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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]

With the development of agriculture, the complexity and dynamism of orchard environments pose challenges to the perception and positioning of inter-row environments for agricultural vehicles. This paper proposes a method for extracting navigation lines and measuring pedestrian obstacles. The improved YOLOv5 algorithm is used to detect tree trunks between left and right rows in orchards. The experimental results show that the average angle deviation of the extracted navigation lines was less than 5 degrees, verifying its accuracy. Due to the variable posture of pedestrians and ineffective camera depth, a distance measurement algorithm based on a four-zone depth comparison is proposed for pedestrian obstacle distance measurement. Experimental results showed that within a range of 6 m, the average relative error of distance measurement did not exceed 1%, and within a range of 9 m, the maximum relative error was 2.03%. The average distance measurement time was 30 ms, which could accurately and quickly achieve pedestrian distance measurement in orchard environments. On the publicly available TUM RGB-D dynamic dataset, YOLOD-SLAM2 significantly reduced the RMSE index of absolute trajectory error compared to the ORB-SLAM2 algorithm, which was less than 0.05 m/s. In actual orchard environments, YOLOD-SLAM2 had a higher degree of agreement between the estimated trajectory and the true trajectory when the vehicle was traveling in straight and circular directions. The RMSE index of the absolute trajectory error was less than 0.03 m/s, and the average tracking time was 47 ms, indicating that the YOLOD-SLAM2 algorithm proposed in this paper could meet the accuracy and real-time requirements of agricultural vehicle positioning in orchard environments. [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]

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]

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]

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]

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]

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]