Literature Review The use of Management Information Systems in dairy farms Author: Efstratios Lales Semester: Autumn 2020 Course code: 4IK524 2 (28) 4IK524 Information Systems Methodology, 7,5hp – Literature review Table of Contents 1. Introduction .................................................................................................................. 2 2. Related work ................................................................................................................. 2 3. Methods........................................................................................................................... 3 3.1 Approach adopted ..................................................................................................................3 3.2 Search strategy ........................................................................................................................3 3.3 Exclusion criteria ....................................................................................................................4 3.4 Data extraction ........................................................................................................................4 3.5 Data synthesis ..........................................................................................................................4 4. Results ................................................................................................................................. 5 4.1 Review question 1: what are the main purposes served by FMIS in dairy farms? .................................................................................................................................................5 4.1.1 Functions served by FMIS ......................................................................................................... 5 4.1.2 Automation ...................................................................................................................................... 6 4.1.3 Smart farm/precision agriculture .......................................................................................... 6 4.1.4 Information management .......................................................................................................... 6 4.2 Review question 2: which are the main problems associated with the use of FMIS?...................................................................................................................................................7 4.2.1 Farmer's skills needed ................................................................................................................ 7 4.2.2 Lack of collaboration ................................................................................................................... 7 4.2.3 Non-acceptance ............................................................................................................................. 8 4.2.4 Internet shortcomings ................................................................................................................ 9 4.2.5 Lack of integration ........................................................................................................................ 9 4.3 Review question 3: what are the effects of using a FMIS on a dairy farm. ...... 10 5. Discussion ....................................................................................................................... 10 6. Conclusions ..................................................................................................................... 11 7. References ....................................................................................................................... 11 Appendix I. Review tables .............................................................................................. 14 4IK524 Information Systems Methodology, 7,5hp – Literature review 1. Introduction Management Information Systems (MIS) use is, nowadays, widely spread among dairy farms, particularly in dairy cattle farms. MISs have been developed for supporting the management and decision making in dairy farms. MIS could be viewed as a combination of human and computer-based resources that support business management and planning by manipulating data. The conversion of data to information serves their purpose to support the decision-making process. MISs are much more than a data processing activity (Shajahan and Priyadharshini, 2004). It is also worth noting that MIS is not just a computer system. Even before the advent of computers and modern technology, MIS techniques existed and were used to promote effective decision-making. The evolution of computer technology, mainly through the ability to process a vast amount of information, has permitted the consideration of many more alternatives and, thus, a more effective decision-making process. MISs used in dairy farms belong to the category of Farm Management Information Systems (FMIS). FMIS are the outcome of an evolutionary process from the simplified farm recordkeeping to a set of complex and highly sophisticated systems. The primary purpose of most current FMIS is to increase the agricultural sector's competitiveness by reducing costs, promoting compliance with agricultural standards, and contributing to product quality and safety (Fountas et al., 2015). The most prominent role of FMIS is to facilitate the decisionmaking process and enable effective monitoring of the various activities within a farm. Thus, efficient use of FMIS results in profit maximization. In the study of FMISs, it is crucial to elaborate on their features and the problems encountered during their development and implementation phase, and the context in which they are used. It must be noted that there is a wide variety of functions that collectively constitute an FMIS. The most often occurring functions that form part of an FMIS are feed and financial management, milk recording and animal breeding data management, and nutrition management. An effective FMIS is a prerequisite for a tight synchronization of a dairy farm's highly specialized functions and avoiding economic redundancies. Thus, the use of an appropriate FMIS could lead to greater economic efficiency. Therefore, a literature review of the use of FMIS in dairy farms is of great importance and should focus on their functions and the obstacles faced during their development. Available FMISs vary in their licensing and delivery modes. They can be accessed through a desktop computer or mobile phones, or both. They can, for example, run on mobile phones or desktop computers. Some of them are commercial; others are not. In recent years, multiple articles on FMISs have been published, though only a few of them deal with the use of FMIS in dairy farms. However, there is a lack of information regarding what constitutes an FMIS and its features, particularly in dairy farms. The identification of the main features of FMIS in dairy farms and the problems associated with their use could prove to be useful for developers of FMISs. In this paper, a systematic literature review is provided to address the issues mentioned above by providing a thorough review of all relevant empirical research. Three review questions emerged from this aim; what are the main purposes served by FMIS in dairy farms, which are the main problems associated with their use and what are the effects of using a FMIS on a dairy farm. 2. Related work A brief description of the limited review literature on FMIS is presented in this section. It is noteworthy that no literature review on the use of FMIS in dairy farms was found. As a result, the present study represents a novel approach for a systematic review of the state-of-the-art 2 4IK524 Information Systems Methodology, 7,5hp – Literature review knowledge on FMISs in dairy farms. There are, however, some closely related scientific papers that are described below in this section. Giua et al. (2020) reviewed scientific articles on the broad definition of FMISs. This study involved a comprehensive review of papers published in the previous two decades. Its aim was to identify theories used to study digital technologies adoption and the identification of drivers and obstacles to the adoption of these technologies. According to this study, diffusion of innovations is the most common theoretical framework used, and digital technologies adoption is mainly dependent on farms' and farmers' traits, as well as on technological features. Tummers et al. (2019) performed a systematic literature review to identify and describe the state-of-the-art of FMIS. They have analyzed 38 papers and identified 81 unique FMIS features and 51 unique obstacles of FMISs. They found the main associated aspects regarding features and obstacles of FMISs include modeling approaches, the agricultural sector concerned, modes of delivery, and relevant stakeholders. Nikkilä et al. (2010) performed a literature review of FMIS and other agricultural sectors' information systems. They aimed to identify the requirements posed on FMIS by precision agriculture and the evaluation of a Web-based approach to address these additional requirements. 3. Methods 3.1 Approach adopted The "input-processing-output" approach by Levi and Ellis (2006) was adopted for carrying out this literature review. This approach was chosen since it is better suited for dealing with the challenges most often faced in information systems research. According to this approach, there are three steps in the literature review process, i.e., inputs, processing, and outputs. The "inputs" phase mainly involves finding and selecting relevant articles for an effective literature review. The "processing" phase entails processing the data contained in the selected articles into information that could provide a basis upon which new research can be built. Finally, the "output" step is the literature review's actual writing, i.e., developing argumentation on the foundation of the information acquired during the previous stage. 3.2 Search strategy To answer the review questions, a systematic search through the available literature was performed during the first week of December in 2020. The search was conducted in the following scholarly databases where high-quality articles and conference papers are included: Scopus, Web of Science, and Science Direct. Management information systems is a fastdeveloping research domain, and therefore, literature from the past decade was used in this review. The targeted sources were exclusively peer-reviewed articles from academic journals or conference papers of good quality. To extent the pool of available literature, both keyword and manual search were performed. Keywords were applied against several categories, i.e., documents' keywords, title, and abstract. The manual search included backward references search, backward authors search and the use of previously used keywords. Additionally, forward references search, and forward authors search was conducted. The main keywords used were: "Management Information System", "Dairy Farm", "Livestock", and "Agriculture". The outcome of the keyword and manual search are presented in the first column of Table 1. The source with the most articles found was Science Direct with 94 articles, and the source with the smallest number of studies was Web of Science with 51. Overall, 202 articles were identified via the keyword and manual search. 3 4IK524 Information Systems Methodology, 7,5hp – Literature review Table 1. Overview of the keyword and manual search results. Source After keyword and After applying inclusion and manual search exclusion criteria Scopus 57 5 Web of Science 51 2 Science Direct 94 7 Total 202 14 3.3 Exclusion criteria The search process had, by intention, a broad scope to avoid missing potentially articles of interest. This lead to a relatively large number of articles being available for review. Out of the pool of available articles, the most relevant were selected by applying the exclusion criteria presented in Table 2. The exclusion criteria were applied manually by reading the title and abstract of the article, and in case of uncertainty, the complete text. The application of exclusion criteria brought down the number of articles under review to 14. Table 2. Exclusion criteria. Exclusion criteria Publication date before 2010 Articles without full text available Articles do not address the research question Articles do not relate to Management Information Systems Articles do not relate to Dairy Farming 3.4 Data extraction The 14 selected articles were read and the required data for the analysis were extracted with the help of a review table (available in Appendix I). This table contains ten elements, including basic information such as author and title, journal, research motivation, research questions, concepts and theories used, methodologies, findings, contribution to knowledge, limitations, and reflections. The resulting data from this table were further analyzed to spot possible trends. 3.5 Data synthesis After the review tables were filled, the selected studies were read again to allow the researcher to immerse in the data. During the reading process, open coding was carried out whereby spotted elements in the study were given a code, and a note was written in the margin. Those codes were then grouped to form themes as related data became apparent. These themes are presented in Table 3. Table 3. Thematic matrix. Theme Description Farmer's skills ICT skills, engagement, and knowledge needed exchange. Lack of A successful development process of a FMIS collaboration should be based on close collaboration among the relevant stakeholders. Non-acceptance Most farmers were unsure about the benefits brought by FMIS 4 Exemplary literature Eastwood et al. 2012 Eastwood et al. 2012 Lawson et al. 2011 4IK524 Information Systems Methodology, 7,5hp – Literature review Automation Internet shortcomings Functions served by FMIS Smart farm/precision agriculture Information management Effects of using FMIS Lack of integration FMIS could be particularly useful in handling Lawson et al. 2011; the data generated by automated milking Sánchez et al. 2020; systems. Steeneveld and Hogeveen 2015; He et al. 2017 The successful implementation of this new Kaloxylos et al. 2012 architecture could enable a farmer to become an actual ''node in an agricultural worldwide web'' The functions served by FMIS are: (1) Cornou et al. 2014; production results forecasting, (2) feeding Hong-liang et al. 2014; budgeting, (3) ration formulation, (4) Paraforos et al. 2016; economic evaluation of production results, (5) Steeneveld and monitoring (6) pasture management (7) Hogeveen 2015; Berger securing production of quality milk (8) and Hovay 2013 streamline production process. Innovation in farming practices could be O'Grady and O'Hare achieved through the collaborative 2017 development of FMIS based on the functionalities provided by novel technologies, i.e., sensors, internet of things. It has been found that farmers do not face a Magne et al. 2010 shortage of information but difficulty selecting the most relevant information. Thus, to support farmers in decision-making, there is a need to focus on managing information. Increased productivity, effective decision Sánchez et al. 2020; making, reduced labor Steeneveld and Hogeveen 2015; Cornou et al. 2014 There is a lack of integration among the Nikander et al. 2015 various automatic and semi-automatic systems in a dairy farm leading to inefficient use of data. 4. Results 4.1 Review question 1: what are the main purposes served by FMIS in dairy farms? 4.1.1 Functions served by FMIS Cornou, et al. (2014) state that the Animal Registration system in Denmark will be replaced by a novel FMIS that would be developed by the Danish Knowledge Centre for Agriculture. This new tool could be used by various stakeholders, apart from farmers, such as public servants, scientific advisors, and veterinarians. The functionalities of this new FMIS include production forecasting, feedstuff budgeting, diet formulation, financial evaluation of production results, and, since 2012, with a monitoring facility of critical control points regarding milk production and quality, reproductive traits, animal health and feedstuff utilization. This system is fully integrated with Denmark's national database. Paraforos, et al. (2016) also mention that the FMIS could also be employed as a financial analysis tool that provides profitability analysis based on farmer's input data or approximations using acceptable default values or values found in official databases. 5 4IK524 Information Systems Methodology, 7,5hp – Literature review The use of FMIS could also enable the real-time monitoring of livestock health and immunological parameters (Hong-liang et al., 2014). Users of FMIS would input the relevant data through its interface, and then, the system would calculate the respective statistics and provide feedback on key health issues of livestock. Steeneveld and Hogeveen (2015) also stated that FMIS could be effectively used to monitor reproductive and behavioral parameters of dairy herds, milk yield, and chemical composition, even the weight through their interconnections with sensors of individual cows. Berger and Hovay (2013) illustrate that FMIS could promote operational efficiency in a dairy farm, streamline production, secure production of quality milk, and contribute to the optimization of product mix. 4.1.2 Automation According to Lawson et al. (2011), a considerable number of dairy farms use robotic milking systems generating a data flow that cannot be handled without the use of sophisticated FMIS. Moreover, Sánchez et al. (2020) have studied the influence of a veterinary automated system on dairy farms and argue that to improve something, you must measure it in the first place. Steeneveld and Hogeveen (2015) state that sensor systems' use was different for farms using an automatic or conventional milking system (AMS or CMS). Reasons for investing in sensor systems varied depending on whether the farm used AMS or not. This is since sensors are embodied in AMS systems or bought at a reduced cost with them. This is why the use of sensors is increasing in parallel with the increase in AMS in Europe. This combined rise in AMS and sensor systems leads to a dairy farm where the vast amount of data produced cannot be handled without FMIS. He et al. (2017) also argue in favor of a management system's need to handle data produced from automated systems. 4.1.3 Smart farm/precision agriculture According to O'Grady and O'Hare (2017), smart farm technologies could provide the basis for the construction and application of farm-specific models leading to radical innovation in farming management practice (improving the efficacy of decision making). It is worthwhile to mention that the end-user, i.e., the farmer, should participate in the design, development, and evaluation of new models. In this way, the internet of things provides the basis for developing a new generation of FMIS. Moreover, incorporating sensing technologies into FMIS is crucial in leading to a truly "smart" farm. Smart farming involves the employment of Information and Communication Technologies as factors enabling the creation of a new type of farm business that utilizes resources more efficiently and it more productive and profitable. However, these technologies cannot deliver the expected benefits in a standalone mode; rather, they must be combined with sophisticated FMIS to deliver meaningful information in a real-time mode. Smart technologies offer an opportunity to develop smart farm-specific models that could replace generic ones and produce information in a near real-time mode. Research on the development process and application of smart farm models is at a preliminary stage. Among the precision agriculture applications in dairy farming the following are included: decreasing methane emission, disease monitoring, behavior inference, and much more. 4.1.4 Information management Magne et al. (2010) argue that farmers do not face a shortage of information but difficulty selecting the most relevant information. Thus, to support farmers in decision-making, there is a need to focus on managing information. Farmers' decision making is based on their experience and the way that they perceive each situation. The available information must be analyzed about farmers' goals (personal development) and production goals and its basic relevance evaluated in terms of supporting 6 4IK524 Information Systems Methodology, 7,5hp – Literature review these two parameters (regardless of whether these are actually explicit or remain implicit). The criteria for defining whether the information could become a resource are content, medium, and origin of the information. The farmer's strategic decision making is based on goals related to the control and development of dairy farm production together with goals linked to the farmer's aspirations (skills, recognition, personal progression, etc.). The informational resources available to a farmer could be subdivided into through three distinct components: medium, origin, and content (Magne et al., 2005): i. Different media (paper or computer-based or any other kind of media) are used to secure and validate the same content. ii. The information source could be internal to the dairy farm (biotechnical subsystem, farmer's own experience), or be external to the dairy farm system, being held by various other stakeholders (including other farmers) and public or private sector agencies, iii. The content of information involves the dairy farm's technical management, a set of farming practices, and various tasks performed. Moreover, as Magne et al. (2010) argue. production goals should be distinguished from farmer's objectives to understand the interaction between subjective, technical and economic rationality Overall, one of the main roles FMIS play in a dairy farm is to help farmers to manage the vast and varied amount of information produced every day. 4.2 Review question 2: which are the main problems associated with the use of FMIS? 4.2.1 Farmer's skills needed For efficient use of decision support systems, the core competencies users need are ICT skills, engagement, and knowledge exchange. It is often the case in the dairy sector that competency traps impede the incorporation of a novel DSS. According to Eastwood et al. (2012), the effective implementation of a new management information system in a dairy farm required that the farmer had three core competencies, i.e., ICT skills, engagement, and knowledge exchange. It is obvious that farmers need to have an elementary understanding of ICT to be reactive to the system's functionalities and incorporate feedback from the system into the decision-making process. However, as Eastwood et al. (2012) have illustrated, there is no need for an extremely high level of computer literacy to use the system. Farmers must adapt their everyday routines to be available for entering the data into the system. It is also important that knowledge exchange's core competency is achieved, which means that the knowledge acquired from everyday practice flows between the people running the dairy farm and between the farm's management team and the system's database. Moreover, it is the case that some farmers install a management information system in their business without having realized that there is a need for investing time and effort to make it worthwhile. There is a need for effective practice networks to support the induction process. Most of the time, it may take several years until the farmer can make full use of the MIS functionalities. 4.2.2 Lack of collaboration As it is stated by Eastwood et al. (2012), a prerequisite for the integration of management information systems into existing farming systems is the conversion of a farmer's tacit-based operational knowledge into explicit forms that would enable the developers to set softwarebased management parameters. Additionally, there is also a necessity for interpreting explicit information produced by FMIS and its integration into everyday management practice. Despite the positive prospects of decision support systems and FMIS in general for the agricultural sector (Higgins, 2007), widespread adoption in everyday farming practice is lagging (McCown, 2012). This is due to the static nature of the technology development and 7 4IK524 Information Systems Methodology, 7,5hp – Literature review adoption process, where the transfer of knowledge is conducted through a 'pipeline' approach. The installation of a FMIS in a dairy farm is merely viewed as successful "adoption." However, the installation of a FMIS should only be viewed as the beginning of a process where a gradual adaptation of FMIS and farming systems occurs to enable effective decision-making in the dairy farm context. This is why, it is necessary to involve the various stakeholders early in the process of adaptation (Jakku and Thorburn, 2010). As Higgins (2007) has described the collaboration between farmers and software developers could facilitate a FMIS. However, in cases where this collaboration ceased after installing the system, the FMIS inflexibility combined with the preconception of dairy farmers led to low use of FMIS. FMIS could become the focus of a social learning process by providing a point of interaction for communities of practice (Jakku and Thorburn, 2010). Therefore, commercially driven FMISs involve less active social learning. Eastwood et al. (2012) argue that FMIS retailers also face a challenge since they have to change their marketing strategy from selling standalone dairy technology to supporting complex FMIS- based technology and the respective collaborative development process. The model used by these retailers is to include in the product early learning support tools to help them reach the necessary "competency" together with an ongoing customer support system. Feedback from the farmers that participated in Eastwood et al. (2012) case study farmers indicates that the current supporting plan is inadequate and more research in needed on the modes of integration of a FMIS in a dairy farm. Nuthall (2006) has identified the need for easy-to-use ICT-enabled learning packages and Flettet et al. (2004) argued for the necessity of co-creation of FMIS. There is an ongoing controversy regarding the delivery of farmers' training as a standalone commodity, and it is questionable whether farmers would be willing to pay for such a commodity. The experience of farmers in Eastwood et al. (2012) indicated that there is significant room for improvement in the collaborative process of developing a FMIS. It is important to match technology to farmers' needs (not "oversell") and promote the learning process. Sterk et al. (2009) identified that the models employed need to have a defined role and be suitable for the context they are going to use. 4.2.3 Non-acceptance Lawson et al. (2011) argue that most farmers were unsure about the benefits brought by FMIS (computer documentation, precision farming) and this is a significant obstacle in the adoption and widespread use of FMIS. Lawson et al. (2011) conducted a survey in Greece, Germany, Finland, and Denmark based on 75, 76, 78 and 184 respondents, respectively. In this survey, larger farms adopted more easily precision agriculture and FMIS technologies. This is because smaller farms do not have the necessary workforce that would enable them to get used to the modern technology. According to Lawson et al. (2011), the effect of farmers' age on the adoption of FMIS will significantly depend on farmers' educational level. Farmers with a high educational background (university degree) would probably be more prone to adopt innovative information systems. According to the results of this survey, over 30% German, fewer than 20% Danish, and over 20% Finish respondents agreed that computer documentation is beneficial in dealing with governmental agencies. However, most of the respondents were unsure, which poses an obstacle for the adoption of FMIS. The analysis of data reveals that the larger the farm's size and the subsidies received, the greater the acceptance for computer documentation. Once more, small size of farms poses an obstacle for FMIS adoption. However, the study reveals that there are potential labor-saving effects from introducing FMIS mainly in relation to budgeting processes, production planning, and bureaucratic activities in relation to subsidy applications and dealing with public sector agencies 8 4IK524 Information Systems Methodology, 7,5hp – Literature review Overall, it seems that the size and the educational level of farmers are the decisive factors that promote or hinder the adoption of management information systems in farms. 4.2.4 Internet shortcomings FMISs focus on various tasks and only use their specifications to implement the advertised functionalities. In recent years, as Kaloxylos et al. (2012) argue, these systems are slowly entering the Internet era and are beginning to incorporate commercial networking solutions to improve their functionalities. However, what causes problems are the shortcomings that the internet faces, especially in handling vast numbers of users and/or networked devices (i.e., Internet of Things). Moreover, a standardized solution to enable simple and unproblematic interoperability among services and stakeholders is still lacking. The Future Internet infrastructures, proposed by Kaloxylos et al. (2012), aims to handle these shortcomings. Several functionalities would be enabled by overcoming the internet's problems and allowing the farmer to perform unfeasible tasks today, such as product advertisement, trustable stakeholder's discovery, and combining functionalities from different information systems, and others). Overall, the shortcomings of the internet pose some obstacles to incorporating new functions into FMIS that you provide significant benefits to dairy farmers. Finally, as Kaloxylos et al. (2012) state, the successful incorporation of internet functionalities into FMIS could enable the farmer to become an actual ''node in a worldwide agricultural web''. 4.2.5 Lack of integration A modern dairy farm contains many autonomous and semi-autonomous systems that are used to reduce human labor. However, the information produced by these systems is not efficiently handled. There is a lack of integration and/or data exchange between systems from different vendors (Nikander, 2015). Modern dairy farming is associated with vast amounts of data gathered from various autonomous and semi-autonomous systems. Different commercial vendors typically provide these systems that are not interoperable and cannot interchange data among them. This means that most of the data must be collected manually. The manual collection of data causes errors and delays due to the human factor and increased workload. As a result, the availability and reliability of data decline. Moreover, the need to manually collect the data causes an increase in labor costs and decreased dairy farm profitability. The combination of FMIS and automatic data gathering methods leads to a reduction of dairy farms' operational costs and an increase in their profitability. Automation provides data in a real-time mode and reduces the number of errors. The introduction of these innovations is hindered by the various commercial ICT packages that control the automation systems in a modern dairy farm. As there are no widely accepted standards and protocols for data sharing, general solutions to the data collection problem are not feasible. Any data collection system implemented under these circumstances has to be able to interface with various systems and protocols; this absorbs a considerable amount of resources. It is challenging to create a single data collection interface in a system that consists of several different parts that were not designed to work together. Thus, it is crucial to develop open standards in modern dairy farms. As the dairy farms' average size increases, management's dependency on the data gathered by various automation systems is increased. Having a holistic view of the entire dataset is a prerequisite for having an awareness of the real situation. In case this is not possible, there is a possibility of negative consequences for livestock's health and the farm's profitability. 9 4IK524 Information Systems Methodology, 7,5hp – Literature review 4.3 Review question 3: what are the effects of using a FMIS on a dairy farm. Sanchez et al. (2020) argue that although it is difficult to assume that the beneficial effect on the dairy farm's parameters could be attributed to the introduction of a FMIS, the positive trend that was observed in the productive and reproductive traits of livestock suggests a strong positive influence of the MIS during the first years following-up its adoption. This is due to the dairy farm management team, gradually learning to use FMIS more effectively to carry out more detailed observations of everyday activities and make informed decisions. According to Steeneveld and Hogeveen (2015), the combined use of a FMIS and an estrus detection sensor system could positively affect detection rates, leading to adequate monitoring of the herd's fertility level, improved profitability, and reduce labor-costs. Labor reduction is one of the primary reasons for using FMIs in combination with sensor systems because individual cow monitoring through physical observations by the dairy farms workforce is time-consuming and ineffective as the herd grows larger. This is especially important in northwest Europe, where wages are high, and farmers are looking for ways to substitute physical labor. FMIS and estrus detection sensor systems could have a labor-saving effect by eliminating the need for physically observing the cows. Cornou et al. (2014) argue that MIS's use in a dairy farm has beneficial effects in terms of efficiency and effectiveness and entails a significant number of different functionalities. The introduction of a FMIS in a dairy farm allows the workforce to perform more effectively livestock reproductive handling daily procedures. It is widely accepted that the monitoring of reproduction traits' critical control points keeps the personnel alert and increases their motivation. The dairy manager would typically monitor the data produced by the FMIS during the week and discuss them with the personnel during a weekly meeting. 5. Discussion This study represents one of the few systematic literature reviews on FMISs in dairy farms to the best of my knowledge. From the results, several interesting themes were identified. Over the decade, a sizable number of high-quality articles have been published on FMIS, whereby the focus has been on arable farming or general-purpose FMIS (i.e., not dealing with a specific agricultural domain). On many occasions, these domains were not explicitly described in the literature. The literature reviewed illustrates that there is a shift happening from the classic desktop application towards cloud applications. Most of the commercial FMISs belong to the category of application software with a predefined set of functionalities, which are not easily extensible. Thus, it seems that vendors do not focus on a generic and reusable platform software enabling the development of a broader set of applications. One of the main characteristics of the present study is that the author has explicitly adopted a systematic literature review protocol that is widely accepted in the field of information systems. Based on this protocol, a search was conducted on the FMISs from a broad set of (more than 200) studies from which 14 articles were selected. The most significant threats to the validity of a literature review are publication and selection bias, data extraction, and the identification of themes (Dybå and Dingsøyr, 2008). The publication and selection bias threats were covered by defining exclusion criteria and carefully screening the selected articles. All selection criteria were discussed among the coauthors to ensure their quality. It was identified that the extracted data adequately answered the review questions. The data synthesis was carried out as objectively as possible with the primary goal of keeping as much distinction between the themes as possible. There is, however, always the possibility that interesting papers were missed after applying the exclusion criteria, but with a total amount of 14 included articles, a reasonable amount of input data for this literature review is achieved. 10 4IK524 Information Systems Methodology, 7,5hp – Literature review 6. Conclusions In this literature review, a systematic search for the past decade's scientific literature was conducted to answer the review questions formulated. The following themes were identified: farmer's skills needed, lack of collaboration, non-acceptance, automation, internet shortcomings, functions served by FMIS, smart farm/precision agriculture, information management, effects of using FMIS, and lack of integration. The main purposes served by FMIS in dairy farms could be summarized into the following points: production results forecasting, feeding budgeting, ration formulation, economic evaluation of production results, monitoring, pasture management, securing production of quality milk, streamline the production process, smart farming/precision agriculture, automation, and information management. The main problems of using FMIS could be summarized into the following points: requirement for farmer's skills, lack of collaboration during the development phase of FMIS, farmers are not convinced about the benefits of FMIS, various automation in a dairy farm are not interoperable, and there are shortcomings associated with the internet. The effects of using a FMIS on a dairy farm are positive and are associated with laborsaving, increased profitability, and more efficient management of the dairy farm. 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Hong-liang, L., Hong-bin, W., Hong-yu, Q., Chao, W., Zhi-nan, Z. and Jian-hua, X., 2014. Design and Implementation of Stud-farm Daily Management System Based on C/S Structure. Journal of Northeast Agricultural University (English Edition), 21(3), pp.50-59. 11 4IK524 Information Systems Methodology, 7,5hp – Literature review Giua, C., Materia, V.C. and Camanzi, L. 2020. Management information system adoption at the farm level: evidence from the literature, British Food Journal (Article in press). Jakku, E., Thorburn, P.J., 2010. A conceptual framework for guiding the participatory development of agricultural decision support systems. Agricultural Systems 103(9), 675–682. Kaloxylos, A., Eigenmann, R., Teye, F., Politopoulou, Z., Wolfert, S., Shrank, C., Dillinger, M., Lampropoulou, I., Antoniou, E., Pesonen, L. and Nicole, H., 2012. Farm management systems and the Future Internet era. Computers and electronics in agriculture, 89, pp.130144. Lawson, L.G., Pedersen, S.M., Sørensen, C.G., Pesonen, L., Fountas, S., Werner, A., Oudshoorn, F.W., Herold, L., Chatzinikos, T., Kirketerp, I.M. and Blackmore, S., 2011. A four nation survey of farm information management and advanced farming systems: a descriptive analysis of survey responses. Computers and Electronics in Agriculture, 77(1), pp.7-20. Levy, Y. & Ellis, T.J. 2006. A Systems Approach to Conduct an Effective Literature Review in Support of Information Systems Research. Informing Science Journal, Vol 9, pp. 181-212. Magne, M.A., Couzy, C. and Ingrand, S. (2005) Comprendre comment les e ĺ eveurs de bovin allaitant mobilisent des informations pour concevoir et piloter leur activité d́ ' elevage: distinguer le Support, l'Origine et le Contenu (SOC). 12e Rencontres autour des Recherches sur les Ruminants, pp. 65–68. Paris, France. Magne, M.A., Cerf, M. and Ingrand, S., 2010. A conceptual model of farmers' informational activity: a tool for improved support of livestock farming management. Animal: an international journal of animal bioscience, 4(6), p.842. McCown, R.L., 2012. A cognitive systems framework to inform delivery of analytic support for farmers' intuitive management under seasonal climatic variability. Agricultural Systems 105 (1), 7–20. Nikander, J., Laajalahti, M., Kajava, S., Sairanen, A.M.J. and Pastell, M., 2015. Development of a general cowshed information management system from proprietary subsystems. In Proceedings of the 7th European Conference on Precision Livestock Farming. Milano, Italy. Nikkilä, R., Seilonen, I. & Koskinen, K., 2010. Software architecture for farm management information systems in precision agriculture. Computers and electronics in agriculture, 70(2), pp.328–336. Nuthall, P.L., 2006. Determining the important management skill competencies: thecase of family farm business in New Zealand. Agricultural Systems 88 (2–3),429–450. O'Grady, M.J. and O'Hare, G.M., 2017. Modelling the smart farm. Information processing in agriculture, 4(3), pp.179-187. Paraforos, D.S., Vassiliadis, V., Kortenbruck, D., Stamkopoulos, K., Ziogas, V., Sapounas, A.A. and Griepentrog, H.W., 2016. A farm management information system using future internet technologies. IFAC-PapersOnLine, 49(16), pp.324-329. Sánchez, Z., Galina, C.S., Vargas, B., Romero, J.J. and Estrada, S., 2020. The Use of Computer Records: A Tool to Increase Productivity in Dairy Herds. Animals, 10(1), p.111. Shajahan, S. & Priyadharshini, R., 2004. Management information systems, New Delhi: New Age International. Steeneveld, W. and Hogeveen, H., 2015. Characterization of Dutch dairy farms using sensor systems for cow management. Journal of Dairy Science, 98(1), pp.709-717. Sterk, B., Leeuwis, C., van Ittersum, M.K., 2009. Land use models in complex societal problem solving: plug and play or networking? Environmental Modelling &Software 24 (2), 165–172. 12 4IK524 Information Systems Methodology, 7,5hp – Literature review Tummers, J., Kassahun, A. and Tekinerdogan, B. 2019. Obstacles and features of Farm Management Information Systems: A systematic literature review, Computers and Electronics in Agriculture, vol. 157, pp. 189-204. 13 4IK524 Information Systems Methodology, 7,5hp – Literature review Appendix I. Review tables Feature of the article Your comment Author(s) and the title of the article Networks of practice for co-construction of agricultural decision support systems: Case studies of precision dairy farms in Australia. Eastwood, C.R., Chapman, D.F. and Paine, M.S. Agricultural Systems Name of the journal/conference Research motivation stated by the author(s) (Why is the research important? What is the knowledge gap?) Objective(s) & research question(s) (What is going to be done?) Concepts and theories used The development of Decision Support Systems is impeded by the n-participation of potentially important stakeholders, in addition to the incomplete links between participating stakeholders such as technology retailers and farmers. The article aims to examine the learning processes associated with using Decision Support Systems on farms by using precision dairy as an example. DSSs are described as dynamic and evolving artifacts that are developed through a collaborative process involving end-users and developers. Adaptive structuration theory Methodology, data collection and data analysis (How was the research conducted?) Exploratory longitudinal case study Key findings or results/ New identified themes (what has been found by the author(s)?) The way new users incorporate DSSs in their daily routine. For efficient use of DSSs the core competencies needed are ICT skills, engagement, and knowledge exchange. Technological barriers and competency traps could impede the incorporation of a novel DSS. It is crucial to convert farmers' implicit knowledge into explicit that would be used to interact with DSS. Efficient knowledge sharing is essential in the development of DSS. It has been shown that a prerequisite for the integration of DSSs into existing farming systems is the conversion of a farmer's tacit-based operational knowledge into explicit forms that would enable the developers to set software-based management parameters. Additionally, there is also a necessity for interpreting explicit information produced by DSS and its integration into everyday management practice. It would be useful to include in future study research and extension professionals as well as agricultural consultants since they play a significant role in knowledge exchange. A successful development process of a Management Information System should be based on close collaboration among the relevant stakeholders. Contributions to knowledge (What is the new knowledge/ How the identified knowledge gap is filled?) Limitations (possibility for future research, still existing knowledge gap) Your own reflections (what have you learnt, what was already known to you, what was 14 4IK524 Information Systems Methodology, 7,5hp – Literature review surprising, what could have been done in a better way?) Feature of the article Your comment Author(s) and the title of the article Name of the journal/conference A four-nation survey of farm information management and advanced farming systems: A descriptive analysis of survey responses Lawson, L.G., Pedersen, S.M., Sørensen, C.G., Pesonen, L., Fountas, S., Werner, A., Oudshoor, F.W., Chatzinikos, T., Kirketerp, I.M., Blackmore, S. Computers and Electronics in Agriculture Research motivation stated by the author(s) (Why is the research important? What is the knowledge gap?) The analysis of the potential use of FMISs in a broader context (time savings, international adoption of precision farming) has not been given the proper emphasis. Objective(s) & research question(s) (What is going to be done?) How farmers perceive FMISs and how much time they dedicate to various activities. Additionally, the investigation of the interaction between different information systems and the use of advanced automated systems. Interpretivism Concepts and theories used Methodology, data collection and data analysis (How was the research conducted?) Survey Key findings or results/ New identified themes (what has been found by the author(s)?) Contributions to knowledge (What is the new knowledge/ How the identified knowledge gap is filled?) Most farmers were unsure about the benefits brought by FMIS (computer documentation, precision farming). Available FMIS have functions ranging from management information from automated data acquisition systems to advanced robotic systems. An advisory service would be helpful, particularly in the case of smaller farms. Many dairy farms use the robotic milking system that could supply a significant amount of data to FMIS. A potential benefit was found resulting from the introduction of labor-saving FMIS to activities such as financial management, planning, and dealing with authorities. Limitations (possibility for future research, still existing knowledge gap) More research is necessary into the feasibility of introducing innovative FMIS into farms to quantify the benefits. Your own reflections (what have you learnt, what was already known to you, what was Most farmers are not convinced about the usefulness of FMIS. FMIS could be particularly useful in handling the data generated by automated milking systems. 15 4IK524 Information Systems Methodology, 7,5hp – Literature review surprising, what could have been done in a better way?) Feature of the article Your comment Author(s) and the title of the article Farm management systems and the Future Internet era Kaloxylos, A., Eigenmann, R., Teye, F., Politopoulou, Z., Wolfert, S., Shrank, C., Dillinger, M., Lampropoulou, I., Antoniou, E., Pesonen, L., Huether, N., Floerchinger T., Alonistioti, N., Kormentzas, G. Name of the journal/conference Computers and Electronics in Agriculture Research motivation stated by the author(s) (Why is the research important? What is the knowledge gap?) The latest trend is to enable FMIS to run over the internet. However, there are several problems associated with handling many networked devices and the integration of systems and services developed by different vendors. This article presents some technological enablers that could potentially address these issues. How could generic software modules be used to build farming related specialized modules. Objective(s) & research question(s) (What is going to be done?) Concepts and theories used Constructivism Methodology, data collection and data analysis (How was the research conducted?) Design research Key findings or results/ New identified themes (what has been found by the author(s)?) Apart from supporting typical everyday farming activities, the designed software could enable the integration of different stakeholders and services with the complete functionality of the FMIS. Contributions to knowledge (What is the new knowledge/ How the identified knowledge gap is filled?) The main contribution is that the conversion of generalpurpose software modules into farming specific could lead us to a cloud operating system where various applications can be integrated. Limitations (possibility for future research, still existing knowledge gap) Implementation and testing of the novel architecture using at least two pilots. Your own reflections (what have you learnt, what was already known to you, what was surprising, what could have been done in a better way?) The successful implementation of this new architecture could enable a farmer to become an actual ''node in an agricultural worldwide web'' 16 4IK524 Information Systems Methodology, 7,5hp – Literature review Feature of the article Your comment Author(s) and the title of the article Dynamic monitoring of reproduction records for dairy cattle Cornou, C., Østergaard, S., Ancker, M.L., Nielsen, J., Kristensen, A.R. Computers and Electronics in Agriculture Name of the journal/conference Research motivation stated by the author(s) (Why is the research important? What is the knowledge gap?) Objective(s) & research question(s) (What is going to be done?) This article's motivation is that the Animal Registration system, which is currently in use, is scheduled to be replaced by a new Dairy Management System (DMS) developed by the Danish Knowledge Centre for Agriculture. The presentation of part of the new DMS. Concepts and theories used Interpretivism Methodology, data collection and data analysis (How was the research conducted?) Experiment Key findings or results/ New identified themes (what has been found by the author(s)?) The use of DMS allows for the improvement of daily procedures. The purposes served by the various modules of DMS are: (1) production results forecasting, (2) feeding budgeting, (3) ration formulation, (4) economic evaluation of production results, (5) surveillance. During 2012, DMS was extended with a surveillance module of Critical Control Points (CCPs) concerning milk production and quality, reproductive traits, health indicators, and efficient utilization of resources. DMS contributes to the improvement of daily procedures in a dairy farm and has many functionalities. Contributions to knowledge (What is the new knowledge/ How the identified knowledge gap is filled?) Limitations (possibility for future research, still existing knowledge gap) Further developments regarding the DMS would be the inclusion of a V mask to monitor potential systematic changes. Your own reflections (what have you learnt, what was already known to you, what was surprising, what could have been done in a better way?) The use of MIS in a dairy farm has beneficial effects in terms of efficiency and effectiveness and entails a significant number of different functionalities. Feature of the article Your comment Author(s) and the title of the article Design and Implementation of Stud-farm Daily Management System Based on C/S Structure 17 4IK524 Information Systems Methodology, 7,5hp – Literature review Name of the journal/conference Luan Hong-liang, Wang Hong-bin, Qin Hong-yu, Wang Chao, Zhai Zhi-nan, and Xiao Jian-hua Journal of Northeast Agricultural University Research motivation stated by the author(s) (Why is the research important? What is the knowledge gap?) There is no research dealing with incorporating the MIS into the network. Database design is necessary, as well as an inference engine. Objective(s) & research question(s) (What is going to be done?) Immunology indicators were observed. Relational databases and a management system were designed. Concepts and theories used Constructivism Methodology, data collection and data analysis (How was the research conducted?) Design research Key findings or results/ New identified themes (what has been found by the author(s)?) The management system has beneficial effect on health management of livestock through improved monitoring of various parameters. Contributions to knowledge (What is the new knowledge/ How the identified knowledge gap is filled?) MIS could prove to be useful for livestock health monitoring. Limitations (possibility for future research, still existing knowledge gap) Further development of the platform is needed. Your own reflections (what have you learnt, what was already known to you, what was surprising, what could have been done in a better way?) Improved monitoring of livestock health leads to greater efficiency. Feature of the article Your comment Author(s) and the title of the article Modelling the smart farm O'Grady, Μ.J. and O'Hare G. Name of the journal/conference Information processing in Agriculture Research motivation stated by the author(s) (Why is the research important? What is the knowledge gap?) The use of sensors offers monitoring abilities to an unprecedented level of detail. There is a knowledge gap in developing farm-specific models that could facilitate the decision-making process and the handling of management overload. 18 4IK524 Information Systems Methodology, 7,5hp – Literature review Objective(s) & research question(s) (What is going to be done?) The author aims to explore developments in modeling and technologies necessary for the construction of farmspecific models. Concepts and theories used Interpretivism Methodology, data collection and data analysis (How was the research conducted?) Case study Key findings or results/ New identified themes (what has been found by the author(s)?) Modelling techniques have been developed for pasture growth, basic activity patterns using GPS-enabled collars, feeding behavior using machine learning techniques and milk production forecasting. Contributions to knowledge (What is the new knowledge/ How the identified knowledge gap is filled?) It has been shown that smart farms technologies could provide the basis for the construction and application of farm-specific models leading to radical innovation in farming management practice (improving the efficacy of decision making), the end-user should participate in the design, development, and evaluation of new models, internet of things provides the basis for the development of a new generation of FMIS, the deployment of technologies in a farm is problematic, sensing technologies are of significant importance in precision agriculture). Limitations (possibility for future research, still existing knowledge gap) Further research is needed in the adoption of a farm and farmer-centric approach in the development of FMIS. Your own reflections (what have you learnt, what was already known to you, what was surprising, what could have been done in a better way?) Innovation in farming practices could be achieved through the collaborative development of FMIS based on the functionalities provided by novel technologies, i.e., sensors, internet of things. Feature of the article Your comment Author(s) and the title of the article Name of the journal/conference Description and evaluation of the Farmax Dairy Pro decision support model Bryant, JR, Ogle, G., Marshall, PR, Glassey, CB, Lancaster, JAS, Garc, SC and Holmes, CW New Zealand Journal of Agricultural Research Research motivation stated by the author(s) (Why is the research important? What is the knowledge gap?) Decision support systems assist dairy farmers in making informed decisions. This article examines a decision support model for pastoral dairy farming systems, Farmax Dairy Pro. 19 4IK524 Information Systems Methodology, 7,5hp – Literature review Objective(s) & research question(s) (What is going to be done?) The study's objective is to evaluate Farmax Dairy Pro by using two independent farm studies and generating industry acceptance. Concepts and theories used Interpretivism Methodology, data collection and data analysis (How was the research conducted?) Case study Key findings or results/ New identified themes (what has been found by the author(s)?) The DSS model under evaluation can be used to predict animal, farm and financial performance for different management scenarios. Contributions to knowledge (What is the new knowledge/ How the identified knowledge gap is filled?) Farma Dairy Pro can accurately predict mean annual yields for milk and constituents. Pasture cover was also reliably predicted. Limitations (possibility for future research, still existing knowledge gap) Further studies in different farms could be conducted Your own reflections (what have you learnt, what was already known to you, what was surprising, what could have been done in a better way?) FMIS could be used in a dairy farm to monitor milk recording activities and pasture management effectively. Feature of the article Your comment Author(s) and the title of the article A conceptual model of farmers' informational activity: a tool for improved support of livestock farming management. Magne, M.A., Cerf, M. and Ingrand, S. Animal Name of the journal/conference Research motivation stated by the author(s) (Why is the research important? What is the knowledge gap?) Objective(s) & research question(s) (What is going to be done?) Concepts and theories used It has been found that farmers do not face a shortage of information but difficulty selecting the most relevant information. Thus, to support farmers in decisionmaking, there is a need to focus on managing information. This study objective is to determine how farmers manage the available information in everyday practice. A conceptual model of FMIS is built using data collected in commercial beef cattle farms. Constructivism 20 4IK524 Information Systems Methodology, 7,5hp – Literature review Methodology, data collection and data analysis (How was the research conducted?) Design research Key findings or results/ New identified themes (what has been found by the author(s)?) The conceptual model built enables the understanding of how farmers handle information by considering both the information flow and how farmers make sense of that information. Contributions to knowledge (What is the new knowledge/ How the identified knowledge gap is filled?) Farmers' decision making is based on their experience and the way that they perceive each situation. The available information must be analyzed regarding farmers' and production goals. The criteria for defining whether the information could become a resource are content, medium, and origin of the information. Further research should consider farmers' perceptions of DSS models management. Limitations (possibility for future research, still existing knowledge gap) Your own reflections (what have you learnt, what was already known to you, what was surprising, what could have been done in a better way?) FMIS development should consider both farmers' and production objectives. Feature of the article Your comment Author(s) and the title of the article The Use of Computer Records: A Tool to Increase Productivity in Dairy Herds Sánchez, Z., Galina, C.S, Vargas, B., Romero, J.J. and Estrada, S. Name of the journal/conference Animals Research motivation stated by the author(s) (Why is the research important? What is the knowledge gap?) There is a need to show the positive effects of using MIS in dairy farms to promote their adoption. Objective(s) & research question(s) (What is going to be done?) This study evaluates the possible effects of intensive use of MIS on productivity levels and, particularly, the influence of the veterinary automated management and production control program on dairy farms. Positivism Concepts and theories used Methodology, data collection and data analysis (How was the research conducted?) Survey 21 4IK524 Information Systems Methodology, 7,5hp – Literature review Key findings or results/ New identified themes (what has been found by the author(s)?) MIS's use has a positive effect on the productive and reproductive performance in a dairy farm, especially in the first years of follow-up. Contributions to knowledge (What is the new knowledge/ How the identified knowledge gap is filled?) Dairy farms are gradually learning to use FMIS more effectively so that effective decision making is enabled. Limitations (possibility for future research, still existing knowledge gap) There is a low level of adoption of FMIS in dairy farms, and there is a need in the dairy sector for a more effective transfer of knowledge and technology. Your own reflections (what have you learnt, what was already known to you, what was surprising, what could have been done in a better way?) Everything measurable, can be improved. Feature of the article Your comment Author(s) and the title of the article Name of the journal/conference A Farm Management Information System Using Future Internet Technologies. Paraforos, D., Vassiliadis, V., Kortenbruck, D., Stamkopoulos, K., Ziogas, V., Sapounas, A. And Griepentrog, H. IFAC-PapersOnLine Research motivation stated by the author(s) (Why is the research important? What is the knowledge gap?) Farmers lack the tools necessary for informed decisionmaking, so developing an innovative FMIS that could incorporate Internet of Things and Big Data could be useful. Objective(s) & research question(s) (What is going to be done?) This study describes the development of a FMIS for small and medium-sized farms that can use Future Internet technologies. Concepts and theories used Constructivism Methodology, data collection and data analysis (How was the research conducted?) Design research Key findings or results/ New identified themes (what has been found by the author(s)?) FMIS could successfully perform profitability analysis. Its capabilities could be further enhanced by connecting and accessing in-field sensors using the Internet of Things connectivity and incorporating a decision support system. 22 4IK524 Information Systems Methodology, 7,5hp – Literature review Contributions to knowledge (What is the new knowledge/ How the identified knowledge gap is filled?) Financial analysis was feasible based on all farm transactions and estimated profitability by using fixed values imported by the farmer. Limitations (possibility for future research, still existing knowledge gap) The application developed uses data imported by the farmer. Future work could involve an automated process of entering the data. Your own reflections (what have you learnt, what was already known to you, what was surprising, what could have been done in a better way?) FMIS could be used for financial analysis; calculation of estimated profitability could be linked to sensors and could incorporate a decision support system. Feature of the article Your comment Author(s) and the title of the article Characterization of Dutch dairy farms using sensor systems for cow management. Steeneveld, W. and Hogeveen, H. Journal of Dairy Science Name of the journal/conference Research motivation stated by the author(s) (Why is the research important? What is the knowledge gap?) Objective(s) & research question(s) (What is going to be done?) Despite the availability of various sensor systems, there is a knowledge gap regarding which systems are used on dairy farms and which are not. Additionally, it remains unclear whether farmers should invest (or not) in sensor systems. This study's objectives were to provide an overview of the sensor systems currently used in the Netherlands to examine the reasons for investing or not investing in sensor systems and the characterization of farms with and without sensor systems. Concepts and theories used Interpretivism Methodology, data collection and data analysis (How was the research conducted?) Survey Key findings or results/ New identified themes (what has been found by the author(s)?) The use of sensor systems was different for farms using an automatic or conventional milking system (AMS or CMS). The main reasons for investing in estrus detection sensor systems were improving detection rates, effective monitoring of the herd's fertility level, improved profitability, and reduced labor. The main reason for not investing in sensor systems was the associated cost. CMS farms with sensor systems were, on average, larger than CMS farms without sensor systems. Contributions to knowledge (What is the new knowledge/ How the identified knowledge gap is filled?) 23 4IK524 Information Systems Methodology, 7,5hp – Literature review Limitations (possibility for future research, still existing knowledge gap) Further research on the topic would be recommended. Your own reflections (what have you learnt, what was already known to you, what was surprising, what could have been done in a better way?) Investing in sensor technology is more economically viable in larger farms. Farms with an AMS use sensors to a larger percentage. Feature of the article Your comment Author(s) and the title of the article Objective(s) & research question(s) (What is going to be done?) Development of a general cowshed information management system from proprietary subsystems. Nikander, J., Laajalahti, M., Kajava, S., Sairanen, A., Järvinen, M. and Pastell, M. Proceedings of the 7th European Conference on Precision Livestock Farming. Milano, Italy. A modern cowshed contains many autonomous and semi-autonomous systems that are used to reduce human labor. However, the information produced by these systems are not efficiently handled. There is a lack of integration and/or data exchange between systems from different vendors. The development of an information system that will address the problem of the non-efficient use of information. Concepts and theories used Constructivism Methodology, data collection and data analysis (How was the research conducted?) Design research Key findings or results/ New identified themes (what has been found by the author(s)?) The Cowlab ACIS system, gathered data automatically, saved considerable resources, provided the necessary information on time, and reduced the number of errors in data sets. Contributions to knowledge (What is the new knowledge/ How the identified knowledge gap is filled?) The system developed provides data automatically to the national database of animal recording and a milk analysis laboratory. Its use has decreased the labor required for data handling and enabled access to all data from a single source. It gives automatic reports and provides alarms in case of hardware failure. Limitations (possibility for future research, still existing knowledge gap) The introduction of information systems is hindered by the closed, commercial ICT systems used to control modern dairy farms' various automation systems. Consequently, it is difficult to provide an overall solution to the data gathering problem. Name of the journal/conference Research motivation stated by the author(s) (Why is the research important? What is the knowledge gap?) 24 4IK524 Information Systems Methodology, 7,5hp – Literature review Your own reflections (what have you learnt, what was already known to you, what was surprising, what could have been done in a better way?) There is a lack of integration among the various automatic and semi-automatic systems in a dairy farm leading to inefficient use of data. Feature of the article Your comment Author(s) and the title of the article Research on cattle farm management information system. He, P., Chang, H., Gao, H. and Wang, Z. In 2017 6th International Conference on Computer Science and Network Technology (ICCSNT) (pp. 508510). IEEE. A reliable management system in dairy farms is urgently needed. Name of the journal/conference Research motivation stated by the author(s) (Why is the research important? What is the knowledge gap?) Objective(s) & research question(s) (What is going to be done?) The design of a system for a reliable management system in dairy farms. Concepts and theories used Constructivism Methodology, data collection and data analysis (How was the research conducted?) Design research Key findings or results/ New identified themes (what has been found by the author(s)?) The results show that the system effectively monitors milk production and quality statistics and could contribute to food safety. Contributions to knowledge (What is the new knowledge/ How the identified knowledge gap is filled?) A system is designed for automation management. Limitations (possibility for future research, still existing knowledge gap) Further research is required in this domain. Your own reflections (what have you learnt, what was already known to you, what was surprising, what could have been done in a better way?) FMIS should effectively interface with automation systems where most data are produced. 25 4IK524 Information Systems Methodology, 7,5hp – Literature review Feature of the article Your comment Author(s) and the title of the article Using a dairy management information system to facilitate precision agriculture: the case of the AfiMilk® system. Berger, R. and Hovav, A. Information systems management Name of the journal/conference Research motivation stated by the author(s) (Why is the research important? What is the knowledge gap?) This study examines a dairy management information system to describe the applicability of a system of this kind in precision agriculture. Objective(s) & research question(s) (What is going to be done?) The goal of this article is the evaluation of a leading global DMIS, AfiMilk®, and answer the following research questions: • To what extent does AfiMilk® implicitly support best practices on the farm? • To what extent does AfiMilk® support the use of TQM/Six Sigma? Concepts and theories used Interpretivism Methodology, data collection and data analysis (How was the research conducted?) Multiple case design Key findings or results/ New identified themes (what has been found by the author(s)?) The use of Six Sigma based processes facilitated by a DMIS could help dairy farmers to implement best practices, improve the operational efficiency of the dairy business, secure the production of quality milk, optimize product mix composition, and streamline the production process. Contributions to knowledge (What is the new knowledge/ How the identified knowledge gap is filled?) This study is exploratory and aims to describe a DMIS in business terms since research in agricultural information systems is in a nascent stage. Limitations (possibility for future research, still existing knowledge gap) The analysis performed is subjective and based on the authors' view of the dairy industry's current situation. Future research should develop objective measures in precision agriculture research. Your own reflections (what have you learnt, what was already known to you, what was surprising, what could have been done in a better way?) It is important to view the dairy farm from a business perspective. This way, we could increase its profitability and secure the production of safe products for human consumption. 26