Leveraging peer-to-peer farmer learning to facilitate better strategies in smallholder dairy husbandry

Nyambo, Devotha G.; Luhanga, Edith Talina; Yonah, Zaipuna O.; Mujibi, Fidalis D. N.; Clemen, Thomas

doi:10.1177/1059712320971369

Cited by 5 publications

(5 citation statements)

References 11 publications

(15 reference statements)

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“…The evaluation metrics used were the support, confidence, and lift values. Support and confidence are the two basic metrics in identifying association rules [2][3][4] while this study aimed to have a lift value greater than 1 to indicate stronger rules [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26]. Support is defined as the proportion of records that contain XY to the overall records in the database, whereas confidence is defined as the proportion of the number of transactions that contain XY to the overall records that contain X [4].…”

Section: Methodsmentioning

confidence: 99%

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Multi-level Association Rule Mining for the Discovery of Strong Underrepresented Patterns

Malamsha

Nyambo

2023

Eng. Technol. Appl. Sci. Res.

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Increasing the milk production of small dairy producers is necessary to cover the increase in milk demand in Tanzania. Currently, the population of people in both Tanzania and the world has increased and is predicted to increase more in the year 2050. The use of multilevel association rule mining methods to mine strong patterns among smallholder dairy farmers could help in identifying the best dairy farming practices and increase their milk production by adopting them. This study employed multi-level association rule mining to discover strong rules in three clusters, resulting in three levels of rules in each cluster. These three clusters were high, medium, and low milk producers. Rules were obtained for feeding practices, milk production, and breeding and health practices. These rules represent strong patterns among smallholder dairy farmers that could help them improve their dairy farming practices and have a gradual increase in milk production, from low to medium and from medium to higher milk production. Smallholder dairy producers would be provided with recommendations on their dairy farming practices, using rules based on the cluster to which they belong that could help them achieve higher milk production.

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Section: Methodsmentioning

confidence: 99%

“…A minimum of 145 days is required for small dairy farmers to learn farming strategies from other farmers in order to increase milk production [8]. This is a lot of time for a farmer who also needs to perform other activities.…”

Section: Introductionmentioning

confidence: 99%

Multi-level Association Rule Mining for the Discovery of Strong Underrepresented Patterns

Malamsha

Nyambo

2023

Eng. Technol. Appl. Sci. Res.

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“…The free (GNU General Public License) and large-scale, agent-based framework MARS [16] has a significant history of applications in a variety of disciplines, e.g., climate change mitigation [4], the modeling of adaptive human behavior [22,27], and traffic simulation [35]. MARS comprises three core concepts: agents, entities, and layers.…”

Section: The Mars Frameworkmentioning

confidence: 99%

Multi-Agent Systems and Digital Twins for Smarter Cities

Clemen

Ahmady-Moghaddam

Lenfers

et al. 2021

Proceedings of the 2021 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation

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An intelligent combination of the Internet of Things (IoT) and approaches to modeling and simulation is one of the most challenging endeavors for future cities, manufacturing industries, and predictive maintenance. Digital Twins take on a unique role here. However, the question of what a Digital Twin is and what differentiates it from a regular model is still open. We present an experimental setup for integrating an existing simulation model of Hamburg's traffic system with the city's real-time sensor network. The Digital Twin is implemented using the large-scale multi-agent framework MARS. The entire process from the model description to retrieving real-time data from the IoT sensors and incorporating it in the simulation is presented. As a first prototypical example, a multi-modal mobility model was connected to real-world bike-sharing locations in Hamburg. We find that the combination of multi-agent systems and IoT sensors as a Digital Twin shows enormous potential for city planners, policy stakeholders, and other decision-makers. By correcting the course of a simulation via real-time data, the corridorof-uncertainty that is intrinsic to some simulation models' use can be reduced significantly. Furthermore, any divergence of simulated and sampled data can lead to a deeper understanding of complex adaptive systems like big cities. CCS CONCEPTS• Computing methodologies → Multi-agent systems; Simulation types and techniques.

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“…The free (GNU General Public License) and large-scale, agent-based framework MARS [21] has a history of applications in a variety of disciplines, e.g., climate change mitigation [22], the modeling of adaptive human behavior [23,24], and traffic simulation [20]. MARS comprises three core concepts: agents, entities, and layers.…”

Section: The Mars Frameworkmentioning

confidence: 99%

Improving Model Predictions—Integration of Real-Time Sensor Data into a Running Simulation of an Agent-Based Model

et al. 2021

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The current trend towards living in big cities contributes to an increased demand for efficient and sustainable space and resource allocation in urban environments. This leads to enormous pressure for resource minimization in city planning. One pillar of efficient city management is a smart intermodal traffic system. Planning and organizing the various kinds of modes of transport in a complex and dynamically adaptive system such as a city is inherently challenging. By deliberately simplifying reality, models can help decision-makers shape the traffic systems of tomorrow. Meanwhile, Smart City initiatives are investing in sensors to observe and manage many kinds of urban resources, making up a part of the Internet of Things (IoT) that produces massive amounts of data relevant for urban planning and monitoring. We use these new data sources of smart cities by integrating real-time data of IoT sensors in an ongoing simulation. In this sense, the model is a digital twin of its real-world counterpart, being augmented with real-world data. To our knowledge, this is a novel instance of real-time correction during simulation of an agent-based model. The process of creating a valid mapping between model components and real-world objects posed several challenges and offered valuable insights, particularly when studying the interaction between humans and their environment. As a proof-of-concept for our implementation, we designed a showcase with bike rental stations in Hamburg-Harburg, a southern district of Hamburg, Germany. Our objective was to investigate the concept of real-time data correction in agent-based modeling, which we consider to hold great potential for improving the predictive capabilities of models. In particular, we hope that the chosen proof-of-concept informs the ongoing politically supported trends in mobility—away from individual and private transport and towards—in Hamburg.

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Leveraging peer-to-peer farmer learning to facilitate better strategies in smallholder dairy husbandry

Cited by 5 publications

References 11 publications

Multi-level Association Rule Mining for the Discovery of Strong Underrepresented Patterns

Multi-level Association Rule Mining for the Discovery of Strong Underrepresented Patterns

Multi-Agent Systems and Digital Twins for Smarter Cities

Improving Model Predictions—Integration of Real-Time Sensor Data into a Running Simulation of an Agent-Based Model

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