Farm Operations Research Articles

Lessons from a participatory community cricket breeding project in Vientiane Province, Lao PDR

Abstract Edible insect production is a key tool for sustainable development and global food security, but its social dimensions and the organisation of farming operations remain understudied. Crickets are among the most recognised edible insects for farming and offer appealing development opportunities for Lao PDR – a Southeast Asian country whose cricket farming sector has lagged behind neighbouring Thailand and which continues to experience challenging economic and food security conditions. Concerning these sustainable development and research priorities, this paper presents the case study analysis of a service evaluation of Cricket Lao Farm, which implemented a project on the distributed (off-site) production of crickets among local community members. Data collection took place from September to November 2023 and included a survey with 30 participants complemented by qualitative information from discussion groups, home visits, informal conversations, and project documentation. While small-scale farming operations ended within a year due to widespread cricket mortality of unknown cause, community-based cricket farming was in principle profitable and demonstrated strong development potential and local relevance. Furthermore, most of the community-based farmers were motivated and interested in resuming operations in the future. The analysis of this case offers important lessons for further development of the cricket farming sector in Lao PDR, highlighting the challenges of the Western social enterprise model in a development setting, the need to actively manage participation, and to develop an understanding of local community structures before commencing operations. To ensure future success, projects should include robust monitoring and evaluation plans; regular and bidirectional communication between community members and the central farm; co-development of breeding strategies, response plans, and good animal husbandry practices; and community engagement activities involving local schools.

Effect of Age at First Calving on the Reproduction Parameters, Metabolic Profile, and Fatty Acid Composition of Polish Holstein Friesian (PHF) and Crossbreds PHF × Swedish Red (SRB) Cattle

Background: The high dairy production of Polish Holstein Friesian (PHF) cows determines high energy requirements in the early stages of lactation. Unfortunately, it is very often difficult to meet this demand through feedstuffs; therefore, homeostasis may be disturbed and metabolic diseases may occur, causing a majority of cows’ health problems. Breeders are, therefore, looking for alternatives to the PHF breed using crossbreeding. Methods: This experiment involved 30 PHF cows and 30 PHF × Swedish Red (SRB) crossbred hybrid cows, divided into two age groups, <2 years and >2 years, at first calving. Milk and blood samples were collected at 35 ± 5 days postpartum for analysis. Data on reproductive performance were also analyzed. Results: This study revealed lower milk production for the crossbreds hybrid (27.44 kg compared to 32.08 kg), with a higher basic composition content than PHF cows (fat: 3.97% compared to 3.83%, protein: 3.53% compared to 3.27%). The heifers of the crossbreds hybrid reached sexual maturity earlier but did not affect the lower age at first calving. Dividing the cows into age categories provided a more detailed perspective of the impact of genotypic differences on reproductive and metabolic profiles in PHF and PHF × SRB cattle. The findings highlight the importance of considering age-specific effects when assessing the performance and health of dairy cattle with diverse genotypes. Conclusions: The choice between PHF and PHF × SRB should depend on the specific goals and priorities of the cattle farming operation. Factors such as overall milk yield requirements, market demands, reproductive management strategies, and health considerations should be carefully evaluated to determine the most suitable breed for a given farming context.

UAV-Based Multispectral Winter Wheat Growth Monitoring with Adaptive Weight Allocation

Comprehensive growth index (CGI) more accurately reflects crop growth conditions than single indicators, which is crucial for precision irrigation, fertilization, and yield prediction. However, many current studies overlook the relationships between different growth parameters and their varying contributions to yield, leading to overlapping information and lower accuracy in monitoring crop growth. Therefore, this study focuses on winter wheat and constructs a comprehensive growth monitoring index (CGIac), based on adaptive weight allocation of growth parameters’ contribution to yield, using data such as leaf area index (LAI), soil plant analysis development (SPAD) values, plant height (PH), biomass (BM), and plant water content (PWC). Using UAV data on vegetation indices, feature selection was performed using the Elastic Net. The growth inversion model was then constructed using machine learning methods, including linear regression (LR), random forest (RF), gradient boosting (GB), and support vector regression (SVR). Based on the optimal growth inversion model for winter wheat, spatial distribution of wheat growth in the study area is obtained. The findings demonstrated that CGIac outperforms CGIav (constructed using equal weighting) and CGIcv (built using the coefficient of variation) in yield correlation and prediction accuracy. Specifically, the yield correlation of CGIac improved by up to 0.76 compared to individual indices, while yield prediction accuracy increased by up to 23.14%. Among the evaluated models, the RF model achieved the best performance, with a coefficient of determination (R2) of 0.895 and a root mean square error (RMSE) of 0.0058. A comparison with wheat orthophotos from the same period confirmed that the inversion results were highly consistent with actual growth conditions in the study area. The proposed method significantly improved the accuracy and applicability of winter wheat growth monitoring, overcoming the limitations of single parameters in growth prediction. Additionally, it provided new technological support and innovative solutions for regional crop monitoring and precision farming operations.

Reliability assessment method of wind power DC collection system based on MLFTA-SMC

Wind power DC collection system, as a crucial component of wind farms, plays a vital role in ensuring the safe and stable operation of the entire wind farm. This paper proposes a reliability assessment method for wind power DC collection systems based on MLFTA-SMC. Firstly, it analyzes the topology and key equipment of wind power DC collection systems. Secondly, based on the topology of different wind power DC collection systems, it constructs multi-level fault tree models to calculate the comprehensive importance of different events, thus providing data support for subsequent reliability assessment. Then, it utilizes the MLFTA-SMC method to assess and analyze the reliability of different wind power DC collection system topologies. Finally, taking a 100 MW wind farm in Northwest China as an example, the proposed reliability assessment method is verified through simulation. The results indicate that this method exhibits good effectiveness and superiority.

Development of agrivoltaic insect net house to enhance sustainable energy-food production: a techno-economic assessment

Agrivoltaics (AV) systems are the simultaneous land utilization for sustainable energy and food production. However, AV may not be suitable for some crops that require rigorous pest management. This study has applied an innovative approach, combining AV with an insect net house to cultivate capsicum. The 1.8 kW AV system has been installed at a 3-m elevation to facilitate unrestricted tractor manoeuvrability for various farming operations. The structure can withstand winds as high as 140.4 km/h. The system has a 3,612-kWh energy capacity and a 51.41 kg food production capacity. The system's energy and food conversion efficiency are 14.09% and 154.8%, respectively, compared to the traditional systems. Economic metrics like a 2.20 benefit-cost ratio and a 9.39-year payback period have been included in the findings. Further, the land equivalence ratio is 1.97, indicating a better system than traditional AV and agriculture approaches.

Anaerobic digestion of dairy cow and goat manure: Comparative assessment of biodegradability and greenhouse gas mitigation

This study evaluated the anaerobic digestion (AD) of dairy cow and goat manure from dairy farming operations. The physicochemical characteristics, biodegradability and greenhouse gas emissions (GHG) reduction potential were assessed. Dairy goat manure exhibited higher volatile solids (16 % vs. 10 %) and 34 % greater chemical oxygen demand content (COD) compared to cow manure, indicating better suitability for AD. However, biochemical methane potential tests revealed higher methane yields from cow manure (81 % biodegradability) compared to goat manure (26 % biodegradability), attributed to slower hydrolysis rates and recalcitrant composition of goat manure. Both manures displayed suboptimal carbon-to-nitrogen ratios (<20:1) and excessive levels of mineral nutrients such as calcium. The ammonia–nitrogen and volatile fatty acid levels remained below threshold limits throughout the anaerobic digestion process, indicating no significant accumulation or inhibition. Recovering biogas through AD showed potential GHG reductions of 64 % (2.21 t CO2-eq/cow·year) and 69 % (0.27 t CO2-eq/goat·year) compared to conventional manure management practices. While pretreatment and co-digestion could enhance biogas production, further research is needed to optimise these strategies for efficient resource recovery and environmental sustainability in dairy farming operations.Abbreviations: AD, anaerobic digestion; AWMS, animal waste management systems; BMP, biochemical methane potential; CHNS, carbon, hydrogen, nitrogen, and sulphur; C/N, carbon-to-nitrogen, COD, chemical oxygen demand; GC-FID, gas chromatography-flame ionisation detection; GHG, greenhouse gas; GWPM, global warming potential of methane; He, helium; ICP-MS, inductively coupled plasma mass spectrometry; I/S, inoculum-to-substrate ratio; IPCC, International Panel on Climate Change; NH3-N, ammonia–nitrogen; SD, standard deviation; TBMP, theoretical biochemical methane potential; TN, total nitrogen; TS, total solids; VFAs, volatile fatty acids; VS, volatile solids.

Numerical modeling of an offshore shellfish farm exposed to extreme wave conditions

Shellfish cultivation is a sustainable method of providing human food and can help remove large amounts of CO2 from the atmosphere. Over the last two decades, longline-based structures have dominated farming systems. So far, the innovative technologies for open-ocean shellfish farming remain stagnant and need to be developed. As such, this paper preliminarily studies the operation and survivability abilities of an innovative shellfish farm under extreme wave conditions. To that end, an efficient numerical scheme with a robust implicit finite element method is established. First, the numerical modeling of a single module of the shellfish farm is conducted and the numerical results are verified against physical model tests. Then, the numerical modeling is implemented in a full-scale shellfish farm containing nine floating rafts with suspended lantern nets in a 3×3 configuration exposed to extreme wave conditions. Different angles of wave attack and shellfish rafts with and without lantern nets are fully considered, allowing an assessment of the operation and survivability abilities of the shellfish farm under extreme wave conditions in various situations. The results highlight that the angle of wave attack significantly affected the energy absorption of the mooring system. Moreover, non-linear instability such as subharmonics, which existed in the motion dynamics, can be manipulated to avoid resonant motions. This study provides insights into the evaluation of the safety design of a shellfish farm at both operational and survivability levels. The numerical method can also model other advanced offshore marine structures with multi-modules, such as floating bridges, airports, and even floating energy islands.

Hydrodynamic exposure – on the quest to deriving quantitative metrics for mariculture sites

This work attempts to define metrics for hydrodynamic exposure, using known oceanographic variables to provide a universal site assessment method for mariculture structures. Understanding environmental conditions driving open-ocean mariculture siting is crucial in establishing consistent ocean governance, minimizing adverse environmental impacts, and facilitating economically sustainable farm operations. To provide a metric of oceanic conditions and associated requirements for structural design and operation of aquaculture systems, six Exposure Indices (EI) are proposed that consider physical energy levels related to hydrodynamic forces at a site. Four of the proposed indices consider only environmental conditions, while the other two also consider the dimensions of the gear that is exposed to the external loads. These indices are: Exposure Velocity (EV), Exposure Velocity at Reference Depth (EVRD), Specific Exposure Energy (SEE), Depth-integrated Energy Flux (DEF), Structure-centered Depth-integrated Energy (SDE), and a Structure-centered Drag-to-Buoyancy Ratio (SDBR). While these indices are derived with a focus on aquaculture structures, they may also have applications for estimating biological stressors and operational challenges. The proposed exposure indices were evaluated for a range of known aquaculture sites around the world. A sensitivity analysis was conducted that quantified the relationship between the exposure indices and storm event return period. At a regional scale, hindcast numerical data for the German Bight combined with calculations of 50-year extreme values were used to calculate and map each proposed index spatially. Resulting maps showed that exposure is not simply a function of distance from shore. The six indices show plausible performance regarding the objective assessment of aquaculture sites. The authors herein present the indices to the aquaculture and ocean engineering communities for discussion, application, and potential adoption of one or more of the proposed indices.

New insights on the role of the farm household-farm operation interface in family farm persistence and a call for future research

Rural social scientists have long sought to understand what the structure of agriculture will look like in the future, who is going to do the work, and under what conditions with a focus on family owned and operated farms. Central to the theorization of farm families' ability to continue farming in the midst of on-going changes are the deep inter-connections between the farm household and the farm operation and farm families’ ability to adjust how resources are used within the farm household and operation spheres. Yet in this paper we argue that the interface between the household and operation has largely been treated as a black box and when it has been considered, it has largely been from the vantage point of the farm operation at the expense of the farm household. We substantiate our argument by drawing on insights from our individual research and collective discussions during a three-day workshop around three themes (i.e. gender, life course and farm business cycle, formal and informal support systems). As an invitation for others to join us in opening a new line of research, we propose a research agenda and discuss considerations around research designs, data, funding, and collaborations to tackle this research agenda.

Farmers Awareness and Perception of Drone Technology

The agricultural sector is increasingly leveraging technological advancements to enhance productivity and sustainability. Among these innovations, drone technology, a crucial component of precision agriculture, is gaining global recognition. This study explores the current awareness and perception of drone technology among farmers in Coimbatore district, Tamil Nadu, where agriculture continues to be a critical livelihood source. Using an ex-post facto research design, the study sampled 60 farmers from various regions within Coimbatore district, selected through a simple random sampling technique to assess their awareness and perception of drone technology. Data were collected through structured interviews and questionnaires, analyzed using statistical tools like frequency, percentage analysis and mean score is to understand response patterns. The findings reveal that awareness of drone technology is highest for its use in spraying agrochemicals (75 per cent), followed by pest and disease control (68 per cent) and irrigation management (66 per cent). Farmers expressed positive perceptions of the efficiency and precision that drones bring to farming operations, particularly in reducing labor and time spent on manual tasks. Drones were recognized for their potential to improve crop health monitoring, optimize resource use, and promote sustainability by minimizing chemical overuse. Nonetheless, logistical challenges such as the durability of drones in extreme weather conditions, connectivity issues in rural areas, and maintenance costs pose significant obstacles to widespread adoption. While government initiatives, including subsidies have begun to alleviate some financial burdens, there is a clear need for further support in terms of training, infrastructure, and policy enhancements. The study underscores the importance of integrating drones into the agricultural sector to ensure more sustainable and productive farming practices that can meet the growing global food demand.

Wind turbine dynamic wake flow estimation (DWFE) from sparse data via reduced-order modeling-based machine learning approach

Wind turbine wake poses a significant challenge in wind farm operations, affecting power generation efficiency. This study introduces a Dynamic Wake Flow Estimation (DWFE) framework designed to predict wind turbine wake evolution from sparse measurement data. The framework integrates Gaussian Process Regression (GPR), Proper Orthogonal Decomposition (POD), and Long Short-Term Memory (LSTM) networks. Specifically, GPR plays a pivotal role in DWFE by enabling the transformation of flow fields discrete sensor signals into coherent, low-dimensional flow fields which is crucial for accurate flow field predictions, while POD aids in dimensionality reduction and LSTM forecasts temporal wake dynamics, improving both predictive accuracy and computational efficiency. Parametric analysis demonstrates that the robustness and adaptability of the framework improve prediction accuracy with increased sensor density and flexible POD modes. The DWFE framework demonstrates high accuracy in wake flow estimation even with limited data, effectively capturing dynamic wake behavior. Future work will extend this approach to various topographic and climatic conditions, optimizing computational efficiency, and improving interpretability. These advancements will expand the framework's applicability in wind energy and other engineering fields requiring precise flow prediction, emphasizing DWFE's potential in advancing wind farm design, operation and renewable energy optimization.

The Impact of Cultivated Land Fragmentation on Farmers’ Ecological Efficiency of Cultivated Land Use Based on the Moderating and Mediating Effects of the Cultivated Land Management Scale

To date, scholars have increasingly focused on the reduction in crop yields caused by cultivated land fragmentation, yet its effects on the ecological efficiency of cultivated land use are often overlooked. This oversight leads to land resource waste and environmental pollution. It is essential to explore this problem to achieve moderate-scale farming operations and promote the green transformation of agricultural land. This study theoretically analyzed the mechanisms by which cultivated land fragmentation and management scales influence the ecological efficiency of cultivated land use. Based on 2023 household data from Changde and Shaoyang, China, empirical tests were conducted using the stochastic frontier analysis method, Tobit model, and structural equation model. The research results indicate that: (1) The mean ecological efficiency of cultivated land use among the total sample households was 0.822, and the eco-efficiency in the plains was slightly lower than that in the hilly areas. (2) The scale of cultivated land management played a moderating role in the impact of cultivated land fragmentation on ecological efficiency, with differences observed between topographical types. The scale of management can offset part of the negative impact of cultivated land fragmentation on the ecological efficiency of cultivated land use. (3) Regarding the impact of cultivated land fragmentation on the ecological efficiency of cultivated land use, cultivated land management scale changes play a complete mediating role. These findings help provide policy implications to improve the ecological efficiency of cultivated land use. Policy support should be strengthened by promoting moderate-scale cultivated land operations, enhancing the comprehensive remediation of cultivated land fragmentation, and developing skilled farmers for long-term environmental sustainability.

BUSINESS PLAN ANALYSIS OF HEALTHYLE

Catfish is a popular and affordable fishery product enjoyed by many. Healthyle, which stands for Healthy Lele, was established in 2023 to sell instant frozen catfish products, including ready-to-fry catfish and catfish katsu. These products are made from catfish farmed according to Good Fish Farming Practices (CBIB) and are halal certified. The catfish farming operation began in 2021. This study aims to analyzing Healthyle's marketing and operational strategies. This research uses data collection techniques carried out is secondary data. The result of this study is Healthyle's business plan highlights the need for comprehensive analysis to refine marketing and operational strategies. Despite a broad market for catfish in Jabodetabek and West Java, Healthyle’s marketing remains traditional and underutilized. The pricing is competitive, and the supply chain for raw materials is secure, suggesting that enhancing marketing efforts could significantly improve sales performance.

Individual Buffalo Identification Through Muzzle Dermatoglyphics Images using Deep Learning Approaches

Animal identification is essential for routine farm operations, residue traceback, insurance, and ownership management. Owing to their uniqueness, incorrigible nature, tamperproof over time, environment-friendly, and pain-free, visual biometrics-based animal identification has recently gained momentum over traditional animal identification methods. Among visual biometrics-based cues, muzzle identification is a simple and relatively low-cost method. Therefore, to address the inherent significant limitations of conventional animal identification systems, we undertook this investigation to collect a database of digital images of muzzles that works as a benchmark, to apply deep learning frameworks to identify individual buffaloes from their muzzle images, and to compare their accuracy in terms of their identification capabilities. Muzzle images of 198 Surti buffaloes were subjected to transfer learning and fine-tuning processes in deep-learning neural networks. The performance was recorded for each pre-train model (ResNet50, InceptionV3, VGG16, AlexNet) with different hyperparameters of the epoch, batch size, and learning rate. A perusal of the data revealed that ResNet50 has the highest train accuracy (99.8%) and test accuracy (99.69%) among all four models used. AlexNet has the lowest train accuracy (90.8%) among the models. The findings concluded that all these four models could be applied to identify individual buffaloes; however, ResNet50 had the highest accuracy, and deep learning applications have great potential for individual buffalo identification and are promising tools for precision livestock farming

Smart Agriculturing Based on KSK Approach: A Novel AI-Driven-IoT(AIIoT) Based Decision-Making Approach

An enormous change is taking place in the agriculture industry as a result of the introduction of the Internet of Things (AIIoT), which is powered by artificial intelligence and provides farmers with unparalleled automation capabilities and insights. The purpose of this research is to present a comprehensive review of artificial intelligence and the internet of things (AIIoT) in smart agriculture, focussing on its applications, benefits, and consequences for decision-making. The concept of smart agricultural decision-making refers to a breakthrough method that enables farmers to optimise their farm operations while also making decisions that are well-informed. Farmers are able to increase crop yields while simultaneously reducing costs and hazards when they make use of advanced analytics, real-time data, and decision-making tools. The development of smart agriculture will result in farmers having increased decision-making authority, which will ultimately lead to an agricultural sector that is more sustainable and productive. The Internet of Things (IoT) and artificial intelligence (AI) in smart agriculture is a cutting-edge technology that has the potential to be a game-changer in terms of how we produce and consume food. Farmers may be able to improve agricultural yields and quality, streamline operations, and contribute to a food supply chain that is more efficient and sustainable if they use artificial intelligence and the internet of things. In spite of the fact that there are still certain problems that need to be cleared up, the Internet of Things (IoT) offers numerous advantages for smart agriculture, and its implementation is anticipated to increase in the years to come. The KSK technique, also known as the Knowledge-Sensors-Knowledge approach, is a suggestion made by Dr. Kutubuddin S. Kazi, who is also using his name. The output of the KSK technique results in an accuracy of 99.9% and a recall of 97.9%, respectively.

Influence of the sound propagation models to predict far field wind turbine noise

During the development process of a wind farm, the noise impact on neighboring residents is of crucial importance to limit annoyance and comply with local regulations. In France, this assessment is carried out by modelling the propagation of sound from each wind turbine to residents. For wind farm owners, this phase will in fine define the operation of the wind farm and therefore its electricity production. However, there are major concerns and uncertainties during this modelling phase. A concrete example of the influence of the choice of propagation model on the production of a wind farm is presented, giving an idea of its economic impact. Comparisons of the impact of wind farm noise modelled by engineering models with measurements taken on wind farms in operation provide a better understanding of the choice of certain methods or parameters. Taken together, these observations suggest areas for further research and development, with the aim of perhaps defining a more restricted framework for the sound propagation modelling stage, in order to help operators predict wind farm noise more accurately.

Methods for addressing variable turbine operations during wind farm noise compliance monitoring

Post-construction wind farm noise measurements at dwelling locations are commonly required in Australia to verify compliance with noise limits. These are often required as soon as the wind farm begins operating. However, commissioning issues and electricity network restrictions are a common occurrence during initial stages of operation. As such, curtailed modes of operation and shutdowns are a regular occurrence, and large sections of a wind farm may only be operating intermittently. This presents challenges to obtaining noise measurement data that is representative of normal operation of the wind farm, and may ultimately limit the conclusions that are able to be reached from measurements during the early stages of operation. To address these challenges, it is necessary to identify and account for any periods when operational noise levels may be lower as a result of turbines being curtailed or shut down. Several methods have been developed for this purpose, and range from simple procedures that remove any data affected by curtailment or shutdown, through to more complex assessments of the magnitude and implications of the uncertainty introduced by variable wind turbine operations. These methods are presented and assessed in this paper with case studies of their application to operational projects.

Contract Farming &amp; Cost of Funding: Evidence from Madurase Solar Salt Small Businesses

This study aimed to estimate the indirect expenses borne by tenant farmers due to credits they receive in community salt farming operations managed through contract farming systems in Pamekasan Regency. Credits constitute a crucial aspect of tenant farmers’ rights within the community salt farming contract farming framework. This study employed a comparative analysis of the cost of funds based on the adopted contract farming models, specifically the two-way and three-way contract farming structures. Moreover, it investigated the correlation between the cultivated salt field’s size and the magnitude of the cost of funds endured by tenant farmers. The results of the analysis reveal that the average costs of funds borne by tenant farmers are notably high, ranging from 5.24% to 6.71% per month. Surprisingly, these figures exceed the loan interest rates offered by two formal financial institutions: BRI and Bank JATIM. Another significant finding is the positive correlation between the size of cultivated salt fields and the magnitude of the cost of funds borne by tenant farmers. Lastly, there is a clear positive relationship between the credit amount received and the corresponding cost of funds endured by tenant farmers. The substantial costs of funds shouldered by tenant farmers involved in the community salt farming, operated through contract farming systems in Pamekasan Regency, signify inefficiencies within the contract farming structure. It is expected that the government can aid these tenant farmers by disbursing subsidized credits through collaborations with relevant ministries and local formal financial institutions.

Assessing the spatial variability of cage movement and velocity attenuation of an off-bottom oyster farm and its influence on eastern oyster (Crassostrea virginica) growth performance

The global shellfish aquaculture industry has expanded over the last seven decades. Similarly, the size of individual shellfish farming operations has grown in some states. Circulation processes in natural water bodies are dynamic and vary across multiple temporal and spatial scales. How gear installation affects the circulation of currents and waves, and subsequent phytoplankton delivery to crops within farms, is largely unknown. Documenting and understanding variability in wave and current energy within an individual farm could help optimize production. Our study sought to understand the variability in oyster growth attributable to hydrodynamic and atmospheric forcing across an active commercial shellfish aquaculture lease in the Patuxent River, Maryland. We observed a 17 % reduction in water flow downstream of the farm (rho = −0.673, p = 0.002, Spearman rank test), suggesting the bottom cages attenuated downstream current velocity during flood tides. Differences in flow speed and the movement of cages and oysters throughout the farm correlated with variances in oyster shell morphology and oyster survival while in bottom cages. Our results demonstrate the occurrence of variability in production within an individual farm location. Further, our findings suggest that insight into the spatial growth patterns across a lease can be practical for forecasting growth and quality of cultured oysters and may enable growers to strategically place oysters within their farm to improve yield and product consistency.

Reliability evaluation of direct current gathering system in onshore wind farm based on reliability block diagram-sequential monte carlo

The full DC wind power generation system has effectively overcome the harmonic resonance, reactive power transmission, and other problems of the traditional AC wind power system, which has broad prospects for development. As a key component of the mentioned system, the reliability of the collection system is critical to the safe and stable operation of the entire onshore wind farm. Firstly, this paper investigates the key equipment and topology of the onshore wind farm DC collection system. Secondly, considering both the internal components and external environment of the wind farm, a component outage probability model based on weather factors is constructed to provide accurate data for the reliability evaluation of the DC collection system of the wind farm. The Reliability Block Diagram is used to analyze the internal logical connection of different topologies of onshore wind farm DC collection systems in detail. Then, a reliability evaluation method of an onshore full DC wind farm collection system based on Reliability Block Diagram-Sequential Monte Carlo is proposed. Finally, a 50 MW onshore wind farm is studied as a sample to compare and analyze the assessment results of the reliability of different collection system topologies. The results show that the reliability of the DC collection system of onshore wind farms has significant advantages.