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Microbial xylanase has wide application in bioenergy, animal feed, environmental protection, the pulp and paper industry, and agricultural development. In this study, three xylanases from the microbiota of T. fuciformis with its companion strains were identified by metagenomics sequencing. The three enzymes were subjected to cloning and expression in E. coli or P. pastoris, purification, and characterization for their properties. The results showed that AsXyn1, from Annulohypoxylon stygium, among the three enzymes possessed high thermostability at 40 °C and broad pH tolerance in the range of 2.0–10.0, exhibiting its application potential. Furthermore, it was found that post-translational modification (such as glycosylation) of AsXyn1 enzyme modulated its activity, kinetic parameters, and thermostability. These results and findings provided a hint for enzyme modification and design in future. [ABSTRACT FROM AUTHOR]

A database of journal articles and conference papers that cover the economics of agricultural development in Africa during the period 2016–2020 was compiled. These papers are first described in terms of demographic and bibliometric criteria, followed by a network analysis of the main centers of origin and of influence of this research. Finally, three different processes were used to test the policy relevance of the work. These include a simple enumeration of the policy recommendations in a sample of the papers, a megatrend analysis, and a comparison of their focus with the standard narrative of the role of agriculture in economic development. The results show that a large proportion of the literature is not forward‐looking, and is weak on policy relevance. We ignore a number of issues that engage the attention of those concerned with implementing structural change in agriculture across the many very different but very real natural resource, political, social, economic, and technological environments around the continent. [ABSTRACT FROM AUTHOR]

The need to address the complex exchanges between natural resource efficiency and economic growth has become increasingly discussed in worldwide strategies and position papers. Resource depletion, environmental challenges and climate change are just a few topics related to fast industrialization and economic expansion. The question is how information regarding crops, climatic factors, or solutions used in plant treatment are managed. The purpose of this paper is to provide an overview of the scientific environment regarding quantitative indicators and models for the assessment of sustainable agriculture practices. The study is based on a bibliometric analysis made using Web of science database and processing using biblioshiny environment from R package. The most relevant works and influential authors in the field of quantitative modelling for sustainable agriculture were identified and a set of indicators and metrics for evaluating efforts in transitioning towards sustainability were synthetised. Integrating practical data into quantitative models can provide insights into trends, correlations, or even predictions, and the results obtained serve as inputs for decision-makers. [ABSTRACT FROM AUTHOR]

The paper presents the current situation as regards the circular economy in agriculture in Romania, while also taking into consideration its global impact. We have here a system globally endorsed by the United Nations. It is also important for regional organisations such as the European Union or la Francophonie as the circular economy seems to have a high number of advantages in tackling climate change, creating a sustainable food chain, improving the organic character of the agriculture, etc. From that perspective Romania represents an interesting case study as it is both a European Union member state and a member of the Francophonie. It thus follows both sets of rules and recommendations in order to improve its status. The paper uses the bibliographic method and reviews the main documents that settle this aspect and presents the legal framework for circular economy. The put into practice of the circular economy in the Romanian agriculture would bring a series of advantages (environment, food safety, etc.) while also requiring systemic transformations. [ABSTRACT FROM AUTHOR]

As we race towards 2030, the target date for achieving the U.N. Sustainable Development Goals, it is critical that we examine our approaches to agricultural development research and practice. We should not expect extraordinary results by using the approaches we have always used. Rather, we must innovate. To that end, Advancements in Agricultural Development held a Symposium on Methods of Agricultural Development Research and Practice in October 2023 on the University of Reading campus, co-hosted by the School of Agriculture, Policy and Development. We invited recognized experts from around the world to develop papers focused on innovative methods of agricultural development practice or innovative methods of agricultural development research. We had ten papers presented that were authored by 26 researchers representing 13 universities/research institutes in 6 countries. Sixteen authors attended the symposium to present their papers. A healthy dialogue followed each presentation to help authors refine their papers, resulting in the articles published in this special issue. A summary of each article is provided. [ABSTRACT FROM AUTHOR]

Purpose The purpose of this paper is to reveal the distributional characteristics and evolutional patterns in source periodicals, topics, authors, funding, and institutes of research papers in Chinese Agricultural Economics so as to understand the current situations and developmental tendency of Chinese agricultural economics research over the past decade.Design/methodology/approach Using the citation analysis method, this paper analyzed the distributional characteristics and evolution of source periodicals, fields, authors and topics of 2,203 highly cited journal papers from the database of China National Knowledge Infrastructure (CNKI) and 189 cited journal papers from database of Social Sciences Citation Index (SSCI) in agricultural economics first-authored by Chinese scholars from 2006 to 2015.Findings First, over the past decade, agricultural economics research in China has seen a rapid development. Specially, 103 scholars and 42 institutes have played key roles in the development, and 12 Chinese periodicals and 3 international journals have been the most influential outlets. Second, the coverage of the topics in Chinese agricultural economics research is broad and has expanded over the past decade. The rural land issue has been the most popular topic, while the issues regarding rural institutional arrangements and industrialization in rural areas have been explored extensively. However, issues in other fields, such as agricultural markets and trade, rural labor, food safety, etc. have to be further studied. Third, the improvements of economic theory and quantitative analytic techniques, the supports from research funding, and an increase in the collaboration between Chinese scholars and those from other countries have made great contribution to the rapid development of Chinese agricultural economics research over the past decade.Originality/value This paper is an original work that identifies the most influential journal papers including highly cited journal papers from CNKI and cited journal papers from SSCI, using citation frequency and standard Essential Science Indicators method. This is a contribution relative to the methods used by previous studies, which did not account for frequency of citation of a paper. Moreover, this study is based on data from two databases, CNKI and SSCI, suggesting that the coverage of sample papers is broader compared to those of previous studies. [ABSTRACT FROM AUTHOR]

Based on the panel data of 30 provinces, municipalities, and autonomous regions in China from 2012 to 2022, from the perspective of harmony between man and nature, this paper selects 20 indicators to measure the level of agricultural green development from five dimensions such as ecological conservation and resource conservation by entropy weight method. On this basis, taking the implementation of green industrial policy in the Yangtze River Economic Belt as a quasi-natural experiment, the policy effect of green industrial policy on agricultural green development was analyzed by using the difference-in-difference method. The study found that: (1) the green development of agriculture is basically increasing year by year in each province, but there are some differences in the green development of agriculture among provinces; (2) Compared with the non-implementation areas of policies, the green development of agriculture in the implementation areas of policies has been significantly improved, and has passed a series of robustness tests; (3) The mechanism analysis shows that the green industrial policy has a crowding-out effect on industrial development, but significantly enhances the ecological construction and protection, thus promoting the green development of agriculture; (4) Heterogeneity analysis shows that the policy has a significant positive incentive effect on the upper and lower reaches of the Yangtze River Economic Belt, and the incentive effect on the middle reaches is not significant; (5) The impact of technological level on agricultural green development shows a positive U-shaped relationship. The improvement of education and information development levels also effectively promotes the green development of agriculture. This paper provides important empirical evidence and factual references for further promoting the green development of agriculture and the improvement of green industrial policies. [ABSTRACT FROM AUTHOR]

Introduction: The primary function and spatial layout of agriculture are essential foundations and prerequisites for achieving green development. This paper explores the growth drivers for the green transformation of agricultural production in Guangdong, China, aiming to guide the continuous green and low-carbon development of agricultural productivity in Guangdong. Methods: Based on the panel data of 21 prefecture-level cities in Guangdong, China, from 2004 to 2022, this paper uses DEA-BCC and DEA-Malmquist to measure the agricultural green total factor productivity (AGTFP) and its decomposition efficiency in Guangdong China. The SDM is employed to analyze its spatial spillover roles of the agricultural industrial structure (AIS) innovation. Results: The results show that the AGTFP varies with an overall fluctuating upward trend, but the imbalance of regional differences still exists, characterized as "Pearl River Delta > Eastern Wing > Western Wing > Mountainous Area," indicating the siphoning role from Pearl River Delta. There is a strong spatial correlation between the AGTFP and the AIS. Discussion: Our evidence suggests that the AIS innovation improves the AGTFP and can have positive spatial consequences in neighboring areas. Besides, human capital hinders AGTFP, accounting for the self-exploitation in agricultural production driven by the excessive labor force input. Our findings highlight the spatial impact of the AIS innovation on the AGTFP when greening agricultural production. [ABSTRACT FROM AUTHOR]

Text matching promotes the research and application of deep understanding of text information, and it provides the basis for information retrieval, recommendation systems and natural language processing by exploring the similar structures in text data. Owning to the outstanding performance and automatically extract text features for the target, the methods based-pre-training models gradually become the mainstream. However, such models usually suffer from the disadvantages of slow retrieval speed and low running efficiency. On the other hand, previous text matching algorithms have mainly focused on horizontal domain research, and there are relatively few vertical domain algorithms for agricultural text, which need to be further investigated. To address this issue, a second-order text matching algorithm has been developed. This paper first obtains a large amount of text about typical agricultural crops and constructs a database by using web crawlers and querying relevant textbooks, etc. Then BM25 algorithm is used to generate a candidate set and BERT model is used to filter the optimal match based on the candidate set. Experiments have shown that the Precision@1 of this second-order algorithm can reach 88.34% on the dataset constructed in this paper, and the average time to match a piece of text is only 2.02 s. Compared with BERT model and BM25 algorithm, there is an increase of 8.81% and 13.73% in Precision@1 respectively. In terms of the average time required for matching a text, it is 55.2 s faster than BERT model and only 2 s slower than BM25 algorithm. It can improve the efficiency and accuracy of agricultural information retrieval, agricultural decision support, agricultural market analysis, etc., and promote the sustainable development of agriculture. [ABSTRACT FROM AUTHOR]

The impact of climate change on agricultural quality development under the constraint of China's "Double Carbon" target has been widely discussed by policy practitioners and academic theorists. This paper attempts to deconstruct the logic of how climate change affects agricultural total factor productivity (TFP) in three dimensions—the structure of agricultural input factors, the change in the cropping system, and the stability of crop supply. This paper also reveals the mechanism through which biased technological progress increases agricultural TFP by weakening the magnitude of climate change and empirically tests it by using China's provincial-level data from 2000 to 2021. This study showed that average annual temperature and annual precipitation had significant negative effects on agricultural TFP, that the number of sunshine hours had a significant positive effect on agricultural TFP, and that obvious regional differences existed in the effect of climate change on agricultural TFP. Further mechanism tests revealed that biased technological progress positively moderated the effect of climate change on agricultural TFP. Based on these findings, the appropriate countermeasures for improving climate early warning mechanisms, promoting the progress of appropriate technology, and fostering new agricultural management bodies. [ABSTRACT FROM AUTHOR]

Pressurized irrigation network systems constitute an important water-saving approach, but their relatively high costs limit their promotion. Pipe diameter optimization is the most direct way to reduce irrigation costs and can bring significant economic benefits. In recent years, material technology developments have improved networks' impact resistance, resulting in traditional flow velocity constraints no longer applying to current network designs, and appropriately increasing the velocity has become an engineering requirement. In addition, with the update of engineering construction standards, the location of pipe diameter changes is no longer limited to pipe section connections and is allowed to change between standard pipes. Therefore, this paper removes the constraint of the upper limit of flow velocity, defines it as a factor related to the maintenance cost of the network, and establishes a pipe section optimization model. Further, this paper changes the minimum optimization unit from pipe sections to standard pipes and establishes a standard pipe optimization model. The results show that compared with traditional optimization methods, the pipe section optimization model that deletes the upper velocity limit can reduce costs by 5.1%, in which the velocity will not increase infinitely but will be stable within a reasonable range. The standard pipe optimization model can reduce irrigation costs by 12.1%. It is worth noting that the two models were optimized to balance the head loss; their energy costs did not change significantly with the reduction in pipe diameter. In addition, this paper changes the structure of the traditional algorithm and designs a structure of double selection and parallel variation, achieving a faster and more accurate solution than the classic genetic algorithm. Practical Applications: Smart irrigation is an irrigation method based on advanced technology and data analysis that can manage water resources more accurately and efficiently. As an essential part of smart irrigation, pressure networks have the characteristics of saving water resources, improving the water utilization coefficient, and facilitating management and control. Their promotion can promote the development of agriculture. However, high network costs limit the popularity of smart network irrigation. Therefore, this study is based on the latest material technology, cancels the upper limit of flow velocity, and establishes a pipe section optimization model. Furthermore, this paper changes the minimum optimization unit from pipe sections to standard pipes and establishes a standard pipe optimization model. The research results show that the standard pipe optimization model can significantly reduce irrigation costs. This has practical significance for increasing farmers' income and promoting the development of smart pipeline irrigation. In addition, this paper changes the structure of the traditional algorithm and designs a structure of double selection and parallel variation, achieving a faster and more accurate solution than the classic genetic algorithm. [ABSTRACT FROM AUTHOR]

Rural communities face problems related to depopulation, lack of jobs, weak digital infrastructure, decline of basic public services. LEADER is an initiative to support rural communities, which takes shape in a Local Development Strategy with a role in using local resources in a way that meets the needs identified in the territory. The support of projects for smart villages must be encouraged through local development strategies that pursue various fields: reducing the depopulation of villages, providing quality services, developing local services, promoting digitization. The paper proposes the role that the LEADER intervention belonging to the Strategic Plan, which is one of the instruments of the European Union, can contribute to supporting these rural development projects, which can represent local initiatives and which can benefit from funding sources that will contribute to the identification of strategies and future solutions to promote smart villages. The information used in the work was collected, processed and interpreted starting from data belonging to the Bureau for the Financing of Rural Investments, along with insights from the Ministry of Agriculture and Rural Development, converge in a rich tapestry of thought at the paper's conclusion. Here, they weave forward-thinking strategies for the cultivation of smart villages, which are envisioned as vibrant oases capable of elevating the quality of life for rural dwellers. These innovative settlements promise to champion the cause of sustainable rural development, casting a verdant future where community and sustainability flourish side by side. [ABSTRACT FROM AUTHOR]

As the most active field in China's economic development, the digital economy is crucial to the high-quality development of enterprises. Taking Chinese-listed agricultural enterprises as research samples, this paper discusses the effect of the digital economy on promoting the high-quality development of agricultural enterprises and the mechanism behind it from the perspective of total factor productivity of enterprises. The study found that the digital economy significantly promoted the improvement of total factor productivity of agricultural enterprises and enabled the high-quality development of agricultural enterprises, and this conclusion remained valid after controlling for endogeneity and conducting robustness tests. In terms of action mechanisms, the digital economy is conducive to promoting green innovation and promoting the high-quality development of agricultural enterprises; However, entrepreneurial activity has a masking effect. Further analysis shows that the digital economy plays a more significant role in promoting non-resource-intensive agricultural enterprises located in northern China. At the same time, the higher the industrial structure and financial development level of the region, the more positive impact the digital economy can play. This study verifies the impact mechanism of the digital economy on the high-quality development of agricultural enterprises and provides enlightenment for China's the digital economy policy-making and enterprises' the high-quality development strategy decision-making. [ABSTRACT FROM AUTHOR]

Sustainable agricultural economic development is the core task for achieving the objective of rural revitalization strategy in China, which cannot be separated from the support and guidance of fiscal policy, and agricultural industry integration is a key path for the fiscal promotion of sustainable agricultural economic development. This paper systematically examines the interaction mechanism between fiscal agricultural expenditures and sustainable agricultural economic development by using 31 provincial panel data in China from 2008 to 2020 and adopting a two-way fixed effect model, a panel quantile model, and a mediating effect model, respectively. The results show that the impact of fiscal agricultural expenditures on sustainable agricultural economic development is significantly positive, and appears a dynamic increasing trend with the agricultural development stage upgrading. Moreover, heterogeneity analysis shows that the effect of fiscal agricultural expenditures is more obvious for the samples in the central region and with a high share of primary industry. Further, a mediating effect test finds that agricultural industry integration plays a mediating mechanism between fiscal agricultural expenditures and sustainable agricultural economic development. Therefore, this paper proposes constructing a long-term investment mechanism for fiscal agricultural expenditures, formulating differentiated fiscal support policies for agriculture, and prioritizing support for agricultural industry integration, which provides theoretical support and policy inspiration for promoting sustainable agricultural economic development. [ABSTRACT FROM AUTHOR]

This special issue contributes to the development economics literature by highlighting the role of information communication and technologies (ICTs) in supporting rural and agricultural development. It is comprised of nine papers. Key findings from this special issue include: (1) internet use increases rural consumption diversity and agricultural productivity; (2) smartphone use empowers rural women in household decision‐making and off‐farm work participation; (3) smartphone‐based agricultural extension services boost rural income growth; (4) a lack of ICT infrastructure and inadequate skills to use digital technologies are two key factors that lead to digital poverty traps for smallholder rural farmers; (5) ICT adoption increases the probability of rural households' access to credit and empowers rural women and farm households in relatively less developed regions to access credit; (6) digital financial inclusion reduces farmers' vulnerability to poverty; and (7) e‐commerce adoption increases both sales prices and marketing costs, but the magnitude of increasing the former is higher than the magnitude of increasing the latter, which finally contributes to a higher gross return. This special issue also proposes practical instruments and implications for advancing the application of ICTs in rural areas to accelerate rural and agricultural development in the digital age. [ABSTRACT FROM AUTHOR]

The occurrence of maize diseases is frequent but challenging to manage. Traditional identification methods have low accuracy and complex model structures with numerous parameters, making them difficult to implement on mobile devices. To address these challenges, this paper proposes a corn leaf disease recognition model SNMPF based on convolutional neural network ShuffleNetV2. In the down-sampling module of the ShuffleNet model, the max pooling layer replaces the deep convolutional layer to perform down-sampling. This improvement helps to extract key features from images, reduce the overfitting of the model, and improve the model's generalization ability. In addition, to enhance the model's ability to express features in complex backgrounds, the Sim AM attention mechanism was introduced. This mechanism enables the model to adaptively adjust focus and pay more attention to local discriminative features. The results on a maize disease image dataset demonstrate that the SNMPF model achieves a recognition accuracy of 98.40%, representing a 4.1 percentage point improvement over the original model, while its size is only 1.56 MB. Compared with existing convolutional neural network models such as EfficientNet, MobileViT, EfficientNetV2, RegNet, and DenseNet, this model offers higher accuracy and a more compact size. As a result, it can automatically detect and classify maize leaf diseases under natural field conditions, boasting high-precision recognition capabilities. Its accurate identification results provide scientific guidance for preventing corn leaf disease and promote the development of precision agriculture. [ABSTRACT FROM AUTHOR]

Plant pests and diseases are important parts of insect disease control and the high-quality development of agriculture. Traditional methods for identifying plant diseases and pests suffer from low accuracy and slow speed, while the existing machine learning methods are constrained by environmental and technological factors, leading to low recognition efficiency. To address the issue of the above problems, this paper has proposed an intelligent recognition algorithm based on the improved YOLOv8 model, which has high recognition accuracy and speed. Firstly, in the Backbone network, the Global Attention Mechanism (GAM) is adopted to weigh the important feature information, thereby improving the accuracy of the model. Secondly, in the mixed feature part of the Neck network, the Receptive-Field Attention Convolutional (RFA Conv) operation is used instead of standard convolution operations to enhance the processing ability for feature information and to reduce computational complexity and costs, thus improving the network performance. After verifying the rice and cotton datasets, the accuracy indicator mean average precision (mAP) reaches 71.27% and 82.91%, respectively, in the two different datasets. Comparing these indices with those of the Faster R-CNN, YOLOv7, and the original YOLOv8 model, the results fully demonstrate the effectiveness and superiority of the improved model in terms of detection accuracy. [ABSTRACT FROM AUTHOR]

The reasonable allocation of grain production factors is of vital importance to food security and agricultural development. To assess the impact of agricultural factor misallocation on food security, this paper, based on the panel data from China spanning from 2005 to 2019, conducted a comprehensive evaluation of agricultural factor misallocation and food security coefficients across 31 provinces in China, using a spatial Durbin model to examine the effects of factor misallocations on food security. The findings are as follows: (1) Production factor misallocation has significant negative impacts on food security, among them, capital misallocation and labor misallocation inhibiting food security in the local and neighboring areas, and land misallocation has a significant negative impact on food security in local areas, while its spatial spillover effect is no longer significant. (2) Mechanism analysis shows that capital misallocation and labor misallocation hinder the development of transportation infrastructure and the transfer of rural labor, thereby reducing food security. Land misallocation has accelerated the construction of transportation infrastructure, promoted the migration of rural labor, and helped ensure food security. (3) The regional heterogeneity test reveals that capital misallocation and labor misallocation hinder food security in major grain-producing areas and both sides of the Hu Huanyong Line (Hu Line). Meanwhile, land misallocation hinders food security development in various grain-producing areas, as well as the southeast of the Hu Line. Based on the above conclusions, this paper proposes suggestions to improve the efficiency of land and labor resource allocation, accelerate the construction of transportation infrastructure, and encourage the transfer speed of surplus agricultural labor. [ABSTRACT FROM AUTHOR]

Since entering the 21st century, many developing countries around the world have begun to enter a stage of rapid urbanization; large-scale "rural-urban" population migration has become a typical social phenomenon in these countries. Against this backdrop, this paper aims to elucidate the spatial migration characteristics of rural populations and to discuss future rural–urban development strategies. For this purpose, this paper takes China as a case and employs methods such as spatial autocorrelation analysis, hotspot analysis, and network analysis to construct an intercity migration network of rural migrants and analyze its spatial characteristics and internal structure. The results indicate that the migration pattern of the rural population exhibits notable spatial clustering features. Cities in the eastern and central regions are, respectively, hotspots for the inflow and outflow of rural populations, with internal migration dominating in western cities and relatively inactive rural population movements in northeastern cities. Municipalities directly under the central government, sub-provincial cities, and provincial capitals show a significant tendency to attract rural populations, while prefecture-level and county-level cities mainly radiate rural populations outward. Cities nationwide form seven major clusters in the migration network, and these clusters exhibit distinct structural characteristics. Rural population migration is influenced by various factors. In the future, considerations should focus on the county as the primary unit, attracting rural populations for local employment, and promoting rural revitalization and agriculture development. The findings of this paper are of reference significance not only to China but also to many developing countries with similar national conditions in the world. [ABSTRACT FROM AUTHOR]

Bird invasion will reduce the yield of high-value crops, which threatens the healthy development of agricultural economy. Sonic bird repellent has the advantages of large range, no time and geographical restrictions, and low cost, which has attracted people's attention in the field of agriculture. At present, there are few studies on the application of sonic bird repellents in pear orchards to minimize economic losses and prolong the adaptive capacity of birds. In this paper, a sound wave bird repellent system based on computer vision is designed, which combines deep learning target recognition technology to accurately identify birds and drive them away. The neural network model that can recognize birds is first trained and deployed to the server. Live video is captured by an installed webcam, and the sonic bird repellent is powered by an ESP-8266 relay switch. In a pear orchard, two experimental areas were divided into two experimental areas to test the designed sonic bird repellent device, and the number of bad fruits pecked by birds was used as an indicator to evaluate the bird repelling effect. The results showed that the pear pecked fruit rate was 6.03% in the pear orchard area that used the acoustic bird repeller based on computer recognition, 7.29% in the pear orchard area of the control group that used the acoustic bird repeller with continuous operation, and 13.07% in the pear orchard area that did not use any bird repellent device. While acoustic bird repellers based on computer vision can be more effective at repelling birds, they can be used in combination with methods such as fruit bags to reduce the economic damage caused by birds. [ABSTRACT FROM AUTHOR]

The downstream concentration of agricultural products markets under the asymmetric competition pattern of the supply chain has a profound impact on upstream agricultural production. Is this centralized market structure sustainable and efficient? The study examines the effects and mechanisms of agricultural product downstream concentration on the high-quality development of agriculture using the dairy industry as an example. Panel data from 10 provinces in China from 2004 to 2021 were selected for analysis. Using Malmquist index, fixed effects model and other methods, the research results prove that: (1) Downstream dairy market concentration is unfavorable to upstream raw milk total factor productivity growth. However, there is scale heterogeneity in this negative effect, with a positive impact for small-scale farming and a negative impact on medium-scale and large-scale farming. (2) Downstream market concentration drives upstream raw milk total factor productivity growth through technical efficiency improvements and market demand expansion, but it also inhibits raw milk total factor productivity growth through mechanisms that squeeze production margins and impede technological progress. Negative mechanisms are the main effect. (3) Higher wages, higher raw milk prices and an improved ratio of concentrate to crude are all helping to mitigate the negative effects of downstream concentration to some extent, but net profit retention and a high proportion of fixed assets will further exacerbate the negative effects. To better address the challenges brought by the trend of downstream market concentration and promote high-quality agricultural development, this paper proposes three suggestions: enhancing the market position of dairy farmers, regulating monopolistic behavior of oligopolies, and building a mechanism for linking interests. [ABSTRACT FROM AUTHOR]

Agricultural modernization can only be attained through the high-quality development of agriculture, which is made possible by the deep integration of the digital economy with traditional agriculture. From the perspective of the entire agricultural industry chain, this paper offers a theoretical and empirical examination of the digital economy's role in enabling high-quality agricultural development. The fixed effects model, convergence test, threshold model, and spatial error model were implemented based on the panel data of 13 cities in Jiangsu Province from 2013 to 2021 to validate the role of the digital economy in enabling high-quality agricultural development. The findings indicated that first, for every 1% increased in the level of digital economy development, the level of high-quality agricultural development increased by approximately 0.183%. Second, the high-quality development of agriculture had the characteristics of absolute convergence and conditional convergence, and there was a non-linear character in the role of the digital economy in the high-quality agricultural development. Third, for every 1% increase in the development level of digital inclusive finance, the level of high-quality agricultural development in surrounding areas with similar economic levels will be improved by about 0.188%.In this regard, Jiangsu Province should grasp the opportunity of the era of digital economic development, continue to strengthen the construction of digital economic development in backward areas, and attach great importance to inter-regional exchanges and cooperation in the construction of digital agriculture. [ABSTRACT FROM AUTHOR]

This research paper introduces a novel approach by combining a Backpropagation (BP) neural network with a non-angular and non-radial directional distance function to construct a BPNN-DDF model. This innovative model evaluates, decomposes, and analyzes China's agricultural sector's carbon emission rate across nine key subregions between 2010 and 2021. The key findings of this study are that China's agricultural carbon emission rate is decreasing, primarily due to technological advancements rather than technological efficiency. Subregions with robust economies and stable climates exhibit higher carbon emission efficiency, whereas those with underdeveloped economies, low agricultural technology, and volatile climates show relatively lower efficiency. The Dagum Gini coefficient analysis reveals a widening disparity in carbon emission rates among agricultural subregions, escalating from 0.174 in 2010 to 0.425 in 2021, indicating a growing gap between subregions that demands immediate attention. The kernel density distribution demonstrates an overall upward trend in China's carbon emission efficiency but also highlights an increasing divergence among subregions, particularly between the South China Area, the Huang-Huai-Hai Plain, and other regions. Therefore, this paper posits that strategies focusing on technological progress, sustainable agricultural development, regional development initiatives, and addressing inter-subregional imbalances will be crucial pathways for China's future low-carbon agricultural development. [ABSTRACT FROM AUTHOR]

The income effect of rice–crayfish co-culture technology (RCT) is directly related to rate of adoption of farmers and the process of China's green development of agriculture. The aim of this study is to explore the income effect and income growth mechanism of rice–crayfish co-culture technology from the perspective of continuous adoption. With the treatment effect model (TEM), this paper empirically analyzes the income effect and income-generating mechanisms of RCT using field survey data from 736 farmers in the Jianghan Plain. As a result of this study, it was discovered that RCT will increase farmers' net agricultural income by RMB 83,430 if they continue to adopt it. Further examinations indicate that the optimal adoption period for RCT is four and a half years. Additionally, it has also been shown that non-farm employment positively moderates the relationship between continuous adoption of RCT and net agricultural income. Farmers who participate in non-farm employment and continue to adopt the RCT will experience an increase in net agricultural income by RMB 104,510. Therefore, our results suggest that it is necessary to encourage farmers to continuously adopt RCT and actively participate in non-farm employment to enhance the income effect of RCT. [ABSTRACT FROM AUTHOR]

Human activities, such as mining, industrialization, industrial waste emissions, and agricultural practices, have caused heavy metals to become widespread and excessively accumulated in soil. The high concentrations of heavy metals in soil can be toxic to plants, severely affecting crop yield and quality. Moreover, these heavy metals can also enter the food chain, affecting animals and humans and leading to various serious illnesses. Selenium (Se) is not only an essential element for animals and humans but is also beneficial for plants, as it promotes their ability to respond actively to biotic and abiotic stresses. The global issue of Se deficiency in diets has made plants the primary source for human Se supplementation. This paper comprehensively reviews the effects of heavy metal stress on plant growth and development, physiological responses of plants to such stress, and the intracellular transport processes of heavy metals within plants. It particularly focuses on the mechanisms by which Se alleviates heavy metal stress in plants. Additionally, the study delves into how Se significantly enhances plant tolerance mechanisms against typical heavy metals, such as cadmium (Cd), lead (Pb), and mercury (Hg). This integrative research not only expands the boundaries of research in the field of plant heavy metal stress and Se application but also provides new perspectives and solutions for understanding and addressing complex environmental heavy metal pollution issues. By integrating these aspects, this paper not only fills existing gaps in the literature but also offers comprehensive scientific basis and strategic recommendations for environmental protection and sustainable agriculture development. [ABSTRACT FROM AUTHOR]

With the development of smart agriculture, autopilot technology is being used more and more widely in agriculture. Because most of the current global path planning only considers the shortest path, it is difficult to meet the articulated steering tractor operation needs in the orchard environment and address other issues, so this paper proposes a hybrid algorithm of an improved bidirectional search A* algorithm and improved differential evolution genetic algorithm(AGADE). First, the integrated priority function and search method of the traditional A* algorithm are improved by adding weight influence to the integrated priority, and the search method is changed to a bidirectional search. Second, the genetic algorithm fitness function and search strategy are improved; the fitness function is set as the path tree row center offset factor; the smoothing factor and safety coefficient are set; and the search strategy adopts differential evolution for cross mutation. Finally, the shortest path obtained by the improved bidirectional search A* algorithm is used as the initial population of an improved differential evolution genetic algorithm, optimized iteratively, and the optimal path is obtained by adding kinematic constraints through a cubic B-spline curve smoothing path. The convergence of the AGADE hybrid algorithm and GA algorithm on four different maps, path length, and trajectory curve are compared and analyzed through simulation tests. The convergence speed of the AGADE hybrid algorithm on four different complexity maps is improved by 92.8%, 64.5%, 50.0%, and 71.2% respectively. The path length is slightly increased compared with the GA algorithm, but the path trajectory curve is located in the center of the tree row, with fewer turns, and it meets the articulated steering tractor operation needs in the orchard environment, proving that the improved hybrid algorithm is effective. [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]

Integrating drones in Indian agriculture presents a transformative potential, ushering in a new era of sustainable farming practices. In India, various incidents tell a story of how pesticides, seeds, and many more applications, once deemed a boon for agricultural progress, have gradually unfolded into a complex web of ecological and health challenges. Presently, the case of drones for spraying purposes in agriculture brings forth compiling advantages. These include enhanced precision in results, alleviation of the diverse adverse effects linked with manual spraying methods, operational feasibility in inclement weather conditions, utility in cultivating tall-standing crops and addressing the complexities of manual spraying for crops such as paddy and sugarcane. This paper explores the role of drones in promoting spraying applications in Indian agriculture. The study addresses two research questions: how can the utilisation of drones for spraying applications contribute to the sustainability of Indian agriculture, and how can drone-assisted spraying applications contribute to achieving the Sustainable Development Goals (SDGs) in Indian agriculture? To accomplish this, a responsible innovation approach is adopted as a theoretical framework to explore the dimensions of innovations in drone mounted spraying in Indian agriculture, juxtaposed by erratic weather conditions and knitted by diverse values and norms. The snowball sampling technique was employed to identify the interviewees and collect qualitative data. For this, in-depth interviews were conducted in person or by using Zoom. The findings suggest that efficiency, capability, effectiveness, gender equality, trust, and accuracy are certain values embedded in drone technology that assist in achieving the SDGs in Indian agriculture. [ABSTRACT FROM AUTHOR]

This paper aims to assess the impact of agricultural investment on agricultural output in the Democratic Republic of Congo using secondary data for the period 1980 to 2019. Using the Johansen and Jusseleus cointegration test, agricultural investment and agricultural output were observed to have a statistically significant long-run relationship. Additionally, the error correction model (ECM) shows that the short-run coefficient of agricultural investment has a statistically significant positive impact on agricultural output, implying that investment (machine, infrastructure etc.) does spur agricultural output in the Democratic Republic of Congo. The results show the presence of unidirectional causality between agricultural output toward agricultural investment. It was also observed that unidirectional causality from labour force to agricultural investment, labour force to education and agricultural investment to land use and a bi-directional causality between land use and labour force exists. The paper concludes that an increase in investment in the agricultural sector will result in agricultural output in the Democratic Republic of Congo. [ABSTRACT FROM AUTHOR]

The problem of high grain production costs, which is not conducive to sustainable agricultural development and food security, is highlighted in the context of China's "large country and small household farmers". Reducing the grain production costs through factor allocation and organizational drive has become particularly important. Based on 768-grain peasant households in China, this paper uses OLS regression and robust regression to examine the effects of agricultural factor inputs and cooperatives on grain production costs. It analyzes the synergistic and substitution effects between farmers' factor inputs and cooperatives in grain production. It was found that: (1) in farmers' grain production, reductions in the grain production costs can be realized by expanding the area under cultivation, improving the use of agricultural machinery, and increasing technological inputs; (2) a reduction in the grain production costs can also be realized through cooperatives driving farmers into grain production; (3) cooperatives can provide farmers with various types of agricultural production services in grain production and cooperative-driven substitution effects between the agricultural factor inputs of farm households. The findings of this paper contribute to the enrichment of research in the field of agricultural production and are important for enhancing agricultural sustainability and reducing grain production costs. [ABSTRACT FROM AUTHOR]

Introduction: Digital agricultural technology service has the characteristics of high efficiency, convenience and flexibility, and plays an important role in the process of Chinese agricultural digitalization, which is of great significance for realizing the modernization and intelligent development of agricultural production. Methods: Based on micro-survey data of small-scale farmers in Sichuan Province, this study utilizes the OLS method and the mediation effect model to investigate the impact of digital agricultural technology services on farmers' willingness to adopt digital production technologies and its underlying mechanisms. Results: The results indicate that digital agricultural technology services effectively enhance farmers' willingness to adopt digital production technologies, especially among those engaged in cooperative. Further analysis of mechanisms suggests that digital agricultural technology services influence farmers' choices of household production and management methods by expanding their information channels, enhancing their cognition of digital production technologies, and facilitating access to technology. Moreover, heterogeneous analysis reveals significant differences in the willingness of farmers with varying levels of digital literacy to adopt digital production technologies. Digital agricultural technology services have a more pronounced impact on the willingness of highly digitally literate farmers. Discussion: The findings of this paper deepen our comprehension of the operational mechanisms of digital agricultural technology services in the context of agricultural development, and advocate for policy recommendations such as enhancing the supply of digital agricultural technology services, strengthening the role of new agricultural entities, improving farmers' digital literacy training, and enhancing policy support systems. [ABSTRACT FROM AUTHOR]

As the main service mode and management mode of socialized service, agricultural production trusteeship services (APTS) are an important way to realize the tight link between farmers and modern agricultural development, which is highly important for ensuring national food security. By constructing a utility model of farmers, this paper deduces the decision-making mechanism of farmers' APTS from the dual mechanism of natural risk and non-agricultural employment income (NAEI) and uses the survey data of 956 farmers in the three northeastern provinces to verify the empirical results by using multivariate logit (Mlogit) and propensity score matching (PSM) methods. The research shows that natural risk hinders farmers' choice of the APTS; NAEI has a positive impact on APST, and an increase in NAEI weakens the negative effect of natural risk on the choice of the APTS, and has an enhanced moderating effect. In addition, the scale heterogeneity analysis revealed that the impacts of natural risk and NAEI on farmers of different scales are significantly different. The NAEI adjustment effect has the greatest impact on small-scale farmers, followed by medium-scale farmers. In view of this, the government should stabilize the nonagricultural employment market and improve the nonagricultural employment opportunities of farmers. APTS organizations should transfer natural risks and improve the agricultural insurance system. According to the differences of different scales of farmers, different promotion policies are formulated, and the expansion of management and deepening services is proposed to further promote the development of APTS and consolidate the foundation of food security in China. [ABSTRACT FROM AUTHOR]

Sustainable agricultural progress is one of the world's greatest challenges in the twenty-first century as the global population is estimated to rise to over nine billion by 2050. Over the years, the Food and Agricultural Organization (FAO) and the African Union (AU) have sought prosperous African Policies based on sustainable agriculture and inclusive growth. Additionally, the AU has adopted the Comprehensive African Agricultural Development Programme (CAADP) policy framework. Given the growing need for innovative policies and studies to reduce the negative environmental effects of agricultural development in Africa, this paper investigates the significance of green finance and digital technologies on green agricultural growth in sub-Saharan Africa (SSA) from 2003 to 2018. Analysis of panel co-integration estimates indicates all variables are integrated. Using the panel-corrected standard errors estimator for long-run equilibrium co-integration assessment, the outcomes indicate mitigated green finance (MGF), internet and mobile phone use (IMU), as well as sustainable energy utilization (RE), individually and collectively exert a positive effect on agriculture, forestry and fishing value added (AVA). Additionally, panel causality estimates indicate (i) a bi-directional causality between MGF and AVA. (ii) A uni-directional causality running from IMU towards AVA without any rebound effect. For policy insights, the AU could urge member states to implement macro-policies to increase agricultural green credit to sustain food production and employment generation. Similarly, the FAO and AU could provide technical support in digital agricultural research and value chains towards ensuring sustainable agriculture development in SSA. [ABSTRACT FROM AUTHOR]

Digitization is becoming the key to achieving rural revitalization, and there is a complex inter-active relationship with farmer enrichment, and agriculture development, making it highly valued by governments around the world. Quantitatively evaluating the symbiotic relationship between rural digitization, farmer enrichment, and agricultural development, and reveal the driving mechanism behind them, which can provide evidence for the rural government, agricultural enterprises, and relevant stakeholders. This paper conducts an empirical study of 84 digital village national pilots in China based on coupling coordination model and geographically weighted regression, with focus on the spatial characteristics, symbiotic relationship and driving mechanisms of the pilots. The symbiotic relationship between rural digitization, farmer enrichment, and agriculture development has significant heterogeneity, with most of the pilots in coordination state. The driving mechanism of rural digitization, farmer enrichment, agriculture development, and symbiotic relationship were complicated, with significant heterogeneity and synergy in the driving forces of different factors, and special attention should be paid to spatial effects (the nature and intensity of regression coefficients in local regions) in the policy design and implementation. It is worth noting that population aging mainly plays a negative role, while industrialization, government support, resident capacity plays a positive role. The other factors play both positive and negative roles. Income gap between urban and rural residents, population aged 65 and above, financial self-sufficiency are the most critical factors. We suggest developing differentiated management policy, accurate management policy, and integrated management policy, which can provide a basis for digital village and smart village planning, construction and management in China. In view of the significant differences in rural digitalization and its driving mechanisms in different pilots as shown in the study, it is necessary to design differentiated spatial policies according to local conditions, to design accurate management policies based on the driving effects of key single factors, and to design integrated development policies by taking into account the interactive effects of multiple factors. [ABSTRACT FROM AUTHOR]

This study aims to provide a scientific basis for the development of county-level agricultural industries through a comprehensive evaluation of the environmental and economic benefits of agricultural products. Focusing on Hancheng City in Shaanxi Province, this paper calculates and analyzes the carbon emission intensity per unit output value and the economic benefits of major agricultural products, assessing their comprehensive advantage indices. The research methods include data collection, data processing, and model construction, utilizing a bi-factor matrix analysis to explore the balance between environmental sustainability and economic profitability of different agricultural products. The results indicate that pepper and vegetables have the highest comprehensive advantages, demonstrating significant economic and environmental benefits, while soybeans show lower comprehensive advantages, requiring improvements in cultivation techniques and management practices. Based on the research findings, this paper proposes policy and management recommendations for different agricultural products, including focusing on the development of high-comprehensive-advantage products, improving cultivation techniques for low-comprehensive-advantage products, promoting green agricultural technologies, establishing a carbon footprint monitoring system for agricultural products, and strengthening agricultural infrastructure construction. The study's conclusions provide theoretical support and practical guidance for the agricultural development strategies of Hancheng City and similar regions, contributing to the achievement of sustainable agricultural development and carbon reduction goals. [ABSTRACT FROM AUTHOR]

This paper examines the relationship between income inequality and consumption, utilizing panel data from rural China over a span of four years to validate the application of relative income theory in the domain of food consumption. Food consumption represents a significant portion of expenditures for the low-income demographic and is of vital importance to China's food security and agricultural development. To ascertain the impact of income inequality on food consumption, this paper employs a bi-directional fixed-effects model, a mediation effect model, and machine learning causal analysis methods. Utilizing four years of rural resident survey data from the China Health and Nutrition Survey database, the study empirically tests the effect of income inequality on various types of food consumption, the channels through which it operates, and the heterogeneity among different income groups and educational backgrounds. The findings indicate that (1) income inequality within rural communities positively influences food consumption, and this conclusion remains robust under endogeneity treatment and robustness checks, positively affecting the transformation of food consumption and healthy intake; (2) income inequality among rural residents promotes food consumption through two mediating channels: the "demonstration effect" and the "ratchet effect;" (3) the impact of income inequality on food consumption exhibits heterogeneity among rural residents of different income levels and educational backgrounds. [ABSTRACT FROM AUTHOR]

Emerging climate-development processes jeopardize water supply, especially in the Global South. In the Vietnamese Mekong Delta, disrupted water flows driven by climate change and hydropower development have caused water scarcity, threatening agricultural systems in both upstream and coastal areas. Based on insights from desk reviews, stakeholder workshops and interviews with local informants, this paper argues that while grey (engineered) solutions take precedence in addressing water scarcity, securing water sustainability (water retention) for agricultural resilience demands integrated 'grey–green' (engineered-nature-based) solutions. This paper suggests demands for translating this approach into the water governance framework for the delta's agricultural development. [ABSTRACT FROM AUTHOR]

The purpose of the paper was to assess the impact of climate changes on agricultural development and food security in Ukraine and Slovakia using statistical data from Statistical Office of the Slovak Republic, State Statistics Service of Ukraine, Eurostat, Food and Agriculture Organization and other sources. The comparison method was used to evaluate changes in agricultural development indicators, as well as climate changes in 1998-2021 in Ukraine and Slovakia. The main indicators of agricultural development used in the research were agricultural raw materials exports(%), agricultural raw materials imports (%), agriculture value added (% of GDP), agriculture value added (annual % growth), agriculture value added (current US$), cereal production (metric tons), cereal yield (kg per hectare), crop production index, food production index. Correlation and regression equations were used to establish the relationship between climatic factors and agricultural development indicators. The multiple correlations between air temperature and indicators of agricultural development was r=0.75 for Ukraine, r=0.69 for Slovakia. Using neuromodeling tools, the indicators of cereal production volumes and cereals yields in Ukraine and Slovakia in 2024-2033 were predicted. Tables were used to summarize the results. In conclusion, it should be noted that climatic conditions should be analyzed in each sector of crop production, and measures should be taken to adapt technologies for the gradual expansion of obligations for the production and export of agricultural products to world markets. [ABSTRACT FROM AUTHOR]

A century ago the New Age philosopher Dr Rudolf Steiner (1861-1925) called for the development of a differentiated. agriculture, one focussed on biology rather than chemistry. At his Agriculture Course at Koberwitz (now Kobierzyce), in the summer of 1924, Steiner founded the Experimental Circle of Anthroposophical Farmers and Gardeners. The Experimental Circle members each signed a non-disclosure agreement (NDA). Their task was to test Steiner's ideas, establish what worked, and to publish the results. That injunction was arguably satisfied by the publication of Ehrenfried Pfeiffer's book 'Bio-Dynamic Farming and Gardening' in 1938. The results reported in the present paper are Experimental Circle results that were subject to the secrecy provisions of the NDA at that time (1936), and are now finally revealed. Immanuel Voegele (1897-1959) recorded yields for five crops under Biodynamic (BD) management in the years 1931-1933, comparing these results to yields in the pre-BD years 1920-1926. He reported yield increases for potatoes to 55%, rye up to 48%, oats to 31%, wheat to 14%, and barley to 9%. Voegele was well credentialed and grounded to report on Biodynamics. He had studied agriculture at Stuttgart, he attended the Agricultural Course of Rudolf Steiner at Koberwitz, and he was an inaugural member of the Experimental Circle. Voegele had served as a farm manager at the Koberwitz estate of Count Carl Keyserlingk (1869-1928) (until 1925). He subsequently worked at the Biodynamic farm of Ernst Stegemann (1882-1943) at Marienstein. The present paper reports longitudinal yield results for five crops at Voegele's farm at Pilgramshain, Silesia, Germany, before and after the conversion to BD. These early BD yield data were shared amongst 'the faithful' at the time, and only now publicly. From the high point of his reported successes with BD, life and prospects for Voegele would rapidly deteriorate. The Nazi regime was hostile to Rudolf Steiner, Anthroposophy, and Anthroposophic ventures (of which Biodynamics was one). All books by Rudolf Steiner were banned by the Nazis in 1935 (including the Agriculture Course). Germany invaded Poland in 1939 and slaughtered millions of Polish civilians, before eventually in 1945 the Russian Army routed the Nazi army. The Russians marched on 'Fortress Breslau' and on to Berlin, sparking a mass westward exodus of Germans, including Immanuel Voegele. Territory, including Pilgramshain, was relinquished to Poland at the Potsdam Conference of 1945. Immanuel Voegele's legacy of reported successes with Biodynamics at a time when secrecy prevailed is now shared. [ABSTRACT FROM AUTHOR]

As a powerful statistical method, meta-analysis has been applied increasingly in agricultural science with remarkable progress. However, meta-analysis research reports in the agricultural discipline still need to be systematically combed. Scientometrics is often used to quantitatively analyze research on certain themes. In this study, the literature from a 30-year period (1992–2021) was retrieved based on the Web of Science database, and a quantitative analysis was performed using the VOSviewer and CiteSpace visual analysis software packages. The objective of this study was to investigate the current application of meta-analysis in agricultural sciences, the latest research hotspots, and trends, and to identify influential authors, research institutions, countries, articles, and journal sources. Over the past 30 years, the volume of the meta-analysis literature in agriculture has increased rapidly. We identified the top three authors (Sauvant D, Kebreab E, and Huhtanen P), the top three contributing organizations (Chinese Academy of Sciences, National Institute for Agricultural Research, and Northwest A&F University), and top three productive countries (the USA, China, and France). Keyword cluster analysis shows that the meta-analysis research in agricultural sciences falls into four categories: climate change, crop yield, soil, and animal husbandry. Jeffrey (2011) is the most influential and cited research paper, with the highest utilization rate for the Journal of Dairy Science. This paper objectively evaluates the development of meta-analysis in the agricultural sciences using bibliometrics analysis, grasps the development frontier of agricultural research, and provides insights into the future of related research in the agricultural sciences. [ABSTRACT FROM AUTHOR]