Efficiency Of Agricultural Production Research Articles

The Impact of Digitalization on Agricultural Green Development: Evidence from China’s Provinces

Agricultural green development is crucial for achieving the United Nations 2030 Sustainable Development Goals, with the digital technology revolution acting as a catalyst for both China’s green agricultural transformation and global sustainable development efforts. This study utilizes panel data from 30 Chinese provinces (including autonomous regions and municipalities) from 2012 to 2022 to assess the digitalization level and agricultural green development through a combined entropy weight and TOPSIS method. It also investigates the spatial agglomeration of agricultural green development using Moran’s I index and empirically evaluates the impact of digitalization on agricultural green development through OLS and spatial Durbin models. The findings reveal that, while China’s agricultural green development has progressed slowly yet steadily during the study period, it demonstrates significant spatial agglomeration, driven primarily by agricultural production efficiency and resource recycling. Notably, a non-linear U-shaped relationship exists between digitalization and agricultural green development, suggesting that digitalization fosters agricultural green development only after surpassing a certain threshold. Additionally, digitalization has spatial spillover effects: advancements in neighboring provinces correlate with local agricultural green development in a U-shaped manner, with an initial “siphon effect” followed by a “trickle-down effect.” These insights inform policy recommendations aimed at optimizing the use of digital technology to facilitate green agricultural transformation, offering valuable guidance for policymakers.

Environmental sustainability, energy efficiency and uncertainty analysis of agricultural residue-based bioethanol production: A comprehensive life cycle assessment study

Bioethanol produced from agro-residue provides a sustainable alternative to fossil fuels, mitigating pollution, reducing waste, and promoting a circular economy. However, challenges arise in sustainable sourcing, resource conflicts, water quality concerns, and managing environmental footprints to maximize agro-residue-based bioethanol benefits. The environmental impacts, net energy ratio (NER), net energy value, water footprint, and uncertainties associated with bioethanol production from agro-residues are analyzed in this study. The analysis followed a cradle-to-gate approach using 1 L of bioethanol produced as a functional unit (FU), allowing for a direct comparison of the environmental performance of the different feedstocks. The result shows that rice straw-based bioethanol exhibited higher environmental impacts, particularly in terms of ecotoxicity, ionizing radiation, human toxicity, and ozone layer depletion, except for greenhouse gas emission, which is lowest for corn stover (0.37 kg CO2 eq. per FU) and highest for cassava straw (1.1 kg CO2 eq. per FU). Furthermore, the Cumulative Energy Demand analysis highlighted a significant reliance on fossil fuels from agricultural biomass throughout the bioethanol production process. The energy balance analysis shows that the agricultural residues are feasible for bioethanol production with NER of 1.20, 3.10, and 3.28 for rice straw, corn stover, and cassava straw, respectively. To enhance the accuracy and reliability of the LCA results, Monte Carlo simulation was employed to account for uncertainty and variability in the data. This methodology provides a better understanding of the impacts of bioethanol production, with a focus on sustainability and advancements in reducing our reliance on fossil fuels.

Agricultural trade liberalization, governance quality, and technical efficiency in the agricultural sector of Southeast Asia

International trade has been regarded as an essential factor in enhancing a country's productivity and efficiency. Nevertheless, developing countries squander money and resources as a result of their deficient institutional quality, impeding their ability to profit from specialization and trade. Therefore, this study intends to investigate the impact of agricultural trade liberalization and governance quality on technical efficiency in Southeast Asia's agricultural sector using balanced panel data spanning from 2002 to 2021. The research utilizes translog stochastic frontier analysis (SFA) with a single-stage maximum likelihood estimation (MLE) to simultaneously calculate the time-varying technical efficiency scores and explore the core factors influencing agricultural inefficiency. The findings reveal that the average output-oriented technical efficiency for ASEAN-8 countries was 94 %. This suggests that there is significant potential to enhance technical efficiency in agricultural production by up to 6 % by addressing the adverse impacts of technical inefficiency. The research findings further point out that Malaysia is the most technically efficient country, having a technical efficiency score of 99.82 %, followed by Vietnam (99.75 %), Thailand (99.70 %), Lao PDR (98.90 %), Myanmar (95.42 %), Indonesia (91.64 %), the Philippines (90.65 %), and Cambodia (76.11 %). The results of disaggregated agricultural trade liberalization demonstrate a significant reduction in agricultural inefficiency in Southeast Asia through agricultural exports and imports. The findings also emphasize that improvements in the rule of law positively contribute to agricultural efficiency, whereas enhancements in terms of voice and accountability and regulatory quality appear to reduce it. Based on these findings, the government should consider enlarging open and liberalized trade policies in order to facilitate the exchange of technology and knowledge within the agriculture sector. Additionally, the government should involve farmers, agricultural cooperatives, local communities, and other relevant stakeholders in the decision-making process to ensure that policies address the specific needs and constraints faced by the sector.

Exploring the Integration of Industry 4.0 Technologies in Agriculture: A Comprehensive Bibliometric Review

While it is essential to increase agricultural production to meet the needs of a growing global population, this task is becoming increasingly difficult due to the environmental challenges faced in recent decades. A promising solution to enhance the efficiency and sustainability of agricultural production is the integration of Industry 4.0 technologies, such as IoT, UAVs, AI, and Blockchain. However, despite their potential, there is a lack of comprehensive bibliometric analyses that cover the full range of these technologies in agriculture. This gap limits understanding of their integration and impact. This study aims to provide a holistic bibliometric analysis of the integration of Industry 4.0 technologies in agriculture, identifying key research trends and gaps. We analyzed relevant literature using the Scopus database and VOSviewer software (version 1.6.20, Centre for Science and Technology Studies, Leiden University, The Netherlands)and identified five major thematic clusters within Agriculture 4.0. These clusters were examined to understand the included technologies and their roles in promoting sustainable agricultural practices. The study also identified unexplored technologies that present opportunities for future research. This paper offers a comprehensive overview of the current research landscape in Agriculture 4.0, highlighting areas for innovation and development, and serves as a valuable resource for enhancing sustainable agricultural practices through technological integration.

Enhancing tomato growth and soil fertility under salinity stress using halotolerant plant growth-promoting rhizobacteria

Soil salinization is a critical issue that not only hampers the efficiency and sustainability of global agricultural production but also poses significant challenges to the achievement of sustainable development goals across environmental, economic, and social dimensions. Halotolerant plant growth-promoting rhizobacteria (HPGPR) have the potential to mitigate abiotic stress, foster plant growth, and bolster the stress resistance capabilities of crops. This study conducted the isolation, identification, and characterization of HPGPR originating from a saline-alkali orchard area in northwest China. The efficacy of the isolated bacterial strains was evaluated through potted plant experiments, assessing the growth of tomato plants under in vitro conditions and under varying salinity stress. Ultimately, the study investigated the influence of these HPGPR on soil physicochemical properties, enzymatic activities, and the structure and composition of the microbial community. Upon isolating 12 bacterial strains, we conducted an in vitro assessment of their salt tolerance, ultimately singling out three robust isolates, which exhibited exceptional salt tolerance. Detailed 16S rRNA gene sequencing and meticulous taxonomic evaluation systematically assigned these isolates to Priestia endophyticus GSCK1 (accession number: OR569048), Bacillus atrophaeus GSCK2 (accession number: OR569061), and Serratia fonticola GSCK6 (accession number: OR569062), respectively. These strains exhibited notable biochemical and plant growth-promoting traits, including enzymatic activities and the production of indole-3-acetic acid. They significantly enhanced plant growth metrics and soil fertilities, particularly strain GSCK6, which also reshaped the soil microbial community, augmenting beneficial microbe abundance. The HPGPR treatment notably improved soil pH, nutrient availability, enzymatic activities, and reduced soil electrical conductivity, underscoring their potential in agricultural resilience against salinity. The eco-friendly salt stress mitigation strategy of HPGPR not only enhances soil quality and promotes plant growth by regulating the composition and function of microbial communities, but also provides a novel solution for global agricultural production. This approach is conducive to increasing crop yield and quality, reducing the limitations of saline-alkali land on agricultural production, and promoting food security and sustainable agricultural development.

Construction of a blockchain based cold chain logistics information platform for Gannan navel oranges to enhance transparency and efficiency

As a high-value agricultural product, the cold chain logistics management of Gannan navel oranges has global significance. Especially in Africa and Europe, food safety and supply chain quality are critical issues. However, the existing Gannan navel orange logistics information management system relies on traditional databases and information systems, with insufficient data sharing and transparency, affecting the overall coordination of the supply chain and customer satisfaction. In addition, the traditional system is vulnerable to hacker attacks and malicious tampering of data by insiders, resulting in serious economic losses and reputation damage. So the blockchain platform constructed in this study improves the transparency and traceability of logistics data through the SMART-PBFT algorithm of the alliance chain, which optimizes the logistics management process, improves the quality of logistics services, and reduces operating costs. It improves the logistics efficiency and data transparency of local agricultural products, and also provides a demonstrative case for the cold chain management of other high-value agricultural products around the world, with broad application potential and reference value.

Optimizing the Agricultural Supply Chain Through the Internet + Model: Enhancing Efficiency with a Case Study of TikTok

This study uses the TikTok platform as a case study to explore the application of the Internet+ model in the agricultural supply chain and analyses the impact of Internet+ model on improving supply chain efficiency and reducing costs. With the development of science and technology, Internet+ has become an important trend in modern industry, which deeply integrates traditional agriculture with information technology and promotes the innovation and efficiency of supply chain management. This study not only discusses the practical application of the Internet + model in agriculture but also proposes to solve the bottlenecks in the traditional agricultural supply chain using logistics informatization, intelligent scheduling system, cold chain monitoring and intelligent warehouse management. Through case studies, the effectiveness of Internet technology in optimizing the supply chain, improving the circulation efficiency of agricultural products and reducing costs are verified, providing theoretical and case study support for promoting the development of agricultural modernization.

Optimization Algorithm of Network Node Layout of Fresh Agricultural Products’ Online Consumption Behavior in E-Commerce Environment

In order to improve the efficiency of online consumption and supply chain efficiency of fresh agricultural products in the e-commerce environment, a network node layout optimization algorithm was designed. In the e-commerce environment, a network node deployment model has been constructed for the online consumption behavior of fresh agricultural products. A network transmission supply chain detection model was established through the rotation scheduling method. By combining distributed transformation protocols, the spatial layout characteristics of network nodes have been reorganized. Further introduction of a multi-coverage scheduling model, combined with sensor information fusion tracking technology, accurately detects the layout characteristics of network nodes. Based on these detection data, forwarding and adaptive control protocols were developed. Real-time control using the node traversal method was developed. Under the optimal node supply chain transmission mechanism, the scheduling of the network node layout has been achieved. Meanwhile, convolutional neural networks were utilized to dynamically adjust layout control to ensure convergence and stability. The simulation results show that the node forwarding ability of the network node layout design for online consumption behavior of fresh agricultural products in the e-commerce environment is good, which improves the output balance of the network supply chain transmission of online consumption behavior of fresh agricultural products in the e-commerce environment. In addition, the optimized delivery cost decreased by 20% compared to before optimization, delivery efficiency increased by 30%, and inventory turnover rate increased by 25%, confirming that the proposed method has good optimization performance.

Trichoderma Rhizosphere Soil Improvement: Regulation of Nitrogen Fertilizer in Saline–Alkali Soil in Semi-Arid Region and Its Effect on the Microbial Community Structure of Maize Roots

In order to reduce the actual impact of a saline–alkali environment on maize production in semi-arid areas, it is particularly important to use the combined fertilization strategy of Trichoderma microbial fertilizer and nitrogen fertilizer. The purpose of this study was to investigate the effects of different concentrations of nitrogen fertilizer combined with Trichoderma on improving the structural characteristics and ecological functions of maize rhizosphere microbial community in semi-arid saline–alkali soil. Through the microbiome analysis of maize rhizosphere soil samples with 60 kg N·ha−1 (N1) and 300 kg N·ha−1 (N2) nitrogen fertilizer combined with Trichoderma (T1) and without Trichoderma (T0), we found that the combination of Trichoderma and different concentrations of nitrogen fertilizer significantly affected the structure of bacterial and fungal communities. The results of this study showed that the combination of Trichoderma and low-concentration nitrogen fertilizer (N1T1) could improve soil nutritional status and enhance its productivity potential, revealing the relationship between beneficial and harmful fungal genera, microbial diversity and abundance, and crop biomass, which is of great significance for improving agricultural production efficiency and sustainable development.

Development of an online prediction system for soil organic matter and soil moisture content based on multi-modal fusion

Realizing accurate determination of in situ soil organic matter (SOM) content and soil moisture (SM) content in the field is of great importance for improving agricultural production efficiency. However, the feature information of a single sensor is limited, and there is still a certain gap in modeling accuracy compared to traditional laboratory methods. The variations of SM in the field also interfere with data collection, limiting the application of single sensor. In order to realize the efficient detection of in-situ SOM and SM content, this study developed an online detection system for SOM and SM content based on the fusion of characteristic wavelengths, visible images and thermal imaging image features. Firstly, based on a vehicle-mounted platform, a visible-thermal imaging camera and a characteristic wavelength integration device were integrated in a deep pine plow to realize the simultaneous acquisition of in-situ soil multi-sensor data. Then, a lightweight multimodal network was constructed to obtain thermal visible light image features and characteristic wavelength features through branch networks, achieving deep fusion of different modal data and predicting SOM and SM content. Finally, output the forecasting results of SOM and SM content, and transmit the predictive information to the cloud platform for storage. It was verified that the Multi-modal proposed in this study worked best in the laboratory environment, with a predicted R2 of 0.91 and RMSE of 2.9 g/kg for SOM, and a predicted R2 of 0.92 and RMSE of 0.77 % for SM. Compared with single image or characteristic spectral data, the fusion of visible image and characteristic wavelength effectively improved the prediction accuracy of SOM. The real-time prediction of SM was realized by fusing thermal imaging data, and the elimination of the moisture effect of visible images and characteristic wavelengths was also realized with the help of deep fusion of Multi-modal network. After field validation, the R2 of the Multi-modal system was 0.84 and the RMSE was 5.0 g/kg for SOM, and the R2 of the SM was 0.88 and the RMSE was 1.03 %. Despite the differences in soil types between the validation field and the sampling field, the system still demonstrated strong generalization and achieved high accuracy prediction of in-situ SOM and SM in the field, which effectively improves the efficiency and applicability of field. The efficiency and applicability of soil testing effectively improve the efficiency and provide technical guidance for precision management in the field.

Digital financial inclusion and residents' health: evidence from rural China

The growth of digital financial inclusion has provided new ways of providing financial services in rural areas, greatly affecting the quality of life and health of rural residents. Using data from the China Household Finance Survey, we examine how digital financial inclusion (DFI) impacts the health of rural residents. The results show that DFI can enhance residents' health. Notably, DFI positively impacts the health of rural residents by promoting commercial insurance, improving medical conditions, and enhancing agricultural production efficiency. Additionally, DFI has a more significant effect on improving the health of low-income residents and those in less-developed areas. This research provides compelling evidence to substantiate the assertion that DFI contributes to enhancing the health of rural residents. It offers empirical support and a basis for decision-making to realize the health benefits of digital financial inclusion.

Rapid and Sensitive On-site Nucleic Acid Detection of Three Main Fusarium Pathogens of Maize Stalk Rot Based on RPA-CRISPR/Cas12a.

Maize stalk rot is a soil-borne disease that poses a serious threat to maize production worldwide, with the most significant cause being fungal stalk rot. The development of a visual and rapid detection method for the maize stalk rot pathogen is significant for its prompt and accurate identification, enhancing agricultural production efficiency, and implementing timely preventive measures. These measures will help safeguard the maize yield and quality, ultimately reducing agricultural losses. In this study, we aimed to develop an efficient method to detect maize stalk rot pathogens. We focused on three pathogenic fungi commonly found in maize-producing regions worldwide: Fusarium verticillioides, Fusarium proliferatum, and Fusarium graminearum. Based on TEF-1α, we developed a rapid detection technique using RPA-CRISPR/Cas12a, combined with test strips to develop an on-site rapid visual detection test for these pathogens. The method showed detection sensitivity for F. verticillioides, F. proliferatum, and F. graminearum within 20 min at concentrations of 7.8 pg/μL, 0.11 ng/μL, and 0.13 ng/μL, respectively. The sensitivity increased with increasing reaction time. Testing of field disease samples indicated that the method is effective in detecting nucleic acids obtained through crude extraction methods. In conclusion, we developed a visually rapid detection technology that does not rely on complex instruments and equipment for the on-site early detection of F. verticillioides, F. proliferatum, and F. graminearum in the field to implement effective control measures, ensuring stable and high maize yields.

The Detection of Maize Seedling Quality from UAV Images Based on Deep Learning and Voronoi Diagram Algorithms

Assessing the quality of maize seedlings is crucial for field management and germplasm evaluation. Traditional methods for evaluating seedling quality mainly rely on manual field surveys, which are not only inefficient but also highly subjective, while large-scale satellite detection often lacks sufficient accuracy. To address these issues, this study proposes an innovative approach that combines the YOLO v8 object detection algorithm with Voronoi spatial analysis to rapidly evaluate maize seedling quality based on high-resolution drone imagery. The YOLO v8 model provides the maize coordinates, which are then used for Voronoi segmentation of the field after applying the Convex Hull difference method. From the generated Voronoi diagram, three key indicators are extracted: Voronoi Polygon Uniformity Index (VPUI), missing seedling rate, and repeated seedling rate to comprehensively evaluate maize seedling quality. The results show that this method effectively extracts the VPUI, missing seedling rate, and repeated seedling rate of maize in the target area. Compared to the traditional plant spacing variation coefficient, VPUI performs better in representing seedling uniformity. Additionally, the R2 for the estimated missing seedling rate and replanting rate based on the Voronoi method were 0.773 and 0.940, respectively. Compared to using the plant spacing method, the R2 increased by 0.09 and 0.544, respectively. The maize seedling quality evaluation method proposed in this study provides technical support for precision maize planting management and is of great significance for improving agricultural production efficiency and reducing labor costs.

Agroecological aspects of nutrients absorption by industrial crops in the conditions of Odesa Region

Modern agroindustrial activities that are focused on intensive methods of agricultural production require a deep understanding of the processes occurring in agroecosystems. Odesa Region is characterised by specific agroclimatic conditions and soil cover features making the study of this issue higly relevant. Intensive use of industrial crops requires knowledge about absorption and removal of biogenic elements such as nitrogen, phosphorus, and potassium. The importance of this research is determined by the need to reveal the dynamics of biogenic elements absorption by industrial crops in the conditions of Odesa Region, taking into account its agroclimatic and soil characteristics. The results of this research are intended to contribute to development of scientifically grounded recommendations for farmers and agricultural enterprises regarding effective management of agroecosystems. This will not only increase productivity of industrial crops but also ensure environmental sustainability of agricultural practices. Identifying the optimal conditions for absorption and removal of biogenic elements will contribute to improvement of soil fertility and preservation of its productivity in a long-term perspective. The conclusions and recommendations obtained during the research can be used to improve agronomic practices, reduce environmental impact, and increase efficiency of agricultural production. Considering current trends of agricultural development, it is necessary to pay special attention to optimization of agrotechnical measures allowing reduction of expenses for fertilizers and improvement of their efficiency. Introduction of new technologies is another important aspect to be considered. It will allow more precise determination of plants' nutrient needs and help to provide them with nutrients according to their growth phases. In addition, it is necessary to consider the interaction between different nutrient elements that can affect their availability and plants' ability to assimilate them.

Farmers Assistance Program Based on Artificial Intelligence (AI) and Big Data

This farmers assistance program aims to build an efficient and intelligent agricultural management system through the deep integration of artificial intelligence (AI) and big data technologies, so as to improve agricultural production efficiency, ensure the quality of agricultural products, promote sustainable agricultural development, and ultimately achieve the goals of increasing farmers' income and prosperity in rural areas.

A method of maize seed variety identification based on near-infrared spectroscopy combined with improved DenseNet model

The development of a real-time online system for rapid and non-destructive identification of seed varieties can significantly improve production efficiency in modern agriculture. Near-infrared spectroscopy technology has become one of the commonly used techniques in seed variety identification due to its fast and non-destructive characteristics. However, existing convolutional neural networks are difficult to reflect the complex nonlinear relationships of the near-infrared (NIR) spectrum, resulting in poor modeling performance, and their high model complexity is not conducive to real-time online identification tasks. Therefore, this study proposes a maize seed variety identification method using near-infrared spectroscopy technology and lightweight deep learning network (BAC-DenseNet). First, a total of 750 samples from 5 different types of maize seeds were taken as the research object. The spectral data were pre-processing using the SGD2-SNV, and the identification accuracy was improved by an average of 15.78 %. Then, the attraction–repulsion optimization algorithm combined with Laplacian Eigenmaps (AROA-LE) was used to perform dimension reduction on the pre-processed data, and the dimensionality was reduced from 1845 to 66. Finally, a lightweight deep learning network model (BAC-DenseNet) was constructed based on DenseNet-121 network with layer pruning and the introduction of batch channel normalization (BCN), self-attention and convolution mixed module (ACmix) and convolutional block attention module (CBAM). The experimental results show that the proposed BAC-DenseNet model has an identification accuracy of 99.33 %. Compared with the original network and seven other classical deep learning models, the proposed method has an average improvement of 2.83 %, 3.52 %, and 3.47 % in accuracy, Kappa, and MCC, respectively. Meanwhile, Params, Size, and FLOPs decreased by an average of 9.09 M, 35.08 MB, and 88.66 M, respectively. This method offered high accuracy and reliability in maize seed variety identification, which can provide qualitative indicators for the breeding, planting, and management of maize seed varieties. This study can provide a reference method for variety identification of other agricultural products.

Research on the Development of Agricultural Products Logistics under the Value Chain Optimization Concept of Logistics Management

The value chain of logistics management is the key to meeting the needs of circulation entities, improving logistics efficiency, optimizing logistics division of labor, and promoting the upgrading of logistics enterprises. It plays a positive role in promoting the development of agricultural product logistics. However, it is understood that there are still many problems in China's agricultural product logistics system, such as opaque information, multiple circulation links, high costs, high losses, and low profits. These will reduce the optimization of logistics management value chain, leading to low efficiency in agricultural product circulation and entering a low consumption cycle mode. Therefore, based on the concept of optimizing the value of logistics management, this article analyzes the development difficulties of the current agricultural product reverse logistics model, explores the design and operation mode of the agricultural product reverse logistics system, proposes corresponding countermeasures to maximize the value chain of agricultural product logistics management, in order to promote the stable and sustainable development of agricultural product logistics and improve the circulation efficiency of agricultural products.

Efficiency and Driving Factors of Agricultural Carbon Emissions: A Study in Chinese State Farms

Promoting low-carbon agriculture is vital for climate action and food security. State farms serve as crucial agricultural production bases in China and are essential in reducing China’s carbon emissions and boosting emission efficiency. This study calculates the carbon emissions of state farms across 29 Chinese provinces using the IPCC method from 2010 to 2022. It also evaluates emission efficiency with the Super-Slack-Based Measure (Super-SBM model) and analyzes influencing factors using the Logarithmic Mean Divisia Index (LMDI) method. The findings suggest that the three largest carbon sources are rice planting, chemical fertilizers, and land tillage. Secondly, agricultural carbon emissions in state farms initially surge, stabilize with fluctuations, and ultimately decline, with higher emissions observed in northern and eastern China. Thirdly, the rise of agricultural carbon emission efficiency is driven primarily by technological progress. Lastly, economic development and industry structure promote agricultural carbon emissions, while production efficiency and labor scale reduce them. To reduce carbon emissions from state farms in China and improve agricultural carbon emission efficiency, the following measures can be taken: (1) Improve agricultural production efficiency and reduce carbon emissions in all links; (2) Optimize the agricultural industrial structure and promote the coordinated development of agriculture; (3) Reduce the agricultural labor scale and promote the specialization, professionalization, and high-quality development of agricultural labor; (4) Accelerate agricultural green technology innovation and guide the green transformation of state farms. This study enriches the theoretical foundation of low-carbon agriculture and develops a framework for assessing carbon emissions in Chinese state farms, offering guidance for future research and policy development in sustainable agriculture.

Rural Industry Revitalization Can Be Energized by Land Transfer: A Case Study in Guangxi Zhuang Autonomous Region, China, 2013–2022

The Chinese government has vigorously promoted the transfer of land use rights, aiming to promote the scale, intensification, and efficiency of agricultural operations and achieve rural industry revitalization (RIR). However, whether and how land transfer energizes RIR remains unclear. Taking five representative cities (Nanning, Guigang, Baise, Fangchenggang, and Guilin) in Guangxi Zhuang Autonomous Region as a case study, we quantificationally characterized RIR during 2013–2022 using eleven variables and land transfer using three difference indices. We evaluated the contribution of land transfer to RIR and compared it among cities. Results showed that all five cities in Guangxi displayed an increasing trend of land transfer and RIR over the last decade. The increase in RIR was largely contributed by the improvement in infrastructure construction and industry convergence, and, to a lesser extent, by the enhancement in agricultural production efficiency. There was a strong city-specific correlation between RIR and the land transfer indices, indicating a beneficial role of land transfer in RIR. Structural equation modeling further indicated that land transfer promoted RIR, likely through facilitating infrastructure construction, enhancing industry convergence, and improving agricultural production efficiency. These results provide strong evidence that the transfer of land use rights can promote RIR and practical reference for advancing farmers’ well-being and the overall goal of rural revitalization in the future. The framework and the methodology proposed here are applicable elsewhere in China and other countries where scattering allocation of land resources represents a key limitation to agricultural production and economic development.

ICPNet: Advanced Maize Leaf Disease Detection with Multidimensional Attention and Coordinate Depthwise Convolution.

Maize is an important crop, and the detection of maize diseases is critical for ensuring food security and improving agricultural production efficiency. To address the challenges of difficult feature extraction due to the high similarity among maize leaf disease species, the blurring of image edge features, and the susceptibility of maize leaf images to noise during acquisition and transmission, we propose a maize disease detection method based on ICPNet (Integrated multidimensional attention coordinate depthwise convolution PSO (Particle Swarm Optimization)-Integrated lion optimisation algorithm network). Firstly, we introduce a novel attention mechanism called Integrated Multidimensional Attention (IMA), which enhances the stability and responsiveness of the model in detecting small speckled disease features by combining cross-attention and spatial channel reconstruction methods. Secondly, we propose Coordinate Depthwise Convolution (CDC) to enhance the accuracy of feature maps through multi-scale convolutional processing, allowing for better differentiation of the fuzzy edges of maize leaf disease regions. To further optimize model performance, we introduce the PSO-Integrated Lion Optimisation Algorithm (PLOA), which leverages the exploratory stochasticity and annealing mechanism of the particle swarm algorithm to enhance the model's ability to handle mutation points while maintaining training stability and robustness. The experimental results demonstrate that ICPNet achieved an average accuracy of 88.4% and a precision of 87.3% on the self-constructed dataset. This method effectively extracts the tiny and fuzzy edge features of maize leaf diseases, providing a valuable reference for disease control in large-scale maize production.