The Impacts and Causes of Land Fragmentation on Farm Productivity: Case Review of East African Countries

This report provides an overall assessment of land fragmentation problems in East Africa. Many parts of East Africa have become highly fragmented, putting development systems and activities in these areas at risk of complete collapse. Land fragmentation occurs when land gets converted for agriculture, industrialization, or urbanization, invaded by non-local plants, or enclosed for individual use and by subdividing farmlands into subsequent smaller units called parcels with varying average farm sizes. Fragmentation results from inappropriate agricultural development processes and ineffective land use planning that fails to recognize how farmland is used, and the importance of its interconnected areas. Insecurity of tenure and resource rights are key factors in making this possible. Land fragmentation is one of the key reasons why the ability of most resources in East Africa becomes scarcer, and those remaining become “privatized” by more powerful community members—keen to maintain their access to them. Such individualistic attitudes are new and disadvantage the poorest even further by affecting the traditional customary safety nets and agricultural outputs. Neither the government nor customary governance systems effectively protect resource access for the poorest. This review summary report identifies the key causes, measures, and implications, government interventions, and the common remedies to land fragmentation problems in the East African Countries of Kenya, Uganda, Rwanda, and Tanzania including neighboring Ethiopia, and the Sudan. The findings indicated from 2005 to 2015, the population kept increasing for all the named countries in East Africa with Rwanda and Uganda having a substantial increase in population density. The study review further explores the trend in the performance of agriculture by average farm sizes within the intervals of five years by highlighting their strong linkages and found that the average farm size has declined drastically, especially for Kenya. This can only mean that small farms kept becoming smaller and smaller and that there were more small-scale farmers. The results further depicted that the major and commonly cultivated food crops among the East African countries include maize, sorghum, rice, cassava, sweet potatoes, bananas, Irish potatoes, beans, peas, etc., with maize yields (Mt/ha) in 2003 for Uganda being the highest (1.79 Mt/ha) and the lowest in Rwanda (0.77 Mt/ha) respectively. Therefore, from the review results, recommendations are being made as to how the negative impacts of land fragmentation on agricultural productivity can be reduced or mitigated. One way is by community sensitization and awareness about the importance of land consolidation and its proposition on farm productivity.

Technical efficiency of cocoa farms at varying elevation levels in Davao City, Philippines: implications to sustainable upland farming systems

Elevation is one of many components that influence agriculture, and this in turn affects the level of both inputs and outputs of farmers. This article focuses on the productivity and technical efficiency of 100 cocoa farms using cross-sectional data from areas ranging from 190 to 1021 m above sea level which were classified as low, medium, and high elevation in Davao City, considered as the chocolate capital of the Philippines. Using stochastic frontier analysis, the results showed that the cost of inputs per ha and the number of cocoa trees per ha significantly increase yield. Farms at high elevations were less technically efficient, as this entails lower temperatures and increased rainfall, and cocoa farming in those areas and conditions can be more challenging, especially with changes in farming practices, terrain, and distance to markets. Other significant variables were age of cocoa farms, married farmers, and age of the farmers. Older farms may be more developed, farmers who are married benefit from their spouses being able to readily contribute as farm labor, and lastly, older farmers' inefficiency may likely stem from nonadaptation of newer farming practices. With an average technical efficiency of 0.61, 0.63, and 0.26 in low, medium, and high elevation areas, respectively, farmers therefore have an incentive to improve farm practices and consider topographical variations found in high elevation areas. Recommendations for the improvement of technical efficiency of cocoa farms are better connectivity to markets, enhancing farm practices, and continuation and improvement of government programs on cocoa with an added emphasis on research. For farmers in high elevation areas, mitigating solutions such as sustainable agriculture practices and ecolabelling are key to improving efficiency and minimizing the potential negative impact on upland farming systems. Moreover, such adaptation measures may also contribute to sustainability of cocoa farming in high elevation areas.

Smart Farming for Sustainable Rice Production:An Insight into Application,Challenge,and Future Prospect

Rice has a huge impact on socio-economic growth,and ensuring its sustainability and optimal utilization is vital.This review provides an insight into the role of smart farming in enhancing rice productivity.The applications of smart farming in rice production including yield estimation,smart irrigation systems,monitoring disease and growth,and predicting rice quality and classifications are highlighted.The challenges of smart farming in sustainable rice production to enhance the understanding of researchers,policymakers,and stakeholders are discussed.Numerous efforts have been exerted to combat the issues in rice production in order to promote rice sector development.The effective implementation of smart farming in rice production has been facilitated by various technical advancements,particularly the integration of the Internet of Things and artificial intelligence.The future prospects of smart farming in transforming existing rice production practices are also elucidated.Through the utilization of smart farming,the rice industry can attain sustainable and resilient production systems that could mitigate environmental impact and safeguard food security.Thus,the rice industry holds a bright future in transforming current rice production practices into a new outlook in rice smart farming development.

Wind farms increase land surface temperature and reduce vegetation productivity in the Inner Mongolia

Wind power has been developing rapidly as a key measure to mitigate human-driven global warming.The under-standing of the development and impacts of wind farms on local climate and vegetation is of great importance for their rational use but is still limited.In this study,we combined remote sensing and on-site investigations to identify wind farm locations in Inner Mongolia and performed landscape pattern analyses using Fragstats.We explored the impacts of wind farms on land surface temperature(LST)and vegetation net primary productivity(NPP)between 1990 and 2020 by contrasting these metrics in wind farms with those in non-wind farm areas.The results showed that the area of wind farms increased rapidly from 1.2 km2 in 1990 to 10,755 km2 in 2020.Spatially,wind farms are mainly clustered in three aggregation areas in the center.Further,wind farms increased nighttime LST,with a mean value of 0.23℃,but had minor impacts on the daytime LST.Moreover,wind farms caused a decline in NPP,especially over forest areas,with an average reduction of 12.37 GC/m^(2).Given the impact of wind farms on LST and NPP,we suggest that the development of wind farms should fully consider their direct and potential impacts.This study provides scientific guidance on the spatial pattern of future wind farms.

Mobile genetic elements facilitate the transmission of antibiotic resistance genes in multidrug-resistant Enterobacteriaceae from duck farms

Multidrug-resistant(MDR)Enterobacteriaceae critically threaten duck farming and public health.The phenotypes,genotypes,and associated mobile genetic elements(MGEs)of MDR Enterobacteriaceae isolated from 6 duck farms in Zhejiang Province,China,were investigated.A total of 215 isolates were identified as Escherichia coli(64.65%),Klebsiella pneumoniae(12.09%),Proteus mirabilis(10.23%),Salmonella(8.84%),and Enterobacter cloacae(4.19%).Meanwhile,all isolates were resistant to at least two antibiotics.Most isolates carried tet(A)(85.12%),blaTEM(78.60%)and sul1(67.44%)resistance genes.Gene co-occurrence analysis showed that the resistance genes were associated with IS26 and integrons.A conjugative IncFII plasmid pSDM004 containing all the above MGEs was detected in Proteus mirabilis isolate SDM004.This isolate was resistant to 18 antibiotics and carried the blaNDM-5 gene.MGEs,especially plasmids,are the primary antibiotic resistance gene transmission route in duck farms.These findings provide a theoretical basis for the rational use of antibiotics in farms which are substantial for evaluating public health and food safety.

Farm buildings and agri-food transitions in Southern France:Mapping dynamics using a stakeholder-based diagnosis

This study's goal is to present a dynamic portrait of the farm-buildings environment in Occitania,in Southern France,in order to better identify the transitions underway in agri-food chains.To this end,we undertook a ter-ritorial diagnosis based on actor statements,using 28 semi-structured interviews across Occitania.This diagnosis was enriched by graphic modelling,which enabled the spatialization of the dynamics described.We show that the process of standardisation of farm buildings prevails in the majority of the territories studied.This phenomenon has intensified in recent years with the development of vast photovoltaic-roofed sheds,accentuating the farm-land conversion and soil sealing.At the same time,in areas with strong environmental,landscape and heritage contexts,a'new adventure in farm buildings'(2022 survey)is taking shape.It is primarily driven by local short food chains,which rely on self-construction,repurposing and refurbishment,the sharing of tools and equipment,and which favour the use and reuse of local resources.This study shows that farm-buildings dynamics crystallise many challenges confronting the reterritorialisation of agriculture and food production.

Transient Stability Preventive Control of Wind Farm Connected Power System Considering the Uncertainty

To address uncertainty as well as transient stability constraints simultaneously in the preventive control of windfarm systems, a novel three-stage optimization strategy is established in this paper. In the first stage, the probabilisticmulti-objective particle swarm optimization based on the point estimate method is employed to cope with thestochastic factors. The transient security region of the system is accurately ensured by the interior point methodin the second stage. Finally, the verification of the final optimal objectives and satisfied constraints are enforcedin the last stage. Furthermore, the proposed strategy is a general framework that can combine other optimizationalgorithms. The proposed methodology is tested on the modified WSCC 9-bus system and the New England 39-bussystem. The results verify the feasibility of the method.

GNN Representation Learning and Multi-Objective Variable Neighborhood Search Algorithm for Wind Farm Layout Optimization

With the increasing demand for electrical services,wind farm layout optimization has been one of the biggest challenges that we have to deal with.Despite the promising performance of the heuristic algorithm on the route network design problem,the expressive capability and search performance of the algorithm on multi-objective problems remain unexplored.In this paper,the wind farm layout optimization problem is defined.Then,a multi-objective algorithm based on Graph Neural Network(GNN)and Variable Neighborhood Search(VNS)algorithm is proposed.GNN provides the basis representations for the following search algorithm so that the expressiveness and search accuracy of the algorithm can be improved.The multi-objective VNS algorithm is put forward by combining it with the multi-objective optimization algorithm to solve the problem with multiple objectives.The proposed algorithm is applied to the 18-node simulation example to evaluate the feasibility and practicality of the developed optimization strategy.The experiment on the simulation example shows that the proposed algorithm yields a reduction of 6.1% in Point of Common Coupling(PCC)over the current state-of-the-art algorithm,which means that the proposed algorithm designs a layout that improves the quality of the power supply by 6.1%at the same cost.The ablation experiments show that the proposed algorithm improves the power quality by more than 8.6% and 7.8% compared to both the original VNS algorithm and the multi-objective VNS algorithm.

Identification of Microorganisms in Poultry Farms in N’djamena and the Border Areas of Hadjer-Lamis and Chari-Baguirmi Chad

Introduction: On the outskirts of Ndjamena, semi-industrial poultry farming and traditional poultry farming are practised informally on almost all poultry farms in Chad. This type of poultry farming is faced with real health problems attributable to a lack of monitoring of the vaccination schedule, inadequate compliance with biosecurity measures and poor application of the Ichikawa rule based on the 5 M’s. Objective: The aim of this article is to identify the microorganisms responsible for contamination of poultry farms in the study area. Method: The study was carried out from 28/04/2022 to 31/01/2023 on the basis of 300 samples taken from feed, drinking water, droppings and scrapings from poultry housing surfaces in the 30 farms that served as a framework for our research. Sampling was of the simple random type, and farms were selected on the basis of the farmers’ consent. The data were recorded on pre-established survey forms. Our study was cross-sectional, descriptive and prospective. Bacteria were isolated using the reference method NF EN ISO 6579 for Salmonella spp. and cultured on the specific medium eosin methylene blue (EMB) for Escherichia coli, Pseudomonas and Citrobacter freundii. Results: The following results emerged from this study: Escherichia coli (5.33%), Pseudomonas (1.33%), Citrobacter freundii (12%) and Salmonella paratyphi (21.68%). Conclusion: Of the 300 samples analysed, 121 (40.33%) were contaminated with pathogens. This high level of contamination is a health problem. The study shows that biosecurity is less satisfactory on the farms visited. Nevertheless, farms with a very satisfactory level of biosafety ensure food safety and variety for the population.

Wake Effects on A Hybrid Semi-Submersible Floating Wind Farm with Multiple Hub Heights

Wind farms generally consist of a single turbine installed with the same hub height. As the scale of turbines increases,wake interference between turbines becomes increasingly significant, especially for floating wind turbines(FWT).Some researchers find that wind farms with multiple hub heights could increase the annual energy production(AEP),while previous studies also indicate that wake meandering could increase fatigue loading. This study investigates the wake interaction within a hybrid floating wind farm with multiple hub heights. In this study, FAST.Farm is employed to simulate a hybrid wind farm which consists of four semi-submersible FWTs(5MW and 15MW) with two different hub heights. Three typical wind speeds(below-rated, rated, and over-rated) are considered in this paper to investigate the wake meandering effects on the dynamics of two FWTs. Damage equivalent loads(DEL) of the turbine critical components are computed and analyzed for several arrangements determined by the different spacing of the four turbines. The result shows that the dynamic wake meandering significantly affects downstream turbines’ global loadings and load effects. Differences in DEL show that blade-root flapwise bending moments and mooring fairlead tensions are sensitive to the spacing of the turbines.

A Chaotic Local Search-Based Particle Swarm Optimizer for Large-Scale Complex Wind Farm Layout Optimization

Wind energy has been widely applied in power generation to alleviate climate problems.The wind turbine layout of a wind farm is a primary factor of impacting power conversion efficiency due to the wake effect that reduces the power outputs of wind turbines located in downstream.Wind farm layout optimization(WFLO)aims to reduce the wake effect for maximizing the power outputs of the wind farm.Nevertheless,the wake effect among wind turbines increases significantly as the number of wind turbines increases in the wind farm,which severely affect power conversion efficiency.Conventional heuristic algorithms suffer from issues of low solution quality and local optimum for large-scale WFLO under complex wind scenarios.Thus,a chaotic local search-based genetic learning particle swarm optimizer(CGPSO)is proposed to optimize large-scale WFLO problems.CGPSO is tested on four larger-scale wind farms under four complex wind scenarios and compares with eight state-of-the-art algorithms.The experiment results indicate that CGPSO significantly outperforms its competitors in terms of performance,stability,and robustness.To be specific,a success and failure memories-based selection is proposed to choose a chaotic map for chaotic search local.It improves the solution quality.The parameter and search pattern of chaotic local search are also analyzed for WFLO problems.

Wind farm active power dispatching algorithm based on Grey Incidence

This study proposes a wind farm active power dispatching(WFAPD) algorithm based on the grey incidence method, which does not rely on an accurate mathematical model of wind turbines. Based on the wind turbine start-stop data at different wind speeds, the weighting coefficients, which are the participation degrees of a variable speed system and a variable pitch system in power regulation, are obtained using the grey incidence method. The incidence coefficient curve is fitted by the B-spline function at a full range of wind speeds, and the power regulation capacity of all wind turbines is obtained. Finally, the WFAPD algorithm, which is based on the regulating capacity of each wind turbine, is compared with the wind speed weighting power dispatching(WSWPD) algorithm in MATLAB. The simulation results show that the active power fluctuation of the wind farm is smaller, the rotating speed of wind turbines is smoother, and the fatigue load of highspeed turbines is effectively reduced.

The role of time preferences in contract breach:Evidence from Chinese poultry farmers participating in contract farming

Farmers’contract breach behavior is cited as one of the major stumbling blocks in the sustainable expansion of contract farming in many developing countries.This paper examines farmers’contract breach decisions from the perspective of time preferences.The empirical analysis is based on a household survey and economic field experiments of poultry households participating in contract farming conducted in Jiangsu Province,China.A discounted utility model and a maximum likelihood technique are applied to estimate farmers’time preferences and the effect of time preferences on contract breach in the production and sales phases are explored with a bivariate probit model.The results show that,on average,the poultry farmers in the sample are generally present biased and impatient regarding future utility.The regression results show that farmers with a higher preference for the present and a higher discount rate are more likely to breach contracts,and time preferences play a greater role in the production phase than in the sales phase.When considering heterogeneity,specific investments and transaction costs promote contract stability only for farmers with a low degree of impatience.Moreover,compared with large-scale farmers,small-scale farmers’contract breach decisions are more significantly affected by their time preferences.These results have implications for contract stability policies and other issues that are impacted by the linking of behavioral preferences to agricultural decisions.

Performance of the CMA-GD Model in Predicting Wind Speed at Wind Farms in Hubei, China

This study assesses the predictive capabilities of the CMA-GD model for wind speed prediction in two wind farms located in Hubei Province,China.The observed wind speeds at the height of 70m in wind turbines of two wind farms in Suizhou serve as the actual observation data for comparison and testing.At the same time,the wind speed predicted by the EC model is also included for comparative analysis.The results indicate that the CMA-GD model performs better than the EC model in Wind Farm A.The CMA-GD model exhibits a monthly average correlation coefficient of 0.56,root mean square error of 2.72 m s^(-1),and average absolute error of 2.11 m s^(-1).In contrast,the EC model shows a monthly average correlation coefficient of 0.51,root mean square error of 2.83 m s^(-1),and average absolute error of 2.21 m s^(-1).Conversely,in Wind Farm B,the EC model outperforms the CMA-GD model.The CMA-GD model achieves a monthly average correlation coefficient of 0.55,root mean square error of 2.61 m s^(-1),and average absolute error of 2.13 m s^(-1).By contrast,the EC model displays a monthly average correlation coefficient of 0.63,root mean square error of 2.04 m s^(-1),and average absolute error of 1.67 m s^(-1).

Paneth cells in farm animals:current status and future direction

A healthy intestine plays an important role in the growth and development of farm animals.In small intestine,Paneth cells are well known for their regulation of intestinal microbiota and intestinal stem cells(ISCs).Although there has been a lot of studies and reviews on human and murine Paneth cells under intestinal homeostasis or disorders,little is known about Paneth cells in farm animals.Most farm animals possess Paneth cells in their small intestine,as identified by various staining methods,and Paneth cells of various livestock species exhibit noticeable differences in cell shape,granule number,and intestinal distribution.Paneth cells in farm animals and their antimicrobial peptides(AMPs)are susceptible to multiple factors such as dietary nutrients and intestinal infection.Thus,the comprehensive understanding of Paneth cells in different livestock species will contribute to the improvement of intestinal health.This review first summarizes the current status of Paneth cells in pig,cattle,sheep,horse,chicken and rabbit,and points out future directions for the investigation of Paneth cells in the reviewed animals.

Pollution characteristics and risk assessment of organophosphate esters in aquaculture farms and natural water bodies adjacent to the Huanghe River delta

To date,little attention has been paid to the effects of organophosphate esters(OPEs)pollution on aquacultural environment and aquatic product safety.Huanghe(Yellow)River delta area is one of the largest aquaculture centers in China,where ecological security protection is crucial in the national strategy of China.To explore the pollution characteristics,bioaccumulation,and health risks of OPEs in aquaculture farms in the Huanghe River delta and natural water bodies in the adjacent seas,five species of organisms from different farm types nearby the Huanghe River delta,and the corresponding culture water and sediments were sampled in this study.The total concentrations of Σ_(13)OPEs in water,sediments,and organisms were 51.53-272.18 ng/L,52.63-63.17 ng/g dry weight(dw),and 46.82-108.90 ng/g dw,respectively.Among the five types of culture ponds,the water samples from the swimming crab and hairy crab culture ponds exhibited higher OPEs,the concentration of OPEs in the sediments from the few ponds was relatively balanced,and the OPEs in the organism from the holothurian ponds was higher.Tris(1,3-dichloro-2-isopropyl pho sphate)(TDCP)was the main contaminant in water samples and tripropyl phosphate(TPrP)in sediments and organisms.However,trisphenyl phosphate(TPhP)showed the strongest bioaccumulation ability,followed by 2-ethylhexyl diphenyl phosphate(EHDPP)and TPrP.The bioaccumulation capacities of the five species were as follows:prawn>holothurian>hairy crab>swimming crab>carp.These five types of organisms,as main seafood in human consumption,were at low risk of negative impacts of pollution.However,the risk from the mixture of organophosphate flame retardants(OPFRs)still requires more attention due to the increasing consumption and production in the world.

Influences of large-scale farming on carbon emissions from cropping:Evidence from China

Reducing agricultural carbon emissions is important to enable carbon emission peaking by 2030 in China.However,China's transformation towards large-scale farming brings uncertainties to carbon emission reduction.This study quantifies the carbon emissions from cropping based on life cycle assessment and estimates the effects of farm size on carbon emissions using a fixed effects model.Furthermore,the variations of the carbon emissions from cropping driven by the changes in farm size in future years are projected through scenario analysis.Results demonstrate an inverted U-shaped change in total carbon emission from cropping as farm size increases,which is dominated by the changes in the carbon emission from fertilizer.Projections illustrate that large-scale farming transformation will postpone the peak year of total carbon emission from cropping until 2048 if the change in farm size follows a historical trend,although it is conducive to reducing total carbon emission in the long run.The findings indicate that environmental regulations to reduce fertilizer usages should be strengthened for carbon emission abatement in the early stage of large-scale farming transformation,which are also informative to other developing countries with small farm size.

Information-Centric IoT-Based Smart Farming with Dynamic Data Optimization

Smart farming has become a strategic approach of sustainable agriculture management and monitoring with the infrastructure to exploit modern technologies,including big data,the cloud,and the Internet of Things(IoT).Many researchers try to integrate IoT-based smart farming on cloud platforms effectively.They define various frameworks on smart farming and monitoring system and still lacks to define effective data management schemes.Since IoT-cloud systems involve massive structured and unstructured data,data optimization comes into the picture.Hence,this research designs an Information-Centric IoT-based Smart Farming with Dynamic Data Optimization(ICISF-DDO),which enhances the performance of the smart farming infrastructure with minimal energy consumption and improved lifetime.Here,a conceptual framework of the proposed scheme and statistical design model has beenwell defined.The information storage and management with DDO has been expanded individually to show the effective use of membership parameters in data optimization.The simulation outcomes state that the proposed ICISF-DDO can surpass existing smart farming systems with a data optimization ratio of 97.71%,reliability ratio of 98.63%,a coverage ratio of 99.67%,least sensor error rate of 8.96%,and efficient energy consumption ratio of 4.84%.

Characteristics of sub-synchronous oscillation in grid-connected wind farm system

With the rapid development of wind power, wind turbines are accompanied by a large quantity of power electronic converters connected to the grid, causing changes in the characteristics of the power system and leading to increasingly serious sub-synchronous oscillation (SSO) problems, which urgently require the generalized classification and characterization of the emerging oscillation problems. This paper classifies and characterizes the emerging types of SSO caused by grid-connected wind turbines to address these issues. Finally, the impact of the typical system parameters changes on the oscillation pattern is analyzed in depth to provide effective support for the subsequent suppression and prevention of SSO.

Enhanced Water Quality Control Based on Predictive Optimization for Smart Fish Farming

The requirement for high-quality seafood is a global challenge in today’s world due to climate change and natural resource limitations.Internet of Things(IoT)based Modern fish farming systems can significantly optimize seafood production by minimizing resource utilization and improving healthy fish production.This objective requires intensive monitoring,prediction,and control by optimizing leading factors that impact fish growth,including temperature,the potential of hydrogen(pH),water level,and feeding rate.This paper proposes the IoT based predictive optimization approach for efficient control and energy utilization in smart fish farming.The proposed fish farm control mechanism has a predictive optimization to deal with water quality control and efficient energy consumption problems.Fish farm indoor and outdoor values are applied to predict the water quality parameters,whereas a novel objective function is proposed to achieve an optimal fish growth environment based on predicted parameters.Fuzzy logic control is utilized to calculate control parameters for IoT actuators based on predictive optimal water quality parameters by minimizing energy consumption.To evaluate the efficiency of the proposed system,the overall approach has been deployed to the fish tank as a case study,and a number of experiments have been carried out.The results show that the predictive optimization module allowed the water quality parameters to be maintained at the optimal level with nearly 30%of energy efficiency at the maximum actuator control rate compared with other control levels.