Development Status and Countermeasures of Farmers' Professional Cooperatives in Shangqiu City

Farmers' professional cooperatives develop in various forms,and with the continuous improvement of development level,social effects have gradually appear.Based on the specific situation of farmers' professional cooperatives in Shangqiu City,this paper expounded the development status of farmers' professional cooperatives in Shangqiu City,analyzed and discussed various problems encountered in the development process of farmers' professional cooperatives,and put forward some countermeasures and suggestions,such as establishing and perfecting cooperative rules and regulations,financing from multiple channels,introducing technical talents in various ways,breaking the information asymmetry restriction by various means,and giving full play to government functions,aiming to further promote standardized,large-scale,healthy and sustainable development of farmers' professional cooperatives.

Cooperative UAV search strategy based on DMPC-AACO algorithm in restricted communication scenarios

Improvement of integrated battlefield situational awareness in complex environments involving dynamic factors such as restricted communications and electromagnetic interference(EMI)has become a contentious research problem.In certain mission environments,due to the impact of many interference sources on real-time communication or mission requirements such as the need to implement communication regulations,the mission stages are represented as a dynamic combination of several communication-available and communication-unavailable stages.Furthermore,the data interaction between unmanned aerial vehicles(UAVs)can only be performed in specific communication-available stages.Traditional cooperative search algorithms cannot handle such situations well.To solve this problem,this study constructed a distributed model predictive control(DMPC)architecture for a collaborative control of UAVs and used the Voronoi diagram generation method to re-plan the search areas of all UAVs in real time to avoid repetition of search areas and UAV collisions while improving the search efficiency and safety factor.An attention mechanism ant-colony optimization(AACO)algorithm is proposed for UAV search-control decision planning.The search strategy is adaptively updated by introducing an attention mechanism for regular instruction information,a priori information,and emergent information of the mission to satisfy different search expectations to the maximum extent.Simulation results show that the proposed algorithm achieves better search performance than traditional algorithms in restricted communication constraint scenarios.

Cooperative Anti-Jamming and Interference Mitigation for UAV Networks: A Local Altruistic Game Approach

To improve the anti-jamming and interference mitigation ability of the UAV-aided communication systems, this paper investigates the channel selection optimization problem in face of both internal mutual interference and external malicious jamming. A cooperative anti-jamming and interference mitigation method based on local altruistic is proposed to optimize UAVs’ channel selection. Specifically, a Stackelberg game is modeled to formulate the confrontation relationship between UAVs and the jammer. A local altruistic game is modeled with each UAV considering the utilities of both itself and other UAVs. A distributed cooperative anti-jamming and interference mitigation algorithm is proposed to obtain the Stackelberg equilibrium. Finally, the convergence of the proposed algorithm and the impact of the transmission power on the system loss value are analyzed, and the anti-jamming performance of the proposed algorithm can be improved by around 64% compared with the existing algorithms.

Characterization of Small-Scale Farmers and Assessment of Their Access to Crop Production Information in Selected Counties of Kenya

Small-scale farming accounts for 78% of total agricultural production in Kenya and contributes to 23.5% of the country’s GDP. Their crop production activities are mostly rainfed subsistence with any surplus being sold to bring in some income. Timely decisions on farm practices such as farm preparation and planting are critical determinants of the seasonal outcomes. In Kenya, most small-scale farmers have no reliable source of information that would help them make timely and accurate decisions. County governments have extension officers who are mandated with giving farmers advisory services to farmers but they are not able to reach most farmers due to facilitation constraints. The mode and format of sharing information is also critical since it’s important to ensure that it’s timely, well-understood and usable. This study sought to assess access to geospatial derived and other crop production information by farmers in four selected counties of Kenya. Specific objectives were to determine the profile of small-scale farmers in terms of age, education and farm size;to determine the type of information that is made available to them by County and Sub-County extension officers including the format and mode of provision;and to determine if the information provided was useful in terms of accuracy, timeliness and adequacy. The results indicated that over 80% of the farmers were over 35 years of age and over 56% were male. Majority had attained primary education (34%) or secondary education (29%) and most farmers in all the counties grew maize (71%). Notably, fellow farmers were a source of information (71%) with the frequency of sharing information being mostly seasonal (37%) and when information was available (43%). Over 66% of interviewed farmers indicating that they faced challenges while using provided information. The results from the study are insightful and helpful in determining effective ways of providing farmers with useful information to ensure maximum benefits.

Dual carbon goal and agriculture in China:Exploring key factors influencing farmers’behavior in adopting low carbon technologies

Identifying the factors influencing farmers’adoption of low-carbon technologies(FA)and understanding their impacts are essential for shaping effective agricultural policies amied at emission reduction and carbon sequestration in China.This study employs a meta-analysis of 122 empirical studies,delves into 23 driving factors affecting FA and addresses the inconsistencies present in the existing literature.We systematically examine the effect size,source of heterogeneity,and time-accumulation effect of the driving factors on FA.We find that significant heterogeneity in the factors influencing FA,except for farming experience,sources of heterogeneity from the survey zone,methodology model,technological attributes,report source,financial support,and the sampling year.Additionally,age,farming experience,and adoption cost negatively correlate with FA.In contrast,educational level,health status,technical training,economic and welfare cognition,land contract,soil quality,terrain,information accessibility,demonstration,government promotion,government regulation,government support,agricultural cooperatives member,peer effect,and agricultural income ratio demonstrate a positive correlation.Especially,demonstration and age show a particularly strong correlation.Finally,the effect of demonstration,age,economic and welfare cognition,farming experience,land contract,soil quality,information accessibility,government promotion,and support,as well as agricultural cooperative membership and peer effects on FA,are generally stable but exhibit varying degrees of attenuation over time.The effect of village cadre,family income,farm scale,gender,health status,technical training,and off-farm work on FA show notable temporal shifts and maintain a weak correlation with FA.This study contributes to shaping China’s current low-carbon agriculture policies across various regions.It encourages policymakers to comprehensively consider the stability of key factors,other potential factors,technological attributes,rural socio-economic context,and their interrelations.

Cholinesterase Activity as an Indicator of Health Risks among Kou Valley Farmers

Introduction: Pesticides are currently an essential component of agricultural production techniques for controlling pests and weeds. In Burkina Faso, non-compliance with good practice in the use of pesticides poses a real health problem for the population. This study examines the health risks associated with pesticide management in rice-growing areas. Material and Methods: A field survey was conducted in Bama, involving farmers, focusing on their socio-demographic characteristics, pesticide usage, and health effects. Cholinesterase levels were measured in subsample of farmers using a portable device. Data were analysed using Microsoft Excel, calculating means and percentages for various practices. Health consultations, protection methods, and pesticide management were studied. Erythrocyte acetylcholinesterase activity was compared before and after treatment. Data were categorised into classes based on inhibition levels, and correlation analyses determined relationships between variables such as age, years of experience, and cholinesterase activity. Results: The results indicate that rice cultivation is mainly carried out by a fairly young population, with nearly 63% being under the age of 50. Common poor practices in pesticide use include improper storage and reuse of leftover pesticides. Seven types of pesticides were identified, including organophosphates such as glyphosate, which was used in 26.7% of cases. This organophosphate has resulted in class B poisoning, causing a 30% - 50% reduction in erythrocyte cholinesterase activity. The health effects of pesticide use are felt by agricultural farmers through various symptoms of poisoning. Conclusion: To reduce the occurrence of pesticide poisoning, it is essential to launch information and awareness campaigns among the population and farmers to promote safe practices in pesticide use in Bama, Burkina Faso.

Local Criteria Used by Farmers to Evaluate the Agronomic Performance and the Fertilizing Capacity of Cowpea Varieties: Diversity, Variability and Proximal Relation with Agronomic Measurements in Contrasted Sahelian Locations

When contributing to participatory research, farmers usually appreciate the performance of cowpea varieties using qualitative scores. The score they attribute to each variety are based on local knowledge. The specific criteria they individually use to attribute a score are not well described. The objectives of this work were to: 1) identify and describe exhaustively the local criteria used by farmers to measure the agronomic performance of cowpea;2) assess the variability and statistical structure of these farmer criteria across local contexts;3) and analyze the association between these farmer criteria and the classical agronomic measurement. To achieve these objectives, an augmented block design was implemented across fifteen locations in the regions of Maradi, Dosso and Tillabéri, representing a diversity of local contexts. From a set of 36 cowpea varieties, fifteen varieties were sown per location, including five varieties (controls) common to all locations. In each location, two replicates were sown in randomized Fisher’s blocks. After agronomic measurement and participatory evaluation (scoring of varieties by farmers), a group survey (focus group) was conducted in each location to identify the criteria considered by farmers to found their discretional scoring of varieties during the participatory evaluation. The analysis of the data identified, across locations, thirteen criteria defined by farmers to characterize the agronomic performance of cowpea. Some of these criteria were different according to location. Farmers ranked the three varieties with the best performance for each agronomical trait (Top 3 varieties). A comparison of the farmer ranking with the ranking based on agronomic measurements revealed similarity and complementary between both methods. This study highlighted the importance of considering both local and scientific knowledge in local varietal evaluations.

Multi-UAV cooperative maneuver decision-making for pursuitevasion using improved MADRL

Aiming at the problem of multi-UAV pursuit-evasion confrontation, a UAV cooperative maneuver method based on an improved multi-agent deep reinforcement learning(MADRL) is proposed. In this method, an improved Comm Net network based on a communication mechanism is introduced into a deep reinforcement learning algorithm to solve the multi-agent problem. A layer of gated recurrent unit(GRU) is added to the actor-network structure to remember historical environmental states. Subsequently,another GRU is designed as a communication channel in the Comm Net core network layer to refine communication information between UAVs. Finally, the simulation results of the algorithm in two sets of scenarios are given, and the results show that the method has good effectiveness and applicability.

Optimization of Cooperative RelayingMolecular Communications for Nanomedical Applications

Recently,nano-systems based on molecular communications via diffusion(MCvD)have been implemented in a variety of nanomedical applications,most notably in targeted drug delivery system(TDDS)scenarios.Furthermore,because the MCvD is unreliable and there exists molecular noise and inter symbol interference(ISI),cooperative nano-relays can acquire the reliability for drug delivery to targeted diseased cells,especially if the separation distance between the nano transmitter and nano receiver is increased.In this work,we propose an approach for optimizing the performance of the nano system using cooperative molecular communications with a nano relay scheme,while accounting for blood flow effects in terms of drift velocity.The fractions of the molecular drug that should be allocated to the nano transmitter and nano relay positioning are computed using a collaborative optimization problem solved by theModified Central Force Optimization(MCFO)algorithm.Unlike the previous work,the probability of bit error is expressed in a closed-form expression.It is used as an objective function to determine the optimal velocity of the drug molecules and the detection threshold at the nano receiver.The simulation results show that the probability of bit error can be dramatically reduced by optimizing the drift velocity,detection threshold,location of the nano-relay in the proposed nano system,and molecular drug budget.

User Preference Aware Hierarchical Edge-User Cooperative Caching Strategy

The emergence of various new services has posed a huge challenge to the existing network architecture.To improve the network delay and backhaul pressure,caching popular contents at the edge of network has been considered as a feasible scheme.However,how to efficiently utilize the limited caching resources to cache diverse contents has been confirmed as a tough problem in the past decade.In this paper,considering the time-varying user requests and the heterogeneous content sizes,a user preference aware hierarchical cooperative caching strategy in edge-user caching architecture is proposed.We divide the caching strategy into three phases,that is,the content placement,the content delivery and the content update.In the content placement phase,a cooperative content placement algorithm for local content popularity is designed to cache contents proactively.In the content delivery phase,a cooperative delivery algorithm is proposed to deliver the cached contents.In the content update phase,a content update algorithm is proposed according to the popularity of the contents.Finally,the proposed caching strategy is validated using the MovieLens dataset,and the results reveal that the proposed strategy improves the delay performance by at least 35.3%compared with the other three benchmark strategies.

Cooperative Rate Splitting Transmit Design for Full-Duplex-Enabled Multiple Multicast Communication Systems

This paper examines the performance of Full-Duplex Cooperative Rate Splitting(FD-CRS)with Simultaneous Wireless Information and Power Transfer(SWIPT)support in Multiple Input Single Output(MISO)networks.In a Rate Splitting Multiple Access(RSMA)multicast system with two local users and one remote user,the common data stream contains the needs of all users,and all users can decode the common data stream.Therefore,each user can receive some information that other users need,and local users with better channel conditions can use this information to further enhance the reception reliability and data rate of users with poor channel quality.Even using Cell-Center-Users(CCUs)as a cooperative relay to assist the transmission of common data can improve the average system speed.To maximize the minimum achievable rate,we optimize the beamforming vector of Base Station(BS),the common streamsplitting vector,the cooperative distributed beamvector and the strong user transmission power under the power budget constraints of BS and relay devices and the service quality requirements constraints of users.Since the whole problem is not convex,we cannot solve it directly.Therefore,we propose a low complexity algorithm based on Successive Convex Approximation(SCA)technology to find the optimal solution to the problemunder consideration.The simulation results show that FD C-RSMA has better gain andmore powerful than FD C-NOMA,HD C-RSMA,RSMA and NOMA.

Navigating Market Choices: Understanding Carrot Market Outlet Selection among Smallholder Farmers in Kenya

This research delves into the hurdles and strategies aimed at augmenting the market involvement of smallholder carrot farmers in Nakuru County, Kenya. Employing a Multinomial Logit (MNL) model, it scrutinizes the factors influencing the selection of marketing outlets among carrot farmers. The findings unveil that a significant majority (81%) of surveyed farmers actively participate in diverse market outlets, encompassing the farm gate, cleaning point, local market, external market, and export market. Notably, pivotal buyers include aggregators, brokers, wholesalers, retailers, and consumers, with transactions predominantly occurring at the farm level. Additionally, the analysis discerns substantial influences of socio-economic characteristics, experiential factors, and geographical proximity on farmers’ choices of market outlets. Specifically, gender, age, land size, farming experience, and distance to markets emerge as critical determinants. Moreover, the study delves into the examination of market margins along the carrot value chain, shedding light on the potential profitability of carrot farming in the region. Remarkably, higher average gross margins are identified in export and external markets, signaling lucrative prospects for farmers targeting these segments. However, disparities in profit distribution between farmers and traders underscore the necessity for interventions to ensure equitable value distribution throughout the value chain. These findings underscore the imperative for tailored interventions to tackle challenges and foster inclusive agricultural development. Strategies such as farmer organizations, contracting, and vertical integration are advocated to enhance market access and profitability for smallholder carrot farmers. Thus, this study enriches our comprehension of the dynamics within carrot value chains and provides valuable insights for policymakers and development practitioners aiming to uplift rural livelihoods and bolster food security.

Climatic and non-climatic factors driving the livelihood vulnerability of smallholder farmers in Ahafo Ano North District,Ghana

Smallholder farmers in Ahafo Ano North District,Ghana,face multiple climatic and non-climatic issues.This study assessed the factors contributing to the livelihood vulnerability of smallholder farmers in this district by household surveys with 200 respondents and focus group discussions(FGDs)with 10 respondents.The Mann–Kendall trend test was used to assess mean annual rainfall and temperature trends from 2002 to 2022.The relative importance index(RII)value was used to rank the climatic and non-climatic factors perceived by respondents.The socioeconomic characteristics affecting smallholder farmers’perceptions of climatic and non-climatic factors were evaluated by the binary logistic regression model.Results showed that mean annual rainfall decreased(P>0.05)but mean annual temperature significantly increased(P<0.05)from 2002 to 2022 in the district.The key climatic factors perceived by smallholder farmers were extreme heat or increasing temperature(RII=0.498),erratic rainfall(RII=0.485),and increased windstorms(RII=0.475).The critical non-climatic factors were high cost of farm inputs(RII=0.485),high cost of healthcare(RII=0.435),and poor condition of roads to farms(RII=0.415).Smallholder farmers’perceptions of climatic and non-climatic factors were significantly affected by their socioeconomic characteristics(P<0.05).This study concluded that these factors negatively impact the livelihoods and well-being of smallholder farmers and socioeconomic characteristics influence their perceptions of these factors.Therefore,to enhance the resilience of smallholder farmers to climate change,it is necessary to adopt a comprehensive and context-specific approach that accounts for climatic and non-climatic factors.

A Pricing-Based Cooperative Relay Selection Scheme for Reliable Communications

With the rapid development and application of energy harvesting technology,it has become a prominent research area due to its significant benefits in terms of green environmental protection,convenience,and high safety and efficiency.However,the uneven energy collection and consumption among IoT devices at varying distances may lead to resource imbalance within energy harvesting networks,thereby resulting in low energy transmission efficiency.To enhance the energy transmission efficiency of IoT devices in energy harvesting,this paper focuses on the utilization of collaborative communication,along with pricing-based incentive mechanisms and auction strategies.We propose a dynamic relay selection scheme,including a ladder pricing mechanism based on energy level and a Kuhn-Munkre Algorithm based on an auction theory employing a negotiation mechanism,to encourage more IoT devices to participate in the collaboration process.Simulation results demonstrate that the proposed algorithm outperforms traditional algorithms in terms of improving the energy efficiency of the system.

Researching the Impact of the Quality of Village Governance on Farmers’Well-Being:From the Perspectives of Income and Occupational Differentiation

The goal of village governance is to improve the well-being of farmers,so this study aims to measure the impact the quality of village governance on the well-being of farmers.It also examines the heterogeneity of this impact across different farmer groups from the perspectives of income levels and occupational differentiation.To this end,this study developed an indicator system based on survey data collected from 1,442 farmers in the Sichuan,Shaanxi,and Gansu provinces,as well as the Ningxia Hui autonomous region.Multiple linear regression models were then used to analyze this data,and the findings revealed that improvements in the quality of village governance significantly increased the well-being of farmers.Specifically,primary-level empowerment and capacity building were shown to contribute the most to the enhancement of the farmers’well-being,followed by social inclusion,and social cohesion was found to have only a minimal effect.In terms of income levels,improving the quality of village governance benefited middle-income farmers the most,followed by low-income farmers,and it had the least effect on high-income farmers.In terms of occupations,full-time farmers gained the most from improvements in the quality of village governance,followed by off-farm farmers,with part-time farmers benefiting the least.Based on these findings,this study suggests that policymakers should improve the quality of village governance to enhance the well-being of farmers,focusing on the impact that level of income and occupational differentiation have on village governance.

Joint computation offloading and parallel scheduling to maximize delay-guarantee in cooperative MEC systems

The growing development of the Internet of Things(IoT)is accelerating the emergence and growth of new IoT services and applications,which will result in massive amounts of data being generated,transmitted and pro-cessed in wireless communication networks.Mobile Edge Computing(MEC)is a desired paradigm to timely process the data from IoT for value maximization.In MEC,a number of computing-capable devices are deployed at the network edge near data sources to support edge computing,such that the long network transmission delay in cloud computing paradigm could be avoided.Since an edge device might not always have sufficient resources to process the massive amount of data,computation offloading is significantly important considering the coop-eration among edge devices.However,the dynamic traffic characteristics and heterogeneous computing capa-bilities of edge devices challenge the offloading.In addition,different scheduling schemes might provide different computation delays to the offloaded tasks.Thus,offloading in mobile nodes and scheduling in the MEC server are coupled to determine service delay.This paper seeks to guarantee low delay for computation intensive applica-tions by jointly optimizing the offloading and scheduling in such an MEC system.We propose a Delay-Greedy Computation Offloading(DGCO)algorithm to make offloading decisions for new tasks in distributed computing-enabled mobile devices.A Reinforcement Learning-based Parallel Scheduling(RLPS)algorithm is further designed to schedule offloaded tasks in the multi-core MEC server.With an offloading delay broadcast mechanism,the DGCO and RLPS cooperate to achieve the goal of delay-guarantee-ratio maximization.Finally,the simulation results show that our proposal can bound the end-to-end delay of various tasks.Even under slightly heavy task load,the delay-guarantee-ratio given by DGCO-RLPS can still approximate 95%,while that given by benchmarked algorithms is reduced to intolerable value.The simulation results are demonstrated the effective-ness of DGCO-RLPS for delay guarantee in MEC.

Layered Coded Cache Placement and Cooperative Delivery with Sharing Links in Satellite-Terrestrial Integrated Networks

Cooperative utilization of multidimensional resources including cache, power and spectrum in satellite-terrestrial integrated networks(STINs) can provide a feasible approach for massive streaming media content delivery over the seamless global coverage area. However, the on-board supportable resources of a single satellite are extremely limited and lack of interaction with others. In this paper, we design a network model with two-layered cache deployment, i.e., satellite layer and ground base station layer, and two types of sharing links, i.e., terrestrial-satellite sharing(TSS) links and inter-satellite sharing(ISS) links, to enhance the capability of cooperative delivery over STINs. Thus, we use rateless codes for the content divided-packet transmission, and derive the total energy efficiency(EE) in the whole transmission procedure, which is defined as the ratio of traffic offloading and energy consumption. We formulate two optimization problems about maximizing EE in different sharing scenarios(only TSS and TSS-ISS),and propose two optimized algorithms to obtain the optimal content placement matrixes, respectively.Simulation results demonstrate that, enabling sharing links with optimized cache placement have more than 2 times improvement of EE performance than other traditional placement schemes. Particularly, TSS-ISS schemes have the higher EE performance than only TSS schemes under the conditions of enough number of satellites and smaller inter-satellite distances.

Pathway and Countermeasure of Farmers Livelihood Transformation in the Ecological Development Area of Northern Guangdong Province from the Perspective of Ecological Protection

[Objectives]To explore pathways and countermeasures for transforming farmers livelihoods in the way of reducing their dependence on land while promoting sustainable development and alleviating ecological degradation.[Methods]A combination of field research,literature review,and policy analysis was employed to identify key factors affecting farmers livelihoods and potential strategies for transformation.[Results]The study found that developing ecological agriculture and modern agriculture,promoting agricultural transformation and upgrading,cultivating alternative industries,strengthening ecological engineering construction,and establishing diversified ecological compensation methods and supporting policies are effective strategies for transforming farmers livelihoods.[Conclusions]Implementing these strategies can help alleviate the contradiction between ecological protection and farmers livelihood development,promoting coordinated development of both.This approach not only benefits farmers but also contributes to sustainable environmental management and biodiversity conservation.

Improved STNModels and Heuristic Rules for Cooperative Scheduling in Automated Container Terminals

Improving the cooperative scheduling efficiency of equipment is the key for automated container terminals to copewith the development trend of large-scale ships. In order to improve the solution efficiency of the existing spacetimenetwork (STN) model for the cooperative scheduling problem of yard cranes (YCs) and automated guidedvehicles (AGVs) and extend its application scenarios, two improved STN models are proposed. The flow balanceconstraints in the original model are decomposed, and the trajectory constraints of YCs and AGVs are added toacquire the model STN_A. The coupling constraint in STN_A is updated, and buffer constraints are added toSTN_A so that themodel STN_B is built.As the size of the problem increases, the solution speed of CPLEX becomesthe bottleneck. So a heuristic method containing three groups of heuristic rules is designed to obtain a near-optimalsolution quickly. Experimental results showthat the computation time of STN_A is shortened by 49.47% on averageand the gap is reduced by 1.69% on average compared with the original model. The gap between the solution ofthe heuristic rules and the solution of CPLEX is less than 3.50%, and the solution time of the heuristic rules is onaverage 99.85% less than the solution time of CPLEX. Compared with STN_A, the computation time for solvingSTN_B increases by 58.93% on average.

Resilience-driven cooperative reconfiguration strategy for unmanned weapon system-of-systems

As the unmanned weap system-of systems(UWSoS)becomes complex,the inevitable uncertain interference gradu-ally increases,which leads to a strong emphasis on the resilience of UWSoS.Hence,this paper presents a resilience-driven cooperative reconfiguration strategy to enhance the resilience of UWSoS.First,a unified resilience-driven coopera-tive reconfiguration strategy framework is designed to guide the UWSoS resilience enhancement.Subsequently,a cooperative reconfiguration strategy algorithm is proposed to identify the optimal cooperative reconfiguration sequence,combining the cooperative pair resilience contribution index(CPRCI)and coop-erative pair importance index(CPII).At last,the effectiveness and superiority of the proposed algorithm are demonstrated through various attack scenario simulations that include differ-ent attack modes and intensities.The analysis results can pro-vide a reference for decision-makers to manage UWSoS.