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Poor research reporting is a major contributing factor to low study reproducibility, financial and animal waste. The ARRIVE (Animal Research: Reporting of In Vivo Experiments) guidelines were developed to improve reporting quality and many journals support these guidelines. The influence of this support is unknown. We hypothesized that papers published in journals supporting the ARRIVE guidelines would show improved reporting compared with those in non-supporting journals. In a retrospective, observational cohort study, papers from 5 ARRIVE supporting (SUPP) and 2 non-supporting (nonSUPP) journals, published before (2009) and 5 years after (2015) the ARRIVE guidelines, were selected. Adherence to the ARRIVE checklist of 20 items was independently evaluated by two reviewers and items assessed as fully, partially or not reported. Mean percentages of items reported were compared between journal types and years with an unequal variance t-test. Individual items and sub-items were compared with a chi-square test. From an initial cohort of 956, 236 papers were included: 120 from 2009 (SUPP; n = 52, nonSUPP; n = 68), 116 from 2015 (SUPP; n = 61, nonSUPP; n = 55). The percentage of fully reported items was similar between journal types in 2009 (SUPP: 55.3 ± 11.5% [SD]; nonSUPP: 51.8 ± 9.0%; p = 0.07, 95% CI of mean difference -0.3–7.3%) and 2015 (SUPP: 60.5 ± 11.2%; nonSUPP; 60.2 ± 10.0%; p = 0.89, 95%CI -3.6–4.2%). The small increase in fully reported items between years was similar for both journal types (p = 0.09, 95% CI -0.5–4.3%). No paper fully reported 100% of items on the ARRIVE checklist and measures associated with bias were poorly reported. These results suggest that journal support for the ARRIVE guidelines has not resulted in a meaningful improvement in reporting quality, contributing to ongoing waste in animal research. [ABSTRACT FROM AUTHOR]

It is important to implement detection and assessment of plant diseases based on remotely sensed data for disease monitoring and control. Hyperspectral data of healthy leaves, leaves in incubation period and leaves in diseased period of wheat stripe rust and wheat leaf rust were collected under in-field conditions using a black-paper-based measuring method developed in this study. After data preprocessing, the models to identify the diseases were built using distinguished partial least squares (DPLS) and support vector machine (SVM), and the disease severity inversion models of stripe rust and the disease severity inversion models of leaf rust were built using quantitative partial least squares (QPLS) and support vector regression (SVR). All the models were validated by using leave-one-out cross validation and external validation. The diseases could be discriminated using both distinguished partial least squares and support vector machine with the accuracies of more than 99%. For each wheat rust, disease severity levels were accurately retrieved using both the optimal QPLS models and the optimal SVR models with the coefficients of determination (R2) of more than 0.90 and the root mean square errors (RMSE) of less than 0.15. The results demonstrated that identification and severity evaluation of stripe rust and leaf rust at the leaf level could be implemented based on the hyperspectral data acquired using the developed method. A scientific basis was provided for implementing disease monitoring by using aerial and space remote sensing technologies. [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]

Biodiversity is in rapid decline globally with agriculture being one of the leading causes. Within agricultural landscapes, some features provide a benefit to biodiversity that is disproportionate to their spatial area. An interesting example is artificial ponds–or farm dams–which can support a large variety of taxa. Here, we present a global review of farm dam research related to biodiversity conservation objectives to provide an overview of the topics, key research insights, and the characteristics of current research. We used a three-stage process to screen literature and identified 104 relevant papers across 27 countries encompassing studies of 13 different taxa. Most of the studies were short-term (less than 5 years) with small sample sizes (less than 20 sites). Of the 104 papers, 88 were focussed primarily on ecological outcomes, such as species richness or abundance, and 15 on primary production outcomes, such as crop and livestock yield, despite addressing or measuring ecological metrics. Only one study measured both ecological and primary production outcomes. Studies frequently examined how the features of dams (79 studies) and attributes of the surrounding landscape (47 studies) impact particular species and communities. Terrestrial mammals (1 study) were under-represented in the literature with macrophytes (28 studies), macroinvertebrates (26 studies), and amphibians (19 studies) receiving the most attention. Our results reveal a growing trend towards recognizing farm dams as habitats for various taxa, including amphibians, beetles, dragonflies, and other macroinvertebrates within agricultural environments. Significant knowledge gaps exist in understanding how dam age, invasive species, and effective management practices impact the biodiversity conservation values of farm dams. Future research should emphasize enhancing biodiversity by collaborating with landholders to increase habitat through strategic vegetation planning, minimizing runoff and nutrient inflow, and restricting stock access. [ABSTRACT FROM AUTHOR]

To facilitate accurate prediction and empirical research on regional agricultural carbon emissions, this paper uses the LLE-PSO-XGBoost carbon emission model, which combines the Local Linear Embedding (LLE), Particle Swarm Algorithm (PSO) and Extreme Gradient Boosting Algorithm (XGBoost), to forecast regional agricultural carbon emissions in Anhui Province under different scenarios. The results show that the regional agricultural carbon emissions in Anhui Province generally show an upward and then downward trend during 2000–2021, and the regional agricultural carbon emissions in Anhui Province in 2030 are expected to fluctuate between 11,342,100 tones and 14,445,700 tones under five different set scenarios. The projections of regional agricultural carbon emissions can play an important role in supporting the development of local regional agriculture, helping to guide the input and policy guidance of local rural low-carbon agriculture and promoting the development of rural areas towards a resource-saving and environment-friendly society. [ABSTRACT FROM AUTHOR]

The challenge of detecting research topics in a specific research field has attracted attention from researchers in the bibliometrics community. In this study, to solve two problems of clustering papers, i.e., the influence of different distributions of citation links and involved textual features on similarity computation, the authors propose a hybrid self-optimized clustering model to detect research topics by extending the hybrid clustering model to identify “core documents”. First, the Amsler network, consisting of bibliographic coupling and co-citation links, is created to calculate the citation-based similarity based on the cosine angle of papers. Second, the cosine similarity is also used to compute the text-based similarity, which consists of the textual statistical and topological features. Then, the cosine angle of the linear combination of citation- and text-based similarity is considered as the hybrid similarity. Finally, the Louvain method is applied to cluster papers, and the terms based on term frequency are used to label clusters. To test the performance of the proposed model, a dataset related to the data envelopment analysis field is used for comparison and analysis of clustering results. Based on the benchmark built, different clustering methods with different citation links or textual features are compared according to evaluation measures. The results show that the proposed model can obtain reasonable and effective clustering results, and the research topics of data envelopment analysis field are also analyzed based on the proposed model. As different features are considered in the proposed model compared with previous hybrid clustering models, the proposed clustering model can provide inspiration for further studies on topic identification by other researchers. [ABSTRACT FROM AUTHOR]

Quantifying the colors of objects is useful in a wide range of applications, including medical diagnosis, agricultural monitoring, and food safety. Accurate colorimetric measurement of objects is a laborious process normally performed through a color matching test in the laboratory. A promising alternative is to use digital images for colorimetric measurement, due to their portability and ease of use. However, image-based measurements suffer from errors caused by the non-linear image formation process and unpredictable environmental lighting. Solutions to this problem often perform relative color correction among multiple images through discrete color reference boards, which may yield biased results due to the lack of continuous observation. In this paper, we propose a smartphone-based solution, that couples a designated color reference board with a novel color correction algorithm, to achieve accurate and absolute color measurements. Our color reference board contains multiple color stripes with continuous color sampling at the sides. A novel correction algorithm is proposed to utilize a first-order spatial varying regression model to perform the color correction, which leverages both the absolute color magnitude and scale to maximize the correction accuracy. The proposed algorithm is implemented as a "human-in-the-loop" smartphone application, where users are guided by an augmented reality scheme with a marker tracking module to take images at an angle that minimizes the impact of non-Lambertian reflectance. Our experimental results show that our colorimetric measurement is device independent and can reduce up to 90% color variance for images collected under different lighting conditions. In the application of reading pH values from test papers, we show that our system performs 200% better than human reading. The designed color reference board, the correction algorithm, and our augmented reality guiding approach form an integrated system as a novel solution to measure color with increased accuracy. This technique has the flexibility to improve color reading performance in systems beyond existing applications, evidenced by both qualitative and quantitative experiments on example applications such as pH-test reading. [ABSTRACT FROM AUTHOR]

In recent decades, we have observed an intensification of science, technology and innovation activities in Brazil. The increase in production of scientific papers indexed in international databases, however, has not been accompanied by an equivalent increase in the impact of publications. This paper presents a methodology for analyzing production and the impact of certain research areas in Brazil related to two aspects: the origin of the journals (national or foreign) and international collaboration. These two variables were selected for being of particular importance in understanding the context of scientific production and communication in countries with emerging economies. The sample consisted of papers written by Brazilian researchers in 19 subfields of knowledge published from 2002 to 2011, totaling 85,082 papers. To calculate the impact, we adopted a normalized indicator called the relative subfield citedness (Rw) using a window of 5 years to obtain measurements evaluated in 2 different years: 2007 and 2012. The data on papers and citations were collected from the Web of Science database. From the results, we note that most of the subfields have presented, from one quinquennium to another, improved performance in the world production rankings. Regarding publication in national and foreign journals, we observed a trend in the distribution maintenance of production of the subfields based on the origin of the journal. Specifically, for impact, we identified a lower Rw pattern for Brazilian papers when they were published in national journals in all subfields. When Brazilian products are published in foreign journals, we observed a higher impact for those papers, even surpassing the average global impact in some subfields. For international collaboration, we analyzed the percentage of participation of foreign researchers and the connection between collaboration and the impact of papers, especially emphasizing the distinction of hyperauthorship papers in terms of production and impact. [ABSTRACT FROM AUTHOR]

Because the complexity of the external environment has put great pressure on the agricultural economy, making it vulnerable, it is necessary to promote a system of resilience in the agricultural economy so that Chinese agriculture can continue to persevere in the face of serious external uncertainties. Therefore, this paper investigates the spatio-temporal pattern and evolution of the distributional dynamics of China's county-level agricultural economic resilience based on 2000–2020 data covering 2,545 counties. The results are as follows: first, from 2000 to 2020, the mean value of China's county-level agricultural economic resilience showed an obvious upward trend, which indicates that China's agricultural economy gradually increased its ability to resist risks and continued to develop in a favourable manner. Specifically, the county-level agricultural economic resilience index of the northeast region grew the most significantly, while the index of county units in the western region was relatively low. Second, the centre of gravity of the spatial distribution of China's agricultural economic resilience gradually migrated to the northwest, showing a dominant direction from northeast to southwest and a tendency to develop from southeast to northwest. Third, the spatial differences in China's agricultural economic resilience generally showed an upward trend, while county-level differences were the main source of the overall differences, followed by inter-provincial differences, inter-municipal differences and inter-regional differences. Additionally, the contribution of county-level differences to the overall differences fluctuated within the range of 54%-58%. Fourth, there is a possibility of localized convergence in China's agricultural economic resilience, which is continuous in spatial effects and has obvious positively correlated spatial effects at different times and in different county spaces. [ABSTRACT FROM AUTHOR]

The Zarafshan River runs from the mountains of Tajikistan and terminates in the sands of the Kyzyl-Kum Desert in Uzbekistan; it served as a communication route and homeland for the Sogdians. The Sogdians are historically depicted as merchants existing from the end of the first millennium BC through the first millennium AD. While recent research has provided the first glimpse into cultivation, commerce, communication, and consumption in the Lower Zarafshan, the agricultural heartland of the Middle Zarafshan Basin has remained unstudied. This paper presents the results of archaeobotanical investigations conducted at five ancient urban sites/areas spanning the fifth to the twelfth centuries AD: Kainar (Penjikent citadel), Penjikent (shahristan), Sanjar-Shah, Kuk-Tosh (pre-Mongol Penjikent), and Afrasiab. Collectively, these data show that cereals, legumes, oil/fiber crops, fruits, and nuts were cultivated on the fertile Zarafshan floodplains. In this paper, we discuss evidence for the diversification of the agricultural assemblage over time, including the introduction of new staple crops and fruits into an already complex cultivation system. In addition, we contrast our data with previously published results from sites along the course of the Zarafshan to determine whether there is a dietary difference between pre-and post-Islamic conquest periods at settlements located along the river. [ABSTRACT FROM AUTHOR]

The long-term and stable development of agriculture is the key to China's economic development and social stability. Agricultural total factor productivity and the digital economy have become new kinetic energy and new engines driving agricultural high-quality development. It is of great significance to verify whether there are significant spatial and threshold effects in the process of high-quality development of agriculture and to explore the intrinsic relationship between high-quality development of agriculture and agricultural total factor productivity and digital economy. This paper takes 31 provinces in China from 2011 to 2020 as the research object. The coefficient of variation method is used to estimate the comprehensive evaluation index of agricultural high-quality development and digital economy. And Dea-Malmquist index method is used to estimate agricultural total factor productivity. On this basis, the spatial Durbin model and threshold regression model are constructed to explore the spatial and threshold effects of agricultural total factor productivity, digital economy and other factors and high-quality agricultural development. The conclusion is as follows: the high-quality development of agriculture has significant spatial autocorrelation. Agricultural total factor productivity and digital economy have significant direct effect and indirect spillover effect on the high-quality development of agriculture. Agricultural total factor productivity has stage differences in each range of digital economy level, but its influence on agricultural high-quality development shows a positive state. Based on this, the paper puts forward some countermeasures to promote the high-quality development of 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., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Guo, Chen. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The karst plateau is dominated by mountainous and hilly landforms, with low mechanization level of cropland, high difficulty of cultivation, and obvious phenomenon of cropland abandonment, which threatens regional food security. This study aims to analyze the spatial-temporal variation and its driving mechanisms of abandoned cropland in the Karst Plateau in Eastern Yunnan, China (KPEYC) between 2001 and 2020. To achieve this goal, 18 key factors from population, economic environment, cropland attributes, and farming conditions are selected. Moreover, correlation analysis, geodetector, and regression analysis methods are applied from three perspectives: temporal change, spatial distribution and spatial-temporal change. The results show that: (i) The cropland abandonment rate (CAR) in the KPEYC shows a fluctuating trend, with an average value of 9.78%, and the spatial distribution shows a pattern of "high in the center and low in the south and north". (ii) From the perspective of temporal change, gross value of agricultural production, and gross value of industrial production have the largest correlation coefficients with CAR. (iii) The explanatory power of gross tertiary industrial production, gross value of industrial production, followed by soil thickness. (iv) Gross value of agricultural production, and gross tertiary industrial production are the core driving forces for the spatial-temporal change of CAR. The higher the gross value of agricultural production and gross tertiary industrial production, the lower the CAR. elevation, soil thickness, and traffic mileage are the main driving factors for the spatial-temporal change of CAR. The study indicates that economic factors are decisive for cropland abandonment in the KPEYC. Based on the results, this study can provide decision-making support for local prevention and control of cropland abandonment, and the local community needs to promote land transfer and concentration and local urbanization according to local conditions, improve agricultural policies, improve farming conditions, etc. in order to increase farmers' enthusiasm for production, promote the rational use of cropland, and solidly push forward ecological restoration and management, optimize ecological spatial patterns, manage serious areas of rocky desertification, and appropriately alleviate the contradiction between people and land., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright: © 2024 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

To explore whether digital finance can reduce agricultural carbon emissions, promote regional convergence, and foster inclusivity in rural revitalization and shared prosperity, this paper uses the provincial-level index of digital financial inclusion to analyze the impact of digital financial inclusion on the intensity of agricultural carbon emissions and the Degum Gini coefficient (D-Gini coefficient) of regional carbon emission intensity in 30 sample provinces from 2010 to 2020. It examines the mechanism of the impact of digital financial inclusion on both variables to understand the underlying factors better. The main conclusions are as follows: (1) Digital financial inclusion significantly reduces the intensity of agricultural carbon emissions and narrows the gap in carbon emission intensity between regions. (2) The unconditional quantile regression coefficients show that the negative coefficients of the digital financial inclusion index and the three-dimensional indices decrease with increasing quantiles. However, the significant effects vary significantly at different quantiles. (3) Technological progress and the government's ability to allocate financial resources play a significant mediating role, and the income gap between urban and rural areas can be further narrowed, as well as the carbon emission intensity gap between provinces. The empirical results are robust and proven by replacing the econometric analysis method, changing the core variables, and other methods., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Tan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Rapid uptake of greenhouse gas (GHG) mitigation measures is central to reducing agricultural and land use emissions and meeting the UK Net Zero policy. The socioeconomic challenges and barriers to uptake are poorly understood, with yet unclear structural pathways to the uptake of GHG mitigation measures. Using an online survey of 201 agricultural land managers across the UK, and applying multiple linear regression and stepwise regression analysis, this research established farm and farmers' factors influencing perceptions and willingness to adopt GHG mitigation measures. The results consistently show that farm sector, farmers' business perception, and labour availability influence willingness to adopt GHG mitigation measures. Based on the farmers' qualitative feedback, other barriers to adoption include costs and concerns for profitability, lack of flexibility in land tenancy contracts, poor awareness and knowledge of the application of some GHG mitigation measures, perception about market demand e.g bioenergy crops, and scepticism about the future impacts of adopting varying GHG mitigation measures. In the midst of the ongoing net zero transition, this study identifies existing barriers to the uptake of GHG mitigation measures, and specifically, a substantial gap between farmers and the science of GHG mitigation measures and the need to incentivise a farm and farming community-led policy interventions to promote adoption of GHG mitigation measures., Competing Interests: The authors declare no known competing interests that could have appeared to influence the work reported in this paper., (Copyright: © 2024 Jebari et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Agricultural non-point source pollution control (ANSPC) is a complex, long-term and dynamic environmental protection process. In order to motivate multiple subjects to participate in ANSPC, this paper constructs a tripartite evolutionary game model of local government, village collectives and farmers, which explores the strategic choices and influencing factors of different subjects through simulation analysis. The results indicate that: There are five stable strategy points in the ANSPC game system, which can be divided into four stages based on subject interactions. Village collectives should play an intermediary role in ANSPC and try to coordinate the behaviour of different subjects. The ideal and stable evolution state is "weak supervise, positive response, and active participate", but it cannot be realized at present. The strategy selection of subjects is determined by relative net income. Providing penalties requires considering the heterogeneity of subjects, but incentives are beneficial for achieving tripartite governance. This study provides new evidence for understanding the role of multi-agency participation in agricultural non-point source pollution control, and provides theoretical guidance for the government to formulate differentiated intervention mechanisms, which is an important reference for achieving sustainable development goals., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Wang, Shang. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The new development pattern has identified two key avenues for the sustained advancement of high-quality agricultural and rural development: digitalisation and low-carbon development. The measurement of the digital economy and the agricultural carbon emission performance, and their spatial and temporal heterogeneity, is a crucial step in promoting the spatial coordination and sustainable development of digitalisation and low-carbon agriculture. This paper employs the entropy value method, SBM model, and coupling coordination degree model to investigate the coupling coordination measurement and spatial-temporal heterogeneity of the performance of the digital economy and agricultural carbon emissions. The data used are provincial panel data from 2013 to 2021. The simulation results demonstrate that, between 2013 and 2021, the digital economy of all provinces exhibited varying degrees of growth, yet the development of the digital economy between provinces exhibited a more pronounced tendency to diverge. Concurrently, the agricultural carbon emission efficiency in China exhibited a fluctuating upward trend. The development of the digital economy and the efficiency of agricultural carbon emission were found to be highly coupled. Their coupling and coordination relationship showed a downward trend followed by an upward trend. In general, it is suggested that we should increase investment in digital economy infrastructure and technology, promote digital agricultural applications, strengthen policy guidance and financial support, establish a coupling coordination mechanism and strengthen farmers' digital literacy and environmental awareness., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Introduction: According to the seventh national population census in China, the proportion of people aged 65 and above in the population reached 13.5%. The aging trend is more pronounced in rural areas, indicating that China has entered an aging society. This article focuses on agricultural carbon emissions in the context of aging, studying the impact of rural population aging on agricultural carbon emissions., Study Objectives: Under the background of deepening population aging, let us discuss how to maintain the green and sustainable development of agriculture in China., Methodology: Fixed effects and mediating effects models are used. Technical efficiency is used as a mediating variable to discuss the relationship between rural population ageing, technical efficiency and agricultural carbon emissions., Results: This paper adopts the classical carbon emission calculation theory of IPCC to measure agricultural carbon emissions from 2010 to 2019, and China's plantation carbon emissions show an "inverted U-shaped" trend, reaching a high level in 2015 and then starting to decline. In addition, the fixed-effects benchmark regression found that the aging of the rural population promotes agricultural carbon emissions, and the technical efficiency of agriculture suppresses agricultural carbon emissions. Finally, the mediating effect model is applied to explore the relationship between the three. Using technical efficiency as the mediating variable, it is found that under the masking effect, rural population aging will weaken agricultural carbon emissions through technical efficiency, thus achieving the suppression of agricultural carbon emissions., Policy Recommendation: The formulation and modification of agricultural carbon-reducing policy row policies should take full account of the broader context of rural population ageing; increase the interconnectedness and interaction between rural population ageing and agricultural production technology, and actively play a positive role in promoting the efficiency of agricultural technology as a result of rural population ageing; and, in accordance with the actual situation of agricultural research, appropriately increase the strength of financial support for agriculture to improve agricultural technology and promote low-carbon development in agriculture., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Song et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Weeds are one of the greatest threats to the growth of rice, and the loss of crops is greater in the early stage of rice growth. Traditional large-area spraying cannot selectively spray weeds and can easily cause herbicide waste and environmental pollution. To realize the transformation from large-area spraying to precision spraying in rice fields, it is necessary to quickly and efficiently detect the distribution of weeds. Benefiting from the rapid development of vision technology and deep learning, this study applies a computer vision method based on deep-learning-driven rice field weed target detection. To address the need to identify small dense targets at the rice seedling stage in paddy fields, this study propose a method for weed target detection based on YOLOX, which is composed of a CSPDarknet backbone network, a feature pyramid network (FPN) enhanced feature extraction network and a YOLO Head detector. The CSPDarknet backbone network extracts feature layers with dimensions of 80 pixels ⊆ 80 pixels, 40 pixels ⊆ 40 pixels and 20 pixels ⊆ 20 pixels. The FPN fuses the features from these three scales, and YOLO Head realizes the regression of the object classification and prediction boxes. In performance comparisons of different models, including YOLOv3, YOLOv4-tiny, YOLOv5-s, SSD and several models of the YOLOX series, namely, YOLOX-s, YOLOX-m, YOLOX-nano, and YOLOX-tiny, the results show that the YOLOX-tiny model performs best. The mAP, F1, and recall values from the YOLOX-tiny model are 0.980, 0.95, and 0.983, respectively. Meanwhile, the intermediate variable memory generated during the model calculation of YOLOX-tiny is only 259.62 MB, making it suitable for deployment in intelligent agricultural devices. However, although the YOLOX-tiny model is the best on the dataset in this paper, this is not true in general. The experimental results suggest that the method proposed in this paper can improve the model performance for the small target detection of sheltered weeds and dense weeds at the rice seedling stage in paddy fields. A weed target detection model suitable for embedded computing platforms is obtained by comparing different single-stage target detection models, thereby laying a foundation for the realization of unmanned targeted herbicide spraying performed by agricultural robots. [ABSTRACT FROM AUTHOR]

Agricultural ecological efficiency is an important tool with which to measure the coordination of the sustainable development of agricultural economies and ecological environments. In this paper, a super-efficiency slacks-based measures model was used to measure the agricultural ecological efficiency in Hebei Province. The characteristics of spatial and temporal evolution patterns were explored using a spatial Markov transfer matrix. The results showed that (i) based on measurements, the agricultural ecological efficiency in Hebei Province showed regional differences in four regions (eastern, northern, central and southern Hebei) and 141 counties; (ii) from the perspective of evolutionary characteristics of agricultural ecological efficiency, the overall development of in Hebei Province was good, with more concentrated spatial distribution and more obvious direction, while the type of transfer of agricultural ecological efficiency in Hebei Province showed strong stability that was significantly affected by geographical neighborhood conditions and the club convergence phenomenon; (iii) from the perspective of the long-term evolutionary trend of agricultural ecological efficiency, the areas adjacent to counties with low efficiency had limited potential for improvement, and the areas adjacent to counties with high grade had great potential. However, it was difficult to achieve large-scale improvement in agricultural ecological efficiency in Hebei Province, whether the impact of geospatial backgrounds was considered or not., Competing Interests: The authors declare no conflict of interest., (Copyright: © 2024 Ma et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

As a new business model, E-commerce brings new changes to the global economy and society. So, can E-commerce development policies promote high-quality agricultural development? This article regards the pilot construction of national e-commerce demonstration cities as a quasi-natural experiment for the development of e-commerce. Based on the E-commerce pilot and economic and social development data of national prefecture-level cities from 2004 to 2018, the agricultural total factor productivity calculated by the Fare-Primont index method is used as the characterization of the quality of urban agricultural development, and an empirical model is constructed under the progressive Differences-in-Differences framework. This paper empirically tests the overall impact of E-commerce development on the high-quality development of agriculture and its sources, analyzes the heterogeneity and dynamics of the impact, and investigates the possible impact mechanism. The result shows that from the overall impact and its sources, the development of E-commerce in cities has a positive impact on the high-quality development of agriculture, and the impact is mainly due to its role in promoting technological innovation and economies of scale. From the perspective of heterogeneity, the larger the population size of a city, the more significant the level of e-conomic development or Internet development. From the perspective of the dynamic impact, the positive impact of e-commerce development becomes more significant over time. With the passage of time, the impact of E-commerce on high-quality agricultural development policies shows a trend of first increasing and then decreasing. Overall, this study provides empirical evidence for the rationality and effectiveness of policies and measures related to E-commerce to promote the high-quality development of agriculture., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Zhong et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The "Three Rural Issues", encompass challenges related to agriculture, farmer, and rural area, which hold significant importance in driving comprehensive rural revitalization efforts in China. Farmer entrepreneurship, as a crucial means to enhance productivity, create job opportunities, and increase residents' income, has gradually become a key driving force in promoting rural revitalization in the new stage of development in China. With the rapid development of rural e-commerce, farmer entrepreneurship has encountered new opportunities. This study utilizes the 2020 China Family Panel Studies (CFPS) data and employs a structural equation model (SEM) to analyze the direct impact of rural e-commerce participation on farmer entrepreneurial behavior, considering factors such as human capital, social capital, and network infrastructure. This study further explores the indirect effects and mechanisms of e-commerce participation as a mediating variable and analyzes the impact and mechanisms on agricultural entrepreneurship behavior. The findings are as follows: (1) E-commerce participation significantly promotes farmer entrepreneurial behavior; (2) E-commerce participation as a mediating variable has a positive indirect effect on the relationship between social trust, network infrastructure, human capital, and farmer entrepreneurial behavior; (3) E-commerce participation has a significant positive influence on farmer entrepreneurship in the agricultural sector, and farmers with higher levels of network infrastructure and human capital have a higher probability of choosing agricultural entrepreneurship under the influence of e-commerce participation. Finally, this study provides policy recommendations in terms of infrastructure construction, entrepreneurial policy environment, and education level, aiming to optimize the situation of farmer entrepreneurship and contribute to the comprehensive promotion of rural revitalization.Overall, the research in this paper effectively combines theory and empirical evidence to outline the direct and indirect impact mechanisms of rural e-commerce participation on farmers' entrepreneurial behavior and agriculture-related entrepreneurial behavior and to test the effects of their impacts. First, most of the existing literature deals with farmers in individual sample areas, while the sample selected in this paper is farmers in the whole country, which is relatively more generalizable; second, most of the previous studies explore the level of e-commerce in the inter-provincial or county areas, while this paper expands the empirical study of rural e-commerce on the entrepreneurial behavior of farmers and the micro-period of agricultural entrepreneurial behavior, and focuses on the impacts of the e-commerce activities of farmers on their entrepreneurial behavior., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Lin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

This study explores the impact of farm households' social capital characteristics and local government policies on the selection of farmland transfer contracts in China's rural industrial revitalization context. Utilizing field research data from 1,979 households in ethnic areas of Hunan Province, this paper constructs an econometric model to assess how farm households' social capital and local governments' involvement in rural industrial revitalization influence farmland transfer contract selections. The findings indicate that, lacking government program support, farmers' social capital significantly affects contract type and duration, but not the rent. Specifically, farmers possessing extensive social capital prefer verbal and short-term contracts (coefficients of 0.525 and 0.643, significant at the 5% level), whereas their influence on rent (coefficient of 2.418, significant at the 5% level) manifests under government program support. These results challenge the conventional theory of farmland transfer contracts and offer substantial empirical support for the development of local government policies in rural industrial revitalization, underlining the critical role of government guidance and social capital in enhancing farmland transfer., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 He, Huang. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Winter wheat is one of the most important crops in the world. It is great significance to obtain the planting area of winter wheat timely and accurately for formulating agricultural policies. Due to the limited resolution of single SAR data and the susceptibility of single optical data to weather conditions, it is difficult to accurately obtain the planting area of winter wheat using only SAR or optical data. To solve the problem of low accuracy of winter wheat extraction only using optical or SAR images, a decision tree classification method combining time series SAR backscattering feature and NDVI (Normalized Difference Vegetation Index) was constructed in this paper. By synergy using of SAR and optical data can compensate for their respective shortcomings. First, winter wheat was distinguished from other vegetation by NDVI at the maturity stage, and then it was extracted by SAR backscattering feature. This approach facilitates the semi-automated extraction of winter wheat. Taking Yucheng City of Shandong Province as study area, 9 Sentinel-1 images and one Sentinel-2 image were taken as the data sources, and the spatial distribution of winter wheat in 2022 was obtained. The results indicate that the overall accuracy (OA) and kappa coefficient (Kappa) of the proposed method are 96.10% and 0.94, respectively. Compared with the supervised classification of multi-temporal composite pseudocolor image and single Sentinel-2 image using Support Vector Machine (SVM) classifier, the OA are improved by 10.69% and 5.66%, respectively. Compared with using only SAR feature for decision tree classification, the producer accuracy (PA) and user accuracy (UA) for extracting the winter wheat are improved by 3.08% and 8.25%, respectively. The method proposed in this paper is rapid and accurate, and provide a new technical method for extracting winter wheat., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Vietnam's agricultural exports to China have remained strong, with the country maintaining its position as the top destination for Agri-products. This article primarily utilizes the Revealed Comparative Advantage (RCA) Index, and Trade Complementarity (TC) index to examine the trade comparative advantage, and the complementary of twenty major agricultural products between China and Vietnam from 2012 to 2021. The study results showed that Vietnam and China frequently exchange agricultural products. Vietnam has more stronger competitiveness than China in terms of agricultural products. China's exports to Vietnam were highly complementary to Vietnam's imports in category 0 whiles Vietnam's exports to China showed strong complementarity with China's imports in category 2. This paper analyzes the complementarity and comparative advantages of agricultural trade between China and Vietnam, and proposes informed suggestions for policy-making to promote agricultural trade between the countries. The proposed suggestions aim to expand agricultural trade between the two countries, reduce the trade imbalance, and achieve mutual benefit and win-win results., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Tian et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Using the panel data of 31 provinces in China between 2000 and 2018, this study theoretically and empirically analyses the impact of environmental regulations on the adjustment of the agriculture industrial structure from the perspectives of rationalisation and optimisation. Overall, variability in the impact of environmental regulation on the adjustment of the agricultural industrial structure is identified: a negative influence on the rationalisation of agricultural industrial structure and a positive influence on the optimisation of agricultural industrial structure. The impact of environmental regulations on the adjustment of agricultural industrial structure also reflects "large country characteristics": environmental regulations are more significant at the medium and low industrial structure levels. Environmental regulation significantly impacts the rationalisation of agricultural industrial structure in central and western regions and its optimisation in eastern and central regions. Through the panel threshold model test, this paper further finds that the effect of environmental regulations on the adjustment of agricultural industrial structure is not invariant. However, there is a non-linear relationship with significant threshold characteristics. Based on the above results, some countermeasures are suggested., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Tan, Jing. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Introduction: As one of the agglomeration models targeting cluster-based rural development, cluster farming has been promoted in Ethiopia and it is already reported to have significant welfare implications, but participation rates are not as high as expected. This study examines the role of land as a constraint to the development of cluster-based development in Ethiopia both using extensive and intensive measures of cluster farming. The study further disaggregates farm households based on their farm size to better understand potential heterogeneities in the relationship between farm size and cluster farming. The paper also documents other household socio-economic and network characteristics that may matter in cluster farming., Methods: We use a large-scale farm household data from 3,969 households coupled with some expert insights on cluster farming in Ethiopia. Households in the study areas grow major staples such as maize, wheat, teff, malt barley, and sesame in four main regions of Ethiopia. We employ a double hurdle model to examine both the decision to participate and the extent to which households participate in cluster farming. By extent of participation, we refer to the amount of land and share of land farm households contribute to cluster farming. For robustness purposes, we also estimate the Tobit and Linear Probability Models., Results: We show a positive association between farm size and cluster farming both at the extensive and intensive margins. This relationship turns negative for large amounts of land. This shows that cluster farming increases with farm size up to a threshold beyond which it declines. We also find suggestive evidence that participation rates are lower for small-scale farms, but also declines for large-scale farms. In addition, we show that access to information and network characteristics also matter in enabling cluster farming., Conclusion: The findings of this study are relevant in the framework of plans to upscale the cluster-based development initiative in Ethiopia. Attention to landholding issues is key and may be an important area where policy action can be geared to boost cluster farming. Moreover, our results inform potential targeting plans that aim to increase the participation of small-scale farmers who are usually the intended targets of such programs., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Dureti, Tabe-Ojong. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

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., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Liu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Lumpy skin disease (LSD) is a critical problem for cattle populations, affecting both individual cows and the entire herd. Given cattle's critical role in meeting human needs, effective management of this disease is essential to prevent significant losses. The study proposes a deep learning approach using the MobileNetV2 model and the RMSprop optimizer to address this challenge. Tests on a dataset of healthy and lumpy cattle images show an impressive accuracy of 95%, outperforming existing benchmarks by 4–10%. These results underline the potential of the proposed methodology to revolutionize the diagnosis and management of skin diseases in cattle farming. Researchers and graduate students are the audience for our paper. [ABSTRACT FROM AUTHOR]

Introduction: Latinos are the fastest growing aging population in the U.S. However, there has been limited attention to conceptualizing successful aging among Latinos, especially those residing in rural communities. Latinos are the largest racial or ethnic group residing in rural underserved communities and rural Latinos experience significant structural barriers to access the conditions they need to age well. The goal of this study is to make unique contributions to the successful aging literature by describing what successful aging means for middle-aged Latinas residing in a rural community. Methods: This qualitative paper used inductive thematic content analysis to examine definitions of successful aging among Latina women (n = 40) residing in an underserved agricultural community and entering mid-life (mean = 49 years old; age range 40–64). Results: With regards to definitions of successful aging, four themes emerged: 1) Having good health; 2) maintaining an active lifestyle; 3) the wellbeing of one's children; and 4) being independent. Discussion: Participants' definitions of successful aging aligned to some extent with existing frameworks, specifically related to health and independence. However, middle-aged Latina participants' unique definitions of successful aging also diverged from existing frameworks, especially around the wellbeing of their children and the importance of work as a way of maintaining an active lifestyle. More research is needed to understand the unique social context and circumstances of middle-aged Latinos residing in rural communities and how they influence their aging journeys. This can provide important information for the development of culturally sensitive services, interventions, and policies to help Latinos age well. [ABSTRACT FROM AUTHOR]

Under the background of the continuous progress of China's agricultural reform, the development of characteristic agriculture is an important field of agricultural development in the country and even the world. Yunnan has unique advantages in geography, location, climate, and human resources, and has unique conditions for the development of agriculture with plateau characteristic. However, the sustainable development of agriculture with plateau characteristic is affected and restricted by many factors. How to promote the sustainable development of agriculture with plateau characteristic is an important research topic, and it is also the main purpose of this study. Therefore, through literature analysis and investigation, this study studies the advantages and disadvantages, development status, main problems, countermeasures and suggestions, macro development direction, and theoretical research methods of characteristic agriculture in the Yunnan plateau. This paper analyzes the advantages and disadvantages of the Decision-making Trial and Evaluation Laboratory (DEMATEL) and the Adversarial Interpretation Structure Modeling Method (AISM) and proposes to combine the two models to make up for each other and improve the accuracy of model analysis. On this basis, a set of identification and evaluation systems of factors affecting the sustainable development of agriculture with plateau characteristic is established, which can comprehensively and accurately identify and evaluate various influencing factors, and provide a scientific basis for the sustainable development of agriculture with plateau characteristic. Finally, combined with the relevant statistical data of Yunnan Province from 2009 to 2020 and many results calculated by the model, the factors affecting the sustainable development of agriculture with plateau characteristic were comprehensively analyzed. The results show that transportation, environment, and insurance support factors are the root factors that affect the sustainable development of agriculture with plateau characteristic, while the regional economy, scientific and technological support, and the development of agricultural enterprises are of high importance. Financial support, the level of agricultural mechanization, the quality of labor, and other factors can not be ignored. Finally, according to the results of the analysis, the corresponding policy recommendations are put forward to provide a reference for the sustainable development of plateau characteristic agriculture in Yunnan Province., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

With the new challenges and crises facing agriculture, digitalization and green transformation have become important ways to solve the problems. This paper uses an international economics perspective to chart a new path for sustainable agricultural development. Specifically, it analyzes whether two-way international direct investment (FDI) can facilitate agricultural digital-green fusion(DGF)? Using a sample of 31 provinces (autonomous regions) from 2012 to 2021, this study finds: (1) Two-way FDI can significantly contribute to agriculture's DGF. (2) In the mechanism test, it is proved that two-way FDI can promote agriculture's DGF level by promoting green technology innovation capacity and overall regional technology innovation capacity. (3) The positive effects of two-way FDI are prominent in the eastern and central regions, coastal regions, and economically developed areas. (4) In the spatial Durbin model, the local two-way FDI growth improves agriculture's DGF level in the surrounding areas to a certain extent. The government is advised to prioritize openness, foster an environment for technological innovation, leverage spatial radiation for agricultural DGF, and advance digitally empowered agricultural modernization., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Chen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Sustainable intensification of agriculture requires understanding of the effect of soil characteristics and nutrient supply on crop growth. As farms are increasing in size by acquiring small fields from various farmers, the soil characteristics and nutrient supply might be very different from field to field, while at the same time specific soil properties might limit the nutrient uptake. As a result, there might be a large number of heterogeneous reasons why crop growth varies significantly. New data analysis techniques can help to explain variability in crop growth among fields. This paper introduces Exceptional Growth Mining (EGM) as a first contribution. EGM instantiates the data mining framework Exceptional Model Mining (EMM) such that subgroups of fields can be found that grow exceptionally in terms of three growth parameters (high/low maximum growth, steep/flat linear growth and early/late midpoint of maximum growth). As second contribution, we apply EGM to a case study by analyzing the dataset of a potato farm in the south of the Netherlands. EGM consists of (i) estimating growth curves by applying nonlinear mixed models, (ii) investigating the correlation between the estimated growth parameters, and (iii) applying EMM on these growth curve parameters using a growth curve-specific quality measure. By applying EGM on the data of the potato farm, we obtain the following results: 1) the estimated growth curves represent the variability in potato tuber growth very well (R2 of 0.92), 2) the steepness of the growth curve has a strong correlation with the maximum growth and the midpoint of maximum growth, and the correlation between the midpoint of maximum growth and maximum growth is weak, 3) the subgroup analyses indicate that: high values of K correspond to high maxima; low values of K correspond to low maxima, steep growth curves', and a late midpoint of halfway growth; Mg influences the midpoint of the growth curve; values of B are higher on dry soils with high tuber growth, while low values of B are found on wet soils with high tuber growth; high values of Zn, Mn, and Fe are found in subgroups with low tuber weight, probably related to the soil's low pH. In summary, this paper introduces EGM to obtain understanding in crop response to soil properties and nutrient supply. In addition, EGM provides a way to analyze only small parts of a large dataset, such that the impact of soil factors on growth can be analyzed on a more detailed level than existing methods., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Mulders et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Landscape changes driven by cash crop plantations have been prevalent in tropical and subtropical regions worldwide in recent decades. Investigating the landscape changes and concluding livelihood outcomes are fundamental to figure out the solutions for rural sustainability. This paper examined the landscape changes which was caused by land use changes in tea plantations as well as investigated the resultant livelihood impacts, based on a case study in Fuding City, Southeast China. A questionnaire survey of 114 rural households in four sampled villages was conducted. Results demonstrated that expansion and intensification of tea plantations were two major proximate causes of landscape changes in recent decade. Our survey indicated that some existing intensively-managed tea plantations had derived from intensification and expansion of tea plantations, respectively. We identified four underlying driving forces of landscape changes, including economic benefit, governmental policies, wildlife destruction on grain crops, and rural return migration. Our study confirmed that landscape changes have significant positive effects on farmers' livelihoods, including increasing employment and incomes, raising living standards, enhancing livelihood assets and livelihood sustainability. Especially, the aged rural populations could have a relatively decent living standard. Meanwhile, the excessive expansion of tea plantations may impair livelihood resilience. Lastly, three policy suggestions based on different time scales have been put forward to promote rural households' livelihood sustainability and resilience., Competing Interests: The authors declare no conflict of interest., (Copyright: © 2023 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Stockmanship is an important determinant for good animal welfare and health. The goal of the FarmMERGE project is to investigate the associations between farmer health and work environment, and the health, productivity and welfare of their livestock. We merged several livestock industry databases with a major total population-based health study in Norway (The Trøndelag Health Study 2017–2019 (HUNT4)). This paper describes the project's collection and merging of data, and the cohort of farmers and farms that were identified as a result of our registry merge. There were 56,042 participants of HUNT4 (Nord-Trøndelag County participants only, participation rate: 54.0%). We merged a list of HUNT4 participants whose self-reported main occupation was "farmer" (n = 2,407) with agricultural databases containing production and health data from sheep, swine, dairy and beef cattle from 2017–2020. The Central Coordinating Register for Legal Entities was used as an intermediary step to achieve a link between the farmer and farming enterprise data. We identified 816 farmers (89.5% male, mean age 51.3 years) who had roles in 771 farming enterprises with documented animal production. The cohort included 675 unique farmer-farm combinations in cattle production, 139 in sheep, and 125 in swine. We linked at least one HUNT4 participant to approximately 63% of the dairy farms, 53% of the beef cattle farms, 30% of the sheep farms, and 38% of the swine farms in Nord-Trøndelag County in the 2017–2019 period. Using existing databases may be an efficient way of collecting large amounts of data for research, and using total population-based human health surveys may decrease response bias. However, the quality of the resulting research data will depend on the quality of the databases used, and thorough knowledge of the databases is required. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive analysis of the agriculture trade dynamic between India and China from 2002-2021. It employed the Revealed Comparative Advantage (RCA) and Revealed Symmetric Comparative Advantage (RSCA) indices and various statistical analyses to assess the trade performance and provide an enhanced comprehension of the specialization pattern. The study has selected 45 agricultural commodities, categorized into seven sections according to the HS Nomenclature 2017. The findings revealed that India boasts a more diversified export portfolio compared to China, with a more significant number of agriculture chapters demonstrating a comparative advantage. Conversely, China's export basket is more concentrated, featuring fewer chapters with a comparative advantage but higher values of RCA and RSCA. The paper also identified the prospective areas for agriculture-trade cooperation and collaboration between the two countries and put forward recommendations. This research aims to offer valuable insights for policymakers, researchers, and traders to enhance bilateral ties and mutual benefits in the agriculture sector., Competing Interests: The authors have delcared that no competing interest exist., (Copyright: © 2023 Shukla et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Hybrid tractors (HT) are regarded as the efficient agricultural machine due to their energy conservation performance and faster torque response to deal with load fluctuations. However, the strategy to allocate the battery and fuel energy for demand power should be discussed. In this paper, an on-line management strategy of the HT is proposed to optimize the energy consumption of engine and motor and to reduce torque ripple for power units. A new architecture for replacing power shift and continuously variable transmission technology is proposed. Then, the modified equivalent consumption minimization strategy (ECMS) is used to optimize the torque distribution in which the equivalent factor is further calculated for the real-time process. Besides, the modification of ECMS in variable working conditions can effectively analyse the torque distribution between the motor and engine. The numerical test is implemented that the effectiveness of the proposed energy strategy is validated in plowing conditions. The consequences indicated that the proposed power distribution strategy can adaptively allocate the torque demand according to the fluctuation load. Comparing with the traditional rule-based strategy, the proposed strategy can reduce 6.2% of the energy, and decrease torque ripple with the proposed tractor architecture., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Dou et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

This paper expounds the theoretical logic among digital inclusive finance, urbanization, and agricultural mechanization level, puts forward the research hypothesis, and then selects the county unbalanced panel data of 1309 counties in China from 2014 to 2020 based on the two-way fixed model with standard error clustering to county level and mediating effect model for empirical data regression analysis. Through baseline regression analysis, mediation effect analysis, and heterogeneity analysis, the findings of this paper are as follows. First, digital financial inclusion has a significant positive effect on the growth of agricultural mechanization. Second, digital inclusive finance at the county level can also indirectly affect the growth of agricultural mechanization through urbanization. That is, agricultural mechanization has an intermediary effect between the financial agglomeration at the county level and the growth of farmers' income. Third, the impact of county-level digital financial inclusion on the growth of agricultural mechanization level is significantly heterogeneous, and the promoting effect is significant in areas with balanced grain production, national-level poor county or contiguous areas of dire poverty, and areas with a good foundation for digital financial inclusion. By analyzing digital inclusive finance, urbanization, and agricultural mechanization, this paper proposes targeted policy recommendations. First, the government can promote agricultural mechanization by developing digital financial inclusion. Second, the government should guide and accelerate the process of digital financial inclusion, promoting urbanization thereby amplifying the positive impact of digital financial inclusion on agricultural mechanization. Third, given the heterogeneity of the impact of digital financial inclusion on agricultural mechanization, local development should focus on developing different dimensions of digital financial inclusion according to specific conditions., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Liu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

This paper contributes to smart greenhouses and IoT (Internet of Things) research. Our pioneering achievement centers on successfully designing, constructing, and testing a 30m2 smart greenhouse, explicitly focusing on the cultivation and development of Brassica Juncea, a mustard variety commonly grown in Vietnam. The construction phase entailed the meticulous integration of diverse IoT technologies and systems, culminating in the creation of a finely tuned environment to meet the unique needs of Brassica Juncea cultivation. Notably, our research team has realized the physical infrastructure and developed and implemented a robust web interface. This interface empowers users to monitor and remotely control the smart greenhouse conveniently. It provides real-time visualization of critical parameters, including temperature, humidity, soil moisture, and light intensity, enabling precise monitoring and supporting informed decision-making in crop management. In addition to the web interface, we have meticulously designed and completed an Android mobile application, further enhancing accessibility and convenience. This mobile app allows users to monitor and control the smart greenhouse while on the move. It is imperative to underscore that this work marks a significant milestone as the first complete smart greenhouse IoT solution dedicated to developing Brassica Juncea. Our pioneering accomplishments not only advance the frontiers of innovative greenhouse and IoT research but also contribute substantially to the progress of intelligent agriculture., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Huynh et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Regarding the mode choice of farmland transfer, the existing literature have more examined the choices between market-based transfer (spontaneous transfer) and government or village committee-led transfer, and between formal contract and informal contract. However, the question that how the two parties choose among various specific transfer modes has not attracted extensive attention of scholars. Based on contract theory and transaction cost economics, this paper uses the public samples of the third national agricultural census data to investigate how large-scale households choose among the specific transfer modes when transferring into farmland, like the transfer of the contracted management right (TCMR), lease and shareholding. The findings of this paper are as follows. Firstly, with the increase in the transfer-in area and the education level of the household head, the probability of choosing lease and shareholding increases relatively, but the latter rises faster. Secondly, compared with large-scale farmers whose aim is to plant crop, the probability of those who transfer into farmland for gardening and forestry operation choosing shareholding has increased significantly. Thirdly, the age of the household head and the number of household laborers have an interactive effect on the mode choice of transferring into farmland. In addition, the mode choice of large-scale households' transferring into farmland is also significantly affected by environmental factors such as local topography, irrigation, traffic conditions, industrial structure, and social security development. Therefore, adhering to the parties to choose the mode of farmland transfer independently is crucial. The government and rural grassroots organizations should help the parties to understand the characteristics, adaptability and supply and demand of different modes, and help the parties to explore the most economical mode. The contribution of this paper is that it expands the study of the transfer of rural land rights to the choices of specific transfer modes, and partially reveals the rule of choices, which provides a reference for the parties to choose the most efficient transfer mode under different conditions and for the government and rural grassroots organizations to play a role., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 He, Xiao. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Green agriculture can minimize the negative impact of agriculture on the environment. As countries around the world strongly advocate green production and green life style, not only is consumers' awareness about green consumption rising, but the demand for green agricultural products at home and abroad is gradually increasing as well. Brand image has been a crucial factor for consumers to make their final purchasing decisions, thus playing a critical role in influencing the purchasing decisions of consumers. Based on the theory of the brand image, this paper undergoes a comprehensive theoretical and positive analysis and explores the influence mechanism of brand image of green agricultural products on consumers' purchasing intention. A hypothetical model with perceived value as mediators is constructed by us to examine the influence of brand image of green agricultural products on consumers' purchasing intention. A quantitative study was conducted for a random sample of 341 consumers who purchased green agricultural products in China according to a questionnaire-based survey using a cluster random sampling technique. The study showed that the overall image of agribusiness, the image of agricultural products, the social image of agribusiness, and the image of consumers are all positively related to consumption intention. The overall image of agribusiness, the image of agricultural products, the social image of agribusiness, and the image of consumers have a positive influence on perceived value. Moreover, perceived value plays a part in the mediating role in the influence of the overall image of agribusiness, the image of agricultural products, the social image of agribusiness, and the image of consumers on consumption intention. These findings shed lights on enterprises in establishing a scientific and effective brand strategy and building an excellent brand image. The research conclusion can provide new insight into how to enhance the consumption willingness for green agricultural products and promote sustainable development., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Yang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

It is of great significance to identify the pest species accurately and control it effectively to reduce the loss of agricultural products. The research results of this project will provide theoretical basis for preventing and controlling the spread of pests and reducing the loss of agricultural products, and have important practical significance for improving the quality of agricultural products and increasing the output of agricultural products. At the same time, it provides a kind of effective prevention and control measures for farmers, so as to ensure the safety and health of crops. Because of the slow speed and high cost of manual identification, it is necessary to establish a set of automatic pest identification system. The traditional image-based insect classifier is mainly realized by machine vision technology, but because of its high complexity, the classification efficiency is low and it is difficult to meet the needs of applications. Therefore, it is necessary to develop a new automatic insect recognition system to improve the accuracy of insect classification. There are many species and forms of insects, and the field living environment is complex. The morphological similarity between species is high, which brings difficulties to the classification of insects. In recent years, with the rapid development of deep learning technology, using artificial neural network to classify pests is an important method to establish a fast and accurate classification model. In this work, we propose a novel convolutional neural network-based model (MSSN), which includes attention mechanism, feature pyramid, and fine-grained model. The model has good scalability, can better capture the semantic information in the image, and achieve more accurate classification. We evaluated our approach on a common data set: large-scale pest data set, PlantVillage benchmark data set, and evaluated model performance using a variety of evaluation indicators, namely, macro mean accuracy (MPre), macro mean recall rate (MRec), macro mean F1-score (MF1), Accuracy (Acc) and geometric mean (GM). Experimental results show that the proposed algorithm has better performance and universality ability than the existing algorithm. For example, on the data set, the maximum accuracy we obtained was 86.35%, which exceeded the corresponding technical level. The ablation experiment was conducted on the experiment itself, and the comprehensive evaluation of the complete MSSN(scale 1+2+3) was the best in various performance indexes, demonstrating the feasibility of the innovative method in this paper., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Linfeng et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)