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Sustaining life on the Earth with its ever-growing population is forcing changes in people’s way of life, industrial and agricultural production, exploitation of energy resources, and approaches to ecology. We face continual growth in the world’s population, and the demand for materials is growing even more rapidly. Every manufacturing and consumer sector is looking for ways to save energy and materials, attempting to minimize their negative impacts on the environment. In the pulp and paper industry, one of the segments in which progress can be made and help protect the planet is to reduce the brightness of paper. Such a reduction would lead to a lowering in the energy and material costs associated with paper production. [ABSTRACT FROM AUTHOR]

Microfluidic analytical devices have gained significant traction by virtue of their unique merits in terms of miniaturization and attractive phenomena at microscale. The utilization of paper as a substrate material has witnessed substantial progress majorly due to its fast fluid transport, capillary action kinetics and improved detection limits bringing forward its techno-economic viability. This article provides light to the state-of-the-art developments in paper-based lab-on-a-chip (pLOC) devices for the detection of agri-food contaminants. Various fabrication processes for paper-based devices are elaborated along with their research progress. An analysis of its potential in detecting pesticides, viruses, food contaminants, foodborne pathogens, mycotoxins, and heavy metals in agricultural and food products has also been highlighted. Finally, the existing challenges and future prospects trending towards digitalization is emphasized to understand its feasibility and commercialization. pLOC devices offer numerous advantages due to its abundant availability of substrate, ease of fabrication, improved surface functionalization processes, as well as the ability to detect multiple analytes simultaneously (i.e., multiplexing) with low-volume requirements. There lies widespread opportunity with paper due to its porous cellulosic matrix and spontaneous channel flow of reagents or analytes enabling the development of standalone analytical devices. The sensitivity, selectivity, and repeatability are the crucial deciding parameters for evaluating the analytical performance of lab-on-a-chip sensors. A frugal engineering approach and protocol is ideally suited for the development of miniaturized sensors for on-site monitoring of contaminants in resource-limited settings. The monitoring of agri-food contaminants may further be combined with nanotechnology for signal enhancement, artificial intelligence and use of algorithms for the smart sensing to make it more user friendly for point-of-care applications. The use of pLOCs for the detection of agri-food contaminants has indeed paved the way to facilitate sustainable precision agriculture. [Display omitted] • pLOCs offer fast fluid transport, capillary action kinetics and high detection limit. • Notable progress is achieved on integrating pLOCs with nanomaterials to improve LOD. • A rapid, straightforward, and high-throughput manufacturing process is needed. • Cost-effective and high sensitive detection/readout mechanisms are required in pLOCs. • Integration of AI & ML can support the design, optimization and fabrication of pLOCs. [ABSTRACT FROM AUTHOR]

Purpose: This case study is based on the Women in Agri-Tech programme, a teacher professional development programme that aimed to build teachers' capabilities to use Agri-Tech in their agriculture/STEM classes to increase student awareness of agriculture technology and its associated career paths. Teachers and agriculture industry partners co-created Agri-Tech modules, which were implemented and evaluated from teachers' and students' perspectives. This paper demonstrates how work-related learning that emphasises technology can increase the visibility of career pathways and how multi-stakeholder benefits can evolve from teacher–industry partnerships. Design/methodology/approach: An action research design using quantitative and qualitative methods was used to construct this case study. Data collection methods included surveys, interviews, peer evaluation and teacher reflections. Findings: Teacher professional development that incorporates teacher-industry partnerships can increase teachers' self-efficacy and build confidence to support authentic work-related learning in their classes. Integrating technology into agriculture/STEM classes can increase the visibility of agriculture career paths for students, which is critical given the serious skills shortage in this sector. Research limitations/implications: This study is limited in that the Women in Agri-Tech professional development programme was one standalone programme tailored specifically for women agriculture/STEM teachers through a competitive process. However, the beneficial implications of such programmes that support teacher–industry engagement have far-reaching benefits. Teacher professional development programmes that provide opportunities to partner with industry can support improvements in integrating career-aligned learning into the curriculum and can specifically address industry skills and knowledge gaps by addressing school-based learning requirements for the future workforce. Originality/value: This paper contributes to the literature on education–industry partnerships and considers how teachers can contribute to an early intervention sector workforce development strategy for future industry sustainability. [ABSTRACT FROM AUTHOR]

[Display omitted] • A real-time grading method of roasted tobacco quality based on near infrared spectroscopy. • A key indicators selection method based on information gain and Fisher criterion was proposed. • A chemical indicator grading table was designed for reference. • Chemical indicator grading based on near infrared spectroscopy is a convenient, rapid and promising grading method. Traditional tobacco quality grading methods have limitations such as high human resource consumption and poor consistency, resulting in the grading accuracy being affected. Near infrared spectroscopy (NIRS) technology, as a rapid and non-destructive analytical method, has been widely used for online inspection. This study aims to accomplish rapid, accurate and reliable real-time grading of tobacco quality by employing online near-infrared spectroscopy to detect key chemical indicators. A key indicators selection method based on information gain and Fisher criterion (IFRI) was proposed in this paper by associating tobacco chemical indicators with traditional quality grades. The key chemical indicators were selected from many indicators by two classical feature selection functions, and a chemical indicator grading table was designed based on a large number of statistical data on tobacco chemical indicators, and the table was applied to grade the tobacco quality of real data from different years. The results show that the IFRI algorithm is an effective method for selecting key indicators, and the selected reducing sugars-to-total nitrogen ratio and nicotine are the key indicators for tobacco quality grading. Moreover, we demonstrated that the designed grading table is reasonable and feasible. Compared with traditional grading, chemical indicator grading based on near infrared spectroscopy can not only be realized quickly by using an online near infrared instrument, but also the tobacco of the same grade is less discrete, better consistency and more representative after grading, and it is applicable to tobacco samples of different years, which is a convenient, rapid and promising grading method that can provide a reference for tobacco quality grading. Furthermore, the formulation design by utilizing different grades after grading can greatly improve the utilization rate of tobacco leaves. Notably, the method can also be applied to other fields, such as quality control of agricultural products, monitoring of food processing processes, etc. [ABSTRACT FROM AUTHOR]

• Reviewed "precision agriculture", "smart agriculture" and "digital agriculture" concepts. • Suggested "smart agriculture" and "digital agriculture" be phases of "precision agriculture". • Proposed a few research focuses on smart and digital technologies for agriculture. [ABSTRACT FROM AUTHOR]

Based on the learning characteristics of the higher quality professional farmer students, this article had taken the course of production technology and equipment of facility agriculture as an example, which was one of the core curriculum of the major of facility agriculture of Beijing Vocational College of Agriculture. The course teaching overall plan was elaborated in this paper in terms of teaching objectives, course content, teaching mode, curriculum politics and assessment program. Also, with the teaching implementation process analysis of the learning module task of installation and operation of soilless culture facilities, the teaching exploration and practice of the teaching team for this course was displayed in the paper, which provided profitable experience and reference for the deeper curriculum reform. [ABSTRACT FROM AUTHOR]

Corn crop row recognition during different growth stages is a major difficulty faced by the current development of visual navigation technology for agricultural robots. In order to solve this problem, an algorithm for recognizing corn crop rows during different growth stages is presented based on the ST-YOLOv8s network. Firstly, a dataset of corn crop rows during different growth stages, including the seedling stage and mid-growth stage, is constructed in this paper; secondly, an improved YOLOv8s network, in which the backbone network is replaced by the swin transformer (ST), is proposed in this paper for detecting corn crop row segments; after that, an improved supergreen method is introduced in this paper, and the segmentation of crop rows and background within the detection frame is achieved utilizing the enhanced method; finally, the corn crop row lines are identified using the proposed local–global detection method, which detects the local crop rows first, and then detects the global crop rows. The corn crop row segment detection experiments show that the mean average precision (MAP) of the ST-YOLOv8s network during different growth stages increases by 7.34%, 11.92%, and 4.03% on average compared to the MAP of YOLOv5s, YOLOv7, and YOLOv8s networks, respectively, indicating that the ST-YOLOv8s network has a better crop row segment detection effect compared to the comparison networks. Corn crop row line detection experiments show that the accuracy of the local–global detection method proposed in this paper is improved by 17.38%, 10.47%, and 5.99%, respectively, compared with the accuracy of the comparison method; the average angle error is reduced by 3.78°, 1.61°, and 0.7°, respectively, compared with the average angle error of the comparison method; and the average fitting time is reduced by 5.30 ms, 18 ms, and 33.77 ms, respectively, compared with the average fitting time of the comparison method, indicating that the local–global detection method has a better crop row line detection effect compared to the comparison method. In summary, the corn crop row recognition algorithm proposed in this paper can well accomplish the task of corn crop row recognition during different growth stages and contribute to the development of crop row detection technology. [ABSTRACT FROM AUTHOR]

The symbiotic relationship between nitrogen-fixing cyanobacteria and plants offers a promising avenue for sustainable agricultural practices and environmental remediation. This review paper explores the molecular interactions between nitrogen-fixing cyanobacteria and nanoparticles, shedding light on their potential synergies in agricultural nanotechnology. Delving into the evolutionary history and specialized adaptations of cyanobacteria, this paper highlights their pivotal role in fixing atmospheric nitrogen, which is crucial for ecosystem productivity. The review discusses the unique characteristics of metal nanoparticles and their emerging applications in agriculture, including improved nutrient delivery, stress tolerance, and disease resistance. It delves into the complex mechanisms of nanoparticle entry into plant cells, intracellular transport, and localization, uncovering the impact on root-shoot translocation and systemic distribution. Furthermore, the paper elucidates cellular responses to nanoparticle exposure, emphasizing oxidative stress, signaling pathways, and enhanced nutrient uptake. The potential of metal nanoparticles as carriers of essential nutrients and their implications for nutrient-use efficiency and crop yield are also explored. Insights into the modulation of plant stress responses, disease resistance, and phytoremediation strategies demonstrate the multifaceted benefits of nanoparticles in agriculture. Current trends, prospects, and challenges in agricultural nanotechnology are discussed, underscoring the need for responsible and safe nanoparticle utilization. By harnessing the power of nitrogen-fixing cyanobacteria and leveraging the unique attributes of nanoparticles, this review paves the way for innovative, sustainable, and efficient agricultural practices. [ABSTRACT FROM AUTHOR]

This paper identified the intangible aspects (IA) that generate impacts in companies of the agricultural sector in the different stages of the companies life cycle. This paper is based on a systematic review methodology to identify intangibles in the agricultural sector. Studies on the life cycle of companies were analyzed to classify the impact of intangible aspects according to the reality of companies. Identify the intangible aspects that impact the agricultural sector through a review of the scientific literature. How intangible aspects affect each stage of the life cycle of companies in the sector shows that they have a different impact depending on the period in which the company is located. Although, much of the previous literature has suggested that intangible aspects have positive effects on a variety of performance variables, there are no scientific studies that specifically address the importance of these intangible aspects in the life cycle of companies in the agricultural sector. [ABSTRACT FROM AUTHOR]

This study evaluates the treatment efficiency of a horizontal subsurface-flow constructed wetland (HSSF-CW) for the removal of chloro-organic compounds from pulp and paper mill wastewater. The surface area of the HSSF-CW unit was 5.25 m² and was planted with Colocasia esculenta. The wastewater was characterized for different chloro-organic compounds, that is, adsorbable organic halides (AOX), chlorophenolics, and chlorinated resin and fatty acids (cRFAs). Under a hydraulic retention time of 5.9 days, the average AOX, chlorophenolics, and cRFA removal from wastewater was 87, 87, and 93%, respectively. Some of the chlorophenolics were found to accumulate in the plant biomass and soil material. The mass balance studies show that a significant fraction of chlorophenolics and cRFA was degraded in the constructed wetland system. Modeling studies were carried out to estimate the first-order area-based removal rate constants (k) for chemical oxygen demand removal. The HSSF-CW was found to be an effective treatment technology for the remediation of pulp and paper mill wastewater. [ABSTRACT FROM AUTHOR]

Oyster Farming is one of important fisheries and aquaculture industries in Taiwan. Each year, approximately 4000–5000 tons of discarded bamboo scaffolding (BS) used in oyster farming, are generated, so the treatment and utilization of BS should be taken seriously. This study evaluates the suitability of BS for pulp and papermaking by assessing the chemical compositions, microstructural, and fiber morphology. The pulping properties is investigated by soda pulping. The chemical composition of BS shows the potential for application in pulping. The BS microstructure shows that can enhance pulping reactions, while the fiber morphology indicates the possibility of producing high-strength paper. Through the pulping experiment, it demonstrated that BS is suitable for pulping with lower NaOH dosage and longer digestion time. The condition at 170 °C with 14% NaOH dosage for 90 min digestion has the highest yield. After refining the highest pulping yield BS pulp, it can improve the handsheet strength and bulk of the OCC-BS mixed pulp, which can achieve the strength property required for industrial paper. In summary, BS exhibits the potential for pulping application and produces a better paper strength than OCC pulp, exhibiting the feasibility of enhancing the circular utilization value of BS in Taiwan. [ABSTRACT FROM AUTHOR]

AbstractThis paper examines the potential of in-store vertical farming technologies in urban food retail as a solution to the increasing demand for fresh, locally sourced food. By enabling a “sell where you produce” model, vertical farming can reduce food miles, enhance traceability, and lower carbon emissions. The study applies the Unified Theory of Acceptance and Use of Technology (UTAUT) to analyze factors influencing the adoption of these technologies among food retailers in Rouen, France, with a particular focus on the role of risk aversion in shaping attitudes and decisions. The findings highlight that performance expectancy and facilitating conditions are the key drivers of adoption intentions among retail store managers. However, perceived risks, such as high investments and operational costs, pose significant barriers, while social influence appears to have little impact on their decisions. The study underscores the importance of conducting economic evaluations and risk assessments of vertical farming practices to ensure their long-term viability. As vertical farming holds the potential to boost fresh food production in urban areas, reduce food miles, and create sustainable supply chains, it is crucial to carefully weigh its benefits and risks to support its successful adoption in urban food retail. [ABSTRACT FROM AUTHOR]

Societal Impact Statement: Maize is the world's second most important agricultural crop. The cereal was unknown to Europeans before the end of the 15th century, but since its arrival in Europe, it has changed agriculture, food and landscapes. Terraces where maize was cultivated in the Northwest of the Iberian Peninsula contributed to the formation of local cultures and identities. The history of maize and maize landscape are mementos that help to recover traditional practices, fostering identities, and are crucial for the successful implementation of sustainable policies to provide prosperous futures. Summary: Maize (Zea mays L.) in the Iberian Peninsula embodies a history of landscape changes where the concepts of 'exotic', 'traditional' and 'hybrid' help to understand the engagements between landscape, farmers, agronomists (since the 19th and 20th centuries) and seeds. Today, landscapes reveal biophysical and ecological changes that reflect a panoply of intentions. A multitude of agents, and their interactions, acted upon those territories over time.Using historical sources from the leading institutions dedicated to agricultural research in the Iberian Peninsula, this paper aims to (1) contribute to a better understanding of the maize landscape and culture in the Iberian Peninsula and (2) interrogate how landscape changes (and the landscape history of maize) can frame local or regional heritage and identities reflecting customs or ways of life.The analysis unveils networks of knowledge, agricultural technologies and seed exchange. Politicians, economists, engineers, agronomists, farmers, governmental officials and agricultural industries planned and transformed traditional rural practices into modern and industrialised ones. Experts and politicians, willing to improve agricultural practices and seeds, using hybrid seeds or building new irrigation systems, led to deep social and landscape changes, allowing maize to cover territories far away from its traditional domains. Moreover, despite farmers' resistance, hybrid maize substituted landraces, eroding agrobiodiversity. Nowadays, the south and east regions of the Iberian Peninsula are the main producers of maize (hybrid), whereas in the Northwest maize is an occasional crop, being replaced by vineyards for economic reasons. [ABSTRACT FROM AUTHOR]

Background: Agriculture is the linchpin of the Indian economic system which extraordinarily dependent on soil, use of fertilizer and climate conditions. Soil fertility depletion is a reason for the problem of Indian agriculture. Not only the usage of plant vitamins per hectare is fairly low and imbalanced, but also the Indian farmers are still using the traditional method to spread the fertilizer in the field which is the predominant reason for low crop yields. It also influences people via consumption. So, there should be a precision and constancy tracking mechanism in agricultural merchandise and use of fertilizers inside the farm. In this paper, the implemented system presents a method for the distribution of required fertilizer for crop acreage. To optimize this, the wireless sensor gives a systematic answer for fertilizer distribution using Lagrange's Interpolation (LI). Methods: The experiment was carried out in the MIT Academy of Engineering's Agrotech Farm in Pune over the last one year (2019-2020). The readings were taken from the tomato crop. We used the LI to evaluate the values between Standard, Conventional and Actual, which delivers the optimal value and reduces fertilizer waste and farmers' financial burden. These LI may result in a decrease in the number of sensor nodes in the agricultural field. The decrease in sensor nodes lowers total operating costs. Result: With an equally and unequally spaced approach, in comparison to a conventional strategy the implemented system saves nitrogen (N), phosphorus (P) and potassium (K) by just giving the crop with the required amount of fertilizer. [ABSTRACT FROM AUTHOR]

The Republic of Moldova has made significant strides in adopting organic farming practices, supported by government subsidies and strategic focus on market demand. This paper aims to analyze the economic performance of organic farms in Moldova and their impact on the sustainable development of the agricultural sector, using primary data collected from 63 organic farms benefiting from subsidies between 2020 and 2022. The agricultural sector in Moldova has experienced a gradual increase in cultivated land, with a noteworthy 9.5% dedicated to organic farming. Economically, farmers have seen substantial growth in income per hectare, credited to favorable climatic conditions and advanced technologies. Although organic farming contributes significantly, constituting approximately one-third of total income, conventional agriculture remains the primary contributor with two-thirds. The impact of subsidies from the National Fund for Agricultural and Rural Development is evident in farm development, as calculated profits per one lei of subsidy have surged from 1.51 to 10.43 lei in 2022. This underscores the effectiveness of government support in fostering growth and development within Moldova's agricultural sector, emphasizing a positive trajectory for sustainable agricultural practices. [ABSTRACT FROM AUTHOR]

In the European agri-food sector, operators with substantial bargaining power often engage in unfair trading practices (UTPs). Our paper aims to empirically examine the occurrence of UTPs and their influence on the performance of cooperatives. To fulfill the goal of our paper, we collected responses from 109 cooperatives in Greece after the transposition of a specialized EU Directive (i.e., Directive (EU) 2019/633). We found that, on average, the sampled cooperatives encountered three prohibited ("black") UTPs, while all reported at least one prohibited UTP. Moreover, the two most commonly reported practices (i.e., "unduly late payments" and "buyers' demand that suppliers pay for the deterioration or loss of products that occurred after ownership transfer") exerted a significant negative influence on cooperative performance, even in the presence of a proficient Board of Directors. Consequently, policymakers may need to pay more attention to UTPs and ensure that the national enforcement authorities are well-equipped to act rapidly and effectively against offenders. [ABSTRACT FROM AUTHOR]

China's agricultural economy has been hindered by insufficient accumulation of agricultural capital and credit constraints. It is worth investigating whether China's county financial marketization reform policy can alleviate these constraints and promote high-quality development of the agricultural economy (HQDAE). This paper presents an empirical analysis of the impact of county financial marketization reform on the HQDAE, based on county panel data. The focus is on the mechanism of county urbanization in the above relationship. The results show that county financial marketization has a significant non-linear impact on the HQDAE. Specifically, it has a 'U-shaped' impact on the overall growth of the agricultural economy and an inverted 'N-shaped' impact on the quality improvement of the agricultural economy. Secondly, the relationship between county financial marketization and the HQDAE is influenced by a threshold effect based on the level of county urbanization. As the level of county urbanization increases, the promoting effect of county financial marketization on HQDAE also increases significantly. Additionally, county financial marketization helps to promote county urbanization and accelerate urban-rural integration, which in turn leads to HQDAE. The research in this paper suggests that county-level local governments should promote a differentiated county financial system. In the early stages of financial market-oriented reform, the government should enhance the capacity of financial services in rural areas through tax breaks, policy incentives, and other measures to prevent financial leakage from agriculture. In the later stages of financial marketization reform, the government should strengthen financial supervision to prevent financial resources from being diverted from industry to capital. Moreover, to achieve the HQDAE, it is necessary to promote county financial market-oriented reform and accelerate the construction of county urbanization. This will help break the dual economic structure of urban and rural areas and promote the flow of financial capital, technology, and human capital from county cities to rural areas. [ABSTRACT FROM AUTHOR]

The relationship between ICT, the financial sector, and output growth has been extensively studied, however, macro-economic studies with an emphasis on the role of rural ICT on agricultural performance are few and yield mixed findings. Additionally, past research has not given sufficient attention to how bank credit affects agricultural performance. This paper highlighted the dynamic effect of rural ICT penetration and bank credit on agricultural performance in Eastern Indonesia. We used secondary data taken from the Central Bureau of Statistics and the Financial Services Authority. The panel data covered 16 provinces of eastern Indonesia from the first semester of 2010 to the second semester of 2022 (2010S1-2022S2). Using the panel autoregressive distributed lag (ARDL) approach, the results showed that in the long run, rural ICT penetration and bank credit played a significant role in boosting agricultural performance. However, in the short run, the impact of rural ICT penetration and bank credit on agricultural performance was statistically insignificant. Finally, we recommended several important policies that can practically impact and contribute to improving agricultural performance. [ABSTRACT FROM AUTHOR]

The paper investigates the technical efficiency of conventional and organic dairy farms in Estonia in the period 2006-2015 using Farm Accountancy Data Network. We analyse self-selection into organic farming using the propensity-score-matching approach and explicitly test the hypothesis that organic and conventional farms apply homogeneous technology. We find that organic farms are less efficient. However, the difference in technical efficiency between organic and conventional farms decreases substantially when the technical efficiency assessment incorporates the use of the appropriate technology. The lack of growth of technical efficiency over time indicates that there might be a lack of knowledge in organic milk production that hinders its development. Since technical efficiency increases with farm size, it is important that organic dairy farms increase their scale. [ABSTRACT FROM AUTHOR]

As the global population is expected to reach 10 billion by 2050, the agricultural sector faces the challenge of achieving an increase of 60% in food production without using much more land. This paper explores the potential of Artificial Intelligence (AI) to bridge this "land gap" and mitigate the environmental implications of agricultural land use. Typically, the problem with using AI in such agricultural sectors is the need for more specific infrastructure to enable developers to design AI and ML engineers to deploy these AIs. It is, therefore, essential to develop dedicated infrastructures to apply AI models that optimize resource extraction in the agricultural sector. This article presents an infrastructure for the execution and development of AI-based models using open-source technology, and this infrastructure has been optimized and tuned for agricultural environments. By embracing the MLOps culture, the automation of AI model development processes is promoted, ensuring efficient workflows, fostering collaboration among multidisciplinary teams, and promoting the rapid deployment of AI-driven solutions adaptable to changing field conditions. The proposed architecture integrates state-of-the-art tools to cover the entire AI model lifecycle, enabling efficient workflows for data scientists and ML engineers. Considering the nature of the agricultural field, it also supports diverse IoT protocols, ensuring communication between sensors and AI models and running multiple AI models simultaneously, optimizing hardware resource utilization. Surveys specifically designed and conducted for this paper with professionals related to AI show promising results. These findings demonstrate that the proposed architecture helps close the gap between data scientists and ML engineers, easing the collaboration between them and simplifying their work through the whole AI model lifecycle. [ABSTRACT FROM AUTHOR]

Summary: The main goal of this strategy is to detect the occurrence of wild animals in farmsteads and human habitats using an improved deep learning in order to avoid loss of life and damage to agricultural crops. This paper formulates a technique for recognizing wild animals in an Internet of Things (IoT) environment utilizing deep batch normalized exponential linear unit AlexNet (DbneAlexnet) and the Gazelle Hunting Optimization Algorithm (GHOA). The IoT‐Multimedia Sensor Networks (WMSN) network is first simulated, with the IoT nodes capturing the images needed for wild animal identification. The suggested GHOA is used for routing, which sends the captured images to the base station (BS). The input wild animal image is transmitted to image preprocessing at the BS, where it is processed using the Weiner filter (WF) to remove undesirable noise from the image. The denoised output is sent into salient map extraction, which extracts salient map to determine the regions that are conspicuous or noticeable at every place in the field of vision and guides the selection of attended location. Finally, the saliency map is forwarded to DbneAlexnet for detecting the wild animals, where the DbneAlexnet is trained using proposed GHOA. The suggested GHOA algorithm is created by combining the Gazelle Optimization Algorithm (GOA) and the deer hunting optimization algorithm (DHOA). Furthermore, detection is appraised by precision, recall, and f1‐score, which provide values of 90.2%, 89.1%, and 89.6%, respectively. [ABSTRACT FROM AUTHOR]

In order to better develop green agricultural products in Jining and promote high-quality agricultural development, this paper based on SWOT to analyze and conclude that there are five advantages to develop green agricultural products in Jining including natural resources, advantageous agricultural industry, modern agricultural technology, human resources, and green agricultural development model, and there are four disadvantages of green agricultural products on small scale of green agricultural products, low popularization rate of green agricultural products production technology, backward management mode of green agricultural products and relatively backward supervision of green agricultural products, and there are three opportunities; the need of the new era, policy guidance and support, and the transformation of consumption concept, and it is threatened by the slowdown of investment in agriculture, the lack of competitiveness of green agricultural products, and the uncertainty of international trade. Strategic suggestions were put forward; in order to develop well green agricultural products in Jining, it was necessary to enlarge and strengthen the superior and high-quality agricultural products industry, improve the quality of agricultural products of small and medium-sized farmers, and continuously improve the certification of "pollution-free, green and organic" products, attach importance to the application and implementation of advanced cultivation technologies and expand effective investment in agriculture, and effectively solve the quality problems in the development of green agricultural products. [ABSTRACT FROM AUTHOR]

To explore whether digital finance can reduce agricultural carbon emissions, promote regional convergence, and foster inclusivity in rural revitalization and shared prosperity, this paper uses the provincial-level index of digital financial inclusion to analyze the impact of digital financial inclusion on the intensity of agricultural carbon emissions and the Degum Gini coefficient (D-Gini coefficient) of regional carbon emission intensity in 30 sample provinces from 2010 to 2020. It examines the mechanism of the impact of digital financial inclusion on both variables to understand the underlying factors better. The main conclusions are as follows: (1) Digital financial inclusion significantly reduces the intensity of agricultural carbon emissions and narrows the gap in carbon emission intensity between regions. (2) The unconditional quantile regression coefficients show that the negative coefficients of the digital financial inclusion index and the three-dimensional indices decrease with increasing quantiles. However, the significant effects vary significantly at different quantiles. (3) Technological progress and the government's ability to allocate financial resources play a significant mediating role, and the income gap between urban and rural areas can be further narrowed, as well as the carbon emission intensity gap between provinces. The empirical results are robust and proven by replacing the econometric analysis method, changing the core variables, and other methods. [ABSTRACT FROM AUTHOR]

It is worthwhile to calculate the execution cost of a manipulator for selecting a planning algorithm to generate trajectories, especially for an agricultural robot. Although there are various off-the-shelf trajectory planning methods, such as pursuing the shortest stroke or the smallest time cost, they often do not consider factors synthetically. This paper uses the state-of-the-art Python version of the Robotics Toolbox for manipulator trajectory planning instead of the traditional D–H method. We propose a cost function with mass, iteration, and residual to assess the effort of a manipulator. We realized three inverse kinematics methods (NR, GN, and LM with variants) and verified our cost function's feasibility and effectiveness. Furthermore, we compared it with state-of-the-art methods such as Double A* and MoveIt. Results show that our method is valid and stable. Moreover, we applied LM (Chan λ = 0.1) in mobile operation on our agricultural robot platform. [ABSTRACT FROM AUTHOR]

As part of sustainable agricultural innovations and the alternative food movement, agroecology provides important tools to achieve the sustainable development goals (SDGs). Yet very few existing studies have explicitly addressed their linkages. Synergies among the SDGs are often examined at the national level, overlooking nuanced opportunities revealed by small agroecology initiatives at the local scale. Based on literature review, field visits and semi-structured interviews with farms and relevant organizations in China, this research investigates how locally embedded agroecology practices lead to advancement of multiple SDG targets and facilitate the synergies among the SDGs. It also highlights their motivations, achievements, and challenges. We argue that the technocentric metrics of SDGs underappreciate the application of traditional knowledge and small-scale low-tech innovations. By bringing visibility to low-tech and small agroecology initiatives, this paper advocates for stronger inclusion of grassroots agroecology initiatives in SDG discussions among researchers and policy makers. [ABSTRACT FROM AUTHOR]

Agriculture has been the backbone of African economies, and agricultural development has the potential to drive overall economic growth. Considering the importance of agriculture in Africa, foreign aid donors have supported the sector to help push economic growth and reduce poverty. Studies indicate governance quality of a recipient country is an important factor for agricultural aid. Building upon this, the paper explores how bilateral donors provided agricultural aid to two African countries that have different institutional quality, Ghana and Uganda. The analysis of agricultural aid in those two countries was carried out with bilateral aidprofile data from 2011 to 2022. Despite the similar economic importance of agriculture in Ghana and Uganda, donors to Ghana invested the largest amount in agriculture among aid sectors, followed by health. On the other hand, donors to Uganda supported mainly health-relevant sectors with agriculture being ranked only fifth. Donors in both countries implemented agricultural aid mostly as project types. Yet, donors disbursed their aid funds through different aid channels. Donors to Ghana disbursed 48% of agricultural aid funds through the public sector institutions channel (or the state channel). By contrast, donors to Uganda disbursed only 24% of agricultural aid funds through the state channel, essentially bypassing Ugandan public agencies. They instead provided 76% of agricultural aid through the non-state channels such as non-governmental organizations, multilateral organizations, and private entities. Similar donor preferences of the aid disbursement channels were observed with total aid across all aid sectors. Overall, the donor aid profiles suggested donors provided Ghana with more flexible agricultural aid, while in Uganda they controlled agricultural aid more tightly. There could exist multiple underlying reasons for this, but the different level of institutional quality is highly likely one of the possible reasons. For foreign aid, governance quality is an important issue equally to both donors and recipients. [ABSTRACT FROM AUTHOR]

Output uncertainty is a major concern for industries prone to exogenous, persistent and large fluctuations in output, such as agriculture, wind and solar power generation, while technology adoption aimed at mitigating output uncertainty can improve social welfare. This paper constructs a competitive market model with random output fluctuations to examine the scale of technology adoption at the long-term equilibrium and its comparison with the social optimum. We show that the First Welfare Theorem no longer holds in general, and depending on the characteristics of the demand function, the scale of technology adoption in the competitive market may be greater or less than the socially optimal scale. • We consider technology adoption aimed at mitigating output uncertainty in the competitive market. • We show that the First Welfare Theorem no longer holds in general. • We show that the scale of technology adoption in the competitive market may be too high or too low. [ABSTRACT FROM AUTHOR]

The new development pattern has identified two key avenues for the sustained advancement of high-quality agricultural and rural development: digitalisation and low-carbon development. The measurement of the digital economy and the agricultural carbon emission performance, and their spatial and temporal heterogeneity, is a crucial step in promoting the spatial coordination and sustainable development of digitalisation and low-carbon agriculture. This paper employs the entropy value method, SBM model, and coupling coordination degree model to investigate the coupling coordination measurement and spatial-temporal heterogeneity of the performance of the digital economy and agricultural carbon emissions. The data used are provincial panel data from 2013 to 2021. The simulation results demonstrate that, between 2013 and 2021, the digital economy of all provinces exhibited varying degrees of growth, yet the development of the digital economy between provinces exhibited a more pronounced tendency to diverge. Concurrently, the agricultural carbon emission efficiency in China exhibited a fluctuating upward trend. The development of the digital economy and the efficiency of agricultural carbon emission were found to be highly coupled. Their coupling and coordination relationship showed a downward trend followed by an upward trend. In general, it is suggested that we should increase investment in digital economy infrastructure and technology, promote digital agricultural applications, strengthen policy guidance and financial support, establish a coupling coordination mechanism and strengthen farmers' digital literacy and environmental awareness. [ABSTRACT FROM AUTHOR]

China is transitioning into the digital economy era. The advancement of the digital economy could offer a fresh mechanism to attain carbon peak and carbon neutrality objectives. Applications of the digital economy, such as smart energy management, intelligent transport systems, and digital agricultural technologies, have significantly reduced carbon emissions by optimizing resource use, reducing energy waste, and improving production efficiency. This research does so by devising a theoretical model that looks into the multi-faceted power of the digital economy under a two-sector paradigm. Utilising a panel model, a mediation effect model and a spatial Durbin model to assess the digital economy's power on carbon emissions. This research has determined that the digital economy can significantly diminish carbon emissions, with green tech innovations and industrial transformation being key contributors. The spatial spillover effect was used for the digital economy to aid in lowering carbon emissions in adjacent districts and upgrading better environmental stewardship. The influence of the digital economy has better performance in lowering carbon emissions in mid-western China than in the eastern area. This paper deepens understanding of the drivers of low-carbon growth and the significance, mechanism and regional disparities of the digital economy's effect on reducing carbon emissions. It offers valuable policy insights and guidance for globally achieving digital economy growth, reducing carbon emissions and reaching carbon peak and neutrality goals. [ABSTRACT FROM AUTHOR]

The integration of Internet of Things (IoT) technology into agriculture has revolutionized farming practices by using connected devices and sensors to optimize processes and facilitate sustainable execution. Because most IoT devices have limited resources, the vital requirement to efficiently manage data traffic while ensuring data security in agricultural IoT solutions creates several challenges. Therefore, it is important to study the data amount that IoT protocols generate for resource-constrained devices, as it has a direct impact on the device performance and overall usability of the IoT solution. In this paper, we present a comprehensive study that focuses on optimizing data transmission in agricultural IoT solutions with the use of compression algorithms and secure technologies. Through experimentation and analysis, we evaluate different approaches to minimize data traffic while protecting sensitive agricultural data. Our results highlight the effectiveness of compression algorithms, especially Huffman coding, in reducing data size and optimizing resource usage. In addition, the integration of encryption techniques, such as AES, provides the security of the transmitted data without incurring significant overhead. By assessing different communication scenarios, we identify the most efficient approach, a combination of Huffman encoding and AES encryption, to strike a balance between data security and transmission efficiency. [ABSTRACT FROM AUTHOR]

Making a living from a small farm is difficult in sub-Saharan Africa. In this paper, we quantify how difficult, using a simple, robust, relation between per capita daily income from farming (FPDI), land per capita and whole-farm net profitability per hectare. This relation allows the calculation of the land area required to generate various levels of household income as a function of farm performance. We use nationally representative household data for Tanzania to investigate the range of whole-farm profitability and to estimate an upper limit for it. For 6818 cases where households with land reported figures for gross crop and livestock revenues and for costs in any of the three years 2009, 2011 or 2013, actual median whole-farm net profitability was only $454/ha/y even without including the opportunity cost of family labour. When those were considered, median net profitability was negative $238/ha/y, i.e. a net loss. The maximum whole-farm profitability achieved was $4485/ha/y without family labour costs and $2742/ha/y with it. We evaluated actual and potential farm performance for their ability to generate a range of values of FPDI up to $10 per person per day. Most farms are not very profitable, particularly when household labour costs are considered, and few would be considered economically prosperous. Our analysis underscores the fact that improving their operations or adopting new technologies alone is unlikely to lift many smallholder farmers out of poverty in developing countries, given typical farm size distributions and reasonable assumptions about the realized economic returns to adoption of currently available agricultural technologies. While continued agricultural R&D investments are certainly worthwhile, such efforts alone will be insufficient to meaningfully address welfare needs of the world's rural poor. This suggests that agricultural development programs should expand their attention to incorporate off-farm and non-farm components of the rural economy. [ABSTRACT FROM AUTHOR]

The digitization of agriculture is widely discussed today. But despite proven benefits, its acceptance in agricultural practice remains low. In small-structured areas, this trend is even more pronounced. There are even known cases where farmers initially purchased and used technology, but then stopped using it due to lack of profitability or other reasons. Interestingly, despite extensive research on precision agriculture technologies (PATs), the processes of adoption and phase-out with their associated economic impacts have never been studied. This paper provides a methodological framework for evaluating the economics of PAT deployment, taking into account changes during the period of use; the framework provides decision rules for determining the appropriate time to phase out technology. Using a selected PAT, a farm model, and defined entry and exit scenarios, it was shown that farms with outdated technology and farms with retrofittable technology are at a significant economic disadvantage during implementation compared to farms already using technology suitable for site-specific fertilization or farms relying on the use of a contractor. And even in the event of a phase-out, the two disadvantaged starting conditions face significantly greater uncertainties and costs. Moreover, the decision to phase out in time is difficult, as making an informed and fact-based decision is not possible after the first year of use. Therefore, it is advisable that farmers are not only accompanied before and during phase-in, but also receive professional support during use. [ABSTRACT FROM AUTHOR]

The current geopolitical and socioeconomic landscape creates a difficult and uncertain operating environment for farming and agri-food businesses. Technological innovation has not been suggested to be a "silver bullet" but is one of the ways organizations can seek to reduce environmental impact, deliver net zero, address the rural skills and labor deficit and produce more output from fewer resources and as a result, make space for nature. But what barriers limit this promissory narrative from delivering in practice? The purpose of the paper is to firstly explore the reported social, technical and systemic barriers to agri-technology adoption in an increasingly uncertain world and then secondly identify potential research gaps that highlight areas for future research and inform key research questions. Socio-technical and infrastructural barriers have been identified within the context of the complex hollowing out and infilling of rural communities across the world. These barriers include seventeen factors that emerge, firstly those external to the farm (economic conditions, external conditions including bureaucracy, market conditions, weather uncertainty and the narratives about farmers), those internal to the farm business (farming conditions, employee relations, general finance, technology and time pressures) and then personal factors (living conditions, personal finances, physical health, role conflict, social isolation and social pressure). Adaptive resilience strategies at personal, organizational and community levels are essential to address these barriers and to navigate agri-technology adoption in an uncertain and dynamic world. [ABSTRACT FROM AUTHOR]

In today's world, agricultural production and operation activities generate a lot of data. As a result, computer-aided agriculture applications have become a hot topic in the study, with various machine learning (ML) algorithms being used to classify agricultural data. This paper presents a comparative study consisting of a combination of ML algorithms with meta-heuristic algorithms for feature selection to improve the classification capability of ML algorithms by finding the features that significantly impact accuracy. We have used six different meta-heuristic algorithms for feature selection. Experiments are conducted on four different agricultural datasets with five classification models. To understand the effect of proposed models, the selected features are fed into the ML algorithms. The results prove that combining ML and meta-heuristic algorithms achieves higher classification accuracy with fewer features on agricultural datasets. [ABSTRACT FROM AUTHOR]

Farmland transfer is a critical component in facilitating agricultural scale management and improving agricultural production efficiency. This study examines the spatial distribution of farmland transfer in China and identifies the factors influencing it, offering valuable guidance for advancing China's farmland transfer practices. Through the application of mathematical statistics and GIS spatial analysis, the study investigates changes in spatial patterns related to the scale, rate, mode, and recipients of farmland transfer across China's 30 provinces from 2015 to 2020. Geographical detectors are also employed to identify the key factors influencing the extent and pace of farmland transfer. The study reveals that between 2015 and 2020, China's farmland transfer area increased from 29,789 to 37,638 million hectares. Provinces with abundant farmland resources generally experienced larger farmland transfers, while economically developed regions and major grain-producing areas saw higher rates of farmland transfers. The predominant mode of farmland transfer in China was leasing (subcontracting), accounting for over 80% of the total transferred area. Large-scale grain growers and family farms were significant participants in farmland transfers, acquiring approximately 60.1% of the transferred lands, followed by professional cooperatives (21.5%), enterprises (10.4%), and other entities (7.9%). Key factors influencing the farmland transfer area include the "regional farmland area", the "proportion of family farms supported by loans", and the "proportion of non-agricultural population", with explanatory powers of 0.663, 0.319, and 0.225, respectively. Notably, there is a substantial interaction between the "regional farmland area" and factors such as the "proportion of family farms supported by loans" and the "grain yield per unit area", with explanatory powers reaching 0.957 and 0.901, respectively. These findings offer valuable insights for promoting farmland transfer in agriculturally rich regions. Factors affecting the farmland transfer rate include "grain yield per unit area", "GDP per capita", and the "proportion of non-agricultural population", each with an explanatory power above 0.500. Moreover, their interactive explanatory powers with other indicators exceed 0.600, indicating that provinces with high agricultural productivity or economic development levels are more likely to undergo farmland transfer. The paper concludes by proposing strategies and recommendations to promote farmland transfer in both "large agricultural areas" and "metropolitan suburbs." [ABSTRACT FROM AUTHOR]

As a new business model, E-commerce brings new changes to the global economy and society. So, can E-commerce development policies promote high-quality agricultural development? This article regards the pilot construction of national e-commerce demonstration cities as a quasi-natural experiment for the development of e-commerce. Based on the E-commerce pilot and economic and social development data of national prefecture-level cities from 2004 to 2018, the agricultural total factor productivity calculated by the Fare-Primont index method is used as the characterization of the quality of urban agricultural development, and an empirical model is constructed under the progressive Differences-in-Differences framework. This paper empirically tests the overall impact of E-commerce development on the high-quality development of agriculture and its sources, analyzes the heterogeneity and dynamics of the impact, and investigates the possible impact mechanism. The result shows that from the overall impact and its sources, the development of E-commerce in cities has a positive impact on the high-quality development of agriculture, and the impact is mainly due to its role in promoting technological innovation and economies of scale. From the perspective of heterogeneity, the larger the population size of a city, the more significant the level of e-conomic development or Internet development. From the perspective of the dynamic impact, the positive impact of e-commerce development becomes more significant over time. With the passage of time, the impact of E-commerce on high-quality agricultural development policies shows a trend of first increasing and then decreasing. Overall, this study provides empirical evidence for the rationality and effectiveness of policies and measures related to E-commerce to promote the high-quality development of agriculture. [ABSTRACT FROM AUTHOR]