Rule-Based Engine for Automatic Allocation of Smallholder Dairy Producers in Preidentified Production Clusters

Mavura, Fatuma; Pandhare, Sanket M.; Mkoba, Elizabeth; Nyambo, Devotha G.

doi:10.1155/2022/6944151

Cited by 4 publications

(4 citation statements)

References 19 publications

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“…Association rule mining enabled the mining of association rules from a dataset that identified underrepresented patterns among small farmers. This rule-based engine gives farmers a platform to learn various farming techniques from each other in their respective farm groups while extension officers can provide timely assistance [25]. Therefore, recommendations can be provided to the farmers according to their various practices.…”

Section: Discussionmentioning

confidence: 99%

“…High dairy producers were identified as cluster 1 (1180 households), medium dairy producers were identified as cluster 2 (516 households), and low dairy producers were identified as cluster 3 (295 households). In [25], cluster 1 had the best performance of 53%, while clusters 2 and 3 had 43% and 33% performance in product traits, respectively. The dataset consisted of 38 production traits that were represented with categorical data values.…”

Section: Methodsmentioning

confidence: 98%

“…This study adopted a pre-clustered dataset with 6 dairy production clusters [22]. The 6 clusters were evaluated based on their dairy production performance using traits such as vaccination frequency and milk production [25]. This study used 3 clusters to represent high, medium, and low dairy producers.…”

Section: Methodsmentioning

confidence: 99%

“…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: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 98%

Section: Methodsmentioning

confidence: 99%

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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A key-feature-based clustering approach to assess the impact of technology integration on cow health in Austrian dairy farms

Matzhold,

Schodl,

Klimek

et al. 2024

Front. Anim. Sci.

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In the domain of precision livestock farming, the integration of diverse data sources is crucial for advancing sustainability and evaluating the implications of farm management practices on cow health. Addressing the challenge of data heterogeneity and management diversity, we propose a key-feature-based clustering method. This approach, merging knowledge-driven feature selection with unsupervised machine learning, enables the systematic investigation of management effects on cow health by forming distinct clusters for analysis. Utilizing data from 3,284 Austrian farms, including 80 features related to feeding, milking, housing, and technology systems, and health information for 56,000 cows, we show how this methodology can be applied to study the impact of technological systems on cow health resulting from the incidence of veterinary diagnoses. Our analysis successfully identified 14 distinct clusters, further divided into four main groups based on their level of technological integration in farm management: “SMART,” “TRADITIONAL,” “AMS (automatic milking system),” and “SENSOR.” We found that “SMART” farms, which integrate both AMS and sensor systems, exhibited a minimally higher disease risk for milk fever (OR 1.09) but lower risks for fertility disorders and udder diseases, indicating a general trend toward reduced disease risks. In contrast, farms with “TRADITIONAL” management, without AMS and sensor systems, showed the lowest risk for milk fever but the highest risk of udder disease (OR 1.12) and a minimally higher incidence of fertility disorders (OR 1.07). Furthermore, across all four groups, we observed that organic farming practices were associated with a reduced incidence of milk fever, udder issues, and particularly fertility diagnoses. However, the size of the effect varied by cluster, highlighting the complex and multifactorial nature of the relationship between farm management practices and disease risk. The study highlights the effectiveness of the key-feature-based clustering approach for high-dimensional data analyses aimed at comparing different management practices and exploring their complex relationships. The adaptable analytical framework of this approach makes it a promising tool for planning optimizing sustainable and efficient animal husbandry practices.

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Differential Assessment of Strategies to Increase Milk Yield in Small-Scale Dairy Farming Systems Using Multi-Agent Modelling and Simulation

Nyambo

Clemen

2023

Agriculture

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Multi-agent-based modelling and simulation provides an adequate environment to study the real world. This paper presents the use of a multi-agent research and simulation (MARS) framework and model design based on the overview, design concepts, design (ODD) protocol to model and simulate small-scale management strategies that are important for increased milk yield per cow. In reality, strategies for farm management at a small-scale level are purely based on heuristics that cost farmers and lead to inadequate milk yields. A differential assessment of the farming strategies was conducted to yield a data-driven approach for selection of the best strategies, which in turn will optimize investments and increase milk yield. The agent-based modelling and simulation revealed that, the studied strategies based on income, farm, and farmer-based characteristics influenced an increase of up to 7.72 L of milk above the average (12.7 ± 4.89). Generally, there was an increase in milk yield based on the identified evolvement strategies; from a baseline data average milk yield of 12.7 ± 4.89 to simulated milk yield average of 17.57 ± 0.72. Evaluating the agent-based models in real-world scenarios will strengthen the assurance that the identified strategies can move small-scale dairy farmers from low to higher milk producers.

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Rule-Based Engine for Automatic Allocation of Smallholder Dairy Producers in Preidentified Production Clusters

Cited by 4 publications

References 19 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

A key-feature-based clustering approach to assess the impact of technology integration on cow health in Austrian dairy farms

Differential Assessment of Strategies to Increase Milk Yield in Small-Scale Dairy Farming Systems Using Multi-Agent Modelling and Simulation

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