Network Of Farms Research Articles

A network evaluation of human and animal movement data across multiple swine farm systems in North America

INTRODUCTIONThe U.S. swine industry is vulnerable to the rapid spread of disease due to systemic structural issues. While animal movement networks are used to identify disease spread risks and design response plans, human movement between farms were rarely accounted for. Human movements, when integrated with animal movement models, create a different, more inclusive, and accurate network structure when compared to animal movements alone. METHODSOne year of propriety farm visit data was analyzed and consisted of anonymized property IDs, location, and user/truck IDs, along with visit dates, property, vehicle, and entry types from three swine management companies. A static directed network was created using the igraph package in R for all movements, with separate sub-networks for each entry type (animal, human, and subsets of vehicle types). Network statistics for each sub-network were compared. RESULTSThe full network included 455 properties, 11 property types, 9 vehicle types, 12 entry types, and 320001 edges (trips between properties). The longest path length was 10 in the animal movement network but decreased to 5 for the full and human movement network, while the average path length decreased from 3.2 to 2.2. Edge density increased from 0.03 to 0.09 for the human network and 0.1 for the full network. For all network properties examined, the full and human movement networks demonstrated higher connectivity than the animal network. A heavy right skew in the degree distributions indicates a 'hub' structure (scale-free-like network) and the shorter path lengths indicates a small-world network topology. DISCUSSIONThe full network is very well connected, more so than expected based on animal movement alone. Hubs may indicate points of disease susceptibility and 'super-spreader' properties. The high connectivity shows that swine farm networks may be more susceptible to spready of an introduced disease than expected from previous analyses. CONCLUSIONSMonitoring human, as well as animal movement, provides for a more complete and accurate understanding of swine farm biosecurity risks.

Tillage and land use management effects on soil organic matter and soil microbial biomass in a field network of practical farms in France, Romania, and Sweden

A reduction in tillage intensity, organic farming, and introduction of ley-grass periods into the crop rotation are means to improve the sustainable use of arable soils. The current study uses an on-farm approach to investigate soils from different practical farms in Northern France, Central Sweden, and Romania in comparison with previously published results from randomized field-experiments nearby. No-tillage generally increased the mean SOC and total N contents of arable fields in comparison with ploughing. However, this increase was only significant for the 70 % increase at 0–10 cm depth in France. No-tillage had no general effects on MBC or MBN and increased the ergosterol content at 0–10 cm depth in France and Sweden but not in Romania. Averaging depths and tillage systems, median MBC significantly increased in the order France (220 μg g−1 soil), Romania (324 μg g−1 soil), and Sweden (384 μg g−1 soil), whereas the median MB-C/N ratios followed the order France (5.6), Sweden (8.3), and Romania (10.4). The study region-specific changes in soil pH significantly affected covariate MBC and MBN in the regression equations for equal slopes ANCOVA models. In Northern France, organic farming and ley-grass implementation into the crop rotation increased SOC and total N contents to the level of the no-tillage fields. On-farm research gives similar results to long-term field experiments in France, Romania, and Sweden. For this reason, on-farm research on practical fields is an important, less expensive alternative to randomized field experiments, with a high potential for investigating actual and relevant research objectives. This will intensify exchange in knowledge between scientists and farmers.

Pallas’s Gull Ichthyaetus ichthyaetus and Fish Farming of the Palearctic: Review

The Pallas’s Gull Ichthyaetus ichthyaetus is a piscivorous predator and scavenger, some breeding populations of which are vulnerable and require protection. This study analyzes the status and distribution of Pallas’s Gull in Palearctic fish ponds—man–made bodies of water where fish concentrations are controlled by humans and can be many times higher than natural values. The aim of the study was to assess the state of the Pallas’s Gull in the fish farming of the Palearctic over the last ≈ 60 years. The review was based on > 1100 publications found in the search engines Yandex, Google, Google Scholar, eLybrary. Between the 1960s and 2020s, the Pallas’s Gull was discovered in 46 fish farms in the Palearctic. Breeding has been established in 3 fish farms, breeding has not been established in 38 fish farms, and birds were present in 5 fish farms, but the status was not specified. There were 35 places of contact with fish farms in Europe, and 11 places of contact in Asia. The average area of fish farms with which Pallas’s Gull came into contact was 10.7 km² (n = 21). Fish farms support non–breeding individuals –– vagrants, visitors, summer or winter residents of different age classes. Fish farms cannot be considered as important places for the reproduction of the species. The network of fish farms plays a certain auxiliary, but far from decisive role in maintaining the population and the current multidirectional expansion of the species’ range in the Palearctic. The hypothesis about the possible important role of reservoirs and fish farms in food supply and the distribution of Pallas’s Gull beyond the historical range was not confirmed in studies of fish farms.

Adoption of smart farm networks: a translational process to inform digital agricultural technologies

AbstractDue to natural phenomena like global warming and climate change, agricultural production is increasingly faced with threats that transcend farm boundaries. Management practices at the landscape or community level are often required to adequately respond to these new challenges (e.g., pest migration). Such decision-making at a community or beyond-farm level—i.e., practices that are jointly developed by farmers within a community—can be aided by computing and communications technology. In this study, we employ a translational research process to examine the social and behavioral drivers of adoption of smart and connected farm networks among commodity crop farmers in the United States. We implement focus groups and questionnaires to bring to the fore views on the use of digital technologies in collaborative contexts. We find that participating farmers are concerned with several issues about the potential features of the network (e.g., the ability to ensure data validity while maintaining data privacy) and the nature of their interactions with the various stakeholders involved in the network management. The participatory approach we adopt helps provide insights into the process of developing technologies that are both actionable and trusted by potential end users.

Crop management strategy redesign enables a reduction in reliance on pesticides: A diachronic approach based on a diversity of French commercial farms

Pesticides are widely used in agriculture to manage pests, weeds and diseases with consequences on ecosystem and human health. Reducing pesticide reliance appears as a necessary step for transitioning to sustainable farming systems. Many non-chemical techniques to control pests, weeds and diseases are individually well documented, but quantitative assessment of how much the reliance on pesticides could be decreased through a holistic approach of cropping system re-design combining several individual techniques was lacking. A diachronic approach was conducted on 913 French commercial farms from the DEPHY network, spanning a diversity of contexts and management strategies. Regression Trees and Random Forest models were run to identify combinations of changes in management strategies associated with the evolution of pesticides reliance over time, in a range of production contexts. The consequences of these changes on pesticide-related risks to human health and the environment were also investigated. Farm reliance on pesticides and pesticide-related risks to human health and the environment were reduced in 65.7% and 60.3% of the farms, respectively. The greatest reductions in pesticide reliance were observed in farms with the highest initial levels of reliance on pesticides. Crop diversification and reduced nitrogen fertilization were often associated with reduced pesticide reliance. This study highlights the value of real farm networks for identifying and promoting combinations of techniques that help reduce pesticide dependency and move towards more sustainable farming systems.

Conservation of genetic resources of farm animals

The problem of preserving the genetic resources of farm animals and breed diversity is one of the priorities in the global livestock industry. As of September 1, 2023, the State Breeding Register of the Russian Federation registered 83 gene pool farms for breeding 63 breeds of 16 species of farm animals, of which 42 are domestic breeds bred in 23 of the 89 constituents of the Russian Federation. As a consequence, it requires the creation of a national system for preserving the gene pool of farm animals based on legislative, economic and biological principles for them. The main centers of breeds formation and the current state of the local breeds in the Russian Federation have been examined in detail in the article. Ways to improve the legislative framework in the fi eld of preserving breed diversity and economic stimulation of gene pool farms were proposed. At the same time, the optimal approach for the selection of small in number local breeds is the use of traditional evaluation of their breeding value together with genotyping for a number of specific genetic markers. The authors have developed appropriate proposals for the conservation of the gene pool of national breeds of farm animals such as: 1) the adoption of the Federal Law on the Conservation of Agrobiodiversity and the creation of an Advisory Council under the Ministry of Agriculture of the Russian Federation and the Russian Academy of Sciences for the conservation of gene pool breeds of animals; 2) the formation of a network of gene pool farms for the main types of farm animals, with their inventory at the genetic level, the creation of information bases and the Federal Bank for the cryopreservation of semen, embryos and DNA of animals of gene pool breeds with budgetary fi nancing of gene pool livestock.

An overlooked poultry trade network of the smallholder farms in the border provinces of Thailand, 2021: implications for avian influenza surveillance.

In Thailand, community-level poultry trade is conducted on a small-scale involving farmers and traders with many trade networks. Understanding the poultry movements may help identify different activities that farmers and traders might contribute to the spread of avian influenza. This study aimed to describe the characteristics of players involved in the poultry trade network at the northeastern border of Thailand using network analysis approaches. Mukdahan and Nakhon Phanom provinces, which border Laos, and Ubon Ratchathani province, which borders both Laos and Cambodia, were selected as survey sites. Local veterinary officers identified and interviewed 338 poultry farmers and eight poultry traders in 2021. A weighted directed network identified incoming and outgoing movements of where the subdistricts traded chickens. Ninety-nine subdistricts and 181 trade links were captured. A self-looping (trader and consumer in the same subdistrict) feedback was found in 56 of 99 subdistricts. The median distance of the movements was 14.02 km (interquartile range (IQR): 6.04-102.74 km), with a maximum of 823.08 km. Most subdistricts in the network had few poultry trade connections, with a median of 1. They typically connected to 1-5 other subdistricts, most often receiving poultry from 1 to 2.5 subdistricts, and sending to 1-2 subdistricts. The subdistricts with the highest overall and in-degree centrality were located in Mukdahan province, whereas one with the highest out-degree centrality was found in Nakhon Phanom province. The poultry movement pattern observed in this network helps explain how avian influenza could spread over the networks once introduced.

Sustainable Task Offloading in Secure UAV-Assisted Smart Farm Networks: A Multi-Agent DRL With Action Mask Approach

Sustainable Task Offloading in Secure UAV-Assisted Smart Farm Networks: A Multi-Agent DRL With Action Mask Approach

Перспективы создания сети рыбоводных предприятий в российской Арктике

Fostering aquaculture aligns with key priorities for Arctic development in Russia, encompassing ecosystem sustainability, job creation, Northern import reduction, enhanced nutritional value of local products, support for small businesses, and advancement of high-tech industries. This study aims to determine the best configuration for a new network of Arctic fish farms in the Russian Arctic. To comprehend the potential of Arctic fish farming, the study starts from identifying feasible farm locations based on an analysis of demand in key Arctic settlements. Criteria-based assessments identified Pevek, Sabetta, Dudinka, Tiksi, Tazovsky, Yamburg, Kharasavey, and Khatanga as potential locations. A comparative economic analysis evaluates logistic options for networks featuring three to eight fish farms in conditions of limited infrastructure. The study determines the attending vessel location (Novosibirsk) and the genetic breeding center location (Pevek). Transportation schemes for feed, fry, and breeding material are developed in accordance with production process requirements. The study proposes the Arctic char as the focus of commercial cultivation at the simulated facility. Economic and mathematical modeling is employed to forecast the effects of creating networks in two configurations at the business, regional, and national levels. The findings reveal that a network of three facilities is more profitable for investors, while a network of eight facilities yields greater social significance with higher fiscal, social, and ecological effects. The quantitative estimates provided serve as a foundation for investment decisions in the development of fish farming in the Russian Arctic.

A novel fault localization method for asymmetric phase‐to‐ground faults on onshore wind farm collector lines without phasor measurement

AbstractExisting fault localization methods for power lines are not applicable to the collector line in onshore wind farms due to the dense access of complex power supplies. Therefore, a fault localization method based on equivalent zero‐sequence injection current (ZIC) is proposed for asymmetric phase‐to‐ground faults on collector lines. Firstly, the idea of load shifting is adopted to generalize the theory of equivalent injection current (EIC) to multi‐terminal and meshed lines. Then, considering features of the zero‐sequence network of wind farms, the EIC theory on two‐terminal and multi‐terminal lines is applied to zero‐sequence networks, and thus faulty areas, faulty sub‐lines, and fault distance can be determined. Attractively, the element correlation in node impedance matrices of zero‐sequence networks is derived from the network branch‐addition process, and the numerical characteristic of matrix elements is further revealed from the particularity of wind farms, based on which root‐mean‐square (RMS) calculation of ZIC can be improved to enable the localization without phasor measurement. Verification results prove that this proposed method can accurately realize the fault localization independent of wind turbine generators with a few measurement points, and the applicability of collector lines with line branches is also considered; additionally, only voltage RMS needs to be measured.

A modeling study of the impact of treatment policies on the evolution of resistance in sea lice on salmon farms.

Salmonid aquaculture is an important source of nutritious food with more than 2 million tonnes of fish produced each year (Food and Agriculture Organisation of the United Nations, 2019). In most salmon producing countries, sea lice represent a major barrier to the sustainability of salmonid aquaculture. This issue is exacerbated by widespread resistance to chemical treatments on both sides of the Atlantic. Regulation for sea lice management mostly involves reporting lice counts and treatment thresholds, which depending on interpretation may encourage preemptive treatments. We have developed a stochastic simulation model of sea lice infestation including the lice life-cycle, genetic resistance to treatment, a wildlife reservoir, salmon growth and stocking practices in the context of infestation, and coordination of treatment between farms. Farms report infestation levels to a central organisation, and may then cooperate or not when coordinated treatment is triggered. Treatment practice then impacts the level of resistance in the surrounding sea lice population. Our simulation finds that treatment drives selection for resistance and coordination between managers is key. We also find that position in the hydrologically-derived network of farms can impact individual farm infestation levels and the topology of this network can impact overall infestation and resistance. We show how coordination and triggering of treatment alongside varying hydrological topology of farm connections affects the evolution of lice resistance, and thus optimise salmon quality within socio-economic and environmental constraints. Network topology drives infestation levels in cages, treatments, and hence treatment-driven resistance. Thus farmer behaviour may be highly dependent on hydrologically position and local level of infestation.

Evolutionary predictions for a parasite metapopulation: Modelling salmon louse resistance to pest controls in aquaculture.

Pests often evolve resistance to pest controls used in agriculture and aquaculture. The rate of pest adaptation is influenced by the type of control, the selective pressure it imposes, and the gene flow between farms. By understanding how these factors influence evolution at the metapopulation level, pest management strategies that prevent resistance from evolving can be developed. We developed a model for the metapopulation and evolutionary dynamics of the salmon louse (Lepeophtheirus salmonis), which is a major parasite affecting salmon aquaculture. Different management scenarios were simulated across a network of salmon farms covering half of Norway, and their effects on louse epidemiology and evolution were investigated. We compared louse controls that differed in how they were deployed through time (discrete vs. continuous), how they impacted the louse life cycle, and in their overall efficacy. We adjusted the strength of selection imposed by treatments, the dominance effect of the resistant allele, and the geographic location at which resistance originated. Continuously acting strategies (e.g., louse-resistant salmon) were generally more effective than discrete strategies at controlling lice, especially when they increased louse mortality during early developmental stages. However, effective strategies also risked imposing frequent and/or strong selection on lice, thus driving rapid adaptation. Resistant alleles were more likely to be lost through genetic drift when they were recessive, had a low-fitness advantage, or originated in low-farm-density areas. The north-flowing current along the Norwegian coastline dispersed resistant genes from south to north, and limited gene flow in the opposite direction. We demonstrate how evolutionary models can produce quantitative predictions over large spatial and temporal scales and for a range of pest control scenarios. Quantitative outputs can be translated into practical management decisions applied at a regional level to minimise the risk of resistance developing.

Short-Term PE Generation Processes in the Soils of a Farmer Plots Network in the Madagascar Highlands: Actors and Drivers

Carbon sequestration in agricultural soils, through the accumulation of high-quality organic matter, represents great potential to mitigate climate change and simultaneously improve soil fertility. Such a challenge is particularly important and relevant in developing tropical countries like Madagascar, where soil carbon storage is vulnerable to climatic variations and where fertilization is generally applied through amendments in organic matter of various origins. The priming effect (PE) is considered here as the stimulation of the mineralization of soil organic matter (SOM) by a supply of fresh organic matter (FOM). PE results from different microbial processes driven by specific biotic and abiotic parameters. Depending on the processes involved, it has been suggested that PE could either counteract SOM accumulation or promote it. The objective of the present study was to explore the relationships between certain agricultural practices (type of crop, quality of fertilization, association with trees), the potential intensity of PE, as well as several abiotic (texture, quantity and quality SOM, nutrient enrichment) a1nd biotic (biomass and phylogenetic composition of microbial communities) factors which have been proposed in the literature as specific determinants of the different PE generation mechanisms. The soils for this study come from a network of farms in a commune in the Highlands of Madagascar. The PE, generated by a supply of 13C-enriched wheat straw, could not directly correlate with agricultural treatments. However, several indirect correlations could be found via several specific abiotic and microbial determinants that are discussed in terms of soil fertility restoration.

The Immense Impact of Reverse Edges on Large Hierarchical Networks

Hierarchical networks are frequently encountered in animal groups, gene networks, and artificial engineering systems such as multiple robots, unmanned vehicle systems, smart grids, wind farm networks, and so forth. The structure of a large directed hierarchical network is often strongly influenced by reverse edges from lower- to higher-level nodes, such as lagging birds’ howl in a flock or the opinions of lower-level individuals feeding back to higher-level ones in a social group. This study reveals that, for most large-scale real hierarchical networks, the majority of the reverse edges do not affect the synchronization process of the entire network; the synchronization process is influenced only by a small part of these reverse edges along specific paths. More surprisingly, a single effective reverse edge can slow down the synchronization of a huge hierarchical network by over 60%. The effect of such edges depends not on the network size but only on the average in-degree of the involved subnetwork. The overwhelming majority of active reverse edges turn out to have some kind of “bunching” effect on the information flows of hierarchical networks, which slows down synchronization processes. This finding refines the current understanding of the role of reverse edges in many natural, social, and engineering hierarchical networks, which might be beneficial for precisely tuning the synchronization rhythms of these networks. Our study also proposes an effective way to attack a hierarchical network by adding a malicious reverse edge to it and provides some guidance for protecting a network by screening out the specific small proportion of vulnerable nodes.

A Location-Allocation Model with Obstacle and Capacity Constraints for the Layout Optimization of a Subsea Transmission Network with Line-Shaped Conduction Structures

The idea of this paper comes from the need for a practical layout design for the subsea pipe line network and the power transmission network of offshore wind farms with subsea cables, which are both subsea transmission networks with line-shaped conduction structures. In this paper, this practical need is treated as an location-allocation problem, with the objective of minimizing the total cost, and a mixed-integer linear programming model (MILP) for layout optimization is developed. Through the model, the locations of service centers and theit corresponding sizes, the allocations between customers and service centers, as well as the transmission routes can all be figured out. This work makes two key contributions. First, facilities’ capacity restrictions and the avoidance of subsea obstacles are both integrated, making the description of the layout closer to practical situations. Secondly, a “global to local” search process based on the Delaunay triangulation method is constructed to solve the model, resulting in a high-quality solution. An offshore field layout design scenario is taken as a case study, through which the validity, feasibility, and stability of the proposed model, as well as the solution strategy, are presented. Furthermore, in the case study, the effect of the manifold number on the layout optimization is analyzed, indicating the flexibility of the model’s applications.

Sub-GHz In-Body to Out-of-Body Communication Channel Modeling for Ruminant Animals for Smart Animal Agriculture.

Sensors in and around the environment becoming ubiquitous has ushered in the age of smart animal agriculture which has the potential to greatly improve animal health and productivity. The data gathered from sensors dwelling in animal agriculture settings have made farms a part of the IoT space leading to active research in developing efficient communication methodologies for farm networks. This study focuses on the first hop of farm networks where data from inside the body of animals is communicated to a node dwelling outside the body. Novel experimental methods are used to calculate the channel loss at sub-GHz frequencies (100-900 MHz) to characterize the in-body to out-of-body (IBOB) communication channel in large animals. A first-of-its-kind 3D bovine modeling is done with computer vision techniques for detailed morphological features of the animal body to perform Finite Element Method based Electromagnetic simulations. The results of the simulations are experimentally validated to build a complete channel modeling methodology for IBOB animal-body-communication. The 3D bovine model is made available publicly on GitHub. The results illustrate that an IBOB communication channel is realizable from the rumen to the collar of ruminants with [Formula: see text] path loss at sub-GHz frequencies making communication feasible. The developed methodology has been illustrated for ruminants but can also be used for other IBOB studies. An efficient communication architecture can be formed using the channel modeling technique illustrated for IBOB communication in animals paving the way for the design and development of future smart animal agriculture systems.

Innovation as a strategic direction for increasing the economic efficiency of the agro-industrial complex

The scientific article is devoted to the study of the processes of innovatization and informatization of the agro-industrial complex. Critical review of publications of academic and business circles and identification of strategic directions of innovative development of the agro-industrial complex; formation of a statistical database and quantitative analysis of indicators of innovative development and digital maturity of the agro-industrial complex; identification of key milestones in the development of the agro-industrial complex in the context of ongoing digital reforms. In the process of writing a scientific article, general scientific (observation, comparison, measurement, analysis and synthesis, the method of logical reasoning) and specifically scientific (static analysis, expert assessments, graphical method) methods of scientific cognition were used. The main directions of innovation and digitalization of the agro-industrial complex are: the development of precision agriculture, the formation of a network of smart farms and greenhouse complexes, the implementation of the federal program of technological cooperation of the agro-industrial complex and the IT sphere "Industrial FoodNet". The main obstacles to the innovative development of the agro-industrial complex are: the dominance of state financing of projects in the field of digital reform; lack of educational programs in the field of cooperation of the agroindustry and IT; insufficient dissemination of ideas and requirements of responsible business policy, expressed in the adoption and compliance with ESG principles; internal resistance of agribusiness management to deep reforms due to the fear of disclosure of trade secrets and the need for admission of partners from the IT sphere to sensitive economic information.

A new power swing detection method in power systems with large‐scale wind farms based on modified empirical‐mode decomposition method

AbstractHigh energy consumption around the world and concerns over climate change and high greenhouse gas emissions have led to a significant increase in using renewable energy resources, particularly wind power plants. In spite of using small wind farms in distribution networks as distributed generation, the interest in employing large‐scale wind farms in transmission levels has grown significantly in recent years. The presence of large‐scale wind farms with a synchronous generator to supply the energy demand can affect the power swing parameters. These effects are in a way that can mislead power swing detection methods and cause undesirable tripping of transmission line due to mal‐operation of distance relay against power swing . Therefore, in this paper, a method has been presented based on modified empirical mode decomposition (EMD) method to differentiate fault from power swing in the networks connected to the large‐scale wind farms. The results prove the efficiency of the presented method in detecting the power swing in large‐scale wind farm networks. Finally, a comparison is conducted between the proposed method and similar methods which proves the efficiency and superiority of the proposed method in terms of noise tolerance, detection fault detection speed, and multi‐mode power swing detection compared to the current methods.

Strengthening agroecological resilience through commons-based seed governance in the Philippines.

The Filipino agricultural sector is exposed to multiple climatic, economic, and social risks that will likely intensify in the near future. Building agroecological resilience has been proposed to protect small-scale farmers' livelihoods and improve food security in the context of (unexpected) shocks and disruptions, and slow system changes such as climate change. This paper argues that commons-based seed production, based on collective management and ownership of seeds and varieties, can play a central role in building resilience capacities in smallholder communities. I explore this by applying an indicator-based framework to assess the contribution of the Filipino farmer network Magsasaka at Siyentipiko para sa Pag-unlad ng Agrikultura (MASIPAG) to agroecological resilience. I find that the networks' commons-based seed governance builds agroecological resilience in various ways. By equipping small-scale farmers with the tools to regain control over seed production and breeding, they become stewards of an actively evolving collection of varieties. The in situ maintenance and development of traditional, open-pollinated varieties and a network of diversified trial and backup farms build up buffering capacities and foster agrobiodiversity and local adaptation. A focus on regionally available natural resources reduces vulnerabilities to external factors. Adaptive capacities are strengthened through a high degree of flexibility and responsiveness achieved by self-organization and polycentric organizational structures. Broad participation, shared learning and spaces for experimentation support the development of farmers' capacities to respond to diverse challenges. Commons-based approaches to seed governance can thus strengthen agroecological resilience and long-term food security in smallholder agricultural systems.

Effective utilization of service vessels in fish farming: fleet design considering the characteristics of the locations

Effective utilization of service vessels in sea-based fish farming requires that the vessels are suited to the operating environments at the fish farms. This paper presents a methodology for assessing service vessel fleet performance when serving a network of farms with different metocean conditions. Fleet performance is defined as the ability to perform operations requested by the fish farms, in due time. An optimization for simulation approach is employed, implementing a routing and scheduling heuristic developed for aquaculture service vessels. A case study was performed assessing the performance of two different fleets serving a set of 21 fish farms. The variation in local metocean conditions between the farms, and how weather changes in time, challenges the operability of the aquaculture infrastructure and the effective routing and scheduling of the vessels. Hence, the results show that proper fleet composition in this context improves fleet performance. Fleet performance is substantially higher when fleet composition, routing, and scheduling is based on the specific weather conditions.