Use of agent – based models in characterizing farm types and evolvement in smallholder dairy systems

A Thesis Submitted in Fulfilment of the Requirements for the Degree of Doctor of philosophy in Information and Communication Science and Engineering of the Nelson Mandela African Institution of Science and TechnologyThe ever-increasing demand for milk and dairy products has attracted research interventions on how milk yield can be increased for the context of smallholder farmers. While bearing significant contribution on milk production and fulfilment of the market demand, the smallholder dairy farmers are faced with challenges that hinder productivity. Among the challenges is the inadequate characterization of the dairy production systems and lack of knowledge on factors attributing to their growth. This has resulted in aggregation of the smallholder dairy farmers and lack of interventions tailored to suit particular farm types. By using Tanzania and Ethiopia as case studies, this research identified the main determinants for evolvement of smallholder dairy farmers. Evolvement in this research refers to, gradual increase in milk yield. The factors that determine evolvement for individual farm typologies were identified by using cluster and frequent pattern analysis. The differential influence of the identified determinants towards increase in milk yield was studied by using Agent-based modelling and simulation where each factor was observed. Six farm types were identified for Tanzania and four for Ethiopia. The characteristics of the farm types were enriched by frequent pattern analysis with confidence level 60% - 97%. Agentbased modelling revealed that, income and farm-based determinants influenced an increase of up to 7.58 litres above the average (13.62 ± 4.47) for Ethiopia. For Tanzania, farm and farmerbased determinants influenced an increase of up to 7.72 litres of milk above the average (12.7 ± 4.89). The identified determinants could predict up to 96% and 93% of the variances in milk yield for Tanzania and Ethiopia, respectively. There was an increase in milk yield based on the identified evolvement determinants; from baseline data average milk yield of 12.7 ± 4.89 and 13.62 ± 4.47 to simulated milk yield average of 17.57 ± 0.72 and 20.34 ± 1.16 for Tanzania and Ethiopia, respectively. Dairy development agencies should consider the disaggregation of dairy farmers and prioritization of the determinants identified in this research for evolvement of dairy farms. In future, it is important to develop a web or mobile application that can inform smallholder dairy farmers about the identified evolvement determinants to aid on-farm decision making

A Review of Characterization Approaches for Smallholder Farmers: Towards Predictive Farm Typologies.

This research article published by Hindawi, 2019Characterization of smallholder farmers has been conducted in various researches by using machine learning algorithms, participatory and expert-based methods. All approaches used end up with the development of some subgroups known as farm typologies. The main purpose of this paper is to highlight the main approaches used to characterize smallholder farmers, presenting the pros and cons of the approaches. By understanding the nature and key advantages of the reviewed approaches, the paper recommends a hybrid approach towards having predictive farm typologies. Search of relevant research articles published between 2007 and 2018 was done on ScienceDirect and Google Scholar. By using a generated search query, 20 research articles related to characterization of smallholder farmers were retained. Cluster-based algorithms appeared to be the mostly used in characterizing smallholder farmers. However, being highly unpredictable and inconsistent, use of clustering methods calls in for a discussion on how well the developed farm typologies can be used to predict future trends of the farmers. A thorough discussion is presented and recommends use of supervised models to validate unsupervised models. In order to achieve predictive farm typologies, three stages in characterization are recommended as tested in smallholder dairy farmers datasets: (a) develop farm types from a comparative analysis of more than two unsupervised learning algorithms by using training models, (b) assess the training models' robustness in predicting farm types for a testing dataset, and (c) assess the predictive power of the developed farm types from each algorithm by predicting the trend of several response variables

Multi-level association rule mining for the discovery of strong underrepresented patterns : the case study of small dairy farms in Tanzania

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.Swedish International Development Cooperation Agency (SIDA

Development of Railway Information System to Improve Railway Data Aggregation and Analysis in Tanzania

This research article was published by Hindawi in 2023For more than three decades, railway transportation in Tanzania has been in an on-and-off state even though a railway network exists. This is due to damaged tracks, a lack of proper management, and railway operational information. Recently, the Tanzanian government made efforts to revive railway transportation by reopening a few train routes and constructing a new and improved railway network. Even with revived operations, the digitalization process of railway data is still at a low pace as most data is populated in excel sheets for analysis; the major source of data being paper-based. With the use of a mixed research method, this paper provides an information system in the form of mobile and web applications, which provide a platform for populating railway data through the web application accessible to the railway corporation and disseminating railway information to the public through the mobile application. With these platforms, data aggregation and analysis have been made easier and more understandable than the use of excel sheets alone. The results show great possibilities for increased use of digital techniques such as web mapping, which contribute to higher data accuracy and better visualization of railway information that can be disseminated to the public

A Deep Learning Model for Predicting Stock Prices in Tanzania

This research article was published by Engineering, Technology & Applied Science Research in Volume: 13 | Issue: 2 | Pages: 10517-10522 | April 2023 |Stock price prediction models help traders to reduce investment risk and choose the most profitable stocks. Machine learning and deep learning techniques have been applied to develop various models. As there is a lack of literature on efforts to utilize such techniques to predict stock prices in Tanzania, this study attempted to fill this gap. This study selected active stocks from the Dar es Salaam Stock Exchange and developed LSTM and GRU deep learning models to predict the next-day closing prices. The results showed that LSTM had the highest prediction accuracy with an RMSE of 4.7524 and an MAE of 2.4377. This study also aimed to examine whether it is significant to account for the outstanding shares of each stock when developing a joint model for predicting the closing prices of multiple stocks. Experimental results with both models revealed that prediction accuracy improved significantly when the number of outstanding shares of each stock was taken into account. The LSTM model achieved an RMSE of 10.4734 when the outstanding shares were not taken into account and 4.7524 when they were taken into account, showing an improvement of 54.62%. However, GRU achieved an RMSE of 12.4583 when outstanding shares were not taken into account and 8.7162 when they were taken into account, showing an improvement of 30.04%. The best model was implemented in a web-based prototype to make it accessible to stockbrokers and investment advisors

IoT-based on boiler fuel monitoring system: A case of Raha Beverages company limited, Arusha-Tanzania

This research article was published in Research Square journal in 2023.RAHA Beverages Company (RABEC) is one of the banana wine production companies that utilize fuel in steam production in Arusha-Tanzania where fuel data conditions like temperature, pressure, discharge, fuel level, and gas leakage with humidity were a challenge to monitor them which provoked boiler malfunction and plant breakdown. Today, RABEC manually uses a dropping stick into the fuel tank to monitor fuel data conditions which is time consuming and gives inaccurate readings, inefficiency, fuel economy discrepancy, and accidents. This study aimed to design and develop an IoT-based fuel monitoring system. The flow meter, ultrasonic level, thermistor fuel temperature, humidity, and pressure sensors were used to gather fuel information where GSM module was employed to send fuel data messages to the operator’s phone. An AT mega 328 microcontroller was used to process and analyze the fuel data and send them to the Thing Speak IoT platform using Wi-Fi connectivity. The results showed that when the fuel level was less than the threshold value, an operator was alerted by a refilling message via GSM technology. At 0.1Psi pressure, fuel temperature of 120℃, and 80% humidity, the system notifies the operator by an alert message to check injector pressure and if the fuel-air mixture was perfect. In addition, these data were observed on LCD and ThingSpeak webpage. To conclude, the developed system proved the best performance with a 99.98% of success rate with high accuracy, security, and efficiency rate compared to the current monitoring syste

Mobile-based Vaccine Registry to Improve Collection and Completeness of Maternal Immunization Data

This research article was published by International Journal of Advanced Computer Science and Applications Vol. 13, No. 3, 2022Immunization during pregnancy and infancy significantly reduces morbidity and mortality of mothers, unborn fetuses, and young infants. Several studies show the merits of getting complete, quality, and accurate data on time to enhance policy and decision-making for society or country development. Despite the efforts by nations to ensure the success of maternal immunization through electronic immunization registries, limited resources such as poor internet access, shortage of electricity, and digital illiteracy in developing countries hinder the goal of full immunization of mothers and infants. Since 2015, immunization programs in Tanzania use internet-based information systems to collect immunization data from health facilities and submit them to the responsible authority for further decision-making such as the allocation of vaccines to health facilities. The internet-based media is not fully achieved in developing countries due to its cost and resource setting, thus, the responsible authority does not receive instant data to update its vaccine inventory and management activities which often results in partial immunization due to the unavailability of vaccines in some facilities. This challenge can be solved by having an affordable system that instantly incorporates and transmits vaccination details such as the utilization of vaccines and demands from each health facility to responsible authority with less resources. The present study proposes a USSD platform to enhance the receipt of real-time data by immunization authorities from both health facilities with poor and good internet connectivity at a lesser cost. A greater number of health facilities in Tanzania prefer to use both online and offline platforms for collecting and recording immunization data. As electronic immunization registry has been introduced in areas with limited resources, it is recommended the use online and offline platforms for data collection so that they can submit immunization data in real-time without the delays caused by poor resource setting

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

This research article was published by Hindawi, 2022Smallholder dairy producers account for around half of all African livestock ventures; nevertheless, they face challenges in producing more milk due to an insufficient framework and infrastructure to maximize their output. Smallholder dairy producers in this scenario use a variety of tactics to boost milk output. However, the attempts need multiple heuristics, time, and financial investment. Furthermore, because of a lack of extension officers, smallholder dairy producers become trapped in failure cycles, unsuccessful attempts, and a diminished motivation to continue farming. Therefore, the interventions were more straightforward as smallholder dairy producers with comparable characteristics grouped. This research aimed to create a rule-based engine that automatically assigns smallholder dairy producers to predefined clusters. About 78 stakeholders were interviewed, including 69 smallholder dairy producers and 9 extension officers from Meru-Arusha, Tanzania. The 10 production features and 6 predefined clusters were adopted from the previous study. Therefore, a rule-based engine used the selected 10 production features. As a result, the rule-based engine automatically assigns the smallholder dairy producers to their respective clusters. Therefore, smallholder dairy producers share their farming skills and experience to increase milk output through these clusters. Furthermore, extension officers in the system provide timely assistance to smallholder dairy producers with farming concerns

Blockchain-based Data Storage Security Architecture for e-Health Care Systems: A Case of Government of Tanzania Hospital Management Information System

A research article was published by IJCSNS International Journal of Computer Science and Network Security, VOL.22 No.3, March 2022Health information systems (HIS) are facing security challenges on data privacy and confidentiality. These challenges are based on centralized system architecture creating a target for malicious attacks. Blockchain technology has emerged as a trending technology with the potential to improve data security. Despite the effectiveness of this technology, still HIS are suffering from a lack of data privacy and confidentiality. This paper presents a blockchain-based data storage security architecture integrated with an e-Health care system to improve its security. The study employed a qualitative research method where data were collected using interviews and document analysis. Execute-order-validate Fabric’s storage security architecture was implemented through private data collection, which is the combination of the actual private data stored in a private state, and a hash of that private data to guarantee data privacy. The key findings of this research show that data privacy and confidentiality are attained through a private data policy. Network peers are decentralized with blockchain only for hash storage to avoid storage challenges. Cost-effectiveness is achieved through data storage within a database of a Hyperledger Fabric. The overall performance of Fabric is higher than Ethereum. Ethereum’s low performance is due to its execute-validate architecture which has high computation power with transaction inconsistencies. E-Health care system administrators should be trained and engaged with blockchain architectural designs for health data storage security. Health policymakers should be aware of blockchain technology and make use of the findings. The scientific contribution of this study is based on; cost-effectiveness of secured data storage, the use of hashes of network data stored in each node, and low energy consumption of Fabric leading to high performanc

Characteristics of smallholder dairy farms by association rules mining based on apriori algorithm

This research article published International Journal of Society Systems Science (IJSSS), Vol. 11, No. 2, 2019Characteristics of smallholder dairy farmers across regions are highly similar. However, introduction of improved farm management practices and extension support can be effective if specific constraints are identified for each farm typology. So far, approaches used to formulate farm types and characterise farming systems are not tailored to studying hidden patterns from farm datasets. Using the apriori association rules mining algorithm, characteristics of four smallholder dairy farm types are studied. Applying the power of the ArulesViz package, frequent items were visualised. These visuals which display some hidden attributes, solidified understanding on the key determinants for change in the studied farm types. The hidden smallholder farm characteristics were identified in addition to those given by cluster analysis in preliminary studies. Characterising smallholder farm data by using association rules mining is recommended in order to understand such systems in terms of what/how the majority practice rather than basing on cluster averages