management recommendations which lend credence to the inclusion of digital technologies in agricultural extension. We find heterogeneity in preferences that is correlated with farmers' resource endowments and access to services. A first group of farmers are strong potential adopters; they are better-off, less sensitive to risk, and are more willing to invest in a high-input maize production system. A second group of farmers are weak potential adopters; they have lower incomes and fewer productive assets, are more sensitive to yield variability, and prefer less capital and labor intensive production techniques. Our empirical findings imply that improving the design of extension tools to enable provision of information on the riskiness of expected outcomes and flexibility in switching between low-risk and high-risk recommendations will help farmers to make better informed decisions, and thereby improve the uptake of extension advice and the efficiency of extension programs. • Choice experiement on farmer preferences for site-specific extension recommendations • High willingness to accept ICT-enabled and site-specific extension recommendations • Observed heterogeneity in farmers' willingness to intensify production systems • Information on riskiness of return crucial for improved design of extension tools [ABSTRACT FROM AUTHOR]

• The diffusion process of biogas technology among Mixed Crop and Livestock (MCL) farm households is evaluated. • Duration analysis is used to model the diffusion rate of biogas technology among MCL farmers. • Technological benefits, as well as the experience testing technologies, support the acceleration of individual adoption. • Technical assistance in the field is required to manage information and communicate biogas technical difficulties to farmers. Research on biogas technology adoption lacks information of the diffusion process in which included the acceleration of the adoption. In this paper, a duration analysis model is employed to model the speed of biogas technology adoption among small mixed crop and livestock farmers in Indonesia. A duration approach can model the adoption in a timely process to determine the biogas technology diffusion. Findings from this study indicate that there is an overall slow rate of biogas diffusion among farmers. There are however several ways to support the diffusion. It is revealed that 1) longer education, 2) higher farm income, 3) women involvement in the diffusion process and 4) access to financial support significantly accelerate the diffusion process. Farmer's perception of the relative advantages of biogas technology and their experience from testing the biogas technology also support the uptake of technology at the farm level. In addition, a technology dissemination strategy of stakeholders, especially local government is essential since the different regional regulations and policies have an important role to accelerate the diffusion process. [ABSTRACT FROM AUTHOR]

Advanced ceramics have many attractive features such as high stability and wear resistance that find broad applications in various fields, e.g. optics, aerospace, etc. However, the accompanying difficult-to-machine property with complex geometry brings great challenges to the commonly used laser machining and rigid wheel based grinding in industry. To achieve optical surface quality with surface roughness below 10 nm Ra, three promising ultra-precision compliant machining technologies using adaptive elastic tools are presented in this paper, including bonnet polishing, compliant pitch polishing and shape adaptive grinding with fine grain size. A comparative study was conducted by machining three different low thermal expansion ceramics while continuously increasing attack angle, spindle speed and tool offset across rectangular regions. Material removal rate (MRR) and surface roughness (Ra) with respect to different process conditions are compared. With sufficient data, the processing ability using above three compliant machining technologies is summarized based on the MRR-Ra plots for different ceramics. In addition, microscopic observation and X-ray diffraction analysis are conducted to characterize differences in material behavior. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to present an exploratory investigation concerning the e-commerce adoption among SMEs together with the factors influencing their readiness to adopt such technology. The context of the study was a developing one. The study revealed that e-commerce adoption among SMEs is affected by technological, financial, cultural, and organizational factors. The financial and technological factors appear as the most critical ones, followed by the cultural and organizational factors. [ABSTRACT FROM AUTHOR]

Abstract Historically, the focus of the agricultural industry has been increasing profit through maximizing crop yield. Costs for energy and water are small compared to equipment and personnel, and are thus often overlooked. However, energy costs for irrigation are increasing and could be exacerbated with declining water levels in many Western states. This trend has motivated many farmers to explore sustainable irrigation water and energy management practices. Much of this new focus has been directed towards the adoption of new agricultural technologies with a misplaced assumption that technology alone will inherently bring all the benefits. On one hand, farms are going through a paradigm shift, and are turning into net electricity generators, and on the other, higher penetration of intermittent renewable sources into the electricity grid, require dynamic loads to help the grid balance its intra-hour variability and short duration ramps. The agricultural industry could be restructured to utilize larger amounts of renewable energy such as wind and solar and provide a great deal of flexibility to the grid. As emerging producers of clean energy, farmers are required to learn and speak the complex language of the electricity grid in order to monetize their energy generation while making the renewable electricity grid more resilient and reliable. In this paper, we develop a foundational approach for understanding and connecting three important concepts that can help the agricultural industry during this critical transition period. Those three concepts are: (a) current and future needs of the electricity grid, (b) available electricity market mechanisms through which farms can provide services to the grid, and (c) understanding electricity consuming/generating equipment on farms. Defining these concepts and condensing them into a standardized framework, can remove a significant barrier for enabling farms to provide services to the electricity grid while improving their bottom line. Highlights • Agricultural demand response is an underutilized grid resource. • Farms are emerging as net clean energy generators. • Farms need to interact with the grid on both demand and supply sides. • A farm to grid framework is necessary for future of the electricity grid. • Farm loads, grid needs, and electricity markets are currently not well defined. [ABSTRACT FROM AUTHOR]